Delinquency Telecom Model.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled2.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iW56DUJRR7xj"
      },
      "source": [
        "Loan Delinquency Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58uK9WPHSD9J"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import seaborn as sn"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1PbfjKSU1PT"
      },
      "source": [
        "df1 = pd.read_csv(\"sample_data_intw.csv\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "so4ima0CU1XA",
        "outputId": "107c40ee-4b3c-42b9-d3f8-758490a51c2c"
      },
      "source": [
        "df1.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>label</th>\n",
              "      <th>msisdn</th>\n",
              "      <th>aon</th>\n",
              "      <th>daily_decr30</th>\n",
              "      <th>daily_decr90</th>\n",
              "      <th>rental30</th>\n",
              "      <th>rental90</th>\n",
              "      <th>last_rech_date_ma</th>\n",
              "      <th>last_rech_date_da</th>\n",
              "      <th>last_rech_amt_ma</th>\n",
              "      <th>cnt_ma_rech30</th>\n",
              "      <th>fr_ma_rech30</th>\n",
              "      <th>sumamnt_ma_rech30</th>\n",
              "      <th>medianamnt_ma_rech30</th>\n",
              "      <th>medianmarechprebal30</th>\n",
              "      <th>cnt_ma_rech90</th>\n",
              "      <th>fr_ma_rech90</th>\n",
              "      <th>sumamnt_ma_rech90</th>\n",
              "      <th>medianamnt_ma_rech90</th>\n",
              "      <th>medianmarechprebal90</th>\n",
              "      <th>cnt_da_rech30</th>\n",
              "      <th>fr_da_rech30</th>\n",
              "      <th>cnt_da_rech90</th>\n",
              "      <th>fr_da_rech90</th>\n",
              "      <th>cnt_loans30</th>\n",
              "      <th>amnt_loans30</th>\n",
              "      <th>maxamnt_loans30</th>\n",
              "      <th>medianamnt_loans30</th>\n",
              "      <th>cnt_loans90</th>\n",
              "      <th>amnt_loans90</th>\n",
              "      <th>maxamnt_loans90</th>\n",
              "      <th>medianamnt_loans90</th>\n",
              "      <th>payback30</th>\n",
              "      <th>payback90</th>\n",
              "      <th>pcircle</th>\n",
              "      <th>pdate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>29.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>UPW</td>\n",
              "      <td>2016-07-20</td>\n",
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              "      <td>UPW</td>\n",
              "      <td>2016-08-10</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>UPW</td>\n",
              "      <td>2016-08-19</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>UPW</td>\n",
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              "      <td>UPW</td>\n",
              "      <td>2016-06-22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   Unnamed: 0  label       msisdn  ...  payback90  pcircle       pdate\n",
              "0           1      0  21408I70789  ...  29.000000      UPW  2016-07-20\n",
              "1           2      1  76462I70374  ...   0.000000      UPW  2016-08-10\n",
              "2           3      1  17943I70372  ...   0.000000      UPW  2016-08-19\n",
              "3           4      1  55773I70781  ...   0.000000      UPW  2016-06-06\n",
              "4           5      1  03813I82730  ...   2.333333      UPW  2016-06-22\n",
              "\n",
              "[5 rows x 37 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-WFypf-Vap0",
        "outputId": "11fcfd86-397c-45b7-8ebc-edb1f029aa53"
      },
      "source": [
        "df1.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(209593, 37)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ff9gBVKEVcRp",
        "outputId": "2f17441e-2041-440b-873e-0b34cc8015be"
      },
      "source": [
        "df1.isnull().sum()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0              0\n",
              "label                   0\n",
              "msisdn                  0\n",
              "aon                     0\n",
              "daily_decr30            0\n",
              "daily_decr90            0\n",
              "rental30                0\n",
              "rental90                0\n",
              "last_rech_date_ma       0\n",
              "last_rech_date_da       0\n",
              "last_rech_amt_ma        0\n",
              "cnt_ma_rech30           0\n",
              "fr_ma_rech30            0\n",
              "sumamnt_ma_rech30       0\n",
              "medianamnt_ma_rech30    0\n",
              "medianmarechprebal30    0\n",
              "cnt_ma_rech90           0\n",
              "fr_ma_rech90            0\n",
              "sumamnt_ma_rech90       0\n",
              "medianamnt_ma_rech90    0\n",
              "medianmarechprebal90    0\n",
              "cnt_da_rech30           0\n",
              "fr_da_rech30            0\n",
              "cnt_da_rech90           0\n",
              "fr_da_rech90            0\n",
              "cnt_loans30             0\n",
              "amnt_loans30            0\n",
              "maxamnt_loans30         0\n",
              "medianamnt_loans30      0\n",
              "cnt_loans90             0\n",
              "amnt_loans90            0\n",
              "maxamnt_loans90         0\n",
              "medianamnt_loans90      0\n",
              "payback30               0\n",
              "payback90               0\n",
              "pcircle                 0\n",
              "pdate                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDu7MmhVVsD1",
        "outputId": "9fcfb958-4598-4a4f-a079-83b2b6164c46"
      },
      "source": [
        "df1.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 209593 entries, 0 to 209592\n",
            "Data columns (total 37 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   Unnamed: 0            209593 non-null  int64  \n",
            " 1   label                 209593 non-null  int64  \n",
            " 2   msisdn                209593 non-null  object \n",
            " 3   aon                   209593 non-null  float64\n",
            " 4   daily_decr30          209593 non-null  float64\n",
            " 5   daily_decr90          209593 non-null  float64\n",
            " 6   rental30              209593 non-null  float64\n",
            " 7   rental90              209593 non-null  float64\n",
            " 8   last_rech_date_ma     209593 non-null  float64\n",
            " 9   last_rech_date_da     209593 non-null  float64\n",
            " 10  last_rech_amt_ma      209593 non-null  int64  \n",
            " 11  cnt_ma_rech30         209593 non-null  int64  \n",
            " 12  fr_ma_rech30          209593 non-null  float64\n",
            " 13  sumamnt_ma_rech30     209593 non-null  float64\n",
            " 14  medianamnt_ma_rech30  209593 non-null  float64\n",
            " 15  medianmarechprebal30  209593 non-null  float64\n",
            " 16  cnt_ma_rech90         209593 non-null  int64  \n",
            " 17  fr_ma_rech90          209593 non-null  int64  \n",
            " 18  sumamnt_ma_rech90     209593 non-null  int64  \n",
            " 19  medianamnt_ma_rech90  209593 non-null  float64\n",
            " 20  medianmarechprebal90  209593 non-null  float64\n",
            " 21  cnt_da_rech30         209593 non-null  float64\n",
            " 22  fr_da_rech30          209593 non-null  float64\n",
            " 23  cnt_da_rech90         209593 non-null  int64  \n",
            " 24  fr_da_rech90          209593 non-null  int64  \n",
            " 25  cnt_loans30           209593 non-null  int64  \n",
            " 26  amnt_loans30          209593 non-null  int64  \n",
            " 27  maxamnt_loans30       209593 non-null  float64\n",
            " 28  medianamnt_loans30    209593 non-null  float64\n",
            " 29  cnt_loans90           209593 non-null  float64\n",
            " 30  amnt_loans90          209593 non-null  int64  \n",
            " 31  maxamnt_loans90       209593 non-null  int64  \n",
            " 32  medianamnt_loans90    209593 non-null  float64\n",
            " 33  payback30             209593 non-null  float64\n",
            " 34  payback90             209593 non-null  float64\n",
            " 35  pcircle               209593 non-null  object \n",
            " 36  pdate                 209593 non-null  object \n",
            "dtypes: float64(21), int64(13), object(3)\n",
            "memory usage: 59.2+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_znb7nasjGz"
      },
      "source": [
        "df = df1"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cD4iF4C0YfnC"
      },
      "source": [
        "object_type=['Unnamed: 0']\n",
        "for i in df.columns:\n",
        "  if df[i].dtype == object:\n",
        "    object_type.append(i)\n",
        "df.drop(labels = object_type,axis = 1,inplace = True)\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZ_xbFyBrqtw",
        "outputId": "54ff6dec-0d40-4c67-d5a2-b5ef6d6c5f22"
      },
      "source": [
        "print(\"Removed columns:\")\n",
        "print(object_type)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Removed columns:\n",
            "['Unnamed: 0', 'msisdn', 'pcircle', 'pdate']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CzfuFtdLYlPu",
        "outputId": "cb8d866f-7594-4e45-b855-e0da030585a8"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 209593 entries, 0 to 209592\n",
            "Data columns (total 33 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   label                 209593 non-null  int64  \n",
            " 1   aon                   209593 non-null  float64\n",
            " 2   daily_decr30          209593 non-null  float64\n",
            " 3   daily_decr90          209593 non-null  float64\n",
            " 4   rental30              209593 non-null  float64\n",
            " 5   rental90              209593 non-null  float64\n",
            " 6   last_rech_date_ma     209593 non-null  float64\n",
            " 7   last_rech_date_da     209593 non-null  float64\n",
            " 8   last_rech_amt_ma      209593 non-null  int64  \n",
            " 9   cnt_ma_rech30         209593 non-null  int64  \n",
            " 10  fr_ma_rech30          209593 non-null  float64\n",
