credit_risk_ensemble.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "LO9bhoEnZsPO"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "7G4qiumaZsPT"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pathlib import Path\n",
        "from collections import Counter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "-VLUlJH5ZsPV"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import balanced_accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from imblearn.metrics import classification_report_imbalanced"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XofUhmwfZsPX"
      },
      "source": [
        "# Read the CSV and Perform Basic Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "dRlQSmuDZsPa"
      },
      "outputs": [],
      "source": [
        "# https://help.lendingclub.com/hc/en-us/articles/215488038-What-do-the-different-Note-statuses-mean-\n",
        "\n",
        "columns = [\n",
        "    \"loan_amnt\", \"int_rate\", \"installment\", \"home_ownership\",\n",
        "    \"annual_inc\", \"verification_status\", \"issue_d\", \"loan_status\",\n",
        "    \"pymnt_plan\", \"dti\", \"delinq_2yrs\", \"inq_last_6mths\",\n",
        "    \"open_acc\", \"pub_rec\", \"revol_bal\", \"total_acc\",\n",
        "    \"initial_list_status\", \"out_prncp\", \"out_prncp_inv\", \"total_pymnt\",\n",
        "    \"total_pymnt_inv\", \"total_rec_prncp\", \"total_rec_int\", \"total_rec_late_fee\",\n",
        "    \"recoveries\", \"collection_recovery_fee\", \"last_pymnt_amnt\", \"next_pymnt_d\",\n",
        "    \"collections_12_mths_ex_med\", \"policy_code\", \"application_type\", \"acc_now_delinq\",\n",
        "    \"tot_coll_amt\", \"tot_cur_bal\", \"open_acc_6m\", \"open_act_il\",\n",
        "    \"open_il_12m\", \"open_il_24m\", \"mths_since_rcnt_il\", \"total_bal_il\",\n",
        "    \"il_util\", \"open_rv_12m\", \"open_rv_24m\", \"max_bal_bc\",\n",
        "    \"all_util\", \"total_rev_hi_lim\", \"inq_fi\", \"total_cu_tl\",\n",
        "    \"inq_last_12m\", \"acc_open_past_24mths\", \"avg_cur_bal\", \"bc_open_to_buy\",\n",
        "    \"bc_util\", \"chargeoff_within_12_mths\", \"delinq_amnt\", \"mo_sin_old_il_acct\",\n",
        "    \"mo_sin_old_rev_tl_op\", \"mo_sin_rcnt_rev_tl_op\", \"mo_sin_rcnt_tl\", \"mort_acc\",\n",
        "    \"mths_since_recent_bc\", \"mths_since_recent_inq\", \"num_accts_ever_120_pd\", \"num_actv_bc_tl\",\n",
        "    \"num_actv_rev_tl\", \"num_bc_sats\", \"num_bc_tl\", \"num_il_tl\",\n",
        "    \"num_op_rev_tl\", \"num_rev_accts\", \"num_rev_tl_bal_gt_0\",\n",
        "    \"num_sats\", \"num_tl_120dpd_2m\", \"num_tl_30dpd\", \"num_tl_90g_dpd_24m\",\n",
        "    \"num_tl_op_past_12m\", \"pct_tl_nvr_dlq\", \"percent_bc_gt_75\", \"pub_rec_bankruptcies\",\n",
        "    \"tax_liens\", \"tot_hi_cred_lim\", \"total_bal_ex_mort\", \"total_bc_limit\",\n",
        "    \"total_il_high_credit_limit\", \"hardship_flag\", \"debt_settlement_flag\"\n",
        "]\n",
        "\n",
        "target = [\"loan_status\"]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "2n5JixYeaLkK"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "\n",
        "uploaded = files.upload()\n"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "X6XZwK7YaL4q",
        "outputId": "2c4b82ba-ea44-415f-b3b3-6a84debfd034"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-79d8f324-854f-4043-a22e-5933519345ee\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-79d8f324-854f-4043-a22e-5933519345ee\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving LoanStats_2019Q1.csv to LoanStats_2019Q1 (1).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "0zE61SuVZsPd",
        "outputId": "2116b04c-6673-4933-e5a1-302656940604"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   loan_amnt  int_rate  installment home_ownership  annual_inc  \\\n",
              "0    10500.0    0.1719       375.35           RENT     66000.0   \n",
              "1    25000.0    0.2000       929.09       MORTGAGE    105000.0   \n",
              "2    20000.0    0.2000       529.88       MORTGAGE     56000.0   \n",
              "3    10000.0    0.1640       353.55           RENT     92000.0   \n",
              "4    22000.0    0.1474       520.39       MORTGAGE     52000.0   \n",
              "\n",
              "  verification_status   issue_d loan_status pymnt_plan    dti  ...  \\\n",
              "0     Source Verified  Mar-2019    low_risk          n  27.24  ...   \n",
              "1            Verified  Mar-2019    low_risk          n  20.23  ...   \n",
              "2            Verified  Mar-2019    low_risk          n  24.26  ...   \n",
              "3            Verified  Mar-2019    low_risk          n  31.44  ...   \n",
              "4        Not Verified  Mar-2019    low_risk          n  18.76  ...   \n",
              "\n",
              "   pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  \\\n",
              "0            85.7             100.0                   0.0        0.0   \n",
              "1            91.2              50.0                   1.0        0.0   \n",
              "2            66.7              50.0                   0.0        0.0   \n",
              "3           100.0              50.0                   1.0        0.0   \n",
              "4           100.0               0.0                   0.0        0.0   \n",
              "\n",
              "   tot_hi_cred_lim  total_bal_ex_mort total_bc_limit  \\\n",
              "0          65687.0            38199.0         2000.0   \n",
              "1         271427.0            60641.0        41200.0   \n",
              "2          60644.0            45684.0         7500.0   \n",
              "3          99506.0            68784.0        19700.0   \n",
              "4         219750.0            25919.0        27600.0   \n",
              "\n",
              "   total_il_high_credit_limit  hardship_flag  debt_settlement_flag  \n",
              "0                     61987.0              N                     N  \n",
              "1                     49197.0              N                     N  \n",
              "2                     43144.0              N                     N  \n",
              "3                     76506.0              N                     N  \n",
              "4                     20000.0              N                     N  \n",
              "\n",
              "[5 rows x 86 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-41a9a7f7-ebad-4563-a8ed-5a9d03839d0f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>pymnt_plan</th>\n",
              "      <th>dti</th>\n",
              "      <th>...</th>\n",
              "      <th>pct_tl_nvr_dlq</th>\n",
