BT2101_Default.ipynb

{
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   "source": [
    "# BUILDING A DEFUALT DETECTION MODEL\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "## Table of Contents\n",
    "1. Problem Description (Brief Write Up)\n",
    "2. Exploratory Data Analysis (EDA)\n",
    "3. Data Pre-processing\n",
    "4. Model Selection\n",
    "5. Evaluation\n",
    "6. Discussion and Possible Improvements\n",
    "\n",
    "## 1. Problem Description\n",
    "\n",
    "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan. \n",
    "\n",
    "Each data sample is described by 23 feature attributes and a binary target feature (default or not) valued 0 (= not default) or 1 (= default). \n",
    "\n",
    "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n",
    "\n",
    "### X1 - X5: Indivual attributes of customer\n",
    "\n",
    "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n",
    "\n",
    "X2: Gender (1 = male; 2 = female). \n",
    "\n",
    "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n",
    "\n",
    "X4: Marital status (1 = married; 2 = single; 3 = others). \n",
    "\n",
    "X5: Age (year). \n",
    "\n",
    "### X6 - X11: Repayment history from April to Septemeber 2005\n",
    "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n",
    "\n",
    "\n",
    "X6 = the repayment status in September, 2005\n",
    "\n",
    "X7 = the repayment status in August, 2005\n",
    "\n",
    "X8 = the repayment status in July, 2005\n",
    "\n",
    "X9 = the repayment status in June, 2005\n",
    "\n",
    "X10 = the repayment status in May, 2005\n",
    "\n",
    "X11 = the repayment status in April, 2005. \n",
    "\n",
    "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n",
    "\n",
    "X12 = amount of bill statement in September, 2005; \n",
    "\n",
    "X13 = amount of bill statement in August, 2005\n",
    "\n",
    ". . .\n",
    "\n",
    "X17 = amount of bill statement in April, 2005. \n",
    "\n",
    "### X18 - X23: Amount of previous payment (NT dollar)\n",
    "X18 = amount paid in September, 2005\n",
    "\n",
    "X19 = amount paid in August, 2005\n",
    "\n",
    ". . .\n",
    "\n",
    "X23 = amount paid in April, 2005. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aM_aIU6UPHe4"
   },
   "source": [
    "## EDA\n",
    "\n",
    "In this section we will explore the data set, its shape and its features to get an idea of the data.\n",
    "\n",
    "### Importing packages and the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Is0wEkk3LJCt"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "x_Z7u_9vRC5m"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KhmX9KWWyrUW"
   },
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n",
    "df = pd.read_csv(url,  header = 1, index_col = 0)\n",
    "# Dataset is now stored in a Pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <td>2</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "ID                                                                         \n",
       "1       20000    2          2         1   24      2      2     -1     -1   \n",
       "2      120000    2          2         2   26     -1      2      0      0   \n",
       "3       90000    2          2         2   34      0      0      0      0   \n",
       "4       50000    2          2         1   37      0      0      0      0   \n",
       "5       50000    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "    PAY_5  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "ID         ...                                                                  \n",
       "1      -2  ...          0          0          0         0       689         0   \n",
       "2       0  ...       3272       3455       3261         0      1000      1000   \n",
       "3       0  ...      14331      14948      15549      1518      1500      1000   \n",
       "4       0  ...      28314      28959      29547      2000      2019      1200   \n",
       "5       0  ...      20940      19146      19131      2000     36681     10000   \n",
       "\n",
       "    PAY_AMT4  PAY_AMT5  PAY_AMT6  Y  \n",
       "ID                                   \n",
       "1          0         0         0  1  \n",
       "2       1000         0      2000  1  \n",
       "3       1000      1000      5000  0  \n",
       "4       1100      1069      1000  0  \n",
       "5       9000       689       679  0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename the target variable to \"Y\" for convenience\n",
    "df[\"Y\"] = df[\"default payment next month\"] \n",
    "df = df.drop(\"default payment next month\", axis = 1)\n",
    "df0 = df #backup of df\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "zcuPyfM86AKj",
    "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data has 24 Columns and 30000 Rows\n"
     ]
    }
   ],
   "source": [
    "size = df.shape\n",
    "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "QVaSnvJP3VbO",
    "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "df.isnull().any().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eVYXnIGH9Zq6"
   },
   "source": [
    "There are no null values in the data.\n",
    "\n",
    "We can also calculate some summary statistics for each attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "HgdgYfpR6hUM",
    "outputId": "0e6655d1-3872-448d-864b-786a54b7cf70"
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   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.00000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>167484.322667</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>1.853133</td>\n",
       "      <td>1.551867</td>\n",
       "      <td>35.485500</td>\n",
       "      <td>-0.016700</td>\n",
       "      <td>-0.133767</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.220667</td>\n",
       "      <td>-0.266200</td>\n",
       "      <td>...</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>38871.760400</td>\n",
       "      <td>5663.580500</td>\n",
       "      <td>5.921163e+03</td>\n",
       "      <td>5225.68150</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>0.221200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>129747.661567</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>0.790349</td>\n",
       "      <td>0.521970</td>\n",
       "      <td>9.217904</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>1.133187</td>\n",
       "      <td>...</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>2.304087e+04</td>\n",
       "      <td>17606.96147</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.415062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</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>...</td>\n",
       "      <td>2326.750000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.330000e+02</td>\n",
       "      <td>390.00000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>252.500000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>140000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</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>...</td>\n",
       "      <td>19052.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>17071.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>1800.00000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>240000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.000000</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>...</td>\n",
       "      <td>54506.000000</td>\n",
       "      <td>50190.500000</td>\n",
       "      <td>49198.250000</td>\n",
       "      <td>5006.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4505.00000</td>\n",
       "      <td>4013.250000</td>\n",
       "      <td>4031.500000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.684259e+06</td>\n",
       "      <td>896040.00000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            LIMIT_BAL           SEX     EDUCATION      MARRIAGE           AGE  \\\n",
       "count    30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean    167484.322667      1.603733      1.853133      1.551867     35.485500   \n",
       "std     129747.661567      0.489129      0.790349      0.521970      9.217904   \n",
       "min      10000.000000      1.000000      0.000000      0.000000     21.000000   \n",
       "25%      50000.000000      1.000000      1.000000      1.000000     28.000000   \n",
       "50%     140000.000000      2.000000      2.000000      2.000000     34.000000   \n",
       "75%     240000.000000      2.000000      2.000000      2.000000     41.000000   \n",
       "max    1000000.000000      2.000000      6.000000      3.000000     79.000000   \n",
       "\n",
       "              PAY_0         PAY_2         PAY_3         PAY_4         PAY_5  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      -0.016700     -0.133767     -0.166200     -0.220667     -0.266200   \n",
       "std        1.123802      1.197186      1.196868      1.169139      1.133187   \n",
       "min       -2.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       -1.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        8.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "       ...      BILL_AMT4      BILL_AMT5      BILL_AMT6       PAY_AMT1  \\\n",
       "count  ...   30000.000000   30000.000000   30000.000000   30000.000000   \n",
       "mean   ...   43262.948967   40311.400967   38871.760400    5663.580500   \n",
       "std    ...   64332.856134   60797.155770   59554.107537   16563.280354   \n",
       "min    ... -170000.000000  -81334.000000 -339603.000000       0.000000   \n",
       "25%    ...    2326.750000    1763.000000    1256.000000    1000.000000   \n",
       "50%    ...   19052.000000   18104.500000   17071.000000    2100.000000   \n",
       "75%    ...   54506.000000   50190.500000   49198.250000    5006.000000   \n",
       "max    ...  891586.000000  927171.000000  961664.000000  873552.000000   \n",
       "\n",
       "           PAY_AMT2      PAY_AMT3       PAY_AMT4       PAY_AMT5  \\\n",
       "count  3.000000e+04   30000.00000   30000.000000   30000.000000   \n",
       "mean   5.921163e+03    5225.68150    4826.076867    4799.387633   \n",
       "std    2.304087e+04   17606.96147   15666.159744   15278.305679   \n",
       "min    0.000000e+00       0.00000       0.000000       0.000000   \n",
       "25%    8.330000e+02     390.00000     296.000000     252.500000   \n",
       "50%    2.009000e+03    1800.00000    1500.000000    1500.000000   \n",
       "75%    5.000000e+03    4505.00000    4013.250000    4031.500000   \n",
       "max    1.684259e+06  896040.00000  621000.000000  426529.000000   \n",
       "\n",
       "            PAY_AMT6             Y  \n",
       "count   30000.000000  30000.000000  \n",
       "mean     5215.502567      0.221200  \n",
       "std     17777.465775      0.415062  \n",
       "min         0.000000      0.000000  \n",
       "25%       117.750000      0.000000  \n",
       "50%      1500.000000      0.000000  \n",
       "75%      4000.000000      0.000000  \n",
       "max    528666.000000      1.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W6hhPNl1Slau"
   },
   "source": [
    "### Exploring the features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1Sp2F3gzXX2F"
   },
   "source": [
    "**1) Exploring target attribute:**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "DCSEICWwXWgX",
    "outputId": "9545da56-f31b-48f2-a271-db0e18677beb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaults : 22.12 %\n",
      "non defaults : 77.88000000000001 %\n"
     ]
    }
   ],
   "source": [
    "All = df.shape[0]\n",
    "default = df[df['Y'] == 1]\n",
    "nondefault = df[df['Y'] == 0]\n",
    "\n",
    "x = len(default)/All\n",
    "y = len(nondefault)/All\n",
    "\n",
    "print('defaults :',x*100,'%')\n",
    "print('non defaults :',y*100,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "id": "W4TWo-gkYTql",
    "outputId": "0f7d6129-f6f2-448a-9236-9f9ef7ae1bb4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting target attribute against frequency\n",
    "labels = ['non default','default']\n",
    "classes = pd.value_counts(df['Y'], sort = True)\n",
    "classes.plot(kind = 'bar', rot=0)\n",
    "plt.title(\"Target attribute distribution\")\n",
    "plt.xticks(range(2), labels)\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tysR0WHw4SGU"
   },
   "source": [
    "**2) Exploring categorical attributes**\n",
    "\n",
    "Categorical attributes are:\n",
    "- Sex\n",
    "- Education\n",
    "- Marriage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oxsZ8GTGarMC"
   },
   "source": [
    "**2a) Checking formatting for categorical attributes:**\n",
    "\n",
    "Since all categorical attributes are in numerical format, there is no need to convert them into numerical factors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TSiH-BxjyJ_e"
   },
   "source": [
    "**2b) Analysis of categorical data groups**\n",
    "\n",
    "- Sex\n",
    "- Education\n",
    "- Marriage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "colab_type": "code",
    "id": "s61SSRII00UB",
    "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    60.373333\n",
      "1    39.626667\n",
      "Name: SEX, dtype: float64\n",
      "--------------------------------------------------------\n",
      "2    46.766667\n",
      "1    35.283333\n",
      "3    16.390000\n",
      "5     0.933333\n",
      "4     0.410000\n",
      "6     0.170000\n",
      "0     0.046667\n",
      "Name: EDUCATION, dtype: float64\n",
      "--------------------------------------------------------\n",
      "2    53.213333\n",
      "1    45.530000\n",
      "3     1.076667\n",
      "0     0.180000\n",
      "Name: MARRIAGE, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n",
    "print(\"--------------------------------------------------------\")\n",
    "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n",
    "print(\"--------------------------------------------------------\")\n",
    "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Uudv5XE828nb"
   },
   "source": [
    "**Conclusion**\n",
    "\n",
    "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n",
    "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n",
    "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z92LGXPKetjL"
   },
   "source": [
    "**2c) Analysing the relationship between categorical attributes and default payment (target attribute)**\n",
    "\n",
    "- Sex\n",
    "- Education\n",
    "- Marriage\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "U3IJzhwwe5KK",
    "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total target attributes:\n",
      "non defaults : 77.88000000000001 %\n",
      "defaults : 22.12 %\n",
      "--------------------------------------------------------\n",
      "SEX                Male     Female\n",
      "Y                                 \n",
      "non defaults  75.832773  79.223719\n",
      "defaults      24.167227  20.776281\n",
      "--------------------------------------------------------\n",
      "EDUCATION         0          1          2          3          4          5  \\\n",
      "Y                                                                            \n",
      "non defaults  100.0  80.765234  76.265146  74.842384  94.308943  93.571429   \n",
      "defaults        0.0  19.234766  23.734854  25.157616   5.691057   6.428571   \n",
      "\n",
      "EDUCATION             6  \n",
      "Y                        \n",
      "non defaults  84.313725  \n",
      "defaults      15.686275  \n",
      "--------------------------------------------------------\n",
      "MARRIAGE        unknown    married     single     others\n",
      "Y                                                       \n",
      "non defaults  90.740741  76.528296  79.071661  73.993808\n",
      "defaults       9.259259  23.471704  20.928339  26.006192\n"
     ]
    }
   ],
   "source": [
    "#proportion of target attribute (for reference)\n",
    "print('Total target attributes:')\n",
    "print('non defaults :',y*100,'%')\n",
    "print('defaults :',x*100,'%')\n",
    "print(\"--------------------------------------------------------\")\n",
    "#analysing default payment with Sex\n",
    "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n",
    "print(sex_target)\n",
    "print(\"--------------------------------------------------------\")\n",
    "#analysing default payment with education\n",
    "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n",
    "print(education_target)\n",
    "print(\"--------------------------------------------------------\")\n",
    "#analysing default payment with marriage\n",
    "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n",
    "print(marriage_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "apWUtjyHPWcE",
    "outputId": "a7e40c77-64d1-4ef3-f31c-b7d1b7f24878"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SEX: 0.6854422910010494\n",
      "EDUCATION: 1.0276815835411287e-08 *** [Significant at the 95% Level]\n",
      "MARRIAGE: 0.016161318279331434 *** [Significant at the 95% Level]\n"
     ]
    }
   ],
   "source": [
    "# we would like to conduct a chi square test of independence using the contingency tables above\n",
    "import scipy.stats as sp_stat\n",
    "\n",
    "def sigf(p, confidence):\n",
    "  return str(p) + \" *** [Significant at the \" + str(int(confidence*100)) + \"% Level]\" if p < 1-confidence else p\n",
    "\n",
    "#print the p values for the test statistic of each chi-sq test\n",
    "print(\"SEX:\", sigf(sp_stat.chi2_contingency(sex_target)[1], 0.95))\n",
    "print(\"EDUCATION:\", sigf(sp_stat.chi2_contingency(education_target)[1],0.95))\n",
    "print(\"MARRIAGE:\", sigf(sp_stat.chi2_contingency(marriage_target)[1],0.95))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kOriUQ0wxbhD"
   },
   "source": [
    "**Conclusion**\n",
    "\n",
    "Categorical attributes EDUCATION and MARRIAGE are associated with the target variable.\n",
    "\n",
    "We will probably omit SEX from our models as it is statistically insignificant.\n",
    "\n",
    "Categorical attributes SEX and MARRIAGE have approximately uniform distributions throughout categories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "77GAylGWnPJO"
   },
   "source": [
    "**3) Analysis of Numerical Attributes**\n",
    "\n",
    "The numerical attributes are:\n",
    "   \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 669
    },
    "colab_type": "code",
    "id": "HEcCl5Rj-N0T",
    "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>AGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>PAY_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>PAY_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>PAY_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>PAY_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>PAY_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>PAY_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>BILL_AMT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>BILL_AMT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>BILL_AMT3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>BILL_AMT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>BILL_AMT5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>BILL_AMT6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>PAY_AMT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>PAY_AMT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>PAY_AMT3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>PAY_AMT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>PAY_AMT5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>PAY_AMT6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0\n",
       "0   LIMIT_BAL\n",
       "1         AGE\n",
       "2       PAY_0\n",
       "3       PAY_2\n",
       "4       PAY_3\n",
       "5       PAY_4\n",
       "6       PAY_5\n",
       "7       PAY_6\n",
       "8   BILL_AMT1\n",
       "9   BILL_AMT2\n",
       "10  BILL_AMT3\n",
       "11  BILL_AMT4\n",
       "12  BILL_AMT5\n",
       "13  BILL_AMT6\n",
       "14   PAY_AMT1\n",
       "15   PAY_AMT2\n",
       "16   PAY_AMT3\n",
       "17   PAY_AMT4\n",
       "18   PAY_AMT5\n",
       "19   PAY_AMT6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#printing numerical attributes\n",
    "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EUl9bX7k_nxw"
   },
   "source": [
    "**3a) Limit Balance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "Csm29blenaJT",
    "outputId": "e84fd570-639f-4a4b-a57f-af2d000d6730"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count      30000.000000\n",
      "mean      167484.322667\n",
      "std       129747.661567\n",
      "min        10000.000000\n",
      "25%        50000.000000\n",
      "50%       140000.000000\n",
      "75%       240000.000000\n",
      "max      1000000.000000\n",
      "Name: LIMIT_BAL, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Find out min and max value of LIMIT BALANCE\n",
    "print(df[\"LIMIT_BAL\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "colab_type": "code",
    "id": "eHrdunFN7Sj0",
    "outputId": "1c087f10-d56c-493a-8f44-45f6c685b8d4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Limit Balance')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"LIMIT_BAL\"]).set_title('Distribution of Limit Balance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xlDZf8ru7Xp6"
   },
   "source": [
    "**3b) Analysis of Age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "colab_type": "code",
    "id": "Iwal1Lhb6ryG",
    "outputId": "b4f48898-27c5-4455-d8d1-9e344f3ddb7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e127780>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.AGE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3c) Rest of the Attributes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              PAY_0         PAY_2         PAY_3         PAY_4         PAY_5  \\\n",
      "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
      "mean      -0.016700     -0.133767     -0.166200     -0.220667     -0.266200   \n",
      "std        1.123802      1.197186      1.196868      1.169139      1.133187   \n",
      "min       -2.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