            " 11  sumamnt_ma_rech30     209593 non-null  float64\n",
            " 12  medianamnt_ma_rech30  209593 non-null  float64\n",
            " 13  medianmarechprebal30  209593 non-null  float64\n",
            " 14  cnt_ma_rech90         209593 non-null  int64  \n",
            " 15  fr_ma_rech90          209593 non-null  int64  \n",
            " 16  sumamnt_ma_rech90     209593 non-null  int64  \n",
            " 17  medianamnt_ma_rech90  209593 non-null  float64\n",
            " 18  medianmarechprebal90  209593 non-null  float64\n",
            " 19  cnt_da_rech30         209593 non-null  float64\n",
            " 20  fr_da_rech30          209593 non-null  float64\n",
            " 21  cnt_da_rech90         209593 non-null  int64  \n",
            " 22  fr_da_rech90          209593 non-null  int64  \n",
            " 23  cnt_loans30           209593 non-null  int64  \n",
            " 24  amnt_loans30          209593 non-null  int64  \n",
            " 25  maxamnt_loans30       209593 non-null  float64\n",
            " 26  medianamnt_loans30    209593 non-null  float64\n",
            " 27  cnt_loans90           209593 non-null  float64\n",
            " 28  amnt_loans90          209593 non-null  int64  \n",
            " 29  maxamnt_loans90       209593 non-null  int64  \n",
            " 30  medianamnt_loans90    209593 non-null  float64\n",
            " 31  payback30             209593 non-null  float64\n",
            " 32  payback90             209593 non-null  float64\n",
            "dtypes: float64(21), int64(12)\n",
            "memory usage: 52.8 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "zOyMx0idZ3P0",
        "outputId": "8c10a9dd-4f47-4bea-be70-d445021facbc"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>label</th>\n",
              "      <th>aon</th>\n",
              "      <th>daily_decr30</th>\n",
              "      <th>daily_decr90</th>\n",
              "      <th>rental30</th>\n",
              "      <th>rental90</th>\n",
              "      <th>last_rech_date_ma</th>\n",
              "      <th>last_rech_date_da</th>\n",
              "      <th>last_rech_amt_ma</th>\n",
              "      <th>cnt_ma_rech30</th>\n",
              "      <th>fr_ma_rech30</th>\n",
              "      <th>sumamnt_ma_rech30</th>\n",
              "      <th>medianamnt_ma_rech30</th>\n",
              "      <th>medianmarechprebal30</th>\n",
              "      <th>cnt_ma_rech90</th>\n",
              "      <th>fr_ma_rech90</th>\n",
              "      <th>sumamnt_ma_rech90</th>\n",
              "      <th>medianamnt_ma_rech90</th>\n",
              "      <th>medianmarechprebal90</th>\n",
              "      <th>cnt_da_rech30</th>\n",
              "      <th>fr_da_rech30</th>\n",
              "      <th>cnt_da_rech90</th>\n",
              "      <th>fr_da_rech90</th>\n",
              "      <th>cnt_loans30</th>\n",
              "      <th>amnt_loans30</th>\n",
              "      <th>maxamnt_loans30</th>\n",
              "      <th>medianamnt_loans30</th>\n",
              "      <th>cnt_loans90</th>\n",
              "      <th>amnt_loans90</th>\n",
              "      <th>maxamnt_loans90</th>\n",
              "      <th>medianamnt_loans90</th>\n",
              "      <th>payback30</th>\n",
              "      <th>payback90</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.00000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "      <td>209593.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.875177</td>\n",
              "      <td>8112.343445</td>\n",
              "      <td>5381.402289</td>\n",
              "      <td>6082.515068</td>\n",
              "      <td>2692.581910</td>\n",
              "      <td>3483.406534</td>\n",
              "      <td>3755.847800</td>\n",
              "      <td>3712.202921</td>\n",
              "      <td>2064.452797</td>\n",
              "      <td>3.978057</td>\n",
              "      <td>3737.355121</td>\n",
              "      <td>7704.501157</td>\n",
              "      <td>1812.817952</td>\n",
              "      <td>3851.927942</td>\n",
              "      <td>6.31543</td>\n",
              "      <td>7.716780</td>\n",
              "      <td>12396.218352</td>\n",
              "      <td>1864.595821</td>\n",
              "      <td>92.025541</td>\n",
              "      <td>262.578110</td>\n",
              "      <td>3749.494447</td>\n",
              "      <td>0.041495</td>\n",
              "      <td>0.045712</td>\n",
              "      <td>2.758981</td>\n",
              "      <td>17.952021</td>\n",
              "      <td>274.658747</td>\n",
              "      <td>0.054029</td>\n",
              "      <td>18.520919</td>\n",
              "      <td>23.645398</td>\n",
              "      <td>6.703134</td>\n",
              "      <td>0.046077</td>\n",
              "      <td>3.398826</td>\n",
              "      <td>4.321485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.330519</td>\n",
              "      <td>75696.082531</td>\n",
              "      <td>9220.623400</td>\n",
              "      <td>10918.812767</td>\n",
              "      <td>4308.586781</td>\n",
              "      <td>5770.461279</td>\n",
              "      <td>53905.892230</td>\n",
              "      <td>53374.833430</td>\n",
              "      <td>2370.786034</td>\n",
              "      <td>4.256090</td>\n",
              "      <td>53643.625172</td>\n",
              "      <td>10139.621714</td>\n",
              "      <td>2070.864620</td>\n",
              "      <td>54006.374433</td>\n",
              "      <td>7.19347</td>\n",
              "      <td>12.590251</td>\n",
              "      <td>16857.793882</td>\n",
              "      <td>2081.680664</td>\n",
              "      <td>369.215658</td>\n",
              "      <td>4183.897978</td>\n",
              "      <td>53885.414979</td>\n",
              "      <td>0.397556</td>\n",
              "      <td>0.951386</td>\n",
              "      <td>2.554502</td>\n",
              "      <td>17.379741</td>\n",
              "      <td>4245.264648</td>\n",
              "      <td>0.218039</td>\n",
              "      <td>224.797423</td>\n",
              "      <td>26.469861</td>\n",
              "      <td>2.103864</td>\n",
              "      <td>0.200692</td>\n",
              "      <td>8.813729</td>\n",
              "      <td>10.308108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-48.000000</td>\n",
              "      <td>-93.012667</td>\n",
              "      <td>-93.012667</td>\n",
              "      <td>-23737.140000</td>\n",
              "      <td>-24720.580000</td>\n",
              "      <td>-29.000000</td>\n",
              "      <td>-29.000000</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>-200.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-200.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>246.000000</td>\n",
              "      <td>42.440000</td>\n",
              "      <td>42.692000</td>\n",
              "      <td>280.420000</td>\n",
              "      <td>300.260000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>770.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1540.000000</td>\n",
              "      <td>770.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2317.000000</td>\n",
              "      <td>773.000000</td>\n",
              "      <td>14.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>527.000000</td>\n",
              "      <td>1469.175667</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>1083.570000</td>\n",
              "      <td>1334.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1539.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4628.000000</td>\n",
              "      <td>1539.000000</td>\n",
              "      <td>33.900000</td>\n",
              "      <td>4.00000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>7226.000000</td>\n",
              "      <td>1539.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>982.000000</td>\n",
              "      <td>7244.000000</td>\n",
              "      <td>7802.790000</td>\n",
              "      <td>3356.940000</td>\n",
              "      <td>4201.790000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2309.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>10010.000000</td>\n",
              "      <td>1924.000000</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>8.00000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>16000.000000</td>\n",
              "      <td>1924.000000</td>\n",
              "      <td>79.310000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>4.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>999860.755168</td>\n",
              "      <td>265926.000000</td>\n",
              "      <td>320630.000000</td>\n",
              "      <td>198926.110000</td>\n",
              "      <td>200148.110000</td>\n",
              "      <td>998650.377733</td>\n",
              "      <td>999171.809410</td>\n",
              "      <td>55000.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>999606.368132</td>\n",
              "      <td>810096.000000</td>\n",
              "      <td>55000.000000</td>\n",
              "      <td>999479.419319</td>\n",
              "      <td>336.00000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>953036.000000</td>\n",
              "      <td>55000.000000</td>\n",
              "      <td>41456.500000</td>\n",
              "      <td>99914.441420</td>\n",
              "      <td>999809.240107</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>306.000000</td>\n",
              "      <td>99864.560864</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4997.517944</td>\n",
              "      <td>438.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>171.500000</td>\n",
              "      <td>171.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               label            aon  ...      payback30      payback90\n",
              "count  209593.000000  209593.000000  ...  209593.000000  209593.000000\n",
              "mean        0.875177    8112.343445  ...       3.398826       4.321485\n",
              "std         0.330519   75696.082531  ...       8.813729      10.308108\n",
              "min         0.000000     -48.000000  ...       0.000000       0.000000\n",
              "25%         1.000000     246.000000  ...       0.000000       0.000000\n",
