Classification Using Pyspark_Home_Quote - v3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification Using Pyspark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pyspark Initializasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to make pyspark importable as a regular library\n",
    "import findspark\n",
    "findspark.init()\n",
    "\n",
    "import pyspark\n",
    "\n",
    "from pyspark import SparkContext\n",
    "sc = SparkContext.getOrCreate()\n",
    "\n",
    "#initializasing SparkSession for creating Spark DataFrame\n",
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.getOrCreate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data Frame spark profiling \n",
    "from pyspark.sql.types import IntegerType, StringType, DoubleType, ShortType, DecimalType\n",
    "import pyspark.sql.functions as func\n",
    "from pyspark.sql.functions import isnull\n",
    "from pyspark.sql.functions import isnan, when, count, col, round\n",
    "from pyspark.sql.functions import mean\n",
    "from pyspark.sql.types import Row\n",
    "import matplotlib.pyplot as plt\n",
    "from pyspark.sql.functions import udf\n",
    "\n",
    "\n",
    "# Pandas DF operation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from numpy import array\n",
    "\n",
    "# Modeling + Evaluation\n",
    "from pyspark.ml.feature import VectorAssembler, VectorIndexer, OneHotEncoder, StringIndexer\n",
    "from pyspark.sql.functions import when\n",
    "from pyspark.sql import functions as F\n",
    "from pyspark.sql.functions import avg\n",
    "from pyspark.ml import Pipeline\n",
    "from pyspark.ml.classification import LogisticRegression, RandomForestClassifier, GBTClassifier\n",
    "from pyspark.ml.classification import DecisionTreeClassifier\n",
    "from pyspark.ml.evaluation import BinaryClassificationEvaluator, MulticlassClassificationEvaluator\n",
    "from pyspark.mllib.evaluation import BinaryClassificationMetrics\n",
    "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder \n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import log_loss\n",
    "from pyspark.sql import Window\n",
    "from pyspark.sql.functions import rank,sum,col\n",
    "from pyspark.ml.linalg import Vectors\n",
    "from pyspark.ml.feature import VectorSlicer\n",
    "\n",
    "window = Window.rowsBetween(Window.unboundedPreceding,Window.unboundedFollowing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Load Data to Spark DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Initializing File Type and path for data train\n",
    "file_type = 'text'\n",
    "path=r'train.csv'\n",
    "delimeter=','"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_data(file_type):\n",
    "    \"\"\"input type of file \"text\" or \"parquet\" and Return pyspark dataframe\"\"\"\n",
    "    if file_type ==\"text\": # use text as file type input\n",
    "        df = spark.read.option(\"header\", \"true\") \\\n",
    "                       .option(\"delimeter\",delimeter)\\\n",
    "                       .option(\"inferSchema\", \"true\") \\\n",
    "                       .csv(path)  #path file that you want import\n",
    "    else:  \n",
    "        df= spark.read.parquet(\"example.parquet\") #path file that you want import\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = load_data(file_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Initializing File Type and path for data test\n",
    "file_type = 'text'\n",
    "path=r'test.csv'\n",
    "delimeter=','"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_data = load_data(file_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function load_data in module __main__:\n",
      "\n",
      "load_data(file_type)\n",
      "    input type of file \"text\" or \"parquet\" and Return pyspark dataframe\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(load_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+-------------------+--------------------+------+------+------+------+-------+-------+-------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+--------------+--------------+----------------+----------------+------------+------------+------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+--------------+--------------+---------------+---------------+--------------+--------------+--------------+--------------+--------------+----------------+----------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+----------------+----------------+---------------+---------------+----------------+----------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+----------------+----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+-----------------+-----------------+\n",
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------------+-----------------+\n",
      "|          1|         16/08/2013|                   0|     B|    23|0.9403|6.0E-4|    965|   1.02|      N|             17|             23|             17|             23|             15|             22|             16|             22|             13|             22|             13|             23|             T|             D|               2|               1|           7|          18|           3|           8|          0|          5|          5|         24|          V|      48649|          0|           0|           0|           0|           0|           0|           0|             0|             0|             -1|             -1|             7|             0|             N|             1|             2|               5|               7|              0|              1|              2|              2|             24|             ZA|             ZE|             XR|             XD|              1|              0|              0|              0|              0|              1|              2|              2|              0|              1|              1|              1|              1|              0|              0|              0|              0|              0|              0|              0|              0|              1|              0|              1|              1|              1|              2|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              1|              1|              1|              2|              0|              0|              0|              0|              1|              2|              5|              7|             -1|             19|             N|             N|             Y|             0|             O|             0|             0|              1|              -1|              21|              4|              2|              C|              4|              14|              22|              0|              3|              0|              0|              17|              23|              2|              4|              15|              21|            3.0|               5|               5|              6|              B|              0|              N|              N|              Y|              G|              Y|              2|              N|              N|              N|               7|               5|                3|                6|               15|               14|               12|               13|               20|               22|               -1|               13|                2|                5|                1|                1|                1|                1|                2|                9|                -1|                25|                 1|                 1|                 1|                 1|                 1|                 1|                -1|                 7|                 1|                 2|                 1|                 2|                13|                22|                -1|                12|                 3|                 6|                 4|                12|                -1|                10|                -1|                15|                -1|                 5|                14|                 9|                16|                16|                15|                18|                17|                16|                11|                18|                24|                25|                 5|                 4|                 7|                10|                13|                20|                14|                21|                12|                14|                14|                22|                 3|                 2|                15|                24|                 2|                 2|                15|                23|                 1|                 1|                15|                13|                 9|                10|                 2|                 2|                 8|                 4|                20|                22|                10|                 8|                 6|                 5|                15|                13|                19|                18|                16|                14|                21|                23|                21|                23|                16|                11|                22|                24|                 7|                14|                -1|                17|                15|                17|                14|                18|                 9|                 9|                -1|                 8|                -1|                18|                -1|                10|                N|               CA|\n",
      "|          2|         22/04/2014|                   0|     F|     7|1.0006| 0.004|    548| 1.2433|      N|              6|              8|              6|              8|              5|              7|              5|              8|             13|             22|             13|             23|             T|             E|               5|               9|           5|          14|           6|          18|          1|          5|          5|         11|          P|      26778|          0|           0|           1|           1|           0|           0|           0|             1|             1|             14|             19|             7|             0|             N|             1|             2|              24|              25|              0|              1|              2|              5|             19|             XB|             YJ|             YE|             XT|              1|              0|              0|              0|              0|              0|       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           Y|             0|             N|             0|             0|              1|              -1|              23|              3|              0|              B|              4|               7|              15|              0|              1|              0|              0|               6|               8|              2|              1|               6|               8|            2.0|               4|               3|              3|              B|           null|              N|              O|              N|              H|              Y|              2|              N|              N|              N|              11|              12|                8|               17|               19|               20|               12|               14|               16|               18|               -1|               19|                4|               13|                9|               13|                5|               13|                5|               12|                -1|                25|                 4|                15|                 8|                13|                 5|                14|                -1|                13|                 9|                11|                 5|                12|                 2|                16|                -1|                22|                23|                24|                20|                22|                -1|                22|                -1|                22|                -1|                13|                10|                 4|                10|                 5|                 5|                 5|                13|                 6|                 7|                10|                21|                24|                10|                19|                 7|                10|                 9|                15|                 4|                 3|                13|                17|                 6|                 9|                17|                23|                11|                21|                13|                21|                 9|                17|                12|                21|                25|                25|                15|                21|                10|                13|                23|                24|                11|                15|                21|                24|                 6|                11|                21|                21|                18|                15|                20|                20|                13|                12|                12|                12|                15|                 9|                13|                11|                11|                20|                -1|                 9|                18|                21|                 8|                 7|                10|                10|                -1|                11|                -1|                17|                -1|                20|                N|               NJ|\n",
      "|          4|         25/08/2014|                   0|     F|     7|1.0006| 0.004|    548| 1.2433|      N|              7|             12|              7|             12|              6|             10|              7|             11|             25|             25|             13|             23|             T|             J|               4|               6|           3|          10|           4|          11|          1|          5|          5|         11|          K|       8751|          0|           0|           2|           2|           0|           0|           0|             1|             1|             16|             21|             7|             0|             N|             1|             2|               7|              16|              0|              1|              2|              3|              7|             ZH|             XS|             YP|             XC|              1|              0|              0|              0|              0|              0|              1|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|           null|             12|             16|             -1|             13|             Y|             Y|             Y|             0|             R|             1|             0|              1|              -1|              21|              4|              2|              C|              4|               9|              17|              1|              3|              0|              0|               7|              12|              2|              1|              15|              21|            1.5|              14|              20|              3|              B|           null|              N|              K|              Y|              H|              Y|              2|              N|              N|              N|              23|              24|                2|                4|               20|               22|               18|               20|               16|               18|               -1|               21|                4|               12|               10|               14|                5|               13|                6|               14|                -1|                25|                 4|                11|                 9|                15|                 5|                12|                -1|                12|                10|                16|                 6|                14|                 2|                13|                -1|                17|                21|                21|                20|                21|                -1|                19|                -1|                19|                -1|                10|                18|                17|                12|                 8|                 7|                 8|                15|                11|                 6|                 9|                 8|                11|                 7|                12|                13|                21|                11|                18|                12|                19|                15|                20|                10|                17|                 9|                13|                 4|                10|                14|                22|                17|                24|                10|                16|                24|                22|                12|                15|                12|                20|                21|                22|                24|                25|                20|                22|                 7|                13|                23|                23|                20|                19|                20|                20|                18|                20|                19|                21|                20|                19|                11|                 8|                 3|                 3|                -1|                 5|                21|                24|                12|                15|                15|                18|                -1|                21|                -1|                11|                -1|                 8|                N|               NJ|\n",
      "|          6|         15/04/2013|                   0|     J|    10|0.9769|4.0E-4|   1165| 1.2665|      N|              3|              2|              3|              2|              2|              2|              3|              2|             13|             22|             13|             23|             Y|             F|              15|              23|           8|          19|          14|          24|          0|          5|          5|         23|          V|      43854|          0|           0|           0|           0|           0|           0|           0|             1|             1|              2|              2|             6|             1|             N|             1|             3|              -1|              -1|              0|              5|              2|              3|             13|             XO|             XE|             YI|             XX|              1|              0|              0|              0|              0|              0|              1|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|           null|              1|              1|             -1|              2|             N|             N|             Y|             0|             R|             1|             0|              1|              -1|              21|              4|              2|              C|              1|               3|               6|              0|              2|              0|              0|               3|               2|              2|              1|               7|               9|            1.0|              13|              18|             10|              B|           null|              N|              O|              Y|              G|              N|              2|              N|              Y|              N|              24|              25|                2|                3|               10|                8|                8|                9|               21|               23|               -1|               23|               13|               21|               22|               22|               14|               21|               18|               21|                -1|                25|                15|                22|                21|                22|                15|                22|                -1|                15|                24|                23|                 9|                18|                 2|                 3|                -1|                 6|                10|                13|                 2|                 3|                -1|                 8|                -1|                15|                -1|                24|                19|                20|                13|                10|                 7|                10|                16|                14|                 3|                 3|                11|                16|                 2|                 1|                10|                15|                 3|                 3|                14|                21|                 1|                 1|                 9|                16|                 3|                 2|                 4|                 8|                 4|                 3|                 9|                18|                 2|                 1|                13|                 9|                 7|                 7|                25|                25|                 3|                 1|                14|                22|                 6|                 2|                 7|                14|                11|                 8|                19|                18|                18|                16|                13|                12|                13|                12|                17|                13|                 5|                 2|                 3|                 4|                -1|                 7|                14|                14|                14|                18|                 6|                 5|                -1|                10|                -1|                 9|                -1|                21|                N|               TX|\n",
      "+-----------+-------------------+--------------------+------+------+------+------+-------+-------+-------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+--------------+--------------+----------------+----------------+------------+------------+------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+--------------+--------------+---------------+---------------+--------------+--------------+--------------+--------------+--------------+----------------+----------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+----------------+----------------+---------------+---------------+----------------+----------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+----------------+----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+-----------------+-----------------+\n",
      "only showing top 4 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#show 5 observation in DataFrame\n",
    "df.show(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+-------------------+------+------+------+------+-------+-------+-------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+--------------+--------------+----------------+----------------+------------+------------+------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+--------------+--------------+---------------+---------------+--------------+--------------+--------------+--------------+--------------+----------------+----------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+----------------+----------------+---------------+---------------+----------------+----------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+----------------+----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+-----------------+-----------------+\n",
      "|QuoteNumber|Original_Quote_Date|Field6|Field7|Field8|Field9|Field10|Field11|Field12|CoverageField1A|CoverageField1B|CoverageField2A|CoverageField2B|CoverageField3A|CoverageField3B|CoverageField4A|CoverageField4B|CoverageField5A|CoverageField5B|CoverageField6A|CoverageField6B|CoverageField8|CoverageField9|CoverageField11A|CoverageField11B|SalesField1A|SalesField1B|SalesField2A|SalesField2B|SalesField3|SalesField4|SalesField5|SalesField6|SalesField7|SalesField8|SalesField9|SalesField10|SalesField11|SalesField12|SalesField13|SalesField14|SalesField15|PersonalField1|PersonalField2|PersonalField4A|PersonalField4B|PersonalField5|PersonalField6|PersonalField7|PersonalField8|PersonalField9|PersonalField10A|PersonalField10B|PersonalField11|PersonalField12|PersonalField13|PersonalField14|PersonalField15|PersonalField16|PersonalField17|PersonalField18|PersonalField19|PersonalField22|PersonalField23|PersonalField24|PersonalField25|PersonalField26|PersonalField27|PersonalField28|PersonalField29|PersonalField30|PersonalField31|PersonalField32|PersonalField33|PersonalField34|PersonalField35|PersonalField36|PersonalField37|PersonalField38|PersonalField39|PersonalField40|PersonalField41|PersonalField42|PersonalField43|PersonalField44|PersonalField45|PersonalField46|PersonalField47|PersonalField48|PersonalField49|PersonalField50|PersonalField51|PersonalField52|PersonalField53|PersonalField54|PersonalField55|PersonalField56|PersonalField57|PersonalField58|PersonalField59|PersonalField60|PersonalField61|PersonalField62|PersonalField63|PersonalField64|PersonalField65|PersonalField66|PersonalField67|PersonalField68|PersonalField69|PersonalField70|PersonalField71|PersonalField72|PersonalField73|PersonalField74|PersonalField75|PersonalField76|PersonalField77|PersonalField78|PersonalField79|PersonalField80|PersonalField81|PersonalField82|PersonalField83|PersonalField84|PropertyField1A|PropertyField1B|PropertyField2A|PropertyField2B|PropertyField3|PropertyField4|PropertyField5|PropertyField6|PropertyField7|PropertyField8|PropertyField9|PropertyField10|PropertyField11A|PropertyField11B|PropertyField12|PropertyField13|PropertyField14|PropertyField15|PropertyField16A|PropertyField16B|PropertyField17|PropertyField18|PropertyField19|PropertyField20|PropertyField21A|PropertyField21B|PropertyField22|PropertyField23|PropertyField24A|PropertyField24B|PropertyField25|PropertyField26A|PropertyField26B|PropertyField27|PropertyField28|PropertyField29|PropertyField30|PropertyField31|PropertyField32|PropertyField33|PropertyField34|PropertyField35|PropertyField36|PropertyField37|PropertyField38|PropertyField39A|PropertyField39B|GeographicField1A|GeographicField1B|GeographicField2A|GeographicField2B|GeographicField3A|GeographicField3B|GeographicField4A|GeographicField4B|GeographicField5A|GeographicField5B|GeographicField6A|GeographicField6B|GeographicField7A|GeographicField7B|GeographicField8A|GeographicField8B|GeographicField9A|GeographicField9B|GeographicField10A|GeographicField10B|GeographicField11A|GeographicField11B|GeographicField12A|GeographicField12B|GeographicField13A|GeographicField13B|GeographicField14A|GeographicField14B|GeographicField15A|GeographicField15B|GeographicField16A|GeographicField16B|GeographicField17A|GeographicField17B|GeographicField18A|GeographicField18B|GeographicField19A|GeographicField19B|GeographicField20A|GeographicField20B|GeographicField21A|GeographicField21B|GeographicField22A|GeographicField22B|GeographicField23A|GeographicField23B|GeographicField24A|GeographicField24B|GeographicField25A|GeographicField25B|GeographicField26A|GeographicField26B|GeographicField27A|GeographicField27B|GeographicField28A|GeographicField28B|GeographicField29A|GeographicField29B|GeographicField30A|GeographicField30B|GeographicField31A|GeographicField31B|GeographicField32A|GeographicField32B|GeographicField33A|GeographicField33B|GeographicField34A|GeographicField34B|GeographicField35A|GeographicField35B|GeographicField36A|GeographicField36B|GeographicField37A|GeographicField37B|GeographicField38A|GeographicField38B|GeographicField39A|GeographicField39B|GeographicField40A|GeographicField40B|GeographicField41A|GeographicField41B|GeographicField42A|GeographicField42B|GeographicField43A|GeographicField43B|GeographicField44A|GeographicField44B|GeographicField45A|GeographicField45B|GeographicField46A|GeographicField46B|GeographicField47A|GeographicField47B|GeographicField48A|GeographicField48B|GeographicField49A|GeographicField49B|GeographicField50A|GeographicField50B|GeographicField51A|GeographicField51B|GeographicField52A|GeographicField52B|GeographicField53A|GeographicField53B|GeographicField54A|GeographicField54B|GeographicField55A|GeographicField55B|GeographicField56A|GeographicField56B|GeographicField57A|GeographicField57B|GeographicField58A|GeographicField58B|GeographicField59A|GeographicField59B|GeographicField60A|GeographicField60B|GeographicField61A|GeographicField61B|GeographicField62A|GeographicField62B|GeographicField63|GeographicField64|\n",
      "+-----------+-------------------+------+------+------+------+-------+-------+-------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+--------------+--------------+----------------+----------------+------------+------------+------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+--------------+--------------+---------------+---------------+--------------+--------------+--------------+--------------+--------------+----------------+----------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+----------------+----------------+---------------+---------------+----------------+----------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+----------------+----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+-----------------+-----------------+\n",
      "|          3|         12/08/2014|     E|    16|0.9364|6.0E-4|   1487| 1.3045|      N|              4|              4|              4|              4|              3|              3|              3|              4|             13|             22|             13|             23|             Y|             K|              13|              22|           6|          16|           9|          21|          0|          5|          5|         11|          P|      67052|          0|           0|           0|           0|           0|           0|           0|             1|             1|             11|             14|             6|             1|             N|             1|             3|              -1|              -1|              0|              5|              2|              5|             14|             YH|             XR|             XQ|             ZQ|              1|              0|              0|              0|              0|              0|              1|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|           null|             18|             23|             -1|              4|             N|             N|             Y|             0|             D|             1|             0|              1|              -1|              24|              1|              2|              A|              1|               3|               5|              0|              2|              0|              0|               4|               4|              2|              1|               6|               8|            1.5|               7|               8|             19|              B|           null|              N|              N|              Y|              G|              N|              2|              N|              Y|              N|              18|              21|               25|               25|                9|                6|                1|                1|               24|               25|               -1|               13|                9|               18|               12|               18|               10|               18|               16|               19|                -1|                25|                 9|                18|                13|                18|                 9|                18|                -1|                19|                10|                13|                 8|                17|                 2|                10|                -1|                20|                19|                18|                 4|                 9|                -1|                25|                -1|                24|                -1|                15|                16|                15|                17|                17|                10|                13|                21|                20|                25|                25|                16|                22|                25|                25|                 8|                11|                10|                17|                11|                17|                24|                25|                15|                23|                25|                25|                -1|                -1|                21|                24|                18|                24|                11|                18|                18|                16|                20|                24|                11|                17|                22|                23|                 9|                12|                25|                25|                 6|                 9|                 4|                 2|                16|                12|                20|                20|                 2|                 2|                 2|                 1|                 1|                 1|                10|                 7|                25|                25|                -1|                19|                19|                22|                12|                15|                 1|                 1|                -1|                 1|                -1|                20|                -1|                25|                Y|               IL|\n",
