Credit_scoring.ipynb

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        "<a href=\"https://colab.research.google.com/github/h4cktivist/credit-scoring-ML/blob/main/Credit_scoring.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/evgpat/edu_stepik_from_idea_to_mvp/main/datasets/credit_scoring.csv')"
      ],
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      "source": [
        "### 📁 Описание данных"
      ],
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        "id": "f75982p2NNrt"
      }
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      "source": [
        "Датасет содержит информацию о клиентах некоторого банка.\n",
        "По различным характеристикам клиентов спрогнозировать целевую переменную - имел клиент просрочку 90 и более дней или нет (и если имел, то банк не будет выдавать кредит этому клиенту, а иначе будет).\n",
        "\n",
        "_Целевая переменная (таргет)_ – `SeriousDlqin2yrs`: клиент будет иметь просрочку по кредиту на 90 и более дней\n",
        "\n",
        "_Признаки_\n",
        "- `RevolvingUtilizationOfUnsecuredLines`: общий остаток по кредиту, деленный на сумму кредитных лимитов\n",
        "- `age`: возраст заемщика в годах\n",
        "- `NumberOfTime30-59DaysPastDueNotWorse`: количество случаев, когда заемщик просрочил платеж на 30-59 дней за последние 2 года\n",
        "- `DebtRatio`: ежемесячные расходы деленные на месячный доход\n",
        "- `MonthlyIncome`: ежемесячный доход\n",
        "- `NumberOfOpenCreditLinesAndLoans`: количество открытых кредитов и кредитных карт\n",
        "- `NumberOfTimes90DaysLate`: количество случаев, когда заемщик просрочил платеж на 90 дней или более за последние 2 года\n",
        "- `RealEstateLoansOrLines`: закодированное количество кредиов (в том числе под залог жилья) ‒ чем больше код буквы, тем больше кредитов\n",
        "- `NumberOfTime60-89DaysPastDueNotWorse`: количество случаев, когда заемщик просрочил платеж на 60-89 дней за последние 2 года\n",
        "- `NumberOfDependents`: количество иждивенцев на попечении (супруги, дети и др.)\n",
        "- `GroupAge`: закодированная возрастная группа ‒ чем больше код, тем больше возраст"
      ],
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        "id": "F316_RHaNSBp"
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        "df.head()"
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              "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines   age  \\\n",
              "0                 0                              0.221813  43.0   \n",
              "1                 0                              0.602794  25.0   \n",
              "2                 1                              0.025656  38.0   \n",
              "3                 0                              0.075427  32.0   \n",
              "4                 0                              0.046560  58.0   \n",
              "\n",
              "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
              "0                                     0   0.527888         3280.0   \n",
              "1                                     0   0.065868          333.0   \n",
              "2                                     0   0.475841         3000.0   \n",
              "3                                     0   0.085512         7916.0   \n",
              "4                                     0   0.241622         2416.0   \n",
              "\n",
              "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
              "0                                7                        0   \n",
              "1                                2                        0   \n",
              "2                                7                        0   \n",
              "3                                6                        0   \n",
              "4                                9                        0   \n",
              "\n",
              "   NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \\\n",
              "0                                     0                 2.0   \n",
              "1                                     0                 0.0   \n",
              "2                                     0                 2.0   \n",
              "3                                     0                 0.0   \n",
              "4                                     0                 0.0   \n",
              "\n",
              "  RealEstateLoansOrLines GroupAge  \n",
              "0                      A        c  \n",
              "1                      A        b  \n",
              "2                      A        c  \n",
              "3                      A        b  \n",
              "4                      A        d  "
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              "      <td>2416.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150000 entries, 0 to 149999\n",
            "Data columns (total 12 columns):\n",
            " #   Column                                Non-Null Count   Dtype  \n",
            "---  ------                                --------------   -----  \n",
            " 0   SeriousDlqin2yrs                      150000 non-null  int64  \n",
            " 1   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float64\n",
            " 2   age                                   135155 non-null  float64\n",
            " 3   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int64  \n",
            " 4   DebtRatio                             150000 non-null  float64\n",
            " 5   MonthlyIncome                         120269 non-null  float64\n",
            " 6   NumberOfOpenCreditLinesAndLoans       150000 non-null  int64  \n",
            " 7   NumberOfTimes90DaysLate               150000 non-null  int64  \n",
            " 8   NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int64  \n",
            " 9   NumberOfDependents                    146076 non-null  float64\n",
            " 10  RealEstateLoansOrLines                150000 non-null  object \n",
            " 11  GroupAge                              150000 non-null  object \n",
            "dtypes: float64(5), int64(5), object(2)\n",
            "memory usage: 13.7+ MB\n"
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      "source": [
        "df.describe()"
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            "text/plain": [
              "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  \\\n",
              "count     150000.000000                         150000.000000  135155.000000   \n",
              "mean           0.066840                              6.048438      52.293618   \n",
              "std            0.249746                            249.755371      14.772577   \n",
              "min            0.000000                              0.000000       0.000000   \n",
              "25%            0.000000                              0.029867      41.000000   \n",
              "50%            0.000000                              0.154181      52.000000   \n",
              "75%            0.000000                              0.559046      63.000000   \n",
              "max            1.000000                          50708.000000     109.000000   \n",
              "\n",
              "       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
              "count                         150000.000000  150000.000000   1.202690e+05   \n",
              "mean                               0.421033     353.005076   6.670221e+03   \n",
              "std                                4.192781    2037.818523   1.438467e+04   \n",
              "min                                0.000000       0.000000   0.000000e+00   \n",
              "25%                                0.000000       0.175074   3.400000e+03   \n",
              "50%                                0.000000       0.366508   5.400000e+03   \n",
              "75%                                0.000000       0.868254   8.249000e+03   \n",
              "max                               98.000000  329664.000000   3.008750e+06   \n",
              "\n",
              "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
              "count                    150000.000000            150000.000000   \n",
              "mean                          8.452760                 0.265973   \n",
              "std                           5.145951                 4.169304   \n",
              "min                           0.000000                 0.000000   \n",
              "25%                           5.000000                 0.000000   \n",
              "50%                           8.000000                 0.000000   \n",
              "75%                          11.000000                 0.000000   \n",
              "max                          58.000000                98.000000   \n",
              "\n",
              "       NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \n",
              "count                         150000.000000       146076.000000  \n",
              "mean                               0.240387            0.757222  \n",
              "std                                4.155179            1.115086  \n",
              "min                                0.000000            0.000000  \n",
              "25%                                0.000000            0.000000  \n",
              "50%                                0.000000            0.000000  \n",
              "75%                                0.000000            1.000000  \n",
              "max                               98.000000           20.000000  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SeriousDlqin2yrs</th>\n",
              "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <th>age</th>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <th>DebtRatio</th>\n",
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              "      <th>NumberOfTimes90DaysLate</th>\n",
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              "      <th>count</th>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
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              "      <td>150000.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.066840</td>\n",
              "      <td>6.048438</td>\n",
              "      <td>52.293618</td>\n",
              "      <td>0.421033</td>\n",
              "      <td>353.005076</td>\n",
              "      <td>6.670221e+03</td>\n",
              "      <td>8.452760</td>\n",
              "      <td>0.265973</td>\n",
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              "      <th>std</th>\n",
              "      <td>0.249746</td>\n",
              "      <td>249.755371</td>\n",
              "      <td>14.772577</td>\n",
              "      <td>4.192781</td>\n",
              "      <td>2037.818523</td>\n",
              "      <td>1.438467e+04</td>\n",
              "      <td>5.145951</td>\n",
              "      <td>4.169304</td>\n",
              "      <td>4.155179</td>\n",
              "      <td>1.115086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.029867</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.175074</td>\n",
              "      <td>3.400000e+03</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.154181</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.366508</td>\n",
              "      <td>5.400000e+03</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.559046</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.868254</td>\n",
              "      <td>8.249000e+03</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>50708.000000</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>329664.000000</td>\n",
              "      <td>3.008750e+06</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
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          "metadata": {},
          "execution_count": 68
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include='object')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "9Dd3Gnc-N55a",
        "outputId": "609ec2bf-ed24-466a-b88d-9d990025524b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       RealEstateLoansOrLines GroupAge\n",
              "count                  150000   150000\n",
              "unique                      5        5\n",
              "top                         A        d\n",
              "freq                   140048    47268"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RealEstateLoansOrLines</th>\n",
              "      <th>GroupAge</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150000</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>A</td>\n",
              "      <td>d</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>140048</td>\n",
              "      <td>47268</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1b152945-b5bf-4380-a24b-6f219bbb27bd 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-1b152945-b5bf-4380-a24b-6f219bbb27bd');\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",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 🚧 Обработка данных"
      ],
      "metadata": {
        "id": "GQbJ3mMyOnT3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Заменяем категориальные признаки, описывающие закодированное число кредитов (`RealEstateLoansOrLines`) и возрастную группу (`GroupAge`), на соотвествующие им числовые значения.\n",
        "\n",
        "Так как чем больше кредитов имеет человек, тем больше код буквы, логично будет изменить их целыми числами от 1 до 5.\n",
        "\n",
        "По той же логике заменяем на числа буквы возрастной группы."
      ],
      "metadata": {
        "id": "ZW-0j8v6dZP_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mapping = {'A': '1', 'B': '2', 'C': '3', 'D': '4', 'E': '5'}\n",
        "\n",
        "for column in ['RealEstateLoansOrLines', 'GroupAge']:\n",
        "  for letter, number in mapping.items():\n",
        "    df[column] = df[column].str.upper().replace(letter, number)\n",
        "\n",
        "  df[column] = df[column].astype(int)"
      ],
      "metadata": {
        "id": "PUoEf0phN7YB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='GroupAge', y='age', data = df, palette='summer')\n",
        "plt.title('GroupAge - age')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ozr60dJbRxOT",
        "outputId": "0d0cacb9-efa9-4460-87a6-b089135391db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Заменяем пустые ячейки признака `age` случайными целыми числами из диапозона, соотвествующего возрастной группе, к которой относится человек.\n",
        "\n"
      ],
      "metadata": {
        "id": "3ELGN9m_ejNs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ranges = dict()\n",
        "for i in range(1, 6):\n",
        "  ranges[i] = (df[df.GroupAge == i]['age'].min(), df[df.GroupAge == i]['age'].max())\n",
        "  print(f\"GroupAge {i}: {df[df.GroupAge == i]['age'].min(), df[df.GroupAge == i]['age'].max()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3oIxux-PT8IW",
        "outputId": "ed15cbc6-58dd-4b42-a64f-2bec3bc3423f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GroupAge 1: (0.0, 0.0)\n",
            "GroupAge 2: (21.0, 34.0)\n",
            "GroupAge 3: (35.0, 49.0)\n",
            "GroupAge 4: (50.0, 64.0)\n",
            "GroupAge 5: (65.0, 109.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from random import randint\n",
        "\n",
        "df['age'] = df.apply(\n",
        "    lambda x: round(randint(ranges[x.GroupAge][0], ranges[x.GroupAge][1])) if np.isnan(x['age']) else x['age'],\n",
        "    axis=1\n",
        ")"
      ],
      "metadata": {
        "id": "sH_XhzGEW3d5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.age.value_counts(dropna=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eT8_DsSpc0nN",
        "outputId": "0457c157-98a3-4bdf-8706-fcbde0f21510"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "49.0     3471\n",
              "48.0     3456\n",
              "63.0     3374\n",
              "50.0     3365\n",
              "46.0     3339\n",
              "         ... \n",
              "103.0     321\n",
              "105.0     303\n",
              "99.0      302\n",
              "21.0      169\n",
              "0.0         1\n",
              "Name: age, Length: 90, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Заполняем пустые значения признака количества иждивенцев (`NumberOfDependents`) средним значением, округленным до целого."
