modulo6/aulas_desafios_modulo6.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "aulas_desafios_modulo6.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOu5fCUf861JdZNFIyuOrux",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/diascarolina/data-science-bootcamp/blob/main/modulo6/aulas_desafios_modulo6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGDDAk20SmUI"
      },
      "source": [
        "# Applied Data Science Bootcamp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7rSkPTS7SqcS"
      },
      "source": [
        "# Module 06 - Data Science in Finance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGJjOLqyTAee"
      },
      "source": [
        "Notebook for the lessons and proposed challenges."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5FGiFum0TBRH"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0Xe0A-0St-7"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import roc_auc_score, classification_report, plot_confusion_matrix\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.pipeline import Pipeline\n",
        "\n",
        "sns.set()"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGDbYWk9nYEk"
      },
      "source": [
        "# Lesson 01"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z64PE0JWS-7c"
      },
      "source": [
        "approved_clients_raw = pd.read_csv('https://github.com/diascarolina/data-science-bootcamp/blob/main/data/approved_clients.csv?raw=true')\n",
        "client_records_raw = pd.read_csv('https://github.com/diascarolina/data-science-bootcamp/blob/main/data/client_record.csv?raw=true')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "3whlZxKvNam3",
        "outputId": "98acbdf4-617b-410a-8165-36900328fc9a"
      },
      "source": [
        "client_records_raw.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>own_car</th>\n",
              "      <th>own_property</th>\n",
              "      <th>children_count</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>income_type</th>\n",
              "      <th>education_type</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>housing_type</th>\n",
              "      <th>age</th>\n",
              "      <th>own_cellphone</th>\n",
              "      <th>own_workphone</th>\n",
              "      <th>own_phone</th>\n",
              "      <th>own_email</th>\n",
              "      <th>occupation_type</th>\n",
              "      <th>family_size</th>\n",
              "      <th>years_working</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>m</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>working</td>\n",
              "      <td>higher_education</td>\n",
              "      <td>civil_marriage</td>\n",
              "      <td>rented_apartment</td>\n",
              "      <td>33.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>others</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>m</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>working</td>\n",
              "      <td>higher_education</td>\n",
              "      <td>civil_marriage</td>\n",
              "      <td>rented_apartment</td>\n",
              "      <td>33.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>others</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>m</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>working</td>\n",
              "      <td>secondary</td>\n",
              "      <td>married</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>59.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>security</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>f</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>commercial_associate</td>\n",
              "      <td>secondary</td>\n",
              "      <td>single</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>sales</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>f</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>commercial_associate</td>\n",
              "      <td>secondary</td>\n",
              "      <td>single</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>sales</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id gender  own_car  ...  occupation_type  family_size  years_working\n",
              "0  5008804      m        1  ...           others          2.0           12.4\n",
              "1  5008805      m        1  ...           others          2.0           12.4\n",
              "2  5008806      m        1  ...         security          2.0            3.1\n",
              "3  5008808      f        0  ...            sales          1.0            8.4\n",
              "4  5008809      f        0  ...            sales          1.0            8.4\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbTKtx1PNnPJ",
        "outputId": "e930bbdd-b7b2-451c-d3fc-a5891e5897e4"
      },
      "source": [
        "client_records_raw.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(438557, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yxE2F3D-N1O9",
        "outputId": "d5729ba4-419a-4404-cca1-856a70dc4dcb"
      },
      "source": [
        "client_records_raw.id.value_counts()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7603224    2\n",
              "7742853    2\n",
              "7836711    2\n",
              "7091721    2\n",
              "7022327    2\n",
              "          ..\n",
              "5372264    1\n",
              "6412652    1\n",
              "6410605    1\n",
              "6367602    1\n",
              "6291456    1\n",
              "Name: id, Length: 438510, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8rmYpOiPUvh"
      },
      "source": [
        "We can see that we have some repeated IDs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wsrc1jrMON4c",
        "outputId": "d3a858d3-452a-40ac-bd98-d80182dab679"
      },
      "source": [
        "client_records_raw.duplicated().sum()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4D0PbCz-QEnZ"
      },
      "source": [
        "This means that we don't have whole rows duplicated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7_VScT17P9AI",
        "outputId": "3bb9305c-6ff3-4e59-9270-ad9c1cc8b73c"
      },
      "source": [
        "client_records_raw[client_records_raw.id.duplicated(keep = False)].shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(94, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e1mqEMRdRLbS"
      },
      "source": [
        "So we have 94 problematic values (47 repeated ones). Let's save them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gyoYiBMxQm7V"
      },
      "source": [
        "repeated_ids = client_records_raw[client_records_raw.id.duplicated(keep = False)].id"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mcK5yfdaSEkm"
      },
      "source": [
        "client_records = client_records_raw.copy()\n",
        "client_records = client_records.drop(repeated_ids.index)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRl8_GiqTv3x",
        "outputId": "30bf7583-c7b8-46ec-eefb-bb375845bad6"
      },
      "source": [
        "client_records.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(438463, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8nJIL5oS3S_",
        "outputId": "561cfca4-e9a4-482e-82c5-83aa6e606dff"
      },
      "source": [
        "client_records.id.value_counts()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6293503    1\n",
              "5166449    1\n",
              "6210931    1\n",
              "5174645    1\n",
              "5170551    1\n",
              "          ..\n",
              "7410037    1\n",
              "6357367    1\n",
              "6375806    1\n",
              "5683584    1\n",
              "6291456    1\n",
              "Name: id, Length: 438463, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lwwFrBSOTzZe"
      },
      "source": [
        "Now we have only one ID per row."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-psn8YECV_B5"
      },
      "source": [
        "Do we have null data?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VgfVWISDTuBd",
        "outputId": "038f7fca-aa44-4083-b232-96a571f69d20"
      },
      "source": [
        "print(f'Number of null values in client_records: {client_records.isnull().sum().sum()}')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of null values in client_records: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NSKYl7tRT9w_",
        "outputId": "ade0ff42-efcf-469a-f2df-04ddbebd23d1"
      },
      "source": [
        "client_records.info()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 438463 entries, 0 to 438556\n",
            "Data columns (total 18 columns):\n",
            " #   Column           Non-Null Count   Dtype  \n",
            "---  ------           --------------   -----  \n",
            " 0   id               438463 non-null  int64  \n",
            " 1   gender           438463 non-null  object \n",
            " 2   own_car          438463 non-null  int64  \n",
            " 3   own_property     438463 non-null  int64  \n",
            " 4   children_count   438463 non-null  int64  \n",
            " 5   annual_income    438463 non-null  float64\n",
            " 6   income_type      438463 non-null  object \n",
            " 7   education_type   438463 non-null  object \n",
            " 8   marital_status   438463 non-null  object \n",
            " 9   housing_type     438463 non-null  object \n",
            " 10  age              438463 non-null  float64\n",
            " 11  own_cellphone    438463 non-null  int64  \n",
            " 12  own_workphone    438463 non-null  int64  \n",
            " 13  own_phone        438463 non-null  int64  \n",
            " 14  own_email        438463 non-null  int64  \n",
            " 15  occupation_type  438463 non-null  object \n",
            " 16  family_size      438463 non-null  float64\n",
            " 17  years_working    438463 non-null  float64\n",
            "dtypes: float64(4), int64(8), object(6)\n",
            "memory usage: 63.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TdlXJBJEWObc"
      },
      "source": [
        "# Lesson 02"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIx15gqnXMak"
      },
      "source": [
        "quantitative_variables = ['age', 'children_count', 'annual_income', 'years_working', 'family_size']"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "E7ZnRNlUYBJS",
        "outputId": "bfa7f2a2-ef16-49fe-d5df-891a08375d0d"
      },
      "source": [
        "client_records[quantitative_variables].describe()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        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>age</th>\n",
              "      <th>children_count</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>years_working</th>\n",
              "      <th>family_size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>438463.000000</td>\n",
              "      <td>438463.000000</td>\n",
              "      <td>4.384630e+05</td>\n",
              "      <td>438463.000000</td>\n",
              "      <td>438463.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>43.834032</td>\n",
              "      <td>0.427384</td>\n",
              "      <td>1.875220e+05</td>\n",
              "      <td>-165.936036</td>\n",
              "      <td>2.194461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.472815</td>\n",
              "      <td>0.724879</td>\n",
              "      <td>1.100832e+05</td>\n",
              "      <td>380.201131</td>\n",
              "      <td>0.897194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.610000e+04</td>\n",
              "      <td>-1000.700000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.215000e+05</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>43.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.611000e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>53.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.250000e+05</td>\n",
              "      <td>8.500000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>69.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>6.750000e+06</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 age  children_count  ...  years_working    family_size\n",
              "count  438463.000000   438463.000000  ...  438463.000000  438463.000000\n",
              "mean       43.834032        0.427384  ...    -165.936036       2.194461\n",
              "std        11.472815        0.724879  ...     380.201131       0.897194\n",
              "min        21.000000        0.000000  ...   -1000.700000       1.000000\n",
              "25%        34.000000        0.000000  ...       1.000000       2.000000\n",
              "50%        43.000000        0.000000  ...       4.000000       2.000000\n",
              "75%        53.000000        1.000000  ...       8.500000       3.000000\n",
              "max        69.000000       19.000000  ...      48.000000      20.000000\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uw7XJtkEYbwf"
      },
      "source": [
        "Taking a look at the table above, how can someone have ```years_working``` equal to -1000.7?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-AjJvnifYJzA",
        "outputId": "109ba629-7532-4579-88e2-00ca735abff7"
      },
      "source": [
        "client_records.query(\"years_working <= 0\")[['years_working', 'income_type']].value_counts()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "years_working  income_type         \n",
              "-1000.7        pensioner               75314\n",
              " 0.0           working                    26\n",
              "               state_servant              11\n",
              "               commercial_associate        7\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMLW_dqQaNV9"
      },
      "source": [
        "We see that all ```years_working == -1000.7``` are pensioners."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G04a8HWxZ9pf",
