Практика_2.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/Keshabl/ML2025/blob/main/%D0%9F%D1%80%D0%B0%D0%BA%D1%82%D0%B8%D0%BA%D0%B0_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# МЛ Практика 2: Статистика в действии и её влияние на модель"
      ],
      "metadata": {
        "id": "vDOnPyEUCBNT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Цель занятия:**\n",
        "Изучить, как статистические метрики (среднее, медиана, квантили, размер выборки, стандартное отклонение) влияют на анализ данных и построение моделей машинного обучения. На практике мы проведем анализ целевой переменной **Y**, обучим модель линейной регрессии и исследуем влияние статистических характеристик на метрики модели."
      ],
      "metadata": {
        "id": "TiIX2NzyCKV3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Задание состоит из следующих частей:**\n",
        "1. Анализ целевой переменной **Y** с точки зрения статистики. (в нашем случае - бинарная классификация)\n",
        "2. Анализ пропусков\n",
        "2. Изучение влияния статистических метрик на данные.\n",
        "3. Обучение модели линейной регрессии и оценка ее точности.\n",
        "4. Проведение теста Пирсона.\n",
        "5. Визуализация результатов и анализ влияния статистики на метрики модели.\n",
        "6. Валидация модели\n",
        "---"
      ],
      "metadata": {
        "id": "gXXq0W21COeO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Анализ целевой переменной Y с точки зрения статистики"
      ],
      "metadata": {
        "id": "M8QATjCVCQ9V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Описание:**\n",
        "В этом разделе вы проведете статистический анализ целевой переменной **Y**, изучите ее распределение и основные статистические характеристики."
      ],
      "metadata": {
        "id": "_dmndy3OCTMn"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "1. **Импортируйте необходимые библиотеки:**"
      ],
      "metadata": {
        "id": "Kl2lHWkZCU0y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas scikit-learn seaborn scipy"
      ],
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        "id": "flQiMFG7CZMI",
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      "execution_count": 33,
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      "source": [
        "   import pandas as pd\n",
        "   import numpy as np\n",
        "   import matplotlib.pyplot as plt\n",
        "   import seaborn as sns\n",
        "   from scipy import stats"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. **Загружаем набор данных:**"
      ],
      "metadata": {
        "id": "rjoWxcqECvS-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Загрузка набора данных\n",
        "df = pd.read_csv('/content/employee.csv')"
      ],
      "metadata": {
        "id": "zLUqWAIhCc4Z"
      },
      "execution_count": 69,
      "outputs": []
    },
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        "df"
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      "execution_count": 70,
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              "       satisfaction_level  last_evaluation  number_project  \\\n",
              "0                    0.38             0.53               2   \n",
              "1                    0.80             0.86               5   \n",
              "2                    0.11             0.88               7   \n",
              "3                    0.72             0.87               5   \n",
              "4                    0.37             0.52               2   \n",
              "...                   ...              ...             ...   \n",
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              "14996                0.37             0.53               2   \n",
              "14997                0.11             0.96               6   \n",
              "14998                0.37             0.52               2   \n",
              "\n",
              "       average_montly_hours  time_spend_company  Work_accident  \\\n",
              "0                       157                   3              0   \n",
              "1                       262                   6              0   \n",
              "2                       272                   4              0   \n",
              "3                       223                   5              0   \n",
              "4                       159                   3              0   \n",
              "...                     ...                 ...            ...   \n",
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              "4                          0      sales     low     1  \n",
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              "14994                      0    support     low     1  \n",
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              "14996                      0    support     low     1  \n",
              "14997                      0    support     low     1  \n",
              "14998                      0    support     low     1  \n",
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              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-366b7215-ef13-4b51-8e2a-7452e3997025 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 14999,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2486306510611418,\n        \"min\": 0.09,\n        \"max\": 1.0,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          0.83,\n          0.13,\n          0.55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17116911062327592,\n        \"min\": 0.36,\n        \"max\": 1.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          0.66,\n          0.44,\n          0.53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49,\n        \"min\": 96,\n        \"max\": 310,\n        \"num_unique_values\": 215,\n        \"samples\": [\n          118,\n          112,\n          222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6,\n          8,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"department\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"marketing\",\n          \"accounting\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"low\",\n          \"medium\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"left\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# для удобства распечатаем все колонки\n",
        "for column in df.columns:\n",
        "  print(f'колонка: {column}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_Am0RHMCkF1",
        "outputId": "2bf46841-407a-428f-8fd5-7689a83107e7"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "колонка: satisfaction_level\n",
            "колонка: last_evaluation\n",
            "колонка: number_project\n",
            "колонка: average_montly_hours\n",
            "колонка: time_spend_company\n",
            "колонка: Work_accident\n",
            "колонка: promotion_last_5years\n",
            "колонка: department\n",
            "колонка: salary\n",
            "колонка: left\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7ynbI9AC2AX",
        "outputId": "5607afd9-a0e7-4a45-e491-1d1f8230deb4"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14999 entries, 0 to 14998\n",
            "Data columns (total 10 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   satisfaction_level     14999 non-null  float64\n",
            " 1   last_evaluation        14999 non-null  float64\n",
            " 2   number_project         14999 non-null  int64  \n",
            " 3   average_montly_hours   14999 non-null  int64  \n",
            " 4   time_spend_company     14999 non-null  int64  \n",
            " 5   Work_accident          14999 non-null  int64  \n",
            " 6   promotion_last_5years  14999 non-null  int64  \n",
            " 7   department             14999 non-null  object \n",
            " 8   salary                 14999 non-null  object \n",
            " 9   left                   14999 non-null  int64  \n",
            "dtypes: float64(2), int64(6), object(2)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. **Проверьте наличие пропущенных значений и работа с пропусками для числовых:**"
      ],
      "metadata": {
        "id": "I-Umxm3KC1K6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверка на пропущенные значения\n",
        "print(df.isnull().sum()) # пропусков нет"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lSxhSMcC9RN",
        "outputId": "045fe326-9926-4dc5-a938-d6e5eedae19c"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "satisfaction_level       0\n",
            "last_evaluation          0\n",
            "number_project           0\n",
            "average_montly_hours     0\n",
            "time_spend_company       0\n",
            "Work_accident            0\n",
            "promotion_last_5years    0\n",
            "department               0\n",
            "salary                   0\n",
            "left                     0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# генерируем случайную последовательность\n",
        "np.random.seed(42)\n",
        "df_test_nan = df.copy()\n",
        "df_test_nan.loc[np.random.choice(df_test_nan.index, size=5, replace=True), 'satisfaction_level'] = np.nan"
      ],
      "metadata": {
        "id": "7fp-OSKxDgN1"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверка на пропущенные значения\n",
        "print(df_test_nan.isnull().sum()) # пропусков нет"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQDlrELvDxHy",
        "outputId": "6de1783f-15de-4511-a90b-033de7559b63"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "satisfaction_level       5\n",
            "last_evaluation          0\n",
            "number_project           0\n",
            "average_montly_hours     0\n",
            "time_spend_company       0\n",
            "Work_accident            0\n",
            "promotion_last_5years    0\n",
            "department               0\n",
            "salary                   0\n",
            "left                     0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Вариант 1: Удаление строк с пропусками\n",
        "df_drop = df_test_nan.dropna()\n",
        "print(\"\\n# Удаление строк с пропусками\")\n",
        "print(df.isnull().sum()) # пропусков нет"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "URBzKPPND0Kx",