      ],
      "metadata": {
        "id": "5cisEHtCfsh6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['NumberOfDependents'].fillna(round(df['NumberOfDependents'].mean()), inplace=True)"
      ],
      "metadata": {
        "id": "AHN_PErPeDxz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Проверяем, есть ли значимые корреляции признака ежемесячного дохода (`MonthlyIncome`) с каким-либо из других, чтобы оценить возможность предсказания данного признака."
      ],
      "metadata": {
        "id": "rCmWMz1GgNNx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[['MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans', 'RealEstateLoansOrLines', 'NumberOfDependents', 'GroupAge']].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qncjqTrEgppw",
        "outputId": "1df12a60-a463-4eec-c2be-241ad406cfdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 MonthlyIncome  \\\n",
              "MonthlyIncome                         1.000000   \n",
              "NumberOfOpenCreditLinesAndLoans       0.091455   \n",
              "RealEstateLoansOrLines                0.089999   \n",
              "NumberOfDependents                    0.062647   \n",
              "GroupAge                              0.035023   \n",
              "\n",
              "                                 NumberOfOpenCreditLinesAndLoans  \\\n",
              "MonthlyIncome                                           0.091455   \n",
              "NumberOfOpenCreditLinesAndLoans                         1.000000   \n",
              "RealEstateLoansOrLines                                  0.262973   \n",
              "NumberOfDependents                                      0.061274   \n",
              "GroupAge                                                0.140525   \n",
              "\n",
              "                                 RealEstateLoansOrLines  NumberOfDependents  \\\n",
              "MonthlyIncome                                  0.089999            0.062647   \n",
              "NumberOfOpenCreditLinesAndLoans                0.262973            0.061274   \n",
              "RealEstateLoansOrLines                         1.000000            0.046646   \n",
              "NumberOfDependents                             0.046646            1.000000   \n",
              "GroupAge                                       0.024184           -0.165870   \n",
              "\n",
              "                                 GroupAge  \n",
              "MonthlyIncome                    0.035023  \n",
              "NumberOfOpenCreditLinesAndLoans  0.140525  \n",
              "RealEstateLoansOrLines           0.024184  \n",
              "NumberOfDependents              -0.165870  \n",
              "GroupAge                         1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ced4fbe-fd22-46d5-aa01-def6b12dec5d\">\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>MonthlyIncome</th>\n",
              "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <th>RealEstateLoansOrLines</th>\n",
              "      <th>NumberOfDependents</th>\n",
              "      <th>GroupAge</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.091455</td>\n",
              "      <td>0.089999</td>\n",
              "      <td>0.062647</td>\n",
              "      <td>0.035023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <td>0.091455</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.262973</td>\n",
              "      <td>0.061274</td>\n",
              "      <td>0.140525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RealEstateLoansOrLines</th>\n",
              "      <td>0.089999</td>\n",
              "      <td>0.262973</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.046646</td>\n",
              "      <td>0.024184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumberOfDependents</th>\n",
              "      <td>0.062647</td>\n",
              "      <td>0.061274</td>\n",
              "      <td>0.046646</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.165870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GroupAge</th>\n",
              "      <td>0.035023</td>\n",
              "      <td>0.140525</td>\n",
              "      <td>0.024184</td>\n",
              "      <td>-0.165870</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ced4fbe-fd22-46d5-aa01-def6b12dec5d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Так как `MonthlyIncome` особо ни с чем не коррелирует, заполняем пустоты медианой."
      ],
      "metadata": {
        "id": "cDxAS5pSgvlI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['MonthlyIncome'].fillna(df['MonthlyIncome'].median(), inplace=True)"
      ],
      "metadata": {
        "id": "4w1WpFIfhpA1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7JJdZCrkut4",
        "outputId": "7dec1ea7-35e2-4c26-a824-46bb16930626"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150000 entries, 0 to 149999\n",
            "Data columns (total 12 columns):\n",
            " #   Column                                Non-Null Count   Dtype  \n",
            "---  ------                                --------------   -----  \n",
            " 0   SeriousDlqin2yrs                      150000 non-null  int64  \n",
            " 1   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float64\n",
            " 2   age                                   150000 non-null  float64\n",
            " 3   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int64  \n",
            " 4   DebtRatio                             150000 non-null  float64\n",
            " 5   MonthlyIncome                         150000 non-null  float64\n",
            " 6   NumberOfOpenCreditLinesAndLoans       150000 non-null  int64  \n",
            " 7   NumberOfTimes90DaysLate               150000 non-null  int64  \n",
            " 8   NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int64  \n",
            " 9   NumberOfDependents                    150000 non-null  float64\n",
            " 10  RealEstateLoansOrLines                150000 non-null  int64  \n",
            " 11  GroupAge                              150000 non-null  int64  \n",
            "dtypes: float64(5), int64(7)\n",
            "memory usage: 13.7 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnH_Up0vzH2K",
        "outputId": "a8418c1d-3944-4085-e2fa-2d2394099fbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  \\\n",
              "count     150000.000000                         150000.000000  150000.000000   \n",
              "mean           0.066840                              6.048438      55.714227   \n",
              "std            0.249746                            249.755371      17.882514   \n",
              "min            0.000000                              0.000000       0.000000   \n",
              "25%            0.000000                              0.029867      43.000000   \n",
              "50%            0.000000                              0.154181      54.000000   \n",
              "75%            0.000000                              0.559046      67.000000   \n",
              "max            1.000000                          50708.000000     109.000000   \n",
              "\n",
              "       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
              "count                         150000.000000  150000.000000   1.500000e+05   \n",
              "mean                               0.421033     353.005076   6.418455e+03   \n",
              "std                                4.192781    2037.818523   1.289040e+04   \n",
              "min                                0.000000       0.000000   0.000000e+00   \n",
              "25%                                0.000000       0.175074   3.903000e+03   \n",
              "50%                                0.000000       0.366508   5.400000e+03   \n",
              "75%                                0.000000       0.868254   7.400000e+03   \n",
              "max                               98.000000  329664.000000   3.008750e+06   \n",
              "\n",
              "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
              "count                    150000.000000            150000.000000   \n",
              "mean                          8.452760                 0.265973   \n",
              "std                           5.145951                 4.169304   \n",
              "min                           0.000000                 0.000000   \n",
              "25%                           5.000000                 0.000000   \n",
              "50%                           8.000000                 0.000000   \n",
              "75%                          11.000000                 0.000000   \n",
              "max                          58.000000                98.000000   \n",
              "\n",
              "       NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \\\n",
              "count                         150000.000000       150000.000000   \n",
              "mean                               0.240387            0.763573   \n",
              "std                                4.155179            1.101086   \n",
              "min                                0.000000            0.000000   \n",
              "25%                                0.000000            0.000000   \n",
              "50%                                0.000000            0.000000   \n",
              "75%                                0.000000            1.000000   \n",
              "max                               98.000000           20.000000   \n",
              "\n",
              "       RealEstateLoansOrLines       GroupAge  \n",
              "count           150000.000000  150000.000000  \n",
              "mean                 1.076920       3.771887  \n",
              "std                  0.306057       0.989830  \n",
              "min                  1.000000       1.000000  \n",
              "25%                  1.000000       3.000000  \n",
              "50%                  1.000000       4.000000  \n",
              "75%                  1.000000       5.000000  \n",
              "max                  5.000000       5.000000  "
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SeriousDlqin2yrs</th>\n",
              "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <th>age</th>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <th>DebtRatio</th>\n",
              "      <th>MonthlyIncome</th>\n",
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              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfDependents</th>\n",
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              "      <th>GroupAge</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150000.000000</td>\n",
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              "      <td>150000.000000</td>\n",
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              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>6.048438</td>\n",
              "      <td>55.714227</td>\n",
              "      <td>0.421033</td>\n",
              "      <td>353.005076</td>\n",
              "      <td>6.418455e+03</td>\n",
              "      <td>8.452760</td>\n",
              "      <td>0.265973</td>\n",
              "      <td>0.240387</td>\n",
              "      <td>0.763573</td>\n",
              "      <td>1.076920</td>\n",
              "      <td>3.771887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.249746</td>\n",
              "      <td>249.755371</td>\n",
              "      <td>17.882514</td>\n",
              "      <td>4.192781</td>\n",
              "      <td>2037.818523</td>\n",
              "      <td>1.289040e+04</td>\n",
              "      <td>5.145951</td>\n",
              "      <td>4.169304</td>\n",
              "      <td>4.155179</td>\n",
              "      <td>1.101086</td>\n",
              "      <td>0.306057</td>\n",
              "      <td>0.989830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.029867</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.175074</td>\n",
              "      <td>3.903000e+03</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.154181</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.366508</td>\n",
              "      <td>5.400000e+03</td>\n",
              "      <td>8.000000</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>67.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.868254</td>\n",
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              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>50708.000000</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>329664.000000</td>\n",
              "      <td>3.008750e+06</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>5.000000</td>\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f0a88f4f-08e3-4e76-b6b3-7a9a409537c5');\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 📈 Анализ данных"
      ],
      "metadata": {
        "id": "8s-hl80hkWM5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Наблюдаем прямую зависимость, между закодированным числом кредитов (`RealEstateLoansOrLines`) и самим количеством кредитов (`NumberOfOpenCreditLinesAndLoans`)."
      ],
      "metadata": {
        "id": "25x4Ko4HdiXc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='RealEstateLoansOrLines', y='NumberOfOpenCreditLinesAndLoans', data = df, palette='summer')\n",
        "plt.title('RealEstateLoansOrLines - NumberOfOpenCreditLinesAndLoans')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "-pLtN0YyGxkp",
        "outputId": "f38460b7-46b7-49c7-de96-41d7f7953db8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Вычисляем медианы количества кредитов по каждой из кредитных групп."
      ],
      "metadata": {
        "id": "A2E272bhd9dz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1, 6):\n",
        "  print(f\"RealEstateLoansOrLines {i} Median: {df[df.RealEstateLoansOrLines == i]['NumberOfOpenCreditLinesAndLoans'].median()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqh2sVTrGlJJ",
        "outputId": "f5a40429-9709-48bd-ca10-7759cdb83f77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RealEstateLoansOrLines 1 Median: 7.0\n",
            "RealEstateLoansOrLines 2 Median: 12.0\n",
            "RealEstateLoansOrLines 3 Median: 15.0\n",
            "RealEstateLoansOrLines 4 Median: 24.0\n",
            "RealEstateLoansOrLines 5 Median: 35.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Распределение признаков, отвечающих за просрочки по платежам (`NumberOfTimes90DaysLate`, `NumberOfTime60-89DaysPastDueNotWorse`, `NumberOfTime30-59DaysPastDueNotWorse`) показывает множественное количество выбросов, однако, можно заметить, что сами признаки между собой распределены похожим образом."
