Credit Risk Prediction.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"15YExFNEfuDHNko09eEeVruE9el_Tnj2_","timestamp":1682338047949}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **Credit Risk Prediction**\n"],"metadata":{"id":"aAmA1Tr1ybJK"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CRRSAood-qCM","outputId":"0d1410b8-1579-49b3-9ca7-d64a5cc2e0b7","executionInfo":{"status":"ok","timestamp":1682375329016,"user_tz":-60,"elapsed":26913,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}],"source":["# Access files from Google Drive\n","from google.colab import drive \n","drive.mount('/content/gdrive')"]},{"cell_type":"markdown","source":["# 1. Exploring the dataset"],"metadata":{"id":"S2Oor1fPEFnR"}},{"cell_type":"markdown","source":["* In this dataset, each entry represents a person who takes a credit by a bank. \n","\n","* The target is: **loan_status** (0 or 1) \n","  * 1 in case of default.\n","  * 0 otherwise.\n","* It is a **binary classification problem**. \n","\n","* The dataset is **highly imbalanced**.\n","\n","* We would like to map the following features to the binary target. \n","  * **person_age** (numerical) refers to the age.\n","  *   **person_income** (numerical) refers to the annual income.\n","  *   **person_home_ownership** (categorical: 'MORTGAGE', 'RENT', 'OWN', 'OTHER').\n","  *   **person_emp_length** (numerical) refers to the employment length (in years). \n","  *   **loan_intent** (categorical: 'VENTURE', 'DEBTCONSOLIDATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'EDUCATION', 'PERSONAL') refers to the loan purpose.\n","  *   **loan_grade** (categorical: 'A', 'C', 'D', 'B', 'E', 'F','G').\n","  *   **loan_amnt** (numerical) refers to the loan amount.\n","  *   **loan_int_rate** (numerical) refers to the loan interest rate.\n","  *   **loan_percent_income** (numerical).\n","  *   **cb_person_default_on_file** (categorical: 'N', 'Y') refers to the historical default.\n","  *   **cb_person_cred_hist_length** (numerical) refers to the credit history length. "],"metadata":{"id":"1RHfks4oZf5y"}},{"cell_type":"markdown","source":["## 1.1 Loading the dataset"],"metadata":{"id":"RZzVq1HfynvM"}},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import seaborn as sn\n","import matplotlib.pyplot as plt\n","import sklearn "],"metadata":{"id":"nvuOqwMk-sl-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Specify the path to your working directory\n","import os\n","os.chdir(\"./gdrive/My Drive/Colab Notebooks/Machine Learning in Finance/\")"],"metadata":{"id":"IH0bTJAGAjTe"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df = pd.read_csv(\"credit_dataset.csv\", index_col=0)\n","df.head(10)"],"metadata":{"id":"x_Sb_nNVAbWo","executionInfo":{"status":"ok","timestamp":1682375333356,"user_tz":-60,"elapsed":586,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"colab":{"base_uri":"https://localhost:8080/","height":427},"outputId":"6e5d99b7-8703-41a2-fded-5dbdd5a9e6a7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   person_age  person_income person_home_ownership  person_emp_length  \\\n","0          24          65000                  RENT                8.0   \n","1          27          72000                  RENT                3.0   \n","2          21          28800              MORTGAGE                0.0   \n","3          30          90000                  RENT               14.0   \n","4          30          61000              MORTGAGE                4.0   \n","5          25         120000              MORTGAGE                2.0   \n","6          22          43200                  RENT                4.0   \n","7          33          45000              MORTGAGE                3.0   \n","8          29         140000              MORTGAGE               13.0   \n","9          25          42000                  RENT                2.0   \n","\n","         loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n","0            VENTURE          E      15000          16.32            1   \n","1  DEBTCONSOLIDATION          D      25000          15.95            1   \n","2  DEBTCONSOLIDATION          A       5000           7.49            0   \n","3           PERSONAL          D      10000          15.62            0   \n","4           PERSONAL          A       4500           6.54            0   \n","5           PERSONAL          F      10000          20.99            0   \n","6          EDUCATION          C       3000          13.72            0   \n","7          EDUCATION          D       8000          16.49            1   \n","8            VENTURE          A      12600          10.99            0   \n","9            MEDICAL          D       6750          17.27            1   \n","\n","   loan_percent_income cb_person_default_on_file  cb_person_cred_hist_length  \n","0                 0.23                         Y                           4  \n","1                 0.35                         Y                           6  \n","2                 0.17                         N                           3  \n","3                 0.11                         Y                           6  \n","4                 0.07                         N                           6  \n","5                 0.08                         N                           4  \n","6                 0.07                         Y                           3  \n","7                 0.18                         N                           7  \n","8                 0.09                         N                           6  \n","9                 0.16                         N                           2  "],"text/html":["\n","  <div id=\"df-2527e389-840c-4c50-8a72-dfb674ea0227\">\n","    <div 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<td>65000</td>\n","      <td>RENT</td>\n","      <td>8.0</td>\n","      <td>VENTURE</td>\n","      <td>E</td>\n","      <td>15000</td>\n","      <td>16.32</td>\n","      <td>1</td>\n","      <td>0.23</td>\n","      <td>Y</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>27</td>\n","      <td>72000</td>\n","      <td>RENT</td>\n","      <td>3.0</td>\n","      <td>DEBTCONSOLIDATION</td>\n","      <td>D</td>\n","      <td>25000</td>\n","      <td>15.95</td>\n","      <td>1</td>\n","      <td>0.35</td>\n","      <td>Y</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>21</td>\n","      <td>28800</td>\n","      <td>MORTGAGE</td>\n","      <td>0.0</td>\n","      <td>DEBTCONSOLIDATION</td>\n","      <td>A</td>\n","      <td>5000</td>\n","      <td>7.49</td>\n","      <td>0</td>\n","      <td>0.17</td>\n","      <td>N</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>30</td>\n","      <td>90000</td>\n","      <td>RENT</td>\n","      <td>14.0</td>\n","      <td>PERSONAL</td>\n","      <td>D</td>\n","      <td>10000</td>\n","      <td>15.62</td>\n","      <td>0</td>\n","      <td>0.11</td>\n","      <td>Y</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>30</td>\n","      <td>61000</td>\n","      <td>MORTGAGE</td>\n","      <td>4.0</td>\n","      <td>PERSONAL</td>\n","      <td>A</td>\n","      <td>4500</td>\n","      <td>6.54</td>\n","      <td>0</td>\n","      <td>0.07</td>\n","      <td>N</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>25</td>\n","      <td>120000</td>\n","      <td>MORTGAGE</td>\n","      <td>2.0</td>\n","      <td>PERSONAL</td>\n","      <td>F</td>\n","      <td>10000</td>\n","      <td>20.99</td>\n","      <td>0</td>\n","      <td>0.08</td>\n","      <td>N</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>22</td>\n","      <td>43200</td>\n","      <td>RENT</td>\n","      <td>4.0</td>\n","      <td>EDUCATION</td>\n","      <td>C</td>\n","      <td>3000</td>\n","      <td>13.72</td>\n","      <td>0</td>\n","      <td>0.07</td>\n","      <td>Y</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>33</td>\n","      <td>45000</td>\n","      <td>MORTGAGE</td>\n","      <td>3.0</td>\n","      <td>EDUCATION</td>\n","      <td>D</td>\n","      <td>8000</td>\n","      <td>16.49</td>\n","      <td>1</td>\n","      <td>0.18</td>\n","      <td>N</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>29</td>\n","      <td>140000</td>\n","      <td>MORTGAGE</td>\n","      <td>13.0</td>\n","      <td>VENTURE</td>\n","      <td>A</td>\n","      <td>12600</td>\n","      <td>10.99</td>\n","      <td>0</td>\n","      <td>0.09</td>\n","      <td>N</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>25</td>\n","      <td>42000</td>\n","      <td>RENT</td>\n","      <td>2.0</td>\n","      <td>MEDICAL</td>\n","      <td>D</td>\n","      <td>6750</td>\n","      <td>17.27</td>\n","      <td>1</td>\n","      <td>0.16</td>\n","      <td>N</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2527e389-840c-4c50-8a72-dfb674ea0227')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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loan_amnt  loan_int_rate  loan_status  \\\n","0          MEDICAL          C      