              "50%         1.000000     527.000000  ...       0.000000       1.666667\n",
              "75%         1.000000     982.000000  ...       3.750000       4.500000\n",
              "max         1.000000  999860.755168  ...     171.500000     171.500000\n",
              "\n",
              "[8 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ftmwab5YP5Tq"
      },
      "source": [
        "Correlation Matrix and its Heatmap."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CH6xLJ9ZsxKn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "36dcbbf8-6230-474e-fcdd-1760e7982155"
      },
      "source": [
        "correlation = df.corr()\n",
        "correlation"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>aon</th>\n",
              "      <th>daily_decr30</th>\n",
              "      <th>daily_decr90</th>\n",
              "      <th>rental30</th>\n",
              "      <th>rental90</th>\n",
              "      <th>last_rech_date_ma</th>\n",
              "      <th>last_rech_date_da</th>\n",
              "      <th>last_rech_amt_ma</th>\n",
              "      <th>cnt_ma_rech30</th>\n",
              "      <th>fr_ma_rech30</th>\n",
              "      <th>sumamnt_ma_rech30</th>\n",
              "      <th>medianamnt_ma_rech30</th>\n",
              "      <th>medianmarechprebal30</th>\n",
              "      <th>cnt_ma_rech90</th>\n",
              "      <th>fr_ma_rech90</th>\n",
              "      <th>sumamnt_ma_rech90</th>\n",
              "      <th>medianamnt_ma_rech90</th>\n",
              "      <th>medianmarechprebal90</th>\n",
              "      <th>cnt_da_rech30</th>\n",
              "      <th>fr_da_rech30</th>\n",
              "      <th>cnt_da_rech90</th>\n",
              "      <th>fr_da_rech90</th>\n",
              "      <th>cnt_loans30</th>\n",
              "      <th>amnt_loans30</th>\n",
              "      <th>maxamnt_loans30</th>\n",
              "      <th>medianamnt_loans30</th>\n",
              "      <th>cnt_loans90</th>\n",
              "      <th>amnt_loans90</th>\n",
              "      <th>maxamnt_loans90</th>\n",
              "      <th>medianamnt_loans90</th>\n",
              "      <th>payback30</th>\n",
              "      <th>payback90</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.003785</td>\n",
              "      <td>0.168298</td>\n",
              "      <td>0.166150</td>\n",
              "      <td>0.058085</td>\n",
              "      <td>0.075521</td>\n",
              "      <td>0.003728</td>\n",
              "      <td>0.001711</td>\n",
              "      <td>0.131804</td>\n",
              "      <td>0.237331</td>\n",
              "      <td>0.001330</td>\n",
              "      <td>0.202828</td>\n",
              "      <td>0.141490</td>\n",
              "      <td>-0.004829</td>\n",
              "      <td>0.236392</td>\n",
              "      <td>0.084385</td>\n",
              "      <td>0.205793</td>\n",
              "      <td>0.120855</td>\n",
              "      <td>0.039300</td>\n",
              "      <td>0.003827</td>\n",
              "      <td>-0.000027</td>\n",
              "      <td>0.002999</td>\n",
              "      <td>-0.005418</td>\n",
              "      <td>0.196283</td>\n",
              "      <td>0.197272</td>\n",
              "      <td>0.000248</td>\n",
              "      <td>0.044589</td>\n",
              "      <td>0.004733</td>\n",
              "      <td>0.199788</td>\n",
              "      <td>0.084144</td>\n",
              "      <td>0.035747</td>\n",
              "      <td>0.048336</td>\n",
              "      <td>0.049183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>aon</th>\n",
              "      <td>-0.003785</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.001104</td>\n",
              "      <td>0.000374</td>\n",
              "      <td>-0.000960</td>\n",
              "      <td>-0.000790</td>\n",
              "      <td>0.001692</td>\n",
              "      <td>-0.001693</td>\n",
              "      <td>0.004256</td>\n",
              "      <td>-0.003148</td>\n",
              "      <td>-0.001163</td>\n",
              "      <td>0.000707</td>\n",
              "      <td>0.004306</td>\n",
              "      <td>0.003930</td>\n",
              "      <td>-0.002725</td>\n",
              "      <td>0.004401</td>\n",
              "      <td>0.001011</td>\n",
              "      <td>0.004909</td>\n",
              "      <td>-0.000859</td>\n",
              "      <td>0.001564</td>\n",
              "      <td>0.000892</td>\n",
              "      <td>0.001121</td>\n",
              "      <td>0.005395</td>\n",
              "      <td>-0.001826</td>\n",
              "      <td>-0.001726</td>\n",
              "      <td>-0.002764</td>\n",
              "      <td>0.004664</td>\n",
              "      <td>-0.000611</td>\n",
              "      <td>-0.002319</td>\n",
              "      <td>-0.001191</td>\n",
              "      <td>0.002771</td>\n",
              "      <td>0.001940</td>\n",
              "      <td>0.002203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>daily_decr30</th>\n",
              "      <td>0.168298</td>\n",
              "      <td>0.001104</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977704</td>\n",
              "      <td>0.442066</td>\n",
              "      <td>0.458977</td>\n",
              "      <td>0.000487</td>\n",
              "      <td>-0.001636</td>\n",
              "      <td>0.275837</td>\n",
              "      <td>0.451385</td>\n",
              "      <td>-0.000577</td>\n",
              "      <td>0.636536</td>\n",
              "      <td>0.295356</td>\n",
              "      <td>-0.001153</td>\n",
              "      <td>0.587338</td>\n",
              "      <td>-0.078299</td>\n",
              "      <td>0.762981</td>\n",
              "      <td>0.257847</td>\n",
              "      <td>0.037495</td>\n",
              "      <td>0.000700</td>\n",
              "      <td>-0.001499</td>\n",
              "      <td>0.038814</td>\n",
              "      <td>0.020673</td>\n",
              "      <td>0.366116</td>\n",
              "      <td>0.471492</td>\n",
              "      <td>-0.000028</td>\n",
              "      <td>-0.011610</td>\n",
              "      <td>0.008962</td>\n",
              "      <td>0.563496</td>\n",
              "      <td>0.400199</td>\n",
              "      <td>-0.037305</td>\n",
              "      <td>0.026915</td>\n",
              "      <td>0.047175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>daily_decr90</th>\n",
              "      <td>0.166150</td>\n",
              "      <td>0.000374</td>\n",
              "      <td>0.977704</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.434685</td>\n",
              "      <td>0.471730</td>\n",
              "      <td>0.000908</td>\n",
              "      <td>-0.001886</td>\n",
              "      <td>0.264131</td>\n",
              "      <td>0.426707</td>\n",
              "      <td>-0.000343</td>\n",
              "      <td>0.603886</td>\n",
              "      <td>0.282960</td>\n",
              "      <td>-0.000746</td>\n",
              "      <td>0.593069</td>\n",
              "      <td>-0.079530</td>\n",
              "      <td>0.768817</td>\n",
              "      <td>0.250518</td>\n",
              "      <td>0.036382</td>\n",
              "      <td>0.000661</td>\n",
              "      <td>-0.001570</td>\n",
              "      <td>0.031155</td>\n",
              "      <td>0.016437</td>\n",
              "      <td>0.340387</td>\n",
              "      <td>0.447869</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>-0.005591</td>\n",
              "      <td>0.009446</td>\n",
              "      <td>0.567204</td>\n",
              "      <td>0.397251</td>\n",
              "      <td>-0.034686</td>\n",
              "      <td>0.019400</td>\n",
              "      <td>0.040800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rental30</th>\n",
              "      <td>0.058085</td>\n",
              "      <td>-0.000960</td>\n",
              "      <td>0.442066</td>\n",
              "      <td>0.434685</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.955237</td>\n",
              "      <td>-0.001095</td>\n",
              "      <td>0.003261</td>\n",
              "      <td>0.127271</td>\n",
              "      <td>0.233343</td>\n",
              "      <td>-0.001219</td>\n",
              "      <td>0.272649</td>\n",
              "      <td>0.129853</td>\n",
              "      <td>-0.001415</td>\n",
              "      <td>0.312118</td>\n",
              "      <td>-0.033530</td>\n",
              "      <td>0.342306</td>\n",
              "      <td>0.110356</td>\n",
              "      <td>0.027170</td>\n",
              "      <td>-0.001105</td>\n",
              "      <td>-0.002558</td>\n",
              "      <td>0.072255</td>\n",
              "      <td>0.046761</td>\n",
              "      <td>0.180203</td>\n",
              "      <td>0.233453</td>\n",
              "      <td>-0.000864</td>\n",
              "      <td>-0.016482</td>\n",
              "      <td>0.004012</td>\n",
              "      <td>0.298943</td>\n",
              "      <td>0.234211</td>\n",
              "      <td>-0.035489</td>\n",
              "      <td>0.072974</td>\n",
              "      <td>0.095147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rental90</th>\n",
              "      <td>0.075521</td>\n",
              "      <td>-0.000790</td>\n",
              "      <td>0.458977</td>\n",
              "      <td>0.471730</td>\n",
              "      <td>0.955237</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.001688</td>\n",
              "      <td>0.002794</td>\n",
              "      <td>0.121416</td>\n",
              "      <td>0.230260</td>\n",
              "      <td>-0.000503</td>\n",
              "      <td>0.259709</td>\n",
              "      <td>0.120242</td>\n",
              "      <td>-0.001237</td>\n",
              "      <td>0.345293</td>\n",
              "      <td>-0.036524</td>\n",
              "      <td>0.360601</td>\n",
              "      <td>0.103151</td>\n",
              "      <td>0.029547</td>\n",
              "      <td>-0.000548</td>\n",
              "      <td>-0.002345</td>\n",