      "|          5|         07/09/2013|     F|    11|0.9919|0.0038|    564| 1.1886|      N|              8|             14|              8|             14|              7|             12|              8|             13|             13|             22|             13|             23|             T|             E|               4|               5|           3|           6|           3|           6|          1|          5|          5|          4|          R|      27288|          1|           0|           0|           0|           0|           0|           0|             1|             1|             15|             20|             6|             1|             N|             1|             2|               9|              18|              0|              1|              2|              2|              4|             YF|             XS|             YP|             XC|              1|              0|              0|              0|              0|              0|              1|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|           null|             22|             24|             -1|             10|             N|             N|             Y|             0|             J|             1|             0|              1|              -1|              21|              1|              1|              A|              4|               7|              15|              1|              1|              1|              0|               8|              14|              2|              1|               5|               6|            1.0|              10|              15|             10|              B|           null|              N|              O|              Y|              H|              N|              1|              N|              N|              N|               5|               3|               17|               24|               17|               17|               15|               17|               10|                9|               -1|               20|                4|               13|                9|               13|                5|               13|                5|               12|                -1|                25|                 4|                15|                 8|                13|                 5|                14|                -1|                13|                 9|                11|                 5|                12|                 2|                16|                -1|                22|                23|                24|                21|                22|                -1|                22|                -1|                22|                -1|                14|                10|                 4|                10|                 5|                 5|                 5|                13|                 6|                 9|                14|                 8|                10|                12|                22|                 7|                10|                 9|                15|                11|                18|                14|                18|                 5|                 7|                19|                24|                 6|                14|                16|                23|                 6|                12|                11|                19|                25|                25|                15|                20|                12|                20|                23|                24|                12|                21|                23|                25|                 7|                11|                16|                14|                13|                 6|                17|                15|                 7|                 5|                 7|                 5|                13|                 7|                14|                14|                 7|                14|                -1|                 4|                 1|                 1|                 5|                 3|                10|                10|                -1|                 5|                -1|                 5|                -1|                21|                N|               NJ|\n",
      "|          7|         29/03/2013|     F|    15|0.8945|0.0038|    564|  1.067|      N|             11|             18|             11|             18|             10|             16|             10|             18|             13|             22|             13|             23|             T|             E|               3|               3|           5|          14|           3|           9|          1|          5|          5|         23|          V|      65264|          0|           1|           2|           2|           0|           0|           0|             1|             1|             12|             15|             6|             1|             N|             1|             2|              22|              24|              0|              1|              2|              4|             10|             XE|             ZH|             YK|             ZN|              1|              0|              0|              0|              0|              0|              1|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|           null|              6|              9|             -1|             23|             N|             N|             Y|             0|             R|             1|             0|              1|              -1|              24|              1|              2|              C|              1|               8|              17|              0|              2|              1|              0|              11|              18|              2|              1|              12|              18|            1.0|              20|              24|             10|              B|           null|              N|              M|              Y|              G|              Y|              1|              N|              N|              N|              11|              12|                3|                6|               16|               16|               15|               18|               14|               16|               -1|               16|                4|               10|               10|               16|                5|               11|                6|               13|                -1|                25|                 4|                10|                10|                16|                 5|                10|                -1|                11|                11|                18|                 5|                13|                 2|                13|                -1|                15|                21|                20|                19|                21|                -1|                16|                -1|                18|                -1|                16|                15|                11|                11|                 7|                 6|                 7|                13|                 7|                 7|                11|                15|                21|                 5|                 5|                 3|                 2|                14|                21|                 8|                12|                12|                15|                14|                22|                 7|                 7|                 5|                11|                 9|                14|                13|                22|                 8|                13|                22|                19|                11|                14|                11|                16|                16|                18|                 9|                10|                14|                16|                 6|                 8|                20|                19|                17|                14|                16|                13|                20|                22|                20|                22|                20|                18|                10|                 7|                 4|                 7|                -1|                11|                13|                12|                18|                22|                10|                11|                -1|                20|                -1|                22|                -1|                11|                N|               NJ|\n",
      "|          9|         21/03/2015|     K|    21| 0.887|4.0E-4|   1113| 1.2665|      Y|             14|             22|             15|             22|             13|             20|             22|             25|             13|             22|             13|             23|             Y|             F|               5|               9|           9|          20|           5|          16|          1|          5|          5|         11|          R|      32725|          1|           1|           1|           1|           0|           0|           0|             1|             1|              5|              6|             6|             1|             N|             1|             2|               7|              15|              0|              1|              2|              3|              5|             XR|             YY|             XT|             XC|              1|              0|              0|              0|              0|              0|              1|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|              0|              0|              0|              0|              1|           null|              5|              8|             -1|              6|             N|             N|             Y|             0|             Q|             1|             0|              1|              -1|              21|              4|              2|              C|              4|               5|              10|              1|              2|              1|              0|              14|              22|              2|              1|              13|              18|            2.0|               8|              11|              4|              B|           null|              N|              M|              N|              G|              N|              0|              N|              N|              N|               7|               5|                9|               18|               11|                9|               13|               15|               22|               24|               -1|               14|               24|               25|               23|               23|               24|               25|               25|               25|                -1|                25|                24|                24|                23|                23|                24|                24|                25|                25|                23|                22|                24|                25|                 2|                 7|                -1|                13|                11|                14|                 6|                16|                -1|                15|                -1|                15|                -1|                22|                11|                 5|                10|                 4|                 5|                 4|                13|                 5|                 7|                10|                 3|                 2|                 5|                 5|                 9|                14|                15|                22|                 7|                10|                 9|                 8|                13|                22|                 6|                 5|                 3|                 7|                 8|                11|                11|                20|                17|                23|                14|                12|                15|                20|                10|                14|                11|                11|                 9|                10|                11|                13|                15|                21|                14|                12|                17|                13|                10|                 6|                20|                22|                20|                22|                19|                16|                12|                11|                 4|                 6|                -1|                13|                10|                 8|                 5|                 3|                 8|                 8|                -1|                13|                -1|                 8|                -1|                21|                N|               TX|\n",
      "+-----------+-------------------+------+------+------+------+-------+-------+-------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+--------------+--------------+----------------+----------------+------------+------------+------------+------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+--------------+--------------+---------------+---------------+--------------+--------------+--------------+--------------+--------------+----------------+----------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+----------------+----------------+---------------+---------------+----------------+----------------+---------------+----------------+----------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+---------------+----------------+----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+-----------------+-----------------+\n",
      "only showing top 4 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Print Schema\n",
    "test_data.show(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- QuoteNumber: integer (nullable = true)\n",
      " |-- Original_Quote_Date: string (nullable = true)\n",
      " |-- QuoteConversion_Flag: integer (nullable = true)\n",
      " |-- Field6: string (nullable = true)\n",
      " |-- Field7: integer (nullable = true)\n",
      " |-- Field8: double (nullable = true)\n",
      " |-- Field9: double (nullable = true)\n",
      " |-- Field10: integer (nullable = true)\n",
      " |-- Field11: double (nullable = true)\n",
      " |-- Field12: string (nullable = true)\n",
      " |-- CoverageField1A: integer (nullable = true)\n",
      " |-- CoverageField1B: integer (nullable = true)\n",
      " |-- CoverageField2A: integer (nullable = true)\n",
      " |-- CoverageField2B: integer (nullable = true)\n",
      " |-- CoverageField3A: integer (nullable = true)\n",
      " |-- CoverageField3B: integer (nullable = true)\n",
      " |-- CoverageField4A: integer (nullable = true)\n",
      " |-- CoverageField4B: integer (nullable = true)\n",
      " |-- CoverageField5A: integer (nullable = true)\n",
      " |-- CoverageField5B: integer (nullable = true)\n",
      " |-- CoverageField6A: integer (nullable = true)\n",
      " |-- CoverageField6B: integer (nullable = true)\n",
      " |-- CoverageField8: string (nullable = true)\n",
      " |-- CoverageField9: string (nullable = true)\n",
      " |-- CoverageField11A: integer (nullable = true)\n",
      " |-- CoverageField11B: integer (nullable = true)\n",
      " |-- SalesField1A: integer (nullable = true)\n",
      " |-- SalesField1B: integer (nullable = true)\n",
      " |-- SalesField2A: integer (nullable = true)\n",
      " |-- SalesField2B: integer (nullable = true)\n",
      " |-- SalesField3: integer (nullable = true)\n",
      " |-- SalesField4: integer (nullable = true)\n",
      " |-- SalesField5: integer (nullable = true)\n",
      " |-- SalesField6: integer (nullable = true)\n",
      " |-- SalesField7: string (nullable = true)\n",
      " |-- SalesField8: integer (nullable = true)\n",
      " |-- SalesField9: integer (nullable = true)\n",
      " |-- SalesField10: integer (nullable = true)\n",
      " |-- SalesField11: integer (nullable = true)\n",
      " |-- SalesField12: integer (nullable = true)\n",
      " |-- SalesField13: integer (nullable = true)\n",
      " |-- SalesField14: integer (nullable = true)\n",
      " |-- SalesField15: integer (nullable = true)\n",
      " |-- PersonalField1: integer (nullable = true)\n",
      " |-- PersonalField2: integer (nullable = true)\n",
      " |-- PersonalField4A: integer (nullable = true)\n",
      " |-- PersonalField4B: integer (nullable = true)\n",
      " |-- PersonalField5: integer (nullable = true)\n",
      " |-- PersonalField6: integer (nullable = true)\n",
      " |-- PersonalField7: string (nullable = true)\n",
      " |-- PersonalField8: integer (nullable = true)\n",
      " |-- PersonalField9: integer (nullable = true)\n",
      " |-- PersonalField10A: integer (nullable = true)\n",
      " |-- PersonalField10B: integer (nullable = true)\n",
      " |-- PersonalField11: integer (nullable = true)\n",
      " |-- PersonalField12: integer (nullable = true)\n",
      " |-- PersonalField13: integer (nullable = true)\n",
      " |-- PersonalField14: integer (nullable = true)\n",
      " |-- PersonalField15: integer (nullable = true)\n",
      " |-- PersonalField16: string (nullable = true)\n",
      " |-- PersonalField17: string (nullable = true)\n",
      " |-- PersonalField18: string (nullable = true)\n",
      " |-- PersonalField19: string (nullable = true)\n",
      " |-- PersonalField22: integer (nullable = true)\n",
      " |-- PersonalField23: integer (nullable = true)\n",
      " |-- PersonalField24: integer (nullable = true)\n",
      " |-- PersonalField25: integer (nullable = true)\n",
      " |-- PersonalField26: integer (nullable = true)\n",
      " |-- PersonalField27: integer (nullable = true)\n",
      " |-- PersonalField28: integer (nullable = true)\n",
      " |-- PersonalField29: integer (nullable = true)\n",
      " |-- PersonalField30: integer (nullable = true)\n",
      " |-- PersonalField31: integer (nullable = true)\n",
      " |-- PersonalField32: integer (nullable = true)\n",
      " |-- PersonalField33: integer (nullable = true)\n",
      " |-- PersonalField34: integer (nullable = true)\n",
      " |-- PersonalField35: integer (nullable = true)\n",
      " |-- PersonalField36: integer (nullable = true)\n",
      " |-- PersonalField37: integer (nullable = true)\n",
      " |-- PersonalField38: integer (nullable = true)\n",
      " |-- PersonalField39: integer (nullable = true)\n",
      " |-- PersonalField40: integer (nullable = true)\n",
      " |-- PersonalField41: integer (nullable = true)\n",
      " |-- PersonalField42: integer (nullable = true)\n",
      " |-- PersonalField43: integer (nullable = true)\n",
      " |-- PersonalField44: integer (nullable = true)\n",
      " |-- PersonalField45: integer (nullable = true)\n",
      " |-- PersonalField46: integer (nullable = true)\n",
      " |-- PersonalField47: integer (nullable = true)\n",
      " |-- PersonalField48: integer (nullable = true)\n",
      " |-- PersonalField49: integer (nullable = true)\n",
      " |-- PersonalField50: integer (nullable = true)\n",
      " |-- PersonalField51: integer (nullable = true)\n",
      " |-- PersonalField52: integer (nullable = true)\n",
      " |-- PersonalField53: integer (nullable = true)\n",
      " |-- PersonalField54: integer (nullable = true)\n",
      " |-- PersonalField55: integer (nullable = true)\n",
      " |-- PersonalField56: integer (nullable = true)\n",
      " |-- PersonalField57: integer (nullable = true)\n",
      " |-- PersonalField58: integer (nullable = true)\n",
      " |-- PersonalField59: integer (nullable = true)\n",
      " |-- PersonalField60: integer (nullable = true)\n",
      " |-- PersonalField61: integer (nullable = true)\n",
      " |-- PersonalField62: integer (nullable = true)\n",
      " |-- PersonalField63: integer (nullable = true)\n",
      " |-- PersonalField64: integer (nullable = true)\n",
      " |-- PersonalField65: integer (nullable = true)\n",
      " |-- PersonalField66: integer (nullable = true)\n",
      " |-- PersonalField67: integer (nullable = true)\n",
      " |-- PersonalField68: integer (nullable = true)\n",
      " |-- PersonalField69: integer (nullable = true)\n",
      " |-- PersonalField70: integer (nullable = true)\n",
      " |-- PersonalField71: integer (nullable = true)\n",
      " |-- PersonalField72: integer (nullable = true)\n",
      " |-- PersonalField73: integer (nullable = true)\n",
      " |-- PersonalField74: integer (nullable = true)\n",
      " |-- PersonalField75: integer (nullable = true)\n",
      " |-- PersonalField76: integer (nullable = true)\n",
      " |-- PersonalField77: integer (nullable = true)\n",
      " |-- PersonalField78: integer (nullable = true)\n",
      " |-- PersonalField79: integer (nullable = true)\n",
      " |-- PersonalField80: integer (nullable = true)\n",
      " |-- PersonalField81: integer (nullable = true)\n",
      " |-- PersonalField82: integer (nullable = true)\n",
      " |-- PersonalField83: integer (nullable = true)\n",
      " |-- PersonalField84: integer (nullable = true)\n",
      " |-- PropertyField1A: integer (nullable = true)\n",
      " |-- PropertyField1B: integer (nullable = true)\n",
      " |-- PropertyField2A: integer (nullable = true)\n",
      " |-- PropertyField2B: integer (nullable = true)\n",
      " |-- PropertyField3: string (nullable = true)\n",
      " |-- PropertyField4: string (nullable = true)\n",
      " |-- PropertyField5: string (nullable = true)\n",
      " |-- PropertyField6: integer (nullable = true)\n",
      " |-- PropertyField7: string (nullable = true)\n",
      " |-- PropertyField8: integer (nullable = true)\n",
      " |-- PropertyField9: integer (nullable = true)\n",
      " |-- PropertyField10: integer (nullable = true)\n",
      " |-- PropertyField11A: integer (nullable = true)\n",
      " |-- PropertyField11B: integer (nullable = true)\n",
      " |-- PropertyField12: integer (nullable = true)\n",
      " |-- PropertyField13: integer (nullable = true)\n",
      " |-- PropertyField14: string (nullable = true)\n",
      " |-- PropertyField15: integer (nullable = true)\n",
      " |-- PropertyField16A: integer (nullable = true)\n",
      " |-- PropertyField16B: integer (nullable = true)\n",
      " |-- PropertyField17: integer (nullable = true)\n",
      " |-- PropertyField18: integer (nullable = true)\n",
      " |-- PropertyField19: integer (nullable = true)\n",
      " |-- PropertyField20: integer (nullable = true)\n",
      " |-- PropertyField21A: integer (nullable = true)\n",
      " |-- PropertyField21B: integer (nullable = true)\n",
      " |-- PropertyField22: integer (nullable = true)\n",
      " |-- PropertyField23: integer (nullable = true)\n",
      " |-- PropertyField24A: integer (nullable = true)\n",
      " |-- PropertyField24B: integer (nullable = true)\n",
      " |-- PropertyField25: double (nullable = true)\n",
      " |-- PropertyField26A: integer (nullable = true)\n",
      " |-- PropertyField26B: integer (nullable = true)\n",
      " |-- PropertyField27: integer (nullable = true)\n",
      " |-- PropertyField28: string (nullable = true)\n",
      " |-- PropertyField29: integer (nullable = true)\n",
      " |-- PropertyField30: string (nullable = true)\n",
      " |-- PropertyField31: string (nullable = true)\n",
      " |-- PropertyField32: string (nullable = true)\n",
      " |-- PropertyField33: string (nullable = true)\n",
      " |-- PropertyField34: string (nullable = true)\n",
      " |-- PropertyField35: integer (nullable = true)\n",
      " |-- PropertyField36: string (nullable = true)\n",
      " |-- PropertyField37: string (nullable = true)\n",
      " |-- PropertyField38: string (nullable = true)\n",
      " |-- PropertyField39A: integer (nullable = true)\n",
      " |-- PropertyField39B: integer (nullable = true)\n",
      " |-- GeographicField1A: integer (nullable = true)\n",
      " |-- GeographicField1B: integer (nullable = true)\n",
      " |-- GeographicField2A: integer (nullable = true)\n",
      " |-- GeographicField2B: integer (nullable = true)\n",
      " |-- GeographicField3A: integer (nullable = true)\n",
      " |-- GeographicField3B: integer (nullable = true)\n",
      " |-- GeographicField4A: integer (nullable = true)\n",
      " |-- GeographicField4B: integer (nullable = true)\n",
      " |-- GeographicField5A: integer (nullable = true)\n",
      " |-- GeographicField5B: integer (nullable = true)\n",
      " |-- GeographicField6A: integer (nullable = true)\n",
      " |-- GeographicField6B: integer (nullable = true)\n",
      " |-- GeographicField7A: integer (nullable = true)\n",
      " |-- GeographicField7B: integer (nullable = true)\n",
      " |-- GeographicField8A: integer (nullable = true)\n",
      " |-- GeographicField8B: integer (nullable = true)\n",
      " |-- GeographicField9A: integer (nullable = true)\n",
      " |-- GeographicField9B: integer (nullable = true)\n",
      " |-- GeographicField10A: integer (nullable = true)\n",
      " |-- GeographicField10B: integer (nullable = true)\n",
      " |-- GeographicField11A: integer (nullable = true)\n",
      " |-- GeographicField11B: integer (nullable = true)\n",
      " |-- GeographicField12A: integer (nullable = true)\n",
      " |-- GeographicField12B: integer (nullable = true)\n",
      " |-- GeographicField13A: integer (nullable = true)\n",
      " |-- GeographicField13B: integer (nullable = true)\n",
      " |-- GeographicField14A: integer (nullable = true)\n",
      " |-- GeographicField14B: integer (nullable = true)\n",
      " |-- GeographicField15A: integer (nullable = true)\n",
      " |-- GeographicField15B: integer (nullable = true)\n",
      " |-- GeographicField16A: integer (nullable = true)\n",
      " |-- GeographicField16B: integer (nullable = true)\n",
      " |-- GeographicField17A: integer (nullable = true)\n",
      " |-- GeographicField17B: integer (nullable = true)\n",
      " |-- GeographicField18A: integer (nullable = true)\n",
      " |-- GeographicField18B: integer (nullable = true)\n",
      " |-- GeographicField19A: integer (nullable = true)\n",
      " |-- GeographicField19B: integer (nullable = true)\n",
      " |-- GeographicField20A: integer (nullable = true)\n",
      " |-- GeographicField20B: integer (nullable = true)\n",
      " |-- GeographicField21A: integer (nullable = true)\n",
      " |-- GeographicField21B: integer (nullable = true)\n",
      " |-- GeographicField22A: integer (nullable = true)\n",
      " |-- GeographicField22B: integer (nullable = true)\n",
      " |-- GeographicField23A: integer (nullable = true)\n",
      " |-- GeographicField23B: integer (nullable = true)\n",
      " |-- GeographicField24A: integer (nullable = true)\n",
      " |-- GeographicField24B: integer (nullable = true)\n",
      " |-- GeographicField25A: integer (nullable = true)\n",
      " |-- GeographicField25B: integer (nullable = true)\n",
      " |-- GeographicField26A: integer (nullable = true)\n",
      " |-- GeographicField26B: integer (nullable = true)\n",
      " |-- GeographicField27A: integer (nullable = true)\n",
      " |-- GeographicField27B: integer (nullable = true)\n",
      " |-- GeographicField28A: integer (nullable = true)\n",
      " |-- GeographicField28B: integer (nullable = true)\n",
      " |-- GeographicField29A: integer (nullable = true)\n",
      " |-- GeographicField29B: integer (nullable = true)\n",
      " |-- GeographicField30A: integer (nullable = true)\n",
      " |-- GeographicField30B: integer (nullable = true)\n",
      " |-- GeographicField31A: integer (nullable = true)\n",
      " |-- GeographicField31B: integer (nullable = true)\n",
      " |-- GeographicField32A: integer (nullable = true)\n",
      " |-- GeographicField32B: integer (nullable = true)\n",
      " |-- GeographicField33A: integer (nullable = true)\n",
      " |-- GeographicField33B: integer (nullable = true)\n",
      " |-- GeographicField34A: integer (nullable = true)\n",
      " |-- GeographicField34B: integer (nullable = true)\n",
      " |-- GeographicField35A: integer (nullable = true)\n",
      " |-- GeographicField35B: integer (nullable = true)\n",
      " |-- GeographicField36A: integer (nullable = true)\n",
      " |-- GeographicField36B: integer (nullable = true)\n",
      " |-- GeographicField37A: integer (nullable = true)\n",
      " |-- GeographicField37B: integer (nullable = true)\n",
      " |-- GeographicField38A: integer (nullable = true)\n",
      " |-- GeographicField38B: integer (nullable = true)\n",
      " |-- GeographicField39A: integer (nullable = true)\n",
      " |-- GeographicField39B: integer (nullable = true)\n",
      " |-- GeographicField40A: integer (nullable = true)\n",
      " |-- GeographicField40B: integer (nullable = true)\n",
      " |-- GeographicField41A: integer (nullable = true)\n",
      " |-- GeographicField41B: integer (nullable = true)\n",
      " |-- GeographicField42A: integer (nullable = true)\n",
      " |-- GeographicField42B: integer (nullable = true)\n",
      " |-- GeographicField43A: integer (nullable = true)\n",
      " |-- GeographicField43B: integer (nullable = true)\n",
      " |-- GeographicField44A: integer (nullable = true)\n",
      " |-- GeographicField44B: integer (nullable = true)\n",
      " |-- GeographicField45A: integer (nullable = true)\n",
      " |-- GeographicField45B: integer (nullable = true)\n",
      " |-- GeographicField46A: integer (nullable = true)\n",
      " |-- GeographicField46B: integer (nullable = true)\n",
      " |-- GeographicField47A: integer (nullable = true)\n",
      " |-- GeographicField47B: integer (nullable = true)\n",
      " |-- GeographicField48A: integer (nullable = true)\n",
      " |-- GeographicField48B: integer (nullable = true)\n",
      " |-- GeographicField49A: integer (nullable = true)\n",
      " |-- GeographicField49B: integer (nullable = true)\n",
      " |-- GeographicField50A: integer (nullable = true)\n",
      " |-- GeographicField50B: integer (nullable = true)\n",
      " |-- GeographicField51A: integer (nullable = true)\n",
      " |-- GeographicField51B: integer (nullable = true)\n",
      " |-- GeographicField52A: integer (nullable = true)\n",
      " |-- GeographicField52B: integer (nullable = true)\n",
      " |-- GeographicField53A: integer (nullable = true)\n",
      " |-- GeographicField53B: integer (nullable = true)\n",
      " |-- GeographicField54A: integer (nullable = true)\n",
      " |-- GeographicField54B: integer (nullable = true)\n",
      " |-- GeographicField55A: integer (nullable = true)\n",
      " |-- GeographicField55B: integer (nullable = true)\n",
      " |-- GeographicField56A: integer (nullable = true)\n",
      " |-- GeographicField56B: integer (nullable = true)\n",
      " |-- GeographicField57A: integer (nullable = true)\n",
      " |-- GeographicField57B: integer (nullable = true)\n",
      " |-- GeographicField58A: integer (nullable = true)\n",
      " |-- GeographicField58B: integer (nullable = true)\n",
      " |-- GeographicField59A: integer (nullable = true)\n",
      " |-- GeographicField59B: integer (nullable = true)\n",
      " |-- GeographicField60A: integer (nullable = true)\n",
      " |-- GeographicField60B: integer (nullable = true)\n",
      " |-- GeographicField61A: integer (nullable = true)\n",
      " |-- GeographicField61B: integer (nullable = true)\n",
      " |-- GeographicField62A: integer (nullable = true)\n",
      " |-- GeographicField62B: integer (nullable = true)\n",
      " |-- GeographicField63: string (nullable = true)\n",
      " |-- GeographicField64: string (nullable = true)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(299, None)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print Schema\n",
    "len(df.columns), df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- QuoteNumber: integer (nullable = true)\n",
      " |-- Original_Quote_Date: string (nullable = true)\n",
      " |-- Field6: string (nullable = true)\n",
      " |-- Field7: integer (nullable = true)\n",
      " |-- Field8: double (nullable = true)\n",
      " |-- Field9: double (nullable = true)\n",
      " |-- Field10: integer (nullable = true)\n",
      " |-- Field11: double (nullable = true)\n",
      " |-- Field12: string (nullable = true)\n",
      " |-- CoverageField1A: integer (nullable = true)\n",
      " |-- CoverageField1B: integer (nullable = true)\n",
      " |-- CoverageField2A: integer (nullable = true)\n",
      " |-- CoverageField2B: integer (nullable = true)\n",
      " |-- CoverageField3A: integer (nullable = true)\n",
      " |-- CoverageField3B: integer (nullable = true)\n",
      " |-- CoverageField4A: integer (nullable = true)\n",
      " |-- CoverageField4B: integer (nullable = true)\n",
      " |-- CoverageField5A: integer (nullable = true)\n",
      " |-- CoverageField5B: integer (nullable = true)\n",
      " |-- CoverageField6A: integer (nullable = true)\n",
      " |-- CoverageField6B: integer (nullable = true)\n",
      " |-- CoverageField8: string (nullable = true)\n",
      " |-- CoverageField9: string (nullable = true)\n",
      " |-- CoverageField11A: integer (nullable = true)\n",
      " |-- CoverageField11B: integer (nullable = true)\n",
      " |-- SalesField1A: integer (nullable = true)\n",
      " |-- SalesField1B: integer (nullable = true)\n",
      " |-- SalesField2A: integer (nullable = true)\n",
      " |-- SalesField2B: integer (nullable = true)\n",
      " |-- SalesField3: integer (nullable = true)\n",
      " |-- SalesField4: integer (nullable = true)\n",
      " |-- SalesField5: integer (nullable = true)\n",
      " |-- SalesField6: integer (nullable = true)\n",
      " |-- SalesField7: string (nullable = true)\n",
      " |-- SalesField8: integer (nullable = true)\n",
      " |-- SalesField9: integer (nullable = true)\n",
      " |-- SalesField10: integer (nullable = true)\n",
      " |-- SalesField11: integer (nullable = true)\n",
      " |-- SalesField12: integer (nullable = true)\n",
      " |-- SalesField13: integer (nullable = true)\n",
      " |-- SalesField14: integer (nullable = true)\n",
      " |-- SalesField15: integer (nullable = true)\n",
      " |-- PersonalField1: integer (nullable = true)\n",
      " |-- PersonalField2: integer (nullable = true)\n",
      " |-- PersonalField4A: integer (nullable = true)\n",
      " |-- PersonalField4B: integer (nullable = true)\n",
      " |-- PersonalField5: integer (nullable = true)\n",
      " |-- PersonalField6: integer (nullable = true)\n",
      " |-- PersonalField7: string (nullable = true)\n",
      " |-- PersonalField8: integer (nullable = true)\n",
      " |-- PersonalField9: integer (nullable = true)\n",
      " |-- PersonalField10A: integer (nullable = true)\n",
      " |-- PersonalField10B: integer (nullable = true)\n",
      " |-- PersonalField11: integer (nullable = true)\n",
      " |-- PersonalField12: integer (nullable = true)\n",
      " |-- PersonalField13: integer (nullable = true)\n",
      " |-- PersonalField14: integer (nullable = true)\n",
      " |-- PersonalField15: integer (nullable = true)\n",
      " |-- PersonalField16: string (nullable = true)\n",
      " |-- PersonalField17: string (nullable = true)\n",
      " |-- PersonalField18: string (nullable = true)\n",
      " |-- PersonalField19: string (nullable = true)\n",
      " |-- PersonalField22: integer (nullable = true)\n",
      " |-- PersonalField23: integer (nullable = true)\n",
      " |-- PersonalField24: integer (nullable = true)\n",
      " |-- PersonalField25: integer (nullable = true)\n",
      " |-- PersonalField26: integer (nullable = true)\n",
      " |-- PersonalField27: integer (nullable = true)\n",
      " |-- PersonalField28: integer (nullable = true)\n",
      " |-- PersonalField29: integer (nullable = true)\n",
      " |-- PersonalField30: integer (nullable = true)\n",
      " |-- PersonalField31: integer (nullable = true)\n",
      " |-- PersonalField32: integer (nullable = true)\n",
      " |-- PersonalField33: integer (nullable = true)\n",