12000          13.11            0   \n","1          MEDICAL          A      10000           5.99            0   \n","2  HOMEIMPROVEMENT          A      10200           7.51            0   \n","3          VENTURE          A      10275           6.62            1   \n","4          MEDICAL          A       2500           7.88            0   \n","\n","   loan_percent_income cb_person_default_on_file  cb_person_cred_hist_length  \n","0                 0.34                         Y                           7  \n","1                 0.22                         N                           2  \n","2                 0.08                         N                          13  \n","3                 0.32                         N                           8  \n","4                 0.05                         N                           8  "],"text/html":["\n","  <div 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<th>0</th>\n","      <td>33</td>\n","      <td>35000</td>\n","      <td>MORTGAGE</td>\n","      <td>15.0</td>\n","      <td>MEDICAL</td>\n","      <td>C</td>\n","      <td>12000</td>\n","      <td>13.11</td>\n","      <td>0</td>\n","      <td>0.34</td>\n","      <td>Y</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>24</td>\n","      <td>45336</td>\n","      <td>MORTGAGE</td>\n","      <td>8.0</td>\n","      <td>MEDICAL</td>\n","      <td>A</td>\n","      <td>10000</td>\n","      <td>5.99</td>\n","      <td>0</td>\n","      <td>0.22</td>\n","      <td>N</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>36</td>\n","      <td>132000</td>\n","      <td>RENT</td>\n","      <td>3.0</td>\n","      <td>HOMEIMPROVEMENT</td>\n","      <td>A</td>\n","      <td>10200</td>\n","      <td>7.51</td>\n","      <td>0</td>\n","      <td>0.08</td>\n","      <td>N</td>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      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32581 non-null  float64\n"," 10  cb_person_default_on_file   32581 non-null  object \n"," 11  cb_person_cred_hist_length  32581 non-null  int64  \n","dtypes: float64(3), int64(5), object(4)\n","memory usage: 3.0+ MB\n"]}]},{"cell_type":"code","source":["df['loan_grade'].unique() # identifies unique values of 'loan_grade'"],"metadata":{"id":"tAgKRtqIAvce","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1682375333793,"user_tz":-60,"elapsed":30,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"00624681-ee89-4eda-85b6-9325b4fc3b42"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['C', 'A', 'B', 'D', 'E', 'G', 'F'], dtype=object)"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["#create quickaccess list with categorical variables labels\n","df.select_dtypes(exclude=['int64', 'float64'])"],"metadata":{"id":"qi9X_z6RBq51","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1682375333794,"user_tz":-60,"elapsed":24,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"efe79df9-c49a-4e4d-91b8-e368a79ec17d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      person_home_ownership        loan_intent loan_grade  \\\n","0                  MORTGAGE            MEDICAL          C   \n","1                  MORTGAGE            MEDICAL          A   \n","2                      RENT    HOMEIMPROVEMENT          A   \n","3                      RENT            VENTURE          A   \n","4                      RENT            MEDICAL          A   \n","...                     ...                ...        ...   \n","32576              MORTGAGE            MEDICAL          B   \n","32577              MORTGAGE            MEDICAL          B   \n","32578              MORTGAGE  DEBTCONSOLIDATION          A   \n","32579                  RENT            MEDICAL          D   \n","32580              MORTGAGE            MEDICAL          B   \n","\n","      cb_person_default_on_file  \n","0                             Y  \n","1                             N  \n","2                             N  \n","3                             N  \n","4                             N  \n","...                         ...  \n","32576                         N  \n","32577                         N  \n","32578                         N  \n","32579                         Y  \n","32580                         N  \n","\n","[32581 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-98e7838f-e879-4625-a168-09d28344a0a5\">\n","    <div class=\"colab-df-container\">\n","      <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","    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    <th>4</th>\n","      <td>RENT</td>\n","      <td>MEDICAL</td>\n","      <td>A</td>\n","      <td>N</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>32576</th>\n","      <td>MORTGAGE</td>\n","      <td>MEDICAL</td>\n","      <td>B</td>\n","      <td>N</td>\n","    </tr>\n","    <tr>\n","      <th>32577</th>\n","      <td>MORTGAGE</td>\n","      <td>MEDICAL</td>\n","      <td>B</td>\n","      <td>N</td>\n","    </tr>\n","    <tr>\n","      <th>32578</th>\n","      <td>MORTGAGE</td>\n","      <td>DEBTCONSOLIDATION</td>\n","      <td>A</td>\n","      <td>N</td>\n","    </tr>\n","    <tr>\n","      <th>32579</th>\n","      <td>RENT</td>\n","      <td>MEDICAL</td>\n","      <td>D</td>\n","      <td>Y</td>\n","    </tr>\n","    <tr>\n","      <th>32580</th>\n","      <td>MORTGAGE</td>\n","      <td>MEDICAL</td>\n","      <td>B</td>\n","      <td>N</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>32581 rows × 4 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98e7838f-e879-4625-a168-09d28344a0a5')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 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categorical"],"metadata":{"id":"94CJ6wjoRqyf","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1682375333794,"user_tz":-60,"elapsed":22,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"a049ffc3-ccfa-45e4-f138-858825dbd0f3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       person_age  person_income  person_emp_length  loan_amnt  loan_int_rate  \\\n","0              33          35000               15.0      12000          13.11   \n","1              24          45336                8.0      10000           5.99   \n","2              36         132000                3.0      10200           7.51   \n","3              29          32400                5.0      10275           6.62   \n","4              34          54600                2.0       2500           7.88   \n","...           ...            ...                ...        ...            ...   \n","32576          41         118075                5.0      28000          11.71   \n","32577          21          56000                6.0       5600          10.37   \n","32578          23          55000                7.0       4000           5.42   \n","32579          28          39000                6.0       8000          14.91   \n","32580          27          35000               11.0       9000          11.49   \n","\n","       loan_status  loan_percent_income  cb_person_cred_hist_length  \n","0                0                 0.34                           7  \n","1                0                 0.22                           2  \n","2                0                 0.08                          13  \n","3                1                 0.32                           8  \n","4                0                 0.05                           8  \n","...            ...                  ...                         ...  \n","32576            0                 0.24                          13  \n","32577            0                 0.10                           4  \n","32578            0                 0.07                           3  \n","32579            1                 0.21                           7  \n","32580            0                 0.26                           5  \n","\n","[32581 rows x 8 columns]"],"text/html":["\n","  <div id=\"df-57605264-72d5-4262-829a-5f42f046beca\">\n","    <div class=\"colab-df-container\">\n","      <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>person_age</th>\n","      <th>person_income</th>\n","      <th>person_emp_length</th>\n","      <th>loan_amnt</th>\n","      <th>loan_int_rate</th>\n","      <th>loan_status</th>\n","      <th>loan_percent_income</th>\n","      <th>cb_person_cred_hist_length</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>33</td>\n","      <td>35000</td>\n","      <td>15.0</td>\n","      <td>12000</td>\n","      <td>13.11</td>\n","      <td>0</td>\n","      <td>0.34</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>24</td>\n","      <td>45336</td>\n","      <td>8.0</td>\n","      <td>10000</td>\n","      <td>5.99</td>\n","      <td>0</td>\n","      <td>0.22</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>36</td>\n","      <td>132000</td>\n","      <td>3.0</td>\n","      <td>10200</td>\n","      <td>7.51</td>\n","      <td>0</td>\n","      <td>0.08</td>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>29</td>\n","      <td>32400</td>\n","      <td>5.0</td>\n","      <td>10275</td>\n","      <td>6.62</td>\n","      <td>1</td>\n","      <td>0.32</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>34</td>\n","      <td>54600</td>\n","      <td>2.0</td>\n","      <td>2500</td>\n","      <td>7.88</td>\n","      <td>0</td>\n","      <td>0.05</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>32576</th>\n","      <td>41</td>\n","      <td>118075</td>\n","      <td>5.0</td>\n","      <td>28000</td>\n","      <td>11.71</td>\n","      <td>0</td>\n","      <td>0.24</td>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>32577</th>\n","      <td>21</td>\n","      <td>56000</td>\n","      <td>6.0</td>\n","      <td>5600</td>\n","      <td>10.37</td>\n","      <td>0</td>\n","      <td>0.10</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>32578</th>\n","      <td>23</td>\n","      <td>55000</td>\n","      <td>7.0</td>\n","      <td>4000</td>\n","      <td>5.42</td>\n","      <td>0</td>\n","      <td>0.07</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>32579</th>\n","      <td>28</td>\n","      <td>39000</td>\n","      <td>6.0</td>\n","      <td>8000</td>\n","      <td>14.91</td>\n","      <td>1</td>\n","      <td>0.21</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>32580</th>\n","      <td>27</td>\n","      <td>35000</td>\n","      <td>11.0</td>\n","      <td>9000</td>\n","      <td>11.49</td>\n","      <td>0</td>\n","      <td>0.26</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>32581 rows × 8 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" 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list(df.select_dtypes(include=['object']).columns)"],"metadata":{"id":"6krTDeehR0iX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print('the list of numerical variables', numvars)\n","print('the list of categorical variables', catvars)"],"metadata":{"id":"0anWw0Y5SMnB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1682375333794,"user_tz":-60,"elapsed":19,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"4c2d46bc-7011-4393-d6ea-3516e685c107"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["the list of numerical variables ['person_age', 'person_income', 'person_emp_length', 'loan_amnt', 'loan_int_rate', 'loan_percent_income', 'cb_person_cred_hist_length']\n","the list of categorical variables ['person_home_ownership', 'loan_intent', 'loan_grade', 'cb_person_default_on_file']\n"]}]},{"cell_type":"code","source":["df.loc[:10,:] # if you want to select certain columns\n","df[:10] # if you dont care about which columns, use .loc"],"metadata":{"id":"fpitWNb5Sex7","colab":{"base_uri":"https://localhost:8080/","height":427},"executionInfo":{"status":"ok","timestamp":1682375333794,"user_tz":-60,"elapsed":16,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"8ed6b475-cebf-45d8-c9f4-3ec4721a0fe0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   person_age  person_income person_home_ownership  person_emp_length  \\\n","0          33          35000              MORTGAGE               15.0   \n","1          24          45336              MORTGAGE                8.0   \n","2          36         132000                  RENT                3.0   \n","3          29          32400                  RENT                5.0   \n","4          34          54600                  RENT                2.0   \n","5          26         180000                   OWN                0.0   \n","6          27         120000              MORTGAGE                0.0   \n","7          23          85500                  RENT                1.0   \n","8          27          40000                  RENT                4.0   \n","9          30          80000              MORTGAGE                0.0   \n","\n","         loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n","0            MEDICAL          C      12000          13.11            0   \n","1            MEDICAL          A      10000           5.99            0   \n","2    HOMEIMPROVEMENT          A      10200           7.51            0   \n","3            VENTURE          A      10275           6.62            1   \n","4            MEDICAL          A       2500           7.88            0   \n","5    HOMEIMPROVEMENT          A      10000           8.00            0   \n","6    HOMEIMPROVEMENT          A      10000           6.62            0   \n","7  DEBTCONSOLIDATION          C      17000          13.11            0   \n","8  DEBTCONSOLIDATION          B       4800          11.71            0   \n","9    HOMEIMPROVEMENT          A      11000          10.99            0   \n","\n","   loan_percent_income cb_person_default_on_file  cb_person_cred_hist_length  \n","0                 0.34                         Y                           7  \n","1                 0.22                         N                           2  \n","2                 0.08                         N                          13  \n","3                 0.32                         N                           8  \n","4                 0.05                         N                           8  \n","5                 0.06                         N                           4  \n","6                 0.08                         N                          10  \n","7                 0.20                         Y                           3  \n","8                 0.12                         N                           8  \n","9                 0.14                         N                           6  "],"text/html":["\n","  <div id=\"df-578e4135-0d27-4c59-8a9a-8beb858b6e6e\">\n","    <div class=\"colab-df-container\">\n","      <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>person_age</th>\n","      <th>person_income</th>\n","      <th>person_home_ownership</th>\n","      <th>person_emp_length</th>\n","      <th>loan_intent</th>\n","      <th>loan_grade</th>\n","      <th>loan_amnt</th>\n","      <th>loan_int_rate</th>\n","      <th>loan_status</th>\n","      <th>loan_percent_income</th>\n","      <th>cb_person_default_on_file</th>\n","      <th>cb_person_cred_hist_length</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>33</td>\n","      <td>35000</td>\n","      <td>MORTGAGE</td>\n","      <td>15.0</td>\n","      <td>MEDICAL</td>\n","      <td>C</td>\n","      <td>12000</td>\n","      <td>13.11</td>\n","      <td>0</td>\n","      <td>0.34</td>\n","      <td>Y</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>24</td>\n","      <td>45336</td>\n","      <td>MORTGAGE</td>\n","      <td>8.0</td>\n","      <td>MEDICAL</td>\n","      <td>A</td>\n","      <td>10000</td>\n","      <td>5.99</td>\n","      <td>0</td>\n","      <td>0.22</td>\n","      <td>N</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>36</td>\n","      <td>132000</td>\n","      <td>RENT</td>\n","      <td>3.0</td>\n","      <td>HOMEIMPROVEMENT</td>\n","      <td>A</td>\n","      <td>10200</td>\n","      <td>7.51</td>\n","      <td>0</td>\n","      <td>0.08</td>\n","      <td>N</td>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>29</td>\n","      <td>32400</td>\n","      <td>RENT</td>\n","      <td>5.0</td>\n","      <td>VENTURE</td>\n","      <td>A</td>\n","      <td>10275</td>\n","      <td>6.62</td>\n","      <td>1</td>\n","      <td>0.32</td>\n","      <td>N</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>34</td>\n","      <td>54600</td>\n","      <td>RENT</td>\n","      <td>2.0</td>\n","      <td>MEDICAL</td>\n","      <td>A</td>\n","      <td>2500</td>\n","      <td>7.88</td>\n","      <td>0</td>\n","      <td>0.05</td>\n","      <td>N</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>26</td>\n","      <td>180000</td>\n","      <td>OWN</td>\n","      <td>0.0</td>\n","      <td>HOMEIMPROVEMENT</td>\n","      <td>A</td>\n","      <td>10000</td>\n","      <td>8.00</td>\n","      <td>0</td>\n","      <td>0.06</td>\n","      <td>N</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>27</td>\n","      <td>120000</td>\n","      <td>MORTGAGE</td>\n","      <td>0.0</td>\n","      <td>HOMEIMPROVEMENT</td>\n","      <td>A</td>\n","      <td>10000</td>\n","      <td>6.62</td>\n","      <td>0</td>\n","      <td>0.08</td>\n","      <td>N</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>23</td>\n","      <td>85500</td>\n","      <td>RENT</td>\n","      <td>1.0</td>\n","      <td>DEBTCONSOLIDATION</td>\n","      <td>C</td>\n","      <td>17000</td>\n","      <td>13.11</td>\n","      <td>0</td>\n","      <td>0.20</td>\n","      <td>Y</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>27</td>\n","      <td>40000</td>\n","      <td>RENT</td>\n","      <td>4.0</td>\n","      <td>DEBTCONSOLIDATION</td>\n","      <td>B</td>\n","      <td>4800</td>\n","      <td>11.71</td>\n","      <td>0</td>\n","      <td>0.12</td>\n","      <td>N</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>30</td>\n","      <td>80000</td>\n","      <td>MORTGAGE</td>\n","      <td>0.0</td>\n","      <td>HOMEIMPROVEMENT</td>\n","      <td>A</td>\n","      <td>11000</td>\n","      <td>10.99</td>\n","      <td>0</td>\n","      <td>0.14</td>\n","      <td>N</td>\n","      <td>6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-578e4135-0d27-4c59-8a9a-8beb858b6e6e')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 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len(df_test))\n","\n","# We do not use the sklearn built in data split as we still have lots of data processing to do."],"metadata":{"id":"5E0A3Ub7BjpL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1682375333795,"user_tz":-60,"elapsed":15,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"033c8840-97d1-472d-e13c-78c097e15e4a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["length of training data is  22806\n","length of test data is  9775\n"]}]},{"cell_type":"markdown","source":["### 1.2 Adding a new categorical feature"],"metadata":{"id":"5g3WyCkt5qCK"}},{"cell_type":"markdown","source":["---\n","Create a new categorical feature named `category_income` assigning a category 'A', 'B', 'C' and 'D' according to the feature `person_income` as follows:\n","  * If `person_income` is in [0, 39000], then `category_income = 'D'`.