              "      <td>0.056282</td>\n",
              "      <td>0.036886</td>\n",
              "      <td>0.171595</td>\n",
              "      <td>0.231906</td>\n",
              "      <td>-0.001411</td>\n",
              "      <td>-0.009467</td>\n",
              "      <td>0.005141</td>\n",
              "      <td>0.327436</td>\n",
              "      <td>0.251029</td>\n",
              "      <td>-0.034122</td>\n",
              "      <td>0.067110</td>\n",
              "      <td>0.099501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_rech_date_ma</th>\n",
              "      <td>0.003728</td>\n",
              "      <td>0.001692</td>\n",
              "      <td>0.000487</td>\n",
              "      <td>0.000908</td>\n",
              "      <td>-0.001095</td>\n",
              "      <td>-0.001688</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.001790</td>\n",
              "      <td>-0.000147</td>\n",
              "      <td>0.004311</td>\n",
              "      <td>-0.001629</td>\n",
              "      <td>0.002105</td>\n",
              "      <td>-0.001358</td>\n",
              "      <td>0.004071</td>\n",
              "      <td>0.004263</td>\n",
              "      <td>0.001414</td>\n",
              "      <td>0.002243</td>\n",
              "      <td>-0.000726</td>\n",
              "      <td>-0.001086</td>\n",
              "      <td>-0.003467</td>\n",
              "      <td>-0.003626</td>\n",
              "      <td>-0.003538</td>\n",
              "      <td>-0.002395</td>\n",
              "      <td>0.001193</td>\n",
              "      <td>0.000903</td>\n",
              "      <td>0.000928</td>\n",
              "      <td>0.001835</td>\n",
              "      <td>-0.000225</td>\n",
              "      <td>0.000870</td>\n",
              "      <td>-0.001123</td>\n",
              "      <td>0.002771</td>\n",
              "      <td>-0.002233</td>\n",
              "      <td>-0.001583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_rech_date_da</th>\n",
              "      <td>0.001711</td>\n",
              "      <td>-0.001693</td>\n",
              "      <td>-0.001636</td>\n",
              "      <td>-0.001886</td>\n",
              "      <td>0.003261</td>\n",
              "      <td>0.002794</td>\n",
              "      <td>0.001790</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000149</td>\n",
              "      <td>0.001549</td>\n",
              "      <td>0.001158</td>\n",
              "      <td>0.000046</td>\n",
              "      <td>0.001037</td>\n",
              "      <td>0.002849</td>\n",
              "      <td>0.001272</td>\n",
              "      <td>0.000798</td>\n",
              "      <td>-0.000414</td>\n",
              "      <td>0.000219</td>\n",
              "      <td>0.004158</td>\n",
              "      <td>-0.003628</td>\n",
              "      <td>-0.000074</td>\n",
              "      <td>-0.001859</td>\n",
              "      <td>-0.000203</td>\n",
              "      <td>0.000380</td>\n",
              "      <td>0.000536</td>\n",
              "      <td>0.000503</td>\n",
              "      <td>0.000061</td>\n",
              "      <td>-0.000972</td>\n",
              "      <td>0.000519</td>\n",
              "      <td>0.001524</td>\n",
              "      <td>-0.002239</td>\n",
              "      <td>0.000077</td>\n",
              "      <td>0.000417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_rech_amt_ma</th>\n",
              "      <td>0.131804</td>\n",
              "      <td>0.004256</td>\n",
              "      <td>0.275837</td>\n",
              "      <td>0.264131</td>\n",
              "      <td>0.127271</td>\n",
              "      <td>0.121416</td>\n",
              "      <td>-0.000147</td>\n",
              "      <td>-0.000149</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.002662</td>\n",
              "      <td>0.002876</td>\n",
              "      <td>0.440821</td>\n",
              "      <td>0.794646</td>\n",
              "      <td>-0.002342</td>\n",
              "      <td>0.016707</td>\n",
              "      <td>0.106267</td>\n",
              "      <td>0.418735</td>\n",
              "      <td>0.818734</td>\n",
              "      <td>0.124646</td>\n",
              "      <td>-0.001837</td>\n",
              "      <td>-0.003230</td>\n",
              "      <td>0.014779</td>\n",
              "      <td>0.016042</td>\n",
              "      <td>-0.027612</td>\n",
              "      <td>0.008502</td>\n",
              "      <td>0.001000</td>\n",
              "      <td>0.028370</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.014067</td>\n",
              "      <td>0.148460</td>\n",
              "      <td>0.021004</td>\n",
              "      <td>-0.027369</td>\n",
              "      <td>-0.014260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cnt_ma_rech30</th>\n",
              "      <td>0.237331</td>\n",
              "      <td>-0.003148</td>\n",
              "      <td>0.451385</td>\n",
              "      <td>0.426707</td>\n",
              "      <td>0.233343</td>\n",
              "      <td>0.230260</td>\n",
              "      <td>0.004311</td>\n",
              "      <td>0.001549</td>\n",
              "      <td>-0.002662</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.001669</td>\n",
              "      <td>0.656986</td>\n",
              "      <td>-0.011792</td>\n",
              "      <td>0.000082</td>\n",
              "      <td>0.886433</td>\n",
              "      <td>-0.152759</td>\n",
              "      <td>0.584060</td>\n",
              "      <td>-0.051347</td>\n",
              "      <td>0.013463</td>\n",
              "      <td>0.002306</td>\n",
              "      <td>-0.002732</td>\n",
              "      <td>0.011981</td>\n",
              "      <td>0.006989</td>\n",
              "      <td>0.765802</td>\n",
              "      <td>0.752296</td>\n",
              "      <td>0.000109</td>\n",
              "      <td>-0.067011</td>\n",
              "      <td>0.014703</td>\n",
              "      <td>0.685257</td>\n",
              "      <td>0.174104</td>\n",
              "      <td>-0.071451</td>\n",
              "      <td>0.043617</td>\n",
              "      <td>0.017362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fr_ma_rech30</th>\n",
              "      <td>0.001330</td>\n",
              "      <td>-0.001163</td>\n",
              "      <td>-0.000577</td>\n",
              "      <td>-0.000343</td>\n",
              "      <td>-0.001219</td>\n",
              "      <td>-0.000503</td>\n",
              "      <td>-0.001629</td>\n",
              "      <td>0.001158</td>\n",
              "      <td>0.002876</td>\n",
              "      <td>0.001669</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000714</td>\n",
              "      <td>-0.000579</td>\n",
              "      <td>0.002613</td>\n",
              "      <td>0.001220</td>\n",
              "      <td>-0.001245</td>\n",
              "      <td>-0.000571</td>\n",
              "      <td>-0.001573</td>\n",
              "      <td>-0.002826</td>\n",
              "      <td>-0.000511</td>\n",
              "      <td>-0.001715</td>\n",
              "      <td>0.001574</td>\n",
              "      <td>-0.001925</td>\n",
              "      <td>0.002666</td>\n",
              "      <td>0.002470</td>\n",
              "      <td>-0.001100</td>\n",
              "      <td>-0.000331</td>\n",
              "      <td>0.003368</td>\n",
              "      <td>0.002634</td>\n",
              "      <td>-0.001890</td>\n",
              "      <td>-0.001292</td>\n",
              "      <td>0.001455</td>\n",
              "      <td>0.000998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sumamnt_ma_rech30</th>\n",
              "      <td>0.202828</td>\n",
              "      <td>0.000707</td>\n",
              "      <td>0.636536</td>\n",
              "      <td>0.603886</td>\n",
              "      <td>0.272649</td>\n",
              "      <td>0.259709</td>\n",
              "      <td>0.002105</td>\n",
              "      <td>0.000046</td>\n",
              "      <td>0.440821</td>\n",
              "      <td>0.656986</td>\n",
              "      <td>0.000714</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.489200</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.596478</td>\n",
              "      <td>-0.071089</td>\n",
              "      <td>0.889989</td>\n",
              "      <td>0.422009</td>\n",
              "      <td>0.090502</td>\n",
              "      <td>0.002537</td>\n",
              "      <td>-0.001578</td>\n",
              "      <td>0.019213</td>\n",
              "      <td>0.017255</td>\n",
              "      <td>0.491864</td>\n",
              "      <td>0.531079</td>\n",
              "      <td>0.001633</td>\n",
              "      <td>-0.034460</td>\n",
              "      <td>0.009419</td>\n",
              "      <td>0.484761</td>\n",
              "      <td>0.257705</td>\n",
              "      <td>-0.042244</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>-0.012596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>medianamnt_ma_rech30</th>\n",
              "      <td>0.141490</td>\n",
              "      <td>0.004306</td>\n",
              "      <td>0.295356</td>\n",
              "      <td>0.282960</td>\n",
              "      <td>0.129853</td>\n",
              "      <td>0.120242</td>\n",
              "      <td>-0.001358</td>\n",
              "      <td>0.001037</td>\n",
              "      <td>0.794646</td>\n",
              "      <td>-0.011792</td>\n",
              "      <td>-0.000579</td>\n",
              "      <td>0.489200</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000737</td>\n",
              "      <td>0.005309</td>\n",
              "      <td>0.149655</td>\n",
              "      <td>0.449012</td>\n",
              "      <td>0.864846</td>\n",
              "      <td>0.156028</td>\n",
              "      <td>-0.000060</td>\n",
              "      <td>-0.001479</td>\n",
              "      <td>0.016727</td>\n",
              "      <td>0.013734</td>\n",
              "      <td>-0.029789</td>\n",
              "      <td>0.007125</td>\n",
              "      <td>0.001985</td>\n",
              "      <td>0.032477</td>\n",
              "      <td>-0.001143</td>\n",
