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    {
      "cell_type": "markdown",
      "source": [
        "**Лекция16. Настройка базового проекта по Data Science.\n",
        "Линейная регрессия.**"
      ],
      "metadata": {
        "id": "vlRD7Bdh-35X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1) Загрузка и подготовка данных\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.datasets import fetch_california_housing\n",
        "# Загрузка данных\n",
        "data = fetch_california_housing()\n",
        "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
        "df['Price'] = data.target\n",
        "# Просмотр первых строк\n",
        "df.head()"
      ],
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          "height": 206
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        "id": "mtvFv9IkuDiL",
        "outputId": "300e3702-ff36-4fe4-9073-b2c26072d77a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  Price  \n",
              "0    -122.23  4.526  \n",
              "1    -122.22  3.585  \n",
              "2    -122.24  3.521  \n",
              "3    -122.25  3.413  \n",
              "4    -122.25  3.422  "
            ],
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              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
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              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
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              "      <td>2401.0</td>\n",
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              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "      <td>3.585</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
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              "      <td>1.073446</td>\n",
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              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "      <td>3.521</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
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              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452732,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.585557612111637,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4741731394243205,\n        \"min\": 0.8461538461538461,\n        \"max\": 141.9090909090909,\n        \"num_unique_values\": 19392,\n        \"samples\": [\n          6.111269614835948,\n          5.912820512820513,\n          5.7924528301886795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47391085679546435,\n        \"min\": 0.3333333333333333,\n        \"max\": 34.06666666666667,\n        \"num_unique_values\": 14233,\n        \"samples\": [\n          0.9906542056074766,\n          1.112099644128114,\n          1.0398230088495575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.4621217653375,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.386049562213591,\n        \"min\": 0.6923076923076923,\n        \"max\": 1243.3333333333333,\n        \"num_unique_values\": 18841,\n        \"samples\": [\n          2.6939799331103678,\n          3.559375,\n          3.297082228116711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571117,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.003531723502581,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1539561587441483,\n        \"min\": 0.14999,\n        \"max\": 5.00001,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          1.943,\n          3.79,\n          2.301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2) Предобработка данных\n",
        "#• Проверим пропущенные значения и основные статистики:\n",
        "print(df.isnull().sum()) # Проверка пропущенных значений"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j9QC6dFVumgt",
        "outputId": "6c085e2c-6cfd-4138-ed9d-5e1ce1af4a2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MedInc        0\n",
            "HouseAge      0\n",
            "AveRooms      0\n",
            "AveBedrms     0\n",
            "Population    0\n",
            "AveOccup      0\n",
            "Latitude      0\n",
            "Longitude     0\n",
            "Price         0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.describe()) # Основные статистики"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cTFL2sWuvEE",
        "outputId": "fef772c7-0b16-43b2-fee9-ffde24b41384"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
            "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
            "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
            "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
            "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
            "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
            "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
            "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
            "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
            "\n",
            "           AveOccup      Latitude     Longitude         Price  \n",
            "count  20640.000000  20640.000000  20640.000000  20640.000000  \n",
            "mean       3.070655     35.631861   -119.569704      2.068558  \n",
            "std       10.386050      2.135952      2.003532      1.153956  \n",
            "min        0.692308     32.540000   -124.350000      0.149990  \n",
            "25%        2.429741     33.930000   -121.800000      1.196000  \n",
            "50%        2.818116     34.260000   -118.490000      1.797000  \n",
            "75%        3.282261     37.710000   -118.010000      2.647250  \n",
            "max     1243.333333     41.950000   -114.310000      5.000010  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Количество записей в базк данных"
      ],
      "metadata": {
        "id": "t0WOuq0rBEEX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdLgk2UfBKSG",
        "outputId": "86dc590c-9943-4b16-d754-121c0cd7087c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20640 entries, 0 to 20639\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   MedInc      20640 non-null  float64\n",
            " 1   HouseAge    20640 non-null  float64\n",
            " 2   AveRooms    20640 non-null  float64\n",
            " 3   AveBedrms   20640 non-null  float64\n",
            " 4   Population  20640 non-null  float64\n",
            " 5   AveOccup    20640 non-null  float64\n",
            " 6   Latitude    20640 non-null  float64\n",
            " 7   Longitude   20640 non-null  float64\n",
            " 8   Price       20640 non-null  float64\n",
            "dtypes: float64(9)\n",
            "memory usage: 1.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N0jmxiadBUJU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3) Визуализация данных. Анализ взаимосвязи между переменными, то есть исследуется как одна переменная влияет на другую.\n",
        "# • Визуализируем зависимость цены от одной из характеристик:\n",
        "import seaborn as sns\n",
        "#sns.scatterplot(x=df['MedInc'], y=df['Price'])\n",
        "sns.scatterplot(x=df['MedInc'][0:200], y=df['Price'][0:200])\n",
        "plt.xlabel('Медианный доход')\n",
        "plt.ylabel('Цена недвижимости')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Rje4PyH2vEsT",
        "outputId": "d11a355f-a43c-470e-d05f-a18c1dc52fd7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# Лекция 16. Линейная регрессия. Продолжение**\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "QvkydRonxR8W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1) Разделение данных\n",
        "from sklearn.model_selection import train_test_split\n",
        "X = df[['MedInc']]\n",
        "y = df['Price']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "4DLWfD4xydOr"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2) Обучение модели\n",
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "id": "EgAqdX17ylY5",
        "outputId": "cdb3cc05-0947-4932-82bb-6df9cd96238f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3) Оценка модели\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "y_pred = model.predict(X_test)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(f'MSE: {mse}')\n",
        "print(f'R^2: {r2}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQstoKdTy_KS",
        "outputId": "c5f65e72-a44a-4c11-ec69-dfee19e21f1a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 0.7091157771765549\n",
            "R^2: 0.45885918903846656\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4) Визуализация результатов\n",
        "plt.scatter(X_test, y_test, color='blue', label='Реальные  значения')\n",
        "plt.plot(X_test, y_pred, color='green', linewidth=2, label='Линейная регрессия')\n",
        "plt.xlabel('Медианный доход')\n",
        "plt.ylabel('Цена недвижимости')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "cFaheeVIzNOi",
        "outputId": "7d509d6d-e530-4524-a627-23c4375f98be"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# Попробовать самостоятельно выполнить коды из задания  \"Визулизация. **\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_nTj3-iP1WL5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Инициализировать\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "# Подготовить данные\n",
        "x=np.linspace(0, 10*np.pi, 1000)\n",
        "y= np.sin(x)\n",
        "# Отрисовать\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(x, y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "Po3bpoLc1bbi",
        "outputId": "529c231f-5201-43f6-d8fc-c21d50ac8391"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}