Credit_Risk_Analysis.ipynb

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        "<hr/>\n",
        "\n",
        "# Credit Risk Analysis for Loan Investments - (Keras - Artificial Neural Network)\n",
        "\n",
        "<hr/>\n",
        "\n",
        "### Table of Contents\n",
        "* **1. [Introduction](#ch1)**\n",
        "* **2. [Dataset](#ch2)**\n",
        "* **3. [Exploratory Data Analysis](#ch3)**\n",
        "* **4. [Data PreProcessing](#ch4)**\n",
        "* **5. [Categorical Variables and Dummy Variables](#ch5)**\n",
        "* **6. [Scaling and Train Test Split](#ch6)**\n",
        "* **7. [Creating a Model](#ch7)**\n",
        "* **8. [Training the Model](#ch8)**\n",
        "* **9. [Evaluation on Test Data](#ch9)**\n",
        "* **10. [Predicting on a New Customer](#ch10)**\n",
        "* **11. [References](#ch11)**"
      ]
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        "<a id=\"ch1\"></a>\n",
        "# 1. Introduction \n",
        "---\n",
        "\n",
        "One of the objectives of this notebook is to **show step-by-step how to visualize the dataset and assess whether or not a new customer is likely to pay back the loan.**\n",
        "\n",
        "LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California. It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission, and to offer loan trading on a secondary market. LendingClub is the world's largest peer-to-peer lending platform.\n",
        "\n",
        "Given historical data on loans given out with information on whether or not the borrower defaulted (charge-off), we can build a model that can predict if a borrower will pay back their loan. This way in the future when we get a new potential customer, **we can assess if they are likely to pay back the loan.**\n",
        "\n",
        "The following questions will be answered throughout the Kernel:\n",
        "* ***Which features are available in the dataset?***\n",
        "* ***What is the distribution of numerical feature values across the samples?***\n",
        "* ***What is the length of the dataframe?***\n",
        "* ***What is the total count of missing values per column?***\n",
        "* ***How many unique employment job titles are there?***\n",
        "* ***Do you wonder how lending companies choose whether to give you money or not?***\n",
        "* ***How does a lending company decide how much money to give you?***\n",
        "* ***Would you offer this person a loan?***\n",
        "* ***Did this person actually end up paying back their loan?***\n",
        "\n"
      ]
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        "<a id=\"ch2\"></a>\n",
        "# 2. Dataset\n",
        "---\n",
        "\n",
        "* We will be using a subset of the LendingClub DataSet obtained from Kaggle: https://www.kaggle.com/wordsforthewise/lending-club\n",
        "\n",
        "There are many LendingClub data sets on Kaggle. Here is the information on this particular data set:\n",
        "\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>LoanStatNew</th>\n",
        "      <th>Description</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>loan_amnt</td>\n",
        "      <td>The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>term</td>\n",
        "      <td>The number of payments on the loan. Values are in months and can be either 36 or 60.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>int_rate</td>\n",
        "      <td>Interest Rate on the loan</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>installment</td>\n",
        "      <td>The monthly payment owed by the borrower if the loan originates.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>grade</td>\n",
        "      <td>LC assigned loan grade</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>sub_grade</td>\n",
        "      <td>LC assigned loan subgrade</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>emp_title</td>\n",
        "      <td>The job title supplied by the Borrower when applying for the loan.*</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>emp_length</td>\n",
        "      <td>Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>home_ownership</td>\n",
        "      <td>The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>annual_inc</td>\n",
        "      <td>The self-reported annual income provided by the borrower during registration.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>verification_status</td>\n",
        "      <td>Indicates if income was verified by LC, not verified, or if the income source was verified</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>issue_d</td>\n",
        "      <td>The month which the loan was funded</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>loan_status</td>\n",
        "      <td>Current status of the loan</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>purpose</td>\n",
        "      <td>A category provided by the borrower for the loan request.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>title</td>\n",
        "      <td>The loan title provided by the borrower</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>zip_code</td>\n",
        "      <td>The first 3 numbers of the zip code provided by the borrower in the loan application.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>addr_state</td>\n",
        "      <td>The state provided by the borrower in the loan application</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>dti</td>\n",
        "      <td>A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>earliest_cr_line</td>\n",
        "      <td>The month the borrower's earliest reported credit line was opened</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>open_acc</td>\n",
        "      <td>The number of open credit lines in the borrower's credit file.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>pub_rec</td>\n",
        "      <td>Number of derogatory public records</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>revol_bal</td>\n",
        "      <td>Total credit revolving balance</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>revol_util</td>\n",
        "      <td>Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>total_acc</td>\n",
        "      <td>The total number of credit lines currently in the borrower's credit file</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>initial_list_status</td>\n",
        "      <td>The initial listing status of the loan. Possible values are – W, F</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>application_type</td>\n",
        "      <td>Indicates whether the loan is an individual application or a joint application with two co-borrowers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>mort_acc</td>\n",
        "      <td>Number of mortgage accounts.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>pub_rec_bankruptcies</td>\n",
        "      <td>Number of public record bankruptcies</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "\n",
        "---"
      ]
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      "source": [
        "## Imports"
      ]
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      "source": [
        "# data analysis and wrangling\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random as rnd\n",
        "\n",
        "# visualization\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# scaling and train test split\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# creating a model\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation\n",
        "from tensorflow.keras.constraints import max_norm\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "# evaluation on test data\n",
        "from sklearn.metrics import classification_report,confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "Before starting, let us make a function to get feature information on the data as a .csv file for easy lookup throughout the notebook. "
      ]
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        "outputId": "c3b7ea07-66a2-48cd-c64d-bacf0044b3d6"
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      "source": [
        "data_info = pd.read_csv('../input/subset-lending-club-loan/lending_club_info.csv',index_col='LoanStatNew')\n",
        "\n",
        "def feat_info(col_name):\n",
        "    print(data_info.loc[col_name]['Description'])\n",
        "\n",
        "# example\n",
        "feat_info('mort_acc')"
      ],
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      "source": [
        "## Loading the data"
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      "source": [
        "df = pd.read_csv('../input/subset-lending-club-loan/lending_club_loan_two.csv')"
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      "source": [
        "**Which features are available in the dataset?**"
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          "start_time": "2021-02-16T21:32:57.200390",
          "status": "completed"
        },
        "tags": [],
        "id": "hRh2S2gQINal",
        "outputId": "6071e0aa-d76b-4e86-d2bd-31958f7b07e8"
      },
      "source": [
        "print(df.info())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 396030 entries, 0 to 396029\n",
            "Data columns (total 27 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   loan_amnt             396030 non-null  float64\n",
            " 1   term                  396030 non-null  object \n",
            " 2   int_rate              396030 non-null  float64\n",
            " 3   installment           396030 non-null  float64\n",
            " 4   grade                 396030 non-null  object \n",
            " 5   sub_grade             396030 non-null  object \n",
            " 6   emp_title             373103 non-null  object \n",
            " 7   emp_length            377729 non-null  object \n",
            " 8   home_ownership        396030 non-null  object \n",
            " 9   annual_inc            396030 non-null  float64\n",
            " 10  verification_status   396030 non-null  object \n",
            " 11  issue_d               396030 non-null  object \n",
            " 12  loan_status           396030 non-null  object \n",
            " 13  purpose               396030 non-null  object \n",
            " 14  title                 394275 non-null  object \n",
            " 15  dti                   396030 non-null  float64\n",
            " 16  earliest_cr_line      396030 non-null  object \n",
            " 17  open_acc              396030 non-null  float64\n",
            " 18  pub_rec               396030 non-null  float64\n",
            " 19  revol_bal             396030 non-null  float64\n",
            " 20  revol_util            395754 non-null  float64\n",
            " 21  total_acc             396030 non-null  float64\n",
            " 22  initial_list_status   396030 non-null  object \n",
            " 23  application_type      396030 non-null  object \n",
            " 24  mort_acc              358235 non-null  float64\n",
            " 25  pub_rec_bankruptcies  395495 non-null  float64\n",
            " 26  address               396030 non-null  object \n",
            "dtypes: float64(12), object(15)\n",
            "memory usage: 81.6+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "RpeZVWQsINal"
      },
      "source": [
        "**Preview the data**"
      ]
    },
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      "cell_type": "code",
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        "_kg_hide-input": true,
        "execution": {
          "iopub.execute_input": "2021-02-16T21:32:58.135056Z",
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          "status": "completed"
        },
        "tags": [],
        "id": "VLOfknININal",
        "outputId": "6d93d777-a285-4fc5-a0cf-4fb4cb1e9af5"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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>loan_amnt</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>...</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>application_type</th>\n",
              "      <th>mort_acc</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>address</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.44</td>\n",
              "      <td>329.48</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>117000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36369.0</td>\n",
              "      <td>41.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>w</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0174 Michelle Gateway\\nMendozaberg, OK 22690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.99</td>\n",
              "      <td>265.68</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>Credit analyst</td>\n",
              "      <td>4 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20131.0</td>\n",
              "      <td>53.3</td>\n",
              "      <td>27.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1076 Carney Fort Apt. 347\\nLoganmouth, SD 05113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15600.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.49</td>\n",
              "      <td>506.97</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>Statistician</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>43057.0</td>\n",
              "      <td>...</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11987.0</td>\n",
              "      <td>92.2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>87025 Mark Dale Apt. 269\\nNew Sabrina, WV 05113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7200.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>6.49</td>\n",
              "      <td>220.65</td>\n",
              "      <td>A</td>\n",
              "      <td>A2</td>\n",
              "      <td>Client Advocate</td>\n",
              "      <td>6 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5472.0</td>\n",
              "      <td>21.5</td>\n",