              "      <td>0.014546</td>\n",
              "      <td>0.159613</td>\n",
              "      <td>0.022692</td>\n",
              "      <td>-0.018398</td>\n",
              "      <td>-0.002861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>medianmarechprebal30</th>\n",
              "      <td>-0.004829</td>\n",
              "      <td>0.003930</td>\n",
              "      <td>-0.001153</td>\n",
              "      <td>-0.000746</td>\n",
              "      <td>-0.001415</td>\n",
              "      <td>-0.001237</td>\n",
              "      <td>0.004071</td>\n",
              "      <td>0.002849</td>\n",
              "      <td>-0.002342</td>\n",
              "      <td>0.000082</td>\n",
              "      <td>0.002613</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>-0.000737</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000899</td>\n",
              "      <td>-0.000902</td>\n",
              "      <td>-0.000665</td>\n",
              "      <td>-0.001459</td>\n",
              "      <td>0.000529</td>\n",
              "      <td>-0.003404</td>\n",
              "      <td>0.000742</td>\n",
              "      <td>-0.003064</td>\n",
              "      <td>-0.001412</td>\n",
              "      <td>-0.000211</td>\n",
              "      <td>0.000134</td>\n",
              "      <td>-0.002176</td>\n",
              "      <td>-0.002289</td>\n",
              "      <td>0.001342</td>\n",
              "      <td>0.000736</td>\n",
              "      <td>-0.002141</td>\n",
              "      <td>-0.001927</td>\n",
              "      <td>0.001516</td>\n",
              "      <td>0.001811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cnt_ma_rech90</th>\n",
              "      <td>0.236392</td>\n",
              "      <td>-0.002725</td>\n",
              "      <td>0.587338</td>\n",
              "      <td>0.593069</td>\n",
              "      <td>0.312118</td>\n",
              "      <td>0.345293</td>\n",
              "      <td>0.004263</td>\n",
              "      <td>0.001272</td>\n",
              "      <td>0.016707</td>\n",
              "      <td>0.886433</td>\n",
              "      <td>0.001220</td>\n",
              "      <td>0.596478</td>\n",
              "      <td>0.005309</td>\n",
              "      <td>0.000899</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.149068</td>\n",
              "      <td>0.687054</td>\n",
              "      <td>-0.035497</td>\n",
              "      <td>0.015461</td>\n",
              "      <td>0.002895</td>\n",
              "      <td>-0.002795</td>\n",
              "      <td>0.004090</td>\n",
              "      <td>0.002865</td>\n",
              "      <td>0.689002</td>\n",
              "      <td>0.707558</td>\n",
              "      <td>-0.000573</td>\n",
              "      <td>-0.053637</td>\n",
              "      <td>0.015754</td>\n",
              "      <td>0.783419</td>\n",
              "      <td>0.244602</td>\n",
              "      <td>-0.072481</td>\n",
              "      <td>0.011943</td>\n",
              "      <td>-0.003141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fr_ma_rech90</th>\n",
              "      <td>0.084385</td>\n",
              "      <td>0.004401</td>\n",
              "      <td>-0.078299</td>\n",
              "      <td>-0.079530</td>\n",
              "      <td>-0.033530</td>\n",
              "      <td>-0.036524</td>\n",
              "      <td>0.001414</td>\n",
              "      <td>0.000798</td>\n",
              "      <td>0.106267</td>\n",
              "      <td>-0.152759</td>\n",
              "      <td>-0.001245</td>\n",
              "      <td>-0.071089</td>\n",
              "      <td>0.149655</td>\n",
              "      <td>-0.000902</td>\n",
              "      <td>-0.149068</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.065566</td>\n",
              "      <td>0.138503</td>\n",
              "      <td>0.004347</td>\n",
              "      <td>0.002230</td>\n",
              "      <td>-0.000081</td>\n",
              "      <td>-0.006727</td>\n",
              "      <td>-0.003822</td>\n",
              "      <td>-0.119083</td>\n",
              "      <td>-0.123664</td>\n",
              "      <td>0.001635</td>\n",
              "      <td>0.020626</td>\n",
              "      <td>-0.002756</td>\n",
              "      <td>-0.132035</td>\n",
              "      <td>-0.045780</td>\n",
              "      <td>0.016964</td>\n",
              "      <td>0.036817</td>\n",
              "      <td>0.077529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sumamnt_ma_rech90</th>\n",
              "      <td>0.205793</td>\n",
              "      <td>0.001011</td>\n",
              "      <td>0.762981</td>\n",
              "      <td>0.768817</td>\n",
              "      <td>0.342306</td>\n",
              "      <td>0.360601</td>\n",
              "      <td>0.002243</td>\n",
              "      <td>-0.000414</td>\n",
              "      <td>0.418735</td>\n",
              "      <td>0.584060</td>\n",
              "      <td>-0.000571</td>\n",
              "      <td>0.889989</td>\n",
              "      <td>0.449012</td>\n",
              "      <td>-0.000665</td>\n",
              "      <td>0.687054</td>\n",
              "      <td>-0.065566</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.429274</td>\n",
              "      <td>0.096385</td>\n",
              "      <td>0.003002</td>\n",
              "      <td>-0.001566</td>\n",
              "      <td>0.012465</td>\n",
              "      <td>0.013136</td>\n",
              "      <td>0.442766</td>\n",
              "      <td>0.508599</td>\n",
              "      <td>0.001152</td>\n",
              "      <td>-0.022133</td>\n",
              "      <td>0.011306</td>\n",
              "      <td>0.562609</td>\n",
              "      <td>0.321739</td>\n",
              "      <td>-0.042386</td>\n",
              "      <td>-0.026409</td>\n",
              "      <td>-0.029997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>medianamnt_ma_rech90</th>\n",
              "      <td>0.120855</td>\n",
              "      <td>0.004909</td>\n",
              "      <td>0.257847</td>\n",
              "      <td>0.250518</td>\n",
              "      <td>0.110356</td>\n",
              "      <td>0.103151</td>\n",
              "      <td>-0.000726</td>\n",
              "      <td>0.000219</td>\n",
              "      <td>0.818734</td>\n",
              "      <td>-0.051347</td>\n",
              "      <td>-0.001573</td>\n",
              "      <td>0.422009</td>\n",
              "      <td>0.864846</td>\n",
              "      <td>-0.001459</td>\n",
              "      <td>-0.035497</td>\n",
              "      <td>0.138503</td>\n",
              "      <td>0.429274</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.209096</td>\n",
              "      <td>-0.002425</td>\n",
              "      <td>-0.002289</td>\n",
              "      <td>0.017533</td>\n",
              "      <td>0.019677</td>\n",
              "      <td>-0.060766</td>\n",
              "      <td>-0.025307</td>\n",
              "      <td>0.001016</td>\n",
              "      <td>0.036475</td>\n",
              "      <td>-0.000123</td>\n",
              "      <td>-0.023450</td>\n",
              "      <td>0.134139</td>\n",
              "      <td>0.031696</td>\n",
              "      <td>-0.035640</td>\n",
              "      <td>-0.023022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>medianmarechprebal90</th>\n",
              "      <td>0.039300</td>\n",
              "      <td>-0.000859</td>\n",
              "      <td>0.037495</td>\n",
              "      <td>0.036382</td>\n",
              "      <td>0.027170</td>\n",
              "      <td>0.029547</td>\n",
              "      <td>-0.001086</td>\n",
              "      <td>0.004158</td>\n",
              "      <td>0.124646</td>\n",
              "      <td>0.013463</td>\n",
              "      <td>-0.002826</td>\n",
              "      <td>0.090502</td>\n",
              "      <td>0.156028</td>\n",
              "      <td>0.000529</td>\n",
              "      <td>0.015461</td>\n",
              "      <td>0.004347</td>\n",
              "      <td>0.096385</td>\n",
              "      <td>0.209096</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.001130</td>\n",
              "      <td>-0.001893</td>\n",
              "      <td>0.007077</td>\n",
              "      <td>0.008492</td>\n",
              "      <td>-0.043381</td>\n",
              "      <td>-0.033878</td>\n",
              "      <td>0.001884</td>\n",
              "      <td>0.030534</td>\n",
              "      <td>-0.000926</td>\n",
              "      <td>-0.036412</td>\n",
              "      <td>0.027130</td>\n",
              "      <td>0.032453</td>\n",
              "      <td>-0.031760</td>\n",
              "      <td>-0.031210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cnt_da_rech30</th>\n",
              "      <td>0.003827</td>\n",
              "      <td>0.001564</td>\n",
              "      <td>0.000700</td>\n",
              "      <td>0.000661</td>\n",
              "      <td>-0.001105</td>\n",
              "      <td>-0.000548</td>\n",
              "      <td>-0.003467</td>\n",
              "      <td>-0.003628</td>\n",
              "      <td>-0.001837</td>\n",
              "      <td>0.002306</td>\n",
              "      <td>-0.000511</td>\n",
              "      <td>0.002537</td>\n",
              "      <td>-0.000060</td>\n",
              "      <td>-0.003404</td>\n",
              "      <td>0.002895</td>\n",
              "      <td>0.002230</td>\n",
              "      <td>0.003002</td>\n",
              "      <td>-0.002425</td>\n",
              "      <td>0.001130</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.005203</td>\n",
              "      <td>0.002153</td>\n",
              "      <td>0.000088</td>\n",
              "      <td>0.001852</td>\n",
              "      <td>0.001487</td>\n",
              "      <td>-0.000394</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>-0.001986</td>\n",
              "      <td>0.003369</td>\n",
              "      <td>-0.000647</td>\n",
              "      <td>-0.001365</td>\n",
              "      <td>-0.000471</td>\n",