      " |-- PersonalField34: integer (nullable = true)\n",
      " |-- PersonalField35: integer (nullable = true)\n",
      " |-- PersonalField36: integer (nullable = true)\n",
      " |-- PersonalField37: integer (nullable = true)\n",
      " |-- PersonalField38: integer (nullable = true)\n",
      " |-- PersonalField39: integer (nullable = true)\n",
      " |-- PersonalField40: integer (nullable = true)\n",
      " |-- PersonalField41: integer (nullable = true)\n",
      " |-- PersonalField42: integer (nullable = true)\n",
      " |-- PersonalField43: integer (nullable = true)\n",
      " |-- PersonalField44: integer (nullable = true)\n",
      " |-- PersonalField45: integer (nullable = true)\n",
      " |-- PersonalField46: integer (nullable = true)\n",
      " |-- PersonalField47: integer (nullable = true)\n",
      " |-- PersonalField48: integer (nullable = true)\n",
      " |-- PersonalField49: integer (nullable = true)\n",
      " |-- PersonalField50: integer (nullable = true)\n",
      " |-- PersonalField51: integer (nullable = true)\n",
      " |-- PersonalField52: integer (nullable = true)\n",
      " |-- PersonalField53: integer (nullable = true)\n",
      " |-- PersonalField54: integer (nullable = true)\n",
      " |-- PersonalField55: integer (nullable = true)\n",
      " |-- PersonalField56: integer (nullable = true)\n",
      " |-- PersonalField57: integer (nullable = true)\n",
      " |-- PersonalField58: integer (nullable = true)\n",
      " |-- PersonalField59: integer (nullable = true)\n",
      " |-- PersonalField60: integer (nullable = true)\n",
      " |-- PersonalField61: integer (nullable = true)\n",
      " |-- PersonalField62: integer (nullable = true)\n",
      " |-- PersonalField63: integer (nullable = true)\n",
      " |-- PersonalField64: integer (nullable = true)\n",
      " |-- PersonalField65: integer (nullable = true)\n",
      " |-- PersonalField66: integer (nullable = true)\n",
      " |-- PersonalField67: integer (nullable = true)\n",
      " |-- PersonalField68: integer (nullable = true)\n",
      " |-- PersonalField69: integer (nullable = true)\n",
      " |-- PersonalField70: integer (nullable = true)\n",
      " |-- PersonalField71: integer (nullable = true)\n",
      " |-- PersonalField72: integer (nullable = true)\n",
      " |-- PersonalField73: integer (nullable = true)\n",
      " |-- PersonalField74: integer (nullable = true)\n",
      " |-- PersonalField75: integer (nullable = true)\n",
      " |-- PersonalField76: integer (nullable = true)\n",
      " |-- PersonalField77: integer (nullable = true)\n",
      " |-- PersonalField78: integer (nullable = true)\n",
      " |-- PersonalField79: integer (nullable = true)\n",
      " |-- PersonalField80: integer (nullable = true)\n",
      " |-- PersonalField81: integer (nullable = true)\n",
      " |-- PersonalField82: integer (nullable = true)\n",
      " |-- PersonalField83: integer (nullable = true)\n",
      " |-- PersonalField84: integer (nullable = true)\n",
      " |-- PropertyField1A: integer (nullable = true)\n",
      " |-- PropertyField1B: integer (nullable = true)\n",
      " |-- PropertyField2A: integer (nullable = true)\n",
      " |-- PropertyField2B: integer (nullable = true)\n",
      " |-- PropertyField3: string (nullable = true)\n",
      " |-- PropertyField4: string (nullable = true)\n",
      " |-- PropertyField5: string (nullable = true)\n",
      " |-- PropertyField6: integer (nullable = true)\n",
      " |-- PropertyField7: string (nullable = true)\n",
      " |-- PropertyField8: integer (nullable = true)\n",
      " |-- PropertyField9: integer (nullable = true)\n",
      " |-- PropertyField10: integer (nullable = true)\n",
      " |-- PropertyField11A: integer (nullable = true)\n",
      " |-- PropertyField11B: integer (nullable = true)\n",
      " |-- PropertyField12: integer (nullable = true)\n",
      " |-- PropertyField13: integer (nullable = true)\n",
      " |-- PropertyField14: string (nullable = true)\n",
      " |-- PropertyField15: integer (nullable = true)\n",
      " |-- PropertyField16A: integer (nullable = true)\n",
      " |-- PropertyField16B: integer (nullable = true)\n",
      " |-- PropertyField17: integer (nullable = true)\n",
      " |-- PropertyField18: integer (nullable = true)\n",
      " |-- PropertyField19: integer (nullable = true)\n",
      " |-- PropertyField20: integer (nullable = true)\n",
      " |-- PropertyField21A: integer (nullable = true)\n",
      " |-- PropertyField21B: integer (nullable = true)\n",
      " |-- PropertyField22: integer (nullable = true)\n",
      " |-- PropertyField23: integer (nullable = true)\n",
      " |-- PropertyField24A: integer (nullable = true)\n",
      " |-- PropertyField24B: integer (nullable = true)\n",
      " |-- PropertyField25: double (nullable = true)\n",
      " |-- PropertyField26A: integer (nullable = true)\n",
      " |-- PropertyField26B: integer (nullable = true)\n",
      " |-- PropertyField27: integer (nullable = true)\n",
      " |-- PropertyField28: string (nullable = true)\n",
      " |-- PropertyField29: integer (nullable = true)\n",
      " |-- PropertyField30: string (nullable = true)\n",
      " |-- PropertyField31: string (nullable = true)\n",
      " |-- PropertyField32: string (nullable = true)\n",
      " |-- PropertyField33: string (nullable = true)\n",
      " |-- PropertyField34: string (nullable = true)\n",
      " |-- PropertyField35: integer (nullable = true)\n",
      " |-- PropertyField36: string (nullable = true)\n",
      " |-- PropertyField37: string (nullable = true)\n",
      " |-- PropertyField38: string (nullable = true)\n",
      " |-- PropertyField39A: integer (nullable = true)\n",
      " |-- PropertyField39B: integer (nullable = true)\n",
      " |-- GeographicField1A: integer (nullable = true)\n",
      " |-- GeographicField1B: integer (nullable = true)\n",
      " |-- GeographicField2A: integer (nullable = true)\n",
      " |-- GeographicField2B: integer (nullable = true)\n",
      " |-- GeographicField3A: integer (nullable = true)\n",
      " |-- GeographicField3B: integer (nullable = true)\n",
      " |-- GeographicField4A: integer (nullable = true)\n",
      " |-- GeographicField4B: integer (nullable = true)\n",
      " |-- GeographicField5A: integer (nullable = true)\n",
      " |-- GeographicField5B: integer (nullable = true)\n",
      " |-- GeographicField6A: integer (nullable = true)\n",
      " |-- GeographicField6B: integer (nullable = true)\n",
      " |-- GeographicField7A: integer (nullable = true)\n",
      " |-- GeographicField7B: integer (nullable = true)\n",
      " |-- GeographicField8A: integer (nullable = true)\n",
      " |-- GeographicField8B: integer (nullable = true)\n",
      " |-- GeographicField9A: integer (nullable = true)\n",
      " |-- GeographicField9B: integer (nullable = true)\n",
      " |-- GeographicField10A: integer (nullable = true)\n",
      " |-- GeographicField10B: integer (nullable = true)\n",
      " |-- GeographicField11A: integer (nullable = true)\n",
      " |-- GeographicField11B: integer (nullable = true)\n",
      " |-- GeographicField12A: integer (nullable = true)\n",
      " |-- GeographicField12B: integer (nullable = true)\n",
      " |-- GeographicField13A: integer (nullable = true)\n",
      " |-- GeographicField13B: integer (nullable = true)\n",
      " |-- GeographicField14A: integer (nullable = true)\n",
      " |-- GeographicField14B: integer (nullable = true)\n",
      " |-- GeographicField15A: integer (nullable = true)\n",
      " |-- GeographicField15B: integer (nullable = true)\n",
      " |-- GeographicField16A: integer (nullable = true)\n",
      " |-- GeographicField16B: integer (nullable = true)\n",
      " |-- GeographicField17A: integer (nullable = true)\n",
      " |-- GeographicField17B: integer (nullable = true)\n",
      " |-- GeographicField18A: integer (nullable = true)\n",
      " |-- GeographicField18B: integer (nullable = true)\n",
      " |-- GeographicField19A: integer (nullable = true)\n",
      " |-- GeographicField19B: integer (nullable = true)\n",
      " |-- GeographicField20A: integer (nullable = true)\n",
      " |-- GeographicField20B: integer (nullable = true)\n",
      " |-- GeographicField21A: integer (nullable = true)\n",
      " |-- GeographicField21B: integer (nullable = true)\n",
      " |-- GeographicField22A: integer (nullable = true)\n",
      " |-- GeographicField22B: integer (nullable = true)\n",
      " |-- GeographicField23A: integer (nullable = true)\n",
      " |-- GeographicField23B: integer (nullable = true)\n",
      " |-- GeographicField24A: integer (nullable = true)\n",
      " |-- GeographicField24B: integer (nullable = true)\n",
      " |-- GeographicField25A: integer (nullable = true)\n",
      " |-- GeographicField25B: integer (nullable = true)\n",
      " |-- GeographicField26A: integer (nullable = true)\n",
      " |-- GeographicField26B: integer (nullable = true)\n",
      " |-- GeographicField27A: integer (nullable = true)\n",
      " |-- GeographicField27B: integer (nullable = true)\n",
      " |-- GeographicField28A: integer (nullable = true)\n",
      " |-- GeographicField28B: integer (nullable = true)\n",
      " |-- GeographicField29A: integer (nullable = true)\n",
      " |-- GeographicField29B: integer (nullable = true)\n",
      " |-- GeographicField30A: integer (nullable = true)\n",
      " |-- GeographicField30B: integer (nullable = true)\n",
      " |-- GeographicField31A: integer (nullable = true)\n",
      " |-- GeographicField31B: integer (nullable = true)\n",
      " |-- GeographicField32A: integer (nullable = true)\n",
      " |-- GeographicField32B: integer (nullable = true)\n",
      " |-- GeographicField33A: integer (nullable = true)\n",
      " |-- GeographicField33B: integer (nullable = true)\n",
      " |-- GeographicField34A: integer (nullable = true)\n",
      " |-- GeographicField34B: integer (nullable = true)\n",
      " |-- GeographicField35A: integer (nullable = true)\n",
      " |-- GeographicField35B: integer (nullable = true)\n",
      " |-- GeographicField36A: integer (nullable = true)\n",
      " |-- GeographicField36B: integer (nullable = true)\n",
      " |-- GeographicField37A: integer (nullable = true)\n",
      " |-- GeographicField37B: integer (nullable = true)\n",
      " |-- GeographicField38A: integer (nullable = true)\n",
      " |-- GeographicField38B: integer (nullable = true)\n",
      " |-- GeographicField39A: integer (nullable = true)\n",
      " |-- GeographicField39B: integer (nullable = true)\n",
      " |-- GeographicField40A: integer (nullable = true)\n",
      " |-- GeographicField40B: integer (nullable = true)\n",
      " |-- GeographicField41A: integer (nullable = true)\n",
      " |-- GeographicField41B: integer (nullable = true)\n",
      " |-- GeographicField42A: integer (nullable = true)\n",
      " |-- GeographicField42B: integer (nullable = true)\n",
      " |-- GeographicField43A: integer (nullable = true)\n",
      " |-- GeographicField43B: integer (nullable = true)\n",
      " |-- GeographicField44A: integer (nullable = true)\n",
      " |-- GeographicField44B: integer (nullable = true)\n",
      " |-- GeographicField45A: integer (nullable = true)\n",
      " |-- GeographicField45B: integer (nullable = true)\n",
      " |-- GeographicField46A: integer (nullable = true)\n",
      " |-- GeographicField46B: integer (nullable = true)\n",
      " |-- GeographicField47A: integer (nullable = true)\n",
      " |-- GeographicField47B: integer (nullable = true)\n",
      " |-- GeographicField48A: integer (nullable = true)\n",
      " |-- GeographicField48B: integer (nullable = true)\n",
      " |-- GeographicField49A: integer (nullable = true)\n",
      " |-- GeographicField49B: integer (nullable = true)\n",
      " |-- GeographicField50A: integer (nullable = true)\n",
      " |-- GeographicField50B: integer (nullable = true)\n",
      " |-- GeographicField51A: integer (nullable = true)\n",
      " |-- GeographicField51B: integer (nullable = true)\n",
      " |-- GeographicField52A: integer (nullable = true)\n",
      " |-- GeographicField52B: integer (nullable = true)\n",
      " |-- GeographicField53A: integer (nullable = true)\n",
      " |-- GeographicField53B: integer (nullable = true)\n",
      " |-- GeographicField54A: integer (nullable = true)\n",
      " |-- GeographicField54B: integer (nullable = true)\n",
      " |-- GeographicField55A: integer (nullable = true)\n",
      " |-- GeographicField55B: integer (nullable = true)\n",
      " |-- GeographicField56A: integer (nullable = true)\n",
      " |-- GeographicField56B: integer (nullable = true)\n",
      " |-- GeographicField57A: integer (nullable = true)\n",
      " |-- GeographicField57B: integer (nullable = true)\n",
      " |-- GeographicField58A: integer (nullable = true)\n",
      " |-- GeographicField58B: integer (nullable = true)\n",
      " |-- GeographicField59A: integer (nullable = true)\n",
      " |-- GeographicField59B: integer (nullable = true)\n",
      " |-- GeographicField60A: integer (nullable = true)\n",
      " |-- GeographicField60B: integer (nullable = true)\n",
      " |-- GeographicField61A: integer (nullable = true)\n",
      " |-- GeographicField61B: integer (nullable = true)\n",
      " |-- GeographicField62A: integer (nullable = true)\n",
      " |-- GeographicField62B: integer (nullable = true)\n",
      " |-- GeographicField63: string (nullable = true)\n",
      " |-- GeographicField64: string (nullable = true)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, 298)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.printSchema(), len (test_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#rename Target to 'label in data train\n",
    "df = df.withColumnRenamed('QuoteConversion_Flag','label')\n",
    "#rename Id number ('QuoteNumber') to 'Id' in data train\n",
    "df = df.withColumnRenamed('QuoteNumber','Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#rename Id number ('QuoteNumber') to 'Id' in data test\n",
    "test_data = test_data.withColumnRenamed('QuoteNumber','Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- QuoteNumber: integer (nullable = true)\n",
      " |-- Original_Quote_Date: string (nullable = true)\n",
      " |-- label: integer (nullable = true)\n",
      " |-- Field6: string (nullable = true)\n",
      " |-- Field7: integer (nullable = true)\n",
      " |-- Field8: double (nullable = true)\n",
      " |-- Field9: double (nullable = true)\n",
      " |-- Field10: integer (nullable = true)\n",
      " |-- Field11: double (nullable = true)\n",
      " |-- Field12: string (nullable = true)\n",
      " |-- CoverageField1A: integer (nullable = true)\n",
      " |-- CoverageField1B: integer (nullable = true)\n",
      " |-- CoverageField2A: integer (nullable = true)\n",
      " |-- CoverageField2B: integer (nullable = true)\n",
      " |-- CoverageField3A: integer (nullable = true)\n",
      " |-- CoverageField3B: integer (nullable = true)\n",
      " |-- CoverageField4A: integer (nullable = true)\n",
      " |-- CoverageField4B: integer (nullable = true)\n",
      " |-- CoverageField5A: integer (nullable = true)\n",
      " |-- CoverageField5B: integer (nullable = true)\n",
      " |-- CoverageField6A: integer (nullable = true)\n",
      " |-- CoverageField6B: integer (nullable = true)\n",
      " |-- CoverageField8: string (nullable = true)\n",
      " |-- CoverageField9: string (nullable = true)\n",
      " |-- CoverageField11A: integer (nullable = true)\n",
      " |-- CoverageField11B: integer (nullable = true)\n",
      " |-- SalesField1A: integer (nullable = true)\n",
      " |-- SalesField1B: integer (nullable = true)\n",
      " |-- SalesField2A: integer (nullable = true)\n",
      " |-- SalesField2B: integer (nullable = true)\n",
      " |-- SalesField3: integer (nullable = true)\n",
      " |-- SalesField4: integer (nullable = true)\n",
      " |-- SalesField5: integer (nullable = true)\n",
      " |-- SalesField6: integer (nullable = true)\n",
      " |-- SalesField7: string (nullable = true)\n",
      " |-- SalesField8: integer (nullable = true)\n",
      " |-- SalesField9: integer (nullable = true)\n",
      " |-- SalesField10: integer (nullable = true)\n",
      " |-- SalesField11: integer (nullable = true)\n",
      " |-- SalesField12: integer (nullable = true)\n",
      " |-- SalesField13: integer (nullable = true)\n",
      " |-- SalesField14: integer (nullable = true)\n",
      " |-- SalesField15: integer (nullable = true)\n",
      " |-- PersonalField1: integer (nullable = true)\n",
      " |-- PersonalField2: integer (nullable = true)\n",
      " |-- PersonalField4A: integer (nullable = true)\n",
      " |-- PersonalField4B: integer (nullable = true)\n",
      " |-- PersonalField5: integer (nullable = true)\n",
      " |-- PersonalField6: integer (nullable = true)\n",
      " |-- PersonalField7: string (nullable = true)\n",
      " |-- PersonalField8: integer (nullable = true)\n",
      " |-- PersonalField9: integer (nullable = true)\n",
      " |-- PersonalField10A: integer (nullable = true)\n",
      " |-- PersonalField10B: integer (nullable = true)\n",
      " |-- PersonalField11: integer (nullable = true)\n",
      " |-- PersonalField12: integer (nullable = true)\n",
      " |-- PersonalField13: integer (nullable = true)\n",
      " |-- PersonalField14: integer (nullable = true)\n",
      " |-- PersonalField15: integer (nullable = true)\n",
      " |-- PersonalField16: string (nullable = true)\n",
      " |-- PersonalField17: string (nullable = true)\n",
      " |-- PersonalField18: string (nullable = true)\n",
      " |-- PersonalField19: string (nullable = true)\n",
      " |-- PersonalField22: integer (nullable = true)\n",
      " |-- PersonalField23: integer (nullable = true)\n",
      " |-- PersonalField24: integer (nullable = true)\n",
      " |-- PersonalField25: integer (nullable = true)\n",
      " |-- PersonalField26: integer (nullable = true)\n",
      " |-- PersonalField27: integer (nullable = true)\n",
      " |-- PersonalField28: integer (nullable = true)\n",
      " |-- PersonalField29: integer (nullable = true)\n",
      " |-- PersonalField30: integer (nullable = true)\n",
      " |-- PersonalField31: integer (nullable = true)\n",
      " |-- PersonalField32: integer (nullable = true)\n",
      " |-- PersonalField33: integer (nullable = true)\n",
      " |-- PersonalField34: integer (nullable = true)\n",
      " |-- PersonalField35: integer (nullable = true)\n",
      " |-- PersonalField36: integer (nullable = true)\n",
      " |-- PersonalField37: integer (nullable = true)\n",
      " |-- PersonalField38: integer (nullable = true)\n",
      " |-- PersonalField39: integer (nullable = true)\n",
      " |-- PersonalField40: integer (nullable = true)\n",
      " |-- PersonalField41: integer (nullable = true)\n",
      " |-- PersonalField42: integer (nullable = true)\n",
      " |-- PersonalField43: integer (nullable = true)\n",
      " |-- PersonalField44: integer (nullable = true)\n",
      " |-- PersonalField45: integer (nullable = true)\n",
      " |-- PersonalField46: integer (nullable = true)\n",
      " |-- PersonalField47: integer (nullable = true)\n",
      " |-- PersonalField48: integer (nullable = true)\n",
      " |-- PersonalField49: integer (nullable = true)\n",
      " |-- PersonalField50: integer (nullable = true)\n",
      " |-- PersonalField51: integer (nullable = true)\n",
      " |-- PersonalField52: integer (nullable = true)\n",
      " |-- PersonalField53: integer (nullable = true)\n",
      " |-- PersonalField54: integer (nullable = true)\n",
      " |-- PersonalField55: integer (nullable = true)\n",
      " |-- PersonalField56: integer (nullable = true)\n",
      " |-- PersonalField57: integer (nullable = true)\n",
      " |-- PersonalField58: integer (nullable = true)\n",
      " |-- PersonalField59: integer (nullable = true)\n",
      " |-- PersonalField60: integer (nullable = true)\n",
      " |-- PersonalField61: integer (nullable = true)\n",
      " |-- PersonalField62: integer (nullable = true)\n",
      " |-- PersonalField63: integer (nullable = true)\n",
      " |-- PersonalField64: integer (nullable = true)\n",
      " |-- PersonalField65: integer (nullable = true)\n",
      " |-- PersonalField66: integer (nullable = true)\n",
      " |-- PersonalField67: integer (nullable = true)\n",
      " |-- PersonalField68: integer (nullable = true)\n",
      " |-- PersonalField69: integer (nullable = true)\n",
      " |-- PersonalField70: integer (nullable = true)\n",
      " |-- PersonalField71: integer (nullable = true)\n",
      " |-- PersonalField72: integer (nullable = true)\n",
      " |-- PersonalField73: integer (nullable = true)\n",
      " |-- PersonalField74: integer (nullable = true)\n",
      " |-- PersonalField75: integer (nullable = true)\n",
      " |-- PersonalField76: integer (nullable = true)\n",
      " |-- PersonalField77: integer (nullable = true)\n",
      " |-- PersonalField78: integer (nullable = true)\n",
      " |-- PersonalField79: integer (nullable = true)\n",
      " |-- PersonalField80: integer (nullable = true)\n",
      " |-- PersonalField81: integer (nullable = true)\n",
      " |-- PersonalField82: integer (nullable = true)\n",
      " |-- PersonalField83: integer (nullable = true)\n",
      " |-- PersonalField84: integer (nullable = true)\n",
      " |-- PropertyField1A: integer (nullable = true)\n",
      " |-- PropertyField1B: integer (nullable = true)\n",
      " |-- PropertyField2A: integer (nullable = true)\n",
      " |-- PropertyField2B: integer (nullable = true)\n",
      " |-- PropertyField3: string (nullable = true)\n",
      " |-- PropertyField4: string (nullable = true)\n",
      " |-- PropertyField5: string (nullable = true)\n",
      " |-- PropertyField6: integer (nullable = true)\n",
      " |-- PropertyField7: string (nullable = true)\n",
      " |-- PropertyField8: integer (nullable = true)\n",
      " |-- PropertyField9: integer (nullable = true)\n",
      " |-- PropertyField10: integer (nullable = true)\n",
      " |-- PropertyField11A: integer (nullable = true)\n",
      " |-- PropertyField11B: integer (nullable = true)\n",
      " |-- PropertyField12: integer (nullable = true)\n",
      " |-- PropertyField13: integer (nullable = true)\n",
      " |-- PropertyField14: string (nullable = true)\n",
      " |-- PropertyField15: integer (nullable = true)\n",
      " |-- PropertyField16A: integer (nullable = true)\n",
      " |-- PropertyField16B: integer (nullable = true)\n",
      " |-- PropertyField17: integer (nullable = true)\n",
      " |-- PropertyField18: integer (nullable = true)\n",
      " |-- PropertyField19: integer (nullable = true)\n",
      " |-- PropertyField20: integer (nullable = true)\n",
      " |-- PropertyField21A: integer (nullable = true)\n",
      " |-- PropertyField21B: integer (nullable = true)\n",
      " |-- PropertyField22: integer (nullable = true)\n",
      " |-- PropertyField23: integer (nullable = true)\n",
      " |-- PropertyField24A: integer (nullable = true)\n",
      " |-- PropertyField24B: integer (nullable = true)\n",
      " |-- PropertyField25: double (nullable = true)\n",
      " |-- PropertyField26A: integer (nullable = true)\n",
      " |-- PropertyField26B: integer (nullable = true)\n",
      " |-- PropertyField27: integer (nullable = true)\n",
      " |-- PropertyField28: string (nullable = true)\n",
      " |-- PropertyField29: integer (nullable = true)\n",
      " |-- PropertyField30: string (nullable = true)\n",
      " |-- PropertyField31: string (nullable = true)\n",
      " |-- PropertyField32: string (nullable = true)\n",
      " |-- PropertyField33: string (nullable = true)\n",
      " |-- PropertyField34: string (nullable = true)\n",
      " |-- PropertyField35: integer (nullable = true)\n",
      " |-- PropertyField36: string (nullable = true)\n",
      " |-- PropertyField37: string (nullable = true)\n",
      " |-- PropertyField38: string (nullable = true)\n",
      " |-- PropertyField39A: integer (nullable = true)\n",
      " |-- PropertyField39B: integer (nullable = true)\n",
      " |-- GeographicField1A: integer (nullable = true)\n",
      " |-- GeographicField1B: integer (nullable = true)\n",
      " |-- GeographicField2A: integer (nullable = true)\n",
      " |-- GeographicField2B: integer (nullable = true)\n",
      " |-- GeographicField3A: integer (nullable = true)\n",
      " |-- GeographicField3B: integer (nullable = true)\n",
      " |-- GeographicField4A: integer (nullable = true)\n",
      " |-- GeographicField4B: integer (nullable = true)\n",
      " |-- GeographicField5A: integer (nullable = true)\n",
      " |-- GeographicField5B: integer (nullable = true)\n",
      " |-- GeographicField6A: integer (nullable = true)\n",
      " |-- GeographicField6B: integer (nullable = true)\n",
      " |-- GeographicField7A: integer (nullable = true)\n",
      " |-- GeographicField7B: integer (nullable = true)\n",
      " |-- GeographicField8A: integer (nullable = true)\n",
      " |-- GeographicField8B: integer (nullable = true)\n",
      " |-- GeographicField9A: integer (nullable = true)\n",
      " |-- GeographicField9B: integer (nullable = true)\n",
      " |-- GeographicField10A: integer (nullable = true)\n",
      " |-- GeographicField10B: integer (nullable = true)\n",
      " |-- GeographicField11A: integer (nullable = true)\n",
      " |-- GeographicField11B: integer (nullable = true)\n",
      " |-- GeographicField12A: integer (nullable = true)\n",
      " |-- GeographicField12B: integer (nullable = true)\n",
      " |-- GeographicField13A: integer (nullable = true)\n",
      " |-- GeographicField13B: integer (nullable = true)\n",
      " |-- GeographicField14A: integer (nullable = true)\n",
      " |-- GeographicField14B: integer (nullable = true)\n",
      " |-- GeographicField15A: integer (nullable = true)\n",
      " |-- GeographicField15B: integer (nullable = true)\n",
      " |-- GeographicField16A: integer (nullable = true)\n",
      " |-- GeographicField16B: integer (nullable = true)\n",
      " |-- GeographicField17A: integer (nullable = true)\n",
      " |-- GeographicField17B: integer (nullable = true)\n",
      " |-- GeographicField18A: integer (nullable = true)\n",
      " |-- GeographicField18B: integer (nullable = true)\n",
      " |-- GeographicField19A: integer (nullable = true)\n",
      " |-- GeographicField19B: integer (nullable = true)\n",
      " |-- GeographicField20A: integer (nullable = true)\n",
      " |-- GeographicField20B: integer (nullable = true)\n",
      " |-- GeographicField21A: integer (nullable = true)\n",
      " |-- GeographicField21B: integer (nullable = true)\n",
      " |-- GeographicField22A: integer (nullable = true)\n",
      " |-- GeographicField22B: integer (nullable = true)\n",
      " |-- GeographicField23A: integer (nullable = true)\n",
      " |-- GeographicField23B: integer (nullable = true)\n",
      " |-- GeographicField24A: integer (nullable = true)\n",
      " |-- GeographicField24B: integer (nullable = true)\n",
      " |-- GeographicField25A: integer (nullable = true)\n",
      " |-- GeographicField25B: integer (nullable = true)\n",
      " |-- GeographicField26A: integer (nullable = true)\n",
      " |-- GeographicField26B: integer (nullable = true)\n",
      " |-- GeographicField27A: integer (nullable = true)\n",
      " |-- GeographicField27B: integer (nullable = true)\n",
      " |-- GeographicField28A: integer (nullable = true)\n",
      " |-- GeographicField28B: integer (nullable = true)\n",
      " |-- GeographicField29A: integer (nullable = true)\n",
      " |-- GeographicField29B: integer (nullable = true)\n",
      " |-- GeographicField30A: integer (nullable = true)\n",
      " |-- GeographicField30B: integer (nullable = true)\n",
      " |-- GeographicField31A: integer (nullable = true)\n",
      " |-- GeographicField31B: integer (nullable = true)\n",
      " |-- GeographicField32A: integer (nullable = true)\n",
      " |-- GeographicField32B: integer (nullable = true)\n",
      " |-- GeographicField33A: integer (nullable = true)\n",
      " |-- GeographicField33B: integer (nullable = true)\n",
      " |-- GeographicField34A: integer (nullable = true)\n",
      " |-- GeographicField34B: integer (nullable = true)\n",
      " |-- GeographicField35A: integer (nullable = true)\n",
      " |-- GeographicField35B: integer (nullable = true)\n",
      " |-- GeographicField36A: integer (nullable = true)\n",
      " |-- GeographicField36B: integer (nullable = true)\n",
      " |-- GeographicField37A: integer (nullable = true)\n",
      " |-- GeographicField37B: integer (nullable = true)\n",
      " |-- GeographicField38A: integer (nullable = true)\n",
      " |-- GeographicField38B: integer (nullable = true)\n",
      " |-- GeographicField39A: integer (nullable = true)\n",
      " |-- GeographicField39B: integer (nullable = true)\n",
      " |-- GeographicField40A: integer (nullable = true)\n",
      " |-- GeographicField40B: integer (nullable = true)\n",
      " |-- GeographicField41A: integer (nullable = true)\n",
      " |-- GeographicField41B: integer (nullable = true)\n",
      " |-- GeographicField42A: integer (nullable = true)\n",
      " |-- GeographicField42B: integer (nullable = true)\n",
      " |-- GeographicField43A: integer (nullable = true)\n",
      " |-- GeographicField43B: integer (nullable = true)\n",
      " |-- GeographicField44A: integer (nullable = true)\n",
      " |-- GeographicField44B: integer (nullable = true)\n",
      " |-- GeographicField45A: integer (nullable = true)\n",
      " |-- GeographicField45B: integer (nullable = true)\n",
      " |-- GeographicField46A: integer (nullable = true)\n",
      " |-- GeographicField46B: integer (nullable = true)\n",
      " |-- GeographicField47A: integer (nullable = true)\n",
      " |-- GeographicField47B: integer (nullable = true)\n",
      " |-- GeographicField48A: integer (nullable = true)\n",
      " |-- GeographicField48B: integer (nullable = true)\n",
      " |-- GeographicField49A: integer (nullable = true)\n",
      " |-- GeographicField49B: integer (nullable = true)\n",
      " |-- GeographicField50A: integer (nullable = true)\n",
      " |-- GeographicField50B: integer (nullable = true)\n",
      " |-- GeographicField51A: integer (nullable = true)\n",
      " |-- GeographicField51B: integer (nullable = true)\n",
      " |-- GeographicField52A: integer (nullable = true)\n",
      " |-- GeographicField52B: integer (nullable = true)\n",
      " |-- GeographicField53A: integer (nullable = true)\n",
      " |-- GeographicField53B: integer (nullable = true)\n",
      " |-- GeographicField54A: integer (nullable = true)\n",
      " |-- GeographicField54B: integer (nullable = true)\n",
      " |-- GeographicField55A: integer (nullable = true)\n",
      " |-- GeographicField55B: integer (nullable = true)\n",
      " |-- GeographicField56A: integer (nullable = true)\n",
      " |-- GeographicField56B: integer (nullable = true)\n",
      " |-- GeographicField57A: integer (nullable = true)\n",
      " |-- GeographicField57B: integer (nullable = true)\n",
      " |-- GeographicField58A: integer (nullable = true)\n",
      " |-- GeographicField58B: integer (nullable = true)\n",
      " |-- GeographicField59A: integer (nullable = true)\n",
      " |-- GeographicField59B: integer (nullable = true)\n",
      " |-- GeographicField60A: integer (nullable = true)\n",
      " |-- GeographicField60B: integer (nullable = true)\n",
      " |-- GeographicField61A: integer (nullable = true)\n",
      " |-- GeographicField61B: integer (nullable = true)\n",
      " |-- GeographicField62A: integer (nullable = true)\n",
      " |-- GeographicField62B: integer (nullable = true)\n",
      " |-- GeographicField63: string (nullable = true)\n",
      " |-- GeographicField64: string (nullable = true)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(299, None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns), df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#drop column Original_Quote_Date from data train\n",
    "df_final=df.drop('Original_Quote_Date')\n",
    "\n",
    "\n",
    "#drop column Original_Quote_Date from data test\n",
    "test_data=test_data.drop('Original_Quote_Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target_percent=df_final.groupBy('label').count().sort(col(\"count\").desc())\\\n",
    "                        .withColumn('total',sum(col('count')).over(window))\\\n",
    "                        .withColumn('Percent',col('count')*100/col('total')) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+------+------+-----------------+\n",
      "|label| count| total|          Percent|\n",
      "+-----+------+------+-----------------+\n",
      "|    0|211859|260753|81.24892139304245|\n",
      "|    1| 48894|260753|18.75107860695754|\n",
      "+-----+------+------+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "target_percent.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### categorical and nummerical variable in df-final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat_cols: ['Field6', 'Field12', 'CoverageField8', 'CoverageField9', 'SalesField7', 'PersonalField7', 'PersonalField16', 'PersonalField17', 'PersonalField18', 'PersonalField19', 'PropertyField3', 'PropertyField4', 'PropertyField5', 'PropertyField7', 'PropertyField14', 'PropertyField28', 'PropertyField30', 'PropertyField31', 'PropertyField32', 'PropertyField33', 'PropertyField34', 'PropertyField36', 'PropertyField37', 'PropertyField38', 'GeographicField63', 'GeographicField64']\n",
