Up_only_v1.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOvyFDO+D+z5nWz3HOQ6OVL"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["Задача кредитного скоринга возникает в банках и других кредитных организациях при принятии решений о выдаче кредитов. Задача заключается в том, чтобы на основе некоторой информации о заявителе обоснованно принять решение — стоит ли ему выдавать кредит, и если да, то на каких условиях. \n","+ Необходимо провести разведочный анализ данных (EDA) \n","+ Придумать новые признаки на основе предложенных данных + поэкспериментировать с различными моделями машинного обучения,\n","+ сделать с помощью этих моделей прогноз на тестовом датасете \n","+ загрузить ответы на страницу с результатами."],"metadata":{"id":"TYLORMnAUSDT"}},{"cell_type":"markdown","source":["# Установка пакетов"],"metadata":{"id":"UOUBJQtdUMC4"}},{"cell_type":"code","source":["pip install catboost"],"metadata":{"id":"1uW1PvqlVC_y","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1680934627893,"user_tz":-420,"elapsed":17788,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"6b8b74c4-6519-4a06-9e6b-8732080c7a8b"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting catboost\n","  Downloading catboost-1.1.1-cp39-none-manylinux1_x86_64.whl (76.6 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.6/76.6 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.9/dist-packages (from catboost) (1.10.1)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (from catboost) (3.7.1)\n","Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.9/dist-packages (from catboost) (1.22.4)\n","Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from catboost) (1.16.0)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.9/dist-packages (from catboost) (0.20.1)\n","Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.9/dist-packages (from catboost) (1.4.4)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.9/dist-packages (from catboost) (5.13.1)\n","Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.24.0->catboost) (2022.7.1)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (8.4.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (3.0.9)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (0.11.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (1.4.4)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (1.0.7)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (4.39.3)\n","Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (5.12.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (23.0)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from plotly->catboost) (8.2.2)\n","Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.9/dist-packages (from importlib-resources>=3.2.0->matplotlib->catboost) (3.15.0)\n","Installing collected packages: catboost\n","Successfully installed catboost-1.1.1\n"]}]},{"cell_type":"markdown","source":["# Загрузка библиотек"],"metadata":{"id":"clJ5ofA_T_un"}},{"cell_type":"code","execution_count":3,"metadata":{"id":"dr4nSS3iglaf","executionInfo":{"status":"ok","timestamp":1680934637209,"user_tz":-420,"elapsed":577,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}}},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","from google.colab import drive\n","from sklearn.model_selection import train_test_split \n","from catboost import CatBoostClassifier\n","from sklearn.model_selection import cross_val_score\n","from catboost import cv\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.metrics import roc_auc_score\n","from sklearn import metrics\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.model_selection import GridSearchCV"]},{"cell_type":"code","source":["drive.mount('/content/drive')"],"metadata":{"id":"qj9My_vvgrGk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1680934689787,"user_tz":-420,"elapsed":46807,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"4a59a97b-2b2d-426f-95ec-71390f741a40"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","source":["# Загрузка данных"],"metadata":{"id":"vPvmRdyFUIOB"}},{"cell_type":"code","source":["df = pd.read_csv('/content/drive/MyDrive/SHIFT/Data/Intensiv_2023/v1/train.csv')"],"metadata":{"id":"AOcTOAdfgvGE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test = pd.read_csv('/content/drive/MyDrive/SHIFT/Data/Intensiv_2023/v1/test.csv')"],"metadata":{"id":"GOopp09pfIwS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["bki = pd.read_csv('/content/drive/MyDrive/SHIFT/Data/Intensiv_2023/v2/bki.csv')"],"metadata":{"id":"Oe_PJFQ_MPCa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sample_answ = pd.read_csv('/content/drive/MyDrive/SHIFT/Data/Intensiv_2023/v1/sample_answer.csv')"],"metadata":{"id":"XqnGDstnnNSx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df.head(3)"],"metadata":{"id":"VAyy8qN9hHo9","colab":{"base_uri":"https://localhost:8080/","height":237},"executionInfo":{"status":"ok","timestamp":1675379465509,"user_tz":-420,"elapsed":67,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"44503ef5-5ba6-48a8-c958-ee7d14a901f8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   reco_id_curr  target contract_type_name gender own_car_flag  \\\n","0        384575       0         Cash loans      M            Y   \n","1        214010       0         Cash loans      F            Y   \n","2        142232       0         Cash loans      F            Y   \n","\n","  own_realty_flag  children_count    income  loan_body  annuity_payment  ...  \\\n","0               N               2  207000.0   465457.5          52641.0  ...   \n","1               Y               0  247500.0  1281712.5          48946.5  ...   \n","2               N               0  202500.0   495000.0          39109.5  ...   \n","\n","   document_18_flag document_19_flag document_20_flag document_21_flag  \\\n","0                 0                0                0                0   \n","1                 0                0                0                0   \n","2                 0                0                0                0   \n","\n","  requests_bki_hour requests_bki_day  requests_bki_week  requests_bki_month  \\\n","0               0.0              0.0                0.0                 1.0   \n","1               0.0              0.0                0.0                 1.0   \n","2               0.0              0.0                0.0                 1.0   \n","\n","   requests_bki_qrt  requests_bki_year  \n","0               0.0                1.0  \n","1               0.0                3.0  \n","2               0.0                3.0  \n","\n","[3 rows x 122 columns]"],"text/html":["\n","  <div id=\"df-1915fd48-a41e-432c-a6b6-81b3bafe14b1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>reco_id_curr</th>\n","      <th>target</th>\n","      <th>contract_type_name</th>\n","      <th>gender</th>\n","      <th>own_car_flag</th>\n","      <th>own_realty_flag</th>\n","      <th>children_count</th>\n","      <th>income</th>\n","      <th>loan_body</th>\n","      <th>annuity_payment</th>\n","      <th>...</th>\n","      <th>document_18_flag</th>\n","      <th>document_19_flag</th>\n","      <th>document_20_flag</th>\n","      <th>document_21_flag</th>\n","      <th>requests_bki_hour</th>\n","      <th>requests_bki_day</th>\n","      <th>requests_bki_week</th>\n","      <th>requests_bki_month</th>\n","      <th>requests_bki_qrt</th>\n","      <th>requests_bki_year</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>384575</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>Y</td>\n","      <td>N</td>\n","      <td>2</td>\n","      <td>207000.0</td>\n","      <td>465457.5</td>\n","      <td>52641.0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>214010</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>Y</td>\n","      <td>Y</td>\n","      <td>0</td>\n","      <td>247500.0</td>\n","      <td>1281712.5</td>\n","      <td>48946.5</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>142232</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>Y</td>\n","      <td>N</td>\n","      <td>0</td>\n","      <td>202500.0</td>\n","      <td>495000.0</td>\n","      <td>39109.5</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>3.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3 rows × 122 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1915fd48-a41e-432c-a6b6-81b3bafe14b1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 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 0                0                0                0   \n","2                0                0                0                0   \n","\n","  requests_bki_hour  requests_bki_day  requests_bki_week  requests_bki_month  \\\n","0               0.0               0.0                0.0                 0.0   \n","1               0.0               0.0                0.0                 0.0   \n","2               0.0               0.0                0.0                 0.0   \n","\n","   requests_bki_qrt  requests_bki_year  \n","0               1.0                1.0  \n","1               0.0                1.0  \n","2               0.0                1.0  \n","\n","[3 rows x 121 columns]"],"text/html":["\n","  <div id=\"df-dcb3d72a-6392-428b-ac0b-d2e7af9b4468\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        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<tr>\n","      <th>0</th>\n","      <td>100008</td>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>N</td>\n","      <td>Y</td>\n","      <td>0</td>\n","      <td>99000.0</td>\n","      <td>490495.5</td>\n","      <td>27517.5</td>\n","      <td>454500.0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>100017</td>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>Y</td>\n","      <td>N</td>\n","      <td>1</td>\n","      <td>225000.0</td>\n","      <td>918468.0</td>\n","      <td>28966.5</td>\n","      <td>697500.0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>100019</td>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>Y</td>\n","      <td>Y</td>\n","      <td>0</td>\n","      <td>157500.0</td>\n","      <td>299772.0</td>\n","      <td>20160.0</td>\n","      <td>247500.0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3 rows × 121 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dcb3d72a-6392-428b-ac0b-d2e7af9b4468')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["sample_answ.head(3)"],"metadata":{"id":"AC1i_kRWnqkB","colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"status":"ok","timestamp":1675379465510,"user_tz":-420,"elapsed":62,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"9eb293b6-181d-4900-c69f-a5ecee314510"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   reco_id_curr  target\n","0        100008     0.5\n","1        100017     0.5\n","2        100019     0.5"],"text/html":["\n","  <div id=\"df-ae606ad3-164c-4b9d-9869-591dc8485cb7\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>reco_id_curr</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>100008</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>100017</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>100019</td>\n","      <td>0.5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae606ad3-164c-4b9d-9869-591dc8485cb7')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-ae606ad3-164c-4b9d-9869-591dc8485cb7 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["df.info()"],"metadata":{"id":"bzFzCe-vIw0_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379465511,"user_tz":-420,"elapsed":61,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"2f23630f-5560-439e-dc7f-2b33b322bf34"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 261384 entries, 0 to 261383\n","Columns: 122 entries, reco_id_curr to requests_bki_year\n","dtypes: float64(65), int64(41), object(16)\n","memory usage: 243.3+ MB\n"]}]},{"cell_type":"markdown","source":["# EDA"],"metadata":{"id":"9lYKeZiCVPSV"}},{"cell_type":"markdown","source":["**Имеем дисбаланс целевого признака**"],"metadata":{"id":"x63KuBVTVbme"}},{"cell_type":"markdown","source":["## Определение категор и числовых признаков"],"metadata":{"id":"_4cMfXtvdHX-"}},{"cell_type":"code","source":["numerical_features = []\n","categorical_features = []\n","target = []"],"metadata":{"id":"zy7FfHbCS6Xf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["numerical_features = []\n","categorical_features = []\n","target = []\n","for