        "outputId": "35a53aa8-2c0d-4908-8aef-8e67f0da6d9a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "# Удаление строк с пропусками\n",
            "satisfaction_level       0\n",
            "last_evaluation          0\n",
            "number_project           0\n",
            "average_montly_hours     0\n",
            "time_spend_company       0\n",
            "Work_accident            0\n",
            "promotion_last_5years    0\n",
            "department               0\n",
            "salary                   0\n",
            "left                     0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Вариант 2: Заполнение пропусков медианой\n",
        "df_median = df_test_nan.copy()\n",
        "df_median.satisfaction_level = df_median.satisfaction_level.fillna(df_median.satisfaction_level.median())\n",
        "\n",
        "print(\"\\n# Заполнение пропусков медианой\")\n",
        "print(df_median.isnull().sum()) # пропусков нет"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_Zu21yGEfrY",
        "outputId": "2fcb77b3-f5d5-4c67-87a1-13443ad2255c"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "# Заполнение пропусков медианой\n",
            "satisfaction_level       0\n",
            "last_evaluation          0\n",
            "number_project           0\n",
            "average_montly_hours     0\n",
            "time_spend_company       0\n",
            "Work_accident            0\n",
            "promotion_last_5years    0\n",
            "department               0\n",
            "salary                   0\n",
            "left                     0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Вариант 3: Заполнение пропусков средним\n",
        "df_mean = df_test_nan.copy()\n",
        "df_mean.satisfaction_level = df_mean.satisfaction_level.fillna(df_median.satisfaction_level.mean())\n",
        "\n",
        "print(\"\\n# Заполнение пропусков средним\")\n",
        "print(df_mean.isnull().sum()) # пропусков нет"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PTPxvki-EzDG",
        "outputId": "33fc6104-e618-4820-ae29-425eaeedabb9"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "# Заполнение пропусков средним\n",
            "satisfaction_level       0\n",
            "last_evaluation          0\n",
            "number_project           0\n",
            "average_montly_hours     0\n",
            "time_spend_company       0\n",
            "Work_accident            0\n",
            "promotion_last_5years    0\n",
            "department               0\n",
            "salary                   0\n",
            "left                     0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "⏰ Заполнение категориальных признаков не требуется, все данные полные.\n",
        "\n",
        "Как вариант - можно использовать KNN (K-ближайших соседей)"
      ],
      "metadata": {
        "id": "FZ9qKGxuu4QM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. **Выбираем целвую переменную**\n",
        "\n",
        "⏰ В качестве целевой выбираем категориальную переменную, либо превращаем числовую в категориальное\n",
        "\n",
        "```В моем датасете была выбрана целевая переменная left, определяющая ушел человек из компании или нет, для будущего предсказания шанса ухода работника.```"
      ],
      "metadata": {
        "id": "sc3IAvF-DG91"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['left_chance'] = df['left']"
      ],
      "metadata": {
        "id": "n4borJ_DDN4d"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qv2hfqKQILE4",
        "outputId": "6230949e-7617-42ee-a04a-d7f6b5ccb9f7"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
              "       'average_montly_hours', 'time_spend_company', 'Work_accident',\n",
              "       'promotion_last_5years', 'department', 'salary', 'left', 'left_chance'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = df['left_chance'] # выбираем целевую переменную (категориальную)\n",
        "X = df.drop(['left_chance','left'], axis=1) # переменные для проверки влияния"
      ],
      "metadata": {
        "id": "9OVe4b_GH2o1"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. **Графический анализ**"
      ],
      "metadata": {
        "id": "swUhFdbAH_Q5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Список числовых колонок для построения графиков\n",
        "numeric_cols = X.select_dtypes(include=['float64', 'int64'])\n",
        "numeric_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Bf4NXIG1It51",
        "outputId": "fad68d15-dabf-4a01-d0e5-4beeca4fbfbf"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       satisfaction_level  last_evaluation  number_project  \\\n",
              "0                    0.38             0.53               2   \n",
              "1                    0.80             0.86               5   \n",
              "2                    0.11             0.88               7   \n",
              "3                    0.72             0.87               5   \n",
              "4                    0.37             0.52               2   \n",
              "...                   ...              ...             ...   \n",
              "14994                0.40             0.57               2   \n",
              "14995                0.37             0.48               2   \n",
              "14996                0.37             0.53               2   \n",
              "14997                0.11             0.96               6   \n",
              "14998                0.37             0.52               2   \n",
              "\n",
              "       average_montly_hours  time_spend_company  Work_accident  \\\n",
              "0                       157                   3              0   \n",
              "1                       262                   6              0   \n",
              "2                       272                   4              0   \n",
              "3                       223                   5              0   \n",
              "4                       159                   3              0   \n",
              "...                     ...                 ...            ...   \n",
              "14994                   151                   3              0   \n",
              "14995                   160                   3              0   \n",
              "14996                   143                   3              0   \n",
              "14997                   280                   4              0   \n",
              "14998                   158                   3              0   \n",
              "\n",
              "       promotion_last_5years  \n",
              "0                          0  \n",
              "1                          0  \n",
              "2                          0  \n",
              "3                          0  \n",
              "4                          0  \n",
              "...                      ...  \n",
              "14994                      0  \n",
              "14995                      0  \n",
              "14996                      0  \n",
              "14997                      0  \n",
              "14998                      0  \n",
              "\n",
              "[14999 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d9e7c59-66dc-48c3-b42f-865eedb245b3\" 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>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
              "      <th>number_project</th>\n",
              "      <th>average_montly_hours</th>\n",
              "      <th>time_spend_company</th>\n",
              "      <th>Work_accident</th>\n",
              "      <th>promotion_last_5years</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.38</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2</td>\n",
              "      <td>157</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>5</td>\n",
              "      <td>262</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.11</td>\n",
              "      <td>0.88</td>\n",
              "      <td>7</td>\n",
              "      <td>272</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.87</td>\n",
              "      <td>5</td>\n",
              "      <td>223</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.52</td>\n",
              "      <td>2</td>\n",
              "      <td>159</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14994</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.57</td>\n",
              "      <td>2</td>\n",
              "      <td>151</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.48</td>\n",
              "      <td>2</td>\n",
              "      <td>160</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14996</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2</td>\n",
              "      <td>143</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14997</th>\n",
              "      <td>0.11</td>\n",
              "      <td>0.96</td>\n",
              "      <td>6</td>\n",
              "      <td>280</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.52</td>\n",
              "      <td>2</td>\n",
              "      <td>158</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14999 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d9e7c59-66dc-48c3-b42f-865eedb245b3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </button>\n",
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              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "      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",
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              "      buttonEl.style.display =\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",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            style=\"display:none;\">\n",
              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "numeric_cols",
              "summary": "{\n  \"name\": \"numeric_cols\",\n  \"rows\": 14999,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2486306510611418,\n        \"min\": 0.09,\n        \"max\": 1.0,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          0.83,\n          0.13,\n          0.55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17116911062327592,\n        \"min\": 0.36,\n        \"max\": 1.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          0.66,\n          0.44,\n          0.53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49,\n        \"min\": 96,\n        \"max\": 310,\n        \"num_unique_values\": 215,\n        \"samples\": [\n          118,\n          112,\n          222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6,\n          8,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Построение boxplot для каждой переменной\n",