              "      <th>percent_bc_gt_75</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>tax_liens</th>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <th>total_bc_limit</th>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "      <th>hardship_flag</th>\n",
              "      <th>debt_settlement_flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10500.0</td>\n",
              "      <td>0.1719</td>\n",
              "      <td>375.35</td>\n",
              "      <td>RENT</td>\n",
              "      <td>66000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Mar-2019</td>\n",
              "      <td>low_risk</td>\n",
              "      <td>n</td>\n",
              "      <td>27.24</td>\n",
              "      <td>...</td>\n",
              "      <td>85.7</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>65687.0</td>\n",
              "      <td>38199.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>61987.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25000.0</td>\n",
              "      <td>0.2000</td>\n",
              "      <td>929.09</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>105000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Mar-2019</td>\n",
              "      <td>low_risk</td>\n",
              "      <td>n</td>\n",
              "      <td>20.23</td>\n",
              "      <td>...</td>\n",
              "      <td>91.2</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>271427.0</td>\n",
              "      <td>60641.0</td>\n",
              "      <td>41200.0</td>\n",
              "      <td>49197.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20000.0</td>\n",
              "      <td>0.2000</td>\n",
              "      <td>529.88</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>56000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Mar-2019</td>\n",
              "      <td>low_risk</td>\n",
              "      <td>n</td>\n",
              "      <td>24.26</td>\n",
              "      <td>...</td>\n",
              "      <td>66.7</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>60644.0</td>\n",
              "      <td>45684.0</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>43144.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>0.1640</td>\n",
              "      <td>353.55</td>\n",
              "      <td>RENT</td>\n",
              "      <td>92000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Mar-2019</td>\n",
              "      <td>low_risk</td>\n",
              "      <td>n</td>\n",
              "      <td>31.44</td>\n",
              "      <td>...</td>\n",
              "      <td>100.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>99506.0</td>\n",
              "      <td>68784.0</td>\n",
              "      <td>19700.0</td>\n",
              "      <td>76506.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22000.0</td>\n",
              "      <td>0.1474</td>\n",
              "      <td>520.39</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>52000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Mar-2019</td>\n",
              "      <td>low_risk</td>\n",
              "      <td>n</td>\n",
              "      <td>18.76</td>\n",
              "      <td>...</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>219750.0</td>\n",
              "      <td>25919.0</td>\n",
              "      <td>27600.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 86 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Load the data\n",
        "file_path = Path('LoanStats_2019Q1.csv')\n",
        "df = pd.read_csv(file_path, skiprows=1)[:-2]\n",
        "df = df.loc[:, columns].copy()\n",
        "\n",
        "# Drop the null columns where all values are null\n",
        "df = df.dropna(axis='columns', how='all')\n",
        "\n",
        "# Drop the null rows\n",
        "df = df.dropna()\n",
        "\n",
        "# Remove the `Issued` loan status\n",
        "issued_mask = df['loan_status'] != 'Issued'\n",
        "df = df.loc[issued_mask]\n",
        "\n",
        "# convert interest rate to numerical\n",
        "df['int_rate'] = df['int_rate'].str.replace('%', '')\n",
        "df['int_rate'] = df['int_rate'].astype('float') / 100\n",
        "\n",
        "\n",
        "# Convert the target column values to low_risk and high_risk based on their values\n",
        "x = {'Current': 'low_risk'}   \n",
        "df = df.replace(x)\n",
        "\n",
        "x = dict.fromkeys(['Late (31-120 days)', 'Late (16-30 days)', 'Default', 'In Grace Period'], 'high_risk')    \n",
        "df = df.replace(x)\n",
        "\n",
        "df.reset_index(inplace=True, drop=True)\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtCQ587sZsPm"
      },
      "source": [
        "# Split the Data into Training and Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "fZGccbwBZsPo"
      },
      "outputs": [],
      "source": [
        "# Create our features\n",
        "X = df.drop(\"loan_status\", axis=1)\n",
        "X = pd.get_dummies(X)\n",
        "\n",
        "# Create our target\n",
        "y = df.loc[:, target].copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "kaEDmRW_ZsPq",
        "outputId": "19aaaf2f-4ddf-4cf6-9032-2bc13f75583f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          loan_amnt      int_rate   installment    annual_inc           dti  \\\n",
              "count  68817.000000  68817.000000  68817.000000  6.881700e+04  68817.000000   \n",
              "mean   16677.594562      0.127718    480.652863  8.821371e+04     21.778153   \n",
              "std    10277.348590      0.048130    288.062432  1.155800e+05     20.199244   \n",
              "min     1000.000000      0.060000     30.890000  4.000000e+01      0.000000   \n",
              "25%     9000.000000      0.088100    265.730000  5.000000e+04     13.890000   \n",
              "50%    15000.000000      0.118000    404.560000  7.300000e+04     19.760000   \n",
              "75%    24000.000000      0.155700    648.100000  1.040000e+05     26.660000   \n",
              "max    40000.000000      0.308400   1676.230000  8.797500e+06    999.000000   \n",
              "\n",
              "        delinq_2yrs  inq_last_6mths      open_acc       pub_rec  \\\n",
              "count  68817.000000    68817.000000  68817.000000  68817.000000   \n",
              "mean       0.217766        0.497697     12.587340      0.126030   \n",
              "std        0.718367        0.758122      6.022869      0.336797   \n",
              "min        0.000000        0.000000      2.000000      0.000000   \n",
              "25%        0.000000        0.000000      8.000000      0.000000   \n",
