{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "f2485344",
      "metadata": {},
      "source": [
        "# Прогнозирование энергопотребления"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "c15bd427",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0.1\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip -q install pandas numpy matplotlib seaborn scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "ad72f151",
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "\n",
        "pd.set_option(\"display.max_columns\", 50)\n",
        "sns.set_style(\"whitegrid\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "79315a77",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Building Type</th>\n",
              "      <th>Square Footage</th>\n",
              "      <th>Number of Occupants</th>\n",
              "      <th>Appliances Used</th>\n",
              "      <th>Average Temperature</th>\n",
              "      <th>Day of Week</th>\n",
              "      <th>Energy Consumption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Residential</td>\n",
              "      <td>7063</td>\n",
              "      <td>76</td>\n",
              "      <td>10</td>\n",
              "      <td>29.84</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>2713.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Commercial</td>\n",
              "      <td>44372</td>\n",
              "      <td>66</td>\n",
              "      <td>45</td>\n",
              "      <td>16.72</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5744.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Industrial</td>\n",
              "      <td>19255</td>\n",
              "      <td>37</td>\n",
              "      <td>17</td>\n",
              "      <td>14.30</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>4101.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Residential</td>\n",
              "      <td>13265</td>\n",
              "      <td>14</td>\n",
              "      <td>41</td>\n",
              "      <td>32.82</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3009.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Commercial</td>\n",
              "      <td>13375</td>\n",
              "      <td>26</td>\n",
              "      <td>18</td>\n",
              "      <td>11.92</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3279.17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Building Type  Square Footage  Number of Occupants  Appliances Used  \\\n",
              "0   Residential            7063                   76               10   \n",
              "1    Commercial           44372                   66               45   \n",
              "2    Industrial           19255                   37               17   \n",
              "3   Residential           13265                   14               41   \n",
              "4    Commercial           13375                   26               18   \n",
              "\n",
              "   Average Temperature Day of Week  Energy Consumption  \n",
              "0                29.84     Weekday             2713.95  \n",
              "1                16.72     Weekday             5744.99  \n",
              "2                14.30     Weekend             4101.24  \n",
              "3                32.82     Weekday             3009.14  \n",
              "4                11.92     Weekday             3279.17  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Загрузка обучающей выборки\n",
        "df = pd.read_csv(\"train_energy_data.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "667f4766",
      "metadata": {},
      "source": [
        "## Очистка и предобработка\n",
        "\n",
        "- Удаляем дубликаты.\n",
        "- Удаляем пропуски (если есть).\n",
        "- Проверяем типы и качество данных."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "e3e30a9f",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 7 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Building Type        1000 non-null   str    \n",
            " 1   Square Footage       1000 non-null   int64  \n",
            " 2   Number of Occupants  1000 non-null   int64  \n",
            " 3   Appliances Used      1000 non-null   int64  \n",
            " 4   Average Temperature  1000 non-null   float64\n",
            " 5   Day of Week          1000 non-null   str    \n",
            " 6   Energy Consumption   1000 non-null   float64\n",
            "dtypes: float64(2), int64(3), str(2)\n",
            "memory usage: 54.8 KB\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "Building Type          0.0\n",
              "Square Footage         0.0\n",
              "Number of Occupants    0.0\n",
              "Appliances Used        0.0\n",
              "Average Temperature    0.0\n",
              "Day of Week            0.0\n",
              "Energy Consumption     0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = df.drop_duplicates()\n",
        "df = df.dropna()\n",
        "display(df.info())\n",
        "display(df.isna().mean().sort_values(ascending=False))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "571cc12d",
      "metadata": {},
      "source": [
        "## Задание 1. Анализ исходных данных. Постановка задачи.\n",
        "\n",