      ],
      "metadata": {
        "id": "4AJv8g-CisHh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.boxplot(df[['NumberOfTimes90DaysLate', 'NumberOfTime60-89DaysPastDueNotWorse', 'NumberOfTime30-59DaysPastDueNotWorse']])\n",
        "plt.setp(ax.get_xticklabels(), rotation=45)\n",
        "plt.title('Numbers of late features distribution')\n",
        "plt.show()\n",
        "\n",
        "df[['NumberOfTimes90DaysLate', 'NumberOfTime60-89DaysPastDueNotWorse', 'NumberOfTime30-59DaysPastDueNotWorse']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 947
        },
        "id": "PJCsjRMajphK",
        "outputId": "fffc453c-59e3-43e8-c74c-fcc5d15601a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       NumberOfTimes90DaysLate  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
              "count            150000.000000                         150000.000000   \n",
              "mean                  0.265973                              0.240387   \n",
              "std                   4.169304                              4.155179   \n",
              "min                   0.000000                              0.000000   \n",
              "25%                   0.000000                              0.000000   \n",
              "50%                   0.000000                              0.000000   \n",
              "75%                   0.000000                              0.000000   \n",
              "max                  98.000000                             98.000000   \n",
              "\n",
              "       NumberOfTime30-59DaysPastDueNotWorse  \n",
              "count                         150000.000000  \n",
              "mean                               0.421033  \n",
              "std                                4.192781  \n",
              "min                                0.000000  \n",
              "25%                                0.000000  \n",
              "50%                                0.000000  \n",
              "75%                                0.000000  \n",
              "max                               98.000000  "
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.265973</td>\n",
              "      <td>0.240387</td>\n",
              "      <td>0.421033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.169304</td>\n",
              "      <td>4.155179</td>\n",
              "      <td>4.192781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>98.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>98.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          document.querySelector('#df-439a3951-e0ef-40b6-b9ca-0672ac90cc9c 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-439a3951-e0ef-40b6-b9ca-0672ac90cc9c');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Распределение ежемесячного дохода (`MonthlyIncome`) также имеет множество выбросов и не является равномерным."
      ],
      "metadata": {
        "id": "jaG2zQKtk21e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(y='MonthlyIncome', data=df)\n",
        "plt.title('Monthly income distribution')\n",
        "plt.show()\n",
        "\n",
        "df['MonthlyIncome'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "8BaLZ9Y3oZv1",
        "outputId": "5b292118-864b-40b8-e755-afe2110bb0d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.500000e+05\n",
              "mean     6.418455e+03\n",
              "std      1.289040e+04\n",
              "min      0.000000e+00\n",
              "25%      3.903000e+03\n",
              "50%      5.400000e+03\n",
              "75%      7.400000e+03\n",
              "max      3.008750e+06\n",
              "Name: MonthlyIncome, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Количество открытых кредитов (`NumberOfOpenCreditLinesAndLoans`) в целом распределено нормально, однако выбросы всё же имеются."
      ],
      "metadata": {
        "id": "DQ0wKKAdnwe3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df['NumberOfOpenCreditLinesAndLoans'])\n",
        "plt.title('Number of open credits distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Wzbj2s7SozUx",
        "outputId": "52e4fe35-b50f-4854-c652-91f110608f4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df['NumberOfOpenCreditLinesAndLoans'])\n",
        "plt.title('Number of open credits distribution')\n",
        "plt.show()\n",
        "\n",
        "df['NumberOfOpenCreditLinesAndLoans'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "JtXOrcJymWNv",
        "outputId": "42169f4c-bd9f-475f-efc6-12cf0bc659ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    150000.000000\n",
              "mean          8.452760\n",
              "std           5.145951\n",
              "min           0.000000\n",
              "25%           5.000000\n",
              "50%           8.000000\n",
              "75%          11.000000\n",
              "max          58.000000\n",
              "Name: NumberOfOpenCreditLinesAndLoans, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Похожую ситуацию можно наблюдать и с распределением количества иждивенцев (`NumberOfDependents`)."
      ],
      "metadata": {
        "id": "TMNWWk-SoIj6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df['NumberOfDependents'])\n",
        "plt.title('Number of dependents distribution')\n",
        "plt.show()\n",
        "\n",
        "df['NumberOfDependents'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "4aq6QpyAp4PV",
        "outputId": "2badfa62-fa9c-4201-f2c4-c05a25f3b3c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    150000.000000\n",
              "mean          0.763573\n",
              "std           1.101086\n",
              "min           0.000000\n",
              "25%           0.000000\n",
              "50%           0.000000\n",
              "75%           1.000000\n",
              "max          20.000000\n",
              "Name: NumberOfDependents, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Возраст заемщиков (`age`) распределен нормально."
      ],
      "metadata": {
        "id": "lV0_Pi5Yo4yC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df['age'], kde=True)\n",
        "plt.title('Age distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Po1i2K-PmJ-J",
        "outputId": "2f3f381f-a62a-45f8-8dfb-a5041b286918"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df['age'])\n",
        "plt.title('Age distribution')\n",
        "plt.show()\n",
        "\n",
        "df['age'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "Fq6rCjxSop8K",
        "outputId": "93bd3c83-23e9-4017-c7fe-45e7863681d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    150000.000000\n",
              "mean         55.714227\n",
              "std          17.882514\n",
              "min           0.000000\n",
              "25%          43.000000\n",
              "50%          54.000000\n",
              "75%          67.000000\n",
              "max         109.000000\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ниже можно видеть, как возрастная группа (`GroupAge`) влияет на целевую переменную, означающую возможность просрочки по платежу на 90 и более дней (`SeriousDlqin2yrs`). Можно заметить, что с возрастом всё меньше людей совершают просрочки."
      ],
      "metadata": {
        "id": "_eiOQUHso_4E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=df, x='GroupAge', y='SeriousDlqin2yrs')\n",
        "plt.title('GroupAge - SeriousDlqin2yrs')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "MsTCvVZGrAou",
        "outputId": "be0bc37f-7b2e-4a51-d87d-b9a1fcad8226"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Такой равномерной зависимости не наблюдается с количеством иждивенцев (`NumberOfDependents`). Больше всего шанс на просрочку у тех, кто содержит 6 человек, а меньше всего у тех, у кого иждивенцев вовсе нет."
      ],
      "metadata": {
        "id": "PYxFMT5GqhKS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=df, x='NumberOfDependents', y='SeriousDlqin2yrs')\n",
        "plt.title('NumberOfDependents - SeriousDlqin2yrs')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "DEcKa5Mbrb27",
        "outputId": "35a0dcfc-bb90-4072-8904-b41af48689c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Закодированное количество кредитов (`RealEstateLoansOrLines`) почти что прямо влияет на риск просрочки, однако отклонения от такой зависимости имеются. Например, заемщики, входящие в 5-ю группу совершили меньше просрочек, чем входящие в 4-ю."
      ],
      "metadata": {
        "id": "xcUlPQ85rMBo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=df, x='RealEstateLoansOrLines', y='SeriousDlqin2yrs')\n",
        "plt.title('RealEstateLoansOrLines - SeriousDlqin2yrs')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "MrU9o-uHrfsi",
        "outputId": "2d2bc568-5c2d-48af-c0f5-ce745f318c3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ниже можно видеть, как различные признаки влияют друг на друга, исходя из корреляции Пирсона.\n",
        "\n",
        "Наибольшая зависимость наблюдается у признаков отвечающих за просрочки по платежам (`NumberOfTimes90DaysLate`, `NumberOfTime60-89DaysPastDueNotWorse`, `NumberOfTime30-59DaysPastDueNotWorse`), что подтверждает сказанное выше.\n",
        "\n",
        "Также, относительно всех остальных показателей, значимую слабую зависимость показывают признаки, отвечающие за кредитную группу (`RealEstateLoansOrLines`) и количество кредитов (`NumberOfOpenCreditLinesAndLoans`), что также является логичным.\n",
        "\n",
        "Целевая же переменная (`SeriousDlqin2yrs`) главным образом зависит от признаков отвечающих за просрочки по платежам, хотя коэффициент корелляции все же достаточно мал."