column in df.columns:\n","    if df[column].dtype == 'object':\n","      categorical_features.append(column)\n","    elif column == 'target':\n","      target.append(column)       \n","    else:\n","        numerical_features.append(column)"],"metadata":{"id":"749Z4XdcVx_a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print('Кол-во категорильных:', len(categorical_features))\n","print('Кол-во числовых:',len(numerical_features))\n","print('Таргет:', len(target))"],"metadata":{"id":"9YIVmxi2V_Qt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379465513,"user_tz":-420,"elapsed":50,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"ca1be89b-dee0-4197-e901-66d7eb97afa7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Кол-во категорильных: 16\n","Кол-во числовых: 105\n","Таргет: 1\n"]}]},{"cell_type":"code","source":["df[categorical_features].head(3)"],"metadata":{"id":"48NO0ZGAbbn6","colab":{"base_uri":"https://localhost:8080/","height":260},"executionInfo":{"status":"ok","timestamp":1675379465514,"user_tz":-420,"elapsed":43,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"58a3f1e8-5953-47f6-8ecc-1878a2ac32f9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  contract_type_name gender own_car_flag own_realty_flag type_suite_name  \\\n","0         Cash loans      M            Y               N   Unaccompanied   \n","1         Cash loans      F            Y               Y   Unaccompanied   \n","2         Cash loans      F            Y               N   Unaccompanied   \n","\n","       income_type_name            education_type_name    family_status_name  \\\n","0  Commercial associate  Secondary / secondary special               Married   \n","1  Commercial associate               Higher education  Single / not married   \n","2               Working  Secondary / secondary special               Married   \n","\n","   housing_type_name type_of_occupation start_weekday_appr_process  \\\n","0  House / apartment        Sales staff                   THURSDAY   \n","1  House / apartment           Managers                   THURSDAY   \n","2  House / apartment        Sales staff                    TUESDAY   \n","\n","     type_of_organization fondkapremon_mode mode_house_type  \\\n","0  Business Entity Type 3               NaN             NaN   \n","1  Business Entity Type 3  reg oper account  block of flats   \n","2           Self-employed               NaN             NaN   \n","\n","  mode_walls_material emergency_state_mode  \n","0                 NaN                  NaN  \n","1          Monolithic                   No  \n","2                 NaN                  NaN  "],"text/html":["\n","  <div id=\"df-7f695023-447e-4be1-a2cb-2f4e3d6420b9\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>contract_type_name</th>\n","      <th>gender</th>\n","      <th>own_car_flag</th>\n","      <th>own_realty_flag</th>\n","      <th>type_suite_name</th>\n","      <th>income_type_name</th>\n","      <th>education_type_name</th>\n","      <th>family_status_name</th>\n","      <th>housing_type_name</th>\n","      <th>type_of_occupation</th>\n","      <th>start_weekday_appr_process</th>\n","      <th>type_of_organization</th>\n","      <th>fondkapremon_mode</th>\n","      <th>mode_house_type</th>\n","      <th>mode_walls_material</th>\n","      <th>emergency_state_mode</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>Y</td>\n","      <td>N</td>\n","      <td>Unaccompanied</td>\n","      <td>Commercial associate</td>\n","      <td>Secondary / secondary special</td>\n","      <td>Married</td>\n","      <td>House / apartment</td>\n","      <td>Sales staff</td>\n","      <td>THURSDAY</td>\n","      <td>Business Entity Type 3</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>Y</td>\n","      <td>Y</td>\n","      <td>Unaccompanied</td>\n","      <td>Commercial associate</td>\n","      <td>Higher education</td>\n","      <td>Single / not married</td>\n","      <td>House / apartment</td>\n","      <td>Managers</td>\n","      <td>THURSDAY</td>\n","      <td>Business Entity Type 3</td>\n","      <td>reg oper account</td>\n","      <td>block of flats</td>\n","      <td>Monolithic</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>Y</td>\n","      <td>N</td>\n","      <td>Unaccompanied</td>\n","      <td>Working</td>\n","      <td>Secondary / secondary special</td>\n","      <td>Married</td>\n","      <td>House / apartment</td>\n","      <td>Sales staff</td>\n","      <td>TUESDAY</td>\n","      <td>Self-employed</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f695023-447e-4be1-a2cb-2f4e3d6420b9')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-7f695023-447e-4be1-a2cb-2f4e3d6420b9 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-7f695023-447e-4be1-a2cb-2f4e3d6420b9');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","source":["## ML"],"metadata":{"id":"FheCD2CpdV6j"}},{"cell_type":"markdown","source":["### Разбиение данных"],"metadata":{"id":"jC_scSEDdaBt"}},{"cell_type":"code","source":["train, valid = train_test_split(df, test_size=0.25, random_state=12)"],"metadata":{"id":"xYijhTpdc4Ec"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(train.shape)\n","print(valid.shape)"],"metadata":{"id":"ZY9OTc0ugQRe","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379465965,"user_tz":-420,"elapsed":27,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"b65ac4dc-f333-45cc-be51-553bde519236"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(196038, 122)\n","(65346, 122)\n"]}]},{"cell_type":"markdown","source":["### Определение признаков"],"metadata":{"id":"zM_xaiqxfAMo"}},{"cell_type":"code","source":["features_train = train.drop('target', axis=1)\n","target_train = train['target']\n","features_valid = valid.drop('target', axis=1)\n","target_valid = valid['target']"],"metadata":{"id":"jBlWG7j0e_Iz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(f'Размероность features_train: {features_train.shape}')\n","print(f'Размероность target_train: {target_train.shape}')\n","print(f'Размероность features_valid: {features_valid.shape}')\n","print(f'Размероность target_valid: {target_valid.shape}')"],"metadata":{"id":"J0JY33jzhKrD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379465966,"user_tz":-420,"elapsed":13,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"4958eec8-5b93-41b3-c588-316d88a13c55"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Размероность features_train: (196038, 121)\n","Размероность target_train: (196038,)\n","Размероность features_valid: (65346, 121)\n","Размероность target_valid: (65346,)\n"]}]},{"cell_type":"markdown","source":["### Подготовка признаков"],"metadata":{"id":"6RpjUDVNjFVS"}},{"cell_type":"markdown","source":["#### Категориальных"],"metadata":{"id":"26mTh0WJtqbh"}},{"cell_type":"code","source":["features_train[categorical_features] = features_train[categorical_features].astype('str')\n","features_valid[categorical_features] = features_valid[categorical_features].astype('str')\n","test[categorical_features] = test[categorical_features].astype('str')"],"metadata":{"id":"6O_auWvDjWIH"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#### Числовых"],"metadata":{"id":"CBCBi0i6tx56"}},{"cell_type":"code","source":["scaler = StandardScaler()"],"metadata":{"id":"6hn-wuMAt4lM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["'''features_train_scaled = features_train.copy()\n","target_train_scaled = target_train.copy()\n","features_valid_scaled = features_valid.copy()\n","target_valid_scaled = target_valid.copy()\n","test_scaled = test.copy()'''"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"A19hXW_2GAUR","executionInfo":{"status":"ok","timestamp":1675379466614,"user_tz":-420,"elapsed":27,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"f1afe874-ac76-4c0b-e5d8-08be387b782f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'features_train_scaled = features_train.copy()\\ntarget_train_scaled = target_train.copy()\\nfeatures_valid_scaled = features_valid.copy()\\ntarget_valid_scaled = target_valid.copy()\\ntest_scaled = test.copy()'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":34}]},{"cell_type":"code","source":["'''scaler.fit(features_train[numerical_features])'''"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"UmliPO3tG4Pm","executionInfo":{"status":"ok","timestamp":1675379466615,"user_tz":-420,"elapsed":26,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"adc54164-503a-46ac-a7c9-36e72292d2e1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'scaler.fit(features_train[numerical_features])'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":35}]},{"cell_type":"code","source":["'''features_train_scaled[numerical_features] =  scaler.transform(features_train[numerical_features])\n","target_train_scaled = np.array(target_train)\n","features_valid_scaled[numerical_features] = scaler.transform(features_valid[numerical_features])\n","target_valid_scaled = np.array(target_valid)\n","test_scaled[numerical_features] = scaler.transform(test[numerical_features])'''"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":70},"id":"Nmt-6QyPG-I3","executionInfo":{"status":"ok","timestamp":1675379466615,"user_tz":-420,"elapsed":24,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"60faf19a-a923-4bef-ba77-cb06320adc57"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'features_train_scaled[numerical_features] =  scaler.transform(features_train[numerical_features])\\ntarget_train_scaled = np.array(target_train)\\nfeatures_valid_scaled[numerical_features] = scaler.transform(features_valid[numerical_features])\\ntarget_valid_scaled = np.array(target_valid)\\ntest_scaled[numerical_features] = scaler.transform(test[numerical_features])'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":36}]},{"cell_type":"code","source":["'''features_train_scaled = pd.DataFrame(features_train_scaled, index = features_train.index, columns = features_train.columns)\n","features_valid_scaled = pd.DataFrame(features_valid_scaled, index = features_valid.index, columns = features_valid.columns)\n","test_scaled = pd.DataFrame(test_scaled, index = test.index, columns = test.columns)'''"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":70},"id":"-v7Fn-RkH7QE","executionInfo":{"status":"ok","timestamp":1675379466616,"user_tz":-420,"elapsed":24,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"4a289f98-992b-4508-a33e-5c2734a8af15"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'features_train_scaled = pd.DataFrame(features_train_scaled, index = features_train.index, columns = features_train.columns)\\nfeatures_valid_scaled = pd.DataFrame(features_valid_scaled, index = features_valid.index, columns = features_valid.columns)\\ntest_scaled = pd.DataFrame(test_scaled, index = test.index, columns = test.columns)'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":37}]},{"cell_type":"markdown","source":["### Разработка модели"],"metadata":{"id":"UfiOb3k-h5t-"}},{"cell_type":"markdown","source":["#### Установка гиперпараметров"],"metadata":{"id":"XS3xb3VUkmFW"}},{"cell_type":"code","source":["Catboost = CatBoostClassifier(random_state = 12,\n","                              verbose=50,\n","                              scale_pos_weight = 12,\n","                              custom_loss = 'AUC',\n","                              iterations=150,\n","                              learning_rate=0.2,\n","                              depth = 5,\n","                              l2_leaf_reg = 4\n","                              )"],"metadata":{"id":"OK7jR2WDhvr9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cat_features = categorical_features"],"metadata":{"id":"twnjwsMriYWB"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#### Обучение"],"metadata":{"id":"l3QPRb9-kzXT"}},{"cell_type":"code","source":["Catboost.fit(features_train, target_train,\n","             cat_features = cat_features,\n","             eval_set=(features_valid, target_valid))"],"metadata":{"id":"dbCOipobigYj","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379509594,"user_tz":-420,"elapsed":42999,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"fa385555-c847-4651-c943-06e9e1e1bf41"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0:\tlearn: 0.6721471\ttest: 0.6719497\tbest: 0.6719497 (0)\ttotal: 342ms\tremaining: 51s\n","50:\tlearn: 0.5874560\ttest: 0.5924714\tbest: 0.5924714 (50)\ttotal: 13.5s\tremaining: 26.2s\n","100:\tlearn: 0.5706509\ttest: 0.5854922\tbest: 0.5854329 (97)\ttotal: 26.2s\tremaining: 12.7s\n","149:\tlearn: 0.5579220\ttest: 0.5851708\tbest: 0.5850879 (121)\ttotal: 40.7s\tremaining: 0us\n","\n","bestTest = 0.5850879015\n","bestIteration = 121\n","\n","Shrink model to first 122 iterations.