        "for col in numeric_cols:\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.boxplot(x='left_chance', y=col, data=df)\n",
        "    plt.title(f'Boxplot {col} относительно left_chance')\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eNse8fXjJkAC",
        "outputId": "2072ff55-39fb-4374-ab17-814f61726034"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Построение диаграмм распределения для каждой переменной\n",
        "for col in numeric_cols:\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.histplot(data=df, x=col, hue='left_chance', element=\"step\", stat=\"density\", common_norm=False)\n",
        "    plt.title(f'Распределение {col} относительно left_chance')\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZczeCnXDJrp4",
        "outputId": "6c7ee24b-4cae-4835-e050-561d285f46c1"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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rV66s4OBg/fXXXy61Nm3aVIULF3aptVWrVkpLS8vyEHtWMvaILly4UMnJyVedt2HDhqpbt67zfpkyZfTQQw9p4cKFSktLkzFGs2fPVvv27WWMcamrdevWSkhIcPlMSJcO310+3ixjj0nGc92wYYOOHTum/v37u8zXs2dPl725ObF48WKdPXtWw4cPz/T+zvgMZvT397//3WWetm3bqkqVKlkeMs7ra5jhmWeecTkS8Le//U2FChVyfoYlOY9+SNLZs2d14sQJNW3aVMnJyfrzzz9dlufv768nn3zSed/T01P169d36btVq1YKDw/X119/7WzbsWOHtm3b5vLYrPznP/+Ru7u7Bg0a5NL+/PPPyxijn3/++aqPz07G87r8llXf9evXV5MmTZxt/v7+euaZZ7Rv3z7t3LlTUt62txs3btTMmTMVFRXl3KbnxqlTp/TLL7+oS5cuLs/l5MmTat26taKjozMNB0lNTb3qNjFDWlqac3kZ29KsnD9/PtM6vHDhgss8+f365Xa7nZ9mz56t2rVrZ7kn+srX+VrbGsn1c5aUlKQTJ06oUaNGMsZo8+bNmfro37+/y/2mTZu6LG/27NlyOBwaNWpUtvXl9rv0ZsKh/Btk4sSJqlSpkgoVKqTixYurcuXKLhutjPEjH374oWJiYlzGl2Qc7pcuDQGoXLmyChXK35eqYsWKLvcdDocqVKigffv2Sbo0flCSevToke0yEhISVLhwYef9EydOZFrulaKjo2WMyXa+Kw+5x8fHS7r64e3o6GglJCSoWLFiWU4/duyYy/1FixapaNGiV63zSqNGjVJ4eLj69euXaUxbdHS0/vjjj2yXeWX/1ytjvNHlgoKCVKpUqUwb1aCgIJfxWtHR0dq2bdt11xoZGamhQ4dq/Pjx+vrrr9W0aVN16NBBTz75ZKbgl9VrXalSJSUnJ+v48eNyc3NTfHy8pkyZoilTpuSorjJlyrjcz3gfZjzX/fv3Z9m3h4eHypUrl6PnmCFjGM7Vxutl9Fe5cuVM06pUqaLVq1e7tF3Pa5jhyufm7++vEiVKOD/D0qVDxP/85z/1yy+/6MyZMy7zXz4WWFKWfRcuXFjbtm1z3ndzc1O3bt00adIkJScny9fXV19//bW8vb3VuXPnTDVebv/+/QoPD3f5g1iSqlat6pyeF717977mPPv371eDBg0ytV/ed40aNfK0vR0+fLiaNm2qdu3aZTlW81r27NkjY4xeeeUVvfLKK1nOc+zYMedODenSdjEiIuKay/7zzz+d7zM3NzdVqFBBo0aNcg4/yfDpp5/q008/zfT4y/vI79cvt9vt/LR371498sgjOZr3WtsaSTpw4IBGjhypH374IdNn9crPWVaf/cKFC7s8bu/evQoPD1dISEi2deX2u/RmQjC9QerXr+8cB5KVMWPG6JVXXlHv3r31+uuvKyQkRG5ubho8eHCuT0K5ETJqGDduXLaX4rg8LKampiouLk733XffNZfrcDj0888/u4xRy2qZknTkyBFJUlhY2FWXWaxYMZe9OJe7ciPQoEEDvfHGGy5t//73vzVv3rwsH//HH39o2rRp+uqrr7L8sKenp6tmzZoaP358lo/P7XjGa8lqvV2t3Vx2YkJ6erruu+8+vfjii1nOW6lSpRzX8c4776hnz56aN2+eFi1apEGDBikqKkq//vqrSpUqlePlZLzXnnzyyWz/EKpVq5bL/Zw8Vzu7ntcwp+Lj49WsWTMFBgbqtddeU/ny5eXt7a1NmzbppZdeyrSdyWnf3bt317hx4zR37lx17dpV33zzjdq1a5frPdH5ZeTIkc69WBnat29fIH0vWrRIS5Ys0dq1a/O8jIzXYdiwYWrdunWW81SoUMHl/pEjR7Kd93Jly5Z1jn09efKk3n//fT311FMqV66c7r77bud8Dz30UKZQ/c9//tO5/b0Rcrvdtsq1PhdpaWm67777dOrUKb300kuqUqWK/Pz8FBsbq549e+b4c5Zbuf0uvZkQTC0ya9YstWjRItNfqfHx8S4Dr8uXL6/ffvtNFy5cyNe/gDL2iGYwxmjPnj3OAJAxyD8wMFCtWrW65vK2bt2qCxcuXDWMZyzXGKPIyMgchaCdO3fK4XBkuSfq8mUuWbJEjRs3djmkkp0iRYpkek5XO0FpxIgRuuOOO/TYY49l2//WrVvVsmXLPA2vuJ4hGblVvnx5JSYm5ug1zYmaNWuqZs2a+uc//6n//ve/aty4sSZPnuwS/K98r0nS7t275evr6/zyCQgIUFpaWr7VlbGnJzo62jmkQro0jCMmJka1a9fO8bIyPgs7duzIFBCu7G/Xrl0u/WW05WTvVm5FR0erRYsWzvuJiYmKi4tzngC5fPlynTx5UnPmzNE999zjnC/jyiB5VaNGDdWpU0dff/21SpUqpQMHDuiDDz645uMiIiK0ZMkSnT171mWvW8aQgryuo5o1a2Z631z5RR0REaFdu3ZleuyVfedme2uM0fDhw/Xwww+7hLzcytiD7+HhkaP3/6FDh3T27Fnnnsqr8fPzc1lm06ZNVbJkSS1atMil5lKlSmXq+7333nMJpvn9+uV2u32l69luli9fXjt27Mjz4y+3fft27d69W59//rm6d+/ubF+8eHGel1m+fHktXLhQp06dynavaW6/S28mjDG1iLu7e6Y9ETNnzsw0luiRRx7RiRMn9O9//zvTMq5n79AXX3yhs2fPOu/PmjVLcXFxatOmjSSpbt26Kl++vP71r38pMTEx0+OPHz+eqXZ3d/csL8V0uU6dOsnd3V2vvvpqpvqNMTp58qTz/sWLFzV79mzVr1//qn/9denSRWlpaXr99dczTbt48aJzOEBerF27VvPmzdPYsWOz3RB26dJFsbGxmc7KlaRz584pKSnpqn34+fllOtxzo3Tp0kVr167VwoULM02Lj4/XxYsXc7ScM2fOZJq3Zs2acnNzy3SJrLVr17qMET148KDmzZun+++/X+7u7nJ3d9cjjzyi2bNnZ/llceV7LSfq1aunokWLavLkyS5j66ZNm5br98P999+vgIAARUVFZRqLnPEerlevnooVK6bJkye7PP+ff/5Zf/zxh9q2bZvr53AtU6ZMcRkHOGnSJF28eNH5Gc4IZ5d/zlJTU/Xhhx9ed99PPfWUFi1apPfee0+hoaHOPq/mwQcfVFpaWqZt2bvvviuHw5GjZeTVgw8+qHXr1rns2UxKStKUKVNUtmxZVatWTVLutrfTp0/Xtm3brnmJrGspVqyYmjdvro8++khxcXGZpl/5/p8+fbokZfoDKCcy9t7lZa9dfr9+17vd9vPzk6Q8bd8feeQRbd26NdPVXaTcf69m9TkzxmjChAm5ruvy+owxevXVV7OtLzffpTcb9phapF27dnrttdfUq1cvNWrUSNu3b9fXX3+dafxb9+7d9cUXX2jo0KFat26dmjZtqqSkJC1ZskR///vf9dBDD+Wp/5CQEDVp0kS9evXS0aNH9d5776lChQrq27evpEvjkT755BO1adNG1atXV69evVSyZEnFxsZq2bJlCgwM1I8//qikpCRNnDhR77//vipVqqTly5c7+8gItNu2bdPatWvVsGFDlS9fXm+88YZGjBihffv2qWPHjgoICFBMTIy+//57PfPMMxo2bJiWLFmiV155Rdu2bdOPP/541efSrFkz9evXT1FRUdqyZYvuv/9+eXh4KDo6WjNnztSECRP06KOP5mk9LVq0SPfdd99V92Q89dRT+u6779S/f38tW7ZMjRs3Vlpamv7880999913Wrhw4VX3JNetW1czZszQ0KFDddddd8nf3/+GHYp84YUX9MMPP6hdu3bq2bOn6tatq6SkJG3fvl2zZs3Svn37XPbYZ+eXX37RwIED1blzZ1WqVEkXL17Ul19+6QyZl6tRo4Zat27tcrkoSS4b3bFjx2rZsmVq0KCB+vbtq2rVqunUqVPatGmTlixZolOnTuXqeXp4eOiNN95Qv379dO+99+qxxx5TTEyMpk6dmusxpoGBgXr33XfVp08f3XXXXXriiSdUuHBhbd26VcnJyfr888/l4eGht956S7169VKzZs3UtWtX5+WiypYte0Mu5ZOamqqWLVuqS5cu2rVrlz788EM1adJEHTp0kHTp0jmFCxdWjx49NGjQIDkcDn355Zf5MtzhiSee0Isvvqjvv/9ef/vb33J0NKd9+/Zq0aKFXn75Ze3bt0+1a9fWokWLNG/ePA0ePDjbSzHlh+HDh+vbb79VmzZtNGjQIIWEhOjzzz9XTEyMZs+e7Rz/n5vt7aJFi9S3b9+rHs3JqYkTJ6pJkyaqWbOm+vbtq3Llyuno0aNau3atDh06pK1bt+ro0aMaNWqUPvnkEz3++OOqUqXKNZebmJioBQsWSLp0ktX7778vDw+PPP2hlN+v3/Vut8uXL6/g4GBNnjxZAQEB8vPzU4MGDTJdVi0rL7zwgmbNmqXOnTurd+/eqlu3rk6dOqUffvhBkydPztURlSpVqqh8+fIaNmyYYmNjFRgYqNmzZ2c5LjynWrRooaeeekrvv/++oqOj9cADDyg9PV2rVq1SixYtNHDgwBx/l96UbvyJ/7eXjMubrF+//qrznT9/3jz//POmRIkSxsfHxzRu3NisXbs2y0uKJCcnm5dfftlERkYaDw8PExYWZh599FGzd+9eY0zeLhf17bffmhEjRphixYoZHx8f07ZtW5fLWmTYvHmz6dSpkwkNDTVeXl4mIiLCdOnSxSxdutSl72vdrryEyuzZs02TJk2Mn5+f8fPzM1WqVDEDBgwwu3btMsYY8+yzz5p77rnHLFiwIFNNV14uKsOUKVNM3bp1jY+PjwkICDA1a9Y0L774ojl8+LBzntxeLsrhcJiNGze6tGf1GqWmppq33nrLVK9e3Xh5eZnChQubunXrmldffdUkJCRk6u9yiYmJ5oknnjDBwcFGkvOSKtldLurKy6Zk1JTVpWGyer5nz541I0aMMBUqVDCenp6mSJEiplGjRuZf//qXy+WHruavv/4yvXv3NuXLlzfe3t4mJCTEtGjRwixZssRlPv3/pWi++uorU7FiRePl5WXq1Kljli1blmmZR48eNQMGDDClS5d2vs9btmxppkyZ4pwnu0sEZbWujDHmww8/NJGRkcbLy8vUq1fPrFy58qqX7bmaH374wTRq1Mj4+PiYwMBAU79+ffPtt9+6zDNjxgxTp04d4+XlZUJCQky3bt2cl4jLcL2vYcY2ZsWKFeaZZ54xhQsXNv7+/qZbt27m5MmTLo9ds2aNufvuu42Pj48JDw83L774ovOST5e/Btn1feUlfi734IMPGknmv//9b5bTs3L27FkzZMgQEx4ebjw8PEzFihXNuHHjXC5Jd7n8ulyUMcbs3bvXPProoyY4ONh4e3ub+vXrm/nz52d6bE63tz4+PiY2NtblsREREXm6XFRGfd27dzdhYWHGw8PDlCxZ0rRr187MmjXLGHPptaxQoYIZPXq0y+WLLq/pystFXb4NDg4ONo0bN850qa+Mz+iVsrqMXW5fv8tl917KyXY7u8/MvHnzTLVq1UyhQoVyfemokydPmoEDB5qSJUsaT09PU6pUKdOjRw/nJetys63ZuXOnadWqlfH39zdFihQxffv2NVu3bs3x9jur77SLFy+acePGmSpVqhhPT09TtGhR06ZNm0zfR9f6Lr0ZOYy5Sc4WQL5Yvny5WrRooZkzZ+Z5L+Ll9u3bp8jISMXExGT6lZAMo0eP1r59+3L1qxy4NTgcDg0YMCDLQ6PIm4wLkq9fv/6aY7pvpIcffljbt2/P8he3ACCvGGMKAMiVuLg4/fTTT3rqqaesLgXALYYxprgu/v7+6tat21VPTqpVq5bzJ1Zhf6dOnbrqxbjd3d1tcymX63X8+PFMv1F9OU9Pz6teS/B2ExMTozVr1uiTTz6Rh4eH+vXrZ3VJgNO5c+eueSJpSEiIywXzYT8EU1yXIkWK6KuvvrrqPJ06dSqgapAfOnXqpBUrVmQ7PSIiwuUi7jezu+6666oXBm/WrJnLCX23uxUrVqhXr14qU6aMPv/886teXxgoaDNmzFCvXr2uOs+yZcvUvHnzgikIecIYUwAuNm7ceNUzSn18fNS4ceMCrOjGWbNmjc6dO5ft9MKFC7v8nCoA+4qLi9Pvv/9+1Xnq1q3r8ouFsB+CKQAAAGyBk58AAABgCzf1GNP09HQdPnxYAQEBBfqzjgAAAMgZY4zOnj2r8PBw5w9aZOemDqaHDx9W6dKlrS4DAAAA13Dw4EGVKlXqqvPc1ME0ICBA0qUnGhgYaHE1AAAAuNKZM2dUunRpZ267mps6mGYcvg8MDCSYAgAA2FhOhl1y8hMAAABsgWAKAAAAWyCYAgAAwBZu6jGmAAAAOZGWlqYLFy5YXcYtyd3dXYUKFcqXS3cSTAEAwC0tMTFRhw4dEj92eeP4+vqqRIkS8vT0vK7lEEwBAMAtKy0tTYcOHZKvr6+KFi3KD/LkM2OMUlNTdfz4ccXExKhixYrXvIj+1RBMAQDALevChQsyxqho0aLy8fGxupxbko+Pjzw8PLR//36lpqbK29s7z8vi5CcAAHDLY0/pjXU9e0ldlpMvSwEAAACuE8EUAAAAtkAwBQAAt6XmzZtr8ODBOZ5/7ty5qlChgtzd3XP1uJwaPXq07rjjjnxf7s2Ek58AAAByoF+/furVq5cGDRqkgIAA9ezZU/Hx8Zo7d67Vpd0yCKYAAADXkJiYqGPHjql169YKDw+3upxbFofyAQDAbS8lJUXDhg1TyZIl5efnpwYNGmj58uWSpOXLlysgIECSdO+998rhcKh58+b6/PPPNW/ePDkcDjkcDuf8V3Po0CF17dpVISEh8vPzU7169fTbb7+5zPPll1+qbNmyCgoK0uOPP66zZ886py1YsEBNmjRRcHCwQkND1a5dO+3du9c5fd++fXI4HJozZ45atGghX19f1a5dW2vXrnXpY82aNWrevLl8fX1VuHBhtW7dWqdPn5YkpaenKyoqSpGRkfLx8VHt2rU1a9asvKzWXCOYAgCA297AgQO1du1aTZ8+Xdu2bVPnzp31wAMPKDo6Wo0aNdKuXbskSbNnz1ZcXJx++OEHdenSRQ888IDi4uIUFxenRo0aXbWPxMRENWvWTLGxsfrhhx+0detWvfjii0pPT3fOs3fvXs2dO1fz58/X/PnztWLFCo0dO9Y5PSkpSUOHDtWGDRu0dOlSubm56eGHH3ZZhiS9/PLLGjZsmLZs2aJKlSqpa9euunjxoiRpy5YtatmypapVq6a1a9dq9erVat++vdLS0iRJUVFR+uKLLzR58mT9/vvvGjJkiJ588kmtWLEiX9b11XAoHwAA3NYOHDigqVOn6sCBA87D9MOGDdOCBQs0depUjRkzRsWKFZMkhYSEKCwsTNKlC8unpKQ471/LN998o+PHj2v9+vUKCQmRJFWoUMFlnvT0dE2bNs25h/app57S0qVL9eabb0qSHnnkEZf5P/vsMxUtWlQ7d+5UjRo1nO3Dhg1T27ZtJUmvvvqqqlevrj179qhKlSp6++23Va9ePX344YfO+atXry7p0p7jMWPGaMmSJWrYsKEkqVy5clq9erU++ugjNWvWLEfPNa8IpgAA4La2fft2paWlqVKlSi7tKSkpCg0Nzbd+tmzZojp16jhDaVbKli3rDKWSVKJECR07dsx5Pzo6WiNHjtRvv/2mEydOOPeUHjhwwCWY1qpVy2UZknTs2DFVqVJFW7ZsUefOnbPsf8+ePUpOTtZ9993n0p6amqo6derk4tnmDcEUAADc1hITE+Xu7q6NGzfK3d3dZZq/v3++9ZOTn0T18PBwue9wOFwO07dv314RERH6+OOPFR4ervT0dNWoUUOpqanZLifjV68ylnO1OhITEyVJP/30k0qWLOkyzcvL65r1Xy+CKa7fyb1SylnXNq8AKbS8NfUAAJALderUUVpamo4dO6amTZvm+HGenp7OcZk5UatWLX3yySc6derUVfeaZufkyZPatWuXPv74Y2edq1evzvVyatWqpaVLl+rVV1/NNK1atWry8vLSgQMHbvhh+6wQTHF9Tu6VPrgz62nPbiKcAgBsr1KlSurWrZu6d++ud955R3Xq1NHx48e1dOlS1apVyzlW80ply5bVwoULtWvXLoWGhiooKCjTHs/Lde3aVWPGjFHHjh0VFRWlEiVKaPPmzQoPD3eO57yawoULKzQ0VFOmTFGJEiV04MABDR8+PNfPd8SIEapZs6b+/ve/q3///vL09NSyZcvUuXNnFSlSRMOGDdOQIUOUnp6uJk2aKCEhQWvWrFFgYKB69OiR6/5yg7PycX0y9pQ2fV5q996lW9PnXacBAGBzU6dOVffu3fX888+rcuXK6tixo9avX68yZcpk+5i+ffuqcuXKqlevnooWLao1a9ZctQ9PT08tWrRIxYoV04MPPqiaNWtq7NixmYYPZMfNzU3Tp0/Xxo0bVaNGDQ0ZMkTjxo3L1fOULgXxRYsWaevWrapfv74aNmyoefPmqVChS/srX3/9db3yyiuKiopS1apV9cADD+inn35SZGRkrvvKLYcxxtzwXm6QM2fOKCgoSAkJCQoMDLS6nNvT4S3SlGaXAmno/59ZeHKPNH+w9MwKKfwO62oDANz2zp8/r5iYGEVGRsrb29vqcm5ZV1vPuclr7DEFAACALRBMAQAA8sGYMWPk7++f5a1NmzZWl3dT4OQnAACAfNC/f3916dIly2k5uVQUCKYAAAD5IiQkJE+XgcL/cCgfAAAAtkAwBQAAgC0QTAEAAGALBFMAAADYAic/AQAA5FJs/DmdTkotsP4K+3mqZPCtf2Y/wRQAACAXYuPPqeU7y3X+QnqB9ent4aalzze/5cMpwRQAACAXTiel6vyFdA1oUaFAgmJs/DlNXLZHp5NSc93fxIkTNW7cOB05ckS1a9fWBx98oPr169+gSq8fwRQAACAPSgb7KLKIn9VlZGvGjBkaOnSoJk+erAYNGui9995T69attWvXLhUrVszq8rLEyU8AAAC3oPHjx6tv377q1auXqlWrpsmTJ8vX11efffaZ1aVli2AKAABwi0lNTdXGjRvVqlUrZ5ubm5tatWqltWvXWljZ1RFMAQAAbjEnTpxQWlqaihcv7tJevHhxHTlyxKKqro0xprhuMelhSjpdSEpPu9SQUEh+6WGKtLYsAABwkyGY4rrExF9Ui9Tx0lJJSvr/1hBJ47Us/qIiw62rDQCA21WRIkXk7u6uo0ePurQfPXpUYWFhFlV1bRzKx3VJSr10DbcBlZM0pqm3xjT11oDKSS7TAABAwfL09FTdunW1dOlSZ1t6erqWLl2qhg0bWljZ1bHHFPmipG+aIoP//++cxDRriwEAoADExp+zdT9Dhw5Vjx49VK9ePdWvX1/vvfeekpKS1KtXr3yuMP8QTAEAAHKhsJ+nvD3cNHHZngLr09vDTYX9PHP1mMcee0zHjx/XyJEjdeTIEd1xxx1asGBBphOi7IRgCgAAkAslg3209PnmOp2UWmB9FvbzzNOvTA0cOFADBw68ARXdGARTAACAXCoZ7HPL/269FTj5CQAAALZAMAUAAIAtEEwBAABgCwRTAAAA2ALBFAAAALZAMAUAAIAtEEwBAABgC1zHFAAAILfiD0rJJwuuP99QKbh0wfVnEYIpAABAbsQflCbeJV3I22/Y54mHjzRg/S0fTgmmAAAAuZF88lIobfq8FFQAQTHhoLTqnUv95jCYrly5UuPGjdPGjRsVFxen77//Xh07dryxdeYDgikAAEBeBJWWQitYXUWWkpKSVLt2bfXu3VudOnWyupwcs/Tkp9GjR8vhcLjcqlSpYmVJAAAAN702bdrojTfe0MMPP2x1Kbli+R7T6tWra8mSJc77hQpZXhIAAAAsYHkKLFSokMLCwnI0b0pKilJSUpz3z5w5c6PKAgAAQAGz/Dqm0dHRCg8PV7ly5dStWzcdOHAg23mjoqIUFBTkvJUufWufmQYAAHA7sTSYNmjQQNOmTdOCBQs0adIkxcTEqGnTpjp79myW848YMUIJCQnO28GDBwu4YgAAANwolh7Kb9OmjfP/tWrVUoMGDRQREaHvvvtOTz/9dKb5vby85OXlVZAlAgAAoIBYPsb0csHBwapUqZL27NljdSkAAABXl1BAR27z0E9iYqJLnoqJidGWLVsUEhKiMmXK5Gd1+cpWwTQxMVF79+7VU089ZXUpAAAAWfMNvfRLTKveKbg+PXwu9ZtDGzZsUIsWLZz3hw4dKknq0aOHpk2blt/V5RtLg+mwYcPUvn17RURE6PDhwxo1apTc3d3VtWtXK8sCAADIXnDpSz8Pmnyy4Pr0Dc3Vz5E2b95cxpgbWNCNYWkwPXTokLp27aqTJ0+qaNGiatKkiX799VcVLVrUyrIAAACuLrj0Lf+79VawNJhOnz7dyu4BAABgI5ZfxxQAAACQCKYAAACwCYIpAAC45d2MJwLdTPJr/RJMAQDALcvd3V2SlJqaanElt7bk5GRJkoeHx3Utx1bXMQUAAMhPhQoVkq+vr44fPy4PDw+5ubFPLj8ZY5ScnKxjx44pODjY+YdAXhFMAQDALcvhcKhEiRKKiYnR/v37rS7nlhUcHKywsLDrXg7BFAAA3NI8PT1VsWJFDuffIB4eHte9pzQDwRQAANzy3Nzc5O3tbXUZuAYGWgAAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWbBNMx44dK4fDocGDB1tdCgAAACxgi2C6fv16ffTRR6pVq5bVpQAAAMAilgfTxMREdevWTR9//LEKFy581XlTUlJ05swZlxsAAABuDZYH0wEDBqht27Zq1arVNeeNiopSUFCQ81a6dOkCqBAAAAAFwdJgOn36dG3atElRUVE5mn/EiBFKSEhw3g4ePHiDKwQAAEBBKWRVxwcPHtRzzz2nxYsXy9vbO0eP8fLykpeX1w2uDAAAAFawLJhu3LhRx44d05133ulsS0tL08qVK/Xvf/9bKSkpcnd3t6o8AAAAFDDLgmnLli21fft2l7ZevXqpSpUqeumllwilAAAAtxnLgmlAQIBq1Kjh0ubn56fQ0NBM7QAAALj1WX5WPgAAACBZuMc0K8uXL7e6BAAAAFiEPaYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsoZHUBN5uYE0lKSrmYqd3Pq5Aii/hZUBEAAMCtgWCaCzEnktTiX8uznb5sWHPCKQAAQB4RTHMhY0/pgBYVVDLYx9keG39OE5ftyXJPKgAAAHKGYJoHJYN92DMKAACQzzj5CQAAALZAMAUAAIAtEEwBAABgCwRTAAAA2ALBFAAAALZAMAUAAIAtEEwBAABgCwRTAAAA2ALBFAAAALZAMAUAAIAtEEwBAABgCwRTAAAA2ALBFAAAALZAMAUAAIAtEEwBAABgCwRTAAAA2ALBFAAAALZAMAUAAIAtEEwBAABgCwRTAAAA2EKegulff/2V33UAAADgNpenYFqhQgW1aNFCX331lc6fP5/fNQEAAOA2lKdgumnTJtWqVUtDhw5VWFiY+vXrp3Xr1uV3bQAAALiN5CmY3nHHHZowYYIOHz6szz77THFxcWrSpIlq1Kih8ePH6/jx4/ldJwAAAG5x13XyU6FChdSpUyfNnDlTb731lvbs2aNhw4apdOnS6t69u+Li4vKrTgAAANziriuYbtiwQX//+99VokQJjR8/XsOGDdPevXu1ePFiHT58WA899FB+1QkAAIBbXKG8PGj8+PGaOnWqdu3apQcffFBffPGFHnzwQbm5Xcq5kZGRmjZtmsqWLZuftQI3h5N7pZSzmdu9AqTQ8gVfDwAAN4k8BdNJkyapd+/e6tmzp0qUKJHlPMWKFdOnn356XcUBN52Te6UP7sx++rObCKcAAGQjT8F08eLFKlOmjHMPaQZjjA4ePKgyZcrI09NTPXr0yJcigZtGxp7Sps9LQaX/155wUFr1TtZ7UgEAgKQ8BtPy5csrLi5OxYoVc2k/deqUIiMjlZaWli/FATetoNJSaAWrqwAA4KaSp5OfjDFZticmJsrb2zvHy5k0aZJq1aqlwMBABQYGqmHDhvr555/zUhIAAABucrnaYzp06FBJksPh0MiRI+Xr6+uclpaWpt9++0133HFHjpdXqlQpjR07VhUrVpQxRp9//rkeeughbd68WdWrV89NaQAAALjJ5SqYbt68WdKlPabbt2+Xp6enc5qnp6dq166tYcOG5Xh57du3d7n/5ptvatKkSfr111+zDKYpKSlKSUlx3j9z5kxuygcAAICN5SqYLlu2TJLUq1cvTZgwQYGBgflWSFpammbOnKmkpCQ1bNgwy3mioqL06quv5lufAAAAsI88jTGdOnVqvoXS7du3y9/fX15eXurfv7++//57VatWLct5R4wYoYSEBOft4MGD+VIDAAAArJfjPaadOnXStGnTFBgYqE6dOl113jlz5uS4gMqVK2vLli1KSEjQrFmz1KNHD61YsSLLcOrl5SUvL68cLxsAAAA3jxwH06CgIDkcDuf/84unp6cqVLh0WZ26detq/fr1mjBhgj766KN86wMAAAD2l+NgOnXq1Cz/n9/S09NdTnACAADA7SFPF9g/d+6cjDHOy0Xt37/fOTb0/vvvz/FyRowYoTZt2qhMmTI6e/asvvnmGy1fvlwLFy7MS1kAAAC4ieUpmD700EPq1KmT+vfvr/j4eNWvX1+enp46ceKExo8fr7/97W85Ws6xY8fUvXt3xcXFKSgoSLVq1dLChQt133335aUsAAAA3MTyFEw3bdqkd999V5I0a9YshYWFafPmzZo9e7ZGjhyZ42D66aef5qV7AAAA3ILyFEyTk5MVEBAgSVq0aJE6deokNzc33X333dq/f3++FgjgFnVyr5RyNnO7V4AUWr7g6wEAWC5PwbRChQqaO3euHn74YS1cuFBDhgyRdOnQfH5edB/ALerkXumDO7Of/uwmwikA3IbyFExHjhypJ554QkOGDFHLli2dv9S0aNEi1alTJ18LBHALythT2vR5Kaj0/9oTDkqr3sl6TyoA4JaXp2D66KOPqkmTJoqLi1Pt2rWd7S1bttTDDz+cb8UBuMUFlZZCK1hdBQDAJvIUTCUpLCxMYWFhLm3169e/7oIAAABwe8pTME1KStLYsWO1dOlSHTt2TOnp6S7T//rrr3wpDgAAALePPAXTPn36aMWKFXrqqadUokQJ50+VAgAAAHmVp2D6888/66efflLjxo3zux4AAADcptzy8qDChQsrJCQkv2sBAADAbSxPwfT111/XyJEjlZycnN/1AAAA4DaVp0P577zzjvbu3avixYurbNmy8vDwcJm+adOmfCkOAAAAt488BdOOHTvmcxkAAAC43eUpmI4aNSq/6wAAAMBtLk9jTCUpPj5en3zyiUaMGKFTp05JunQIPzY2Nt+KAwAAwO0jT3tMt23bplatWikoKEj79u1T3759FRISojlz5ujAgQP64osv8rtOAAAA3OLytMd06NCh6tmzp6Kjo+Xt7e1sf/DBB7Vy5cp8Kw4AAAC3jzwF0/Xr16tfv36Z2kuWLKkjR45cd1EAAAC4/eQpmHp5eenMmTOZ2nfv3q2iRYted1EAAAC4/eQpmHbo0EGvvfaaLly4IElyOBw6cOCAXnrpJT3yyCP5WiAAAABuD3kKpu+8844SExNVtGhRnTt3Ts2aNVOFChUUEBCgN998M79rBAAAwG0gT2flBwUFafHixVqzZo22bt2qxMRE3XnnnWrVqlV+1wcAAIDbRK6DaXp6uqZNm6Y5c+Zo3759cjgcioyMVFhYmIwxcjgcN6JOAAAA3OJydSjfGKMOHTqoT58+io2NVc2aNVW9enXt379fPXv21MMPP3yj6gQAAMAtLld7TKdNm6aVK1dq6dKlatGihcu0X375RR07dtQXX3yh7t2752uRAAAAuPXlao/pt99+q3/84x+ZQqkk3XvvvRo+fLi+/vrrfCsOAAAAt49cBdNt27bpgQceyHZ6mzZttHXr1usuCgAAALefXAXTU6dOqXjx4tlOL168uE6fPn3dRQEAAOD2k6tgmpaWpkKFsh+W6u7urosXL153UQAAALj95OrkJ2OMevbsKS8vryynp6Sk5EtRAAAAuP3kKpj26NHjmvNwRj4AAADyIlfBdOrUqTeqDgAAANzmcjXGFAAAALhRCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBUuDaVRUlO666y4FBASoWLFi6tixo3bt2mVlSQAAALCIpcF0xYoVGjBggH799VctXrxYFy5c0P3336+kpCQrywIAAIAFClnZ+YIFC1zuT5s2TcWKFdPGjRt1zz33ZJo/JSVFKSkpzvtnzpy54TUCAACgYNhqjGlCQoIkKSQkJMvpUVFRCgoKct5Kly5dkOUBAADgBrJNME1PT9fgwYPVuHFj1ahRI8t5RowYoYSEBOft4MGDBVwlAAAAbhRLD+VfbsCAAdqxY4dWr16d7TxeXl7y8vIqwKoAAABQUGwRTAcOHKj58+dr5cqVKlWqlNXlAAAAwAKWBlNjjJ599ll9//33Wr58uSIjI60sBwAAABayNJgOGDBA33zzjebNm6eAgAAdOXJEkhQUFCQfHx8rSwMAAEABs/Tkp0mTJikhIUHNmzdXiRIlnLcZM2ZYWRYAAAAsYPmhfAAAAECy0eWiAAAAcHsjmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBUuvYwrcimLSw5R0upCUnva/xoRC8ksPEz+6CwBA9gimQD6Kib+oFqnjpaWSlHTZlBBJ47Us/qIiw62pDQAAuyOYAvkoKTVdkjSgcpJKFgt1tsceO6mJu/yc0wEAQGYEU+AGKOmbpsjgy4ZwJ6ZlPzMAAJDEyU8AAACwCYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbIFgCgAAAFsgmAIAAMAWCKYAAACwBYIpAAAAbMHSYLpy5Uq1b99e4eHhcjgcmjt3rpXlAAAAwEKWBtOkpCTVrl1bEydOtLIMAAAA2EAhKztv06aN2rRpY2UJAAAAsAlLg2lupaSkKCUlxXn/zJkzFlYDAACA/HRTnfwUFRWloKAg56106dJWlwQAAIB8clMF0xEjRighIcF5O3jwoNUlAQAAIJ/cVIfyvby85OXlZXUZAAAAuAFuqj2mAAAAuHVZusc0MTFRe/bscd6PiYnRli1bFBISojJlylhYGQAAAAqapcF0w4YNatGihfP+0KFDJUk9evTQtGnTLKoKAAAAVrA0mDZv3lzGGCtLAAAAgE0wxhQAAAC2QDAFAACALRBMAQAAYAsEUwAAANgCwRQAAAC2QDAFAACALRBMAQAAYAsEUwAAANgCwRQAAAC2QDAFAACALRBMAQAAYAsEUwAAANgCwRQAAAC2QDAFAACALRSyugAAgKSTe6WUs5nbvQKk0PIFXw8AWIBgCgBWO7lX+uDO7Kc/u4lwCuC2QDAFYImY9DAlnS4kpaf9rzGhkPzSwxRpXVnWyNhT2vR5Kaj0/9oTDkqr3sl6TyoA3IIIpgAKXEz8RbVIHS8tlaSky6aESBqvZfEXFRluTW2WCiothVawugoAsAzBFECBS0pNlyQNqJykksVCne2xx05q4i4/53QAwO2FYArAMiV90xQZfNnFQRLTsp8ZAHDL43JRAAAAsAWCKQAAAGyBYAoAAABbIJgCAADAFgimAAAAsAWCKQAAAGyBYAoAAABb4DqmAGAD/EQrABBMAcBy/EQrAFxCMAUAi/ETrQBwCcEUAGyCn2gFcLvj5CcAAADYAsEUAAAAtkAwBQAAgC0QTAEAAGALBFMAAADYAsEUAAAAtkAwBQAAgC0QTAEAAGALBFMAAADYAsEUAAAAtkAwBQAAgC0QTAEAAGALBFMAAADYAsEUAAAAtkAwBQAAgC0QTAEAAGALBFMAAADYAsEUAAAAtkAwBQAAgC0QTAEAAGALBFMAAADYAsEUAAAAtlDI6gIAAABwY8ScSFJSysVM7X5ehRRZxM+Ciq6OYAoAAHALijmRpBb/Wp7t9GXDmtsunBJMAQC2lNWeHrvu5QHsKOPzM6BFBZUM9nG2x8af08Rle7Lck2o1gikAwHautqfHjnt5ADsrGexz03xmCKZ5EX9Qcly26uLt9xcHANzMstrTY+e9PADyB8E0NxIOXfp31TjJceR/7SZMUt9L00sGWVIaANyKbqY9PVZhyANuJQTT3EhNuvRvxfukopcF0OMJ0h+XTQcAoAAw5AG3GoJpXniHSEFh/7t/1t26WgAAty2GPOBWQzAFAOAmx5AH3Cr45ScAAADYAsEUAAAAtkAwBQAAgC0QTAEAAGALBFMAAADYAsEUAAAAtkAwBQAAgC3YIphOnDhRZcuWlbe3txo0aKB169ZZXRIAAAAKmOXBdMaMGRo6dKhGjRqlTZs2qXbt2mrdurWOHTtmdWkAAAAoQJYH0/Hjx6tv377q1auXqlWrpsmTJ8vX11efffaZ1aUBAACgAFn6k6SpqanauHGjRowY4Wxzc3NTq1attHbt2kzzp6SkKCUlxXk/ISFBknTmzJkbX6ykxMREpacka+/h40pOTna27z+ZqPSUQpq+5FeV27qjQGqxi7+OJyo9pYT+OHjMuU5YH67rQ7q910lWWE+uWB+Z/ZVYSOkpPvpjwzIl+6VJkvYnuSs9xVfT585TOX9+B15iPeHqsnp/SFLcuUvvkcSzZ3TmjOOG15GR04wx157ZWCg2NtZIMv/9739d2l944QVTv379TPOPGjXKSOLGjRs3bty4ceN2k90OHjx4zWxo6R7T3BoxYoSGDh3qvJ+enq5Tp04pNDRUDkfBJP7SpUvr4MGDCgwMvOH93UxYN1ljvWSPdZM11kv2WDdZY71kj3WTtYJeL8YYnT17VuHh4dec19JgWqRIEbm7u+vo0aMu7UePHlVYWFim+b28vOTl5eXSFhwcfCNLzFJgYCBv8GywbrLGeske6yZrrJfssW6yxnrJHusmawW5XoKCgnI0n6UnP3l6eqpu3bpaunSpsy09PV1Lly5Vw4YNLawMAAAABc3yQ/lDhw5Vjx49VK9ePdWvX1/vvfeekpKS1KtXL6tLAwAAQAGyPJg+9thjOn78uEaOHKkjR47ojjvu0IIFC1S8eHGrS8vEy8tLo0aNyjScAKyb7LBesse6yRrrJXusm6yxXrLHusmandeLw5icnLsPAAAA3FiWX2AfAAAAkAimAAAAsAmCKQAAAGyBYAoAAABbIJjmQFRUlO666y4FBASoWLFi6tixo3bt2mV1WZabNGmSatWq5bxAb8OGDfXzzz9bXZYtjR07Vg6HQ4MHD7a6FEuNHj1aDofD5ValShWry7KN2NhYPfnkkwoNDZWPj49q1qypDRs2WF2WpcqWLZvpPeNwODRgwACrS7NcWlqaXnnlFUVGRsrHx0fly5fX66+/nrPfI7/FnT17VoMHD1ZERIR8fHzUqFEjrV+/3uqyCtzKlSvVvn17hYeHy+FwaO7cuS7TjTEaOXKkSpQoIR8fH7Vq1UrR0dHWFPv/CKY5sGLFCg0YMEC//vqrFi9erAsXLuj+++9XUlKS1aVZqlSpUho7dqw2btyoDRs26N5779VDDz2k33//3erSbGX9+vX66KOPVKtWLatLsYXq1asrLi7OeVu9erXVJdnC6dOn1bhxY3l4eOjnn3/Wzp079c4776hw4cJWl2ap9evXu7xfFi9eLEnq3LmzxZVZ76233tKkSZP073//W3/88Yfeeustvf322/rggw+sLs1yffr00eLFi/Xll19q+/btuv/++9WqVSvFxsZaXVqBSkpKUu3atTVx4sQsp7/99tt6//33NXnyZP3222/y8/NT69atdf78+QKu9DIGuXbs2DEjyaxYscLqUmyncOHC5pNPPrG6DNs4e/asqVixolm8eLFp1qyZee6556wuyVKjRo0ytWvXtroMW3rppZdMkyZNrC7D9p577jlTvnx5k56ebnUplmvbtq3p3bu3S1unTp1Mt27dLKrIHpKTk427u7uZP3++S/udd95pXn75ZYuqsp4k8/333zvvp6enm7CwMDNu3DhnW3x8vPHy8jLffvutBRVewh7TPEhISJAkhYSEWFyJfaSlpWn69OlKSkri52QvM2DAALVt21atWrWyuhTbiI6OVnh4uMqVK6du3brpwIEDVpdkCz/88IPq1aunzp07q1ixYqpTp44+/vhjq8uyldTUVH311Vfq3bu3HA6H1eVYrlGjRlq6dKl2794tSdq6datWr16tNm3aWFyZtS5evKi0tDR5e3u7tPv4+HCE5jIxMTE6cuSIy/dTUFCQGjRooLVr11pWl+W//HSzSU9P1+DBg9W4cWPVqFHD6nIst337djVs2FDnz5+Xv7+/vv/+e1WrVs3qsmxh+vTp2rRp0205rik7DRo00LRp01S5cmXFxcXp1VdfVdOmTbVjxw4FBARYXZ6l/vrrL02aNElDhw7VP/7xD61fv16DBg2Sp6enevToYXV5tjB37lzFx8erZ8+eVpdiC8OHD9eZM2dUpUoVubu7Ky0tTW+++aa6detmdWmWCggIUMOGDfX666+ratWqKl68uL799lutXbtWFSpUsLo82zhy5IgkZfqlzeLFizunWYFgmksDBgzQjh07+Kvr/1WuXFlbtmxRQkKCZs2apR49emjFihW3fTg9ePCgnnvuOS1evDjTX+23s8v35NSqVUsNGjRQRESEvvvuOz399NMWVma99PR01atXT2PGjJEk1alTRzt27NDkyZMJpv/v008/VZs2bRQeHm51Kbbw3Xff6euvv9Y333yj6tWra8uWLRo8eLDCw8Nv+/fMl19+qd69e6tkyZJyd3fXnXfeqa5du2rjxo1Wl4Zr4FB+LgwcOFDz58/XsmXLVKpUKavLsQVPT09VqFBBdevWVVRUlGrXrq0JEyZYXZblNm7cqGPHjunOO+9UoUKFVKhQIa1YsULvv/++ChUqpLS0NKtLtIXg4GBVqlRJe/bssboUy5UoUSLTH3RVq1ZlqMP/279/v5YsWaI+ffpYXYptvPDCCxo+fLgef/xx1axZU0899ZSGDBmiqKgoq0uzXPny5bVixQolJibq4MGDWrdunS5cuKBy5cpZXZpthIWFSZKOHj3q0n706FHnNCsQTHPAGKOBAwfq+++/1y+//KLIyEirS7Kt9PR0paSkWF2G5Vq2bKnt27dry5Ytzlu9evXUrVs3bdmyRe7u7laXaAuJiYnau3evSpQoYXUplmvcuHGmy9Dt3r1bERERFlVkL1OnTlWxYsXUtm1bq0uxjeTkZLm5uX6Nu7u7Kz093aKK7MfPz08lSpTQ6dOntXDhQj300ENWl2QbkZGRCgsL09KlS51tZ86c0W+//WbpuSIcys+BAQMG6JtvvtG8efMUEBDgHHsRFBQkHx8fi6uzzogRI9SmTRuVKVNGZ8+e1TfffKPly5dr4cKFVpdmuYCAgExjkP38/BQaGnpbj00eNmyY2rdvr4iICB0+fFijRo2Su7u7unbtanVplhsyZIgaNWqkMWPGqEuXLlq3bp2mTJmiKVOmWF2a5dLT0zV16lT16NFDhQrxtZWhffv2evPNN1WmTBlVr15dmzdv1vjx49W7d2+rS7PcwoULZYxR5cqVtWfPHr3wwguqUqWKevXqZXVpBSoxMdHliFRMTIy2bNmikJAQlSlTRoMHD9Ybb7yhihUrKjIyUq+88orCw8PVsWNH64q27HoANxFJWd6mTp1qdWmW6t27t4mIiDCenp6maNGipmXLlmbRokVWl2VbXC7KmMcee8yUKFHCeHp6mpIlS5rHHnvM7Nmzx+qybOPHH380NWrUMF5eXqZKlSpmypQpVpdkCwsXLjSSzK5du6wuxVbOnDljnnvuOVOmTBnj7e1typUrZ15++WWTkpJidWmWmzFjhilXrpzx9PQ0YWFhZsCAASY+Pt7qsgrcsmXLsswvPXr0MMZcumTUK6+8YooXL268vLxMy5YtLf+cOYzhJyIAAABgPcaYAgAAwBYIpgAAALAFgikAAABsgWAKAAAAWyCYAgAAwBYIpgAAALAFgikAAABsgWAKAAAAWyCYArDM8uXL5XA4FB8fb3UptrNv3z45HA5t2bLF6lIAoMAQTAEUmObNm2vw4MHO+40aNVJcXJyCgoKsKwoAYBuFrC4AwO3L09NTYWFhVpcBALAJ9pgCKBA9e/bUihUrNGHCBDkcDjkcDk2bNs3lUP60adMUHBys+fPnq3LlyvL19dWjjz6q5ORkff755ypbtqwKFy6sQYMGKS0tzbnslJQUDRs2TCVLlpSfn58aNGig5cuX56iu/fv3q3379ipcuLD8/PxUvXp1/ec//5H0v6EGP/30k2rVqiVvb2/dfffd2rFjh8syVq9eraZNm8rHx0elS5fWoEGDlJSU5JxetmxZjRkzRr1791ZAQIDKlCmjKVOmuCxj3bp1qlOnjry9vVWvXj1t3rw5V+v3999/V7t27RQYGKiAgAA1bdpUe/fulSSlp6frtddeU6lSpeTl5aU77rhDCxYscD42Y9jAd99953wed911l3bv3q3169erXr168vf3V5s2bXT8+HHn43r27KmOHTvq1VdfVdGiRRUYGKj+/fsrNTXVOc+CBQvUpEkTBQcHKzQ0VO3atXPWdXnfc+bMUYsWLeTr66vatWtr7dq1kqSkpCQFBgZq1qxZLs937ty58vPz09mzZ3O1ngDYnAGAAhAfH28aNmxo+vbta+Li4kxcXJxZsmSJkWROnz5tjDFm6tSpxsPDw9x3331m06ZNZsWKFSY0NNTcf//9pkuXLub33383P/74o/H09DTTp093LrtPnz6mUaNGZuXKlWbPnj1m3LhxxsvLy+zevfuadbVt29bcd999Ztu2bWbv3r3mxx9/NCtWrDDGGLNs2TIjyVStWtUsWrTIbNu2zbRr186ULVvWpKamGmOM2bNnj/Hz8zPvvvuu2b17t1mzZo2pU6eO6dmzp7OPiIgIExISYiZOnGiio6NNVFSUcXNzM3/++acxxpizZ8+aokWLmieeeMLs2LHD/Pjjj6ZcuXJGktm8efM1n8OhQ4dMSEiI6dSpk1m/fr3ZtWuX+eyzz5zLHz9+vAkMDDTffvut+fPPP82LL75oPDw8nOsnJibGSDJVqlQxCxYsMDt37jR33323qVu3rmnevLlZvXq12bRpk6lQoYLp37+/s98ePXoYf39/89hjj5kdO3aY+fPnm6JFi5p//OMfznlmzZplZs+ebaKjo83mzZtN+/btTc2aNU1aWlqmvufPn2927dplHn30URMREWEuXLhgjDGmb9++5sEHH3R5zh06dDDdu3e/5roBcHMhmAIoMM2aNTPPPfec835G8Ls8mEoye/bscc7Tr18/4+vra86ePetsa926tenXr58xxpj9+/cbd3d3Exsb69JXy5YtzYgRI65ZU82aNc3o0aOznJZR3+Uh+OTJk8bHx8fMmDHDGGPM008/bZ555hmXx61atcq4ubmZc+fOGWMuBdMnn3zSOT09Pd0UK1bMTJo0yRhjzEcffWRCQ0Od8xtjzKRJk3IcTEeMGGEiIyOdYflK4eHh5s0333Rpu+uuu8zf//53Y8z/wuEnn3zinP7tt98aSWbp0qXOtqioKFO5cmXn/R49epiQkBCTlJTkUre/v78zeF7p+PHjRpLZvn17tn3//vvvRpL5448/jDHG/Pbbb8bd3d0cPnzYGGPM0aNHTaFChczy5cuvuW4A3Fw4lA/AVnx9fVW+fHnn/eLFi6ts2bLy9/d3aTt27Jgkafv27UpLS1OlSpXk7+/vvK1YscLlkHF2Bg0apDfeeEONGzfWqFGjtG3btkzzNGzY0Pn/kJAQVa5cWX/88YckaevWrZo2bZpL361bt1Z6erpiYmKcj6tVq5bz/w6HQ2FhYc7n8McffziHCmTV57Vs2bJFTZs2lYeHR6ZpZ86c0eHDh9W4cWOX9saNGzufQ1Y1Fi9eXJJUs2ZNl7aMmjPUrl1bvr6+LnUnJibq4MGDkqTo6Gh17dpV5cqVU2BgoMqWLStJOnDgQLZ9lyhRQpKcfdWvX1/Vq1fX559/Lkn66quvFBERoXvuuSe7VQLgJsXJTwBs5cpw5XA4smxLT0+XJCUmJsrd3V0bN26Uu7u7y3yXh9ns9OnTR61bt9ZPP/2kRYsWKSoqSu+8846effbZHNWbmJiofv36adCgQZmmlSlT5qrPK+M5XC8fH598Wc7lNTocjizbcltz+/btFRERoY8//ljh4eFKT09XjRo1XMahZtf35X316dNHEydO1PDhwzV16lT16tXLOR+AWwd7TAEUGE9PT5eTlvJDnTp1lJaWpmPHjqlChQout5ye8V+6dGn1799fc+bM0fPPP6+PP/7YZfqvv/7q/P/p06e1e/duVa1aVZJ05513aufOnZn6rlChgjw9PXPUf9WqVbVt2zadP38+yz6vpVatWlq1apUuXLiQaVpgYKDCw8O1Zs0al/Y1a9aoWrVqOe4jO1u3btW5c+ec93/99Vf5+/urdOnSOnnypHbt2qV//vOfatmypapWrarTp0/nqZ8nn3xS+/fv1/vvv6+dO3eqR48e1107APshmAIoMGXLltVvv/2mffv26cSJE/myx7BSpUrq1q2bunfvrjlz5igmJkbr1q1TVFSUfvrpp2s+fvDgwVq4cKFiYmK0adMmLVu2zBk6M7z22mtaunSpduzYoZ49e6pIkSLq2LGjJOmll17Sf//7Xw0cOFBbtmxRdHS05s2bp4EDB+b4OTzxxBNyOBzq27evdu7cqf/85z/617/+lePHDxw4UGfOnNHjjz+uDRs2KDo6Wl9++aV27dolSXrhhRf01ltvacaMGdq1a5eGDx+uLVu26LnnnstxH9lJTU3V008/7ax71KhRGjhwoNzc3FS4cGGFhoZqypQp2rNnj3755RcNHTo0T/0ULlxYnTp10gsvvKD7779fpUqVuu7aAdgPwRRAgRk2bJjc3d1VrVo1FS1aNNM4w7yaOnWqunfvrueff16VK1dWx44dtX79epdD6dlJS0vTgAEDVLVqVT3wwAOqVKmSPvzwQ5d5xo4dq+eee05169bVkSNH9OOPPzr3htaqVUsrVqzQ7t271bRpU9WpU0cjR45UeHh4juv39/fXjz/+qO3bt6tOnTp6+eWX9dZbb+X48aGhofrll1+UmJioZs2aqW7duvr444+dh8cHDRqkoUOH6vnnn1fNmjW1YMEC/fDDD6pYsWKO+8hOy5YtVbFiRd1zzz167LHH1KFDB40ePVqS5ObmpunTp2vjxo2qUaOGhgwZonHjxuW5r6efflqpqanq3bv3ddcNwJ4cxhhjdREAYEfLly9XixYtdPr0aQUHB1tdju307NlT8fHxmjt3boH09+WXX2rIkCE6fPhwjodJALi5cPITAMDWkpOTFRcXp7Fjx6pfv36EUuAWxqF8ALe0Nm3auFzK6fLbmDFjrC4vR/r375/tc+jfv7/V5d1wb7/9tqpUqaKwsDCNGDHC6nIA3EAcygdwS4uNjXU5a/xyISEhCgkJKeCKcu/YsWM6c+ZMltMCAwNVrFixAq4IAG4MgikAAABsgUP5AAAAsAWCKQAAAGyBYAoAAABbIJgCAADAFgimAAAAsAWCKQAAAGyBYAoAAABb+D+RXyOUs17M/gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "⏰ проанализировать график, выделить значимые признаки относительно Y\n",
        "\n",
        "time_spend_company\n",
        "\n",
        "average_monthly_hours\n",
        "\n",
        "number project\n",
        "\n",
        "last_evaluation\n",
        "\n",
        "satisfaction_level"
      ],
      "metadata": {
        "id": "VaRCEhhPthDQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Описательная статистика для числовых переменных, разделенная по целевой переменной\n",
        "desc_stats_numeric = df.groupby('left_chance')[df.select_dtypes(include=[np.number]).columns].describe()\n",
        "\n",
        "# Описательная статистика для категориальных переменных, разделенная по целевой переменной\n",
        "desc_stats_categorical = df.groupby('left_chance')[df.select_dtypes(include=['object']).columns].describe()"
      ],
      "metadata": {
        "id": "9lBY1IdeJ3Mb"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Описание полученных результатов\n",
        "⏰ ```Необходимо сделать подобное описание для своих переменных```\n",
        "\n",
        "time_spend_company хорошо показывает, что если человек провел в компании слишком мало или слишком много времени, то шансов на увольнение больше\n",
        "\n",
        "average_monthly_hours показывает, что если человек работает слишком много или слишком мало, то шансов больше\n",
        "\n",
        "number project слишком мало или слишком много проектов - больше шанс на увольнение\n",
        "\n",
        "last_evaluation слишком низко или высоко оценен - больше шанс на увольнение\n",
        "\n",
        "satisfaction_level слишком недоволен - больше шанс на увольнение"
      ],
      "metadata": {
        "id": "gAkuvGAwLDP5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Проходим по каждой числовой переменной и выводим статистику отдельно\n",
        "for col in numeric_cols:\n",
        "    print(f\"Описательная статистика для {col}:\\n\")\n",
        "    print(df.groupby('left_chance')[col].describe())\n",
        "    print(\"\\n\" + \"=\"*50 + \"\\n\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36FjYF_vLrYa",
        "outputId": "77877210-fdfc-46a1-8661-7e856a3fdd98"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Описательная статистика для satisfaction_level:\n",
            "\n",
            "               count      mean       std   min   25%   50%   75%   max\n",
            "left_chance                                                           \n",
            "0            11428.0  0.666810  0.217104  0.12  0.54  0.69  0.84  1.00\n",
            "1             3571.0  0.440098  0.263933  0.09  0.13  0.41  0.73  0.92\n",
            "\n",
            "==================================================\n",
            "\n",
            "Описательная статистика для last_evaluation:\n",
            "\n",
            "               count      mean       std   min   25%   50%   75%  max\n",
            "left_chance                                                          \n",
            "0            11428.0  0.715473  0.162005  0.36  0.58  0.71  0.85  1.0\n",
            "1             3571.0  0.718113  0.197673  0.45  0.52  0.79  0.90  1.0\n",
            "\n",
            "==================================================\n",
            "\n",
            "Описательная статистика для number_project:\n",
            "\n",
            "               count      mean       std  min  25%  50%  75%  max\n",
            "left_chance                                                      \n",
            "0            11428.0  3.786664  0.979884  2.0  3.0  4.0  4.0  6.0\n",
            "1             3571.0  3.855503  1.818165  2.0  2.0  4.0  6.0  7.0\n",
            "\n",
            "==================================================\n",
            "\n",
            "Описательная статистика для average_montly_hours:\n",
            "\n",
            "               count        mean        std    min    25%    50%    75%    max\n",
            "left_chance                                                                   \n",
            "0            11428.0  199.060203  45.682731   96.0  162.0  198.0  238.0  287.0\n",
            "1             3571.0  207.419210  61.202825  126.0  146.0  224.0  262.0  310.0\n",
            "\n",
            "==================================================\n",
            "\n",
            "Описательная статистика для time_spend_company:\n",
            "\n",
            "               count      mean       std  min  25%  50%  75%   max\n",
            "left_chance                                                       \n",
            "0            11428.0  3.380032  1.562348  2.0  2.0  3.0  4.0  10.0\n",
            "1             3571.0  3.876505  0.977698  2.0  3.0  4.0  5.0   6.0\n",
            "\n",
            "==================================================\n",
            "\n",
            "Описательная статистика для Work_accident:\n",
            "\n",
            "               count      mean       std  min  25%  50%  75%  max\n",
            "left_chance                                                      \n",
            "0            11428.0  0.175009  0.379991  0.0  0.0  0.0  0.0  1.0\n",
            "1             3571.0  0.047326  0.212364  0.0  0.0  0.0  0.0  1.0\n",
            "\n",
            "==================================================\n",
            "\n",
            "Описательная статистика для promotion_last_5years:\n",
            "\n",
            "               count      mean       std  min  25%  50%  75%  max\n",
            "left_chance                                                      \n",
            "0            11428.0  0.026251  0.159889  0.0  0.0  0.0  0.0  1.0\n",
            "1             3571.0  0.005321  0.072759  0.0  0.0  0.0  0.0  1.0\n",
            "\n",
            "==================================================\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nКатегориальные переменные:\")\n",
        "desc_stats_categorical"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "9jFLJ6qYKPbZ",
