EDA_и_начало_спецификации_v3.ipynb

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      "source": [
        "#Импортируем всякое разное (A)\n",
        "(в этом разделе импортируем всякие библиотечки)"
      ],
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from patsy import dmatrices\n",
        "import warnings\n",
        "\n",
        "from sklearn import preprocessing\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Подгружаем данные (B)\n",
        "(в этом разделе импортируем данные, немного корректируем исходную табличку и понимаем, с чем имеем дело)"
      ],
      "metadata": {
        "id": "jLfOE38_rW5b"
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      "source": [
        "df = pd.read_csv('/content/Drom_data.csv')\n",
        "df.head()"
      ],
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          "height": 446
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        "outputId": "4b29d31b-fdba-4101-ce88-2b1f08373bea"
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      "execution_count": 6,
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              "   Unnamed: 0     Цена  Мощность   Пробег  Объём двигателя   Год  \\\n",
              "0           0  1350000     122.0  86684.0              1.8  2018   \n",
              "1           1  1350000     122.0  77400.0              1.8  2019   \n",
              "2           2   950000      90.0   9200.0              1.6  2023   \n",
              "3           4  1165000     122.0  84594.0              1.8  2019   \n",
              "4           7  1145000     122.0  58195.0              1.8  2019   \n",
              "\n",
              "   Коробка передач_вариатор  Коробка передач_механика  Коробка передач_робот  \\\n",
              "0                         0                         1                      0   \n",
              "1                         0                         0                      1   \n",
              "2                         0                         1                      0   \n",
              "3                         0                         0                      1   \n",
              "4                         0                         1                      0   \n",
              "\n",
              "   Привод_передний  ...  0_Характеристики совпадают с ПТС  \\\n",
              "0                1  ...                                 1   \n",
              "1                1  ...                                 1   \n",
              "2                1  ...                                 1   \n",
              "3                1  ...                                 0   \n",
              "4                1  ...                                 1   \n",
              "\n",
              "   0_Цвет не совпадает с ПТС  1_3-6 записей о регистрации  \\\n",
              "0                          0                            0   \n",
              "1                          0                            0   \n",
              "2                          0                            0   \n",
              "3                          0                            0   \n",
              "4                          0                            0   \n",
              "\n",
              "   1_более 6 записей о регистрации  2_Стоит на учете в ГИБДД  \\\n",
              "0                                0                         1   \n",
              "1                                0                         1   \n",
              "2                                0                         1   \n",
              "3                                0                         0   \n",
              "4                                0                         0   \n",
              "\n",
              "   3_Не был во владении у юр. лица  4_Последний период владения до 1 года  \\\n",
              "0                                1                                      0   \n",
              "1                                1                                      0   \n",
              "2                                1                                      0   \n",
              "3                                0                                      0   \n",
              "4                                1                                      0   \n",
              "\n",
              "   4_Последний период владения до 6 лет  \\\n",
              "0                                     1   \n",
              "1                                     1   \n",
              "2                                     0   \n",
              "3                                     1   \n",
              "4                                     1   \n",
              "\n",
              "                                              Ссылка   Название машины  \n",
              "0  https://auto.drom.ru/moscow/lada/vesta_cross/5...  Лада Веста Кросс  \n",
              "1  https://auto.drom.ru/moscow/lada/vesta/2447192...        Лада Веста  \n",
              "2  https://auto.drom.ru/moscow/lada/granta/290328...       Лада Гранта  \n",
              "3  https://auto.drom.ru/moscow/lada/vesta_cross/9...  Лада Веста Кросс  \n",
              "4  https://auto.drom.ru/moscow/lada/vesta/6242073...        Лада Веста  \n",
              "\n",
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          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#в первом столбике стоят индексы ранней версии таблицы, сейчас индексация новая, поэтому дропнем его\n",
        "#код нельзя перепрогонять, иначе столбцы будут удаляться и дальше\n",
        "del df[df.columns[0]]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "CbYk-pSbtt9v",
        "outputId": "d97e7896-69eb-428e-acae-301eb6e68e0a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Цена  Мощность   Пробег  Объём двигателя   Год  \\\n",
              "0  1350000     122.0  86684.0              1.8  2018   \n",
              "1  1350000     122.0  77400.0              1.8  2019   \n",
              "2   950000      90.0   9200.0              1.6  2023   \n",
              "3  1165000     122.0  84594.0              1.8  2019   \n",
              "4  1145000     122.0  58195.0              1.8  2019   \n",
              "\n",
              "   Коробка передач_вариатор  Коробка передач_механика  Коробка передач_робот  \\\n",
              "0                         0                         1                      0   \n",
              "1                         0                         0                      1   \n",
              "2                         0                         1                      0   \n",
              "3                         0                         0                      1   \n",
              "4                         0                         1                      0   \n",
              "\n",
              "   Привод_передний  Привод_задний  ...  0_Характеристики совпадают с ПТС  \\\n",
              "0                1              0  ...                                 1   \n",
              "1                1              0  ...                                 1   \n",
              "2                1              0  ...                                 1   \n",
              "3                1              0  ...                                 0   \n",
              "4                1              0  ...                                 1   \n",
              "\n",
              "   0_Цвет не совпадает с ПТС  1_3-6 записей о регистрации  \\\n",
              "0                          0                            0   \n",
              "1                          0                            0   \n",
              "2                          0                            0   \n",
              "3                          0                            0   \n",
              "4                          0                            0   \n",
              "\n",
              "   1_более 6 записей о регистрации  2_Стоит на учете в ГИБДД  \\\n",
              "0                                0                         1   \n",
              "1                                0                         1   \n",
              "2                                0                         1   \n",
              "3                                0                         0   \n",
              "4                                0                         0   \n",
              "\n",
              "   3_Не был во владении у юр. лица  4_Последний период владения до 1 года  \\\n",
              "0                                1                                      0   \n",
              "1                                1                                      0   \n",
              "2                                1                                      0   \n",
              "3                                0                                      0   \n",
              "4                                1                                      0   \n",
              "\n",
              "   4_Последний период владения до 6 лет  \\\n",
              "0                                     1   \n",
              "1                                     1   \n",
              "2                                     0   \n",
              "3                                     1   \n",
              "4                                     1   \n",
              "\n",
              "                                              Ссылка   Название машины  \n",
              "0  https://auto.drom.ru/moscow/lada/vesta_cross/5...  Лада Веста Кросс  \n",
              "1  https://auto.drom.ru/moscow/lada/vesta/2447192...        Лада Веста  \n",
              "2  https://auto.drom.ru/moscow/lada/granta/290328...       Лада Гранта  \n",
              "3  https://auto.drom.ru/moscow/lada/vesta_cross/9...  Лада Веста Кросс  \n",
              "4  https://auto.drom.ru/moscow/lada/vesta/6242073...        Лада Веста  \n",
              "\n",
              "[5 rows x 43 columns]"
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              "      <td>2018</td>\n",
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              "      <td>https://auto.drom.ru/moscow/lada/vesta/2447192...</td>\n",
              "      <td>Лада Веста</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>950000</td>\n",
              "      <td>90.0</td>\n",
              "      <td>9200.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2023</td>\n",
              "      <td>0</td>\n",
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              "      <td>https://auto.drom.ru/moscow/lada/granta/290328...</td>\n",
              "      <td>Лада Гранта</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1165000</td>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 43 columns</p>\n",
              "</div>\n",
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              "\n",
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              "        document.querySelector('#df-2de77f05-75b7-49cd-9bb5-90ddc450cb1f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2de77f05-75b7-49cd-9bb5-90ddc450cb1f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    80% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d1ca871f-6eb1-4f43-9e51-f3eb1c1c9d16 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#теперь давайте поймем, как выглядят признаки каждого объекта\n",
        "df.loc[0].tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGSvH2ztzham",
        "outputId": "567693b8-9281-48ab-8d78-c711f7f61363"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 0,\n",
              " 1,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 1,\n",
              " 1,\n",
              " 0,\n",
              " 1,\n",
              " 'https://auto.drom.ru/moscow/lada/vesta_cross/553824953.html',\n",
              " 'Лада Веста Кросс']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Заметим, что это несколько количественных переменных (3 штуки и цена), год покупки автомобиля, ссылка на объявление, название автомобиля и много бинарных переменных"
      ],
      "metadata": {
        "id": "XYw1EY3wiNce"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Первичный анализ данных (C)\n",
        "(в этом разделе уже внимательнее смотрим на данные и делаем предварительные преобразования для того, чтобы в дальнейшем можно было строить регрессии и всякое разное)"
      ],
      "metadata": {
        "id": "4TUxj1_0x6N_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#сначала посмотрим, какие масштабы данных для понимания, с чем будем работать\n",
        "df.describe()"
      ],
      "metadata": {
        "id": "DMNlL6jjxk32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "8c562be9-6201-4e95-e530-925afd6bd169"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Цена    Мощность         Пробег  Объём двигателя          Год  \\\n",
              "count  6.410000e+02  641.000000     641.000000       641.000000   641.000000   \n",
              "mean   8.269794e+05   97.541342  106336.375975         1.613417  2016.531981   \n",
              "std    3.958286e+05   12.755217   79313.194940         0.069424     6.061352   \n",
              "min    6.000000e+04   59.000000      69.000000         1.200000  1978.000000   \n",
              "25%    5.250000e+05   87.000000   46749.000000         1.600000  2014.000000   \n",
              "50%    8.750000e+05   98.000000   89589.000000         1.600000  2018.000000   \n",
              "75%    1.115000e+06  106.000000  152000.000000         1.600000  2021.000000   \n",
              "max    2.150000e+06  122.000000  400000.000000         1.800000  2024.000000   \n",
              "\n",
              "       Коробка передач_вариатор  Коробка передач_механика  \\\n",
              "count                641.000000                641.000000   \n",
              "mean                   0.068643                  0.825273   \n",
              "std                    0.253043                  0.380030   \n",
              "min                    0.000000                  0.000000   \n",
              "25%                    0.000000                  1.000000   \n",
              "50%                    0.000000                  1.000000   \n",
              "75%                    0.000000                  1.000000   \n",
              "max                    1.000000                  1.000000   \n",
              "\n",
              "       Коробка передач_робот  Привод_передний  Привод_задний  ...  \\\n",
              "count             641.000000       641.000000      641.00000  ...   \n",
              "mean                0.068643         0.934477        0.00624  ...   \n",
              "std                 0.253043         0.247639        0.07881  ...   \n",
              "min                 0.000000         0.000000        0.00000  ...   \n",
              "25%                 0.000000         1.000000        0.00000  ...   \n",
              "50%                 0.000000         1.000000        0.00000  ...   \n",
              "75%                 0.000000         1.000000        0.00000  ...   \n",
              "max                 1.000000         1.000000        1.00000  ...   \n",
              "\n",
              "       0_Модель не совпадает с ПТС  0_Мощность двигателя не совпадает с ПТС  \\\n",
              "count                   641.000000                               641.000000   \n",
              "mean                      0.012480                                 0.004680   \n",
              "std                       0.111104                                 0.068305   \n",
              "min                       0.000000                                 0.000000   \n",
              "25%                       0.000000                                 0.000000   \n",
              "50%                       0.000000                                 0.000000   \n",
              "75%                       0.000000                                 0.000000   \n",
              "max                       1.000000                                 1.000000   \n",
              "\n",
              "       0_Характеристики совпадают с ПТС  0_Цвет не совпадает с ПТС  \\\n",
              "count                        641.000000                 641.000000   \n",
              "mean                           0.942278                   0.039002   \n",
              "std                            0.233400                   0.193750   \n",
              "min                            0.000000                   0.000000   \n",
              "25%                            1.000000                   0.000000   \n",
              "50%                            1.000000                   0.000000   \n",
              "75%                            1.000000                   0.000000   \n",
              "max                            1.000000                   1.000000   \n",
              "\n",
              "       1_3-6 записей о регистрации  1_более 6 записей о регистрации  \\\n",