              "      <td>13.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>823 Reid Ford\\nDelacruzside, MA 00813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24375.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>17.27</td>\n",
              "      <td>609.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>Destiny Management Inc.</td>\n",
              "      <td>9 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24584.0</td>\n",
              "      <td>69.8</td>\n",
              "      <td>43.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>679 Luna Roads\\nGreggshire, VA 11650</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_amnt        term  int_rate  installment grade sub_grade  \\\n",
              "0    10000.0   36 months     11.44       329.48     B        B4   \n",
              "1     8000.0   36 months     11.99       265.68     B        B5   \n",
              "2    15600.0   36 months     10.49       506.97     B        B3   \n",
              "3     7200.0   36 months      6.49       220.65     A        A2   \n",
              "4    24375.0   60 months     17.27       609.33     C        C5   \n",
              "\n",
              "                 emp_title emp_length home_ownership  annual_inc  ...  \\\n",
              "0                Marketing  10+ years           RENT    117000.0  ...   \n",
              "1          Credit analyst     4 years       MORTGAGE     65000.0  ...   \n",
              "2             Statistician   < 1 year           RENT     43057.0  ...   \n",
              "3          Client Advocate    6 years           RENT     54000.0  ...   \n",
              "4  Destiny Management Inc.    9 years       MORTGAGE     55000.0  ...   \n",
              "\n",
              "  open_acc pub_rec revol_bal revol_util total_acc  initial_list_status  \\\n",
              "0     16.0     0.0   36369.0       41.8      25.0                    w   \n",
              "1     17.0     0.0   20131.0       53.3      27.0                    f   \n",
              "2     13.0     0.0   11987.0       92.2      26.0                    f   \n",
              "3      6.0     0.0    5472.0       21.5      13.0                    f   \n",
              "4     13.0     0.0   24584.0       69.8      43.0                    f   \n",
              "\n",
              "  application_type  mort_acc  pub_rec_bankruptcies  \\\n",
              "0       INDIVIDUAL       0.0                   0.0   \n",
              "1       INDIVIDUAL       3.0                   0.0   \n",
              "2       INDIVIDUAL       0.0                   0.0   \n",
              "3       INDIVIDUAL       0.0                   0.0   \n",
              "4       INDIVIDUAL       1.0                   0.0   \n",
              "\n",
              "                                           address  \n",
              "0     0174 Michelle Gateway\\nMendozaberg, OK 22690  \n",
              "1  1076 Carney Fort Apt. 347\\nLoganmouth, SD 05113  \n",
              "2  87025 Mark Dale Apt. 269\\nNew Sabrina, WV 05113  \n",
              "3            823 Reid Ford\\nDelacruzside, MA 00813  \n",
              "4             679 Luna Roads\\nGreggshire, VA 11650  \n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
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          "status": "completed"
        },
        "tags": [],
        "id": "gq4fEQZnINal"
      },
      "source": [
        "**What is the distribution of numerical feature values across the samples?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "execution": {
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          "status": "completed"
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        "tags": [],
        "id": "tIU7RlFpINam",
        "outputId": "63bdeb6b-5296-4d7c-fb74-f850cca34307"
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      "source": [
        "df.describe().transpose()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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",
              "        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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>loan_amnt</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>14113.888089</td>\n",
              "      <td>8357.441341</td>\n",
              "      <td>500.00</td>\n",
              "      <td>8000.00</td>\n",
              "      <td>12000.00</td>\n",
              "      <td>20000.00</td>\n",
              "      <td>40000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int_rate</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>13.639400</td>\n",
              "      <td>4.472157</td>\n",
              "      <td>5.32</td>\n",
              "      <td>10.49</td>\n",
              "      <td>13.33</td>\n",
              "      <td>16.49</td>\n",
              "      <td>30.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>431.849698</td>\n",
              "      <td>250.727790</td>\n",
              "      <td>16.08</td>\n",
              "      <td>250.33</td>\n",
              "      <td>375.43</td>\n",
              "      <td>567.30</td>\n",
              "      <td>1533.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_inc</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>74203.175798</td>\n",
              "      <td>61637.621158</td>\n",
              "      <td>0.00</td>\n",
              "      <td>45000.00</td>\n",
              "      <td>64000.00</td>\n",
              "      <td>90000.00</td>\n",
              "      <td>8706582.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>17.379514</td>\n",
              "      <td>18.019092</td>\n",
              "      <td>0.00</td>\n",
              "      <td>11.28</td>\n",
              "      <td>16.91</td>\n",
              "      <td>22.98</td>\n",
              "      <td>9999.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_acc</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>11.311153</td>\n",
              "      <td>5.137649</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>14.00</td>\n",
              "      <td>90.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>0.178191</td>\n",
              "      <td>0.530671</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>86.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol_bal</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>15844.539853</td>\n",
              "      <td>20591.836109</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6025.00</td>\n",
              "      <td>11181.00</td>\n",
              "      <td>19620.00</td>\n",
              "      <td>1743266.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol_util</th>\n",
              "      <td>395754.0</td>\n",
              "      <td>53.791749</td>\n",
              "      <td>24.452193</td>\n",
              "      <td>0.00</td>\n",
              "      <td>35.80</td>\n",
              "      <td>54.80</td>\n",
              "      <td>72.90</td>\n",
              "      <td>892.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_acc</th>\n",
              "      <td>396030.0</td>\n",
              "      <td>25.414744</td>\n",
              "      <td>11.886991</td>\n",
              "      <td>2.00</td>\n",
              "      <td>17.00</td>\n",
              "      <td>24.00</td>\n",
              "      <td>32.00</td>\n",
              "      <td>151.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mort_acc</th>\n",
              "      <td>358235.0</td>\n",
              "      <td>1.813991</td>\n",
              "      <td>2.147930</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>34.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <td>395495.0</td>\n",
              "      <td>0.121648</td>\n",
              "      <td>0.356174</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         count          mean           std     min       25%  \\\n",
              "loan_amnt             396030.0  14113.888089   8357.441341  500.00   8000.00   \n",
              "int_rate              396030.0     13.639400      4.472157    5.32     10.49   \n",
              "installment           396030.0    431.849698    250.727790   16.08    250.33   \n",
              "annual_inc            396030.0  74203.175798  61637.621158    0.00  45000.00   \n",
              "dti                   396030.0     17.379514     18.019092    0.00     11.28   \n",
              "open_acc              396030.0     11.311153      5.137649    0.00      8.00   \n",
              "pub_rec               396030.0      0.178191      0.530671    0.00      0.00   \n",
              "revol_bal             396030.0  15844.539853  20591.836109    0.00   6025.00   \n",
              "revol_util            395754.0     53.791749     24.452193    0.00     35.80   \n",
              "total_acc             396030.0     25.414744     11.886991    2.00     17.00   \n",
              "mort_acc              358235.0      1.813991      2.147930    0.00      0.00   \n",
              "pub_rec_bankruptcies  395495.0      0.121648      0.356174    0.00      0.00   \n",
              "\n",
              "                           50%       75%         max  \n",
              "loan_amnt             12000.00  20000.00    40000.00  \n",
              "int_rate                 13.33     16.49       30.99  \n",
              "installment             375.43    567.30     1533.81  \n",
              "annual_inc            64000.00  90000.00  8706582.00  \n",
              "dti                      16.91     22.98     9999.00  \n",
              "open_acc                 10.00     14.00       90.00  \n",
              "pub_rec                   0.00      0.00       86.00  \n",
              "revol_bal             11181.00  19620.00  1743266.00  \n",
              "revol_util               54.80     72.90      892.30  \n",
              "total_acc                24.00     32.00      151.00  \n",
              "mort_acc                  1.00      3.00       34.00  \n",
              "pub_rec_bankruptcies      0.00      0.00        8.00  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.052971,
          "end_time": "2021-02-16T21:32:58.717478",
          "exception": false,
          "start_time": "2021-02-16T21:32:58.664507",
          "status": "completed"
        },
        "tags": [],
        "id": "Hi7ug5nAINam"
      },
      "source": [
        "<a id=\"ch3\"></a>\n",
        "# 3. Exploratory Data Analysis\n",
        "---\n",
        "\n",
        "## Analyze by visualizing data\n",
        "Get an understanding for which variables are important, view summary statistics, and visualize the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.053447,
          "end_time": "2021-02-16T21:32:58.824831",
          "exception": false,
          "start_time": "2021-02-16T21:32:58.771384",
          "status": "completed"
        },
        "tags": [],
        "id": "BoiELGGSINam"
      },
      "source": [
        "## Pearson correlation matrix\n",
        "We use the Pearson correlation coefficient to examine the strength and direction of the linear relationship between two continuous variables.\n",
        "\n",
        "The correlation coefficient can range in value from −1 to +1. The larger the absolute value of the coefficient, the stronger the relationship between the variables. For the Pearson correlation, an absolute value of 1 indicates a perfect linear relationship. A correlation close to 0 indicates no linear relationship between the variables. \n",
        "\n",
        "The sign of the coefficient indicates the direction of the relationship. If both variables tend to increase or decrease together, the coefficient is positive, and the line that represents the correlation slopes upward. If one variable tends to increase as the other decreases, the coefficient is negative, and the line that represents the correlation slopes downward.\n",
        "\n",
        "* We can see a strong correlation between loan_amnt and installment. (The monthly payment owed by the borrower if the loan originates)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "execution": {
          "iopub.execute_input": "2021-02-16T21:32:58.941907Z",
          "iopub.status.busy": "2021-02-16T21:32:58.941258Z",
          "iopub.status.idle": "2021-02-16T21:32:59.967630Z",
          "shell.execute_reply": "2021-02-16T21:32:59.968211Z"
        },
        "papermill": {
          "duration": 1.088649,
          "end_time": "2021-02-16T21:32:59.968364",
          "exception": false,
          "start_time": "2021-02-16T21:32:58.879715",
          "status": "completed"
        },
        "tags": [],
        "id": "miqqHStfINam",
        "outputId": "5b99240f-a7d1-4e84-f47c-00974de68ace"
      },
      "source": [
        "sns.set(style=\"whitegrid\", font_scale=1)\n",
        "\n",
        "plt.figure(figsize=(12,12))\n",
        "plt.title('Pearson Correlation Matrix',fontsize=25)\n",
        "sns.heatmap(df.corr(),linewidths=0.25,vmax=0.7,square=True,cmap=\"GnBu\",linecolor='w',\n",
        "            annot=True, annot_kws={\"size\":10}, cbar_kws={\"shrink\": .7})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa993d50f10>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.056795,
          "end_time": "2021-02-16T21:33:00.083200",
          "exception": false,
          "start_time": "2021-02-16T21:33:00.026405",
          "status": "completed"
        },
        "tags": [],
        "id": "ssbqQJj4INan"
      },
      "source": [
        "## Loan status and loan amount distribution\n",
        "* This is an imbalance problem, because we have a lot more entries of people that fully paid their loans then people that did not pay back.\n",
        "* We can expect to probably do very well in terms of accuracy but our precision and recall are going to be the true metrics that we will have to evaluate our model based off of.\n",
        "* In the loan amount distribution we can see spikes in even ten thousend dollar, so this is indicating that there are certain amounts that are basically standard loans."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:00.213463Z",