        "outputId": "45597ea8-23a6-463c-e1b6-86f8ed86d5e0"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Категориальные переменные:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            department                     salary                  \n",
              "                 count unique    top  freq  count unique  top  freq\n",
              "left_chance                                                        \n",
              "0                11428     10  sales  3126  11428      3  low  5144\n",
              "1                 3571     10  sales  1014   3571      3  low  2172"
            ],
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
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              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "desc_stats_categorical",
              "summary": "{\n  \"name\": \"desc_stats_categorical\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": [\n        \"left_chance\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"department\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"3571\",\n        \"max\": \"11428\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"3571\",\n          \"11428\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"department\",\n        \"unique\"\n      ],\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 10,\n        \"max\": 10,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"department\",\n        \"top\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"sales\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"department\",\n        \"freq\"\n      ],\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1014\",\n        \"max\": \"3126\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1014\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"salary\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"3571\",\n        \"max\": \"11428\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"3571\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"salary\",\n        \"unique\"\n      ],\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"salary\",\n        \"top\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"low\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"salary\",\n        \"freq\"\n      ],\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2172\",\n        \"max\": \"5144\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"2172\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Расчет теста Пирсона"
      ],
      "metadata": {
        "id": "b34BwyK0MF0P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Описание:**\n",
        "Вы рассчитаете коэффициенты корреляции Пирсона между Y и каждым из признаков X, чтобы оценить значимость этих корреляций.\n",
        "\n",
        "### **Шаги:**\n",
        "\n",
        "1. **Рассчитайте коэффициенты корреляции Пирсона для каждого признака:**"
      ],
      "metadata": {
        "id": "ixhxJl6lMtNV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df.select_dtypes(include=[np.number]).columns].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "A9e2_NoPMRfK",
        "outputId": "cf34ca1a-7328-4102-d902-70d825c05420"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       satisfaction_level  last_evaluation  number_project  \\\n",
              "satisfaction_level               1.000000         0.105021       -0.142970   \n",
              "last_evaluation                  0.105021         1.000000        0.349333   \n",
              "number_project                  -0.142970         0.349333        1.000000   \n",
              "average_montly_hours            -0.020048         0.339742        0.417211   \n",
              "time_spend_company              -0.100866         0.131591        0.196786   \n",
              "Work_accident                    0.058697        -0.007104       -0.004741   \n",
              "promotion_last_5years            0.025605        -0.008684       -0.006064   \n",
              "left                            -0.388375         0.006567        0.023787   \n",
              "left_chance                     -0.388375         0.006567        0.023787   \n",
              "\n",
              "                       average_montly_hours  time_spend_company  \\\n",
              "satisfaction_level                -0.020048           -0.100866   \n",
              "last_evaluation                    0.339742            0.131591   \n",
              "number_project                     0.417211            0.196786   \n",
              "average_montly_hours               1.000000            0.127755   \n",
              "time_spend_company                 0.127755            1.000000   \n",
              "Work_accident                     -0.010143            0.002120   \n",
              "promotion_last_5years             -0.003544            0.067433   \n",
              "left                               0.071287            0.144822   \n",
              "left_chance                        0.071287            0.144822   \n",
              "\n",
              "                       Work_accident  promotion_last_5years      left  \\\n",
              "satisfaction_level          0.058697               0.025605 -0.388375   \n",
              "last_evaluation            -0.007104              -0.008684  0.006567   \n",
              "number_project             -0.004741              -0.006064  0.023787   \n",
              "average_montly_hours       -0.010143              -0.003544  0.071287   \n",
              "time_spend_company          0.002120               0.067433  0.144822   \n",
              "Work_accident               1.000000               0.039245 -0.154622   \n",
              "promotion_last_5years       0.039245               1.000000 -0.061788   \n",
              "left                       -0.154622              -0.061788  1.000000   \n",
              "left_chance                -0.154622              -0.061788  1.000000   \n",
              "\n",
              "                       left_chance  \n",
              "satisfaction_level       -0.388375  \n",
              "last_evaluation           0.006567  \n",
              "number_project            0.023787  \n",
              "average_montly_hours      0.071287  \n",
              "time_spend_company        0.144822  \n",
              "Work_accident            -0.154622  \n",
              "promotion_last_5years    -0.061788  \n",
              "left                      1.000000  \n",
              "left_chance               1.000000  "
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              "summary": "{\n  \"name\": \"df[df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.41008541423153855,\n        \"min\": -0.3883749834241161,\n        \"max\": 1.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.10502121397148648,\n          0.05869724105197295,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3269404689802771,\n        \"min\": -0.008683767904798018,\n        \"max\": 1.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.0,\n          -0.0071042885196038325,\n          0.10502121397148648\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.348790672757976,\n        \"min\": -0.14296958603690096,\n        \"max\": 1.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.34933258851626237,\n          -0.004740547719769696,\n          -0.14296958603690096\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3307020671252602,\n        \"min\": -0.020048113219472644,\n        \"max\": 1.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.3397417998383594,\n          -0.01014288818580297,\n          -0.020048113219472644\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3167089552965548,\n        \"min\": -0.10086607257796669,\n        \"max\": 1.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.13159072244765863,\n          0.0021204180967097077,\n          -0.10086607257796669\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3513017752826785,\n        \"min\": -0.15462163370513443,\n        \"max\": 1.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.0071042885196038325,\n          1.0,\n          0.05869724105197295\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3364357201101894,\n        \"min\": -0.06178810657920049,\n        \"max\": 1.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.008683767904798018,\n          0.039245434583548434,\n          0.025605185709040485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"left\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48807893733451163,\n        \"min\": -0.3883749834241161,\n        \"max\": 1.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.006567120447529851,\n          -0.15462163370513443,\n          -0.3883749834241161\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"left_chance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48807893733451163,\n        \"min\": -0.3883749834241161,\n        \"max\": 1.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.006567120447529851,\n          -0.15462163370513443,\n          -0.3883749834241161\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. ⏰ **Интерпретируйте результаты:**\n",
        "\n",
        "\n",
        "### Интерпретация:\n",
        "- **Коэффициент корреляции Пирсона** показывает степень линейной связи между двумя переменными."
      ],
      "metadata": {
        "id": "v2KxpxnBMyDL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Описание корреляций для значимых признаков:\n"
      ],
      "metadata": {
        "id": "nSVkl345PZOK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "1. **satisfaction_level и left_chance (-0.388)**:\n",
        "   - Есть умеренная положительная корреляция между уровнем удовлетворенности и нашей целевой переменной шанса на увольнение. Видим что чем ниже уровень удовлетворенности, тем выше шанс на уход из компании.\n",
        "\n",
        "2. **number_project и average_montly_hours (0.417)**:\n",
        "   - Тоже довольно логично, чем над большим количеством проектов человек работает, тем больше у него часов рабочих в месяц.\n",
        "\n",
        "3. **last_evaluation и average_montly_hours (0.339)**:\n",
        "   - Тоже логично, чем больше человек работает, тем выше его оценка эффективности.\n",
        "\n",
        "4. **last_evaluation и number_project\t (0.349)**:\n",
        "   - Как и с прошлым, чем больше проектов берет на себя человек, тем больше его оценка."