      "25%       -1.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
      "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
      "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
      "max        8.000000      8.000000      8.000000      8.000000      8.000000   \n",
      "\n",
      "              PAY_6      BILL_AMT1      BILL_AMT2     BILL_AMT3  \\\n",
      "count  30000.000000   30000.000000   30000.000000  3.000000e+04   \n",
      "mean      -0.291100   51223.330900   49179.075167  4.701315e+04   \n",
      "std        1.149988   73635.860576   71173.768783  6.934939e+04   \n",
      "min       -2.000000 -165580.000000  -69777.000000 -1.572640e+05   \n",
      "25%       -1.000000    3558.750000    2984.750000  2.666250e+03   \n",
      "50%        0.000000   22381.500000   21200.000000  2.008850e+04   \n",
      "75%        0.000000   67091.000000   64006.250000  6.016475e+04   \n",
      "max        8.000000  964511.000000  983931.000000  1.664089e+06   \n",
      "\n",
      "           BILL_AMT4      BILL_AMT5      BILL_AMT6       PAY_AMT1  \\\n",
      "count   30000.000000   30000.000000   30000.000000   30000.000000   \n",
      "mean    43262.948967   40311.400967   38871.760400    5663.580500   \n",
      "std     64332.856134   60797.155770   59554.107537   16563.280354   \n",
      "min   -170000.000000  -81334.000000 -339603.000000       0.000000   \n",
      "25%      2326.750000    1763.000000    1256.000000    1000.000000   \n",
      "50%     19052.000000   18104.500000   17071.000000    2100.000000   \n",
      "75%     54506.000000   50190.500000   49198.250000    5006.000000   \n",
      "max    891586.000000  927171.000000  961664.000000  873552.000000   \n",
      "\n",
      "           PAY_AMT2      PAY_AMT3       PAY_AMT4       PAY_AMT5       PAY_AMT6  \n",
      "count  3.000000e+04   30000.00000   30000.000000   30000.000000   30000.000000  \n",
      "mean   5.921163e+03    5225.68150    4826.076867    4799.387633    5215.502567  \n",
      "std    2.304087e+04   17606.96147   15666.159744   15278.305679   17777.465775  \n",
      "min    0.000000e+00       0.00000       0.000000       0.000000       0.000000  \n",
      "25%    8.330000e+02     390.00000     296.000000     252.500000     117.750000  \n",
      "50%    2.009000e+03    1800.00000    1500.000000    1500.000000    1500.000000  \n",
      "75%    5.000000e+03    4505.00000    4013.250000    4031.500000    4000.000000  \n",
      "max    1.684259e+06  896040.00000  621000.000000  426529.000000  528666.000000  \n"
     ]
    }
   ],
   "source": [
    "print(df.drop(['LIMIT_BAL', 'AGE','SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Meaning of attributes PAY_0 to PAY_6**\n",
    "\n",
    "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on.\n",
    "\n",
    "However, the presence of -2, -1 in these columns mean we must conduct some data transformation to use these columns as the months of payment delay.\n",
    "\n",
    "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3d) Relationships with Target**\n",
    "\n",
    "To find out the relationships between the features, we calculate the absolute value of R with the target for all attributes. (R = Correlation Coefficient)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 669
    },
    "colab_type": "code",
    "id": "awXnqvLOS-wB",
    "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>PAY_0</td>\n",
       "      <td>0.324794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_2</td>\n",
       "      <td>0.263551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_3</td>\n",
       "      <td>0.235253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_4</td>\n",
       "      <td>0.216614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_5</td>\n",
       "      <td>0.204149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_6</td>\n",
       "      <td>0.186866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>0.153520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_AMT1</td>\n",
       "      <td>0.072929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_AMT2</td>\n",
       "      <td>0.058579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_AMT4</td>\n",
       "      <td>0.056827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_AMT3</td>\n",
       "      <td>0.056250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_AMT5</td>\n",
       "      <td>0.055124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_AMT6</td>\n",
       "      <td>0.053183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BILL_AMT1</td>\n",
       "      <td>0.019644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BILL_AMT2</td>\n",
       "      <td>0.014193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BILL_AMT3</td>\n",
       "      <td>0.014076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AGE</td>\n",
       "      <td>0.013890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BILL_AMT4</td>\n",
       "      <td>0.010156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BILL_AMT5</td>\n",
       "      <td>0.006760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BILL_AMT6</td>\n",
       "      <td>0.005372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "PAY_0      0.324794\n",
       "PAY_2      0.263551\n",
       "PAY_3      0.235253\n",
       "PAY_4      0.216614\n",
       "PAY_5      0.204149\n",
       "PAY_6      0.186866\n",
       "LIMIT_BAL  0.153520\n",
       "PAY_AMT1   0.072929\n",
       "PAY_AMT2   0.058579\n",
       "PAY_AMT4   0.056827\n",
       "PAY_AMT3   0.056250\n",
       "PAY_AMT5   0.055124\n",
       "PAY_AMT6   0.053183\n",
       "BILL_AMT1  0.019644\n",
       "BILL_AMT2  0.014193\n",
       "BILL_AMT3  0.014076\n",
       "AGE        0.013890\n",
       "BILL_AMT4  0.010156\n",
       "BILL_AMT5  0.006760\n",
       "BILL_AMT6  0.005372"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#absolute correlation coefficient\n",
    "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).apply(lambda x: x.corr(df.Y) if x.corr(df.Y) > 0 else -x.corr(df.Y)).sort_values(ascending = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "C6c_Gz6wUrJ8"
   },
   "source": [
    "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n",
    "\n",
    "The least correlated attributes are the bill amounts.\n",
    "\n",
    "We can also generate a correlation matrix (heatmap) to see which features are the most correlated with each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 964
    },
    "colab_type": "code",
    "id": "C2Mg6Zi_Q_rf",
    "outputId": "a06ab39f-82c5-43a7-cc4f-a267b2a967f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Correlation Matrix')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(19, 15))\n",
    "plt.matshow(df.corr(), fignum=f.number)\n",
    "plt.xticks(range(df.shape[1]), df.columns, fontsize=14, rotation=45)\n",
    "plt.yticks(range(df.shape[1]), df.columns, fontsize=14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "plt.title('Correlation Matrix', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gXQQgn7kU0Uc"
   },
   "source": [
    "The heatmap reveals that the payment and billed amounts across the 5 months are highly correlated with each other.  Highly Correlated factors should be removed before carrying out any regression to prevent multicollinearity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AQBksEyEf4Sf"
   },
   "source": [
    "## Data Preprocessing\n",
    "It was previously mentioned that our data had a bit of noise. In this part we will clean up the data so that it can be correctly applied to ML models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AG82bczx53gl"
   },
   "source": [
    "???\n",
    "1. get rid of unknown attributes\n",
    "2. One-hot encoding\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [

=======
    "### 1. Filter method\n",
    "Selecting relevant attributes before moving on to learning phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "from sklearn.datasets import load_boston\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import RidgeCV, LassoCV, Ridge, Lasso"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1a. Using Pearson Correlation\n",
    "Compute the correlation between each input attribute and the target attribute. Those with correlations below a threshold are excluded from the learning phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Pearson Correlation\n",
    "plt.figure(figsize= (19, 15))\n",
    "cor = df.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If correlation set as 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAY_0    0.324794\n",
       "PAY_2    0.263551\n",
       "Y        1.000000\n",
       "Name: Y, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation with output variable\n",
    "cor_target = abs(cor[\"Y\"])\n",
    "#Selecting highly correlated features\n",
    "relevant_features = cor_target[cor_target>0.25]\n",
    "relevant_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the assumptions of linear regression is that the independent variables need to be uncorrelated with each other. If these variables are correlated with each other, then we need to keep only one of them and drop the rest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          PAY_0     PAY_2\n",
      "PAY_0  1.000000  0.672164\n",
      "PAY_2  0.672164  1.000000\n"
     ]
    }
   ],
   "source": [
    "print(df[[\"PAY_0\",\"PAY_2\"]].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since PAY_0 and PAY_2 are highly correlated, we drop PAY_2 and keep PAY_0 as it is more closely correlated to the target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Wrapper method\n",
    "The selection of predictive attributes is based not only on the relevance of each single attribute, but also on the performance of the specific algorithm being applied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2a. Backward method\n",
    "Start with all attributes, and eliminate one at a time, stop when no more attributes can be eliminated according to a pre-fixed threshold for removal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance metric used here to evaluate feature performance is pvalue. If the pvalue is above 0.05 then we remove the feature, else we keep it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "const         1.537569e-68\n",
       "LIMIT_BAL     2.755133e-05\n",
       "SEX           1.747109e-03\n",
       "EDUCATION     5.147797e-07\n",
       "MARRIAGE      5.875418e-07\n",
       "AGE           2.946747e-07\n",
       "PAY_0        3.326642e-257\n",
       "PAY_2         5.681012e-09\n",
       "PAY_3         1.130550e-03\n",
       "PAY_4         3.975500e-01\n",
       "PAY_5         1.854490e-01\n",
       "PAY_6         8.220139e-01\n",
       "BILL_AMT1     4.983321e-08\n",
       "BILL_AMT2     3.222480e-01\n",
       "BILL_AMT3     8.422220e-01\n",
       "BILL_AMT4     6.658746e-01\n",
       "BILL_AMT5     9.115911e-01\n",
       "BILL_AMT6     4.299775e-01\n",
       "PAY_AMT1      2.666502e-05\n",
       "PAY_AMT2      1.509489e-01\n",
       "PAY_AMT3      8.649222e-01\n",
       "PAY_AMT4      1.704694e-01\n",
       "PAY_AMT5      7.393028e-02\n",
       "PAY_AMT6      4.742209e-01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding constant column of ones, mandatory for sm.OLS model\n",
    "X = df.drop(\"Y\",1)   #Feature Matrix\n",
    "y = df[\"Y\"]          #Target Variable\n",
    "X_1 = sm.add_constant(X)\n",
    "#Fitting sm.OLS model\n",
    "model = sm.OLS(y,X_1).fit()\n",
    "model.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_5', 'BILL_AMT1', 'BILL_AMT2', 'PAY_AMT1', 'PAY_AMT2']\n"
     ]
    }
   ],
   "source": [
    "#Backward Elimination\n",
    "cols = list(X.columns)\n",
    "pmax = 1\n",
    "while (len(cols)>0):\n",
    "    p= []\n",
    "    X_1 = X[cols]\n",
    "    X_1 = sm.add_constant(X_1)\n",
    "    model = sm.OLS(y,X_1).fit()\n",
    "    p = pd.Series(model.pvalues.values[1:],index = cols)      \n",
    "    pmax = max(p)\n",
    "    feature_with_p_max = p.idxmax()\n",
    "    if(pmax>0.05):\n",
    "        cols.remove(feature_with_p_max)\n",
    "    else:\n",
    "        break\n",
    "selected_features_BE = cols\n",
    "print(selected_features_BE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables with a p-value above 0.5 are removed and model is rebuilt in an iterative fashion. The final variables produced are as follows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2b. Forward-backward method (Recursive Feature Elimination)\n",
    "At each step the best attribute among those excluded is introduced and the worst attribute among those included is eliminated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim is to find the optimum number of features where accuracy is the highest. We achieve that by checking each feature iteratively and select each feature where accuracy is the highest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimum number of features: 20\n",
      "Score with 20 features: 0.123130\n"
     ]
    }
   ],
   "source": [
    "#no of features\n",
    "nof_list=np.arange(1,23)            \n",
    "high_score=0\n",
    "#Variable to store the optimum features\n",
    "nof=0           \n",
    "score_list =[]\n",
    "for n in range(len(nof_list)):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state = 0)\n",
    "    model = LinearRegression()\n",
    "    rfe = RFE(model,nof_list[n])\n",
    "    X_train_rfe = rfe.fit_transform(X_train,y_train)\n",
    "    X_test_rfe = rfe.transform(X_test)\n",
    "    model.fit(X_train_rfe,y_train)\n",
    "    score = model.score(X_test_rfe,y_test)\n",
    "    score_list.append(score)\n",
    "    if(score>high_score):\n",
    "        high_score = score\n",
    "        nof = nof_list[n]\n",
    "print(\"Optimum number of features: %d\" %nof)\n",
    "print(\"Score with %d features: %f\" % (nof, high_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimum number of features is 20. We now feed 20 as number of features to RFE and get the final set of features given by RFE method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2',\n",
      "       'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
      "       'BILL_AMT4', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4',\n",
      "       'PAY_AMT5', 'PAY_AMT6'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "cols = list(X.columns)\n",
    "model = LinearRegression()\n",
    "#Initializing RFE model\n",
    "rfe = RFE(model, 20)             \n",
    "#Transforming data using RFE\n",
    "X_rfe = rfe.fit_transform(X,y)  \n",
    "#Fitting the data to model\n",
    "model.fit(X_rfe,y)              \n",
    "temp = pd.Series(rfe.support_,index = cols)\n",
    "selected_features_rfe = temp[temp==True].index\n",
    "print(selected_features_rfe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Embedded method\n",
    "Feature selection using Lasso regularization. If the feature is irrelevant, Lasso penalizes it’s coefficient and make it 0. Hence the features with coefficient = 0 are removed and the rest are taken."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha using built-in LassoCV: 41.146428\n",
      "Best score using built-in LassoCV: 0.030792\n"
     ]
    }
   ],
   "source": [
    "reg = LassoCV()\n",
    "reg.fit(X, y)\n",
    "print(\"Best alpha using built-in LassoCV: %f\" % reg.alpha_)\n",
    "print(\"Best score using built-in LassoCV: %f\" %reg.score(X,y))\n",
    "coef = pd.Series(reg.coef_, index = X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso picked 12 variables and eliminated the other 11 variables\n"
     ]
    }
   ],
   "source": [
    "print(\"Lasso picked \" + str(sum(coef != 0)) + \" variables and eliminated the other \" +  str(sum(coef == 0)) + \" variables\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature importance using Lasso Model')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp_coef = coef.sort_values()\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (8.0, 10.0)\n",
    "imp_coef.plot(kind = \"barh\")\n",
    "plt.title(\"Feature importance using Lasso Model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasso method thus takes all variables except BILL_AMT3, BILL_AMT4, PAY_3, PAY_4, PAY_5, PAY_6, AGE, PAY_2, PAY_0, MARRIAGE, EDUCATION and SEX."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AG82bczx53gl"
   },
   "source": [
    "### Feature Selection\n",
    "\n",
    "Automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [

    "### Removing Noise\n",
    "#### Inconsistency\n",
    "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3, 4])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n",
    "df[\"EDUCATION\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"MARRIAGE\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n",
    "\n",
    "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,7):\n",
    "    try:\n",
    "        df[\"S_\" + str(i)] = [x  if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S_0</th>\n",
       "      <th>S_2</th>\n",
       "      <th>S_3</th>\n",
       "      <th>S_4</th>\n",
       "      <th>S_5</th>\n",
       "      <th>S_6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    S_0  S_2  S_3  S_4  S_5  S_6\n",
       "ID                              \n",
       "1     1    1   -1   -1   -2   -2\n",
       "2    -1    1    0    0    0    1\n",
       "3     0    0    0    0    0    0\n",
       "4     0    0    0    0    0    0\n",
       "5    -1    0   -1    0    0    0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              PAY_0         PAY_2         PAY_3         PAY_4        PAY_5  \\\n",
      "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.00000   \n",
      "mean       0.356767      0.320033      0.304067      0.258767      0.22150   \n",
      "std        0.760594      0.801727      0.790589      0.761113      0.71772   \n",
      "min        0.000000      0.000000      0.000000      0.000000      0.00000   \n",
      "25%        0.000000      0.000000      0.000000      0.000000      0.00000   \n",
      "50%        0.000000      0.000000      0.000000      0.000000      0.00000   \n",
      "75%        0.000000      0.000000      0.000000      0.000000      0.00000   \n",
      "max        8.000000      8.000000      8.000000      8.000000      8.00000   \n",
      "\n",
      "              PAY_6  \n",
      "count  30000.000000  \n",
      "mean       0.226567  \n",
      "std        0.715438  \n",
      "min        0.000000  \n",
      "25%        0.000000  \n",
      "50%        0.000000  \n",
      "75%        0.000000  \n",
      "max        8.000000  \n"
     ]
    }
   ],
   "source": [
    "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n",
    "    df[col].replace([0,-1,-2], 0, regex=True, inplace=True)\n",
    "\n",
    "print(df[[\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]].describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outliers\n",
    "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>Y</th>\n",
       "      <th>S_0</th>\n",
       "      <th>S_2</th>\n",
       "      <th>S_3</th>\n",
       "      <th>S_4</th>\n",
       "      <th>S_5</th>\n",
       "      <th>S_6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "ID                                                                         \n",
       "1       20000    2          2         1   24      2      2      0      0   \n",
       "2      120000    2          2         2   26      0      2      0      0   \n",
       "3       90000    2          2         2   34      0      0      0      0   \n",
       "4       50000    2          2         1   37      0      0      0      0   \n",
       "5       50000    1          2         1   57      0      0      0      0   \n",
       "\n",
       "    PAY_5  ...  PAY_AMT4  PAY_AMT5  PAY_AMT6  Y  S_0  S_2  S_3  S_4  S_5  S_6  \n",
       "ID         ...                                                                 \n",
       "1       0  ...         0         0         0  1    1    1   -1   -1   -2   -2  \n",
       "2       0  ...      1000         0      2000  1   -1    1    0    0    0    1  \n",
       "3       0  ...      1000      1000      5000  0    0    0    0    0    0    0  \n",
       "4       0  ...      1100      1069      1000  0    0    0    0    0    0    0  \n",
       "5       0  ...      9000       689       679  0   -1    0   -1    0    0    0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#KIV might not be useful\n",
    "from scipy import stats\n",
    "#we are only concerned with the ordinal data\n",
    "o = pd.DataFrame(df.drop(['EDUCATION', 'MARRIAGE',\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"SEX\"], axis=1))\n",
    "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n",
    "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n",
    "df = df[rows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              PAY_0         PAY_2         PAY_3         PAY_4         PAY_5  \\\n",
      "count  25128.000000  25128.000000  25128.000000  25128.000000  25128.000000   \n",
      "mean       0.299507      0.250398      0.237345      0.195599      0.163403   \n",
      "std        0.619521      0.661300      0.646757      0.594064      0.547830   \n",
      "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
      "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
      "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
      "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
      "max        2.000000      2.000000      2.000000      2.000000      2.000000   \n",
      "\n",
      "              PAY_6  \n",
      "count  25128.000000  \n",
      "mean       0.174069  \n",
      "std        0.563782  \n",
      "min        0.000000  \n",
      "25%        0.000000  \n",
      "50%        0.000000  \n",
      "75%        0.000000  \n",
      "max        2.000000  \n"
     ]
    }
   ],
   "source": [
    "print(df[[\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]].describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n",
    "\n",
    "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n",
    "\n",
    "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n",
    "\n",