      "num_cols: ['QuoteNumber', 'label', 'Field7', 'Field8', 'Field9', 'Field10', 'Field11', 'CoverageField1A', 'CoverageField1B', 'CoverageField2A', 'CoverageField2B', 'CoverageField3A', 'CoverageField3B', 'CoverageField4A', 'CoverageField4B', 'CoverageField5A', 'CoverageField5B', 'CoverageField6A', 'CoverageField6B', 'CoverageField11A', 'CoverageField11B', 'SalesField1A', 'SalesField1B', 'SalesField2A', 'SalesField2B', 'SalesField3', 'SalesField4', 'SalesField5', 'SalesField6', 'SalesField8', 'SalesField9', 'SalesField10', 'SalesField11', 'SalesField12', 'SalesField13', 'SalesField14', 'SalesField15', 'PersonalField1', 'PersonalField2', 'PersonalField4A', 'PersonalField4B', 'PersonalField5', 'PersonalField6', 'PersonalField8', 'PersonalField9', 'PersonalField10A', 'PersonalField10B', 'PersonalField11', 'PersonalField12', 'PersonalField13', 'PersonalField14', 'PersonalField15', 'PersonalField22', 'PersonalField23', 'PersonalField24', 'PersonalField25', 'PersonalField26', 'PersonalField27', 'PersonalField28', 'PersonalField29', 'PersonalField30', 'PersonalField31', 'PersonalField32', 'PersonalField33', 'PersonalField34', 'PersonalField35', 'PersonalField36', 'PersonalField37', 'PersonalField38', 'PersonalField39', 'PersonalField40', 'PersonalField41', 'PersonalField42', 'PersonalField43', 'PersonalField44', 'PersonalField45', 'PersonalField46', 'PersonalField47', 'PersonalField48', 'PersonalField49', 'PersonalField50', 'PersonalField51', 'PersonalField52', 'PersonalField53', 'PersonalField54', 'PersonalField55', 'PersonalField56', 'PersonalField57', 'PersonalField58', 'PersonalField59', 'PersonalField60', 'PersonalField61', 'PersonalField62', 'PersonalField63', 'PersonalField64', 'PersonalField65', 'PersonalField66', 'PersonalField67', 'PersonalField68', 'PersonalField69', 'PersonalField70', 'PersonalField71', 'PersonalField72', 'PersonalField73', 'PersonalField74', 'PersonalField75', 'PersonalField76', 'PersonalField77', 'PersonalField78', 'PersonalField79', 'PersonalField80', 'PersonalField81', 'PersonalField82', 'PersonalField83', 'PersonalField84', 'PropertyField1A', 'PropertyField1B', 'PropertyField2A', 'PropertyField2B', 'PropertyField6', 'PropertyField8', 'PropertyField9', 'PropertyField10', 'PropertyField11A', 'PropertyField11B', 'PropertyField12', 'PropertyField13', 'PropertyField15', 'PropertyField16A', 'PropertyField16B', 'PropertyField17', 'PropertyField18', 'PropertyField19', 'PropertyField20', 'PropertyField21A', 'PropertyField21B', 'PropertyField22', 'PropertyField23', 'PropertyField24A', 'PropertyField24B', 'PropertyField25', 'PropertyField26A', 'PropertyField26B', 'PropertyField27', 'PropertyField29', 'PropertyField35', 'PropertyField39A', 'PropertyField39B', 'GeographicField1A', 'GeographicField1B', 'GeographicField2A', 'GeographicField2B', 'GeographicField3A', 'GeographicField3B', 'GeographicField4A', 'GeographicField4B', 'GeographicField5A', 'GeographicField5B', 'GeographicField6A', 'GeographicField6B', 'GeographicField7A', 'GeographicField7B', 'GeographicField8A', 'GeographicField8B', 'GeographicField9A', 'GeographicField9B', 'GeographicField10A', 'GeographicField10B', 'GeographicField11A', 'GeographicField11B', 'GeographicField12A', 'GeographicField12B', 'GeographicField13A', 'GeographicField13B', 'GeographicField14A', 'GeographicField14B', 'GeographicField15A', 'GeographicField15B', 'GeographicField16A', 'GeographicField16B', 'GeographicField17A', 'GeographicField17B', 'GeographicField18A', 'GeographicField18B', 'GeographicField19A', 'GeographicField19B', 'GeographicField20A', 'GeographicField20B', 'GeographicField21A', 'GeographicField21B', 'GeographicField22A', 'GeographicField22B', 'GeographicField23A', 'GeographicField23B', 'GeographicField24A', 'GeographicField24B', 'GeographicField25A', 'GeographicField25B', 'GeographicField26A', 'GeographicField26B', 'GeographicField27A', 'GeographicField27B', 'GeographicField28A', 'GeographicField28B', 'GeographicField29A', 'GeographicField29B', 'GeographicField30A', 'GeographicField30B', 'GeographicField31A', 'GeographicField31B', 'GeographicField32A', 'GeographicField32B', 'GeographicField33A', 'GeographicField33B', 'GeographicField34A', 'GeographicField34B', 'GeographicField35A', 'GeographicField35B', 'GeographicField36A', 'GeographicField36B', 'GeographicField37A', 'GeographicField37B', 'GeographicField38A', 'GeographicField38B', 'GeographicField39A', 'GeographicField39B', 'GeographicField40A', 'GeographicField40B', 'GeographicField41A', 'GeographicField41B', 'GeographicField42A', 'GeographicField42B', 'GeographicField43A', 'GeographicField43B', 'GeographicField44A', 'GeographicField44B', 'GeographicField45A', 'GeographicField45B', 'GeographicField46A', 'GeographicField46B', 'GeographicField47A', 'GeographicField47B', 'GeographicField48A', 'GeographicField48B', 'GeographicField49A', 'GeographicField49B', 'GeographicField50A', 'GeographicField50B', 'GeographicField51A', 'GeographicField51B', 'GeographicField52A', 'GeographicField52B', 'GeographicField53A', 'GeographicField53B', 'GeographicField54A', 'GeographicField54B', 'GeographicField55A', 'GeographicField55B', 'GeographicField56A', 'GeographicField56B', 'GeographicField57A', 'GeographicField57B', 'GeographicField58A', 'GeographicField58B', 'GeographicField59A', 'GeographicField59B', 'GeographicField60A', 'GeographicField60B', 'GeographicField61A', 'GeographicField61B', 'GeographicField62A', 'GeographicField62B']\n"
     ]
    }
   ],
   "source": [
    "#Categorical and numerical variable\n",
    "#just will select string data type\n",
    "cat_cols = [item[0] for item in df_final.dtypes if item[1].startswith('string')] \n",
    "print(\"cat_cols:\", cat_cols)\n",
    "\n",
    "#just will select integer or double data type\n",
    "num_cols = [item[0] for item in df_final.dtypes if item[1].startswith('int') | item[1].startswith('double')] \n",
    "print(\"num_cols:\", num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_id: QuoteNumber\n",
      "['QuoteNumber']\n",
      "num_cols: ['Field7', 'Field8', 'Field9', 'Field10', 'Field11', 'CoverageField1A', 'CoverageField1B', 'CoverageField2A', 'CoverageField2B', 'CoverageField3A', 'CoverageField3B', 'CoverageField4A', 'CoverageField4B', 'CoverageField5A', 'CoverageField5B', 'CoverageField6A', 'CoverageField6B', 'CoverageField11A', 'CoverageField11B', 'SalesField1A', 'SalesField1B', 'SalesField2A', 'SalesField2B', 'SalesField3', 'SalesField4', 'SalesField5', 'SalesField6', 'SalesField8', 'SalesField9', 'SalesField10', 'SalesField11', 'SalesField12', 'SalesField13', 'SalesField14', 'SalesField15', 'PersonalField1', 'PersonalField2', 'PersonalField4A', 'PersonalField4B', 'PersonalField5', 'PersonalField6', 'PersonalField8', 'PersonalField9', 'PersonalField10A', 'PersonalField10B', 'PersonalField11', 'PersonalField12', 'PersonalField13', 'PersonalField14', 'PersonalField15', 'PersonalField22', 'PersonalField23', 'PersonalField24', 'PersonalField25', 'PersonalField26', 'PersonalField27', 'PersonalField28', 'PersonalField29', 'PersonalField30', 'PersonalField31', 'PersonalField32', 'PersonalField33', 'PersonalField34', 'PersonalField35', 'PersonalField36', 'PersonalField37', 'PersonalField38', 'PersonalField39', 'PersonalField40', 'PersonalField41', 'PersonalField42', 'PersonalField43', 'PersonalField44', 'PersonalField45', 'PersonalField46', 'PersonalField47', 'PersonalField48', 'PersonalField49', 'PersonalField50', 'PersonalField51', 'PersonalField52', 'PersonalField53', 'PersonalField54', 'PersonalField55', 'PersonalField56', 'PersonalField57', 'PersonalField58', 'PersonalField59', 'PersonalField60', 'PersonalField61', 'PersonalField62', 'PersonalField63', 'PersonalField64', 'PersonalField65', 'PersonalField66', 'PersonalField67', 'PersonalField68', 'PersonalField69', 'PersonalField70', 'PersonalField71', 'PersonalField72', 'PersonalField73', 'PersonalField74', 'PersonalField75', 'PersonalField76', 'PersonalField77', 'PersonalField78', 'PersonalField79', 'PersonalField80', 'PersonalField81', 'PersonalField82', 'PersonalField83', 'PersonalField84', 'PropertyField1A', 'PropertyField1B', 'PropertyField2A', 'PropertyField2B', 'PropertyField6', 'PropertyField8', 'PropertyField9', 'PropertyField10', 'PropertyField11A', 'PropertyField11B', 'PropertyField12', 'PropertyField13', 'PropertyField15', 'PropertyField16A', 'PropertyField16B', 'PropertyField17', 'PropertyField18', 'PropertyField19', 'PropertyField20', 'PropertyField21A', 'PropertyField21B', 'PropertyField22', 'PropertyField23', 'PropertyField24A', 'PropertyField24B', 'PropertyField25', 'PropertyField26A', 'PropertyField26B', 'PropertyField27', 'PropertyField29', 'PropertyField35', 'PropertyField39A', 'PropertyField39B', 'GeographicField1A', 'GeographicField1B', 'GeographicField2A', 'GeographicField2B', 'GeographicField3A', 'GeographicField3B', 'GeographicField4A', 'GeographicField4B', 'GeographicField5A', 'GeographicField5B', 'GeographicField6A', 'GeographicField6B', 'GeographicField7A', 'GeographicField7B', 'GeographicField8A', 'GeographicField8B', 'GeographicField9A', 'GeographicField9B', 'GeographicField10A', 'GeographicField10B', 'GeographicField11A', 'GeographicField11B', 'GeographicField12A', 'GeographicField12B', 'GeographicField13A', 'GeographicField13B', 'GeographicField14A', 'GeographicField14B', 'GeographicField15A', 'GeographicField15B', 'GeographicField16A', 'GeographicField16B', 'GeographicField17A', 'GeographicField17B', 'GeographicField18A', 'GeographicField18B', 'GeographicField19A', 'GeographicField19B', 'GeographicField20A', 'GeographicField20B', 'GeographicField21A', 'GeographicField21B', 'GeographicField22A', 'GeographicField22B', 'GeographicField23A', 'GeographicField23B', 'GeographicField24A', 'GeographicField24B', 'GeographicField25A', 'GeographicField25B', 'GeographicField26A', 'GeographicField26B', 'GeographicField27A', 'GeographicField27B', 'GeographicField28A', 'GeographicField28B', 'GeographicField29A', 'GeographicField29B', 'GeographicField30A', 'GeographicField30B', 'GeographicField31A', 'GeographicField31B', 'GeographicField32A', 'GeographicField32B', 'GeographicField33A', 'GeographicField33B', 'GeographicField34A', 'GeographicField34B', 'GeographicField35A', 'GeographicField35B', 'GeographicField36A', 'GeographicField36B', 'GeographicField37A', 'GeographicField37B', 'GeographicField38A', 'GeographicField38B', 'GeographicField39A', 'GeographicField39B', 'GeographicField40A', 'GeographicField40B', 'GeographicField41A', 'GeographicField41B', 'GeographicField42A', 'GeographicField42B', 'GeographicField43A', 'GeographicField43B', 'GeographicField44A', 'GeographicField44B', 'GeographicField45A', 'GeographicField45B', 'GeographicField46A', 'GeographicField46B', 'GeographicField47A', 'GeographicField47B', 'GeographicField48A', 'GeographicField48B', 'GeographicField49A', 'GeographicField49B', 'GeographicField50A', 'GeographicField50B', 'GeographicField51A', 'GeographicField51B', 'GeographicField52A', 'GeographicField52B', 'GeographicField53A', 'GeographicField53B', 'GeographicField54A', 'GeographicField54B', 'GeographicField55A', 'GeographicField55B', 'GeographicField56A', 'GeographicField56B', 'GeographicField57A', 'GeographicField57B', 'GeographicField58A', 'GeographicField58B', 'GeographicField59A', 'GeographicField59B', 'GeographicField60A', 'GeographicField60B', 'GeographicField61A', 'GeographicField61B', 'GeographicField62A', 'GeographicField62B']\n"
     ]
    }
   ],
   "source": [
    "#Select column 'Id' from num_cols\n",
    "num_id=num_cols.pop(0)\n",
    "print(\"num_id:\", num_id)\n",
    "\n",
    "#save column 'Id' in num_id variable\n",
    "num_id=[num_id]\n",
    "#print num_id\n",
    "print(num_id)\n",
    "\n",
    "#Remove column 'label' from numerical columns group\n",
    "num_cols.remove('label') #label is removed because it's the target to validate the model\n",
    "\n",
    "#print num_cols variable\n",
    "print(\"num_cols:\", num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260753"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count number of observation\n",
    "df_final.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Categorical and numerical variables in test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat_cols_test: ['Field6', 'Field12', 'CoverageField8', 'CoverageField9', 'SalesField7', 'PersonalField7', 'PersonalField16', 'PersonalField17', 'PersonalField18', 'PersonalField19', 'PropertyField3', 'PropertyField4', 'PropertyField5', 'PropertyField7', 'PropertyField14', 'PropertyField28', 'PropertyField30', 'PropertyField31', 'PropertyField32', 'PropertyField33', 'PropertyField34', 'PropertyField36', 'PropertyField37', 'PropertyField38', 'GeographicField63', 'GeographicField64']\n",
      "num_cols_test: ['QuoteNumber', 'Field7', 'Field8', 'Field9', 'Field10', 'Field11', 'CoverageField1A', 'CoverageField1B', 'CoverageField2A', 'CoverageField2B', 'CoverageField3A', 'CoverageField3B', 'CoverageField4A', 'CoverageField4B', 'CoverageField5A', 'CoverageField5B', 'CoverageField6A', 'CoverageField6B', 'CoverageField11A', 'CoverageField11B', 'SalesField1A', 'SalesField1B', 'SalesField2A', 'SalesField2B', 'SalesField3', 'SalesField4', 'SalesField5', 'SalesField6', 'SalesField8', 'SalesField9', 'SalesField10', 'SalesField11', 'SalesField12', 'SalesField13', 'SalesField14', 'SalesField15', 'PersonalField1', 'PersonalField2', 'PersonalField4A', 'PersonalField4B', 'PersonalField5', 'PersonalField6', 'PersonalField8', 'PersonalField9', 'PersonalField10A', 'PersonalField10B', 'PersonalField11', 'PersonalField12', 'PersonalField13', 'PersonalField14', 'PersonalField15', 'PersonalField22', 'PersonalField23', 'PersonalField24', 'PersonalField25', 'PersonalField26', 'PersonalField27', 'PersonalField28', 'PersonalField29', 'PersonalField30', 'PersonalField31', 'PersonalField32', 'PersonalField33', 'PersonalField34', 'PersonalField35', 'PersonalField36', 'PersonalField37', 'PersonalField38', 'PersonalField39', 'PersonalField40', 'PersonalField41', 'PersonalField42', 'PersonalField43', 'PersonalField44', 'PersonalField45', 'PersonalField46', 'PersonalField47', 'PersonalField48', 'PersonalField49', 'PersonalField50', 'PersonalField51', 'PersonalField52', 'PersonalField53', 'PersonalField54', 'PersonalField55', 'PersonalField56', 'PersonalField57', 'PersonalField58', 'PersonalField59', 'PersonalField60', 'PersonalField61', 'PersonalField62', 'PersonalField63', 'PersonalField64', 'PersonalField65', 'PersonalField66', 'PersonalField67', 'PersonalField68', 'PersonalField69', 'PersonalField70', 'PersonalField71', 'PersonalField72', 'PersonalField73', 'PersonalField74', 'PersonalField75', 'PersonalField76', 'PersonalField77', 'PersonalField78', 'PersonalField79', 'PersonalField80', 'PersonalField81', 'PersonalField82', 'PersonalField83', 'PersonalField84', 'PropertyField1A', 'PropertyField1B', 'PropertyField2A', 'PropertyField2B', 'PropertyField6', 'PropertyField8', 'PropertyField9', 'PropertyField10', 'PropertyField11A', 'PropertyField11B', 'PropertyField12', 'PropertyField13', 'PropertyField15', 'PropertyField16A', 'PropertyField16B', 'PropertyField17', 'PropertyField18', 'PropertyField19', 'PropertyField20', 'PropertyField21A', 'PropertyField21B', 'PropertyField22', 'PropertyField23', 'PropertyField24A', 'PropertyField24B', 'PropertyField25', 'PropertyField26A', 'PropertyField26B', 'PropertyField27', 'PropertyField29', 'PropertyField35', 'PropertyField39A', 'PropertyField39B', 'GeographicField1A', 'GeographicField1B', 'GeographicField2A', 'GeographicField2B', 'GeographicField3A', 'GeographicField3B', 'GeographicField4A', 'GeographicField4B', 'GeographicField5A', 'GeographicField5B', 'GeographicField6A', 'GeographicField6B', 'GeographicField7A', 'GeographicField7B', 'GeographicField8A', 'GeographicField8B', 'GeographicField9A', 'GeographicField9B', 'GeographicField10A', 'GeographicField10B', 'GeographicField11A', 'GeographicField11B', 'GeographicField12A', 'GeographicField12B', 'GeographicField13A', 'GeographicField13B', 'GeographicField14A', 'GeographicField14B', 'GeographicField15A', 'GeographicField15B', 'GeographicField16A', 'GeographicField16B', 'GeographicField17A', 'GeographicField17B', 'GeographicField18A', 'GeographicField18B', 'GeographicField19A', 'GeographicField19B', 'GeographicField20A', 'GeographicField20B', 'GeographicField21A', 'GeographicField21B', 'GeographicField22A', 'GeographicField22B', 'GeographicField23A', 'GeographicField23B', 'GeographicField24A', 'GeographicField24B', 'GeographicField25A', 'GeographicField25B', 'GeographicField26A', 'GeographicField26B', 'GeographicField27A', 'GeographicField27B', 'GeographicField28A', 'GeographicField28B', 'GeographicField29A', 'GeographicField29B', 'GeographicField30A', 'GeographicField30B', 'GeographicField31A', 'GeographicField31B', 'GeographicField32A', 'GeographicField32B', 'GeographicField33A', 'GeographicField33B', 'GeographicField34A', 'GeographicField34B', 'GeographicField35A', 'GeographicField35B', 'GeographicField36A', 'GeographicField36B', 'GeographicField37A', 'GeographicField37B', 'GeographicField38A', 'GeographicField38B', 'GeographicField39A', 'GeographicField39B', 'GeographicField40A', 'GeographicField40B', 'GeographicField41A', 'GeographicField41B', 'GeographicField42A', 'GeographicField42B', 'GeographicField43A', 'GeographicField43B', 'GeographicField44A', 'GeographicField44B', 'GeographicField45A', 'GeographicField45B', 'GeographicField46A', 'GeographicField46B', 'GeographicField47A', 'GeographicField47B', 'GeographicField48A', 'GeographicField48B', 'GeographicField49A', 'GeographicField49B', 'GeographicField50A', 'GeographicField50B', 'GeographicField51A', 'GeographicField51B', 'GeographicField52A', 'GeographicField52B', 'GeographicField53A', 'GeographicField53B', 'GeographicField54A', 'GeographicField54B', 'GeographicField55A', 'GeographicField55B', 'GeographicField56A', 'GeographicField56B', 'GeographicField57A', 'GeographicField57B', 'GeographicField58A', 'GeographicField58B', 'GeographicField59A', 'GeographicField59B', 'GeographicField60A', 'GeographicField60B', 'GeographicField61A', 'GeographicField61B', 'GeographicField62A', 'GeographicField62B']\n"
     ]
    }
   ],
   "source": [
    "#Categorical and numerical variable\n",
    "#just will select string data type\n",
    "cat_cols_test = [item[0] for item in test_data.dtypes if item[1].startswith('string')] \n",
    "print(\"cat_cols_test:\", cat_cols_test)\n",
    "\n",
    "#just will select integer or double data type\n",
    "num_cols_test = [item[0] for item in test_data.dtypes if item[1].startswith('int') | item[1].startswith('double')] \n",
    "print(\"num_cols_test:\", num_cols_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_id_test: QuoteNumber\n",
      "['QuoteNumber']\n",
      "['Field7', 'Field8', 'Field9', 'Field10', 'Field11', 'CoverageField1A', 'CoverageField1B', 'CoverageField2A', 'CoverageField2B', 'CoverageField3A', 'CoverageField3B', 'CoverageField4A', 'CoverageField4B', 'CoverageField5A', 'CoverageField5B', 'CoverageField6A', 'CoverageField6B', 'CoverageField11A', 'CoverageField11B', 'SalesField1A', 'SalesField1B', 'SalesField2A', 'SalesField2B', 'SalesField3', 'SalesField4', 'SalesField5', 'SalesField6', 'SalesField8', 'SalesField9', 'SalesField10', 'SalesField11', 'SalesField12', 'SalesField13', 'SalesField14', 'SalesField15', 'PersonalField1', 'PersonalField2', 'PersonalField4A', 'PersonalField4B', 'PersonalField5', 'PersonalField6', 'PersonalField8', 'PersonalField9', 'PersonalField10A', 'PersonalField10B', 'PersonalField11', 'PersonalField12', 'PersonalField13', 'PersonalField14', 'PersonalField15', 'PersonalField22', 'PersonalField23', 'PersonalField24', 'PersonalField25', 'PersonalField26', 'PersonalField27', 'PersonalField28', 'PersonalField29', 'PersonalField30', 'PersonalField31', 'PersonalField32', 'PersonalField33', 'PersonalField34', 'PersonalField35', 'PersonalField36', 'PersonalField37', 'PersonalField38', 'PersonalField39', 'PersonalField40', 'PersonalField41', 'PersonalField42', 'PersonalField43', 'PersonalField44', 'PersonalField45', 'PersonalField46', 'PersonalField47', 'PersonalField48', 'PersonalField49', 'PersonalField50', 'PersonalField51', 'PersonalField52', 'PersonalField53', 'PersonalField54', 'PersonalField55', 'PersonalField56', 'PersonalField57', 'PersonalField58', 'PersonalField59', 'PersonalField60', 'PersonalField61', 'PersonalField62', 'PersonalField63', 'PersonalField64', 'PersonalField65', 'PersonalField66', 'PersonalField67', 'PersonalField68', 'PersonalField69', 'PersonalField70', 'PersonalField71', 'PersonalField72', 'PersonalField73', 'PersonalField74', 'PersonalField75', 'PersonalField76', 'PersonalField77', 'PersonalField78', 'PersonalField79', 'PersonalField80', 'PersonalField81', 'PersonalField82', 'PersonalField83', 'PersonalField84', 'PropertyField1A', 'PropertyField1B', 'PropertyField2A', 'PropertyField2B', 'PropertyField6', 'PropertyField8', 'PropertyField9', 'PropertyField10', 'PropertyField11A', 'PropertyField11B', 'PropertyField12', 'PropertyField13', 'PropertyField15', 'PropertyField16A', 'PropertyField16B', 'PropertyField17', 'PropertyField18', 'PropertyField19', 'PropertyField20', 'PropertyField21A', 'PropertyField21B', 'PropertyField22', 'PropertyField23', 'PropertyField24A', 'PropertyField24B', 'PropertyField25', 'PropertyField26A', 'PropertyField26B', 'PropertyField27', 'PropertyField29', 'PropertyField35', 'PropertyField39A', 'PropertyField39B', 'GeographicField1A', 'GeographicField1B', 'GeographicField2A', 'GeographicField2B', 'GeographicField3A', 'GeographicField3B', 'GeographicField4A', 'GeographicField4B', 'GeographicField5A', 'GeographicField5B', 'GeographicField6A', 'GeographicField6B', 'GeographicField7A', 'GeographicField7B', 'GeographicField8A', 'GeographicField8B', 'GeographicField9A', 'GeographicField9B', 'GeographicField10A', 'GeographicField10B', 'GeographicField11A', 'GeographicField11B', 'GeographicField12A', 'GeographicField12B', 'GeographicField13A', 'GeographicField13B', 'GeographicField14A', 'GeographicField14B', 'GeographicField15A', 'GeographicField15B', 'GeographicField16A', 'GeographicField16B', 'GeographicField17A', 'GeographicField17B', 'GeographicField18A', 'GeographicField18B', 'GeographicField19A', 'GeographicField19B', 'GeographicField20A', 'GeographicField20B', 'GeographicField21A', 'GeographicField21B', 'GeographicField22A', 'GeographicField22B', 'GeographicField23A', 'GeographicField23B', 'GeographicField24A', 'GeographicField24B', 'GeographicField25A', 'GeographicField25B', 'GeographicField26A', 'GeographicField26B', 'GeographicField27A', 'GeographicField27B', 'GeographicField28A', 'GeographicField28B', 'GeographicField29A', 'GeographicField29B', 'GeographicField30A', 'GeographicField30B', 'GeographicField31A', 'GeographicField31B', 'GeographicField32A', 'GeographicField32B', 'GeographicField33A', 'GeographicField33B', 'GeographicField34A', 'GeographicField34B', 'GeographicField35A', 'GeographicField35B', 'GeographicField36A', 'GeographicField36B', 'GeographicField37A', 'GeographicField37B', 'GeographicField38A', 'GeographicField38B', 'GeographicField39A', 'GeographicField39B', 'GeographicField40A', 'GeographicField40B', 'GeographicField41A', 'GeographicField41B', 'GeographicField42A', 'GeographicField42B', 'GeographicField43A', 'GeographicField43B', 'GeographicField44A', 'GeographicField44B', 'GeographicField45A', 'GeographicField45B', 'GeographicField46A', 'GeographicField46B', 'GeographicField47A', 'GeographicField47B', 'GeographicField48A', 'GeographicField48B', 'GeographicField49A', 'GeographicField49B', 'GeographicField50A', 'GeographicField50B', 'GeographicField51A', 'GeographicField51B', 'GeographicField52A', 'GeographicField52B', 'GeographicField53A', 'GeographicField53B', 'GeographicField54A', 'GeographicField54B', 'GeographicField55A', 'GeographicField55B', 'GeographicField56A', 'GeographicField56B', 'GeographicField57A', 'GeographicField57B', 'GeographicField58A', 'GeographicField58B', 'GeographicField59A', 'GeographicField59B', 'GeographicField60A', 'GeographicField60B', 'GeographicField61A', 'GeographicField61B', 'GeographicField62A', 'GeographicField62B']\n"
     ]
    }
   ],
   "source": [
    "#Select 'Id' from num_cols_test and save in variable called 'num_id_test'\n",
    "num_id_test=num_cols_test.pop(0)\n",
    "print(\"num_id_test:\", num_id_test)\n",
    "\n",
    "#save num_id_test to list called 'num_id_test'\n",
    "num_id_test=[num_id_test]\n",
    "print(num_id_test)\n",
    "print(num_cols_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173836"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(270, 26)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(num_cols_test), len(cat_cols_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Sample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ratio=0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_sample=df_final.sample(False, ratio, 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26239"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_sample=test_data.sample(False, ratio, 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17454"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_sample.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Missing Value in data train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are using two method to check missing values:\n",
    "1. Using pandas dataframe\n",
    "2. Using pyspark dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#convert to pandas DataFrame\n",
    "df_pd=df_sample.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Check Missing value with proportion in Pandas DataFrame\n",
    "\n",
    "def missing_values_table(df_pd):\n",
    "    \"\"\"Input pandas dataframe and Return columns with missing value&percentage and stored as pandas dataframe\"\"\"\n",
    "    mis_val = df_pd.isnull().sum() #count total of null in each columns in dataframe\n",
    "    mis_val_percent = 100 * df_pd.isnull().sum() / len(df_pd) #count percentage of null in each columns\n",
    "    mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)  #join to left (as column) between mis_val and mis_val_percent and create it as dataframe\n",
    "    mis_val_table_ren_columns = mis_val_table.rename(\n",
    "    columns = {0 : 'Missing Values', 1 : '% of Total Values'}) #rename columns in table, mis_val to Missing Values and mis_val_percent to % of Total Values\n",
    "    mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "    mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "    '% of Total Values', ascending=False).round(1)         #sort column % of Total Values descending and round 1 after point(coma)\n",
    "    print (\"Your selected dataframe has \" + str(df_pd.shape[1]) + \" columns.\\n\"    #.shape[1] : just view total columns in dataframe  \n",
    "    \"There are \" + str(mis_val_table_ren_columns.shape[0]) +              \n",
    "    \" columns that have missing values.\") #.shape[0] : just view total rows in dataframe\n",
    "    return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function missing_values_table in module __main__:\n",
      "\n",
      "missing_values_table(df_pd)\n",
      "    Input pandas dataframe and Return columns with missing value&percentage and stored as pandas dataframe\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(missing_values_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 298 columns.\n",
      "There are 9 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PropertyField29</th>\n",
       "      <td>20199</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PersonalField84</th>\n",
       "      <td>12515</td>\n",
       "      <td>47.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField38</th>\n",
       "      <td>131</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PersonalField7</th>\n",
       "      <td>17</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField36</th>\n",
       "      <td>17</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField3</th>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField32</th>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField34</th>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField4</th>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Missing Values  % of Total Values\n",
       "PropertyField29           20199               77.0\n",
       "PersonalField84           12515               47.7\n",
       "PropertyField38             131                0.5\n",
       "PersonalField7               17                0.1\n",
       "PropertyField36              17                0.1\n",
       "PropertyField3               10                0.0\n",
       "PropertyField32              10                0.0\n",
       "PropertyField34              10                0.0\n",
       "PropertyField4                8                0.0"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=missing_values_table(df_pd)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Check Missing Value in Pyspark Dataframe\n",
    "def count_nulls(c):\n",
    "    \"\"\"Input pyspark dataframe and return list of columns with missing value and it's total value\"\"\"\n",
    "    null_counts = []          #make an empty list to hold our results\n",
    "    for col in c.dtypes:     #iterate through the column data types we saw above, e.g. ('C0', 'bigint')\n",
    "        cname = col[0]        #splits out the column name, e.g. 'C0'    \n",
    "        ctype = col[1]        #splits out the column type, e.g. 'bigint'\n",
    "        nulls = c.where( c[cname].isNull()).count() #check count of null in column name\n",
    "        result = tuple([cname, nulls])  #new tuple, (column name, null count)\n",
    "        null_counts.append(result)      #put the new tuple in our result list\n",
    "    null_counts=[(x,y) for (x,y) in null_counts if y!=0]  #view just columns that have missing values\n",
    "    return null_counts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function count_nulls in module __main__:\n",
      "\n",
      "count_nulls(c)\n",
      "    Input pyspark dataframe and return list of columns with missing value and it's total value\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(count_nulls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 8min 11s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('PersonalField7', 113),\n",
       " ('PersonalField84', 124208),\n",
       " ('PropertyField3', 81),\n",
       " ('PropertyField4', 63),\n",
       " ('PropertyField29', 200685),\n",
       " ('PropertyField32', 70),\n",
       " ('PropertyField34', 70),\n",
       " ('PropertyField36', 113),\n",
       " ('PropertyField38', 1220)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Call function count_nulls and apply it to data train (df_final)\n",
    "%time null_counts = count_nulls(df_final)\n",
    "null_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From function count_nulls above we get information all columns that has missing values with it's numbers and save in list \"null_counts\".\n",
    "\n",
    "From null_counts, we just take information of columns name and save in list \"list_cols_miss\", like in the script below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PersonalField7',\n",
       " 'PersonalField84',\n",
       " 'PropertyField3',\n",
       " 'PropertyField4',\n",
       " 'PropertyField29',\n",
       " 'PropertyField32',\n",
       " 'PropertyField34',\n",
       " 'PropertyField36',\n",
       " 'PropertyField38']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%time\n",
    "#import time\n",
    "list_cols_miss=[x[0] for x in null_counts]\n",
    "list_cols_miss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From list_cols_miss create dataframe called \"df_miss\". The aim is to define categorical and numerical columns which have missing values. \n",
    "As the process of handling missing values will be diffrent betwen  numerical dan categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create dataframe which just has list_cols_miss\n",
    "df_miss= df_final.select(*list_cols_miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('PersonalField7', 'string'),\n",
       " ('PersonalField84', 'int'),\n",
       " ('PropertyField3', 'string'),\n",
       " ('PropertyField4', 'string'),\n",
       " ('PropertyField29', 'int'),\n",
       " ('PropertyField32', 'string'),\n",
       " ('PropertyField34', 'string'),\n",
       " ('PropertyField36', 'string'),\n",
       " ('PropertyField38', 'string')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_miss.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we create \"df_miss\", we split to categorical and numerical columns which have missing value.\n",
    "for categorical columns we  called \"catcolums_miss\" and for numerical columns we called \"numcolumns_miss\". \n",
    "Like in script below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "catcolums_miss: ['PersonalField7', 'PropertyField3', 'PropertyField4', 'PropertyField32', 'PropertyField34', 'PropertyField36', 'PropertyField38']\n",
      "numcolumns_miss: ['PersonalField84', 'PropertyField29']\n"
     ]
    }
   ],
   "source": [
    "#Define categorical columns and numerical columns which have missing value.\n",
    "### for categorical columns\n",
    "catcolums_miss=[item[0] for item in df_miss.dtypes if item[1].startswith('string')]  #will select name of column with string data type\n",
    "print(\"catcolums_miss:\", catcolums_miss)\n",
    "\n",
    "### for numerical columns\n",
    "numcolumns_miss = [item[0] for item in df_miss.dtypes if item[1].startswith('int') | item[1].startswith('double')] #will select name of column with integer or double data type\n",
    "print(\"numcolumns_miss:\", numcolumns_miss)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handle Missing Values\n",
    "So, in this module we will use pyspark dataframe to check missing values.\n",
    "\n",
    "For categorical columns we use most frequent to fill missing values. Therefore we have to count categories which has max values in each columns by counting and sorting descenting each columns in dataframe with no has missing values. Therefore we drop missing values and save in new dataframe called \"df_Nomiss\".\n",
    "For numerical columns we fill missing values with average in it's columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Drop missing value\n",
    "df_Nomiss=df_final.na.drop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n",
      "PersonalField7 N\n",
      "PropertyField3 N\n",
      "PropertyField4 N\n",
      "PropertyField32 Y\n",
      "PropertyField34 N\n",
      "PropertyField36 N\n",
      "PropertyField38 N\n"
     ]
    }
   ],
   "source": [
    "#fill missing value in categorical variable with most frequent\n",
    "%time\n",
    "for x in catcolums_miss:\n",
    "    mode=df_Nomiss.groupBy(x).count().sort(col(\"count\").desc()).collect()[0][0] #group by based on categories and count each categories and sort descending then take the first value in column\n",
    "    print(x, mode) #print name of columns and it's most categories \n",
    "    df_final = df_final.na.fill({x:mode}) #fill missing value in each columns with most frequent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PersonalField84 2.0\n",
      "PropertyField29 0.0\n"
     ]
    }
   ],
   "source": [
    "#fill missing value in numerical variable with average\n",
    "for i in numcolumns_miss:\n",
    "    meanvalue = df_final.select(round(mean(i))).collect()[0][0] #calculate average in each numerical column\n",
    "    print(i, meanvalue) #print name of columns and it's average value\n",
    "    df_final=df_final.na.fill({i:meanvalue}) #fill missing value in each columns with it's average value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 8min 41s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Missing value after filling\n",