      ],
      "metadata": {
        "id": "KzFPVKvgsmZY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr = df[[col for col in df.columns]].corr()\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "sns.heatmap(corr, annot=True, fmt='.2f', cmap='crest')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "id": "BYGFoE5yYbel",
        "outputId": "86d710c7-d786-464a-eb65-4c24f7e1388b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[[col for col in df.columns]].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "OQwi1kPAYdn4",
        "outputId": "14abeabc-cc8b-4dae-fa34-4ec1cb8cc1d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      SeriousDlqin2yrs  \\\n",
              "SeriousDlqin2yrs                              1.000000   \n",
              "RevolvingUtilizationOfUnsecuredLines         -0.001802   \n",
              "age                                          -0.085043   \n",
              "NumberOfTime30-59DaysPastDueNotWorse          0.125587   \n",
              "DebtRatio                                    -0.007602   \n",
              "MonthlyIncome                                -0.017151   \n",
              "NumberOfOpenCreditLinesAndLoans              -0.029669   \n",
              "NumberOfTimes90DaysLate                       0.117175   \n",
              "NumberOfTime60-89DaysPastDueNotWorse          0.102261   \n",
              "NumberOfDependents                            0.045103   \n",
              "RealEstateLoansOrLines                        0.027718   \n",
              "GroupAge                                     -0.096787   \n",
              "\n",
              "                                      RevolvingUtilizationOfUnsecuredLines  \\\n",
              "SeriousDlqin2yrs                                                 -0.001802   \n",
              "RevolvingUtilizationOfUnsecuredLines                              1.000000   \n",
              "age                                                              -0.002146   \n",
              "NumberOfTime30-59DaysPastDueNotWorse                             -0.001314   \n",
              "DebtRatio                                                         0.003961   \n",
              "MonthlyIncome                                                     0.006513   \n",
              "NumberOfOpenCreditLinesAndLoans                                  -0.011281   \n",
              "NumberOfTimes90DaysLate                                          -0.001061   \n",
              "NumberOfTime60-89DaysPastDueNotWorse                             -0.001048   \n",
              "NumberOfDependents                                                0.001646   \n",
              "RealEstateLoansOrLines                                           -0.000876   \n",
              "GroupAge                                                         -0.001645   \n",
              "\n",
              "                                           age  \\\n",
              "SeriousDlqin2yrs                     -0.085043   \n",
              "RevolvingUtilizationOfUnsecuredLines -0.002146   \n",
              "age                                   1.000000   \n",
              "NumberOfTime30-59DaysPastDueNotWorse -0.046327   \n",
              "DebtRatio                             0.015706   \n",
              "MonthlyIncome                         0.018026   \n",
              "NumberOfOpenCreditLinesAndLoans       0.110718   \n",
              "NumberOfTimes90DaysLate              -0.044549   \n",
              "NumberOfTime60-89DaysPastDueNotWorse -0.041679   \n",
              "NumberOfDependents                   -0.153693   \n",
              "RealEstateLoansOrLines                0.013760   \n",
              "GroupAge                              0.911802   \n",
              "\n",
              "                                      NumberOfTime30-59DaysPastDueNotWorse  \\\n",
              "SeriousDlqin2yrs                                                  0.125587   \n",
              "RevolvingUtilizationOfUnsecuredLines                             -0.001314   \n",
              "age                                                              -0.046327   \n",
              "NumberOfTime30-59DaysPastDueNotWorse                              1.000000   \n",
              "DebtRatio                                                        -0.006542   \n",
              "MonthlyIncome                                                    -0.008370   \n",
              "NumberOfOpenCreditLinesAndLoans                                  -0.055312   \n",
              "NumberOfTimes90DaysLate                                           0.983603   \n",
              "NumberOfTime60-89DaysPastDueNotWorse                              0.987005   \n",
              "NumberOfDependents                                               -0.001853   \n",
              "RealEstateLoansOrLines                                           -0.004847   \n",
              "GroupAge                                                         -0.050028   \n",
              "\n",
              "                                      DebtRatio  MonthlyIncome  \\\n",
              "SeriousDlqin2yrs                      -0.007602      -0.017151   \n",
              "RevolvingUtilizationOfUnsecuredLines   0.003961       0.006513   \n",
              "age                                    0.015706       0.018026   \n",
              "NumberOfTime30-59DaysPastDueNotWorse  -0.006542      -0.008370   \n",
              "DebtRatio                              1.000000      -0.018006   \n",
              "MonthlyIncome                         -0.018006       1.000000   \n",
              "NumberOfOpenCreditLinesAndLoans        0.049565       0.086949   \n",
              "NumberOfTimes90DaysLate               -0.008320      -0.010500   \n",
              "NumberOfTime60-89DaysPastDueNotWorse  -0.007533      -0.009252   \n",
              "NumberOfDependents                    -0.036195       0.064794   \n",
              "RealEstateLoansOrLines                 0.086480       0.084085   \n",
              "GroupAge                               0.024194       0.027120   \n",
              "\n",
              "                                      NumberOfOpenCreditLinesAndLoans  \\\n",
              "SeriousDlqin2yrs                                            -0.029669   \n",
              "RevolvingUtilizationOfUnsecuredLines                        -0.011281   \n",
              "age                                                          0.110718   \n",
              "NumberOfTime30-59DaysPastDueNotWorse                        -0.055312   \n",
              "DebtRatio                                                    0.049565   \n",
              "MonthlyIncome                                                0.086949   \n",
              "NumberOfOpenCreditLinesAndLoans                              1.000000   \n",
              "NumberOfTimes90DaysLate                                     -0.079984   \n",
              "NumberOfTime60-89DaysPastDueNotWorse                        -0.071077   \n",
              "NumberOfDependents                                           0.061274   \n",
              "RealEstateLoansOrLines                                       0.262973   \n",
              "GroupAge                                                     0.140525   \n",
              "\n",
              "                                      NumberOfTimes90DaysLate  \\\n",
              "SeriousDlqin2yrs                                     0.117175   \n",
              "RevolvingUtilizationOfUnsecuredLines                -0.001061   \n",
              "age                                                 -0.044549   \n",
              "NumberOfTime30-59DaysPastDueNotWorse                 0.983603   \n",
              "DebtRatio                                           -0.008320   \n",
              "MonthlyIncome                                       -0.010500   \n",
              "NumberOfOpenCreditLinesAndLoans                     -0.079984   \n",
              "NumberOfTimes90DaysLate                              1.000000   \n",
              "NumberOfTime60-89DaysPastDueNotWorse                 0.992796   \n",
              "NumberOfDependents                                  -0.008787   \n",
              "RealEstateLoansOrLines                              -0.012930   \n",
              "GroupAge                                            -0.048597   \n",
              "\n",
              "                                      NumberOfTime60-89DaysPastDueNotWorse  \\\n",
              "SeriousDlqin2yrs                                                  0.102261   \n",
              "RevolvingUtilizationOfUnsecuredLines                             -0.001048   \n",
              "age                                                              -0.041679   \n",
              "NumberOfTime30-59DaysPastDueNotWorse                              0.987005   \n",
              "DebtRatio                                                        -0.007533   \n",
              "MonthlyIncome                                                    -0.009252   \n",
              "NumberOfOpenCreditLinesAndLoans                                  -0.071077   \n",
              "NumberOfTimes90DaysLate                                           0.992796   \n",
              "NumberOfTime60-89DaysPastDueNotWorse                              1.000000   \n",
              "NumberOfDependents                                               -0.009477   \n",