\n"]},{"output_type":"execute_result","data":{"text/plain":["<catboost.core.CatBoostClassifier at 0x7f977d211880>"]},"metadata":{},"execution_count":40}]},{"cell_type":"markdown","source":["## Предсказание и оценка качества"],"metadata":{"id":"FbyVsfElk3Yh"}},{"cell_type":"code","source":["predict = Catboost.predict(test)\n","print(predict)"],"metadata":{"id":"ZdMxQphEiw9P","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379509596,"user_tz":-420,"elapsed":29,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"a5b24161-c73d-42ef-bcce-0c2a69097879"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[0 0 1 ... 1 1 0]\n"]}]},{"cell_type":"code","source":["predict_probability = Catboost.predict_proba(test)[:, 1]\n","print(predict_probability)"],"metadata":{"id":"m30evwJDlIc0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379510013,"user_tz":-420,"elapsed":438,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"f53478a4-7064-44d5-8b68-a600cf2e0d87"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[0.47892532 0.27866164 0.50935372 ... 0.66948872 0.55220975 0.416838  ]\n"]}]},{"cell_type":"code","source":["auc_roc = metrics.roc_auc_score(target_valid, Catboost.predict_proba(features_valid)[:, 1])\n","\n","print(auc_roc)"],"metadata":{"id":"h6p6tigwlkJM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379510444,"user_tz":-420,"elapsed":434,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"11938706-1f93-4417-d451-702acf75fb56"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.7583059962324478\n"]}]},{"cell_type":"markdown","source":["roc_auc-best = 0.76151\n","\n","```\n","# Выбран кодовый формат\n","```\n","\n"],"metadata":{"id":"MSOv1w3Ad0RE"}},{"cell_type":"markdown","source":["roc_auc_scaled_feat = 0.5666132601027234"],"metadata":{"id":"UubaYKydKghG"}},{"cell_type":"markdown","source":["roc_auc = 0.7560301695695976"],"metadata":{"id":"W9Lp_CInovMq"}},{"cell_type":"markdown","source":["# Загрузка результатов"],"metadata":{"id":"7SjbUPUkqKM0"}},{"cell_type":"code","source":["sample_answ['target'] = predict_probability"],"metadata":{"id":"bOnIKhiaomZM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sample_answ.head()"],"metadata":{"id":"EnbILOlwtRe1","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1675379510448,"user_tz":-420,"elapsed":43,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"414128e9-ef91-49f5-e560-d796ba5576ab"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   reco_id_curr    target\n","0        100008  0.478925\n","1        100017  0.278662\n","2        100019  0.509354\n","3        100022  0.189132\n","4        100023  0.364499"],"text/html":["\n","  <div id=\"df-6ca383f6-1015-42ac-a367-d2551f8cd1c7\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n"," 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-6ca383f6-1015-42ac-a367-d2551f8cd1c7');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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Чем выше значимость - тем выше корреляция с target. "],"metadata":{"id":"APP_zJfLN9R1"}},{"cell_type":"code","source":["feature_importances = pd.DataFrame({'features': test.columns,'feature_importance':Catboost.feature_importances_})\n","print(feature_importances.sort_values('feature_importance',ascending=False).head(25))\n","'''#plot(feature importance)\n","importance = Catboost.feature_importances_\n","plt.bar([x for x in range(len(importance))], importance)\n","plt.show()'''\n","feature_importances.plot(kind='bar', figsize=(18,10), color='indigo')"],"metadata":{"id":"g2ShDZHAJsD-","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1675379511786,"user_tz":-420,"elapsed":1378,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"de3247bc-1bea-45c0-a428-8a02738672b3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["                        features  feature_importance\n","42             external_source_3           21.576870\n","41             external_source_2           13.446410\n","40             external_source_1            7.512264\n","16                    days_birth            5.747395\n","7                      loan_body            5.344897\n","9                    goods_price            4.913321\n","8                annuity_payment            3.747424\n","17                 days_employed            3.300648\n","12           education_type_name            2.350785\n","19         publication_timestamp            1.901542\n","2                         gender            1.645122\n","94      last_phone_number_change            1.378885\n","39          type_of_organization            1.260567\n","30   rating_client_w_city_region            1.046520\n","96               document_3_flag            1.005574\n","1             contract_type_name            0.922815\n","3                   own_car_flag            0.901273\n","18        registration_timestamp            0.855236\n","119             requests_bki_qrt            0.771216\n","27            type_of_occupation            0.766179\n","6                         income            0.762680\n","120            requests_bki_year            0.762433\n","31    start_weekday_appr_process            0.743756\n","74         median_years_building            0.738687\n","13            family_status_name            0.725239\n"]},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f977b4f6610>"]},"metadata":{},"execution_count":47},{"output_type":"display_data","data":{"text/plain":["<Figure size 1296x720 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Получение сильных признаков"],"metadata":{"id":"v5GNo8A3VFdk"}},{"cell_type":"markdown","source":["Получим список самых значисых признаков, значимость которых больше 1%."],"metadata":{"id":"U2ijy8maUNrU"}},{"cell_type":"code","source":["features_best = feature_importances.query('feature_importance > 1')['features']"],"metadata":{"id":"HNP6MxakVte5"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Значения признаков"],"metadata":{"id":"y8PX5Yd1gmkR"}},{"cell_type":"markdown","source":["+ contract_type_name - Идентификация, если заём выдается наличными или по кредитной карте\n","+ loan_body - сумма займа\n","+ goods_price -Для POS-займов это цена товара, на который оформляется заём\n","+ days_birth - Возраст клиента в днях на момент оформления займа\n","+ days_employed - За сколько дней до оформления займа человек начал текущую работу\n","+ publication_timestamp - За сколько дней до оформления займа изменился документ, с помощью которого клиент проходил идентификацию\n","+ age_own_car - Возраст автомобиля клиента\n","+ type_of_occupation - Профессия клиента\n","+ external_source - Нормализованный балл из внешнего источника данных(скор клиента)\n","+ median_years_beginexpluatation - Нормализованная информация о здании, в котором живет клиент(лет)\n","+ last_phone_number_change - За сколько дней до оформления текущего займа клиент сменил телефон\n","+ document_3_flag - Предоставил ли клиент документ 3\n","+ requests_bki_qrt - Количество запросов в БКИ о клиенте за 3 месяца до оформления займа"],"metadata":{"id":"oqxTVJmuX4sN"}},{"cell_type":"markdown","source":[" # Предобрабортка df"],"metadata":{"id":"su9UXxiDhzrA"}},{"cell_type":"code","source":["df[features_best].info()"],"metadata":{"id":"4zvCIw-vcotP","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379511788,"user_tz":-420,"elapsed":20,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"0690a5a4-b926-43e3-d2ac-1e8029729d37"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 261384 entries, 0 to 261383\n","Data columns (total 15 columns):\n"," #   Column                       Non-Null Count   Dtype  \n","---  ------                       --------------   -----  \n"," 0   gender                       261384 non-null  object \n"," 1   loan_body                    261384 non-null  float64\n"," 2   annuity_payment              261373 non-null  float64\n"," 3   goods_price                  261152 non-null  float64\n"," 4   education_type_name          261384 non-null  object \n"," 5   days_birth                   261384 non-null  int64  \n"," 6   days_employed                261384 non-null  int64  \n"," 7   publication_timestamp        261384 non-null  int64  \n"," 8   rating_client_w_city_region  261384 non-null  int64  \n"," 9   type_of_organization         261384 non-null  object \n"," 10  external_source_1            113996 non-null  float64\n"," 11  external_source_2            260825 non-null  float64\n"," 12  external_source_3            209684 non-null  float64\n"," 13  last_phone_number_change     261383 non-null  float64\n"," 14  document_3_flag              261384 non-null  int64  \n","dtypes: float64(7), int64(5), object(3)\n","memory usage: 29.9+ MB\n"]}]},{"cell_type":"markdown","source":["**Типы данных в норме**"],"metadata":{"id":"7IQei6BTiyfA"}},{"cell_type":"markdown","source":["## Обработка пропусков"],"metadata":{"id":"Xf57baALi4jV"}},{"cell_type":"code","source":["df[features_best].isna().mean()"],"metadata":{"id":"CH7FpHNshT_u","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379511788,"user_tz":-420,"elapsed":17,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"8ad368ae-9d64-42b1-c583-d80e461c4296"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["gender                         0.000000\n","loan_body                      0.000000\n","annuity_payment                0.000042\n","goods_price                    0.000888\n","education_type_name            0.000000\n","days_birth                     0.000000\n","days_employed                  0.000000\n","publication_timestamp          0.000000\n","rating_client_w_city_region    0.000000\n","type_of_organization           0.000000\n","external_source_1              0.563875\n","external_source_2              0.002139\n","external_source_3              0.197793\n","last_phone_number_change       0.000004\n","document_3_flag                0.000000\n","dtype: float64"]},"metadata":{},"execution_count":50}]},{"cell_type":"markdown","source":["Заполним пропуски в признаке annuity_payment рассчетом с помощью признака loan_body, умножив его на среднее значение отношения df_1['annuity_payment'] / df_1['loan_body']."],"metadata":{"id":"Cn-HMeW87-TI"}},{"cell_type":"code","source":["df['annuity_payment'] = df['annuity_payment'].fillna(df['loan_body'] * ((df['annuity_payment']/df['loan_body']).mean()))"],"metadata":{"id":"BVk0OFD3jLxf"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Пропуски в признаке цена товара, заполним суммой выданного кредита:"],"metadata":{"id":"C22OX0h09d3j"}},{"cell_type":"code","source":["df['goods_price'] = df['goods_price'].fillna(df['loan_body'])"],"metadata":{"id":"oUlxuzSZACld"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Пропуски в признаке age_own_car говорят нам о том,что вероятно у клиента нет автомобиля. Заполним пропуски групповым большим числом, например 100."],"metadata":{"id":"EyPAR3KtDASS"}},{"cell_type":"code","source":["df['age_own_car'] = df['age_own_car'].fillna(100)"],"metadata":{"id":"r6RIuyHTCViG"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":[],"metadata":{"id":"41qpHOodG0US"}},{"cell_type":"markdown","source":["Создадим новый признак, показывающий безработный или работающимй человек."],"metadata":{"id":"tW20Oun1aHj0"}},{"cell_type":"code","source":["def set_professia(type_of_organisation, type_of_occupation):\n","  if type_of_organisation == 'XNA':\n","     type_of_occupation = 'Unemployed'\n","  else: \n","    type_of_occupation ='Worker'\n","\n","  return type_of_occupation\n","\n","df['work_status'] = df.apply(lambda x: set_professia(x['type_of_organization'], x['type_of_occupation']), axis=1)"],"metadata":{"id":"C7q22D8wWOMy"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df['work_status'].value_counts()"],"metadata":{"id":"5DXMyOQvZ7cH","executionInfo":{"status":"ok","timestamp":1675379516459,"user_tz":-420,"elapsed":54,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"7843b11c-22f5-449b-a47b-86a01737be0d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Worker        