              "      <td>0.000382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fr_da_rech30</th>\n",
              "      <td>-0.000027</td>\n",
              "      <td>0.000892</td>\n",
              "      <td>-0.001499</td>\n",
              "      <td>-0.001570</td>\n",
              "      <td>-0.002558</td>\n",
              "      <td>-0.002345</td>\n",
              "      <td>-0.003626</td>\n",
              "      <td>-0.000074</td>\n",
              "      <td>-0.003230</td>\n",
              "      <td>-0.002732</td>\n",
              "      <td>-0.001715</td>\n",
              "      <td>-0.001578</td>\n",
              "      <td>-0.001479</td>\n",
              "      <td>0.000742</td>\n",
              "      <td>-0.002795</td>\n",
              "      <td>-0.000081</td>\n",
              "      <td>-0.001566</td>\n",
              "      <td>-0.002289</td>\n",
              "      <td>-0.001893</td>\n",
              "      <td>0.005203</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000770</td>\n",
              "      <td>0.005505</td>\n",
              "      <td>-0.003212</td>\n",
              "      <td>-0.003395</td>\n",
              "      <td>-0.001070</td>\n",
              "      <td>-0.001141</td>\n",
              "      <td>-0.001623</td>\n",
              "      <td>-0.001593</td>\n",
              "      <td>-0.001069</td>\n",
              "      <td>-0.001623</td>\n",
              "      <td>0.001709</td>\n",
              "      <td>-0.000725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cnt_da_rech90</th>\n",
              "      <td>0.002999</td>\n",
              "      <td>0.001121</td>\n",
              "      <td>0.038814</td>\n",
              "      <td>0.031155</td>\n",
              "      <td>0.072255</td>\n",
              "      <td>0.056282</td>\n",
              "      <td>-0.003538</td>\n",
              "      <td>-0.001859</td>\n",
              "      <td>0.014779</td>\n",
              "      <td>0.011981</td>\n",
              "      <td>0.001574</td>\n",
              "      <td>0.019213</td>\n",
              "      <td>0.016727</td>\n",
              "      <td>-0.003064</td>\n",
              "      <td>0.004090</td>\n",
              "      <td>-0.006727</td>\n",
              "      <td>0.012465</td>\n",
              "      <td>0.017533</td>\n",
              "      <td>0.007077</td>\n",
              "      <td>0.002153</td>\n",
              "      <td>0.000770</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.348885</td>\n",
              "      <td>0.017976</td>\n",
              "      <td>0.026249</td>\n",
              "      <td>-0.001855</td>\n",
              "      <td>-0.003351</td>\n",
              "      <td>0.000907</td>\n",
              "      <td>0.020909</td>\n",
              "      <td>0.038019</td>\n",
              "      <td>-0.003094</td>\n",
              "      <td>0.013430</td>\n",
              "      <td>0.024046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fr_da_rech90</th>\n",
              "      <td>-0.005418</td>\n",
              "      <td>0.005395</td>\n",
              "      <td>0.020673</td>\n",
              "      <td>0.016437</td>\n",
              "      <td>0.046761</td>\n",
              "      <td>0.036886</td>\n",
              "      <td>-0.002395</td>\n",
              "      <td>-0.000203</td>\n",
              "      <td>0.016042</td>\n",
              "      <td>0.006989</td>\n",
              "      <td>-0.001925</td>\n",
              "      <td>0.017255</td>\n",
              "      <td>0.013734</td>\n",
              "      <td>-0.001412</td>\n",
              "      <td>0.002865</td>\n",
              "      <td>-0.003822</td>\n",
              "      <td>0.013136</td>\n",
              "      <td>0.019677</td>\n",
              "      <td>0.008492</td>\n",
              "      <td>0.000088</td>\n",
              "      <td>0.005505</td>\n",
              "      <td>0.348885</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010021</td>\n",
              "      <td>0.014935</td>\n",
              "      <td>-0.000754</td>\n",
              "      <td>-0.002234</td>\n",
              "      <td>0.001993</td>\n",
              "      <td>0.011501</td>\n",
              "      <td>0.021785</td>\n",
              "      <td>-0.000949</td>\n",
              "      <td>0.000354</td>\n",
              "      <td>0.001226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cnt_loans30</th>\n",
              "      <td>0.196283</td>\n",
              "      <td>-0.001826</td>\n",
              "      <td>0.366116</td>\n",
              "      <td>0.340387</td>\n",
              "      <td>0.180203</td>\n",
              "      <td>0.171595</td>\n",
              "      <td>0.001193</td>\n",
              "      <td>0.000380</td>\n",
              "      <td>-0.027612</td>\n",
              "      <td>0.765802</td>\n",
              "      <td>0.002666</td>\n",
              "      <td>0.491864</td>\n",
              "      <td>-0.029789</td>\n",
              "      <td>-0.000211</td>\n",
              "      <td>0.689002</td>\n",
              "      <td>-0.119083</td>\n",
              "      <td>0.442766</td>\n",
              "      <td>-0.060766</td>\n",
              "      <td>-0.043381</td>\n",
              "      <td>0.001852</td>\n",
              "      <td>-0.003212</td>\n",
              "      <td>0.017976</td>\n",
              "      <td>0.010021</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.957742</td>\n",
              "      <td>0.000251</td>\n",
              "      <td>-0.093146</td>\n",
              "      <td>0.015595</td>\n",
              "      <td>0.852905</td>\n",
              "      <td>0.143590</td>\n",
              "      <td>-0.097871</td>\n",
              "      <td>0.067842</td>\n",
              "      <td>0.033369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>amnt_loans30</th>\n",
              "      <td>0.197272</td>\n",
              "      <td>-0.001726</td>\n",
              "      <td>0.471492</td>\n",
              "      <td>0.447869</td>\n",
              "      <td>0.233453</td>\n",
              "      <td>0.231906</td>\n",
              "      <td>0.000903</td>\n",
              "      <td>0.000536</td>\n",
              "      <td>0.008502</td>\n",
              "      <td>0.752296</td>\n",
              "      <td>0.002470</td>\n",
              "      <td>0.531079</td>\n",
              "      <td>0.007125</td>\n",
              "      <td>0.000134</td>\n",
              "      <td>0.707558</td>\n",
              "      <td>-0.123664</td>\n",
              "      <td>0.508599</td>\n",
              "      <td>-0.025307</td>\n",
              "      <td>-0.033878</td>\n",
              "      <td>0.001487</td>\n",
              "      <td>-0.003395</td>\n",
              "      <td>0.026249</td>\n",
              "      <td>0.014935</td>\n",
              "      <td>0.957742</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000073</td>\n",
              "      <td>-0.078531</td>\n",
              "      <td>0.014625</td>\n",
              "      <td>0.897961</td>\n",
              "      <td>0.329170</td>\n",
              "      <td>-0.087989</td>\n",
              "      <td>0.060425</td>\n",
              "      <td>0.030901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>maxamnt_loans30</th>\n",
              "      <td>0.000248</td>\n",
              "      <td>-0.002764</td>\n",
              "      <td>-0.000028</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>-0.000864</td>\n",
              "      <td>-0.001411</td>\n",
              "      <td>0.000928</td>\n",
              "      <td>0.000503</td>\n",
              "      <td>0.001000</td>\n",
              "      <td>0.000109</td>\n",
              "      <td>-0.001100</td>\n",
              "      <td>0.001633</td>\n",
              "      <td>0.001985</td>\n",
              "      <td>-0.002176</td>\n",
              "      <td>-0.000573</td>\n",
              "      <td>0.001635</td>\n",
              "      <td>0.001152</td>\n",
              "      <td>0.001016</td>\n",
              "      <td>0.001884</td>\n",
              "      <td>-0.000394</td>\n",
              "      <td>-0.001070</td>\n",
              "      <td>-0.001855</td>\n",
              "      <td>-0.000754</td>\n",
              "      <td>0.000251</td>\n",
              "      <td>-0.000073</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.007782</td>\n",
              "      <td>0.003987</td>\n",
              "      <td>-0.001850</td>\n",
              "      <td>-0.000487</td>\n",
              "      <td>0.009330</td>\n",
              "      <td>-0.001512</td>\n",
              "      <td>-0.000915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>medianamnt_loans30</th>\n",
              "      <td>0.044589</td>\n",
              "      <td>0.004664</td>\n",
              "      <td>-0.011610</td>\n",
              "      <td>-0.005591</td>\n",
              "      <td>-0.016482</td>\n",
              "      <td>-0.009467</td>\n",
              "      <td>0.001835</td>\n",
              "      <td>0.000061</td>\n",
              "      <td>0.028370</td>\n",
              "      <td>-0.067011</td>\n",
              "      <td>-0.000331</td>\n",
              "      <td>-0.034460</td>\n",
              "      <td>0.032477</td>\n",
              "      <td>-0.002289</td>\n",
              "      <td>-0.053637</td>\n",
              "      <td>0.020626</td>\n",
              "      <td>-0.022133</td>\n",
              "      <td>0.036475</td>\n",
              "      <td>0.030534</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>-0.001141</td>\n",
              "      <td>-0.003351</td>\n",
              "      <td>-0.002234</td>\n",
              "      <td>-0.093146</td>\n",
              "      <td>-0.078531</td>\n",
              "      <td>0.007782</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.003670</td>\n",
              "      <td>-0.067580</td>\n",
              "      <td>0.058378</td>\n",
              "      <td>0.911604</td>\n",
              "      <td>-0.006007</td>\n",
              "      <td>0.003178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cnt_loans90</th>\n",
              "      <td>0.004733</td>\n",
              "      <td>-0.000611</td>\n",
              "      <td>0.008962</td>\n",