              "RealEstateLoansOrLines                                           -0.010986   \n",
              "GroupAge                                                         -0.045230   \n",
              "\n",
              "                                      NumberOfDependents  \\\n",
              "SeriousDlqin2yrs                                0.045103   \n",
              "RevolvingUtilizationOfUnsecuredLines            0.001646   \n",
              "age                                            -0.153693   \n",
              "NumberOfTime30-59DaysPastDueNotWorse           -0.001853   \n",
              "DebtRatio                                      -0.036195   \n",
              "MonthlyIncome                                   0.064794   \n",
              "NumberOfOpenCreditLinesAndLoans                 0.061274   \n",
              "NumberOfTimes90DaysLate                        -0.008787   \n",
              "NumberOfTime60-89DaysPastDueNotWorse           -0.009477   \n",
              "NumberOfDependents                              1.000000   \n",
              "RealEstateLoansOrLines                          0.046646   \n",
              "GroupAge                                       -0.165870   \n",
              "\n",
              "                                      RealEstateLoansOrLines  GroupAge  \n",
              "SeriousDlqin2yrs                                    0.027718 -0.096787  \n",
              "RevolvingUtilizationOfUnsecuredLines               -0.000876 -0.001645  \n",
              "age                                                 0.013760  0.911802  \n",
              "NumberOfTime30-59DaysPastDueNotWorse               -0.004847 -0.050028  \n",
              "DebtRatio                                           0.086480  0.024194  \n",
              "MonthlyIncome                                       0.084085  0.027120  \n",
              "NumberOfOpenCreditLinesAndLoans                     0.262973  0.140525  \n",
              "NumberOfTimes90DaysLate                            -0.012930 -0.048597  \n",
              "NumberOfTime60-89DaysPastDueNotWorse               -0.010986 -0.045230  \n",
              "NumberOfDependents                                  0.046646 -0.165870  \n",
              "RealEstateLoansOrLines                              1.000000  0.024184  \n",
              "GroupAge                                            0.024184  1.000000  "
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              "      <th>SeriousDlqin2yrs</th>\n",
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              "      <th>age</th>\n",
              "      <td>-0.085043</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
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              "      <td>-0.004847</td>\n",
              "      <td>-0.050028</td>\n",
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              "      <th>DebtRatio</th>\n",
              "      <td>-0.007602</td>\n",
              "      <td>0.003961</td>\n",
              "      <td>0.015706</td>\n",
              "      <td>-0.006542</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.018006</td>\n",
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              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <td>0.102261</td>\n",
              "      <td>-0.001048</td>\n",
              "      <td>-0.041679</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[[col for col in df.columns]].corr()['SeriousDlqin2yrs']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8fQYqY4ymRM",
        "outputId": "9721e7cd-c81a-49ad-ff97-e54bc9041262"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SeriousDlqin2yrs                        1.000000\n",
              "RevolvingUtilizationOfUnsecuredLines   -0.001802\n",
              "age                                    -0.085043\n",
              "NumberOfTime30-59DaysPastDueNotWorse    0.125587\n",
              "DebtRatio                              -0.007602\n",
              "MonthlyIncome                          -0.017151\n",
              "NumberOfOpenCreditLinesAndLoans        -0.029669\n",
              "NumberOfTimes90DaysLate                 0.117175\n",
              "NumberOfTime60-89DaysPastDueNotWorse    0.102261\n",
              "NumberOfDependents                      0.045103\n",
              "RealEstateLoansOrLines                  0.027718\n",
              "GroupAge                               -0.096787\n",
              "Name: SeriousDlqin2yrs, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 🎓 Обучение модели"
      ],
      "metadata": {
        "id": "Qsw9r9wPzqPN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='SeriousDlqin2yrs',data=df)\n",
        "plt.title('SeriousDlqin2yrs (target) counts')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Rwha88OQAPVY",
        "outputId": "c7599428-c29f-4f95-953f-5d4a75edd037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SeriousDlqin2yrs'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGhPcy6Y__lQ",
        "outputId": "fe3afdd4-c969-40fc-ec07-e0422dc7ed51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    139974\n",
              "1     10026\n",
              "Name: SeriousDlqin2yrs, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Количество `SeriousDlqin2yrs` равных `0` в исходном датасете намного больше, чем равных `1`. Создаем новый, урезая количество нулей примерно до количества единиц для качественного обучения."
      ],
      "metadata": {
        "id": "HeDm8UhbrYZy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.drop(df[(df.SeriousDlqin2yrs == 0) & (df.index < 139000)].index)"
      ],
      "metadata": {
        "id": "lweHJefAGgx5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1['SeriousDlqin2yrs'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BoYKMeyLHMk1",
        "outputId": "51e49c8c-02c7-4a75-9871-a0be7335211e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    10250\n",
              "1    10026\n",
              "Name: SeriousDlqin2yrs, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df1.drop(['SeriousDlqin2yrs'], axis=1)\n",
        "y = df1['SeriousDlqin2yrs']"
      ],
      "metadata": {
        "id": "yZ96Bu6mzY6G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "nDzjSqd_0l3n",
        "outputId": "063eaa98-c87b-4de5-b20c-66d8ebd7c07a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    RevolvingUtilizationOfUnsecuredLines   age  \\\n",
              "2                               0.025656  38.0   \n",
              "5                               0.392248  50.0   \n",
              "8                               0.695321  38.0   \n",
              "12                              0.957570  54.0   \n",
              "53                              0.234539  53.0   \n",
              "\n",
              "    NumberOfTime30-59DaysPastDueNotWorse    DebtRatio  MonthlyIncome  \\\n",
              "2                                      0     0.475841         3000.0   \n",
              "5                                      0     1.595253         4676.0   \n",
              "8                                      0     0.618575         7859.0   \n",
              "12                                     0     0.371126         5000.0   \n",
              "53                                     0  1513.000000         5400.0   \n",
              "\n",
              "    NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
              "2                                 7                        0   \n",
              "5                                14                        0   \n",
              "8                                 8                        0   \n",
              "12                               11                        2   \n",
              "53                               10                        0   \n",
              "\n",
              "    NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \\\n",
              "2                                      0                 2.0   \n",
              "5                                      0                 1.0   \n",
              "8                                      0                 3.0   \n",
              "12                                     1                 2.0   \n",
              "53                                     0                 1.0   \n",
              "\n",
              "    RealEstateLoansOrLines  GroupAge  \n",
              "2                        1         3  \n",
              "5                        2         4  \n",
              "8                        1         3  \n",
              "12                       1         4  \n",
              "53                       1         4  "
            ],
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              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0.234539</td>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XWoPuLrg0sGT",
        "outputId": "2d41d704-6464-440c-98bf-52c4e3c02653"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2     1\n",
              "5     1\n",
              "8     1\n",
              "12    1\n",
              "53    1\n",
              "Name: SeriousDlqin2yrs, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
        "\n",
        "X_train.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFOFKrxA0yoz",
        "outputId": "cf222662-38af-4211-a780-6d7d26b6bcad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((15207, 11), (5069, 11))"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "9qB6LmcJ018z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1ho616m1BeC",