              "count                   641.000000                       641.000000   \n",
              "mean                      0.224649                         0.067083   \n",
              "std                       0.417677                         0.250361   \n",
              "min                       0.000000                         0.000000   \n",
              "25%                       0.000000                         0.000000   \n",
              "50%                       0.000000                         0.000000   \n",
              "75%                       0.000000                         0.000000   \n",
              "max                       1.000000                         1.000000   \n",
              "\n",
              "       2_Стоит на учете в ГИБДД  3_Не был во владении у юр. лица  \\\n",
              "count                641.000000                       641.000000   \n",
              "mean                   0.716069                         0.861154   \n",
              "std                    0.451256                         0.346055   \n",
              "min                    0.000000                         0.000000   \n",
              "25%                    0.000000                         1.000000   \n",
              "50%                    1.000000                         1.000000   \n",
              "75%                    1.000000                         1.000000   \n",
              "max                    1.000000                         1.000000   \n",
              "\n",
              "       4_Последний период владения до 1 года  \\\n",
              "count                             641.000000   \n",
              "mean                                0.193448   \n",
              "std                                 0.395309   \n",
              "min                                 0.000000   \n",
              "25%                                 0.000000   \n",
              "50%                                 0.000000   \n",
              "75%                                 0.000000   \n",
              "max                                 1.000000   \n",
              "\n",
              "       4_Последний период владения до 6 лет  \n",
              "count                            641.000000  \n",
              "mean                               0.556942  \n",
              "std                                0.497135  \n",
              "min                                0.000000  \n",
              "25%                                0.000000  \n",
              "50%                                1.000000  \n",
              "75%                                1.000000  \n",
              "max                                1.000000  \n",
              "\n",
              "[8 rows x 41 columns]"
            ],
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              "\n",
              "  <div id=\"df-2beb5a55-5078-4a20-8f77-10f0e57a7290\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Цена</th>\n",
              "      <th>Мощность</th>\n",
              "      <th>Пробег</th>\n",
              "      <th>Объём двигателя</th>\n",
              "      <th>Год</th>\n",
              "      <th>Коробка передач_вариатор</th>\n",
              "      <th>Коробка передач_механика</th>\n",
              "      <th>Коробка передач_робот</th>\n",
              "      <th>Привод_передний</th>\n",
              "      <th>Привод_задний</th>\n",
              "      <th>...</th>\n",
              "      <th>0_Модель не совпадает с ПТС</th>\n",
              "      <th>0_Мощность двигателя не совпадает с ПТС</th>\n",
              "      <th>0_Характеристики совпадают с ПТС</th>\n",
              "      <th>0_Цвет не совпадает с ПТС</th>\n",
              "      <th>1_3-6 записей о регистрации</th>\n",
              "      <th>1_более 6 записей о регистрации</th>\n",
              "      <th>2_Стоит на учете в ГИБДД</th>\n",
              "      <th>3_Не был во владении у юр. лица</th>\n",
              "      <th>4_Последний период владения до 1 года</th>\n",
              "      <th>4_Последний период владения до 6 лет</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.410000e+02</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.269794e+05</td>\n",
              "      <td>97.541342</td>\n",
              "      <td>106336.375975</td>\n",
              "      <td>1.613417</td>\n",
              "      <td>2016.531981</td>\n",
              "      <td>0.068643</td>\n",
              "      <td>0.825273</td>\n",
              "      <td>0.068643</td>\n",
              "      <td>0.934477</td>\n",
              "      <td>0.00624</td>\n",
              "      <td>...</td>\n",
              "      <td>0.012480</td>\n",
              "      <td>0.004680</td>\n",
              "      <td>0.942278</td>\n",
              "      <td>0.039002</td>\n",
              "      <td>0.224649</td>\n",
              "      <td>0.067083</td>\n",
              "      <td>0.716069</td>\n",
              "      <td>0.861154</td>\n",
              "      <td>0.193448</td>\n",
              "      <td>0.556942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.958286e+05</td>\n",
              "      <td>12.755217</td>\n",
              "      <td>79313.194940</td>\n",
              "      <td>0.069424</td>\n",
              "      <td>6.061352</td>\n",
              "      <td>0.253043</td>\n",
              "      <td>0.380030</td>\n",
              "      <td>0.253043</td>\n",
              "      <td>0.247639</td>\n",
              "      <td>0.07881</td>\n",
              "      <td>...</td>\n",
              "      <td>0.111104</td>\n",
              "      <td>0.068305</td>\n",
              "      <td>0.233400</td>\n",
              "      <td>0.193750</td>\n",
              "      <td>0.417677</td>\n",
              "      <td>0.250361</td>\n",
              "      <td>0.451256</td>\n",
              "      <td>0.346055</td>\n",
              "      <td>0.395309</td>\n",
              "      <td>0.497135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.000000e+04</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>1978.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.250000e+05</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>46749.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2014.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.750000e+05</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>89589.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.115000e+06</td>\n",
              "      <td>106.000000</td>\n",
              "      <td>152000.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.150000e+06</td>\n",
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              "      <td>400000.000000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>2024.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 41 columns</p>\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3454ed8f-622b-42f3-9b2e-327bc5d53a63 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#теперь посмотрим, какие признаки есть у автомобилей\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LRV1equH4AG1",
        "outputId": "1dfaf6d9-e48d-4dee-eafe-06900bbd891c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Цена', 'Мощность', 'Пробег', 'Объём двигателя', 'Год',\n",
              "       'Коробка передач_вариатор', 'Коробка передач_механика',\n",
              "       'Коробка передач_робот', 'Привод_передний', 'Привод_задний',\n",
              "       'Тип кузова_джип/suv 5 дв.', 'Тип кузова_лифтбек', 'Тип кузова_минивэн',\n",
              "       'Тип кузова_седан', 'Тип кузова_универсал', 'Тип кузова_хэтчбек 3 дв.',\n",
              "       'Тип кузова_хэтчбек 5 дв.', 'Цвет_бордовый', 'Цвет_голубой',\n",
              "       'Цвет_желтый', 'Цвет_зеленый', 'Цвет_золотистый', 'Цвет_коричневый',\n",
              "       'Цвет_красный', 'Цвет_оранжевый', 'Цвет_серебристый', 'Цвет_серый',\n",
              "       'Цвет_синий', 'Цвет_фиолетовый', 'Цвет_черный', 'Цвет_белый',\n",
              "       '0_Модель не совпадает с ПТС',\n",
              "       '0_Мощность двигателя не совпадает с ПТС',\n",
              "       '0_Характеристики совпадают с ПТС', '0_Цвет не совпадает с ПТС',\n",
              "       '1_3-6 записей о регистрации', '1_более 6 записей о регистрации',\n",
              "       '2_Стоит на учете в ГИБДД', '3_Не был во владении у юр. лица',\n",
              "       '4_Последний период владения до 1 года',\n",
              "       '4_Последний период владения до 6 лет', 'Ссылка', 'Название машины'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Также следует отметить, что в процессе парсинга данных в каждой группе одна из дамми была \"дропнута\", чтобы избежать линейной связи между признаками, поэтому в дополнении к указанным колонкам есть еще следующий список: ***'0_Год не совпадает с ПТС', '1_1-2 записей о регистрации', '2_Не стоит на учете в ГИБДД', '3_Был во владении у юр. лица', '4_Последний период владения больше 6 лет', 'Коробка передач_АКПП', 'Привод_4WD', 'Тип кузова_джип/suv 3 дв.', 'Цвет_бежевый'.***\n",
        "\n",
        "Данная особенность сбора данных не влияет на дальнейший анализ, потому что не меняет возможные принимаемые значения, однако ее следует принять к рассмотрению в процессе спецификации модели и размышлениях о релевантности переменных."
      ],
      "metadata": {
        "id": "ud1OJ3hJ8xgg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#изучим, можно ли сделать название машины используемым признаком или таких названий слишком много\n",
        "df['Название машины'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPNpsHG-5X6X",
        "outputId": "30a42b5d-b7a3-4991-85bb-98827c7cd02d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#судя по количеству наблюдений, да, потому что количество машин всего около 640, а \"классов\" всего 13, посмотрим на гистограмму распределения\n",
        "plt.hist(df['Название машины'])\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()\n",
        "#для большей части названий наблюдений достаточно, однако для 5 наименований маловато, позже решим, что с этим делать"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "PVctkmCv5CJp",
        "outputId": "25f46dad-ecd2-455a-984b-2e388963506f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#дропнем ссылку как неинформативный для регрессии признак:\n",
        "df_no_link = df.drop(['Ссылка'], axis=1)\n",
        "df_link = df['Ссылка']\n",
        "#разделение выборки на признаки и таргет будет позже, когда преобразуем столбик с названиями машин"
      ],
      "metadata": {
        "id": "4tOvrbrg3tAb"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Создание базовой регрессии (D)\n",
        "(в этом разделе создадим модель со всеми исходными (и даже чуть больше) признаками и посмотрим, есть ли в этом смысл)"
      ],
      "metadata": {
        "id": "pTVqCHGOug94"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#попробуем преобразовать название машин в бинарные признаки через onehotencoder, код частично взят из https://habr.com/ru/articles/666234/\n",
        "enc = OneHotEncoder(sparse=False)\n",
        "\n",
        "encoded_column = enc.fit_transform(df_no_link[['Название машины']])\n",
        "one_hot_df = pd.DataFrame(encoded_column, columns=[f'Название машины_{i}' for i in range(len(df_no_link['Название машины'].unique()))])\n",
        "df_enc = pd.concat([df_no_link.drop(['Название машины'], axis=1), one_hot_df], axis=1)\n",
        "\n",
        "df_enc.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "TCf2AMWU_eyC",
        "outputId": "cc0903a2-f503-4af2-9893-bc3d76d127c6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Цена  Мощность   Пробег  Объём двигателя   Год  \\\n",
              "0  1350000     122.0  86684.0              1.8  2018   \n",
              "1  1350000     122.0  77400.0              1.8  2019   \n",
              "2   950000      90.0   9200.0              1.6  2023   \n",
              "3  1165000     122.0  84594.0              1.8  2019   \n",
              "4  1145000     122.0  58195.0              1.8  2019   \n",
              "\n",
              "   Коробка передач_вариатор  Коробка передач_механика  Коробка передач_робот  \\\n",
              "0                         0                         1                      0   \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_enc"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Заметим, что количество признаков увеличилось на 11, это получилось так: убрав название машин из признаков изначально получим 42 признака (df_no_link), прибавим 12 наименований (13-1 из-за того, что можно \"отнормировать\" к одному названию) и получим 54 (или +11) признака"
      ],
      "metadata": {
        "id": "xST0QeC6iDxA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#теперь у нас есть огромная таблица с кучей признаков. попробуем прогнать регрессию, что называется, в лоб,\n",
        "#чтобы прикинуть на глаз, есть ли мультиколлинеарность или все и так хорошо\n",
        "df_y = df_enc['Цена']\n",
        "df_X = df_enc.drop(['Цена'], axis=1)\n",
        "\n",
        "X = sm.add_constant(df_X)\n",
        "model_0 = sm.OLS(df_y, X).fit()\n",
        "\n",
        "print(model_0.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LIqjPAct8PaA",
        "outputId": "784aab05-799e-457f-c630-73a7692a12b7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                   Цена   R-squared:                       0.920\n",
            "Model:                            OLS   Adj. R-squared:                  0.913\n",
            "Method:                 Least Squares   F-statistic:                     138.2\n",
            "Date:                Fri, 10 May 2024   Prob (F-statistic):          5.97e-290\n",
            "Time:                        01:39:15   Log-Likelihood:                -8362.2\n",
            "No. Observations:                 641   AIC:                         1.682e+04\n",
            "Df Residuals:                     591   BIC:                         1.705e+04\n",
            "Df Model:                          49                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================================\n",
            "                                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "const                                   -6.103e+07   3.87e+06    -15.769      0.000   -6.86e+07   -5.34e+07\n",
            "Мощность                                 3585.6900    900.919      3.980      0.000    1816.297    5355.083\n",
            "Пробег                                     -0.9407      0.090    -10.469      0.000      -1.117      -0.764\n",
            "Объём двигателя                         -2.819e+05   1.38e+05     -2.047      0.041   -5.52e+05   -1.14e+04\n",
            "Год                                      3.812e+04   2393.330     15.929      0.000    3.34e+04    4.28e+04\n",
            "Коробка передач_вариатор                 5966.2828   3.52e+04      0.170      0.865   -6.31e+04     7.5e+04\n",
            "Коробка передач_механика                 -1.26e+05   2.73e+04     -4.607      0.000    -1.8e+05   -7.23e+04\n",
            "Коробка передач_робот                   -1.491e+05   3.32e+04     -4.495      0.000   -2.14e+05   -8.39e+04\n",
            "Привод_передний                         -1.084e+07   6.87e+05    -15.783      0.000   -1.22e+07   -9.49e+06\n",
            "Привод_задний                           -5.712e+06    3.5e+05    -16.334      0.000    -6.4e+06   -5.03e+06\n",
            "Тип кузова_джип/suv 5 дв.               -4.557e+04   5.02e+04     -0.909      0.364   -1.44e+05    5.29e+04\n",
            "Тип кузова_лифтбек                      -2.753e+06   1.73e+05    -15.959      0.000   -3.09e+06   -2.41e+06\n",
            "Тип кузова_минивэн                      -2.751e+06   2.04e+05    -13.485      0.000   -3.15e+06   -2.35e+06\n",
            "Тип кузова_седан                        -2.752e+06   1.72e+05    -16.043      0.000   -3.09e+06   -2.41e+06\n",
            "Тип кузова_универсал                    -2.693e+06   1.73e+05    -15.585      0.000   -3.03e+06   -2.35e+06\n",
            "Тип кузова_хэтчбек 3 дв.                -2.849e+06   1.96e+05    -14.510      0.000   -3.23e+06   -2.46e+06\n",
            "Тип кузова_хэтчбек 5 дв.                -2.758e+06   1.74e+05    -15.833      0.000    -3.1e+06   -2.42e+06\n",
            "Цвет_бордовый                           -3.657e+04   5.72e+04     -0.639      0.523   -1.49e+05    7.58e+04\n",
            "Цвет_голубой                            -6.098e+04   5.31e+04     -1.149      0.251   -1.65e+05    4.32e+04\n",
            "Цвет_желтый                              2.502e+05   9.36e+04      2.674      0.008    6.64e+04    4.34e+05\n",
            "Цвет_зеленый                               77.1368   4.52e+04      0.002      0.999   -8.86e+04    8.88e+04\n",
            "Цвет_золотистый                         -1.028e+04   1.26e+05     -0.081      0.935   -2.58e+05    2.38e+05\n",
