colab/Вариант 1/car_price_solution.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "f10cf91e",
      "metadata": {},
      "source": [
        "# Прогнозирование цены автомобиля"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "0757256f",
      "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\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": 19,
      "id": "ad0e2ade",
      "metadata": {},
      "outputs": [],
      "source": [
        "import re\n",
        "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": 20,
      "id": "2180fd89",
      "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>Car ID</th>\n",
              "      <th>Brand</th>\n",
              "      <th>Year</th>\n",
              "      <th>Engine Size</th>\n",
              "      <th>Fuel Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Condition</th>\n",
              "      <th>Price</th>\n",
              "      <th>Model</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>Tesla</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>114832.0</td>\n",
              "      <td>New</td>\n",
              "      <td>26613.92</td>\n",
              "      <td>Model X</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>BMW</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>Electric</td>\n",
              "      <td>Manual</td>\n",
              "      <td>143190.0</td>\n",
              "      <td>Used</td>\n",
              "      <td>14679.61</td>\n",
              "      <td>5 Series</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>Audi</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>Electric</td>\n",
              "      <td>Manual</td>\n",
              "      <td>181601.0</td>\n",
              "      <td>New</td>\n",
              "      <td>44402.61</td>\n",
              "      <td>A4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>Tesla</td>\n",
              "      <td>2011.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>68682.0</td>\n",
              "      <td>New</td>\n",
              "      <td>86374.33</td>\n",
              "      <td>Model Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>223009.0</td>\n",
              "      <td>Like New</td>\n",
              "      <td>73577.10</td>\n",
              "      <td>Mustang</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Car ID  Brand    Year  Engine Size Fuel Type Transmission   Mileage  \\\n",
              "0     1.0  Tesla  2016.0          2.3    Petrol       Manual  114832.0   \n",
              "1     2.0    BMW  2018.0          4.4  Electric       Manual  143190.0   \n",
              "2     3.0   Audi  2013.0          4.5  Electric       Manual  181601.0   \n",
              "3     4.0  Tesla  2011.0          4.1    Diesel    Automatic   68682.0   \n",
              "4     5.0   Ford  2009.0          2.6    Diesel       Manual  223009.0   \n",
              "\n",
              "  Condition     Price     Model  \n",
              "0       New  26613.92   Model X  \n",
              "1      Used  14679.61  5 Series  \n",
              "2       New  44402.61        A4  \n",
              "3       New  86374.33   Model Y  \n",
              "4  Like New  73577.10   Mustang  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 1) Загрузка датасета\n",
        "df = pd.read_csv(\"car_price_prediction_with_missing.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "19d24a44",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.DataFrame'>\n",
            "RangeIndex: 2500 entries, 0 to 2499\n",
            "Data columns (total 10 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Car ID        2250 non-null   float64\n",
            " 1   Brand         2250 non-null   str    \n",
            " 2   Year          2250 non-null   float64\n",
            " 3   Engine Size   2250 non-null   float64\n",
            " 4   Fuel Type     2250 non-null   str    \n",
            " 5   Transmission  2250 non-null   str    \n",
            " 6   Mileage       2250 non-null   float64\n",
            " 7   Condition     2250 non-null   str    \n",
            " 8   Price         2250 non-null   float64\n",
            " 9   Model         2250 non-null   str    \n",
            "dtypes: float64(5), str(5)\n",
            "memory usage: 195.4 KB\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "Car ID          0.1\n",
              "Brand           0.1\n",
              "Year            0.1\n",
              "Engine Size     0.1\n",
              "Fuel Type       0.1\n",
              "Transmission    0.1\n",
              "Mileage         0.1\n",
              "Condition       0.1\n",
              "Price           0.1\n",
              "Model           0.1\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Общая информация и пропуски\n",
        "display(df.info())\n",
        "display(df.isna().mean().sort_values(ascending=False))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c0dffc81",
      "metadata": {},
      "source": [
        "## Очистка и предобработка\n",
        "Исходный набор данных имеет пропуски\n",
        "- Удаляем дубликаты.\n",
