light_model.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "! pip install category_encoders"
      ],
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        },
        "id": "dEwLjRUJMXf0",
        "outputId": "e42d39e4-8252-4c94-a331-e779a7436e4f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: category_encoders in /usr/local/lib/python3.7/dist-packages (2.5.0)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.0.2)\n",
            "Requirement already satisfied: pandas>=1.0.5 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.3.5)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.10.2)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.5.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.7.3)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.5->category_encoders) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.5->category_encoders) (2022.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.5.1->category_encoders) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category_encoders) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.1.0)\n"
          ]
        }
      ]
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      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "TA_REpsXFvEF",
        "outputId": "99a2fb4f-d0ea-484c-fb74-f245f7cf3f91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "pd.set_option('display.max_columns', 500)\n",
        "from category_encoders.target_encoder import TargetEncoder\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PATH_TO_DATA = 'covid_data_train_new.csv'\n",
        "PATH_TO_TEST = 'covid_data_test.csv'"
      ],
      "metadata": {
        "id": "nCpFAqv4MFm9"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "covid_df = pd.read_csv(PATH_TO_DATA)\n",
        "covid_df = covid_df.drop(columns=['Unnamed: 0'])\n",
        "\n",
        "# исключим дубликаты городов и записи без таргета \n",
        "covid_df = covid_df[covid_df['inf_rate'].notna()]\n",
        "covid_df = covid_df.drop_duplicates(subset='lat')"
      ],
      "metadata": {
        "id": "E5X2IsRfbpDx"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Сгенерируем признаки"
      ],
      "metadata": {
        "id": "d0lbl5qrIdAJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# строки нельзя подать в Random Forest, поэтому будем использовать для них TargetEncoder\n",
        "covid_df.select_dtypes('object')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "HWG6VCkv_wZl",
        "outputId": "06a0a66b-ccba-4fa9-fde9-159d379fdf44"
      },
      "execution_count": 5,
      "outputs": [
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              "                name         district               subject  \\\n",
              "0              Абаза        Сибирский               Хакасия   \n",
              "1             Абакан        Сибирский               Хакасия   \n",
              "2           Абдулино      Приволжский  Оренбургская область   \n",
              "3             Абинск            Южный    Краснодарский край   \n",
              "4              Агрыз      Приволжский             Татарстан   \n",
              "..               ...              ...                   ...   \n",
              "446         Макарьев      Центральный   Костромская область   \n",
              "447         Макушино        Уральский    Курганская область   \n",
              "448     Малая Вишера  Северо-Западный  Новгородская область   \n",
              "449  Малоархангельск      Центральный     Орловская область   \n",
              "450    Малоярославец      Центральный     Калужская область   \n",
              "\n",
              "                 region_x  \n",
              "0      Республика Хакасия  \n",
              "1      Республика Хакасия  \n",
              "2    Оренбургская область  \n",
              "3      Краснодарский край  \n",
              "4    Республика Татарстан  \n",
              "..                    ...  \n",
              "446   Костромская область  \n",
              "447    Курганская область  \n",
              "448  Новгородская область  \n",
              "449     Орловская область  \n",
              "450     Калужская область  \n",
              "\n",
              "[395 rows x 4 columns]"
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              "      <th>name</th>\n",
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              "      <td>Абаза</td>\n",
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              "      <td>Абдулино</td>\n",
              "      <td>Приволжский</td>\n",
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              "      <td>Костромская область</td>\n",