            "Цвет_коричневый                           427.1534   4.21e+04      0.010      0.992   -8.23e+04    8.31e+04\n",
            "Цвет_красный                              643.0048   4.32e+04      0.015      0.988   -8.42e+04    8.54e+04\n",
            "Цвет_оранжевый                           8.587e+04    5.1e+04      1.684      0.093   -1.43e+04    1.86e+05\n",
            "Цвет_серебристый                        -4844.1436   4.07e+04     -0.119      0.905   -8.48e+04    7.51e+04\n",
            "Цвет_серый                               2.418e+04   4.01e+04      0.603      0.547   -5.46e+04    1.03e+05\n",
            "Цвет_синий                               3.916e+04   4.28e+04      0.915      0.361   -4.49e+04    1.23e+05\n",
            "Цвет_фиолетовый                          1.294e+05   1.24e+05      1.041      0.298   -1.15e+05    3.74e+05\n",
            "Цвет_черный                             -6424.6280   4.03e+04     -0.159      0.873   -8.56e+04    7.27e+04\n",
            "Цвет_белый                               9048.0197   3.93e+04      0.230      0.818   -6.82e+04    8.63e+04\n",
            "0_Модель не совпадает с ПТС             -1.418e+04   1.28e+05     -0.110      0.912   -2.66e+05    2.38e+05\n",
            "0_Мощность двигателя не совпадает с ПТС  7.846e+04   1.39e+05      0.564      0.573   -1.95e+05    3.52e+05\n",
            "0_Характеристики совпадают с ПТС         6.181e+04    1.2e+05      0.515      0.607   -1.74e+05    2.98e+05\n",
            "0_Цвет не совпадает с ПТС                4.117e+04   1.23e+05      0.336      0.737      -2e+05    2.82e+05\n",
            "1_3-6 записей о регистрации             -4.681e+04   1.58e+04     -2.970      0.003   -7.78e+04   -1.59e+04\n",
            "1_более 6 записей о регистрации         -3.172e+04   2.55e+04     -1.246      0.213   -8.17e+04    1.83e+04\n",
            "2_Стоит на учете в ГИБДД                 2.756e+04   1.09e+04      2.520      0.012    6080.114     4.9e+04\n",
            "3_Не был во владении у юр. лица          3.101e+04    1.6e+04      1.938      0.053    -416.951    6.24e+04\n",
            "4_Последний период владения до 1 года    4.358e+04   1.51e+04      2.878      0.004    1.38e+04    7.33e+04\n",
            "4_Последний период владения до 6 лет     1442.7163   1.23e+04      0.117      0.907   -2.28e+04    2.57e+04\n",
            "Название машины_0                       -5.712e+06    3.5e+05    -16.334      0.000    -6.4e+06   -5.03e+06\n",
            "Название машины_1                       -9.879e+05   9.98e+04     -9.901      0.000   -1.18e+06   -7.92e+05\n",
            "Название машины_2                       -1.249e+06   6.88e+04    -18.154      0.000   -1.38e+06   -1.11e+06\n",
            "Название машины_3                       -1.339e+06   7.46e+04    -17.951      0.000   -1.49e+06   -1.19e+06\n",
            "Название машины_4                       -1.484e+07   9.47e+05    -15.670      0.000   -1.67e+07    -1.3e+07\n",
            "Название машины_5                        -1.48e+07   9.43e+05    -15.686      0.000   -1.67e+07   -1.29e+07\n",
            "Название машины_6                       -1.136e+06   9.32e+04    -12.181      0.000   -1.32e+06   -9.53e+05\n",
            "Название машины_7                       -9.713e+05   9.49e+04    -10.234      0.000   -1.16e+06   -7.85e+05\n",
            "Название машины_8                       -1.422e+06   9.15e+04    -15.538      0.000    -1.6e+06   -1.24e+06\n",
            "Название машины_9                       -1.347e+06    1.1e+05    -12.223      0.000   -1.56e+06   -1.13e+06\n",
            "Название машины_10                      -1.067e+06    9.2e+04    -11.603      0.000   -1.25e+06   -8.87e+05\n",
            "Название машины_11                      -1.484e+07   9.49e+05    -15.638      0.000   -1.67e+07    -1.3e+07\n",
            "Название машины_12                      -1.326e+06   7.81e+04    -16.975      0.000   -1.48e+06   -1.17e+06\n",
            "==============================================================================\n",
            "Omnibus:                       85.657   Durbin-Watson:                   2.054\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              166.771\n",
            "Skew:                           0.786   Prob(JB):                     6.11e-37\n",
            "Kurtosis:                       4.942   Cond. No.                     1.25e+16\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 7.24e-20. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Несмотря на то, что R^2 оказался довольно большим, мы видим, что многие коэффициенты оказались отрицательными, хотя по идее они должны вносить положительный вклад (например, объем двигателя). Более того, в категориальных группах (например, типов кузовов и названий машин) отрицательными являются все коэффициенты, а значит в таких группах никакой признак не является \"предпочтительным\", то есть вся группа вносит отрицательный вклад (это так не должно работать).\n",
        "\n",
        "Получается, что так как многие коэффициенты очень большие по модулю и отрицательные, причем их отрицательность противоречит экономической интуиции, то, скорее всего, в данных присутствует мультиколлинеарность."
      ],
      "metadata": {
        "id": "FDPDFqeAhbhu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#проверим предположение о мультиколлинеарности, посчитав VIF-ы\n",
        "#ниже мы увидим, что VIF -> inf, а значит будет присутствовать деление на ноль, чтобы избежать предупреждений поставим заглушку\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "vif = pd.DataFrame()\n",
        "vif[\"VIF\"] = [variance_inflation_factor(df_X.values, i) for i in range(df_X.shape[1])]\n",
        "vif[\"features\"] = df_X.columns\n",
        "\n",
        "vif = vif.round(1)\n",
        "vif"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "d5ubop_fiTPs",
        "outputId": "9ee65667-03d3-4172-fb2a-80c3ade7c21c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        VIF                                 features\n",
              "0       6.2                                 Мощность\n",
              "1       2.4                                   Пробег\n",
              "2       4.3                          Объём двигателя\n",
              "3       9.9                                      Год\n",
              "4       3.7                 Коробка передач_вариатор\n",
              "5       5.1                 Коробка передач_механика\n",
              "6       3.3                    Коробка передач_робот\n",
              "7       inf                          Привод_передний\n",
              "8       inf                            Привод_задний\n",
              "9       1.3                Тип кузова_джип/suv 5 дв.\n",
              "10      inf                       Тип кузова_лифтбек\n",
              "11      inf                       Тип кузова_минивэн\n",
              "12      inf                         Тип кузова_седан\n",
              "13      inf                     Тип кузова_универсал\n",
              "14      inf                 Тип кузова_хэтчбек 3 дв.\n",
              "15      inf                 Тип кузова_хэтчбек 5 дв.\n",
              "16      1.9                            Цвет_бордовый\n",
              "17      2.2                             Цвет_голубой\n",
              "18      1.3                              Цвет_желтый\n",
              "19      4.1                             Цвет_зеленый\n",
              "20      1.2                          Цвет_золотистый\n",
              "21      5.2                          Цвет_коричневый\n",
              "22      4.7                             Цвет_красный\n",
              "23      3.0                           Цвет_оранжевый\n",
              "24      7.4                         Цвет_серебристый\n",
              "25      9.8                               Цвет_серый\n",
              "26      4.9                               Цвет_синий\n",
              "27      1.1                          Цвет_фиолетовый\n",
              "28     10.7                              Цвет_черный\n",
              "29     14.2                               Цвет_белый\n",
              "30      9.6              0_Модель не совпадает с ПТС\n",
              "31      4.2  0_Мощность двигателя не совпадает с ПТС\n",
              "32     36.9         0_Характеристики совпадают с ПТС\n",
              "33     26.5                0_Цвет не совпадает с ПТС\n",
              "34      2.0              1_3-6 записей о регистрации\n",
              "35      1.9          1_более 6 записей о регистрации\n",
              "36      1.1                 2_Стоит на учете в ГИБДД\n",
              "37      1.4          3_Не был во владении у юр. лица\n",
              "38      1.7    4_Последний период владения до 1 года\n",
              "39      1.8     4_Последний период владения до 6 лет\n",
              "40      inf                        Название машины_0\n",
              "41      inf                        Название машины_1\n",
              "42      inf                        Название машины_2\n",
              "43      inf                        Название машины_3\n",
              "44   5110.5                        Название машины_4\n",
              "45  10208.9                        Название машины_5\n",
              "46      inf                        Название машины_6\n",
              "47      inf                        Название машины_7\n",
              "48      inf                        Название машины_8\n",
              "49      inf                        Название машины_9\n",
              "50      inf                       Название машины_10\n",
              "51  49468.4                       Название машины_11\n",
              "52      inf                       Название машины_12"
            ],
            "text/html": [
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              "    <div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>VIF</th>\n",
              "      <th>features</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.2</td>\n",
              "      <td>Мощность</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.4</td>\n",
              "      <td>Пробег</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.3</td>\n",
              "      <td>Объём двигателя</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.9</td>\n",
              "      <td>Год</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.7</td>\n",
              "      <td>Коробка передач_вариатор</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.1</td>\n",
              "      <td>Коробка передач_механика</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3.3</td>\n",
              "      <td>Коробка передач_робот</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>inf</td>\n",
              "      <td>Привод_передний</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>inf</td>\n",
              "      <td>Привод_задний</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.3</td>\n",
              "      <td>Тип кузова_джип/suv 5 дв.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>inf</td>\n",
              "      <td>Тип кузова_лифтбек</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>inf</td>\n",
              "      <td>Тип кузова_минивэн</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>inf</td>\n",
              "      <td>Тип кузова_седан</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>inf</td>\n",
              "      <td>Тип кузова_универсал</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>inf</td>\n",
              "      <td>Тип кузова_хэтчбек 3 дв.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>inf</td>\n",
              "      <td>Тип кузова_хэтчбек 5 дв.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.9</td>\n",
              "      <td>Цвет_бордовый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2.2</td>\n",
              "      <td>Цвет_голубой</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1.3</td>\n",
              "      <td>Цвет_желтый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>4.1</td>\n",
              "      <td>Цвет_зеленый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1.2</td>\n",
              "      <td>Цвет_золотистый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5.2</td>\n",
              "      <td>Цвет_коричневый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>4.7</td>\n",
              "      <td>Цвет_красный</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>3.0</td>\n",
              "      <td>Цвет_оранжевый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>7.4</td>\n",
              "      <td>Цвет_серебристый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>9.8</td>\n",
              "      <td>Цвет_серый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>4.9</td>\n",
              "      <td>Цвет_синий</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1.1</td>\n",
              "      <td>Цвет_фиолетовый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>10.7</td>\n",
              "      <td>Цвет_черный</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>14.2</td>\n",
              "      <td>Цвет_белый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>9.6</td>\n",
              "      <td>0_Модель не совпадает с ПТС</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>4.2</td>\n",
              "      <td>0_Мощность двигателя не совпадает с ПТС</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>36.9</td>\n",
              "      <td>0_Характеристики совпадают с ПТС</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>26.5</td>\n",
              "      <td>0_Цвет не совпадает с ПТС</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1_3-6 записей о регистрации</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1.9</td>\n",
              "      <td>1_более 6 записей о регистрации</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1.1</td>\n",
              "      <td>2_Стоит на учете в ГИБДД</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1.4</td>\n",
              "      <td>3_Не был во владении у юр. лица</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1.7</td>\n",
              "      <td>4_Последний период владения до 1 года</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1.8</td>\n",
              "      <td>4_Последний период владения до 6 лет</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>5110.5</td>\n",
              "      <td>Название машины_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>10208.9</td>\n",
              "      <td>Название машины_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>49468.4</td>\n",
              "      <td>Название машины_11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-225930ea-d39e-4030-a02d-b50f7cb72216')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-225930ea-d39e-4030-a02d-b50f7cb72216 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-225930ea-d39e-4030-a02d-b50f7cb72216');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-43ffab46-d944-4fa5-b929-8c6681ab6b61\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-43ffab46-d944-4fa5-b929-8c6681ab6b61')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-43ffab46-d944-4fa5-b929-8c6681ab6b61 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "vif",
              "repr_error": "Out of range float values are not JSON compliant: inf"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#посмотрим VIF-ы в виде массива\n",
        "vif['VIF'].values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "whABK246n1S5",
        "outputId": "242654cd-47fb-4474-bf06-b85220f1fcfd"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6.20000e+00, 2.40000e+00, 4.30000e+00, 9.90000e+00, 3.70000e+00,\n",
              "       5.10000e+00, 3.30000e+00,         inf,         inf, 1.30000e+00,\n",
              "               inf,         inf,         inf,         inf,         inf,\n",
              "               inf, 1.90000e+00, 2.20000e+00, 1.30000e+00, 4.10000e+00,\n",
              "       1.20000e+00, 5.20000e+00, 4.70000e+00, 3.00000e+00, 7.40000e+00,\n",
              "       9.80000e+00, 4.90000e+00, 1.10000e+00, 1.07000e+01, 1.42000e+01,\n",
              "       9.60000e+00, 4.20000e+00, 3.69000e+01, 2.65000e+01, 2.00000e+00,\n",
              "       1.90000e+00, 1.10000e+00, 1.40000e+00, 1.70000e+00, 1.80000e+00,\n",
              "               inf,         inf,         inf,         inf, 5.11050e+03,\n",
              "       1.02089e+04,         inf,         inf,         inf,         inf,\n",
              "               inf, 4.94684e+04,         inf])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Заметим, что много VIF-ов стремятся к бесконечности, особенно это касается названия машин и типов кузовов. В принципе, это было понятно еще из таблички с результатами оценивания регрессии, потому что именно эти группы имели самые подозрительные коэффициенты. Также присутствует много больших VIF-ов, с ними тоже что-то надо делать."