    "The following categorical columns will be one-hot encoded\n",
    "1. MARRIAGE\n",
    "2. EDUCATION\n",
    "3. S_0 to S_6 (ex. S_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "onenc = OneHotEncoder(categories='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   x0_1  x0_2  x0_3  x0_4  x1_0  x1_1  x1_2  x1_3  x2_-2  x2_-1  ...  x5_0  \\\n",
       "0   0.0   1.0   0.0   0.0   0.0   1.0   0.0   0.0    0.0    0.0  ...   0.0   \n",
       "1   0.0   1.0   0.0   0.0   0.0   0.0   1.0   0.0    0.0    1.0  ...   1.0   \n",
       "2   0.0   1.0   0.0   0.0   0.0   0.0   1.0   0.0    0.0    0.0  ...   1.0   \n",
       "3   0.0   1.0   0.0   0.0   0.0   1.0   0.0   0.0    0.0    0.0  ...   1.0   \n",
       "4   0.0   1.0   0.0   0.0   0.0   1.0   0.0   0.0    0.0    1.0  ...   1.0   \n",
       "\n",
       "   x5_1  x6_-2  x6_-1  x6_0  x6_1  x7_-2  x7_-1  x7_0  x7_1  \n",
       "0   0.0    1.0    0.0   0.0   0.0    1.0    0.0   0.0   0.0  \n",
       "1   0.0    0.0    0.0   1.0   0.0    0.0    0.0   0.0   1.0  \n",
       "2   0.0    0.0    0.0   1.0   0.0    0.0    0.0   1.0   0.0  \n",
       "3   0.0    0.0    0.0   1.0   0.0    0.0    0.0   1.0   0.0  \n",
       "4   0.0    0.0    0.0   1.0   0.0    0.0    0.0   1.0   0.0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE',\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).toarray())\n",
    "onehot.columns= onenc.fit(df[['EDUCATION', 'MARRIAGE',\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n",
    "\n",
    "onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
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       "    <tr>\n",
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       "      <td>50000</td>\n",
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       "      <td>0</td>\n",
       "      <td>47929</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25128 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  SEX  AGE  PAY_0  PAY_2  PAY_3  PAY_4  PAY_5  PAY_6  \\\n",
       "0          20000    2   24      2      2      0      0      0      0   \n",
       "1         120000    2   26      0      2      0      0      0      2   \n",
       "2          90000    2   34      0      0      0      0      0      0   \n",
       "3          50000    2   37      0      0      0      0      0      0   \n",
       "4          50000    1   57      0      0      0      0      0      0   \n",
       "...          ...  ...  ...    ...    ...    ...    ...    ...    ...   \n",
       "25123      10000    1   43      0      0      0      0      0      0   \n",
       "25124      80000    1   34      2      2      2      2      2      2   \n",
       "25125     220000    1   39      0      0      0      0      0      0   \n",
       "25126     150000    1   43      0      0      0      0      0      0   \n",
       "25127      50000    1   46      0      0      0      0      0      0   \n",
       "\n",
       "       BILL_AMT1  ...  x5_0  x5_1  x6_-2  x6_-1  x6_0  x6_1  x7_-2  x7_-1  \\\n",
       "0           3913  ...   0.0   0.0    1.0    0.0   0.0   0.0    1.0    0.0   \n",
       "1           2682  ...   1.0   0.0    0.0    0.0   1.0   0.0    0.0    0.0   \n",
       "2          29239  ...   1.0   0.0    0.0    0.0   1.0   0.0    0.0    0.0   \n",
       "3          46990  ...   1.0   0.0    0.0    0.0   1.0   0.0    0.0    0.0   \n",
       "4           8617  ...   1.0   0.0    0.0    0.0   1.0   0.0    0.0    0.0   \n",
       "...          ...  ...   ...   ...    ...    ...   ...   ...    ...    ...   \n",
       "25123       8802  ...   0.0   0.0    1.0    0.0   0.0   0.0    1.0    0.0   \n",
       "25124      72557  ...   0.0   1.0    0.0    0.0   0.0   1.0    0.0    0.0   \n",
       "25125     188948  ...   1.0   0.0    0.0    0.0   1.0   0.0    0.0    0.0   \n",
       "25126       1683  ...   0.0   0.0    0.0    0.0   1.0   0.0    0.0    0.0   \n",
       "25127      47929  ...   1.0   0.0    0.0    0.0   1.0   0.0    0.0    0.0   \n",
       "\n",
       "       x7_0  x7_1  \n",
       "0       0.0   0.0  \n",
       "1       0.0   1.0  \n",
       "2       1.0   0.0  \n",
       "3       1.0   0.0  \n",
       "4       1.0   0.0  \n",
       "...     ...   ...  \n",
       "25123   0.0   0.0  \n",
       "25124   0.0   1.0  \n",
       "25125   1.0   0.0  \n",
       "25126   1.0   0.0  \n",
       "25127   1.0   0.0  \n",
       "\n",
       "[25128 rows x 54 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.drop(['EDUCATION', 'MARRIAGE',\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"], axis = 1)\n",
    "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>...</th>\n",
       "      <th>x5_0</th>\n",
       "      <th>x5_1</th>\n",
       "      <th>x6_-2</th>\n",
       "      <th>x6_-1</th>\n",
       "      <th>x6_0</th>\n",
       "      <th>x6_1</th>\n",
       "      <th>x7_-2</th>\n",
       "      <th>x7_-1</th>\n",
       "      <th>x7_0</th>\n",
       "      <th>x7_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>PAY_5</td>\n",
       "      <td>-0.137504</td>\n",
       "      <td>-0.020715</td>\n",
       "      <td>-0.018503</td>\n",
       "      <td>0.329063</td>\n",
       "      <td>0.376564</td>\n",
       "      <td>0.404531</td>\n",
       "      <td>0.600780</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.609907</td>\n",
       "      <td>0.033914</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.176377</td>\n",
       "      <td>0.600671</td>\n",
       "      <td>-0.132974</td>\n",
       "      <td>-0.144588</td>\n",
       "      <td>-0.338040</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.124204</td>\n",
       "      <td>-0.119272</td>\n",
       "      <td>-0.153801</td>\n",
       "      <td>0.609907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PAY_6</td>\n",
       "      <td>-0.141827</td>\n",
       "      <td>-0.016436</td>\n",
       "      <td>-0.024559</td>\n",
       "      <td>0.293515</td>\n",
       "      <td>0.336756</td>\n",
       "      <td>0.372171</td>\n",
       "      <td>0.427355</td>\n",
       "      <td>0.609907</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034888</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.081909</td>\n",
       "      <td>0.427271</td>\n",
       "      <td>-0.137645</td>\n",
       "      <td>-0.088522</td>\n",
       "      <td>-0.163521</td>\n",
       "      <td>0.609907</td>\n",
       "      <td>-0.144620</td>\n",
       "      <td>-0.153292</td>\n",
       "      <td>-0.331383</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x6_1</td>\n",
       "      <td>-0.137504</td>\n",
       "      <td>-0.020715</td>\n",
       "      <td>-0.018503</td>\n",
       "      <td>0.329063</td>\n",
       "      <td>0.376564</td>\n",
       "      <td>0.404531</td>\n",
       "      <td>0.600780</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.609907</td>\n",
       "      <td>0.033914</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.176377</td>\n",
       "      <td>0.600671</td>\n",
       "      <td>-0.132974</td>\n",
       "      <td>-0.144588</td>\n",
       "      <td>-0.338040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.124204</td>\n",
       "      <td>-0.119272</td>\n",
       "      <td>-0.153801</td>\n",
       "      <td>0.609907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>x7_1</td>\n",
       "      <td>-0.141827</td>\n",
       "      <td>-0.016436</td>\n",
       "      <td>-0.024559</td>\n",
       "      <td>0.293515</td>\n",
       "      <td>0.336756</td>\n",
       "      <td>0.372171</td>\n",
       "      <td>0.427355</td>\n",
       "      <td>0.609907</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034888</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.081909</td>\n",
       "      <td>0.427271</td>\n",
       "      <td>-0.137645</td>\n",
       "      <td>-0.088522</td>\n",
       "      <td>-0.163521</td>\n",
       "      <td>0.609907</td>\n",
       "      <td>-0.144620</td>\n",
       "      <td>-0.153292</td>\n",
       "      <td>-0.331383</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL       SEX       AGE     PAY_0     PAY_2     PAY_3     PAY_4  \\\n",
       "PAY_5  -0.137504 -0.020715 -0.018503  0.329063  0.376564  0.404531  0.600780   \n",
       "PAY_6  -0.141827 -0.016436 -0.024559  0.293515  0.336756  0.372171  0.427355   \n",
       "x6_1   -0.137504 -0.020715 -0.018503  0.329063  0.376564  0.404531  0.600780   \n",
       "x7_1   -0.141827 -0.016436 -0.024559  0.293515  0.336756  0.372171  0.427355   \n",
       "\n",
       "          PAY_5     PAY_6  BILL_AMT1  ...      x5_0      x5_1     x6_-2  \\\n",
       "PAY_5  0.000000  0.609907   0.033914  ... -0.176377  0.600671 -0.132974   \n",
       "PAY_6  0.609907  0.000000   0.034888  ... -0.081909  0.427271 -0.137645   \n",
       "x6_1   1.000000  0.609907   0.033914  ... -0.176377  0.600671 -0.132974   \n",
       "x7_1   0.609907  1.000000   0.034888  ... -0.081909  0.427271 -0.137645   \n",
       "\n",
       "          x6_-1      x6_0      x6_1     x7_-2     x7_-1      x7_0      x7_1  \n",
       "PAY_5 -0.144588 -0.338040  1.000000 -0.124204 -0.119272 -0.153801  0.609907  \n",
       "PAY_6 -0.088522 -0.163521  0.609907 -0.144620 -0.153292 -0.331383  1.000000  \n",
       "x6_1  -0.144588 -0.338040  0.000000 -0.124204 -0.119272 -0.153801  0.609907  \n",
       "x7_1  -0.088522 -0.163521  0.609907 -0.144620 -0.153292 -0.331383  0.000000  \n",
       "\n",
       "[4 rows x 54 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df1.corr()\n",
    "for i in range(len(corr)):\n",
    "    corr.iloc[i,i] = 0\n",
    "#corr[corr == 1] = 0\n",
    "corr[corr.eq(1).any(1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x6_1 and pay_5 are perfectly correlated\n",
    "#x7_1 and pay_6 are perfectly correlated\n",
    "\n",
    "df1 = df1.drop([\"x6_1\", \"x7_1\"], axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n",
    "is reached if the data is not normalized. Additionaly, for neural networks Multi-layer Perceptron is sensitive to feature scaling, so we will use the built-in StandardScaler for standardization. Other than the training set, the test set values are scaled as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>...</th>\n",
       "      <th>x5_-2</th>\n",
       "      <th>x5_-1</th>\n",
       "      <th>x5_0</th>\n",
       "      <th>x5_1</th>\n",
       "      <th>x6_-2</th>\n",
       "      <th>x6_-1</th>\n",
       "      <th>x6_0</th>\n",
       "      <th>x7_-2</th>\n",
       "      <th>x7_-1</th>\n",
       "      <th>x7_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3913</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29239</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46990</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LIMIT_BAL  SEX  AGE  PAY_0  PAY_2  PAY_3  PAY_4  PAY_5  PAY_6  BILL_AMT1  \\\n",
       "0      20000    2   24      2      2      0      0      0      0       3913   \n",
       "1     120000    2   26      0      2      0      0      0      2       2682   \n",
       "2      90000    2   34      0      0      0      0      0      0      29239   \n",
       "3      50000    2   37      0      0      0      0      0      0      46990   \n",
       "4      50000    1   57      0      0      0      0      0      0       8617   \n",
       "\n",
       "   ...  x5_-2  x5_-1  x5_0  x5_1  x6_-2  x6_-1  x6_0  x7_-2  x7_-1  x7_0  \n",
       "0  ...    0.0    1.0   0.0   0.0    1.0    0.0   0.0    1.0    0.0   0.0  \n",
       "1  ...    0.0    0.0   1.0   0.0    0.0    0.0   1.0    0.0    0.0   0.0  \n",
       "2  ...    0.0    0.0   1.0   0.0    0.0    0.0   1.0    0.0    0.0   1.0  \n",
       "3  ...    0.0    0.0   1.0   0.0    0.0    0.0   1.0    0.0    0.0   1.0  \n",
       "4  ...    0.0    0.0   1.0   0.0    0.0    0.0   1.0    0.0    0.0   1.0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mbhlIlQzZz7c"
   },
   "source": [
    "## Model Selection\n",
    "\n",
    "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n",
    "\n",
    "We will be attempting to fit the following models:\n",
    "- K-Nearest Neighbour\n",
    "- Support Vector Machine\n",
    "- Decision Tree \n",
    "- Naive Bayes Classifier\n",
    "- Logistic Regression\n",
    "- Neural Network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import *\n",
    "from sklearn.model_selection import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VOB68z_hM1jW"
   },
   "outputs": [],
   "source": [
    "#using holdout sampling for train test split\n",
    "ft = df1.drop(\"Y\", axis = 1)\n",
    "target = df1[\"Y\"]\n",
    "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.20)\n",
    "#make the results reproducible\n",
    "np.random.seed(123) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_roc(model, y_test, X_test, name):\n",
    "    try:\n",
    "        fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n",
    "        tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n",
    "        thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n",
    "    except:\n",
    "        fpr = roc_curve(y_test,model.predict(X_test))[0]\n",
    "        tpr = roc_curve(y_test,model.predict(X_test))[1]\n",
    "        thresholds = roc_curve(y_test,model.predict(X_test))[2]\n",
    "    plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\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('Receiver operating characteristic for ' + name)\n",
    "    plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    \n",
    "    #find- best threshold\n",
    "    optimal_idx = np.argmax(tpr - fpr)\n",
    "    optimal_threshold = thresholds[optimal_idx]\n",
    "    print(\"Optimal Threshold: \" + str(optimal_threshold))\n",
    "    \n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def confusion(y_test, predictions, name):\n",
    "    conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n",
    "    print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n",
    "    return conf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation \n",
    "We will select the model based on the model evaluation. The key metrics we will compute are:\n",
    "\n",
    "1. Accuracy\n",
    "2. Recall\n",
    "3. AUROC\n",
    "\n",
    "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n",
    "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n",
    "\n",
    "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PEyK8hM1BHj3"
   },
   "source": [
    "### Model 1 - K-Nearest Neighbour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "mgcr1jtzcVpL",
    "outputId": "669a1298-dc36-4dfc-e9fd-298089a515dc"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "neighbours = np.arange(1,25)\n",
    "train_accuracy =np.empty(len(neighbours))\n",
    "test_accuracy = np.empty(len(neighbours))\n",
    "                                \n",
    "for i,k in enumerate(neighbours):\n",
    "    #Setup a knn classifier with k neighbors\n",
    "    knn=KNeighborsClassifier(n_neighbors=k,algorithm=\"kd_tree\",n_jobs=-1)\n",
    "    \n",
    "    #Fit the model\n",
    "    knn.fit(X_train,y_train.ravel())\n",
    "    \n",
    "    #Compute accuracy on the training set\n",
    "    train_accuracy[i] = knn.score(X_train, y_train.ravel())\n",
    "    \n",
    "    #Compute accuracy on the test set\n",
    "    test_accuracy[i] = knn.score(X_test, y_test.ravel())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "OUhQMYZhPKUS",
    "outputId": "ea071750-1327-4907-8934-c329416798f2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Generate plot\n",
    "plt.title('k-NN Varying number of neighbors')\n",
    "plt.plot(neighbours, test_accuracy, label='Testing Accuracy')\n",
    "plt.plot(neighbours, train_accuracy, label='Training accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of neighbors')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "AKG1zgjxPcPE",
    "outputId": "eac2fe99-1db9-4160-e3ff-da85bb9c2459"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.98      0.87      3899\n",
      "           1       0.52      0.06      0.11      1127\n",
      "\n",
      "    accuracy                           0.78      5026\n",
      "   macro avg       0.65      0.52      0.49      5026\n",
      "weighted avg       0.73      0.78      0.70      5026\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# best k:\n",
    "idx = np.where(test_accuracy == max(test_accuracy))\n",
    "k = neighbours[idx][0]\n",
    "knn = KNeighborsClassifier(n_neighbors=k,algorithm=\"kd_tree\",n_jobs=-1)\n",
    "knn.fit(X_train,y_train.ravel())\n",
    "print(classification_report(y_test,knn.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0LZH_AAXUL72"
   },
   "source": [
    "Despite a reasonably high accuracy, the KNN model has very low recall for defaults."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Of 1127 Defaulters, the KNN identified 69\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3836</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1058</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted     0   1\n",
       "Actual             \n",
       "0          3836  63\n",
       "1          1058  69"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion(y_test, knn.predict(X_test), \"KNN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "H89tM6NvaN17"
   },
   "source": [
    "### Model 2 - Decision Trees\n",
    "\n",
    "#### Theory:\n",
    "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n",
    "\n",
    "Below is a binary decision tree that has been split for a few iterations.\n",
    "\n",
    "![image.png](https://elf11.github.io/images/decisionTree.png)\n",
    "\n",
    "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n",
    "\n",
    "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n",
    "\n",
    "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n",
    "\n",
    "#### Training\n",
    "We will now construct a simple decision tree using the GINI index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier()\n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     15890\n",
      "           1       1.00      1.00      1.00      4212\n",
      "\n",
      "    accuracy                           1.00     20102\n",
      "   macro avg       1.00      1.00      1.00     20102\n",
      "weighted avg       1.00      1.00      1.00     20102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, tree.predict(X_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Of 1127 Defaulters, the Decision Tree (GINI) identified 428\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3163</td>\n",
       "      <td>736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>699</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted     0    1\n",
       "Actual              \n",
       "0          3163  736\n",
       "1           699  428"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Threshold: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.81      0.82      3899\n",
      "           1       0.37      0.38      0.37      1127\n",
      "\n",
      "    accuracy                           0.71      5026\n",
      "   macro avg       0.59      0.60      0.59      5026\n",
      "weighted avg       0.72      0.71      0.72      5026\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, tree.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier\n",
    "\n",
    "#### Theory\n",
    "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n",
    "\n",
    "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n",
    "\n",
    "#### Training\n",
    "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "randf = RandomForestClassifier(n_estimators=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=300,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     15890\n",
      "           1       1.00      1.00      1.00      4212\n",
      "\n",
      "    accuracy                           1.00     20102\n",
      "   macro avg       1.00      1.00      1.00     20102\n",
      "weighted avg       1.00      1.00      1.00     20102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, randf.predict(X_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.95      0.89      3899\n",
      "           1       0.67      0.33      0.44      1127\n",
      "\n",
      "    accuracy                           0.81      5026\n",
      "   macro avg       0.75      0.64      0.67      5026\n",
      "weighted avg       0.80      0.81      0.79      5026\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, randf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Of 1127 Defaulters, the Decision Tree (Random Forest) identified 374\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3718</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>753</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted     0    1\n",
       "Actual              \n",
       "0          3718  181\n",
       "1           753  374"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Threshold: 0.24333333333333335\n"
     ]
    },
    {
     "data": {