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              "    <tr>\n",
              "      <th>447</th>\n",
              "      <td>Макушино</td>\n",
              "      <td>Уральский</td>\n",
              "      <td>Курганская область</td>\n",
              "      <td>Курганская область</td>\n",
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              "      <td>Новгородская область</td>\n",
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              "      <th>449</th>\n",
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              "      <td>Центральный</td>\n",
              "      <td>Орловская область</td>\n",
              "      <td>Орловская область</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450</th>\n",
              "      <td>Малоярославец</td>\n",
              "      <td>Центральный</td>\n",
              "      <td>Калужская область</td>\n",
              "      <td>Калужская область</td>\n",
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              "<p>395 rows × 4 columns</p>\n",
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    {
      "cell_type": "code",
      "source": [
        "te = TargetEncoder().fit(covid_df.select_dtypes('object'), covid_df['inf_rate'])\n",
        "cat_f = te.transform(covid_df.select_dtypes('object'))\n",
        "covid_df_rf = covid_df.drop(columns=covid_df.select_dtypes('object').columns).join(cat_f).drop(columns='name')\n",
        "# добавим обратно все признаки, кроме name, потому что это айдишник, будет лик"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24Ezm3K7bw5s",
        "outputId": "f5e3e05c-d3f3-448b-fcc2-f79ef7d798db"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/category_encoders/target_encoder.py:94: FutureWarning: Default parameter min_samples_leaf will change in version 2.6.See https://github.com/scikit-learn-contrib/category_encoders/issues/327\n",
            "  category=FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/category_encoders/target_encoder.py:99: FutureWarning: Default parameter smoothing will change in version 2.6.See https://github.com/scikit-learn-contrib/category_encoders/issues/327\n",
            "  category=FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# доля больных туберкулезом в городе\n",
        "covid_df_rf['patients_percent_2017'] = covid_df_rf.apply(\n",
        "    lambda x: x['num_patients_tubercul_2017']/x['population'], axis=1)"
      ],
      "metadata": {
        "id": "eYwR8Ynj3Q8l"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# объем предлагаемых населению услуг, суммарно\n",
        "covid_df_rf['volume_serv_total'] = covid_df_rf.apply(lambda x: x['volume_serv_household_2017'] +\\\n",
        "                            x['volume_serv_chargeable_2017']+\\\n",
        "                            x['volume_serv_transport_2017'] +\\\n",
        "                            x['volume_serv_post_2017']+\\\n",
        "                            x['volume_serv_accommodation_2017'] +\\\n",
        "                            x['volume_serv_telecom_2017']+\\\n",
        "                            x['volume_serv_others_2017'] +\\\n",
        "                            x['volume_serv_veterinary_2017']+\\\n",
        "                            x['volume_serv_housing_2017'] +\\\n",
        "                            x['volume_serv_education_2017']+\\\n",
        "                            x['volume_serv_medicine_2017'] +\\\n",
        "                            x['volume_serv_disabled_2017']+\\\n",
        "                            x['volume_serv_culture_2017'] +\\\n",
        "                            x['volume_serv_sport_2017']+\\\n",
        "                            x['volume_serv_hotels_2017'] +\\\n",
        "                            x['volume_serv_tourism_2017']+\\\n",
        "                            x['volume_serv_sanatorium_2017'],\n",
        "                            axis=1\n",
        "                        )"
      ],
      "metadata": {
        "id": "r1oIabCS72wZ"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "[x for x in covid_df_rf.columns if 'urban' in x]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsYsVu8bFLRs",
        "outputId": "8d223039-bde4-4cb7-8969-cec4c522e77c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['urban_50-54_years',\n",
              " 'urban_55-59_years',\n",
              " 'urban_60-64_years',\n",
              " 'urban_65-69_years',\n",
              " 'urban_70-74_years',\n",
              " 'urban_75-79_years',\n",
              " 'urban_80-84_years',\n",
              " 'urban_85-89_years',\n",
              " 'urban_90-94_years',\n",
              " 'num_phones_urban_2019',\n",