        "outputId": "7ad25c57-99c8-4885-ed4b-9699b387fd14"
      },
      "source": [
        "client_records.query(\"income_type == 'pensioner'\")['income_type'].value_counts()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pensioner    75478\n",
              "Name: income_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_paiyZkauw4"
      },
      "source": [
        "Almost all pensioners have ```years_working == -1000.7```."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JdqD0eWAbDbJ"
      },
      "source": [
        "client_records['years_working'] = client_records['years_working'].replace(-1000.7, -1)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "95H98x-cbOwS",
        "outputId": "9812b3a4-e67e-4728-b47b-122151392e46"
      },
      "source": [
        "client_records[quantitative_variables].describe()"
      ],
      "execution_count": 19,
      "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>age</th>\n",
              "      <th>children_count</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>years_working</th>\n",
              "      <th>family_size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>438463.000000</td>\n",
              "      <td>438463.000000</td>\n",
              "      <td>4.384630e+05</td>\n",
              "      <td>438463.000000</td>\n",
              "      <td>438463.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>43.834032</td>\n",
              "      <td>0.427384</td>\n",
              "      <td>1.875220e+05</td>\n",
              "      <td>5.780633</td>\n",
              "      <td>2.194461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.472815</td>\n",
              "      <td>0.724879</td>\n",
              "      <td>1.100832e+05</td>\n",
              "      <td>6.733151</td>\n",
              "      <td>0.897194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.610000e+04</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.215000e+05</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>43.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.611000e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>53.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.250000e+05</td>\n",
              "      <td>8.500000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>69.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>6.750000e+06</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 age  children_count  ...  years_working    family_size\n",
              "count  438463.000000   438463.000000  ...  438463.000000  438463.000000\n",
              "mean       43.834032        0.427384  ...       5.780633       2.194461\n",
              "std        11.472815        0.724879  ...       6.733151       0.897194\n",
              "min        21.000000        0.000000  ...      -1.000000       1.000000\n",
              "25%        34.000000        0.000000  ...       1.000000       2.000000\n",
              "50%        43.000000        0.000000  ...       4.000000       2.000000\n",
              "75%        53.000000        1.000000  ...       8.500000       3.000000\n",
              "max        69.000000       19.000000  ...      48.000000      20.000000\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dTOcIqm4bSfn",
        "outputId": "d44325e4-b33c-4548-ad35-7a15f0428c2d"
      },
      "source": [
        "sns.histplot(data = client_records, x = 'annual_income', bins = 10)\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N0VG65kjcitN"
      },
      "source": [
        "Let's remove tha outliers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6SE2l080cU_1",
        "outputId": "f678d205-07d2-42d5-fbdc-eaaa30b38660"
      },
      "source": [
        "annual_income_column = client_records['annual_income']\n",
        "avg_annual_income = annual_income_column.mean()\n",
        "std_annual_income = annual_income_column.std()\n",
        "\n",
        "upper_limit = avg_annual_income + (2 * std_annual_income)\n",
        "lower_limit = avg_annual_income - (2 * std_annual_income)\n",
        "\n",
        "outlier_index = []\n",
        "for index, value in annual_income_column.items():\n",
        "    if value > upper_limit or value < lower_limit:\n",
        "        outlier_index.append(index)\n",
        "\n",
        "print(f'We have {len(outlier_index)} outliers in the annual income column.')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have 12641 outliers in the annual income column.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MfJoVIq6dNa6",
        "outputId": "e70353b8-5381-4715-8679-9fae05ce4379"
      },
      "source": [
        "client_records = client_records.drop(outlier_index)\n",
        "client_records.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(425822, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "KCp2UvjJeyQ_",
        "outputId": "112c189e-91e8-46ae-d874-f31b970318cf"
      },
      "source": [
        "client_records[quantitative_variables].describe()"
      ],
      "execution_count": 23,
      "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>age</th>\n",
              "      <th>children_count</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>years_working</th>\n",
              "      <th>family_size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>425822.000000</td>\n",
              "      <td>425822.000000</td>\n",
              "      <td>425822.000000</td>\n",
              "      <td>425822.000000</td>\n",
              "      <td>425822.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>43.838707</td>\n",
              "      <td>0.425314</td>\n",
              "      <td>176384.728083</td>\n",
              "      <td>5.744956</td>\n",
              "      <td>2.191683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.525400</td>\n",
              "      <td>0.723577</td>\n",
              "      <td>75033.530551</td>\n",
              "      <td>6.736649</td>\n",
              "      <td>0.896300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>26100.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>117000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>43.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>157500.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>53.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>225000.000000</td>\n",
              "      <td>8.500000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>69.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>405000.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 age  children_count  ...  years_working    family_size\n",
              "count  425822.000000   425822.000000  ...  425822.000000  425822.000000\n",
              "mean       43.838707        0.425314  ...       5.744956       2.191683\n",
              "std        11.525400        0.723577  ...       6.736649       0.896300\n",
              "min        21.000000        0.000000  ...      -1.000000       1.000000\n",
              "25%        34.000000        0.000000  ...       1.000000       2.000000\n",
              "50%        43.000000        0.000000  ...       4.000000       2.000000\n",
              "75%        53.000000        1.000000  ...       8.500000       3.000000\n",
              "max        69.000000       19.000000  ...      48.000000      20.000000\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Y-3q8_4yfBib",
        "outputId": "6d3df9f8-532c-4adb-dba7-d7a80c6ceaf3"
      },
      "source": [
        "sns.histplot(data = client_records, x = 'annual_income', bins = 10)\n",
        "plt.xticks(rotation = 20)\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "2hP6quLefJTO",
        "outputId": "b5c5e781-b328-4f3d-dcf7-21b64566cbcf"
      },
      "source": [
        "sns.histplot(data = client_records, x = 'age', bins = 10)\n",
        "plt.xticks(rotation = 20)\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xJ65n6PmfbCn",
        "outputId": "16a0bc2f-2957-4d5a-bfa5-d822ee6abe65"
      },
      "source": [
        "sns.histplot(data = client_records.query(\"years_working > 0\"), x = 'years_working')\n",
        "plt.xticks(rotation = 20)\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-phygmChu7t",
        "outputId": "24a0908c-5f0e-4a83-c60e-13803017d348"
      },
      "source": [
        "client_records.info()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 425822 entries, 2 to 438556\n",
            "Data columns (total 18 columns):\n",
            " #   Column           Non-Null Count   Dtype  \n",
            "---  ------           --------------   -----  \n",
            " 0   id               425822 non-null  int64  \n",
            " 1   gender           425822 non-null  object \n",
            " 2   own_car          425822 non-null  int64  \n",
            " 3   own_property     425822 non-null  int64  \n",
            " 4   children_count   425822 non-null  int64  \n",
            " 5   annual_income    425822 non-null  float64\n",
            " 6   income_type      425822 non-null  object \n",
            " 7   education_type   425822 non-null  object \n",
            " 8   marital_status   425822 non-null  object \n",
            " 9   housing_type     425822 non-null  object \n",
            " 10  age              425822 non-null  float64\n",
            " 11  own_cellphone    425822 non-null  int64  \n",
            " 12  own_workphone    425822 non-null  int64  \n",
            " 13  own_phone        425822 non-null  int64  \n",
            " 14  own_email        425822 non-null  int64  \n",
            " 15  occupation_type  425822 non-null  object \n",
            " 16  family_size      425822 non-null  float64\n",
            " 17  years_working    425822 non-null  float64\n",
            "dtypes: float64(4), int64(8), object(6)\n",
            "memory usage: 61.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwFIhdPufoB7"
      },
      "source": [
        "categorical_variables = ['gender', 'own_car', 'own_property', 'income_type',\n",
        "                         'education_type', 'marital_status', 'housing_type',\n",
        "                         'own_cellphone', 'own_workphone', 'own_phone', 'own_email',\n",
        "                         'occupation_type']"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Lu-p3fISirAc",
        "outputId": "40f8f3d5-24c2-4b7d-9a6a-3d74456029a9"
      },
      "source": [
        "sns.countplot(data = client_records, x = 'marital_status')\n",
        "plt.xticks(rotation = 45, ha = 'right')\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "roBzhLW_kQs3",
        "outputId": "80547372-a20f-4d92-889d-89f910c40542"
      },
      "source": [
        "sns.countplot(data = client_records, x = 'own_cellphone')\n",
        "plt.xticks(ha = 'right')\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "oS_nmD5-kfOS",
        "outputId": "7babcdea-6846-4416-c068-e9e122929d6a"
      },
      "source": [
        "sns.countplot(data = client_records, x = 'occupation_type')\n",
        "plt.xticks(rotation = 45, ha = 'right')\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Zsw9h70Akluv",
        "outputId": "0990d9af-3776-4455-959f-1394b96e297f"
      },
      "source": [
        "sns.histplot(data = client_records.query(\"occupation_type == 'others'\"), x = 'annual_income', bins = 10)\n",
        "plt.xticks(rotation = 45)\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "meIpxB25m_Q8"
      },
      "source": [
        "We have seen that the variable ``own_cellphone``` has only one alue, so it is not relevant for our future machine learning model.\n",
        "\n",
        "Also, we will drop the gender variable in order to avoid a bias."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xN3WW15KlazX"
      },
      "source": [
        "client_records_treatment1 = client_records.drop(['gender', 'own_cellphone'], axis = 1)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1KO0ytCSnqGh",
        "outputId": "42229f99-5018-479b-ec31-d2a2e7650dec"
      },
      "source": [
        "client_records_treatment1.shape"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(425822, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSg9yh2nn3X4"
      },
      "source": [
        "Let's update our categorical variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5up-uLJntao"
      },
      "source": [
        "categorical_variables.remove('gender')\n",
        "categorical_variables.remove('own_cellphone')"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-j_8x_SoQw2"
      },
      "source": [
        "# Lesson 03"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2x-AqWnyTD2"
      },
      "source": [
        "Let's now take a look at the approved clients."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sI90Yo-wn1ZF"
      },
      "source": [
        "approved_clients = approved_clients_raw.copy()"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "j2G-ehKwpw5N",
        "outputId": "86ca1d7b-0ffa-4aec-89cd-284d5e1fb07f"
      },
      "source": [
        "approved_clients.head()"
      ],
      "execution_count": 37,
      "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>id</th>\n",
              "      <th>month</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001711</td>\n",
              "      <td>0</td>\n",
              "      <td>no_loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-1</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-2</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-3</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001712</td>\n",
              "      <td>0</td>\n",
              "      <td>paid_loan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  month     status\n",
              "0  5001711      0    no_loan\n",
              "1  5001711     -1   1-29days\n",
              "2  5001711     -2   1-29days\n",
              "3  5001711     -3   1-29days\n",
              "4  5001712      0  paid_loan"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaJmlzRmp1mL",
        "outputId": "8c6e47b4-e7ea-4565-85b1-f65721fb5ec0"