      "image/png": 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Q63nSUex9f7Q9bBpWcdtoY0zqSS7TSbMT+yisCll7sI7Jd/nnoucVrMcNv4lIFlatshNdsL6L9UzlRF4GHrYv/CbwT2WwH/mnRMeZ2H/CKhpbiHUs/FpmkDuwihd328N8hHWedRn7XH4+1jk8DSsxjjbGHDrBaNdgPb8sPed9zT/Frb2AFSKSg3Vuu9MYs8/uNwH43D6+Lra7XY11YXFCpTXmVDlE5Aas2jn93B3LybKv2tOxiuV2uzsepaoaEQkE1mBVtkh2dzzViYh0Bf7rzLm1SjaLpE6NiJwnIkF2ccLLWFfPe9wblVJVk30X3VYTVOUzxqxx9uJfk5R3uQCruGY/VhHlFUZvlZVSHkyL+5RSSlVZeiellFKqyvLIRk9DQ0NNdHS0u8NQSimPsmrVqlRjTMOKh6w6PDJJRUdHs3LlSneHoZRSHkVEyrYwUeVpcZ9SSqkqS5OUUkqpKkuTlFJKqSpLk5RSSqkqS5OUUkqpKkuTlFJKqSrL5UlKRD4UkWQR2XCc/iIib4jIDhFZJyLdXB2TUkopz1AZd1JTsT6RcDznYLUzFwPchvVRPaWUUmdAXmHx3/88kctf5jXGLBKR6BMMcgHwsd0Q6p8iUldEGtsfFVRKKVVGXmEx6xMzOJCRR3ZeEdn5hexPz2PLwUySMq1PyBWXGNKy88kp8MzkVKoqtDgRwdEfwUuwux2VpETkNqw7LaKiHL+8rJRS3iszr5CdydnssP+t2nuYdQkZFBSXHDVcUIAvsY1q075JCD4iiECdQD82rjrIgvm78fX1zCoIVSFJlfeJ82OaZjfGvAe8BxAXF6dNtyulPEp+UTH70/NIPJxLclYeKVn5pGTlk5ZTQFZeEUcKisjJLyKnoJgjpf8XFFFY/M/pLsDXh3ZNQrihbzQ9ousT3SCI4EA/agf6E+Tvi4/P0afTl15azLeTlnD55e155ZURRETcWtmLfdqqQpJKAJo6/I7E+h6SUkpVeUcKikjLLiAtp4C07HxSs/P/TkAp2fkczMgj4XAuyVn5x4wb6O9DaHANQgL9qVXDl7pBAUTU8yUowI/gGn4EBfgSUtOfFqG1aBUWTFT9IPwquCNKTs4hMTGTrl0bc+edPenatTFDh7Zw1eK7XFVIUj8Cd4nINKAXkKHPo5RS7pRbUExi+hH2p+eRlpNPWnYBqdlWEkrL+SchpWUXkHucCgkhgX6EhQTSKKQGg1o3JLJeEBF1axJRrybhIYGE1q5BrQBfRMorTDp5JSWGDz5YzcMPzyU8PJgNG+4gKMjfoxMUVEKSEpEvgEFAqIgkABMAfwBjzGRgJjAK2AEcAW50dUxKKQWQkpXPloOZbD6QyeYDWexOzSHhcC6p2cfe9fj7Cg1q1aBBcAANgmvQIrQWDWpZfzcIDvjn71oBNKxdg0B/30pbjnXrkhg7dgZLlyYwaFA077xz7jFFf56qMmr3XVlBfwPc6eo4lFLVV2FxCbtScuxklMkmOyk5JqPwkEBahtViaNswIuvVJLJeEE3q1qRh7RrUrxVASKDfGbvrOZOWLUugb98PqVevJh99dCHXXtupSsZ5qqpCcZ9SSp0xhcUlbEvKYn1CBmsTMlifmM62g9l/14YL8PWhVVgwA2Mb0rZxbdo1DqFN4xDq1wpwc+QnJyEhk8jIEHr0iOCZZwZz++1x1K9f091hnXGapJRSHi39SAEr9hxm+e40Vuw5zKYDmRQUWQkpJNCPTpF1ubFvNG0bh9C2cQgtGtbC30OrYwPs25fBPffMYuHCvWzdehdhYbV45JH+7g7LZTRJKaU8yuGcApbuSuPPXWks332ILQezAAjw86FzZB2u792MjpF16RxZh6j6QV5T9FVYWMzrry9jwoQFGGOYOHEQ9eoFujssl9MkpZSq0rLzi1ix+xCLd6SyZGcamw5kAtbLq92b1WN0p8b0bN6ATpF1KrWyQmXKzi6gb98PWbcuidGjY/nvf88hOrquu8OqFJqklFJVSkmJYcWeQ/xhJ6W18ekUlRgC/HzoHlWPB4fH0rtlKJ0i63h0sZ0zCguL8ff3JTg4gCFDmjNx4kAuvLCN19wdOkOTlFKqStidmsM3qxL4bk0iiem5+PoInSLrcPvAFvRtGUq3ZvW89k6pLGMMn3++nvHjf2PWrKvp0CGMV14Z4e6w3EKTlFLKbTLzCpmx9gDfrE5g1d7D+Aj0i2nI/41szeA2YYQE+rs7xEq3dWsqd9wxk3nzdtOzZwTV6KapXJqklFIuV1BUwsq9h9iZnM3+jDwOpOeyPyOP5bsPARATFsz4c9pwUdcIGoV4f2WA43n22UU8/fQiatb04+23R3Hbbd09tmHYM0WTlFLKJQ5m5LFgazLztybzx/bUvz8Z4ecjNAoJpEndQC7o0oRzOoQzon14tXrOcjzZ2QWMGdOO//xnOOHhwe4Op0rQJKWUOmN2JGcxa/1BZm88yMb9Vi28JnUCubBrBINbh9Epsg6hwTW8psme03XwYDYPPPAL113XiREjWvHss0N03ZShSUopdcqMMWzcn8nsDQeZteEAO1NyAOjerB4Pj2zD2W3CiG0UrHdJZZSUGN59dyWPPPIbublF9O1rfQhCE9SxNEkppU5KSYlhTfxhZm+w7pjiD+XiI3BWiwZc3yeaEe3Dq/VzpYr89ddBxo6dwbJliZx9dnPefnsUrVuHujusKkuTlFKqQhlHCvl9RwoLt6awcFsKyVn5+PsKfVuFctfgVgxrF+5xbd+5y7JlCezenc6nn17EVVd11LvMCojVCLlniYuLMytXrnR3GEp5reISw4bEDBZtS2HBthTW7DtMibHawusf05Bh7RpxdtvqWUX8ZBlj+P77LeTlFXHllR0pKTFkZuZTt27l322KyCpjTFylz/g06J2UUgpjDLtSc1i8I5XFO1JZujONzLwiADpF1uHOwa0Y1LohnSPrVvhlWPWPvXvTueuuWcyYsY1Bg6K54ooO+PiIWxKUp9IkpVQ1lVdYzNKdafy2JYn5W1JITM8FIKJuTUZ2CKdvq1D6tgolNLiGmyP1PIWFxbz66p889dRCRODll4dx771nadHeKdAkpVQ1YYxhR3I2S3elsXBrCot3ppJXWEJQgC/9WoVyx+CW9GsV6lUth7vL4sXxPPzwXC68sA2vvz6SqKg67g7JY2mSUsqL5eQX8ePa/fyxPZU/d6WRllMAQFT9IK7oEcXZbcLo1aI+NfyqR5t4rpSWdoRFi/Zy0UVtGTQomuXLb6FHjwh3h+XxNEkp5YWSs/KYungPn/65l8y8IhrXCWRgbEPOatGA3i0bEFmvpt4tnSHGGD75ZB0PPPALOTkF7Nt3H6GhQZqgzhBNUkp5kZ0p2UxZtItvVydSWFLCyPbh3DagBV2a1tWk5AJbtqTyr3/9zIIFe+jdO5LJk0cTGhrk7rC8iiYppbzAyj2HeHfRLn7dlEQNPx8ujYvklv4taB5ay92hea1Dh3KJi3sPf39f3n13NLfc0k1bjHABTVJKeaiSEsOvm5N4d+FOVu9Lp26QP/cMieG63s20Rp4LrV17kM6dw6lfvyb/+98FDBwYTViYXgy4iiYppTyIMYYtB7P4ce1+fvxrP4npuTStX5Onzm/PpXGRBAXoIe0qBw5kcd99c/jyy43Mm3cdgwc359JL27s7LK+ne7RSHiAjt5DPl+3j29UJbE/OxtdH6B8TyiOj2jCyfbi+YOtCxcUlvPPOSh57bB75+UU89dQg+vRp6u6wqg1NUkpVYcmZeXzwx24+W7aP7PwiekTX45kLOzCqQzgNtEjP5YwxjBr1Ob/8spOhQ1vw9tujiIlp4O6wqhVNUkpVQelHCnh+1ha+XZ1IUUkJozs1YezAlrRrEuLu0KqFrKx8atUKwMdHuO66TtxwQ2euuKKD1pB0A01SSlUhOflFfLZsL6/N3c6RgmLO6RDO+HPa0KyBPpivDMYYvvlmM/feO5uJEwdy663dufrqTu4Oq1rTJKVUFZBfVMz/Fu9h8sKdpB8ppF+rUO46uxVntdCipcqye/dh7rprFjNnbqdLl3C6dAl3d0gKTVJKud28LUk8/dMm9qQd4ew2Ydx9diu6RtVzd1jVyvvvr+aee2bh6+vDq6+O4K67euLnp5VRqgJNUkq5ye7UHJ7+aSPzt6bQsmEtPr6pJwNiG7o7rGrFGIOI0LRpCCNHtuKNN84hMlKf+1UlmqSUqkR5hcXM3ZzE9FUJLNqWQlCAH4+f25br+0Tjr9XIK01q6hH+7/9+pUmT2kyadDYjRrRixIhW7g5LlUOTlFKVYHtSFp/8uZcf/tpPRm4hjesEcsegVlzXpxlhtfUDeJXFGMPUqX/x0EO/kpGRz/jxfd0dkqqAy5OUiIwEXgd8gfeNMc+X6R8FfATUtYcZb4yZ6eq4lKoMq/YeZvLCnX+3qTeifTiXxkXSp2UovtrOW6Xati2NW275kd9/30ffvk2ZPHk0HTqEuTssVQGXJikR8QXeAoYBCcAKEfnRGLPJYbDHga+MMe+ISDtgJhDtyriUcrVF21J4c/4Olu8+RN0gf+4dEsP1faKpXyvA3aFVWwUFxWzblsb775/HjTd21cZgPYSr76R6AjuMMbsARGQacAHgmKQMUPqksg6w38UxKeUy+9KO8PaCHUxbEU+gvw9Pjm7H5T2aUquGlqy7w6xZ25k/fw8vvjiMDh3C2Lt3HDV0W3gUV2+tCCDe4XcC0KvMMBOBX0TkbqAWMLS8CYnIbcBtAFFRUWc8UKVOVXJWHr9vS+Wb1Qks2ZmGj8Cg1g3598UdaVynprvDq5YSEzMZN24O06dvok2bUB5/fAAhITU0QXkgV2+x8u6nTZnfVwJTjTH/EZHewCci0sEYU3LUSMa8B7wHEBcXV3YaSlWa7Pwilu1K448dqSzekcq2pGwAIuvV5IFhsYyJi9Tk5CZFRSW8/fYKHn98HoWFJUyaNJiHHupLQICvu0NTp8jVSSoBcGwuOJJji/NuBkYCGGOWikggEAokuzg2pZxSWFzCX/Hp/LHdSkp/xadTVGKo4edDj+j6XNQ1kr6tGtChSR19zuFmhw7l8uST8+nTpylvvTWKli3ruzskdZpcnaRWADEi0hxIBK4AriozzD5gCDBVRNoCgUCKi+NSqkJ5hcV8snQv7yzcyaGcAnwEOkbU4bYBLejXKpRuzeoR6K9X6O6WkZHHlCmruf/+3oSF1WLNmtuJjq6rjcF6CZcmKWNMkYjcBczBql7+oTFmo4g8Daw0xvwIPABMEZH7sIoCbzDGaHGecpvC4hK+WhnPG79tJykzn/4xoVzdK4reLUKpE+Tv7vCUzRjDV19tZNy4OSQlZdOnT1P69GlK8+bapJQ3cflTRPudp5lluj3p8PcmQN+oU26XmJ7LVyvi+XplPPsz8ujerB6vXd6V3i21kdeqZufOQ9x550zmzNlJt26N+emnK4mLa+LusJQLaFUXVa0VFpfw2+Zkpq3Yx8JtKRgD/WNCmXRRBwa3DtMioyrIGMMFF0xj374M3nhjJHfc0QNfbVLKa2mSUtXSntQcpq2IZ/qqBFKz82kUUoO7BrfisrimNK0f5O7wVDl+/30v3bs3ISjIn6lTL6RJk9o0aVLb3WEpF9MkpaqNkhLDwm0p/G/JHhZtS8HXRxjcOowrezZlYGxD/PRqvEpKScnhoYd+5aOP1vLvfw9h/Ph+WrRXjWiSUl6vqLiEL1fGM2XRLvakHSGsdg3uHxbL5T2a0ihEG3etqkpKDB9+uIb/+79fyc4u4NFH+3HPPWXbAlDeTpOU8lrGGBZsTeHZmZvZkZxNl6Z1eWN4a87pEK6fxfAA48bN5r//XU7//lFMnjyadu30W1vVkSYp5RWMMRzKKSDhcK797wiLtqeweEcazUNr8d613RnWrpFWhKjicnIKyM8vpn79mtx6aze6dWvM9dd31u1WjWmSUh7JGMPOlGyW7jrEn7vSWLbrEKnZ+UcNE1a7BhPOa8fVvZoRoJ8Cr/J+/nkbd945k759o/jss4vp2LERHTs2cndYys2cTlIiEgBEGWN2uDAepU4oLTuf79Yk8tXK+L/bzAsPCaRfqwZ0jKxL03o1aVo/iIh6NQkJ1BdvPUFCQib33jubb7/dTLt2DWKKESQAACAASURBVBk7tru7Q1JViFNJSkTOBV4BAoDmItIFmGCMuciVwSlVas2+w7y3aBdzNydRWGzo0rQuz1zYgQExoUTVD9LiIA81e/YOLr30a4qLS/j3v4dw//29tTFYdRRn76SexvrExnwAY8xfItLKZVEpZTPGMHXJHp79eTMhNf25vnc0l/VoSmwjfT/GkxUWFuPv70uXLuGMGhXD888P0eaMVLmcTVKFxpj0Mler2r6ecqm8wmIe/XY9365JZGjbRrxyeWctwvNw6el5PProb2zYkMyCBTcQHh7Ml1+OcXdYqgpzNkltFpHLAB+7RfN7gT9dF5aq7valHeH2T1ex+UAm9w+L5a7BrfQzGB7MGMO0aRu47745pKQc4e67e1JQUExgoNbdUifm7B5yF/AkUAJ8i9Wq+SOuCkpVX1l5hbw1fycf/rEbP1/hxTGduCyuacUjqirr4MFsrrvuO379dRdxcU2YOfNqunVr7O6wlIdwNkmNMMY8DDxc2kFELsZKWEqdlm1JWSzffYhVew+zaFsKaTkFXNwtgodHttEWIbxASEgNUlOP8Oab5zB2bJw2BqtOirNJ6nGOTUiPldNNKacVlxgm/riRT/7cC0BocA16tajPbQNa0qVpXTdHp07HvHm7efnlJXzzzWUEBfmzcuVtWlyrTskJk5SIjMD6tHuEiLzi0CsEq+hPqVNyMCOPe6etYdnuQwxu3ZCJ57fXquReICkpmwcf/JVPP11Hy5b12Lcvg9atQzVBqVNW0Z1UMrAByAM2OnTPAsa7KijlvY4UFPHpn3t5e8FOCopK+M+lnbmke6S7w1KnqaTEMGXKKsaP/42cnAKeeGIAjzzSj5o1tTamOj0nTFLGmDXAGhH5zBiTV0kxKS9UmpzeXbiLtJwC+seEMvH89rRsGOzu0NQZ8vHH6+jSJZx33jmXNm1C3R2O8hLOPpOKEJFngXbA30+yjTGxLolKeY0jBUV89uc+3l20k9RsKzmNGxpD92b13R2aOk3Z2QU899zv3HtvLxo1Cuann66kXr1ALbJVZ5SzSWoqMAl4GTgHuBF9JqVOIDE9l+9WJzB1yR5NTl7ohx+2cPfds4iPz6RVq/rcdFNX6tev6e6wlBdyNkkFGWPmiMjLxpidwOMi8rsrA1OeJykzj4VbU/huTSJLd6UB0D8mlHuHxBAXrcnJG+zbl8E998zihx+20rFjGNOmjaFPH32PTbmOs0kqX6x7+J0iMhZIBMJcF5byFAcz8nj9t20s2ZnG3rQjADQPrcX9w2K5qGsETesHuTlCdSZNmLCAX3/dxYsvDmXcuLPw99fGYJVriTEVN8EnIr2ATUA94FmgDvCCMWaxa8MrX1xcnFm5cqU7Zq0cJGflcdWUZexIzmZAbEMGxIRyVosGtG8Sos8lvMjSpfHUqRNIu3YNSUrKJi+viGbN9D02TyQiq4wxce6O42Q4dSdljFlm/5kFXAsgIlpvuBoqKTFsOpDJwm0pfL5sH4ePFPDxTT0ZEKuf9vY2hw/nMn78XN57bzVjxrTj668vpVEjrY2pKleFSUpEegARwB/GmFQRaY/VPNLZgCaqasIYw+fL9/Ha3O2kZFlfwO0QEcKbV3Wla5R+YsGbGGP47LP13H//HA4dyuX++89i4sRB7g5LVVMVtTjxb+ASYC1WZYnvsFpAfwEY6/rwVFWQk1/EY9+t5/u/9nNWi/qMH9mG/rGhhNXWdvW80fvvr+a222bQs2cEv/xyLV26hLs7JFWNVXQndQHQ2RiTKyL1gf32762uD025y57UHGZvPMjsDQfZkJhBsTEI8ODwWO4YpJ/M8EZ5eUXs3ZtO69ahXH11J/z8fLjuus7aGKxyu4qSVJ4xJhfAGHNIRLZogvJOO1Oy+WntfmZvOMiWg1kAdIyow839muPv68PA1g3podXIvdKvv+7kjjtmUlJi2LLlToKC/Lnxxq7uDkspoOIk1UJESls6FyDa4TfGmItdFplyOWMMczcnM2XRLpbvOYQI9GhWnydGt2NE+0ZE1tPq497s4MFs7r9/Dl98sYGYmPq8++5orVKuqpyKktQlZX6/6apAVOX6Kz6d537ezPI9h2jWIIjx57Th4q4RhOn3m6qFzZtT6N37A3Jzi5gwYSDjx/fTr+SqKqmiBmZ/q6xAVOVYn5DBm/O3M2djEqHBAUy6sAOX92iKvz57qBYyM/MJCalB69ah3HRTV8aOjSM2toG7w1LquPTSqZooKTG8MGcL7y7cRe1AP+4dEsOtA1oQXEN3geogKyufCRMW8Mkn69iw4V80ahTMK6+McHdYSlXI5WcoERkJvA74Au8bY54vZ5jLgImAAdYaY65ydVzVSWFxCeOm/cXP6w9wZc8oHhnVhpBA/c5PdWCM4bvvtnDPPbPYvz+L22/vTg29MFEe5KT2VhGpYYzJP4nhfYG3gGFAArBCRH40xmxyGCYGeAToa4w5LCLaJuAZknGkkM+X7+PDxbtJycrnhj7RTDivnTZZVE0UFBRzySVfMWPGNjp3bsT06Zdx1ln6/r3yLE4lKRHpCXyA1WZflIh0Bm4xxtxdwag9gR3GmF32dKZhvXu1yWGYW4G3jDGHAYwxySe3CKqs3IJiXpi9hS9XxJNbWEzfVg148ZJODGrdUBNUNWCMQUQICPAlPLwW//nPcO65pxd+fvrcUXkeZ++k3gBGA98DGGPWishgJ8aLAOIdficAvcoMEwsgIouxigQnGmNmOxmXKiOvsJhbPl7Bkp1pXNQlgpv7N6d9kzruDktVksWL93H33bP4+OOL6NAhjClTznd3SEqdFmeTlI8xZm+Zq/BiJ8Yr77K9bLPrfkAMMAirLcDfRaSDMSb9qAmJ3AbcBhAVFeVk2NXLuoR0Hvl2PZsOZPLSmM6M6a5FO9VFWtoRxo+fy/vvryEqqg6HDuW6OySlzghnk1S8XeRn7OdMdwPbnBgvAXD8IlokVtNKZYf50xhTCOwWka1YSWuF40DGmPeA98D6VIeTcVcL6xLS+XjpXr5dnUCD4Bq8e013hrfX9taqi88+W8e4cXM4fDiXhx7qw5NPDiQ4OMDdYSl1RjibpP6FVeQXBSQBc+1uFVkBxIhIc6wPJV4BlK259z1wJTBVREKxiv92ORlXtZZw+AhP/rCReVuSqenvy/V9orlvWKzW3KtmNm5MISamPpMnj6ZTp0buDkepM8rZJFVkjLniZCdujCkSkbuAOVjPmz40xmwUkaeBlcaYH+1+w0VkE1YR4kPGmLSTnVd1szY+nWs+WEZxiWH8OW24qleUJqdqIje3kGef/Z0BA5oxfHhLJk4chJ+fjzb8q7ySs0lqhV0M9yXwrTEmy9kZGGNmAjPLdHvS4W8D3G//U06YvyWZe6atoU5Nfz6/5SyiGmgbe9XFnDk7uOOOmezadZiSEsPw4S0JCND29pT3cvbLvC1FpA9Wcd1TIvIXMM0YM82l0amjFBWXMO7Lv5ix7gAtG9bio5t6aiOw1cSBA1mMGzeHr77aSOvWDZg37zoGD27u7rCUcjmnX5wwxiwxxtwDdAMygc9cFpUq12tztzNj3QHuHRLDzHv7a4KqRmbM2MYPP2zh6acHsXbtWE1Qqtpw9mXeYKyXcK8A2gI/AH1cGJdysC0pi7fn7+CHtfu5tHsk9w2LdXdIqhKsWrWf+PhMLrywDTff3I1hw1oSHV3X3WEpVamcfSa1AfgJeNEY87sL41EO9qfn8v1fibw+dzsBfj5c2TOKx89t6+6wlItlZubzxBPzePPNFbRu3YDzz2+Nj49oglLVkrNJqoUxpsSlkaijbEjM4Mr3/iQrv4jBrRvy4pjONKxdw91hKRcyxjB9+ibuvXc2Bw9m869/xfHss0O01p6q1k6YpETkP8aYB4BvROSYF2j1y7xnnjGGWRsO8sT3Gwip6c9XY3vTJry2trlXDaxadYDLLptOly7hfP/9FfTsGeHukJRyu4rupL60/9cv8laS9xbt4t+zthDbKJjJ13SnRcNgd4ekXKigoJglS+IZNCiauLgmzJhxJSNGtNLGYJWynfBIMMYst/9sa4z5zfEfVgUKdQZ9tyaB52dv4dyOjZl17wBNUF5u0aK9dOkymWHDPmHfvgwAzj03VhOUUg6cPRpuKqfbzWcykOosv6iYx75bz31frqVHdH1eurQTvvocwmulph7hppt+YODAqeTmFvH995cTFaUt1StVnoqeSV2OVe28uYh869CrNpBe/ljqZCzdmcaDX68lMT2X8zs34T+XdcbfV6+kvVVOTgEdO75DauoRHn64L08+OZCgIG3OSqnjqeiZ1HIgDav18rccumcBa1wVlLczxrBkZxqLtqXwwR+7iWoQxKc396JfTKi7Q1MukpCQSWRkCLVqBTBp0mB69YqkQwf9CLVSFTlhkjLG7AZ2Y7V6rs6AXSnZPPrdev7cdQhfH+HsNmG8fGln6tTUq2lvdORIIZMmLeLll5cwY8ZVDB/ekptv7ubusJTyGBUV9y00xgwUkcMc/bFCwWobtr5Lo/MSJSWGL1fG89vmZBZtT6GGnw+TLuzARV0jqFXD2VfVlKeZOXM7d945kz170rnhhi507arf+FLqZFV0hiz9RLyWQ52i1Ox87v58DUt3pdEitBYXdYnggeGxhIUEujs05UK33fYTU6aspm3bUBYsuJ6BA6PdHZJSHqmi4r7SViaaAvuNMQUi0g/oBHyK1dCsOo6UrHzGTF5CUmYez1/ckct7NNWXcr1YcXEJIoKPj9C7dyTR0XV58ME++ikNpU6Ds9XIvsf6dHxL4GOsd6Q+d1lUXiCvsJhbP15JUmYen91yFlf0jNIE5cVWrEikZ8/3ef/91QDceGNXHn20vyYopU6Tsw9ESowxhSJyMfCaMeYNEdHafeXYcjCTD37fzZr4dHamZPPO1d3p3qyeu8NSLpKRkcdjj83j7bdXEB4eTFhYLXeHpJRXcfrz8SJyKXAtcKHdTaujOThSUMSMdQd4+Jt1GAM9m9fntgGdGNlBH5Z7q59/3sYtt/xEcnIOd93Vk0mTziYkRBsBVupMcjZJ3QTcgfWpjl0i0hz4wnVheZYdydlcNeVPkrPy6dK0Lq9e3oXmoXpF7e18fX2IiKjNTz9dSVxcE3eHo5RXEmOOady8/AFF/IBW9s8dxpgil0VVgbi4OLNy5Up3zf4YD3y1ll82HuTVy7swILYhAdr2mlfKzy/ipZeWUFxcwoQJgwDr9QL9lIbyFCKyyhgT5+44ToazX+btD3wCJGK9IxUuItcaYxa7MjhPsGrvIb5ZncD5nZswtF0jd4ejXGTBgj2MHTuDrVvTuOqqjhhj/q7Jp5RyHWeL+14FRhljNgGISFuspOVRGflM+2ZVAg98vRaA3i0buDka5QopKTk8+OCvfPzxWpo3r8vMmVdxzjkx7g5LqWrD2SQVUJqgAIwxm0UkwEUxeYSlO9N4cPpa+rRswJtXdaN+rWq9OrxWUlIO06dv4rHH+vPYY/2pqc1XKVWpnE1Sq0XkXay7J4CrqcYNzB7KKeCx79cTWa8mH1zfg5r6LoxXWb8+iR9/3Mpjjw2gQ4cw4uPvo379mu4OS6lqydkn/GOBncD/AQ8Du4DbXRVUVZZw+AhjJi8h4XAuz1/cSROUF8nJKeDhh3+lW7f3ePXVP0lOzgHQBKWUG1V4JyUiHYGWwHfGmBddH1LV9eLsLUz5fRd+Pj58enMvejbX9nW9xU8/beWuu2axb18GN93UhRdfHEaDBkHuDkupaq+iVtAfxfoC72qgh4g8bYz5sFIiq0KKSwxP/7SRj5buZWjbMJ4Y3Y5mDfQ9KG+Rnp7Hddd9T5MmtVm06Ab692/m7pCUUraK7qSuBjoZY3JEpCEwE6h2SerHtYl8tHQvN/SJ5pFRbajhp0V8nq6oqIRp0zZw1VUdqVs3kHnzrqN9+zBta0+pKqaiJJVvjMkBMMakiEi1eku1uMQwdckeJv28ifCQQJ4c3U7fi/ECy5YlcPvtM1i7Nol69QI599xYunZt7O6wlFLlqChJtRCRb+2/BWjp8BtjzMUui6wKuPXjlczbksyg1g15/Ny2mqA8XHp6Ho8++huTJ6+kcePaTJ9+KaNG6TtPSlVlFSWpS8r8ftNVgVQ1G/dnMG9LMvcOiWHc0Bj9zIYXGD36c5YuTeCee3rxzDODqV1bG4NVqqqr6KOHv1VWIFXNom2pAFzbu5kmKA+2fXsaEREhBAX588ILQ6lZ059u3bRoTylPUa2eMZ2MFXsO0aJhLUKD9WrbE+XnF/HUUwvo2PEdXnjhDwD69o3SBKWUh3F5khKRkSKyVUR2iMj4Eww3RkSMiLi9PcCpi3czb0syw9pqg7GeaN683XTqNJmJExdy0UVtGTvW7buUUuoUnVSSEpGTuq0QEV/gLeAcoB1wpYi0K2e42sA9wLKTmb4r/PBXIk/N2ESAnw/3D491dzjqJD3//B8MGfIxxcUlzJlzDV98cQmNG9d2d1hKqVPk7Kc6egIfAHWAKBHpDNxijLm7glF7Yn17apc9nWnABcCmMsM9A7wIPHgSsZ9xO5KzePDrtXRpWpcPru+h70N5iJISw5EjhQQHBzB6dCxHjhTyyCP9tDFYpbyAs3dSbwCjgTQAY8xaYLAT40UA8Q6/E+xufxORrkBTY8yME01IRG4TkZUisjIlJcXJsJ1njOHR7zYQFODHlOvitFVzD7F27UH69v2Q22+3dp8OHcJ4+unBmqCU8hLOJikfY8zeMt2KnRivvGpxf38K2H45+FXggYomZIx5zxgTZ4yJa9iwoROzPjlbk7JYvvsQ44bGaGUJD5CdXcCDD/5C9+7vsXPnIc45p1XFIymlPI6zn+qIt4v8jP2c6W5gmxPjJQBNHX5HAvsdftcGOgAL7Gre4cCPInK+MaZSvw+/5UAWAJ2b1q3M2apTsHx5ImPGfEV8fCa33tqN558fqi2VK+WlnE1S/8Iq8osCkoC5dreKrABiRKQ51qfnrwCuKu1pjMkAQkt/i8gC4MHKTlAAHy/dQ7MGQXSJ1CRVVZV+sj0qqg7R0XWZNm0Mffo0rXhEpZTHcipJGWOSsRLMSTHGFInIXcAcwBf40BizUUSeBlYaY3482Wm6wqq9h1i9L52J52nbfFVRYWExr7++jF9+2cns2dcQHh7MokU3ujsspVQlcLZ23xQcniWVMsbcVtG4xpiZWK2nO3Z78jjDDnImnjNp4/4MrpqyjLpB/oyJ06vyqmbJknjGjp3B+vXJjB4dS1ZWPnXqBLo7LKVUJXG2uG+uw9+BwEUcXWvPY32/JpH8ohJ+ursfwTWcXR3K1TIz83nooV94773VREaG8N13l3PBBa21iSqlqhlni/u+dPwtIp8Av7okokq0Zt9hpq2IZ1i7RsQ20hc+qxI/Px8WLNjLAw/0ZuLEQQQH6ysBSlVHp3rr0Bzw+M+XPvzNOgL9fXni3GMawVBusHVrKs899wfvvHMuQUH+rF07lsBAvbtVqjpz6j0pETksIofsf+lYd1GPujY015q94QDbkrK5uV9zohoEuTucai0vr4gJE+bTqdNkfvhhC+vXJwFoglJKVXwnJdZDgM5YVcgBSowxx1Si8CSJ6blM+HEjQQG+XHuWx98QerRfftnJHXf8zM6dh7n66o68/PJwwsOD3R2WUqqKqDBJGWOMiHxnjOleGQFVhqmLd3M4p5Af7+5LLa0s4TbGGJ5+eiE+PsLcudcyZEgLd4eklKpinD1DLxeRbsaY1S6NphIYY5i7OZmuUXVpEx7i7nCqneLiEt5/fzUXXNCG8PBgvvxyDA0aBGnRnlKqXCd8JiUipWeOfliJaquIrBaRNSLikQnrqZ82sTs1h9Gd9ON3lW3NmgP06fMhY8f+zP/+twaAiIgQTVBKqeOq6OywHOgGXFgJsbjcD38lMnXJHhqF1OBSfXG30mRl5fPkk/N5443lhIYG8dlnF3PllR3cHZZSygNUlKQEwBizsxJicSljDF+tjCesdg2Wjh+izR9Vokce+Y23317B7bd357nnhlCvnjYGq5RyTkVJqqGI3H+8nsaYV85wPC6zet9hFu9I4/5hsZqgKsGePekUFhYTE9OAxx8fwDXXdOKssyLdHZZSysNU9J6ULxCM9UmN8v55jC+Wx1MrwJeb+zV3dyherbCwmBde+IN27d7irrtmARAeHqwJSil1Siq6kzpgjHm6UiJxoYMZecxYt5+LukZolXMXWrx4H7ffPoONG1O48MI2vPHGSHeHpJTycE49k/J0i7alkFdYwnW9o90ditf67rvNXHzxV0RF1eGHH67g/PNbuzskpZQXqChJDamUKFxs04FMggJ8aa2NyJ5Rxhj2788iIiKEESNaMWnSYMaNO4tatbQxWKXUmXHCZ1LGmEOVFYgrrU/MoEXDWlph4gzavDmFwYM/YsCAqeTmFhIU5M9jjw3QBKWUOqOcamDWk83bksSqvYfpH9PQ3aF4hdzcQh5/fB6dO09m3bokHnmkHzX0OZ9SykW8/uwyY90BAMZ019plpys+PoNBgz5i167DXHttJ15+eThhYbXcHZZSyot5fZJKzS6gc2QdWjbUlrVPVWFhMf7+vkREhNC/fxTvv38egwdrVX6llOt5dXGfMYatBzNpUldbODgVxcUl/Pe/y2jV6r8kJWXj4yNMnXqhJiilVKXx6jupT5ftIykzn25R9dwdisdZtWo/t98+g1WrDjBsWAsKCordHZJSqhry2iR1KKeAZ37aRGhwANf3iXZ3OB6jpMRw332zefPNFYSF1WLatEu47LL2WN++VEqpyuW1SWrhtmQKikt466puBPh5danmGeXjI6Sl5fKvf8UxadLZ1K0b6O6QlFLVmNeevX/ZmETD2jXoEV3f3aFUebt2Heb8879g48ZkAD7++CLefHOUJiillNt5ZZLKKyxmwdYURrRvpC/wnkBBQTHPPfc77du/zfz5e9iyJRVA15lSqsrwyuK+RdtSyC0sZmR7/fru8SxatJexY2eweXMql1zSltdeG0lkZIi7w1JKqaN4ZZJasjONoABferXQor7jmT17B7m5RcyYcSXnnhvr7nCUUqpcXlncl5VXRL2gAPx9vXLxTokxhv/9bw1z5+4C4IknBrBx4x2aoJRSVZpXnsUT04/QIFgbOi21cWMyAwdO5aabfuSjj9YCULOmP0FB/m6OTCmlTszrklRxiWH13nR6aq0+jhwp5JFH5tKly7ts3JjCBx+cz0cfXejusJRSymle90xqf3ouBcUlNK0f5O5Q3O7rrzfy/POLueGGLrz00jBCQ3WdKKU8i9clqa9XxgNwVosGbo7EPRITM9m0KYVhw1py7bWdadu2IT17Rrg7LKWUOiUuL+4TkZEislVEdojI+HL63y8im0RknYj8JiLNTnVeKVn5vDFvByGBfrQKq16tnhcVlfD663/Sps1bXH/99xQUFOPjI5qglFIezaVJSkR8gbeAc4B2wJUi0q7MYGuAOGNMJ2A68OKpzi8jtwCA8ee0xbcavZC6YkUivXq9z7hxc+jXL4o//riJgABfd4ellFKnzdXFfT2BHcaYXQAiMg24ANhUOoAxZr7D8H8C15zqzBIO5wIQWo1q9m3dmspZZ31Ao0a1+OqrMYwZ004bg1VKeQ1XJ6kIIN7hdwLQ6wTD3wzMKq+HiNwG3AYQFRVV7sjztyTj7yt0b+bdn+YwxrBhQzIdOzaidetQPvzwfC66qC0hITXcHZpSSp1Rrn4mVd4lvSl3QJFrgDjgpfL6G2PeM8bEGWPiGjZsWO7M9mfk0bJhMA2CvfdkvWPHIUaO/Ixu3d77u62966/voglKKeWVXH0nlQA0dfgdCewvO5CIDAUeAwYaY/JPdWYHM/IIr+OdLXfn5xfx0ktLmDRpEQEBvrz66ghiYvRdMKWUd3N1kloBxIhIcyARuAK4ynEAEekKvAuMNMYkn87MDmTk0SHC+xpJLSwspkePKaxfn8yll7bjtddG0qRJbXeHpZRSLufSJGWMKRKRu4A5gC/woTFmo4g8Daw0xvyIVbwXDHxtP/DfZ4w5/2TnVVBUQmp2PuEhNc/gErhXZmY+ISE18Pf35eabuxIb24Bzzolxd1hKKVVpXP4yrzFmJjCzTLcnHf4eeibmk5SZB0B4Hc9/NlNSYvjgg9U8/PBcpk0bw/DhLbn33rPcHZZSSlU6r2lxYkdKNgDNQz37Jd7165MYO/ZnliyJZ8CAZjRt6n3Fl0op5SyvSVLbDmYBENvIc5PUs88uYuLEhdStG8jUqRdw3XWd9Z0npVS15j1JKimbsNo1qBvkeS/yGmMQEcLCanH99Z154YWhNGigjcEqpZTXfKpjW1IWrcM9q8ZbfHwGF1/8JVOmrAbg1lu78/7752uCUkopm1ckqZISw/bkLGLCPCNJFRWV8MorS2nb9i1mz95BQUGxu0NSSqkqySuK+5bvOUReYYlHPI9auXI/t9zyI2vXJnHuuTG8+eYooqPrujsspZSqkrwiSf24dj8Bfj6M7BDu7lAqdOhQLqmpR/jmm8u46KI2WjFCKaVOwCuS1Ko9h+nTskGVrDRhjOGLLzaQmJjJQw/1ZfjwluzYcQ+BgV6x6pVSyqU8/plUYXEJu1Kzadu46r1PtH17GsOHf8rVV3/LDz9spbi4BEATlFJKOcnjk9TmA5kUFpsqlaTy84t46qkFdOz4DsuXJ/LWW6NYuPAGfH09fnUrpVSl8vhL+uRMq9H0ZvWrTrXtnTsPM2nS74wZ045XXhlO48aeUetQKaWqGo9PUonp1td469dy7/OopKRsvvlmM3fc0YN27RqyZcudtGypn9JQSqnT4fHlT+sSMqgX5E9TN91JlZQYJk9eSevWb3LffXPYvfswgCYopZQ6Azz+TmrZ7jR6NndPQli79iBjx/7Mn38mMHhwNG+/fS7Nm3v3p+vVqSksLCQhIYG8vDx3bYTvRwAAE79JREFUh6KqgcDAQCIjI/H393d3KKfNo5NUbkExiem5XNq9acUDn+l55xYydOgniMAnn1zE1Vd31Hee1HElJCRQu3ZtoqOjdT9RLmWMIS0tjYSEBJo3b+7ucE6bRyepvYdyMAaa1q+8Dx3Om7ebQYOiqVnTn+nTL6Vjx0bUr8T5K8+Ul5enCUpVChGhQYMGpKSkuDuUM8Kjn0nN2ZAEQIuGrm8Oae/edC64YBpDhnzMtGkbABg4MFoTlHKaJihVWbxpX/PoO6mdKdk0CqlBl6aua/uusLCY1177k4kTFwLw0kvDuPTSdi6bn1JKqX947J2UMYYVew4R18y1lSYuu2w6//d/cxk6tAWbN9/Jgw/2wd/f16XzVMoVfH196dKlCx06dOC8884jPT39734bN27k7LPPJjY2lpiYGJ555hmMMX/3nzVrFnFxcbRt25Y2bdrw4IMPumMRTmjNmjXccsstR3W74IIL6N2791HdbrjhBqZPn35Ut+Dgf0pjtm3bxqhRo2jVqhVt27blsssuIykp6bRiO3ToEMOGDSMmJoZhw4Zx+PDhY4aZP38+Xbp0+ftfYGAg33//PWCd7x577DFiY2Np27Ytb7zxBgAzZsxgwoQJpxVblWfM/7d378FV1dcCx7+Lh4RAQATNgKgRwiMkSATshaKXABVQi8qjRKAQEBGR6lQUeh3vDPgYqVS0paFS2usEbw2JgFHEVisvRQQllDSJiWLEyEVjgJhKeFRCsu4fe+fkTU4kOY+wPjNn5ux99tl7nTXnZGX/9m//fhp0jyFDhuiZs+f0ml9t0cTtn2lTKyo6radOnVVV1e3bD2laWm6TH8NcXHJycvwdgnbo0MHzfNasWfrUU0+pqurp06e1V69e+vbbb6uq6qlTp3T8+PGamJioqqpZWVnaq1cvzc11fgelpaW6evXqJo2ttLT0gvcxZcoUzcjI8CwXFxdrz549tX///nro0CHP+oSEBN2wYUO191bk5syZMxoZGambN2/2vLZ9+3bNysq6oNgWL16sy5cvV1XV5cuX65IlS867fVFRkXbp0kVPnTqlqqovvviizpw5U8vKylRVtbCwUFVVy8vLNTY21rNdVXV954B0DYC/4Y15BG1z37ESZ6SJju2a7iOoKn/5SyYPP/x37rlnME8/PYZRo4K/d4wJLI+/8TE5X59o0n0O6NGJpROivd5++PDhZGZmApCcnMyIESMYO3YsAKGhoSQmJhIXF8fChQtZsWIFjz32GP379wegTZs23H///bX2efLkSR544AHS09MREZYuXcrkyZPp2LEjJ0+eBGDjxo1s2bKFpKQkZs+ezWWXXcaBAweIjY0lLS2NjIwMLr3Uab6PjIxk9+7dtGrVivvuu4/Dhw8D8Nvf/pYRI0ZUO3ZJSQmZmZkMGjTIs27Tpk1MmDCB8PBwUlJSePTRRxvMS3JyMsOHD2fChAmedaNGjfI6r/V5/fXX2blzJwAJCQnExcXxzDPP1Lv9xo0bueWWWwgNde7/fOGFF0hOTqZVK6fx64orrgCca09xcXFs2bKFqVOnXnCcgShoi1Taga8AiOjWoUn29+mnx1mw4E127Mhn2LCexMd7/4M3JpiUlZWxbds25s6dCzhNfUOGDKm2Te/evTl58iQnTpwgOzubhx9+uMH9Pvnkk3Tu3JmsrCyAOpu0ajp48CBbt26ldevWlJeXk5aWxpw5c/jwww+JiIggPDyc6dOn89BDD3HjjTdy+PBhxo0bR25ubrX9pKenExMTU23d+vXrWbp0KeHh4UyZMsWrIpWdnV0rF3UpKSnhpptuqvO15ORkBgyoft26sLCQ7t27A9C9e3eOHj163v2npKSwaNEiz/Lnn39OamoqaWlpXH755axatYo+ffoAMHToUHbt2mVFKtDkHz8FwI97d73gfSUlZTB//hZCQ9uyZs1tzJs3hFatWk7vGBNYGnPG05TOnDlDbGws+fn5DBkyhJtvvhlwWhDq6w3WmF5iW7duJSUlxbPcpUvDN7b/7Gc/o3Vr5xpvfHw8TzzxBHPmzCElJYX4+HjPfnNycjzvOXHiBCUlJYSFVY6JWVBQwOWXX+5ZLiwsJC8vjxtvvBERoU2bNmRnZxMTE1PnZ2psb7iwsDAyMjIa9R5vFRQUkJWVxbhx4zzrvv/+e0JCQkhPT+fVV1/l7rvvZteuXYBzVvX11183SyyBIGg7TuQUnGDoNV1oewEji5eWOtO2Dx3ag7vuiuGTTxYyf/5QK1CmRWrfvj0ZGRl8+eWXnD17ltWrVwMQHR1Nenp6tW0PHTpEx44dCQsLIzo6mv379ze4//qKXdV1NUfc6NChsiVk+PDh5OXlcezYMV577TUmTZoEQHl5OXv27CEjI4OMjAy++uqragWq4rNV3XdqairFxcVce+21REREkJ+f7ymgXbt2rXaW9+2339KtWzdPLrz5rCUlJdU6OVR9VC2oFcLDwykoKACcIlTRXFeXV155hYkTJ1YbLaJnz55MnjwZgIkTJ3qaasHJafv2LfdWmKAsUqe+P8cn35QwcfCVP+j9BQUlTJu2iYQEp+dMTMwVrFt3J+FBMP28MReqc+fOrFq1imeffZbS0lJmzJjB+++/z9atWwHnjOvBBx9kyZIlACxevJinn36agwcPAk7ReO6552rtd+zYsSQmJnqWKwpBeHg4ubm5nua8+ogIEydOZNGiRURFRdG1a9c691vXGUxUVBR5eXme5fXr1/PWW2+Rn59Pfn4++/fv9xSpuLg4UlNTOXv2LABJSUme607Tp0/ngw8+4M033/Ts66233vI0YVaoOJOq61GzqQ/g9ttvZ926dQCsW7eOO+64o948rF+/nmnTplVbd+edd7J9+3YA3n33Xfr27et57eDBg7WaOlsUf/fc+CGPq/vF6DW/2qL/On22Vu+V8zl3rkxXr/5IO3Varu3aPamPP75Ty8vLG7UPY36IQOvdp6r605/+VF966SVVVc3MzNSRI0dq3759tXfv3rps2bJqv4033nhDBw8erP3799eoqCh95JFHau2/pKREZ82apdHR0Xrdddfppk2bVFV1w4YN2qtXLx05cqQuXLhQExISVLXuXnb79u1TQJOSkjzrjh07plOnTtWBAwdqVFSUzp8/v87PFxMToydOnNAvvvhCe/ToUeu3ff311+vevXtVVXXZsmUaExOjgwYN0kmTJunRo0c92+Xm5uq4ceM0MjJSo6KiND4+Xr/55pvz5rYhx48f19GjR2tkZKSOHj1ai4qKPJ937ty5nu0qYq/oxVehuLhYb731Vo2JidFhw4ZV68V42223aWZmZq1jtpTefeLEHVx69InWLtNWkv34OK/bkj/7rIgZM15l376vGTPmWl544Tb69Lnw61nGeCM3N5eoqCh/h9GiPf/884SFhdW6V6olKywsZPr06Wzbtq3Wa3V950Rkv6oO9VV8TSEom/tKzyk9Lm3fqIudnTq1o6TkLC+/PIl33plpBcqYFmbBggW0a9fO32H41OHDh1m5cqW/w2hWQdm7r6xc6drx/JMcqippaZ+QkpJNSsoUwsM78vHH91unCGNaqJCQEGbOnOnvMHzqhhtu8HcIzS4oz6TKVbmkTf1DE+Xn/4sJE9YzefIrHDxYxLFjTnd1K1DGn4Kxad0Ep5b0XQvKInWmtIxLWtcuOKWlZTzzzPsMGLCanTvzee65saSn32u99ozfhYSEUFRU1KL+eJjApOrMJxUSEuLvUJpEUDb3tRKhvI7f+rlz5axd+w/Gj4/kd78bz1VXdfZ9cMbUoWfPnhw5cqTFzPFjAlvFzLwtQVAWqXJVBl/tjO9VVHSaFSt2s3RpHKGhbfnoo3vo2jXUzxEaU13btm1bxCypxvhaszf3ich4EflURPJE5L/qeL2diKS6r38oIhHe7HfglZ1JSsqgX79EVq7cw3vvfQlgBcoYY1qQZi1SItIaWA3cAgwApolIzdux5wLFqhoJPA/UPzRwFUsWv8OcOa/Tr183DhyYz/jxkU0ZujHGmADQ3GdSPwLyVPWQqp4FUoCa44HcAaxzn28ExogXN0AdyvuWP/1pArt2zWHgwPAmDdoYY0xgaO5rUlcC/1dl+QjwH/Vto6rnROQ7oCtwvOpGInIvcK+7+P3xbx7MnjcP5s1rlriDSTdq5OoiZrmoZLmoZLmo1M/fATRWcxepus6IavbL82YbVHUtsBZARNKDbWiP5mK5qGS5qGS5qGS5qCQi6Q1vFViau7nvCHBVleWeQM2JTzzbiEgboDPwbTPHZYwxJgg0d5HaB/QRkWtF5BLgLmBzjW02Awnu8ynAdrU7Ho0xxtDMzX3uNaZfAG8DrYEXVfVjEXkCZ8j4zcD/AP8rInk4Z1B3ebHrtc0WdPCxXFSyXFSyXFSyXFQKulwE5VQdxhhjLg5BOXafMcaYi4MVKWOMMQEroItUcw2pFIy8yMUiEckRkUwR2SYi1/gjTl9oKBdVtpsiIioiLbb7sTe5EJGp7nfjYxFJ9nWMvuLFb+RqEdkhIgfc38mt/oizuYnIiyJyVESy63ldRGSVm6dMERns6xgbxd/z19f3wOlo8TnQC7gE+CcwoMY29wNr3Od3Aan+jtuPuRgFhLrPF1zMuXC3CwPeA/YCQ/0dtx+/F32AA0AXd/kKf8ftx1ysBRa4zwcA+f6Ou5ly8Z/AYCC7ntdvBf6Gc4/qMOBDf8d8vkcgn0k125BKQajBXKjqDlU97S7uxbknrSXy5nsB8CSwAvi3L4PzMW9yMQ9YrarFAKp61Mcx+oo3uVCgk/u8M7Xv2WwRVPU9zn+v6R3AS+rYC1wqIt19E13jBXKRqmtIpSvr20ZVzwEVQyq1NN7koqq5OP8ptUQN5kJErgeuUtUtvgzMD7z5XvQF+orIbhHZKyLjfRadb3mTi2XAz0XkCPBX4AHfhBZwGvv3xK8CeT6pJhtSqQXw+nOKyM+BocDIZo3If86bCxFphTOa/mxfBeRH3nwv2uA0+cXhnF3vEpEYVf1XM8fma97kYhqQpKorRWQ4zv2ZMapa3vzhBZSg+rsZyGdSNqRSJW9ygYj8BHgMuF1Vv/dRbL7WUC7CgBhgp4jk47S5b26hnSe8/Y28rqqlqvoF8ClO0WppvMnFXOAVAFXdA4TgDD57sfHq70mgCOQiZUMqVWowF24T1x9xClRLve4ADeRCVb9T1W6qGqGqETjX525X1aAbWNML3vxGXsPpVIOIdMNp/jvk0yh9w5tcHAbGAIhIFE6ROubTKAPDZmCW28tvGPCdqhb4O6j6BGxznzbfkEpBx8tc/AboCGxw+44cVtXb/RZ0M/EyFxcFL3PxNjBWRHKAMmCxqhb5L+rm4WUuHgb+JCIP4TRvzW6J/9SKyHqc5t1u7vW3pUBbAFVdg3M97lYgDzgNzPFPpN6xYZGMMcYErEBu7jPGGHORsyJljDEmYFmRMsYYE7CsSBljjAlYVqSMMcYELCtSJmCJSJmIZFR5RJxn24j6Rn1u5DF3uiNp/9MdSqjfD9jHfSIyy30+W0R6VHntzyIyoInj3CcisV6855ciEnqhxzbGl6xImUB2RlVjqzzyfXTcGao6CGfw4t809s2qukZVX3IXZwM9qrx2j6rmNEmUlXH+Ae/i/CVgRcoEFStSJqi4Z0y7ROQf7uPHdWwTLSIfuWdfmSLSx13/8yrr/ygirRs43HtApPveMe48RFnufD3t3PW/lsp5vJ511y0TkUdEZArOOIovu8ds754BDRWRBSKyokrMs0Xk9z8wzj1UGSBURF4QkXRx5o963F33IE6x3CEiO9x1Y0Vkj5vHDSLSsYHjGONzVqRMIGtfpakvzV13FLhZVQcD8cCqOt53H/A7VY3FKRJH3GFw4oER7voyYEYDx58AZIlICJAExKvqQJyRWhaIyGXARCBaVa8Dnqr6ZlXdCKTjnPHEquqZKi9vBCZVWY4HUn9gnONxhj+q8JiqDgWuA0aKyHWqugpnfLZRqjrKHSLpv4GfuLlMBxY1cBxjfC5gh0UyBre5r8a6tkCiew2mDGcsupr2AI+JSE/gVVX9TETGAEOAfe6wUe1xCl5dXhaRM0A+znQO/YAvVPWg+/o6YCGQiDNf1Z9F5E3A66lBVPWYiBxyx077zD3Gbne/jYmzA84wQFVnV50qIvfi/L6740zwl1njvcPc9bvd41yCkzdjAooVKRNsHgIKgUE4LQG1JjVU1WQR+RC4DXhbRO7BmZ5gnao+6sUxZlQdkFZE6pyjzB0v7kc4g5beBfwCGN2Iz5IKTAU+AdJUVcWpGF7HiTMD7a+B1cAkEbkWeAS4QVWLRSQJZyDVmgR4R1WnNSJeY3zOmvtMsOkMFLhzAM3EOYuoRkR6AYfcJq7NOM1e24ApInKFu81lInKNl8f8BIgQkUh3eSbwrnsNp7Oq/hWnU0JdPexKcKYPqcurwJ048xyluusaFaeqluI02w1zmwo7AaeA70QkHLilnlj2AiMqPpOIhIpIXWelxviVFSkTbP4AJIjIXpymvlN1bBMPZItIBtAfZ6rsHJw/5n8XkUzgHZymsAap6r9xRoreICJZQDmwBucP/hZ3f+/inOXVlASsqeg4UWO/xUAOcI2qfuSua3Sc7rWulcAjqvpP4ADwMfAiThNihbXA30Rkh6oew+l5uN49zl6cXBkTUGwUdGOMMQHLzqSMMcYELCtSxhhjApYVKWOMMQHLipQxxpiAZUXKGGNMwLIiZYwxJmBZkTLGGBOw/h/3Dfeb4vce5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosted Trees Classifier\n",