\n","  * If `person_income` is in [39000, 55000], then `category_income = 'C'`.\n","  * If `person_income` is in [55000, 79000], then `category_income = 'B'`.\n","  * If `person_income` is in [79000, +$\\infty$], then `category_income = 'A'`.\n","\n","---"],"metadata":{"id":"YqOwL6flKF1N"}},{"cell_type":"code","source":["# Loop way \n","df_train['category_income'] = ''\n","\n","# Create the new feature\n","for index , row in df_train.iterrows():\n","    person_income = row['person_income']\n","    if person_income < 39_000:\n","        category_income = 'D'\n","    elif person_income < 55_000:\n","        category_income = 'C'\n","    elif person_income < 79_000:\n","        category_income = 'B'\n","    else:\n","        category_income = 'A'\n","    \n","    # .at is like .loc\n","    df_train.at[index , 'category_income'] = category_income\n"],"metadata":{"id":"aD89OAEECb8t","executionInfo":{"status":"ok","timestamp":1682375335458,"user_tz":-60,"elapsed":1672,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"696db6e6-e457-43ca-94ca-94ecc5ffdc87"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-20-bf180b5814d6>:2: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  df_train['category_income'] = ''\n"]}]},{"cell_type":"code","source":["df_train"],"metadata":{"id":"6Twhi9jmCtU8","colab":{"base_uri":"https://localhost:8080/","height":488},"executionInfo":{"status":"ok","timestamp":1682375335459,"user_tz":-60,"elapsed":20,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"feabc6b7-84e2-46b4-a86b-3baf846c0ae0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       person_age  person_income person_home_ownership  person_emp_length  \\\n","0              33          35000              MORTGAGE               15.0   \n","1              24          45336              MORTGAGE                8.0   \n","2              36         132000                  RENT                3.0   \n","3              29          32400                  RENT                5.0   \n","4              34          54600                  RENT                2.0   \n","...           ...            ...                   ...                ...   \n","22801          27         103000              MORTGAGE                8.0   \n","22802          49          72000                  RENT                4.0   \n","22803          23          53000                  RENT                0.0   \n","22804          23          56000                  RENT                3.0   \n","22805          30          42000                  RENT                5.0   \n","\n","             loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n","0                MEDICAL          C      12000          13.11            0   \n","1                MEDICAL          A      10000           5.99            0   \n","2        HOMEIMPROVEMENT          A      10200           7.51            0   \n","3                VENTURE          A      10275           6.62            1   \n","4                MEDICAL          A       2500           7.88            0   \n","...                  ...        ...        ...            ...          ...   \n","22801            MEDICAL          A       8800           7.29            0   \n","22802  DEBTCONSOLIDATION          A       8000           7.49            0   \n","22803            VENTURE          C       9000          12.09            0   \n","22804          EDUCATION          D       5875          14.54            0   \n","22805            MEDICAL          B       8000          10.00            0   \n","\n","       loan_percent_income cb_person_default_on_file  \\\n","0                     0.34                         Y   \n","1                     0.22                         N   \n","2                     0.08                         N   \n","3                     0.32                         N   \n","4                     0.05                         N   \n","...                    ...                       ...   \n","22801                 0.09                         N   \n","22802                 0.11                         N   \n","22803                 0.17                         Y   \n","22804                 0.10                         N   \n","22805                 0.19                         N   \n","\n","       cb_person_cred_hist_length category_income  \n","0                               7               D  \n","1                               2               C  \n","2                              13               A  \n","3                               8               D  \n","4                               8               C  \n","...                           ...             ...  \n","22801                           8               A  \n","22802                          17               B  \n","22803                           3               C  \n","22804                           2               B  \n","22805                           5               C  \n","\n","[22806 rows x 13 columns]"],"text/html":["\n","  <div id=\"df-deab852a-2f55-486e-b6ce-ab14ee4f406e\">\n","    <div class=\"colab-df-container\">\n","      <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>person_age</th>\n","      <th>person_income</th>\n","      <th>person_home_ownership</th>\n","      <th>person_emp_length</th>\n","      <th>loan_intent</th>\n","      <th>loan_grade</th>\n","      <th>loan_amnt</th>\n","      <th>loan_int_rate</th>\n","      <th>loan_status</th>\n","      <th>loan_percent_income</th>\n","      <th>cb_person_default_on_file</th>\n","      <th>cb_person_cred_hist_length</th>\n","      <th>category_income</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>33</td>\n","      <td>35000</td>\n","      <td>MORTGAGE</td>\n","      <td>15.0</td>\n","      <td>MEDICAL</td>\n","      <td>C</td>\n","      <td>12000</td>\n","      <td>13.11</td>\n","      <td>0</td>\n","      <td>0.34</td>\n","      <td>Y</td>\n","      <td>7</td>\n","      <td>D</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>24</td>\n","      <td>45336</td>\n","      <td>MORTGAGE</td>\n","      <td>8.0</td>\n","      <td>MEDICAL</td>\n","      <td>A</td>\n","      <td>10000</td>\n","      <td>5.99</td>\n","      <td>0</td>\n","      <td>0.22</td>\n","      <td>N</td>\n","      <td>2</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>36</td>\n","      <td>132000</td>\n","      <td>RENT</td>\n","      <td>3.0</td>\n","      <td>HOMEIMPROVEMENT</td>\n","      <td>A</td>\n","      <td>10200</td>\n","      <td>7.51</td>\n","      <td>0</td>\n","      <td>0.08</td>\n","      <td>N</td>\n","      <td>13</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>29</td>\n","      <td>32400</td>\n","      <td>RENT</td>\n","      <td>5.0</td>\n","      <td>VENTURE</td>\n","      <td>A</td>\n","      <td>10275</td>\n","      <td>6.62</td>\n","      <td>1</td>\n","      <td>0.32</td>\n","      <td>N</td>\n","      <td>8</td>\n","      <td>D</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>34</td>\n","      <td>54600</td>\n","      <td>RENT</td>\n","      <td>2.0</td>\n","      <td>MEDICAL</td>\n","      <td>A</td>\n","      <td>2500</td>\n","      <td>7.88</td>\n","      <td>0</td>\n","      <td>0.05</td>\n","      <td>N</td>\n","      <td>8</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>22801</th>\n","      <td>27</td>\n","      <td>103000</td>\n","      <td>MORTGAGE</td>\n","      <td>8.0</td>\n","      <td>MEDICAL</td>\n","      <td>A</td>\n","      <td>8800</td>\n","      <td>7.29</td>\n","      <td>0</td>\n","      <td>0.09</td>\n","      <td>N</td>\n","      <td>8</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>22802</th>\n","      <td>49</td>\n","      <td>72000</td>\n","      <td>RENT</td>\n","      <td>4.0</td>\n","      <td>DEBTCONSOLIDATION</td>\n","      <td>A</td>\n","      <td>8000</td>\n","      <td>7.49</td>\n","      <td>0</td>\n","      <td>0.11</td>\n","      <td>N</td>\n","      <td>17</td>\n","      <td>B</td>\n","    </tr>\n","    <tr>\n","      <th>22803</th>\n","      <td>23</td>\n","      <td>53000</td>\n","      <td>RENT</td>\n","      <td>0.0</td>\n","      <td>VENTURE</td>\n","      <td>C</td>\n","      <td>9000</td>\n","      <td>12.09</td>\n","      <td>0</td>\n","      <td>0.17</td>\n","      <td>Y</td>\n","      <td>3</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>22804</th>\n","      <td>23</td>\n","      <td>56000</td>\n","      <td>RENT</td>\n","      <td>3.0</td>\n","      <td>EDUCATION</td>\n","      <td>D</td>\n","      <td>5875</td>\n","      <td>14.54</td>\n","      <td>0</td>\n","      <td>0.10</td>\n","      <td>N</td>\n","      <td>2</td>\n","      <td>B</td>\n","    </tr>\n","    <tr>\n","      <th>22805</th>\n","      <td>30</td>\n","      <td>42000</td>\n","      <td>RENT</td>\n","      <td>5.0</td>\n","      <td>MEDICAL</td>\n","      <td>B</td>\n","      <td>8000</td>\n","      <td>10.00</td>\n","      <td>0</td>\n","      <td>0.19</td>\n","      <td>N</td>\n","      <td>5</td>\n","      <td>C</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>22806 rows × 13 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-deab852a-2f55-486e-b6ce-ab14ee4f406e')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" 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pd.cut(df_train.person_income  ,intervals ,labels = labels)\n","df_test['category_income'] = pd.cut(df_test.person_income ,  intervals , labels = labels)"],"metadata":{"id":"4xBld4PSfkql"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_train"],"metadata":{"id":"S7Td4IKNfq01","colab":{"base_uri":"https://localhost:8080/","height":488},"executionInfo":{"status":"ok","timestamp":1682375335459,"user_tz":-60,"elapsed":8,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"39e5cdb4-6f74-485a-ab0f-130fd3cfa543"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       person_age  person_income person_home_ownership  person_emp_length  \\\n","0              33          35000              MORTGAGE               15.0   \n","1              24          45336              MORTGAGE                8.0   \n","2              36         132000                  RENT                3.0   \n","3              29          32400                  RENT                5.0   \n","4              34          54600                  RENT                2.0   \n","...           ...            ...                   ...                ...   \n","22801          27         103000              MORTGAGE                8.0   \n","22802          49          72000                  RENT                4.0   \n","22803          23          53000                  RENT                0.0   \n","22804          23          56000                  RENT                3.0   \n","22805          30          42000                  RENT                5.0   \n","\n","             loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n","0                MEDICAL          C      12000          13.11            0   \n","1                MEDICAL          A      10000           5.99            0   \n","2        HOMEIMPROVEMENT          A      10200           7.51            0   \n","3                VENTURE          A      10275           6.62            1   \n","4                MEDICAL          A       2500           7.88            0   \n","...                  ...        ...        ...            ...          ...   \n","22801            MEDICAL          A       8800           7.29            0   \n","22802  DEBTCONSOLIDATION          A       8000           7.49            0   \n","22803            VENTURE          C       9000          12.09            0   \n","22804          EDUCATION          D       5875          14.54            0   \n","22805            MEDICAL          B       8000          10.00            0   \n","\n","       loan_percent_income cb_person_default_on_file  \\\n","0                     0.34                         Y   \n","1                     0.22                         N   \n","2                     0.08                         N   \n","3                     0.32                         N   \n","4                     0.05                         N   \n","...                    ...                       ...   \n","22801                 0.09                         N   \n","22802                 0.11                         N   \n","22803                 0.17                         Y   \n","22804                 0.10                         N   \n","22805                 0.19                         N   \n","\n","       cb_person_cred_hist_length category_income  \n","0                               7               D  \n","1                               2               C  \n","2                              13               A  \n","3                               8               D  \n","4                               8               C  \n","...                           ...             ...  \n","22801                           8               A  \n","22802                          17               B  \n","22803                           3               C  \n","22804                           2               B  \n","22805                           5               C  \n","\n","[22806 rows x 13 columns]"],"text/html":["\n","  <div id=\"df-5c61b6c2-0c67-466d-9b7e-b82aba08a1c2\">\n","    <div class=\"colab-df-container\">\n","      <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>person_age</th>\n","      <th>person_income</th>\n","      <th>person_home_ownership</th>\n","      <th>person_emp_length</th>\n","      <th>loan_intent</th>\n","      <th>loan_grade</th>\n","      <th>loan_amnt</th>\n","      <th>loan_int_rate</th>\n","      <th>loan_status</th>\n","      <th>loan_percent_income</th>\n","      <th>cb_person_default_on_file</th>\n","      <th>cb_person_cred_hist_length</th>\n","      <th>category_income</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>33</td>\n","      <td>35000</td>\n","      <td>MORTGAGE</td>\n","      <td>15.0</td>\n","      <td>MEDICAL</td>\n","      <td>C</td>\n","      <td>12000</td>\n","      <td>13.11</td>\n","      <td>0</td>\n","      <td>0.34</td>\n","      <td>Y</td>\n","      <td>7</td>\n","      <td>D</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>24</td>\n","      <td>45336</td>\n","      <td>MORTGAGE</td>\n","      <td>8.0</td>\n","      <td>MEDICAL</td>\n","      <td>A</td>\n","      <td>10000</td>\n","      <td>5.99</td>\n","      <td>0</td>\n","      <td>0.22</td>\n","      <td>N</td>\n","      <td>2</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>36</td>\n","      <td>132000</td>\n","      <td>RENT</td>\n","      <td>3.0</td>\n","      <td>HOMEIMPROVEMENT</td>\n","      <td>A</td>\n","      <td>10200</td>\n","      <td>7.51</td>\n","      <td>0</td>\n","      <td>0.08</td>\n","      <td>N</td>\n","      <td>13</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>29</td>\n","      <td>32400</td>\n","      <td>RENT</td>\n","      <td>5.0</td>\n","      <td>VENTURE</td>\n","      <td>A</td>\n","      <td>10275</td>\n","      <td>6.62</td>\n","      <td>1</td>\n","      <td>0.32</td>\n","      <td>N</td>\n","      <td>8</td>\n","      <td>D</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>34</td>\n","      <td>54600</td>\n","      <td>RENT</td>\n","      <td>2.0</td>\n","      <td>MEDICAL</td>\n","      <td>A</td>\n","      <td>2500</td>\n","      <td>7.88</td>\n","      <td>0</td>\n","      <td>0.05</td>\n","      <td>N</td>\n","      <td>8</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      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<td>23</td>\n","      <td>53000</td>\n","      <td>RENT</td>\n","      <td>0.0</td>\n","      <td>VENTURE</td>\n","      <td>C</td>\n","      <td>9000</td>\n","      <td>12.09</td>\n","      <td>0</td>\n","      <td>0.17</td>\n","      <td>Y</td>\n","      <td>3</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>22804</th>\n","      <td>23</td>\n","      <td>56000</td>\n","      <td>RENT</td>\n","      <td>3.0</td>\n","      <td>EDUCATION</td>\n","      <td>D</td>\n","      <td>5875</td>\n","      <td>14.54</td>\n","      <td>0</td>\n","      <td>0.10</td>\n","      <td>N</td>\n","      <td>2</td>\n","      <td>B</td>\n","    </tr>\n","    <tr>\n","      <th>22805</th>\n","      <td>30</td>\n","      <td>42000</td>\n","      <td>RENT</td>\n","      <td>5.0</td>\n","      <td>MEDICAL</td>\n","      <td>B</td>\n","      <td>8000</td>\n","      <td>10.00</td>\n","      <td>0</td>\n","      <td>0.19</td>\n","      <td>N</td>\n","      <td>5</td>\n","      <td>C</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>22806 rows × 13 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c61b6c2-0c67-466d-9b7e-b82aba08a1c2')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  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Visualization"],"metadata":{"id":"5o1pvnyjEBF3"}},{"cell_type":"markdown","metadata":{"id":"k5HEklJbE1sX"},"source":["### 2.1 Count plot for a categorical variable. "]},{"cell_type":"markdown","metadata":{"id":"awAi2fVNfj7L"},"source":["A **count plot** can be thought of as a histogram across a categorical variable. It shows the counts of observations in each category using bars.