      },
      "source": [
        "approved_clients.info()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1048575 entries, 0 to 1048574\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count    Dtype \n",
            "---  ------  --------------    ----- \n",
            " 0   id      1048575 non-null  int64 \n",
            " 1   month   1048575 non-null  int64 \n",
            " 2   status  1048575 non-null  object\n",
            "dtypes: int64(2), object(1)\n",
            "memory usage: 24.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PEKX11a_p50z",
        "outputId": "e54edbd5-9ec0-41d6-891e-fbe199a7ecca"
      },
      "source": [
        "approved_clients.id.value_counts().shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45985,)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "th_CL6TZyfi-",
        "outputId": "e93a13c9-8a39-436b-9964-fbe72c9daf08"
      },
      "source": [
        "client_records_treatment1.shape"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(425822, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "Aq4UtAn0zCi1",
        "outputId": "f267077f-17d1-499c-987b-9a8a673908cb"
      },
      "source": [
        "# example of a client\n",
        "approved_clients.query(\"id == 5001712\")"
      ],
      "execution_count": 41,
      "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>id</th>\n",
              "      <th>month</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001712</td>\n",
              "      <td>0</td>\n",
              "      <td>paid_loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-1</td>\n",
              "      <td>paid_loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-2</td>\n",
              "      <td>paid_loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-3</td>\n",
              "      <td>paid_loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-4</td>\n",
              "      <td>paid_loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-5</td>\n",
              "      <td>paid_loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-6</td>\n",
              "      <td>paid_loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-7</td>\n",
              "      <td>paid_loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-8</td>\n",
              "      <td>paid_loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-9</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-10</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-11</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-12</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-13</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-14</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-15</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-16</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-17</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-18</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id  month     status\n",
              "4   5001712      0  paid_loan\n",
              "5   5001712     -1  paid_loan\n",
              "6   5001712     -2  paid_loan\n",
              "7   5001712     -3  paid_loan\n",
              "8   5001712     -4  paid_loan\n",
              "9   5001712     -5  paid_loan\n",
              "10  5001712     -6  paid_loan\n",
              "11  5001712     -7  paid_loan\n",
              "12  5001712     -8  paid_loan\n",
              "13  5001712     -9   1-29days\n",
              "14  5001712    -10   1-29days\n",
              "15  5001712    -11   1-29days\n",
              "16  5001712    -12   1-29days\n",
              "17  5001712    -13   1-29days\n",
              "18  5001712    -14   1-29days\n",
              "19  5001712    -15   1-29days\n",
              "20  5001712    -16   1-29days\n",
              "21  5001712    -17   1-29days\n",
              "22  5001712    -18   1-29days"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "b4svxy1LzNas",
        "outputId": "0161ab50-31fa-422b-b132-d91c02055a52"
      },
      "source": [
        "approved_clients.query(\"id == 5001711\")"
      ],
      "execution_count": 42,
      "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>id</th>\n",
              "      <th>month</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001711</td>\n",
              "      <td>0</td>\n",
              "      <td>no_loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-1</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-2</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-3</td>\n",
              "      <td>1-29days</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  month    status\n",
              "0  5001711      0   no_loan\n",
              "1  5001711     -1  1-29days\n",
              "2  5001711     -2  1-29days\n",
              "3  5001711     -3  1-29days"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Se9JnpTzcT6",
        "outputId": "9128519b-64ad-4f92-8603-c237810c99bc"
      },
      "source": [
        "approved_clients.status.value_counts().index.to_list()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['paid_loan',\n",
              " '1-29days',\n",
              " 'no_loan',\n",
              " '30-59days',\n",
              " '>150days',\n",
              " '60-89days',\n",
              " '90-119days',\n",
              " '120-149days']"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFvhAw470Vol"
      },
      "source": [
        "## Creating Auxiliary Variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k3xMLXIxzlKK",
        "outputId": "bdbb2f76-9d43-4184-c805-cf66a1135e5c"
      },
      "source": [
        "# month in which the client's account was opened\n",
        "approved_clients_by_id = approved_clients.copy()\n",
        "approved_clients_by_id = approved_clients_by_id.groupby('id')\n",
        "opening_series = approved_clients_by_id.apply(lambda x: min(x['month']))\n",
        "opening_series.name = 'opening'\n",
        "opening_series"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id\n",
              "5001711    -3\n",
              "5001712   -18\n",
              "5001713   -21\n",
              "5001714   -14\n",
              "5001715   -59\n",
              "           ..\n",
              "5150482   -28\n",
              "5150483   -17\n",
              "5150484   -12\n",
              "5150485    -1\n",
              "5150487   -29\n",
              "Name: opening, Length: 45985, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "XrUma7vE08Mc",
        "outputId": "37030c4a-de77-4f22-c043-d3bdd578e6a1"
      },
      "source": [
        "approved_clients = approved_clients.merge(opening_series, on = 'id')\n",
        "approved_clients.head()"
      ],
      "execution_count": 45,
      "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>id</th>\n",
              "      <th>month</th>\n",
              "      <th>status</th>\n",
              "      <th>opening</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001711</td>\n",
              "      <td>0</td>\n",
              "      <td>no_loan</td>\n",
              "      <td>-3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-1</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-2</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-3</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001712</td>\n",
              "      <td>0</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  month     status  opening\n",
              "0  5001711      0    no_loan       -3\n",
              "1  5001711     -1   1-29days       -3\n",
              "2  5001711     -2   1-29days       -3\n",
              "3  5001711     -3   1-29days       -3\n",
              "4  5001712      0  paid_loan      -18"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8aOYKXf1GX7",
        "outputId": "6ae749db-2434-42b9-f401-522e4b2134a7"
      },
      "source": [
        "last_registered_series = approved_clients_by_id.apply(lambda x: max(x['month']))\n",
        "last_registered_series.name = 'final'\n",
        "last_registered_series"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id\n",
              "5001711     0\n",
              "5001712     0\n",
              "5001713     0\n",
              "5001714     0\n",
              "5001715     0\n",
              "           ..\n",
              "5150482   -11\n",
              "5150483     0\n",
              "5150484     0\n",
              "5150485     0\n",
              "5150487     0\n",
              "Name: final, Length: 45985, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Ys8E2Vba2Mx2",
        "outputId": "a824c6f1-7010-4541-ff45-8f4cae6196f3"
      },
      "source": [
        "approved_clients = approved_clients.merge(last_registered_series, on = 'id')\n",
        "approved_clients.head()"
      ],
      "execution_count": 47,
      "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>id</th>\n",
              "      <th>month</th>\n",
              "      <th>status</th>\n",
              "      <th>opening</th>\n",
              "      <th>final</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001711</td>\n",
              "      <td>0</td>\n",
              "      <td>no_loan</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-1</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-2</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-3</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001712</td>\n",
              "      <td>0</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-18</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  month     status  opening  final\n",
              "0  5001711      0    no_loan       -3      0\n",
              "1  5001711     -1   1-29days       -3      0\n",
              "2  5001711     -2   1-29days       -3      0\n",
              "3  5001711     -3   1-29days       -3      0\n",
              "4  5001712      0  paid_loan      -18      0"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "WwvHmdIR2UE4",
        "outputId": "0846a03e-c998-490a-dc21-6820be06d68d"
      },
      "source": [
        "# window of number of opening months\n",
        "approved_clients['window'] = approved_clients['final'] - approved_clients['opening']\n",
        "approved_clients.head()"
      ],
      "execution_count": 48,
      "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>id</th>\n",
              "      <th>month</th>\n",
              "      <th>status</th>\n",
              "      <th>opening</th>\n",
              "      <th>final</th>\n",
              "      <th>window</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001711</td>\n",
              "      <td>0</td>\n",
              "      <td>no_loan</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-1</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-2</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-3</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001712</td>\n",
              "      <td>0</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-18</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  month     status  opening  final  window\n",
              "0  5001711      0    no_loan       -3      0       3\n",
              "1  5001711     -1   1-29days       -3      0       3\n",
              "2  5001711     -2   1-29days       -3      0       3\n",
              "3  5001711     -3   1-29days       -3      0       3\n",
              "4  5001712      0  paid_loan      -18      0      18"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "TQPBnnUA2jPK",
        "outputId": "5a82aa84-d047-4663-cded-f0f7482c09c8"
      },
      "source": [
        "# month on book (mob)\n",
        "approved_clients['mob'] = approved_clients['month'] - approved_clients['opening']\n",
        "approved_clients.head()"
      ],
      "execution_count": 49,
      "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>id</th>\n",
              "      <th>month</th>\n",
              "      <th>status</th>\n",
              "      <th>opening</th>\n",
              "      <th>final</th>\n",
              "      <th>window</th>\n",
              "      <th>mob</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001711</td>\n",
              "      <td>0</td>\n",
              "      <td>no_loan</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-1</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-2</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-3</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001712</td>\n",
              "      <td>0</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-18</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  month     status  opening  final  window  mob\n",
              "0  5001711      0    no_loan       -3      0       3    3\n",
              "1  5001711     -1   1-29days       -3      0       3    2\n",
              "2  5001711     -2   1-29days       -3      0       3    1\n",
              "3  5001711     -3   1-29days       -3      0       3    0\n",
              "4  5001712      0  paid_loan      -18      0      18   18"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stX2040m3mXP",
        "outputId": "393d4313-19d1-4a22-fd57-e84f854acdae"
      },
      "source": [
        "approved_clients['status'].unique()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no_loan', '1-29days', 'paid_loan', '30-59days', '60-89days',\n",
              "       '90-119days', '120-149days', '>150days'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60rrgqy02_dk"
      },
      "source": [
        "status_dict = {\n",
        "    'no_loan': 0,\n",
        "    'paid_loan': 1,\n",
        "    '1-29days': 2,\n",
        "    '30-59days': 3,\n",
        "    '60-89days': 4,\n",
        "    '90-119days': 5,\n",
        "    '120-149days': 6,\n",
        "    '>150days': 7\n",
        "}\n",
        "\n",
        "approved_clients['status_int'] = approved_clients['status'].map(status_dict)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Dax1Fqd54K96",