        "- Удалить пропуски"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "1ab1ead2",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.DataFrame'>\n",
            "Index: 2250 entries, 0 to 2499\n",
            "Data columns (total 10 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Car ID        2250 non-null   float64\n",
            " 1   Brand         2250 non-null   str    \n",
            " 2   Year          2250 non-null   float64\n",
            " 3   Engine Size   2250 non-null   float64\n",
            " 4   Fuel Type     2250 non-null   str    \n",
            " 5   Transmission  2250 non-null   str    \n",
            " 6   Mileage       2250 non-null   float64\n",
            " 7   Condition     2250 non-null   str    \n",
            " 8   Price         2250 non-null   float64\n",
            " 9   Model         2250 non-null   str    \n",
            "dtypes: float64(5), str(5)\n",
            "memory usage: 193.4 KB\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "Car ID          0.0\n",
              "Brand           0.0\n",
              "Year            0.0\n",
              "Engine Size     0.0\n",
              "Fuel Type       0.0\n",
              "Transmission    0.0\n",
              "Mileage         0.0\n",
              "Condition       0.0\n",
              "Price           0.0\n",
              "Model           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": "d7fdb1a8",
      "metadata": {},
      "source": [
        "## Задание 1. Анализ и визуализация\n",
        "\n",
        "Ниже 3 графика для изучения датасета\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "83c5b8b2",
      "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 цены\n",
        "plt.figure(figsize=(7, 4))\n",
        "sns.histplot(df[\"Price\"], bins=50)\n",
        "plt.title(\"Распределение цены\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4cf27c93",
      "metadata": {},
      "source": [
        "Видно, что распределение цен на автомобили примерно равномерное, близкое к случайному"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "926cf42e",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# График 2: связь цены и года выпуска\n",
        "plt.figure(figsize=(7, 4))\n",
        "sns.scatterplot(data=df, x=\"Year\", y=\"Price\", alpha=0.6)\n",
        "plt.title(\"Цена vs Год выпуска\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "69a27e4a",
      "metadata": {},
      "source": [
        "Цена на автомобиль совсем не зависит от года выпуска"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "fb59d4c3",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# График 3: цена в зависимости от состояния авто\n",
        "plt.figure(figsize=(7, 4))\n",
        "sns.boxplot(data=df, x=\"Condition\", y=\"Price\")\n",
        "plt.title(\"Цена vs Состояние\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4948afde",
      "metadata": {},
      "source": [
        "От состояния цена также не зависит"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ab37182d",
      "metadata": {},
      "source": [
        "### Краткие выводы по графикам\n",
        "- В данных есть пропуски.\n",
        "- Данные не коррелируют между собой, они выглядят случайными"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "daa3521f",
      "metadata": {},
      "source": [
        "## Задание 2. Модели прогнозирования"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "4dade46e",
      "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>23677.424433</td>\n",
              "      <td>27261.296466</td>\n",
              "      <td>0.001463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LinearRegression</td>\n",
              "      <td>test</td>\n",
              "      <td>23386.369896</td>\n",
              "      <td>26995.898047</td>\n",
              "      <td>-0.000709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>train</td>\n",
              "      <td>8877.283827</td>\n",
              "      <td>10364.788027</td>\n",
              "      <td>0.855658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>test</td>\n",
              "      <td>23676.962601</td>\n",
              "      <td>27982.335432</td>\n",
              "      <td>-0.075178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GradientBoosting</td>\n",
              "      <td>train</td>\n",
              "      <td>20860.335930</td>\n",
              "      <td>24310.182306</td>\n",
              "      <td>0.205951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>GradientBoosting</td>\n",
              "      <td>test</td>\n",
              "      <td>23625.922747</td>\n",
              "      <td>27750.650439</td>\n",
              "      <td>-0.057447</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              model  split           MAE          RMSE        R2\n",
              "0  LinearRegression  train  23677.424433  27261.296466  0.001463\n",
              "1  LinearRegression   test  23386.369896  26995.898047 -0.000709\n",
              "2      RandomForest  train   8877.283827  10364.788027  0.855658\n",
              "3      RandomForest   test  23676.962601  27982.335432 -0.075178\n",