        "Ниже представлены не менее 3 графиков с описанием зависимостей и распределений."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "861b82a1",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# График 1: распределение целевой переменной (энергопотребление)\n",
        "plt.figure(figsize=(7, 4))\n",
        "sns.histplot(df[\"Energy Consumption\"], bins=50)\n",
        "plt.title(\"Распределение энергопотребления\")\n",
        "plt.xlabel(\"Energy Consumption\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "72db1caa",
      "metadata": {},
      "source": [
        "**Описание графика 1:** Распределение целевой переменной Energy Consumption. Видно, что больше всего потребление около 4000 единиц\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "d75488be",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# График 2: зависимость энергопотребления от типа здания и площади\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
        "sns.boxplot(data=df, x=\"Building Type\", y=\"Energy Consumption\", ax=axes[0])\n",
        "axes[0].set_title(\"Энергопотребление по типу здания\")\n",
        "sns.scatterplot(data=df, x=\"Square Footage\", y=\"Energy Consumption\", alpha=0.4, ax=axes[1])\n",
        "axes[1].set_title(\"Энергопотребление от площади (кв. футы)\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "462cc702",
      "metadata": {},
      "source": [
        "**Описание графика 2:** Слева — зависимость энергопотребления от типа здания (Residential, Commercial, Industrial). Справа — связь между площадью здания и энергопотреблением; \n",
        "Можно заметить, что у жилых помещений потребление меньше, чем у коммерческих, а у коммерческих меньше чем у индустриальных. Также видно, что бОльшая площадь ведёт к бОльшему потреблению."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "8837110e",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# График 3: среднее энергопотребление по дню недели и тепловая карта корреляций числовых признаков\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
        "sns.barplot(data=df, x=\"Day of Week\", y=\"Energy Consumption\", estimator=\"mean\", ax=axes[0])\n",
        "axes[0].set_title(\"Среднее энергопотребление: будни vs выходные\")\n",
        "num_cols = [\"Square Footage\", \"Number of Occupants\", \"Appliances Used\", \"Average Temperature\", \"Energy Consumption\"]\n",
        "sns.heatmap(df[num_cols].corr(), annot=True, fmt=\".2f\", cmap=\"coolwarm\", center=0, ax=axes[1])\n",
        "axes[1].set_title(\"Корреляция числовых признаков с целевой переменной\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e8ade242",
      "metadata": {},
      "source": [
        "**Описание графика 3:** Слева — сравнение среднего энергопотребления в будни и выходные. Справа — корреляционная матрица числовых признаков; видно, какие факторы сильнее связаны с энергопотреблением: площадь, количество проживающих, количество бытовой техники - положительно коррелируют с потреблением. А вот средняя температура - не коррелирует.\n",
        "\n",
        "---\n",
        "\n",
        "### Выводы о качестве датасета\n",
        "\n",
        "- **Очистка и предобработка:** выполнены удаление дубликатов и пропусков; после очистки датасет готов к моделированию.\n",
        "- **Качество:** датасет имеет не случайные данные, видна зависимость энергопотребления от других данных."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c4f56adf",
      "metadata": {},
      "source": [
        "## Задание 2. Построить прогнозную модель для оценки энергопотребления\n",
        "\n",
        "Целевая переменная — **Energy Consumption** (количественная). Используем несколько моделей для сравнения точности на train и test."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "8479ddf7",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        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>model</th>\n",
              "      <th>split</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LinearRegression</td>\n",
              "      <td>train</td>\n",
              "      <td>0.011691</td>\n",
              "      <td>0.013645</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LinearRegression</td>\n",
              "      <td>test</td>\n",
              "      <td>0.011412</td>\n",
              "      <td>0.013731</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>train</td>\n",
              "      <td>38.263438</td>\n",
              "      <td>48.845146</td>\n",
              "      <td>0.997297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>test</td>\n",
              "      <td>97.208261</td>\n",
              "      <td>124.591866</td>\n",
              "      <td>0.980937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GradientBoosting</td>\n",
              "      <td>train</td>\n",
              "      <td>38.677028</td>\n",
              "      <td>49.438269</td>\n",
              "      <td>0.997230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>GradientBoosting</td>\n",
              "      <td>test</td>\n",
              "      <td>74.769433</td>\n",
              "      <td>94.078591</td>\n",
              "      <td>0.989131</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              model  split        MAE        RMSE        R2\n",
              "0  LinearRegression  train   0.011691    0.013645  1.000000\n",
              "1  LinearRegression   test   0.011412    0.013731  1.000000\n",
              "2      RandomForest  train  38.263438   48.845146  0.997297\n",