        "outputId": "5605c9aa-c216-41bc-e0fe-7727e87997b7"
      },
      "source": [
        "approved_clients.head()"
      ],
      "execution_count": 52,
      "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>id</th>\n",
              "      <th>month</th>\n",
              "      <th>status</th>\n",
              "      <th>opening</th>\n",
              "      <th>final</th>\n",
              "      <th>window</th>\n",
              "      <th>mob</th>\n",
              "      <th>status_int</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001711</td>\n",
              "      <td>0</td>\n",
              "      <td>no_loan</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-1</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-2</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-3</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001712</td>\n",
              "      <td>0</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-18</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  month     status  opening  final  window  mob  status_int\n",
              "0  5001711      0    no_loan       -3      0       3    3           0\n",
              "1  5001711     -1   1-29days       -3      0       3    2           2\n",
              "2  5001711     -2   1-29days       -3      0       3    1           2\n",
              "3  5001711     -3   1-29days       -3      0       3    0           2\n",
              "4  5001712      0  paid_loan      -18      0      18   18           1"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZzGvqJEt5Chj"
      },
      "source": [
        "# approved_clients['bad'] = approved_clients.apply(lambda x: x['status_int'] > 3, axis = 1)\n",
        "approved_clients['bad'] = approved_clients.apply(lambda x: 1 if x['status_int'] > 3 else 0, axis = 1)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rlrSraZm50JA",
        "outputId": "fee1409c-eede-4743-d799-0221a497f492"
      },
      "source": [
        "approved_clients"
      ],
      "execution_count": 54,
      "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>id</th>\n",
              "      <th>month</th>\n",
              "      <th>status</th>\n",
              "      <th>opening</th>\n",
              "      <th>final</th>\n",
              "      <th>window</th>\n",
              "      <th>mob</th>\n",
              "      <th>status_int</th>\n",
              "      <th>bad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001711</td>\n",
              "      <td>0</td>\n",
              "      <td>no_loan</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-1</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-2</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-3</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001712</td>\n",
              "      <td>0</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-18</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048570</th>\n",
              "      <td>5150487</td>\n",
              "      <td>-25</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048571</th>\n",
              "      <td>5150487</td>\n",
              "      <td>-26</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048572</th>\n",
              "      <td>5150487</td>\n",
              "      <td>-27</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048573</th>\n",
              "      <td>5150487</td>\n",
              "      <td>-28</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048574</th>\n",
              "      <td>5150487</td>\n",
              "      <td>-29</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1048575 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              id  month     status  opening  ...  window  mob  status_int  bad\n",
              "0        5001711      0    no_loan       -3  ...       3    3           0    0\n",
              "1        5001711     -1   1-29days       -3  ...       3    2           2    0\n",
              "2        5001711     -2   1-29days       -3  ...       3    1           2    0\n",
              "3        5001711     -3   1-29days       -3  ...       3    0           2    0\n",
              "4        5001712      0  paid_loan      -18  ...      18   18           1    0\n",
              "...          ...    ...        ...      ...  ...     ...  ...         ...  ...\n",
              "1048570  5150487    -25  paid_loan      -29  ...      29    4           1    0\n",
              "1048571  5150487    -26  paid_loan      -29  ...      29    3           1    0\n",
              "1048572  5150487    -27  paid_loan      -29  ...      29    2           1    0\n",
              "1048573  5150487    -28  paid_loan      -29  ...      29    1           1    0\n",
              "1048574  5150487    -29  paid_loan      -29  ...      29    0           1    0\n",
              "\n",
              "[1048575 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uTIjJP_u65Ac"
      },
      "source": [
        "## Vintage Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "et3yrgT556Ef",
        "outputId": "b03b272a-4de3-41f4-f458-96fba95ad845"
      },
      "source": [
        "approved_clients_unique_id = approved_clients[['id', 'opening', 'final', 'window']].groupby('id').apply(lambda x: x.iloc[0]).reset_index(drop = True)\n",
        "approved_clients_unique_id"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>opening</th>\n",
              "      <th>final</th>\n",
              "      <th>window</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001712</td>\n",
              "      <td>-18</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001713</td>\n",
              "      <td>-21</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001714</td>\n",
              "      <td>-14</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001715</td>\n",
              "      <td>-59</td>\n",
              "      <td>0</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45980</th>\n",
              "      <td>5150482</td>\n",
              "      <td>-28</td>\n",
              "      <td>-11</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45981</th>\n",
              "      <td>5150483</td>\n",
              "      <td>-17</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45982</th>\n",
              "      <td>5150484</td>\n",
              "      <td>-12</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45983</th>\n",
              "      <td>5150485</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45984</th>\n",
              "      <td>5150487</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45985 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            id  opening  final  window\n",
              "0      5001711       -3      0       3\n",
              "1      5001712      -18      0      18\n",
              "2      5001713      -21      0      21\n",
              "3      5001714      -14      0      14\n",
              "4      5001715      -59      0      59\n",
              "...        ...      ...    ...     ...\n",
              "45980  5150482      -28    -11      17\n",
              "45981  5150483      -17      0      17\n",
              "45982  5150484      -12      0      12\n",
              "45983  5150485       -1      0       1\n",
              "45984  5150487      -29      0      29\n",
              "\n",
              "[45985 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "nDdJ8W777p_M",
        "outputId": "cce1042d-9954-43df-e79b-e1cb8694f65b"
      },
      "source": [
        "approved_clients_denominator = approved_clients_unique_id.groupby('opening').apply(lambda x: x['id'].count()).reset_index()\n",
        "approved_clients_denominator.columns = ['opening', 'number_of_clients']\n",
        "approved_clients_denominator"
      ],
      "execution_count": 56,
      "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",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>opening</th>\n",
              "      <th>number_of_clients</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-60</td>\n",
              "      <td>415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-59</td>\n",
              "      <td>406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-58</td>\n",
              "      <td>440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-57</td>\n",
              "      <td>400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-56</td>\n",
              "      <td>470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>-4</td>\n",
              "      <td>874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>-3</td>\n",
              "      <td>919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>-2</td>\n",
              "      <td>771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>-1</td>\n",
              "      <td>686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0</td>\n",
              "      <td>370</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    opening  number_of_clients\n",
              "0       -60                415\n",
              "1       -59                406\n",
              "2       -58                440\n",
              "3       -57                400\n",
              "4       -56                470\n",
              "..      ...                ...\n",
              "56       -4                874\n",
              "57       -3                919\n",
              "58       -2                771\n",
              "59       -1                686\n",
              "60        0                370\n",
              "\n",
              "[61 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "IVS8kWpO8MW_",
        "outputId": "13610cc5-12d1-4a1a-d856-8e9bb56b0292"
      },
      "source": [
        "vintage = approved_clients.groupby(['opening', 'mob']).apply(lambda x: x['id'].count()).reset_index()\n",
        "vintage.columns = ['opening', 'mob', 'number_of_clients']\n",
        "vintage"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        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>opening</th>\n",
              "      <th>mob</th>\n",
              "      <th>number_of_clients</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-60</td>\n",
              "      <td>0</td>\n",
              "      <td>415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-60</td>\n",
              "      <td>1</td>\n",
              "      <td>414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-60</td>\n",
              "      <td>2</td>\n",
              "      <td>410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-60</td>\n",
              "      <td>3</td>\n",
              "      <td>408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-60</td>\n",
              "      <td>4</td>\n",
              "      <td>403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1886</th>\n",
              "      <td>-2</td>\n",
              "      <td>1</td>\n",
              "      <td>770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1887</th>\n",
              "      <td>-2</td>\n",
              "      <td>2</td>\n",
              "      <td>750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1888</th>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1889</th>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1890</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1891 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      opening  mob  number_of_clients\n",
              "0         -60    0                415\n",
              "1         -60    1                414\n",
              "2         -60    2                410\n",
              "3         -60    3                408\n",
              "4         -60    4                403\n",
              "...       ...  ...                ...\n",
              "1886       -2    1                770\n",
              "1887       -2    2                750\n",
              "1888       -1    0                686\n",
              "1889       -1    1                682\n",
              "1890        0    0                370\n",
              "\n",
              "[1891 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gqnITEHa87x6"
      },
      "source": [
        "To explain the above table, let's look at the index 1886:\n",
        "- opening -2 = 2 months ago\n",
        "- mob 1 = account open for one month\n",
        "- number_of_clients 770\n",
        "\n",
        "So, two months ago we had 770 clients with accounts open for a period of one month."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MgLxTnVT8sKl",
        "outputId": "6f83fd88-ede9-4888-b633-1972e1f4f714"
      },
      "source": [
        "vintage = pd.merge(vintage[['opening', 'mob']],\n",
        "                   approved_clients_denominator,\n",
        "                   on = ['opening'],\n",
        "                   how = 'left')\n",
        "vintage"
      ],
      "execution_count": 58,
      "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",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>opening</th>\n",
              "      <th>mob</th>\n",
              "      <th>number_of_clients</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-60</td>\n",
              "      <td>0</td>\n",
              "      <td>415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-60</td>\n",
              "      <td>1</td>\n",
              "      <td>415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-60</td>\n",
              "      <td>2</td>\n",
              "      <td>415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-60</td>\n",
              "      <td>3</td>\n",
              "      <td>415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-60</td>\n",
              "      <td>4</td>\n",
              "      <td>415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1886</th>\n",
              "      <td>-2</td>\n",