214280\n","Unemployed     47104\n","Name: work_status, dtype: int64"]},"metadata":{},"execution_count":55}]},{"cell_type":"markdown","source":["Признаки external_source - это скор клиента из БКИ. Отсутствующие значения - это отсутсвие информации, заполним их значением \"нет\""],"metadata":{"id":"1Q-qtHxTOQdf"}},{"cell_type":"code","source":["df['external_source_1'] = df['external_source_1'].fillna(0)\n","df['external_source_2'] = df['external_source_2'].fillna(0)\n","df['external_source_3'] = df['external_source_3'].fillna(0)\n","print('кол-во пропусков external_source_1:', df['external_source_1'].isna().mean())\n","print('кол-во пропусков external_source_2 :', df['external_source_2'].isna().mean())\n","print('кол-во пропусков external_source_3:', df['external_source_3'].isna().mean())"],"metadata":{"id":"xxwqbSCMN7_9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379516460,"user_tz":-420,"elapsed":53,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"f75565ce-03d9-458e-e972-1bece7f97fd7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["кол-во пропусков external_source_1: 0.0\n","кол-во пропусков external_source_2 : 0.0\n","кол-во пропусков external_source_3: 0.0\n"]}]},{"cell_type":"markdown","source":["Пропуски в признаке median_years_beginexpluatation заполним самым часто встречающимся значением."],"metadata":{"id":"UhAeK53SCdwO"}},{"cell_type":"code","source":["df['median_years_beginexpluatation'] = df['median_years_beginexpluatation'].fillna(0.98)\n","print('кол-во пропусков median_years_beginexpluatation:', df['median_years_beginexpluatation'].isna().mean())"],"metadata":{"id":"WcmZAR4vOPmf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379516461,"user_tz":-420,"elapsed":46,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"9708476f-e63f-4a8b-94ab-33c48295c631"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["кол-во пропусков median_years_beginexpluatation: 0.0\n"]}]},{"cell_type":"markdown","source":["Пропуски в признаке last_phone_number_change заполняем самым часто стречающимся значением."],"metadata":{"id":"_xNDr4UKCxId"}},{"cell_type":"code","source":["df['last_phone_number_change'] = df['last_phone_number_change'].fillna(0)\n","print('кол-во пропусков last_phone_number_change:', df['last_phone_number_change'].isna().mean())"],"metadata":{"id":"t3StnWR3QVI7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379516461,"user_tz":-420,"elapsed":39,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"48d256a1-86d1-4e2c-a319-0f3e820c1018"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["кол-во пропусков last_phone_number_change: 0.0\n"]}]},{"cell_type":"markdown","source":["Пропуски в признаке requests_bki_qrt означают, очевидно, что запросов в БКИ не было. Заполним их нулями."],"metadata":{"id":"rgLnZcJ6C_4p"}},{"cell_type":"code","source":["df['requests_bki_qrt'] =  df['requests_bki_qrt'].fillna(0)\n","print('кол-во пропусков requests_bki_qrt:', df['requests_bki_qrt'].isna().mean())"],"metadata":{"id":"1AiqP0qGTT0k","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379516462,"user_tz":-420,"elapsed":34,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"c12548be-414b-4196-d97d-cae910260a8d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["кол-во пропусков requests_bki_qrt: 0.0\n"]}]},{"cell_type":"markdown","source":["## Исключение выбросов"],"metadata":{"id":"RIySxAhGbocn"}},{"cell_type":"code","source":["t = df.query('days_birth > days_employed')"],"metadata":{"id":"TgswABBTdzil"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df['days_employed'].plot.hist(title = 'Days Employment Histogram');\n","plt.xlabel('Days Employment');"],"metadata":{"id":"YApuH1d6fj8Q","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1675379516800,"user_tz":-420,"elapsed":364,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"871bf61a-2553-46d8-efbf-adcb9dda97ba"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["Видим большое число выбросов одинакового значения. Их примерно 20%. Создадим новый признак  - это будет флаг наличия аномального опыта."],"metadata":{"id":"_GURgdBaDcR0"}},{"cell_type":"code","source":["df['days_employed_anom'] = df[\"days_employed\"] == 365243"],"metadata":{"id":"VeD-9Gs2gHl8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Сам выброс заменим на медианное значение."],"metadata":{"id":"ohOT494pD3W8"}},{"cell_type":"code","source":["df['days_employed'].replace({365243: -1647.0}, inplace = True)"],"metadata":{"id":"xZceC461gnSy"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df['days_employed'].plot.hist(title = 'Days Employment Histogram');\n","plt.xlabel('Days Employment');"],"metadata":{"id":"J1UGWTdxgxWD","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1675379517313,"user_tz":-420,"elapsed":521,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"9aa7a0c8-63cf-4499-d8c1-b07311e12eb4"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Предобработка test"],"metadata":{"id":"ca-0IgevPu1R"}},{"cell_type":"markdown","source":["### Предобработка пропусков"],"metadata":{"id":"GMl-aXIhJt1_"}},{"cell_type":"code","source":["test[features_best].isna().mean()"],"metadata":{"id":"o2csvLtKP7EM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379517314,"user_tz":-420,"elapsed":20,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"7e6aa91c-4b70-48a9-d87b-5579452ab69c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["gender                         0.000000\n","loan_body                      0.000000\n","annuity_payment                0.000022\n","goods_price                    0.000997\n","education_type_name            0.000000\n","days_birth                     0.000000\n","days_employed                  0.000000\n","publication_timestamp          0.000000\n","rating_client_w_city_region    0.000000\n","type_of_organization           0.000000\n","external_source_1              0.563444\n","external_source_2              0.002190\n","external_source_3              0.200858\n","last_phone_number_change       0.000000\n","document_3_flag                0.000000\n","dtype: float64"]},"metadata":{},"execution_count":65}]},{"cell_type":"markdown","source":["Заполним пропуски в признаке annuity_payment рассчетом с помощью признака loan_body, умножив его на среднее значение отношения test['annuity_payment'] / test['loan_body']."],"metadata":{"id":"pMmo5cg_SaqM"}},{"cell_type":"code","source":["test['annuity_payment'] = test['annuity_payment'].fillna(test['loan_body'] * ((test['annuity_payment']/test['loan_body']).mean()))"],"metadata":{"id":"pYeglMMLSaqP"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Пропуски в признаке цена товара, заполним суммой выданного кредита:"],"metadata":{"id":"ec2wXJNxSaqR"}},{"cell_type":"code","source":["test['goods_price'] = test['goods_price'].fillna(test['loan_body'])"],"metadata":{"id":"KigXKogMSaqS"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Пропуски в признаке age_own_car говорят нам о том,что вероятно у клиента нет автомобиля. Заполним пропуски групповым большим числом, например 100."],"metadata":{"id":"5efzL47eSaqU"}},{"cell_type":"code","source":["test['age_own_car'] = test['age_own_car'].fillna(100)"],"metadata":{"id":"YSIjcnK9SaqV"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":[],"metadata":{"id":"JEsMLotFSaqW"}},{"cell_type":"markdown","source":["Создадим новый признак, показывающий безработный или работающимй человек."],"metadata":{"id":"b5endGaESaqZ"}},{"cell_type":"code","source":["def set_professia(type_of_organisation, type_of_occupation):\n","  if type_of_organisation == 'XNA':\n","     type_of_occupation = 'Unemployed'\n","  else: \n","    type_of_occupation ='Worker'\n","\n","  return type_of_occupation\n","\n","test['work_status'] = test.apply(lambda x: set_professia(x['type_of_organization'], x['type_of_occupation']), axis=1)"],"metadata":{"id":"K41JtvN6Saqa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test['work_status'].value_counts()"],"metadata":{"id":"swmr0duNSaqb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379518150,"user_tz":-420,"elapsed":66,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"18782298-4155-4fd9-8cad-159d868ed9a4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Worker        37857\n","Unemployed     8270\n","Name: work_status, dtype: int64"]},"metadata":{},"execution_count":70}]},{"cell_type":"markdown","source":["Признаки external_source - это скор клиента из БКИ. Отсутствующие значения - это отсутсвие информации, т.е 0."],"metadata":{"id":"VZYtBIB7Saqc"}},{"cell_type":"code","source":["test['external_source_1'] = test['external_source_1'].fillna(0)\n","test['external_source_2'] = test['external_source_2'].fillna(0)\n","test['external_source_3'] = test['external_source_3'].fillna(0)\n","print('кол-во пропусков external_source_1:', test['external_source_1'].isna().mean())\n","print('кол-во пропусков external_source_2 :', test['external_source_2'].isna().mean())\n","print('кол-во пропусков external_source_3:', test['external_source_3'].isna().mean())"],"metadata":{"id":"14CAC7F0Saqe","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379518152,"user_tz":-420,"elapsed":60,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"ff423805-8506-4408-ab56-48919dbe0dc6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["кол-во пропусков external_source_1: 0.0\n","кол-во пропусков external_source_2 : 0.0\n","кол-во пропусков external_source_3: 0.0\n"]}]},{"cell_type":"code","source":["test['median_years_beginexpluatation'] = test['median_years_beginexpluatation'].fillna(0.98)\n","print('кол-во пропусков median_years_beginexpluatation:', test['median_years_beginexpluatation'].isna().mean())"],"metadata":{"id":"Cevfw00tSaqf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379518153,"user_tz":-420,"elapsed":51,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"e9241175-36fb-4312-870c-54260c6dc04e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["кол-во пропусков median_years_beginexpluatation: 0.0\n"]}]},{"cell_type":"code","source":["test['last_phone_number_change'] = test['last_phone_number_change'].fillna(0)\n","print('кол-во пропусков last_phone_number_change:', test['last_phone_number_change'].isna().mean())"],"metadata":{"id":"j0N69eyZSaqg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379518155,"user_tz":-420,"elapsed":42,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"aff76a22-080c-45e0-81c5-970cdf14c69e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["кол-во пропусков last_phone_number_change: 0.0\n"]}]},{"cell_type":"code","source":["test['requests_bki_qrt'] =  test['requests_bki_qrt'].fillna(0)\n","print('кол-во пропусков requests_bki_qrt:', test['requests_bki_qrt'].isna().mean())"],"metadata":{"id":"njC8LcWeSaqi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379518155,"user_tz":-420,"elapsed":29,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"6ba72254-aec4-4fba-d9c9-26c2bd36aeca"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["кол-во пропусков requests_bki_qrt: 0.0\n"]}]},{"cell_type":"markdown","source":["## Исключение выбросов"],"metadata":{"id":"l_0O_tbnSaqj"}},{"cell_type":"code","source":["test['days_employed'].plot.hist(title = 'Days Employment Histogram');\n","plt.xlabel('Days Employment');"],"metadata":{"id":"DKsE0aA1Saqn","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1675379518668,"user_tz":-420,"elapsed":531,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"67c0e433-cbbd-4bd1-d165-4d4632db4e73"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["test['days_employed_anom'] = df[\"days_employed\"] == 365243"],"metadata":{"id":"WOEVFzOjSaqo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test['days_employed'].replace({365243: -1647.0}, inplace = True)"],"metadata":{"id":"-P9pwqX9Saqo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test['days_employed'].plot.hist(title = 'Days Employment Histogram');\n","plt.xlabel('Days Employment');"],"metadata":{"id":"tbENZTVuSaqo","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1675379518670,"user_tz":-420,"elapsed":24,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"441fcb57-edaf-464c-b3dd-8b90a602caf9"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Заполнение пропусков остальных признаков"],"metadata":{"id":"AB3pwdASDrhB"}},{"cell_type":"markdown","source":["#### Заполнение пропусков отстальных