              "3      RandomForest   test  97.208261  124.591866  0.980937\n",
              "4  GradientBoosting  train  38.677028   49.438269  0.997230\n",
              "5  GradientBoosting   test  74.769433   94.078591  0.989131"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Выделяем целевую переменную и признаки\n",
        "df_model = df.dropna(subset=[\"Energy Consumption\"]).copy()\n",
        "X = df_model.drop(columns=[\"Energy Consumption\"])\n",
        "y = df_model[\"Energy Consumption\"]\n",
        "\n",
        "cat_cols = X.select_dtypes(include=[\"object\", \"string\"]).columns.tolist()\n",
        "num_cols = X.select_dtypes(exclude=[\"object\", \"string\"]).columns.tolist()\n",
        "\n",
        "preprocess = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"num\", Pipeline([\n",
        "            (\"imputer\", SimpleImputer(strategy=\"median\"))\n",
        "        ]), num_cols),\n",
        "        (\"cat\", Pipeline([\n",
        "            (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
        "            (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"))\n",
        "        ]), cat_cols)\n",
        "    ]\n",
        ")\n",
        "\n",
        "models = {\n",
        "    \"LinearRegression\": LinearRegression(),\n",
        "    \"RandomForest\": RandomForestRegressor(n_estimators=200, random_state=42),\n",
        "    \"GradientBoosting\": GradientBoostingRegressor(random_state=42)\n",
        "}\n",
        "\n",
        "def evaluate_models(X, y, models, random_state=42):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(\n",
        "        X, y, test_size=0.2, random_state=random_state\n",
        "    )\n",
        "    rows = []\n",
        "    for name, model in models.items():\n",
        "        pipe = Pipeline([\n",
        "            (\"preprocess\", preprocess),\n",
        "            (\"model\", model)\n",
        "        ])\n",
        "        pipe.fit(X_train, y_train)\n",
        "        for split, (X_s, y_s) in {\n",
        "            \"train\": (X_train, y_train),\n",
        "            \"test\": (X_test, y_test)\n",
        "        }.items():\n",
        "            pred = pipe.predict(X_s)\n",
        "            rows.append({\n",
        "                \"model\": name,\n",
        "                \"split\": split,\n",
        "                \"MAE\": mean_absolute_error(y_s, pred),\n",
        "                \"RMSE\": np.sqrt(mean_squared_error(y_s, pred)),\n",
        "                \"R2\": r2_score(y_s, pred)\n",
        "            })\n",
        "    return pd.DataFrame(rows)\n",
        "\n",
        "results_before = evaluate_models(X, y, models)\n",
        "results_before"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "1fd412bc",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">MAE</th>\n",
              "      <th colspan=\"2\" halign=\"left\">R2</th>\n",
              "      <th colspan=\"2\" halign=\"left\">RMSE</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>split</th>\n",
              "      <th>test</th>\n",
              "      <th>train</th>\n",
              "      <th>test</th>\n",
              "      <th>train</th>\n",
              "      <th>test</th>\n",
              "      <th>train</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>GradientBoosting</th>\n",
              "      <td>74.769433</td>\n",
              "      <td>38.677028</td>\n",
              "      <td>0.989131</td>\n",
              "      <td>0.997230</td>\n",
              "      <td>94.078591</td>\n",
              "      <td>49.438269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.011412</td>\n",
              "      <td>0.011691</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.013731</td>\n",
              "      <td>0.013645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForest</th>\n",
              "      <td>97.208261</td>\n",
              "      <td>38.263438</td>\n",
              "      <td>0.980937</td>\n",
              "      <td>0.997297</td>\n",
              "      <td>124.591866</td>\n",
              "      <td>48.845146</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        MAE                   R2                  RMSE  \\\n",
              "split                  test      train      test     train        test   \n",
              "model                                                                    \n",
              "GradientBoosting  74.769433  38.677028  0.989131  0.997230   94.078591   \n",
              "LinearRegression   0.011412   0.011691  1.000000  1.000000    0.013731   \n",
              "RandomForest      97.208261  38.263438  0.980937  0.997297  124.591866   \n",
              "\n",
              "                             \n",
              "split                 train  \n",
              "model                        \n",
              "GradientBoosting  49.438269  \n",
              "LinearRegression   0.013645  \n",
              "RandomForest      48.845146  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Таблица результатов (train / test)\n",
        "pivot_before = results_before.pivot_table(index=\"model\", columns=\"split\", values=[\"MAE\", \"RMSE\", \"R2\"])\n",
        "pivot_before"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "eabdd5b5",
      "metadata": {},