\n","\n"]},{"cell_type":"code","source":["fig = plt.figure(figsize=(8, 4))\n","g = sn.countplot(x=\"person_home_ownership\", data=df_train, palette=\"hls\", hue=\"loan_status\")\n","g.set_title(\"Count Plot for person_home_ownership\")\n","plt.show()"],"metadata":{"id":"Q4C-TyO-k-8X","colab":{"base_uri":"https://localhost:8080/","height":410},"executionInfo":{"status":"ok","timestamp":1682375336403,"user_tz":-60,"elapsed":525,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"bb668c42-67d1-4647-d686-19615a94fea2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"mjwv_Tw39FNT"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"G-ky4dPucJUH"},"source":["### 2.2 Numerical variable distribution"]},{"cell_type":"markdown","metadata":{"id":"q9mt9f_ydrAC"},"source":["We want to plot a univariate distribution of a numerical variable associated to the \"0\" and \"1\" targets. "]},{"cell_type":"code","metadata":{"id":"BraoDr6GcaYD","colab":{"base_uri":"https://localhost:8080/","height":822},"executionInfo":{"status":"ok","timestamp":1682375336897,"user_tz":-60,"elapsed":502,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"26ad95a7-21d3-4b4e-a397-6e2d0b6ab375"},"source":["fig = plt.figure(figsize=(8, 4))\n","g = sn.distplot(df_train[df_train[\"loan_status\"]==0][\"person_income\"], color=\"g\")\n","g = sn.distplot(df_train[df_train[\"loan_status\"]==1][\"person_income\"], color=\"r\")\n","g.set_xlabel(\"Person Income\")\n","g.set(xlim=(0, 200000))\n","g.set_ylabel(\"Frequency\")\n","g.set_title(\"Person Income distribution according to loan status\")\n","fig.legend(labels=[\"0\", \"1\"])\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-27-e6692b53dc12>:2: UserWarning: \n","\n","`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n","\n","Please adapt your code to use either `displot` (a figure-level function with\n","similar flexibility) or `histplot` (an axes-level function for histograms).\n","\n","For a guide to updating your code to use the new functions, please see\n","https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n","\n","  g = sn.distplot(df_train[df_train[\"loan_status\"]==0][\"person_income\"], color=\"g\")\n","<ipython-input-27-e6692b53dc12>:3: UserWarning: \n","\n","`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n","\n","Please adapt your code to use either `displot` (a figure-level function with\n","similar flexibility) or `histplot` (an axes-level function for histograms).\n","\n","For a guide to updating your code to use the new functions, please see\n","https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n","\n","  g = sn.distplot(df_train[df_train[\"loan_status\"]==1][\"person_income\"], color=\"r\")\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"44-3sDdqWFdS"},"source":["### 2.3 Boxplots "]},{"cell_type":"code","metadata":{"id":"rhEdDWrz9FLI","colab":{"base_uri":"https://localhost:8080/","height":641},"executionInfo":{"status":"ok","timestamp":1682375337372,"user_tz":-60,"elapsed":481,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"bb1a9f8f-a879-47ff-a7ac-20e048da5782"},"source":["fig = plt.figure(figsize=(14, 7))\n","g = sn.boxplot(x=\"category_income\", y=\"loan_amnt\", data=df_train, palette=\"hls\",hue=\"loan_status\")\n","g.set_title(\"Loan Amount by Category Income\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x700 with 1 Axes>"],"image/png":"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3. Preprocessing the data"],"metadata":{"id":"vDFtR_RjGCkY"}},{"cell_type":"markdown","metadata":{"id":"v8HcghXHgkk3"},"source":["### 3.1. Preprocessing categorical variables "]},{"cell_type":"markdown","metadata":{"id":"Q9Ow6MljoSSY"},"source":["* Let's suppose we have a categorical variable X that can take K different values $\\{1, \\dots, K\\}$\n","\n","* **One hot encoding** a category consists in transforming the category into a vector $\\in \\mathbb{R}^K$ where we have 1 in the position of the category and zeros elswhere. \n","\n","* For instance, if $K=4$, the category \"3\" for one sample is one hot encoded into the vector $[0, 0, 1, 0]$\n","\n","\n","* Therefore, we can turn a catogorical feature into K features. \n","\n","* As each sample is only mapped to one category, the K features are not independent (the K-th feature can be deduced from the K-1 first features). Therefore, we usually drop one of the columns."]},{"cell_type":"code","source":["for categorical_variable in catvars:\n","  print(\"Feature: {} ... Categories: {}\".format(categorical_variable, df_train[categorical_variable].unique()))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lY8IS17mXpD4","executionInfo":{"status":"ok","timestamp":1682375337373,"user_tz":-60,"elapsed":13,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"6cc46e45-2e62-4acd-ba28-293b97a05efd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Feature: person_home_ownership ... Categories: ['MORTGAGE' 'RENT' 'OWN' 'OTHER']\n","Feature: loan_intent ... Categories: ['MEDICAL' 'HOMEIMPROVEMENT' 'VENTURE' 'DEBTCONSOLIDATION' 'EDUCATION'\n"," 'PERSONAL']\n","Feature: loan_grade ... Categories: ['C' 'A' 'B' 'D' 'E' 'G' 'F']\n","Feature: cb_person_default_on_file ... Categories: ['Y' 'N']\n","Feature: category_income ... Categories: ['D', 'C', 'A', 'B']\n","Categories (4, object): ['D' < 'C' < 'B' < 'A']\n"]}]},{"cell_type":"markdown","metadata":{"id":"ijzgiaUButpB"},"source":["The number of categories:"]},{"cell_type":"code","metadata":{"id":"DlTOd5mfhs7-"},"source":["df_train = pd.get_dummies(df_train,\n","                          columns=catvars,\n","                          dummy_na=False,\n","                          drop_first=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1ToPF7IV9E-w"},"source":["df_test = pd.get_dummies(df_test,\n","                         columns=catvars,\n","                         dummy_na=False,\n","                         drop_first=True)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["---\n","* Number of Dummy variables: 18.\n","  * person_home_ownership     ---->   4 possible values ----> 3 Dummy variables \n","  * loan_intent               ---->   6 possible values ----> 5 Dummy variables\n","  * loan_grade                ---->   7 possible values ----> 6 Dummy variables\n","  * cb_person_default_on_file ---->   2 possible values ----> 1 Dummy variable\n","  * category_income           ---->   4 possible values ----> 3 Dummy variables\n","\n","* Number of Numerical variables: 7. \n","\n","* One column for the target `loan_status`. \n","\n","* Total number of columns: 26\n","---"],"metadata":{"id":"jFCjcTXKo8b8"}},{"cell_type":"markdown","metadata":{"id":"MRnlqR7f0_xM"},"source":["### 3.2 Preprocessing Numerical Variables"]},{"cell_type":"code","source":["df_train_targets = df_train['loan_status']\n","df_train_features = df_train.drop('loan_status',axis=1)\n","\n","df_test_targets = df_test['loan_status']\n","df_test_features = df_test.drop('loan_status',axis=1)"],"metadata":{"id":"FcGkGV0sZ9Zh"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_train_features_numvars = df_train_features[numvars]\n","df_train_features_rest = df_train_features[df_train_features.columns[~df_train_features.columns.isin(numvars)]]\n","\n","\n","df_test_features_numvars = df_test_features[numvars]\n","df_test_features_rest = df_test_features[df_test_features.columns[~df_test_features.columns.isin(numvars)]]"],"metadata":{"id":"-iyUvVzCZ9mS"},"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":699},"id":"5NWrJKx_1F8R","outputId":"2ed61f9a-6acc-46ba-f437-a1b88877d99f","executionInfo":{"status":"ok","timestamp":1682375647383,"user_tz":-60,"elapsed":3292,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}}},"source":["df_train_features_numvars.hist(figsize=(16, 8))\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 9 Axes>"],"image/png":"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are two ways of scaling a numerical variable:"]},{"cell_type":"markdown","source":["<center><img width=\"500\" src = \"https://drive.google.com/uc?export=view&id=1UiUAyNligNF9TQkavRdatKR-4WI3pxqz\"></center>"],"metadata":{"id":"_eJffUfDnlie"}},{"cell_type":"markdown","source":["---\n","\n","Plot the histograms of the numerical features after the scaling process.