    "\n",
    "#### Theory\n",
    "In this part we train a gradient boosted trees classifier using xgBoost. xgBoost is short for \"Extreme Gradient Boosting\". It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n",
    "\n",
    "xgBoost uses the gradient descent algorithm that we learnt in BT2101 at each iteration to maximise the reduction in the error term. (More details? math?)\n",
    " \n",
    "#### Training\n",
    "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=4,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=300,\n",
       "                           n_iter_no_change=None, presort='auto',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.97      0.91     15890\n",
      "           1       0.79      0.40      0.53      4212\n",
      "\n",
      "    accuracy                           0.85     20102\n",
      "   macro avg       0.82      0.69      0.72     20102\n",
      "weighted avg       0.84      0.85      0.83     20102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, xgb.predict(X_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the xgBoost ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.96      0.89      3899\n",
      "           1       0.68      0.32      0.44      1127\n",
      "\n",
      "    accuracy                           0.81      5026\n",
      "   macro avg       0.75      0.64      0.66      5026\n",
      "weighted avg       0.80      0.81      0.79      5026\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, xgb.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Threshold: 0.2492940726546302\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Of 1127 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 366\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3724</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>761</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted     0    1\n",
       "Actual              \n",
       "0          3724  175\n",
       "1           761  366"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the accuracy and AUROC, we observe that the XGBoost performs similarly to the random forest ensemble. It has a slight bump in AUROC at 0.76, but the accuracy is the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 3 - Logistic Regression\n",
    "\n",
    "#### Theory\n",
    "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n",
    "\n",
    "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n",
    "\n",
    "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n",
    "\n",
    "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n",
    "\n",
    "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n",
    "\n",
    "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n",
    "\n",
    "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n",
    "\n",
    "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n",
    "\n",
    "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n",
    "\n",
    "\n",
    "#### Training\n",
    "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Chew/anaconda3/lib/python3.7/site-packages/statsmodels/compat/pandas.py:23: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  data_klasses = (pandas.Series, pandas.DataFrame, pandas.Panel)\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.429279\n",
      "         Iterations: 35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Chew/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/Users/Chew/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py:1286: RuntimeWarning: invalid value encountered in sqrt\n",
      "  bse_ = np.sqrt(np.diag(self.cov_params()))\n",
      "/Users/Chew/anaconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "/Users/Chew/anaconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "/Users/Chew/anaconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:1892: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>Y</td>        <th>  No. Observations:  </th>  <td> 20102</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td> 20059</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>    42</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 12 Nov 2019</td> <th>  Pseudo R-squ.:     </th>  <td>0.1637</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>12:59:19</td>     <th>  Log-Likelihood:    </th> <td> -8629.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>False</td>      <th>  LL-Null:           </th> <td> -10319.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td> 0.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LIMIT_BAL</th> <td>-1.984e-06</td> <td> 2.22e-07</td> <td>   -8.924</td> <td> 0.000</td> <td>-2.42e-06</td> <td>-1.55e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SEX</th>       <td>   -0.1555</td> <td>    0.040</td> <td>   -3.915</td> <td> 0.000</td> <td>   -0.233</td> <td>   -0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AGE</th>       <td>    0.0016</td> <td>    0.003</td> <td>    0.621</td> <td> 0.534</td> <td>   -0.003</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_0</th>     <td>    1.2697</td> <td>    0.084</td> <td>   15.074</td> <td> 0.000</td> <td>    1.105</td> <td>    1.435</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_2</th>     <td>    0.0464</td> <td>    0.685</td> <td>    0.068</td> <td> 0.946</td> <td>   -1.295</td> <td>    1.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_3</th>     <td>    0.6671</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_4</th>     <td>    0.6514</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_5</th>     <td>   -0.0163</td> <td> 3.13e+05</td> <td>-5.21e-08</td> <td> 1.000</td> <td>-6.14e+05</td> <td> 6.14e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_6</th>     <td>   -0.0863</td> <td> 1.05e+06</td> <td>-8.19e-08</td> <td> 1.000</td> <td>-2.07e+06</td> <td> 2.07e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BILL_AMT1</th> <td>-4.749e-06</td> <td>    2e-06</td> <td>   -2.380</td> <td> 0.017</td> <td>-8.66e-06</td> <td>-8.38e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BILL_AMT2</th> <td> 3.006e-06</td> <td> 2.77e-06</td> <td>    1.085</td> <td> 0.278</td> <td>-2.42e-06</td> <td> 8.44e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BILL_AMT3</th> <td> 4.718e-06</td> <td> 2.54e-06</td> <td>    1.856</td> <td> 0.063</td> <td>-2.63e-07</td> <td>  9.7e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BILL_AMT4</th> <td> 3.509e-07</td> <td>  2.6e-06</td> <td>    0.135</td> <td> 0.893</td> <td>-4.75e-06</td> <td> 5.46e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BILL_AMT5</th> <td>-2.688e-06</td> <td> 3.47e-06</td> <td>   -0.776</td> <td> 0.438</td> <td>-9.48e-06</td> <td> 4.11e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BILL_AMT6</th> <td>   1.6e-06</td> <td> 2.84e-06</td> <td>    0.563</td> <td> 0.574</td> <td>-3.97e-06</td> <td> 7.17e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_AMT1</th>  <td>-2.604e-05</td> <td> 6.04e-06</td> <td>   -4.313</td> <td> 0.000</td> <td>-3.79e-05</td> <td>-1.42e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_AMT2</th>  <td>  -3.2e-05</td> <td> 5.68e-06</td> <td>   -5.635</td> <td> 0.000</td> <td>-4.31e-05</td> <td>-2.09e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_AMT3</th>  <td>-1.081e-05</td> <td> 5.69e-06</td> <td>   -1.899</td> <td> 0.058</td> <td> -2.2e-05</td> <td> 3.49e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_AMT4</th>  <td>-1.428e-05</td> <td>  6.2e-06</td> <td>   -2.302</td> <td> 0.021</td> <td>-2.64e-05</td> <td>-2.12e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_AMT5</th>  <td>-1.118e-05</td> <td> 6.16e-06</td> <td>   -1.814</td> <td> 0.070</td> <td>-2.33e-05</td> <td> 8.99e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_AMT6</th>  <td>-5.935e-06</td> <td> 4.69e-06</td> <td>   -1.267</td> <td> 0.205</td> <td>-1.51e-05</td> <td> 3.25e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0_1</th>      <td>   -0.5913</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0_2</th>      <td>   -0.6073</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0_3</th>      <td>   -0.6865</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0_4</th>      <td>   -1.9583</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1_0</th>      <td>   -2.0173</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1_1</th>      <td>   -0.5746</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1_2</th>      <td>   -0.7621</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1_3</th>      <td>   -0.4894</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_-2</th>     <td>    4.0609</td> <td> 1.65e+06</td> <td> 2.45e-06</td> <td> 1.000</td> <td>-3.24e+06</td> <td> 3.24e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_-1</th>     <td>    4.4303</td> <td> 1.63e+06</td> <td> 2.72e-06</td> <td> 1.000</td> <td>-3.19e+06</td> <td> 3.19e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_0</th>      <td>    3.6782</td> <td> 1.65e+06</td> <td> 2.23e-06</td> <td> 1.000</td> <td>-3.24e+06</td> <td> 3.24e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_1</th>      <td>    3.4291</td> <td> 1.59e+06</td> <td> 2.15e-06</td> <td> 1.000</td> <td>-3.12e+06</td> <td> 3.12e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3_-2</th>     <td>   -1.0755</td> <td> 1.06e+06</td> <td>-1.01e-06</td> <td> 1.000</td> <td>-2.08e+06</td> <td> 2.08e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3_-1</th>     <td>   -1.2471</td> <td> 1.11e+06</td> <td>-1.13e-06</td> <td> 1.000</td> <td>-2.17e+06</td> <td> 2.17e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3_0</th>      <td>   -0.9771</td> <td> 1.07e+06</td> <td>-9.12e-07</td> <td> 1.000</td> <td> -2.1e+06</td> <td>  2.1e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3_1</th>      <td>   -1.0962</td> <td> 1.08e+06</td> <td>-1.01e-06</td> <td> 1.000</td> <td>-2.12e+06</td> <td> 2.12e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4_-2</th>     <td>   -2.0947</td> <td> 2.51e+06</td> <td>-8.35e-07</td> <td> 1.000</td> <td>-4.92e+06</td> <td> 4.92e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4_-1</th>     <td>   -1.9440</td> <td>  2.6e+06</td> <td>-7.48e-07</td> <td> 1.000</td> <td> -5.1e+06</td> <td>  5.1e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4_0</th>      <td>   -1.9146</td> <td> 2.48e+06</td> <td>-7.73e-07</td> <td> 1.000</td> <td>-4.86e+06</td> <td> 4.86e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4_1</th>      <td>   -2.9571</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5_-2</th>     <td>    0.0772</td> <td> 1.61e+06</td> <td> 4.78e-08</td> <td> 1.000</td> <td>-3.17e+06</td> <td> 3.17e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5_-1</th>     <td>   -0.0961</td> <td> 1.61e+06</td> <td>-5.95e-08</td> <td> 1.000</td> <td>-3.16e+06</td> <td> 3.16e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5_0</th>      <td>   -0.0530</td> <td> 1.62e+06</td> <td>-3.27e-08</td> <td> 1.000</td> <td>-3.18e+06</td> <td> 3.18e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5_1</th>      <td>   -1.0071</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6_-2</th>     <td>   -0.3831</td> <td> 6.33e+05</td> <td>-6.06e-07</td> <td> 1.000</td> <td>-1.24e+06</td> <td> 1.24e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6_-1</th>     <td>   -0.3752</td> <td> 6.39e+05</td> <td>-5.88e-07</td> <td> 1.000</td> <td>-1.25e+06</td> <td> 1.25e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6_0</th>      <td>   -0.3123</td> <td> 6.25e+05</td> <td>   -5e-07</td> <td> 1.000</td> <td>-1.23e+06</td> <td> 1.23e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7_-2</th>     <td>   -0.2115</td> <td> 2.11e+06</td> <td>   -1e-07</td> <td> 1.000</td> <td>-4.13e+06</td> <td> 4.13e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7_-1</th>     <td>   -0.3358</td> <td> 2.11e+06</td> <td>-1.59e-07</td> <td> 1.000</td> <td>-4.13e+06</td> <td> 4.13e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7_0</th>      <td>   -0.4883</td> <td> 2.11e+06</td> <td>-2.32e-07</td> <td> 1.000</td> <td>-4.13e+06</td> <td> 4.13e+06</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                      Y   No. Observations:                20102\n",
       "Model:                          Logit   Df Residuals:                    20059\n",
       "Method:                           MLE   Df Model:                           42\n",
       "Date:                Tue, 12 Nov 2019   Pseudo R-squ.:                  0.1637\n",
       "Time:                        12:59:19   Log-Likelihood:                -8629.4\n",
       "converged:                      False   LL-Null:                       -10319.\n",
       "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "LIMIT_BAL  -1.984e-06   2.22e-07     -8.924      0.000   -2.42e-06   -1.55e-06\n",
       "SEX           -0.1555      0.040     -3.915      0.000      -0.233      -0.078\n",
       "AGE            0.0016      0.003      0.621      0.534      -0.003       0.007\n",
       "PAY_0          1.2697      0.084     15.074      0.000       1.105       1.435\n",
       "PAY_2          0.0464      0.685      0.068      0.946      -1.295       1.388\n",
       "PAY_3          0.6671        nan        nan        nan         nan         nan\n",
       "PAY_4          0.6514        nan        nan        nan         nan         nan\n",
       "PAY_5         -0.0163   3.13e+05  -5.21e-08      1.000   -6.14e+05    6.14e+05\n",
       "PAY_6         -0.0863   1.05e+06  -8.19e-08      1.000   -2.07e+06    2.07e+06\n",
       "BILL_AMT1  -4.749e-06      2e-06     -2.380      0.017   -8.66e-06   -8.38e-07\n",
       "BILL_AMT2   3.006e-06   2.77e-06      1.085      0.278   -2.42e-06    8.44e-06\n",
       "BILL_AMT3   4.718e-06   2.54e-06      1.856      0.063   -2.63e-07     9.7e-06\n",
       "BILL_AMT4   3.509e-07    2.6e-06      0.135      0.893   -4.75e-06    5.46e-06\n",
       "BILL_AMT5  -2.688e-06   3.47e-06     -0.776      0.438   -9.48e-06    4.11e-06\n",
       "BILL_AMT6     1.6e-06   2.84e-06      0.563      0.574   -3.97e-06    7.17e-06\n",
       "PAY_AMT1   -2.604e-05   6.04e-06     -4.313      0.000   -3.79e-05   -1.42e-05\n",
       "PAY_AMT2     -3.2e-05   5.68e-06     -5.635      0.000   -4.31e-05   -2.09e-05\n",
       "PAY_AMT3   -1.081e-05   5.69e-06     -1.899      0.058    -2.2e-05    3.49e-07\n",
       "PAY_AMT4   -1.428e-05    6.2e-06     -2.302      0.021   -2.64e-05   -2.12e-06\n",
       "PAY_AMT5   -1.118e-05   6.16e-06     -1.814      0.070   -2.33e-05    8.99e-07\n",
       "PAY_AMT6   -5.935e-06   4.69e-06     -1.267      0.205   -1.51e-05    3.25e-06\n",
       "x0_1          -0.5913        nan        nan        nan         nan         nan\n",
       "x0_2          -0.6073        nan        nan        nan         nan         nan\n",
       "x0_3          -0.6865        nan        nan        nan         nan         nan\n",
       "x0_4          -1.9583        nan        nan        nan         nan         nan\n",
       "x1_0          -2.0173        nan        nan        nan         nan         nan\n",
       "x1_1          -0.5746        nan        nan        nan         nan         nan\n",
       "x1_2          -0.7621        nan        nan        nan         nan         nan\n",
       "x1_3          -0.4894        nan        nan        nan         nan         nan\n",
       "x2_-2          4.0609   1.65e+06   2.45e-06      1.000   -3.24e+06    3.24e+06\n",
       "x2_-1          4.4303   1.63e+06   2.72e-06      1.000   -3.19e+06    3.19e+06\n",
       "x2_0           3.6782   1.65e+06   2.23e-06      1.000   -3.24e+06    3.24e+06\n",
       "x2_1           3.4291   1.59e+06   2.15e-06      1.000   -3.12e+06    3.12e+06\n",
       "x3_-2         -1.0755   1.06e+06  -1.01e-06      1.000   -2.08e+06    2.08e+06\n",
       "x3_-1         -1.2471   1.11e+06  -1.13e-06      1.000   -2.17e+06    2.17e+06\n",
       "x3_0          -0.9771   1.07e+06  -9.12e-07      1.000    -2.1e+06     2.1e+06\n",
       "x3_1          -1.0962   1.08e+06  -1.01e-06      1.000   -2.12e+06    2.12e+06\n",
       "x4_-2         -2.0947   2.51e+06  -8.35e-07      1.000   -4.92e+06    4.92e+06\n",
       "x4_-1         -1.9440    2.6e+06  -7.48e-07      1.000    -5.1e+06     5.1e+06\n",
       "x4_0          -1.9146   2.48e+06  -7.73e-07      1.000   -4.86e+06    4.86e+06\n",
       "x4_1          -2.9571        nan        nan        nan         nan         nan\n",
       "x5_-2          0.0772   1.61e+06   4.78e-08      1.000   -3.17e+06    3.17e+06\n",
       "x5_-1         -0.0961   1.61e+06  -5.95e-08      1.000   -3.16e+06    3.16e+06\n",
       "x5_0          -0.0530   1.62e+06  -3.27e-08      1.000   -3.18e+06    3.18e+06\n",
       "x5_1          -1.0071        nan        nan        nan         nan         nan\n",
       "x6_-2         -0.3831   6.33e+05  -6.06e-07      1.000   -1.24e+06    1.24e+06\n",
       "x6_-1         -0.3752   6.39e+05  -5.88e-07      1.000   -1.25e+06    1.25e+06\n",
       "x6_0          -0.3123   6.25e+05     -5e-07      1.000   -1.23e+06    1.23e+06\n",
       "x7_-2         -0.2115   2.11e+06     -1e-07      1.000   -4.13e+06    4.13e+06\n",
       "x7_-1         -0.3358   2.11e+06  -1.59e-07      1.000   -4.13e+06    4.13e+06\n",
       "x7_0          -0.4883   2.11e+06  -2.32e-07      1.000   -4.13e+06    4.13e+06\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glm = sm.Logit(y_train,X_train).fit()\n",
    "glm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.96      0.90     15890\n",
      "           1       0.67      0.30      0.42      4212\n",
      "\n",
      "    accuracy                           0.82     20102\n",
      "   macro avg       0.76      0.63      0.66     20102\n",
      "weighted avg       0.80      0.82      0.80     20102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.96      0.89      3899\n",
      "           1       0.71      0.31      0.43      1127\n",
      "\n",
      "    accuracy                           0.82      5026\n",
      "   macro avg       0.77      0.64      0.66      5026\n",
      "weighted avg       0.80      0.82      0.79      5026\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,list(glm.predict(X_test)>0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The logisitc model with all features performs quite well on both the train and test set with an accuracy of 0.81. We will now try removing all the insignificant features to see how that affects the model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.448196\n",
      "         Iterations 6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>Y</td>        <th>  No. Observations:  </th>  <td> 20102</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td> 20095</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 12 Nov 2019</td> <th>  Pseudo R-squ.:     </th>  <td>0.1269</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>12:59:19</td>     <th>  Log-Likelihood:    </th> <td> -9009.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -10319.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td> 0.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LIMIT_BAL</th> <td>-2.743e-06</td> <td> 1.77e-07</td> <td>  -15.502</td> <td> 0.000</td> <td>-3.09e-06</td> <td> -2.4e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SEX</th>       <td>   -0.6629</td> <td>    0.020</td> <td>  -32.463</td> <td> 0.000</td> <td>   -0.703</td> <td>   -0.623</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_0</th>     <td>    1.1246</td> <td>    0.026</td> <td>   42.486</td> <td> 0.000</td> <td>    1.073</td> <td>    1.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BILL_AMT1</th> <td>-1.052e-06</td> <td> 4.62e-07</td> <td>   -2.277</td> <td> 0.023</td> <td>-1.96e-06</td> <td>-1.46e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_AMT1</th>  <td>-2.652e-05</td> <td> 5.25e-06</td> <td>   -5.049</td> <td> 0.000</td> <td>-3.68e-05</td> <td>-1.62e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_AMT2</th>  <td>-3.622e-05</td> <td> 5.07e-06</td> <td>   -7.143</td> <td> 0.000</td> <td>-4.62e-05</td> <td>-2.63e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PAY_AMT4</th>  <td>-2.454e-05</td> <td> 5.35e-06</td> <td>   -4.584</td> <td> 0.000</td> <td> -3.5e-05</td> <td> -1.4e-05</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                      Y   No. Observations:                20102\n",
       "Model:                          Logit   Df Residuals:                    20095\n",
       "Method:                           MLE   Df Model:                            6\n",
       "Date:                Tue, 12 Nov 2019   Pseudo R-squ.:                  0.1269\n",
       "Time:                        12:59:19   Log-Likelihood:                -9009.6\n",
       "converged:                       True   LL-Null:                       -10319.\n",
       "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "LIMIT_BAL  -2.743e-06   1.77e-07    -15.502      0.000   -3.09e-06    -2.4e-06\n",
       "SEX           -0.6629      0.020    -32.463      0.000      -0.703      -0.623\n",
       "PAY_0          1.1246      0.026     42.486      0.000       1.073       1.177\n",
       "BILL_AMT1  -1.052e-06   4.62e-07     -2.277      0.023   -1.96e-06   -1.46e-07\n",
       "PAY_AMT1   -2.652e-05   5.25e-06     -5.049      0.000   -3.68e-05   -1.62e-05\n",
       "PAY_AMT2   -3.622e-05   5.07e-06     -7.143      0.000   -4.62e-05   -2.63e-05\n",
       "PAY_AMT4   -2.454e-05   5.35e-06     -4.584      0.000    -3.5e-05    -1.4e-05\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove all insignificant attributes\n",
    "sig = glm.pvalues[glm.pvalues < 0.05]\n",
    "glm_2 = sm.Logit(y_train,X_train[sig.index]).fit()\n",
    "glm_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.95      0.89     15890\n",
      "           1       0.62      0.30      0.41      4212\n",
      "\n",
      "    accuracy                           0.82     20102\n",
      "   macro avg       0.73      0.63      0.65     20102\n",