      ],
      "metadata": {
        "id": "AbIRUf2wrimi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#проверим, сколько VIF-ов стремятся к бесконечности и по каким признакам:\n",
        "vif[vif['VIF']==np.inf]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "X_n6iUC0j4sC",
        "outputId": "f7f337e1-0aa4-46c6-8341-dbec4291ec3e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    VIF                  features\n",
              "7   inf           Привод_передний\n",
              "8   inf             Привод_задний\n",
              "10  inf        Тип кузова_лифтбек\n",
              "11  inf        Тип кузова_минивэн\n",
              "12  inf          Тип кузова_седан\n",
              "13  inf      Тип кузова_универсал\n",
              "14  inf  Тип кузова_хэтчбек 3 дв.\n",
              "15  inf  Тип кузова_хэтчбек 5 дв.\n",
              "40  inf         Название машины_0\n",
              "41  inf         Название машины_1\n",
              "42  inf         Название машины_2\n",
              "43  inf         Название машины_3\n",
              "46  inf         Название машины_6\n",
              "47  inf         Название машины_7\n",
              "48  inf         Название машины_8\n",
              "49  inf         Название машины_9\n",
              "50  inf        Название машины_10\n",
              "52  inf        Название машины_12"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bdad450b-4823-49c5-bc8e-ec51677018c5\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "      <th>7</th>\n",
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              "      <th>42</th>\n",
              "      <td>inf</td>\n",
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              "    </tr>\n",
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              "    <tr>\n",
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              "      <td>inf</td>\n",
              "      <td>Название машины_10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bdad450b-4823-49c5-bc8e-ec51677018c5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bdad450b-4823-49c5-bc8e-ec51677018c5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bdad450b-4823-49c5-bc8e-ec51677018c5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-819c0ce2-234d-4826-80e2-55b16424a04c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-819c0ce2-234d-4826-80e2-55b16424a04c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-819c0ce2-234d-4826-80e2-55b16424a04c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: inf"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Получилось, что в данных и правда присутствует мультиколлинеарность, значит базовая регрессия со всеми признаками едва ли хорошая идея. Попробуем что-то придумать!\n",
        "\n",
        "Самое логичное - отредактировать признаки так, чтобы снизить мультиколлинеарность и сделать их более агрегированными, общими. Приглашаем в следующий раздел!"
      ],
      "metadata": {
        "id": "HzozD80azvIf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Вторичный анализ данных (E)\n",
        "(в этом разделе отредактируем признаки, уберем самые скоррелированные и постараемся починить мультиколлинеарность как сможем)"
      ],
      "metadata": {
        "id": "3phKlzEI0tlZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#для начала еще раз выведем признаки с большими VIF-ми, потому что именно в этом множестве и содержатся скоррелированные переменные\n",
        "#положим, что VIF чрезмерный, если он больше 5\n",
        "vif[vif['VIF']>5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cD-mhEn-1J6i",
        "outputId": "65dd5ba9-0442-47e0-8eb1-64c340998f0f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        VIF                          features\n",
              "0       6.2                          Мощность\n",
              "3       9.9                               Год\n",
              "5       5.1          Коробка передач_механика\n",
              "7       inf                   Привод_передний\n",
              "8       inf                     Привод_задний\n",
              "10      inf                Тип кузова_лифтбек\n",
              "11      inf                Тип кузова_минивэн\n",
              "12      inf                  Тип кузова_седан\n",
              "13      inf              Тип кузова_универсал\n",
              "14      inf          Тип кузова_хэтчбек 3 дв.\n",
              "15      inf          Тип кузова_хэтчбек 5 дв.\n",
              "21      5.2                   Цвет_коричневый\n",
              "24      7.4                  Цвет_серебристый\n",
              "25      9.8                        Цвет_серый\n",
              "28     10.7                       Цвет_черный\n",
              "29     14.2                        Цвет_белый\n",
              "30      9.6       0_Модель не совпадает с ПТС\n",
              "32     36.9  0_Характеристики совпадают с ПТС\n",
              "33     26.5         0_Цвет не совпадает с ПТС\n",
              "40      inf                 Название машины_0\n",
              "41      inf                 Название машины_1\n",
              "42      inf                 Название машины_2\n",
              "43      inf                 Название машины_3\n",
              "44   5110.5                 Название машины_4\n",
              "45  10208.9                 Название машины_5\n",
              "46      inf                 Название машины_6\n",
              "47      inf                 Название машины_7\n",
              "48      inf                 Название машины_8\n",
              "49      inf                 Название машины_9\n",
              "50      inf                Название машины_10\n",
              "51  49468.4                Название машины_11\n",
              "52      inf                Название машины_12"
            ],
            "text/html": [
              "\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>VIF</th>\n",
              "      <th>features</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>6.2</td>\n",
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              "      <td>Коробка передач_механика</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>inf</td>\n",
              "      <td>Тип кузова_хэтчбек 5 дв.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5.2</td>\n",
              "      <td>Цвет_коричневый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>7.4</td>\n",
              "      <td>Цвет_серебристый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>9.8</td>\n",
              "      <td>Цвет_серый</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>10.7</td>\n",
              "      <td>Цвет_черный</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>14.2</td>\n",
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              "      <th>30</th>\n",
              "      <td>9.6</td>\n",
              "      <td>0_Модель не совпадает с ПТС</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>36.9</td>\n",
              "      <td>0_Характеристики совпадают с ПТС</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>26.5</td>\n",
              "      <td>0_Цвет не совпадает с ПТС</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
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              "      <td>Название машины_0</td>\n",
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              "      <td>inf</td>\n",
              "      <td>Название машины_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>5110.5</td>\n",
              "      <td>Название машины_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>10208.9</td>\n",
              "      <td>Название машины_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_6</td>\n",
              "    </tr>\n",
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              "      <td>inf</td>\n",
              "      <td>Название машины_7</td>\n",
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              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_9</td>\n",
              "    </tr>\n",
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              "      <th>50</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_10</td>\n",
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              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>49468.4</td>\n",
              "      <td>Название машины_11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>inf</td>\n",
              "      <td>Название машины_12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85d995ab-0a38-4adc-a52c-adde3bda1113')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-85d995ab-0a38-4adc-a52c-adde3bda1113 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-85d995ab-0a38-4adc-a52c-adde3bda1113');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9800fea1-69cb-44c9-80c5-aff567eaded2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9800fea1-69cb-44c9-80c5-aff567eaded2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9800fea1-69cb-44c9-80c5-aff567eaded2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: inf"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#теперь посмотрим на корреляции переменных\n",
        "#значения возьмем по модулю, потому что важно само отклонение, а не его знак (ну он тоже важен, но пока возьмем значение)\n",
        "corr_matrix_abs = df_X.corr().abs()\n",
        "\n",
        "sns.heatmap(corr_matrix_abs, cmap=\"crest\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "2RNaOHZa1lLe",
        "outputId": "f36e8a31-dcc5-41d8-89a7-178d89c779c0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "По тепловой матрице видим, что в целом корреляции не везде большие, однако присутствуют отдельные \"подозрительные\" точки, которые надо отправить в корреляционный патруль и разобраться, что с ними делать."
      ],
      "metadata": {
        "id": "hKP_r1on81XR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#выведем 10 самых скоррелированных пар\n",
        "n_E = 10\n",
        "\n",
        "corr_matrix = df_X.corr()\n",
        "mask_E = np.triu(np.ones_like(corr_matrix_abs, dtype=bool), k=1)\n",
        "corr_values = corr_matrix_abs.where(mask_E)\n",
        "pairs = corr_values.unstack().sort_values(ascending=False).dropna()\n",
        "pairs_top_10 = pairs[:10]\n",
        "\n",
        "print(f\"Топ {n_E} пар наиболее скоррелированных признаков:\")\n",
        "for index, pair in enumerate(pairs_top_10.index, start=1):\n",
        "    col1, col2 = pair\n",
        "    val = corr_matrix[col1][col2]\n",
        "    print(f\"{index}. {col1} и {col2}: {val:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96VVXPlM8wrA",
        "outputId": "d8715d3b-08d2-4514-d3d4-720b32c44bdd"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Топ 10 пар наиболее скоррелированных признаков:\n",
            "1. Название машины_0 и Привод_задний: 1.00\n",
            "2. Название машины_1 и Тип кузова_хэтчбек 3 дв.: 0.86\n",
            "3. Название машины_11 и Привод_передний: -0.82\n",
            "4. 0_Цвет не совпадает с ПТС и 0_Характеристики совпадают с ПТС: -0.81\n",
            "5. Название машины_3 и Тип кузова_хэтчбек 5 дв.: 0.66\n",
            "6. Название машины_10 и Тип кузова_универсал: 0.66\n",
            "7. Коробка передач_механика и Коробка передач_вариатор: -0.59\n",
            "8. Коробка передач_робот и Коробка передач_механика: -0.59\n",
            "9. Год и Пробег: -0.57\n",
            "10. 4_Последний период владения до 6 лет и 4_Последний период владения до 1 года: -0.55\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#а теперь выведем все пары, у которых корреляция больше 0.5 по модулю:\n",
        "pairs_thres = pairs[pairs>0.5]\n",
        "\n",
        "print(f\"Пары с корреляцией по модулю >0.5:\")\n",
        "for index, pair in enumerate(pairs_thres.index, start=1):\n",
        "    col1, col2 = pair\n",
        "    val = corr_matrix[col1][col2]\n",
        "    print(f\"{index}. {col1} и {col2}: {val:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_K0nklh8uFo",
        "outputId": "db9251b4-7dbe-490c-ee84-a1f0fc6d28f9"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Пары с корреляцией по модулю >0.5:\n",
            "1. Название машины_0 и Привод_задний: 1.00\n",
            "2. Название машины_1 и Тип кузова_хэтчбек 3 дв.: 0.86\n",
            "3. Название машины_11 и Привод_передний: -0.82\n",
            "4. 0_Цвет не совпадает с ПТС и 0_Характеристики совпадают с ПТС: -0.81\n",
            "5. Название машины_3 и Тип кузова_хэтчбек 5 дв.: 0.66\n",
            "6. Название машины_10 и Тип кузова_универсал: 0.66\n",
            "7. Коробка передач_механика и Коробка передач_вариатор: -0.59\n",
            "8. Коробка передач_робот и Коробка передач_механика: -0.59\n",
            "9. Год и Пробег: -0.57\n",
            "10. 4_Последний период владения до 6 лет и 4_Последний период владения до 1 года: -0.55\n",
            "11. Тип кузова_универсал и Тип кузова_седан: -0.52\n",
            "12. Объём двигателя и Мощность: 0.51\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Итак, нужно что-то сделать с названиями машин, типами кузовов, приводами и кое-чем еще.\n",
        "\n",
        "Приступим!"