              "4  GradientBoosting  train  20860.335930  24310.182306  0.205951\n",
              "5  GradientBoosting   test  23625.922747  27750.650439 -0.057447"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Удаляем строки без цены\n",
        "df_model = df.dropna(subset=[\"Price\"]).copy()\n",
        "\n",
        "X = df_model.drop(columns=[\"Price\"])\n",
        "y = df_model[\"Price\"]\n",
        "\n",
        "cat_cols = X.select_dtypes(include=[\"object\", \"string\"]).columns\n",
        "num_cols = X.select_dtypes(exclude=[\"object\", \"string\"]).columns\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",
        "    mask = y.notna()\n",
        "    X = X.loc[mask]\n",
        "    y = y.loc[mask]\n",
        "\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",
        "\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",
        "\n",
        "    return pd.DataFrame(rows)\n",
        "\n",
        "results_before = evaluate_models(X, y, models)\n",
        "results_before"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "2d537e27",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    .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>23625.922747</td>\n",
              "      <td>20860.335930</td>\n",
              "      <td>-0.057447</td>\n",
              "      <td>0.205951</td>\n",
              "      <td>27750.650439</td>\n",
              "      <td>24310.182306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>23386.369896</td>\n",
              "      <td>23677.424433</td>\n",
              "      <td>-0.000709</td>\n",
              "      <td>0.001463</td>\n",
              "      <td>26995.898047</td>\n",
              "      <td>27261.296466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForest</th>\n",
              "      <td>23676.962601</td>\n",
              "      <td>8877.283827</td>\n",
              "      <td>-0.075178</td>\n",
              "      <td>0.855658</td>\n",
              "      <td>27982.335432</td>\n",
              "      <td>10364.788027</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           MAE                      R2            \\\n",
              "split                     test         train      test     train   \n",
              "model                                                              \n",
              "GradientBoosting  23625.922747  20860.335930 -0.057447  0.205951   \n",
              "LinearRegression  23386.369896  23677.424433 -0.000709  0.001463   \n",
              "RandomForest      23676.962601   8877.283827 -0.075178  0.855658   \n",
              "\n",
              "                          RMSE                \n",
              "split                     test         train  \n",
              "model                                         \n",
              "GradientBoosting  27750.650439  24310.182306  \n",
              "LinearRegression  26995.898047  27261.296466  \n",
              "RandomForest      27982.335432  10364.788027  "
            ]
          },
          "execution_count": 27,
          "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": 28,
      "id": "92be75ab",
      "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",
        "\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",
        "\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": "0fd28587",
      "metadata": {},
      "source": [
        "### Почему выбраны эти метрики\n",
        "\n",
        "- **MAE** показывает среднюю абсолютную ошибку в тех же единицах, что и цена.\n",
        "- **RMSE** сильнее штрафует большие ошибки.\n",
        "- **R2** — доля объяснённой дисперсии: 0 означает, что модель не лучше предсказания средним, 1 — полное совпадение с целевой переменной."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "39b556c4",
      "metadata": {},
      "source": [
        "## Задание 3. Добавляем `cars_new.csv` и сравниваем качество"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a4377b3",
      "metadata": {},
      "source": [
        "cars_new.csv имеет другой формат, все данные в первом столбце и переведены на другой язык. Исправим это для объединения:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "a9f78135",
      "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>Car ID</th>\n",
              "      <th>Brand</th>\n",
              "      <th>Year</th>\n",
              "      <th>Engine Size</th>\n",
              "      <th>Fuel Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Condition</th>\n",
              "      <th>Price</th>\n",
              "      <th>Model</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Lada</td>\n",
              "      <td>2000</td>\n",
              "      <td>1.5 л</td>\n",
              "      <td>Бензин</td>\n",
              "      <td>Механика</td>\n",
              "      <td>150000</td>\n",
              "      <td>Отличное</td>\n",
              "      <td>70000</td>\n",
              "      <td>2110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Lada</td>\n",
              "      <td>2001</td>\n",
              "      <td>1.6 л</td>\n",
              "      <td>Бензин</td>\n",