        "outputId": "59b166a8-af30-4722-9add-efd17f207890"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "cf = confusion_matrix(y_test, pred, normalize='true')\n",
        "sns.heatmap(cf, annot=True, cmap='crest')\n",
        "plt.show()\n",
        "\n",
        "confusion_matrix(y_test, pred)"
      ],
      "metadata": {
        "id": "S7UbNCkR1Evq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "1fe79f7a-3713-4d14-dffd-adc97de633e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2011,  561],\n",
              "       [ 592, 1905]])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, pred))"
      ],
      "metadata": {
        "id": "RpVAR8jW3qg3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "264b6d4b-7074-476b-dd80-c11018f1280b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.78      0.78      2572\n",
            "           1       0.77      0.76      0.77      2497\n",
            "\n",
            "    accuracy                           0.77      5069\n",
            "   macro avg       0.77      0.77      0.77      5069\n",
            "weighted avg       0.77      0.77      0.77      5069\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 🔧 Оптимизация гиперпараметров"
      ],
      "metadata": {
        "id": "e4YB0kLmfTNZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "params = {\n",
        "    'n_estimators': range(50,500),\n",
        "    'max_depth': range(1,20)\n",
        "}\n",
        "\n",
        "search = RandomizedSearchCV(\n",
        "    model,\n",
        "    param_distributions=params,\n",
        "    n_iter=5,\n",
        "    cv=5\n",
        ")\n",
        "\n",
        "search.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "Nos_gUOlfdaA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = search.best_estimator_\n",
        "search.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "941dAHrqgpac",
        "outputId": "029bc1d6-8013-4562-89a2-90f12717dc6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_estimators': 400, 'max_depth': 13}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(X_test)\n",
        "\n",
        "cf = confusion_matrix(y_test, pred, normalize='true')\n",
        "sns.heatmap(cf, annot=True, cmap='crest')\n",
        "plt.show()\n",
        "\n",
        "confusion_matrix(y_test, pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "9boThbTYhyzd",
        "outputId": "045efd2b-48b2-4cb9-ba76-d5bda590de9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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6WpmZmfrss880d+5clZSUtPs8hAEAAKzCuGbAbre3680/KSlJ0dHRqqurCxivq6tTcnJym8ekpKQoNjZW0dHR/rFhw4aptrZWLS0tiouLa9ccaRMAAGBlC+PWTnFxccrMzFR5ebl/zOv1qry8XLm5uW0e861vfUs7duyQ1+v1j23btk0pKSntDgISYQAAgE7D6XRq4cKFevLJJ/Xhhx/qtttuU3Nzs//pgilTpqi4uNi//2233aYDBw5oxowZ2rZtm1555RU9+OCDmj59ekjXpU0AAEAnUVBQoPr6es2ePVu1tbXKyMjQihUr/IsK9+zZo6io/3yO79+/v1auXKmZM2dq1KhRSk1N1YwZM3T33XeHdF3CAAAAFrYIfh1xUVGRioqK2nxt9erVQWO5ublat27dSV2TNgEAAIYjDAAAYDjaBAAAWPGrhQAAwCRUBgAAsDKsMkAYAADAwrAsQJsAAADTURkAAMAqkl80EAGEAQAALAzLArQJAAAwHWEAAADD0SYAAMDKsDYBYQAAAAvDsgBtAgAATEdlAAAAK8NKA4QBAAAseLQQAAAYhTAAAIDhaBMAAGBBmwAAABiFMAAAgOFoEwAAYGFam4AwAACAlWFhgDYBAACGozIAAICFzbDSAGEAAAArs7IAbQIAAExHZQAAAAvDCgOEAQAArEx7tJA2AQAAhqMyAACAlWGVAcIAAAAWhmUBwgAAAEEMSwOsGQAAwHBUBgAAsDCsMEAYAADAikcLAQCAUagMAABgZVhlgDAAAICFYVmANgEAAKajMgAAgAULCAEAgFEIAwAAGI42AQAAFqa1CQgDAABYEQYAADCbzbA0wJoBAAAMR2UAAAAL09YMUBkAAMBwhAEAAAxHmwAAAAvT2gSEAQAALAzLArQJAAAwHZUBAACsDCsNUBkAAMDCZgvfFqrS0lKlpaUpPj5eOTk5qqysPOa+ixcvls1mC9ji4+NDviZhAACATmLJkiVyOp0qKSlRdXW10tPTlZ+fr3379h3zmO7du6umpsa/7d69O+TrEgYAALCwhXELxbx58zRt2jQVFhZq+PDhWrBggRISElRWVnbsudpsSk5O9m8OhyPEqxIGAAAIFsY04Ha71dTUFLC53e6gS7a0tKiqqkp5eXn+saioKOXl5amiouKYUz18+LAGDhyo/v37a+LEidqyZUvIt9tpFhD+/I8DIz0FoNNx3bIn0lMAOqXr13fs+cO5ftDlcum+++4LGCspKdG9994bMNbQ0CCPxxP0yd7hcOijjz5q89znnXeeysrKNGrUKDU2Nurhhx/WmDFjtGXLFvXr16/dc+w0YQAAgDNRcXGxnE5nwJjdbg/LuXNzc5Wbm+v/5zFjxmjYsGF64okndP/997f7PIQBAAAswvkNhHa7vV1v/klJSYqOjlZdXV3AeF1dnZKTk9t1rdjYWF144YXasWNHSHNkzQAAAFYRWEEYFxenzMxMlZeX+8e8Xq/Ky8sDPv0fj8fj0aZNm5SSktL+C4vKAAAAnYbT6dTUqVOVlZWl7OxszZ8/X83NzSosLJQkTZkyRampqXK5XJKkOXPm6OKLL9aQIUP0+eefa+7cudq9e7d+8pOfhHRdwgAAABaR+gLCgoIC1dfXa/bs2aqtrVVGRoZWrFjhX1S4Z88eRUX9p6h/8OBBTZs2TbW1terZs6cyMzO1du1aDR8+PKTr2nw+ny+sd3KCnlr/aKSnAHQ6v7mVpwmAtmxa/3CHnv/iX88L27nW3e385p0ijDUDAAAYjjYBAABBzPqlIsIAAAAW4Xy08HRAmwAAAMNRGQAAwMqwygBhAAAAC8OyAGEAAAAr1gwAAACjEAYAADAcbQIAACxoEwAAAKNQGQAAwMKwwgBhAACAIIalAdoEAAAYjsoAAAAWpi0gJAwAAGBhWBagTQAAgOmoDAAAYGVYaYAwAACAhWFZgDAAAICVaQsIWTMAAIDhqAwAAGBlWGmAMAAAgIVZUYA2AQAAxqMyAACAlWGlAcIAAAAWhmUB2gQAAJiOygAAABaGPUxAGAAAIIhhYYA2AQAAhqMyAACAhWGFAcIAAABWpq0ZoE0AAIDhCAMAABiONgEAABamtQkIAwAAWJgWBmgTAABgOMIAAACGo00AAIAFbQIAAGAUKgMAAFgYVhggDAAAEMSwNECbAAAAw1EZAADAwrQFhIQBAAAsDMsChAEAAIIYVhpgzQAAAIajMgAAgIVZdQHCAAAAQQzrEtAmAADAdFQGAACwoDIAAACMQhgAAMBwtAkAALCgTQAAgOFsYdxCVVpaqrS0NMXHxysnJ0eVlZXtOu6ZZ56RzWbTpEmTQr4mYQAAgE5iyZIlcjqdKikpUXV1tdLT05Wfn699+/Yd97hdu3bpzjvv1NixY0/ouoQBAACsIlQamDdvnqZNm6bCwkINHz5cCxYsUEJCgsrKyo55jMfj0Q033KD77rtPgwYNCu2C/x9hAAAAi3BmAbfbraampoDN7XYHXbOlpUVVVVXKy8vzj0VFRSkvL08VFRXHnOucOXPUt29f/fjHPz7h+yUMAABgYbOFb3O5XEpMTAzYXC5X0DUbGhrk8XjkcDgCxh0Oh2pra9uc55o1a7Ro0SItXLjwpO6XpwkAAOhAxcXFcjqdAWN2u/2kz3vo0CHddNNNWrhwoZKSkk7qXIQBAAAswvlood1ub9ebf1JSkqKjo1VXVxcwXldXp+Tk5KD9//Wvf2nXrl266qqr/GNer1eSFBMTo61bt2rw4MHtmiNtAgAAOoG4uDhlZmaqvLzcP+b1elVeXq7c3Nyg/c8//3xt2rRJGzZs8G8TJkzQpZdeqg0bNqh///7tvjaVAQAAOgmn06mpU6cqKytL2dnZmj9/vpqbm1VYWChJmjJlilJTU+VyuRQfH68RI0YEHN+jRw9JChr/JoQBAAAsIvUNhAUFBaqvr9fs2bNVW1urjIwMrVixwr+ocM+ePYqKCn9RnzAAAIBFJL+NuKioSEVFRW2+tnr16uMeu3jx4hO6JmsGAAAwHJUBAAAsTPuhIsIAAAAWpoUB2gQAABiOMAAAgOFoEwAAYGFam4AwAACAhWFZgDYBAACmozIAAIAFbQIAAAxnWBagTQAAgOmoDAAAYGVYaYAwAACAhWlrBmgTAABgOCoDAABYGFYYIAyc6dav2qSKV97T4cYv5BjQW/lTL1HqYEeb+1a/sUWb1mxV/ScHJEnJ5/TRpQUX+/f3tHq0+rl3tGPDbn1e3yR7lzidM6K/vj05V916dj1l9wSEw+Rrx+jmm8YrqXc3bd1eI9fcF7V5yydt7lv2xG0anTk4aPytNR9q+h2LJEldusRp5u1X6NvjLlBiYld9tveA/rJkjZ57vqJD7wMdw7Q2AWHgDLalYrte+8safe9H45U62KHKFRv19EMv67aHr1fXxISg/Xd/+JkuyB2qflOSFRMXo7UvV+uvDy3T//n1dere6ywdbWlV7a56jb06S44BSfqy2a1VT/1Tz/72Ff34Vz+MwB0CJyb/snTdNXOC7nc9r/c379FN143VE7+fpqt+8BsdOHg4aP877lqs2Nj//O+yR2KClv7VqVWvb/SP/XzmBGWPHqJZs5/W3r0HNObic3XP3deovr5Rq9/64JTcF3CiWDNwBnvn1Q268NILlDFumPr066Xv/2i8Yu0x2vDmh23uf/X0y5V12Uglp/VR0tk9deW0S+Xz+rRry6eSpPgEu24onqjhFw9V77N7qt/QZH136iWq2VmvxoZDp/LWgJMy5YZxev6ld/TSy+/q4511muN6Xl8eOaqrJ4xuc/+mpi+1f/8h/5abc66OHDmqVa+/798nPT1Ny/6+Xuur/qW9NQe19MV3tG17jUZeMOBU3RbCyBbG7XRAGDhDeVo9qtlZr3NG9POP2aJsShvRT59tr23XOY66W+X1eNWla/wx9znyZYtk+yooAKeDmJhoDT8/Veve2eYf8/l8Wle5XemjBrbrHNdMzNaKVRv05ZEW/9jGjbs0/pIL1LdPd0nS6MzBGjggSWvXbTvWadCJ2Wzh204HtAnOUF8cOiKf1xfUDjire4L27z3YrnO88UyFzurZNSBQfF1rS6veeLpCF+QOlT0h7qTnDJwKPXt0VUxMtPYfCGwH7D9wSOek9f3G40dc0F9Dh6Ro9v3PBow/OPdFldxzrcpfna2jrR75vD7d+8Bzqnrv47DOH6fGafIeHjZhDwOffPKJSkpKVFZWdsx93G633G53wNjRllbFxpFNOou3l1VpS8V23fSLSYpp49+Lp9Wj53+/UpJP3y8cf6qnB0TMNROztW373qDFhtcX/C+NGjlARTPLVFNzUJkXDdI9P79a9fVNWle5PUKzBdon7G2CAwcO6MknnzzuPi6XS4mJiQHby4tfC/dUjJbQLV62KJuaG78IGD/c9IXOamPx4NdVvPKe1r5cretnTZBjQFLQ655Wj174/Uo1NhzS9bMmUhXAaeXg581qbfWod6+zAsZ79+qm/fubjntsl/g4fffyDL3wt8qAcbs9RjOmf09z572sN//5gbbtqNHTz76tFa9t1NQbx4X9HtDxaBN8g2XLlh339Y8//uaSWHFxsZxOZ8DY0s1/CnUqOI7omGilnNNHO7d8qvOyBknSV4sBN3+qrMtHHvO4tS9X6+2/Vem6u6/S2YOCS6b/DgIHaht14z2TlNDt2OsJgM6otdWjDz76TDnZQ/XGm1skSTabTRePHqKnn337uMdenjdKcbEx+vur1QHjMTHRio2Nkc/nCxj3er2KijpN3g0QyLB/bSGHgUmTJslmswX9R/91tm+IQna7XXZ74IIzWgThl/O9DC17olwp5/RV6uC+emfFRh11typ93DBJ0t/+8Lq69eyqb0/OlfRVEHhz6TuaNP1y9ejTTYc/b5YkxcXHKi4+7qvWwCMrVLOrQZPvvEI+r9e/T5ez4hUdEx2ZGwVC9D9/eVMP3DtZWz74VJu27NFN149Vly5xeunldyVJD9w3Wfv2NeqR0lcDjrt6YrbeeHOzGi0Vt+Zmt96t+pecM67UEfdR1dQcVNZFg3TV97M093fH/wAFdAYhvwOnpKTo8ccf18SJE9t8fcOGDcrMzDzpieHkXZA7VF8c+lJvLn1HzY1fyDEwSdfdfaW/TdC4/1BAcKt6fbM8rV49/8iKgPOMvWa0xv0gW4cONmtb9S5J0sL/XhKwz433TFLa8NSOvSEgTFa+tlG9ep6l6bfmK6l3N320ba9uvf1P/kWFKck95fMGfuBJG9hHmRcO0i3Tn2jznHf99591x/Tv66H7r1di9wTV1B7U7//wqp7lS4dOS4YVBmTzHe8jfhsmTJigjIwMzZkzp83XN27cqAsvvFBerzekiTy1/tGQ9gdM8Jtb90R6CkCntGn9wx16/mkvPBK2cy28ZkbYztVRQq4M3HXXXWpubj7m60OGDNE//vGPk5oUAAA4dUIOA2PHjj3u6127dtW4cayeBQCcvkxrE7BqDwAAi9PlkcBw4euIAQAwHJUBAAAsDCsMEAYAALAyrU1AGAAAwMK0MMCaAQAADEdlAAAAC8MKA4QBAACsaBMAAACjUBkAAMDCsMIAYQAAACvaBAAAwChUBgAAsDCsMEAYAADAijYBAAAwCpUBAAAsDCsMEAYAALAyrU1AGAAAwMKwLMCaAQAATEdlAAAAC9oEAAAYzrQwQJsAAADDURkAAMDCsMIAYQAAACubYX0C2gQAABiOygAAABZm1QWoDAAAEMRmC98WqtLSUqWlpSk+Pl45OTmqrKw85r4vvPCCsrKy1KNHD3Xt2lUZGRl66qmnQr4mYQAAgE5iyZIlcjqdKikpUXV1tdLT05Wfn699+/a1uX+vXr10zz33qKKiQu+//74KCwtVWFiolStXhnRdwgAAABa2MG6hmDdvnqZNm6bCwkINHz5cCxYsUEJCgsrKytrcf/z48br66qs1bNgwDR48WDNmzNCoUaO0Zs2akK5LGAAAwCLKFr6tvVpaWlRVVaW8vLz/zCMqSnl5eaqoqPjG430+n8rLy7V161ZdcsklId0vCwgBALAI5wJCt9stt9sdMGa322W32wPGGhoa5PF45HA4AsYdDoc++uijY56/sbFRqampcrvdio6O1uOPP67LLrsspDlSGQAAoAO5XC4lJiYGbC6XK2zn79atmzZs2KB3331XDzzwgJxOp1avXh3SOagMAABgEc7vHCouLpbT6QwYs1YFJCkpKUnR0dGqq6sLGK+rq1NycvIxzx8VFaUhQ4ZIkjIyMvThhx/K5XJp/Pjx7Z4jlQEAACzCuYDQbrere/fuAVtbYSAuLk6ZmZkqLy/3j3m9XpWXlys3N7fdc/d6vUFtiW9CZQAAgE7C6XRq6tSpysrKUnZ2tubPn6/m5mYVFhZKkqZMmaLU1FR/m8HlcikrK0uDBw+W2+3W8uXL9dRTT+kPf/hDSNclDAAAYBGpnyYoKChQfX29Zs+erdraWmVkZGjFihX+RYV79uxRVNR/ivrNzc366U9/qk8//VRdunTR+eefrz//+c8qKCgI6bo2n8/nC+udnKCn1j8a6SkAnc5vbt0T6SkAndKm9Q936Pnvez1870klef8VtnN1FNYMAABgONoEAABYGPYLxoQBAACsDMsCtAkAADAdlQEAACxC+U2BMwFhAAAAC8OyAGEAAAAr0xYQsmYAAADDURkAAMDCsMIAYQAAACvaBAAAwChUBgAAsDCsMEAYAADAijYBAAAwCpUBAAAsTKsMEAYAALAwrWxu2v0CAAALKgMAAFjQJgAAwHCGZQHCAAAAVqZVBlgzAACA4agMAABgYVhhgDAAAIAVbQIAAGAUKgMAAFgYVhggDAAAYEWbAAAAGIXKAAAAFoYVBggDAABY0SYAAABGoTIAAICFaZ+UCQMAAFiY1iYgDAAAYGFYFjCuEgIAACyoDAAAYEGbAAAAwxmWBWgTAABgOioDAABY0CYAAMBwpoUB2gQAABiOygAAABaGFQYIAwAAWNEmAAAARqEyAACAhWmflAkDAABYmNYmIAwAAGBhky/SUzilTKuEAAAACyoDAABYmNYmsPl8PrNqITgut9stl8ul4uJi2e32SE8H6BT4u8CZjjCAAE1NTUpMTFRjY6O6d+8e6ekAnQJ/FzjTsWYAAADDEQYAADAcYQAAAMMRBhDAbrerpKSERVLA1/B3gTMdCwgBADAclQEAAAxHGAAAwHCEAQAADEcYAADAcIQB+JWWliotLU3x8fHKyclRZWVlpKcERNRbb72lq666SmeffbZsNpteeumlSE8J6BCEAUiSlixZIqfTqZKSElVXVys9PV35+fnat29fpKcGRExzc7PS09NVWloa6akAHYpHCyFJysnJ0ejRo/XYY49Jkrxer/r376/bb79ds2bNivDsgMiz2Wx68cUXNWnSpEhPBQg7KgNQS0uLqqqqlJeX5x+LiopSXl6eKioqIjgzAMCpQBiAGhoa5PF45HA4AsYdDodqa2sjNCsAwKlCGAAAwHCEASgpKUnR0dGqq6sLGK+rq1NycnKEZgUAOFUIA1BcXJwyMzNVXl7uH/N6vSovL1dubm4EZwYAOBViIj0BdA5Op1NTp05VVlaWsrOzNX/+fDU3N6uwsDDSUwMi5vDhw9qxY4f/n3fu3KkNGzaoV69eGjBgQARnBoQXjxbC77HHHtPcuXNVW1urjIwMPfroo8rJyYn0tICIWb16tS699NKg8alTp2rx4sWnfkJAByEMAABgONYMAABgOMIAAACGIwwAAGA4wgAAAIYjDAAAYDjCAAAAhiMMAABgOMIAAACGIwwAAGA4wgAAAIYjDAAAYDjCAAAAhvt/wq5gxO6gqlUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2017,  555],\n",