              "50%        0.000000        0.000000     11.000000      0.000000   \n",
              "75%        0.000000        1.000000     16.000000      0.000000   \n",
              "max       18.000000        5.000000     72.000000      4.000000   \n",
              "\n",
              "           revol_bal  ...  issue_d_Mar-2019  pymnt_plan_n  \\\n",
              "count   68817.000000  ...      68817.000000       68817.0   \n",
              "mean    17604.142828  ...          0.177238           1.0   \n",
              "std     21835.880400  ...          0.381873           0.0   \n",
              "min         0.000000  ...          0.000000           1.0   \n",
              "25%      6293.000000  ...          0.000000           1.0   \n",
              "50%     12068.000000  ...          0.000000           1.0   \n",
              "75%     21735.000000  ...          0.000000           1.0   \n",
              "max    587191.000000  ...          1.000000           1.0   \n",
              "\n",
              "       initial_list_status_f  initial_list_status_w  next_pymnt_d_Apr-2019  \\\n",
              "count           68817.000000           68817.000000           68817.000000   \n",
              "mean                0.123879               0.876121               0.383161   \n",
              "std                 0.329446               0.329446               0.486161   \n",
              "min                 0.000000               0.000000               0.000000   \n",
              "25%                 0.000000               1.000000               0.000000   \n",
              "50%                 0.000000               1.000000               0.000000   \n",
              "75%                 0.000000               1.000000               1.000000   \n",
              "max                 1.000000               1.000000               1.000000   \n",
              "\n",
              "       next_pymnt_d_May-2019  application_type_Individual  \\\n",
              "count           68817.000000                 68817.000000   \n",
              "mean                0.616839                     0.860340   \n",
              "std                 0.486161                     0.346637   \n",
              "min                 0.000000                     0.000000   \n",
              "25%                 0.000000                     1.000000   \n",
              "50%                 1.000000                     1.000000   \n",
              "75%                 1.000000                     1.000000   \n",
              "max                 1.000000                     1.000000   \n",
              "\n",
              "       application_type_Joint App  hardship_flag_N  debt_settlement_flag_N  \n",
              "count                68817.000000          68817.0                 68817.0  \n",
              "mean                     0.139660              1.0                     1.0  \n",
              "std                      0.346637              0.0                     0.0  \n",
              "min                      0.000000              1.0                     1.0  \n",
              "25%                      0.000000              1.0                     1.0  \n",
              "50%                      0.000000              1.0                     1.0  \n",
              "75%                      0.000000              1.0                     1.0  \n",
              "max                      1.000000              1.0                     1.0  \n",
              "\n",
              "[8 rows x 95 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>...</th>\n",
              "      <th>issue_d_Mar-2019</th>\n",
              "      <th>pymnt_plan_n</th>\n",
              "      <th>initial_list_status_f</th>\n",
              "      <th>initial_list_status_w</th>\n",
              "      <th>next_pymnt_d_Apr-2019</th>\n",
              "      <th>next_pymnt_d_May-2019</th>\n",
              "      <th>application_type_Individual</th>\n",
              "      <th>application_type_Joint App</th>\n",
              "      <th>hardship_flag_N</th>\n",
              "      <th>debt_settlement_flag_N</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>6.881700e+04</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.0</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.000000</td>\n",
              "      <td>68817.0</td>\n",
              "      <td>68817.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>16677.594562</td>\n",
              "      <td>0.127718</td>\n",
              "      <td>480.652863</td>\n",
              "      <td>8.821371e+04</td>\n",
              "      <td>21.778153</td>\n",
              "      <td>0.217766</td>\n",
              "      <td>0.497697</td>\n",
              "      <td>12.587340</td>\n",
              "      <td>0.126030</td>\n",
              "      <td>17604.142828</td>\n",
              "      <td>...</td>\n",
              "      <td>0.177238</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.123879</td>\n",
              "      <td>0.876121</td>\n",
              "      <td>0.383161</td>\n",
              "      <td>0.616839</td>\n",
              "      <td>0.860340</td>\n",
              "      <td>0.139660</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10277.348590</td>\n",
              "      <td>0.048130</td>\n",
              "      <td>288.062432</td>\n",
              "      <td>1.155800e+05</td>\n",
              "      <td>20.199244</td>\n",
              "      <td>0.718367</td>\n",
              "      <td>0.758122</td>\n",
              "      <td>6.022869</td>\n",
              "      <td>0.336797</td>\n",
              "      <td>21835.880400</td>\n",
              "      <td>...</td>\n",
              "      <td>0.381873</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.329446</td>\n",
              "      <td>0.329446</td>\n",
              "      <td>0.486161</td>\n",
              "      <td>0.486161</td>\n",
              "      <td>0.346637</td>\n",
              "      <td>0.346637</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>30.890000</td>\n",
              "      <td>4.000000e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9000.000000</td>\n",
              "      <td>0.088100</td>\n",
              "      <td>265.730000</td>\n",
              "      <td>5.000000e+04</td>\n",
              "      <td>13.890000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6293.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>15000.000000</td>\n",
              "      <td>0.118000</td>\n",
              "      <td>404.560000</td>\n",
              "      <td>7.300000e+04</td>\n",