      ],
      "metadata": {
        "id": "C6akAJqERMrS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#еще раз выведем все колонки, чтобы понять, какие из них можно агрегировать и выделить общую бинарную переменную:\n",
        "df_X.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGUi0oenRZEA",
        "outputId": "dafe92bd-f1f5-4251-f94e-079b002d2206"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Мощность', 'Пробег', 'Объём двигателя', 'Год',\n",
              "       'Коробка передач_вариатор', 'Коробка передач_механика',\n",
              "       'Коробка передач_робот', 'Привод_передний', 'Привод_задний',\n",
              "       'Тип кузова_джип/suv 5 дв.', 'Тип кузова_лифтбек', 'Тип кузова_минивэн',\n",
              "       'Тип кузова_седан', 'Тип кузова_универсал', 'Тип кузова_хэтчбек 3 дв.',\n",
              "       'Тип кузова_хэтчбек 5 дв.', 'Цвет_бордовый', 'Цвет_голубой',\n",
              "       'Цвет_желтый', 'Цвет_зеленый', 'Цвет_золотистый', 'Цвет_коричневый',\n",
              "       'Цвет_красный', 'Цвет_оранжевый', 'Цвет_серебристый', 'Цвет_серый',\n",
              "       'Цвет_синий', 'Цвет_фиолетовый', 'Цвет_черный', 'Цвет_белый',\n",
              "       '0_Модель не совпадает с ПТС',\n",
              "       '0_Мощность двигателя не совпадает с ПТС',\n",
              "       '0_Характеристики совпадают с ПТС', '0_Цвет не совпадает с ПТС',\n",
              "       '1_3-6 записей о регистрации', '1_более 6 записей о регистрации',\n",
              "       '2_Стоит на учете в ГИБДД', '3_Не был во владении у юр. лица',\n",
              "       '4_Последний период владения до 1 года',\n",
              "       '4_Последний период владения до 6 лет', 'Название машины_0',\n",
              "       'Название машины_1', 'Название машины_2', 'Название машины_3',\n",
              "       'Название машины_4', 'Название машины_5', 'Название машины_6',\n",
              "       'Название машины_7', 'Название машины_8', 'Название машины_9',\n",
              "       'Название машины_10', 'Название машины_11', 'Название машины_12'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Еще раз напомним о списке дропнутых дамми, которые здесь, как то самое ружье на стене, выстреливают: ***'0_Год не совпадает с ПТС', '1_1-2 записей о регистрации', '2_Не стоит на учете в ГИБДД', '3_Был во владении у юр. лица', '4_Последний период владения больше 6 лет', 'Коробка передач_АКПП', 'Привод_4WD', 'Тип кузова_джип/suv 3 дв.', 'Цвет_бежевый'.***"
      ],
      "metadata": {
        "id": "KEVTf5xq9yXg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Итак, исходя из логических соображений, можно предположить, что релевантны следующие преобразования:**\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "1) **Удалить в целом признак \"Название машины\"**.\n",
        "Во-первых, данная категория сильно повышает мультиколлинеарность, так как хорошо описывается другими переменными (например, мощностью, коробкой передач, объемом двигателя, приводом и кузовом. Да, кузовы могут быть разными у модели, но все равно варианты ограничены: едва ли мини купер можно купить в комплектации минивэна.\n",
        "Во-вторых, у данной переменной нет экономическо-исследовательского смысла с точки зрения построения функции гедонистического спроса. У нас присутствует 13 машин, каждая из которых, по факту, есть совокупность упомянутых выше характеристик (концептуально 13 (хотя бы такого порядка) совокупностей). Проще говоря, каждая X_i модель в конкретной комплектации стоит на рынке (отличие первичного от вторичного в данном контексте минимально) Y_i рублей, эта цена известна и стабильна, а значит анализировать влияние названия машины нет смысла (оно и так известно).\n",
        "\n",
        "2) **В группе коробок передач сделать бинарную переменную \"Механика или не механика\"**. Предполагается выделение именно механики, потому что она сильнее всего отличается от остальных типов, а они, в свою очередь, довольно похожи друг на друга.\n",
        "\n",
        "3) **В группе приводов сделать бинарную переменную \"Передний или не передний\"**. Выделение основывается на том, что передний привод сильнее всего отличается от остальных в представленности в наблюдениях и лучше выделяет класс условно \"обычных\" машин от \"необычных\" в данной группе.\n",
        "\n",
        "4) **В группе кузовов сделать несколько бинарных переменных, которые обозначают \"Джип\", \"Лифтбек\", \"Хэтчбек\", \"Минивэн\", \"Седан\" и \"Универсал\".**\n",
        "Было решено укрупнить отдельные группы (по дверям) и оставить типы как бинарные переменные. Почему? Потому что одна и та же модель машины может быть представлена в разных кузовах, причем данный признак хоть и является вариативным (можно купить схожую машину, но с другим кузовом), но эта вариативность ограничена (на вторичном рынке подобрать такой же пробег или юридическую историю сложнее), а также признак не может быть изменен как, например, цвет.\n",
        "То есть основная причина рассмотрения данного признака это существенное различие комплектаций на первичном и вторичном рынках (на вторичном нельзя (очень сложно) купить такую же, но с другим кузовом).\n",
        "\n",
        "5) **В группе цветов сделать бинарную переменную \"Классический цвет или нет\"**. К классическим цветам отнесем черный, белый, серый и серебристый. На эти 4 цвета приходится около 70% всех автомобилей в России (см. https://news.drom.ru/79717.html)\n",
        "\n",
        "6) **В группе юридических аспектов автомобиля (ПТС) сделать бинарную переменную \"Есть ли несовпадения или нет\"**. Тогда любое нарушение и несовпадение будет означать 1, а их полное отсутствие 0.\n",
        "\n",
        "7) Остальные признаки оставить без изменений, так как они относительно независимы и могут рассматриваться отдельно от подавляющего большинства первичных и вторичных характеристик объявления.\n",
        "\n",
        "Теперь внесем упомянутые коррективы!\n"
      ],
      "metadata": {
        "id": "59ezRJ_rRUvN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#удаляем название машины:\n",
        "df_X_new = df_X.iloc[:, :-13]\n",
        "\n",
        "#сделаем бинарную переменную механика или нет:\n",
        "df_X_new.rename(columns = {'Коробка передач_механика':'КП механическая'}, inplace = True)\n",
        "df_X_new.drop(['Коробка передач_вариатор', 'Коробка передач_робот'], axis=1, inplace=True)\n",
        "\n",
        "#сделаем бинарную переменную передний или задний привод:\n",
        "df_X_new.rename(columns = {'Привод_передний':'Привод передний'}, inplace = True)\n",
        "df_X_new.drop(['Привод_задний'], axis=1, inplace=True)\n",
        "\n",
        "#сделаем бинарные переменные о типах кузовов:\n",
        "kuz = ['Кузов_лифтбек', 'Кузов_минивен', 'Кузов_седан', 'Кузов_универсал', 'Кузов_хэтчбек'] #список с типами понадобится ниже\n",
        "\n",
        "df_X_new.rename(columns = {'Тип кузова_лифтбек':'Кузов_лифтбек'}, inplace = True) #тут просто переименовываем столбцы\n",
        "df_X_new.rename(columns = {'Тип кузова_минивэн':'Кузов_минивен'}, inplace = True)\n",
        "df_X_new.rename(columns = {'Тип кузова_седан':'Кузов_седан'}, inplace = True)\n",
        "df_X_new.rename(columns = {'Тип кузова_универсал':'Кузов_универсал'}, inplace = True)\n",
        "\n",
        "df_X_new['Кузов_хэтчбек'] = df_X['Тип кузова_хэтчбек 5 дв.'] + df_X['Тип кузова_хэтчбек 3 дв.'] #здесь мерджим в одну группу хэтчбеки\n",
        "df_X_new.drop(['Тип кузова_хэтчбек 5 дв.', 'Тип кузова_хэтчбек 3 дв.'], axis=1, inplace=True)\n",
        "\n",
        "df_X_new['Кузов_джип'] = df_X['Тип кузова_джип/suv 5 дв.'] + np.prod([df_X_new[i]==0 for i in kuz], axis=0) #восстановим дропнутый класс джипов\n",
        "df_X_new.drop(['Тип кузова_джип/suv 5 дв.'], axis=1, inplace=True)\n",
        "\n",
        "df_X_new.drop(['Кузов_минивен'], axis=1, inplace=True)\n",
        "\n",
        "#сделаем бинарную переменную о цвете:\n",
        "cl_col = ['Цвет_белый', 'Цвет_черный', 'Цвет_серебристый', 'Цвет_серый']\n",
        "colors = ['Цвет_бордовый', 'Цвет_голубой', 'Цвет_желтый', 'Цвет_зеленый',\n",
        "          'Цвет_золотистый', 'Цвет_коричневый', 'Цвет_красный', 'Цвет_оранжевый',\n",
        "          'Цвет_серебристый', 'Цвет_серый', 'Цвет_синий', 'Цвет_фиолетовый',\n",
        "          'Цвет_черный', 'Цвет_белый']\n",
        "\n",
        "df_X_new['Классический цвет'] = np.sum([df_X_new[i] for i in cl_col], axis=0)\n",
        "df_X_new.drop(colors, axis=1, inplace=True)\n",
        "\n",
        "#сделаем бинарную переменную о существовании несовпадений:\n",
        "df_X_new.rename(columns = {'0_Характеристики совпадают с ПТС':'ПТС без нарушений'}, inplace = True)\n",
        "df_X_new.drop(['0_Модель не совпадает с ПТС', '0_Мощность двигателя не совпадает с ПТС', '0_Цвет не совпадает с ПТС'], axis=1, inplace=True)\n",
        "\n",
        "#для красоты переименуем оставшиеся колонки:\n",
        "df_X_new.rename(columns = {'2_Стоит на учете в ГИБДД':'Стоит на учете в ГИБДД'}, inplace = True)\n",
        "\n",
        "df_X_new['Был во владении у юр. лица'] = 1 - df_X_new['3_Не был во владении у юр. лица']\n",
        "df_X_new.drop('3_Не был во владении у юр. лица', axis=1, inplace=True)\n",
        "\n",
        "df_X_new.rename(columns = {'4_Последний период владения до 1 года':'Последний период владения до 1 года'}, inplace = True)\n",
        "df_X_new.rename(columns = {'4_Последний период владения до 6 лет':'Последний период владения до 6 лет'}, inplace = True)\n",
        "\n",
        "df_X_new['1-2 записей о регистрации'] = 1 - df_X_new['1_3-6 записей о регистрации'] - df_X_new['1_более 6 записей о регистрации']\n",
        "df_X_new.rename(columns = {'1_3-6 записей о регистрации':'3-6 записей о регистрации'}, inplace = True)\n",
        "df_X_new.drop('1_более 6 записей о регистрации', axis=1, inplace=True)\n",
        "\n",
        "df_X_new = df_X_new[['Мощность', 'Объём двигателя', 'Пробег', 'Год',\n",
        "                    'КП механическая', 'Привод передний', 'Стоит на учете в ГИБДД', 'Классический цвет', 'Был во владении у юр. лица',\n",
        "                    'Кузов_универсал', 'Кузов_седан', 'Кузов_лифтбек', 'Кузов_хэтчбек', 'Кузов_джип',\n",
        "                    '1-2 записей о регистрации', '3-6 записей о регистрации',\n",
        "                    'Последний период владения до 1 года', 'Последний период владения до 6 лет']]\n",
        "\n",
        "df_X_new.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "HG_Sa-xAACCV",
        "outputId": "16a675e6-6ba2-4f09-9e11-c0e2921a06a5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Мощность  Объём двигателя   Пробег   Год  КП механическая  Привод передний  \\\n",
              "0     122.0              1.8  86684.0  2018                1                1   \n",
              "1     122.0              1.8  77400.0  2019                0                1   \n",
              "2      90.0              1.6   9200.0  2023                1                1   \n",
              "3     122.0              1.8  84594.0  2019                0                1   \n",
              "4     122.0              1.8  58195.0  2019                1                1   \n",
              "\n",
              "   Стоит на учете в ГИБДД  Классический цвет  Был во владении у юр. лица  \\\n",
              "0                       1                  1                           0   \n",
              "1                       1                  1                           0   \n",
              "2                       1                  1                           0   \n",
              "3                       0                  1                           1   \n",
              "4                       0                  1                           0   \n",
              "\n",
              "   Кузов_универсал  Кузов_седан  Кузов_лифтбек  Кузов_хэтчбек  Кузов_джип  \\\n",
              "0                1            0              0              0           0   \n",