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1400x400 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Визуализация метрик на тесте\n",
        "test_before = results_before[results_before[\"split\"] == \"test\"]\n",
        "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
        "sns.barplot(data=test_before, x=\"model\", y=\"MAE\", ax=axes[0])\n",
        "sns.barplot(data=test_before, x=\"model\", y=\"RMSE\", ax=axes[1])\n",
        "sns.barplot(data=test_before, x=\"model\", y=\"R2\", ax=axes[2])\n",
        "axes[0].set_title(\"MAE (test)\")\n",
        "axes[1].set_title(\"RMSE (test)\")\n",
        "axes[2].set_title(\"R2 (test)\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "23888437",
      "metadata": {},
      "source": [
        "### Почему выбраны эти метрики\n",
        "\n",
        "- **MAE** (Mean Absolute Error) — средняя абсолютная ошибка в тех же единицах, что и энергопотребление; интерпретируема и устойчива к выбросам.\n",
        "- **RMSE** (Root Mean Squared Error) — корень из средней квадратичной ошибки; сильнее штрафует большие ошибки, полезна для оценки риска крупных промахов.\n",
        "- **R2** (коэффициент детерминации) — доля объяснённой дисперсии: 0 означает, что модель не лучше предсказания средним, 1 — полное совпадение с целевой переменной."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eadb5756",
      "metadata": {},
      "source": [
        "## Задание 3. Добавление 20 новых записей и анализ изменений точности\n",
        "\n",
        "Добавляем 20 новых записей. Признаки генерируются по следующему **принципу**:\n",
        "- **Building Type**, **Day of Week** — случайный выбор из тех же категорий, что и в исходных данных (равновероятно или пропорционально).\n",
        "- **Square Footage**, **Number of Occupants**, **Appliances Used**, **Average Temperature** — случайные значения из диапазонов, совпадающих с минимумом и максимумом по обучающей выборке (равномерное распределение), чтобы новые точки не выходили за пределы уже наблюдаемых.\n",
        "- **Energy Consumption** — для новых записей задаём по упрощённой формуле, согласованной с данными: базовая составляющая от площади и числа жильцов плюс случайный шум в диапазоне стандартного отклонения целевой переменной, чтобы новые строки были «правдоподобны» и не ломали распределение."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "357262bb",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Building Type</th>\n",
              "      <th>Square Footage</th>\n",
              "      <th>Number of Occupants</th>\n",
              "      <th>Appliances Used</th>\n",
              "      <th>Average Temperature</th>\n",
              "      <th>Day of Week</th>\n",
              "      <th>Energy Consumption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Industrial</td>\n",
              "      <td>47560.463166</td>\n",
              "      <td>73</td>\n",
              "      <td>30</td>\n",
              "      <td>13.94</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>6346.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Industrial</td>\n",
              "      <td>3348.819232</td>\n",
              "      <td>72</td>\n",
              "      <td>46</td>\n",
              "      <td>10.07</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>4607.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Residential</td>\n",
              "      <td>15600.823765</td>\n",
              "      <td>52</td>\n",
              "      <td>22</td>\n",
              "      <td>17.31</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3895.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Industrial</td>\n",
              "      <td>5039.310304</td>\n",
              "      <td>62</td>\n",
              "      <td>19</td>\n",
              "      <td>34.57</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>4711.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Residential</td>\n",
              "      <td>43072.873888</td>\n",
              "      <td>68</td>\n",
              "      <td>23</td>\n",
              "      <td>10.38</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5582.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Commercial</td>\n",
              "      <td>1349.323611</td>\n",
              "      <td>23</td>\n",
              "      <td>12</td>\n",
              "      <td>27.09</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3261.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Residential</td>\n",
              "      <td>2260.065318</td>\n",
              "      <td>91</td>\n",
              "      <td>13</td>\n",
              "      <td>26.57</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>4543.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Commercial</td>\n",
              "      <td>9698.649718</td>\n",
              "      <td>96</td>\n",
              "      <td>38</td>\n",
              "      <td>33.48</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5503.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Commercial</td>\n",
              "      <td>30118.381252</td>\n",
              "      <td>92</td>\n",
              "      <td>5</td>\n",
              "      <td>14.94</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5043.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Residential</td>\n",
              "      <td>27240.759949</td>\n",
              "      <td>59</td>\n",
              "      <td>48</td>\n",