    "\n",
    "%time null_counts = count_nulls(df_final)\n",
    "null_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, output null_counts is null, it's mean no more columns that have missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Missing Value in data test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Check missing values Using Pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_pd=test_data.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 297 columns.\n",
      "There are 11 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PropertyField29</th>\n",
       "      <td>133945</td>\n",
       "      <td>77.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PersonalField84</th>\n",
       "      <td>82812</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField38</th>\n",
       "      <td>846</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PersonalField7</th>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField3</th>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField36</th>\n",
       "      <td>67</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField4</th>\n",
       "      <td>52</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField32</th>\n",
       "      <td>41</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField34</th>\n",
       "      <td>41</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField5</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropertyField30</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Missing Values  % of Total Values\n",
       "PropertyField29          133945               77.1\n",
       "PersonalField84           82812               47.6\n",
       "PropertyField38             846                0.5\n",
       "PersonalField7               69                0.0\n",
       "PropertyField3               69                0.0\n",
       "PropertyField36              67                0.0\n",
       "PropertyField4               52                0.0\n",
       "PropertyField32              41                0.0\n",
       "PropertyField34              41                0.0\n",
       "PropertyField5                1                0.0\n",
       "PropertyField30               1                0.0"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss=missing_values_table(test_pd)\n",
    "miss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Check Missing Values Using Pyspark Dataframe\n",
    "We will cleansing missing values in pyspark dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5min 27s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('PersonalField7', 69),\n",
       " ('PersonalField84', 82812),\n",
       " ('PropertyField3', 69),\n",
       " ('PropertyField4', 52),\n",
       " ('PropertyField5', 1),\n",
       " ('PropertyField29', 133945),\n",
       " ('PropertyField30', 1),\n",
       " ('PropertyField32', 41),\n",
       " ('PropertyField34', 41),\n",
       " ('PropertyField36', 67),\n",
       " ('PropertyField38', 846)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Call function to count missing values in test_data\n",
    "%time null_test= count_nulls(test_data)\n",
    "null_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PersonalField7',\n",
       " 'PersonalField84',\n",
       " 'PropertyField3',\n",
       " 'PropertyField4',\n",
       " 'PropertyField5',\n",
       " 'PropertyField29',\n",
       " 'PropertyField30',\n",
       " 'PropertyField32',\n",
       " 'PropertyField34',\n",
       " 'PropertyField36',\n",
       " 'PropertyField38']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#take just name of columns that have missing values\n",
    "list_miss_test=[x[0] for x in null_test]\n",
    "list_miss_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('PersonalField7', 'string'),\n",
       " ('PersonalField84', 'int'),\n",
       " ('PropertyField3', 'string'),\n",
       " ('PropertyField4', 'string'),\n",
       " ('PropertyField5', 'string'),\n",
       " ('PropertyField29', 'int'),\n",
       " ('PropertyField30', 'string'),\n",
       " ('PropertyField32', 'string'),\n",
       " ('PropertyField34', 'string'),\n",
       " ('PropertyField36', 'string'),\n",
       " ('PropertyField38', 'string')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create dataframe which just has list_cols_miss\n",
    "test_miss= test_data.select(*list_miss_test)\n",
    "\n",
    "#view data types in df_miss\n",
    "test_miss.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "catcolums_miss_test: ['PersonalField7', 'PropertyField3', 'PropertyField4', 'PropertyField5', 'PropertyField30', 'PropertyField32', 'PropertyField34', 'PropertyField36', 'PropertyField38']\n",
      "numcolumns_miss_test: ['PersonalField84', 'PropertyField29']\n"
     ]
    }
   ],
   "source": [
    "#Define categorical columns and numerical columns which have missing value.\n",
    "### for categorical columns\n",
    "catcolums_miss_test=[item[0] for item in test_miss.dtypes if item[1].startswith('string')]  #will select name of column with string data type\n",
    "print(\"catcolums_miss_test:\", catcolums_miss_test)\n",
    "\n",
    "### for numerical columns\n",
    "numcolumns_miss_test = [item[0] for item in test_miss.dtypes if item[1].startswith('int') | item[1].startswith('double')] #will select name of column with integer or double data type\n",
    "print(\"numcolumns_miss_test:\", numcolumns_miss_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handle Missing Values\n",
    "So, in this module we will use pyspark dataframe to check missing values.\n",
    "\n",
    "For categorical columns we use most frequent to fill missing values. Therefore we have to count categories which has max values in each columns by counting and sorting descenting each columns in dataframe with no has missing values. Therefore we drop missing values and save in new dataframe called \"test_Nomiss\".\n",
    "For numerical columns we fill missing values with average in it's columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Drop missing value\n",
    "test_Nomiss=test_data.na.drop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PersonalField7 N\n",
      "PropertyField3 N\n",
      "PropertyField4 N\n",
      "PropertyField5 Y\n",
      "PropertyField30 N\n",
      "PropertyField32 Y\n",
      "PropertyField34 N\n",
      "PropertyField36 N\n",
      "PropertyField38 N\n"
     ]
    }
   ],
   "source": [
    "#fill missing value in categorical variable with most frequent\n",
    "for x in catcolums_miss_test:\n",
    "    mode=test_Nomiss.groupBy(x).count().sort(col(\"count\").desc()).collect()[0][0] #group by based on categories and count each categories and sort descending then take the first value in column\n",
    "    print(x, mode) #print name of columns and it's most categories \n",
    "    test_data = test_data.na.fill({x:mode}) #fill missing value in each columns with most frequent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PersonalField84 2.0\n",
      "PropertyField29 0.0\n"
     ]
    }
   ],
   "source": [
    "#fill missing value in numerical variable with average\n",
    "for i in numcolumns_miss_test:\n",
    "    meanvalue_test = test_data.select(round(mean(i))).collect()[0][0] #calculate average in each numerical column\n",
    "    print(i, meanvalue_test) #print name of columns and it's average value\n",
    "    test_data=test_data.na.fill({i:meanvalue_test}) #fill missing value in each columns with it's average value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5min 56s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Missing value after filling\n",
    "%time null_test = count_nulls(test_data)\n",
    "null_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, output null_test is null, it's mean no more columns that have missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare categorical columns in df_final and test_data\n",
    "\n",
    "In this step, we check whether categories between data train and data test same or not. Categories in data test will be equated with data train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "def check_category2(a1,a2,y):\n",
    "    \"\"\"input are two dataframe you want to compare categorical variables and the colomn category name\"\"\"\n",
    "    print('column:',y)\n",
    "    #distinct1=a1.select([y]).distinct().count() #count distinct column in dataframe1\n",
    "    #distinct2=a2.select([y]).distinct().count() #count distinct column in dataframe2\n",
    "    #if distinct1 == distinct2:\n",
    "    var1=a1.select([y]).distinct() #define distinct category in column in dataframe1\n",
    "    var2=a2.select([y]).distinct() #define distinct category in column in dataframe2\n",
    "    diff2=var2.subtract(var1).collect() #define the different category in dataframe2, return is list\n",
    "    diff2=[r[y] for r in diff2] #just take the values\n",
    "    diff1=var1.subtract(var2).collect() #define the different category in dataframe1, return is list\n",
    "    diff1=[r[y] for r in diff1] #just take the values\n",
    "    if diff1 == diff2:\n",
    "        print('diff2:', diff2)\n",
    "        print('diff1:', diff1)\n",
    "        print('Columns match!!')\n",
    "    else:\n",
    "        if len(diff1)!=0 and len(diff2)==len(diff1):\n",
    "            print('diff2:', diff2)\n",
    "            print('diff1:', diff1)\n",
    "            a2=a2.replace(diff2, diff1, y) #replace the different category in dataframe2 with category in dataframe1\n",
    "            print('Columns match now!!')\n",
    "        else:\n",
    "            if len(diff2)!=len(diff1) and len(diff2)!=0:\n",
    "                print('diff2:', diff2)\n",
    "                print('diff1:', diff1)\n",
    "                dominant1=a1.groupBy(y).count().sort(col(\"count\").desc()).collect()[0][0]\n",
    "                dominant2=a2.groupBy(y).count().sort(col(\"count\").desc()).collect()[0][0] #define category dominant in dataframe2\n",
    "                print('dominant2:', dominant2)\n",
    "                print('dominant1:', dominant1)\n",
    "                a2=a2.replace(diff2, dominant1, y) #replace different category in dataframe2 with dominant category\n",
    "                print('Columns match now!!')\n",
    "            else:     \n",
    "                print('diff1:', diff1)\n",
    "                print('diff2:', diff2)\n",
    "    return a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column: Field6\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: Field12\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: CoverageField8\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: CoverageField9\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: SalesField7\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PersonalField7\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PersonalField16\n",
      "diff2: ['ZM', 'XG', 'YG']\n",
      "diff1: ['XU', 'XN', 'XY', 'ZO', 'ZQ', 'ZV', 'ZL', 'ZS', 'ZP']\n",
      "dominant2: ZA\n",
      "dominant1: ZA\n",
      "Columns match now!!\n",
      "column: PersonalField17\n",
      "diff2: ['XZ', 'XF', 'YO', 'ZJ']\n",
      "diff1: ['XP', 'XY', 'ZR', 'XJ', 'YP', 'XO']\n",
      "dominant2: ZE\n",
      "dominant1: ZE\n",
      "Columns match now!!\n",
      "column: PersonalField18\n",
      "diff2: ['XB']\n",
      "diff1: ['ZG', 'XV', 'ZI', 'XH']\n",
      "dominant2: XR\n",
      "dominant1: XR\n",
      "Columns match now!!\n",
      "column: PersonalField19\n",
      "diff2: ['ZS']\n",
      "diff1: ['XL', 'YI', 'ZC']\n",
      "dominant2: XD\n",
      "dominant1: XD\n",
      "Columns match now!!\n",
      "column: PropertyField3\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PropertyField4\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PropertyField5\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PropertyField7\n",
      "diff2: ['T']\n",
      "diff1: ['B']\n",
      "Columns match now!!\n",
      "column: PropertyField14\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PropertyField28\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PropertyField30\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PropertyField31\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PropertyField32\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PropertyField33\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PropertyField34\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PropertyField36\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: PropertyField37\n",
      "diff2: [' ']\n",
      "diff1: []\n",
      "dominant2: N\n",
      "dominant1: N\n",
      "Columns match now!!\n",
      "column: PropertyField38\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: GeographicField63\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n",
      "column: GeographicField64\n",
      "diff2: []\n",
      "diff1: []\n",
      "Columns match!!\n"
     ]
    }
   ],
   "source": [
    "#call function to check catgories in data train and test, whether same or not, if not, the different categories will be replaced.\n",
    "for y in cat_cols_test:\n",
    "    test_data=check_category2(df_final,test_data,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check distribution in each variables\n",
    "Pyspark dataframe has limitation in visualization. Then to create visualization we have to convert pyspark dataframe to pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert spark dataframe to pandas for visualization\n",
    "df_pd=df_final.toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2080b5cbb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barchart for categorical variable\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(221)\n",
    "sns.countplot(x='label', data=df_pd, order=df_pd['label'].value_counts().index)\n",
    "plt.title('TARGET', fontsize=15)\n",
    "plt.subplot(222)\n",
    "sns.countplot(y='Field6', data=df_pd, order=df_pd['Field6'].value_counts().index)\n",
    "plt.title('Field6', fontsize=15)\n",
    "plt.subplot(223)\n",
    "sns.countplot(x='Field12', data=df_pd, order=df_pd['Field12'].value_counts().index)\n",
    "plt.title('Field12', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2080621b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barchart for categorical variable\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(221)\n",
    "sns.countplot(y='CoverageField8', data=df_pd, order=df_pd['CoverageField8'].value_counts().index)\n",
    "plt.title('CoverageField8', fontsize=15)\n",
    "plt.subplot(222)\n",
    "sns.countplot(y='CoverageField9', data=df_pd, order=df_pd['CoverageField9'].value_counts().index)\n",
    "plt.title('CoverageField9', fontsize=15)\n",
    "plt.subplot(223)\n",
    "sns.countplot(y='SalesField7', data=df_pd, order=df_pd['SalesField7'].value_counts().index)\n",
    "plt.title('SalesField7', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+ffsUGxurhIQEGQyG2+rr5Wc/5wcGAACqKIskm95++239+eefWrp0qZYuXSpJ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\ndD3uuOMO2xhYLBYNGzZMRqPxhvoEAAC41fzyyy86duyYvWM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20819dfd5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barchart for categorical variable\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(221)\n",
    "sns.countplot(x='PersonalField7', data=df_pd, order=df_pd['PersonalField7'].value_counts().index)\n",
    "plt.title('PersonalField7', fontsize=15)\n",
    "plt.subplot(222)\n",
    "sns.countplot(y='PersonalField16', data=df_pd, order=df_pd['PersonalField16'].value_counts().index)\n",
    "plt.title('PersonalField16', fontsize=15)\n",
    "plt.subplot(223)\n",
    "sns.countplot(y='PersonalField17', data=df_pd, order=df_pd['PersonalField17'].value_counts().index)\n",
    "plt.title('PersonalField17', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iBQEBAUWYrYiIiEjxFxISQlZWlmlAeOvWrfHz88PHx4egoCCeeeYZ07np6elUqlSpCLMV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mi+8pWvtOhgAAAAALQ/zYpNY8aMycMPP5yf/exnKSsry9e//vUcddRRLT0bAAAAAO1Ms2JTkhxz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\nAAAAhRGbAAAAACiM2AQAAABAYf4faM4X7rHCxDgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2080d468b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barchart for categorical variable\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(221)\n",
    "sns.countplot(y='PersonalField18', data=df_pd, order=df_pd['PersonalField18'].value_counts().index)\n",
    "plt.title('PersonalField18', fontsize=15)\n",
    "plt.subplot(222)\n",
    "sns.countplot(y='PersonalField19', data=df_pd, order=df_pd['PersonalField19'].value_counts().index)\n",
    "plt.title('PersonalField19', fontsize=15)\n",
    "plt.subplot(223)\n",
    "sns.countplot(x='PropertyField3', data=df_pd, order=df_pd['PropertyField3'].value_counts().index)\n",
    "plt.title('PropertyField3', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorical vs Target visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20819c99da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2081636d978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pd.crosstab(df_pd['Field6'], df_pd['label'], normalize='index').plot.bar(rot=0, stacked=True,\n",
    "            color=['green', 'red'], figsize=(4,4), title=\"Field6 VS label\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.2, 1))\n",
    "\n",
    "\n",
    "pd.crosstab(df_pd['Field12'], df_pd['label'], normalize='index').plot.bar(rot=0, stacked=True,                            \n",
    "            color=['green', 'red'], figsize=(4,4), title=\"Field12 VS label\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.2, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BYIBOp4NOp8Pw4cPRrFkztG7dGkOGDGmIcRNRE2I1qOzs7DB//vwa+9zd3ZU/Dxs2DMOG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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20805d9a0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vtaq7J7dKrFWddSqz1sP8npelVlXu38McXHZPpnt7eyMtLQ0AkJWVBU9PT2VZvXr1UKdO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\nx6AiIukxqIhIev8PW9hgQJejX2MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20829c6dcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pd.crosstab(df_pd['CoverageField8'], df_pd['label'], normalize='index').plot.bar(rot=0, stacked=True,\n",
    "            color=['green', 'red'], figsize=(4,4), title=\"CoverageField8 VS label\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.2, 1))\n",
    "\n",
    "\n",
    "pd.crosstab(df_pd['CoverageField9'], df_pd['label'], normalize='index').plot.bar(rot=0, stacked=True,                            \n",
    "            color=['green', 'red'], figsize=(4,4), title=\"CoverageField9 VS label\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.2, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lojrJZlCVlZVBo9HIyyqVCpWVlVCr1SguLsbZs2cREREBX19fTJs2De3bt0e3bt2qPF6j\nRg2gVqveTPcv4O3tZrdjsz7ri1y/ps/dnmwGlUajgdFolJctFgvU6p928/DwQPPmzeHn5wcA0Gq1\nyM3NrTaoiotNr9tztYqKSu16fNZnfVHr26pdm4PM5ndUQUFByMzMBADk5OTA399fHvPx8YHRaER+\nfj4A4PTp02jdurWdWiWiusrmjCo4OBhZWVkICwuDJEmIiYlBWloaTCYT9Ho9Fi9ejLlz50KSJHTo\n0AG9evVyQNtEVJfYDCqlUomoqCirdU8v9QCgW7duSE5OfvOdERH9D2/4JCLhMaiISHgMKiISHoOK\niITHoCIi4TGoiEh4DCoiEh6DioiEx6AiIuExqIhIeAwqIhIeg4qIhMegIiLhMaiISHgMKiISHoOK\niITHoCIi4TGoiEh4DCoiEh6DioiEx6AiIuExqIhIeAwqIhIeg4qIhMegIiLhMaiISHgMKiISHoOK\niITHoCIi4TGoiEh4DCoiEh6DioiEx6AiIuHZDCqLxYKIiAjo9XoYDAbk5+e/cLtFixZh2bJlb7xB\nIiKbQZWRkYGKigokJSVh7ty5iI2NfW6bxMREXLp0yS4NEhHZDKrs7GxotVoAQGBgIHJzc63Gz5w5\ng3PnzkGv19unQyKq89S2NigrK4NGo5GXVSoVKisroVarUVhYiDVr1uCrr75Cenr6SxVs1KgB1GrV\nL+/YBm9vN7sdm/VZX+T6NX3u9mQzqDQaDYxGo7xssVigVv+02969e1FcXIwpU6agqKgI5eXlaNmy\nJYYNG1bl8YqLTW+g7aoVFZXa9fisz/qi1rdVuzYHmc2gCgoKwqFDhzBgwADk5OTA399fHhs3bhzG\njRsHAEhJScG1a9eqDSkiol/CZlAFBwcjKysLYWFhkCQJMTExSEtLg8lk4vdSROQQNoNKqVQiKirK\nap2fn99z23EmRUT2whs+iUh4DCoiEh6DioiEx6AiIuExqIhIeAwqIhIeg4qIhMegIiLhMaiISHgM\nKiISHoOKiITHoCIi4TGoiEh4DCoiEh6DioiEx6AiIuExqIhIeAwqIhIeg4qIhMegIiLhMaiISHgM\nKiISHoOKiITHoCIi4TGoiEh4DCoiEh6DioiEx6AiIuExqIhIeAwqIhIeg4qIhMegIiLhMaiISHgM\nKiISntrWBhaLBZGRkbh48SKcnJwQHR2N5s2by+PffvstNm3aBJVKBX9/f0RGRkKpZP4R0ZtjM1Ey\nMjJQUVGBpKQkzJ07F7GxsfJYeXk5/vKXv2Dz5s1ITExEWVkZDh06ZNeGiajusTmjys7OhlarBQAE\nBgYiNzdXHnNyckJiYiJcXFwAAJWVlXB2dq72eI0aNYBarXqdnqvl7e1mt2OzPuuLXL+mz92ebAZV\nWVkZNBqNvKxSqVBZWQm1Wg2lUgkvLy8AQEJCAkwmE7p3717t8YqLTa/ZcvWKikrtenzWZ31R69uq\nXZuDzGZQaTQaGI1GedlisUCtVlstL126FNevX8fq1auhUCjs0ykR1Vk2v6MKCgpCZmYmACAnJwf+\n/v5W4xEREXj8+DHWrl0rXwISEb1JNmdUwcHByMrKQlhYGCRJQkxMDNLS0mAymdC+fXskJyejU6dO\nGD9+PABg3LhxCA4OtnvjRFR32AwqpVKJqKgoq3V+fn7yn/Py8t58V0REz+ANT0QkPAYVEQmPQUVE\nwmNQEZHwGFREJDwGFREJj0FFRMJjUBGR8BhURCQ8BhURCY9BRUTCY1ARkfAYVEQkPAYVEQmPQUVE\nwmNQEZHwGFREJDwGFREJj0FFRMJjUBGR8BhURCQ8BhURCY9BRUTCY1ARkfAYVEQkPAYVEQmPQUVE\nwmNQEZHwGFREJDwGFREJj0FFRMJjUBGR8BhURCQ8m0FlsVgQEREBvV4Pg8GA/Px8q/GDBw9Cp9NB\nr9dj+/btdmuUiOoum0GVkZGBiooKJCUlYe7cuYiNjZXHnjx5giVLlmDDhg1ISEhAUlIS7t27Z9eG\niajusRlU2dnZ0Gq1AIDAwEDk5ubKY1evXoWvry/c3d3h5OSEjh074tSpU/brlojqJLWtDcrKyqDR\naORllUqFyspKqNVqlJWVwc3NTR5zdXVFWVlZtcfz9nardlz6XLLVkl2xPuvXxdqiszmj0mg0MBqN\n8rLFYoFarX7hmNFotAouIqI3wWZQBQUFITMzEwCQk5MDf39/eczPzw/5+fkoKSlBRUUFTp8+jQ4d\nOtivWyKqkxSSJFU737RYLIiMjMSlS5cgSRJiYmJw4cIFmEwm6PV6HDx4EGvWrIEkSdDpdBgzZoyj\neieiOsJmUBER1TTe8ElEwmNQEZHwGFREJLxaE1QnTpzAJ598Ii/v3bsXgwYNwp07dxxSOyAgAHv2\n7LFaHxoaivnz5zukfrdu3WAwGGAwGDBy5EgkJCTYve5Tubm5mDhxIkaNGoWwsDCsXLkSFRUVDqv/\n8/MfNmwYZs2a5bAeYmNjYTAY0K9fP/Tq1QsGgwGzZs1ySO2xY8fi2LFjVuuio6OxY8cOh9QXhlRL\nHD9+XJo9e7YkSZKUlpYmDR06VCoqKnJY7X79+kkff/yxvC4vL0/q27evNG/ePIfUf3rukiRJjx8/\nlnr37i09ePDA7rXv3r0r9evXT7p27ZokSZJksVik1atXS5GRkXav/dTPz1+SJGnOnDlSenq6w3qQ\nJEnauXOntHTpUofWTE1Ntfo79vjxY6lPnz6S0Wh0aB81rdbMqJ5KTU3FN998g40bN8LLy8thddu0\naYM7d+6gtLQUALB7926EhoY6rP6zysrKoFQqoVKp7F5r165dGDFiBFq0aAEAUCgUmD59Ov71r3+h\nvLzc7vVfpKKiAoWFhXB3d6+R+o7Ur18/HD9+HI8ePQIAHDhwAN27d0eDBg1quDPHsvkIjUhOnz6N\ngoICPHjwAGaz2eH1f/vb32Lfvn0YNmwYzp8/j8mTJ+Pu3bsOqX38+HEYDAYoFArUq1cPixYtgqur\nq93r3r59W37W8ymFQgEvLy8UFRXBx8fH7j0A/3/+//3vf6FUKjFy5Eh069bNIbVrkrOzMz788EPs\n378fgwcPRkpKitVXIHVFrQoqb29vbNy4ETt27EB4eDjWr18PpdJxk8LQ0FBERkbCx8cHnTp1clhd\nAOjatStWrlzp0JoA8M477+DmzZtW6ywWC+7cuYPGjRs7rI+n519cXIyJEyeiWbNmDqtd00aMGIG4\nuDh06dIFDx8+xHvvvVfTLTlcrbr0a968OZydnTF27FjUq1cP8fHxDq3v4+MDk8mEhIQEDB482KG1\na8rvfvc77NixAzdu3MDDhw8xceJE/OlPf0Lv3r1r5PKjUaNGWLp0KRYuXIjCwkKH168JAQEBMBqN\n2Lx5M3Q6XU23UyNqVVA9KyYmBklJSTh+/LhD6w4YMAB3796Vv7N5273zzjtYunQpvvjiC0yZMgX3\n799HUVERnjx5gpKSkhrpqVWrVjAYDIiOjq6R+jVBp9Nhx44dGDhwYE23UiP4CA39Inl5efDx8XHI\n92REDCoiEl6tvfQjorqDQUVEwmNQEZHwGFREJLxadcMnvdjevXuxbt06VFZWQpIkDBkyBJMmTapy\ne4PBgBkzZqBLly6vVGf+/Pk4fvy41aMrvXr1wujRo7Fw4UKsX7++yn1Xr14NAJg5c6bV+pSUFJw8\neRLh4eGYOHGivL60tBTFxcU4e/bsK/VIbycGVS1XUFCAL7/8EikpKWjUqBGMRiMMBgNatGiBvn37\nvvF6s2bNwrBhw55bX11IvYzGjRtj165dAH668338+PF18lERejEGVS1XXFyMJ0+eyA8Iu7q6IjY2\nFs7OzkhPT8fGjRtRXl6Ox48fIzo6Gp07d7baf926dUhPT4fZbEaPHj0QHh4Oo9GIOXPmyL9Mdvr0\n6dWG3q1btzBu3DgcPHgQ9+7dQ0REBH788UcoFArMnTsXH3zwgdX2qampiI+Ph0ajQdOmTZ+7w33n\nzp1wcXGpsYe+STwMqlquTZs26Nu3Lz788EO0bdsWXbp0QWhoKHx8fBAREYG//vWv8PT0RHJyMr7+\n+muroMrMzERubi6Sk5OhUCgQHh6O3bt3w2KxoGnTpli3bh2uXr2K5ORkOahWrVqFTZs2ycfYsmWL\nVT+LFy+GTqdD3759UVhYiNGjRyM1NVUeLygowLJly5CamgoPDw9MnTrVKqjMZjPi4+Md/ngUiY1B\n9Rb485//jD/84Q84cuQIjhw5gpEjR2LZsmVYs2YNDh48iOvXr+PkyZPPPcB97NgxnD9/Xr6UKy8v\nx7vvvgudTocVK1agoKAAvXr1wvTp0+V9XnTp9+yjNEePHsW1a9ewatUqAEBlZaXVQ81nz55Fhw4d\n5Ff0hIaGWj0G9cMPP6BFixYICAh4Q58OvQ0YVLXc4cOHYTKZMGDAAOh0Ouh0Omzfvh1btmzB8uXL\nMWTIEHTu3BkBAQHPzX7MZjPGjx+PCRMmAAAePnwIlUoFV1dXpKen44cffsChQ4ewYcMGpKenv1Q/\nFosFmzZtgoeHB4CfZlBeXl7IyMgA8NMrYiwWi7z9019m+1RGRgYGDBjwiz8Pejvx9oRarn79+li+\nfDlu3boFAJAkCVeuXIGTkxOUSiWmTZuGrl27IjMz87l3eHXt2hW7du2C0WhEZWUlpk+fju+//x7/\n+Mc/sHr1avTv3x+ff/457t+/L78w0JauXbti69atAIArV65g8ODB8kvfAKBjx444d+4cCgoKYLFY\n8N1331ntn5OT4/BX6JD4OKOq5bp27YoZM2Zg2rRpePLkCQBAq9VizZo1mD9/Pvr374/69eujc+fO\nz71fvk+fPsjLy8PIkSNhNpuh1WoxdOhQ+cv00NBQqNVqzJgxAw0bNnypfhYuXIiIiAj5i/C4uDho\nNBp53MvLCwsXLsTvf/97uLi4oFWrVlb737x5E02aNHmdj4TeQnwomYiEx0s/IhIeg4qIhMegIiLh\nMaiISHgMKiISHoOKiITHoCIi4f0f/1KdvA6+e8wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20825f1cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20809a21d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pd.crosstab(df_pd['SalesField7'], df_pd['label'], normalize='index').plot.bar(rot=0, stacked=True,\n",
    "            color=['green', 'red'], figsize=(4,4), title=\"SalesField7 VS label\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.2, 1))\n",
    "\n",
    "\n",
    "pd.crosstab(df_pd['PersonalField7'], df_pd['label'], normalize='index').plot.bar(rot=0, stacked=True,                            \n",
    "            color=['green', 'red'], figsize=(4,4), title=\"PersonalField7 VS label\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.2, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Insignificant Categories in Data train\n",
    "\n",
    "From visualization above we can see, there are much categories with minimal number let's say under 0.7 per cent from total observations 260753. Those minimal numbers of categories called insignificant categories and will replace with the largest numbers of catories in each categorical columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "threshold=98\n",
    "threshold2=0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def replace_cat2(f,cols):\n",
    "    \"\"\"input are dataframe and categorical variables, replace insignificant categories (percentage <=0.7) with largest number\n",
    "    of catgories and output is new dataframe \"\"\"\n",
    "    df_percent=f.groupBy(cols).count().sort(col(\"count\").desc())\\\n",
    "                .withColumn('total',sum(col('count')).over(window))\\\n",
    "                .withColumn('Percent',col('count')*100/col('total')) #calculate the percentage-save in Percent columns from each categories\n",
    "    dominant_cat=df_percent.select(df_percent['Percent']).collect()[0][0] #calculate the highest percentage of category\n",
    "    count_dist=f.select([cols]).distinct().count() #calculate distinct values in that columns\n",
    "    if count_dist > 2 and dominant_cat <= threshold :\n",
    "        print('column:', cols)\n",
    "        cols_names.append(cols)  #combine with previous list\n",
    "        replacement=f.groupBy(cols).count().sort(col(\"count\").desc()).collect()[0][0] #define dominant category \n",
    "        print(\"replacement:\",replacement)\n",
    "        replacing.append(replacement) #combine with previous list\n",
    "        insign_cat=df_percent.filter(df_percent['Percent']< threshold2).select(df_percent[cols]).collect() #calculate insignificant categories\n",
    "        insign_cat=[r[cols] for r in insign_cat] #just take the values\n",
    "        category.append(insign_cat) #combine with previous list\n",
    "        print(\"insign_cat:\",insign_cat)\n",
    "        f=f.replace(insign_cat,replacement, cols) #replace insignificant categories with dominant categories\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+-----+------+--------------------+\n",
      "|Field6|count| total|             Percent|\n",
      "+------+-----+------+--------------------+\n",
      "|     B|94694|260753|  36.315593684444664|\n",
      "|     F|69053|260753|  26.482149773923982|\n",
      "|     J|51782|260753|   19.85864016904887|\n",
      "|     E|26063|260753|   9.995282892239015|\n",
      "|     K|13226|260753|   5.072233109494426|\n",
      "|     C| 5660|260753|   2.170636579444915|\n",
      "|     D|  240|260753| 0.09204112704360065|\n",
      "|     A|   35|260753|0.013422664360525095|\n",
      "+------+-----+------+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_percent=df_final.groupBy('Field6').count().sort(col(\"count\").desc())\\\n",
    "                    .withColumn('total',sum(col('count')).over(window))\\\n",
    "                    .withColumn('Percent',col('count')*100/col('total')) #calculate the percentage-save in Percent columns from each categories\n",
    "df_percent.show()\n",
    "#dominant_cat=df_percent.select(df_percent['Percent']).collect()[0][0]\n",
    "#dominant_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column: Field6\n",
      "replacement: B\n",
      "insign_cat: ['D', 'A']\n",
      "column: CoverageField8\n",
      "replacement: Y\n",
      "insign_cat: ['Z', 'U', 'V', 'W']\n",
      "column: CoverageField9\n",
      "replacement: E\n",
      "insign_cat: ['B', 'I', 'H', 'C', 'L']\n",
      "column: SalesField7\n",
      "replacement: K\n",
      "insign_cat: []\n",
      "column: PersonalField16\n",
      "replacement: ZA\n",
      "insign_cat: ['XC', 'ZK', 'XI', 'ZD', 'ZE', 'XZ', 'ZJ', 'XL', 'XP', 'ZU', 'YI', 'XK', 'XF', 'ZB', 'XN', 'XT', 'ZI', 'XU', 'XY', 'ZO', 'ZQ', 'ZV', 'XV', 'ZL', 'ZS', 'ZP']\n",
      "column: PersonalField17\n",
      "replacement: ZE\n",
      "insign_cat: ['YU', 'YS', 'YL', 'XH', 'ZL', 'ZW', 'XB', 'XW', 'XL', 'ZS', 'YK', 'YX', 'ZN', 'ZO', 'ZG', 'ZC', 'ZP', 'YI', 'XT', 'XN', 'ZU', 'XC', 'YM', 'XI', 'XQ', 'XX', 'YT', 'ZM', 'YE', 'ZT', 'YZ', 'ZA', 'YQ', 'ZD', 'YW', 'YG', 'XD', 'ZR', 'YP', 'XM', 'XP', 'XY', 'XJ', 'ZB', 'XO']\n",
      "column: PersonalField18\n",
      "replacement: XR\n",
      "insign_cat: ['XK', 'XW', 'ZD', 'XO', 'ZL', 'XP', 'ZJ', 'ZF', 'YK', 'XN', 'ZV', 'ZQ', 'ZT', 'XY', 'XJ', 'XS', 'YL', 'XC', 'ZK', 'ZE', 'ZA', 'XT', 'ZU', 'ZR', 'XL', 'XI', 'ZO', 'XM', 'YH', 'XE', 'YR', 'ZC', 'YO', 'ZS', 'XG', 'ZM', 'YJ', 'YM', 'XD', 'XX', 'XV', 'ZB', 'ZG', 'ZI', 'XH']\n",
      "column: PersonalField19\n",
      "replacement: XD\n",
      "insign_cat: ['ZO', 'XT', 'ZA', 'YG', 'XZ', 'XY', 'ZJ', 'ZB', 'XF', 'YK', 'ZW', 'YE', 'XP', 'XN', 'XW', 'ZI', 'XI', 'YL', 'XM', 'YN', 'ZL', 'ZE', 'ZH', 'ZK', 'XJ', 'ZG', 'XV', 'ZF', 'XR', 'ZP', 'XK', 'YM', 'XB', 'ZM', 'ZR', 'ZU', 'XE', 'ZD', 'XO', 'XQ', 'XG', 'XH', 'YI', 'XL', 'ZC', 'XS']\n",
      "column: PropertyField7\n",
      "replacement: O\n",
      "insign_cat: ['K', 'H', 'E', 'L', 'F', 'G', 'M', 'P', 'C', 'B']\n",
      "column: PropertyField14\n",
      "replacement: C\n",
      "insign_cat: ['D']\n",
      "column: PropertyField28\n",
      "replacement: B\n",
      "insign_cat: ['C']\n",
      "column: PropertyField31\n",
      "replacement: O\n",
      "insign_cat: []\n",
      "column: PropertyField33\n",
      "replacement: H\n",
      "insign_cat: []\n",
      "column: GeographicField63\n",
      "replacement: N\n",
      "insign_cat: [' ']\n",
      "column: GeographicField64\n",
      "replacement: CA\n",
      "insign_cat: []\n"
     ]
    }
   ],
   "source": [
    "#call function replacing insignificant categories \n",
    "replacing=[]\n",
    "cols_names=[]\n",
    "category=[]\n",
    "for cols in cat_cols:\n",
    "    df_final=replace_cat2(df_final,cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+------+------+------------------+\n",
      "|PersonalField17| count| total|           Percent|\n",
      "+---------------+------+------+------------------+\n",
      "|             ZE|143089|260753| 54.87530344809072|\n",
      "|             XV| 20857|260753|7.9987574447849115|\n",
      "|             XS| 15733|260753| 6.033679382404038|\n",
      "|             YY| 12619|260753| 4.839445759013319|\n",
      "|             ZK| 10780|260753| 4.134180623041729|\n",
      "|             ZH|  6925|260753|2.6557700199038936|\n",
      "|             XE|  5978|260753|2.2925910727776864|\n",
      "|             XG|  5667|260753|  2.17332111231702|\n",
      "|             YV|  4937|260753|1.8933626842260682|\n",
      "|             ZF|  4180|260753|1.6030496293427112|\n",
      "|             YJ|  3946|260753|1.5133095304752007|\n",
      "|             ZQ|  3695|260753| 1.417049851775435|\n",
      "|             YH|  3045|260753|1.1677717993656833|\n",
      "|             ZI|  2923|260753|1.1209842264518528|\n",
      "|             YF|  2729|260753|1.0465843154249423|\n",
      "|             YR|  2547|260753|0.9767864607502119|\n",
      "|             YN|  2495|260753|0.9568442165574318|\n",
      "|             XR|  2446|260753|0.9380524864526966|\n",
      "|             XK|  2290|260753|0.8782257538743562|\n",
      "|             ZV|  1998|260753|0.7662423826379754|\n",
      "+---------------+------+------+------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_percent=df_final.groupBy('PersonalField17').count().sort(col(\"count\").desc())\\\n",
    "                    .withColumn('total',sum(col('count')).over(window))\\\n",
    "                    .withColumn('Percent',col('count')*100/col('total')) #calculate the percentage-save in Percent columns from each categories\n",
    "df_percent.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 15, 15)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check length in list cols_names, category and replacing\n",
    "len(cols_names), len(category), len(replacing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From **replace_cat2** function above we got 3 list,  are:\n",
    "1. list of columns, actually this list same with list categorical columns --> cols_names\n",
    "2. list of replaced categories --> category\n",
    "3. list of replacing categories --> replacing\n",
    "\n",
    "Those three list will be created into dataframe called **g**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+---------+--------------------+\n",
      "|     cols_names|replacing|            category|\n",
      "+---------------+---------+--------------------+\n",
      "|         Field6|        B|              [D, A]|\n",
      "| CoverageField8|        Y|        [Z, U, V, W]|\n",
      "| CoverageField9|        E|     [B, I, H, C, L]|\n",
      "|    SalesField7|        K|                  []|\n",
      "|PersonalField16|       ZA|[XC, ZK, XI, ZD, ...|\n",
      "|PersonalField17|       ZE|[YU, YS, YL, XH, ...|\n",
      "|PersonalField18|       XR|[XK, XW, ZD, XO, ...|\n",
      "|PersonalField19|       XD|[ZO, XT, ZA, YG, ...|\n",
      "| PropertyField7|        O|[K, H, E, L, F, G...|\n",
      "+---------------+---------+--------------------+\n",
      "only showing top 9 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Create dataframe of replaced categories\n",
    "g=spark.createDataFrame(list(zip(cols_names, replacing, category)),['cols_names', 'replacing', 'category'])\n",
    "g.show(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('cols_names', 'string'),\n",
       " ('replacing', 'string'),\n",
       " ('category', 'array<string>')]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- cols_names: string (nullable = true)\n",