              "1                1            0              0              0           0   \n",
              "2                0            1              0              0           0   \n",
              "3                1            0              0              0           0   \n",
              "4                0            1              0              0           0   \n",
              "\n",
              "   1-2 записей о регистрации  3-6 записей о регистрации  \\\n",
              "0                          1                          0   \n",
              "1                          1                          0   \n",
              "2                          1                          0   \n",
              "3                          1                          0   \n",
              "4                          1                          0   \n",
              "\n",
              "   Последний период владения до 1 года  Последний период владения до 6 лет  \n",
              "0                                    0                                   1  \n",
              "1                                    0                                   1  \n",
              "2                                    0                                   0  \n",
              "3                                    0                                   1  \n",
              "4                                    0                                   1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d7f038b3-7c9d-4ba7-82e2-817531ed8870\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Мощность</th>\n",
              "      <th>Объём двигателя</th>\n",
              "      <th>Пробег</th>\n",
              "      <th>Год</th>\n",
              "      <th>КП механическая</th>\n",
              "      <th>Привод передний</th>\n",
              "      <th>Стоит на учете в ГИБДД</th>\n",
              "      <th>Классический цвет</th>\n",
              "      <th>Был во владении у юр. лица</th>\n",
              "      <th>Кузов_универсал</th>\n",
              "      <th>Кузов_седан</th>\n",
              "      <th>Кузов_лифтбек</th>\n",
              "      <th>Кузов_хэтчбек</th>\n",
              "      <th>Кузов_джип</th>\n",
              "      <th>1-2 записей о регистрации</th>\n",
              "      <th>3-6 записей о регистрации</th>\n",
              "      <th>Последний период владения до 1 года</th>\n",
              "      <th>Последний период владения до 6 лет</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>122.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>86684.0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>122.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>77400.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>90.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>9200.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>122.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>84594.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>122.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>58195.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7f038b3-7c9d-4ba7-82e2-817531ed8870')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d7f038b3-7c9d-4ba7-82e2-817531ed8870 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d7f038b3-7c9d-4ba7-82e2-817531ed8870');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-44cea51f-cb86-41a5-ad3c-e2cbbe24f875')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
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            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Получается, что имеем следующие переменные:\n",
        "\n",
        "***'Мощность', 'Пробег', 'Объём двигателя', 'Год'*** - количественные переменные\n",
        "\n",
        "***'КП механическая', 'Привод передний', 'Стоит на учете в ГИБДД', 'Классический цвет' и 'Был во владении у юр. лица'*** - бинарные переменные\n",
        "\n",
        "***'Кузов_лифтбек', 'Кузов_седан', 'Кузов_универсал', 'Кузов_хэтчбек' и  'Кузов_джип'*** - бинарные переменные общего признака \"Тип кузова\" с нулевым вариантом \"Минивен\"\n",
        "\n",
        " ***'1-2 записей о регистрации' и  '3-6 записей о регистрации'*** - бинарные переменные общего признака \"Количество записей о регистрации\" с нулевым вариантом \"Больше 6 записей о регистрации\"\n",
        "\n",
        " ***'Последний период владения до 1 года' и 'Последний период владения до 6 лет'*** - бинарные переменные общего признака \"Время последнего периода владения\" с нулевым вариантом \"Последний период владения больше 6 лет\""
      ],
      "metadata": {
        "id": "xKhPsmVK7bO8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#прогоним регрессию по новой таблице:\n",
        "\n",
        "X_new = sm.add_constant(df_X_new)\n",
        "model_new = sm.OLS(df_y, X_new).fit()\n",
        "\n",
        "print(model_new.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jAsG6LHc7P65",
        "outputId": "a00e23b1-25bf-45b8-df72-e43c85e72f03"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                   Цена   R-squared:                       0.859\n",
            "Model:                            OLS   Adj. R-squared:                  0.855\n",
            "Method:                 Least Squares   F-statistic:                     210.2\n",
            "Date:                Fri, 10 May 2024   Prob (F-statistic):          2.62e-250\n",
            "Time:                        01:39:17   Log-Likelihood:                -8543.2\n",
            "No. Observations:                 641   AIC:                         1.712e+04\n",
            "Df Residuals:                     622   BIC:                         1.721e+04\n",
            "Df Model:                          18                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "=======================================================================================================\n",
            "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------------------\n",
            "const                                -6.65e+07   4.01e+06    -16.595      0.000   -7.44e+07   -5.86e+07\n",
            "Мощность                             1.033e+04    813.523     12.692      0.000    8727.640    1.19e+04\n",
            "Объём двигателя                     -8.001e+05   1.33e+05     -6.015      0.000   -1.06e+06   -5.39e+05\n",
            "Пробег                                 -1.1115      0.107    -10.368      0.000      -1.322      -0.901\n",
            "Год                                  3.371e+04   2012.224     16.751      0.000    2.98e+04    3.77e+04\n",
            "КП механическая                     -4.592e+04    1.8e+04     -2.554      0.011   -8.12e+04   -1.06e+04\n",
            "Привод передний                     -5.157e+05   9.23e+04     -5.589      0.000   -6.97e+05   -3.35e+05\n",
            "Стоит на учете в ГИБДД               1.651e+04   1.35e+04      1.219      0.223   -1.01e+04    4.31e+04\n",
            "Классический цвет                   -3.524e+04   1.35e+04     -2.608      0.009   -6.18e+04   -8700.754\n",
            "Был во владении у юр. лица            894.1739   1.92e+04      0.047      0.963   -3.68e+04    3.86e+04\n",
            "Кузов_универсал                      4.385e+05   1.03e+05      4.252      0.000    2.36e+05    6.41e+05\n",
            "Кузов_седан                          2.072e+05   1.04e+05      1.985      0.048    2189.488    4.12e+05\n",
            "Кузов_лифтбек                        8.645e+04   1.07e+05      0.811      0.417   -1.23e+05    2.96e+05\n",
            "Кузов_хэтчбек                        2.039e+05    1.1e+05      1.857      0.064   -1.18e+04     4.2e+05\n",
            "Кузов_джип                          -1.113e+05   5.91e+04     -1.883      0.060   -2.27e+05    4795.418\n",
            "1-2 записей о регистрации            4.369e+04   3.09e+04      1.413      0.158    -1.7e+04    1.04e+05\n",
            "3-6 записей о регистрации           -2.033e+04   2.76e+04     -0.736      0.462   -7.46e+04    3.39e+04\n",
            "Последний период владения до 1 года  5.435e+04   1.89e+04      2.872      0.004    1.72e+04    9.15e+04\n",
            "Последний период владения до 6 лет   1.066e+04   1.52e+04      0.703      0.482   -1.91e+04    4.04e+04\n",
            "==============================================================================\n",
            "Omnibus:                       43.589   Durbin-Watson:                   2.052\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               84.064\n",
            "Skew:                           0.434   Prob(JB):                     5.57e-19\n",
            "Kurtosis:                       4.547   Cond. No.                     8.92e+07\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 8.92e+07. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "vif_new = pd.DataFrame()\n",
        "vif_new[\"VIF\"] = [variance_inflation_factor(df_X_new.values, i) for i in range(df_X_new.shape[1])]\n",
        "vif_new[\"features\"] = df_X_new.columns\n",
        "\n",
        "vif_new = vif_new.round(1)\n",
        "vif_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "jmammf1A7P1C",
        "outputId": "9764e476-a593-4a6b-c55a-65f6691e87ab"
      },
      "execution_count": 26,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "       VIF                             features\n",
              "0    180.4                             Мощность\n",
              "1   1269.7                      Объём двигателя\n",
              "2      4.4                               Пробег\n",
              "3   1191.4                                  Год\n",
              "4      7.5                      КП механическая\n",
              "5    161.3                      Привод передний\n",
              "6      3.7               Стоит на учете в ГИБДД\n",
              "7      3.5                    Классический цвет\n",
              "8      1.4           Был во владении у юр. лица\n",
              "9     71.7                      Кузов_универсал\n",
              "10   101.7                          Кузов_седан\n",
              "11    20.2                        Кузов_лифтбек\n",
              "12    47.5                        Кузов_хэтчбек\n",
              "13     9.0                           Кузов_джип\n",
              "14    16.4            1-2 записей о регистрации\n",
              "15     4.6            3-6 записей о регистрации\n",
              "16     1.9  Последний период владения до 1 года\n",
              "17     3.5   Последний период владения до 6 лет"
            ],
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              "      <th>VIF</th>\n",
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              "      <th>11</th>\n",
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              "      <th>13</th>\n",
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              "    <tr>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "vif_new",
              "summary": "{\n  \"name\": \"vif_new\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"VIF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 389.18863245730466,\n        \"min\": 1.4,\n        \"max\": 1269.7,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          180.4,\n          1269.7,\n          161.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"features\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"\\u041c\\u043e\\u0449\\u043d\\u043e\\u0441\\u0442\\u044c\",\n          \"\\u041e\\u0431\\u044a\\u0451\\u043c \\u0434\\u0432\\u0438\\u0433\\u0430\\u0442\\u0435\\u043b\\u044f\",\n          \"\\u0411\\u044b\\u043b \\u0432\\u043e \\u0432\\u043b\\u0430\\u0434\\u0435\\u043d\\u0438\\u0438 \\u0443 \\u044e\\u0440. \\u043b\\u0438\\u0446\\u0430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "VIF-ы стали лучше. Присутствует несколько очень больших значений, но они хотя бы не бесконечность, теперь будем думать дальше. Для начала давайте посмотрим на корреляции."