              "      <td>25.19</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5990.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Residential</td>\n",
              "      <td>15206.873301</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>20.61</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>2556.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Industrial</td>\n",
              "      <td>10390.171931</td>\n",
              "      <td>71</td>\n",
              "      <td>39</td>\n",
              "      <td>25.16</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>4978.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Commercial</td>\n",
              "      <td>32747.294910</td>\n",
              "      <td>91</td>\n",
              "      <td>42</td>\n",
              "      <td>21.26</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>6544.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Commercial</td>\n",
              "      <td>33481.203544</td>\n",
              "      <td>59</td>\n",
              "      <td>14</td>\n",
              "      <td>24.05</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5094.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Industrial</td>\n",
              "      <td>48598.530860</td>\n",
              "      <td>85</td>\n",
              "      <td>36</td>\n",
              "      <td>15.94</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>6667.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Residential</td>\n",
              "      <td>2113.764653</td>\n",
              "      <td>64</td>\n",
              "      <td>16</td>\n",
              "      <td>22.73</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3928.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Commercial</td>\n",
              "      <td>12884.259932</td>\n",
              "      <td>41</td>\n",
              "      <td>38</td>\n",
              "      <td>15.76</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>4165.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Commercial</td>\n",
              "      <td>46521.462839</td>\n",
              "      <td>81</td>\n",
              "      <td>32</td>\n",
              "      <td>31.78</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>7000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Commercial</td>\n",
              "      <td>27223.462414</td>\n",
              "      <td>80</td>\n",
              "      <td>44</td>\n",
              "      <td>17.98</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5941.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Commercial</td>\n",
              "      <td>41000.195983</td>\n",
              "      <td>86</td>\n",
              "      <td>1</td>\n",
              "      <td>22.79</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5669.67</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Building Type  Square Footage  Number of Occupants  Appliances Used  \\\n",
              "0     Industrial    47560.463166                   73               30   \n",
              "1     Industrial     3348.819232                   72               46   \n",
              "2    Residential    15600.823765                   52               22   \n",
              "3     Industrial     5039.310304                   62               19   \n",
              "4    Residential    43072.873888                   68               23   \n",
              "5     Commercial     1349.323611                   23               12   \n",
              "6    Residential     2260.065318                   91               13   \n",
              "7     Commercial     9698.649718                   96               38   \n",
              "8     Commercial    30118.381252                   92                5   \n",
              "9    Residential    27240.759949                   59               48   \n",
              "10   Residential    15206.873301                   17                1   \n",
              "11    Industrial    10390.171931                   71               39   \n",
              "12    Commercial    32747.294910                   91               42   \n",
              "13    Commercial    33481.203544                   59               14   \n",
              "14    Industrial    48598.530860                   85               36   \n",
              "15   Residential     2113.764653                   64               16   \n",
              "16    Commercial    12884.259932                   41               38   \n",
              "17    Commercial    46521.462839                   81               32   \n",
              "18    Commercial    27223.462414                   80               44   \n",
              "19    Commercial    41000.195983                   86                1   \n",
              "\n",
              "    Average Temperature Day of Week  Energy Consumption  \n",
              "0                 13.94     Weekend             6346.46  \n",
              "1                 10.07     Weekend             4607.10  \n",
              "2                 17.31     Weekday             3895.37  \n",
              "3                 34.57     Weekend             4711.79  \n",
              "4                 10.38     Weekday             5582.57  \n",
              "5                 27.09     Weekday             3261.35  \n",