      "weighted avg       0.79      0.82      0.79     20102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train,list(glm_2.predict(X_train[sig.index])>0.5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.95      0.88      3899\n",
      "           1       0.65      0.31      0.42      1127\n",
      "\n",
      "    accuracy                           0.81      5026\n",
      "   macro avg       0.74      0.63      0.65      5026\n",
      "weighted avg       0.79      0.81      0.78      5026\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n",
    "\n",
    "We now Calculate the AUROC for the train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Threshold: 0.2792741288710482\n"
     ]
    },
    {
     "data": {
      "image/png": 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kSAVEoE6QL96lPQj0K0PtQF/KeJZCRIiK+pyDO0/w6qvX88gjrSld+p7Ljs+ViSKGczsmD8bqv0AppYqt1Iwsdh1PJjEtkyW74th5LJkVe+PPmaaavzddG1ahok8ZalQsS50gX0IqlqVyee8C571ixSGaNKmMn58XX3zRm8BAH0JCLqVH5vy5MlHMwuqkfArQGkjU+gmlVHEjIuw6nsK0dYdYeyCBfw+dOm+aVrUqcWPDKtzUtBpVy3tjTH4FLhcWH3+aZ55ZyBdfbGDMmI68/HInmjevdrVWwXmJwhjzI9AJCDTGxGB1gVgaQEQ+wepkvSdWZ+OngUHOikUppQpDZnYOR0+lsyo6nl83HuZQwmkOnUw7Z5r724ZRJ6gc9auWp5q/NyGVfC57eSLCt9/+y5NPLiAhIY3Ro9syenTbK12N8zjzqacBFxkvwHBnLV8ppZwpPTObY4np7DyezPI9J/hlw2GS07POmaaiT2m6N6pKVX9vujSoTJvaAXh6XL23Ep5+eiFvv72Ctm1D+OSTm2jSpMpVm7c9t+uPQimlCltOjvD3nhMcPpXGn9uPs+5AAqdOZ543XfPQClxfvzJ1K/tyfURlvEtf2dNG+UlLyyQ1NZPAQB+GDGlO3bqVGDKkBaVKXVpx1aXQRKGUUnmcycphd2wyGw6eYu7mo+dVNgM0C/anbXggzYL9qVfFj1qB5S65buFSzZu3h+HD5xIZWZUZM26nfv1A6tcPdOoyQROFUkoRn5LB3C3H2HksiSW74s6rV6gVWI6+LWrQtWFValQsi69X4Z46jxxJ5rHH5jFt2jbq1w9gxIhrCnX5miiUUiVKVnYOW48ksXRXHIt3xbE5JvGcdxaCK5alf8tg6lT2pV4VXyJDKlKpXBmXxfvnn9HcdttUzpzJZuzY6xk9ui1ehZyoNFEopYqtjKxsth1JIjoulczsHCat2M+OY8nnTONftjR3NQ+lSQ1/bmxUBT/v0i6K9lyZmdmULu1Bs2ZV6dmzLq+91pnw8EouiUUThVKqWImOS+H1uTtYf+AkCflUOHuWMjzSpS49m1SjVmA5PJxYCXw5kpIyePHFv1i9+jDLlw8mMNCHKVP6uTQmTRRKqWJj3pajDP3+HwBCK/lwQ4MqhFTy4draAbl1C/5li8YdQ14iwvTp23j00XkcO5bCsGHXkJGRjY+P67sN0kShlHJryemZvPH7DhZuP87xJKu11F+HtyMypIKLI3NcXFwq9933K7//vofmzasyc+adXHNNDVeHlUsThVLK7WTnCIt2xLL9aBJfr9jPydQzANzQoAqPdAmnabD7JAmA8uW9OHHiNO+/343hw1vh6en6uwh7miiUUkXegfhUVu6NZ/HOOFLPZLFs94nccWEBPrzdrymdIyo7/T2Gq2np0gOMG7eMGTNux9e3DKtWPeDUl+auhCYKpVSRdPhUGot3xvLpkmgOnjwNQCkD3qU9aFM7gPpV/RjULozQSj5ulSBOnDjN6NELmDRpI2FhFdi//xSNG1cuskkCNFEopYoAEWFTTCLbjibxz4EE1h9MIDouNXd8h3pBDG4XRps6AXhdYSc8riIifP31RkaPXkBSUgbPPtueF17ogI9P0axct6eJQilV6ESEL5btY09sClPXHTpvfKBvGYZfX4fWtQJoW+fqNqTnSt9/v4mGDYP45JObaNSosqvDcZgmCqVUoRARDp1MY9vRRMbN3Z7bTEZIpbKU9ijFTU2q0bNJNUIr+VCukN88dpbTpzN5/fVlDB0aRXBweWbMuB1/f+8iXcyUn+KxN5RSRZKIsCr6JNPWH+KPLcdIPZMNgEcpw+B2tXi2ZwSli8ndQl5z5+5m+PC57N9/iho1/Hj44WuoWLGsq8O6LJoolFJXVXpmNj+uOcjsTUdZfyDhnHGD29WiTZ0A2tQJKPSG9QpLTEwSjz02jxkzttOgQSBLltxPhw41XR3WFSmee0opVajikjOYt/UYXy6LZn/86dzhzYL9uSasEne2CiG8sp8LIyw848YtZc6c3bz+emdGjWpLmTLuWfluz1gdzbmPqKgoWbdunavDUKrES8nI4psV+/lmxX5ikzNyhwdXLMuQ9rW4q3Wo2z6hdKnWrDlM2bKeNGlShfj40yQmZlC7dkVXh3UOY8x6EYm6nO/qHYVSyiF7YlNYf+AkO4+lsHb/STYfTgSs7j7vbVOT7o2q0jKsYolJDgCJiek899yffPzxOnr1qsesWQMICPAhIODy+8EuijRRKKXylZmdw/erDrAqOp6/dsSSmf2/0odG1ctz5zUhNKpengGtQovN46uOEhGmTt3K44//QWxsKiNHtmLs2M6uDstpNFEopXKdPpPFvC3H+GTJXnYdT8kd3j48kHpV/OhYP4jI4Ar4u8FLYs70/febuPfeX4mKqs7s2QNo2bK6q0NyKk0USpVwIsLKvfEMn/zPOf031AosR7+WwTzcsY7bPffvDBkZWURHJ9CgQRC3396IrKwc7r23GR4l4G5KE4VSJVBsUjoro+OZu/kof2w9nju8abA/91xbk64NqlDRhd1/FjWLFu3j4YfncPp0Jrt3j8TLy5NBg5q7OqxCo4lCqRJCRNh8OJFPl0QzZ/PR3OFtagfQLjyAvi2Dqebvni+EOUtsbCpPPjmf777bRO3aFfnss96F3l91UVDy1lipEmbnsWR+2XCYT5bszR3WqX4Q97SuSbvwQMoWg+f8nWHPnpO0avU5KSlneP7563j++esoW0R7x3M2TRRKFTPHEtOZ9e9hNh9OYvHOWJLTswCoW9mXZiEVGNqxdol5+e1yJCVlUL68F3XqVGTIkOYMHtycBg2CXB2WS2miUMqNpWdm8/M/h1l34CQr98ZzNDH9nPHV/b25NbIG/7muNqHF7Nn+qy019QyvvrqEzz//h02bHiY4uDxvv32jq8MqEjRRKOVmRIRZ/x5hyppDrIyOzx0eUdWPHo2rUdrT0CK0Ih3rBeFdWouVHPHbbzsZMeJ3Dh5MZMiQ5m7RR0Rh0kShlJuIS85g2vpD/LT2UG57SjUDfOjVtBqD29UiwNfLxRG6n6ysHG6/fRq//LKDRo2CWLZsEO3bh7o6rCJHE4VSRVRyeiYzNx7h9y1HWbc/gYysHMBqontI+1o80bVesem3obCJCMYYPD1LUa2aL2+80YXHH29TLBrwcwY9ypQqYpLTM3nm583M2fS/R1iD/LxoFVaJqLCK3NsmDA99Ae6yrVoVw/Dhc/n88960aFGNiRNvcnVIRZ4mCqWKiD2xKUxbf4gfVh0kJSMLL89SDL8+nCHta+mdw1WQkJDGc8/9yaefrqd6dT8SEtJcHZLbcOrRZ4zpDnwAeABfiMgbecaHAt8AFWzTPCMic50Zk1JFSXpmNvO3HefFX7eQmGY1n9E02J9Hu9Slc0RljNE7h6th6tQtPPLIPE6cOM1jj13LK690ws9P63Qc5bREYYzxACYCXYEYYK0xZpaIbLOb7AXgJxH52BjTEJgLhDkrJqWKipiE0zzy4wY2HjpFjq1R1iA/LyYNuoZG1f1dG1wxtGPHCcLCKjBv3t00b17N1eG4HWfeUbQC9ohINIAxZgpwC2CfKAQob/vbHzjixHiUcqnsHOGH1Qf4aNFejiVZ7zt4ly7Fi70a0iWiClX9vV0cYfGRnp7Fm2/+TYsW1ejduz7PPXcdL7zQoUQ04OcMzkwUNYBDdp9jgNZ5pnkZmG+MGQmUA27Ib0bGmAeBBwFCQ/XRNeVe9p1IZdbGI3zxd3TuW9Kta1Xiia71aF07wMXRFT8LF0YzbNgcdu8+yahRbejduz6l9X2SK+LMRJFf4WreflcHAJNE5F1jTBvgO2NMYxHJOedLIp8Bn4HVFapTolXqKkrPzObNeTuYvekocXbdhD7cqQ4Pd6pDeW99oetqO348hSeemM/kyZsJD6/E/Pn30LVrHVeHVSw4M1HEACF2n4M5v2hpCNAdQERWGmO8gUAg1olxKeU0h0+lMeGvPUxdezC37qF3s+r0aV6DDvWC9LFWJ1qwIJrp07fx0ksdePbZ6/D21ifFrhZnbsm1QF1jTC3gMHAncFeeaQ4CXYBJxpgGgDcQ58SYlLrqRIS1+xP4aPEeFu+0Dt9q/t6M7lafPi2CXRxd8fbvv8fYvfsk/fo15O67m9CuXQi1alV0dVjFjtMShYhkGWNGAH9gPfr6lYhsNca8CqwTkVnAKOBzY8zjWMVS94uIFi0pt5CVncOyPScY9PXa3GH3tqnJzc2qExVWyYWRFX8pKWcYM2YRH3ywmrCwCtx6awSenqU0STiJU+/NbO9EzM0z7CW7v7cB7ZwZg1JX285jyXy8eA+/brRKUiuVK0PniMo81b0+lf30ySVn+/XXHYwc+TsxMUk8+GALxo+/AU9PfZrJmbQQTykHnD6TxeTVB/lxzUH2xqUCUMazFHdeE8LobvXx08rpQrF583Fuu20qTZpUZurUfrRtG3LxL6krpolCqQJEx6UwYdEefv7nMACepQz3tqlJnxbBRIZUcHF0JUNmZjbLlh2kc+daNGlShTlz7qJr19r6yGsh0kShVD6yc4Q35+3gs6XRucNe6tWQ+9pqg3yFacWKQwwdOputW+PYuXME4eGV6NmzrqvDKnE0UShlk50jfL183znFSxFV/RjTuxGta1WilCaIQnPyZBrPPLOQzz//h5CQ8vz88+2Eh+sDAq6iiUKVeDuOJbFoRxz/99duTp/JBiAypAINq5dn3K2NtWG+QpaenkVk5CccOZLMqFFtePnlTvj6lnF1WCWaJgpVIp199+G1OdvYFJMIWO8+PNShDiM6h2vxkgvExCQRHFweb29Pxo69nsjIqjRrVtXVYSk0UagSaPHOWD5bGs2KvVZ/09fXD+KJrvVpEqyttrpCWlom48f/zZtvLmf69P707l2f++6LdHVYyo5DicIYUwYIFZE9To5HKaeZufEwv244zCLb29N9WtTg8RvqEVLJx8WRlVzz5+9l2LA57N2bwD33NKVVqxquDknl46KJwhhzE/AeUAaoZYyJBMaIyG3ODk6pK5WVncPS3XF8s+IAS3ZZCeKu1qGM7BxONf+yLo6uZBs5ci4TJqylbt1KLFw4kC5dars6JHUBjtxRvIrVPPgiABHZaIwJd2pUSl2B7Bxh0or9fL18HzF23V0+0L4WT3arj7c+f+8y2dlWw9AeHqW49tpgAgN9ePrp9tqAXxHnyN7JFJFTeZ780PaYVJETn5LBD6sPMuGvPZyxnZDahQfQtk4gNzerrkVMLvbPP0cZOnQ2Awc2ZeTI1tx9d1NXh6Qc5Eii2G6MuR0oZWsJ9lFglXPDUspxZ7JyePbnzczdfJS0zGwq+pTmvpY1GXWj3j0UBcnJGbz00iI+/HANQUE+VKvm5+qQ1CVyJFGMAF4CcoCfsVqDfdaZQSnliPTMbP7cHsvwyf8AUNGnNF/eH0Wb2gH67kMRMX/+XgYPnsmRI8kMHRrF6693oUIFbTjR3TiSKLqJyNPA02cHGGP6YCUNpQrd+gMneXnWNjYfTswd9vgN9XikS7gmiCKmTBkPKlcux4wZt9O6tfbN4a7Mxbp/MMb8IyIt8gxbLyItnRrZBURFRcm6detcsWjlYrHJ6XR9bymJaZl4lDK0CK3ATU2q0aVBFa1/KCIyM7N5772VJCVlMG5cFwByckSbPykCbOftqMv57gXvKIwx3bC6Ka1hjHnPblR5rGIopQrFPwcTeH3OdtYdSACgbmVffnqoDRXLabMORcnffx/MbcCvf/+GuQlCk4T7K6joKRbYAqQDW+2GJwPPODMopXJyhPnbjvHhn3vYdjQJgIbVyjOiczg9m1RzcXTKXnz8aZ5+eiFffrmB0FB/fvttAL161XN1WOoqumCiEJENwAZjzA8ikl6IMakSbv2Bk9z/1VqSM7IAuKlpNZ7uFkFogBYvFUXx8WlMmbKFp55qy0svdaSc3ukVO45UZtcwxowDGgK5jyuIiF4yqKtGRPh25QHGzLJuXksZuL9tGI92qatFTEXQ9u1x/PTTVsaM6US9egEcPPg4lSrpm+7FlSOJYhLwGvAO0AMYhNZRqKtoypqDvLtgF3HJGfiXLU2rWpV45eZGVK+gJ56i5vTpTN5yLw0AACAASURBVMaNW8rbb6/A17cMQ4a0IDi4vCaJYs6RROEjIn8YY94Rkb3AC8aYZc4OTBV/f24/zsPf/5P7FvXIzuE80bWePuJaRM2bt4dhw+awb98p7ruvGW+/3ZWgoHKuDksVAkcSRYaxfrl7jTFDgcNAZeeGpYqzFXtP8MPqg8zZdBSAWyKr8+rNjfH3Ke3iyNSFpKScYeDAXwgIKMuiRffRqVOYq0NShciRRPE44As8AowD/IHBzgxKFU+pGVk8PnUj87cdB+CmJtV4urtWUhdV2dk5/PjjFgYMaIyvbxkWLhxIREQgXl7agF9Jc9E9LiKrbX8mAwMBjDH6iqVymIjw26ajvP3HDg6dTKN7o6q83qcJlbSSushav/4IDz00m/Xrj1K2rCd9+zbU3uZKsAIThTHmGqAG8LeInDDGNMJqyqMzoMlCXdTB+NN0/2Bpbl/UH9wZyS2R2jlNUZWYmM6LLy5i4sS1VK5cjilT+tKnTwNXh6VcrKA3s8cDfYF/sSqwf8FqOfZNYGjhhKfcVVZ2Du8t2MX3qw5w+kw2PZtU5cVeDbWzoCKub9+f+OuvfQwffg2vvdYZf39twE8VfEdxC9BMRNKMMZWAI7bPOwsnNOWOMrNzGDdnO0t3xRF9IhWAqQ9eS+vaAS6OTF1IdHQCQUE++Pl5MW5cZ0qVMlxzjd71qf8pKFGki0gagIicNMbs0CShLuT0mSwmrz7Ia3O25w57sVdDBrcL08ddi6gzZ7J5550VjB27lEceacWbb3bVFl5VvgpKFLWNMWebEjdAmN1nRKSPUyNTbmPmxsM8OmVj7ufB7Wrx/E0N8NDG4IqspUsPMHTobLZvP0G/fg155JHWrg5JFWEFJYq+eT5PcGYgyv0s2x3H+wt3s/5AAkF+Xgy4JoSRXepS2qOUq0NTBfjvf1fyxBPzCQurwJw5d9GzZ11Xh6SKuIIaBfyzMANR7iMjK5vXZm/nu1UHABjULoxnezSgjKcmiKIqJ0dITT2Dn58XN91Uj7i407zwQgd89CVH5QB9c0Y5bMXeE/zfn3tYGR0PgH/Z0nw7uBXNQiq4ODJVkK1bYxk6dE5uT3P16gXw+utdXB2WciNOTRTGmO7AB4AH8IWIvJHPNLcDLwMC/CsidzkzJnVp0jOz+Xr5fpbvOcHfe04AcHfrUCJDKtA/KsTF0amCnD6dydixS3jnnZX4+3sxeHAkIqIPF6hL5nCiMMZ4iUjGJUzvAUwEugIxwFpjzCwR2WY3TV3gWaCdiCQYY7QNqSLkeFI6Hd9eRHqm1Whf3xbBPNIlnJoB2hBcUbdhw1H69PmJ/ftPMWhQJG+91ZXAQG0qRV2eiyYKY0wr4EusNp5CjTHNgAdEZORFvtoK2CMi0bb5TMF6N2Ob3TT/ASaKSAKAiMRe+iqoq23Z7jgenbKRk6lnAHioY21G31gfT62kLvLO3jGEhvoTGurPN9/cSocONV0dlnJzjtxRfAj0An4FEJF/jTHXO/C9GsAhu88xQN5n8OoBGGOWYxVPvSwi8xyYt3KCBduOM/737UTHWS/KtakdwNBOdehYL8jFkamLycrKYcKENcyatZMFCwYSEODDkiX3uzosVUw4kihKiciBPOWa2Q58L7+CUMln+XWBTlhtRy0zxjQWkVPnzMiYB4EHAUJDQx1YtLoUiWmZPPjtOlbvO0lFn9LceU0I/+lQmzpBvq4OTTlgzZrDDB06mw0bjtGjRzhJSRlUrKhNpairx5FEcchW/CS2eoeRwC4HvhcD2Nd2BmM1A5J3mlUikgnsM8bsxEoca+0nEpHPgM8AoqKi8iYbdQX2xCZz31drOZKYxt2tQ3m6RwTlvfWRSXeQknKGp59ewMcfr6NaNT+mTetP374NtLJaXXWOJIqHsYqfQoHjwELbsItZC9Q1xtTC6uzoTiDvE02/AgOAScaYQKyiqGjHQldXIidHeOW3rfy49hAIfHhnc3o3q+7qsNQlKF26FIsXH2DkyFaMHduZ8uW9XB2SKqYcSRRZInLnpc5YRLKMMSOAP7DqH74Ska3GmFeBdSIyyzbuRmPMNqzirNEiEn+py1KOy8kRnv15M4t2xhKbbD3ENveR62hYvbyLI1OO2LPnJK++uoSJE3vi5+fF+vUP4u2tr0Mp5zIiBZfkGGP2AjuBqcDPIpJcGIFdSFRUlKxbt86VIbil/SdS+XHtQX5cfZCk9CwA3u3fjFub19A2mdxARkYWb721nHHjllGmjAdz5tzFddfp00zKccaY9SISdTnfdaSHuzrGmLZYRUevGGM2AlNEZMrlLFAVrtSMLCYs2sPHi/cC4OflycOd6vBUt/palu0mFi3ax8MPz2HnznjuuKMR773XjerV/VwdlipBHLpnFZEVwApjzMvA+8APgCaKIiw9M5tR0/5lzqajAFTz9+b125rQqX6QJgg3IiKMG7eMzMwc5s27m27dwl0dkiqBHHnhzhfrRbk7gQbATKCtk+NSlyk9M5u35u3kq+X7coe9f0ckvZtV1yImN5GTI3z55T907x5OSIg/3313GxUqeFO2rD6NplzDkTuKLcBvwFsisszJ8agr8MuGGJ6avonMbCGkUlnualWToR1r6x2EG9m06ThDh85m5coYXnqpA6+8cj3Vqmkxk3ItRxJFbRHJcXok6rLk5Ah/7znB41M3Em9rcmNI+1q82KuhiyNTlyIl5QyvvLKY//53FRUrlmXSpFu4995mrg5LKaCARGGMeVdERgEzjDHnPRqlPdy53uKdsdz/tfVuokcpQ8d6QbzTvxlBfvo8vbt5+eXFvPvuSh54oDlvvHEDAQHagJ8qOgq6o5hq+197titiTqRk8NnSaD5bar2beH39ID4Y0FzfqHYzhw4lkpqaSUREIM88055bb42gfXttokYVPQX1cLfG9mcDETknWdhepNMe8AqZiPDu/F1MWLQHgNpB5ZgwoIW+LOdmsrJy+PDD1bz00iJatqzOkiX3Exjoo0lCFVmO1FEM5vy7iiH5DFNOkp6Zzbcr9/P2HzvJzBZqVCjLU93rc0tkDVeHpi7RqlUxDB06m3//Pc5NN9VlwoSerg5JqYsqqI7iDqxHYmsZY362G+UHnMr/W+pqy8jK5taJy9lxLBk/L0+6N67MB3dEUkofdXU7c+bsonfvH6le3Y+ff76dW2+N0CfSlFso6I5iDRCP1errRLvhycAGZwZV0uXkCCuj4xkzayt7YlMACPT1Yu3zXfTE4mZEhCNHkqlRozw33FCbV1+9nkcfbY2fPnCg3MhF23oqaop7W0/rD5yk78crcz9X8CnNf66rzYMdalNae5hzK7t2xTNs2Bx27Ypn27bh+PqWcXVIqgRzSltPxpglItLRGJPAuR0OGUBEpNLlLFBd2HsLdvHhn7sBuK15DZ7oWo+QSvqYpLtJT8/ijTf+Zvz4vylb1pPx47tQtqy28KrcV0FH79nuTgMLI5CS7GhiGq/P3cFv/x6hok9pvhvSmsY1/F0dlroMx46l0KHD1+zefZIBAxrz3nvdqFpVewpU7q2gx2PPvo0dAhwRkTPGmPZAU+B7IKkQ4ivW0jOz+e+CXfy45iCnz2RzX5uaPNuzAd6lPVwdmrpEmZnZlC7tQZUq5ejQoSYTJ/aka9c6rg5LqavCkfvhX4FrjDF1gG+BOcBkoJczAyvuFu2IZdS0fzmZeoa6lX357N4oagWWc3VY6hLl5Aiffbae119fxooVQwgOLs8XX9zs6rCUuqocSRQ5IpJpjOkDvC8iHxpj9Kmny7TlcCJ9P15BRpZ1wzbhrub0aqpdkLqjf/89xkMPzWb16sN07lyLzMxsV4eklFM41BWqMaY/MBC41TZM24q4DL9uOMwTP23E06MUA1qF8EiXulTzL+vqsNQlEhFGj17A+++volKlsnz33W3cfXcTfXRZFVuOvpk9DKuZ8WhjTC3gR+eGVbyICBP+2sO7C3ZR3d+bn4a2IbiiPs3krowxJCSkMWSI1YBfxYqa7FXx5tB7FMYYT+Bs11p7RCTLqVEVwN3eozh8Ko2BX6wm+kQqlcqVYdaIdpok3NCBA6d49NF5vPRSR1q0qEZOjujb8cqtXMl7FBd9g8sYcx2wB/gS+ArYZYxpdzkLK2lmbjxM1/eWEH0ilf4tg1n1bBdNEm4mMzObt95aTsOGH7FgQTQ7d54A0CShShRHip7+C/QUkW0AxpgGwHfAZWWmkuKTJXt54/cd+Hp5MuPhNrSsqe8nupsVKw7x0EOz2bIllltuqc+HH/YgNFTfb1EljyOJoszZJAEgItuNMdoWQQE+XxrNG7/voHloBb4Z3Er7iXBTCxdGk5iYzq+/3sEtt0S4OhylXOaidRTGmElABtZdBMDdgI+I3Ofc0PJXlOsoktIzGTF5A0t3xRFQrgxzH72OKuW9XR2WcpCI8N13mwgK8qFHj7pkZGSRmZmjbTSpYsEpbT3ZGQo8AjyF1c7TUuD/Lmdhxdk3K/YzZtZWADrUC+Kju1vg66Xt+7iLHTtO8PDDc1i8eD/9+zekR4+6eHl54qWNvCpVcKIwxjQB6gC/iMhbhROSexERRvy4gTmbjgLwUq+GDG5fy8VRKUelpWXy+uvLePPN5ZQrV4ZPP+3FAw+0cHVYShUpBbUe+xxWT3b/YDXh8aqIfFVokbmJcXO2M2fTUYIrluXnYW2p7KdFTe7kt9928dpry7jnnqa8805XqlTRBvyUyqugO4q7gaYikmqMCQLmYj0eq2xW7D3BF3/vo1mwP78Ob6dv5rqJY8dS2LjxGN27h9O/f0PCwh6gVSvtVlapCynoPYoMEUkFEJG4i0xb4vy9+wRDv1tPjQpl+eK+azRJuIHs7Bw++mgt9etPYODAX0hLy8QYo0lCqYso6I6itl1f2QaoY993toj0cWpkRdiiHbEMmrQWY+D3xzoQpN1aFnn//HOUoUNns3btEW64oTYffdSTsmX1sWWlHFFQouib5/MEZwbiDrJzhF82HOblWVup7OfF7JHtqayPvxZ5+/Yl0KrV5wQG+jB5ch/uvLOx3gEqdQkK6rjoz8IMpKg7dPI0I3/cwMZDpyjjUYqZI9ppkijCRITNm2Np2rQKtWpV5Ouvb6F37/pUqKD7TKlLpfUODsjOEZ6avomNh05xf9sw/n7meuoE6dMxRdW+fQn06vUjzZt/yqZNxwEYOLCZJgmlLpNTE4UxprsxZqcxZo8x5pkCputnjBFjTJFsP+rV37ayMjqeZ3pE8PLNjfQR2CLqzJls3njjbxo1+oglS/bzzjtdadgwyNVhKeX2HH512BjjJSIZlzC9BzAR6ArEAGuNMbPs242yTeeH9eb3akfnXZhiEk7z/eqD1Akqx0Mdars6HHUB2dk5tG37JevXH6VPnwa8/343QkK0AT+lrgZHmhlvZYzZDOy2fW5mjHGkCY9WWH1XRIvIGWAKcEs+040F3gLSHQ+7cMSnZHDvl2sQET4d2FIrQIugpCTr2sXDoxSDBzfnt98GMGPG7ZoklLqKHCl6+hDoBcQDiMi/wPUOfK8GcMjuc4xtWC5jTHMgRERmFzQjY8yDxph1xph1cXFxDiz6yokI93+9lugTqbx7ezPCK/sVynKVY0SESZM2Urv2B8ycuQOAYcOuoVevei6OTKnix5FEUUpEDuQZ5kgv8vldfuc2VWuMKYXV18Woi81IRD4TkSgRiQoKKpwy56+X72fz4URGda3Hbc2DC2WZyjHbtsXRqdM3DBo0k4iIQOrU0b4+lHImR+ooDhljWgFiq3cYCexy4HsxQIjd52DgiN1nP6AxsNhWpFMVmGWMuVlEXNqO+KKdsYyds40q5b0Ydn34xb+gCs1bby3n+ef/onx5L774ojeDBjXX3uaUcjJHEsXDWMVPocBxYKFt2MWsBeoaY2oBh4E7gbvOjhSRRCDw7GdjzGLgSVcnieT0TB76dj0iMH1oWzz0JFQkiAjGGKpW9eXuu5vw9ttdCQoq5+qwlCoRLpooRCQW6yR/SUQkyxgzAvgD8AC+EpGtxphXgXUiMuuSoy0E7y3YxZnsHJ68sR4hlbR/a1c7ciSZRx+dx3XXhfLII625995m3HtvM1eHpVSJctFEYYz5HLu6hbNE5MGLfVdE5mK1Oms/7KULTNvpYvNztqOJaXy9fD/NQyswXIucXOpsA37PP/8XmZk5tG2r9URKuYojRU8L7f72Bm7j3KeZio0xM60e6l64qaE+CutCGzce44EHZrF+/VFuvLEOH33UUyuslXIhR4qeptp/NsZ8ByxwWkQusuVwIvO3Haeavzcta1Z0dTglWmJiOkeOJDN1aj/699ekrZSrXU6nzrWAmlc7EFcSEe76fBWlDPw8rK2rwylxRIRp07axe3c8zz/fgY4dw4iOfhRvb+1zXKmiwJE3sxOMMSdt/05h3U085/zQCs8nS6JJSs9i4LU1qeZf1tXhlCh7956kZ8/J3HHHdGbO3ElmpvWKjiYJpYqOAn+Nxrrnb4b1eCtAjoicV7Htzv7efYI35+2gQ70gXurdyNXhlBgZGVm8884KXnttGaVLl+KDD7ozbNg1eHpqg8ZKFTUFJgoREWPMLyLSsrACKkwiwutztwPwTr+m+s5EITp0KImxY5fSu3d93n+/GzVqlHd1SEqpC3Dk8m2NMaaF0yNxga+W72fb0SReu7WxdkJUCOLiUpkwYQ0A4eGV2LZtONOm9dckoVQRd8E7CmOMp4hkAe2B/xhj9gKpWG04iYi4dfJIychi7OxthFQqy4BWoa4Op1jLyRG+/noDTz21kOTkDLp2rU39+oHUrq1PlynlDgoqeloDtABuLaRYCtUni/cC8Ejnulrk5ERbtsTy8MNz+Pvvg1x3XSiffNKL+vUDL/5FpVSRUVCiMAAisreQYik0Wdk5/LTuELUDy9E/KuTiX1CX5cyZbG688TvOnMnmq69u5v77I/WdCKXcUEGJIsgY88SFRorIe06Ip1CMmbWV2OQMXr5Zn3Jyhr/+2kfHjjUpU8aDn37qT0REIIGB2m6WUu6qoMpsD8AXqznw/P65pUU7Y/lh9UFqBvjQrVFVV4dTrMTEJNG370906fIt3377LwDt24dqklDKzRV0R3FURF4ttEgKyYd/7gbghwdaa93EVZKVlcOECWt48cVFZGfnMH58F+6+u6mrw1JKXSUXraMoTiYt38eGg6cY3K4WwRX1KvdqGTjwF6ZM2UKPHuFMnNiTWrX0aSalipOCEkWXQouikLz82zYARt2o/SpfqVOn0vH0LIWvbxmGD7+Gvn0b0LdvA62sVqoYumAdhYicLMxAnG35nhMA9GxSlXJe2o7Q5RIRpkzZQoMGE3nxxb8Aqx6iXz9t5VWp4qrENKzzxbJoAF6/rYmLI3Ffe/acpFu37xkwYAbBweW55x6th1CqJCgRl9Y5OcKinXE0qFaeCj5lXB2OW5o8eTODB8/Ey8uTCRN6MHRoFB4eJeY6Q6kSrUQkip3HkwHoUFffCL5UmZnZlC7tQVRUdfr1a8hbb3WlenW3fTpaKXUZSkSieHf+LgBt0+kSxMamMmrUfFJTz/Dzz3dQr14A33/fx9VhKaVcoNiXHew4lsTC7ce5uVl1wgLLuTqcIi8nR/jss/XUrz+BqVO30KhRENnZOa4OSynlQsX+juKN33cA8EKvBi6OpOiLjk7gnnt+ZuXKGDp1CuPjj28iIkKL65Qq6Yp1osjOEbYfTaKiT2kq+2l/Exfj7+/FqVPpfPPNrQwc2FQfd1VKAcW86Gnh9uMcT8pgdLcIV4dSZM2atZM+faaSnZ1DQIAPW7YM4957m2mSUErlKtaJ4myxU69m1VwcSdFz8GAit946hVtumcKuXfEcPZoCQClt/0oplUexLXqavPog+06kckdUCOW9S7s6nCIjKyuH999fxZgxixER3nzzBh5//FpKl/ZwdWhKqSKq2CaKqWsPAmifE3lkZ+fwxRf/0LlzLf7v/3oQFlbB1SEppYq4Yln0lJGVzb8xidzYsAply+iVckJCGk8/vYDk5Ay8vDxZvnwws2bdqUlCKeWQYpko5m89DsD1EZVdHIlriQg//LCJiIiJvPvuShYt2g9AQICPVlYrpRxW7IqeRIRPlljdfN/WvIaLo3GdXbviGTZsDn/+uY9WrWrwxx/3EBmpPfoppS5dsUsUW48ksfVIEiOuD8e7BFfQPvbYPNatO8JHH/XkwQdbagN+SqnLVuwSxZ/bYzEG7m8X5upQCt2CBXuJiAgkJMSfjz++CS8vT6pW9XV1WEopN+fUy0xjTHdjzE5jzB5jzDP5jH/CGLPNGLPJGPOnMabmlS5z2vpDRIZUINDX60pn5TaOHUvhrrtmcOON3/Pmm8sBqFmzgiYJpdRV4bREYYzxACYCPYCGwABjTMM8k20AokSkKTAdeOtKlrn+wEliEtKIqFoymsHOyRE++WQdERETmDFjO2PGdOSdd250dVhKqWLGmXcUrYA9IhItImeAKcAt9hOIyCIROW37uAoIvpIF/r75GADDOoVfyWzcxvjxy3j44Tm0bFmdTZuG8vLLnfD2LnaliUopF3PmWaUGcMjucwzQuoDphwC/5zfCGPMg8CBAaOiF+5SYvekoDaqVJ6SSzyUH6y6SkzM4ceI0tWpVZOjQKGrVqsiAAY31cVellNM4844ivzOX5DuhMfcAUcDb+Y0Xkc9EJEpEooKCgvJdmIhwLCkdP6/ieUUtIvzyy3YaNvyIO+6YjogQEODDXXc10SShlHIqZyaKGCDE7nMwcCTvRMaYG4DngZtFJONyF7YpJhGAjvXzTyTu7MCBU9x88xT69PmJSpXK8uGHPTQ5KKUKjTMvv9cCdY0xtYDDwJ3AXfYTGGOaA58C3UUk9koWtjI6HoD24cWro52VKw9xww3fAfDOO1159NFr8fTUdyKUUoXHaYlCRLKMMSOAPwAP4CsR2WqMeRVYJyKzsIqafIFptivkgyJy8+Usb9EOK8/ULyZPPCUlZVC+vBctWlRj8OBIRo9uR2iov6vDUkqVQE4t0BeRucDcPMNesvv7hqu1rKwcwaeMh9u/jR0ff5pnnlnI/PnRbN06DF/fMvzf//V0dVhKqRKs2NT87jqWTKtalVwdxmUTEb77bhOjRs0nISGNJ55og1ZDKKWKgmKRKESE5IwsfNy0SfHExHRuvXUqixfvp02bYD75pBdNm1ZxdVhKKQUUk0QRl2w9LBVQzr2a7RARjDGUL+9FYKAPn33WiyFDWmh3pEqpIqVYPD6zO9bq77lxjfIujsRxf/yxhxYtPiMmJgljDNOm9ec//2mpSUIpVeQUi0QRn3oGgIiqRT9RHD2azJ13Tqd79x84fTqT2NhUV4eklFIFKhZFT/8cSACgdlA5F0dSsIkT1/Dcc3+RkZHFK6904umn2+FVTN8kV0oVH8XiLLXzWDIAft6lXRxJwdavP0rr1jWYOLEndesGuDocpZRySLFIFIdPpVGjQllXh3GepKQMXnppEQMHNqVly+p89NFNeHl5aPMbSim34vaJIis7h4MnT3Nd3aLTdIeIMGPGdh59dB5HjyYTGupPy5bVtQlwpZRbcvsz154464mna8KKxst2+/YlMGLE78ydu5vIyKr8/PPttG59Rd1sKKWUS7l9oth+NAmATkWk1dgfftjM0qUH+O9/uzFiRCttwE8p5fbcPlEs2RlHuTIeNKzmukdjly07QEZGNjfcUJvRo9ty//2RBAcX/Ud1lVLKEW5/ubt4Vxzt6wbi6VH4q3LixGkGD55Jhw6TePXVJQB4eXlqklBKFStufUcRm5zOqdOZtAitWKjLFREmTdrI6NELSEzM4Omn2/Hiix0KNQblPjIzM4mJiSE9Pd3VoagSwNvbm+DgYEqXvnqvC7h1oth1zKrIrl7Ij8bOnbubwYNn0a5dCJ980ovGjSsX6vKVe4mJicHPz4+wsDB9NFo5lYgQHx9PTEwMtWrVumrzdeuipzX7TwIQXtnX6cs6fTqT5csPAtCzZ11mzryTpUsHaZJQF5Wenk5AQIAmCeV0xhgCAgKu+t2rWyeKbUesJ57qBDk3Ufz++24aN/6IHj1+4NSpdIwx3HxzfW3ATzlMk4QqLM441tw6URyIT6VuZV/KOOkR1MOHk+jffxo9e07Gy8uT334bQIUK3k5ZllJKFVVumyjSzmSzJy6FLg2c08FPbGwqDRt+xOzZu3jttev599+hdOwY5pRlKeVMHh4eREZG0rhxY3r37s2pU6dyx23dupXOnTtTr1496taty9ixYxGR3PG///47UVFRNGjQgIiICJ588klXrEKBNmzYwAMPPHDOsFtuuYU2bdqcM+z+++9n+vTp5wzz9f1facSuXbvo2bMn4eHhNGjQgNtvv53jx49fUWwnT56ka9eu1K1bl65du5KQkHDeNIsWLSIyMjL3n7e3N7/++isAQ4YMoVmzZjRt2pR+/fqRkmLVy06YMIGvv/76imK7JCLiVv9atmwpIiKbY05Jzadny8yNh+VqiolJzP37gw9WyZ498Vd1/qrk2bZtm0uXX65cudy/7733XnnttddEROT06dNSu3Zt+eOPP0REJDU1Vbp37y4TJkwQEZHNmzdL7dq1Zfv27SIikpmZKRMnTryqsWVmZl7xPPr16ycbN27M/ZyQkCDBwcESEREh0dHRucPvu+8+mTZt2jnfPbtt0tLSJDw8XGbNmpU77q+//pLNmzdfUWyjR4+W8ePHi4jI+PHj5amnnipw+vj4eKlYsaKkpqaKiEhi4v/OR48//njuvFJTUyUyMvKC88nvmAPWyWWed932qacNB21NiwdenabFExPTeeGFv/j00/WsWvUALVpU45FHWl+VeSt11iu/bc2tW7taGlYvz5jejRyatk2bNmzatAmAyZMn065dO2688UYAfHx8mDBhAp06dWL48OG89dZbPP/880RERADg6enJsGHDzptnSkoK3XQXewAAEGxJREFUI0eOZN26dRhjGDNmDH379sXX1zf3Cnj69OnMnj2bSZMmcf/991OpUiU2bNhAZGQkv/zyCxs3bqRChQoAhIeHs3z5ckqVKsXQoUM5eNB6iOT999+nXbt25yw7OTmZTZs20axZs9xhM2bMoHfv3lSpUoUpU6bw7LPPXnS7TJ48mTZt2tC7d+/cYddff71D27QgM2fOZPHixQDcd999dOrUiTfffPOC00+fPp0ePXrg4+MDQPny1jtZIkJaWlpu/YOPjw9hYWGsWbOGVq1aXXGcF+O2iSL1TDbAFbcaKyJMm7aNxx6bx7FjKYwY0Yo6dQr3vQylCkN2djZ//vknQ4YMAaxip5YtW54zTZ06dUhJSSEpKYktW7YwatSoi8537Nix+Pv7s3nzZoB8i1fy2rVrFwsXLsTDw4OcnBx++eUXBg0axOrVqwkLC6NKlSrcddddPP7447Rv356DBw/SrVs3tm/ffs581q1bR+PGjc8Z9uOPPzJmzBiqVKlCv379HEoUW7ZsOW9b5Cc5OZnrrrsu33GTJ0+mYcOG5ww7fvw41apVA6BatWrExsYWOP8pU6bwxBNPnDNs0KBBzJ07l4YNG/Luu+/mDo+KimLZsmWaKAry9+4TAPiXvfyXSkSEPn1+4tdfd9CiRTVmzRpAVFT1qxWiUudx9Mr/akpLSyMyMpL9+/fTsmVLunbtCvyvz/b8XMqTMwsXLmTKlCm5nytWvPiFVv/+/fHw+P/2zj+6qurK459t+BF+RGYKQouogBF5JAZB2gKloGDBWgcGlovIb1hYFSjUIjgzi1lrMjK1raU4peAgYzXgamNEivJDoZViqUoEMkRAQmkaIo2NECADkZ8h7Pnj3ry8hJe8m0zee3kv+7PWW+vec889d9+93rv7nX3O/Z4EANLT03nmmWeYNWsWr732Gunp6f52Dx8+7D/n3LlzlJeXk5SU5C8rKSnhppuqdd5OnDhBQUEBw4YNQ0Ro1aoVhw4dIjU1Neg9NXSGUFJSEnl5eQ06xyslJSUcPHiQMWPG1Ch/5ZVXqKysZP78+WRnZzNr1iwAunbtypEjR8JiS21idjC7uOwCbVvd0KgpqhUVTm9ERBg27BZWrHiAPXsetSBhxCXt2rUjLy+PTz/9lCtXrrBq1SoAUlJS2LdvX426hYWFdOzYkaSkJFJSUsjNzQ3Zfl0BJ7Cs9rz+Dh2qU8ZDhgyhoKCA0tJS3nzzTSZMmADAtWvX2L17N3l5eeTl5fHZZ5/VCBJV9xbYdnZ2NmVlZfTq1YuePXtSVFTkD2KdO3eu0ds5c+YMXbp08fvCy72Wl5fXGHgO/AQGtSq6detGSUkJ4ASCrl3rfu/q9ddfZ/z48UHfqE5ISCA9PZ0NGzb4yy5dukS7dpF52TgmA8X5y1c5fuYC9zdixtN77xWRlraat95yIvFTTw1l/vyvkxAFrSjDiCSdOnVixYoVLFu2jIqKCqZMmcL777/Pu+++Czg9jwULFvD0008DsHjxYp599lmOHj0KOA/u5cuXX9fu6NGjWblypX+/6mHcrVs38vPz/amluhARxo8fz8KFC/H5fHTu3Dlou8H+yft8PgoKCvz7WVlZbNu2jaKiIoqKisjNzfUHinvvvZfs7GyuXLkCQGZmpn8cYvLkyXz44Yds3brV39a2bdv86bQqqnoUwT61004AY8eOZe3atQCsXbuWcePG1emHrKwsJk2a5N9XVf+9qSqbN2/2jxeBk76rnXYLG40dBY/W55577tHf55/Q2/5pi7594G91jvrX5uTJL3T69I0KGdqr13/qjh2FoU8yjCagOc16UlV96KGHdN26daqqeuDAAR0xYoT26dNHb7/9ds3IyNBr1675627evFkHDhyoffv2VZ/Pp4sWLbqu/fLycp0+fbqmpKRoWlqabtiwQVVV169fr71799YRI0bovHnzdMaMGaoafPbR3r17FdDMzEx/WWlpqU6cOFHvuusu9fl8+vjjjwe9v9TUVD137pweO3ZMu3fvXsN+VdUBAwZoTk6OqqpmZGRoamqq9u/fXydMmKAnT57018vPz9cxY8ZocnKy+nw+TU9P188//7xe34bi1KlTOnLkSE1OTtaRI0fq6dOn/fc7e/Zsf70q2ysrK/1llZWVOnToUE1NTdWUlBSdPHlyjVlQAwYM0NLS0qDXbepZT6IBc6ZjgUGDBunM57JY9tujbH9yOHd+OSnkOVlZB5k3722++OIKixcPZcmS4bRv37zX1zbih/z8fHw+X7TNiFuef/55kpKSrnuXIp7Zv38/y5cv59VXXw16PNh3TkRyVXVQY64Xk/mWotMXALjDo8bT1avXSE3tSl7eE/zwh6MsSBhGHDFnzhzatm0bbTMiyqlTp1i6dGnErheTs552HS2l75eT6hzIPn/+CkuX7uLWWzsxd+5XmTo1jalT00xvxzDikMTERKZNmxZtMyJK1cy1SBFzPYqKSuVk+WVSuncKenzLlqOkpLzAT37yAUePngacwTILEkY0ibUUrxG7hOO7FnM9irMXnRkLY1JqzngqLj7HggXvsHHjEfr1u4ldu2byzW/eFg0TDaMGiYmJnD592qTGjbCj6qxHkZjYtOKlMRcozl+upANwX9+a85ELC8vYvv0v/OhHo1i4cAht2iREx0DDqEWPHj0oLi6mtLQ02qYYLYCqFe6akpgLFOcuVXD7jYm0TriBPXs+Y/fuv/L97w9m+PDbOH78STp3bh9tEw2jBq1bt27S1cYMI9KEdYxCRB4QkT+JSIGI/HOQ421FJNs9/pGI9PTSbvJNHZg7dyuDB7/E8uU5nD/vpKMsSBiGYTQ9YQsUIpIArAK+DfQDJolI7VcXZwNlqpoMPA/ULasYwI5f5PLii7ksWPB1Dh6cQ4cObZrSdMMwDCOAcKaevgYUqGohgIi8BowDAgVRxgEZ7vYbwEoREQ0xbH9z2zZs3ftdBg78StNbbRiGYdQgnIHiZuCvAfvFQO0FHvx1VPWqiJwFOgOnAiuJyGPAY+7u5dzPHzvkQRG4JdCFWr5qwZgvqjFfVGO+qObOxp4YzkARbB5g7Z6Clzqo6hpgDYCI7Gvsa+jxhvmiGvNFNeaLaswX1YjIvtC1ghPOwexi4JaA/R7A3+qqIyKtgE7AmTDaZBiGYTSQcAaKvcAdItJLRNoAjwCbatXZBMxwtx8Gfh9qfMIwDMOILGFLPbljDt8DtgMJwMuq+omIPIMjd7sJ+CXwqogU4PQkHvHQ9Jpw2RyDmC+qMV9UY76oxnxRTaN9EXMy44ZhGEZkiTlRQMMwDCOyWKAwDMMw6qXZBopwyX/EIh58sVBEDovIARHZISJxK5sbyhcB9R4WERWRuJ0a6cUXIjLR/W58IiK/jrSNkcLDb+RWEdkpIvvd38mD0bAz3IjIyyJyUkQO1XFcRGSF66cDIjLQU8ONXUM1nB+cwe+/AL2BNsDHQL9adeYCq93tR4DsaNsdRV/cB7R3t+e0ZF+49ZKAXUAOMCjadkfxe3EHsB/4e3e/a7TtjqIv1gBz3O1+QFG07Q6TL4YDA4FDdRx/EHgH5x22wcBHXtptrj0Kv/yHql4BquQ/AhkHrHW33wBGSXyK/Yf0haruVNUL7m4Ozjsr8YiX7wXAUuA54FIkjYswXnzxXWCVqpYBqOrJCNsYKbz4QoEb3e1OXP9OV1ygqruo/120ccA6dcgB/k5EQmohNddAEUz+4+a66qjqVaBK/iPe8OKLQGbj/GOIR0L6QkQGALeo6pZIGhYFvHwv+gB9ROQDEckRkQciZl1k8eKLDGCqiBQDbwPzI2Nas6OhzxOg+a5H0WTyH3GA5/sUkanAIGBEWC2KHvX6QkRuwFEhnhkpg6KIl+9FK5z00704vcw/ikiqqv5vmG2LNF58MQnIVNWficgQnPe3UlX1WvjNa1Y06rnZXHsUJv9RjRdfICL3A0uAsap6OUK2RZpQvkgCUoH3RKQIJwe7KU4HtL3+Rt5S1QpVPQb8CSdwxBtefDEbeB1AVXcDiTiCgS0NT8+T2jTXQGHyH9WE9IWbbnkRJ0jEax4aQvhCVc+qahdV7amqPXHGa8aqaqPF0JoxXn4jb+JMdEBEuuCkogojamVk8OKL48AoABHx4QSKlrg27SZgujv7aTBwVlVLQp3ULFNPGj75j5jDoy9+CnQE1rvj+cdVdWzUjA4THn3RIvDoi+3AaBE5DFQCi1X1dPSsDg8effEU8N8i8gOcVMvMePxjKSJZOKnGLu54zL8BrQFUdTXO+MyDQAFwAZjlqd049JVhGIbRhDTX1JNhGIbRTLBAYRiGYdSLBQrDMAyjXixQGIZhGPVigcIwDMOoFwsURrNDRCpFJC/g07Oeuj3rUsps4DXfc9VHP3YlL+5sRBtPiMh0d3umiHQPOPaSiPRrYjv3isjdHs55UkTa/3+vbbRcLFAYzZGLqnp3wKcoQtedoqr9ccQmf9rQk1V1taquc3dnAt0Djj2qqoebxMpqO1/Am51PAhYojEZjgcKICdyewx9F5H/cz9AgdVJEZI/bCzkgIne45VMDyl8UkYQQl9sFJLvnjnLXMDjoav23dct/LNVrgCxzyzJEZJGIPIyjufUr95rt3J7AIBGZIyLPBdg8U0R+0Ug7dxMg6CYi/yUi+8RZe+Lf3bIFOAFrp4jsdMtGi8hu14/rRaRjiOsYLRwLFEZzpF1A2mmjW3YS+JaqDgTSgRVBznsC+Lmq3o3zoC525RrSgW+45ZXAlBDX/wfgoIgkAplAuqrehaNkMEdEvgSMB1JUNQ34j8CTVfUNYB/OP/+7VfViwOE3gAkB++lAdiPtfABHpqOKJao6CEgDRohImqquwNHyuU9V73OlPP4VuN/15T5gYYjrGC2cZinhYbR4LroPy0BaAyvdnHwljm5RbXYDS0SkB/AbVf2ziIwC7gH2uvIm7XCCTjB+JSIXgSIcGeo7gWOqetQ9vhaYB6zEWeviJRHZCniWNFfVUhEpdHV2/uxe4wO33YbY2QFHriJwhbKJIvIYzu/6KzgL9Byode5gt/wD9zptcPxmGHVigcKIFX4AnAD64/SEr1uUSFV/LSIfAd8BtovIoziyymtV9V88XGNKoICgiARd38TVFvoajsjcI8D3gJENuJdsYCJwBNioqirOU9uznTiruP0YWAVMEJFewCLgq6paJiKZOMJ3tRHgd6o6qQH2Gi0cSz0ZsUInoMRdP2Aazr/pGohIb6DQTbdswknB7AAeFpGubp0vifc1xY8APUUk2d2fBvzBzel3UtW3cQaKg808KseRPQ/Gb4B/xFkjIdsta5CdqlqBk0Ia7KatbgTOA2dFpBvw7TpsyQG+UXVPItJeRIL1zgzDjwUKI1Z4AZghIjk4aafzQeqkA4dEJA/oi7Pk42GcB+pvReQA8DuctExIVPUSjrrmehE5CFwDVuM8dLe47f0Bp7dTm0xgddVgdq12y4DDwG2quscta7Cd7tjHz4BFqvoxzvrYnwAv46SzqlgDvCMiO1W1FGdGVpZ7nRwcXxlGnZh6rGEYhlEv1qMwDMMw6sUChWEYhlEvFigMwzCMerFAYRiGYdSLBQrDMAyjXixQGIZhGPVigcIwDMOol/8D1K3HacMUrcsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_roc(glm_2, y_train, X_train[sig.index], \"Logistic Regression\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the optimal cut off was found to be 0.276 using the training data, we will use that as our cut off for our evaluation of the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.83      0.85      3899\n",