              "       [ 556, 1941]])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SlgpkakWiSa-",
        "outputId": "125087f8-43b4-4f48-8988-ba76a27a3390"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.78      0.78      2572\n",
            "           1       0.78      0.78      0.78      2497\n",
            "\n",
            "    accuracy                           0.78      5069\n",
            "   macro avg       0.78      0.78      0.78      5069\n",
            "weighted avg       0.78      0.78      0.78      5069\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ❕ Улучшение целевой метрики"
      ],
      "metadata": {
        "id": "5b9bRIS8hK_Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Главной целью предсказания было выявление наибольшего возможного числа заемщиков, имеющих риск просрочки платежа на 90 и более дней, т.е. для которых признак `SeriousDlqin2yrs` равен `1.` Поэтому было принято решение улучшить показатель метрики recall путем уменьшения порога вероятности, при которой клиент начинал иметь приск просрочки."
      ],
      "metadata": {
        "id": "DlLOaLBFV40n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "probs = model.predict_proba(X_test)"
      ],
      "metadata": {
        "id": "1ayNvJGRKvsQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import PrecisionRecallDisplay\n",
        "\n",
        "PrecisionRecallDisplay.from_estimator(model, X_test, y_test)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "nO4mT2_5MRJy",
        "outputId": "7cf63502-83da-49c2-ecb4-0d3af29b3f3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "prec, rec, threshholds = precision_recall_curve(y_true=y_test, probas_pred=probs[:,1])"
      ],
      "metadata": {
        "id": "QIITELBtJ9IL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_prob = threshholds[np.where(rec > 0.85)[0][-1]]\n",
        "target_prob"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PaVd1wgXIllh",
        "outputId": "56bebb32-eebb-4d54-b6b1-ef25a42e2dd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.39074379276042953"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = probs[:,1] > target_prob"
      ],
      "metadata": {
        "id": "Ab914norNNAI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cf = confusion_matrix(y_test, classes, normalize='true')\n",
        "sns.heatmap(cf, annot=True, cmap='crest')\n",
        "plt.show()\n",
        "\n",
        "confusion_matrix(y_test, classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "_rotMI0wgpn4",
        "outputId": "d5cd49cb-c632-4f87-f250-7765d4062462"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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c6eGfr829QJL0wrqVemHdP47QlQGHxtYV5TMMq6qq9Otf/1oNDQ0KBoOSJIfDoezsbLndbl122WWHNJGMqzxfvRNgmOmXJH/1ToCBHrpkQa8ef3TRXTE7VvPDN8fsWL0l6tp8YWGhCgsLtXfvXrW1ff6ozdTUVCUkJMR8cgAAoPcdcqM+ISFBI0eOjOVcAADoG1gzAACA6cxKAzyOGAAAw1EZAADAyqzCAGEAAIBuDAsDtAkAADAclQEAACyiegDPUYAwAACAFW0CAABgEsIAAACGo00AAICVzaw+AWEAAAArs7IAbQIAAExHGAAAwHC0CQAAsKJNAAAATEJlAAAAKyoDAADAJIQBAAAMR5sAAAArw9oEhAEAACxshj2BkDYBAACGIwwAAGA42gQAAFiZ1SUgDAAA0I1hYYA2AQAAhiMMAABgONoEAABYGHZnIZUBAABMRxgAAKAPKS8vV0ZGhpKSkpSbm6v6+voD7r9jxw5df/31GjlypJxOp0466SQtW7YsqnPSJgAAwCpObYKqqiq53W5VVFQoNzdXZWVlKigo0Pr16zVixIhu+3d2duq8887TiBEj9Oc//1np6enavHmzhgwZEtV5CQMAAPQRixcv1rx581RUVCRJqqio0IsvvqjKykrdfPPN3favrKzUtm3btGLFCiUkJEiSMjIyoj4vbQIAAKxsMdwOUmdnpxoaGpSfnx8es9vtys/PV11dXY+fef7555WXl6frr79eLpdLp556qu68804Fg8GoLpfKAAAAFrHsEgQCAQUCgYgxp9Mpp9MZMdbW1qZgMCiXyxUx7nK59N577/V47I0bN+rVV1/VrFmztGzZMm3YsEHz58/X3r17VVpaetBzpDIAAEAv8ng8SklJidg8Hk9Mjh0KhTRixAg99NBDys7OVmFhoW655RZVVFREdRwqAwAAWMXwQQMlJSVyu90RY9aqgCSlpqbK4XDI5/NFjPt8PqWlpfV47JEjRyohIUEOhyM8Nn78eHm9XnV2dioxMfGg5khlAAAAC5stdpvT6dTgwYMjtp7CQGJiorKzs1VTUxMeC4VCqqmpUV5eXo/zPOuss7RhwwaFQqHw2Pvvv6+RI0cedBCQCAMAAPQZbrdbS5Ys0aOPPqp169bpuuuuk9/vD99dMHv2bJWUlIT3v+6667Rt2zYtWLBA77//vl588UXdeeeduv7666M6L20CAAD6iMLCQrW2tmrhwoXyer3KyspSdXV1eFFhc3Oz7PYv/44fNWqUli9frhtvvFGTJk1Senq6FixYoJtuuimq8xIGAACwiuO7CYqLi1VcXNzj72pra7uN5eXlaeXKlYd1TsIAAAAWhr2niDUDAACYjsoAAABWhpUGCAMAAFjE8DEDXwu0CQAAMBxhAAAAw9EmAADAgjYBAAAwCmEAAADD0SYAAMDCtDYBYQAAACvDwgBtAgAADEdlAAAAC5thpQHCAAAAVmZlAdoEAACYjsoAAAAWhhUGCAMAAFiZdmshbQIAAAxHZQAAACvDKgOEAQAALAzLAoQBAAC6MSwNsGYAAADDURkAAMDCsMIAYQAAACtuLQQAAEahMgAAgJVhlQHCAAAAFoZlAdoEAACYjsoAAAAWLCAEAABGIQwAAGA42gQAAFiY1iYgDAAAYEUYAADAbDbD0gBrBgAAMByVAQAALExbM0BlAAAAwxEGAAAwHG0CAAAsTGsTEAYAALAwLAvQJgAAwHRUBgAAsDKsNEBlAAAAC5stdlu0ysvLlZGRoaSkJOXm5qq+vn6/+z7yyCOy2WwRW1JSUtTnJAwAANBHVFVVye12q7S0VI2NjcrMzFRBQYFaWlr2+5nBgwdr69at4W3z5s1Rn5cwAACAhS2GWzQWL16sefPmqaioSBMmTFBFRYWSk5NVWVm5/7nabEpLSwtvLpcryrMSBgAA6C6GaSAQCKijoyNiCwQC3U7Z2dmphoYG5efnh8fsdrvy8/NVV1e336nu2rVLxx9/vEaNGqWLL75Ya9eujfpy+8wCwjvdyfGeAtDneOY2x3sKQN90Se8ePpbrBz0ej26//faIsdLSUv3iF7+IGGtra1MwGOz2l73L5dJ7773X47FPPvlkVVZWatKkSWpvb9e9996rKVOmaO3atTruuOMOeo59JgwAAHA0KikpkdvtjhhzOp0xOXZeXp7y8vLCP0+ZMkXjx4/X73//e91xxx0HfRzCAAAAFrF8AqHT6TyoL//U1FQ5HA75fL6IcZ/Pp7S0tIM6V0JCgk477TRt2LAhqjmyZgAAAKs4rCBMTExUdna2ampqwmOhUEg1NTURf/0fSDAY1OrVqzVy5MiDP7GoDAAA0Ge43W7NmTNHOTk5mjx5ssrKyuT3+1VUVCRJmj17ttLT0+XxeCRJixYt0plnnqmxY8dqx44duueee7R582bNnTs3qvMSBgAAsIjXAwgLCwvV2tqqhQsXyuv1KisrS9XV1eFFhc3NzbLbvyzqb9++XfPmzZPX69XQoUOVnZ2tFStWaMKECVGd19bV1dUV0ys5RE82/ibeUwD6HO4mAHq2uvG+Xj3+mXcvjtmxVt7k/uqd4ow1AwAAGI42AQAA3Zj1piLCAAAAFrG8tfDrgDYBAACGozIAAICVYZUBwgAAABaGZQHCAAAAVqwZAAAARiEMAABgONoEAABY0CYAAABGoTIAAICFYYUBwgAAAN0YlgZoEwAAYDgqAwAAWJi2gJAwAACAhWFZgDYBAACmozIAAICVYaUBwgAAABaGZQHCAAAAVqYtIGTNAAAAhqMyAACAlWGlAcIAAAAWZkUB2gQAABiPygAAAFaGlQYIAwAAWBiWBWgTAABgOioDAABYGHYzAWEAAIBuDAsDtAkAADAclQEAACwMKwwQBgAAsDJtzQBtAgAADEcYAADAcLQJAACwMK1NQBgAAMDCtDBAmwAAAMMRBgAAMBxtAgAALGgTAAAAo1AZAADAwrDCAGEAAIBuDEsDtAkAAOhDysvLlZGRoaSkJOXm5qq+vv6gPvfUU0/JZrNp5syZUZ+TMAAAgIXNFrstGlVVVXK73SotLVVjY6MyMzNVUFCglpaWA35u06ZN+ulPf6qpU6ce0vUSBgAAsLDFcIvG4sWLNW/ePBUVFWnChAmqqKhQcnKyKisr9/uZYDCoWbNm6fbbb9cJJ5wQ5Rk/RxgAAMAqhqWBQCCgjo6OiC0QCHQ7ZWdnpxoaGpSfnx8es9vtys/PV11d3X6numjRIo0YMULXXHPNIV8uYQAAgF7k8XiUkpISsXk8nm77tbW1KRgMyuVyRYy7XC55vd4ej/3mm29q6dKlWrJkyWHNkbsJAACwiOXNBCUlJXK73RFjTqfzsI+7c+dOXX311VqyZIlSU1MP61iEAQAALGL5BEKn03lQX/6pqalyOBzy+XwR4z6fT2lpad32//DDD7Vp0ybNmDEjPBYKhSRJ/fr10/r16zVmzJiDmiNtAgAA+oDExERlZ2erpqYmPBYKhVRTU6O8vLxu+48bN06rV69WU1NTeLvooov0rW99S01NTRo1atRBn5vKAAAAFvF6N4Hb7dacOXOUk5OjyZMnq6ysTH6/X0VFRZKk2bNnKz09XR6PR0lJSTr11FMjPj9kyBBJ6jb+VQgDAAD0EYWFhWptbdXChQvl9XqVlZWl6urq8KLC5uZm2e2xL+oTBgAA6EOKi4tVXFzc4+9qa2sP+NlHHnnkkM5JGAAAwMK0VxgTBgAAsDAsC3A3AQAApqMyAACAlWGlAcIAAAAWhmUBwgAAAFamLSBkzQAAAIajMgAAgAWVAQAAYBTCAAAAhqNNAACAhWltAsIAAAAWhmUB2gQAAJiOygAAABa0CQAAMJxpYYA2AQAAhiMMAABgONoEAABYmNYmIAwAAGBhWBagTQAAgOmoDAAAYEGbAAAAwxmWBWgTAABgOioDAABYGVYaIAwAAGBh2poB2gQAABiOygAAABaGFQYIA0e7+pdWa8ULTdrVvltpo4/Rd34wVeljXT3u2/LxNtX+uV6fbmxVe9tOFVx9ls68IDNin9o/1+v1v/wzYuyYY4eo+L4re+0agN5w+WVn6Qezpyn1mEFa//6n8vzqWa1Z+/F+97/qyqm67PtTNDJtqHbs8OvlmlUq++0ydXbukyRd96Ppmv+jgojPfPRRiy76j7t79TrQO0xrExAGjmJr6j7QS4/9XRdec46OG+vSyr+9o8fv+quK77tCA1KSu+2/t3OvhowYrAm5Y7T8sb/v97jDjxum2bdcFP7ZbjfsXw2+9gqmZ+ln7ot0x51/1jurm3X1rKn6ffm1mvG9u7Vt+65u+19w/mn6rxsu1MLbq9S0apOOP364/vv2y9XVJd2z+Pnwfh9s2Kp51/0+/HMwGDoi1wMcLtYMHMVWvrhKp397gk6bNl7Djxum715zjhIS++nt2vd63D99jEvTZ03RqVNOlKOfY7/HtTtsGjgkObwlD+7fW5cA9IrZs87WX55dqeeef0sbP/Jp0S//on/t2avvXTy5x/2zMjP09qpNWlb9tj7dul11K9/X36rf1qmnjI7YLxgM6bPPdoa3HTv8R+Jy0AtsMdy+DqgMHKWC+4L69KNWffPi08NjNrtNJ5x6nD75wHtYx97mbdd91z2ifon9NOpEl869/EylpA463CkDR0S/fg5NGH+clj78anisq6tLK//xvjInHd/jZ5pWbdKFF2Tr1FNGac3aj3Vc+jBN/eZ4vfBiQ8R+o0enqmb5QnUG9mnVO5tV9sCL8np39ObloJfQJsBRYXfHHnWFurq1Awak9Ffbp9sP+bjpY126+MffVurIIdq5Y7de/8tbevj2Z3Xdry6Xs3/i4U4b6HVDhwxQv34OfbZtZ8T4Z9t26RsZI3r8zLLqtzVkyAD9sbJYkk0JCQ5VPb1Cf6isCe+zenWzbit9Sps2tyo1dbCuu3a6Hl16vb536b3avTvQm5eEXmBYFoh9m+Djjz/WD3/4wwPuEwgE1NHREbHt/WIRDvq2E7OO1ylnjpXr+FSNzRytWTd9V3v8nVq7ckO8pwb0mpzsMZr3w3P1355nVDhrsRb85GGd/c3x+tHc/PA+b654Ty+98o7e/2CrVtSt1/wblmjQwP4qOC/zAEcG+oaYh4Ft27bp0UcfPeA+Ho9HKSkpEdvzD78c66kYLXlwkmx2m/ztuyPG/e3/0sAh3RcPHqqkAU4dMzJF27ztMTsm0Ju27/Br376gjhkW2do6ZthAffbZzh4/Uzz/fL2wrEHPPPcPfbDBq1dfW6P7y5fpmqJzZdtPPXnnrj3a3Nyq0aNSY34N6H02W+y2r4Oo2wTPP//8AX+/cePGrzxGSUmJ3G53xNiz7y6Jdio4AEc/h479xnBtXLNF4844QZLUFerSxrWfaPL0iTE7T+eevdrm69CkqQNidkygN+3bF9S76z5R7uQT9WrtGkmSzWbTmZNP1P9U9XwXTf+kBHWFuiLGgsGuLz4rdXX18Jn+iRp1XGq3dQX4mviafInHStRhYObMmbLZbOrq6b/+L+wvKf+b0+mU0+mMGEtIZPlCrJ15Yaaee/BVHXvCcKWPHaGVf3tHewP7lHXOOEnSs797RYOGDlD+FXmSPl902PrJ9vD/7tjul3dTmxKTEjQsLUWS9NLjf9dJp2doyPBB2rndr9qn35LdbtOpU06Mz0UCh+CPT7yhX95+uda++7FWr23W1Veerf79E/Xc8/WSpF8uukItLe36zQPLJEm1b7yr2bPO0br3tmj1mmaNHpWq4vnn6/X/fVehL0LCT/5rhl5/Y60+3bpdw4en6PofFygYCulv1W/H7TqBgxX1N/DIkSP1u9/9ThdffHGPv29qalJ2dvZhTwyH79S8E7W7Y49q/1yvXTt2K+34VM26+bvhNkF7266I4LZzu1+/L/lT+Oe6vzap7q9NOn78sfrBwpmSpI5tfv3lty/rX7v2KHlwf40+eaSuueM/NIDbC/E1svylJg0bOkDXX1eg1GMG6731W/Tj4iX6bNvnzxgYmTYkohLw0B9eUVeXdMP139GI4Snavn2XXv/fd3X/F2FBklyuFN3tuUpDUgZo+/Zdamz6SLPm3K/t3F74