признаков"],"metadata":{"id":"KliuV84QD3tv"}},{"cell_type":"markdown","source":["Выведем рейтинг признаков с самой высокой долей пропусков:"],"metadata":{"id":"j3hjeEBkD3tw"}},{"cell_type":"code","source":["df.isna().mean().sort_values(ascending=False).head(50)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379518670,"user_tz":-420,"elapsed":22,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"6fe3f4c2-fed3-424e-9043-5c0470730e2d","id":"0-IaD1jJD3tx"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["mode_commonarea                    0.698884\n","median_commonarea                  0.698884\n","average_commonarea                 0.698884\n","non_living_apartments_mode         0.694492\n","non_living_apartments_avg          0.694492\n","non_living_apartments_medi         0.694492\n","fondkapremon_mode                  0.684059\n","median_living_apartments           0.683814\n","mode_living_apartments             0.683814\n","average_living_apartments          0.683814\n","mode_min_floors                    0.678802\n","average_min_floors                 0.678802\n","median_min_floors                  0.678802\n","average_years_building             0.665309\n","mode_years_building                0.665309\n","median_years_building              0.665309\n","mode_land_area                     0.593816\n","average_land_area                  0.593816\n","median_land_area                   0.593816\n","median_basementarea                0.585070\n","mode_basementarea                  0.585070\n","average_basementarea               0.585070\n","non_living_area_medi               0.551495\n","non_living_area_mode               0.551495\n","non_living_area_avg                0.551495\n","average_elevator_count             0.532810\n","mode_elevator_count                0.532810\n","median_elevator_count              0.532810\n","mode_walls_material                0.508111\n","median_apartments                  0.507391\n","average_apartments                 0.507391\n","mode_apartments                    0.507391\n","average_entrance_count             0.503164\n","median_entrance_count              0.503164\n","mode_entrance_count                0.503164\n","average_living_area                0.501748\n","mode_living_area                   0.501748\n","median_living_area                 0.501748\n","mode_house_type                    0.501599\n","median_max_floors                  0.497276\n","mode_max_floors                    0.497276\n","average_max_floors                 0.497276\n","mode_years_beginexpluatation       0.487639\n","average_years_beginexpluatation    0.487639\n","mode_total_area                    0.482505\n","emergency_state_mode               0.473832\n","type_of_occupation                 0.313573\n","requests_bki_day                   0.134798\n","requests_bki_year                  0.134798\n","requests_bki_week                  0.134798\n","dtype: float64"]},"metadata":{},"execution_count":79}]},{"cell_type":"markdown","source":["Самая высокая доля пропусков у признаков характеризующих жилье заемщика. Очевидно, что пропуски присутствуют у клиентов, которые не владеют жильем. Можно заполнить их групповым значением \"Отсутствует\". Всего их 46."],"metadata":{"id":"sccxLo9CD3ty"}},{"cell_type":"code","source":["housing_features = list(\n","    df.isna().mean().\n","    sort_values(ascending=False).\n","    head(46).\n","    reset_index()['index']\n","    )"],"metadata":{"id":"54WkdBEOD3tz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[housing_features] = df[housing_features].fillna(-1)\n","test[housing_features] = test[housing_features].fillna(-1)"],"metadata":{"id":"363Qr_sSD3t0"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Следующая группа признаков с пропусками - это количесиво запросов в БКИ. Пропуски - отсутствие запросов. Заполним значением 0."],"metadata":{"id":"OBQx_bQUD3t1"}},{"cell_type":"code","source":["requests_bki_features = ['requests_bki_year', 'requests_bki_month', 'requests_bki_week', 'requests_bki_day', 'requests_bki_hour']"],"metadata":{"id":"32WjXBy4D3t1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[requests_bki_features] = df[requests_bki_features].fillna(0)\n","test[requests_bki_features] = test[requests_bki_features].fillna(0)"],"metadata":{"id":"38ukvaxCD3t2"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Узнаем, какие признаки с пропусками у нас остались:"],"metadata":{"id":"23sE1L43D3t3"}},{"cell_type":"code","source":["df.isna().mean().sort_values(ascending=False).head(7)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379519734,"user_tz":-420,"elapsed":297,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"e369513b-16f0-4fe4-9964-a7c156900dc5","id":"0ihamkuuD3t3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["type_of_occupation                 0.313573\n","type_suite_name                    0.004124\n","observes_60_count_social_circle    0.003332\n","social_circle_defaults_60_days     0.003332\n","observes_30_count_social_circle    0.003332\n","social_circle_defaults_30_days     0.003332\n","family_members__count              0.000008\n","dtype: float64"]},"metadata":{},"execution_count":84}]},{"cell_type":"code","source":["df['type_of_occupation'] = df['type_of_occupation'].astype(str)\n","test['type_of_occupation'] = test['type_of_occupation'].astype(str)\n","print('Пропуски в type_of_occupation:', df['type_of_occupation'].isna().mean())\n","print('Пропуски в type_of_occupation:', test['type_of_occupation'].isna().mean())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379520119,"user_tz":-420,"elapsed":388,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"b0338119-754d-47c6-a66e-15632b365567","id":"tDAZ9lL6D3t4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Пропуски в type_of_occupation: 0.0\n","Пропуски в type_of_occupation: 0.0\n"]}]},{"cell_type":"code","source":["df['type_suite_name'] = df['type_suite_name'].astype(str).replace('nan', 'Unaccompanied')\n","test['type_suite_name'] = test['type_suite_name'].astype(str).replace('nan', 'Unaccompanied')\n","print('Пропуски в type_suite_name:', df['type_suite_name'].isna().mean())\n","print('Пропуски в type_suite_name:', test['type_suite_name'].isna().mean())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379520119,"user_tz":-420,"elapsed":27,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"e234bdfe-6c36-40a4-c397-7ab449cfa37e","id":"ABt1vB4ID3t5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Пропуски в type_suite_name: 0.0\n","Пропуски в type_suite_name: 0.0\n"]}]},{"cell_type":"code","source":["delays_features = ['social_circle_defaults_60_days', 'observes_60_count_social_circle',\n","                   'observes_30_count_social_circle', 'social_circle_defaults_30_days']"],"metadata":{"id":"YzKmjSGYD3t6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[delays_features] = df[delays_features].fillna(0)\n","test[delays_features] = test[delays_features].fillna(0)\n","print('Пропуски в delays_features:', df[delays_features].isna().mean())\n","print('Пропуски в delays_features:', test[delays_features].isna().mean())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379520120,"user_tz":-420,"elapsed":18,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"58456434-fe5c-40db-f81a-1b15faf24e33","id":"V2loC3pfD3t7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Пропуски в delays_features: social_circle_defaults_60_days     0.0\n","observes_60_count_social_circle    0.0\n","observes_30_count_social_circle    0.0\n","social_circle_defaults_30_days     0.0\n","dtype: float64\n","Пропуски в delays_features: social_circle_defaults_60_days     0.0\n","observes_60_count_social_circle    0.0\n","observes_30_count_social_circle    0.0\n","social_circle_defaults_30_days     0.0\n","dtype: float64\n"]}]},{"cell_type":"code","source":["df['family_members__count'] = df['family_members__count'].fillna(1)\n","test['family_members__count'] = test['family_members__count'].fillna(1)\n","print('Пропуски в family_members__count:', df['family_members__count'].isna().mean())\n","print('Пропуски в family_members__count:', test['family_members__count'].isna().mean())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675379520990,"user_tz":-420,"elapsed":882,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"bc7f297a-633a-4c5d-a2ae-d920fc9f0ff0","id":"nHLSRUYDD3t8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Пропуски в family_members__count: 0.0\n","Пропуски в family_members__count: 0.0\n"]}]},{"cell_type":"markdown","source":["# Предсказание на преобработанном дата-сете"],"metadata":{"id":"AyGXvta2JeZP"}},{"cell_type":"markdown","source":["Создадим функцию для предсказания:"],"metadata":{"id":"CPOuUAkyJ7T7"}},{"cell_type":"code","source":["def predict_func(train_, test):\n","  numerical_features = []\n","  categorical_features = []\n","  for column in train_.columns:\n","    if train_[column].dtype == 'object':\n","      categorical_features.append(column)     \n","    else:\n","        numerical_features.append(column)\n","  #############     \n","  train, valid = train_test_split(train_, test_size=0.25, random_state=12)\n","  #############\n","  features_train = train.drop('target', axis=1)\n","  target_train = train['target']\n","  features_valid = valid.drop('target', axis=1)\n","  target_valid = valid['target']\n","  ############\n","  features_train[categorical_features] = features_train[categorical_features].astype('str')\n","  features_valid[categorical_features] = features_valid[categorical_features].astype('str')\n","  test[categorical_features] = test[categorical_features].astype('str')\n","  ###########\n","  Catboost = CatBoostClassifier(random_state = 12,\n","                              verbose=50,\n","                              scale_pos_weight = 12,\n","                              custom_loss = 'AUC',\n","                              iterations=200,\n","                              learning_rate=0.2,\n","                              depth = 5,\n","                              l2_leaf_reg = 4)\n","  cat_features = categorical_features\n","  ###########\n","  Catboost.fit(features_train, target_train, cat_features = cat_features, eval_set=(features_valid, target_valid))\n","  predict_probability = Catboost.predict_proba(test)[:, 1]\n","  auc_roc = metrics.roc_auc_score(target_valid, Catboost.predict_proba(features_valid)[:, 1])\n","  return print('AUC_ROC:', auc_roc)"],"metadata":{"id":"vRDVGRkfWKz0"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Определим качество предсказаний после заполнения пропусков самых сильных признаков:"],"metadata":{"id":"CaKNZLR3eksz"}},{"cell_type":"code","source":["predict_func(df, test)"],"metadata":{"id":"41FUdXgoK1Qk"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**Качество предсказаний улучшилось незначительно.**"],"metadata":{"id":"2TDT97kle0AF"}},{"cell_type":"markdown","source":["## Предсказание только на самых сильных обработанных признаках"],"metadata":{"id":"a3zJ4UA-fOry"}},{"cell_type":"code","source":["df[features_best].columns"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a9CGQ_-0gtDI","executionInfo":{"status":"ok","timestamp":1675379520992,"user_tz":-420,"elapsed":18,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"adfee77e-c1fe-4018-c18f-6fadd53b5b20"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['gender', 'loan_body', 'annuity_payment', 'goods_price',\n","       'education_type_name', 'days_birth', 'days_employed',\n","       'publication_timestamp', 'rating_client_w_city_region',\n","       'type_of_organization', 'external_source_1', 'external_source_2',\n","       'external_source_3', 'last_phone_number_change', 'document_3_flag'],\n","      dtype='object')"]},"metadata":{},"execution_count":92}]},{"cell_type":"code","source":["df_best_features = df[['gender', 'loan_body', 'annuity_payment', 'goods_price',\n","       'education_type_name', 'days_birth', 'days_employed',\n","       'publication_timestamp', 'rating_client_w_city_region',\n","       'type_of_organization', 'external_source_1', 'external_source_2',\n","       'external_source_3', 'last_phone_number_change', 'document_3_flag', 'target']]\n","test_best_features = test[features_best]"],"metadata":{"id":"fj_oE3EhK_GN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["predict_func(df_best_features, test_best_features)"],"metadata":{"id":"oZ54m2bJgOGu"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**Результат не изменился. Значит остальные признаки не влияют на работу модели.