      "           1       0.47      0.52      0.50      1127\n",
      "\n",
      "    accuracy                           0.76      5026\n",
      "   macro avg       0.67      0.68      0.67      5026\n",
      "weighted avg       0.77      0.76      0.77      5026\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.276)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. The recall here is more important since we prioritise detecting defaulters at the expense of misclassifying non-defaulters. Looking at recall, our lower cutoff is able to correctly catch more defaulters.\n",
    "\n",
    "\n",
    "Calculate the confusion matrices for both cut offs to better compare their performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Of 1127 Defaulters, the Logistic Regression identified 588\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3248</td>\n",
       "      <td>651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>539</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  False  True \n",
       "Actual                 \n",
       "0           3248    651\n",
       "1            539    588"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion(y_test,glm_2.predict(X_test[sig.index])>0.276, \"Logistic Regression\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Of 1127 Defaulters, the Logistic Regression identified 350\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3707</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>777</td>\n",
       "      <td>350</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  False  True \n",
       "Actual                 \n",
       "0           3707    192\n",
       "1            777    350"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion(y_test,glm_2.predict(X_test[sig.index])>0.5, \"Logistic Regression\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is evident that the cutoff of 0.276 is better able to detect defualts, with 688 detected in the test set for 0.276 but only 346 detected for 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Threshold: 0.279103444545237\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_roc(glm_2, y_test, X_test[sig.index], \"Logistic Regression\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iCxBcin11EI8"
   },
   "source": [
    "### Model x - Support Vector Machine\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardizing attributes to prep for PCA\n",
    "SVM is computationally expensive for large datasets like ours. Therefore, we would like to reduce the dimensionality of our dataset before training an SVM on it. This can be done through Principle Component Analysis (PCA). The idea would be to reduce the number of features without loss of information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using training set to determine parameters of standardization\n",
    "apply the parameters to standardize all attributes in training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3LfxfeMZ1Kol"
   },
   "outputs": [],
   "source": [
    "#Standardising data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# Standardizing the features\n",
    "names = X_train.columns\n",
    "scalar = StandardScaler()\n",
    "scalar = scalar.fit(X_train)\n",
    "X_train_standardize = scalar.transform(X_train)\n",
    "X_train_standardize = pd.DataFrame(data = X_train_standardize, columns = names)\n",
    "X_test_standardize = scalar.transform(X_test)\n",
    "X_test_standardize = pd.DataFrame(data = X_test_standardize, columns = names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>...</th>\n",
       "      <th>x5_-2</th>\n",
       "      <th>x5_-1</th>\n",
       "      <th>x5_0</th>\n",
       "      <th>x5_1</th>\n",
       "      <th>x6_-2</th>\n",
       "      <th>x6_-1</th>\n",
       "      <th>x6_0</th>\n",
       "      <th>x7_-2</th>\n",
       "      <th>x7_-1</th>\n",
       "      <th>x7_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "      <td>2.010200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.144521e-16</td>\n",
       "      <td>-1.269615e-16</td>\n",
       "      <td>-3.593009e-16</td>\n",
       "      <td>-1.799432e-16</td>\n",
       "      <td>-9.886077e-19</td>\n",
       "      <td>-1.838236e-15</td>\n",
       "      <td>1.594746e-15</td>\n",
       "      <td>1.162255e-15</td>\n",
       "      <td>-2.980625e-16</td>\n",
       "      <td>3.461784e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.311419e-16</td>\n",
       "      <td>-1.118546e-15</td>\n",
       "      <td>5.865813e-16</td>\n",
       "      <td>-3.025637e-16</td>\n",
       "      <td>2.000798e-16</td>\n",
       "      <td>7.437699e-16</td>\n",
       "      <td>-3.886719e-16</td>\n",
       "      <td>-3.167853e-16</td>\n",
       "      <td>-9.253313e-16</td>\n",
       "      <td>1.004077e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "      <td>1.000025e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-1.217650e+00</td>\n",
       "      <td>-1.251659e+00</td>\n",
       "      <td>-1.595443e+00</td>\n",
       "      <td>-4.822372e-01</td>\n",
       "      <td>-3.777715e-01</td>\n",
       "      <td>-3.639056e-01</td>\n",
       "      <td>-3.298725e-01</td>\n",
       "      <td>-2.992312e-01</td>\n",
       "      <td>-3.099583e-01</td>\n",
       "      <td>-1.054810e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.327697e-01</td>\n",
       "      <td>-4.900767e-01</td>\n",
       "      <td>-1.106531e+00</td>\n",
       "      <td>-3.298957e-01</td>\n",
       "      <td>-4.471068e-01</td>\n",
       "      <td>-4.824128e-01</td>\n",
       "      <td>-1.133736e+00</td>\n",
       "      <td>-4.692822e-01</td>\n",
       "      <td>-4.929631e-01</td>\n",
       "      <td>-1.075577e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-8.772721e-01</td>\n",
       "      <td>-1.251659e+00</td>\n",
       "      <td>-7.969099e-01</td>\n",
       "      <td>-4.822372e-01</td>\n",
       "      <td>-3.777715e-01</td>\n",
       "      <td>-3.639056e-01</td>\n",
       "      <td>-3.298725e-01</td>\n",
       "      <td>-2.992312e-01</td>\n",
       "      <td>-3.099583e-01</td>\n",
       "      <td>-7.543990e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.327697e-01</td>\n",
       "      <td>-4.900767e-01</td>\n",
       "      <td>-1.106531e+00</td>\n",
       "      <td>-3.298957e-01</td>\n",
       "      <td>-4.471068e-01</td>\n",
       "      <td>-4.824128e-01</td>\n",
       "      <td>-1.133736e+00</td>\n",
       "      <td>-4.692822e-01</td>\n",
       "      <td>-4.929631e-01</td>\n",
       "      <td>-1.075577e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-1.965158e-01</td>\n",
       "      <td>7.989398e-01</td>\n",
       "      <td>-1.124531e-01</td>\n",
       "      <td>-4.822372e-01</td>\n",
       "      <td>-3.777715e-01</td>\n",
       "      <td>-3.639056e-01</td>\n",
       "      <td>-3.298725e-01</td>\n",
       "      <td>-2.992312e-01</td>\n",
       "      <td>-3.099583e-01</td>\n",
       "      <td>-4.110548e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.327697e-01</td>\n",
       "      <td>-4.900767e-01</td>\n",
       "      <td>9.037253e-01</td>\n",
       "      <td>-3.298957e-01</td>\n",
       "      <td>-4.471068e-01</td>\n",
       "      <td>-4.824128e-01</td>\n",
       "      <td>8.820397e-01</td>\n",
       "      <td>-4.692822e-01</td>\n",
       "      <td>-4.929631e-01</td>\n",
       "      <td>9.297331e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>4.842405e-01</td>\n",
       "      <td>7.989398e-01</td>\n",
       "      <td>6.860798e-01</td>\n",
       "      <td>-4.822372e-01</td>\n",
       "      <td>-3.777715e-01</td>\n",
       "      <td>-3.639056e-01</td>\n",
       "      <td>-3.298725e-01</td>\n",
       "      <td>-2.992312e-01</td>\n",
       "      <td>-3.099583e-01</td>\n",
       "      <td>3.571947e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.327697e-01</td>\n",
       "      <td>-4.900767e-01</td>\n",
       "      <td>9.037253e-01</td>\n",
       "      <td>-3.298957e-01</td>\n",
       "      <td>-4.471068e-01</td>\n",
       "      <td>-4.824128e-01</td>\n",
       "      <td>8.820397e-01</td>\n",
       "      <td>-4.692822e-01</td>\n",
       "      <td>-4.929631e-01</td>\n",
       "      <td>9.297331e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2.951982e+00</td>\n",
       "      <td>7.989398e-01</td>\n",
       "      <td>2.739450e+00</td>\n",
       "      <td>2.755009e+00</td>\n",
       "      <td>2.651332e+00</td>\n",
       "      <td>2.748296e+00</td>\n",
       "      <td>3.031900e+00</td>\n",
       "      <td>3.341898e+00</td>\n",
       "      <td>3.226240e+00</td>\n",
       "      <td>4.169400e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.310698e+00</td>\n",
       "      <td>2.040497e+00</td>\n",
       "      <td>9.037253e-01</td>\n",
       "      <td>3.031261e+00</td>\n",
       "      <td>2.236602e+00</td>\n",
       "      <td>2.072913e+00</td>\n",
       "      <td>8.820397e-01</td>\n",
       "      <td>2.130914e+00</td>\n",
       "      <td>2.028549e+00</td>\n",
       "      <td>9.297331e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          LIMIT_BAL           SEX           AGE         PAY_0         PAY_2  \\\n",
       "count  2.010200e+04  2.010200e+04  2.010200e+04  2.010200e+04  2.010200e+04   \n",
       "mean   1.144521e-16 -1.269615e-16 -3.593009e-16 -1.799432e-16 -9.886077e-19   \n",
       "std    1.000025e+00  1.000025e+00  1.000025e+00  1.000025e+00  1.000025e+00   \n",
       "min   -1.217650e+00 -1.251659e+00 -1.595443e+00 -4.822372e-01 -3.777715e-01   \n",
       "25%   -8.772721e-01 -1.251659e+00 -7.969099e-01 -4.822372e-01 -3.777715e-01   \n",
       "50%   -1.965158e-01  7.989398e-01 -1.124531e-01 -4.822372e-01 -3.777715e-01   \n",
       "75%    4.842405e-01  7.989398e-01  6.860798e-01 -4.822372e-01 -3.777715e-01   \n",
       "max    2.951982e+00  7.989398e-01  2.739450e+00  2.755009e+00  2.651332e+00   \n",
       "\n",
       "              PAY_3         PAY_4         PAY_5         PAY_6     BILL_AMT1  \\\n",
       "count  2.010200e+04  2.010200e+04  2.010200e+04  2.010200e+04  2.010200e+04   \n",
       "mean  -1.838236e-15  1.594746e-15  1.162255e-15 -2.980625e-16  3.461784e-17   \n",
       "std    1.000025e+00  1.000025e+00  1.000025e+00  1.000025e+00  1.000025e+00   \n",
       "min   -3.639056e-01 -3.298725e-01 -2.992312e-01 -3.099583e-01 -1.054810e+00   \n",
       "25%   -3.639056e-01 -3.298725e-01 -2.992312e-01 -3.099583e-01 -7.543990e-01   \n",
       "50%   -3.639056e-01 -3.298725e-01 -2.992312e-01 -3.099583e-01 -4.110548e-01   \n",
       "75%   -3.639056e-01 -3.298725e-01 -2.992312e-01 -3.099583e-01  3.571947e-01   \n",
       "max    2.748296e+00  3.031900e+00  3.341898e+00  3.226240e+00  4.169400e+00   \n",
       "\n",
       "       ...         x5_-2         x5_-1          x5_0          x5_1  \\\n",
       "count  ...  2.010200e+04  2.010200e+04  2.010200e+04  2.010200e+04   \n",
       "mean   ... -5.311419e-16 -1.118546e-15  5.865813e-16 -3.025637e-16   \n",
       "std    ...  1.000025e+00  1.000025e+00  1.000025e+00  1.000025e+00   \n",
       "min    ... -4.327697e-01 -4.900767e-01 -1.106531e+00 -3.298957e-01   \n",
       "25%    ... -4.327697e-01 -4.900767e-01 -1.106531e+00 -3.298957e-01   \n",
       "50%    ... -4.327697e-01 -4.900767e-01  9.037253e-01 -3.298957e-01   \n",
       "75%    ... -4.327697e-01 -4.900767e-01  9.037253e-01 -3.298957e-01   \n",
       "max    ...  2.310698e+00  2.040497e+00  9.037253e-01  3.031261e+00   \n",
       "\n",
       "              x6_-2         x6_-1          x6_0         x7_-2         x7_-1  \\\n",
       "count  2.010200e+04  2.010200e+04  2.010200e+04  2.010200e+04  2.010200e+04   \n",
       "mean   2.000798e-16  7.437699e-16 -3.886719e-16 -3.167853e-16 -9.253313e-16   \n",
       "std    1.000025e+00  1.000025e+00  1.000025e+00  1.000025e+00  1.000025e+00   \n",
       "min   -4.471068e-01 -4.824128e-01 -1.133736e+00 -4.692822e-01 -4.929631e-01   \n",
       "25%   -4.471068e-01 -4.824128e-01 -1.133736e+00 -4.692822e-01 -4.929631e-01   \n",
       "50%   -4.471068e-01 -4.824128e-01  8.820397e-01 -4.692822e-01 -4.929631e-01   \n",
       "75%   -4.471068e-01 -4.824128e-01  8.820397e-01 -4.692822e-01 -4.929631e-01   \n",
       "max    2.236602e+00  2.072913e+00  8.820397e-01  2.130914e+00  2.028549e+00   \n",
       "\n",
       "               x7_0  \n",
       "count  2.010200e+04  \n",
       "mean   1.004077e-15  \n",
       "std    1.000025e+00  \n",
       "min   -1.075577e+00  \n",
       "25%   -1.075577e+00  \n",
       "50%    9.297331e-01  \n",
       "75%    9.297331e-01  \n",
       "max    9.297331e-01  \n",
       "\n",
       "[8 rows x 51 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if numerical attributes are normailised with mean = 0 and sd = 1\n",
    "X_train_standardize.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perfrom initial PCA for 2 components for visualization\n",
    "giving 2 pca components, we will visualize the information retained after performing 2 pca components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#perform pca\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "principalComponents =  pca.fit_transform(X_train_standardize)\n",
    "principalDf = pd.DataFrame(data = principalComponents\n",
    "             , columns = ['principal component 1', 'principal component 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variation per principal component: [0.20419394 0.12301281]\n"
     ]
    }
   ],
   "source": [
    "#amount of information each principal component holds after projecting the data to a lower dimensional subspace.\n",
    "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that the information of principal component 1 retained is 28.4% and principal component 2 retained is 17.8%, both of which is quite low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing pca\n",
    "pca_visualize_df = pd.concat([principalDf, y_train], axis = 1)\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "targets = [1,0]\n",
    "colors = ['r', 'g']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = pca_visualize_df['Y'] == target\n",
    "    ax.scatter(pca_visualize_df.loc[indicesToKeep, 'principal component 1']\n",
    "               , pca_visualize_df.loc[indicesToKeep, 'principal component 2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there is no clear linear separation for the target attributes for 2 pca components, justifying the above percentages. Nonetherless, we will continue to use PCA by finding the  optimmum number of PC components which retains 90% of information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform PCA to retain 90% of information\n",
    "perform PCA to reduce components so we can run SVM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting pca threshold to 90%\n",
    "pca = PCA(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=0.9, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(X_train_standardize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of components before pca: 51\n",
      "No. of components after pca: 22\n"
     ]
    }
   ],
   "source": [
    "#get number of components after pca\n",
    "print('No. of components before pca: {}'.format(len(X_train.columns)))\n",
    "print('No. of components after pca: {}'.format(pca.n_components_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the number of components is reduced from 23 components to 13 components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#perform pca on training and test attributes\n",
    "X_train_pca = pca.transform(X_train_standardize)\n",
    "X_test_pca = pca.transform(X_test_standardize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply SVM model\n",
    "Next, we will used the reduced attributes train set to train our SVM model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we train our SVM model without parameter tuning\n",
    "nor pca reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Chew/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "#train svm model without standardization and no parameter tuning\n",
    "clf_original = svm.SVC(kernel = 'rbf', probability = True)\n",
    "clf_original.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot roc for svm\n",
    "get_roc(clf_original, y_test, X_test, \"SVM original data RBF kernal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#confusion matrix\n",
    "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evidently, SVM model fit with no tuning or feature reduction with RBF kernal shows low performance. Now, we will fit SVM model with reduced standardized features and access accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "note that the default values of gamma = 1/13 and c= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train svm model with feature reduction and no parameter tuning\n",
    "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n",
    "clf_reduced.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot roc for svm\n",
    "get_roc(clf_reduced, y_test, X_test_pca, \n",
    "        \"SVM reduced features no tuning RBF kernal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#confusion matrix\n",
    "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, by reducing features through pca, the AUROC curve improves, suggesting a better prediction model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now try to find best parameters for SVM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "def svc_param_selection(X, y, nfolds):\n",
    "    Cs = [0.001, 0.01, 0.1, 1, 10]\n",
    "    gammas = [0.001, 0.01, 0.1, 1,10]\n",
    "    param_grid = {'C': Cs, 'gamma' : gammas}\n",
    "    grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n",
    "    grid_search.fit(X, y)\n",
    "    grid_search.best_params_\n",
    "    return grid_search.best_params_\n",
    "svc_param_selection(X_train_pca, y_train,10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train svm model with feature reduction and cost = 10, gamma = 0.01\n",
    "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.01)\n",
    "clf_reduced_tuned.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_roc(clf_reduced_tuned, y_test, X_test_pca, \n",
    "        \"SVM reduced features and tuning RBF kernal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#confusion matrix\n",
    "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Networks\n",
    "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The neural network may have difficulty converging before the maximum number \n",
    "# of iterations allowed is reached if the data is not normalized. Additionaly, \n",
    "# Multi-layer Perceptron is sensitive to feature scaling, so we will use the built-in \n",
    "# StandardScaler for standardization. Other than the training set, the test set values are scaled as well.\n",
    "\n",
    "# from sklearn.preprocessing import StandardScaler I SHIFTED THIS ON TOP\n",
    "# scaler = StandardScaler()\n",
    "# scaler.fit(X_train)\n",
    "# X_train = scaler.transform(X_train)\n",
    "# X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create an instance of our model, with 5 layers of 23 neurons each, identical to that of our training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(23,23,23,23,23))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Chew/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "              hidden_layer_sizes=(23, 23, 23, 23, 23), learning_rate='constant',\n",
       "              learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "              n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "              random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
       "              validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = mlp.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here, we produce the confusion matrix and tabulate the accuracy and reliability of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3572  327]\n",
      " [ 741  386]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.92      0.87      3899\n",
      "           1       0.54      0.34      0.42      1127\n",
      "\n",
      "    accuracy                           0.79      5026\n",
      "   macro avg       0.68      0.63      0.64      5026\n",
      "weighted avg       0.76      0.79      0.77      5026\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this table the results look decent , but the model can still be improved upon. Hence, we will \n",
    "double the number of hidden layers to see if there is an improvement in performance.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3560  339]\n",
      " [ 750  377]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.91      0.87      3899\n",
      "           1       0.53      0.33      0.41      1127\n",
      "\n",
      "    accuracy                           0.78      5026\n",