      " |-- replacing: string (nullable = true)\n",
      " |-- category: array (nullable = true)\n",
      " |    |-- element: string (containsNull = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "g.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_percent=df_final.groupBy('CoverageField8').count().sort(col(\"count\").desc())\\\n",
    "                       .withColumn('total',sum(col('count')).over(window))\\\n",
    "                       .withColumn('Percent',col('count')*100/col('total'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------+------+------+------------------+\n",
      "|CoverageField8| count| total|           Percent|\n",
      "+--------------+------+------+------------------+\n",
      "|             Y|145350|260753|55.742407565780645|\n",
      "|             T| 93778|260753|35.964303382894926|\n",
      "|             X| 21625|260753| 8.293289051324434|\n",
      "+--------------+------+------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "f_percent.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing Insignificant Categories in data test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We already have a dataframe containing any categories that need to be replaced, we got it when the process of replacing the insignificant categories in the data train, the data frame is called **g**. \n",
    "Based on those information, insignificant categories on data test will be replaced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+---------+--------------------+\n",
      "|       cols_names|replacing|            category|\n",
      "+-----------------+---------+--------------------+\n",
      "|           Field6|        B|              [D, A]|\n",
      "|   CoverageField8|        Y|        [Z, U, V, W]|\n",
      "|   CoverageField9|        E|     [B, I, H, C, L]|\n",
      "|      SalesField7|        K|                  []|\n",
      "|  PersonalField16|       ZA|[XC, ZK, XI, ZD, ...|\n",
      "|  PersonalField17|       ZE|[YU, YS, YL, XH, ...|\n",
      "|  PersonalField18|       XR|[XK, XW, ZD, XO, ...|\n",
      "|  PersonalField19|       XD|[ZO, XT, ZA, YG, ...|\n",
      "|   PropertyField7|        O|[K, H, E, L, F, G...|\n",
      "|  PropertyField14|        C|                 [D]|\n",
      "|  PropertyField28|        B|                 [C]|\n",
      "|  PropertyField31|        O|                  []|\n",
      "|  PropertyField33|        H|                  []|\n",
      "|GeographicField63|        N|                 [ ]|\n",
      "|GeographicField64|       CA|                  []|\n",
      "+-----------------+---------+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#view g dataframe\n",
    "g.show(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cols_names: Field6\n",
      "replacement_cat: B\n",
      "insignificant_cat: ['D', 'A']\n",
      "cols_names: CoverageField8\n",
      "replacement_cat: Y\n",
      "insignificant_cat: ['Z', 'U', 'V', 'W']\n",
      "cols_names: CoverageField9\n",
      "replacement_cat: E\n",
      "insignificant_cat: ['B', 'I', 'H', 'C', 'L']\n",
      "cols_names: SalesField7\n",
      "replacement_cat: K\n",
      "insignificant_cat: []\n",
      "cols_names: PersonalField16\n",
      "replacement_cat: ZA\n",
      "insignificant_cat: ['XC', 'ZK', 'XI', 'ZD', 'ZE', 'XZ', 'ZJ', 'XL', 'XP', 'ZU', 'YI', 'XK', 'XF', 'ZB', 'XN', 'XT', 'ZI', 'XU', 'XY', 'ZO', 'ZQ', 'ZV', 'XV', 'ZL', 'ZS', 'ZP']\n",
      "cols_names: PersonalField17\n",
      "replacement_cat: ZE\n",
      "insignificant_cat: ['YU', 'YS', 'YL', 'XH', 'ZL', 'ZW', 'XB', 'XW', 'XL', 'ZS', 'YK', 'YX', 'ZN', 'ZO', 'ZG', 'ZC', 'ZP', 'YI', 'XT', 'XN', 'ZU', 'XC', 'YM', 'XI', 'XQ', 'XX', 'YT', 'ZM', 'YE', 'ZT', 'YZ', 'ZA', 'YQ', 'ZD', 'YW', 'YG', 'XD', 'ZR', 'YP', 'XM', 'XP', 'XY', 'XJ', 'ZB', 'XO']\n",
      "cols_names: PersonalField18\n",
      "replacement_cat: XR\n",
      "insignificant_cat: ['XK', 'XW', 'ZD', 'XO', 'ZL', 'XP', 'ZJ', 'ZF', 'YK', 'XN', 'ZV', 'ZQ', 'ZT', 'XY', 'XJ', 'XS', 'YL', 'XC', 'ZK', 'ZE', 'ZA', 'XT', 'ZU', 'ZR', 'XL', 'XI', 'ZO', 'XM', 'YH', 'XE', 'YR', 'ZC', 'YO', 'ZS', 'XG', 'ZM', 'YJ', 'YM', 'XD', 'XX', 'XV', 'ZB', 'ZG', 'ZI', 'XH']\n",
      "cols_names: PersonalField19\n",
      "replacement_cat: XD\n",
      "insignificant_cat: ['ZO', 'XT', 'ZA', 'YG', 'XZ', 'XY', 'ZJ', 'ZB', 'XF', 'YK', 'ZW', 'YE', 'XP', 'XN', 'XW', 'ZI', 'XI', 'YL', 'XM', 'YN', 'ZL', 'ZE', 'ZH', 'ZK', 'XJ', 'ZG', 'XV', 'ZF', 'XR', 'ZP', 'XK', 'YM', 'XB', 'ZM', 'ZR', 'ZU', 'XE', 'ZD', 'XO', 'XQ', 'XG', 'XH', 'YI', 'XL', 'ZC', 'XS']\n",
      "cols_names: PropertyField7\n",
      "replacement_cat: O\n",
      "insignificant_cat: ['K', 'H', 'E', 'L', 'F', 'G', 'M', 'P', 'C', 'B']\n",
      "cols_names: PropertyField14\n",
      "replacement_cat: C\n",
      "insignificant_cat: ['D']\n",
      "cols_names: PropertyField28\n",
      "replacement_cat: B\n",
      "insignificant_cat: ['C']\n",
      "cols_names: PropertyField31\n",
      "replacement_cat: O\n",
      "insignificant_cat: []\n",
      "cols_names: PropertyField33\n",
      "replacement_cat: H\n",
      "insignificant_cat: []\n",
      "cols_names: GeographicField63\n",
      "replacement_cat: N\n",
      "insignificant_cat: [' ']\n",
      "cols_names: GeographicField64\n",
      "replacement_cat: CA\n",
      "insignificant_cat: []\n"
     ]
    }
   ],
   "source": [
    "cols_names_list=g.select('cols_names').collect() #select just cols_names from dataframe g\n",
    "cols_names_list=[r['cols_names'] for r in cols_names_list] #take just the values\n",
    "\n",
    "#function to replace insignificant categories in data test\n",
    "for z in cols_names_list:\n",
    "    print('cols_names:',z)\n",
    "    replacement_cat=g.filter(g['cols_names']== z).select(g['replacing']).collect()[0][0] #select values of replacing columns accoring to z in cols_names \n",
    "    print('replacement_cat:', replacement_cat)\n",
    "    insignificant_cat=g.filter(g['cols_names']== z).select(g['category']).collect()[0][0] #select values of category columns accoring to z in cols_names\n",
    "    print('insignificant_cat:',insignificant_cat)\n",
    "    test_data=test_data.replace(insignificant_cat,replacement_cat, z) #replace insignificant cat with replacement value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+------+------+------------------+\n",
      "|GeographicField63| count| total|           Percent|\n",
      "+-----------------+------+------+------------------+\n",
      "|                N|254714|260753|  97.6840151407654|\n",
      "|                Y|  6039|260753|2.3159848592346015|\n",
      "+-----------------+------+------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "percent=df_final.groupBy('GeographicField63').count().sort(col(\"count\").desc())\\\n",
    "                       .withColumn('total',sum(col('count')).over(window))\\\n",
    "                       .withColumn('Percent',col('count')*100/col('total'))\n",
    "percent.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+------+------+-----------------+\n",
      "|GeographicField63| count| total|          Percent|\n",
      "+-----------------+------+------+-----------------+\n",
      "|                N|169856|173836|97.71048574518511|\n",
      "|                Y|  3980|173836|2.289514254814883|\n",
      "+-----------------+------+------+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "f_percent=test_data.groupBy('GeographicField63').count().sort(col(\"count\").desc())\\\n",
    "                       .withColumn('total',sum(col('count')).over(window))\\\n",
    "                       .withColumn('Percent',col('count')*100/col('total'))\n",
    "f_percent.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numerical Variables\n",
    "\n",
    "We have 260 numerical variables, and we will plot just some variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7LLu2fYHhTDQOfTYHXj1QDrVKwHfvzBvTYK/s5AjkpESgZ2AYr79XAUnizemn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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae2505ba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#density plot Field7\n",
    "#plt.figure(figsize=(24,5))\n",
    "sns.distplot(df_pd['Field7'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Numerical vs Target visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/f3+ioqJcM+ro6Ghqa2ux22VPVyFE83Ki8gQAbSPa+qwP7dEjGLZsxjZkKEps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\nmfl/f80e1Qi7V/sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae6fa0ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show distribution 'Field7' vs 'label'\n",
    "#plt.figure(figsize=(20,8))\n",
    "sns.kdeplot(df_pd[df_pd[\"label\"]==0][\"Field7\"], label=\"0\", color=\"green\")\n",
    "sns.kdeplot(df_pd[df_pd[\"label\"]==1][\"Field7\"], label=\"1\", color=\"red\")\n",
    "plt.title(\"Field7 VS label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae10584630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check outlier in numerical variable\n",
    "df_pd[[\"Field7\"]].boxplot(sym='g-*', grid=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Handle of outlier\n",
    "Outlier is observations that fall below lower side or above upper side.\n",
    "\n",
    "To handle outlier we approach by replacing the value greater than upper side with upper side value and also replacing the value lower than lower side with lower side value.\n",
    "So, we need calculate upper and lower side from quantile value, quantile is probability distribution of variable. In General, there are three quantile:\n",
    "1. Q1 = the value that cut off 25% of the first data when it is sorted in ascending order. \n",
    "2. Q2 = cut off data, or median, it's 50 % of the data \n",
    "3. Q3 = the value that cut off 75% of the first data when it is sorted in ascending order.\n",
    "\n",
    "IQR or interquartile range is range between Q1 and Q3. IQR = Q3 - Q1.\n",
    "\n",
    "Upper side = Q3 + 1.5 * IQR\n",
    "\n",
    "Lower side = Q1 - 1.5 * IQR\n",
    "\n",
    "To calculate upper and lower side we use pandas dataframe and pyspark dataframe. Both ways have same result.\n",
    "Then, we will use result from pyspark to handle outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle of outlier in data train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate Upper&Lower side in pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>QuoteNumber</th>\n",
       "      <th>label</th>\n",
       "      <th>Field7</th>\n",
       "      <th>Field8</th>\n",
       "      <th>Field9</th>\n",
       "      <th>Field10</th>\n",
       "      <th>Field11</th>\n",
       "      <th>CoverageField1A</th>\n",
       "      <th>CoverageField1B</th>\n",
       "      <th>CoverageField2A</th>\n",
       "      <th>...</th>\n",
       "      <th>GeographicField58A</th>\n",
       "      <th>GeographicField58B</th>\n",
       "      <th>GeographicField59A</th>\n",
       "      <th>GeographicField59B</th>\n",
       "      <th>GeographicField60A</th>\n",
       "      <th>GeographicField60B</th>\n",
       "      <th>GeographicField61A</th>\n",
       "      <th>GeographicField61B</th>\n",
       "      <th>GeographicField62A</th>\n",
       "      <th>GeographicField62B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "      <td>26239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>216470.226952</td>\n",
       "      <td>0.188460</td>\n",
       "      <td>18.284767</td>\n",
       "      <td>0.938466</td>\n",
       "      <td>0.001465</td>\n",
       "      <td>950.580700</td>\n",
       "      <td>1.162168</td>\n",
       "      <td>9.011471</td>\n",
       "      <td>13.010671</td>\n",
       "      <td>9.055795</td>\n",
       "      <td>...</td>\n",
       "      <td>11.507794</td>\n",
       "      <td>12.900720</td>\n",
       "      <td>11.690728</td>\n",
       "      <td>13.036129</td>\n",
       "      <td>-0.361866</td>\n",
       "      <td>12.934068</td>\n",
       "      <td>-0.358893</td>\n",
       "      <td>12.986242</td>\n",
       "      <td>-0.344030</td>\n",
       "      <td>14.094897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>125864.692290</td>\n",
       "      <td>0.391086</td>\n",
       "      <td>7.375948</td>\n",
       "      <td>0.037184</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>290.298805</td>\n",
       "      <td>0.116729</td>\n",
       "      <td>5.568995</td>\n",
       "      <td>7.204728</td>\n",
       "      <td>5.552631</td>\n",
       "      <td>...</td>\n",
       "      <td>5.455040</td>\n",
       "      <td>7.236423</td>\n",
       "      <td>5.640301</td>\n",
       "      <td>7.237964</td>\n",
       "      <td>4.023045</td>\n",
       "      <td>7.232334</td>\n",
       "      <td>4.032168</td>\n",
       "      <td>7.142827</td>\n",
       "      <td>4.077445</td>\n",
       "      <td>5.816711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.874600</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>107131.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.915300</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>216172.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.940300</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>965.000000</td>\n",
       "      <td>1.188600</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>325870.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.968500</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>1113.000000</td>\n",
       "      <td>1.266500</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>434588.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.010100</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>1487.000000</td>\n",
       "      <td>1.304500</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 272 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         QuoteNumber         label        Field7        Field8        Field9  \\\n",
       "count   26239.000000  26239.000000  26239.000000  26239.000000  26239.000000   \n",
       "mean   216470.226952      0.188460     18.284767      0.938466      0.001465   \n",
       "std    125864.692290      0.391086      7.375948      0.037184      0.001493   \n",
       "min        13.000000      0.000000      1.000000      0.874600      0.000400   \n",
       "25%    107131.000000      0.000000     11.000000      0.915300      0.000600   \n",
       "50%    216172.000000      0.000000     23.000000      0.940300      0.000600   \n",
       "75%    325870.500000      0.000000     24.000000      0.968500      0.003800   \n",
       "max    434588.000000      1.000000     28.000000      1.010100      0.004000   \n",
       "\n",
       "            Field10       Field11  CoverageField1A  CoverageField1B  \\\n",
       "count  26239.000000  26239.000000     26239.000000     26239.000000   \n",
       "mean     950.580700      1.162168         9.011471        13.010671   \n",
       "std      290.298805      0.116729         5.568995         7.204728   \n",
       "min      548.000000      1.000000        -1.000000        -1.000000   \n",
       "25%      564.000000      1.020000         5.000000         7.000000   \n",
       "50%      965.000000      1.188600         8.000000        13.000000   \n",
       "75%     1113.000000      1.266500        12.000000        19.000000   \n",
       "max     1487.000000      1.304500        25.000000        25.000000   \n",
       "\n",
       "       CoverageField2A         ...          GeographicField58A  \\\n",
       "count     26239.000000         ...                26239.000000   \n",
       "mean          9.055795         ...                   11.507794   \n",
       "std           5.552631         ...                    5.455040   \n",
       "min           1.000000         ...                   -1.000000   \n",
       "25%           5.000000         ...                    8.000000   \n",
       "50%           8.000000         ...                   11.000000   \n",
       "75%          12.000000         ...                   15.000000   \n",
       "max          25.000000         ...                   25.000000   \n",
       "\n",
       "       GeographicField58B  GeographicField59A  GeographicField59B  \\\n",
       "count        26239.000000        26239.000000        26239.000000   \n",
       "mean            12.900720           11.690728           13.036129   \n",
       "std              7.236423            5.640301            7.237964   \n",
       "min             -1.000000            1.000000            1.000000   \n",
       "25%              7.000000            7.000000            7.000000   \n",
       "50%             13.000000           11.000000           13.000000   \n",
       "75%             19.000000           15.000000           19.000000   \n",
       "max             25.000000           25.000000           25.000000   \n",
       "\n",
       "       GeographicField60A  GeographicField60B  GeographicField61A  \\\n",
       "count        26239.000000        26239.000000        26239.000000   \n",
       "mean            -0.361866           12.934068           -0.358893   \n",
       "std              4.023045            7.232334            4.032168   \n",
       "min             -1.000000           -1.000000           -1.000000   \n",
       "25%             -1.000000            7.000000           -1.000000   \n",
       "50%             -1.000000           13.000000           -1.000000   \n",
       "75%             -1.000000           19.000000           -1.000000   \n",
       "max             25.000000           25.000000           25.000000   \n",
       "\n",
       "       GeographicField61B  GeographicField62A  GeographicField62B  \n",
       "count        26239.000000        26239.000000        26239.000000  \n",
       "mean            12.986242           -0.344030           14.094897  \n",
       "std              7.142827            4.077445            5.816711  \n",
       "min             -1.000000           -1.000000           -1.000000  \n",
       "25%              7.000000           -1.000000            8.000000  \n",
       "50%             13.000000           -1.000000           13.000000  \n",
       "75%             19.000000           -1.000000           19.000000  \n",
       "max             25.000000           25.000000           25.000000  \n",
       "\n",
       "[8 rows x 272 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_describe=df_pd.describe()\n",
    "df_describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create quantile dataframe\n",
    "def quantile(e):\n",
    "    \"\"\"Input is dataframe and return new dataframe with value of quantile from numerical columns\"\"\"\n",
    "    percentiles = [0.25, 0.5, 0.75]\n",
    "    quant=spark.createDataFrame(zip(percentiles, *e.approxQuantile(num_cols, percentiles, 0.0)),\n",
    "                               ['percentile']+num_cols) #calculate quantile from pyspark dataframe, 0.0 is relativeError,\n",
    "                                                        #The relative target precision to achieve (>= 0). If set to zero, \n",
    "                                                        #the exact quantiles are computed, which could be very expensive\n",
    "                                                        #and aggregate the result with percentiles variable, \n",
    "                                                        #then create pyspark dataframe\n",
    "    return quant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 52.1 s\n"
     ]
    }
   ],
   "source": [
    "#call function quantile and apply to data train sample(df_sample)\n",
    "%time quantile=quantile(df_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#function to calculate uppler side\n",
    "def upper_value(b,c):\n",
    "    \"\"\"Input is quantile dataframe and name of numerical column and Retrun upper value from the column\"\"\"\n",
    "    q1 = b.select(c).collect()[0][0] #select value of q1 from the column\n",
    "    q2 = b.select(c).collect()[1][0] #select value of q2 from the column\n",
    "    q3 = b.select(c).collect()[2][0] #select value of q3 from the column\n",
    "    IQR=q3-q1  #calculate the value of IQR\n",
    "    upper= q3 + (IQR*1.5)   #calculate the value of upper side\n",
    "    return upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function upper_value in module __main__:\n",
      "\n",
      "upper_value(b, c)\n",
      "    Input is quantile dataframe and name of numerical column and Retrun upper value from the column\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(upper_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#function to calculate lower side\n",
    "def lower_value(b,c):\n",
    "    \"\"\"Input is quantile dataframe and name of numerical column and Retrun lower value from the column\"\"\"\n",
    "    q1 = b.select(c).collect()[0][0] #select value of q1 from the column\n",
    "    q2 = b.select(c).collect()[1][0] #select value of q2 from the column\n",
    "    q3 = b.select(c).collect()[2][0] #select value of q3 from the column\n",
    "    IQR=q3-q1                   #calculate the value of IQR\n",
    "    lower= q1 - (IQR*1.5)       #calculate the value of lower side\n",
    "    return lower"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replacing the outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#function for replacing outlier by upper side\n",
    "def replce_outlier_up2(d,col, value):\n",
    "    \"\"\"Input is name of numerical column and it's upper side value\"\"\"\n",
    "    d=d.withColumn(col, F.when(d[col] > value , value).otherwise(d[col]))\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#function for replacing outlier by lower side\n",
    "def replce_outlier_low2(d,col, value):\n",
    "    \"\"\"Input is name of numerical column and it's lower side value\"\"\"\n",
    "    d=d.withColumn(col, F.when(d[col] < value , value).otherwise(d[col]))\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 18.6 s\n"
     ]
    }
   ],
   "source": [
    "%time lower_Field7=lower_value(quantile,'Field7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-8.5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time lower_Field7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 206 ms\n"
     ]
    }
   ],
   "source": [
    "%time df_final=replce_outlier_low2(df_final, 'Field7', lower_Field7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 16.8 s\n"
     ]
    }
   ],
   "source": [
    "%time upper_Field7=upper_value(quantile,'Field7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 104 ms\n"
     ]
    }
   ],
   "source": [
    "%time df_final=replce_outlier_up2(df_final, 'Field7', upper_Field7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#call function to calculate lower side and replace value under lower side with value lower side at all numerical variables\n",
    "for i in num_cols:\n",
    "    lower=lower_value(quantile,i)\n",
    "    df_final=replce_outlier_low2(df_final, i, lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#call function to calculate upper side and replace value above upper side with value upper side at all numerical variables\n",
    "for x in num_cols:\n",
    "    upper=upper_value(quantile,x)\n",
    "    df_final=replce_outlier_up2(df_final, x, upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+\n",
      "|QuoteNumber|\n",
      "+-----------+\n",
      "|          1|\n",
      "|          2|\n",
      "|          4|\n",
      "|          6|\n",
      "|          8|\n",
      "+-----------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_final.select('QuoteNumber').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle of outlier in data test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd_9=test_data.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2190.000000</td>\n",
       "      <td>57.378341</td>\n",
       "      <td>68.645648</td>\n",
       "      <td>9819.161069</td>\n",
       "      <td>1971.357779</td>\n",
       "      <td>1983.662783</td>\n",
       "      <td>100.712132</td>\n",
       "      <td>439.203564</td>\n",
       "      <td>52.619602</td>\n",
       "      <td>554.294722</td>\n",
       "      <td>...</td>\n",
       "      <td>472.769020</td>\n",
       "      <td>93.174777</td>\n",
       "      <td>48.313914</td>\n",
       "      <td>24.243317</td>\n",
       "      <td>1.794380</td>\n",
       "      <td>17.064428</td>\n",
       "      <td>1.744345</td>\n",
       "      <td>58.167923</td>\n",
       "      <td>6.104181</td>\n",
       "      <td>2007.769705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.321334</td>\n",
       "      <td>42.746880</td>\n",
       "      <td>20.561791</td>\n",
       "      <td>4955.517327</td>\n",
       "      <td>30.390071</td>\n",
       "      <td>21.130467</td>\n",
       "      <td>176.709827</td>\n",
       "      <td>455.111888</td>\n",
       "      <td>176.693301</td>\n",
       "      <td>437.110508</td>\n",
       "      <td>...</td>\n",
       "      <td>216.974165</td>\n",
       "      <td>127.744882</td>\n",
       "      <td>68.883364</td>\n",
       "      <td>67.227765</td>\n",
       "      <td>20.207842</td>\n",
       "      <td>56.609763</td>\n",
       "      <td>30.491646</td>\n",
       "      <td>630.806978</td>\n",
       "      <td>2.722432</td>\n",
       "      <td>1.301740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1461.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1879.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1825.500000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>7391.000000</td>\n",
       "      <td>1953.000000</td>\n",
       "      <td>1963.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>219.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>318.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2190.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9399.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2554.500000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>11517.500000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>752.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>797.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2919.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>56600.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1290.000000</td>\n",
       "      <td>4010.000000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>2140.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>1424.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage       LotArea    YearBuilt  \\\n",
       "count  1459.000000  1459.000000  1459.000000   1459.000000  1459.000000   \n",
       "mean   2190.000000    57.378341    68.645648   9819.161069  1971.357779   \n",
       "std     421.321334    42.746880    20.561791   4955.517327    30.390071   \n",
       "min    1461.000000    20.000000    21.000000   1470.000000  1879.000000   \n",
       "25%    1825.500000    20.000000    60.000000   7391.000000  1953.000000   \n",
       "50%    2190.000000    50.000000    69.000000   9399.000000  1973.000000   \n",
       "75%    2554.500000    70.000000    78.000000  11517.500000  2001.000000   \n",
       "max    2919.000000   190.000000   200.000000  56600.000000  2010.000000   \n",
       "\n",
       "       YearRemodAdd   MasVnrArea   BsmtFinSF1   BsmtFinSF2    BsmtUnfSF  \\\n",
       "count   1459.000000  1459.000000  1459.000000  1459.000000  1459.000000   \n",
       "mean    1983.662783   100.712132   439.203564    52.619602   554.294722   \n",
       "std       21.130467   176.709827   455.111888   176.693301   437.110508   \n",
       "min     1950.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%     1963.000000     0.000000     0.000000     0.000000   219.500000   \n",
       "50%     1992.000000     0.000000   351.000000     0.000000   460.000000   \n",
       "75%     2004.000000   162.000000   752.000000     0.000000   797.500000   \n",
       "max     2010.000000  1290.000000  4010.000000  1526.000000  2140.000000   \n",
       "\n",
       "          ...        GarageArea   WoodDeckSF  OpenPorchSF  EnclosedPorch  \\\n",
       "count     ...       1459.000000  1459.000000  1459.000000    1459.000000   \n",
       "mean      ...        472.769020    93.174777    48.313914      24.243317   \n",
       "std       ...        216.974165   127.744882    68.883364      67.227765   \n",
       "min       ...          0.000000     0.000000     0.000000       0.000000   \n",
       "25%       ...        318.000000     0.000000     0.000000       0.000000   \n",
       "50%       ...        480.000000     0.000000    28.000000       0.000000   \n",
       "75%       ...        576.000000   168.000000    72.000000       0.000000   \n",
       "max       ...       1488.000000  1424.000000   742.000000    1012.000000   \n",
       "\n",
       "         3SsnPorch  ScreenPorch     PoolArea       MiscVal       MoSold  \\\n",
       "count  1459.000000  1459.000000  1459.000000   1459.000000  1459.000000   \n",
       "mean      1.794380    17.064428     1.744345     58.167923     6.104181   \n",
       "std      20.207842    56.609763    30.491646    630.806978     2.722432   \n",
       "min       0.000000     0.000000     0.000000      0.000000     1.000000   \n",
       "25%       0.000000     0.000000     0.000000      0.000000     4.000000   \n",
       "50%       0.000000     0.000000     0.000000      0.000000     6.000000   \n",
       "75%       0.000000     0.000000     0.000000      0.000000     8.000000   \n",
       "max     360.000000   576.000000   800.000000  17000.000000    12.000000   \n",
       "\n",
       "            YrSold  \n",
       "count  1459.000000  \n",
       "mean   2007.769705  \n",
       "std       1.301740  \n",
       "min    2006.000000  \n",
       "25%    2007.000000  \n",
       "50%    2008.000000  \n",
       "75%    2009.000000  \n",
       "max    2010.000000  \n",
       "\n",
       "[8 rows x 35 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_9.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create quantile dataframe\n",
    "def quantile(e):\n",
    "    \"\"\"Input is dataframe and return new dataframe with value of quantile from numerical columns\"\"\"\n",
    "    percentiles = [0.25, 0.5, 0.75]\n",
    "    quant=spark.createDataFrame(zip(percentiles, *e.approxQuantile(num_cols_test, percentiles, 0.0)),\n",
    "                               ['percentile']+num_cols_test) #calculate quantile from pyspark dataframe, 0.0 is relativeError,\n",
    "                                                        #The relative target precision to achieve (>= 0). If set to zero, \n",
    "                                                        #the exact quantiles are computed, which could be very expensive\n",
    "                                                        #and aggregate the result with percentiles variable, \n",
    "                                                        #then create pyspark dataframe\n",
    "    return quant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#call funtion quantile\n",
    "quantile=quantile(test_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#call function to calculate lower side and replace value under lower side with value lower side at all numerical variables\n",
    "for i in num_cols_test:\n",
    "    lower=lower_value(quantile,i)\n",
    "    test_data=replce_outlier_low2(test_data, i, lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#call function to calculate upper side and replace value above upper side with value upper side at all numerical variables\n",
    "for x in num_cols_test:\n",
    "    upper=upper_value(quantile,x)\n",
    "    test_data=replce_outlier_up2(test_data, x, upper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "Before splitting the data train, all categorical variables must be made numerical. There are several approaches to categorical variables in SparkML, including:\n",
    "1. StringIndexer, which is to encode the string label into the index label by sequencing the string frequency descending and giving the smallest index (0)\n",
    "at most string frequency.\n",
    "2. One-hot Encoding, which is mapping the label column (string label) on the binary column.\n",
    "3. Vector assembler, which is mapping all columns in vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def check_distinct(a1,a2):\n",
    "    \"\"\"input are two dataframe that you want to compare categorical variables and the output is \n",
    "    total distinct categories in both dataframe\"\"\"\n",
    "    total1=0\n",
    "    total2=0\n",
    "    for y in cat_cols:\n",
    "        distinct1=a1.select([y]).distinct().count() #count distinct column in dataframe1\n",
    "        distinct2=a2.select([y]).distinct().count() #count distinct column in dataframe2\n",
    "        var1=a1.select([y]).distinct().collect() #define distinct category in column in dataframe1\n",
    "        var1=[r[y] for r in var1]\n",
    "        var2=a2.select([y]).distinct().collect()\n",
    "        var2=[r[y] for r in var2]\n",
    "        total1=total1+distinct1\n",
    "        total2=total2+distinct2   \n",
    "    return total1, total2    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#function to execute feature engineering\n",
    "def feature_engineering(a1):    \n",
    "    \"\"\"Function for feature engineering (StringIndexer and OneHotEncoder process)\"\"\"\n",
    "    cat_columns_string_vec = []\n",
    "    for c in cat_cols:\n",
    "        cat_columns_string= c+\"_vec\"\n",
    "        cat_columns_string_vec.append(cat_columns_string)\n",
    "    stringIndexer = [StringIndexer(inputCol=x, outputCol=x+\"_Index\")\n",
    "                  for x in cat_cols]\n",
    "    #use oneHotEncoder to convert categorical variable to binary\n",
    "    encoder = [OneHotEncoder(inputCol=x+\"_Index\", outputCol=y)\n",
    "           for x,y in zip(cat_cols, cat_columns_string_vec)]\n",
    "    #create list of stringIndexer and encoder with 2 dimension\n",
    "    tmp = [[i,j] for i,j in zip(stringIndexer, encoder)]\n",
    "    tmp = [i for sublist in tmp for i in sublist]\n",
    "    cols_assember=num_id + num_cols + cat_columns_string_vec\n",
    "    assembler=VectorAssembler(inputCols=cols_assember, outputCol='features')\n",
    "    tmp += [assembler]\n",
    "    pipeline=Pipeline(stages=tmp)\n",
    "    df_final_feat=pipeline.fit(a1).transform(a1)\n",
    "    return df_final_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#fucntion to call fucntion feature_engineering and check_distinct\n",