      ],
      "metadata": {
        "id": "gyMn6Ie5DyJj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix_abs_new = df_X_new.corr().abs()\n",
        "\n",
        "sns.heatmap(corr_matrix_abs_new, cmap=\"crest\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "id": "xi9PO9_eGwhX",
        "outputId": "cca6cffb-8a1b-47de-e8bf-b316b109ba59"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix_new = df_X_new.corr()\n",
        "mask_new = np.triu(np.ones_like(corr_matrix_abs_new, dtype=bool), k=1)\n",
        "corr_values_new = corr_matrix_abs_new.where(mask_new)\n",
        "pairs_new = corr_values_new.unstack().sort_values(ascending=False).dropna()\n",
        "pairs_top_10_new = pairs_new[:10]\n",
        "\n",
        "print(f\"Топ 10 пар наиболее скоррелированных признаков:\")\n",
        "for index, pair in enumerate(pairs_top_10_new.index, start=1):\n",
        "    col1, col2 = pair\n",
        "    val = corr_matrix_new[col1][col2]\n",
        "    print(f\"{index}. {col1} и {col2}: {val:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WRMLSlF17PvC",
        "outputId": "f58765a1-cd4e-4c62-abf5-cf2b9f58f568"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Топ 10 пар наиболее скоррелированных признаков:\n",
            "1. Кузов_джип и Привод передний: -0.90\n",
            "2. 3-6 записей о регистрации и 1-2 записей о регистрации: -0.84\n",
            "3. 1-2 записей о регистрации и Год: 0.62\n",
            "4. Год и Пробег: -0.57\n",
            "5. Последний период владения до 6 лет и Последний период владения до 1 года: -0.55\n",
            "6. Кузов_хэтчбек и Год: -0.55\n",
            "7. Кузов_седан и Кузов_универсал: -0.52\n",
            "8. Объём двигателя и Мощность: 0.51\n",
            "9. 1-2 записей о регистрации и Пробег: -0.49\n",
            "10. Год и Объём двигателя: 0.49\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Итак, корреляции все еще очень большие как и некоторые VIF-ы, однако сейчас уже понятно, что надо делать. В целом, главный инсайт такой: сделать общую регрессию с такими признаками сложно, возможно, вообще невозможно, поэтому нужно конкретизировать модели, для которых мы строим регрессии и признаки, которые мы используем. Происходит это из-за того, что у нас слишком разные машины и признаки сильно варьируются для них. Джипам, например, свойственен соответствующий кузов, но вместе переднего привода там полный (4WD) и так далее.\n",
        "\n",
        "В общем, бейзлайн сделан, дальше нужно лучше специфицировать модели и признаки."
      ],
      "metadata": {
        "id": "L2-3bluRFs2Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Разные регрессии (F)\n",
        "(здесь построены варианты моделей для разных данных и признаков)"
      ],
      "metadata": {
        "id": "EWZbvWR7sEjz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#регрессия только для цветов\n",
        "colors = df.columns[17:31]\n",
        "\n",
        "X_color = sm.add_constant(df_enc[colors])\n",
        "model_color = sm.OLS(df_enc['Цена'], X_color).fit()\n",
        "\n",
        "print(model_color.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5zBqzlCeSSw",
        "outputId": "f3e98038-c7fc-4459-b313-0f94ffe6c263"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                   Цена   R-squared:                       0.147\n",
            "Model:                            OLS   Adj. R-squared:                  0.128\n",
            "Method:                 Least Squares   F-statistic:                     7.714\n",
            "Date:                Fri, 10 May 2024   Prob (F-statistic):           3.69e-15\n",
            "Time:                        01:39:18   Log-Likelihood:                -9119.7\n",
            "No. Observations:                 641   AIC:                         1.827e+04\n",
            "Df Residuals:                     626   BIC:                         1.834e+04\n",
            "Df Model:                          14                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "====================================================================================\n",
            "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------------\n",
            "const             6.981e+05   1.07e+05      6.543      0.000    4.89e+05    9.08e+05\n",
            "Цвет_бордовый    -1.662e+05   1.69e+05     -0.985      0.325   -4.98e+05    1.65e+05\n",
            "Цвет_голубой     -1.454e+04   1.54e+05     -0.094      0.925   -3.18e+05    2.88e+05\n",
            "Цвет_желтый       3.669e+05   2.82e+05      1.300      0.194   -1.87e+05    9.21e+05\n",
            "Цвет_зеленый     -1.098e+05   1.27e+05     -0.866      0.387   -3.59e+05    1.39e+05\n",
            "Цвет_золотистый   1.739e+05   3.85e+05      0.452      0.651   -5.82e+05    9.29e+05\n",
            "Цвет_коричневый   2.631e+05   1.21e+05      2.180      0.030    2.61e+04       5e+05\n",
            "Цвет_красный      1.698e+05   1.23e+05      1.378      0.169   -7.21e+04    4.12e+05\n",
            "Цвет_оранжевый    5.251e+05   1.41e+05      3.720      0.000    2.48e+05    8.02e+05\n",
            "Цвет_серебристый -5.179e+04   1.16e+05     -0.447      0.655   -2.79e+05    1.75e+05\n",
            "Цвет_серый        1.152e+05   1.13e+05      1.019      0.308   -1.07e+05    3.37e+05\n",
            "Цвет_синий        6.543e+04   1.22e+05      0.536      0.592   -1.74e+05    3.05e+05\n",
            "Цвет_фиолетовый  -2.481e+05   3.85e+05     -0.645      0.519      -1e+06    5.07e+05\n",
            "Цвет_черный       1.101e+04   1.12e+05      0.098      0.922    -2.1e+05    2.32e+05\n",
            "Цвет_белый        2.917e+05    1.1e+05      2.642      0.008    7.49e+04    5.09e+05\n",
            "==============================================================================\n",
            "Omnibus:                        4.447   Durbin-Watson:                   1.999\n",
            "Prob(Omnibus):                  0.108   Jarque-Bera (JB):                3.690\n",
            "Skew:                           0.092   Prob(JB):                        0.158\n",
            "Kurtosis:                       2.677   Cond. No.                         34.3\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#регрессия только для названий машин\n",
        "names = df_enc.columns[-13:]\n",
        "\n",
        "X_name = sm.add_constant(df_enc[names])\n",
        "model_name = sm.OLS(df_enc['Цена'], X_name).fit()\n",
        "\n",
        "print(model_name.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdeCIVHrU5__",
        "outputId": "43a54da5-fe1a-40a7-b460-d99ab35d9d62"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                   Цена   R-squared:                       0.768\n",
            "Model:                            OLS   Adj. R-squared:                  0.763\n",
            "Method:                 Least Squares   F-statistic:                     173.0\n",
            "Date:                Fri, 10 May 2024   Prob (F-statistic):          5.44e-190\n",
            "Time:                        01:39:18   Log-Likelihood:                -8702.7\n",
            "No. Observations:                 641   AIC:                         1.743e+04\n",
            "Df Residuals:                     628   BIC:                         1.749e+04\n",
            "Df Model:                          12                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "======================================================================================\n",
            "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------\n",
            "const               6.264e+05   1.63e+04     38.346      0.000    5.94e+05    6.58e+05\n",
            "Название машины_0  -4.621e+05   9.06e+04     -5.100      0.000    -6.4e+05   -2.84e+05\n",
            "Название машины_1  -5.064e+05   7.46e+04     -6.789      0.000   -6.53e+05    -3.6e+05\n",
            "Название машины_2  -3.966e+05   4.14e+04     -9.586      0.000   -4.78e+05   -3.15e+05\n",
            "Название машины_3  -3.979e+05   2.98e+04    -13.339      0.000   -4.57e+05   -3.39e+05\n",
            "Название машины_4   1.033e+05   1.04e+05      0.991      0.322   -1.01e+05    3.08e+05\n",
            "Название машины_5    2.35e+05   7.46e+04      3.150      0.002    8.85e+04    3.81e+05\n",
            "Название машины_6   5.088e+05   2.21e+04     23.006      0.000    4.65e+05    5.52e+05\n",
            "Название машины_7   7.017e+05   3.05e+04     23.024      0.000    6.42e+05    7.62e+05\n",
            "Название машины_8   1.082e+05   2.13e+04      5.081      0.000    6.64e+04     1.5e+05\n",
            "Название машины_9   2.103e+05   1.04e+05      2.018      0.044    5647.991    4.15e+05\n",
            "Название машины_10  4.187e+05   2.44e+04     17.174      0.000    3.71e+05    4.67e+05\n",
            "Название машины_11  3.802e+05   3.69e+04     10.300      0.000    3.08e+05    4.53e+05\n",
            "Название машины_12 -2.769e+05   2.84e+04     -9.755      0.000   -3.33e+05   -2.21e+05\n",
            "==============================================================================\n",
            "Omnibus:                       43.489   Durbin-Watson:                   2.118\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               62.606\n",
            "Skew:                           0.533   Prob(JB):                     2.54e-14\n",
            "Kurtosis:                       4.100   Cond. No.                     5.18e+15\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 2.8e-29. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Черновик"
      ],
      "metadata": {
        "id": "jGICdnVFT0qA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(np.log(df['Пробег']), 50)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "ZNsyJ8ZXrX6g",
        "outputId": "3c559690-7fca-427a-ec03-40f1e3e624db"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['Пробег'], 50)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "jvNWj_-DreoP",
        "outputId": "2678f69b-18fd-41c2-dc02-45a5703621a9"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv('/content/Drom_data_nodrop.csv')\n",
        "df_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OtFpvANOI4bJ",
        "outputId": "04a40352-c3a5-401d-848a-d49e3e13bb45"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             Unnamed: 0  Мощность  \\\n",
              "0                                                     0     122.0   \n",
              "1                                                     1     122.0   \n",
              "2     2,90.0,механика,передний,седан,белый,9200.0,ле...       NaN   \n",
              "3                                                     3      83.0   \n",
              "4                                                     4     122.0   \n",
              "...                                                 ...       ...   \n",
              "1308  1308,83.0,механика,4WD,,синий,39500.0,левый,\"1...       NaN   \n",
              "1309                                               1309     122.0   \n",
              "1310                                               1310      98.0   \n",
              "1311                                               1311      72.0   \n",
              "1312                                               1312      82.0   \n",
              "\n",
              "     Коробка передач    Привод     Тип кузова        Цвет    Пробег   Руль  \\\n",
              "0           механика  передний      универсал       серый   86684.0  левый   \n",
              "1              робот  передний      универсал       белый   77400.0  левый   \n",
              "2                NaN       NaN            NaN         NaN       NaN    NaN   \n",
              "3           механика       4WD            NaN     зеленый   76916.0  левый   \n",
              "4              робот  передний      универсал      черный   84594.0  левый   \n",
              "...              ...       ...            ...         ...       ...    ...   \n",
              "1308             NaN       NaN            NaN         NaN       NaN    NaN   \n",
              "1309           робот  передний      универсал     красный   71127.0  левый   \n",
              "1310        механика  передний  хэтчбек 5 дв.       серый  237000.0  левый   \n",
              "1311        механика    задний            NaN     зеленый  127000.0    NaN   \n",
              "1312        механика  передний          седан  коричневый   52000.0  левый   \n",
              "\n",
              "        Поколение       Цена  ...                                           4  \\\n",
              "0     1 поколение  1350000.0  ...      Последний период владения больше 5 лет   \n",
              "1     1 поколение  1350000.0  ...      Последний период владения больше 4 лет   \n",
              "2             NaN        NaN  ...                                         NaN   \n",
              "3     1 поколение   655000.0  ...      Последний период владения больше 4 лет   \n",
              "4     1 поколение  1165000.0  ...      Последний период владения больше 2 лет   \n",
              "...           ...        ...  ...                                         ...   \n",
              "1308          NaN        NaN  ...                                         NaN   \n",
              "1309  1 поколение  1249000.0  ...      Последний период владения больше 5 лет   \n",
              "1310  1 поколение   360000.0  ...  Последний период владения больше 6 месяцев   \n",
              "1311  1 поколение   295000.0  ...  Последний период владения больше 6 месяцев   \n",
              "1312  1 поколение   760000.0  ...      Последний период владения больше 5 лет   \n",
              "\n",
              "                          5                          6  \\\n",
              "0     Не числится в розыске  Ограничений не обнаружено   \n",
              "1     Не числится в розыске  Ограничений не обнаружено   \n",
              "2                       NaN                        NaN   \n",
              "3     Не числится в розыске  Ограничений не обнаружено   \n",
              "4     Не числится в розыске  Ограничений не обнаружено   \n",
              "...                     ...                        ...   \n",
              "1308                    NaN                        NaN   \n",
              "1309  Не числится в розыске  Ограничений не обнаружено   \n",
              "1310  Не числится в розыске  Ограничений не обнаружено   \n",
              "1311  Не числится в розыске  Ограничений не обнаружено   \n",
              "1312  Не числится в розыске  Ограничений не обнаружено   \n",
              "\n",
              "                                                 Ссылка  \\\n",
              "0     https://auto.drom.ru/moscow/lada/vesta_cross/5...   \n",
              "1     https://auto.drom.ru/moscow/lada/vesta/2447192...   \n",
              "2                                                   NaN   \n",
              "3     https://auto.drom.ru/moscow/lada/2121_4x4_niva...   \n",
              "4     https://auto.drom.ru/moscow/lada/vesta_cross/9...   \n",
              "...                                                 ...   \n",
              "1308                                                NaN   \n",
              "1309  https://auto.drom.ru/moscow/lada/vesta_cross/2...   \n",
              "1310  https://auto.drom.ru/moscow/lada/priora/268769...   \n",
              "1311  https://auto.drom.ru/moscow/lada/2107/12622377...   \n",
              "1312  https://auto.drom.ru/moscow/lada/granta/104925...   \n",
              "\n",
              "                  Комплектация Особые отметки Топливо двигателя  \\\n",