      "   macro avg       0.68      0.62      0.64      5026\n",
      "weighted avg       0.76      0.78      0.76      5026\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Chew/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(23,23,23,23,23,23,23,23,23,23))\n",
    "mlp.fit(X_train,y_train)\n",
    "predictions = mlp.predict(X_test)\n",
    "print(confusion_matrix(y_test,predictions))\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The avergae accuracy of the model can be said to be similiar to that of the previous one, and is even performing slightly worse, hence we might be overfitting the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3720  179]\n",
      " [ 789  338]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.95      0.88      3899\n",
      "           1       0.65      0.30      0.41      1127\n",
      "\n",
      "    accuracy                           0.81      5026\n",
      "   macro avg       0.74      0.63      0.65      5026\n",
      "weighted avg       0.79      0.81      0.78      5026\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(23,23,23,23,23))\n",
    "mlp.fit(X_train,y_train)\n",
    "predictions = mlp.predict(X_test)\n",
    "print(confusion_matrix(y_test,predictions))\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "We try to use Keras instead to improve our deep learning performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "25128/25128 [==============================] - 4s 158us/step - loss: 64.3604 - accuracy: 0.6887 1s - loss: 82.7019 \n",
      "Epoch 2/150\n",
      "25128/25128 [==============================] - 3s 136us/step - loss: 6.8673 - accuracy: 0.6882\n",
      "Epoch 3/150\n",
      "25128/25128 [==============================] - 3s 136us/step - loss: 3.0394 - accuracy: 0.6851\n",
      "Epoch 4/150\n",
      "25128/25128 [==============================] - 4s 141us/step - loss: 1.0035 - accuracy: 0.7036\n",
      "Epoch 5/150\n",
      "25128/25128 [==============================] - 3s 134us/step - loss: 0.5581 - accuracy: 0.7851\n",
      "Epoch 6/150\n",
      "25128/25128 [==============================] - 4s 142us/step - loss: 0.5963 - accuracy: 0.7865\n",
      "Epoch 7/150\n",
      "25128/25128 [==============================] - 3s 132us/step - loss: 0.5663 - accuracy: 0.78740s - loss: 0.5696 - accuracy\n",
      "Epoch 8/150\n",
      "25128/25128 [==============================] - 4s 145us/step - loss: 0.5204 - accuracy: 0.7875\n",
      "Epoch 9/150\n",
      "25128/25128 [==============================] - 4s 160us/step - loss: 0.5499 - accuracy: 0.7876\n",
      "Epoch 10/150\n",
      "25128/25128 [==============================] - 3s 134us/step - loss: 0.5172 - accuracy: 0.7876\n",
      "Epoch 11/150\n",
      "25128/25128 [==============================] - 4s 146us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 12/150\n",
      "25128/25128 [==============================] - 3s 132us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 13/150\n",
      "25128/25128 [==============================] - 3s 135us/step - loss: 0.5182 - accuracy: 0.7876\n",
      "Epoch 14/150\n",
      "25128/25128 [==============================] - 3s 133us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 15/150\n",
      "25128/25128 [==============================] - 3s 132us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 16/150\n",
      "25128/25128 [==============================] - 3s 132us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 17/150\n",
      "25128/25128 [==============================] - 3s 134us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 18/150\n",
      "25128/25128 [==============================] - 3s 133us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 19/150\n",
      "25128/25128 [==============================] - 3s 129us/step - loss: 0.5265 - accuracy: 0.7876\n",
      "Epoch 20/150\n",
      "25128/25128 [==============================] - 4s 146us/step - loss: 0.5183 - accuracy: 0.7876\n",
      "Epoch 21/150\n",
      "25128/25128 [==============================] - 7s 292us/step - loss: 0.5184 - accuracy: 0.7876\n",
      "Epoch 22/150\n",
      "25128/25128 [==============================] - 6s 250us/step - loss: 0.5256 - accuracy: 0.7876\n",
      "Epoch 23/150\n",
      "25128/25128 [==============================] - 7s 289us/step - loss: 0.5174 - accuracy: 0.7876\n",
      "Epoch 24/150\n",
      "25128/25128 [==============================] - 6s 222us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 25/150\n",
      "25128/25128 [==============================] - 4s 177us/step - loss: 0.5171 - accuracy: 0.78760s - loss: 0.5168 - accura\n",
      "Epoch 26/150\n",
      "25128/25128 [==============================] - 5s 194us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 27/150\n",
      "25128/25128 [==============================] - 5s 195us/step - loss: 0.5170 - accuracy: 0.78760s - loss: 0.517\n",
      "Epoch 28/150\n",
      "25128/25128 [==============================] - 4s 150us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 29/150\n",
      "25128/25128 [==============================] - 4s 171us/step - loss: 0.5207 - accuracy: 0.7876\n",
      "Epoch 30/150\n",
      "25128/25128 [==============================] - 4s 154us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 31/150\n",
      "25128/25128 [==============================] - 4s 171us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 32/150\n",
      "25128/25128 [==============================] - 4s 176us/step - loss: 0.5179 - accuracy: 0.78760s - loss: 0.5\n",
      "Epoch 33/150\n",
      "25128/25128 [==============================] - 5s 214us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 34/150\n",
      "25128/25128 [==============================] - 4s 171us/step - loss: 0.5171 - accuracy: 0.78760s - loss: 0.5170 - accuracy: \n",
      "Epoch 35/150\n",
      "25128/25128 [==============================] - 4s 175us/step - loss: 0.5171 - accuracy: 0.78760s - loss: 0\n",
      "Epoch 36/150\n",
      "25128/25128 [==============================] - 4s 161us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 37/150\n",
      "25128/25128 [==============================] - 4s 159us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 38/150\n",
      "25128/25128 [==============================] - 4s 158us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 39/150\n",
      "25128/25128 [==============================] - 4s 154us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 40/150\n",
      "25128/25128 [==============================] - 3s 139us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 41/150\n",
      "25128/25128 [==============================] - 3s 130us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 42/150\n",
      "25128/25128 [==============================] - 3s 130us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 43/150\n",
      "25128/25128 [==============================] - 3s 131us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 44/150\n",
      "25128/25128 [==============================] - 4s 159us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 45/150\n",
      "25128/25128 [==============================] - 3s 135us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 46/150\n",
      "25128/25128 [==============================] - 3s 132us/step - loss: 0.5171 - accuracy: 0.7876TA: 0s - loss: 0.5159 - accuracy: 0. - ETA: 0s - los\n",
      "Epoch 47/150\n",
      "25128/25128 [==============================] - 3s 117us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 48/150\n",
      "25128/25128 [==============================] - 3s 134us/step - loss: 0.5171 - accuracy: 0.7876TA\n",
      "Epoch 49/150\n",
      "25128/25128 [==============================] - 3s 139us/step - loss: 0.5171 - accuracy: 0.78760s - los\n",
      "Epoch 50/150\n",
      "25128/25128 [==============================] - 3s 116us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 51/150\n",
      "25128/25128 [==============================] - 3s 128us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 52/150\n",
      "25128/25128 [==============================] - 3s 121us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 53/150\n",
      "25128/25128 [==============================] - 3s 128us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 54/150\n",
      "25128/25128 [==============================] - 3s 113us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 55/150\n",
      "25128/25128 [==============================] - 3s 116us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 56/150\n",
      "25128/25128 [==============================] - 3s 125us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 57/150\n",
      "25128/25128 [==============================] - 10s 381us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 58/150\n",
      "25128/25128 [==============================] - 6s 219us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 59/150\n",
      "25128/25128 [==============================] - 4s 176us/step - loss: 0.5171 - accuracy: 0.78760s - l\n",
      "Epoch 60/150\n",
      "25128/25128 [==============================] - 5s 180us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 61/150\n",
      "25128/25128 [==============================] - 4s 159us/step - loss: 0.5170 - accuracy: 0.78760s -\n",
      "Epoch 62/150\n",
      "25128/25128 [==============================] - 5s 193us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 63/150\n",
      "25128/25128 [==============================] - 5s 199us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 64/150\n",
      "25128/25128 [==============================] - 5s 189us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 65/150\n",
      "25128/25128 [==============================] - 6s 221us/step - loss: 0.5171 - accuracy: 0.78760s - loss: 0.5\n",
      "Epoch 66/150\n",
      "25128/25128 [==============================] - 5s 190us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 67/150\n",
      "25128/25128 [==============================] - 6s 224us/step - loss: 0.5171 - accuracy: 0.78760s - loss: 0\n",
      "Epoch 68/150\n",
      "25128/25128 [==============================] - 4s 172us/step - loss: 0.5171 - accuracy: 0.78760s - loss: 0.5157 - \n",
      "Epoch 69/150\n",
      "25128/25128 [==============================] - 4s 178us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 70/150\n",
      "25128/25128 [==============================] - 5s 180us/step - loss: 0.5171 - accuracy: 0.78761s - loss: - ETA: 0s - loss: 0\n",
      "Epoch 71/150\n",
      "25128/25128 [==============================] - 6s 238us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 72/150\n",
      "25128/25128 [==============================] - 4s 173us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 73/150\n",
      "25128/25128 [==============================] - 5s 186us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 74/150\n",
      "25128/25128 [==============================] - 4s 178us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 75/150\n",
      "25128/25128 [==============================] - 6s 255us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 76/150\n",
      "25128/25128 [==============================] - 5s 198us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 77/150\n",
      "25128/25128 [==============================] - 5s 210us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 78/150\n",
      "25128/25128 [==============================] - 4s 176us/step - loss: 0.5171 - accuracy: 0.78760s - loss: 0.5167 \n",
      "Epoch 79/150\n",
      "25128/25128 [==============================] - 5s 205us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 80/150\n",
      "25128/25128 [==============================] - 5s 189us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 81/150\n",
      "25128/25128 [==============================] - ETA: 0s - loss: 0.5168 - accuracy: 0.78 - 5s 205us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 82/150\n",
      "25128/25128 [==============================] - 5s 214us/step - loss: 0.5171 - accuracy: 0.78760s - loss: 0.5164 - accuracy: \n",
      "Epoch 83/150\n",
      "25128/25128 [==============================] - 6s 228us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 84/150\n",
      "25128/25128 [==============================] - 5s 188us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 85/150\n",
      "25128/25128 [==============================] - 4s 175us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 86/150\n",
      "25128/25128 [==============================] - 5s 206us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 87/150\n",
      "25128/25128 [==============================] - 6s 242us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 88/150\n",
      "25128/25128 [==============================] - 10s 401us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 89/150\n",
      "25128/25128 [==============================] - 9s 357us/step - loss: 0.5171 - accuracy: 0.78760s - loss: 0.5170 - accuracy:  - ETA: 0s - loss: 0.5168 - accuracy: \n",
      "Epoch 90/150\n",
      "25128/25128 [==============================] - ETA: 0s - loss: 0.5169 - accuracy: 0.78 - 5s 203us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 91/150\n",
      "25128/25128 [==============================] - 4s 175us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 92/150\n",
      "25128/25128 [==============================] - 6s 219us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 93/150\n",
      "25128/25128 [==============================] - 5s 219us/step - loss: 0.5171 - accuracy: 0.78761s - loss: 0\n",
      "Epoch 94/150\n",
      "25128/25128 [==============================] - 6s 228us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 95/150\n",
      "25128/25128 [==============================] - 6s 236us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 96/150\n",
      "25128/25128 [==============================] - 5s 217us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 97/150\n",
      "25128/25128 [==============================] - 6s 229us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 98/150\n",
      "25128/25128 [==============================] - 5s 209us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 99/150\n",
      "25128/25128 [==============================] - 4s 178us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 100/150\n",
      "25128/25128 [==============================] - 5s 192us/step - loss: 0.5178 - accuracy: 0.7876\n",
      "Epoch 101/150\n",
      "25128/25128 [==============================] - 4s 171us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 102/150\n",
      "25128/25128 [==============================] - 4s 163us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 103/150\n",
      "25128/25128 [==============================] - 4s 153us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 104/150\n",
      "25128/25128 [==============================] - 4s 148us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 105/150\n",
      "25128/25128 [==============================] - 3s 135us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 106/150\n",
      "25128/25128 [==============================] - 3s 135us/step - loss: 0.5197 - accuracy: 0.7876\n",
      "Epoch 107/150\n",
      "25128/25128 [==============================] - 3s 136us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 108/150\n",
      "25128/25128 [==============================] - 4s 147us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 109/150\n",
      "25128/25128 [==============================] - 4s 140us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 110/150\n",
      "25128/25128 [==============================] - 4s 155us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 111/150\n",
      "25128/25128 [==============================] - 5s 202us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 112/150\n",
      "25128/25128 [==============================] - 4s 149us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 113/150\n",
      "25128/25128 [==============================] - 4s 143us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 114/150\n",
      "25128/25128 [==============================] - 4s 148us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 115/150\n",
      "25128/25128 [==============================] - 4s 143us/step - loss: 0.5171 - accuracy: 0.78761s - loss: 0\n",
      "Epoch 116/150\n",
      "25128/25128 [==============================] - 3s 137us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 117/150\n",
      "25128/25128 [==============================] - 4s 149us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 118/150\n",
      "25128/25128 [==============================] - 3s 135us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 119/150\n",
      "25128/25128 [==============================] - 4s 140us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 120/150\n",
      "25128/25128 [==============================] - 3s 137us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 121/150\n",
      "25128/25128 [==============================] - 3s 136us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 122/150\n",
      "25128/25128 [==============================] - 3s 134us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 123/150\n",
      "25128/25128 [==============================] - 3s 135us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 124/150\n",
      "25128/25128 [==============================] - 4s 155us/step - loss: 0.5170 - accuracy: 0.78760s - loss: 0.5183 - ac\n",
      "Epoch 125/150\n",
      "25128/25128 [==============================] - 4s 145us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 126/150\n",
      "25128/25128 [==============================] - 4s 142us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 127/150\n",
      "25128/25128 [==============================] - 4s 154us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 128/150\n",
      "25128/25128 [==============================] - 3s 137us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 129/150\n",
      "25128/25128 [==============================] - 3s 135us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 130/150\n",
      "25128/25128 [==============================] - 3s 134us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 131/150\n",
      "25128/25128 [==============================] - 3s 133us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 132/150\n",
      "25128/25128 [==============================] - 3s 134us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 133/150\n",
      "25128/25128 [==============================] - 3s 133us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 134/150\n",
      "25128/25128 [==============================] - 3s 131us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 135/150\n",
      "25128/25128 [==============================] - 3s 131us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 136/150\n",
      "25128/25128 [==============================] - 3s 134us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 137/150\n",
      "25128/25128 [==============================] - 3s 133us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 138/150\n",
      "25128/25128 [==============================] - 3s 132us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 139/150\n",
      "25128/25128 [==============================] - 3s 132us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 140/150\n",
      "25128/25128 [==============================] - 3s 135us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 141/150\n",
      "25128/25128 [==============================] - 3s 138us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 142/150\n",
      "25128/25128 [==============================] - 3s 138us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 143/150\n",
      "25128/25128 [==============================] - 4s 144us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 144/150\n",
      "25128/25128 [==============================] - 4s 146us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 145/150\n",
      "25128/25128 [==============================] - 4s 165us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 146/150\n",
      "25128/25128 [==============================] - 4s 157us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 147/150\n",
      "25128/25128 [==============================] - 3s 134us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 148/150\n",
      "25128/25128 [==============================] - 4s 141us/step - loss: 0.5171 - accuracy: 0.7876\n",
      "Epoch 149/150\n",
      "25128/25128 [==============================] - 3s 132us/step - loss: 0.5170 - accuracy: 0.7876\n",
      "Epoch 150/150\n",
      "25128/25128 [==============================] - 3s 128us/step - loss: 0.5171 - accuracy: 0.7876\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-108-74f44494e9cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# evaluate the keras model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Accuracy: %.2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "#!pip3 install keras\n",
    "from numpy import loadtxt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "# define the keras model\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=52, activation='relu'))\n",
    "model.add(Dense(8, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "# compile the keras model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "# fit the keras model on the dataset\n",
    "model.fit(df1, target, epochs=150, batch_size=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25128/25128 [==============================] - 0s 18us/step\n",
      "Accuracy: 78.76\n",
      "Recall: 51.70\n"
     ]
    }
   ],
   "source": [
    "# evaluate the keras model\n",
    "recall, accuracy = model.evaluate(df1, target)\n",
    "print('Accuracy: %.2f' % (accuracy*100))\n",
    "print('Recall: %.2f' % (recall*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the Keras model outperforms the scikit-learn model, we will opt for the keras model with 12 hidden layers. "
   ]
  }
 ],
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