              "      <td>Механика</td>\n",
              "      <td>180000</td>\n",
              "      <td>Хорошее</td>\n",
              "      <td>80000</td>\n",
              "      <td>2107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Lada</td>\n",
              "      <td>2002</td>\n",
              "      <td>1.5 л</td>\n",
              "      <td>Бензин</td>\n",
              "      <td>Механика</td>\n",
              "      <td>120000</td>\n",
              "      <td>Отличное</td>\n",
              "      <td>75000</td>\n",
              "      <td>2115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Lada</td>\n",
              "      <td>2008</td>\n",
              "      <td>1.6 л</td>\n",
              "      <td>Бензин</td>\n",
              "      <td>Механика/Автомат</td>\n",
              "      <td>130000</td>\n",
              "      <td>Хорошее</td>\n",
              "      <td>180000</td>\n",
              "      <td>Kalina</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Lada</td>\n",
              "      <td>2009</td>\n",
              "      <td>98 л.с.,1.6 л</td>\n",
              "      <td>Бензин</td>\n",
              "      <td>Механика/Автомат</td>\n",
              "      <td>100000</td>\n",
              "      <td>Хорошее</td>\n",
              "      <td>200000</td>\n",
              "      <td>Priora</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Car ID Brand  Year    Engine Size Fuel Type      Transmission Mileage  \\\n",
              "0      1  Lada  2000          1.5 л    Бензин          Механика  150000   \n",
              "1      2  Lada  2001          1.6 л    Бензин          Механика  180000   \n",
              "2      3  Lada  2002          1.5 л    Бензин          Механика  120000   \n",
              "3      4  Lada  2008          1.6 л    Бензин  Механика/Автомат  130000   \n",
              "4      5  Lada  2009  98 л.с.,1.6 л    Бензин  Механика/Автомат  100000   \n",
              "\n",
              "  Condition   Price   Model  \n",
              "0  Отличное   70000    2110  \n",
              "1   Хорошее   80000    2107  \n",
              "2  Отличное   75000    2115  \n",
              "3   Хорошее  180000  Kalina  \n",
              "4   Хорошее  200000  Priora  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def parse_cars_new(path):\n",
        "    raw = pd.read_csv(path, header=None)\n",
        "    lines = raw[0].astype(str).str.strip('\"')\n",
        "    rows = []\n",
        "    for line in lines:\n",
        "        parts = [p.strip() for p in line.split(\",\")]\n",
        "        if not parts or parts[0] == \"Car ID\":\n",
        "            continue\n",
        "        if len(parts) < 10:\n",
        "            continue\n",
        "        # Если внутри Engine Size есть запятая, будет больше 10 частей\n",
        "        head = parts[:3]\n",
        "        tail = parts[-6:]\n",
        "        engine = \",\".join(parts[3:-6]).strip()\n",
        "        row = head + [engine] + tail\n",
        "        if len(row) == 10:\n",
        "            rows.append(row)\n",
        "\n",
        "    cols = [\"Car ID\", \"Brand\", \"Year\", \"Engine Size\", \"Fuel Type\", \"Transmission\", \"Mileage\", \"Condition\", \"Price\", \"Model\"]\n",
        "    df_new = pd.DataFrame(rows, columns=cols)\n",
        "    return df_new\n",
        "\n",
        "df_new = parse_cars_new(\"cars_new.csv\")\n",
        "df_new.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "ca74b22f",
      "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>Car ID</th>\n",
              "      <th>Brand</th>\n",
              "      <th>Year</th>\n",
              "      <th>Engine Size</th>\n",
              "      <th>Fuel Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Condition</th>\n",
              "      <th>Price</th>\n",
              "      <th>Model</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Lada</td>\n",
              "      <td>2000</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>150000</td>\n",
              "      <td>New</td>\n",
              "      <td>70000</td>\n",
              "      <td>2110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Lada</td>\n",
              "      <td>2001</td>\n",
              "      <td>1.6</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>180000</td>\n",
              "      <td>Used</td>\n",
              "      <td>80000</td>\n",
              "      <td>2107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Lada</td>\n",
              "      <td>2002</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>120000</td>\n",
              "      <td>New</td>\n",
              "      <td>75000</td>\n",
              "      <td>2115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Lada</td>\n",
              "      <td>2008</td>\n",
              "      <td>1.6</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>130000</td>\n",
              "      <td>Used</td>\n",
              "      <td>180000</td>\n",
              "      <td>Kalina</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Lada</td>\n",