              "      <td>0.009446</td>\n",
              "      <td>0.004012</td>\n",
              "      <td>0.005141</td>\n",
              "      <td>-0.000225</td>\n",
              "      <td>-0.000972</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.014703</td>\n",
              "      <td>0.003368</td>\n",
              "      <td>0.009419</td>\n",
              "      <td>-0.001143</td>\n",
              "      <td>0.001342</td>\n",
              "      <td>0.015754</td>\n",
              "      <td>-0.002756</td>\n",
              "      <td>0.011306</td>\n",
              "      <td>-0.000123</td>\n",
              "      <td>-0.000926</td>\n",
              "      <td>-0.001986</td>\n",
              "      <td>-0.001623</td>\n",
              "      <td>0.000907</td>\n",
              "      <td>0.001993</td>\n",
              "      <td>0.015595</td>\n",
              "      <td>0.014625</td>\n",
              "      <td>0.003987</td>\n",
              "      <td>-0.003670</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.017697</td>\n",
              "      <td>0.001450</td>\n",
              "      <td>-0.002571</td>\n",
              "      <td>0.000587</td>\n",
              "      <td>-0.000476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>amnt_loans90</th>\n",
              "      <td>0.199788</td>\n",
              "      <td>-0.002319</td>\n",
              "      <td>0.563496</td>\n",
              "      <td>0.567204</td>\n",
              "      <td>0.298943</td>\n",
              "      <td>0.327436</td>\n",
              "      <td>0.000870</td>\n",
              "      <td>0.000519</td>\n",
              "      <td>0.014067</td>\n",
              "      <td>0.685257</td>\n",
              "      <td>0.002634</td>\n",
              "      <td>0.484761</td>\n",
              "      <td>0.014546</td>\n",
              "      <td>0.000736</td>\n",
              "      <td>0.783419</td>\n",
              "      <td>-0.132035</td>\n",
              "      <td>0.562609</td>\n",
              "      <td>-0.023450</td>\n",
              "      <td>-0.036412</td>\n",
              "      <td>0.003369</td>\n",
              "      <td>-0.001593</td>\n",
              "      <td>0.020909</td>\n",
              "      <td>0.011501</td>\n",
              "      <td>0.852905</td>\n",
              "      <td>0.897961</td>\n",
              "      <td>-0.001850</td>\n",
              "      <td>-0.067580</td>\n",
              "      <td>0.017697</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.323417</td>\n",
              "      <td>-0.095160</td>\n",
              "      <td>0.052142</td>\n",
              "      <td>0.029653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>maxamnt_loans90</th>\n",
              "      <td>0.084144</td>\n",
              "      <td>-0.001191</td>\n",
              "      <td>0.400199</td>\n",
              "      <td>0.397251</td>\n",
              "      <td>0.234211</td>\n",
              "      <td>0.251029</td>\n",
              "      <td>-0.001123</td>\n",
              "      <td>0.001524</td>\n",
              "      <td>0.148460</td>\n",
              "      <td>0.174104</td>\n",
              "      <td>-0.001890</td>\n",
              "      <td>0.257705</td>\n",
              "      <td>0.159613</td>\n",
              "      <td>-0.002141</td>\n",
              "      <td>0.244602</td>\n",
              "      <td>-0.045780</td>\n",
              "      <td>0.321739</td>\n",
              "      <td>0.134139</td>\n",
              "      <td>0.027130</td>\n",
              "      <td>-0.000647</td>\n",
              "      <td>-0.001069</td>\n",
              "      <td>0.038019</td>\n",
              "      <td>0.021785</td>\n",
              "      <td>0.143590</td>\n",
              "      <td>0.329170</td>\n",
              "      <td>-0.000487</td>\n",
              "      <td>0.058378</td>\n",
              "      <td>0.001450</td>\n",
              "      <td>0.323417</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.032256</td>\n",
              "      <td>0.013376</td>\n",
              "      <td>0.029679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>medianamnt_loans90</th>\n",
              "      <td>0.035747</td>\n",
              "      <td>0.002771</td>\n",
              "      <td>-0.037305</td>\n",
              "      <td>-0.034686</td>\n",
              "      <td>-0.035489</td>\n",
              "      <td>-0.034122</td>\n",
              "      <td>0.002771</td>\n",
              "      <td>-0.002239</td>\n",
              "      <td>0.021004</td>\n",
              "      <td>-0.071451</td>\n",
              "      <td>-0.001292</td>\n",
              "      <td>-0.042244</td>\n",
              "      <td>0.022692</td>\n",
              "      <td>-0.001927</td>\n",
              "      <td>-0.072481</td>\n",
              "      <td>0.016964</td>\n",
              "      <td>-0.042386</td>\n",
              "      <td>0.031696</td>\n",
              "      <td>0.032453</td>\n",
              "      <td>-0.001365</td>\n",
              "      <td>-0.001623</td>\n",
              "      <td>-0.003094</td>\n",
              "      <td>-0.000949</td>\n",
              "      <td>-0.097871</td>\n",
              "      <td>-0.087989</td>\n",
              "      <td>0.009330</td>\n",
              "      <td>0.911604</td>\n",
              "      <td>-0.002571</td>\n",
              "      <td>-0.095160</td>\n",
              "      <td>0.032256</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.013388</td>\n",
              "      <td>-0.010443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>payback30</th>\n",
              "      <td>0.048336</td>\n",
              "      <td>0.001940</td>\n",
              "      <td>0.026915</td>\n",
              "      <td>0.019400</td>\n",
              "      <td>0.072974</td>\n",
              "      <td>0.067110</td>\n",
              "      <td>-0.002233</td>\n",
              "      <td>0.000077</td>\n",
              "      <td>-0.027369</td>\n",
              "      <td>0.043617</td>\n",
              "      <td>0.001455</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>-0.018398</td>\n",
              "      <td>0.001516</td>\n",
              "      <td>0.011943</td>\n",
              "      <td>0.036817</td>\n",
              "      <td>-0.026409</td>\n",
              "      <td>-0.035640</td>\n",
              "      <td>-0.031760</td>\n",
              "      <td>-0.000471</td>\n",
              "      <td>0.001709</td>\n",
              "      <td>0.013430</td>\n",
              "      <td>0.000354</td>\n",
              "      <td>0.067842</td>\n",
              "      <td>0.060425</td>\n",
              "      <td>-0.001512</td>\n",
              "      <td>-0.006007</td>\n",
              "      <td>0.000587</td>\n",
              "      <td>0.052142</td>\n",
              "      <td>0.013376</td>\n",
              "      <td>-0.013388</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.830363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>payback90</th>\n",
              "      <td>0.049183</td>\n",
              "      <td>0.002203</td>\n",
              "      <td>0.047175</td>\n",
              "      <td>0.040800</td>\n",
              "      <td>0.095147</td>\n",
              "      <td>0.099501</td>\n",
              "      <td>-0.001583</td>\n",
              "      <td>0.000417</td>\n",
              "      <td>-0.014260</td>\n",
              "      <td>0.017362</td>\n",
              "      <td>0.000998</td>\n",
              "      <td>-0.012596</td>\n",
              "      <td>-0.002861</td>\n",
              "      <td>0.001811</td>\n",
              "      <td>-0.003141</td>\n",
              "      <td>0.077529</td>\n",
              "      <td>-0.029997</td>\n",
              "      <td>-0.023022</td>\n",
              "      <td>-0.031210</td>\n",
              "      <td>0.000382</td>\n",
              "      <td>-0.000725</td>\n",
              "      <td>0.024046</td>\n",
              "      <td>0.001226</td>\n",
              "      <td>0.033369</td>\n",
              "      <td>0.030901</td>\n",
              "      <td>-0.000915</td>\n",
              "      <td>0.003178</td>\n",
              "      <td>-0.000476</td>\n",
              "      <td>0.029653</td>\n",
              "      <td>0.029679</td>\n",
              "      <td>-0.010443</td>\n",
              "      <td>0.830363</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         label       aon  ...  payback30  payback90\n",
              "label                 1.000000 -0.003785  ...   0.048336   0.049183\n",
              "aon                  -0.003785  1.000000  ...   0.001940   0.002203\n",
              "daily_decr30          0.168298  0.001104  ...   0.026915   0.047175\n",
              "daily_decr90          0.166150  0.000374  ...   0.019400   0.040800\n",
              "rental30              0.058085 -0.000960  ...   0.072974   0.095147\n",
              "rental90              0.075521 -0.000790  ...   0.067110   0.099501\n",
              "last_rech_date_ma     0.003728  0.001692  ...  -0.002233  -0.001583\n",
              "last_rech_date_da     0.001711 -0.001693  ...   0.000077   0.000417\n",
              "last_rech_amt_ma      0.131804  0.004256  ...  -0.027369  -0.014260\n",
              "cnt_ma_rech30         0.237331 -0.003148  ...   0.043617   0.017362\n",
              "fr_ma_rech30          0.001330 -0.001163  ...   0.001455   0.000998\n",
              "sumamnt_ma_rech30     0.202828  0.000707  ...   0.000112  -0.012596\n",
              "medianamnt_ma_rech30  0.141490  0.004306  ...  -0.018398  -0.002861\n",
              "medianmarechprebal30 -0.004829  0.003930  ...   0.001516   0.001811\n",
              "cnt_ma_rech90         0.236392 -0.002725  ...   0.011943  -0.003141\n",
              "fr_ma_rech90          0.084385  0.004401  ...   0.036817   0.077529\n",
              "sumamnt_ma_rech90     0.205793  0.001011  ...  -0.026409  -0.029997\n",
              "medianamnt_ma_rech90  0.120855  0.004909  ...  -0.035640  -0.023022\n",
              "medianmarechprebal90  0.039300 -0.000859  ...  -0.031760  -0.031210\n",
              "cnt_da_rech30         0.003827  0.001564  ...  -0.000471   0.000382\n",
              "fr_da_rech30         -0.000027  0.000892  ...   0.001709  -0.000725\n",