          "iopub.status.busy": "2021-02-16T21:33:00.212357Z",
          "iopub.status.idle": "2021-02-16T21:33:00.871063Z",
          "shell.execute_reply": "2021-02-16T21:33:00.871625Z"
        },
        "papermill": {
          "duration": 0.731365,
          "end_time": "2021-02-16T21:33:00.871787",
          "exception": false,
          "start_time": "2021-02-16T21:33:00.140422",
          "status": "completed"
        },
        "tags": [],
        "id": "RqqEDCi_INan",
        "outputId": "88f3707d-9db1-4562-f58f-ebf26dd63204"
      },
      "source": [
        "f, axes = plt.subplots(1, 2, figsize=(15,5))\n",
        "sns.countplot(x='loan_status', data=df, ax=axes[0])\n",
        "sns.distplot(df['loan_amnt'], kde=False, bins=40, ax=axes[1])\n",
        "sns.despine()\n",
        "axes[0].set(xlabel='Status', ylabel='')\n",
        "axes[0].set_title('Count of Loan Status', size=20)\n",
        "axes[1].set(xlabel='Loan Amount', ylabel='')\n",
        "axes[1].set_title('Loan Amount Distribution', size=20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loan Amount Distribution')"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.058264,
          "end_time": "2021-02-16T21:33:00.988638",
          "exception": false,
          "start_time": "2021-02-16T21:33:00.930374",
          "status": "completed"
        },
        "tags": [],
        "id": "nJMTmg-KINan"
      },
      "source": [
        "## Relationship between loan_amnt, loan_status and installment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:01.110166Z",
          "iopub.status.busy": "2021-02-16T21:33:01.109178Z",
          "iopub.status.idle": "2021-02-16T21:33:02.552428Z",
          "shell.execute_reply": "2021-02-16T21:33:02.552941Z"
        },
        "papermill": {
          "duration": 1.505576,
          "end_time": "2021-02-16T21:33:02.553107",
          "exception": false,
          "start_time": "2021-02-16T21:33:01.047531",
          "status": "completed"
        },
        "tags": [],
        "id": "uMEFbC65INan",
        "outputId": "6a316c6c-3000-4d9f-d337-9a456783af85"
      },
      "source": [
        "f, axes = plt.subplots(1, 2, figsize=(15,5))\n",
        "sns.scatterplot(x='installment', y='loan_amnt', data=df, ax=axes[0])\n",
        "sns.boxplot(x='loan_status', y='loan_amnt', data=df, ax=axes[1])\n",
        "sns.despine()\n",
        "axes[0].set(xlabel='Installment', ylabel='Loan Amount')\n",
        "axes[0].set_title('Scatterplot between Loan Amount and Installment', size=15)\n",
        "axes[1].set(xlabel='Loan Status', ylabel='Loan Amount')\n",
        "axes[1].set_title('Boxplot between Loan Amount and Loan Status', size=15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Boxplot between Loan Amount and Loan Status')"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.062867,
          "end_time": "2021-02-16T21:33:02.678674",
          "exception": false,
          "start_time": "2021-02-16T21:33:02.615807",
          "status": "completed"
        },
        "tags": [],
        "id": "UNRZGSRJINao"
      },
      "source": [
        "In case that the boxplot is a little hard to read you can always compare the averages here: \n",
        "* So you can see the charged off average price is a little higher than the fully paid loan."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:02.815026Z",
          "iopub.status.busy": "2021-02-16T21:33:02.814327Z",
          "iopub.status.idle": "2021-02-16T21:33:02.894508Z",
          "shell.execute_reply": "2021-02-16T21:33:02.894989Z"
        },
        "papermill": {
          "duration": 0.154701,
          "end_time": "2021-02-16T21:33:02.895138",
          "exception": false,
          "start_time": "2021-02-16T21:33:02.740437",
          "status": "completed"
        },
        "tags": [],
        "id": "HXC8I8AZINao",
        "outputId": "ffbcda90-745b-402d-dc1c-d0507438428d"
      },
      "source": [
        "df.groupby('loan_status')['loan_amnt'].describe()"
      ],
      "execution_count": null,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</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",
              "      <th>Charged Off</th>\n",
              "      <td>77673.0</td>\n",
              "      <td>15126.300967</td>\n",
              "      <td>8505.090557</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>8525.0</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>40000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fully Paid</th>\n",
              "      <td>318357.0</td>\n",
              "      <td>13866.878771</td>\n",
              "      <td>8302.319699</td>\n",
              "      <td>500.0</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>19225.0</td>\n",
              "      <td>40000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                count          mean          std     min     25%      50%  \\\n",
              "loan_status                                                                 \n",
              "Charged Off   77673.0  15126.300967  8505.090557  1000.0  8525.0  14000.0   \n",
              "Fully Paid   318357.0  13866.878771  8302.319699   500.0  7500.0  12000.0   \n",
              "\n",
              "                 75%      max  \n",
              "loan_status                    \n",
              "Charged Off  20000.0  40000.0  \n",
              "Fully Paid   19225.0  40000.0  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.062877,
          "end_time": "2021-02-16T21:33:03.020991",
          "exception": false,
          "start_time": "2021-02-16T21:33:02.958114",
          "status": "completed"
        },
        "tags": [],
        "id": "8k2BD9gqINao"
      },
      "source": [
        "## Countplot per grade and subgrade\n",
        "* Essentially this is showing the percentage of charged off loans.\n",
        "* Looks like it is increasing as the letter grade gets higher.\n",
        "* Better grades are bluer and the worse grades are redder."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:03.156946Z",
          "iopub.status.busy": "2021-02-16T21:33:03.155308Z",
          "iopub.status.idle": "2021-02-16T21:33:04.457493Z",
          "shell.execute_reply": "2021-02-16T21:33:04.456829Z"
        },
        "papermill": {
          "duration": 1.373653,
          "end_time": "2021-02-16T21:33:04.457602",
          "exception": false,
          "start_time": "2021-02-16T21:33:03.083949",
          "status": "completed"
        },
        "tags": [],
        "id": "3pK7dhYmINao",
        "outputId": "76ce54a4-1793-4fc4-aad9-f2b7136356ef"
      },
      "source": [
        "f, axes = plt.subplots(1, 2, figsize=(15,5), gridspec_kw={'width_ratios': [1, 2]})\n",
        "sns.countplot(x='grade', hue='loan_status', data=df, order=sorted(df['grade'].unique()), palette='seismic', ax=axes[0])\n",
        "sns.countplot(x='sub_grade', data=df, palette='seismic', order=sorted(df['sub_grade'].unique()), ax=axes[1])\n",
        "sns.despine()\n",
        "axes[0].set(xlabel='Grade', ylabel='Count')\n",
        "axes[0].set_title('Count of Loan Status per Grade', size=20)\n",
        "axes[1].set(xlabel='Sub Grade', ylabel='Count')\n",
        "axes[1].set_title('Count of Loan Status per Sub Grade', size=20)\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.064332,
          "end_time": "2021-02-16T21:33:04.585973",
          "exception": false,
          "start_time": "2021-02-16T21:33:04.521641",
          "status": "completed"
        },
        "tags": [],
        "id": "ZuHEtzxVINao"
      },
      "source": [
        "Now we are going to create a new column called 'loan_repaid' which will contain a 1 if the loan status was \"Fully Paid\" and a 0 if it was \"Charged Off\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:04.766694Z",
          "iopub.status.busy": "2021-02-16T21:33:04.727989Z",
          "iopub.status.idle": "2021-02-16T21:33:04.782490Z",
          "shell.execute_reply": "2021-02-16T21:33:04.781948Z"
        },
        "papermill": {
          "duration": 0.132268,
          "end_time": "2021-02-16T21:33:04.782598",
          "exception": false,
          "start_time": "2021-02-16T21:33:04.650330",
          "status": "completed"
        },
        "tags": [],
        "id": "HU18NBuEINao",
        "outputId": "b35e3750-5f45-4700-fcc6-f8c855d2fb97"
      },
      "source": [
        "df['loan_repaid'] = df['loan_status'].map({'Fully Paid':1,'Charged Off':0})\n",
        "df[['loan_repaid','loan_status']].head()"
      ],
      "execution_count": null,
      "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>loan_repaid</th>\n",
              "      <th>loan_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Fully Paid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Charged Off</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_repaid  loan_status\n",
              "0            1   Fully Paid\n",
              "1            1   Fully Paid\n",
              "2            1   Fully Paid\n",
              "3            1   Fully Paid\n",
              "4            0  Charged Off"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.066658,
          "end_time": "2021-02-16T21:33:04.913848",
          "exception": false,
          "start_time": "2021-02-16T21:33:04.847190",
          "status": "completed"
        },
        "tags": [],
        "id": "nR84hp-KINap"
      },
      "source": [
        "* The interest rate has essentially the highest negative correlation with whether or not someone is going to repay their loan.\n",
        "* If you have a extremely high interest rate you are going to find it harder to pay off that loan."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:05.049825Z",
          "iopub.status.busy": "2021-02-16T21:33:05.049170Z",
          "iopub.status.idle": "2021-02-16T21:33:05.522648Z",
          "shell.execute_reply": "2021-02-16T21:33:05.521733Z"
        },
        "papermill": {
          "duration": 0.54406,
          "end_time": "2021-02-16T21:33:05.522833",
          "exception": false,
          "start_time": "2021-02-16T21:33:04.978773",
          "status": "completed"
        },
        "tags": [],
        "id": "pLMcZYFQINap",
        "outputId": "4b37e16d-7dfc-4bbe-d6f4-63d66e45bc3c"
      },
      "source": [
        "df.corr()['loan_repaid'].sort_values(ascending=True).drop('loan_repaid').plot.bar(color='green')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa991b856d0>"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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TjNecX1tTiVdRUXHPL9bAAySIxMTEes+7uroiKysLTk5OAICcnBz07du3QYW0t7fX/+zm5gYfHx/8+uuvDU4QREQkHaP7IPz8/BAXFwegppno3LlzGDhwYIPucfPmTQiCAAAoLCzEqVOn0LlzZ2OLRkRERjC6D2LSpEmIiYmBr68vLCwsEBsbq68RrFmzBs7OzggPD4dGo8HgwYNRWVmJ4uJieHt7IzQ0FDNmzEBycjJ27doFS0tLaDQaBAYGwsfHx+gXR0RED8/oBGFra4u1a9eKnouKitL/rFarRUc3AcC4ceMwbtw4Y4tCREQS4kxqIiISxQRBRESimCCIiEgUEwQREYligiAiIlFMEEREJIoJgoiIRDFBEBGRKCYIIiISxQRBRESimCCIiEgUEwQREYligiAiIlFMEEREJIoJgoiIRDFBEBGRKCYIIiISxQRBRESimCCIiEgUEwQREYligiAiIlFGJ4iysjLMmjULvr6+8PPzw/Hjx0WvO3r0KEJCQhAQEIARI0Zg+/btBud3794NX19f+Pj4IDY2Flqt1tiiERGRESyNvcG2bdtgZ2eHb7/9FhkZGYiMjERycjLs7OwMrmvbti02btyIdu3aoaioCCEhIejWrRu8vLyQmZmJ9evXIykpCa1atcLkyZOxf/9+BAUFGVs8IiJ6SEbXIA4fPoywsDAAgLu7Ozw9PXHy5Mk613Xv3h3t2rUDADg4OMDDwwNZWVkAgCNHjsDHxwdOTk6wsLBAaGgoDh06ZGzRiIjICEYniOzsbLRv317/2MXFBbm5ufU+58qVK/jtt9/Qr18/AEBOTg5cXV31511dXZGTk2Ns0YiIyAj3bWIKDg5Gdna26LnTp083OODNmzcxbdo0LFy4UF+jkMr58+dFj/fu3fuh75mamvrQz1XynozHeE0xXnN+bc0h3n0TRGJiYr3nXV1dkZWVBScnJwA1tYG+ffuKXnvr1i1MmDABr776Kvz9/fXHXVxcDJJQdnY2XFxcHugF1Obp6QkbG5sGP68+xiQXMampqZLfk/EYrynGa86vranEq6iouOcXa0CCJiY/Pz/ExcUBADIyMnDu3DkMHDiwznUFBQWYMGECIiMjERoaanBu2LBhOHr0KPLz86HVahEfH4/hw4cbWzQiIjKC0Qli0qRJuHPnDnx9ffHaa68hNjYW9vb2AIA1a9Zg165dAIAtW7YgIyMDcXFxCAwMRGBgIPbu3QsAcHNzw7Rp0zBmzBgMHToUjz32GEaNGmVs0YiIyAhGD3O1tbXF2rVrRc9FRUXpf46OjkZ0dPQ97xMWFqYfDUVERKbHmdRERCSKCYKIiEQxQRARkSgmCCIiEsUEQUREopggiIhIFBMEERGJYoIgIiJRTBBERCSKCYKIiEQxQRARkSgmCCIiEsUEQUREopggiIhIFBMEERGJYoIgIiJRTBBERCSKCYKIiEQxQRARkSgmCCIiEsUEQUREopggiIhIlKWxNygrK8O8efNw4cIFqNVqREdHY/DgwXWuO3r0KDZs2IDKykoIgoAXX3wREydOBACcOXMGU6ZMgbu7OwDA2toa8fHxxhaNiIiMYHSC2LZtG+zs7PDtt98iIyMDkZGRSE5Ohp2dncF1bdu2xcaNG9GuXTsUFRUhJCQE3bp1g5eXFwDAw8MDCQkJxhaHiIgkYnSCOHz4MN5//30AgLu7Ozw9PXHy5EkMHz7c4Lru3bvrf3ZwcICHhweysrL0CUJO5dXlEBYJD/W8FpYtZCgREVHjZ3QfRHZ2Ntq3b69/7OLigtzc3Hqfc+XKFfz222/o16+f/lhGRgaCg4MRGhqKxMREY4tloL4P+dTU1Id6HhFRc6cSBKHer9bBwcHIzs4WPXf69Gl4eXnhu+++g5OTEwBg8eLFeOKJJzBhwgTR59y8eRPjx49HVFQU/P39AQDFxcUQBAEODg7IzMzEhAkTEBsbiwEDBjzQi6ioqMD58+cf6FoiIjLk6ekJGxubuicEI/n7+wu///67/vGUKVOEQ4cOiV77n//8R/D39xd2795d7z2XL18urFu37oHLUF5eLqSkpAjl5eUP/BydlJSUBj/HGIzHeIynfCzGE3e/z06jm5j8/PwQFxcHoKaZ6Ny5cxg4cGCd6woKCjBhwgRERkYiNDTU4NzNmzch/P+KTGFhIU6dOoXOnTsbWzQiIjKC0Z3UkyZNQkxMDHx9fWFhYYHY2FjY29sDANasWQNnZ2eEh4djy5YtyMjIQFxcnD6hvPTSS3jxxReRnJyMXbt2wdLSEhqNBoGBgfDx8TG2aEREZASjE4StrS3Wrl0rei4qKkr/c3R0NKKjo0WvGzduHMaNG2dsUYiISEKcSU1ERKKYIIiISJTRTUyNga6Du7Ky8qGeX1FRIWVxGI/xGK8RxmK8unSfmcI9Zjvcdx5EU1BUVITLly+buhhERE1Sp06d4ODgUOd4s0gQWq0WJSUlsLKygkqlMnVxiIiaBEEQUFVVBTs7O1hY1O1xaBYJgoiIpMdOaiIiEsUEQUREopggiIhIFBMEERGJYoIgIiJRTBBERCSKCYKIiEQxQRARkSgmCCIiEsUEQUb73//93wc6JpXw8PAHOiaF06dPo6ioSP/4zp07+PHHH2WJZSrp6en6Rd6+//57bNmyBbdv3zZxqaSRn59vsIhnZWUl8vPzZYuXlJRk8H9XWFiI/fv3yxLr8uXLKC0t1T8uKSnBn3/+KWmMZrGa68PIyMjAvHnzkJeXh2PHjuHChQs4duwYZsyYIWmclStX1nt+7ty5ksU6fPgwhg8fjp07d4qej4yMlCxWbSkpKXWO/fLLL7LEAoDy8nKDx1qtVrYPtJUrVyIxMVH/2N7evs4xqYWHh2PTpk1wdHQEUPMh88Ybb9zz92qsWbNmYc+ePcjMzMSiRYvw3HPPITo6Gps2bZI0Tr9+/UTXShMEASqVSpbE+9prr2HHjh36x9XV1Zg6dSp2794teSwA2L59O4KCgvSPW7Vqhe3bt2PUqFGSx4qJidHvzgkAVlZWiI6ORkJCgmQxzDZBLF68GK+//jo+/PBDAECXLl0wd+5cyROEra2tpPerz59//onhw4fj/PnzisQ7fPgwDh8+jKysLIPdA4uLi9GiRQvJ423duhVbt25FcXEx+vfvrz9eXl6OkSNHSh4P+O+Hl46FhQU0Go0ssXRKS0v1yQGo+ZApLi6WLZ6FhQWsrKxw4sQJhIeHY/LkyQgMDJQ8zt69eyW/5/1UVlaiZcuW+se2traKL8Et19+LRqOBlZWV/rG1tbXkscw2QRQVFcHb2xurV68G8N83idSmT58u+T3vZebMmQCABQsW6PcF15HjA6ZDhw4YNGgQzp07h0GDBumP29vbG3yAS2Xs2LHw8/PD0qVLsXDhQoN4tT9QpWRnZ4ezZ8+ie/fuAICzZ8/KnvS1Wi1KS0v1cUpKSmRNShUVFfqa9OzZswHce38AY7Rv317yez6I/Px8ODk5AQBu3boFrVYrW6y2bdsiOTkZQ4cOBQAcOXIEjz76qCyxLC0tkZmZCTc3NwDA9evXoVarpY0h6d2aELVajaqqKv23w7y8PNHlbo1limaf8ePH12kCETtmrM6dO6Nz584YMmQIWrVqJem9xTg4OMDBwQGbN2+WPZbOnDlz8MYbb+Cf//wnAOCvv/7C+vXrZY0ZEBCAiRMn6vtVdu3aJUsThc7LL7+MESNGoH///ujatSsyMzNF9waQSk5ODlatWoW0tDSDb/Pfffed5LHGjx+P8PBwfY1o3759mDJliuRxdObPn49p06Zh1apVAGo+ZzZs2CBLrOnTpyM8PBwvvPACAODEiRNYtmyZpDHMdrnvpKQkHD58GH/88QdefPFFJCUlYfbs2QgICJA0ztq1azFz5kzMmzdP9Pzy5csli1VdXY2qqiqEhYUhLi5O/y2wqKgIL730Er755hvJYtV269YtfPHFF8jMzER1dbX++Jo1a2SJ9+uvv2LVqlXIzMyERqORtQ0bAG7fvo3ffvsNgiCgZ8+estVWaktMTMS///1vCIKAIUOGGLRry02j0UCj0cDa2lqW+0+YMAH+/v7Yvn073nvvPezatQuPP/64bLXtM2fO4MSJE/r/yz59+sgSR0ej0SA9PR2CIODJJ5+U/Ft9bRkZGTh16hQA4Pnnn8cTTzwh6f3NNkEANZ2rx48f1//heHl5yRaruLhYtNnn7mPGWL9+vei3W3t7e7zyyiuyvQEjIiLg4eGB7t27G7wZgoODZYk3fPhwTJs2DT169DCo9cnRhHH58mU89thj+uae0tJSZGVloWPHjpLHMpWysjJs3rwZmZmZ+PDDD3HlyhWkp6fDx8dHlnhBQUFISkrCyJEjceDAAWi1WrzyyisGnclNWVlZGXJzcw2aBXU1UCnl5+fD3t5en8irqqpQVFSkb06ThGCmkpKSHuiYVIKCgh7omDFKS0uFkpISYcGCBfqfdf9KS0sljVXbiBEjZLu3GKn/3+oTHBwsVFZW6h9XVFQIwcHBssa8evWqEBYWJgwePFgQBEE4f/68sHbtWtnizZ07V1i1apXg7+8vCIIgFBcXC4GBgbLFGz16tCAIgjBmzBghKytLqKioEP7nf/5HllhhYWFCYWGh/nFBQYEQEREhSyxBEIQvv/xS6NGjhzBo0CBh8ODBwuDBg4UhQ4bIEmv06NEG7+uSkhIhNDRU0hhm2wfx2Wef1RmpIXbMWLpmH61Wi/LycoNmn7KyMklj9ezZEyqVCoIgiI4YuXTpkqTxdDp27Ii8vDy0a9dOlvvfzdvbGydOnNC3vcpJiZEid1uyZIkiI+x0Ll++jBUrVuCHH34AUNMxL2dHrpeXFwoLCxEeHo6QkBBYW1vDz89PllhKjwjbvn07Dh48qEiHvBIjtMwuQZw7dw6///47CgoKDDqOi4uLUVVVJXm8TZs2Yf369VCpVOjRo4f+uL29PSZMmCBprLS0NADAxo0bYWVlhbFjx0IQBMTHx8PSUr5f9Z07dzBq1Cj07NkTNjY2+uNy9UHExcVh8+bNsLOzg7W1tax9EEqMFLmbUiPsdO6+d0VFhSyjmHSio6MB1DQ1PfvssyguLkanTp1kiaX0iLC2bdsqOlpL7hFaZpcg8vLycP78eZSVlRnMF7Czs5O0w1hn+vTpmD59OmJjYw2GZsrp5MmT2LVrl/7xpEmTEB4ejpdfflmWeAEBAZJ37tdHyfH0SowUuZtSI+x0vLy8sGnTJlRWVuLMmTP49NNPMWTIENniATW1lp9//hkqlQrPPvusbHGUHhE2YMAArFy5EiNGjDD4siRHH4QSI7TMtpP6hx9+wPPPP69YvHs1J9WuIkpl+PDh2LRpk35Ew/Xr1/Haa6/h8OHDkscyleLiYly7dg3PPPOM7LHS09Nx+vRpAPKMFLmbUiPsdKqqqrB161YcO3ZMP2BjypQpstU6d+7ciU2bNmHQoEEQBAHff/89XnvtNURERMgST8kRYWKJVaVSyTKEF5B/hJbZJggAuHr1KtLS0gzWapHrj6dz5876/oHaM3Pl6BdITk7Gu+++C09PTwDAxYsXsXTpUtlGpdy6dQtffvklrl+/rsgw1xMnTmDhwoVQq9U4duwYzp07h//7v/+TfGkIU1JyhJ3Shg0bhq+++ko/gSw/Px/h4eE4cuSIiUtGdzO7JiadHTt2IC4uDn///Te6du2KlJQU9OnTR7YEoesfAGraeA8cOICCggJZYg0dOhReXl4GY/clHfp2l2nTpuHpp59G//79ZW+fB2rmluzZsweTJ08GAHTt2hXXr1+XJZaSk7qAmk7xMWPGYO/evbInBVOt3dW2bVuD2cVOTk5o06aNLLGqq6uxd+9eXLp0yeD3J3VzcmVlJaytrRVtKSgqKsInn3xS57VJOVzYbBPE7t27ER8fj/DwcGzbtg2XL19WbIaujY0NRo8ejXHjxuk/5KTm5OQkezuyTllZGRYtWqRILJ22bdsaPJZrUtf8+fPh7++PS5cu4YMPPtBP6pKLWq1G69atUVFRYdCGLQel1+7666+/AAC9evXCggULMHr0aAA1TUByNfcuXLgQGo0GZ86cQXh4OA4ePChL4h07diwSExMNRhLqqFQqWVoK5s+fDwJRevwAABkhSURBVA8PD2RkZCAqKgp79+6VvMnVbBOEtbU1bG1todVqIQgCOnXqJNu3UMCwD0Kr1eLcuXO4efOmbPGU1L17d/zxxx946qmnFIlnZ2eH//znP/qmujNnzsi2NERBQQFCQ0OxY8cO9OzZE927d8crr7wiSywdd3d3REZGYtiwYQbrPkn9jV63dpccgzPE3N2BWnvUmUqlwuuvvy55zHPnzuHAgQMYOXKkvp9j1qxZksfRLWNTu6VATO1RR8a6du0a1q1bh++++w4BAQEYOnSo5J3UZpsgWrZsiaqqKnTu3BmrVq2Ci4tLnWWkpVT7m4Varcbjjz+OBQsWyBZPSWFhYRg3bhz+8Y9/GHzr3bNnjyzx3n77bUyePBk3btzA+PHjkZGRgY0bN8oSSzcE1NbWFtnZ2WjTpg2ys7NliaVTUlKCjh074urVq7LG0dmyZQvGjBmjX0+roKAAe/fuxauvvippnGPHjj3QdWlpaejcubMkMXV/j2q1GmVlZXBwcDDpF7NJkyZJtiaartZsZWWFwsJCODo6Ijc3V5J765htgli0aBGqqqoQExOD1atX48aNG/fdu8EY9/tmIeWbQmlz5szB1KlT8fTTTyvSB9GtWzfs2LEDv/76K4Ca5PvII4/IEkvJSV069/tGv2fPHn3zjBS+/vprg2+erVu3xsGDByVPEA9q3rx5kn2IOjo64vbt2xg4cCAmT56M1q1by9bf8SCkHBPk7u6OwsJCjBw5EmPHjoWDgwO6dOki2f0BmOdSG9XV1cKaNWtMXQwDSi4fITVTlb2iokIoLS3V/5NbVlaW8Mcffxgcu3Tpkuxx7yb1//fIkSPrHAsICJA0RkNIucxHdXW1IAiCoNFohKSkJOGLL74QioqKJLt/Q8n1Xvnll1+EY8eO6V+vIAjCrVu3jL6vWW45qlarZd3x7GEITXi08cCBA3Hy5EnF4h05cgQvvPACunfvjl69eqFnz57o1auX7HFdXV3rzPi91yq9cpL6b8Xd3R2ffvopBEGAVqvF9u3bZe2Ivx+xXecelq5Ga2FhgcDAQIwbN85ggUwpa2Km5OXlhcGDBxvU4CdNmmT0fc22iWnQoEHYtm0bgoKCDDoC5RiO9iCkfFMobffu3diyZYsiS18ANduArlu3Dp6enrLOMH4QpkjsUv+tLFiwAHPmzMHq1auhUqnQs2dPWZtbG5Pa83aUoOTfixSxzDZB6Db0WLVqlcEENrkWtGvOlN5Ksm3btujWrZuiMe+lKSd2nXbt2mHHjh0oLS0FoOw2uWKU/BBV+vfn6+urWCwpXpvZJgglh6M9iKbcxKT0VpLjx4/Hxx9/DF9fX9nXu2mMpP5bCQ8Px65duwwSg+6YKcg1QU9O95psqKN7TW+88YYSxZGM2SaI+5FyONqDaIpvCh2lZxvn5eXhs88+Q1JSkr6JSc71buojV2KvqqpCeno6VCoVOnToYLAu0vvvvy9prLuHd2s0Gty+fVvSGADu22w1d+5cAEBoaKjkse9Fqt+fUpMNG4JNTDKS6g+nMb4ppKb0bOMvvvgCycnJcHZ2li3Gg5IjsaekpOCtt95CixYtIAgCKisrsXr1an1HvFTDobdu3YqtW7eiuLgY/fv31x8vLy/HyJEjJYlRm6mbrsR0795dkvsoNdmwtitXrsDDw+Oex6RozjLrxfrqExwcLEkN4n4b3Mu1DaiSlN5CMjIy8r5Veqkovd82AIwaNQrvvvuufmXOlJQUxMbGYv/+/ZLGKSoqwu3bt7F06VKDpejt7e0V2XdbTidOnKj3vJybTSm1CKjYZ5RUn1s6rEHIrDkkgPtRerZxt27d8Oabb8LPz8+gD0KON/2MGTPg4eGh2EKEQM3s39rLNnt5eaFFixaSx3FwcICDgwM2b95s0KQl986AZWVl2LBhA06fPg2VSoXnnnsOU6dOlXQE4datW+95TqVSyZYglFgEND8/H/n5+aioqMCVK1cMdqnUDTSQChPEPUhdsVLiTWEqSs821rX3fvHFF/pjcr3p79y5g6VLl0p+3/p4eXlh//79+o1tDhw4AG9vb9nipaam4s0337xnk5bUli5dCo1Gg/nz5wOomRkeGxsraTNN7b8NJSmxCOiBAwfw+eef4+bNmwaLfTo4OEg/+93oqXZN1F9//VXvsfXr10sab968ecLcuXOFlJQUISUlRYiJiRFiYmIkjdEYNJbZxlKZNWuWkJubq0isvn37Cv369RP69u0rPPXUU0LXrl2Frl27Ck899ZTQr18/2eKOHDlS+Pnnn/WPf/nlF9HZ1VK5e5a2VquVdeb2nTt3hLNnzwo///yz/p9cgoODBUGoeY1arVYQBEEYPXq05HG0Wq2wceNGye97N7OtQbz99tt12upqH5N6OJpuVUmdXr16ybr1oam4urrWOSbV2jq65aLvRY5hrkrut630fBIdpZq0aqu9T/S99lCQwqFDh7BixQrcuXMHzs7OuH79Ojp37izbCEUlFwE9dOgQpk6dKsu9dcwuQSjZfnc3pd4UjY0gUXNdfUsZyzXMVcn9tpWeT6KjdJOWbnG5ESNGQKVS4euvv9bvqyy1TZs2ISEhAZMmTUJSUhJOnTqF5ORkWWIByi0CqlKp4Obmhtu3b8s6oMDsRjF9/vnn+va72sMkHRwcMG7cONmGm27ZsgUHDhwweFOMGjXKZCtmKknqkRXNXb9+/URnwcq1dEm/fv1QWFioXz66srJSv/S3XEumnDhxAj/99BMEQcCAAQNkS0ghISFISEjQj7ADapan/9e//iVLPCXNmTMHqamp8Pb2NhhCrBs6LwWzq0G8/PLLePnll7Fp0ybZq2e1TZkyBU899ZT+TfH222/L+i2tObpfrUuODv+MjAzMmzcPeXl5OHbsGC5cuIBjx45hxowZksfSqd3UpNuetvZEOTnjKSElJQUvvPCCrENNdXRrgz3xxBP44osv0L59e9m2+gVqNmESS+5yNEk+8cQTeOKJJyS/b21mV4OoraysDLm5udBoNPpjci3XkJKS0qw2nm8I3TwJY3Xu3LnOdo46cq2j9corr2DixIn48MMPsW/fPmi1WowcORJff/215LHq89JLL8k2r0RpISEhKC4uRnBwMIKDg/GPf/xDtlg//vgjPD09cevWLSxevBhFRUV46623MGDAAFni1a4pV1RU4MiRI/Dw8MA777wjSzy5mV0NQmfnzp344IMP4OjoqMhyDe+9955ib4rGRqrZxvdbP0sORUVF8Pb2xurVqwHULButm/ehlMzMTGRlZcl2f6WbtBISEnD58mUkJiYiNDQUHTt2REhIiCx9Pc7Ozvr5Hp999hmAmtnGcgkODjZ4HBISIstWqoAyQ+fNNkFs374dBw8eVKxjUMk3hdLuN9u4KS8jolarUVVVpf8AzcvLk32J8dof2FqtFtXV1bJuT6t0kxYAdOrUCdHR0Zg9ezaWLVuGOXPmyPJeuN9oRbmpVCrcuHFDlnsrMZ/EbBNE27ZtFR81otSbQmlKzzZOS0vDokWL6ixnIEcTU0REBKZPn46CggKsW7cOSUlJmD17tuRxaqv9gW1paYk2bdrI+v969/sgKioKL730kqwrj+q+LB08eBD//Oc/sWLFCknvb6rRirX7IARBwB9//GGwzpWUlBg6b7YJYsCAAVi5ciVGjBih2JLRcr8pTEXp2caLFy/GrFmzsHz5cmzduhU7d+6EnZ2dLLGCgoLw2GOP4fjx4ygrK8OKFStk70tq37498vPzcfbsWahUKnTv3h2tW7eWNWZtcjdpBQcHo7S0FIGBgdi9ezdcXFwkj6HobONaBg8erP9ZrVZj4sSJ6NGjh2zx5B46b7ad1EOGDKlzTM4+iNpviuDgYFneFKYye/ZsxMTEyL6Gj47Y0MXx48fLurxCSUkJAMiWiGpLTk7Gu+++C09PT2i1WqSlpWHp0qXw8fGRJd69mrRCQkJkiafkgA2lRysqSYmh82Zbgzh27Jii8RYsWNBsRzEpOdsY+O8+w46OjkhLS0O7du1k+8Z75coVzJ07F3/++SeAmmbCFStW1FlmWUofffQR/vWvf6FDhw4Aaobavv7667IlCKWbtHr37o34+HhkZGRgzpw5uHHjBm7evCnL2k9Tp07FX3/9hTNnzgCoSYZy/u6uXr2KjRs31umP27Nnj+SxlBg6b3YJorKyEtbW1vesjsm1eJ6SbwqlKTnbGAD8/f1RUFCAKVOmIDw8HFqtVrZ5CfPmzcP48eP1M33379+PefPmYffu3bLEA2oSny45AIC7u7t+4prUNBoNZs6cqehciOXLl+PWrVu4cOEC5syZAzs7O7z33nuyfIgmJSXhgw8+wKBBgwAAmzdvxttvvy3bMje6VYZDQkIU6Y+Tez6J2SWIsWPHIjExET179qwzpl7OPamVfFMo7e6hfXKbMGECAMDb2xs///wzKioqYG9vL0us6upqg6WaAwMD8fnnn8sSS+f555/Hxo0bMXr0aAiCgISEBPj6+uq/1Ej5JUatVqN169aoqKgwqP3J6cyZM0hKStL/3ejiy2H79u1ITExE27ZtAQB///03Jk2aJFuC0Gq1ijVpXb16FZs2bcL169dlq62YXYLQDW9Teky9km8KpVVXV2Pv3r24dOmSwWuSa5et2vslW1lZwcrKSrY9lJ966imDNvPU1FRZOx2B/24ydXcT3cqVK2X5EuPu7o7IyEgMGzbMYMkGubbBtbGxMZh3odVqZYmjo0sOd/8shx49eiAtLU2yXf/qExUVpe/TlKu2YnYJwlSUflMoaeHChdBoNDhz5gzCw8Nx8OBBWftblNpDGQAuXryIxMRE/ZIG165dw9NPP43Ro0cDkKdtWekvLyUlJejYsSOuXr2qSLxOnTph//79EAQBN27cwJYtW9C7d29ZYj3++ONYu3Ytxo4dC5VKhd27d8PNzU2WWADw+++/IyEhAR06dDCokcnxd2JpaSn7Wm5MEApR8k2hNN147JEjR+K1115DREQEZs2aJXkcpfdQBqCfoFZSUgKVStUo91U21pIlS/QL9encuXNHtngxMTF4//338ffff2PMmDEYMmQIYmJiZIm1ZMkSLFu2DKNGjYJKpcKAAQNkHZKtm7SmhIEDB+LkyZPyrukm+44TJAiCIBQVFQkLFiwQ+vfvL/Tv319YsGCBUFJSYupiSeLFF18UBEEQAgMDhdLSUkEQ6m4KI4U7d+4ImZmZwpQpU4QbN27o/xUWFkoeS+fatWtCaGio4OnpKXTt2lUYO3ascP36ddnimcKbb75p8LioqEgIDQ2VJVZ1dbWwZs0aWe4tJigo6IGONUWnT58WevToIfTq1Uu/0ZTUG0uxBqEAjUaD7du3Y9myZaYuiiwcHR1x+/ZtDBw4EJMnT0br1q3Rpk0byePU3kNZ59atW0hPT5etX2DRokUYM2YMXnzxRQA1S6YsXLgQn376qSzxTKFNmzZYuXIl5s6di7KyMkydOlW2GplarcYvv/wiy71rq66uRlVVFbRaLcrLyyEIAlQqFe7cuSPrXixKDnNduHAhli9fjmeeeUa25V/MdqKc0uSeyGVKGo0GarUaWq0WBw4cQFFREYKCgmQbWRQREYHNmzdDEASMGDECjzzyCLy9vREdHS15rMDAQOzbt+++x5oyQRAQFRWFnj174vvvv0f//v0NZh9Lbdu2bQBqZqnXbrKTcnTW+vXrsX79+jojFe3t7TFhwgTZlhEJCgqCn58funfvbtBx/Oyzz0oea/To0bKPgmSCUIgSbwpTqq6uRnp6OlQqFdzd3WVd7E23fPi+ffvwxx9/4O2330ZgYKDBujRSCQ4Oxocffognn3wSAJCeno633noLCQkJksdSWu1v0uXl5Zg8eTL69u2L6dOnA5Dvb7P2CB/dB7hcQ8xjY2OxcOFCye97L6NGjcL+/fsVibVp0yY4Ojpi+PDhBh3iUv7emCAUouSbQmnnzp3DzJkz9ctgV1dXY926dXjmmWdkiefv749Dhw5h/vz58Pf3x/PPPy/ZnhN3O3nyJKKjo9GlSxf972vlypV4/vnnJY+ltNr7ayg5J6g5W7hwISIiIhQZ5qrEZwoTBBktLCwMUVFR+pFFP/30Ez7++GPZtnVcvHgxfvzxRwiCgIMHD6K8vByvvPKKbN/qdQvnCYKAHj16wMnJSZY41PQFBQXhr7/+0g9z1X1oN9UJsUwQZDSl2+kFQUBaWhrc3Nxgb2+P/Px85Obm4umnn5YlnrmorKw02F2xuTR/Kunnn3+uc0ylUqFPnz4mKI3xOIqJjNayZUv89NNP6NevH4CaN4mcHy66fo7c3Fzk5uYCQJ1x/PTgkpOTsWzZMty8ebPZNX8qrUuXLtiyZQvS0tIMVhVoqtvFMkGQ0ebPn4+oqCj9h3RVVRXWrl0rWzzddrGtWrXSz06Xc6n25m7VqlX4+OOP0aNHD9l3y2vu5s+fDw8PD2RkZCAqKgp79+6VrS9OCUwQZLRu3bohOTkZ6enpEAQBTz75pKz7Niu9XWxz5+jo2CxWFW4Mrl27hnXr1uG7775DQEAAhg4diilTppi6WA+NXxfooZWVlen/VVdXw83NDY8//jiqq6tlnYxkiu1imzNfX1989dVXKCwsNPidUsPpatFWVlYoLCyElZWVvhm0KWINgh7a3Uum196LV842bFNsF9ucffTRRwBq5gywD8I47u7uKCwsxMiRIzF27Fg4ODigS5cupi7WQ+MoJmpylN4uluhhpKSkoKioCN7e3opsHiQHJggiIhLFJiZqMky1XWxzl5aWhkWLFiEtLQ2VlZX642xiIiYIajJMtV1sc7d48WLMmjULy5cvx9atW7Fz507Y2dmZuljUCLCJicjMhYSEICEhASNHjtQveNicVx+mB8dhrkRmTteB6ujoiLS0NBQUFCArK8vEpaLGgE1MRGbO398fBQUFmDJlCsLDw6HVajFz5kxTF4saATYxEZFeVVUVKioqZNvsiZoW1iCICCdOnMBPP/0EAOjXrx9eeOEFE5eIGgP2QRCZuY8++ggffPABHB0d8cgjj2D16tX4+OOPTV0sagTYxERk5oYNG4bExET9VrilpaUIDg7GkSNHTFwyMjXWIIjMnLOzs8EkQxsbGzg7O5uwRNRYsA+CyEydOHECQM0mN6+++iqCg4MBAPv27ePy3wSATUxEZmv8+PH3PKdSqZrsLmgkHSYIIqrXyZMn4e3tbepikAmwD4KI6qXbL4LMDxMEEdWLjQzmiwmCiOql2ymQzA8TBBERiWKCIKJ6sYnJfDFBEFG9Zs+ebeoikIkwQRCZufDwcNy+fVv/uLCwEJGRkfrHXLjPfDFBEJm50tJSODo66h+3atUKxcXFJiwRNRZMEERmTqvVorS0VP+4pKQEGo3GhCWixoJrMRGZuYCAAEycOBHh4eEAgF27dmHUqFEmLhU1Blxqg4iQmJiIf//73xAEAUOGDEFQUJCpi0SNABMEERGJYh8EkZlLT09HeHg4hgwZAgC4cOEC1q1bZ+JSUWPABEFk5pYsWYLXX38dDg4OAGr2h/jmm29MXCpqDJggiMxcUVERvL299WsuWVhYwMrKysSlosaACYLIzKnValRVVekTRF5eHiws+NFATBBEZi8iIgLTp09HQUEB1q1bh4iICEycONHUxaJGgKOYiAgpKSk4fvy4fpirl5eXqYtEjQATBJEZ02g0GDNmDPbu3WvqolAjxCYmIjOmVqvRunVrVFRUmLoo1AhxqQ0iM+fu7o7IyEgMGzYMtra2+uO1V3Ql88QEQWTmSkpK0LFjR1y9etXURaFGhn0QRFSvPXv2YPTo0aYuBpkA+yCIqF47d+40dRHIRJggiKhebGQwX0wQRFQv3QxrMj9MEEREJIoJgojqxSYm88VRTESEqqoqpKenQ6VSoUOHDrC0/O8I+LS0NHTu3NmEpSNTYYIgMnMpKSl466230KJFCwiCgMrKSqxevRq9evUyddHIxJggiMzcqFGj8O6776JPnz4AahJGbGws9u/fb+KSkamxD4LIzNnY2OiTAwB4eXmhRYsWJiwRNRZMEERmzsvLy6C2cODAAXh7e5uwRNRYsImJyEz169cPKpUKgiCgsLAQ1tbWAIDKykq0bt0aP/74o4lLSKbGBEFkprKysuo93759e4VKQo0VEwQREYnict9EZk7X1HQ3NjEREwSRmau93WhFRQUOHDhgMFGOzBebmIiojpdeegk7duwwdTHIxDjMlYgMZGZm3rcDm8wD65FEZq52H4RWq0V1dTUWLFhg4lJRY8AmJiIzV7u2YGlpiTZt2kCtVpuwRNRYsImJyMy1b98eLVu2xOXLl3Hp0iXcuXPH1EWiRoIJgsjMJScnY/jw4fjyyy/x+eefw9/fH0ePHjV1sagRYBMTkZkbPnw4NmzYgA4dOgAAMjIy8Prrr+Pw4cMmLhmZGmsQRGbO0dFRnxwAwN3dHa1atTJhiaixYA2CyMytX78earUao0ePhiAISEhIgLW1NcLDwwEALVu2NHEJyVSYIIjMXH3biapUKly6dEnB0lBjwgRBRESi2AdBRESimCCIiEgUEwQREYligiAiIlFMEEREJOr/ASSCM6KI0TfGAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.074305,
          "end_time": "2021-02-16T21:33:05.676520",
          "exception": false,
          "start_time": "2021-02-16T21:33:05.602215",
          "status": "completed"
        },
        "tags": [],
        "id": "zm6W8jKiINap"
      },
      "source": [
        "<a id=\"ch4\"></a>\n",
        "# 4. Data preprocessing\n",
        "---\n",
        "Remove or fill any missing data. Remove unnecessary or repetitive features. Convert categorical string features to dummy variables.\n",
        "\n",
        "## Missing data\n",
        "***What is the length of the dataframe?***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:05.823620Z",
          "iopub.status.busy": "2021-02-16T21:33:05.822960Z",
          "iopub.status.idle": "2021-02-16T21:33:05.826169Z",
          "shell.execute_reply": "2021-02-16T21:33:05.827036Z"
        },
        "papermill": {
          "duration": 0.076793,
          "end_time": "2021-02-16T21:33:05.827239",
          "exception": false,
          "start_time": "2021-02-16T21:33:05.750446",
          "status": "completed"
        },
        "tags": [],
        "id": "L-L_yBddINap",
        "outputId": "cd430a18-98c6-47dc-9443-08259bc77ab6"
      },
      "source": [
        "print(len(df))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "396030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.069391,
          "end_time": "2021-02-16T21:33:05.966183",
          "exception": false,
          "start_time": "2021-02-16T21:33:05.896792",
          "status": "completed"
        },
        "tags": [],
        "id": "QIbQ9wk0INap"
      },
      "source": [
        "***What is the total count of missing values per column?***\n",
        "\n",
        "We have missing values in emp_title, emp_length, title, revol_util, mort_acc and pub_rec_bankruptcies. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:06.105543Z",
          "iopub.status.busy": "2021-02-16T21:33:06.104904Z",
          "iopub.status.idle": "2021-02-16T21:33:06.761520Z",
          "shell.execute_reply": "2021-02-16T21:33:06.762077Z"
        },
        "papermill": {
          "duration": 0.728544,
          "end_time": "2021-02-16T21:33:06.762222",
          "exception": false,
          "start_time": "2021-02-16T21:33:06.033678",
          "status": "completed"
        },
        "tags": [],
        "id": "hex8lXLfINap",
        "outputId": "a8cfe20b-74d2-4f90-d393-9a83c01a8189"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "loan_amnt                   0\n",
              "term                        0\n",
              "int_rate                    0\n",
              "installment                 0\n",
              "grade                       0\n",
              "sub_grade                   0\n",
              "emp_title               22927\n",
              "emp_length              18301\n",
              "home_ownership              0\n",
              "annual_inc                  0\n",