              "      <td>1</td>\n",
              "      <td>771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1887</th>\n",
              "      <td>-2</td>\n",
              "      <td>2</td>\n",
              "      <td>771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1888</th>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1889</th>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1890</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1891 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      opening  mob  number_of_clients\n",
              "0         -60    0                415\n",
              "1         -60    1                415\n",
              "2         -60    2                415\n",
              "3         -60    3                415\n",
              "4         -60    4                415\n",
              "...       ...  ...                ...\n",
              "1886       -2    1                771\n",
              "1887       -2    2                771\n",
              "1888       -1    0                686\n",
              "1889       -1    1                686\n",
              "1890        0    0                370\n",
              "\n",
              "[1891 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xUSSqxyG9t79",
        "outputId": "38604cfa-f893-4b66-8cc9-24931408afa4"
      },
      "source": [
        "vintage['number_of_bad_payers'] = np.nan\n",
        "\n",
        "for j in range(-60, 1):\n",
        "    ls = []\n",
        "    for i in range(0, 61):\n",
        "        due = list(approved_clients.query(\"bad == 1 and mob == @i and opening == @j\")['id'])\n",
        "        ls.extend(due)\n",
        "        vintage.loc[(vintage['mob'] == i) & (vintage['opening'] == j), 'number_of_bad_payers'] = len(set(ls))\n",
        "\n",
        "vintage['bad_payers_rate'] = vintage['number_of_bad_payers'] / vintage['number_of_clients']\n",
        "vintage"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</table>\n",
              "<p>1891 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      opening  mob  number_of_clients  number_of_bad_payers  bad_payers_rate\n",
              "0         -60    0                415                   0.0         0.000000\n",
              "1         -60    1                415                   0.0         0.000000\n",
              "2         -60    2                415                   0.0         0.000000\n",
              "3         -60    3                415                   0.0         0.000000\n",
              "4         -60    4                415                   0.0         0.000000\n",
              "...       ...  ...                ...                   ...              ...\n",
              "1886       -2    1                771                   0.0         0.000000\n",
              "1887       -2    2                771                   1.0         0.001297\n",
              "1888       -1    0                686                   0.0         0.000000\n",
              "1889       -1    1                686                   0.0         0.000000\n",
              "1890        0    0                370                   0.0         0.000000\n",
              "\n",
              "[1891 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "23pmj7qi-2W2",
        "outputId": "71821a51-3f44-4688-adc0-2c2b8067ef68"
      },
      "source": [
        "vintage_pivot = vintage.pivot(index = 'opening',\n",
        "                              columns = 'mob',\n",
        "                              values = 'bad_payers_rate')\n",
        "\n",
        "vintage_pivot"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-1</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 61 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "mob       0    1         2         3   ...        57        58        59        60\n",
              "opening                                ...                                        \n",
              "-60      0.0  0.0  0.000000  0.000000  ...  0.028916  0.031325  0.031325  0.031325\n",
              "-59      0.0  0.0  0.000000  0.004926  ...  0.032020  0.032020  0.032020       NaN\n",
              "-58      0.0  0.0  0.000000  0.000000  ...  0.025000  0.025000       NaN       NaN\n",
              "-57      0.0  0.0  0.000000  0.000000  ...  0.025000       NaN       NaN       NaN\n",
              "-56      0.0  0.0  0.004255  0.004255  ...       NaN       NaN       NaN       NaN\n",
              "...      ...  ...       ...       ...  ...       ...       ...       ...       ...\n",
              "-4       0.0  0.0  0.000000  0.003432  ...       NaN       NaN       NaN       NaN\n",
              "-3       0.0  0.0  0.001088  0.001088  ...       NaN       NaN       NaN       NaN\n",
              "-2       0.0  0.0  0.001297       NaN  ...       NaN       NaN       NaN       NaN\n",
              "-1       0.0  0.0       NaN       NaN  ...       NaN       NaN       NaN       NaN\n",
              " 0       0.0  NaN       NaN       NaN  ...       NaN       NaN       NaN       NaN\n",
              "\n",
              "[61 rows x 61 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "pcLVCyva_dCQ",
        "outputId": "5a7e2c67-c0aa-4ec4-c452-551b59429ea8"
      },
      "source": [
        "lst = [i for i in range(0, 61)]\n",
        "vintage_pivot[lst].T.iloc[:, :10].plot(legend = True,\n",
        "                                       grid = True,\n",
        "                                       title = 'Cumulative Percentage of Bad Clients (late pay > 60 days)',\n",
        "                                       figsize = (10, 8))\n",
        "\n",
        "plt.xlabel('MOB')\n",
        "plt.ylabel('Cumulative Percentage of Bad Clients')\n",
        "plt.legend(bbox_to_anchor = (1.12, 1), title = 'Opening')\n",
        "plt.show()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "jfxa8v8QAS4c",
        "outputId": "6a406574-9677-43b7-a09c-2ded324408d3"
      },
      "source": [
        "sns.histplot(data = approved_clients_unique_id,\n",
        "             x = 'window',\n",
        "             cumulative = True,\n",
        "             stat = 'density',\n",
        "             kde = True)\n",
        "plt.title('Distribution of Clients by Window')\n",
        "plt.xlabel('Window')\n",
        "plt.ylabel('Percentage of Clients')\n",
        "plt.show()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ukjbfmNBG_L"
      },
      "source": [
        "late_interval_dict = {\n",
        "    'more_than_30_days': 3,\n",
        "    'more_than_60_days': 4,\n",
        "    'more_than_90_days': 5,\n",
        "    'more_than_120_days': 6,\n",
        "    'more_than_150_days': 7\n",
        "}"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wdKp4hm-CE4-"
      },
      "source": [
        "for key, value in late_interval_dict.items():\n",
        "    approved_clients[f'bad_{key}'] = approved_clients.apply(lambda x: 1 if x['status_int'] >= value else 0, axis = 1) "
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "IckBsv6iCq9I",
        "outputId": "c680576b-34c8-4a3f-83b1-2b69d21e9f82"
      },
      "source": [
        "approved_clients"
      ],
      "execution_count": 65,
      "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>id</th>\n",
              "      <th>month</th>\n",
              "      <th>status</th>\n",
              "      <th>opening</th>\n",
              "      <th>final</th>\n",
              "      <th>window</th>\n",
              "      <th>mob</th>\n",
              "      <th>status_int</th>\n",
              "      <th>bad</th>\n",
              "      <th>bad_more_than_30_days</th>\n",
              "      <th>bad_more_than_60_days</th>\n",
              "      <th>bad_more_than_90_days</th>\n",
              "      <th>bad_more_than_120_days</th>\n",
              "      <th>bad_more_than_150_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001711</td>\n",
              "      <td>0</td>\n",
              "      <td>no_loan</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-1</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-2</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-3</td>\n",
              "      <td>1-29days</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001712</td>\n",
              "      <td>0</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-18</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048570</th>\n",
              "      <td>5150487</td>\n",
              "      <td>-25</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048571</th>\n",
              "      <td>5150487</td>\n",
              "      <td>-26</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048572</th>\n",
              "      <td>5150487</td>\n",
              "      <td>-27</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048573</th>\n",
              "      <td>5150487</td>\n",
              "      <td>-28</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048574</th>\n",
              "      <td>5150487</td>\n",
              "      <td>-29</td>\n",
              "      <td>paid_loan</td>\n",
              "      <td>-29</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1048575 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              id  month  ... bad_more_than_120_days  bad_more_than_150_days\n",
              "0        5001711      0  ...                      0                       0\n",
              "1        5001711     -1  ...                      0                       0\n",
              "2        5001711     -2  ...                      0                       0\n",
              "3        5001711     -3  ...                      0                       0\n",
              "4        5001712      0  ...                      0                       0\n",
              "...          ...    ...  ...                    ...                     ...\n",
              "1048570  5150487    -25  ...                      0                       0\n",
              "1048571  5150487    -26  ...                      0                       0\n",
              "1048572  5150487    -27  ...                      0                       0\n",
              "1048573  5150487    -28  ...                      0                       0\n",
              "1048574  5150487    -29  ...                      0                       0\n",
              "\n",
              "[1048575 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dXPX3D9jCrOS",
        "outputId": "53424ca3-f683-46fa-c40d-f313aff158a9"
      },
      "source": [
        "bad_rate_dict = {}\n",
        "id_sum = len(set(approved_clients['id']))\n",
        "\n",
        "for key in late_interval_dict.keys():\n",
        "  min_bad = approved_clients.query(f'bad_{key} == 1').groupby('id')['mob'].min().reset_index()\n",
        "  mob_bad_rate = pd.DataFrame({'mob':range(0,61), 'bad_rate': np.nan})\n",
        "  lst = []\n",
        "  for i in range(0,61):\n",
        "      due = min_bad.query('mob == @i')['id'].to_list()\n",
        "      lst.extend(due)\n",
        "      mob_bad_rate.loc[mob_bad_rate['mob'] == i, 'bad_rate'] = len(set(lst)) / id_sum\n",
        "  bad_rate_dict[key] = mob_bad_rate['bad_rate']\n",
        "\n",
        "bad_rate = pd.DataFrame(bad_rate_dict)\n",
        "bad_rate"
      ],
      "execution_count": 66,
      "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>more_than_30_days</th>\n",
              "      <th>more_than_60_days</th>\n",
              "      <th>more_than_90_days</th>\n",
              "      <th>more_than_120_days</th>\n",
              "      <th>more_than_150_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000304</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.011612</td>\n",
              "      <td>0.000174</td>\n",
              "      <td>0.000087</td>\n",
              "      <td>0.000087</td>\n",
              "      <td>0.000087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.025878</td>\n",
              "      <td>0.001153</td>\n",
              "      <td>0.000196</td>\n",
              "      <td>0.000174</td>\n",
              "      <td>0.000174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.036860</td>\n",
              "      <td>0.002240</td>\n",
              "      <td>0.000696</td>\n",
              "      <td>0.000304</td>\n",
              "      <td>0.000304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.047385</td>\n",
              "      <td>0.003392</td>\n",
              "      <td>0.001044</td>\n",
              "      <td>0.000696</td>\n",
              "      <td>0.000348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>0.116321</td>\n",
              "      <td>0.014483</td>\n",
              "      <td>0.007176</td>\n",
              "      <td>0.005263</td>\n",
              "      <td>0.004219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0.116321</td>\n",
              "      <td>0.014483</td>\n",
              "      <td>0.007176</td>\n",
              "      <td>0.005263</td>\n",
              "      <td>0.004219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>0.116321</td>\n",
              "      <td>0.014505</td>\n",
              "      <td>0.007198</td>\n",
              "      <td>0.005284</td>\n",
              "      <td>0.004241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>0.116321</td>\n",
              "      <td>0.014505</td>\n",
              "      <td>0.007198</td>\n",
              "      <td>0.005284</td>\n",
              "      <td>0.004241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.116342</td>\n",
              "      <td>0.014505</td>\n",
              "      <td>0.007198</td>\n",
              "      <td>0.005284</td>\n",
              "      <td>0.004241</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    more_than_30_days  ...  more_than_150_days\n",
              "0            0.000304  ...            0.000022\n",