              "cnt_da_rech90         0.002999  0.001121  ...   0.013430   0.024046\n",
              "fr_da_rech90         -0.005418  0.005395  ...   0.000354   0.001226\n",
              "cnt_loans30           0.196283 -0.001826  ...   0.067842   0.033369\n",
              "amnt_loans30          0.197272 -0.001726  ...   0.060425   0.030901\n",
              "maxamnt_loans30       0.000248 -0.002764  ...  -0.001512  -0.000915\n",
              "medianamnt_loans30    0.044589  0.004664  ...  -0.006007   0.003178\n",
              "cnt_loans90           0.004733 -0.000611  ...   0.000587  -0.000476\n",
              "amnt_loans90          0.199788 -0.002319  ...   0.052142   0.029653\n",
              "maxamnt_loans90       0.084144 -0.001191  ...   0.013376   0.029679\n",
              "medianamnt_loans90    0.035747  0.002771  ...  -0.013388  -0.010443\n",
              "payback30             0.048336  0.001940  ...   1.000000   0.830363\n",
              "payback90             0.049183  0.002203  ...   0.830363   1.000000\n",
              "\n",
              "[33 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "tV8Br-sIvylk",
        "outputId": "490ee389-b699-4723-cae7-6642984ee62c"
      },
      "source": [
        "sn.heatmap(correlation)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4a8df4fcd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ui4yy6kld0Ri"
      },
      "source": [
        "y = df[\"label\"]\n",
        "x = df.drop(labels=[\"label\"],axis=1)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5rpG4mnPtGW"
      },
      "source": [
        "Normalizing the Input Variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MoyZ0QK8SFv6"
      },
      "source": [
        "from sklearn.preprocessing import Normalizer\n",
        "norm_x = Normalizer()\n",
        "dt=norm_x.fit_transform(x)\n",
        "col_name=[str(i) for i in df.columns]\n",
        "data=pd.DataFrame(dt,columns=col_name[1:])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a91mLj28lR-8"
      },
      "source": [
        "Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QarH86h6XrE6",
        "outputId": "af619ebc-3a46-4694-be05-23f0ed70f6c0"
      },
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "array=x.values\n",
        "X=array\n",
        "Y=y.values\n",
        "model=ExtraTreesClassifier()\n",
        "model.fit(X,Y)\n",
        "score=model.feature_importances_\n",
        "score"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.07618218, 0.12226881, 0.11648878, 0.07629464, 0.07424043,\n",
              "       0.04986471, 0.00384383, 0.02729224, 0.03549795, 0.01616628,\n",
              "       0.02603862, 0.02753344, 0.03815509, 0.03445368, 0.03367795,\n",
              "       0.0336888 , 0.02792536, 0.0444995 , 0.00282671, 0.00197302,\n",
              "       0.00325379, 0.00102244, 0.02198819, 0.02511275, 0.00220168,\n",
              "       0.00401191, 0.0071293 , 0.02425603, 0.00741138, 0.00411815,\n",
              "       0.01443063, 0.01615174])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CaFre8OKagd8",
        "outputId": "11870438-2d68-44bc-b920-e1c77ac5a081"
      },
      "source": [
        "temp=list(zip(x.columns,score))\n",
        "from operator import itemgetter\n",
        "sorted(temp,key=itemgetter(1),reverse=True)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('daily_decr30', 0.12226880542313884),\n",
              " ('daily_decr90', 0.11648877660852447),\n",
              " ('rental30', 0.07629463864953122),\n",
              " ('aon', 0.07618217884617874),\n",
              " ('rental90', 0.07424042872851358),\n",
              " ('last_rech_date_ma', 0.04986470868951346),\n",
              " ('medianmarechprebal90', 0.044499499448494156),\n",
              " ('medianmarechprebal30', 0.0381550949167394),\n",
              " ('cnt_ma_rech30', 0.035497950019082895),\n",
              " ('cnt_ma_rech90', 0.034453684938571184),\n",
              " ('sumamnt_ma_rech90', 0.03368880007401036),\n",
              " ('fr_ma_rech90', 0.033677953694325885),\n",
              " ('medianamnt_ma_rech90', 0.027925356232041048),\n",
              " ('medianamnt_ma_rech30', 0.02753344170119414),\n",
              " ('last_rech_amt_ma', 0.027292237439315973),\n",
              " ('sumamnt_ma_rech30', 0.02603861812953617),\n",
              " ('amnt_loans30', 0.02511274600375574),\n",
              " ('amnt_loans90', 0.024256030076881437),\n",
              " ('cnt_loans30', 0.021988193498438724),\n",
              " ('fr_ma_rech30', 0.016166282336192987),\n",
              " ('payback90', 0.016151736101922276),\n",
              " ('payback30', 0.014430630558581457),\n",
              " ('maxamnt_loans90', 0.00741138445442633),\n",
              " ('cnt_loans90', 0.007129299022128563),\n",
              " ('medianamnt_loans90', 0.0041181454065559245),\n",
              " ('medianamnt_loans30', 0.004011909653870165),\n",
              " ('last_rech_date_da', 0.0038438257080308496),\n",
              " ('cnt_da_rech90', 0.0032537945137315893),\n",
              " ('cnt_da_rech30', 0.002826705485915485),\n",
              " ('maxamnt_loans30', 0.002201680969969645),\n",
              " ('fr_da_rech30', 0.001973020556787435),\n",
              " ('fr_da_rech90', 0.0010224421140998907)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SDctMAGalWbs"
      },
      "source": [
        "Removing the columns having score less than 0.01"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7fy61Vf35kf"
      },
      "source": [
        "x.drop(['last_rech_date_da','cnt_loans90','fr_da_rech30','maxamnt_loans30','medianamnt_loans30','maxamnt_loans90','medianamnt_loans90','cnt_da_rech30','cnt_da_rech90','fr_da_rech90'],axis=1,inplace=True)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1piLhrQl1MI"
      },
      "source": [
        "Splitting the training and test sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Te8OIyp22yHE",
        "outputId": "aed55cb8-612b-4ed2-9b60-8a2c5b28745c"
      },
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state = 0)\n",
        "print(\"size of training and valid sets:\"+str(x_train.shape)+\", \" + str(x_test.shape))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "size of training and valid sets:(167674, 22), (41919, 22)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHXSHTZNPZZu"
      },
      "source": [
        "Applying Random Forest Classifier (Decided by Manually applying several classification methods and checking their accuracy score.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eI34ljmd0Z_9"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import model_selection\n",
        "from sklearn.metrics import classification_report,accuracy_score\n",
        "RFC = RandomForestClassifier(n_estimators=100)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4A8pWCMs-aRr",
        "outputId": "65d4c6dd-f494-4f4e-f4c7-5216153b594e"
      },
      "source": [
        "RFC.fit(x_train,y_train)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGyT6DLPPlLr"
      },
      "source": [
        "Cross Validation Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aE0vmdyT_oGX",
        "outputId": "fe96506e-bef0-4756-b7a6-4149de8b3198"
      },
      "source": [
        "cv_score = model_selection.cross_val_score(RFC, x_train,y_train, scoring='f1_weighted',n_jobs=-1)\n",
        "print(np.mean(cv_score))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9015132772147068\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgs_ZgSjPN2g"
      },
      "source": [
        "Prediction on the Test Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vc6nXH7_EhKN",
        "outputId": "9dce4d77-f4fd-47bc-aba8-cd1c79ca1e78"
      },
      "source": [
        "prediction=RFC.predict(x_test)\n",
        "print(classification_report(y_true = y_test, y_pred = prediction))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.45      0.56      5311\n",
            "           1       0.92      0.98      0.95     36608\n",
            "\n",
            "    accuracy                           0.91     41919\n",
            "   macro avg       0.84      0.71      0.76     41919\n",
            "weighted avg       0.90      0.91      0.90     41919\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DSPNIg62Lpld",
        "outputId": "76d534b2-7179-48cd-dc23-253df6ed36c2"
      },
      "source": [
        "print(\"Mean Accuracy score of:\")\n",
        "print(\"Training Data:\"+str(RFC.score(x_train,y_train)))\n",
        "print(\"Test Data:\"+str(RFC.score(x_test,y_test)))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy score of:\n",
            "Training Data:0.999594451137326\n",
            "Test Data:0.9110665807867554\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "653SFgahOOg4"
      },
      "source": [
        "Comparison of Real and Predicted values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "9tEd6vUTMCF_",
        "outputId": "3eceabfb-96ef-4ae0-91e7-e7cd1df9a4fc"
      },
      "source": [
        "real_vs_pred=pd.DataFrame({'Actual':y_test, 'Predicted':prediction})\n",
        "real_vs_pred"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23412</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106457</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160064</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46825</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86927</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157031</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43221</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129880</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60199</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13524</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41919 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Actual  Predicted\n",
              "23412        1          1\n",
              "106457       1          1\n",
              "160064       0          0\n",
              "46825        1          1\n",
              "86927        0          1\n",
              "...        ...        ...\n",
              "157031       0          1\n",
              "43221        1          1\n",
              "129880       0          0\n",
              "60199        1          0\n",
              "13524        1          1\n",
              "\n",
              "[41919 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    }
  ]
}