              "verification_status         0\n",
              "issue_d                     0\n",
              "loan_status                 0\n",
              "purpose                     0\n",
              "title                    1755\n",
              "dti                         0\n",
              "earliest_cr_line            0\n",
              "open_acc                    0\n",
              "pub_rec                     0\n",
              "revol_bal                   0\n",
              "revol_util                276\n",
              "total_acc                   0\n",
              "initial_list_status         0\n",
              "application_type            0\n",
              "mort_acc                37795\n",
              "pub_rec_bankruptcies      535\n",
              "address                     0\n",
              "loan_repaid                 0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:06.902005Z",
          "iopub.status.busy": "2021-02-16T21:33:06.901261Z",
          "iopub.status.idle": "2021-02-16T21:33:06.912023Z",
          "shell.execute_reply": "2021-02-16T21:33:06.911501Z"
        },
        "papermill": {
          "duration": 0.082321,
          "end_time": "2021-02-16T21:33:06.912138",
          "exception": false,
          "start_time": "2021-02-16T21:33:06.829817",
          "status": "completed"
        },
        "tags": [],
        "id": "Dbhhm8nGINap",
        "outputId": "c3b1d6fa-58b8-4933-c228-d74255bfdc59"
      },
      "source": [
        "feat_info('emp_title')\n",
        "print('\\n')\n",
        "feat_info('emp_length')\n",
        "print('\\n')\n",
        "feat_info('title')\n",
        "print('\\n')\n",
        "feat_info('revol_util')\n",
        "print('\\n')\n",
        "feat_info('mort_acc')\n",
        "print('\\n')\n",
        "feat_info('pub_rec_bankruptcies')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The job title supplied by the Borrower when applying for the loan.*\n",
            "\n",
            "\n",
            "Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. \n",
            "\n",
            "\n",
            "The loan title provided by the borrower\n",
            "\n",
            "\n",
            "Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.\n",
            "\n",
            "\n",
            "Number of mortgage accounts.\n",
            "\n",
            "\n",
            "Number of public record bankruptcies\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.067996,
          "end_time": "2021-02-16T21:33:07.053260",
          "exception": false,
          "start_time": "2021-02-16T21:33:06.985264",
          "status": "completed"
        },
        "tags": [],
        "id": "DTNiUvu5INap"
      },
      "source": [
        "## Percentage of missing values per column\n",
        "* In the plot we can see how much data is missing as a percentage of the total data.\n",
        "* Notice that there is missing almost 10% of mortgage accounts, so we can not drop all those rows. \n",
        "* On the other hand, we could drop missing values in revol_util or pub_rec_bankruptcies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:07.243469Z",
          "iopub.status.busy": "2021-02-16T21:33:07.242797Z",
          "iopub.status.idle": "2021-02-16T21:33:08.300439Z",
          "shell.execute_reply": "2021-02-16T21:33:08.299842Z"
        },
        "papermill": {
          "duration": 1.131642,
          "end_time": "2021-02-16T21:33:08.300567",
          "exception": false,
          "start_time": "2021-02-16T21:33:07.168925",
          "status": "completed"
        },
        "tags": [],
        "id": "SBl8b2eEINaq",
        "outputId": "83660bbc-1e51-49ea-96a6-72aced691a43"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "((df.isnull().sum())/len(df)*100).plot.bar(title='Percentage of missing values per column', color='green')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa991a9e590>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.070568,
          "end_time": "2021-02-16T21:33:08.442072",
          "exception": false,
          "start_time": "2021-02-16T21:33:08.371504",
          "status": "completed"
        },
        "tags": [],
        "id": "kV28Ib4gINaq"
      },
      "source": [
        "Let's examine emp_title and emp_length to see whether it will be okay to drop them.\n",
        "\n",
        "***How many unique employment job titles are there?***\n",
        "\n",
        "Realistically there are too many unique job titles to try to convert this to a dummy variable feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:08.706632Z",
          "iopub.status.busy": "2021-02-16T21:33:08.602134Z",
          "iopub.status.idle": "2021-02-16T21:33:09.027599Z",
          "shell.execute_reply": "2021-02-16T21:33:09.027053Z"
        },
        "papermill": {
          "duration": 0.514985,
          "end_time": "2021-02-16T21:33:09.027722",
          "exception": false,
          "start_time": "2021-02-16T21:33:08.512737",
          "status": "completed"
        },
        "tags": [],
        "id": "aI4QdQ7uINaq",
        "outputId": "18f14588-c2a1-4cfb-f4f0-cf9d53fa190a"
      },
      "source": [
        "print(df['emp_title'].nunique())\n",
        "df['emp_title'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "173105\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Teacher                                4389\n",
              "Manager                                4250\n",
              "Registered Nurse                       1856\n",
              "RN                                     1846\n",
              "Supervisor                             1830\n",
              "                                       ... \n",
              "Meta Interfaces, LLC                      1\n",
              "lululemon athletica                       1\n",
              "Ten Oaks                                  1\n",
              "property accountability specialist        1\n",
              "DIRECTOR OF PROGRAMS                      1\n",
              "Name: emp_title, Length: 173105, dtype: int64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.07172,
          "end_time": "2021-02-16T21:33:09.171247",
          "exception": false,
          "start_time": "2021-02-16T21:33:09.099527",
          "status": "completed"
        },
        "tags": [],
        "id": "QqLzG-5TINaq"
      },
      "source": [
        "Let's drop emp_title:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:09.386105Z",
          "iopub.status.busy": "2021-02-16T21:33:09.360605Z",
          "iopub.status.idle": "2021-02-16T21:33:09.389931Z",
          "shell.execute_reply": "2021-02-16T21:33:09.389261Z"
        },
        "papermill": {
          "duration": 0.147413,
          "end_time": "2021-02-16T21:33:09.390046",
          "exception": false,
          "start_time": "2021-02-16T21:33:09.242633",
          "status": "completed"
        },
        "tags": [],
        "id": "pKUdVJmuINaq"
      },
      "source": [
        "df = df.drop('emp_title',axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.073189,
          "end_time": "2021-02-16T21:33:09.534727",
          "exception": false,
          "start_time": "2021-02-16T21:33:09.461538",
          "status": "completed"
        },
        "tags": [],
        "id": "O6TpAz0vINaq"
      },
      "source": [
        "Now we want the percentage of charge offs per category. Essentially informing us what percent of people per employment category didn't pay back their loan. \n",
        "* We can see that across the extremes it looks to be extremely similar.\n",
        "* Looks like this particular feature of employment length doesn't actually have some extreme differences on the charge off rates.\n",
        "* Looks like regardless of what actual employment length you have if you were to pick someone, about 20% of them are going to have not paid back their loans."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:09.686131Z",
          "iopub.status.busy": "2021-02-16T21:33:09.685407Z",
          "iopub.status.idle": "2021-02-16T21:33:10.140328Z",
          "shell.execute_reply": "2021-02-16T21:33:10.139673Z"
        },
        "papermill": {
          "duration": 0.533463,
          "end_time": "2021-02-16T21:33:10.140463",
          "exception": false,
          "start_time": "2021-02-16T21:33:09.607000",
          "status": "completed"
        },
        "tags": [],
        "id": "P0SIEQ9oINaq",
        "outputId": "6ecb7b15-bc64-497c-9fd4-dba7f5cd3ef9"
      },
      "source": [
        "per_charge_off = df[df['loan_repaid'] == 0]['emp_length'].value_counts() / df[df['loan_repaid'] == 1]['emp_length'].value_counts()\n",
        "per_charge_off.plot.bar(color='green')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa991a90f90>"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.073734,
          "end_time": "2021-02-16T21:33:10.287988",
          "exception": false,
          "start_time": "2021-02-16T21:33:10.214254",
          "status": "completed"
        },
        "tags": [],
        "id": "tsA2LmFFINaq"
      },
      "source": [
        "Let's drop emp_length:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:10.513432Z",
          "iopub.status.busy": "2021-02-16T21:33:10.512650Z",
          "iopub.status.idle": "2021-02-16T21:33:10.515709Z",
          "shell.execute_reply": "2021-02-16T21:33:10.516261Z"
        },
        "papermill": {
          "duration": 0.155952,
          "end_time": "2021-02-16T21:33:10.516407",
          "exception": false,
          "start_time": "2021-02-16T21:33:10.360455",
          "status": "completed"
        },
        "tags": [],
        "id": "0htxSv6hINaq"
      },
      "source": [
        "df = df.drop('emp_length', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.074247,
          "end_time": "2021-02-16T21:33:10.665347",
          "exception": false,
          "start_time": "2021-02-16T21:33:10.591100",
          "status": "completed"
        },
        "tags": [],
        "id": "fGaHW8rkINar"
      },
      "source": [
        "If we review the title column vs the purpose column looks like there is repeated information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:10.817101Z",
          "iopub.status.busy": "2021-02-16T21:33:10.816435Z",
          "iopub.status.idle": "2021-02-16T21:33:10.830628Z",
          "shell.execute_reply": "2021-02-16T21:33:10.830076Z"
        },
        "papermill": {
          "duration": 0.092349,
          "end_time": "2021-02-16T21:33:10.830772",
          "exception": false,
          "start_time": "2021-02-16T21:33:10.738423",
          "status": "completed"
        },
        "tags": [],
        "id": "IeFkzPe5INar",
        "outputId": "472a2c20-a7d9-4715-da0a-964a571f2fda"
      },
      "source": [
        "df[['title', 'purpose']].head(10)"
      ],
      "execution_count": null,
      "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>title</th>\n",
              "      <th>purpose</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Vacation</td>\n",
              "      <td>vacation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>debt_consolidation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>credit_card</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>credit_card</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Credit Card Refinance</td>\n",
              "      <td>credit_card</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>debt_consolidation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Home improvement</td>\n",
              "      <td>home_improvement</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>No More Credit Cards</td>\n",
              "      <td>credit_card</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>debt_consolidation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Debt Consolidation</td>\n",
              "      <td>debt_consolidation</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     title             purpose\n",
              "0                 Vacation            vacation\n",
              "1       Debt consolidation  debt_consolidation\n",
              "2  Credit card refinancing         credit_card\n",
              "3  Credit card refinancing         credit_card\n",
              "4    Credit Card Refinance         credit_card\n",
              "5       Debt consolidation  debt_consolidation\n",
              "6         Home improvement    home_improvement\n",
              "7     No More Credit Cards         credit_card\n",
              "8       Debt consolidation  debt_consolidation\n",
              "9       Debt Consolidation  debt_consolidation"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.073174,
          "end_time": "2021-02-16T21:33:10.977876",
          "exception": false,
          "start_time": "2021-02-16T21:33:10.904702",
          "status": "completed"
        },
        "tags": [],
        "id": "nIf02YjVINar"
      },
      "source": [
        "The title column is simply a string subcategory/description of the purpose column. Let's drop the column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:11.201540Z",
          "iopub.status.busy": "2021-02-16T21:33:11.145436Z",
          "iopub.status.idle": "2021-02-16T21:33:11.204639Z",
          "shell.execute_reply": "2021-02-16T21:33:11.203997Z"
        },
        "papermill": {
          "duration": 0.153423,
          "end_time": "2021-02-16T21:33:11.204769",
          "exception": false,
          "start_time": "2021-02-16T21:33:11.051346",
          "status": "completed"
        },
        "tags": [],
        "id": "9lp8qGA6INar"
      },
      "source": [
        "df = df.drop('title', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.072863,
          "end_time": "2021-02-16T21:33:11.350837",
          "exception": false,
          "start_time": "2021-02-16T21:33:11.277974",
          "status": "completed"
        },
        "tags": [],
        "id": "KLfaNZURINar"
      },
      "source": [