              "      <td>19.760000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12068.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>24000.000000</td>\n",
              "      <td>0.155700</td>\n",
              "      <td>648.100000</td>\n",
              "      <td>1.040000e+05</td>\n",
              "      <td>26.660000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21735.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>40000.000000</td>\n",
              "      <td>0.308400</td>\n",
              "      <td>1676.230000</td>\n",
              "      <td>8.797500e+06</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>587191.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 95 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed869de6-4edc-4ec8-aec3-e571fecd69d8')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ed869de6-4edc-4ec8-aec3-e571fecd69d8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ed869de6-4edc-4ec8-aec3-e571fecd69d8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "X.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bhKb-Wc9ZsPs",
        "outputId": "29c8912c-bb04-4f0a-84a0-82dc9418fd1d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "low_risk     68470\n",
              "high_risk      347\n",
              "Name: loan_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Check the balance of our target values\n",
        "y['loan_status'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6RKlUewZsPu",
        "outputId": "0ca2b80c-1c8f-4a2a-d2b5-a22654dbeac3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({'low_risk': 51366, 'high_risk': 246})\n",
            "Counter({'low_risk': 17104, 'high_risk': 101})\n"
          ]
        }
      ],
      "source": [
        "# Normal train test split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
        "print(Counter(y_train['loan_status']))\n",
        "print(Counter(y_test['loan_status']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaeVRBX6ZsPv"
      },
      "source": [
        "# Ensemble Learners\n",
        "\n",
        "In this section, you will compare two ensemble algorithms to determine which algorithm results in the best performance. You will train a Balanced Random Forest Classifier and an Easy Ensemble AdaBoost classifier . For each algorithm, be sure to complete the folliowing steps:\n",
        "\n",
        "1. Train the model using the training data. \n",
        "2. Calculate the balanced accuracy score from sklearn.metrics.\n",
        "3. Print the confusion matrix from sklearn.metrics.\n",
        "4. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
        "5. For the Balanced Random Forest Classifier onely, print the feature importance sorted in descending order (most important feature to least important) along with the feature score\n",
        "\n",
        "Note: Use a random state of 1 for each algorithm to ensure consistency between tests"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOvOosmVZsPz"
      },
      "source": [
        "### Balanced Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cirgrCIAZsP1",
        "outputId": "0463086d-a566-42bc-f2f0-8e27c82dce7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({'low_risk': 51366, 'high_risk': 246})\n"
          ]
        }
      ],
      "source": [
        "# Resample the training data with the BalancedRandomForestClassifier\n",
        "from imblearn.ensemble import BalancedRandomForestClassifier\n",
        "BRFC_model = BalancedRandomForestClassifier(n_estimators=100, random_state=1)\n",
        "BRFC_model.fit(X_train, y_train)\n",
        "print(Counter(y_train['loan_status']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "S5z__5Q8ZsP5"
      },
      "outputs": [],
      "source": [
        "# Calculate predictions\n",
        "y_pred = BRFC_model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fqUAdofZsP8",
        "outputId": "177c89fb-25a0-48fd-e317-133aa46b7f7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7885466545953005"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Calculated the balanced accuracy score\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "balanced_accuracy_score(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Q11w3cpZZsP9",
        "outputId": "cd987c40-1aaa-4d2f-9ffb-9fb234c01221"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Predicted High_Risk  Predicted Low_Risk\n",
              "Actual High_Risk                   71                  30\n",
              "Actual Low_Risk                  2153               14951"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cc9a1b7f-0c04-4172-b09e-5c30a09b9738\">\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>Predicted High_Risk</th>\n",
              "      <th>Predicted Low_Risk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual High_Risk</th>\n",
              "      <td>71</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual Low_Risk</th>\n",
              "      <td>2153</td>\n",
              "      <td>14951</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cc9a1b7f-0c04-4172-b09e-5c30a09b9738')\"\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",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cc9a1b7f-0c04-4172-b09e-5c30a09b9738 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cc9a1b7f-0c04-4172-b09e-5c30a09b9738');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Display the confusion matrix\n",
        "cmatrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Create a DataFrame from the confusion matrix.\n",
        "cmatrix_df = pd.DataFrame(\n",
        "    cmatrix, index=[\"Actual High_Risk\", \"Actual Low_Risk\"], columns=[\"Predicted High_Risk\", \"Predicted Low_Risk\"])\n",
        "\n",
        "cmatrix_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZLok99sZsP-",
        "outputId": "cbae3492-f867-4399-ebc6-d23ce49ce04d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   pre       rec       spe        f1       geo       iba       sup\n",
            "\n",
            "  high_risk       0.03      0.70      0.87      0.06      0.78      0.60       101\n",
            "   low_risk       1.00      0.87      0.70      0.93      0.78      0.62     17104\n",
            "\n",
            "avg / total       0.99      0.87      0.70      0.93      0.78      0.62     17205\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Print the imbalanced classification report\n",
        "print(classification_report_imbalanced(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This will show the imbalanced classification report as a Pandas Dataframe for better readability\n",