              "      <td>2009</td>\n",
              "      <td>1.6</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>100000</td>\n",
              "      <td>Used</td>\n",
              "      <td>200000</td>\n",
              "      <td>Priora</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Car ID Brand  Year  Engine Size Fuel Type Transmission  Mileage Condition  \\\n",
              "0      1  Lada  2000          1.5    Petrol       Manual   150000       New   \n",
              "1      2  Lada  2001          1.6    Petrol       Manual   180000      Used   \n",
              "2      3  Lada  2002          1.5    Petrol       Manual   120000       New   \n",
              "3      4  Lada  2008          1.6    Petrol    Automatic   130000      Used   \n",
              "4      5  Lada  2009          1.6    Petrol    Automatic   100000      Used   \n",
              "\n",
              "    Price   Model  \n",
              "0   70000    2110  \n",
              "1   80000    2107  \n",
              "2   75000    2115  \n",
              "3  180000  Kalina  \n",
              "4  200000  Priora  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Приводим к единому формату\n",
        "fuel_map = {\n",
        "    \"Бензин\": \"Petrol\",\n",
        "    \"Дизель\": \"Diesel\",\n",
        "    \"Электро\": \"Electric\",\n",
        "    \"Гибрид\": \"Hybrid\"\n",
        "}\n",
        "trans_map = {\n",
        "    \"Механика\": \"Manual\",\n",
        "    \"Автомат\": \"Automatic\",\n",
        "    \"Механика/Автомат\": \"Automatic\"\n",
        "}\n",
        "cond_map = {\n",
        "    \"Отличное\": \"New\",\n",
        "    \"Хорошее\": \"Used\"\n",
        "}\n",
        "\n",
        "def extract_engine_size(x):\n",
        "    if pd.isna(x):\n",
        "        return np.nan\n",
        "    nums = re.findall(r\"\\d+(?:[.,]\\d+)?\", str(x))\n",
        "    if not nums:\n",
        "        return np.nan\n",
        "    return float(nums[-1].replace(\",\", \".\"))\n",
        "\n",
        "df_new[\"Fuel Type\"] = df_new[\"Fuel Type\"].map(fuel_map)\n",
        "df_new[\"Transmission\"] = df_new[\"Transmission\"].map(trans_map)\n",
        "df_new[\"Condition\"] = df_new[\"Condition\"].map(cond_map)\n",
        "\n",
        "df_new[\"Engine Size\"] = df_new[\"Engine Size\"].apply(extract_engine_size)\n",
        "df_new[\"Year\"] = pd.to_numeric(df_new[\"Year\"], errors=\"coerce\")\n",
        "df_new[\"Mileage\"] = pd.to_numeric(df_new[\"Mileage\"], errors=\"coerce\")\n",
        "df_new[\"Price\"] = pd.to_numeric(df_new[\"Price\"], errors=\"coerce\")\n",
        "\n",
        "df_new.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "c807c43d",
      "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>24385.641471</td>\n",
              "      <td>32867.632333</td>\n",
              "      <td>0.004216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LinearRegression</td>\n",
              "      <td>test</td>\n",
              "      <td>24857.121340</td>\n",
              "      <td>37804.530574</td>\n",
              "      <td>-0.007647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>train</td>\n",
              "      <td>9174.571497</td>\n",
              "      <td>12278.185487</td>\n",
              "      <td>0.861038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>test</td>\n",
              "      <td>24465.982962</td>\n",
              "      <td>34010.448955</td>\n",
              "      <td>0.184460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GradientBoosting</td>\n",
              "      <td>train</td>\n",
              "      <td>21391.653978</td>\n",
              "      <td>24988.338946</td>\n",
              "      <td>0.424423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>GradientBoosting</td>\n",
              "      <td>test</td>\n",
              "      <td>23904.569889</td>\n",
              "      <td>29600.017743</td>\n",
              "      <td>0.382262</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              model  split           MAE          RMSE        R2\n",
              "0  LinearRegression  train  24385.641471  32867.632333  0.004216\n",
              "1  LinearRegression   test  24857.121340  37804.530574 -0.007647\n",
              "2      RandomForest  train   9174.571497  12278.185487  0.861038\n",
              "3      RandomForest   test  24465.982962  34010.448955  0.184460\n",
              "4  GradientBoosting  train  21391.653978  24988.338946  0.424423\n",
              "5  GradientBoosting   test  23904.569889  29600.017743  0.382262"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Объединяем и повторяем оценку\n",
        "df_all = pd.concat([df, df_new], ignore_index=True)\n",
        "\n",
        "# Удаляем строки без цены\n",
        "df_all = df_all.dropna(subset=[\"Price\"]).copy()\n",
        "\n",
        "X_all = df_all.drop(columns=[\"Price\"])\n",
        "y_all = df_all[\"Price\"]\n",
        "\n",
        "results_after = evaluate_models(X_all, y_all, models)\n",
        "results_after"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "4a4a7c79",