        "Now we are going to deal with the missing data of mort_acc. Since mort_acc has a strong correlation with total_acc we will group the dataframe by the total_acc and calculate the mean value for the mort_acc per total_acc entry. To get the result below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:11.561231Z",
          "iopub.status.busy": "2021-02-16T21:33:11.515979Z",
          "iopub.status.idle": "2021-02-16T21:33:11.616027Z",
          "shell.execute_reply": "2021-02-16T21:33:11.614921Z"
        },
        "papermill": {
          "duration": 0.192333,
          "end_time": "2021-02-16T21:33:11.616276",
          "exception": false,
          "start_time": "2021-02-16T21:33:11.423943",
          "status": "completed"
        },
        "tags": [],
        "id": "vKSc3hbXINar",
        "outputId": "57a2d9ce-48b0-4736-8ffd-2b6473c10c85"
      },
      "source": [
        "print(\"Mean of mort_acc column per total_acc\")\n",
        "total_acc_avg = df.groupby('total_acc').mean()['mort_acc']\n",
        "print(total_acc_avg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean of mort_acc column per total_acc\n",
            "total_acc\n",
            "2.0      0.000000\n",
            "3.0      0.052023\n",
            "4.0      0.066743\n",
            "5.0      0.103289\n",
            "6.0      0.151293\n",
            "           ...   \n",
            "124.0    1.000000\n",
            "129.0    1.000000\n",
            "135.0    3.000000\n",
            "150.0    2.000000\n",
            "151.0    0.000000\n",
            "Name: mort_acc, Length: 118, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.07373,
          "end_time": "2021-02-16T21:33:11.787556",
          "exception": false,
          "start_time": "2021-02-16T21:33:11.713826",
          "status": "completed"
        },
        "tags": [],
        "id": "khQVagtgINar"
      },
      "source": [
        "Let's fill in the missing mort_acc values based on their total_acc value. If the mort_acc is missing, then we will fill in that missing value with the mean value corresponding to its total_acc value from the Series we created above. This involves using an .apply() method with two columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:11.979521Z",
          "iopub.status.busy": "2021-02-16T21:33:11.957121Z",
          "iopub.status.idle": "2021-02-16T21:33:21.467382Z",
          "shell.execute_reply": "2021-02-16T21:33:21.466468Z"
        },
        "papermill": {
          "duration": 9.606045,
          "end_time": "2021-02-16T21:33:21.467580",
          "exception": false,
          "start_time": "2021-02-16T21:33:11.861535",
          "status": "completed"
        },
        "tags": [],
        "id": "JoTN0d3KINar"
      },
      "source": [
        "total_acc_avg = df.groupby('total_acc').mean()['mort_acc']\n",
        "\n",
        "def fill_mort_acc(total_acc,mort_acc):\n",
        "    '''\n",
        "    Accepts the total_acc and mort_acc values for the row.\n",
        "    Checks if the mort_acc is NaN , if so, it returns the avg mort_acc value\n",
        "    for the corresponding total_acc value for that row.\n",
        "    \n",
        "    total_acc_avg here should be a Series or dictionary containing the mapping of the\n",
        "    groupby averages of mort_acc per total_acc values.\n",
        "    '''\n",
        "    if np.isnan(mort_acc):\n",
        "        return total_acc_avg[total_acc]\n",
        "    else:\n",
        "        return mort_acc\n",
        "    \n",
        "df['mort_acc'] = df.apply(lambda x: fill_mort_acc(x['total_acc'], x['mort_acc']), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.074571,
          "end_time": "2021-02-16T21:33:21.616405",
          "exception": false,
          "start_time": "2021-02-16T21:33:21.541834",
          "status": "completed"
        },
        "tags": [],
        "id": "oJse887_INar"
      },
      "source": [
        "revol_util and the pub_rec_bankruptcies have missing data points, but they account for less than 0.5% of the total data. Let's remove the rows that are missing those values in those columns with dropna()."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:21.771863Z",
          "iopub.status.busy": "2021-02-16T21:33:21.770611Z",
          "iopub.status.idle": "2021-02-16T21:33:22.358648Z",
          "shell.execute_reply": "2021-02-16T21:33:22.358075Z"
        },
        "papermill": {
          "duration": 0.667161,
          "end_time": "2021-02-16T21:33:22.358780",
          "exception": false,
          "start_time": "2021-02-16T21:33:21.691619",
          "status": "completed"
        },
        "tags": [],
        "id": "aFkMwcIJINar"
      },
      "source": [
        "df = df.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:22.513141Z",
          "iopub.status.busy": "2021-02-16T21:33:22.511955Z",
          "iopub.status.idle": "2021-02-16T21:33:23.057960Z",
          "shell.execute_reply": "2021-02-16T21:33:23.057263Z"
        },
        "papermill": {
          "duration": 0.625011,
          "end_time": "2021-02-16T21:33:23.058084",
          "exception": false,
          "start_time": "2021-02-16T21:33:22.433073",
          "status": "completed"
        },
        "tags": [],
        "id": "2QYHpf6OINas",
        "outputId": "4a674c37-75c5-4dd5-c4d3-174268923ce2"
      },
      "source": [
        "# check for missing values\n",
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "loan_amnt               0\n",
              "term                    0\n",
              "int_rate                0\n",
              "installment             0\n",
              "grade                   0\n",
              "sub_grade               0\n",
              "home_ownership          0\n",
              "annual_inc              0\n",
              "verification_status     0\n",
              "issue_d                 0\n",
              "loan_status             0\n",
              "purpose                 0\n",
              "dti                     0\n",
              "earliest_cr_line        0\n",
              "open_acc                0\n",
              "pub_rec                 0\n",
              "revol_bal               0\n",
              "revol_util              0\n",
              "total_acc               0\n",
              "initial_list_status     0\n",
              "application_type        0\n",
              "mort_acc                0\n",
              "pub_rec_bankruptcies    0\n",
              "address                 0\n",
              "loan_repaid             0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.075002,
          "end_time": "2021-02-16T21:33:23.208460",
          "exception": false,
          "start_time": "2021-02-16T21:33:23.133458",
          "status": "completed"
        },
        "tags": [],
        "id": "6mm1V2u4INas"
      },
      "source": [
        "<a id=\"ch5\"></a>\n",
        "# 5. Categorical variables and dummy variables\n",
        "---\n",
        "We're done working with the missing data! Now we just need to deal with the string values due to the categorical columns."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.074821,
          "end_time": "2021-02-16T21:33:23.358799",
          "exception": false,
          "start_time": "2021-02-16T21:33:23.283978",
          "status": "completed"
        },
        "tags": [],
        "id": "upoLomjjINas"
      },
      "source": [
        "## Term feature\n",
        "Convert the term feature into either a 36 or 60 integer numeric data type using .apply() or .map()."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:23.616198Z",
          "iopub.status.busy": "2021-02-16T21:33:23.614133Z",
          "iopub.status.idle": "2021-02-16T21:33:24.041279Z",
          "shell.execute_reply": "2021-02-16T21:33:24.040459Z"
        },
        "papermill": {
          "duration": 0.608181,
          "end_time": "2021-02-16T21:33:24.041412",
          "exception": false,
          "start_time": "2021-02-16T21:33:23.433231",
          "status": "completed"
        },
        "tags": [],
        "id": "63Dg7QyUINas",
        "outputId": "ceea2a33-18e6-48a8-d137-f7125d5bf31a"
      },
      "source": [
        "print(df['term'].value_counts())\n",
        "print('\\n')\n",
        "print('\\n')\n",
        "\n",
        "df['term'] = df['term'].apply(lambda term: int(term[:3]))\n",
        "\n",
        "print(df['term'].value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " 36 months    301247\n",
            " 60 months     93972\n",
            "Name: term, dtype: int64\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "36    301247\n",
            "60     93972\n",
            "Name: term, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.075868,
          "end_time": "2021-02-16T21:33:24.194558",
          "exception": false,
          "start_time": "2021-02-16T21:33:24.118690",
          "status": "completed"
        },
        "tags": [],
        "id": "90l1fn9KINas"
      },
      "source": [
        "## Grade feature\n",
        "We already know grade is part of sub_grade, so just drop the grade feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:24.352132Z",
          "iopub.status.busy": "2021-02-16T21:33:24.351401Z",
          "iopub.status.idle": "2021-02-16T21:33:24.421135Z",
          "shell.execute_reply": "2021-02-16T21:33:24.420310Z"
        },
        "papermill": {
          "duration": 0.150623,
          "end_time": "2021-02-16T21:33:24.421260",
          "exception": false,
          "start_time": "2021-02-16T21:33:24.270637",
          "status": "completed"
        },
        "tags": [],
        "id": "IPgrLdININas"
      },
      "source": [
        "df = df.drop('grade', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.075722,
          "end_time": "2021-02-16T21:33:24.572461",
          "exception": false,
          "start_time": "2021-02-16T21:33:24.496739",
          "status": "completed"
        },
        "tags": [],
        "id": "RYWYQ7OpINas"
      },
      "source": [
        "Let's convert the subgrade into dummy variables. Then concatenate these new columns to the original dataframe. Remember to drop the original subgrade column and to add drop_first=True to your get_dummies call."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:24.739353Z",
          "iopub.status.busy": "2021-02-16T21:33:24.738637Z",
          "iopub.status.idle": "2021-02-16T21:33:24.976161Z",
          "shell.execute_reply": "2021-02-16T21:33:24.975432Z"
        },
        "papermill": {
          "duration": 0.327127,
          "end_time": "2021-02-16T21:33:24.976278",
          "exception": false,
          "start_time": "2021-02-16T21:33:24.649151",
          "status": "completed"
        },
        "tags": [],
        "id": "-2hYPrc2INas"
      },
      "source": [
        "subgrade_dummies = pd.get_dummies(df['sub_grade'],drop_first=True)\n",
        "df = pd.concat([df.drop('sub_grade',axis=1),subgrade_dummies],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.074726,
          "end_time": "2021-02-16T21:33:25.131409",
          "exception": false,
          "start_time": "2021-02-16T21:33:25.056683",
          "status": "completed"
        },
        "tags": [],
        "id": "hZ6mUL1NINas"
      },
      "source": [
        "## Verification_status, application_type, initial_list_status, purpose features\n",
        "Let's convert these columns into dummy variables and concatenate them with the original dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:25.289735Z",
          "iopub.status.busy": "2021-02-16T21:33:25.289072Z",
          "iopub.status.idle": "2021-02-16T21:33:25.688033Z",
          "shell.execute_reply": "2021-02-16T21:33:25.687425Z"
        },
        "papermill": {
          "duration": 0.481647,
          "end_time": "2021-02-16T21:33:25.688162",
          "exception": false,
          "start_time": "2021-02-16T21:33:25.206515",
          "status": "completed"
        },
        "tags": [],
        "id": "BkYT3PMDINas"
      },
      "source": [
        "dummies = pd.get_dummies(df[['verification_status', 'application_type','initial_list_status','purpose']], drop_first=True)\n",
        "\n",
        "df = df.drop(['verification_status', 'application_type','initial_list_status','purpose'],axis=1)\n",
        "\n",
        "df = pd.concat([df,dummies],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.075589,
          "end_time": "2021-02-16T21:33:25.840168",
          "exception": false,
          "start_time": "2021-02-16T21:33:25.764579",
          "status": "completed"
        },
        "tags": [],
        "id": "44oknHQuINas"
      },
      "source": [
        "## Home_ownership feature\n",
        "Convert these to dummy variables, but replace NONE and ANY with OTHER, so that we end up with just 4 categories, MORTGAGE, RENT, OWN, OTHER. Then concatenate them with the original dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:26.034125Z",
          "iopub.status.busy": "2021-02-16T21:33:26.033382Z",
          "iopub.status.idle": "2021-02-16T21:33:26.227458Z",
          "shell.execute_reply": "2021-02-16T21:33:26.226790Z"
        },
        "papermill": {
          "duration": 0.31071,
          "end_time": "2021-02-16T21:33:26.227570",
          "exception": false,
          "start_time": "2021-02-16T21:33:25.916860",
          "status": "completed"
        },
        "tags": [],
        "id": "-XW6PHeoINat"
      },
      "source": [
        "df['home_ownership'] = df['home_ownership'].replace(['NONE', 'ANY'], 'OTHER')\n",
        "dummies = pd.get_dummies(df['home_ownership'],drop_first=True)\n",
        "df = df.drop('home_ownership',axis=1)\n",
        "df = pd.concat([df,dummies],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.075559,
          "end_time": "2021-02-16T21:33:26.379189",
          "exception": false,
          "start_time": "2021-02-16T21:33:26.303630",
          "status": "completed"
        },
        "tags": [],
        "id": "MEGe7ZH9INat"
      },
      "source": [
        "## Address feature\n",