              " 'urban']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# те же признаки, но в процентах от населения города\n",
        "covid_df_rf['urban_50-54_years_percent'] = covid_df.apply(\n",
        "    lambda x: x['urban_50-54_years']/x['whole_population'], axis=1)\n",
        "covid_df_rf['urban_55-59_years_percent'] = covid_df.apply(\n",
        "    lambda x: x['urban_55-59_years']/x['whole_population'], axis=1)\n",
        "covid_df_rf['urban_60-64_years_percent'] = covid_df.apply(\n",
        "    lambda x: x['urban_60-64_years']/x['whole_population'], axis=1)\n",
        "covid_df_rf['urban_65-69_years_percent'] = covid_df.apply(\n",
        "    lambda x: x['urban_65-69_years']/x['whole_population'], axis=1)\n",
        "covid_df_rf['urban_70-74_years_percent'] = covid_df.apply(\n",
        "    lambda x: x['urban_70-74_years']/x['whole_population'], axis=1)\n",
        "covid_df_rf['urban_75-79_years_percent'] = covid_df.apply(\n",
        "    lambda x: x['urban_75-79_years']/x['whole_population'], axis=1)\n",
        "covid_df_rf['urban_80-84_years_percent'] = covid_df.apply(\n",
        "    lambda x: x['urban_80-84_years']/x['whole_population'], axis=1)\n",
        "covid_df_rf['urban_85-89_years_percent'] = covid_df.apply(\n",
        "    lambda x: x['urban_85-89_years']/x['whole_population'], axis=1)\n",
        "covid_df_rf['urban_90-94_years_percent'] = covid_df.apply(\n",
        "    lambda x: x['urban_90-94_years']/x['whole_population'], axis=1)\n",
        "covid_df_rf['urban_percent'] = covid_df.apply(\n",
        "    lambda x: x['urban']/x['whole_population'], axis=1)"
      ],
      "metadata": {
        "id": "6iBsr2wJB3_S"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Удалим ненужные признаки"
      ],
      "metadata": {
        "id": "s1gHaFw9H_Hq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "covid_df_rf = covid_df_rf.drop(columns=[\n",
        "    'num_patients_tubercul_1992',\n",
        "    'num_patients_tubercul_1993',\n",
        "    'num_patients_tubercul_1994',\n",
        "    'num_patients_tubercul_1995',\n",
        "    'num_patients_tubercul_1996',\n",
        "    'num_patients_tubercul_1997',\n",
        "    'num_patients_tubercul_1998',\n",
        "    'num_patients_tubercul_1999',\n",
        "    'num_patients_tubercul_2000',\n",
        "    'num_patients_tubercul_2001',\n",
        "    'num_patients_tubercul_2002',\n",
        "    'num_patients_tubercul_2003',\n",
        "    'num_patients_tubercul_2004',\n",
        "    'num_patients_tubercul_2005',\n",
        "    'num_patients_tubercul_2006',\n",
        "    'num_patients_tubercul_2007',\n",
        "    'num_patients_tubercul_2008',\n",
        "    'num_patients_tubercul_2009',\n",
        "    'num_patients_tubercul_2010',\n",
        "    'num_patients_tubercul_2011',\n",
        "    'num_patients_tubercul_2012',\n",
        "    'num_patients_tubercul_2013',\n",
        "    'num_patients_tubercul_2014',\n",
        "    'num_patients_tubercul_2015',\n",
        "    'num_patients_tubercul_2016',\n",
        "    'num_patients_tubercul_2017',\n",
        "             ])"
      ],
      "metadata": {
        "id": "EvLTD3kf3ZW-"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in covid_df_rf.columns:\n",
        "  if covid_df_rf[covid_df_rf[col].isna()].shape[0] != 0:\n",
        "    print(f\"COLUMN: {col}\")\n",
        "    print(covid_df_rf[covid_df_rf[col].isna()].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3e6kxlY6ipv",
        "outputId": "626503c7-3d19-49d7-f437-ee0a0a69930d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "COLUMN: ivl_per_100k\n",
            "(68, 105)\n",
            "COLUMN: ivl_number\n",
            "(68, 105)\n",
            "COLUMN: ekmo_per_100k\n",
            "(68, 105)\n",
            "COLUMN: ekmo_number\n",
            "(217, 105)\n",
            "COLUMN: life_quality_place_rating\n",
            "(295, 105)\n",
            "COLUMN: ecology\n",
            "(295, 105)\n",
            "COLUMN: cleanness\n",
            "(295, 105)\n",
            "COLUMN: public_services\n",
            "(295, 105)\n",
            "COLUMN: neighbourhood\n",
            "(295, 105)\n",
            "COLUMN: children_places\n",
            "(295, 105)\n",
            "COLUMN: sport_and_outdoor\n",
            "(295, 105)\n",
            "COLUMN: shops_and_malls\n",
            "(295, 105)\n",
            "COLUMN: public_transport\n",
            "(295, 105)\n",
            "COLUMN: security\n",
            "(295, 105)\n",
            "COLUMN: life_costs\n",
            "(295, 105)\n",
            "COLUMN: epirank_avia\n",
            "(352, 105)\n",
            "COLUMN: epirank_bus\n",
            "(88, 105)\n",