        "# Source: https://stackoverflow.com/questions/39662398/scikit-learn-output-metrics-classification-report-into-csv-tab-delimited-format'''\n",
        "from sklearn import metrics\n",
        "report = metrics.classification_report(y_test, y_pred, output_dict=True)\n",
        "df_classification_report_imbalanced = pd.DataFrame(report).transpose()\n",
        "df_classification_report_imbalanced = df_classification_report_imbalanced.sort_values(by=['f1-score'], ascending=False)\n",
        "\n",
        "df_classification_report_imbalanced\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jlVLz5S9e18g",
        "outputId": "0c9dd85d-edca-4d03-d600-49317e89d6e3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              precision    recall  f1-score       support\n",
              "low_risk       0.997997  0.874123  0.931962  17104.000000\n",
              "weighted avg   0.992326  0.873118  0.926850  17205.000000\n",
              "accuracy       0.873118  0.873118  0.873118      0.873118\n",
              "macro avg      0.514961  0.788547  0.496519  17205.000000\n",
              "high_risk      0.031924  0.702970  0.061075    101.000000"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>low_risk</th>\n",
              "      <td>0.997997</td>\n",
              "      <td>0.874123</td>\n",
              "      <td>0.931962</td>\n",
              "      <td>17104.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.992326</td>\n",
              "      <td>0.873118</td>\n",
              "      <td>0.926850</td>\n",
              "      <td>17205.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.873118</td>\n",
              "      <td>0.873118</td>\n",
              "      <td>0.873118</td>\n",
              "      <td>0.873118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.514961</td>\n",
              "      <td>0.788547</td>\n",
              "      <td>0.496519</td>\n",
              "      <td>17205.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>high_risk</th>\n",
              "      <td>0.031924</td>\n",
              "      <td>0.702970</td>\n",
              "      <td>0.061075</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-8e851438-f0c4-43cd-a8f9-109cc046ac5c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Zb1sQLjOZsQA",
        "outputId": "3fb84345-129b-430f-c784-d5643979473d"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     Importance\n",
              "total_rec_prncp                        0.078768\n",
              "total_pymnt                            0.058838\n",
              "total_pymnt_inv                        0.056256\n",
              "total_rec_int                          0.053555\n",
              "last_pymnt_amnt                        0.050033\n",
              "int_rate                               0.029670\n",
              "issue_d_Jan-2019                       0.021129\n",
              "installment                            0.019802\n",
              "dti                                    0.017471\n",
              "out_prncp_inv                          0.016858\n",
              "total_bal_ex_mort                      0.016413\n",
              "mths_since_recent_inq                  0.015221\n",
              "issue_d_Mar-2019                       0.015115\n",
              "revol_bal                              0.014927\n",
              "max_bal_bc                             0.014899\n",
              "out_prncp                              0.014881\n",
              "mo_sin_old_rev_tl_op                   0.014859\n",
              "annual_inc                             0.014833\n",
              "bc_util                                0.014614\n",
              "tot_hi_cred_lim                        0.014488\n",
              "mo_sin_old_il_acct                     0.013921\n",
              "mths_since_rcnt_il                     0.013534\n",
              "total_rev_hi_lim                       0.013365\n",
              "avg_cur_bal                            0.013323\n",
              "bc_open_to_buy                         0.013266\n",
              "il_util                                0.013115\n",
              "total_bc_limit                         0.013045\n",
              "all_util                               0.013045\n",
              "pct_tl_nvr_dlq                         0.013030\n",
              "tot_cur_bal                            0.012856\n",
              "total_il_high_credit_limit             0.012799\n",
              "inq_last_12m                           0.012774\n",
              "total_bal_il                           0.011969\n",
              "mo_sin_rcnt_tl                         0.010983\n",
              "loan_amnt                              0.010580\n",
              "mths_since_recent_bc                   0.010575\n",
              "num_rev_accts                          0.010320\n",
              "total_acc                              0.010209\n",
              "open_acc_6m                            0.009754\n",
              "num_il_tl                              0.009662\n",
              "num_actv_rev_tl                        0.009632\n",
              "mo_sin_rcnt_rev_tl_op                  0.009393\n",
              "num_bc_tl                              0.008724\n",
              "num_sats                               0.008629\n",
              "num_op_rev_tl                          0.008331\n",
              "num_bc_sats                            0.007571\n",
              "num_tl_op_past_12m                     0.007549\n",
              "open_act_il                            0.007490\n",
              "open_acc                               0.007382\n",
              "num_rev_tl_bal_gt_0                    0.007273\n",
              "total_rec_late_fee                     0.006999\n",
              "num_actv_bc_tl                         0.006867\n",
              "acc_open_past_24mths                   0.006714\n",
              "open_il_24m                            0.006561\n",
              "inq_fi                                 0.006241\n",
              "inq_last_6mths                         0.006216\n",
              "mort_acc                               0.006171\n",
              "total_cu_tl                            0.006083\n",
              "issue_d_Feb-2019                       0.005640\n",
              "open_rv_24m                            0.005635\n",
              "percent_bc_gt_75                       0.005131\n",
              "next_pymnt_d_Apr-2019                  0.005106\n",
              "next_pymnt_d_May-2019                  0.005037\n",
              "open_il_12m                            0.004860\n",
              "tot_coll_amt                           0.004199\n",