              "6                 26.57     Weekend             4543.40  \n",
              "7                 33.48     Weekend             5503.54  \n",
              "8                 14.94     Weekend             5043.68  \n",
              "9                 25.19     Weekend             5990.45  \n",
              "10                20.61     Weekday             2556.43  \n",
              "11                25.16     Weekday             4978.12  \n",
              "12                21.26     Weekday             6544.77  \n",
              "13                24.05     Weekend             5094.18  \n",
              "14                15.94     Weekday             6667.79  \n",
              "15                22.73     Weekday             3928.83  \n",
              "16                15.76     Weekday             4165.87  \n",
              "17                31.78     Weekend             7000.00  \n",
              "18                17.98     Weekend             5941.62  \n",
              "19                22.79     Weekday             5669.67  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Генерация 20 новых записей\n",
        "np.random.seed(42)\n",
        "n_new = 20\n",
        "\n",
        "building_types = df[\"Building Type\"].unique()\n",
        "day_of_week = df[\"Day of Week\"].unique()\n",
        "sq_min, sq_max = df[\"Square Footage\"].min(), df[\"Square Footage\"].max()\n",
        "occ_min, occ_max = df[\"Number of Occupants\"].min(), df[\"Number of Occupants\"].max()\n",
        "app_min, app_max = df[\"Appliances Used\"].min(), df[\"Appliances Used\"].max()\n",
        "temp_min, temp_max = df[\"Average Temperature\"].min(), df[\"Average Temperature\"].max()\n",
        "y_mean, y_std = df[\"Energy Consumption\"].mean(), df[\"Energy Consumption\"].std()\n",
        "\n",
        "new_rows = []\n",
        "for _ in range(n_new):\n",
        "    bt = np.random.choice(building_types)\n",
        "    dow = np.random.choice(day_of_week)\n",
        "    sq = np.random.uniform(sq_min, sq_max)\n",
        "    occ = int(np.random.uniform(occ_min, occ_max + 1))\n",
        "    app = int(np.random.uniform(app_min, app_max + 1))\n",
        "    temp = np.random.uniform(temp_min, temp_max)\n",
        "    # Правдоподобное энергопотребление: линейная комбинация признаков + шум в масштабе y_std\n",
        "    energy = 0.05 * sq + 20 * occ + 30 * app + 50 * temp + np.random.normal(y_mean * 0.2, y_std * 0.3)\n",
        "    energy = max(1500, min(7000, energy))  # в разумных границах\n",
        "    new_rows.append({\n",
        "        \"Building Type\": bt, \"Square Footage\": sq, \"Number of Occupants\": occ,\n",
        "        \"Appliances Used\": app, \"Average Temperature\": round(temp, 2), \"Day of Week\": dow,\n",
        "        \"Energy Consumption\": round(energy, 2)\n",
        "    })\n",
        "\n",
        "df_new = pd.DataFrame(new_rows)\n",
        "df_new"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "53f17cc6",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Размер до: 1000 после: 1020\n"
          ]
        }
      ],
      "source": [
        "# Объединяем исходные данные и 20 новых записей\n",
        "df_extended = pd.concat([df, df_new], ignore_index=True)\n",
        "print(\"Размер до:\", len(df), \"после:\", len(df_extended))\n",
        "X_ext = df_extended.drop(columns=[\"Energy Consumption\"])\n",
        "y_ext = df_extended[\"Energy Consumption\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "5bea9f00",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>split</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LinearRegression</td>\n",
              "      <td>train</td>\n",
              "      <td>30.798960</td>\n",
              "      <td>114.159172</td>\n",
              "      <td>0.985531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LinearRegression</td>\n",
              "      <td>test</td>\n",
              "      <td>44.691906</td>\n",
              "      <td>210.873053</td>\n",
              "      <td>0.948965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>train</td>\n",
              "      <td>42.712283</td>\n",
              "      <td>67.727599</td>\n",
              "      <td>0.994907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>test</td>\n",
              "      <td>125.177327</td>\n",
              "      <td>249.809220</td>\n",
              "      <td>0.928379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GradientBoosting</td>\n",
              "      <td>train</td>\n",
              "      <td>53.938732</td>\n",
              "      <td>88.165860</td>\n",
              "      <td>0.991370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>GradientBoosting</td>\n",
              "      <td>test</td>\n",
              "      <td>107.484864</td>\n",
              "      <td>243.285940</td>\n",
              "      <td>0.932071</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              model  split         MAE        RMSE        R2\n",
              "0  LinearRegression  train   30.798960  114.159172  0.985531\n",
              "1  LinearRegression   test   44.691906  210.873053  0.948965\n",
              "2      RandomForest  train   42.712283   67.727599  0.994907\n",
              "3      RandomForest   test  125.177327  249.809220  0.928379\n",
              "4  GradientBoosting  train   53.938732   88.165860  0.991370\n",