tWRYYUC2rgP9id+Diy66SFlZWVq0aFGPv1+1apVOO+00hULRPWzjycbfRLU/YALP3OZ4TwHok1Y33terx5/3TOy+k5ZcsiBmx+otUVcGfvazn8nv33/SHTt2rF577bXDmhQAADhyog4DU6dOPeDvBwwYoHPOOeeQJwQAQLyZ1iZg1R4AABZfl1sCY4V3EwAAYDjCAAAAFvF8UVF5ebkyMjKUlJSk3Nxc1dfX73ffZ555Rjk5ORoyZIgGDBigrKwsPfbYY1GfkzAAAIBFvJ5AWFVVJbfbrdLSUjU2NiozM1MFBQVqaWnpcf9hw4bplltuUV1dnd555x0VFRWpqKhIy5cvj+q8hAEAACziFQYWL16sefPmqaioSBMmTFBFRYWSk5NVWVnZ4/7Tpk3T9773PY0fP15jxozRggULNGnSJL355ptRnZcwAABAL+rp5XyBQPc3WXZ2dqqhoUH5+V++AMtutys/P191dXVfeZ6uri7V1NRo/fr1Ovvss6OaI2EAAACLWK4Z6OnlfB6Pp9s529raFAwG5XK5IsZdLpe8Xu9+59re3q6BAwcqMTFRF154oX7729/qvPPOi+p6ubUQAACLWN5a2NPL+azv5zkcgwYNUlNTk3bt2qWamhq53W6dcMIJmjZt2kEfgzAAAEAv6unlfD1JTU2Vw+GQz+eLGPf5fEpLS9vv5+x2u8aOHStJysrK0rp16+TxeKIKA7QJAACwiMethYmJicrOzlZNTU14LBQKqaamRnl5eQd9nFAo1OOahAOhMgAAgEW8nkDodrs1Z84c5eTkaPLkySorK5Pf71dRUZEkafbs2UpPTw+vOfB4PMrJydGYMWMUCAS0bNkyPfbYY3rwwQejOi9hAACAPqKwsFCtra1auHChvF6vsrKyVF1dHV5U2NzcLLv9y6K+3+/X/Pnz9cknn6h///4aN26cHn/8cRUWFkZ13qhfYdxbeIUx0B2vMAZ61tuvMF7wQuy+k34z4yh8hTEAAEc7XlQEAACMQmUAAAALwwoDhAEAAKxMaxMQBgAAsDAsC7BmAAAA01EZAADAgjYBAACGMy0M0CYAAMBwVAYAALAwrDBAGAAAwMpmWJ+ANgEAAIajMgAAgIVZdQHCAAAA3RjWJaBNAACA6agMAABgYVhhgDAAAICV3bA0QBgAAMDCsCzAmgEAAExHZQAAAAvT7iYgDAAAYGFYFqBNAACA6agMAABgQZsAAADDGZYFaBMAAGA6KgMAAFjQJgAAwHCGZQHaBAAAmI7KAAAAFrybAAAAwxmWBQgDAABYmbaAkDUDAAAYjsoAAAAWhhUGCAMAAFjRJgAAAEahMgAAgIVhhQHCAAAAVrQJAACAUagMAABgYVplgDAAAICFaWVz064XAABYUBkAAMDCtDYBlQEAACxsMdyiVV5eroyMDCUlJSk3N1f19fX73XfJkiWaOnWqhg4dqqFDhyo/P/+A++8PYQAAAAubLXZbNKqqquR2u1VaWqrGxkZlZmaqoKBALS0tPe5fW1urK664Qq+99prq6uo0atQoTZ8+XVu2bInqvIQBAAD6iMWLF2vevHkqKirShAkTVFFRoeTkZFVWVva4/xNPPKH58+crKytL48aN0x/+8AeFQiHV1NREdV7CAAAAFrFsEwQCAXV0dERsgUCg2zk7OzvV0NCg/Pz88Jjdbld+fr7q6uoOat67d+/W3r17NWzYsKiulzAAAIBFLNsEHo9HKSkpEZvH4+l2zra2NgWDQblcrohxl8slr9d7UPO+6aabdOyxx0YEioPB3QQAAPSikpISud3uiDGn0xnz89x111166qmnVFtbq6SkpKg+SxgAAMAilncWOp3Og/ryT01NlcPhkM/nixj3+XxKS0s74Gfvvfde3XXXXXrllVc0adKkqOdImwAAAIt43E2QmJio7OzsiMV//14MmJeXt9/P/epXv9Idd9yh6upq5eTkHNL1UhkAAKCPcLvdmjNnjnJycjR58mSVlZXJ7/erqKhIkjR79mylp6eH1xzcfffdWrhwoZ588kllZGSE1xYMHDhQAwcOPOjzEgYAALCI1wMICwsL1draqoULF8rr9SorK0vV1dXhRYXNzc2y278s6j/44IPq7OzU97///YjjlJaW6he/+MVBn5cwAACARTwfR1xcXKzi4uIef1dbWxvx86ZNm2JyTtYMAABgOCoDAABYmPaXMmEAAAAL095aSBgAAMDCsCxgXCUEAABYUBkAAMCCNgEAAIYzLAvQJgAAwHRUBgAAsKBNAACA4UwLA7QJAAAwHJUBAAAsDCsMEAYAALCiTQAAAIxCZQAAAAvT/lImDAAAYGFam4AwAACAhU1d8Z7CEWVaJQQAAFhQGQAAwMK0NoGtq6vLrFoIDigQCMjj8aikpEROpzPe0wH6BP5d4GhHGECEjo4OpaSkqL29XYMHD473dIA+gX8XONqxZgAAAMMRBgAAMBxhAAAAwxEGEMHpdKq0tJRFUsD/wb8LHO1YQAgAgOGoDAAAYDjCAAAAhiMMAABgOMIAAACGIwwgrLy8XBkZGUpKSlJubq7q6+vjPSUgrt544w3NmDFDxx57rGw2m5577rl4TwnoFYQBSJKqqqrkdrtVWlqqxsZGZWZmqqCgQC0tLfGeGhA3fr9fmZmZKi8vj/dUgF7FrYWQJOXm5uqMM87QAw88IEkKhUIaNWqUbrjhBt18881xnh0QfzabTc8++6xmzpwZ76kAMUdlAOrs7FRDQ4Py8/PDY3a7Xfn5+aqrq4vjzAAARwJhAGpra1MwGJTL5YoYd7lc8nq9cZoVAOBIIQwAAGA4wgCUmpoqh8Mhn88XMe7z+ZSWlhanWQEAjhTCAJSYmKjs7GzV1NSEx0KhkGpqapSXlxfHmQEAjoR+8Z4A+ga32605c+YoJydHkydPVllZmfx+v4qKiuI9NSBudu3apQ0bNoR//uijj9TU1KRhw4Zp9OjRcZwZEFvcWoiwBx54QPfcc4+8Xq+ysrJ0//33Kzc3N97TAuKmtrZW3/rWt7qNz5kzR4888siRnxDQSwgDAAAYjjUDAAAYjjAAAIDhCAMAABiOMAAAgOEIAwAAGI4wAACA4QgDAAAYjjAAAIDhCAMAABiOMAAAgOEIAwAAGI4wAACA4f4/3VwTNq/pn/YAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1768,  804],\n",
              "       [ 375, 2122]])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oNjNEWh_g0gw",
        "outputId": "80888990-d267-4155-9fff-37dbe37730b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.69      0.75      2572\n",
            "           1       0.73      0.85      0.78      2497\n",
            "\n",
            "    accuracy                           0.77      5069\n",
            "   macro avg       0.78      0.77      0.77      5069\n",
            "weighted avg       0.78      0.77      0.77      5069\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 📊 Оценка важности признаков"
      ],
      "metadata": {
        "id": "Ym8txPinjFap"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "ss = MinMaxScaler()\n",
        "ss.fit(X_train)\n",
        "\n",
        "X_train = pd.DataFrame(ss.transform(X_train), columns=X_train.columns)\n",
        "X_test = pd.DataFrame(ss.transform(X_test), columns=X_test.columns)\n",
        "\n",
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "dyZjT9tSjVbz",
        "outputId": "aa7de21c-871e-40af-c5fd-65b1a18bf916"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RevolvingUtilizationOfUnsecuredLines       age  \\\n",
              "0                          3.893764e-08  0.534091   \n",
              "1                          1.114602e-05  0.750000   \n",
              "2                          1.012321e-07  0.988636   \n",
              "3                          2.069600e-06  0.284091   \n",
              "4                          4.580128e-05  0.170455   \n",
              "\n",
              "   NumberOfTime30-59DaysPastDueNotWorse     DebtRatio  MonthlyIncome  \\\n",
              "0                                   0.0  1.706704e-07       0.016185   \n",
              "1                                   0.0  1.211722e-02       0.023018   \n",
              "2                                   0.0  5.461235e-07       0.012148   \n",
              "3                                   0.0  4.301431e-06       0.005328   \n",
              "4                                   0.0  2.038066e-06       0.021667   \n",
              "\n",
              "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
              "0                         0.070175                 0.000000   \n",
              "1                         0.280702                 0.000000   \n",
              "2                         0.192982                 0.000000   \n",
              "3                         0.122807                 0.000000   \n",
              "4                         0.315789                 0.081633   \n",
              "\n",
              "   NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \\\n",
              "0                                   0.0            0.000000   \n",
              "1                                   0.0            0.000000   \n",
              "2                                   0.0            0.111111   \n",
              "3                                   0.0            0.111111   \n",
              "4                                   0.0            0.111111   \n",
              "\n",
              "   RealEstateLoansOrLines  GroupAge  \n",
              "0                     0.0  1.000000  \n",
              "1                     0.0  1.000000  \n",
              "2                     0.0  1.000000  \n",
              "3                     0.0  0.333333  \n",
              "4                     0.0  0.333333  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0ec289ba-1a1f-4906-aebe-a51f697e3e4c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <th>age</th>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <th>DebtRatio</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfDependents</th>\n",
              "      <th>RealEstateLoansOrLines</th>\n",
              "      <th>GroupAge</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.893764e-08</td>\n",
              "      <td>0.534091</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.706704e-07</td>\n",
              "      <td>0.016185</td>\n",
              "      <td>0.070175</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.114602e-05</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.211722e-02</td>\n",
              "      <td>0.023018</td>\n",
              "      <td>0.280702</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.012321e-07</td>\n",
              "      <td>0.988636</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.461235e-07</td>\n",
              "      <td>0.012148</td>\n",
              "      <td>0.192982</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.069600e-06</td>\n",
              "      <td>0.284091</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.301431e-06</td>\n",
              "      <td>0.005328</td>\n",
              "      <td>0.122807</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.580128e-05</td>\n",
              "      <td>0.170455</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.038066e-06</td>\n",
              "      <td>0.021667</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.081633</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ec289ba-1a1f-4906-aebe-a51f697e3e4c')\"\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-0ec289ba-1a1f-4906-aebe-a51f697e3e4c 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-0ec289ba-1a1f-4906-aebe-a51f697e3e4c');\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": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)\n",
        "\n",
        "pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "_iPQLXnnjXZS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = list(zip([col for col in X_train.columns], model.feature_importances_))\n",
        "features.sort(key=lambda x: x[1])\n",
        "\n",
        "plt.barh([x[0] for x in features], [x[1] for x in features])\n",
        "plt.title('Feature importances')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "Egs3vUJ4jdca",
        "outputId": "db4f5ab5-82bb-4f93-cf54-2b288bd7f810"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 💾 Сохранение модели"
      ],
      "metadata": {
        "id": "OSnos4M2bzW6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('model.pickle', 'wb') as f:\n",
        "    pickle.dump(model, f)"
      ],
      "metadata": {
        "id": "GkxtpSwzaEoo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}