              "1            0.011612  ...            0.000087\n",
              "2            0.025878  ...            0.000174\n",
              "3            0.036860  ...            0.000304\n",
              "4            0.047385  ...            0.000348\n",
              "..                ...  ...                 ...\n",
              "56           0.116321  ...            0.004219\n",
              "57           0.116321  ...            0.004219\n",
              "58           0.116321  ...            0.004241\n",
              "59           0.116321  ...            0.004241\n",
              "60           0.116342  ...            0.004241\n",
              "\n",
              "[61 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "_nRTSwEeEaMB",
        "outputId": "38073a26-8812-44c1-f747-e7e057291a81"
      },
      "source": [
        "bad_rate.plot(grid = True,\n",
        "              title = 'Cumulative Percentage of Bad Clients by Late Window',\n",
        "              figsize = (10, 6))\n",
        "plt.xlabel('Mob')\n",
        "plt.ylabel('Cumulative Percentage of Clients')\n",
        "plt.show()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FvH0t5B7Fwud"
      },
      "source": [
        "## Defining the Target Variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5OmcfKoFDEg",
        "outputId": "72cf1e70-57e3-4d9e-cb45-7193b1e58ff8"
      },
      "source": [
        "len(approved_clients.query('window >= 12').groupby('id').count().index)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30918"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "It49tS5BFNLf",
        "outputId": "92b2e073-af93-4c0d-9f4e-466f321d70bb"
      },
      "source": [
        "approved_clients_treatment1 = approved_clients.query('window >= 12').copy()\n",
        "approved_clients_treatment1.shape"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(943061, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "9BRNVKQtFfYT",
        "outputId": "157c631d-cb5e-4e6a-90ca-b549ee15bf2c"
      },
      "source": [
        "def verify(registers):\n",
        "  status_list = registers['status'].to_list()\n",
        "  if '60-89days' in status_list or '90-119days' in status_list or '120-149days' in status_list or '>150days' in status_list:\n",
        "    return 1\n",
        "  else:\n",
        "    return 0\n",
        "  \n",
        "client_records_unique_id = pd.DataFrame(approved_clients_treatment1.groupby('id').apply(verify)).reset_index()\n",
        "client_records_unique_id.columns = ['id', 'bad']\n",
        "client_records_unique_id.head()"
      ],
      "execution_count": 70,
      "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>id</th>\n",
              "      <th>bad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001712</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001713</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001714</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001715</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001717</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  bad\n",
              "0  5001712    0\n",
              "1  5001713    0\n",
              "2  5001714    0\n",
              "3  5001715    0\n",
              "4  5001717    0"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCSvk9zGGoRG",
        "outputId": "24a6055a-a3bd-44ff-b5f7-5acd2d920972"
      },
      "source": [
        "client_records_unique_id.shape"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30918, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jvH90dX2HVLD",
        "outputId": "c5fdb1f4-53d9-4f9a-bdec-f4ca2e2751a0"
      },
      "source": [
        "client_records_treatment1.shape"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(425822, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "fozoGPzpHbhh",
        "outputId": "0eb00d58-66e6-4be0-c1bc-c01e26b9ca68"
      },
      "source": [
        "target_client_records = client_records_treatment1.merge(client_records_unique_id, on = 'id')\n",
        "target_client_records.head()"
      ],
      "execution_count": 73,
      "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>id</th>\n",
              "      <th>own_car</th>\n",
              "      <th>own_property</th>\n",
              "      <th>children_count</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>income_type</th>\n",
              "      <th>education_type</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>housing_type</th>\n",
              "      <th>age</th>\n",
              "      <th>own_workphone</th>\n",
              "      <th>own_phone</th>\n",
              "      <th>own_email</th>\n",
              "      <th>occupation_type</th>\n",
              "      <th>family_size</th>\n",
              "      <th>years_working</th>\n",
              "      <th>bad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008806</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>working</td>\n",
              "      <td>secondary</td>\n",
              "      <td>married</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>security</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008810</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>commercial_associate</td>\n",
              "      <td>secondary</td>\n",
              "      <td>single</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>sales</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008811</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>commercial_associate</td>\n",
              "      <td>secondary</td>\n",
              "      <td>single</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>sales</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008812</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>283500.0</td>\n",
              "      <td>pensioner</td>\n",
              "      <td>higher_education</td>\n",
              "      <td>separated</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>others</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008813</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>283500.0</td>\n",
              "      <td>pensioner</td>\n",
              "      <td>higher_education</td>\n",
              "      <td>separated</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>others</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  own_car  own_property  ...  family_size  years_working bad\n",
              "0  5008806        1             1  ...          2.0            3.1   0\n",
              "1  5008810        0             1  ...          1.0            8.4   0\n",
              "2  5008811        0             1  ...          1.0            8.4   0\n",
              "3  5008812        0             1  ...          1.0           -1.0   0\n",
              "4  5008813        0             1  ...          1.0           -1.0   0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2ZglplFNmGM",
        "outputId": "3a9b6e79-2554-4b62-fe57-1fba8dcd8b25"
      },
      "source": [
        "target_client_records['bad'].value_counts(normalize = True) * 100"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    97.726673\n",
              "1     2.273327\n",
              "Name: bad, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PucMmvHvKENe"
      },
      "source": [
        "We can see that our data is pretty unbalanced."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c30feWaIJ8Xv"
      },
      "source": [
        "# Lesson 04"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WEyZ6dLDN_Sq",
        "outputId": "ad2c8cf5-3d1f-44c2-85a2-062563f65591"
      },
      "source": [
        "!pip install scikit-learn==0.23.2 imbalanced-learn==0.7.0"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn==0.23.2 in /usr/local/lib/python3.7/dist-packages (0.23.2)\n",
            "Requirement already satisfied: imbalanced-learn==0.7.0 in /usr/local/lib/python3.7/dist-packages (0.7.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.23.2) (1.19.5)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.23.2) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.23.2) (1.0.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.23.2) (2.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s38FQ7umOGgy"
      },
      "source": [
        "import six\n",
        "import sys\n",
        "sys.modules['sklearn.externals.six'] = six"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03200EKyJ-X3"
      },
      "source": [
        "binary_columns = ['own_car', 'own_property', 'own_workphone',\n",
        "                  'own_phone', 'own_email']"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9qaZUV0Kg34"
      },
      "source": [
        "non_binary_columns = ['income_type', 'education_type', 'marital_status',\n",
        "                      'housing_type', 'occupation_type']"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anXUuTfLLR7D"
      },
      "source": [
        "target_client_records = target_client_records[['id'] + quantitative_variables + binary_columns + non_binary_columns + ['bad']]"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "ssxw6O_eL9K1",
        "outputId": "81db1ce6-4d54-47cc-87d0-5357a6ea5032"
      },
      "source": [
        "target_client_records.head()"
      ],
      "execution_count": 80,
      "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>id</th>\n",
              "      <th>age</th>\n",
              "      <th>children_count</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>years_working</th>\n",
              "      <th>family_size</th>\n",
              "      <th>own_car</th>\n",
              "      <th>own_property</th>\n",
              "      <th>own_workphone</th>\n",
              "      <th>own_phone</th>\n",
              "      <th>own_email</th>\n",
              "      <th>income_type</th>\n",
              "      <th>education_type</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>housing_type</th>\n",
              "      <th>occupation_type</th>\n",
              "      <th>bad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008806</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>3.1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>working</td>\n",
              "      <td>secondary</td>\n",
              "      <td>married</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>security</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008810</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>8.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>commercial_associate</td>\n",
              "      <td>secondary</td>\n",
              "      <td>single</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>sales</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008811</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>8.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>commercial_associate</td>\n",
              "      <td>secondary</td>\n",
              "      <td>single</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>sales</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008812</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>283500.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>pensioner</td>\n",
              "      <td>higher_education</td>\n",
              "      <td>separated</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>others</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008813</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>283500.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>pensioner</td>\n",
              "      <td>higher_education</td>\n",
              "      <td>separated</td>\n",
              "      <td>house_apartment</td>\n",
              "      <td>others</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id   age  children_count  ...     housing_type  occupation_type  bad\n",
              "0  5008806  59.0               0  ...  house_apartment         security    0\n",
              "1  5008810  52.0               0  ...  house_apartment            sales    0\n",
              "2  5008811  52.0               0  ...  house_apartment            sales    0\n",
              "3  5008812  62.0               0  ...  house_apartment           others    0\n",
              "4  5008813  62.0               0  ...  house_apartment           others    0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckGH4n09L-U0"
      },
      "source": [
        "SEED = 1561651\n",
        "\n",
        "def run_model(model, data):\n",
        "\n",
        "    X = data.drop(['id', 'bad'], axis=1)\n",
        "    y = data.drop('id', axis = 1)['bad']\n",
        "    \n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y, random_state = SEED)\n",
        "\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    prob_predic = model.predict_proba(X_test)\n",
        "    auc = roc_auc_score(y_test, prob_predic[:,1])\n",
        "    print(f'AUC score: {auc}')\n",
        "    \n",
        "    good_data = np.sort(model.predict_proba(X_test)[:, 1])\n",
        "    bad_data = np.sort(model.predict_proba(X_test)[:, 0])\n",
        "    kstest = stats.ks_2samp(good_data, bad_data)\n",
        "    print(f\"KS: {kstest}\")\n",
        "\n",
        "    CM = plot_confusion_matrix(model,\n",
        "                               X_test,\n",
        "                               y_test,\n",
        "                               values_format = '.0f',\n",
        "                               display_labels = ['Good', 'Bad'])\n",
        "    plt.grid(False)    \n",
        "    plt.show(CM)\n",
        "\n",
        "    prediction = model.predict(X_test)\n",
        "    print('\\nClassification Report')\n",
        "    print(classification_report(y_test, prediction, zero_division = 0))"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OTMB-kYNb-Q"
      },
      "source": [
        "class Transformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, quantitative_variables, categorical_variables):\n",