              "0                          NaN            NaN            бензин   \n",
              "1                          NaN            NaN            бензин   \n",
              "2                          NaN            NaN               NaN   \n",
              "3                          NaN            NaN            бензин   \n",
              "4                          NaN            NaN            бензин   \n",
              "...                        ...            ...               ...   \n",
              "1308                       NaN            NaN               NaN   \n",
              "1309                       NaN            NaN            бензин   \n",
              "1310  1.6 MT Люкс 21723-83-039            NaN            бензин   \n",
              "1311       1.6 MT 21074-40-015            NaN            бензин   \n",
              "1312                       NaN            NaN            бензин   \n",
              "\n",
              "     Объём двигателя     Год     Название машины  \n",
              "0                1.8  2018.0    Лада Веста Кросс  \n",
              "1                1.8  2019.0          Лада Веста  \n",
              "2                NaN     NaN                 NaN  \n",
              "3                1.7  2016.0  Лада 4x4 2121 Нива  \n",
              "4                1.8  2019.0    Лада Веста Кросс  \n",
              "...              ...     ...                 ...  \n",
              "1308             NaN     NaN                 NaN  \n",
              "1309             1.8  2018.0    Лада Веста Кросс  \n",
              "1310             1.6  2009.0         Лада Приора  \n",
              "1311             1.6  2010.0           Лада 2107  \n",
              "1312             1.6  2018.0         Лада Гранта  \n",
              "\n",
              "[1313 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6777e67d-b61e-40b1-b845-91f29c748584\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Мощность</th>\n",
              "      <th>Коробка передач</th>\n",
              "      <th>Привод</th>\n",
              "      <th>Тип кузова</th>\n",
              "      <th>Цвет</th>\n",
              "      <th>Пробег</th>\n",
              "      <th>Руль</th>\n",
              "      <th>Поколение</th>\n",
              "      <th>Цена</th>\n",
              "      <th>...</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>Ссылка</th>\n",
              "      <th>Комплектация</th>\n",
              "      <th>Особые отметки</th>\n",
              "      <th>Топливо двигателя</th>\n",
              "      <th>Объём двигателя</th>\n",
              "      <th>Год</th>\n",
              "      <th>Название машины</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>механика</td>\n",
              "      <td>передний</td>\n",
              "      <td>универсал</td>\n",
              "      <td>серый</td>\n",
              "      <td>86684.0</td>\n",
              "      <td>левый</td>\n",
              "      <td>1 поколение</td>\n",
              "      <td>1350000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Последний период владения больше 5 лет</td>\n",
              "      <td>Не числится в розыске</td>\n",
              "      <td>Ограничений не обнаружено</td>\n",
              "      <td>https://auto.drom.ru/moscow/lada/vesta_cross/5...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>бензин</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>Лада Веста Кросс</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>122.0</td>\n",
              "      <td>робот</td>\n",
              "      <td>передний</td>\n",
              "      <td>универсал</td>\n",
              "      <td>белый</td>\n",
              "      <td>77400.0</td>\n",
              "      <td>левый</td>\n",
              "      <td>1 поколение</td>\n",
              "      <td>1350000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Последний период владения больше 4 лет</td>\n",
              "      <td>Не числится в розыске</td>\n",
              "      <td>Ограничений не обнаружено</td>\n",
              "      <td>https://auto.drom.ru/moscow/lada/vesta/2447192...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>бензин</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>Лада Веста</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2,90.0,механика,передний,седан,белый,9200.0,ле...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>83.0</td>\n",
              "      <td>механика</td>\n",
              "      <td>4WD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>зеленый</td>\n",
              "      <td>76916.0</td>\n",
              "      <td>левый</td>\n",
              "      <td>1 поколение</td>\n",
              "      <td>655000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Последний период владения больше 4 лет</td>\n",
              "      <td>Не числится в розыске</td>\n",
              "      <td>Ограничений не обнаружено</td>\n",
              "      <td>https://auto.drom.ru/moscow/lada/2121_4x4_niva...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>бензин</td>\n",
              "      <td>1.7</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>Лада 4x4 2121 Нива</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>122.0</td>\n",
              "      <td>робот</td>\n",
              "      <td>передний</td>\n",
              "      <td>универсал</td>\n",
              "      <td>черный</td>\n",
              "      <td>84594.0</td>\n",
              "      <td>левый</td>\n",
              "      <td>1 поколение</td>\n",
              "      <td>1165000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Последний период владения больше 2 лет</td>\n",
              "      <td>Не числится в розыске</td>\n",
              "      <td>Ограничений не обнаружено</td>\n",
              "      <td>https://auto.drom.ru/moscow/lada/vesta_cross/9...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>бензин</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>Лада Веста Кросс</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>1308,83.0,механика,4WD,,синий,39500.0,левый,\"1...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1309</th>\n",
              "      <td>1309</td>\n",
              "      <td>122.0</td>\n",
              "      <td>робот</td>\n",
              "      <td>передний</td>\n",
              "      <td>универсал</td>\n",
              "      <td>красный</td>\n",
              "      <td>71127.0</td>\n",
              "      <td>левый</td>\n",
              "      <td>1 поколение</td>\n",
              "      <td>1249000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Последний период владения больше 5 лет</td>\n",
              "      <td>Не числится в розыске</td>\n",
              "      <td>Ограничений не обнаружено</td>\n",
              "      <td>https://auto.drom.ru/moscow/lada/vesta_cross/2...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>бензин</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>Лада Веста Кросс</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1310</th>\n",
              "      <td>1310</td>\n",
              "      <td>98.0</td>\n",
              "      <td>механика</td>\n",
              "      <td>передний</td>\n",
              "      <td>хэтчбек 5 дв.</td>\n",
              "      <td>серый</td>\n",
              "      <td>237000.0</td>\n",
              "      <td>левый</td>\n",
              "      <td>1 поколение</td>\n",
              "      <td>360000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Последний период владения больше 6 месяцев</td>\n",
              "      <td>Не числится в розыске</td>\n",
              "      <td>Ограничений не обнаружено</td>\n",
              "      <td>https://auto.drom.ru/moscow/lada/priora/268769...</td>\n",
              "      <td>1.6 MT Люкс 21723-83-039</td>\n",
              "      <td>NaN</td>\n",
              "      <td>бензин</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Лада Приора</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1311</th>\n",
              "      <td>1311</td>\n",
              "      <td>72.0</td>\n",
              "      <td>механика</td>\n",
              "      <td>задний</td>\n",
              "      <td>NaN</td>\n",
              "      <td>зеленый</td>\n",
              "      <td>127000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1 поколение</td>\n",
              "      <td>295000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Последний период владения больше 6 месяцев</td>\n",
              "      <td>Не числится в розыске</td>\n",
              "      <td>Ограничений не обнаружено</td>\n",
              "      <td>https://auto.drom.ru/moscow/lada/2107/12622377...</td>\n",
              "      <td>1.6 MT 21074-40-015</td>\n",
              "      <td>NaN</td>\n",
              "      <td>бензин</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>Лада 2107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1312</th>\n",
              "      <td>1312</td>\n",
              "      <td>82.0</td>\n",
              "      <td>механика</td>\n",
              "      <td>передний</td>\n",
              "      <td>седан</td>\n",
              "      <td>коричневый</td>\n",
              "      <td>52000.0</td>\n",
              "      <td>левый</td>\n",
              "      <td>1 поколение</td>\n",
              "      <td>760000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Последний период владения больше 5 лет</td>\n",
              "      <td>Не числится в розыске</td>\n",
              "      <td>Ограничений не обнаружено</td>\n",
              "      <td>https://auto.drom.ru/moscow/lada/granta/104925...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>бензин</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>Лада Гранта</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1313 rows × 24 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6777e67d-b61e-40b1-b845-91f29c748584')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6777e67d-b61e-40b1-b845-91f29c748584 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6777e67d-b61e-40b1-b845-91f29c748584');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-57194f1e-9c3e-410a-b1df-9a9ae979b9b7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-57194f1e-9c3e-410a-b1df-9a9ae979b9b7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-57194f1e-9c3e-410a-b1df-9a9ae979b9b7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P-_0lBmMJgWe",
        "outputId": "29029f3b-3d2f-4b4b-c2ff-35da961afe8b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0              0\n",
              "Мощность              239\n",
              "Коробка передач       233\n",
              "Привод                234\n",
              "Тип кузова            640\n",
              "Цвет                  250\n",
              "Пробег                235\n",
              "Руль                  416\n",
              "Поколение             246\n",
              "Цена                  230\n",
              "0                     259\n",
              "1                     259\n",
              "2                     259\n",
              "3                     259\n",
              "4                     259\n",
              "5                     259\n",
              "6                     259\n",
              "Ссылка                230\n",
              "Комплектация          885\n",
              "Особые отметки       1275\n",
              "Топливо двигателя     232\n",
              "Объём двигателя       233\n",
              "Год                   230\n",
              "Название машины       259\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['Особые отметки'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQk6uewPJqYW",
        "outputId": "7556960f-fe1c-4574-dd18-1f9c4cac15b6"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'требуется ремонт или не на ходу',\n",
              "       'документы с проблемами или отсутствуют'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['Название машины'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0mKrwQFEKdr2",
        "outputId": "bcaad94f-2c77-4cc3-d910-d89d84c0406a"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Название машины\n",
              "Лада Веста            156\n",
              "Лада Ларгус           111\n",
              "Лада 2107              80\n",
              "Лада Гранта            79\n",
              "Лада 2114 Самара       70\n",
              "Лада Приора            66\n",
              "Лада Веста Кросс       55\n",
              "Лада Калина            47\n",
              "Лада 2115 Самара       43\n",
              "Лада Нива Легенд       36\n",
              "Лада 2105              34\n",
              "Лада 2112              29\n",
              "Лада Ларгус Кросс      29\n",
              "Лада 4x4 2121 Нива     28\n",
              "Лада 2106              25\n",
              "Лада 21099             23\n",
              "Лада 2104              22\n",
              "Лада 2110              18\n",
              "Лада 2101              15\n",
              "Лада Нива Тревел       14\n",
              "Лада 4x4 2131 Нива     13\n",
              "Лада 2113 Самара       10\n",
              "рей Кросс               9\n",
              "Лада Веста Спорт        8\n",
              "Лада 2111               8\n",
              "Лада Калина Кросс       6\n",
              "Лада Гранта Кросс       5\n",
              "Лада 4x4 Урбан          4\n",
              "Лада 2108               2\n",
              "Лада 2109               2\n",
              "Лада Гранта Спорт       2\n",
              "Лада 2103               2\n",
              "Лада Калина Спорт       1\n",
              "Лада 4x4 Бронто         1\n",
              "Лада 1111 Ока           1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['Цвет'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qA1mf6mLbKD",
        "outputId": "e01d11f3-47a3-4514-972b-893a070d798f"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Цвет\n",
              "белый          218\n",
              "черный         134\n",
              "серебристый    123\n",
              "серый          122\n",
              "синий          111\n",
              "зеленый         98\n",
              "коричневый      69\n",
              "красный         69\n",
              "бордовый        38\n",
              "бежевый         25\n",
              "оранжевый       23\n",
              "голубой         16\n",
              "фиолетовый      13\n",
              "желтый           3\n",
              "золотистый       1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.groupby(['Цвет'])['Цена'].mean().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yOG-HNKuK8Fh",
        "outputId": "d3ba9905-dbce-45ac-fd39-45fc36e0288f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Цвет\n",
              "оранжевый      1.092913e+06\n",
              "коричневый     9.265030e+05\n",
              "белый          8.598489e+05\n",
              "золотистый     7.980000e+05\n",
              "желтый         6.683333e+05\n",
              "голубой        6.532500e+05\n",
              "серый          6.444836e+05\n",
              "красный        5.792029e+05\n",
              "бежевый        5.732400e+05\n",
              "черный         5.631626e+05\n",
              "синий          5.474063e+05\n",
              "серебристый    5.102493e+05\n",
              "зеленый        4.536860e+05\n",
              "бордовый       2.578158e+05\n",
              "фиолетовый     1.822308e+05\n",
              "Name: Цена, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yYjMGvrtKZor"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}