        "Let's feature engineer a zip code column from the address in the data set. Create a column called 'zip_code' that extracts the zip code from the address column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:26.536929Z",
          "iopub.status.busy": "2021-02-16T21:33:26.536283Z",
          "iopub.status.idle": "2021-02-16T21:33:26.878376Z",
          "shell.execute_reply": "2021-02-16T21:33:26.877654Z"
        },
        "papermill": {
          "duration": 0.424019,
          "end_time": "2021-02-16T21:33:26.878504",
          "exception": false,
          "start_time": "2021-02-16T21:33:26.454485",
          "status": "completed"
        },
        "tags": [],
        "id": "f00zXNCxINat"
      },
      "source": [
        "df['zip_code'] = df['address'].apply(lambda address:address[-5:])\n",
        "\n",
        "dummies = pd.get_dummies(df['zip_code'],drop_first=True)\n",
        "df = df.drop(['zip_code','address'],axis=1)\n",
        "df = pd.concat([df,dummies],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.075763,
          "end_time": "2021-02-16T21:33:27.030927",
          "exception": false,
          "start_time": "2021-02-16T21:33:26.955164",
          "status": "completed"
        },
        "tags": [],
        "id": "tS_BXUFpINat"
      },
      "source": [
        "## Issue_d feature\n",
        "This would be data leakage, we wouldn't know beforehand whether or not a loan would be issued when using our model, so in theory we wouldn't have an issue_date, drop this feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:27.187881Z",
          "iopub.status.busy": "2021-02-16T21:33:27.187226Z",
          "iopub.status.idle": "2021-02-16T21:33:27.265038Z",
          "shell.execute_reply": "2021-02-16T21:33:27.264323Z"
        },
        "papermill": {
          "duration": 0.158532,
          "end_time": "2021-02-16T21:33:27.265154",
          "exception": false,
          "start_time": "2021-02-16T21:33:27.106622",
          "status": "completed"
        },
        "tags": [],
        "id": "Rj_ZsG7mINat"
      },
      "source": [
        "df = df.drop('issue_d', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.075496,
          "end_time": "2021-02-16T21:33:27.416681",
          "exception": false,
          "start_time": "2021-02-16T21:33:27.341185",
          "status": "completed"
        },
        "tags": [],
        "id": "DyRuh9OZINat"
      },
      "source": [
        "## Earliest_cr_line feature\n",
        "This appears to be a historical time stamp feature. Extract the year from this feature using a .apply function, then convert it to a numeric feature. Set this new data to a feature column called 'earliest_cr_year'. Then drop the earliest_cr_line feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:27.574512Z",
          "iopub.status.busy": "2021-02-16T21:33:27.573873Z",
          "iopub.status.idle": "2021-02-16T21:33:27.997585Z",
          "shell.execute_reply": "2021-02-16T21:33:27.998128Z"
        },
        "papermill": {
          "duration": 0.506055,
          "end_time": "2021-02-16T21:33:27.998269",
          "exception": false,
          "start_time": "2021-02-16T21:33:27.492214",
          "status": "completed"
        },
        "tags": [],
        "id": "bh3B0lSdINat",
        "outputId": "db242d7a-0ca9-4b7c-8e2e-8d86a348befd"
      },
      "source": [
        "df['earliest_cr_year'] = df['earliest_cr_line'].apply(lambda date:int(date[-4:]))\n",
        "df = df.drop('earliest_cr_line', axis=1)\n",
        "\n",
        "df.select_dtypes(['object']).columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['loan_status'], dtype='object')"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:28.170503Z",
          "iopub.status.busy": "2021-02-16T21:33:28.168326Z",
          "iopub.status.idle": "2021-02-16T21:33:28.189018Z",
          "shell.execute_reply": "2021-02-16T21:33:28.188400Z"
        },
        "papermill": {
          "duration": 0.114328,
          "end_time": "2021-02-16T21:33:28.189133",
          "exception": false,
          "start_time": "2021-02-16T21:33:28.074805",
          "status": "completed"
        },
        "tags": [],
        "id": "LfmJX-8WINat"
      },
      "source": [
        "df = df.drop('loan_status',axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.077153,
          "end_time": "2021-02-16T21:33:28.343353",
          "exception": false,
          "start_time": "2021-02-16T21:33:28.266200",
          "status": "completed"
        },
        "tags": [],
        "id": "WHzX9UTlINat"
      },
      "source": [
        "<a id=\"ch6\"></a>\n",
        "# 6. Scaling and train test split\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:28.519690Z",
          "iopub.status.busy": "2021-02-16T21:33:28.519006Z",
          "iopub.status.idle": "2021-02-16T21:33:29.036425Z",
          "shell.execute_reply": "2021-02-16T21:33:29.035836Z"
        },
        "papermill": {
          "duration": 0.616029,
          "end_time": "2021-02-16T21:33:29.036548",
          "exception": false,
          "start_time": "2021-02-16T21:33:28.420519",
          "status": "completed"
        },
        "tags": [],
        "id": "Cu0va8gDINat"
      },
      "source": [
        "# Features\n",
        "X = df.drop('loan_repaid',axis=1).values\n",
        "\n",
        "# Label\n",
        "y = df['loan_repaid'].values\n",
        "\n",
        "# Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:29.196477Z",
          "iopub.status.busy": "2021-02-16T21:33:29.195843Z",
          "iopub.status.idle": "2021-02-16T21:33:29.200028Z",
          "shell.execute_reply": "2021-02-16T21:33:29.199394Z"
        },
        "papermill": {
          "duration": 0.086289,
          "end_time": "2021-02-16T21:33:29.200137",
          "exception": false,
          "start_time": "2021-02-16T21:33:29.113848",
          "status": "completed"
        },
        "tags": [],
        "id": "6VduZQ1yINat",
        "outputId": "f90b15f2-949d-4ca3-876f-e23d4c6eda43"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(316175, 78)\n",
            "(79044, 78)\n",
            "(316175,)\n",
            "(79044,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.076661,
          "end_time": "2021-02-16T21:33:29.354096",
          "exception": false,
          "start_time": "2021-02-16T21:33:29.277435",
          "status": "completed"
        },
        "tags": [],
        "id": "w0xEE2ubINat"
      },
      "source": [
        "## Normalizing / scaling the data\n",
        "We scale the feature data. To prevent data leakage from the test set, we only fit our scaler to the training set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:29.514597Z",
          "iopub.status.busy": "2021-02-16T21:33:29.513974Z",
          "iopub.status.idle": "2021-02-16T21:33:29.857691Z",
          "shell.execute_reply": "2021-02-16T21:33:29.858309Z"
        },
        "papermill": {
          "duration": 0.427182,
          "end_time": "2021-02-16T21:33:29.858459",
          "exception": false,
          "start_time": "2021-02-16T21:33:29.431277",
          "status": "completed"
        },
        "tags": [],
        "id": "ZOVW_P44INat",
        "outputId": "a2750f80-96c3-4566-da49-bde37133fe31"
      },
      "source": [
        "scaler = MinMaxScaler()\n",
        "\n",
        "# fit and transfrom\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# everything has been scaled between 1 and 0\n",
        "print('Max: ',X_train.max())\n",
        "print('Min: ', X_train.min())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max:  1.0\n",
            "Min:  0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.077531,
          "end_time": "2021-02-16T21:33:30.014089",
          "exception": false,
          "start_time": "2021-02-16T21:33:29.936558",
          "status": "completed"
        },
        "tags": [],
        "id": "w0yaKJVBINau"
      },
      "source": [
        "<a id=\"ch7\"></a>\n",
        "# 7. Creating a model\n",
        "---\n",
        "**Dropout Layers**\n",
        "* Dropout is a technique where randomly selected neurons are ignored during training. They are “dropped-out” randomly.\n",
        "* Simply put, dropout refers to ignoring units (i.e. neurons) during the training phase of certain set of neurons which is chosen at random.\n",
        "* Helps prevent overfitting."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:30.178891Z",
          "iopub.status.busy": "2021-02-16T21:33:30.178005Z",
          "iopub.status.idle": "2021-02-16T21:33:30.265031Z",
          "shell.execute_reply": "2021-02-16T21:33:30.264470Z"
        },
        "papermill": {
          "duration": 0.173012,
          "end_time": "2021-02-16T21:33:30.265152",
          "exception": false,
          "start_time": "2021-02-16T21:33:30.092140",
          "status": "completed"
        },
        "tags": [],
        "id": "b2TIjeU0INau"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "# input layer\n",
        "model.add(Dense(78,activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# hidden layer\n",
        "model.add(Dense(39,activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# hidden layer\n",
        "model.add(Dense(19,activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# output layer\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# compile model\n",
        "model.compile(optimizer=\"adam\", loss='binary_crossentropy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.078069,
          "end_time": "2021-02-16T21:33:30.421513",
          "exception": false,
          "start_time": "2021-02-16T21:33:30.343444",
          "status": "completed"
        },
        "tags": [],
        "id": "Sx0xrsZmINau"
      },
      "source": [
        "## Early stopping\n",
        "This callback allows you to specify the performance measure to monitor, the trigger, and once triggered, it will stop the training process. Basically, it stop training when a monitored quantity has stopped improving."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:30.583563Z",
          "iopub.status.busy": "2021-02-16T21:33:30.582596Z",
          "iopub.status.idle": "2021-02-16T21:33:30.585697Z",
          "shell.execute_reply": "2021-02-16T21:33:30.585209Z"
        },
        "papermill": {
          "duration": 0.08619,
          "end_time": "2021-02-16T21:33:30.585846",
          "exception": false,
          "start_time": "2021-02-16T21:33:30.499656",
          "status": "completed"
        },
        "tags": [],
        "id": "8B_T5zRqINau"
      },
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=25)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.077667,
          "end_time": "2021-02-16T21:33:30.743376",
          "exception": false,
          "start_time": "2021-02-16T21:33:30.665709",
          "status": "completed"
        },
        "tags": [],
        "id": "HJ-1wrkxINau"
      },
      "source": [
        "<a id=\"ch8\"></a>\n",
        "# 8. Training the model\n",
        "---\n",
        "Now that the model is ready, we can fit the model into the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-output": true,
        "execution": {
          "iopub.execute_input": "2021-02-16T21:33:30.906779Z",
          "iopub.status.busy": "2021-02-16T21:33:30.905823Z",
          "iopub.status.idle": "2021-02-16T21:37:29.750479Z",
          "shell.execute_reply": "2021-02-16T21:37:29.749946Z"
        },
        "papermill": {
          "duration": 238.928975,
          "end_time": "2021-02-16T21:37:29.750605",
          "exception": false,
          "start_time": "2021-02-16T21:33:30.821630",
          "status": "completed"
        },
        "tags": [],
        "id": "8REPjVfAINau",
        "outputId": "6823278e-323c-4612-bce0-8c6e7248b0ff"
      },
      "source": [
        "model.fit(x=X_train, \n",
        "          y=y_train, \n",
        "          epochs=400,\n",
        "          verbose = 2,\n",
        "          batch_size=256,\n",
        "          validation_data=(X_test, y_test),\n",
        "          callbacks=[early_stop])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/400\n",
            "1236/1236 - 3s - loss: 0.2972 - val_loss: 0.2642\n",
            "Epoch 2/400\n",
            "1236/1236 - 3s - loss: 0.2650 - val_loss: 0.2632\n",
            "Epoch 3/400\n",
            "1236/1236 - 4s - loss: 0.2627 - val_loss: 0.2623\n",
            "Epoch 4/400\n",
            "1236/1236 - 3s - loss: 0.2616 - val_loss: 0.2615\n",
            "Epoch 5/400\n",
            "1236/1236 - 3s - loss: 0.2611 - val_loss: 0.2614\n",
            "Epoch 6/400\n",
            "1236/1236 - 3s - loss: 0.2604 - val_loss: 0.2617\n",
            "Epoch 7/400\n",
            "1236/1236 - 3s - loss: 0.2597 - val_loss: 0.2615\n",
            "Epoch 8/400\n",
            "1236/1236 - 3s - loss: 0.2594 - val_loss: 0.2612\n",
            "Epoch 9/400\n",
            "1236/1236 - 3s - loss: 0.2593 - val_loss: 0.2612\n",
            "Epoch 10/400\n",
            "1236/1236 - 3s - loss: 0.2590 - val_loss: 0.2614\n",
            "Epoch 11/400\n",
            "1236/1236 - 3s - loss: 0.2587 - val_loss: 0.2617\n",
            "Epoch 12/400\n",
            "1236/1236 - 3s - loss: 0.2583 - val_loss: 0.2615\n",
            "Epoch 13/400\n",
            "1236/1236 - 3s - loss: 0.2583 - val_loss: 0.2617\n",
            "Epoch 14/400\n",
            "1236/1236 - 3s - loss: 0.2580 - val_loss: 0.2608\n",
            "Epoch 15/400\n",
            "1236/1236 - 3s - loss: 0.2578 - val_loss: 0.2616\n",
            "Epoch 16/400\n",
            "1236/1236 - 3s - loss: 0.2576 - val_loss: 0.2610\n",
            "Epoch 17/400\n",
            "1236/1236 - 3s - loss: 0.2570 - val_loss: 0.2611\n",
            "Epoch 18/400\n",
            "1236/1236 - 3s - loss: 0.2572 - val_loss: 0.2612\n",
            "Epoch 19/400\n",
            "1236/1236 - 3s - loss: 0.2569 - val_loss: 0.2618\n",
            "Epoch 20/400\n",
            "1236/1236 - 3s - loss: 0.2569 - val_loss: 0.2625\n",
            "Epoch 21/400\n",
            "1236/1236 - 3s - loss: 0.2565 - val_loss: 0.2620\n",
            "Epoch 22/400\n",