    "def Main_feature_engineering(df,df2): \n",
    "    \"\"\"Function for calling check_distinct and feature_engineering. Then Join data train and data test if distinct categories \n",
    "    between data train and data test not same then do feature engineering, If distinct same will do feature engineering data train\n",
    "    and data test separately\"\"\"\n",
    "    dist_total1, dist_total2=check_distinct(df,df2)   \n",
    "    if dist_total1!=dist_total2:\n",
    "        Label_df=df.select('Id', 'label')\n",
    "        df_final2=df.drop('label')\n",
    "        all_df =df_final2.union(df2)\n",
    "        all_df_feat=feature_engineering(all_df)\n",
    "        id_train=df.select('Id').collect()\n",
    "        id_train=[r['Id'] for r in id_train]\n",
    "        id_test=df2.select('Id').collect()\n",
    "        id_test=[r['Id'] for r in id_test]\n",
    "        a=all_df_feat.filter(all_df['Id'].isin(id_train))\n",
    "        b=all_df_feat.filter(all_df['Id'].isin(id_test))\n",
    "        a=a.join(Label_df, 'Id')\n",
    "    else:\n",
    "        a=feature_engineering(df)\n",
    "        b=feature_engineering(df2)        \n",
    "    return a,b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Call function feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 8min 43s\n"
     ]
    }
   ],
   "source": [
    "#call function feature engineering\n",
    "%time data2, test2=Main_feature_engineering(df_final, test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+--------------------+\n",
      "| Id|            features|\n",
      "+---+--------------------+\n",
      "|  1|(391,[0,1,2,3,4,5...|\n",
      "|  2|(391,[0,1,2,3,4,5...|\n",
      "|  4|(391,[0,1,2,3,4,5...|\n",
      "|  6|(391,[0,1,2,3,4,5...|\n",
      "|  8|(391,[0,1,2,3,4,5...|\n",
      "+---+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#view result of feature engineering in data train\n",
    "data2.select('Id', 'features').show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+--------------------+\n",
      "| Id|            features|\n",
      "+---+--------------------+\n",
      "|  3|(391,[0,1,2,3,4,5...|\n",
      "|  5|(391,[0,1,2,3,4,5...|\n",
      "|  7|(391,[0,1,2,3,4,5...|\n",
      "|  9|(391,[0,1,2,3,4,5...|\n",
      "| 10|(391,[0,1,2,3,4,5...|\n",
      "+---+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#view result of feature engineering in data test\n",
    "test2.select('Id', 'features').show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Data train to train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Split df_final to train and test, train 70% and test 30%. Define seed 24 so the random data that we split will not change.\n",
    "#we can define seed with any value\n",
    "data_train, data_test=data2.randomSplit([0.7,0.3], 24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling \n",
    "We will use four algorithm to make a model and prediction, they are:\n",
    "\n",
    "1. Logistic Regression\n",
    "Logistic regression used logit function in prediction the probability. \n",
    "2. Decision Tree\n",
    "This algorithm will find the most significant independent variable to  create a group. \n",
    "3. Random Forest\n",
    "This algorithm build multiple decision trees and merges them together and use bagging method.\n",
    "4. Gradient Boosting \n",
    "This algorithm use boosting ensemble technic. This technique employs the logic in which the subsequent predictors learn from the mistakes of the previous predictors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation\n",
    "To evaluate model we use four metrics, they are:\n",
    "\n",
    "1. ##### ROC\n",
    "ROC (Receiver Operating Characteristic)\n",
    "The graph shows the true positive rate versus the false positive rate.\n",
    "This metric is between 0 and 1 with a better model scoring higher.\n",
    "An area of 1 represents a perfect test; an area of .5 represents a worthless test. So, The model is said to be good enaught if the value of the area under the curve is above 0.5\n",
    "\n",
    "2. ##### Gini Coefficient\n",
    "Gini is ratio between the ROC curve and the diagnol line & the area of the above triangle. So, we can calculate Gini by this formula:\n",
    "Gini = 2*AUC - 1\n",
    "Such as AUC ROC, Gini above 50% or 60% is good model.\n",
    "\n",
    "3. ##### Confusion Matrix\n",
    "Confusion Matrix is a table is used to describe performance of a classification model. Some definition are:\n",
    "  a. Accuracy = Proportion of total number of predictions that were correct\n",
    "  b. Precision (Positive Predictive Value) : Proportion of positive cases that were correctly identified.\n",
    "  c. Negative Predictive Value : Proportion of negative cases that were correctly identified.\n",
    "  d. Sensitivity (Recall) : Proportion of actual positive cases which are correctly identified.\n",
    "  e. Specificity : Proportion of actual negative cases which are correctly identified.\n",
    "  \n",
    "4. ##### Log Loss\n",
    "Log Loss is one of model performance evaluation in classification model. The purpose of model is to minimize log loss value. A perfect model would have of log loss of 0.\n",
    "Log Loss increase when predicted probability diverges from actual label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling & Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create logistic regression model to data train\n",
    "lr=LogisticRegression(featuresCol='features', labelCol='label')\n",
    "lr_model = lr.fit(data_train)\n",
    "\n",
    "#Transform model to data test\n",
    "lr_result = lr_model.transform(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+-----+----------+--------------------+\n",
      "|QuoteNumber|label|prediction|         probability|\n",
      "+-----------+-----+----------+--------------------+\n",
      "|          1|    0|       0.0|[0.98970992249340...|\n",
      "|          2|    0|       0.0|[0.94277193429369...|\n",
      "|          6|    0|       0.0|[0.96793973374689...|\n",
      "|         12|    0|       0.0|[0.95114651013774...|\n",
      "|         13|    0|       0.0|[0.99965320682235...|\n",
      "+-----------+-----+----------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#view id, label, prediction and probability from result of modelling\n",
    "lr_result.select('Id', 'label', 'prediction', 'probability').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Performance Measure\n",
      "Accuracy = 0.89\n",
      "AUC = 0.89\n"
     ]
    }
   ],
   "source": [
    "#Evaluate model by checking accuracy and AUC value\n",
    "lr_eval = BinaryClassificationEvaluator(rawPredictionCol=\"probability\", labelCol=\"label\")\n",
    "lr_eval2= MulticlassClassificationEvaluator(predictionCol=\"prediction\", labelCol=\"label\")\n",
    "lr_AUC  = lr_eval.evaluate(lr_result)\n",
    "lr_ACC  = lr_eval2.evaluate(lr_result, {lr_eval2.metricName:\"accuracy\"})\n",
    "\n",
    "print(\"Logistic Regression Performance Measure\")\n",
    "print(\"Accuracy = %0.2f\" % lr_ACC)\n",
    "print(\"AUC = %.2f\" % lr_AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ROC Grafik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Area Under ROC\n",
      "Area under ROC = 0.89\n"
     ]
    },
    {
     "data": {
      "image/png": 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Uqbv9II6Tfz2kgKMV/rVKGDuOkM8cPXqEQYP6EhX1mCZNmlGpUmVjR8qXMiye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\npQzQ4qmUUgZo8VRKKQP+B8g7qbzMbmkgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2081d729e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create ROC grafik from lr_result\n",
    "PredAndLabels           = lr_result.select(\"probability\", \"label\")\n",
    "PredAndLabels_collect   = PredAndLabels.collect()\n",
    "PredAndLabels_list      = [(float(i[0][0]), 1.0-float(i[1])) for i in PredAndLabels_collect]\n",
    "PredAndLabels           = sc.parallelize(PredAndLabels_list)\n",
    "\n",
    "metrics = BinaryClassificationMetrics(PredAndLabels)\n",
    "\n",
    "# Area under ROC\n",
    "print(\"Logistic Regression Area Under ROC\")\n",
    "print(\"Area under ROC = %.2f\" % metrics.areaUnderROC)\n",
    "\n",
    "# Visualization\n",
    "FPR = dict()                                                        # FPR: False Positive Rate\n",
    "tpr = dict()                                                        # TPR: True Positive Rate\n",
    "roc_auc = dict()\n",
    " \n",
    "y_test = [i[1] for i in PredAndLabels_list]\n",
    "y_score = [i[0] for i in PredAndLabels_list]\n",
    " \n",
    "fpr, tpr, _ = roc_curve(y_test, y_score)\n",
    "roc_auc = auc(fpr, tpr)\n",
    " \n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Logistic Regression')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction_label</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1918</td>\n",
       "      <td>8294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>61886</td>\n",
       "      <td>6451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  prediction_label      0     1\n",
       "0              1.0   1918  8294\n",
       "1              0.0  61886  6451"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_lr_result = lr_result.crosstab(\"prediction\", \"label\")\n",
    "cm_lr_result = cm_lr_result.toPandas()\n",
    "cm_lr_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.89\n",
      "Sensitivity = 0.56\n",
      "Specificity = 0.97\n",
      "Precision = 0.81\n"
     ]
    }
   ],
   "source": [
    "#calculate Accuracy, Sensitivity, Specificity, Precision\n",
    "TP = cm_lr_result[\"1\"][0]\n",
    "FP = cm_lr_result[\"0\"][0]\n",
    "TN = cm_lr_result[\"0\"][1]\n",
    "FN = cm_lr_result[\"1\"][1]\n",
    "Accuracy = (TP+TN)/(TP+FP+TN+FN)\n",
    "Sensitivity = TP/(TP+FN)\n",
    "Specificity = TN/(TN+FP)\n",
    "Precision = TP/(TP+FP)\n",
    "\n",
    "print (\"Accuracy = %0.2f\" %Accuracy )\n",
    "print (\"Sensitivity = %0.2f\" %Sensitivity )\n",
    "print (\"Specificity = %0.2f\" %Specificity )\n",
    "print (\"Precision = %0.2f\" %Precision )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Gini  Coefficient from AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC=0.89\n",
      "GINI ~=0.78\n"
     ]
    }
   ],
   "source": [
    "AUC = lr_AUC\n",
    "Gini = (2 * AUC - 1)\n",
    "print(\"AUC=%.2f\" % AUC)\n",
    "print(\"GINI ~=%.2f\" % Gini)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Log Loss in pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create Dataframe to Calculate Log Loss\n",
    "y_test= data_test.select('label')\n",
    "lr_proba=lr_result.select('probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Convert lr_probaspark dataframe to numpy array\n",
    "lr_proba= np.array(lr_result.select('probability').collect())\n",
    "\n",
    "#Convert numpy array 3 dimentional to 2 dimentional\n",
    "lr_proba=lr_proba.reshape(-1, lr_proba.shape[-1])\n",
    "\n",
    "#Convert y_test dataframe to pandas dataframe\n",
    "y_test=y_test.toPandas()\n",
    "\n",
    "#Convert y_test pandas dataframe to pandas series\n",
    "y_test=pd.Series(y_test['label'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Loss Linear Regression:0.2821\n"
     ]
    }
   ],
   "source": [
    "#Calculate log loss from logistic regression\n",
    "LogLoss = log_loss(y_test, lr_proba) \n",
    "\n",
    "print(\"Log Loss Linear Regression:%.4f\" % LogLoss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression With Hyper-Parameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#define logistic regression model\n",
    "lr_hyper=LogisticRegression(featuresCol='features', labelCol='label')\n",
    "\n",
    "\n",
    "#Hyper-Parameter Tuning\n",
    "paramGrid_lr = ParamGridBuilder() \\\n",
    "    .addGrid(lr_hyper.regParam, [0.1, 0.01]) \\\n",
    "    .addGrid(lr_hyper.elasticNetParam, [0.8, 0.7]) \\\n",
    "    .build()\n",
    "crossval_lr = CrossValidator(estimator=lr_hyper,\n",
    "                             estimatorParamMaps=paramGrid_lr,\n",
    "                             evaluator=BinaryClassificationEvaluator(),\n",
    "                             numFolds=3)\n",
    "#fit model to data train\n",
    "lr_model_hyper = crossval_lr.fit(data_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Transform model to data test\n",
    "lr_result_hyper = lr_model_hyper.transform(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#view id, label, prediction and probability from result of modelling\n",
    "lr_result_hyper.select('Id', 'label', 'prediction', 'probability').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression With Hyper-Parameter Tuning Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Evaluate model by checking accuracy and AUC value\n",
    "lr_hyper_eval = BinaryClassificationEvaluator(rawPredictionCol=\"probability\", labelCol=\"label\")\n",
    "lr_hyper_eval2= MulticlassClassificationEvaluator(predictionCol=\"prediction\", labelCol=\"label\")\n",
    "lr_hyper_AUC  = lr_hyper_eval.evaluate(lr_result_hyper)\n",
    "lr_hyper_ACC  = lr_hyper_eval2.evaluate(lr_result_hyper, {lr_hyper_eval2.metricName:\"accuracy\"})\n",
    "\n",
    "print(\"Logistic Regression Performance Measure\")\n",
    "print(\"Accuracy = %0.2f\" % lr_hyper_ACC)\n",
    "print(\"AUC = %.2f\" % lr_hyper_AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ROC Grafik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PredAndLabels           = lr_result_hyper.select(\"probability\", \"label\")\n",
    "PredAndLabels_collect   = PredAndLabels.collect()\n",
    "PredAndLabels_list      = [(float(i[0][0]), 1.0-float(i[1])) for i in PredAndLabels_collect]\n",
    "PredAndLabels           = sc.parallelize(PredAndLabels_list)\n",
    "\n",
    "metrics = BinaryClassificationMetrics(PredAndLabels)\n",
    "\n",
    "# Area under ROC\n",
    "print(\"Logistic Regression Area Under ROC\")\n",
    "print(\"Area under ROC = %.2f\" % metrics.areaUnderROC)\n",
    "\n",
    "# Visualization\n",
    "FPR = dict()                                                        # FPR: False Positive Rate\n",
    "tpr = dict()                                                        # TPR: True Positive Rate\n",
    "roc_auc = dict()\n",
    " \n",
    "y_test = [i[1] for i in PredAndLabels_list]\n",
    "y_score = [i[0] for i in PredAndLabels_list]\n",
    " \n",
    "fpr, tpr, _ = roc_curve(y_test, y_score)\n",
    "roc_auc = auc(fpr, tpr)\n",
    " \n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Logistic Regression')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confution Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm_lr_result_hyper = lr_result_hyper.crosstab(\"prediction\", \"label\")\n",
    "cm_lr_result_hyper = cm_lr_result_hyper.toPandas()\n",
    "cm_lr_result_hyper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#calculate Accuracy, Sensitivity, Specificity, Precision\n",
    "TP = cm_lr_result_hyper[\"1\"][0]\n",
    "FP = cm_lr_result_hyper[\"0\"][0]\n",
    "TN = cm_lr_result_hyper[\"0\"][1]\n",
    "FN = cm_lr_result_hyper[\"1\"][1]\n",
    "Accuracy = (TP+TN)/(TP+FP+TN+FN)\n",
    "Sensitivity = TP/(TP+FN)\n",
    "Specificity = TN/(TN+FP)\n",
    "Precision = TP/(TP+FP)\n",
    "\n",
    "print (\"Accuracy = %0.2f\" %Accuracy )\n",
    "print (\"Sensitivity = %0.2f\" %Sensitivity )\n",
    "print (\"Specificity = %0.2f\" %Specificity )\n",
    "print (\"Precision = %0.2f\" %Precision )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Gini Coefisient from AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AUC = lr_hyper_AUC\n",
    "Gini_lr_hyper = (2 * AUC - 1)\n",
    "print(\"AUC=%.2f\" % AUC)\n",
    "print(\"GINI ~=%.2f\" % Gini_lr_hyper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Log Loss in pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create Dataframe to Calculate Log Loss\n",
    "y_test= titanic_test.select('label')\n",
    "lr_hyper_proba=lr_result_hyper.select('probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Convert lr_probaspark dataframe to numpy array\n",
    "lr_hyper_proba= np.array(lr_hyper_proba.select('probability').collect())\n",
    "\n",
    "#Convert numpy array 3 dimentional to 2 dimentional\n",
    "lr_hyper_proba=lr_hyper_proba.reshape(-1, lr_hyper_proba.shape[-1])\n",
    "\n",
    "#Convert y_test dataframe to pandas dataframe\n",
    "y_test=y_test.toPandas()\n",
    "\n",
    "#Convert y_test pandas dataframe to pandas series\n",
    "y_test=pd.Series(y_test['label'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Calculate log loss from logistic regression hyper parameter\n",
    "LogLoss = log_loss(y_test, lr_hyper_proba) \n",
    "\n",
    "print(\"Log Loss Linear Regression:%.4f\" % LogLoss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create decision tree model to data train\n",
    "dt=DecisionTreeClassifier(featuresCol = 'features', labelCol = 'label', maxDepth = 3)\n",
    "dt_model = dt.fit(data_train)\n",
    "\n",
    "##Transform model to data test\n",
    "dt_result = dt_model.transform(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+-----+----------+--------------------+\n",
      "|QuoteNumber|label|prediction|         probability|\n",
      "+-----------+-----+----------+--------------------+\n",
      "|          1|    0|       0.0|[0.93437569103893...|\n",
      "|          2|    0|       0.0|[0.93437569103893...|\n",
      "|          6|    0|       0.0|           [1.0,0.0]|\n",
      "|         12|    0|       0.0|[0.69059559980964...|\n",
      "|         13|    0|       0.0|[0.93437569103893...|\n",
      "+-----------+-----+----------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#view id, label, prediction and probability from result of modelling\n",
    "dt_result.select('Id', 'label', 'prediction', 'probability').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Performance Measure\n",
      "Accuracy = 0.88\n",
      "AUC = 0.86\n"
     ]
    }
   ],
   "source": [
    "#Evaluate model by calculating accuracy and area under curve (AUC)\n",
    "dt_eval = BinaryClassificationEvaluator(rawPredictionCol=\"probability\", labelCol=\"label\")\n",
    "dt_eval2= MulticlassClassificationEvaluator(predictionCol=\"prediction\", labelCol=\"label\")\n",
    "dt_AUC  = dt_eval.evaluate(dt_result)\n",
    "dt_ACC  = dt_eval2.evaluate(dt_result, {dt_eval2.metricName:\"accuracy\"})\n",
    "\n",
    "print(\"Decision Tree Performance Measure\")\n",
    "print(\"Accuracy = %0.2f\" % dt_ACC)\n",
    "print(\"AUC = %.2f\" % dt_AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ROC Grafik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Area Under ROC\n",
      "Area under ROC = 0.86\n"
     ]
    },
    {
     "data": {
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rN2FiYqLtsACRPAUdk5CSwanLMZyMiCE2Pg0ABzszvOo50cLdCVtLUWKkLw4e/J0xY0aQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20805d07080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PredAndLabels           = dt_result.select(\"probability\", \"label\")\n",
    "PredAndLabels_collect   = PredAndLabels.collect()\n",
    "PredAndLabels_list      = [(float(i[0][0]), 1.0-float(i[1])) for i in PredAndLabels_collect]\n",
    "PredAndLabels           = sc.parallelize(PredAndLabels_list)\n",
    "\n",
    "metrics = BinaryClassificationMetrics(PredAndLabels)\n",
    "\n",
    "# Area under ROC\n",
    "print(\"Decision Tree Area Under ROC\")\n",
    "print(\"Area under ROC = %.2f\" % metrics.areaUnderROC)\n",
    "\n",
    "# Visualization\n",
    "FPR = dict()                                                        # FPR: False Positive Rate\n",
    "tpr = dict()                                                        # TPR: True Positive Rate\n",
    "roc_auc = dict()\n",
    " \n",
    "y_test = [i[1] for i in PredAndLabels_list]\n",
    "y_score = [i[0] for i in PredAndLabels_list]\n",
    " \n",
    "fpr, tpr, _ = roc_curve(y_test, y_score)\n",
    "roc_auc = auc(fpr, tpr)\n",
    " \n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Decision Tree')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction_label</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>5263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>63779</td>\n",
       "      <td>9482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  prediction_label      0     1\n",
       "0              1.0     25  5263\n",
       "1              0.0  63779  9482"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_dt_result = dt_result.crosstab(\"prediction\", \"label\")\n",
    "cm_dt_result = cm_dt_result.toPandas()\n",
    "cm_dt_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.88\n",
      "Sensitivity = 0.36\n",
      "Specificity = 1.00\n",
      "Precision = 1.00\n"
     ]
    }
   ],
   "source": [
    "#calculate accuracy, sensitivity, specificity and precision\n",
    "TP = cm_dt_result[\"1\"][0]\n",
    "FP = cm_dt_result[\"0\"][0]\n",
    "TN = cm_dt_result[\"0\"][1]\n",
    "FN = cm_dt_result[\"1\"][1]\n",
    "Accuracy = (TP+TN)/(TP+FP+TN+FN)\n",
    "Sensitivity = TP/(TP+FN)\n",
    "Specificity = TN/(TN+FP)\n",
    "Precision = TP/(TP+FP)\n",
    "\n",
    "print (\"Accuracy = %0.2f\" %Accuracy )\n",
    "print (\"Sensitivity = %0.2f\" %Sensitivity )\n",
    "print (\"Specificity = %0.2f\" %Specificity )\n",
    "print (\"Precision = %0.2f\" %Precision )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Gini Coeffiecient from AUC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC=0.86\n",
      "GINI ~=0.72\n"
     ]
    }
   ],
   "source": [
    "AUC = dt_AUC\n",
    "Gini_dt = (2 * AUC - 1)\n",
    "print(\"AUC=%.2f\" % AUC)\n",
    "print(\"GINI ~=%.2f\" % Gini_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Log Loss in pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create Dataframe to Calculate Log Loss\n",
    "y_test= data_test.select('label')\n",
    "dt_proba=dt_result.select('probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Convert lr_probaspark dataframe to numpy array\n",
    "dt_proba= np.array(dt_proba.select('probability').collect())\n",
    "\n",
    "#Convert numpy array 3 dimentional to 2 dimentional\n",
    "dt_proba=dt_proba.reshape(-1, dt_proba.shape[-1])\n",
    "\n",
    "#Convert y_test dataframe to pandas dataframe\n",
    "y_test=y_test.toPandas()\n",
    "\n",
    "#Convert y_test pandas dataframe to pandas series\n",
    "y_test=pd.Series(y_test['label'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Loss Decision Tree:0.2929\n"
     ]
    }
   ],
   "source": [
    "#Calculate log loss from Decision Tree\n",
    "LogLoss = log_loss(y_test, dt_proba) \n",
    "\n",
    "print(\"Log Loss Decision Tree:%.4f\" % LogLoss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree With Hyper-Parameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#define desicion tree model\n",
    "dt_hyper=DecisionTreeClassifier(featuresCol = 'features', labelCol = 'label', impurity='gini')\n",
    "\n",
    "#Hyper-Parameter Tuning\n",
    "paramGrid_dt = ParamGridBuilder() \\\n",
    "    .addGrid(dt_hyper.maxDepth, [5, 7]) \\\n",
    "    .addGrid(dt_hyper.maxBins, [10,20]) \\\n",
    "    .build()\n",
    "crossval_dt = CrossValidator(estimator=dt_hyper,\n",
    "                             estimatorParamMaps=paramGrid_dt,\n",
    "                             evaluator=BinaryClassificationEvaluator(),\n",
    "                             numFolds=5)\n",
    "#fit model to data train\n",
    "dt_model_hyper = crossval_dt.fit(data_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#transform model to data test\n",
    "dt_result_hyper = dt_model_hyper.transform(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#view id, label, prediction and probability from result of modelling \n",
    "dt_result_hyper.select('Id', 'label', 'prediction', 'probability').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree With Hyper-Parameter Tuning Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Evaluate model by calculating accuracy and area under curve (AUC)\n",
    "dt_hyper_eval = BinaryClassificationEvaluator(rawPredictionCol=\"probability\", labelCol=\"label\")\n",
    "dt_hyper_eval2= MulticlassClassificationEvaluator(predictionCol=\"prediction\", labelCol=\"label\")\n",
    "dt_hyper_AUC  = dt_hyper_eval.evaluate(dt_result_hyper)\n",
    "dt_hyper_ACC  = dt_hyper_eval2.evaluate(dt_result_hyper, {dt_hyper_eval2.metricName:\"accuracy\"})\n",
    "\n",
    "print(\"Decision Tree Performance Measure\")\n",
    "print(\"Accuracy = %0.2f\" % dt_hyper_ACC)\n",
    "print(\"AUC = %.2f\" % dt_hyper_AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ROC Grafik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PredAndLabels           = dt_result_hyper.select(\"probability\", \"label\")\n",
    "PredAndLabels_collect   = PredAndLabels.collect()\n",
    "PredAndLabels_list      = [(float(i[0][0]), 1.0-float(i[1])) for i in PredAndLabels_collect]\n",
    "PredAndLabels           = sc.parallelize(PredAndLabels_list)\n",
    "\n",
    "metrics = BinaryClassificationMetrics(PredAndLabels)\n",
    "\n",
    "# Area under ROC\n",
    "print(\"Decision Tree Area Under ROC\")\n",
    "print(\"Area under ROC = %.2f\" % metrics.areaUnderROC)\n",
    "\n",
    "# Visualization\n",
    "FPR = dict()                                                        # FPR: False Positive Rate\n",
    "tpr = dict()                                                        # TPR: True Positive Rate\n",
    "roc_auc = dict()\n",
    " \n",
    "y_test = [i[1] for i in PredAndLabels_list]\n",
    "y_score = [i[0] for i in PredAndLabels_list]\n",
    " \n",
    "fpr, tpr, _ = roc_curve(y_test, y_score)\n",
    "roc_auc = auc(fpr, tpr)\n",
    " \n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Decision Tree')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm_dt_result_hyper = dt_result_hyper.crosstab(\"prediction\", \"label\")\n",
    "cm_dt_result_hyper = cm_dt_result_hyper.toPandas()\n",
    "cm_dt_result_hyper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#calculate accuracy, sensitivity, specificity and precision\n",
    "TP = cm_dt_result_hyper[\"1\"][0]\n",
    "FP = cm_dt_result_hyper[\"0\"][0]\n",
    "TN = cm_dt_result_hyper[\"0\"][1]\n",
    "FN = cm_dt_result_hyper[\"1\"][1]\n",
    "Accuracy = (TP+TN)/(TP+FP+TN+FN)\n",
    "Sensitivity = TP/(TP+FN)\n",
    "Specificity = TN/(TN+FP)\n",
    "Precision = TP/(TP+FP)\n",
    "\n",
    "print (\"Accuracy = %0.2f\" %Accuracy )\n",
    "print (\"Sensitivity = %0.2f\" %Sensitivity )\n",
    "print (\"Specificity = %0.2f\" %Specificity )\n",
    "print (\"Precision = %0.2f\" %Precision )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Gini Coefficient from AUC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AUC = dt_hyper_AUC\n",
    "Gini_dt_hyper= (2 * AUC -1)\n",
    "\n",
    "print(\"AUC=%.2f\" % AUC)\n",
    "print(\"GINI ~=%.2f\" % Gini_dt_hyper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Log Loss in pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create Dataframe to Calculate Log Loss\n",
    "y_test= data_test.select('label')\n",
    "dt_hyper_proba=dt_result_hyper.select('probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Convert lr_probaspark dataframe to numpy array\n",
    "dt_hyper_proba= np.array(dt_hyper_proba.select('probability').collect())\n",
    "\n",
    "#Convert numpy array 3 dimentional to 2 dimentional\n",
    "dt_hyper_proba=dt_hyper_proba.reshape(-1, dt_hyper_proba.shape[-1])\n",
    "\n",
    "#Convert y_test dataframe to pandas dataframe\n",
    "y_test=y_test.toPandas()\n",
    "\n",
    "#Convert y_test pandas dataframe to pandas series\n",
    "y_test=pd.Series(y_test['label'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Calculate log loss from Decision Tree hyper parameter\n",
    "LogLoss = log_loss(y_test, dt_hyper_proba) \n",
    "\n",
    "print(\"Log Loss Decision Tree:%.4f\" % LogLoss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create decision tree model to data train\n",
    "rf = RandomForestClassifier(featuresCol='features', labelCol=\"label\")\n",
    "rf_model = rf.fit(data_train)\n",
    "\n",
    "#transform model to data test\n",
    "rf_result = rf_model.transform(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+-----+----------+--------------------+\n",
      "|QuoteNumber|label|prediction|         probability|\n",
      "+-----------+-----+----------+--------------------+\n",
      "|          1|    0|       0.0|[0.91517273017559...|\n",
      "|          2|    0|       0.0|[0.82291268107459...|\n",
      "|          6|    0|       0.0|[0.97064099517392...|\n",
      "|         12|    0|       0.0|[0.84192959588552...|\n",
      "|         13|    0|       0.0|[0.99085569112231...|\n",
      "+-----------+-----+----------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#view id, label, prediction and probability from result of modelling\n",
    "rf_result.select('Id', 'label', 'prediction', 'probability').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Performance Measure\n",
      "Accuracy = 0.84\n",
      "AUC = 0.91\n"
     ]
    }
   ],
   "source": [
    "#Evaluate model by calculatin accuracy and area under curve (AUC)\n",
    "rf_eval = BinaryClassificationEvaluator(rawPredictionCol=\"probability\", labelCol=\"label\")\n",
    "rf_eval2= MulticlassClassificationEvaluator(predictionCol=\"prediction\", labelCol=\"label\")\n",
    "rf_AUC  = rf_eval.evaluate(rf_result)\n",
    "rf_ACC  = rf_eval2.evaluate(rf_result, {rf_eval2.metricName:\"accuracy\"})\n",
    "\n",
    "print(\"Decision Tree Performance Measure\")\n",
    "print(\"Accuracy = %0.2f\" % rf_ACC)\n",
    "print(\"AUC = %.2f\" % rf_AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ROC Grafik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Area Under ROC\n",
      "Area under ROC = 0.91\n"
     ]
    },
    {
     "data": {