              "open_rv_12m                            0.004019\n",
              "num_accts_ever_120_pd                  0.003757\n",
              "delinq_2yrs                            0.003083\n",
              "home_ownership_RENT                    0.002913\n",
              "home_ownership_OWN                     0.002825\n",
              "verification_status_Not Verified       0.002205\n",
              "application_type_Joint App             0.002091\n",
              "pub_rec                                0.002015\n",
              "verification_status_Verified           0.001933\n",
              "application_type_Individual            0.001902\n",
              "verification_status_Source Verified    0.001505\n",
              "home_ownership_MORTGAGE                0.001459\n",
              "pub_rec_bankruptcies                   0.001373\n",
              "initial_list_status_w                  0.001152\n",
              "initial_list_status_f                  0.001101\n",
              "num_tl_90g_dpd_24m                     0.000996\n",
              "collections_12_mths_ex_med             0.000715\n",
              "home_ownership_ANY                     0.000496\n",
              "chargeoff_within_12_mths               0.000273\n",
              "pymnt_plan_n                           0.000000\n",
              "hardship_flag_N                        0.000000\n",
              "num_tl_30dpd                           0.000000\n",
              "tax_liens                              0.000000\n",
              "recoveries                             0.000000\n",
              "num_tl_120dpd_2m                       0.000000\n",
              "collection_recovery_fee                0.000000\n",
              "policy_code                            0.000000\n",
              "acc_now_delinq                         0.000000\n",
              "delinq_amnt                            0.000000\n",
              "debt_settlement_flag_N                 0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7a3e7f8-b082-4ea6-988e-346a4b736614\">\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>total_rec_prncp</th>\n",
              "      <td>0.078768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_pymnt</th>\n",
              "      <td>0.058838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <td>0.056256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rec_int</th>\n",
              "      <td>0.053555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_pymnt_amnt</th>\n",
              "      <td>0.050033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int_rate</th>\n",
              "      <td>0.029670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>issue_d_Jan-2019</th>\n",
              "      <td>0.021129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>0.019802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>0.017471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <td>0.016858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <td>0.016413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mths_since_recent_inq</th>\n",
              "      <td>0.015221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>issue_d_Mar-2019</th>\n",
              "      <td>0.015115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol_bal</th>\n",
              "      <td>0.014927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max_bal_bc</th>\n",
              "      <td>0.014899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>out_prncp</th>\n",
              "      <td>0.014881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mo_sin_old_rev_tl_op</th>\n",
              "      <td>0.014859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_inc</th>\n",
              "      <td>0.014833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bc_util</th>\n",
              "      <td>0.014614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <td>0.014488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mo_sin_old_il_acct</th>\n",
              "      <td>0.013921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mths_since_rcnt_il</th>\n",
              "      <td>0.013534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rev_hi_lim</th>\n",
              "      <td>0.013365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_cur_bal</th>\n",
              "      <td>0.013323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bc_open_to_buy</th>\n",
              "      <td>0.013266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>il_util</th>\n",
              "      <td>0.013115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_bc_limit</th>\n",
              "      <td>0.013045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all_util</th>\n",
              "      <td>0.013045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pct_tl_nvr_dlq</th>\n",
              "      <td>0.013030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tot_cur_bal</th>\n",
              "      <td>0.012856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "      <td>0.012799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inq_last_12m</th>\n",
              "      <td>0.012774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_bal_il</th>\n",
              "      <td>0.011969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mo_sin_rcnt_tl</th>\n",
              "      <td>0.010983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_amnt</th>\n",
              "      <td>0.010580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mths_since_recent_bc</th>\n",
              "      <td>0.010575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_rev_accts</th>\n",
              "      <td>0.010320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_acc</th>\n",
              "      <td>0.010209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_acc_6m</th>\n",
              "      <td>0.009754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_il_tl</th>\n",
              "      <td>0.009662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_actv_rev_tl</th>\n",
              "      <td>0.009632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
              "      <td>0.009393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_bc_tl</th>\n",
              "      <td>0.008724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_sats</th>\n",
              "      <td>0.008629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_op_rev_tl</th>\n",
              "      <td>0.008331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_bc_sats</th>\n",
              "      <td>0.007571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_tl_op_past_12m</th>\n",