\n","\n","---"],"metadata":{"id":"XuD9xZKGSrEE"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":699},"id":"mpY88zlz1F0N","outputId":"cc12c834-1114-4aae-b347-54701fde639d","executionInfo":{"status":"ok","timestamp":1682282853234,"user_tz":-60,"elapsed":1473,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"source":["df_train_features_numvars.hist(figsize=(16, 8))\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 9 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4. Fine Tuning a Supervised Model"],"metadata":{"id":"QgibaIZ2Z3hS"}},{"cell_type":"markdown","metadata":{"id":"cnZyF17Fqf-1"},"source":["### 4.1 Evaluation Metrics"]},{"cell_type":"markdown","metadata":{"id":"TqFyLtbVCjjz"},"source":["First, let's start by a review of the different evaluation metrics"]},{"cell_type":"markdown","metadata":{"id":"XI52SFn-DXPT"},"source":["The F1 score is calculated as follows: \n","\n","$$\n","\\text{F1} = 2 \\frac{\\text{precision} \\times \\text{recall}}{\\text{precision} + \\text{recall}}\n","$$"]},{"cell_type":"code","metadata":{"id":"2DF2RbgZqgwq"},"source":["# Import module \n","from sklearn.model_selection import cross_val_score\n","# Cross Validation to test and anticipate overfitting problem\n","def crossvalidate(clf, X, y):\n","    precision = cross_val_score(clf, X, y, cv=5, scoring='precision')\n","    recall = cross_val_score(clf, X, y, cv=5, scoring='recall')\n","    f1 = cross_val_score(clf, X, y, cv=5, scoring='f1')\n","    roc_auc = cross_val_score(clf, X, y, cv=5, scoring='roc_auc')\n","    accuracy = cross_val_score(clf, X, y, cv=5, scoring='accuracy')\n","    # The mean score and standard deviation of the score estimate\n","    print(\"Cross Validation Precision: %0.2f (+/- %0.2f)\" % (precision.mean(), precision.std()))\n","    print(\"Cross Validation Recall: %0.2f (+/- %0.2f)\" % (recall.mean(), recall.std()))\n","    print(\"Cross Validation f1 score: %0.2f (+/- %0.2f)\" % (f1.mean(), f1.std()))\n","    print(\"Cross Validation roc_auc: %0.2f (+/- %0.2f)\" % (roc_auc.mean(), roc_auc.std()))\n","    print(\"Cross Validation Accuracy: %0.2f (+/- %0.2f)\" % (accuracy.mean(), accuracy.std()))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KNO3MNZCEH-S"},"source":["# Import module\n","from sklearn.metrics import confusion_matrix\n","\n","def plot_confusion_matrix(y_test, y_hat):\n","  fig = plt.figure(figsize=(10, 8))\n","  g = sns.heatmap(confusion_matrix(y_test, y_hat),\n","              annot=True,\n","              fmt=\"0.0f\")\n","  g.set_xlabel(\"Predicted\")\n","  g.set_ylabel(\"Actual\")\n","  g.set_title(\"Confusion Matrix on the test set\")\n","  plt.show()\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"yE0io5hzEIZg"},"source":["### 4.2 Fine Tuning Random Forest"]},{"cell_type":"markdown","source":["We would like to tune the following hyperparameters:"],"metadata":{"id":"dNxhnSU8433V"}},{"cell_type":"markdown","source":["* n_estimators = number of trees in the foreset\n","* max_features = max number of features considered for splitting a node\n","* max_depth = max number of levels in each decision tree\n","* bootstrap = method for sampling data points (with or without replacement)"],"metadata":{"id":"RRMnb0Qqhxti"}},{"cell_type":"code","source":["from pprint import pprint\n","# Number of trees in random forest\n","n_estimators = [int(x) for x in np.linspace(start = 200, stop = 400, num = 5)]\n","# Number of features to consider at every split\n","max_features = ['auto', 'sqrt']\n","# Maximum number of levels in tree\n","max_depth = [int(x) for x in np.linspace(10, 110, num = 5)]\n","max_depth.append(None)\n","# Method of selecting samples for training each tree\n","bootstrap = [True, False]\n","# Create the random grid\n","random_grid = {'n_estimators': n_estimators,\n","               'max_features': max_features,\n","               'max_depth': max_depth,\n","               'bootstrap': bootstrap}\n","pprint(random_grid)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hC2yc4U6iB4h","outputId":"7b8bd361-8cfa-4280-9484-23161dabacca","executionInfo":{"status":"ok","timestamp":1682282853727,"user_tz":-60,"elapsed":8,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'bootstrap': [True, False],\n"," 'max_depth': [10, 35, 60, 85, 110, None],\n"," 'max_features': ['auto', 'sqrt'],\n"," 'n_estimators': [200, 250, 300, 350, 400]}\n"]}]},{"cell_type":"code","source":["# Import Modules \n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.model_selection import RandomizedSearchCV"],"metadata":{"id":"0UiWWQbRhiwU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Use the random grid to search for best hyperparameters\n","# First create the base model to tune\n","model_rf = RandomForestClassifier(random_state=42)\n","# Random search of parameters, using 3 fold cross validation, \n","# search across 5 different combinations, and use all available cores\n","rf_random = RandomizedSearchCV(estimator = model_rf, \n","                               param_distributions = random_grid, # grid of all possible combinations of hp (defined in a dictionary)\n","                               n_iter = 10, # Number of hp that you want to test \n","                               cv = 3, # Number of folds in cross validation\n","                               verbose=2, \n","                               scoring=\"f1\",# Evaluation metric \n","                               random_state=42, # fixing the seed\n","                               n_jobs = -1)\n","# Fit the random search model\n","rf_random.fit(X_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":136},"id":"ejs_VZ1tiCAg","outputId":"5dc85b0f-6367-4c01-bb5c-ce627eb60dcb","executionInfo":{"status":"ok","timestamp":1682282943312,"user_tz":-60,"elapsed":89290,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 3 folds for each of 5 candidates, totalling 15 fits\n"]},{"output_type":"execute_result","data":{"text/plain":["RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),\n","                   n_iter=5, n_jobs=-1,\n","                   param_distributions={'bootstrap': [True, False],\n","                                        'max_depth': [10, 35, 60, 85, 110,\n","                                                      None],\n","                                        'max_features': ['auto', 'sqrt'],\n","                                        'n_estimators': [200, 250, 300, 350,\n","                                                         400]},\n","                   random_state=42, scoring='f1', verbose=2)"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),\n","                   n_iter=5, n_jobs=-1,\n","                   param_distributions={&#x27;bootstrap&#x27;: [True, False],\n","                                        &#x27;max_depth&#x27;: [10, 35, 60, 85, 110,\n","                                                      None],\n","                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n","                                        &#x27;n_estimators&#x27;: [200, 250, 300, 350,\n","                                                         400]},\n","                   random_state=42, scoring=&#x27;f1&#x27;, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),\n","                   n_iter=5, n_jobs=-1,\n","                   param_distributions={&#x27;bootstrap&#x27;: [True, False],\n","                                        &#x27;max_depth&#x27;: [10, 35, 60, 85, 110,\n","                                                      None],\n","                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n","                                        &#x27;n_estimators&#x27;: [200, 250, 300, 350,\n","                                                         400]},\n","                   random_state=42, scoring=&#x27;f1&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":53}]},{"cell_type":"code","source":["rf_random.best_estimator_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"id":"JRIENzI8oyoR","outputId":"1fe6820e-a30c-497f-85f7-2c55fb2537d5","executionInfo":{"status":"ok","timestamp":1682282943312,"user_tz":-60,"elapsed":6,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier(max_depth=60, n_estimators=250, random_state=42)"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=60, n_estimators=250, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=60, n_estimators=250, random_state=42)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":54}]},{"cell_type":"code","source":["rf_best = rf_random.best_estimator_ # Random forest corresponding to the best combination of HP"],"metadata":{"id":"ltL5deQkomB9"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["---\n","<font color=green>Q39:</font>\n","<br><font color='green'>\n","Using `classification_report` from `sklearn.metrics`, display the different performance measures and the confusion matrix for the \"optimal\" model `rf_best`. \n","</font>\n","\n","---"],"metadata":{"id":"YLJvPm_LS7gs"}},{"cell_type":"code","metadata":{"id":"gHGOzqRoEJZk","colab":{"base_uri":"https://localhost:8080/","height":879},"outputId":"cd834b69-f6ca-4cfe-e268-e2354d38e7cb","executionInfo":{"status":"ok","timestamp":1682282944111,"user_tz":-60,"elapsed":803,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"source":["# Evaluation