      "image/png": 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tLS2Jj4+nUqVKXLp0CZlMZrKDg3y5IZw0VSZt3nHHs6y4GF4wLmq1milTPuWj\nj/qgUqWzePEKVqxYi51d/mZ3FPRLZ3gOGDCAcePG0bJlS/bs2UP79u2pVatWYdRWoG7cjycuKZ0y\nJW3o1sLT0OUIwkvkcjmPHj2iWrXq/PLLcXr27CP2joxYnuZtfzGoqlKpJCoqCnd3d+zs7Aqjvmze\nZO7oLzeEc/dxEqO61KJ+NeM71lkc5sYW7Xu1S5cuULt2XQASExMwN7cwqgnZisP3Lj9y7Hk+f/6c\nBQsWsG7dOjIzM4Gs458XLlwwuQvkL9+K5e7jJLwqOBllcArFk1KpZPz4j/H3f4+ffw4Fsm6xNKbg\nFHKW41HoTz/9FFtbW+Li4sjIyOC9995j0qRJpKamMmXKlMKs8Y1oNBKbf7mOudxM7K4LRuP69UiG\nDOlPZOQ1atV6m7feesvQJQmvKcfwvHfvHkeOHCE5OZkePXqwbds2+vbty4ABA1AoFIVZ4xs5e/Ux\nzxLTafZ2GTzFiEmCgUmSxPbtW5k8eQKpqakMGjSUL74IxsrKytClCa8px/B8cUzTzs6O+Ph4li5d\nSt26dQutsIKQqdHw0+k7ALQXdxIJRmDv3t188slIHBwcWb58LR06dDR0SUI+5Rie/z7LV6pUKZML\nToBTl6OJTUijcU03McCxYBTat+/IwIGDGTlyDB4eFQ1djvAGcgzPlJQUzp8/j0ajITU1lfPnz2eb\nSKphw4aFUmB+aSSJoxEPAPjg3YqGLUYotiRJYsOGb1EqlYwaNQZzc3O+/vob3S8UjF6O4enm5sbi\nxYsBcHV11X4NWb3STZs26b+6N3D5r2c8jEmhTpVSlCkpRk0SCl9CQjzjx48hNHQPLi6u9O8/UFzw\nXoTkGJ6bN28uzDoK3KqfrgDQQfQ6BQO4cCGCIUMGcu9eFI0bv8uqVd+K4CxiiuSQQn9GPUel1uBR\n2p7KZR0MXY5QjEiSxKpVy+jQ4X3u37/L+PET2bVrH2XLljN0aUIBK5KjDew6eRuANo3cDVyJUNzI\nZDKuXr2Ck5MzK1as5b33Whq6JEFP9BaeGo2GGTNmcP36dRQKBcHBwXh4/HO50OXLl5kzZw6SJOHi\n4sK8efOwtLR84/dNU6m5/SgRGdCguovO9QWhINy4cQMnp9LIZDLmzFlAcnIybm5uhi5L0COdu+0J\nCQlMmzaNfv36ERcXx5QpU0hISNC54SNHjqBSqdixYwcTJkxgzpw52mWSJDF9+nRmz57N999/j4+P\nDw8fPnwKeGHfAAAgAElEQVSzlvzt3NUnADSvUxa5mElQ0DONRsOSJd9Qo0YNduzYBoCtra0IzmJA\nZ7pMnz4db29v4uPjsbW1xdXVlYkTJ+rccEREBD4+PgDUqVOHK1euaJfduXMHJycnNmzYQJ8+fYiP\nj6dy5cpv0Ix/XPorFoDmtYvO3PKCcYqJiaFHj64EB8+gdOnS4rrNYkbnbvuDBw8IDAzk+++/R6FQ\nMG7cODp21H1XRHJycraRl+RyOWq1GnNzc+Li4rhw4QJBQUG4u7szfPhwatWqpXOuJF2jn0iSxP2Y\nFAAa1CprclMJ53d0F1NRlNoXFhZG7969efz4Me3atWPjxo2UKlXK0GXpTVH63hUUneEpl8tJSkrS\n3nEUFRWFWR52h+3s7EhJSdE+1mg02tGwnZyc8PDwwNMza6AOHx8frly5ojM8dQ2Ldf1eHM8T02hS\n041nz5J11mhMisOwX0Wlfb/+eo6OHVsjl8uZMeMrhg8fRalSjkWmff9VlL53r1LgQ9K98PHHH9O3\nb18ePXrEyJEj6dWrF2PHjtW54Xr16nHy5EkALl68iJeXl3ZZhQoVSElJ4e7duwCcP3+eqlWr5qsB\n/3bi0iMAvCqImQUF/WnYsBF9+vRn796DjBz5cZ46E0LRo3Mw5MzMTBISErh8+TKZmZnUrl07T7sn\nL86237hxA0mSCAkJ4c8//0SpVBIYGMjZs2dZsGABkiRRt25dpk2bpnObuf31U2dqGDrvOAoLMxZ/\n7IOlQq5ze8akOPx1N+X2hYUd5vLlS4wd++krl5t6+3JTlNsG+e956gxPHx8f/P396dixI3Xq1MnX\nmxSU3L6BJy89YsOBSBrXdGPoBzULsaqCURx+QE2xfRkZGcyZE8zSpQuxsrLi3LkLr7zg3VTblxdF\nuW2gx932ffv28dZbb7Fw4ULatGnD0qVLtbvbxuTY7w8xk8no3KySoUsRioj79+/RqVNbli5dSKVK\nlfn558PiTiFBS2d4Ojo60q1bNzZu3Mi8efM4duwYbdu2LYza8iwhRcXdJ0l4lnPAVQw9JxSAAwd+\nxs+vGefP/0bXrh9y9OgpvL1rG7oswYjoPNv+/PlzDhw4wP79+0lISKBDhw4sW7asMGrLs/ORTwHE\nfexCgTlz5iTp6eksXLiMXr36ilkshZfoDM9OnTrRtm1bpkyZYrRTDv98NgqAlvXKG7QOwbRFRz+i\ndOkyyGQygoJm0b//IKpW9dL9QqFY0rnbfuLECT7//HOjDU6ANFXW7J6uTtYGrkQwVT/9tIumTRuy\nZs0KABQKhQhOIVc59jy7dOnC7t27qVGjRrZdlhdzuF+7dq1QCtQlIUVFmiqTKuXF5G7C60tNTWX6\n9Cls2vQdNja2lCxZdO8SEgpWjuG5e/duACIjI19aplKp9FfRazpxIWtAEe9KJQxciWBqbt68weDB\n/bl27So1atRi3bqNVKny5jdrCMWDzt32wMDAbI81Gg0BAQF6K+h1nb8eA0ClMuJkkZB3t2//hb9/\nc65du0r//oM4cOCoCE7hteTY8+zXrx+//fYbANWrV//nBebm+Pr66r+yPJAkiehnWffP1xQ9T+E1\nVKrkSUBAd5o3b0GnTl0NXY5ggnIMzxcTvAUHB+fp1klDiE1II1MjUadKKXEpiaDT1atX+OWX/Ywf\nPwmZTMaCBUsMXZJgwnIMz2PHjtGyZUtq1qzJnj17XlreuXNnvRaWF5F34wBwKyHOsgs5kySJzZs3\nMG3aZ6SlpeHv31pc8C68sRzD848//qBly5baXff/MobwvP80a9i5WpVLGrgSwVglJSUyYcIY9uzZ\nhZOTE2vWbBDBKRSIHMNzzJgxAMyePVv7XHJyMtHR0QUyfFxBuPcka7CCyuJkkfAKly5dYMiQAURF\n3aFhw3dYvfo7ypevYOiyhCJC59n2//3vf0yZMoXnz5/Trl07xowZw8KFCwujtlxJksS9p8m4Oltj\nbVkkJwEV3tDhw78QFXWHTz6ZwJ49+0VwCgVKZ3h+//33fPbZZ+zbtw8/Pz9CQ0M5depUYdSWq3tP\nkklTZYr72YVsEhLiyczMuuNs3LiJ7Nt3mKlTv8DCwsLAlQlFTZ6GwHZycuLEiRO0aNECc3Nz0tPT\n9V2XTtf+PllUqbQITyFLePivtGzZlAULvgayppBp1OgdA1clFFU6w7NKlSoMGzaMBw8e0KRJEz75\n5BO8vb0Lo7ZcxSVlBXgJBysDVyIYWtb0vwvp2LENjx491M6VJQj6pPOnLCQkhAsXLuDl5YVCoaBT\np040b968MGrL1Z3oREBcplTcxcTEMHr0UI4dO4qbW2lWrfqWpk19DF2WUAzoDM+MjAyOHTvG7Nmz\nyczM5J133qFx48YG/+v+18MEAEqKnmex9fz5M/z8mvH4cTS+vq1YtmxNkZ7+VzAuOhNw5syZWFtb\nExISAsAPP/zAF198wbx58/ReXE7UmRogawg6caa9+CpRoiTt2nWgXLkKjBo1RsxiKRQqnclz9epV\n9u7dq30cFBREu3bt9FqULncfZ13fWdJR9DqLmydPHvP991v45JMJyGQy5sxZYOiShGJKZ3hKkkRi\nYiIODllntRMTE5HLDTut759/n2n3FncWFSvHjh1l1KghxMbGUrNmLfz92xi6JKEY0xmeAwYM4MMP\nP9SOpBQWFsbQoUP1XlhuYuJSAShbSkz2Vhyo1Wq+/vorFi9egIWFBV999TWtWrU2dFlCMaczPAMC\nAvD29iY8PByNRsPSpUupVq1aYdSWo9N/RGOlkFOrkuh5FnUPHz5g2LCP+O23c3h4VGTt2g3UqVPP\n0GUJQs7hqdFo2Lp1K1FRUdSvX5/evXsXZl05kiQJAJlMhpmZGIauqDt4cD+//XaOTp26smDBYhwc\nxHQrgnHIMTxnzJjBrVu3qFu3LqtWreL27duMHj26MGt7pRcXx7uIk0VFlkqlQpIkLC0t+eijIXh4\neODn974Ys1UwKjle2xEeHs6WLVv49NNP2bhxI4cOHSrMunL0Yhi6t6uIXfaiKCrqDh06+DNz5nQg\naw+jVavWIjgFo5NjeFpaWmp/YJ2dnY3mh/f6vXgAypSwNXAlQkHbu3c3fn4+XLx4gcTERDQajaFL\nEoQc5bjb/t+wNJYLkNV//0I52SkMXIlQUNLS0ggKmsKGDd9iY2PDkiUr6dHDOI6xC0JOcgzPR48e\nMWXKlBwf/3uQ5MIUG58GQFkXO4O8v1Cw0tPTadeuFVeuXOatt2qybt1Gqlb1MnRZgqBTjuE5efLk\nbI8bNWqk92Ly4uJfsViYm2FnLW7LLAosLS1p2dKPevUaMGvWbKytxUAvgmnIMYG6dOlSmHXkiUZ7\nmRLIjeQwgvD6UlJS2Lx5PUOHjsTMzIxp02YYzTF1Qcgrk+q+paarAXBxEr0TUxUZeY0hQ/pz/Xok\ndnb29OnTXwSnYJL01n3TaDQEBQURGBhI3759uXv37ivXmz59OvPnz8/TNh8/UwJQpZy4UNrUSJLE\n1q2baN26BdevRzJ06Ai6deth6LIEId/yFJ5KpZLIyEgkSUKpVOZpw0eOHEGlUrFjxw4mTJjAnDlz\nXlpn+/bt3LhxI8/FPo3PuqfdTPRUTEpychJ9+vRh3LjRKBSWbNiwjeDgr7G0tDR0aYKQbzrD8+zZ\ns3Tq1ImRI0cSExODr68vp0+f1rnhiIgIfHyyRvSuU6cOV65cybb8999/59KlSwQGBua52OTUDABK\nOYm7i0zJ0aOH2bZtGw0aNCIs7DTt2nUwdEmC8MZ0HvP85ptv2LZtG0OGDMHV1ZUtW7Ywfvx4mjVr\nluvrkpOTsbP753IiuVyOWq3G3Nycp0+fsnz5cpYtW8aBAwfyXKzcImsovOqVS+HiYp/n15mKotQm\nSZLIyMhAoVAweHB/HBys6dKlS5GexbIoff/+qyi3Lb90hqdGo8HFxUX7uEqVKnnasJ2dHSkpKdm2\n82LqjoMHDxIXF8fQoUOJiYkhLS2NypUr07Vr11y3GfX31BsKJGJikvJUh6lwcbEvMm1KSIhn7NjR\n2NnZsXTpKgC6d+/+d/vSDFucnhSl799/FeW2Qf7/MOgMz9KlS3Ps2DFkMhmJiYls3bqVsmXL6txw\nvXr1OHbsGO3atePixYt4ef1z4XO/fv3o168fALt27eL27ds6gxPgWULWL14pcbbdaEVEhDNs2Efc\nu3eXJk2aolQqsbER464KRY/OY54zZ84kNDSU6OhoWrVqxbVr15g5c6bODfv7+6NQKOjRowezZ89m\nypQphIaGsmPHjnwXeyc6EWd7SywtDDuSvfAyjUbD8uVL+OCD1ty/f48JEz7jxx9DRXAKRZZMejFA\nppFTZWQSMHkfHqXt+WJAQ0OXU+BMeddIkiT69+/FwYM/4+rqxsqV6/DxeS/bOqbcvrwoyu0rym0D\nPe62+/r6vvIi5qNHj+brDfPrxQXy1grR6zQ2MpmMBg0akpaWyvLla7MdIxeEokpneG7evFn7tVqt\n5vDhw6hUKr0W9SqqjKzRlJztxbWBxiAzM5Pt27fSvXtPLCwsGD16LKNHjzWa0bcEQd90/qSXK1dO\n+8/Dw4PBgwdz5MiRwqgtmzRVVs9TIY53GtyTJ0/o3r0L48aNZtmyRUDWkIUiOIXiRGfPMzw8XPu1\nJEncvHmT9PR0vRb1Ko9iskaQd7ITPU9DOnHiGCNGDCY2NobWrdvSv/9Hhi5JEAxCZ3guWbJE+7VM\nJsPZ2fmVt1rqW+zft2a6OovLlAxBrVYzb14IixYtwNzcnFmzZjN06EgxqIdQbOkMz7Zt29KrV6/C\nqCVXMX+HZykx8ZtBnDv3fyxcOB9394qsXbueunXrG7okQTAonQeptm3bVhh16JSmygTAWmFSo+iZ\nvMzMrM+9WbPmLFmykqNHT4rgFATyeIdRv379qF27drZRcAp7GmJlWtagIFbiUqVCoVKpCAmZye3b\nt9i4cRsymUzMKyQI/6IzPOvUqVMYdeh06+/72m2ti+7AEsbi3r27DBs2kIiI83h6ViEmJgZXV1dD\nlyUIRiXH8Ny9ezddunQp9B6mLqLnqV/79u1l7NhRJCYmEBDQnXnzFmJnJ0bUEYT/yvGY56ZNmwqz\nDp0yMyUszM3E2V09mjFjGh991IeMDBWLF69gxYq1IjgFIQcmc/YlJS2DEuLuIr2qVKky1au/xdq1\nG6lWrbqhyxEEo5ZjeN68eRM/P7+XnpckCZlMVuj3titTM3AsZVuo71kcHDjwMy1a+GJtbU2/fgMJ\nDOyFlZW4HEwQdMkxPD08PFizZk1h1pIrlVqDg43C0GUUGUqlkqlTJ7F16yaGDBnOV1/NRSaTieAU\nhDzKMTwtLCwoV65cYdaikzI9w9AlFAmRkdcYOnQAkZHX8PauzaBBQw1dkiCYnBxPGNWrV68w68gT\nd1dx8uJNSJLEtm2bad26BZGR1xg8eBj79x+hcuW8Ta0iCMI/cux5BgUFFWYdeSJOtL+Z69cjGTdu\nNA4OjixfvpYOHToauiRBMFkmc7YdQGMSY94bnxcn+apXf4uFC5fRrFlz3N09DF2WIJg0kxqA0VVM\n/PZaJEli/fp19OwZoL1HvVevviI4BaEAmFTP88WAyIJuCQnxjB8/htDQPZQoUYLbt29RtaqX7hcK\ngpAnJtXzdHMWMzHmxYULEfj5NSc0dA+NG79LWNgZEZyCUMBMKjzFFBy6rV+/jg4d3uf+/buMHz+R\nXbv2UbascV1yJghFgUnttttamVS5BuHk5ISTkzMrVqzlvfdaGrocQSiyTKrnaWFhUuUWmvDwX0lM\nzBqyr0uXDzl37ncRnIKgZyaVRmbiQs9sNBoNixcvoGPHNkyaNE77vL29gwGrEoTiwaT2g8Uxz388\nffqU0aOHcvx4GGXKlGXAgMGGLkkQihWT6nkKWU6dOoGvb1OOHw/D3781YWFnaNz4XUOXJQjFikn1\nPMVOOzx58oTevbuhVquZMeMrhg8fhZmZ+BsoCIXNpMJTADc3N+bOXUjVql7Ur9/Q0OUIQrFlUl2W\n4nq+6OjRQ/TsGUB6ejoAPXr0FsEpCAZmUuFZ3GRkZDBzZhA9e37IqVMnuHAhwtAlCYLwN7HbbqTu\n37/H0KEDiYgIp3JlT9au3YC3d21DlyUIwt9Ez9MIHTp0AF/fZkREhNO1azeOHDkpglMQjIzoeRoh\nuVxORoaKRYuW07NnHzHdsiAYIb2Fp0ajYcaMGVy/fh2FQkFwcDAeHv+MI7lv3z42btyIXC7Hy8uL\nGTNm6LzkpiiHyK1bt0hPl+Hi4oKf3/ucP3+FUqVKGbosQRByoLfd9iNHjqBSqdixYwcTJkxgzpw5\n2mVpaWksWrSITZs2sX37dpKTkzl27Ji+SjF6e/b8SN26dfn442FIUtZw+SI4BcG46a3nGRERgY+P\nDwB16tThypUr2mUKhYLt27djbZ01MrxarcbS0lLnNkuWsMXFxU4/BRtAamoqY8eOZc2aNdja2jJw\nYH9cXYvufekuLkV7Ar+i3L6i3Lb80lt4JicnY2f3T9DJ5XLUajXm5uaYmZlpe1abN29GqVTStGlT\nndt8/jwFC4rGREY3blxnyJABXLt2lZo1vfnxx/9RokRZYmKSDF2aXri42BfZtkHRbl9Rbhvk/w+D\n3nbb7ezsSElJ0T7WaDSYm5tne/z1119z5swZli5dmrfjmUXkkKdSqaRz53Zcu3aVgQMHc+DAUapV\nq2bosgRBeA1663nWq1ePY8eO0a5dOy5evIiXV/ZpIIKCglAoFKxYsaLY3ZttY2PDzJkhKBQKOnbs\nYuhyBEHIB72Fp7+/P2fOnKFHjx5IkkRISAihoaEolUpq1arFzp07adCgAf379wegX79++Pv757pN\nU+54Xr16ha+//oqVK9dha2vLhx8GGrokQRDegN7C08zMjJkzZ2Z7ztPTU/t1ZGSkvt7aqEiSxKZN\n65k27TPS09M5fPggnTsHGLosQRDekLhIXo8SExOYMOETfvppF87Ozqxbt4nWrdsauixBEAqAaYWn\nCV0kf/nyRQYN6sfdu1E0atSY1au/o1y58oYuSxCEAlK8ztQUIqUylUePHjJ27Kfs2bNfBKcgFDEm\n1fM09n5nXNxz0tLSKFOmLI0bN+HcuQtUqOBu6LIEQdAD0fMsIOHhv+Ln58Pgwf3JyMgAEMEpCEWY\nSYWnMfY8NRoNS5YspGPHNjx69JCWLf2K3XWrglAcmdRuu7GJiYlh9OihHDt2FDe30qxa9S1Nm/oY\nuixBEAqBaYWnEXU9NRoNAQEdiIy8hq9vK5YtWyNGQhKEYsS0wtOImJmZMXnydG7d+otRo8aIXXVB\nKGbEb/xrePw4mpEjhxAX9xyAdu068PHHY0VwCkIxZFK/9TID7reHhR3B17cpO3fuYMuWTQarQxAE\n42BS4WkIGRkZBAfPoEePriQmJhISMpfRoz8xdFmCIBiYSR3zLOy7Mx88uM+wYR8RHv4rFStWYu3a\nDdSuXbdwixAEwSiJnmcu4uLiuHz5Ip07d+Xo0VMiOAVB0DKpnmdhUKlUPHsWS5kyZfH2fpujR09T\ntapXkZ65UxCE1yd6nv8SFXWHDz54n549PyQ1NRUAL69qIjgFQXiJSYWnPkNs797d+Pn5cOHC73h7\nv41Go9HbewmCYPqK/W57amoqQUGfs3Hjt9jY2LJ06SoCA3sZuixBEIxcsQ/PAQN6cezYUWrUqMXa\ntRuoWtVL94sEQSj2in14jhr1Ce7uFZk5MwRra2tDlyMYqd9/P09Q0BQqVqyETCYjJSWFsmXL8cUX\nwVhYWPD8+XOCg4N5/DgajUaDq6sbH388jpIls8Y7uHTpAuvXr0WtVpOWlka7dh/QtWs3g7YpISGe\n1auXM2nSVIPWkZ6exsyZ04mLi8PGxoapU7/E2dk52zpbtmzgyJFD2Nra0qtXv2wD8Jw4cYxjx44w\nY8ZXAHz77Wp8ff2pVKmyXusuduGZkpLC7Nkz+fjjcbi5laZ58xY0b97C0GUJr+GHsL8Ij3xaoNts\nWN2V7r5Vcl2nfv0GfPnlbO3jGTOmcvr0CVq08GP06NF8+GFPfHxaAFnju06aNI41azbw+HE0ixbN\nY8GCpZQoUZL09DQ+/ng4ZcuWo3Hjdwu0Ha9j7dqVdO3a3WDv/8Lu3TupXLkKgwYN48iRX9i48VvG\njv1Uu/zWrb84fPgX1qzZAMCIER9Rv35DrKysWLRoPr/9djbbHmP37r348supzJ+/RK91m1R4vun5\nomvX/mTIkP7cuHGdzMxMZs+eXzCFCcVORkYGz57FYm/vwPXr17C3t9cGJ0DDhu8QGrqHS5cucPHi\n77Rp054SJUoCYGlpxTffLHtpT+f+/Xt8/XUwGRkZWFlZMWNGCCtWLMbP730aN36Xc+f+j6NHDzF1\n6gwCAjrg4VGRihUrcebMKTZs+B5ra2u2bduMXG5GixZ+zJ0bQnp6GpaWVkya9DlubqW175WSksy1\na3/y6adVAfjxxx2cOHGM1NRUnJycCAmZz+HDB/n5573I5TL69RtMYmIiO3ZsxczMjLffrsOIER/z\n9OkT5s+fg0qVzrNnsQwZMjJbZ+TBg/vMmTMrWzv9/dvQqVNX7ePLly/Rq1c/ABo3bsqGDd9mWz8q\n6g5169bH0tISgPLl3fnrr5vUquWNt/fbNG/egp9++lG7vr29PZaWlvz1102qVKn6ut/aPDOp8Mwv\nSZLYunUTn38+kbS0NIYNG8m0aV8auiwhn7r7VtHZS9SHiIjzjB49lPj4OGQyGR07dqVBg0YcPXqY\nChUqvLR+2bLlePw4mtjYmJeOpdvZ2b20/vLli+jTZwCNG7/L6dMnuHnzeo61PH36hO++24KjoxPm\n5hYcP36Utm07cOTIQRYuXM6CBV/z4YeBNGnSlPPnf2PVqmV88UWw9vVXr17B3d0DyBpeMSEhgUWL\nVmBmZsb48aO5du0qkBVE3367llu3HjBy5GDWrduMlZUVs2ZNJzz8HCCjR4/e1KvXgD/+uMS3367O\nFp7ly1dg2bI1uX6uKSkp2s/DxsaGlJTkbMs9PauwZct6lMoUMjIyuHLlMh07dgHAz+99fv/9/Evb\n9PSsyoULESI8X8hPxzMpKZGJE8eya9dOnJycWL16PW3bti/w2oSi78Vue0JCPOPGjaJMmbIAuLi4\ncOLEw5fWf/DgHg0bvkNsbAxPnz7JtuzmzRtIkgYvr+ra5+7du0utWm8D0KzZewAcPnxQu1ySJO3X\njo5OODo6AfDBB52ZP38OHh4VqVDBA0dHJ27f/ovNm9ezdetGAOTy7L/q8fHxlChRAsgaXtHCwoIZ\nM6ZibW3N06dPUavVANqAffDgPvHxcXz66RgAlEolDx8+4O2367Jx47f8/PNPgEz7un8+A909T1tb\nW5TKFO12//uHpWLFSgQEdGfChI9xdS1NjRo1tW3PScmSpYiNjcl1nTdlUuGZHzExMRw69AsNGjRi\nzZr1lC//cg9BEF6Ho6MT06fPYsyY4VSvvg1v79qsWrWE06dP0qxZcwDOnfs/Hjx4QJ069ShbthxT\npnyKr+/7ODs7o1QqmTcvhIEDB2fbrodHJa5du0rDhu9w6NABEhMTUCgUPHsWC8CNG5Hadf89DGLW\nXFkS27ZtpkuXDwFwd69Iz5598Pauzd27UVy4EJHtvZydnUlKSgLgr79ucvLkcdau3UhaWhqDBvXR\nrieTZb1PmTLlcHV1Y9GiFZibm7N/fyhVq3qxbt0qPvigM02aNOXnn/dy4MC+bO+Tl56nt3dtzp49\nQ40atTh37sxLt0HHxcWhVCpZufI7kpOTGTduFJUre+a6zaSkRJycnHNd502ZVnjm8aCnJEnExsbi\n4uJC5cqe/PTTfqpXr4GFhYWeCxSKi0qVKvPhh4EsWjSP4OCvWbVqFUFBX7J583oAXF3dmDdvEXK5\nnDJlyjJy5BimTp2ImZkZSqXy78Bplm2bo0Z9wrx5IWzc+C1WVlYEBc3i0aOHzJ49k0OHDuY6oWD7\n9p349ttV1KvXQLutBQvmoFKpSE9P45NPPs22fs2a3qxcuRTICjhra2tGjPgIeHWvzdnZmcDA3owe\nPZTMzEzKlCmLr68/LVv6sXz5YrZs2YCLiyvx8fGv/Vl26fIhwcFfMGLEICwsLLSHF7Zv30L58hVo\n2rQ5UVF3GDy4HxYW5owa9QlyuTzXbf7551WGDRv12rW8Dpn0730BI/bBhJ9Y+HEzHG0Vua4XHx/H\n2LFZx2yOHDmJvb1DIVX4Zlxc7ImJSTJ0GXoj2md85s0LoVOnrtkOHbyKqbUtMTGB4OAZzJ27ME/r\nu7jY5+t9TOv2TB3Lz5//DT8/H/bvD6VMmbKkpqYVSl2CYIoGDx7O7t07DV1GgduxY5vee51gYuGZ\nE41Gw/LlS+jYsQ0PHtzn008n8+OPobi6uhq6NEEwWs7OJfjss2mGLqPADRkyAk9P/V+NYVrHPHPw\n2WcT2LjxW1xd3Vi5ch0+Pu8ZuiRBEIo40wrPHPbbe/bsTXT0QxYuXI6Li0vh1iQIQrFkkrvtmZmZ\nLFnyDVFRdwCoV68BW7b8IIJTEIRCY1I9Txnw5MkTRo4cwqlTx4mIOM/GjdsMXZYgCMWQ3nqeGo2G\noKAgAgMD6du3L3fv3s22PCwsjICAAAIDA/nhhx/ytM0zp0/QsuW7nDp1nNat27Jw4VJ9lC4IgqCT\n3nqeR44cQaVSsWPHDi5evMicOXNYuXIlkDWowuzZs9m5cyfW1tb07NkTX19fSpUqleP2Is9spf/C\nnZibmzNr1myGDh0ppscQBMFg9NbzjIiIwMcna8y9OnXqcOXKFe2yW7du4e7ujqOjIwqFgvr16xMe\nHp7r9u5eOkj58u7s23eIYcNGieAUBMGg9NbzTE5OznaDv1wuR61WY25uTnJyMvb2/1zVb2trS3Jy\n8qs2o6VKTdRXqUYjv3c6mArRPtNVlNuWX3rredrZ2ZGSkqJ9rNFoMDc3f+WylJSUbGEqCIJg7PQW\nnnxku3oAAAmdSURBVPXq1ePkyZMAXLx4ES+vf8Yz9PT05O7du8THx6NSqTh//jx169bNaVOCIAhG\nR28Dg2g0GmbMmMGNGzeQJImQkBD+/PNPlEolgYGBhIWFsXz5ciRJIiAggN69e+ujDEEQBL0wmVGV\nBEEQjIlJ3mEkCIJgaCI8BUEQ8kGEpyAIQj4YXXjq47ZOY6Grbfv27aNbt2706NGDoKAgNBqNgSrN\nH13te2H69OnMn2960z7rat/ly5fp1asXPXv2ZMyYMaSnpxuo0vzR1b69e/fSpUsXAgIC2LbNNMeU\nuHTpEn379n3p+XzlimRkfvnlF+mzzz6TJEmSLly4IA0fPly7TKVSSa1atZLi4+Ol9PR0qWvXrlJM\nTIyhSn1tubUtNTVV8vPzk5RKpSRJkjRu3DjpyJEjBqkzv3Jr3wvff/+91L17d2nevHmFXd4by619\nGo1G6tixoxQVFSVJkiT98MMP0q1btwxSZ37p+v41bdpUiouLk9LT07W/h6ZkzZo1UocOHaRu3bpl\nez6/uWJ0Pc+Cvq3TmOTWNoVCwfbt27G2tgZArVZjaWlpkDrzK7f2Afz+++9cunSJwMBAQ5T3xnJr\n3507d3BycmLDhg306dOH+Ph4KleubKhS80XX969atWokJSWhUqmQJMnkbpF2d3dn6dKXBxPKb64Y\nXXjmdFvni2Wve1unMcmtbWZmZtqBUTZv3oxSqaRp06YGqTO/cmvf06dPWb58OUFBQYYq743l1r64\nuDguXLhAnz59WL9+PefOnePs2bOGKjVfcmsfQNWqVQkICKB9+/a0aNECBwfTmFzxhdatW2vvcvy3\n/OaK0YVnUb6tM7e2vXj89ddfc+bMGZYuXWpyf9lza9/BgweJi4tj6NChrFmzhn379rFr1y5DlZov\nubXPyckJDw8PPD09sbCwwMfH56Wem7HLrX2RkZEcP36co0ePEhYWxvPnzzlw4IChSi1Q+c0VowvP\nonxbZ25tAwgKCiI9PZ0VK1Zod99NSW7t69evH7t27WLz5s0MHTqUDh060LVrV0OVmi+5ta9ChQqk\npKRoT7KcP3+eqlWrGqTO/Mqtffb29lhZWWFpaYlcLqdEiRIkJhaNwXrymytGN5K8v78/Z86coUeP\nHtrbOkNDQ7W3dU6ePJlBgwZpb+t0c3MzdMl5llvbatWqxc6dO2nQoAH9+/cHsgLH39/fwFXnna7v\nnanT1b6vvvqKCRMmIEkSdevWpUWLFoYu+bXoal9gYCC9evXCwsICd3d3unTpYuiS38ib5oq4PVMQ\nBCEfjG63XRAEwRSI8BQEQcgHEZ6CIAj5IMJTEAQhH0R4CoIg5IPRXaokGIcHDx7Qpk0bPD09sz2/\natUqypQp88rXvLj17eOPP873++7atYs5c+Zo3yMtLY1GjRrxxRdfvPLukNz8f3v3F9LkFwZw/KvC\nDCNLyLqoLrqoJfZXcIqBaa6gtT9EVCBOkCi7aQ0yNDEtNoZYCI6KogsJGWUyyGhEVIsKrbzoHxT9\nIyUjFXIZwWxM9/wuopcsTV1B8eN8YHDg7Ow577l4OO/e933epqYmli9fTlFREXa7nZaWFgBsNhvt\n7e1xzxHAbrfT399PSkoK8PUplUWLFnHs2LFfvkK7tbWVmTNnYjabfyu+8vep5KlMaN68eb+dZOKx\nfv166uvrARgdHcVut+Pz+bT7X6dq3759Wrurq0tr/6ljcrvd5OTkAF+fxnE4HDQ3N3PgwIEJxzx8\n+BCDwfBH4it/l0qeyrS9fPkSl8tFOBwmFApRVlZGaWmp1h+NRqmurubVq1cAFBcXs337dj58+EBt\nbS39/f0kJCSwf/9+8vLyfhkrKSmJNWvW0NPTA4Df76e5uZmEhAQyMzM5dOgQOp1u3HhVVVUYDAae\nPXsGwLZt22hra0Ov1/P06VMKCgq4ePEic+fOZWhoCLPZzM2bN7l79y5er5eRkREWLlyIy+UiLS3t\nl/MMh8N8/PiRlStXAnDlyhWam5v58uULkUgEt9tNNBolGAxy79490tPTycjImPZ6KP+QP1rzSfnf\n6O3tlczMTLFardrnzJkzIiLidruls7NTRETevn0rq1evFhERr9crXq9X7t+/L7t27RIRkVAopJU5\nczqdWpm9gYEBKSoqks+fP4+J6/f7te9/G282m+XSpUvy/PlzMRqNEgqFRETk8OHDUl9fP2G8yspK\n8fv9IiKydOlS7Te/tV0ul7S0tIiISGtrq9TV1cng4KBYrVat3Nq5c+ekurr6p/UpKSkRo9EoFotF\n1q5dKyaTSY4fPy6RSERGR0eltLRUBgcHRUSkra1NysvLf5rTVNZD+XepnacyoYlO26uqqrhz5w6n\nT5/mxYsXhMPhMf1Lliyhu7ubnTt3kp+fT0VFBQCdnZ28efMGr9cLfC2719vbS0ZGxpjxwWAQm82G\niCAibNiwAbPZjM/no7CwUNsF7tixg4MHD7J79+5x403GZrPh8XgoKSnh8uXLOJ1OHj9+TF9fn7aT\njsVizJ49e9zx307bHzx4gMPhYN26deh0OgBOnDhBMBiku7ubrq4uEhN/vjY71fVQ/k0qeSrT5nQ6\nSU1NpbCwEJPJRCAQGNOflpZGIBCgo6ODW7dusWXLFgKBALFYjLNnzzJnzhwABgYGxr248v1/nt/7\nsbK+iDAyMjJhvMmsWLGCT58+8eTJEwYGBsjKyuL69etkZWVx6tQpACKRyJiKO+PJysrCbrdTWVlJ\ne3s7kUiErVu3YrPZyM7ORq/X4/P5xj2eqayH8m9Styop09bR0YHD4cBoNGpFY0dHR7X+GzduUFFR\nQUFBATU1NaSkpNDX10dubq72+obXr19jtVoZHh6eclyDwUAwGGRoaAiACxcukJOTM2G87/1Ym/Ib\ni8VCXV0dJpMJgFWrVvHo0SO6u7sBOHnyJA0NDZPOraysjOHhYc6fP09PTw+JiYns2bOH3Nxcbt++\nra1PUlKS1v7d9VD+LrXzVKZt7969FBcXk5qayuLFi1mwYAHv3r3T+vPz87l69SqbN28mOTmZjRs3\notfrqampoba2FovFAkBDQ8OY4ruTWbZsGeXl5djtdqLRKJmZmRw5coTk5ORx432vqKgIm832Uw1R\nq9VKU1MTjY2NAKSnp+PxeHA6ncRiMebPn8/Ro0cnnZtOp8PpdOLxeLh27RoZGRls2rSJGTNmkJ2d\nzfv37wHIy8ujsbGRWbNm/fZ6KH+XqqqkKIoSB3XariiKEgeVPBVFUeKgkqeiKEocVPJUFEWJg0qe\niqIocVDJU1EUJQ4qeSqKosThP5vf9PFl0bK/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2081d6045f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PredAndLabels           = rf_result.select(\"probability\", \"label\")\n",
    "PredAndLabels_collect   = PredAndLabels.collect()\n",
    "PredAndLabels_list      = [(float(i[0][0]), 1.0-float(i[1])) for i in PredAndLabels_collect]\n",
    "PredAndLabels           = sc.parallelize(PredAndLabels_list)\n",
    "\n",
    "metrics = BinaryClassificationMetrics(PredAndLabels)\n",
    "\n",
    "# Area under ROC\n",
    "print(\"Random Forest Area Under ROC\")\n",
    "print(\"Area under ROC = %.2f\" % metrics.areaUnderROC)\n",
    "\n",
    "# Visualization\n",
    "FPR = dict()                                                        # FPR: False Positive Rate\n",
    "tpr = dict()                                                        # TPR: True Positive Rate\n",
    "roc_auc = dict()\n",
    " \n",
    "y_test = [i[1] for i in PredAndLabels_list]\n",
    "y_score = [i[0] for i in PredAndLabels_list]\n",
    " \n",
    "fpr, tpr, _ = roc_curve(y_test, y_score)\n",
    "roc_auc = auc(fpr, tpr)\n",
    " \n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Random Forest')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction_label</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>63798</td>\n",
       "      <td>12278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  prediction_label      0      1\n",
       "0              1.0      6   2467\n",
       "1              0.0  63798  12278"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_rf_result = rf_result.crosstab(\"prediction\", \"label\")\n",
    "cm_rf_result = cm_rf_result.toPandas()\n",
    "cm_rf_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.84\n",
      "Sensitivity = 0.17\n",
      "Specificity = 1.00\n",
      "Precision = 1.00\n"
     ]
    }
   ],
   "source": [
    "#calculate accurary,sensitivity, specificity and precision \n",
    "TP = cm_rf_result[\"1\"][0]\n",
    "FP = cm_rf_result[\"0\"][0]\n",
    "TN = cm_rf_result[\"0\"][1]\n",
    "FN = cm_rf_result[\"1\"][1]\n",
    "Accuracy = (TP+TN)/(TP+FP+TN+FN)\n",
    "Sensitivity = TP/(TP+FN)\n",
    "Specificity = TN/(TN+FP)\n",
    "Precision = TP/(TP+FP)\n",
    "\n",
    "print (\"Accuracy = %0.2f\" %Accuracy )\n",
    "print (\"Sensitivity = %0.2f\" %Sensitivity )\n",
    "print (\"Specificity = %0.2f\" %Specificity )\n",
    "print (\"Precision = %0.2f\" %Precision )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Gini Coefficient from AUC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC=0.91\n",
      "GINI ~=0.82\n"
     ]
    }
   ],
   "source": [
    "AUC = rf_AUC\n",
    "Gini_rf= (2 * AUC -1)\n",
    "\n",
    "print(\"AUC=%.2f\" % AUC)\n",
    "print(\"GINI ~=%.2f\" % Gini_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Log Loss in pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create Dataframe to Calculate Log Loss\n",
    "y_test= data_test.select('label')\n",
    "rf_proba=rf_result.select('probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Convert rf_probaspark dataframe to numpy array\n",
    "rf_proba= np.array(rf_proba.select('probability').collect())\n",
    "\n",
    "#Convert numpy array 3 dimentional to 2 dimentional\n",
    "rf_proba=rf_proba.reshape(-1, rf_proba.shape[-1])\n",
    "\n",
    "#Convert y_test dataframe to pandas dataframe\n",
    "y_test=y_test.toPandas()\n",
    "\n",
    "#Convert y_test pandas dataframe to pandas series\n",
    "y_test=pd.Series(y_test['label'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Loss Random Forest:0.3317\n"
     ]
    }
   ],
   "source": [
    "#Calculate log loss from Random Forest\n",
    "LogLoss = log_loss(y_test, rf_proba) \n",
    "\n",
    "print(\"Log Loss Random Forest:%.4f\" % LogLoss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest With Hyper-Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#define random forest model\n",
    "rf_hyper= RandomForestClassifier(featuresCol='features', labelCol=\"label\")\n",
    "\n",
    "# Hyper-Parameter Tuning\n",
    "paramGrid_rf = ParamGridBuilder() \\\n",
    "    .addGrid(rf_hyper.numTrees, [40, 60, 80, 100]) \\\n",
    "    .build()\n",
    "crossval_rf = CrossValidator(estimator=rf_hyper,\n",
    "                             estimatorParamMaps=paramGrid_rf,\n",
    "                             evaluator=BinaryClassificationEvaluator(),\n",
    "                             numFolds=3) \n",
    "#fit model to data train\n",
    "rf_model_hyper=crossval_rf.fit(data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#transfrom model to data test\n",
    "rf_result_hyper = rf_model_hyper.transform(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#view id, label, prediction and probability from result of modelling\n",
    "rf_result_hyper.select('Id', 'label', 'prediction', 'probability').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest With Hyper-Parameter Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Evaluate model by calculating accuracy and area under curve (AUC)\n",
    "rf_hyper_eval = BinaryClassificationEvaluator(rawPredictionCol=\"probability\", labelCol=\"label\")\n",
    "rf_hyper_eval2= MulticlassClassificationEvaluator(predictionCol=\"prediction\", labelCol=\"label\")\n",
    "rf_hyper_AUC  = rf_hyper_eval.evaluate(rf_result_hyper)\n",
    "rf_hyper_ACC  = rf_hyper_eval2.evaluate(rf_result_hyper, {rf_hyper_eval2.metricName:\"accuracy\"})\n",
    "\n",
    "print(\"Decision Tree Performance Measure\")\n",
    "print(\"Accuracy = %0.2f\" % rf_hyper_ACC)\n",
    "print(\"AUC = %.2f\" % rf_hyper_AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ROC Grafik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PredAndLabels           = rf_result_hyper.select(\"probability\", \"label\")\n",
    "PredAndLabels_collect   = PredAndLabels.collect()\n",
    "PredAndLabels_list      = [(float(i[0][0]), 1.0-float(i[1])) for i in PredAndLabels_collect]\n",
    "PredAndLabels           = sc.parallelize(PredAndLabels_list)\n",
    "\n",
    "metrics = BinaryClassificationMetrics(PredAndLabels)\n",
    "\n",
    "# Area under ROC\n",
    "print(\"Random Forest Area Under ROC\")\n",
    "print(\"Area under ROC = %.2f\" % metrics.areaUnderROC)\n",
    "\n",
    "# Visualization\n",
    "FPR = dict()                                                        # FPR: False Positive Rate\n",
    "tpr = dict()                                                        # TPR: True Positive Rate\n",
    "roc_auc = dict()\n",
    " \n",
    "y_test = [i[1] for i in PredAndLabels_list]\n",
    "y_score = [i[0] for i in PredAndLabels_list]\n",
    " \n",
    "fpr, tpr, _ = roc_curve(y_test, y_score)\n",
    "roc_auc = auc(fpr, tpr)\n",
    " \n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Random Forest')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm_rf_result_hyper = rf_result_hyper.crosstab(\"prediction\", \"label\")\n",
    "cm_rf_result_hyper = cm_rf_result_hyper.toPandas()\n",
    "cm_rf_result_hyper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#calculate accuracy, sensitivity, specificity and precision\n",
    "TP = cm_rf_result_hyper[\"1\"][0]\n",
    "FP = cm_rf_result_hyper[\"0\"][0]\n",
    "TN = cm_rf_result_hyper[\"0\"][1]\n",
    "FN = cm_rf_result_hyper[\"1\"][1]\n",
    "Accuracy = (TP+TN)/(TP+FP+TN+FN)\n",
    "Sensitivity = TP/(TP+FN)\n",
    "Specificity = TN/(TN+FP)\n",
    "Precision = TP/(TP+FP)\n",
    "\n",
    "print (\"Accuracy = %0.2f\" %Accuracy )\n",
    "print (\"Sensitivity = %0.2f\" %Sensitivity )\n",
    "print (\"Specificity = %0.2f\" %Specificity )\n",
    "print (\"Precision = %0.2f\" %Precision )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Gini Coefficient from AUC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AUC = rf_hyper_AUC\n",
    "Gini_rf_hyper= (2 * AUC -1)\n",
    "\n",
    "print(\"AUC=%.2f\" % AUC)\n",
    "print(\"GINI ~=%.2f\" % Gini_rf_hyper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Log Loss in pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create Dataframe to Calculate Log Loss\n",
    "y_test= data_test.select('label')\n",
    "rf_hyper_proba=rf_result_hyper.select('probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Convert pyspark dataframe to numpy array\n",
    "rf_hyper_proba= np.array(rf_hyper_proba.select('probability').collect())\n",
    "\n",
    "#Convert numpy array 3 dimentional to 2 dimentional\n",
    "rf_hyper_proba=rf_hyper_proba.reshape(-1, rf_hyper_proba.shape[-1])\n",
    "\n",
    "#Convert y_test dataframe to pandas dataframe\n",
    "y_test=y_test.toPandas()\n",
    "\n",
    "#Convert y_test pandas dataframe to pandas series\n",
    "y_test=pd.Series(y_test['label'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Calculate log loss from Random Forest hyper parameter\n",
    "LogLoss = log_loss(y_test, rf_hyper_proba) \n",
    "\n",
    "print(\"Log Loss Random Forest:%.4f\" % LogLoss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create gradient boosting model in data train\n",
    "gbt = GBTClassifier(featuresCol=\"features\", labelCol=\"label\",  maxIter=10)\n",
    "gbt_model = gbt.fit(data_train)\n",
    "\n",
    "#transfrom model to data test\n",
    "gbt_result = gbt_model.transform(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+-----+----------+--------------------+\n",
      "|QuoteNumber|label|prediction|         probability|\n",
      "+-----------+-----+----------+--------------------+\n",
      "|          1|    0|       0.0|[0.92947436919380...|\n",
      "|          2|    0|       0.0|[0.89769279941372...|\n",
      "|          6|    0|       0.0|[0.92664383201093...|\n",
      "|         12|    0|       0.0|[0.89098783539883...|\n",
      "|         13|    0|       0.0|[0.92957194874559...|\n",
      "+-----------+-----+----------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#view id, label, prediction and probability from result of modelling\n",
    "gbt_result.select('Id', 'label', 'prediction', 'probability').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gradient Boosting Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosted Tree Performance Measure\n",
      "Accuracy = 0.91\n",
      "AUC = 0.95\n"
     ]
    }
   ],
   "source": [
    "#Evaluate model by calculating accuracy and area under curve (AUC)\n",
    "gbt_eval = BinaryClassificationEvaluator(rawPredictionCol=\"probability\",labelCol=\"label\")\n",
    "gbt_eval2= MulticlassClassificationEvaluator(predictionCol=\"prediction\", labelCol=\"label\")\n",
    "gbt_AUC  = gbt_eval.evaluate(gbt_result)\n",
    "gbt_ACC  = gbt_eval2.evaluate(gbt_result, {gbt_eval2.metricName:\"accuracy\"})\n",
    "\n",
    "print(\"Gradient Boosted Tree Performance Measure\")\n",
    "print(\"Accuracy = %0.2f\" % gbt_ACC)\n",
    "print(\"AUC = %.2f\" % gbt_AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ROC Grafik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Area Under ROC\n",
      "Area under ROC = 0.95\n"
     ]
    },
    {
     "data": {
      "image/png": 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o0qRJXtSWY6evRpGgUNGoRnGsLcVFwoLpuXfvLq1bN2Px4gWsWfM9arXa0CUJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x208225fdb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PredAndLabels           = gbt_result.select(\"probability\", \"label\")\n",
    "PredAndLabels_collect   = PredAndLabels.collect()\n",
    "PredAndLabels_list      = [(float(i[0][0]), 1.0-float(i[1])) for i in PredAndLabels_collect]\n",
    "PredAndLabels           = sc.parallelize(PredAndLabels_list)\n",
    "\n",
    "metrics = BinaryClassificationMetrics(PredAndLabels)\n",
    "\n",
    "# Area under ROC\n",
    "print(\"Gradient Boosting Area Under ROC\")\n",
    "print(\"Area under ROC = %.2f\" % metrics.areaUnderROC)\n",
    "\n",
    "# Visualization\n",
    "FPR = dict()                                                        # FPR: False Positive Rate\n",
    "tpr = dict()                                                        # TPR: True Positive Rate\n",
    "roc_auc = dict()\n",
    " \n",
    "y_test = [i[1] for i in PredAndLabels_list]\n",
    "y_score = [i[0] for i in PredAndLabels_list]\n",
    " \n",
    "fpr, tpr, _ = roc_curve(y_test, y_score)\n",
    "roc_auc = auc(fpr, tpr)\n",
    " \n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Gradient Boosting')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confusion Matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction_label</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>266</td>\n",
       "      <td>8294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>63538</td>\n",
       "      <td>6451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  prediction_label      0     1\n",
       "0              1.0    266  8294\n",
       "1              0.0  63538  6451"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_gbt_result = gbt_result.crosstab(\"prediction\", \"label\")\n",
    "cm_gbt_result = cm_gbt_result.toPandas()\n",
    "cm_gbt_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.91\n",
      "Sensitivity = 0.56\n",
      "Specificity = 1.00\n",
      "Precision = 0.97\n"
     ]
    }
   ],
   "source": [
    "#calculate accuracy, sensitivity, specificity and precision\n",
    "TP = cm_gbt_result[\"1\"][0]\n",
    "FP = cm_gbt_result[\"0\"][0]\n",
    "TN = cm_gbt_result[\"0\"][1]\n",
    "FN = cm_gbt_result[\"1\"][1]\n",
    "Accuracy = (TP+TN)/(TP+FP+TN+FN)\n",
    "Sensitivity = TP/(TP+FN)\n",
    "Specificity = TN/(TN+FP)\n",
    "Precision = TP/(TP+FP)\n",
    "\n",
    "print (\"Accuracy = %0.2f\" %Accuracy )\n",
    "print (\"Sensitivity = %0.2f\" %Sensitivity )\n",
    "print (\"Specificity = %0.2f\" %Specificity )\n",
    "print (\"Precision = %0.2f\" %Precision )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Gini Coefficient from AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC=0.95\n",
      "GINI ~=0.89\n"
     ]
    }
   ],
   "source": [
    "AUC = gbt_AUC\n",
    "Gini_gbt= (2 * AUC -1)\n",
    "\n",
    "print(\"AUC=%.2f\" % AUC)\n",
    "print(\"GINI ~=%.2f\" % Gini_gbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Log Loss in pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create Dataframe to Calculate Log Loss\n",
    "y_test= data_test.select('label')\n",
    "gbt_proba=gbt_result.select('probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Convert pyspark dataframe to numpy array\n",
    "gbt_proba= np.array(gbt_proba.select('probability').collect())\n",
    "\n",
    "#Convert numpy array 3 dimentional to 2 dimentional\n",
    "gbt_proba=gbt_proba.reshape(-1, gbt_proba.shape[-1])\n",
    "\n",
    "#Convert y_test dataframe to pandas dataframe\n",
    "y_test=y_test.toPandas()\n",
    "\n",
    "#Convert y_test pandas dataframe to pandas series\n",
    "y_test=pd.Series(y_test['label'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Loss Gradient Boosting:0.2402\n"
     ]
    }
   ],
   "source": [
    "#Calculate log loss from Gradient Boosting\n",
    "LogLoss = log_loss(y_test, gbt_proba) \n",
    "\n",
    "print(\"Log Loss Gradient Boosting:%.4f\" % LogLoss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gradient Boosting With Hyper-Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#define gradient boosting model\n",
    "gbt_hyper= GBTClassifier(featuresCol=\"features\", labelCol=\"label\")\n",
    "\n",
    "# Hyper-Parameter Tuning\n",
    "paramGrid_gbt = ParamGridBuilder() \\\n",
    "    .addGrid(gbt_hyper.maxIter, [10])\\\n",
    "    .addGrid(gbt_hyper.maxDepth, [6, 7,10]) \\\n",
    "    .build()\n",
    "crossval_gbt = CrossValidator(estimator=gbt_hyper,\n",
    "                             estimatorParamMaps=paramGrid_gbt,\n",
    "                             evaluator=BinaryClassificationEvaluator(),\n",
    "                             numFolds=3)\n",
    "#fit model to data train\n",
    "gbt_model_hyper = crossval_gbt.fit(data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#transfrom model to data test\n",
    "gbt_result_hyper = gbt_model_hyper.transform(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#view id, label, prediction and probability from result of modelling\n",
    "gbt_result_hyper.select('Id', 'label', 'prediction', 'probability').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gradient Boosting With Hyper-Parameter Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Evaluate model by calculating accuracy and area under curve (AUC)\n",
    "gbt_eval_hyper = BinaryClassificationEvaluator(rawPredictionCol=\"probability\", labelCol=\"label\")\n",
    "gbt_eval_hyper2= MulticlassClassificationEvaluator(predictionCol=\"prediction\", labelCol=\"label\")\n",
    "gbt_hyper_AUC  = gbt_eval_hyper.evaluate(gbt_result_hyper)\n",
    "gbt_hyper_ACC  = gbt_eval_hyper2.evaluate(gbt_result_hyper, {gbt_eval_hyper2.metricName:\"accuracy\"})\n",
    "\n",
    "\n",
    "print(\"Gradient Boosted Tree Performance Measure\")\n",
    "print(\"Accuracy = %0.2f\" % gbt_hyper_ACC)\n",
    "print(\"AUC = %.2f\" % gbt_hyper_AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ROC Grafik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PredAndLabels           = gbt_result_hyper.select(\"probability\", \"label\")\n",
    "PredAndLabels_collect   = PredAndLabels.collect()\n",
    "PredAndLabels_list      = [(float(i[0][0]), 1.0-float(i[1])) for i in PredAndLabels_collect]\n",
    "PredAndLabels           = sc.parallelize(PredAndLabels_list)\n",
    "\n",
    "metrics = BinaryClassificationMetrics(PredAndLabels)\n",
    "\n",
    "# Area under ROC\n",
    "print(\"Gradient Boosting Area Under ROC\")\n",
    "print(\"Area under ROC = %.2f\" % metrics.areaUnderROC)\n",
    "\n",
    "# Visualization\n",
    "FPR = dict()                                                        # FPR: False Positive Rate\n",
    "tpr = dict()                                                        # TPR: True Positive Rate\n",
    "roc_auc = dict()\n",
    " \n",
    "y_test = [i[1] for i in PredAndLabels_list]\n",
    "y_score = [i[0] for i in PredAndLabels_list]\n",
    " \n",
    "fpr, tpr, _ = roc_curve(y_test, y_score)\n",
    "roc_auc = auc(fpr, tpr)\n",
    " \n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Gradient Boosting')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm_gbt_result_hyper = gbt_result_hyper.crosstab(\"prediction\", \"label\")\n",
    "cm_gbt_result_hyper = cm_gbt_result_hyper.toPandas()\n",
    "cm_gbt_result_hyper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#calculate accuracy, sensitivity, specificity and precision\n",
    "TP = cm_gbt_result_hyper[\"1\"][0]\n",
    "FP = cm_gbt_result_hyper[\"0\"][0]\n",
    "TN = cm_gbt_result_hyper[\"0\"][1]\n",
    "FN = cm_gbt_result_hyper[\"1\"][1]\n",
    "Accuracy = (TP+TN)/(TP+FP+TN+FN)\n",
    "Sensitivity = TP/(TP+FN)\n",
    "Specificity = TN/(TN+FP)\n",
    "Precision = TP/(TP+FP)\n",
    "\n",
    "print (\"Accuracy = %0.2f\" %Accuracy )\n",
    "print (\"Sensitivity = %0.2f\" %Sensitivity )\n",
    "print (\"Specificity = %0.2f\" %Specificity )\n",
    "print (\"Precision = %0.2f\" %Precision )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Gini Coefficient from AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AUC = gbt_hyper_AUC\n",
    "Gini_gbt_hyper= (2 * AUC -1)\n",
    "\n",
    "print(\"AUC=%.2f\" % AUC)\n",
    "print(\"GINI ~=%.2f\" % Gini_gbt_hyper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate Log Loss in pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create Dataframe to Calculate Log Loss\n",
    "y_test= data_test.select('label')\n",
    "gbt_hyper_proba=gbt_result_hyper.select('probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Convert pyspark dataframe to numpy array\n",
    "gbt_hyper_proba= np.array(gbt_hyper_proba.select('probability').collect())\n",
    "\n",
    "#Convert numpy array 3 dimentional to 2 dimentional\n",
    "gbt_hyper_proba=gbt_hyper_proba.reshape(-1, gbt_hyper_proba.shape[-1])\n",
    "\n",
    "#Convert y_test dataframe to pandas dataframe\n",
    "y_test=y_test.toPandas()\n",
    "\n",
    "#Convert y_test pandas dataframe to pandas series\n",
    "y_test=pd.Series(y_test['label'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Calculate log loss from Gradient Boosting hyper parameter\n",
    "LogLoss = log_loss(y_test, gbt_hyper_proba) \n",
    "\n",
    "print(\"Log Loss Gradient Boosting:%.4f\" % LogLoss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation Modelling to data test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#transform logistic regression to data test\n",
    "lr_predict = lr_model.transform(test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+----------+--------------------+\n",
      "|QuoteNumber|prediction|         probability|\n",
      "+-----------+----------+--------------------+\n",
      "|          3|       0.0|[0.83791364398138...|\n",
      "|          5|       0.0|[0.92952193248116...|\n",
      "|          7|       0.0|[0.95741665141917...|\n",
      "|          9|       0.0|[0.97971792414083...|\n",
      "|         10|       0.0|[0.99136899854054...|\n",
      "+-----------+----------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#view id, label, prediction and probability from result of modelling\n",
    "lr_predict.select('Id', 'prediction', 'probability').show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#select id and prediction from result of modelling and save in data frame called my_submission\n",
    "my_submission=lr_predict.select(\"Id\",\"prediction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#convert to Pandas dataframe\n",
    "my_submission=my_submission.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#save to csv\n",
    "my_submission.to_csv('E:/Datalabs/Classification/Home_Quote_conversion/my_submission.csv', index = False, header = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction using Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#transfrom gradient boosting model to data test\n",
    "gbt_predict = gbt_model.transform(test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+----------+--------------------+\n",
      "|QuoteNumber|prediction|         probability|\n",
      "+-----------+----------+--------------------+\n",
      "|          3|       0.0|[0.92769229359160...|\n",
      "|          5|       0.0|[0.90001675024220...|\n",
      "|          7|       0.0|[0.90051101019602...|\n",
      "|          9|       0.0|[0.91877741316781...|\n",
      "|         10|       0.0|[0.80122160794555...|\n",
      "+-----------+----------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#view id, label, prediction and probability from result of modelling\n",
    "gbt_predict.select('Id', 'prediction', 'probability').show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#select id and prediction from result of modelling and save in data frame called my_submission\n",
    "my_submission2=gbt_predict.select(\"Id\",\"prediction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#convert to Pandas dataframe\n",
    "my_submission2=my_submission2.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#save to csv\n",
    "my_submission2.to_csv('E:/Datalabs/Classification/Home_Quote_conversion/my_submission2.csv', index = False, header = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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