              "      <td>0.007549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_act_il</th>\n",
              "      <td>0.007490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_acc</th>\n",
              "      <td>0.007382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_rev_tl_bal_gt_0</th>\n",
              "      <td>0.007273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rec_late_fee</th>\n",
              "      <td>0.006999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_actv_bc_tl</th>\n",
              "      <td>0.006867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acc_open_past_24mths</th>\n",
              "      <td>0.006714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_il_24m</th>\n",
              "      <td>0.006561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inq_fi</th>\n",
              "      <td>0.006241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <td>0.006216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mort_acc</th>\n",
              "      <td>0.006171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_cu_tl</th>\n",
              "      <td>0.006083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>issue_d_Feb-2019</th>\n",
              "      <td>0.005640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_rv_24m</th>\n",
              "      <td>0.005635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>percent_bc_gt_75</th>\n",
              "      <td>0.005131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>next_pymnt_d_Apr-2019</th>\n",
              "      <td>0.005106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>next_pymnt_d_May-2019</th>\n",
              "      <td>0.005037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_il_12m</th>\n",
              "      <td>0.004860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tot_coll_amt</th>\n",
              "      <td>0.004199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_rv_12m</th>\n",
              "      <td>0.004019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_accts_ever_120_pd</th>\n",
              "      <td>0.003757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <td>0.003083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home_ownership_RENT</th>\n",
              "      <td>0.002913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home_ownership_OWN</th>\n",
              "      <td>0.002825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>verification_status_Not Verified</th>\n",
              "      <td>0.002205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>application_type_Joint App</th>\n",
              "      <td>0.002091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec</th>\n",
              "      <td>0.002015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>verification_status_Verified</th>\n",
              "      <td>0.001933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>application_type_Individual</th>\n",
              "      <td>0.001902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>verification_status_Source Verified</th>\n",
              "      <td>0.001505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home_ownership_MORTGAGE</th>\n",
              "      <td>0.001459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <td>0.001373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>initial_list_status_w</th>\n",
              "      <td>0.001152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>initial_list_status_f</th>\n",
              "      <td>0.001101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_tl_90g_dpd_24m</th>\n",
              "      <td>0.000996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>collections_12_mths_ex_med</th>\n",
              "      <td>0.000715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home_ownership_ANY</th>\n",
              "      <td>0.000496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chargeoff_within_12_mths</th>\n",
              "      <td>0.000273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pymnt_plan_n</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hardship_flag_N</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_tl_30dpd</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tax_liens</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>recoveries</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_tl_120dpd_2m</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>collection_recovery_fee</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>policy_code</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acc_now_delinq</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>delinq_amnt</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>debt_settlement_flag_N</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# List the features sorted in descending order by feature importance\n",
        "# First, set option to print entire DataFrame and not show \"...\" in middle to truncate:\n",
        "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
        "\n",
        "importances = pd.DataFrame(BRFC_model.feature_importances_, index = X_train.columns, columns=['Importance']).sort_values('Importance', ascending=False)\n",
        "importances.loc[:,:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzwomvHVZsQB"
      },
      "source": [
        "### Easy Ensemble AdaBoost Classifier"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# There was an error in previous runs that said: AttributeError: 'BalancedBaggingClassifier' object has no attribute 'n_features_in_'\n",
        "# It seems that the attribute n_features_in_ is deprecated and its support was lost in sklearn version 1.2\n",
        "# We need to downgrade the scikit-learn package using: pip install scikit-learn==1.0 -U to solve the issue\n",
        "# Source of the fix: https://github.com/scikit-learn-contrib/imbalanced-learn/issues/872\n",
        "# Direct link to the fix comment: https://github.com/scikit-learn-contrib/imbalanced-learn/issues/872#:~:text=You%20have%20to%20downgrade%20the%20scikit%2Dlearn%20package%20using%3A%20pip3%20install%20scikit%2Dlearn%3D%3D1.0%20%2DU%0AThe%20attribute%20n_features_in_%20is%20deprecated%20and%20its%20support%20was%20lost%20in%20sklearn%20version%201.2\n"
      ],
      "metadata": {
        "id": "C-UcbKd1925E"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install scikit-learn==1.0 -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqFBThxTjLB8",