        "        self.quantitative_variables = quantitative_variables\n",
        "        self.categorical_variables = categorical_variables\n",
        "        self.encoder = OneHotEncoder()\n",
        "        self.scaler = MinMaxScaler()\n",
        "\n",
        "    def fit(self, X, y = None):\n",
        "        self.encoder.fit(X[self.categorical_variables])\n",
        "        self.scaler.fit(X[self.quantitative_variables])\n",
        "        return self \n",
        "\n",
        "    def transform(self, X, y = None):\n",
        "      X_categorical = pd.DataFrame(data = self.encoder.transform(X[self.categorical_variables]).toarray(),\n",
        "                                  columns = self.encoder.get_feature_names(self.categorical_variables))\n",
        "      \n",
        "      X_quantitative = pd.DataFrame(data = self.scaler.transform(X[self.quantitative_variables]),\n",
        "                                  columns = self.quantitative_variables)\n",
        "      \n",
        "      X = pd.concat([X_quantitative, X_categorical], axis = 1)\n",
        "\n",
        "      return X"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5tD7gidDRrtQ"
      },
      "source": [
        "## Dummy Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "FtREF8tpPXVy",
        "outputId": "5a1a1b71-3abf-4431-cb16-c6c9bce237d9"
      },
      "source": [
        "pipeline = Pipeline([('Transformer', Transformer(quantitative_variables, categorical_variables)),\n",
        "                     ('Model', DummyClassifier(strategy = 'prior'))])   \n",
        "run_model(pipeline, target_client_records)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC score: 0.5\n",
            "KS: Ks_2sampResult(statistic=1.0, pvalue=0.0)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99      5557\n",
            "           1       0.00      0.00      0.00       129\n",
            "\n",
            "    accuracy                           0.98      5686\n",
            "   macro avg       0.49      0.50      0.49      5686\n",
            "weighted avg       0.96      0.98      0.97      5686\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j5ORyLoRR5dD"
      },
      "source": [
        "## Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "no5-JEZfQWhh",
        "outputId": "ed708e58-6131-412f-c768-0d2c48a81086"
      },
      "source": [
        "pipeline = Pipeline([('Transformer', Transformer(quantitative_variables, categorical_variables)),\n",
        "                     ('Model', LogisticRegression(random_state = SEED, max_iter = 1000))])   \n",
        "run_model(pipeline, target_client_records)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC score: 0.5847293657137516\n",
            "KS: Ks_2sampResult(statistic=1.0, pvalue=0.0)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99      5557\n",
            "           1       0.00      0.00      0.00       129\n",
            "\n",
            "    accuracy                           0.98      5686\n",
            "   macro avg       0.49      0.50      0.49      5686\n",
            "weighted avg       0.96      0.98      0.97      5686\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fp7z1GZcSUQD"
      },
      "source": [
        "## Oversampling and Undersampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "f8kE00I_R_gi",
        "outputId": "863668e0-dc29-4582-f20a-a4998be1e371"
      },
      "source": [
        "pipeline = Pipeline([('Transformer', Transformer(quantitative_variables, categorical_variables)),\n",
        "                     ('Sampling', SMOTE(random_state = SEED)),\n",
        "                     ('Model', LogisticRegression(random_state = SEED, max_iter = 1000))])   \n",
        "run_model(pipeline, target_client_records)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC score: 0.5815990168137679\n",
            "KS: Ks_2sampResult(statistic=0.1290889905029898, pvalue=1.09559025330554e-41)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.55      0.70      5557\n",
            "           1       0.03      0.58      0.06       129\n",
            "\n",
            "    accuracy                           0.55      5686\n",
            "   macro avg       0.51      0.56      0.38      5686\n",
            "weighted avg       0.96      0.55      0.69      5686\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "NJW09TpVTaix",
        "outputId": "77c90b90-8b41-4b5e-ce40-0a81867c12e9"
      },
      "source": [
        "pipeline = Pipeline([('Transformer', Transformer(quantitative_variables, categorical_variables)),\n",
        "                     ('Model', RandomForestClassifier(random_state = SEED))])   \n",
        "run_model(pipeline, target_client_records)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC score: 0.8130676721726771\n",
            "KS: Ks_2sampResult(statistic=0.979071403447063, pvalue=0.0)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wmDFjqrMOEZHLMnE9xOCHHWD3x4C3bNnCmjVryMnJITU1lT179pCbm0vnzp2rsj4REUzDguniVmRX7b7IrcuGb7zxBk888QQxMTFs374dgNDQUF544YUqLU5EBH6ZhuZq8zduBfA///lPXn31VUaOHInVWvqWFi1akJmZWaXFiYjALzdiuNr8jVtDEHl5edjtdoCymRElJSUEBQVVXWUiIj+7Wm/EcKsH3KFDB/7xj3+Ue+3111+nU6dOVVKUiEh5ltJZDhVtV+tqaI899hijRo1i+fLl5OXlkZiYSJ06dXjxxReruj4RkZq9FkRkZCTvvPMOe/bs4fDhw9jtdq677rqy8WARkap0tc6CcHsammEYFBcXA+B0OjH98a8bEfFPV+lEYLcCeO/evYwePZqioiIaN27M0aNHCQkJYdGiRVx77bVVXaOI1HQ17U648yUlJTF06FDuvfdeLBYLpmny2muvkZSUxLvvvlvVNYpITXeV9oDdGsQ9cOAAI0aMKJuCZrFYGD58OAcOHKjK2kREzmNxsfkftwL41ltvZdOmTeVe+/DDD+nWrVtV1CQiUp4JGC42P+wBu7UcpdPpZPz48bRr144mTZpw9OhR0tLS6NGjR7UVKiI1WNlcXxf7XIFHHnmEQ4cOYbVaqV27No8//jhxcXFkZmYyefJkTp48SXh4OLNmzSImJgag0m2X4/ZylLGxsWVft2rViq5du17RyYqIVFZVzAOeNWsWdevWBWDjxo0kJSWxYsUKpk2bxpAhQ+jfvz8rV64kOTmZ119/HaDSbZej5ShFxPdVwUW4c+ELkJubi8Vi4cSJE6Snp/Pqq68C0LdvX2bMmEFOTg6maVaqLSIi4rI1uD0PuKioiMzMTH766adyc4C1HKWIVLkrGIJwOBw4nc5yTTabDZvNdtFbpk6dypYtWzBNk8WLF+NwOGjcuDEBAQEABAQEEBkZicPhwDTNSrX96gDesWMHf/rTnygqKiI3N5ewsDDy8vJo0qQJH3zwgTuHEBGpNItZurnaB2Do0KEcPny4XNuYMWMYO3bsRe956qmnAHjvvfeYPXs2jz76qEfqdZdbAfzMM8/wwAMPcM8999ChQwe2bdvGwoULqVWrVlXXJyIChqV0c7UPsHTp0kv2gCvy+9//nuTkZJo0aUJ2djZOp5OAgACcTifHjh3Dbrdjmmal2iri9jzg4cOHl3tt5MiRvPbaa+68XUTk1zNdbD+z2+00a9as3HZhAOfl5eFwOMq+37RpE/Xq1aNBgwbExcWxevVqAFavXk1cXBwRERGVbquIWz3gunXrkpubi81mo1GjRmRkZBAeHk5+fr47bxcR+XU8fBHu7NmzPProo5w9exar1Uq9evVITU3FYrHwxBNPMHnyZFJSUrDZbMyaNavsfZVtuxy3Arhnz55s3ryZ//mf/+Guu+5i+PDhBAYGkpiY6P4Zi4hUlocDuGHDhrz99tuXbGvZsiXLly/3aNvluBXAU6dOLfv6/vvv5/rrrycvL4+bb775ij5MRKRSquBGDF/g9jS08yUkJHi6DhGRy3NjFsRVdSvykCFDym5FrsjSpUs9WpCIyEWu0tXQLhvAAwcOrM46vGZEl/ZkZ1U8VUR8SYm3CxAvuJJ5wP7ksgE8YMCA6qxDROTyNAYsIuJFftjDdUUBLCK+r6aNAYuI+AqLUbq52sffKIBFxPddpT1gt9aCKCoq4rnnnqNHjx7ceOONAHz66acsWbKkSosTEYFfZkG42vyNWwH89NNPs2/fPubOnVs2N7h169a89dZbVVqciAjwyywIV5ufcWsIYuPGjWzYsIHatWtjtZZmduPGjcnOzq7S4kREgKt2CMKtAA4KCrpofc2cnBzCw8OrpCgRkfNZcONGjGqpxLPcGoLo3bs3f/3rX/nhhx8AOHbsGNOnT6dPnz5VWpyICPwyC8LV5m/cCuDx48fTrFkz+vXrx+nTp0lMTCQyMpLRo0dXdX0iIq4XY3dniMIHuTUEERwcTFJSEklJSeTk5FC/fn23FuoREfGImjwGfG7o4Zy8vLyyr5s3b+7ZikRELlDjFuM5X8+ePbFYLOUeR3+uB/ztt99WTWUiIlc5twJ479695b7/8ccfWbhwoRZmF5HqcZUOQbh1Ee5CjRo1YurUqcybN8/T9YiIXMRiujELwg8DuNJrQXz//fecPXvWk7WIiFzaVdoDdiuAL3w80dmzZ8nIyNA0NBGpHjXtmXDnu/DxRLVq1eLaa68lJiamKmoSESmvpvaAnU4nn3/+OTNmzCA4OLg6ahIRKafGTkMLCAhgy5YtuvFCRLzH+HlztY+fcWsWxIgRI1iwYAHFxcVVXY+IyEWu1vWAK+wBr169mr59+7JkyRKOHz/Oq6++SkRERLne8EcffVTVNYqI+OUYrysVBnBycjJ9+/Zlzpw51VWPiMjFauJFuHO3Hnfs2LFaihERuZQaeRHOMAw+//zzcmtAXKhz584eL0pEpJya2AMuKipi6tSplw1gi8XCBx98UCWFiYicUyMfS1+rVi0FrIh4n4d7wD/99BOTJk3i4MGDBAcHEx0dzfTp04mIiGDXrl0kJydTWFhI06ZNmTNnDg0aNACodNvlVGoxHhGR6mRxc3P7eBYLDzzwAOvXr2fVqlU0b96cuXPnYhgGEydOJDk5mfXr15OQkMDcuXMBKt1WkQoDuKKxXxGRanMFjyRyOBwcOnSo3Hb69OlyhwsPD6dTp05l38fHx3PkyBHS0tIICQkpW2p38ODB/Oc//wGodFtFKhyC2Llzp8sDiIhUtSt5KvLQoUM5fPhwubYxY8YwduzYS77PMAzeeustunfvjsPhICoqqqwtIiICwzA4efJkpdsqenp8pZejFBGpNlcwBrx06VKcTme5JpvNdtm3zZgxg9q1a/PHP/6R999//9fVeYUUwCLi865kFoTdbnf7uLNmzSIrK4vU1FSsVit2u50jR46Utefk5GC1WgkPD690W0V0EU5EfF8VPJZ+3rx5pKWlsWjRorKVHtu1a0dBQQE7duwAYNmyZfTu3ftXtVVEPWAR8X0eXpD9u+++48UXXyQmJobBgwcD0KxZMxYtWsTs2bOZNm1auelkAFartVJtFbGYNXyqw7DWY8nOOu7tMsRNZkmJt0sQNzWObsSSzBSPHOuOx17GkXO6wn3sETbWzrzfI59XXdQDFhGfVyPXghAR8QkmrhdcVwCLiHieesAiIt5SE1dDExHxBRbTxOJivoCrdl+kABYR36cesIiId2gMWETESyymG7ciK4BFRKqAhiBERLxDQxAiIt6iHrCIiHeoBywi4i2GicVwkbCu2n2QAtiPNW91ltEzfqB1+zxO5QSx+KmmfLa+PgA3981h2HgHDe1F/OgI5rVZTdm6oeLFoaXqTVqQRXzXXEJrG/x0LJDlf4/kP282IDDIYPKig7S+Pp8mzYuZeFdLdm8N83a5vkNDEFWne/fuBAcHExISQmFhIQkJCUybNo2goCC3jzFs2DDuu+8+brvttiqs1HdYA0ymLd7P2iWNSBramvY3neHJV/Yz+r9rUZBvZdLzB3jygZbs+MhGx+6nSfr7fkZ0ac+pE+7/TMXz/rWgMc/9pTnFRVaatypg9r/3k7GnFgf2hvLNtjqsWNyQqS9mebtMn3O1TkPzmSdizJ8/n5UrV7JmzRoyMjKq/dlM/qZ5ywIaNC7m3cWRGIaFrz+z8c2OOvS48wQN7cXknQ5gx0f1AAvbNtWjID+AqOhCb5dd42XtC6W4qPTXzjRLt6iYIkqKraxY3IhvtoVhOK/kAes1RBU8EcMX+EQP+HyFhYUUFhZis9nYunUrzz//PIWFhTidTkaNGkWfPn0AyMjIYMqUKeTn5xMbG0thocLFYoHoNgUsea42BzNCuannSbZ9UI9Ot5+iuMjC99/W8naJAox5+hA9B+UQWsvkuz212PZBXW+X5PN0Ea6KjRs3jpCQEA4ePEjXrl3p2rUrp06d4s033yQgIIDjx49z55130rVrV+rVq8ekSZMYNmwYAwYMYNeuXfzhD3/w9ilUq0Pfh3LyRCD/OyqbFYsbc33nM7TvlMvurXUxDAsfvNOAv87PJDjEoLjYwtMPt6TwbIC3yxZgYVIzUh5rSlxCHtd1zivrEUsFzv1zwdU+fsZn/sufG4L4/PPPKSws5LXXXiMnJ4dx48bRt29f7r//fk6dOkVmZia5ubns27eP/v37AxAfH09sbKyXz6B6OUssTH+gJR27n+KtL7/mrpHZfLK6PscdQdzQ9TT3Jx1i0qBY+rb8HZMGtuFPsw/Qom2+t8uWnxmGhW+2hdHIXkzfEXoklivnnorsavM3PtMDPickJIRu3brx0Ucf8eGHH9K9e3cWLlyIxWIhMTFRQw3nydxbm0l3tyn7ft67e9n4TgNatM1nzxd1+W53HQD27a7D3p11uKHrab5Pr+2tcuUSrIEm9ugib5fh867WIQif6QGfYxgG27dvJyYmhjNnztC0aVMsFgtbtmwhK6v06nBYWBixsbGsWrUKgN27d7Nv3z5vlu0Vv7k2n6AQg5BQg7tGHiUispj3lzdg39d1aNfxTFmPt+Vv82nXMZfMbxW+3lSvQTG39v+J0NpOrFaTG289zW2/P8muT0unmwUFGwSFlHbjAoPNn7/2w1SpEuYvwxCX2/zwZ+UzPeBzY8DFxcW0bt2a0aNHk5aWxpNPPsmCBQto3749bdr80tubPXs2U6ZM4aWXXiI2Npb27dt7sXrv6HFnDol/OE5goEnatjCmDG1NcZGVPV/UZclzUTyW+j3hDYs5lRPIsoV2vvrE5u2SazbTQt/hJxj3t0NYrHDsUDCpyVF8vqEeAIs/2UuT5sUAPPPW9wAM7xhH9qFgr5XsK67WHrAeS6/H0vsVPZbef3jysfR3P/AiR49V/Fj6JpE23l78kEc+r7r4TA9YRORyrtYesAJYRHyf0yzdXO3jZxTAIuLz1AMWEfEaN27E0CwIEZEq4EYP2A/zVwEsIn5Ay1GKiHiHxQkWFxfZLM5qKsaDfO5OOBGRC1lM063NXbNmzaJ79+60adOm3F20mZmZDBo0iMTERAYNGsSBAwd+dVtFFMAi4vs8vB5wjx49WLp0KU2bNi33+rRp0xgyZAjr169nyJAhJCcn/+q2iiiARcQPeHYtiISEBOx2e7nXTpw4QXp6On379gWgb9++pKenk5OTU+k2VzQGLCI+70rmATscDpzO8gPCNpsNm63itVAcDgeNGzcmIKB03eyAgAAiIyNxOByYplmptoiIiAo/UwEsIr7vChZkHzp0KIcPHy7XNGbMGMaOHVtV1VWaAlhEfJ7FaboxC6K0fenSpZfsAbtit9vJzs7G6XQSEBCA0+nk2LFj2O12TNOsVJsrGgMWEd93BRfh7HY7zZo1K7e5E8ANGjQgLi6O1atXA7B69Wri4uKIiIiodJsrWo5Sy1H6FS1H6T88uRzlsAHzyT56quLPa1KPN1aMc+t4M2fOZMOGDRw/fpz69esTHh7OmjVr2L9/P5MnT+b06dPYbDZmzZpFixYtACrdVhEFsALYryiA/YdnA/gFsh0uAthejzdWPOqRz6suGgMWEd9n/Ly52sfPKIBFxOe5c6fbldwJ5ysUwCLi+wwTDBddXEMBLCLieRqCEBHxDgtuDEH44XqUCmAR8X1XcCecP1EAi4jvUwCLiHiJnoosIuIl7iy4rh6wiEgV0BCEiIiXmLie5+t/+asAFhE/oB6wiIiXKIBFRLzEaZRurvbxMwpgEfF9plG6udrHzyiARcQPuDEE4YdX4RTAIuL7DFzPgvC/DrACWET8gC7CiYh4iQJYRMRLnM7SzdU+fkYBLCJ+QBfhRES8Q0MQIiJeolkQIiJeYhqYuhFDRMQLdCuyiIiXmIbrx9KrBywiUgV0EU5ExDtMw8R00QM2XV2k80EKYBHxfeoBi4h4iWG6MQ1NASwi4nGm4cR0cauxaehWZBERzzNNNxZkVw/Y7zRsGuHtEuQKmCX+18upqTz5u9Ugqr7Li2wNoup77POqi8U0/fCvDRGRq4DV2wWIiNRUCmARES9RAIuIeIkCWETESxTAIiJeogAWEfESBbCIiJcogEVEvEQBLCLiJTX+VvWMqfQAAAkZSURBVGRfV1xcTGpqKqtXryYwMJCAgABiYmIYN24crVq18shntGnThq+++oo6dep45Hjyi+7duxMcHExISAiFhYUkJCQwbdo0goKC3D7GsGHDuO+++7jtttuqsFLxBgWwj5syZQoFBQUsX74cm82GaZps3ryZzMxMjwWwVK358+cTGxuL0+lk6NChvP/++9xxxx3eLkt8gALYhx04cICNGzeyefNmbDYbABaLhW7dugGQl5fHzJkz2bNnDwD9+/fnwQcfBCArK4vk5GRycnIIDAxk/Pjx3HLLLQBs2LCBefPmERISQq9evar/xGqowsJCCgsLsdlsbN26leeff57CwkKcTiejRo2iT58+AGRkZDBlyhTy8/OJjY2lsLDQy5VLVVEA+7D09HSio6OpV6/eJdtTUlIwDINVq1aRl5fHoEGDiI2N5dZbb2XChAncfffdDBw4kIyMDIYOHcq6deswDIPHH3+ct956ixYtWvDSSy9V81nVPOPGjSMkJISDBw/StWtXunbtyqlTp3jzzTcJCAjg+PHj3HnnnXTt2pV69eoxadIkhg0bxoABA9i1axd/+MMfvH0KUkV0Ec6PZGRk0L9/fxITE5k5cyZbt25l4MCBWCwWwsLC6NOnD1u3biU3N5dvv/2Wu+66C4BWrVoRFxfHrl27+Prrr2nbti0tWrQAYNCgQd48pRph/vz5rFy5ks8//5zCwkJee+01cnJyGDduHH379uX+++/n1KlTZGZmkpuby759++jfvz8A8fHxxMbGevkMpKoogH1Y27ZtycrK4vTp00BpkK5cuZJhw4aRm5vr5erkSoWEhNCtWzc+++wznnjiCTp27MiqVatYuXIlTZo00VBDDaQA9mExMTH06NGDxx57jDNnzpS9np+fD0Dnzp155513ME2T3Nxc1q5dS5cuXQgLCyMuLo4VK1YAsH//fvbu3Ut8fDzx8fGkp6dz4MABAJYvX17t51VTGYbB9u3biYmJ4cyZMzRt2hSLxcKWLVvIysoCICwsjNjYWFatWgXA7t272bdvnzfLliqkBdl9XFFRESkpKaxbt47AwEBsNhuRkZGMHDmSmJgYZsyYQVpaGgD9+vVj5MiRgHsX4UJDQ+nVqxcvvPCCpqFVkfOnoRUXF9O6dWumT59OWloaTz75JLVq1aJ9+/bs2bOHpKQkOnXqVHYR7uzZs8TGxnLo0CEefvhhTUO7CimARUS8REMQIiJeogAWEfESBbCIiJcogEVEvEQBLCLiJQpgqTKTJ0/mueeeA2DHjh0kJiZWy+e2adOmbF7thYYNG+b23Ofu3bvz2WefVaqGX/NeqTkUwDVc9+7due6667jhhhvo0qULkydPJi8vz+Ofk5CQwPr1613u9+6772rtA6kxFMBCamoqO3fuZMWKFaSlpfH3v//9on1KSkq8UJnI1U0BLGUaN27MzTffzHfffQeU/lN+6dKl9OrVq2zZyg8//JD+/fuTkJDA4MGD2bt3b9n709PTGTBgADfccAN/+tOfyq1t8MUXX5TdiQfgcDgYM2YMN910E506dWL69Ons37+fadOmsWvXLm644QYSEhKA0rsBZ82aRbdu3ejSpQvJyckUFBSUHWvx4sVlq4z9+9//dvt8Dx48yPDhw+nUqROdOnXiL3/5S9m6G+fs2bOHO+64gw4dOjBlypRy51TRz0LEHQpgKeNwOPj444+Ji4sre23jxo28/fbbrF27lvT0dJKSkpg+fTpffPEFgwYN4pFHHqGoqIiioiJGjx5N//792bZtG71792bDhg2X/Byn08lDDz1EVFQUmzZt4uOPP+aOO+6gZcuWPPnkk8THx7Nz50527NgBwNy5c8nMzOS9995jw4YNHDt2jEWLFgHw8ccf88orr/DKK6+wYcMGtm7d6vb5mqbJQw89xCeffMK6des4evQoCxYsKLfPqlWrePnll3n//ffJzMwkJSUFoMKfhYi7FMDC6NGjSUhIYMiQIXTo0IFRo0aVtY0cOZLw8HBCQ0P517/+xaBBg7j++usJCAhgwIABBAUFlS1zWVxczIgRIwgKCqJ37960b9/+kp+3e/dujh07xqRJk6hduzYhISFlvd0LmabJ22+/TVJSEuHh4YSFhfHQQw+xZs0aANatW8edd95JbGwstWvXZsyYMW6fd3R0NP/1X/9FcHAwERER3HvvvWzfvr3cPkOHDsVutxMeHs7DDz9c9rkV/SxE3KUF2YVFixbRpUuXS7bZ7fayr48cOcJ7773HkiVLyl4rLi7m2LFjWCwWGjdujMViKWuLioq65DEdDgdRUVEEBrr+45eTk8PZs2e58847y14zTRPDMAA4duwY7dq1K2tr2rSpy2Oec/z4cZ566il27NhBXl4epmmWPXnknPPPPyoqimPHjgEV/yxE3KUAlgqdH6h2u51Ro0bx8MMPX7Tftm3byM7OxjTNsvccOXKE5s2bX7Sv3W7H4XBQUlJyUQif/3kA9evXJzQ0lDVr1tC4ceOLjhUZGYnD4Sj7/siRI26f27x587BYLKxatYrw8HA2btzI9OnTy+1z4bEjIyPLzuFyPwsRd2kIQtw2cOBAli1bxtdff41pmuTn5/PRRx+Rm5tLfHw8gYGBvP766xQXF7Nhw4ayZ9Vd6LrrrqNRo0Y8++yz5OfnU1hYyJdffglAgwYNyM7OLhtLtVqtDBw4kKeffpoTJ04AkJ2dzSeffAJA7969WbFiBRkZGZw9e5aFCxe6fT55eXnUrl2bunXrkp2dzeLFiy/a58033+To0aOcPHmS1NTUsodpVvSzEHGXAljc1r59e2bMmMH06dPp0KEDvXr14t133wUgODiYBQsWsGLFCjp27MjatWvp2bPnJY8TEBBAamoqWVlZ3Hbbbdxyyy2sW7cOgJtuuolWrVrRtWtXOnXqBMDEiROJjo7m7rvv5ne/+x333HMPmZmZANx6662MGDGCESNG0LNnT2666Sa3z2fMmDGkp6eTkJDAyJEjL/mA0r59+3Lfffdx++23c80115T1eCv6WYi4S+sBi4h4iXrAIiJeogAWEfESBbCIiJcogEVEvEQBLCLiJQpgEREvUQCLiHiJAlhExEsUwCIiXvL/AexPKq9RT15JAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99      5557\n",