**"],"metadata":{"id":"VkjFlaAVhQzu"}},{"cell_type":"markdown","source":["## Предсказание на чистом дата-сете"],"metadata":{"id":"dNTix9Fw0ckU"}},{"cell_type":"code","source":["predict_func(df, test)"],"metadata":{"id":"VZNNI_mXyKmP"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**Качество предсказаний на чистых дата-сетах не улучшилось.**"],"metadata":{"id":"vNv2rfO41bnt"}},{"cell_type":"markdown","source":["# Создание новых признаков"],"metadata":{"id":"q8zxw1v1auke"}},{"cell_type":"markdown","source":["+ процент суммы кредита относительно дохода клиента\n","+ процент аннуитетного платежа кредита относительно дохода клиента\n","+ продолжительность платежа в месяцах\n","+ процент отработанных дней по отношению к возрасту клиента"],"metadata":{"id":"iMVT0Sq-MWx3"}},{"cell_type":"code","source":["df['credit_sum__income_part'] = df['income'] / df['loan_body']\n","test['credit_sum__income_part'] = test['income'] / test['loan_body']"],"metadata":{"id":"WXvKgn5-y4Gu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df['payment_part_annuity'] = df['annuity_payment'] / df['income']\n","test['payment_part_annuity'] = test['annuity_payment'] / test['income']"],"metadata":{"id":"bCubNGBFPw-H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df['duration_credit'] = df['loan_body'] / df['annuity_payment']\n","test['duration_credit'] = test['loan_body'] / test['annuity_payment']"],"metadata":{"id":"ybnXzmCGQXCx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df['spent_part'] = df['days_employed'] / df['days_birth']\n","test['spent_part'] = test['days_employed'] / test['days_birth']"],"metadata":{"id":"Z9jRhtatQ1JO"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":[" Предскажем с добавленными признаками:"],"metadata":{"id":"G_S9F-wjQuvi"}},{"cell_type":"code","source":["predict_func(df, test)"],"metadata":{"id":"xs_fkZyQRbkc"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**Создание новых признаков улучшило результаты на примерно на 0,5%**"],"metadata":{"id":"xj1y8ZbyRnxv"}},{"cell_type":"markdown","source":["# Присоединение нового дата-сета"],"metadata":{"id":"NHnWCY6I1ueu"}},{"cell_type":"code","source":["bki.sort_values(by='reco_id_curr').head(4)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":326},"id":"_txtLnPT2bFe","executionInfo":{"status":"ok","timestamp":1675379521975,"user_tz":-420,"elapsed":992,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"4a07709b-e552-4e46-ec14-eb96ccab07ba"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Unnamed: 0  reco_id_curr  reco_bureau_id credit_status  \\\n","248487      248487        100001         5896633        Closed   \n","248485      248485        100001         5896631        Closed   \n","248490      248490        100001         5896636        Active   \n","248489      248489        100001         5896635        Active   \n","\n","       credit_currency  days_credit  credit_day_overdue  days_credit_enddate  \\\n","248487      currency 1        -1572                   0              -1329.0   \n","248485      currency 1         -909                   0               -179.0   \n","248490      currency 1         -320                   0                411.0   \n","248489      currency 1          -49                   0               1778.0   \n","\n","        days_enddate_fact  credit_limit_max_overdue  credit_prolong_count  \\\n","248487            -1328.0                       NaN                     0   \n","248485             -877.0                       NaN                     0   \n","248490                NaN                       NaN                     0   \n","248489                NaN                       NaN                     0   \n","\n","        credit_sum  credit_sum_debt  credit_sum_limit  credit_sum_overdue  \\\n","248487     85500.0              0.0               0.0                 0.0   \n","248485    279720.0              0.0               0.0                 0.0   \n","248490    168345.0         110281.5               NaN                 0.0   \n","248489    378000.0         373239.0               0.0                 0.0   \n","\n","            credit_type  days_credit_update  annuity_payment  \n","248487  Consumer credit                -155              0.0  \n","248485  Consumer credit                -155              0.0  \n","248490  Consumer credit                 -10           9364.5  \n","248489  Consumer credit                 -16          10822.5  "],"text/html":["\n","  <div id=\"df-0fbb97d0-935c-4143-a2a2-7d7351e10f7a\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>reco_id_curr</th>\n","      <th>reco_bureau_id</th>\n","      <th>credit_status</th>\n","      <th>credit_currency</th>\n","      <th>days_credit</th>\n","      <th>credit_day_overdue</th>\n","      <th>days_credit_enddate</th>\n","      <th>days_enddate_fact</th>\n","      <th>credit_limit_max_overdue</th>\n","      <th>credit_prolong_count</th>\n","      <th>credit_sum</th>\n","      <th>credit_sum_debt</th>\n","      <th>credit_sum_limit</th>\n","      <th>credit_sum_overdue</th>\n","      <th>credit_type</th>\n","      <th>days_credit_update</th>\n","      <th>annuity_payment</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>248487</th>\n","      <td>248487</td>\n","      <td>100001</td>\n","      <td>5896633</td>\n","      <td>Closed</td>\n","      <td>currency 1</td>\n","      <td>-1572</td>\n","      <td>0</td>\n","      <td>-1329.0</td>\n","      <td>-1328.0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>85500.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-155</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>248485</th>\n","      <td>248485</td>\n","      <td>100001</td>\n","      <td>5896631</td>\n","      <td>Closed</td>\n","      <td>currency 1</td>\n","      <td>-909</td>\n","      <td>0</td>\n","      <td>-179.0</td>\n","      <td>-877.0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>279720.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-155</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>248490</th>\n","      <td>248490</td>\n","      <td>100001</td>\n","      <td>5896636</td>\n","      <td>Active</td>\n","      <td>currency 1</td>\n","      <td>-320</td>\n","      <td>0</td>\n","      <td>411.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>168345.0</td>\n","      <td>110281.5</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-10</td>\n","      <td>9364.5</td>\n","    </tr>\n","    <tr>\n","      <th>248489</th>\n","      <td>248489</td>\n","      <td>100001</td>\n","      <td>5896635</td>\n","      <td>Active</td>\n","      <td>currency 1</td>\n","      <td>-49</td>\n","      <td>0</td>\n","      <td>1778.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>378000.0</td>\n","      <td>373239.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-16</td>\n","      <td>10822.5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0fbb97d0-935c-4143-a2a2-7d7351e10f7a')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      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Егоров","userId":"00573607421615031768"}},"outputId":"1f2c35a0-632f-4bb9-b592-773f89083d31"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         Unnamed: 0  reco_id_curr  reco_bureau_id credit_status  \\\n","675684       675684        100002         6158904        Closed   \n","675685       675685        100002         6158905        Closed   \n","675686       675686        100002         6158906        Closed   \n","675687       675687        100002         6158907        Closed   \n","675688       675688        100002         6158908        Closed   \n","675689       675689        100002         6158909        Active   \n","1337779     1337779        100002         6158903        Active   \n","1486113     1486113        100002         6113835        Closed   \n","\n","        credit_currency  days_credit  credit_day_overdue  days_credit_enddate  \\\n","675684       currency 1        -1125                   0              -1038.0   \n","675685       currency 1         -476                   0                  NaN   \n","675686       currency 1        -1437                   0              -1072.0   \n","675687       currency 1        -1121                   0               -911.0   \n","675688       currency 1         -645                   0                 85.0   \n","675689       currency 1         -103                   0                  NaN   \n","1337779      currency 1        -1042                   0                780.0   \n","1486113      currency 1        -1043                   0                 62.0   \n","\n","         days_enddate_fact  credit_limit_max_overdue  credit_prolong_count  \\\n","675684             -1038.0                       NaN                     0   \n","675685               -48.0                       NaN                     0   \n","675686             -1185.0                     0.000                     0   \n","675687              -911.0                  3321.000                     0   \n","675688               -36.0                  5043.645                     0   \n","675689                 NaN                    40.500                     0   \n","1337779                NaN                       NaN                     0   \n","1486113             -967.0                     0.000                     0   \n","\n","         credit_sum  credit_sum_debt  credit_sum_limit  credit_sum_overdue  \\\n","675684    40761.000              NaN               NaN                 0.0   \n","675685        0.000              0.0               NaN                 0.0   \n","675686   135000.000              0.0             0.000                 0.0   \n","675687    19071.000              NaN               NaN                 0.0   \n","675688   120735.000              0.0             0.000                 0.0   \n","675689    31988.565              0.0         31988.565                 0.0   \n","1337779  450000.000         245781.0             0.000                 0.0   \n","1486113   67500.000              NaN               NaN                 0.0   \n","\n","             credit_type  days_credit_update  annuity_payment  \n","675684       Credit card               -1038              0.0  \n","675685       Credit card                 -47              NaN  \n","675686   Consumer credit               -1185              0.0  \n","675687   Consumer credit                -906              0.0  \n","675688   Consumer credit                 -34              0.0  \n","675689       Credit card                 -24              0.0  \n","1337779  Consumer credit                  -7              0.0  \n","1486113      Credit card                -758              0.0  "],"text/html":["\n","  <div id=\"df-45b65b79-6e90-4e9a-98da-7bbfde7256d1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        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<td>100002</td>\n","      <td>6158904</td>\n","      <td>Closed</td>\n","      <td>currency 1</td>\n","      <td>-1125</td>\n","      <td>0</td>\n","      <td>-1038.0</td>\n","      <td>-1038.0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>40761.000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>Credit card</td>\n","      <td>-1038</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>675685</th>\n","      <td>675685</td>\n","      <td>100002</td>\n","      <td>6158905</td>\n","      <td>Closed</td>\n","      <td>currency 1</td>\n","      <td>-476</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>-48.0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0.000</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>Credit card</td>\n","      <td>-47</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>675686</th>\n","      <td>675686</td>\n","  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<th>675688</th>\n","      <td>675688</td>\n","      <td>100002</td>\n","      <td>6158908</td>\n","      <td>Closed</td>\n","      <td>currency 1</td>\n","      <td>-645</td>\n","      <td>0</td>\n","      <td>85.0</td>\n","      <td>-36.0</td>\n","      <td>5043.645</td>\n","      <td>0</td>\n","      <td>120735.000</td>\n","      <td>0.0</td>\n","      <td>0.000</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-34</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>675689</th>\n","      <td>675689</td>\n","      <td>100002</td>\n","      <td>6158909</td>\n","      <td>Active</td>\n","      <td>currency 1</td>\n","      <td>-103</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>40.500</td>\n","      <td>0</td>\n","      <td>31988.565</td>\n","      <td>0.0</td>\n","      <td>31988.565</td>\n","      <td>0.0</td>\n","      <td>Credit card</td>\n","      <td>-24</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1337779</th>\n","      <td>1337779</td>\n","      <td>100002</td>\n","      <td>6158903</td>\n","      <td>Active</td>\n","      <td>currency 1</td>\n","      <td>-1042</td>\n","      <td>0</td>\n","      <td>780.