            "COLUMN: epirank_train\n",
            "(144, 105)\n",
            "COLUMN: epirank_avia_cat\n",
            "(352, 105)\n",
            "COLUMN: epirank_bus_cat\n",
            "(88, 105)\n",
            "COLUMN: epirank_train_cat\n",
            "(144, 105)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# много пропущенных значений\n",
        "covid_df_rf = covid_df_rf.drop(columns=[\n",
        "    'life_costs',\n",
        "    'epirank_avia',\n",
        "    'epirank_bus',\n",
        "    'epirank_train',\n",
        "    'epirank_avia_cat',\n",
        "    'epirank_bus_cat',\n",
        "    'epirank_train_cat',\n",
        "    'life_quality_place_rating',\n",
        "    'ecology',\n",
        "    'public_services',\n",
        "    'neighbourhood',\n",
        "    'children_places',\n",
        "    'shops_and_malls',\n",
        "    'public_transport',\n",
        "    'security'\n",
        "])"
      ],
      "metadata": {
        "id": "tQHMBnGZypF3"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Обучим модель"
      ],
      "metadata": {
        "id": "PoEpTUaYL6-l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "covid_df_rf = covid_df_rf[[\n",
        "  'region_x',\n",
        "  'subject',\n",
        "  'work_ratio_15-72_years',\n",
        "  'density', \n",
        "  'inf_rate']]"
      ],
      "metadata": {
        "id": "svd_dWaPVzjf"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    covid_df_rf.drop(columns=['inf_rate']),\n",
        "    covid_df_rf['inf_rate'],\n",
        "    test_size=0.33,\n",
        "     random_state=42,\n",
        "     )"
      ],
      "metadata": {
        "id": "PnMATZJsckn0"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "covid_df_rf.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xCqcFxZH7XDp",
        "outputId": "1c3e3302-3ba8-4ddd-faa0-53a13ada3299"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(395, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RandomForestRegressor(max_depth=5, n_estimators=20, random_state=42)\n",
        "clf.fit(X_train.fillna(-1), y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGlYN4OLc25w",
        "outputId": "39f8bb6f-4ac9-46bd-8a21-f6d1b79365a4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=5, n_estimators=20, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test, clf.predict(X_test.fillna(-1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBgZfS6Nc_Ho",
        "outputId": "154eb7ac-8a8a-43d7-b38c-44c968de5c7c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.012083507049890927"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_train, clf.predict(X_train.fillna(-1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZFmdPqAGdG0y",
        "outputId": "5026e580-fa6c-4aec-a2ca-cb8568c21fd2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.012963212336592166"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "GridSearch"
      ],
      "metadata": {
        "id": "EY33ROTwhewu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RandomForestRegressor(random_state=0)\n",
        "params = [{'max_depth': [2, 3, 4, 5],\n",
        "         'n_estimators': [8, 10, 12, 15, 18, 20]}]\n",
        "clf_gs = GridSearchCV(clf,\n",
        "                      param_grid=params,\n",
        "                      scoring='neg_mean_absolute_error',\n",
        "                      cv=5)\n",
        "clf_gs.fit(X_train.fillna(-1), y_train)\n",
        "clf_gs.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lh1gf_eLdNpv",
        "outputId": "b5b0a736-7597-485d-9dcc-e5365496f59d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 5, 'n_estimators': 20}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RandomForestRegressor(max_depth=5, n_estimators=20, random_state=0)\n",
        "clf.fit(X_train.fillna(-1), y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvng7a7jh92f",
        "outputId": "4ab24564-059a-4216-9728-836f06b69f30"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=5, n_estimators=20, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test, clf.predict(X_test.fillna(-1)))"
      ],
      "metadata": {
        "outputId": "da78380d-5796-4a58-dcee-28d22cfa41fe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vz7OPQ3PicZk"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01297693724753621"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_train, clf.predict(X_train.fillna(-1)))"
      ],
      "metadata": {
        "outputId": "38fa67d0-e178-4dd5-9136-f7b746e5b676",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y59uFXTMicZl"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.013237713437158845"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    }
  ]
}