              "5  GradientBoosting   test  107.484864  243.285940  0.932071"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Обучаем модели на расширенной выборке и собираем метрики\n",
        "results_after = evaluate_models(X_ext, y_ext, models)\n",
        "results_after"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "90d16f0d",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\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>MAE_before</th>\n",
              "      <th>MAE_after</th>\n",
              "      <th>RMSE_before</th>\n",
              "      <th>RMSE_after</th>\n",
              "      <th>R2_before</th>\n",
              "      <th>R2_after</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.011412</td>\n",
              "      <td>27.794149</td>\n",
              "      <td>0.013731</td>\n",
              "      <td>33.091760</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.998655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForest</th>\n",
              "      <td>97.208261</td>\n",
              "      <td>113.184960</td>\n",
              "      <td>124.591866</td>\n",
              "      <td>149.632163</td>\n",
              "      <td>0.980937</td>\n",
              "      <td>0.972504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoosting</th>\n",
              "      <td>74.769433</td>\n",
              "      <td>88.355225</td>\n",
              "      <td>94.078591</td>\n",
              "      <td>116.712240</td>\n",
              "      <td>0.989131</td>\n",
              "      <td>0.983272</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  MAE_before   MAE_after  RMSE_before  RMSE_after  R2_before  \\\n",
              "LinearRegression    0.011412   27.794149     0.013731   33.091760   1.000000   \n",
              "RandomForest       97.208261  113.184960   124.591866  149.632163   0.980937   \n",
              "GradientBoosting   74.769433   88.355225    94.078591  116.712240   0.989131   \n",
              "\n",
              "                  R2_after  \n",
              "LinearRegression  0.998655  \n",
              "RandomForest      0.972504  \n",
              "GradientBoosting  0.983272  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Честное сравнение: одна и та же тестовая выборка (из исходных данных)\n",
        "# Разбиваем исходные данные один раз и запоминаем индексы теста\n",
        "X_train_orig, X_test_fixed, y_train_orig, y_test_fixed = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=42\n",
        ")\n",
        "# Обучаем на исходных train\n",
        "metrics_before = {}\n",
        "for name, model in models.items():\n",
        "    pipe = Pipeline([(\"preprocess\", preprocess), (\"model\", model)])\n",
        "    pipe.fit(X_train_orig, y_train_orig)\n",
        "    pred = pipe.predict(X_test_fixed)\n",
        "    metrics_before[name] = {\"MAE\": mean_absolute_error(y_test_fixed, pred),\n",
        "                           \"RMSE\": np.sqrt(mean_squared_error(y_test_fixed, pred)),\n",
        "                           \"R2\": r2_score(y_test_fixed, pred)}\n",
        "# Обучаем на исходный train + 20 новых записей, тест тот же\n",
        "X_train_ext = pd.concat([X_train_orig, df_new.drop(columns=[\"Energy Consumption\"])], ignore_index=True)\n",
        "y_train_ext = pd.concat([y_train_orig, df_new[\"Energy Consumption\"]], ignore_index=True)\n",
        "metrics_after = {}\n",
        "for name, model in models.items():\n",
        "    pipe = Pipeline([(\"preprocess\", preprocess), (\"model\", model)])\n",
        "    pipe.fit(X_train_ext, y_train_ext)\n",
        "    pred = pipe.predict(X_test_fixed)\n",
        "    metrics_after[name] = {\"MAE\": mean_absolute_error(y_test_fixed, pred),\n",
        "                          \"RMSE\": np.sqrt(mean_squared_error(y_test_fixed, pred)),\n",
        "                          \"R2\": r2_score(y_test_fixed, pred)}\n",
        "\n",
        "comparison = pd.DataFrame({\n",
        "    \"MAE_before\": [metrics_before[m][\"MAE\"] for m in models],\n",
        "    \"MAE_after\": [metrics_after[m][\"MAE\"] for m in models],\n",
        "    \"RMSE_before\": [metrics_before[m][\"RMSE\"] for m in models],\n",
        "    \"RMSE_after\": [metrics_after[m][\"RMSE\"] for m in models],\n",
        "    \"R2_before\": [metrics_before[m][\"R2\"] for m in models],\n",
        "    \"R2_after\": [metrics_after[m][\"R2\"] for m in models]\n",
        "}, index=list(models.keys()))\n",
        "comparison"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a5852867",
      "metadata": {},
      "source": [
        "### Анализ изменений в точности моделей\n",
        "\n",
        "- Добавление 20 новых записей, сгенерированных в тех же диапазонах признаков и с правдоподобной целевой переменной, немного увеличивает объём обучающей выборки.\n",
        "- Изменения метрик (MAE, RMSE, R2) на тестовой выборке могут незначительно улучшиться или ухудшиться в зависимости от случайного разбиения train/test и от того, насколько новые точки согласованы с истинным распределением. Обычно при небольшом числе добавленных строк сильных сдвигов не ожидается.\n",
        "- Для корректного сравнения важно смотреть метрики на одной и той же тестовой выборке или использовать фиксированный `random_state` при `train_test_split` (как сделано выше)."
      ]
    }
  ],
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