            "1236/1236 - 3s - loss: 0.2565 - val_loss: 0.2607\n",
            "Epoch 23/400\n",
            "1236/1236 - 3s - loss: 0.2561 - val_loss: 0.2611\n",
            "Epoch 24/400\n",
            "1236/1236 - 3s - loss: 0.2562 - val_loss: 0.2613\n",
            "Epoch 25/400\n",
            "1236/1236 - 3s - loss: 0.2558 - val_loss: 0.2614\n",
            "Epoch 26/400\n",
            "1236/1236 - 3s - loss: 0.2557 - val_loss: 0.2610\n",
            "Epoch 27/400\n",
            "1236/1236 - 3s - loss: 0.2556 - val_loss: 0.2618\n",
            "Epoch 28/400\n",
            "1236/1236 - 3s - loss: 0.2553 - val_loss: 0.2616\n",
            "Epoch 29/400\n",
            "1236/1236 - 3s - loss: 0.2554 - val_loss: 0.2614\n",
            "Epoch 30/400\n",
            "1236/1236 - 3s - loss: 0.2555 - val_loss: 0.2613\n",
            "Epoch 31/400\n",
            "1236/1236 - 3s - loss: 0.2550 - val_loss: 0.2610\n",
            "Epoch 32/400\n",
            "1236/1236 - 4s - loss: 0.2550 - val_loss: 0.2609\n",
            "Epoch 33/400\n",
            "1236/1236 - 4s - loss: 0.2547 - val_loss: 0.2611\n",
            "Epoch 34/400\n",
            "1236/1236 - 3s - loss: 0.2548 - val_loss: 0.2607\n",
            "Epoch 35/400\n",
            "1236/1236 - 3s - loss: 0.2544 - val_loss: 0.2615\n",
            "Epoch 36/400\n",
            "1236/1236 - 3s - loss: 0.2544 - val_loss: 0.2611\n",
            "Epoch 37/400\n",
            "1236/1236 - 3s - loss: 0.2541 - val_loss: 0.2611\n",
            "Epoch 38/400\n",
            "1236/1236 - 3s - loss: 0.2542 - val_loss: 0.2611\n",
            "Epoch 39/400\n",
            "1236/1236 - 3s - loss: 0.2540 - val_loss: 0.2619\n",
            "Epoch 40/400\n",
            "1236/1236 - 3s - loss: 0.2541 - val_loss: 0.2615\n",
            "Epoch 41/400\n",
            "1236/1236 - 3s - loss: 0.2537 - val_loss: 0.2616\n",
            "Epoch 42/400\n",
            "1236/1236 - 4s - loss: 0.2536 - val_loss: 0.2609\n",
            "Epoch 43/400\n",
            "1236/1236 - 3s - loss: 0.2534 - val_loss: 0.2614\n",
            "Epoch 44/400\n",
            "1236/1236 - 3s - loss: 0.2537 - val_loss: 0.2614\n",
            "Epoch 45/400\n",
            "1236/1236 - 3s - loss: 0.2535 - val_loss: 0.2607\n",
            "Epoch 46/400\n",
            "1236/1236 - 3s - loss: 0.2535 - val_loss: 0.2615\n",
            "Epoch 47/400\n",
            "1236/1236 - 3s - loss: 0.2532 - val_loss: 0.2612\n",
            "Epoch 48/400\n",
            "1236/1236 - 3s - loss: 0.2533 - val_loss: 0.2606\n",
            "Epoch 49/400\n",
            "1236/1236 - 3s - loss: 0.2529 - val_loss: 0.2627\n",
            "Epoch 50/400\n",
            "1236/1236 - 3s - loss: 0.2530 - val_loss: 0.2609\n",
            "Epoch 51/400\n",
            "1236/1236 - 3s - loss: 0.2531 - val_loss: 0.2611\n",
            "Epoch 52/400\n",
            "1236/1236 - 3s - loss: 0.2527 - val_loss: 0.2609\n",
            "Epoch 53/400\n",
            "1236/1236 - 3s - loss: 0.2528 - val_loss: 0.2619\n",
            "Epoch 54/400\n",
            "1236/1236 - 3s - loss: 0.2526 - val_loss: 0.2611\n",
            "Epoch 55/400\n",
            "1236/1236 - 3s - loss: 0.2527 - val_loss: 0.2616\n",
            "Epoch 56/400\n",
            "1236/1236 - 3s - loss: 0.2524 - val_loss: 0.2618\n",
            "Epoch 57/400\n",
            "1236/1236 - 3s - loss: 0.2525 - val_loss: 0.2625\n",
            "Epoch 58/400\n",
            "1236/1236 - 3s - loss: 0.2526 - val_loss: 0.2622\n",
            "Epoch 59/400\n",
            "1236/1236 - 3s - loss: 0.2527 - val_loss: 0.2616\n",
            "Epoch 60/400\n",
            "1236/1236 - 3s - loss: 0.2521 - val_loss: 0.2617\n",
            "Epoch 61/400\n",
            "1236/1236 - 3s - loss: 0.2525 - val_loss: 0.2610\n",
            "Epoch 62/400\n",
            "1236/1236 - 3s - loss: 0.2522 - val_loss: 0.2627\n",
            "Epoch 63/400\n",
            "1236/1236 - 3s - loss: 0.2523 - val_loss: 0.2617\n",
            "Epoch 64/400\n",
            "1236/1236 - 3s - loss: 0.2520 - val_loss: 0.2612\n",
            "Epoch 65/400\n",
            "1236/1236 - 3s - loss: 0.2519 - val_loss: 0.2617\n",
            "Epoch 66/400\n",
            "1236/1236 - 3s - loss: 0.2519 - val_loss: 0.2613\n",
            "Epoch 67/400\n",
            "1236/1236 - 3s - loss: 0.2521 - val_loss: 0.2618\n",
            "Epoch 68/400\n",
            "1236/1236 - 3s - loss: 0.2518 - val_loss: 0.2606\n",
            "Epoch 69/400\n",
            "1236/1236 - 3s - loss: 0.2521 - val_loss: 0.2612\n",
            "Epoch 70/400\n",
            "1236/1236 - 3s - loss: 0.2517 - val_loss: 0.2613\n",
            "Epoch 71/400\n",
            "1236/1236 - 3s - loss: 0.2516 - val_loss: 0.2617\n",
            "Epoch 72/400\n",
            "1236/1236 - 4s - loss: 0.2520 - val_loss: 0.2618\n",
            "Epoch 73/400\n",
            "1236/1236 - 3s - loss: 0.2516 - val_loss: 0.2617\n",
            "Epoch 00073: early stopping\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fa9918fccd0>"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.127216,
          "end_time": "2021-02-16T21:37:30.005382",
          "exception": false,
          "start_time": "2021-02-16T21:37:29.878166",
          "status": "completed"
        },
        "tags": [],
        "id": "Xm3DkK5jINau"
      },
      "source": [
        "## Training loss per epoch\n",
        "* This plot shows the training loss per epoch.\n",
        "* This plot helps us to see if there is overfitting in the model. In this case there is no overfitting because both lines go down at the same time. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:37:30.267652Z",
          "iopub.status.busy": "2021-02-16T21:37:30.266615Z",
          "iopub.status.idle": "2021-02-16T21:37:30.629800Z",
          "shell.execute_reply": "2021-02-16T21:37:30.628689Z"
        },
        "papermill": {
          "duration": 0.498052,
          "end_time": "2021-02-16T21:37:30.629937",
          "exception": false,
          "start_time": "2021-02-16T21:37:30.131885",
          "status": "completed"
        },
        "tags": [],
        "id": "S5gp8k4UINau",
        "outputId": "57f0e47d-79e2-4b78-a395-1b974bf4b043"
      },
      "source": [
        "losses = pd.DataFrame(model.history.history)\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "sns.lineplot(data=losses,lw=3)\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('')\n",
        "plt.title('Training Loss per Epoch')\n",
        "sns.despine()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.128835,
          "end_time": "2021-02-16T21:37:30.892302",
          "exception": false,
          "start_time": "2021-02-16T21:37:30.763467",
          "status": "completed"
        },
        "tags": [],
        "id": "AGDPROxyINau"
      },
      "source": [
        "<a id=\"ch9\"></a>\n",
        "# 9. Evaluation on test data\n",
        "---\n",
        "***Classification Report***\n",
        "* **Accuracy** is just the actual percent that we got right, in this case it was 89%.\n",
        "* Note that since the data is imbalance if we were to make a model that approve all the loans, it would have a 80% accuracy.\n",
        "* For example: (317696/len(df)) = 0.80 where 317696 is `df[loan_repaid].value_counts()`\n",
        "* So do not be fooled by a model that returns back 80% accuracy because by default a model that always reports back to the loan will be repaid itself will be 80% accurate on this actual test data set.\n",
        "\n",
        "\n",
        "* The **recall** means \"how many of this class you find over the whole number of element of this class\"\n",
        "* The **precision** will be \"how many are correctly classified among that class\"\n",
        "* The **f1-score** is the harmonic mean between precision & recall\n",
        "* The **support** is the number of occurence of the given class in your dataset.\n",
        "\n",
        "\n",
        "* Precision is 0.96, which is really good. On the other hand, recall is not good. \n",
        "* We should focus in improving the f1-score in the 0 class. We should improve the 0.62.\n",
        "\n",
        "***Confusion Matirx***\n",
        "* A confusion matrix is a technique for summarizing the performance of a classification algorithm.\n",
        "* Classification accuracy alone can be misleading if you have an unequal number of observations in each class, which is our case. \n",
        "\n",
        "\n",
        "* We have 308 Type I errors (False Positive) and 8562 Type II errors (False Negative). \n",
        "* 7096 True Positive and 63078 True Negative. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "execution": {
          "iopub.execute_input": "2021-02-16T21:37:31.161923Z",
          "iopub.status.busy": "2021-02-16T21:37:31.160590Z",
          "iopub.status.idle": "2021-02-16T21:37:32.975272Z",
          "shell.execute_reply": "2021-02-16T21:37:32.975810Z"
        },
        "papermill": {
          "duration": 1.953116,
          "end_time": "2021-02-16T21:37:32.975971",
          "exception": false,
          "start_time": "2021-02-16T21:37:31.022855",
          "status": "completed"
        },
        "tags": [],
        "id": "ntdZM6p3INau",
        "outputId": "f912330b-42d6-4bf7-89c1-d915e889c0c5"
      },
      "source": [
        "predictions = model.predict_classes(X_test)\n",
        "\n",
        "print('Classification Report:')\n",
        "print(classification_report(y_test, predictions))\n",
        "print('\\n')\n",
        "print('Confusion Matirx:')\n",
        "print(confusion_matrix(y_test, predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.44      0.61     15658\n",
            "           1       0.88      1.00      0.93     63386\n",
            "\n",
            "    accuracy                           0.89     79044\n",
            "   macro avg       0.93      0.72      0.77     79044\n",
            "weighted avg       0.90      0.89      0.87     79044\n",
            "\n",
            "\n",
            "\n",
            "Confusion Matirx:\n",
            "[[ 6919  8739]\n",
            " [  181 63205]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.128299,
          "end_time": "2021-02-16T21:37:33.233710",
          "exception": false,
          "start_time": "2021-02-16T21:37:33.105411",
          "status": "completed"
        },
        "tags": [],
        "id": "KOklbOR5INau"
      },
      "source": [
        "<a id=\"ch10\"></a>\n",
        "# 10. Predicting on a  new customer\n",
        "---\n",
        "***Would you offer this person a loan?***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:37:33.518987Z",
          "iopub.status.busy": "2021-02-16T21:37:33.517537Z",
          "iopub.status.idle": "2021-02-16T21:37:33.551819Z",
          "shell.execute_reply": "2021-02-16T21:37:33.552386Z"
        },
        "papermill": {
          "duration": 0.18998,
          "end_time": "2021-02-16T21:37:33.552543",
          "exception": false,
          "start_time": "2021-02-16T21:37:33.362563",
          "status": "completed"
        },
        "tags": [],
        "id": "gsUGp02OINau",
        "outputId": "5f344f71-c94a-4261-e798-4601961e5900"
      },
      "source": [
        "rnd.seed(101)\n",
        "random_ind = rnd.randint(0,len(df))\n",
        "\n",
        "new_customer = df.drop('loan_repaid',axis=1).iloc[random_ind]\n",
        "new_customer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "loan_amnt           25000.00\n",
              "term                   60.00\n",
              "int_rate               18.24\n",
              "installment           638.11\n",
              "annual_inc          61665.00\n",
              "                      ...   \n",
              "48052                   0.00\n",
              "70466                   0.00\n",
              "86630                   0.00\n",
              "93700                   0.00\n",
              "earliest_cr_year     1996.00\n",
              "Name: 305323, Length: 78, dtype: float64"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:37:33.825027Z",
          "iopub.status.busy": "2021-02-16T21:37:33.823958Z",
          "iopub.status.idle": "2021-02-16T21:37:33.867543Z",
          "shell.execute_reply": "2021-02-16T21:37:33.866688Z"
        },
        "papermill": {
          "duration": 0.182681,
          "end_time": "2021-02-16T21:37:33.867667",
          "exception": false,
          "start_time": "2021-02-16T21:37:33.684986",
          "status": "completed"
        },
        "tags": [],
        "id": "Q5H27uqeINau",
        "outputId": "19efb85f-5d6a-444a-e6dd-3b16bdd35da9"
      },
      "source": [
        "# we need to reshape this to be in the same shape of the training data that the model was trained on\n",
        "model.predict_classes(new_customer.values.reshape(1,78))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1]], dtype=int32)"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.131149,
          "end_time": "2021-02-16T21:37:34.128642",
          "exception": false,
          "start_time": "2021-02-16T21:37:33.997493",
          "status": "completed"
        },
        "tags": [],
        "id": "qC-gHCY-INav"
      },
      "source": [
        "***Did this person actually end up paying back their loan?***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-16T21:37:34.395673Z",
          "iopub.status.busy": "2021-02-16T21:37:34.394920Z",
          "iopub.status.idle": "2021-02-16T21:37:34.400507Z",
          "shell.execute_reply": "2021-02-16T21:37:34.399842Z"
        },
        "papermill": {
          "duration": 0.142196,
          "end_time": "2021-02-16T21:37:34.400622",
          "exception": false,
          "start_time": "2021-02-16T21:37:34.258426",
          "status": "completed"
        },
        "tags": [],
        "id": "ukOJB748INav",
        "outputId": "804ab264-3dc9-4dc5-d31c-be0820bd5db2"
      },
      "source": [
        "# the prediction was right\n",
        "df.iloc[random_ind]['loan_repaid']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    }
  ]
}