            "           1       0.52      0.24      0.33       129\n",
            "\n",
            "    accuracy                           0.98      5686\n",
            "   macro avg       0.75      0.62      0.66      5686\n",
            "weighted avg       0.97      0.98      0.97      5686\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "19DO6zAKTQgH",
        "outputId": "cf69ee2a-3363-4c33-abd6-60558ec3f287"
      },
      "source": [
        "pipeline = Pipeline([('Transformer', Transformer(quantitative_variables, categorical_variables)),\n",
        "                     ('Sampling', SMOTE(random_state = SEED)),\n",
        "                     ('Model', RandomForestClassifier(random_state = SEED))])   \n",
        "run_model(pipeline, target_client_records)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC score: 0.8419941047885691\n",
            "KS: Ks_2sampResult(statistic=0.9651776292648611, pvalue=0.0)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uekvbtm0pKCio0+BERODHhRjuSrDxaAiipKQEq9UK4JoZUVlZSYMGDeouMhGR0y7XhRge9YA7d+7Mn//852qvvfHGG3Tp0qVOghIRqc5UNcuhpnK57ob2xBNPMGbMGJYtW0ZJSQkpKSk0adKEF198sa7jExGp33tBtGzZknfffZcdO3awf/9+rFYr119/vWs8WESkLl2usyA8nobmdDqpqKgAwOFwYATjf25EJDhdphOBPUrAO3fuZNy4cZSXlxMTE8OBAwcIDw9n0aJFXHPNNXUdo4jUd/VtJdxPTZ06lbS0NO677z5MJhOGYfDaa68xdepU3nvvvbqOUUTqu8u0B+zRIO6ePXsYOXKkawqayWRixIgR7Nmzpy5jExH5CZObEnw8SsC33347H3/8cbXXPvnkE3r06FEXMYmIVGcATjclCHvAHm1H6XA4eOyxx+jYsSOtWrXiwIED5Obm0qtXL58FKiL1mGuur5s2F2Hs2LHs27cPs9lM48aNefLJJ0lMTKSgoIDJkydz7NgxoqKimD17NvHx8QC1rrsQj7ejTEhIcP25ffv2dO/e/aJuVkSktupiHvDs2bNp2rQpAOvXr2fq1KksX76czMxMhg0bRmpqKtnZ2WRkZPDGG28A1LruQrQdpYgEvjp4CHcm+QIUFxdjMpk4cuQIeXl5vPrqqwD079+fGTNmUFRUhGEYtaqLjo6+YAwezwMuLy+noKCAo0ePVpsDrO0oRaTOXcQQhM1mw+FwVKuyWCxYLJZz3jJt2jQ2btyIYRi89NJL2Gw2YmJiCAkJASAkJISWLVtis9kwDKNWdZecgLdu3cqjjz5KeXk5xcXFREREUFJSQqtWrfjoo488uYSISK2ZjKrirg1AWloa+/fvr1aXnp7O+PHjz3nPM888A8D777/PnDlzeOSRR7wSr6c8SsDPPvssDzzwAPfeey+dO3fmq6++YuHChTRq1Kiu4xMRAaepqrhrA2RlZZ23B1yT//3f/yUjI4NWrVpx8OBBHA4HISEhOBwODh06hNVqxTCMWtXVxON5wCNGjKj22ujRo3nttdc8ebuIyKUz3JTTrFYrbdq0qVbOTsAlJSXYbDbX1x9//DGRkZE0b96cxMREVq5cCcDKlStJTEwkOjq61nU18agH3LRpU4qLi7FYLLRo0YL8/HyioqIoLS315O0iIpfGyw/hTp06xSOPPMKpU6cwm81ERkayZMkSTCYTTz31FJMnT2bx4sVYLBZmz57tel9t6y7EowTcu3dvNmzYwK9+9SvuvvtuRowYQWhoKCkpKZ7fsYhIbXk5AV9xxRW88847561r164dy5Yt82rdhXiUgKdNm+b68/33388NN9xASUkJt95660V9mIhIrdTBQoxA4PE0tJ9KTk72dhwiIhfmwSyIy2op8rBhw1xLkWuSlZXl1YBERM5xme6GdsEEPGjQIF/G4TcjulzHwb0t/R2GeCwIf8vkkl3MPOBgcsEEPHDgQF/GISJyYRoDFhHxoyDs4bqjBCwiga++jQGLiAQKk7OquGsTbJSARSTwXaY9YI/2gigvL+e5556jV69e3HTTTQB88cUXvPnmm3UanIgI/DgLwl0JNh4l4FmzZrFr1y7mzZvnmht89dVX8/bbb9dpcCIiwI+zINyVIOPREMT69etZt24djRs3xmyuytkxMTEcPHiwToMTEQEu2yEIjxJwgwYNztlfs6ioiKioqDoJSkTkp0x4sBDDJ5F4l0dDEH379uV3v/sd//3vfwE4dOgQ06dPp1+/fnUanIgI/DgLwl0JNh4l4Mcee4w2bdowYMAATpw4QUpKCi1btmTcuHF1HZ+IiPvN2D0ZoghAHg1BhIWFMXXqVKZOnUpRURHNmjXzaKMeERGvqM9jwGeGHs4oKSlx/fnKK6/0bkQiImepd5vx/FTv3r0xmUzVjqM/0wP+7rvv6iYyEZHLnEcJeOfOndW+/uGHH1i4cKE2ZhcR37hMhyA8egh3thYtWjBt2jTmz5/v7XhERM5hMjyYBRGECbjWe0H85z//4dSpU96MRUTk/C7THrBHCfjs44lOnTpFfn6+pqGJiG/UtzPhfurs44kaNWrENddcQ3x8fF3EJCJSXX3tATscDjZv3syMGTMICwvzRUwiItXU22loISEhbNy4UQsvRMR/nKeLuzZBxqNZECNHjmTBggVUVFTUdTwiIue4XPcDrrEHvHLlSvr378+bb77J4cOHefXVV4mOjq7WG/7000/rOkYRkaAc43WnxgSckZFB//79mTt3rq/iERE5V318CHdm6fHNN9/sk2BERM6nXj6EczqdbN68udoeEGfr2rWr14MSEammPvaAy8vLmTZt2gUTsMlk4qOPPqqTwEREzqiXx9I3atRICVZE/M/LPeCjR4/y+OOP8/333xMWFkZcXBzTp08nOjqanJwcMjIysNvttG7dmrlz59K8eXOAWtddSK024xER8SWTh8Xj65lMPPDAA6xdu5YVK1Zw5ZVXMm/ePJxOJ5MmTSIjI4O1a9eSnJzMvHnzAGpdV5MaE3BNY78iIj5zEUcS2Ww29u3bV62cOHGi2uWioqLo0qWL6+ukpCQKCwvJzc0lPDzctdXu0KFD+eCDDwBqXVeTGocgtm3b5vYCIiJ17WJORU5LS2P//v3V6tLT0xk/fvx53+d0Onn77bfp2bMnNpuN2NhYV110dDROp5Njx47Vuq6m0+NrvR2liIjPXMQYcFZWFg6Ho1qVxWK54NtmzJhB48aN+fWvf82HH354aXFeJCVgEQl4FzMLwmq1enzd2bNns3fvXpYsWYLZbMZqtVJYWOiqLyoqwmw2ExUVVeu6mughnIgEvjo4ln7+/Pnk5uayaNEi106PHTt2pKysjK1btwKwdOlS+vbte0l1NVEPWEQCn5c3ZP/3v//Niy++SHx8PEOHDgWgTZs2LFq0iDlz5pCZmVltOhmA2WyuVV1NTEY9n+rw67bjOLj3B3+HIZ6q3z+uQSUmrgVvFiz2yrXufOJlbEUnamxjjbaweub9Xvk8X1EPWEQCXr3cC0JEJCAYuN9wXQlYRMT71AMWEfGX+rgbmohIIDAZBiY3D2Dd1QciJWARCXzqAYuI+IfGgEVE/MRkeLAUWQlYRKQOaAhCRMQ/NAQhIuIv6gGLiPiHesAiIv7iNDA53WRYd/UBSAk4iMW0sZM+ax+JN5VSUW7ii1VR/CmzNdY4Ow8+UUhicgkhZvjXN435U0Zr9u1u6O+Q5bTYn9l58aN/8fmqSOaMj2Po+IMMnXDIVW82GzQINxhy/XWcKNKvqYYg6lDPnj0JCwsjPDwcu91OcnIymZmZNGjQwONrDB8+nFGjRnHHHXfUYaSBJX3WPo4dCeWen19HhMXBs2/v5lcjD7Pz68ZsWhfJH35zFaXFIaQ9doCnXinggdsT/R2ynJY+ax+7vmns+nrpghiWLohxff3r3x6gU5cSJd/TLtdpaAFzIsYLL7xAdnY2q1atIj8/3+dnMwWjVleV89mKZlTYzRz9oQFbP21KXIcy/pXThLVLm3PyWCiOShPL/9KCK9vbadqs0t8hC3B76lFKjoew7YuIC7Qw+OX/HeXDZc18GldAq4MTMQJBwP3n1W63Y7fbsVgsbNq0iT/+8Y/Y7XYcDgdjxoyhX79+AOTn5zNlyhRKS0tJSEjAbrf7OXLfW/5SC3qkHmX7lxFERFXS+Y6TvD631TntOnYp5sjBUE4eDbi/7nqncYSDERMP8LvB7eg7rOi8bTp2KSHqikq+WBXp4+gClx7C1bEJEyYQHh7O999/T/fu3enevTvHjx/nrbfeIiQkhMOHD3PXXXfRvXt3IiMjefzxxxk+fDgDBw4kJyeHe+65x9+34HM7NkdwZ9oRlv9rOyGhsO6dZnz5QfVf2ius5aQ/s58/P93aT1HKT414/ABrlzbnsC3sgm16Dz7K5ysjKSsN8WFkAc4w3J+GEoSb8QTcEMTmzZux2+289tprFBUVMWHCBPr378/999/P8ePHKSgooLi4mF27dpGamgpAUlISCQkJfr4D3zKZDJ7J2s3GNZGkXn09/9exI00jHdw/zeZqExldyay3drPyjeZ8mq1/zvpb2+tO8fNbT/Len6+4YJvwRk5u7X+M9cuifRhZ4DtzKrK7EmwCpgd8Rnh4OD169ODTTz/lk08+oWfPnixcuBCTyURKSkq9HGo4n6ZRDmLaVJD9agsqys1UlJtZ+7do7n38AC8/E0tEZCWz3t7N5nWRvP3CucMS4ns3dC0m5soK/rrlOwAaNXFiNhtclbCL9JSqDkS3vsc5eSyEb75s4s9QA87lOgQRMD3gM5xOJ1u2bCE+Pp6TJ0/SunVrTCYTGzduZO/evQBERESQkJDAihUrANi+fTu7du3yZ9g+d+JoKLa9YfQfcRhziEETSyW9Bx2l4LuGNI5wMCvrP+RtacIrz8b6O1Q5bXVWc+7teg1jeycwtncCq/7anK8+sjDtnrauNr0HF/HR36MBk/8CDUjGj8MQFypB+BQuYHrAZ8aAKyoquPrqqxk3bhy5ubk8/fTTLFiwgE6dOtGhQwdX+zlz5jBlyhT+8pe/kJCQQKdOnfwYvX9MfzCeMU/tZ/DYQzid8M3Gpix5qjXd/uc4HW4sJa5DGb0H//ig58Ee1/BD4YXHHqVu2U+ZsZ/6sc9zqsRMud3E8dNTzZq3qiDpF8UsnNLGXyEGrMu1B6xj6XUsfXCp3z+uQcWbx9IPfuBFDhyq+Vj6Vi0tvPPSQ175PF8JmB6wiMiFXK49YCVgEQl8DqOquGsTZJSARSTgqQcsIuI3HizE0CwIEZE64EEPOAjzrxKwiAQBbUcpIuIfJgeY3DxkMzl8FIwXKQGLSMAzGQYmN2PA7uoDUcAtRRYROYeX9wOePXs2PXv2pEOHDtW2MSgoKGDIkCGkpKQwZMgQ9uzZc8l1NVECFpEg4N29IHr16kVWVhatW1ffpjUzM5Nhw4axdu1ahg0bRkZGxiXX1UQJWEQC3pl5wO4KgM1mY9++fdXKiRPVlzEnJydjtVqrvXbkyBHy8vLo378/AP379ycvL4+ioqJa17mjMWARCXwXsSF7Wloa+/fvr1aVnp7O+PHja3y7zWYjJiaGkJCqjfBDQkJo2bIlNpsNwzBqVRcdXfO+zkrAIhLwTA7Dg1kQVfVZWVk4HNWnRFgsljqL7VIoAYtI4LuIecBnDy14ymq1cvDgQRwOByEhITgcDg4dOoTVasUwjFrVuaMxYBEJeGemobkrl6J58+YkJiaycuVKAFauXEliYiLR0dG1rvPgvoJw8pwXaT/gIFO/f1yDijf3Ax4+8HkO2o7X/HnWSP66/BGPrjdz5kzWrVvH4cOHadasGVFRUaxatYrdu3czefJkTpw4gcViYfbs2bRtW3ViSW3raqIErAQcXOr3j2tQ8WoCTvUwAWd7loADhcaARSTgXa4r4ZSARSTwOQ1wujl33qkELCLifc7TxV2bIKMELCIBz4QHQxBBuB+lErCIBL6LWAkXTJSARSTwKQGLiPiJTkUWEfETT1a6qQcsIlIHNAQhIuInBu7n+QZf/lUCFpEgoB6wiIifKAGLiPiJw1lV3LUJMkrAIhL4DGdVcdcmyCgBi0gQ8GAIIgifwikBi0jgc+J+FkTwdYCVgEUkCOghnIiInygBi4j4icNRVdy1CTJKwCISBPQQTkTEPzQEISLiJ5oFISLiJ4YTQwsxRET8QEuRRUT8xHC6P5ZePWARkTqgh3AiIv5hOA0MNz1gw91DugCkBCwigU89YBERP3EaHkxDUwIWEfE6w+nAcLPU2HBqKbKIiPcZhgcbsqsHHHSuaB3t7xDkYgThL1l95c3freaxzdw+ZGse28xrn+crJsPQT7SIiD+Y/R2AiEh9pQQsIuInSsAiIn6iBCwi4idKwCIifqIELCLiJ0rAIiJ+ogQsIuInSsAiIn5S75ciB7qKigqWLFnCypUrCQ0NJSQkhPj4eCZMmED79u298hkdOnTg66+/pkmTJl65nvyoZ8+ehIWFER4ejt1uJzk5mczMTBo0aODxNYYPH86oUaO444476jBS8Qcl4AA3ZcoUysrKWLZsGRaLBcMw2LBhAwUFBV5LwFK3XnjhBRISEiGCHIYAAAicSURBVHA4HKSlpfHhhx9y5513+jssCQBKwAFsz549rF+/ng0bNmCxWAAwmUz06NEDgJKSEmbOnMmOHTsASE1N5cEHHwRg7969ZGRkUFRURGhoKI899hi33XYbAOvWrWP+/PmEh4fTp08f399YPWW327Hb7VgsFjZt2sQf//hH7HY7DoeDMWPG0K9fPwDy8/OZMmUKpaWlJCQkYLfb/Ry51BUl4ACWl5dHXFwckZGR561fvHgxTqeTFStWUFJSwpAhQ0hISOD2229n4sSJDB48mEGDBpGfn09aWhpr1qzB6XTy5JNP8vbbb9O2bVv+8pe/+Piu6p8JEyYQHh7O999/T/fu3enevTvHjx/nrbfeIiQkhMOHD3PXXXfRvXt3IiMjefzxxxk+fDgDBw4kJyeHe+65x9+3IHVED+GCSH5+PqmpqaSkpDBz5kw2bdrEoEGDMJlMRERE0K9fPzZt2kRxcTHfffcdd999NwDt27cnMTGRnJwcvvnmG6699lratm0LwJAhQ/x5S/XCCy+8QHZ2Nps3b8Zut/Paa69RVFTEhAkT6N+/P/fffz/Hjx+noKCA4uJidu3aRWpqKgBJSUkkJCT4+Q6krigBB7Brr72WvXv3cuLECaAqkWZnZzN8+HCKi4v9HJ1crPDwcHr06MGXX37JU089xc0338yKFSvIzs6mVatWGmqoh5SAA1h8fDy9evXiiSee4OTJk67XS0tLAejatSvvvvsuhmFQXFzM6tWr6datGxERESQmJrJ8+XIAdu/ezc6dO0lKSiIpKYm8vDz27NkDwLJly3x+X/WV0+lky5YtxMfHc/LkSVq3bo3JZGLjxo3s3bsXgIiICBISElixYgUA27dvZ9euXf4MW+qQNmQPcOXl5SxevJg1a9YQGhqKxWKhZcuWjB49mvj4eGbMmEFubi4AAwYMYPTo0YBnD+EaNmxInz59eP755zUNrY78dBpaRUUFV199NdOnTyc3N5enn36aRo0a0alTJ3bs2MHUqVPp0qWL6yHcqVOnSEhIYN++fTz88MOahnYZUgIWEfETDUGIiPiJErCIiJ8oAYuI+IkSsIiInygBi4j4iRKw1JnJkyfz3HPPAbB161ZSUlJ88rkdOnRwzas92/Dhwz2e+9yzZ0++/PLLWsVwKe+V+kMJuJ7r2bMn119/PTfeeCPdunVj8uTJlJSUeP1zkpOTWbt2rdt27733nvY+kHpDCVhYsmQJ27ZtY/ny5eTm5vKnP/3pnDaVlZV+iEzk8qYELC4xMTHceuut/Pvf/waq/imflZVFnz59XNtWfvLJJ6SmppKcnMzQoUPZuXOn6/15eXkMHDiQG2+8kUcffbTa3gb/+Mc/XCvxAGw2G+np6dxyyy106dKF6dOns3v3bjIzM8nJyeHGG28kOTkZqFoNOHv2bHr06EG3bt3IyMigrKzMda2XXnrJtcvY3//+d4/v9/vvv2fEiBF06dKFLl268Nvf/ta178YZO3bs4M4776Rz585MmTKl2j3V9L0Q8YQSsLjYbDY+++wzEhMTXa+tX7+ed955h9WrV5OXl8fUqVOZPn06//jHPxgyZAhjx46lvLyc8vJyxo0bR2pqKl999RV9+/Zl3bp15/0ch8PBQw89RGxsLB9//DGfffYZd955J+3atePpp58mKSmJbdu2sXXrVgDmzZtHQUEB77//PuvWrePQoUMsWrQIgM8++4xXXnmFV155hXXr1rFp0yaP79cwDB566CE+//xz1qxZw4EDB1iwYEG1NitWrODll1/mww8/pKCggMWLFwPU+L0Q8ZQSsDBu3DiSk5MZNmwYnTt3ZsyYMa660aNHExUVRcOGDfnb3/7GkCFDuOGGGwgJCWHgwIE0aNDAtc1lRUUFI0eOpEGDBvTt25dOnTqd9/O2b9/OoUOHePzxx2ncuDHh4eGu3u7ZDMPgnXfeYerUqURFRREREcFDDz3EqlWrAFizZg133XUXCQkJNG7cmPT0dI/vOy4ujl/84heEhYURHR3Nfffdx5YtW6q1SUtLw2q1EhUVxcMPP+z63Jq+FyKe0obswqJFi+jWrdt566xWq+vPhYWFvP/++7z55puu1yoqKjh06BAmk4mYmBhMJpOrLjY29rzXtNlsxMbGEhrq/sevqKiIU6dOcdddd7leMwwDp9MJwKFDh+jYsaOrrnXr1m6vecbhw4d55pln2Lp1KyUlJRiG4Tp55Iyf3n9sbCyHDh0Cav5eiHhKCVhq9NOEarVaGTNmDA8//PA57b766isOHjyIYRiu9xQWFnLllVee09ZqtWKz2aisrDwnCf/08wCaNWtGw4YNWbVqFTExMedcq2XLlthsNtfXhYWFHt/b/PnzMZlMrFixgqioKNavX8/06dOrtTn72i1btnTdw4W+FyKe0hCEeGzQoEEsXbqUb775BsMwKC0t5dNPP6W4uJikpCRCQ0N54403qKioYN26da6z6s52/fXX06JFC/7whz9QWlqK3W7nn//8JwDNmzfn4MGDrrFUs9nMoEGDmDVrFkeOHAHg4MGDfP755wD07duX5cuXk5+fz6lTp1i4cKHH91NSUkLjxo1p2rQpBw8e5KWXXjqnzVtvvcWBAwc4duwYS5YscR2mWdP3QsRTSsDisU6dOjFjxgymT59O586d6dOnD++99x4AYWFhLFiwgOXLl3PzzTezevVqevfufd7rhISEsGTJEvbu3csdd9zBbbfdxpo1awC45ZZbaN++Pd27d6dLly4ATJo0ibi4OAYPHszPf/5z7r33XgoKCgC4/fbbGTlyJCNHjqR3797ccsstHt9Peno6eXl5JCcnM3r06PMeUNq/f39GjRrFL3/5S6666ipXj7em74WIp7QfsIiIn6gHLCLiJ0rAIiJ+ogQsIuInSsAiIn6iBCwi4idKwCIifqIELCLiJ0rAIiJ+ogQsIuIn/w/tMeiqzdsYLQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      5557\n",
            "           1       0.47      0.36      0.41       129\n",
            "\n",
            "    accuracy                           0.98      5686\n",
            "   macro avg       0.73      0.68      0.70      5686\n",
            "weighted avg       0.97      0.98      0.97      5686\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1P8onUUUfLy"
      },
      "source": [
        "# Lesson 05"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L257aJ2BUgVj"
      },
      "source": [
        "pipeline = Pipeline([('Transformer', Transformer(quantitative_variables, categorical_variables)),\n",
        "                     ('Sampling', SMOTE(random_state = SEED)),\n",
        "                     ('Model', RandomForestClassifier(random_state = SEED))])   "
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXnrcyfCUtsx",
        "outputId": "963a5ae7-8feb-4170-ec3f-6346fbe0371e"
      },
      "source": [
        "X = target_client_records.drop(['id', 'bad'], axis = 1)\n",
        "y = target_client_records['bad']\n",
        "\n",
        "pipeline.fit(X, y)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('Transformer',\n",
              "                 Transformer(categorical_variables=['own_car', 'own_property',\n",
              "                                                    'income_type',\n",
              "                                                    'education_type',\n",
              "                                                    'marital_status',\n",
              "                                                    'housing_type',\n",
              "                                                    'own_workphone',\n",
              "                                                    'own_phone', 'own_email',\n",
              "                                                    'occupation_type'],\n",
              "                             quantitative_variables=['age', 'children_count',\n",
              "                                                     'annual_income',\n",
              "                                                     'years_working',\n",
              "                                                     'family_size'])),\n",
              "                ('Sampling', SMOTE(random_state=1561651)),\n",
              "                ('Model', RandomForestClassifier(random_state=1561651))])"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ei62MksVJPm"
      },
      "source": [
        "## Saving the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhsNOo23VCIR"
      },
      "source": [
        "from joblib import dump"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vAze4gOrVNrx",
        "outputId": "948a994e-53cc-40e6-9f73-dda2aad8970b"
      },
      "source": [
        "dump(pipeline, 'model.joblib')"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCK9UHPoVeKO"
      },
      "source": [
        "features = X.columns"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "igfu2WNIVgPj",
        "outputId": "92d07e92-9a10-429b-c997-96435565a611"
      },
      "source": [
        "dump(features, 'features.joblib')"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['features.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMRozGF-VjXe"
      },
      "source": [
        "categories_list = target_client_records[non_binary_columns].apply(lambda x: x.unique(), axis = 0)"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDL3FI9tV0Xr",
        "outputId": "de552897-973a-45dd-eee1-5e14c19d0588"
      },
      "source": [
        "dump(categories_list, 'categories_list.joblib')"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['categories_list.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    }
  ]
}