      ],
      "metadata": {
        "id": "Faea4u5mPd5B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Почему мультиколлинеарность это плохо?"
      ],
      "metadata": {
        "id": "LUZs5O0zPp3h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Мультиколлинеарность возникает, когда два или более независимых признака сильно коррелируют друг с другом. Это приводит к нескольким проблемам:\n",
        "\n",
        "1. **Переоценка значимости признаков**:\n",
        "   - Когда признаки сильно коррелированы, модель может \"перепутать\" влияние каждого из них на целевую переменную, что приведет к ненадежным коэффициентам. Это затрудняет интерпретацию модели, поскольку сложно понять, какой из признаков действительно важен.\n",
        "\n",
        "2. **Нестабильность модели**:\n",
        "   - При наличии мультиколлинеарности небольшие изменения в данных могут привести к большим изменениям в коэффициентах модели. Это делает модель нестабильной и менее устойчивой.\n",
        "\n",
        "3. **Затруднение выбора значимых признаков**:\n",
        "   - Модель может затрудняться при выделении важности отдельных признаков, если они сильно связаны друг с другом. Это может усложнить процесс отбора признаков и снизить точность модели.\n",
        "\n",
        "4. **Увеличение дисперсии коэффициентов**:\n",
        "   - Мультиколлинеарность увеличивает дисперсию оценок коэффициентов регрессии, что делает их менее надежными. В результате, некоторые признаки могут казаться незначимыми, хотя на самом деле они играют важную роль."
      ],
      "metadata": {
        "id": "iMQop79XPrbr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Немного машинного обучения"
      ],
      "metadata": {
        "id": "Jy_5Ydv0Q6pl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- отберем признаки (числовые и категориальные отдельно)\n",
        "- обработаем числовые и категориальыне\n",
        "- обучим модель\n",
        "- узнаем что влияет на Y"
      ],
      "metadata": {
        "id": "pIQF0u7rQ9bE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report"
      ],
      "metadata": {
        "id": "EaXN0RZRRdeV"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Выбираем категориальные признаки\n",
        "categorical_features = X.select_dtypes(include=['object']).columns.tolist() + [col for col in X.select_dtypes(include=['int64']).columns.tolist() if col != 'average_montly_hours']\n",
        "\n",
        "\n",
        "print(\"Категориальные признаки:\", categorical_features)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DdR9yP9pQ9Ds",
        "outputId": "09f8bf30-995a-46a3-a940-b2d4f4d23f80"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Категориальные признаки: ['department', 'salary', 'number_project', 'time_spend_company', 'Work_accident', 'promotion_last_5years']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Создаем копию данных\n",
        "X_processed = X.copy()\n",
        "\n",
        "# Применяем One-Hot Encoding\n",
        "X_processed = pd.get_dummies(X_processed, columns=categorical_features, drop_first=True) # drop_first=True позволяет избежать мультиколлинеарности, удаляя первый уровень категориальной переменной.\n"
      ],
      "metadata": {
        "id": "lTk0CBB0RGXN"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_processed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "eNMqQxUERKPG",
        "outputId": "40b664d4-38eb-4bfb-c8e0-6a8dd31f2279"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       satisfaction_level  last_evaluation  average_montly_hours  \\\n",
              "0                    0.38             0.53                   157   \n",
              "1                    0.80             0.86                   262   \n",
              "2                    0.11             0.88                   272   \n",
              "3                    0.72             0.87                   223   \n",
              "4                    0.37             0.52                   159   \n",
              "...                   ...              ...                   ...   \n",
              "14994                0.40             0.57                   151   \n",
              "14995                0.37             0.48                   160   \n",
              "14996                0.37             0.53                   143   \n",
              "14997                0.11             0.96                   280   \n",
              "14998                0.37             0.52                   158   \n",
              "\n",
              "       department_RandD  department_accounting  department_hr  \\\n",
              "0                 False                  False          False   \n",
              "1                 False                  False          False   \n",
              "2                 False                  False          False   \n",
              "3                 False                  False          False   \n",
              "4                 False                  False          False   \n",
              "...                 ...                    ...            ...   \n",
              "14994             False                  False          False   \n",
              "14995             False                  False          False   \n",
              "14996             False                  False          False   \n",
              "14997             False                  False          False   \n",
              "14998             False                  False          False   \n",
              "\n",
              "       department_management  department_marketing  department_product_mng  \\\n",
              "0                      False                 False                   False   \n",
              "1                      False                 False                   False   \n",
              "2                      False                 False                   False   \n",
              "3                      False                 False                   False   \n",
              "4                      False                 False                   False   \n",
              "...                      ...                   ...                     ...   \n",
              "14994                  False                 False                   False   \n",
              "14995                  False                 False                   False   \n",
              "14996                  False                 False                   False   \n",
              "14997                  False                 False                   False   \n",
              "14998                  False                 False                   False   \n",
              "\n",
              "       department_sales  ...  number_project_7  time_spend_company_3  \\\n",
              "0                  True  ...             False                  True   \n",
              "1                  True  ...             False                 False   \n",
              "2                  True  ...              True                 False   \n",
              "3                  True  ...             False                 False   \n",
              "4                  True  ...             False                  True   \n",
              "...                 ...  ...               ...                   ...   \n",
              "14994             False  ...             False                  True   \n",
              "14995             False  ...             False                  True   \n",
              "14996             False  ...             False                  True   \n",
              "14997             False  ...             False                 False   \n",
              "14998             False  ...             False                  True   \n",
              "\n",
              "       time_spend_company_4  time_spend_company_5  time_spend_company_6  \\\n",
              "0                     False                 False                 False   \n",
              "1                     False                 False                  True   \n",
              "2                      True                 False                 False   \n",
              "3                     False                  True                 False   \n",
              "4                     False                 False                 False   \n",
              "...                     ...                   ...                   ...   \n",
              "14994                 False                 False                 False   \n",
              "14995                 False                 False                 False   \n",
              "14996                 False                 False                 False   \n",
              "14997                  True                 False                 False   \n",
              "14998                 False                 False                 False   \n",
              "\n",
              "       time_spend_company_7  time_spend_company_8  time_spend_company_10  \\\n",
              "0                     False                 False                  False   \n",
              "1                     False                 False                  False   \n",
              "2                     False                 False                  False   \n",
              "3                     False                 False                  False   \n",
              "4                     False                 False                  False   \n",
              "...                     ...                   ...                    ...   \n",
              "14994                 False                 False                  False   \n",
              "14995                 False                 False                  False   \n",
              "14996                 False                 False                  False   \n",
              "14997                 False                 False                  False   \n",
              "14998                 False                 False                  False   \n",
              "\n",
              "       Work_accident_1  promotion_last_5years_1  \n",
              "0                False                    False  \n",
              "1                False                    False  \n",
              "2                False                    False  \n",
              "3                False                    False  \n",
              "4                False                    False  \n",
              "...                ...                      ...  \n",
              "14994            False                    False  \n",
              "14995            False                    False  \n",
              "14996            False                    False  \n",
              "14997            False                    False  \n",
              "14998            False                    False  \n",
              "\n",
              "[14999 rows x 28 columns]"
            ],
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              "\n",
              "  <div id=\"df-f821e4c1-f3c7-4924-a753-65f0bb96a9df\" 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",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
              "      <th>average_montly_hours</th>\n",
              "      <th>department_RandD</th>\n",
              "      <th>department_accounting</th>\n",
              "      <th>department_hr</th>\n",
              "      <th>department_management</th>\n",
              "      <th>department_marketing</th>\n",
              "      <th>department_product_mng</th>\n",
              "      <th>department_sales</th>\n",
              "      <th>...</th>\n",
              "      <th>number_project_7</th>\n",
              "      <th>time_spend_company_3</th>\n",
              "      <th>time_spend_company_4</th>\n",
              "      <th>time_spend_company_5</th>\n",
              "      <th>time_spend_company_6</th>\n",
              "      <th>time_spend_company_7</th>\n",
              "      <th>time_spend_company_8</th>\n",
              "      <th>time_spend_company_10</th>\n",
              "      <th>Work_accident_1</th>\n",
              "      <th>promotion_last_5years_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.38</td>\n",
              "      <td>0.53</td>\n",
              "      <td>157</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>262</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.11</td>\n",
              "      <td>0.88</td>\n",
              "      <td>272</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>True</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.87</td>\n",
              "      <td>223</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.52</td>\n",
              "      <td>159</td>\n",
              "      <td>False</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14994</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.57</td>\n",
              "      <td>151</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.48</td>\n",
              "      <td>160</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14996</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.53</td>\n",
              "      <td>143</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14997</th>\n",
              "      <td>0.11</td>\n",
              "      <td>0.96</td>\n",
              "      <td>280</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.52</td>\n",
              "      <td>158</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14999 rows × 28 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "      fill: #1967D2;\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-f821e4c1-f3c7-4924-a753-65f0bb96a9df 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-f821e4c1-f3c7-4924-a753-65f0bb96a9df');\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",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d2282e4e-261f-45cc-b125-476448bfbef9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_processed"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "IhOZkrl-RUZP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Выбираем числовые признаки\n",
        "numeric_features = X_processed.select_dtypes(include=['float64']).columns.tolist() + [col for col in X.select_dtypes(include=['int64']).columns.tolist() if col == 'average_montly_hours']\n",
        "\n",
        "print(\"Числовые признаки:\", numeric_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJP2t855RNIQ",
        "outputId": "1a03d4d4-3080-4aa8-ec4e-1aca64ceb923"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Числовые признаки: ['satisfaction_level', 'last_evaluation', 'average_montly_hours']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Инициализируем scaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Применяем нормализацию\n",
        "X_processed[numeric_features] = scaler.fit_transform(X_processed[numeric_features])\n"
      ],
      "metadata": {
        "id": "I6T1wnUbRXMz"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_processed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "pZ7BfPUbRfUL",
        "outputId": "1de5e581-3e69-4f7a-b46c-8f3cb5cb650a"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       satisfaction_level  last_evaluation  average_montly_hours  \\\n",
              "0                0.318681         0.265625              0.285047   \n",
              "1                0.780220         0.781250              0.775701   \n",
              "2                0.021978         0.812500              0.822430   \n",
              "3                0.692308         0.796875              0.593458   \n",
              "4                0.307692         0.250000              0.294393   \n",
              "...                   ...              ...                   ...   \n",
              "14994            0.340659         0.328125              0.257009   \n",
              "14995            0.307692         0.187500              0.299065   \n",
              "14996            0.307692         0.265625              0.219626   \n",
              "14997            0.021978         0.937500              0.859813   \n",
              "14998            0.307692         0.250000              0.289720   \n",
              "\n",
              "       department_RandD  department_accounting  department_hr  \\\n",
              "0                 False                  False          False   \n",
              "1                 False                  False          False   \n",
              "2                 False                  False          False   \n",
              "3                 False                  False          False   \n",
              "4                 False                  False          False   \n",
              "...                 ...                    ...            ...   \n",
              "14994             False                  False          False   \n",
              "14995             False                  False          False   \n",
              "14996             False                  False          False   \n",
              "14997             False                  False          False   \n",
              "14998             False                  False          False   \n",
              "\n",
              "       department_management  department_marketing  department_product_mng  \\\n",
              "0                      False                 False                   False   \n",
              "1                      False                 False                   False   \n",
              "2                      False                 False                   False   \n",
              "3                      False                 False                   False   \n",
              "4                      False                 False                   False   \n",
              "...                      ...                   ...                     ...   \n",
              "14994                  False                 False                   False   \n",
              "14995                  False                 False                   False   \n",
              "14996                  False                 False                   False   \n",
              "14997                  False                 False                   False   \n",
              "14998                  False                 False                   False   \n",
              "\n",
              "       department_sales  ...  number_project_7  time_spend_company_3  \\\n",
              "0                  True  ...             False                  True   \n",
              "1                  True  ...             False                 False   \n",
              "2                  True  ...              True                 False   \n",
              "3                  True  ...             False                 False   \n",
              "4                  True  ...             False                  True   \n",
              "...                 ...  ...               ...                   ...   \n",
              "14994             False  ...             False                  True   \n",
              "14995             False  ...             False                  True   \n",
              "14996             False  ...             False                  True   \n",
              "14997             False  ...             False                 False   \n",
              "14998             False  ...             False                  True   \n",
              "\n",
              "       time_spend_company_4  time_spend_company_5  time_spend_company_6  \\\n",
              "0                     False                 False                 False   \n",
              "1                     False                 False                  True   \n",
              "2                      True                 False                 False   \n",
              "3                     False                  True                 False   \n",
              "4                     False                 False                 False   \n",
              "...                     ...                   ...                   ...   \n",
              "14994                 False                 False                 False   \n",
              "14995                 False                 False                 False   \n",
              "14996                 False                 False                 False   \n",
              "14997                  True                 False                 False   \n",
              "14998                 False                 False                 False   \n",
              "\n",
              "       time_spend_company_7  time_spend_company_8  time_spend_company_10  \\\n",
              "0                     False                 False                  False   \n",
              "1                     False                 False                  False   \n",
              "2                     False                 False                  False   \n",
              "3                     False                 False                  False   \n",
              "4                     False                 False                  False   \n",
              "...                     ...                   ...                    ...   \n",
              "14994                 False                 False                  False   \n",
              "14995                 False                 False                  False   \n",
              "14996                 False                 False                  False   \n",
              "14997                 False                 False                  False   \n",
              "14998                 False                 False                  False   \n",
              "\n",
              "       Work_accident_1  promotion_last_5years_1  \n",
              "0                False                    False  \n",
              "1                False                    False  \n",
              "2                False                    False  \n",
              "3                False                    False  \n",
              "4                False                    False  \n",
              "...                ...                      ...  \n",
              "14994            False                    False  \n",
              "14995            False                    False  \n",
              "14996            False                    False  \n",
              "14997            False                    False  \n",
              "14998            False                    False  \n",
              "\n",
              "[14999 rows x 28 columns]"
            ],
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              "\n",
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              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
              "      <th>average_montly_hours</th>\n",
              "      <th>department_RandD</th>\n",
              "      <th>department_accounting</th>\n",
              "      <th>department_hr</th>\n",
              "      <th>department_management</th>\n",
              "      <th>department_marketing</th>\n",
              "      <th>department_product_mng</th>\n",
              "      <th>department_sales</th>\n",
              "      <th>...</th>\n",
              "      <th>number_project_7</th>\n",
              "      <th>time_spend_company_3</th>\n",
              "      <th>time_spend_company_4</th>\n",
              "      <th>time_spend_company_5</th>\n",
              "      <th>time_spend_company_6</th>\n",
              "      <th>time_spend_company_7</th>\n",
              "      <th>time_spend_company_8</th>\n",
              "      <th>time_spend_company_10</th>\n",
              "      <th>Work_accident_1</th>\n",
              "      <th>promotion_last_5years_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.318681</td>\n",
              "      <td>0.265625</td>\n",
              "      <td>0.285047</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.780220</td>\n",
              "      <td>0.781250</td>\n",
              "      <td>0.775701</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.021978</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.822430</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.692308</td>\n",
              "      <td>0.796875</td>\n",
              "      <td>0.593458</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.307692</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.294393</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14994</th>\n",
              "      <td>0.340659</td>\n",
              "      <td>0.328125</td>\n",
              "      <td>0.257009</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>0.307692</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.299065</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14996</th>\n",
              "      <td>0.307692</td>\n",
              "      <td>0.265625</td>\n",
              "      <td>0.219626</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14997</th>\n",
              "      <td>0.021978</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.859813</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>0.307692</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.289720</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14999 rows × 28 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8162b1cf-fdec-440c-8047-466d0351377a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-8162b1cf-fdec-440c-8047-466d0351377a 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-8162b1cf-fdec-440c-8047-466d0351377a');\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",
              "\n",
              "\n",
              "<div id=\"df-45a695bd-561a-4a3b-a15a-54eae1fd8729\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-45a695bd-561a-4a3b-a15a-54eae1fd8729')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-45a695bd-561a-4a3b-a15a-54eae1fd8729 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_processed"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Шаг 3: Объяснение логистической регрессии\n"
      ],
      "metadata": {
        "id": "4fCIa6HtRmjw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Логистическая регрессия — это статистический метод для анализа набора данных, в котором одна или более независимых переменных определяют исход (зависимую переменную), которая является бинарной (т.е. принимает только два возможных значения, например, 0 и 1). Целью логистической регрессии является нахождение наилучшей модели, описывающей связь между независимыми переменными и вероятностью определенного исхода.\n",
        "\n",
        "Основные характеристики:\n",
        "\n",
        "Функция логита (логистическая функция): Используется для моделирования вероятности принадлежности наблюдения к определенному классу.\n",
        "Выводы о влиянии признаков: Коэффициенты модели показывают, как изменение признака влияет на логарифм отношения шансов."