        "outputId": "11a9ac4f-85e7-4cd4-d232-a446641b5ccc"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-learn==1.0 in /usr/local/lib/python3.7/dist-packages (1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==1.0) (3.1.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==1.0) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==1.0) (1.21.6)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==1.0) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
      ],
      "metadata": {
        "id": "PTLzMinvkCtf"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHdonO7UZsQD",
        "outputId": "a47451ad-13de-4466-a806-b38b4a3074f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EasyEnsembleClassifier(n_estimators=100, random_state=1)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Train the EasyEnsembleClassifier\n",
        "from imblearn.ensemble import EasyEnsembleClassifier\n",
        "\n",
        "eec = EasyEnsembleClassifier(n_estimators=100, random_state=1)\n",
        "eec.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KMs4n_3jZsQE",
        "outputId": "3394a2ff-3861-4e31-f03f-50e775cd7828"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9103728756944658"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# Calculated the balanced accuracy score\n",
        "y_pred = eec.predict(X_test)\n",
        "balanced_accuracy_score(y_test, y_pred)  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZ8lq7lsZsQF",
        "outputId": "9ffa2b4d-c23b-4782-f2f0-4938903521f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  116,    15],\n",
              "       [ 1462, 21117]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Display the confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "y_pred = eec.predict(X_test)\n",
        "cmatrix2 = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "cmatrix2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This will show the confusion matrix as a Pandas Dataframe for better readability\n",
        "cmatrix_df2 = pd.DataFrame(\n",
        "    cmatrix2, index=[\"Actual High_Risk\", \"Actual Low_Risk\"], columns=[\"Predicted High_Risk\", \"Predicted Low_Risk\"])\n",
        "\n",
        "cmatrix_df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "lZyUpogtlvi8",
        "outputId": "ed773da5-07ec-48ff-a619-42c7c99d88d2"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Predicted High_Risk  Predicted Low_Risk\n",
              "Actual High_Risk                  116                  15\n",
              "Actual Low_Risk                  1462               21117"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Predicted High_Risk</th>\n",
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              "    <tr>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-023b1c21-c63f-48c8-a096-94e2c6a53abc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-023b1c21-c63f-48c8-a096-94e2c6a53abc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYbAoMAiZsQG",
        "outputId": "94a600fc-232a-4959-82ea-f8a16f0a1248"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   pre       rec       spe        f1       geo       iba       sup\n",
            "\n",
            "  high_risk       0.07      0.89      0.94      0.14      0.91      0.82       131\n",
            "   low_risk       1.00      0.94      0.89      0.97      0.91      0.83     22579\n",
            "\n",
            "avg / total       0.99      0.93      0.89      0.96      0.91      0.83     22710\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Print the imbalanced classification report\n",
        "from imblearn.metrics import classification_report_imbalanced\n",
        "print(classification_report_imbalanced(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jNvn5gCiZsQH",
        "outputId": "bc0a580b-e2a6-429a-d854-55c16a7945b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              precision    recall  f1-score       support\n",
              "low_risk       0.999290  0.935250  0.966210  22579.000000\n",
              "weighted avg   0.993950  0.934963  0.961419  22710.000000\n",
              "accuracy       0.934963  0.934963  0.934963      0.934963\n",
              "macro avg      0.536400  0.910373  0.550981  22710.000000\n",
              "high_risk      0.073511  0.885496  0.135752    131.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4eea4408-9007-4fa5-9b58-72a0984b45ad\">\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>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>low_risk</th>\n",
              "      <td>0.999290</td>\n",
              "      <td>0.935250</td>\n",
              "      <td>0.966210</td>\n",
              "      <td>22579.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.993950</td>\n",
              "      <td>0.934963</td>\n",
              "      <td>0.961419</td>\n",
              "      <td>22710.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.934963</td>\n",
              "      <td>0.934963</td>\n",
              "      <td>0.934963</td>\n",
              "      <td>0.934963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.536400</td>\n",
              "      <td>0.910373</td>\n",
              "      <td>0.550981</td>\n",
              "      <td>22710.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>high_risk</th>\n",
              "      <td>0.073511</td>\n",
              "      <td>0.885496</td>\n",
              "      <td>0.135752</td>\n",
              "      <td>131.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4eea4408-9007-4fa5-9b58-72a0984b45ad')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-4eea4408-9007-4fa5-9b58-72a0984b45ad button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4eea4408-9007-4fa5-9b58-72a0984b45ad');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# This will show the classification report as a Pandas Dataframe for better readability\n",
        "# Source: https://stackoverflow.com/questions/39662398/scikit-learn-output-metrics-classification-report-into-csv-tab-delimited-format'''\n",
        "from sklearn import metrics\n",
        "report2 = metrics.classification_report(y_test, y_pred, output_dict=True)\n",
        "df_classification_report2 = pd.DataFrame(report2).transpose()\n",
        "df_classification_report2 = df_classification_report2.sort_values(by=['f1-score'], ascending=False)\n",
        "\n",
        "df_classification_report2\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python37ImbalancedMachineLearni",
      "language": "python",
      "name": "python37imbalancedmachinelearni"
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.13"
    },
    "colab": {
      "name": "credit_risk_ensemble.ipynb",
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      "collapsed_sections": []
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  "nbformat_minor": 0
}