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>450000.000</td>\n","      <td>245781.0</td>\n","      <td>0.000</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-7</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1486113</th>\n","      <td>1486113</td>\n","      <td>100002</td>\n","      <td>6113835</td>\n","      <td>Closed</td>\n","      <td>currency 1</td>\n","      <td>-1043</td>\n","      <td>0</td>\n","      <td>62.0</td>\n","      <td>-967.0</td>\n","      <td>0.000</td>\n","      <td>0</td>\n","      <td>67500.000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>Credit card</td>\n","      <td>-758</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45b65b79-6e90-4e9a-98da-7bbfde7256d1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      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<th>requests_bki_qrt</th>\n","      <th>requests_bki_year</th>\n","      <th>work_status</th>\n","      <th>days_employed_anom</th>\n","      <th>credit_sum__income_part</th>\n","      <th>payment_part_annuity</th>\n","      <th>duration_credit</th>\n","      <th>spent_part</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>17677</th>\n","      <td>100002</td>\n","      <td>1</td>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>N</td>\n","      <td>Y</td>\n","      <td>0</td>\n","      <td>202500.0</td>\n","      <td>406597.5</td>\n","      <td>24700.5</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Worker</td>\n","      <td>False</td>\n","      <td>0.498036</td>\n","      <td>0.121978</td>\n","      <td>16.461104</td>\n","      <td>0.067329</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1 rows × 128 columns</p>\n","</div>\n","      <button 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  \n","109178        456255       0         Cash loans      F            N   \n","\n","       own_realty_flag  children_count    income  loan_body  annuity_payment  \\\n","17677                Y               0  202500.0   406597.5          24700.5   \n","140587               N               0  270000.0  1293502.5          35698.5   \n","12943                Y               0   67500.0   135000.0           6750.0   \n","195574               Y               0  135000.0   312682.5          29686.5   \n","91683                Y               0  121500.0   513000.0          21865.5   \n","...                ...             ...       ...        ...              ...   \n","208926               Y               0  112500.0   225000.0          22050.0   \n","181487               Y               0   72000.0   269550.0          12001.5   \n","114106               Y               0  153000.0   677664.0          29979.0   \n","207594               Y               0  171000.0   370107.0          20205.0   \n","109178               N               0  157500.0   675000.0          49117.5   \n","\n","        ...  requests_bki_week requests_bki_month requests_bki_qrt  \\\n","17677   ...                0.0                0.0              0.0   \n","140587  ...                0.0                0.0              0.0   \n","12943   ...                0.0                0.0              0.0   \n","195574  ...                0.0                0.0              0.0   \n","91683   ...                0.0                0.0              0.0   \n","...     ...                ...                ...              ...   \n","208926  ...                0.0                2.0              0.0   \n","181487  ...                0.0                0.0              0.0   \n","114106  ...                0.0                1.0              0.0   \n","207594  ...                0.0                0.0              0.0   \n","109178  ...                0.0                2.0              0.0   \n","\n","       requests_bki_year work_status days_employed_anom  \\\n","17677                1.0      Worker              False   \n","140587               0.0      Worker              False   \n","12943                0.0      Worker              False   \n","195574               0.0      Worker              False   \n","91683                0.0      Worker              False   \n","...                  ...         ...                ...   \n","208926               0.0  Unemployed               True   \n","181487               0.0  Unemployed               True   \n","114106               1.0      Worker              False   \n","207594               0.0      Worker              False   \n","109178               1.0      Worker              False   \n","\n","        credit_sum__income_part  payment_part_annuity  duration_credit  \\\n","17677                  0.498036              0.121978        16.461104   \n","140587                 0.208736              0.132217        36.234085   \n","12943                  0.500000              0.100000        20.000000   \n","195574                 0.431748              0.219900        10.532818   \n","91683                  0.236842              0.179963        23.461618   \n","...                         ...                   ...              ...   \n","208926                 0.500000              0.196000        10.204082   \n","181487                 0.267112              0.166687        22.459693   \n","114106                 0.225776              0.195941        22.604623   \n","207594                 0.462029              0.118158        18.317595   \n","109178                 0.233333              0.311857        13.742556   \n","\n","        spent_part  \n","17677     0.067329  \n","140587    0.070862  \n","12943     0.011814  \n","195574    0.159905  \n","91683     0.152418  \n","...            ...  \n","208926    0.067544  \n","181487    0.079278  \n","114106    0.529266  \n","207594    0.400134  \n","109178    0.074869  \n","\n","[261384 rows x 128 columns]"],"text/html":["\n","  <div id=\"df-31402d95-f398-4653-9c29-4456e86ee707\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>reco_id_curr</th>\n","      <th>target</th>\n","      <th>contract_type_name</th>\n","      <th>gender</th>\n","      <th>own_car_flag</th>\n","      <th>own_realty_flag</th>\n","      <th>children_count</th>\n","      <th>income</th>\n","      <th>loan_body</th>\n","      <th>annuity_payment</th>\n","      <th>...</th>\n","      <th>requests_bki_week</th>\n","      <th>requests_bki_month</th>\n","      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<td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>N</td>\n","      <td>0</td>\n","      <td>270000.0</td>\n","      <td>1293502.5</td>\n","      <td>35698.5</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Worker</td>\n","      <td>False</td>\n","      <td>0.208736</td>\n","      <td>0.132217</td>\n","      <td>36.234085</td>\n","      <td>0.070862</td>\n","    </tr>\n","    <tr>\n","      <th>12943</th>\n","      <td>100004</td>\n","      <td>0</td>\n","      <td>Revolving loans</td>\n","      <td>M</td>\n","      <td>Y</td>\n","      <td>Y</td>\n","      <td>0</td>\n","      <td>67500.0</td>\n","      <td>135000.0</td>\n","      <td>6750.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Worker</td>\n","      <td>False</td>\n","      <td>0.500000</td>\n","      <td>0.100000</td>\n","      <td>20.000000</td>\n","      <td>0.011814</td>\n","    </tr>\n","    <tr>\n","      <th>195574</th>\n","      <td>100006</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>Y</td>\n","      <td>0</td>\n","      <td>135000.0</td>\n","      <td>312682.5</td>\n","      <td>29686.5</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Worker</td>\n","      <td>False</td>\n","      <td>0.431748</td>\n","      <td>0.219900</td>\n","      <td>10.532818</td>\n","      <td>0.159905</td>\n","    </tr>\n","    <tr>\n","      <th>91683</th>\n","      <td>100007</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>N</td>\n","      <td>Y</td>\n","      <td>0</td>\n","      <td>121500.0</td>\n","      <td>513000.0</td>\n","      <td>21865.5</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Worker</td>\n","      <td>False</td>\n","      <td>0.236842</td>\n","      <td>0.179963</td>\n","      <td>23.461618</td>\n","      <td>0.152418</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","      <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>208926</th>\n","      <td>456249</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>Y</td>\n","      <td>0</td>\n","      <td>112500.0</td>\n","      <td>225000.0</td>\n","      <td>22050.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Unemployed</td>\n","      <td>True</td>\n","      <td>0.500000</td>\n","      <td>0.196000</td>\n","      <td>10.204082</td>\n","      <td>0.067544</td>\n","    </tr>\n","    <tr>\n","      <th>181487</th>\n","      <td>456252</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>Y</td>\n","      <td>0</td>\n","      <td>72000.0</td>\n","      <td>269550.0</td>\n","      <td>12001.5</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Unemployed</td>\n","      <td>True</td>\n","      <td>0.267112</td>\n","      <td>0.166687</td>\n","      <td>22.459693</td>\n","      <td>0.079278</td>\n","    </tr>\n","    <tr>\n","      <th>114106</th>\n","      <td>456253</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>Y</td>\n","      <td>0</td>\n","      <td>153000.0</td>\n","      <td>677664.0</td>\n","      <td>29979.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Worker</td>\n","      <td>False</td>\n","      <td>0.225776</td>\n","      <td>0.195941</td>\n","      <td>22.604623</td>\n","      <td>0.529266</td>\n","    </tr>\n","    <tr>\n","      <th>207594</th>\n","      <td>456254</td>\n","      <td>1</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>Y</td>\n","      <td>0</td>\n","      <td>171000.0</td>\n","      <td>370107.0</td>\n","      <td>20205.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Worker</td>\n","      <td>False</td>\n","      <td>0.462029</td>\n","      <td>0.118158</td>\n","      <td>18.317595</td>\n","      <td>0.400134</td>\n","    </tr>\n","    <tr>\n","      <th>109178</th>\n","      <td>456255</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>N</td>\n","      <td>0</td>\n","      <td>157500.0</td>\n","      <td>675000.0</td>\n","      <td>49117.5</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Worker</td>\n","      <td>False</td>\n","      <td>0.233333</td>\n","      <td>0.311857</td>\n","      <td>13.742556</td>\n","      <td>0.074869</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>261384 rows × 128 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31402d95-f398-4653-9c29-4456e86ee707')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  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NaN                      NaN   \n","2                  52641.0  ...            -1036.0                      NaN   \n","3                  52641.0  ...                NaN                      NaN   \n","4                  52641.0  ...             -225.0                      0.0   \n","...                    ...  ...                ...                      ...   \n","1283238            15151.5  ...             -932.0                      0.0   \n","1283239            15151.5  ...            -1645.0                      NaN   \n","1283240            15151.5  ...            -1247.0                      NaN   \n","1283241            15151.5  ...            -1175.0                      NaN   \n","1283242            15151.5  ...                