performances\n","# rf_best.predict() is gonna output binary values 0/1 based on a threshold of 0.5 vs rf_best.predict_proba() outputs probailities \n","y_hat = rf_best.predict(X_test)\n","print(classification_report(y_test, y_hat))\n","# Confusion Matrix\n","plot_confusion_matrix(y_test, y_hat)\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.93      0.99      0.96      7652\n","           1       0.95      0.73      0.82      2122\n","\n","    accuracy                           0.93      9774\n","   macro avg       0.94      0.86      0.89      9774\n","weighted avg       0.93      0.93      0.93      9774\n","\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x800 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# 5. Adjusting the threshold"],"metadata":{"id":"5BKVilJLgUDi"}},{"cell_type":"markdown","source":["### 5.2 Adjusting the probability threshold"],"metadata":{"id":"aOhuR142XmPA"}},{"cell_type":"markdown","source":["**Our objective in this section is to adjust the threshold in order to match a desired recall**"],"metadata":{"id":"BNOgptF099ta"}},{"cell_type":"markdown","source":["In order to plot the ROC curve, you will have to install `plot_metric` using the following command line:\n","\n","```\n","!pip install plot_metric\n","```"],"metadata":{"id":"lqjBuXr8XuNJ"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V0SI38AGELzU","outputId":"0211d525-e279-4589-b44f-c6b08aaabf71","executionInfo":{"status":"ok","timestamp":1682282949385,"user_tz":-60,"elapsed":5280,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting plot_metric\n","  Downloading plot_metric-0.0.6-py3-none-any.whl (13 kB)\n","Requirement already satisfied: seaborn>=0.9.0 in /usr/local/lib/python3.9/dist-packages (from plot_metric) (0.12.2)\n","Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.9/dist-packages (from plot_metric) (1.22.4)\n","Requirement already satisfied: pandas>=0.23.4 in /usr/local/lib/python3.9/dist-packages (from plot_metric) (1.5.3)\n","Requirement already satisfied: scikit-learn>=0.21.2 in /usr/local/lib/python3.9/dist-packages (from plot_metric) (1.2.2)\n","Requirement already satisfied: colorlover>=0.3.0 in /usr/local/lib/python3.9/dist-packages (from plot_metric) (0.3.0)\n","Requirement already satisfied: matplotlib>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from plot_metric) (3.7.1)\n","Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.9/dist-packages (from plot_metric) (1.10.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=3.0.2->plot_metric) (3.0.9)\n","Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=3.0.2->plot_metric) (5.12.0)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=3.0.2->plot_metric) (2.8.2)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=3.0.2->plot_metric) (1.4.4)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=3.0.2->plot_metric) (0.11.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=3.0.2->plot_metric) (23.1)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=3.0.2->plot_metric) (8.4.0)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=3.0.2->plot_metric) (1.0.7)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=3.0.2->plot_metric) (4.39.3)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.23.4->plot_metric) (2022.7.1)\n","Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=0.21.2->plot_metric) (1.2.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=0.21.2->plot_metric) (3.1.0)\n","Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.9/dist-packages (from importlib-resources>=3.2.0->matplotlib>=3.0.2->plot_metric) (3.15.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7->matplotlib>=3.0.2->plot_metric) (1.16.0)\n","Installing collected packages: plot_metric\n","Successfully installed plot_metric-0.0.6\n"]}],"source":["!pip install plot_metric"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qVv7L7lYEL9A","colab":{"base_uri":"https://localhost:8080/","height":564},"outputId":"fbc9bd15-e83f-42c4-978b-2180b160ea07","executionInfo":{"status":"ok","timestamp":1682282950571,"user_tz":-60,"elapsed":1198,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 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\n"},"metadata":{}}],"source":["# Import module\n","from plot_metric.functions import BinaryClassification\n","\n","# Plot the ROC curve\n","bc = BinaryClassification(y_test, rf_best.predict_proba(X_test)[:, 1], labels=[0, 1])\n","plt.figure(figsize=(10, 6))\n","bc.plot_roc_curve()\n","plt.show()"]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","y_hat = rf_best.predict(X_test)\n","print(classification_report(y_test, y_hat))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RfMj-PDixqKz","outputId":"4cb5d2dd-7b3c-49f0-985e-2f132ef61217","executionInfo":{"status":"ok","timestamp":1682282951439,"user_tz":-60,"elapsed":876,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.93      0.99      0.96      7652\n","           1       0.95      0.73      0.82      2122\n","\n","    accuracy                           0.93      9774\n","   macro avg       0.94      0.86      0.89      9774\n","weighted avg       0.93      0.93      0.93      9774\n","\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"srCThYfBEL_m","outputId":"a3a7371c-1cd8-48fe-f908-966c84dca3dd","executionInfo":{"status":"ok","timestamp":1682282951728,"user_tz":-60,"elapsed":293,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["The best Threshold is 0.28...The best recall is 0.8034872761545712\n"]}],"source":["# Let us find the threshold with the highest recall\n","from sklearn.metrics import recall_score\n","# Calculate the prediction scores y_hat_proba associated with X_test\n","y_hat_proba = rf_best.predict_proba(X_test)[:, 1]\n","\n","# The objective recall\n","OBJECTIVE_RECALL = 0.8\n","\n","# Initialize the best threshold to 0.5\n","best_threshold = 0.5\n","# Calculate the binary prediction y_hat associated with the threshold 0.5\n","y_hat = (y_hat_proba > best_threshold)\n","# Calculate the recall associated with the threshold 0.5\n","best_recall = recall_score(y_test, y_hat)\n","\n","# Find the best threshold \n","for th in np.arange(0.01, 1, 0.01)[::-1]:\n","  # Calculate the binary prediction y_hat associated with the threshold th\n","  y_pred = (y_hat_proba > th)\n","  recall = recall_score(y_test, y_pred)\n","  if recall > OBJECTIVE_RECALL:\n","    best_recall = recall\n","    best_threshold = th\n","    break\n","print(\"The best Threshold is {}...The best recall is {}\".format(best_threshold, best_recall))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"iYuTmBudEMRO","outputId":"9b73ddd0-dbc5-41eb-a3cf-44dd144a5c68","executionInfo":{"status":"ok","timestamp":1682282953080,"user_tz":-60,"elapsed":1354,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# let's use another probability threshold so that we can get to the elbow position in the above curve\n","bc = BinaryClassification(y_test, rf_best.predict_proba(X_test)[:, 1], threshold=best_threshold, labels = [0, 1])\n","plt.figure(figsize=(10, 6))\n","bc.plot_roc_curve()\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rKY68pOZCfmu"},"outputs":[],"source":["# Calculate the prediction scores y_hat_proba associated with X_test\n","y_hat_proba = rf_best.predict_proba(X_test)[:, 1]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZcXHq2biCn3D"},"outputs":[],"source":["# Calculate the binary prediction y_hat associated with the threshold th\n","y_hat = (y_hat_proba >= best_threshold)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":718},"id":"745o8G5DCprX","outputId":"d7eb8d27-69d1-4445-95ed-590f96af2d6c","executionInfo":{"status":"ok","timestamp":1682282954433,"user_tz":-60,"elapsed":1085,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x800 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# plot the confusion matrix\n","fig = plt.figure(figsize=(10, 8))\n","g = sns.heatmap(confusion_matrix(y_test, y_hat),\n","            annot=True,\n","            fmt=\"0.0f\")\n","g.set_xlabel(\"Predicted\")\n","g.set_ylabel(\"Actual\")\n","g.set_title(\"Confusion Matrix on the test set\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uvQ4-LDJCrGz","outputId":"aba2bd34-d355-4c73-91e6-eaeeb681b645","executionInfo":{"status":"ok","timestamp":1682282954434,"user_tz":-60,"elapsed":14,"user":{"displayName":"Hachem Madmoun","userId":"01826424375397677797"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.95      0.94      0.94      7652\n","           1       0.78      0.81      0.79      2122\n","\n","    accuracy                           0.91      9774\n","   macro avg       0.86      0.87      0.87      9774\n","weighted avg       0.91      0.91      0.91      9774\n","\n"]}],"source":["# print the classification report\n","from sklearn.metrics import classification_report\n","print(classification_report(y_test, y_hat))"]}]}