      ],
      "metadata": {
        "id": "7iMew3iZRoq4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Функция для обучения и оценки модели**"
      ],
      "metadata": {
        "id": "-3f-a3jkRsDf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_and_evaluate(X, Y):\n",
        "    # Разделение данных на обучающую и тестовую выборки\n",
        "    X_train, X_test, Y_train, Y_test = train_test_split(\n",
        "        X, Y, test_size=0.2, random_state=42, stratify=Y)\n",
        "\n",
        "    # Инициализация модели\n",
        "    model = LogisticRegression(max_iter=1000)\n",
        "\n",
        "    # Обучение модели\n",
        "    model.fit(X_train, Y_train)\n",
        "\n",
        "    # Предсказания на обучающей выборке\n",
        "    Y_train_pred = model.predict(X_train)\n",
        "    train_accuracy = accuracy_score(Y_train, Y_train_pred)\n",
        "\n",
        "    # Предсказания на тестовой выборке\n",
        "    Y_test_pred = model.predict(X_test)\n",
        "    test_accuracy = accuracy_score(Y_test, Y_test_pred)\n",
        "\n",
        "    # Вывод результатов\n",
        "    print(f\"Точность на обучающей выборке: {train_accuracy:.4f}\")\n",
        "    print(f\"Точность на тестовой выборке: {test_accuracy:.4f}\")\n",
        "\n",
        "    # Классификационный отчет\n",
        "    print(\"\\nКлассификационный отчет на тестовой выборке:\")\n",
        "    print(classification_report(Y_test, Y_test_pred))\n",
        "\n",
        "    return model\n"
      ],
      "metadata": {
        "id": "ISOh8FAUR6C8"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Обучение модели\n",
        "model = train_and_evaluate(X_processed, Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zY3_h4I8R9hI",
        "outputId": "cdbd552f-99ec-45a2-dc28-042b97667ece"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Точность на обучающей выборке: 0.8949\n",
            "Точность на тестовой выборке: 0.9037\n",
            "\n",
            "Классификационный отчет на тестовой выборке:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.93      0.94      2286\n",
            "           1       0.79      0.82      0.80       714\n",
            "\n",
            "    accuracy                           0.90      3000\n",
            "   macro avg       0.86      0.87      0.87      3000\n",
            "weighted avg       0.91      0.90      0.90      3000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Пояснение:**\n",
        "\n",
        "- Точность (Accuracy): Доля правильных предсказаний от общего числа предсказаний.\n",
        "- Precision (Точность): Доля правильных положительных предсказаний среди всех положительных предсказаний.\n",
        "- Recall (Полнота): Доля правильно предсказанных положительных случаев среди всех реальных положительных случаев.\n",
        "- F1-score: Гармоническое среднее между точностью и полнотой."
      ],
      "metadata": {
        "id": "dN1PqgwRSB2M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Получение коэффициентов модели\n",
        "coefficients = pd.DataFrame({\n",
        "    'Feature': X_processed.columns,\n",
        "    'Coefficient': model.coef_[0]\n",
        "})\n",
        "\n",
        "# Сортировка по абсолютному значению коэффициента\n",
        "coefficients['Abs_Coefficient'] = coefficients['Coefficient'].abs()\n",
        "coefficients = coefficients.sort_values(by='Abs_Coefficient', ascending=False)\n",
        "\n",
        "print(coefficients[['Feature', 'Coefficient']])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KwHGCQZR_iP",
        "outputId": "cf425ddc-7d23-4554-8864-baeab2a2f82e"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                    Feature  Coefficient\n",
            "14         number_project_3    -5.127709\n",
            "21     time_spend_company_5     4.420782\n",
            "15         number_project_4    -3.854700\n",
            "22     time_spend_company_6     3.281380\n",
            "16         number_project_5    -3.207488\n",
            "20     time_spend_company_4     2.519304\n",
            "17         number_project_6    -2.442384\n",
            "19     time_spend_company_3     2.385114\n",
            "0        satisfaction_level    -1.835905\n",
            "2      average_montly_hours     1.800284\n",
            "18         number_project_7     1.699650\n",
            "12               salary_low     1.658079\n",
            "26          Work_accident_1    -1.515081\n",
            "1           last_evaluation     1.308634\n",
            "13            salary_medium     1.229544\n",
            "23     time_spend_company_7    -0.964653\n",
            "25    time_spend_company_10    -0.895424\n",
            "24     time_spend_company_8    -0.848163\n",
            "27  promotion_last_5years_1    -0.822867\n",
            "5             department_hr     0.523926\n",
            "3          department_RandD    -0.414886\n",
            "11     department_technical     0.263477\n",
            "7      department_marketing     0.258378\n",
            "4     department_accounting     0.196191\n",
            "10       department_support     0.171310\n",
            "9          department_sales     0.170565\n",
            "8    department_product_mng    -0.141320\n",
            "6     department_management     0.016090\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Пояснение:**\n",
        "\n",
        "- Коэффициенты показывают влияние признака на вероятность принадлежности к классу 1 (лучшие вина).\n",
        "- Положительный коэффициент: Увеличение признака повышает вероятность принадлежности к классу 1.\n",
        "- Отрицательный коэффициент: Увеличение признака снижает вероятность принадлежности к классу 1."
      ],
      "metadata": {
        "id": "OrC2hDwySNdE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Построение модели логистической регрессии для предсказания качества вина\n",
        "\n",
        "- **Модель логистической регрессии успешно обучена для предсказания лучших вин.**\n",
        "- **Точность на обучающей и тестовой выборках высока, что указывает на хорошую способность модели обобщать данные.**\n",
        "- **Анализ коэффициентов показал, какие признаки наиболее влияют на качество вина:**\n",
        "  - **Содержание алкоголя и сульфатов положительно влияют на качество.**\n",
        "  - **Плотность и летучая кислотность отрицательно влияют на качество.**\n",
        "- **Преобразование категориальных признаков и нормализация числовых признаков улучшили качество модели и упростили интерпретацию результатов.**\n"
      ],
      "metadata": {
        "id": "_T6lnsZXVLSY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1 Кросс-валидация модели"
      ],
      "metadata": {
        "id": "jUgvVuJpVP_d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Кросс-валидация с 5 фолдами\n",
        "scores = cross_val_score(model, X_processed, Y, cv=5, scoring='accuracy')\n",
        "\n",
        "print(f\"Средняя точность при кросс-валидации: {scores.mean():.4f}\")\n",
        "print(f\"Отклонение точности: {scores.std():.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZKKWlv5VSj5",
        "outputId": "304797f3-1d46-408c-ba9d-74dd5c086044"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Средняя точность при кросс-валидации: 0.8943\n",
            "Отклонение точности: 0.0088\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X_processed, Y, test_size=0.2, random_state=42, stratify=Y)"
      ],
      "metadata": {
        "id": "ym4BKDocVbhN"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.2 Построение ROC-кривой"
      ],
      "metadata": {
        "id": "_xZekN3yVler"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "# Предсказания вероятностей для тестовой выборки\n",
        "Y_test_prob = model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "# Расчет ROC-кривой\n",
        "fpr, tpr, thresholds = roc_curve(Y_test, Y_test_prob)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Построение графика\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.plot(fpr, tpr, label=f'ROC curve (area = {roc_auc:.2f})')\n",
        "plt.plot([0,1], [0,1], 'k--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC-кривая')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "lpzY-X-0VVTb",
        "outputId": "791a9033-f054-4f50-9c2d-ddb431116cc2"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "BcmdZJVKt8k-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ⏰ Доп задание"
      ],
      "metadata": {
        "id": "iqVTUBAXt-8q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Изучить различные метрики качества классификационных моделей и понять, в каких ситуациях они наиболее информативны.\n",
        "\n",
        "**Метрики для исследования:**\n",
        "- Accuracy (Точность)\n",
        "- Precision (Точность)\n",
        "- Recall (Полнота)\n",
        "- F1 Score\n",
        "- ROC-AUC\n",
        "- Log Loss\n",
        "\n",
        "**Попробовать другие модели и сравнить результат метрик**\n",
        "\n",
        "**Попробовать другие методы отбора признаков**\n",
        "\n",
        "**Использовать параметры модели и исследовать влияние на предсказание**"
      ],
      "metadata": {
        "id": "2goKgvPOuCKc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Заключение\n",
        "\n",
        "Мы успешно построили модель логистической регрессии для предсказания лучших вин на основе химических характеристик. Преобразование данных и анализ коэффициентов модели позволили нам понять влияние различных признаков на качество вина."
      ],
      "metadata": {
        "id": "FcSAAnQWVqHP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "---\n",
        "\n",
        "# Примечания\n",
        "\n",
        "- **Логистическая регрессия** является мощным инструментом для бинарной классификации и позволяет интерпретировать результаты через коэффициенты модели.\n",
        "- **Преобразование категориальных признаков** (One-Hot Encoding) и **нормализация числовых признаков** являются стандартными шагами предобработки данных для многих алгоритмов машинного обучения.\n",
        "- **Анализ коэффициентов модели** помогает понять, какие признаки наиболее значимы и как они влияют на предсказания.\n",
        "\n",
        "---\n",
        "\n",
        "# Дополнительная литература\n",
        "\n",
        "- **Логистическая регрессия:**\n",
        "  - [Документация scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)\n",
        "- **Предобработка данных:**\n",
        "  - [One-Hot Encoding](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)\n",
        "  - [Min-Max Scaling](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)\n",
        "- **Метрики качества модели:**\n",
        "  - [Classification Report](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html)\n",
        "  - [ROC-кривая и AUC](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html)\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "x3j0CQnhVE7B"
      }
    }
  ]
}