NaN                      NaN   \n","\n","        credit_prolong_count credit_sum credit_sum_debt credit_sum_limit  \\\n","0                        0.0    19453.5             0.0              NaN   \n","1                        0.0    49374.0             0.0              NaN   \n","2                        0.0    19449.0             0.0              0.0   \n","3                        0.0   405000.0        276988.5              0.0   \n","4                        0.0   225000.0             0.0              0.0   \n","...                      ...        ...             ...              ...   \n","1283238                  0.0    59400.0             0.0              0.0   \n","1283239                  0.0   209308.5             NaN              NaN   \n","1283240                  0.0    96615.0             0.0              NaN   \n","1283241                  0.0   180000.0             NaN              NaN   \n","1283242                  0.0   458685.0             NaN              NaN   \n","\n","         credit_sum_overdue      credit_type  days_credit_update  \\\n","0                       0.0  Consumer credit             -1033.0   \n","1                       0.0  Consumer credit              -610.0   \n","2                       0.0  Consumer credit              -906.0   \n","3                       0.0  Consumer credit               -12.0   \n","4                       0.0  Consumer credit              -225.0   \n","...                     ...              ...                 ...   \n","1283238                 0.0  Consumer credit              -932.0   \n","1283239                 0.0  Consumer credit             -1645.0   \n","1283240                 0.0  Consumer credit             -1247.0   \n","1283241                 0.0  Consumer credit             -1175.0   \n","1283242                 0.0  Consumer credit               -31.0   \n","\n","         annuity_payment_y  \n","0                  37305.0  \n","1                      0.0  \n","2                      0.0  \n","3                      NaN  \n","4                      0.0  \n","...                    ...  \n","1283238                0.0  \n","1283239                NaN  \n","1283240                NaN  \n","1283241                NaN  \n","1283242                0.0  \n","\n","[1283243 rows x 145 columns]"],"text/html":["\n","  <div id=\"df-32538f4c-a214-405a-b057-f70e16f28299\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>reco_id_curr</th>\n","      <th>target</th>\n","      <th>contract_type_name</th>\n","      <th>gender</th>\n","      <th>own_car_flag</th>\n","      <th>own_realty_flag</th>\n","      <th>children_count</th>\n","      <th>income</th>\n","      <th>loan_body</th>\n","      <th>annuity_payment_x</th>\n","      <th>...</th>\n","      <th>days_enddate_fact</th>\n","      <th>credit_limit_max_overdue</th>\n","      <th>credit_prolong_count</th>\n","      <th>credit_sum</th>\n","      <th>credit_sum_debt</th>\n","      <th>credit_sum_limit</th>\n","      <th>credit_sum_overdue</th>\n","      <th>credit_type</th>\n","      <th>days_credit_update</th>\n","      <th>annuity_payment_y</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>384575</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>Y</td>\n","      <td>N</td>\n","      <td>2</td>\n","      <td>207000.0</td>\n","      <td>465457.5</td>\n","      <td>52641.0</td>\n","      <td>...</td>\n","      <td>-1036.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>19453.5</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-1033.0</td>\n","      <td>37305.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>384575</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>Y</td>\n","      <td>N</td>\n","      <td>2</td>\n","      <td>207000.0</td>\n","      <td>465457.5</td>\n","      <td>52641.0</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>49374.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-610.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>384575</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>Y</td>\n","      <td>N</td>\n","      <td>2</td>\n","      <td>207000.0</td>\n","      <td>465457.5</td>\n","      <td>52641.0</td>\n","      <td>...</td>\n","      <td>-1036.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>19449.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-906.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>384575</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>Y</td>\n","      <td>N</td>\n","      <td>2</td>\n","      <td>207000.0</td>\n","      <td>465457.5</td>\n","      <td>52641.0</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>405000.0</td>\n","      <td>276988.5</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-12.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>384575</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>M</td>\n","      <td>Y</td>\n","      <td>N</td>\n","      <td>2</td>\n","      <td>207000.0</td>\n","      <td>465457.5</td>\n","      <td>52641.0</td>\n","      <td>...</td>\n","      <td>-225.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>225000.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-225.0</td>\n","      <td>0.0</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","      <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>1283238</th>\n","      <td>291070</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>N</td>\n","      <td>1</td>\n","      <td>112500.0</td>\n","      <td>315000.0</td>\n","      <td>15151.5</td>\n","      <td>...</td>\n","      <td>-932.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>59400.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-932.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1283239</th>\n","      <td>291070</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>N</td>\n","      <td>1</td>\n","      <td>112500.0</td>\n","      <td>315000.0</td>\n","      <td>15151.5</td>\n","      <td>...</td>\n","      <td>-1645.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>209308.5</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-1645.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1283240</th>\n","      <td>291070</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>N</td>\n","      <td>1</td>\n","      <td>112500.0</td>\n","      <td>315000.0</td>\n","      <td>15151.5</td>\n","      <td>...</td>\n","      <td>-1247.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>96615.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-1247.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1283241</th>\n","      <td>291070</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>N</td>\n","      <td>1</td>\n","      <td>112500.0</td>\n","      <td>315000.0</td>\n","      <td>15151.5</td>\n","      <td>...</td>\n","      <td>-1175.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>180000.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-1175.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1283242</th>\n","      <td>291070</td>\n","      <td>0</td>\n","      <td>Cash loans</td>\n","      <td>F</td>\n","      <td>N</td>\n","      <td>N</td>\n","      <td>1</td>\n","      <td>112500.0</td>\n","      <td>315000.0</td>\n","      <td>15151.5</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>458685.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>Consumer credit</td>\n","      <td>-31.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1283243 rows × 145 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" 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random_state=12)\n","  #############\n","features_train = train.drop('target', axis=1)\n","target_train = train['target']\n","features_valid = valid.drop('target', axis=1)\n","target_valid = valid['target']\n","  ############\n","features_train[categorical_features] = features_train[categorical_features].astype('str')\n","features_valid[categorical_features] = features_valid[categorical_features].astype('str')\n","test_1[categorical_features] = test_1[categorical_features].astype('str')\n","  ###########\n","Catboost = CatBoostClassifier(random_state = 12,\n","                              verbose=50,\n","                              scale_pos_weight = 12,\n","                              custom_loss = 'AUC',\n","                              iterations=250,\n","                              learning_rate=0.2,\n","                              depth = 5,\n","                              l2_leaf_reg = 4)\n","cat_features = categorical_features\n","  ###########\n","Catboost.fit(features_train, target_train, cat_features = cat_features, eval_set=(features_valid, target_valid))\n","predict_probability_1 = Catboost.predict_proba(test_1)[:, 1]\n","auc_roc = metrics.roc_auc_score(target_valid, Catboost.predict_proba(features_valid)[:, 1])\n","print('AUC_ROC:', auc_roc)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Njo-I5M6Pwa1","executionInfo":{"status":"ok","timestamp":1675380348132,"user_tz":-420,"elapsed":817540,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"71ebba7b-d245-494d-f600-3ac3e04ec295"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0:\tlearn: 0.6679823\ttest: 0.6681248\tbest: 0.6681248 (0)\ttotal: 3.47s\tremaining: 14m 23s\n","50:\tlearn: 0.5571514\ttest: 0.5564810\tbest: 0.5564810 (50)\ttotal: 2m 43s\tremaining: 10m 38s\n","100:\tlearn: 0.5253579\ttest: 0.5221736\tbest: 0.5221736 (100)\ttotal: 5m 22s\tremaining: 7m 55s\n","150:\tlearn: 0.5082048\ttest: 0.5060062\tbest: 0.5060062 (150)\ttotal: 8m\tremaining: 5m 14s\n","200:\tlearn: 0.4931180\ttest: 0.4918405\tbest: 0.4918405 (200)\ttotal: 10m 38s\tremaining: 2m 35s\n","249:\tlearn: 0.4817970\ttest: 0.4822024\tbest: 0.4822024 (249)\ttotal: 13m 14s\tremaining: 0us\n","\n","bestTest = 0.4822024057\n","bestIteration = 249\n","\n","AUC_ROC: 0.8551145421347607\n"]}]},{"cell_type":"markdown","source":["AUC_ROC: 0.8551145421347607"],"metadata":{"id":"3YK9E06-dQba"}},{"cell_type":"code","source":["sample_answ['target'] = predict_probability_1"],"metadata":{"executionInfo":{"status":"error","timestamp":1675380410756,"user_tz":-420,"elapsed":335,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"colab":{"base_uri":"https://localhost:8080/","height":311},"id":"RIO1wCS0c_wH","outputId":"5909dd45-b5f3-4a4c-9817-8535614e4aec"},"execution_count":null,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-110-26c0f0412a86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msample_answ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_probability_1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3610\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3611\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3612\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3613\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3614\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3782\u001b[0m         \u001b[0mensure\u001b[0m \u001b[0mhomogeneity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3783\u001b[0m         \"\"\"\n\u001b[0;32m-> 3784\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3786\u001b[0m         if (\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   4507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4508\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4509\u001b[0;31m             \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire_length_match\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4510\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/common.py\u001b[0m in \u001b[0;36mrequire_length_match\u001b[0;34m(data, index)\u001b[0m\n\u001b[1;32m    529\u001b[0m     \"\"\"\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    532\u001b[0m             \u001b[0;34m\"Length of values \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m             \u001b[0;34mf\"({len(data)}) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Length of values (226102) does not match length of index (46127)"]}]},{"cell_type":"code","source":["sample_answ.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1675379510448,"user_tz":-420,"elapsed":43,"user":{"displayName":"Александр Егоров","userId":"00573607421615031768"}},"outputId":"414128e9-ef91-49f5-e560-d796ba5576ab","id":"lquZx90jc_wK"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   reco_id_curr    target\n","0        100008  0.478925\n","1        100017  0.278662\n","2        100019  0.509354\n","3        100022  0.189132\n","4        100023  0.364499"],"text/html":["\n","  <div 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