      "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>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>0</th>\n",
              "      <td>LinearRegression</td>\n",
              "      <td>23386.369896</td>\n",
              "      <td>24857.121340</td>\n",
              "      <td>26995.898047</td>\n",
              "      <td>37804.530574</td>\n",
              "      <td>-0.000709</td>\n",
              "      <td>-0.007647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>23676.962601</td>\n",
              "      <td>24465.982962</td>\n",
              "      <td>27982.335432</td>\n",
              "      <td>34010.448955</td>\n",
              "      <td>-0.075178</td>\n",
              "      <td>0.184460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GradientBoosting</td>\n",
              "      <td>23625.922747</td>\n",
              "      <td>23904.569889</td>\n",
              "      <td>27750.650439</td>\n",
              "      <td>29600.017743</td>\n",
              "      <td>-0.057447</td>\n",
              "      <td>0.382262</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              model    MAE_before     MAE_after   RMSE_before    RMSE_after  \\\n",
              "0  LinearRegression  23386.369896  24857.121340  26995.898047  37804.530574   \n",
              "1      RandomForest  23676.962601  24465.982962  27982.335432  34010.448955   \n",
              "2  GradientBoosting  23625.922747  23904.569889  27750.650439  29600.017743   \n",
              "\n",
              "   R2_before  R2_after  \n",
              "0  -0.000709 -0.007647  \n",
              "1  -0.075178  0.184460  \n",
              "2  -0.057447  0.382262  "
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Сравнение метрик на тесте ДО и ПОСЛЕ добавления новых данных\n",
        "test_before = results_before[results_before[\"split\"] == \"test\"].copy()\n",
        "test_after = results_after[results_after[\"split\"] == \"test\"].copy()\n",
        "\n",
        "compare = test_before.merge(test_after, on=\"model\", suffixes=(\"_before\", \"_after\"))\n",
        "compare[[\"model\", \"MAE_before\", \"MAE_after\", \"RMSE_before\", \"RMSE_after\", \"R2_before\", \"R2_after\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "0821f421",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Визуализация влияния новых данных (R2)\n",
        "plt.figure(figsize=(7, 4))\n",
        "compare_melt = compare.melt(id_vars=\"model\", value_vars=[\"R2_before\", \"R2_after\"], var_name=\"stage\", value_name=\"R2\")\n",
        "sns.barplot(data=compare_melt, x=\"model\", y=\"R2\", hue=\"stage\")\n",
        "plt.title(\"R2 на тесте: до и после добавления новых данных\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7905ea14",
      "metadata": {},
      "source": [
        "### Итоговый анализ\n",
        "\n",
        "- Новые данные добавлены и приведены к единому формату.\n",
        "- Для корректного сравнения смотрим метрики `до` и `после`.\n",
        "\n",
        "Качество предсказания моделей после объединения данных заметно выросло, особенно у градиентного бустинга. Причина — в дополнительных данных (cars_new.csv) признаки связаны с ценой (год, состояние, пробег), что позволяет моделям лучше обобщать. Ниже — анализ корреляций в новых данных."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "9d37afe8",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Анализ и визуализация cars_new.csv (как в Задании 1)\n",
        "df_plot = df_new.dropna(subset=[\"Price\"]).copy()\n",
        "\n",
        "# 1. Распределение цены\n",
        "plt.figure(figsize=(7, 4))\n",
        "sns.histplot(df_plot[\"Price\"], bins=50)\n",
        "plt.title(\"Распределение цены (cars_new.csv)\")\n",
        "plt.show()\n",
        "\n",
        "# 2. Цена vs Год выпуска\n",
        "plt.figure(figsize=(7, 4))\n",
        "sns.scatterplot(data=df_plot, x=\"Year\", y=\"Price\", alpha=0.6)\n",
        "plt.title(\"Цена vs Год выпуска (cars_new.csv)\")\n",
        "plt.show()\n",
        "\n",
        "# 3. Цена vs Состояние\n",
        "plt.figure(figsize=(7, 4))\n",
        "sns.boxplot(data=df_plot, x=\"Condition\", y=\"Price\")\n",
        "plt.title(\"Цена vs Состояние (cars_new.csv)\")\n",
        "plt.show()\n",
        "\n",
        "# 4. Тепловая карта корреляций\n",
        "numeric_cols = df_plot.select_dtypes(include=[np.number]).columns\n",
        "if len(numeric_cols) > 1:\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    corr = df_plot[numeric_cols].corr()\n",
        "    sns.heatmap(corr, annot=True, fmt=\".2f\", cmap=\"coolwarm\", center=0, square=True)\n",
        "    plt.title(\"Тепловая карта корреляций (cars_new.csv)\")\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e6e0089c",
      "metadata": {},
      "source": [
        "## Анализ новых данных\n",
        "\n",
        "В дополнительных данных видно сильную корреляцию состояния автомобиля и цены; года выпуска и цены; что приводит к более точным прогнозам"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": ".venv (3.14.2)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.14.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}