Moscow_house_pricing.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":7529522,"sourceType":"datasetVersion","datasetId":4385516}],"dockerImageVersionId":30732,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nimport warnings\nimport matplotlib.pyplot as plt\nwarnings.filterwarnings('ignore')\nfiles= list()\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        files.append(dirname + '/' + filename)\n        print(os.path.join(dirname, filename))\n        \nprint(files)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-07-02T14:59:00.540529Z","iopub.execute_input":"2024-07-02T14:59:00.541605Z","iopub.status.idle":"2024-07-02T14:59:00.567165Z","shell.execute_reply.started":"2024-07-02T14:59:00.541570Z","shell.execute_reply":"2024-07-02T14:59:00.566157Z"},"trusted":true},"execution_count":156,"outputs":[{"name":"stdout","text":"/kaggle/input/moscow-housing-price-dataset/data.csv\n['/kaggle/input/moscow-housing-price-dataset/data.csv']\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearnex import patch_sklearn, unpatch_sklearn\npatch_sklearn()\n#unpatch_sklearn()","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:00.581115Z","iopub.execute_input":"2024-07-02T14:59:00.582304Z","iopub.status.idle":"2024-07-02T14:59:00.588260Z","shell.execute_reply.started":"2024-07-02T14:59:00.582259Z","shell.execute_reply":"2024-07-02T14:59:00.587079Z"},"trusted":true},"execution_count":157,"outputs":[{"name":"stderr","text":"Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.read_csv(files[0])\nprint(df.shape)\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:00.608388Z","iopub.execute_input":"2024-07-02T14:59:00.608765Z","iopub.status.idle":"2024-07-02T14:59:00.669427Z","shell.execute_reply.started":"2024-07-02T14:59:00.608733Z","shell.execute_reply":"2024-07-02T14:59:00.668266Z"},"trusted":true},"execution_count":158,"outputs":[{"name":"stdout","text":"(22676, 12)\n","output_type":"stream"},{"execution_count":158,"output_type":"execute_result","data":{"text/plain":"        Price Apartment type Metro station  Minutes to metro         Region  \\\n0   6300000.0      Secondary       Опалиха               6.0  Moscow region   \n1   9000000.0      Secondary       Павшино               2.0  Moscow region   \n2  11090000.0      Secondary      Мякинино              14.0  Moscow region   \n3   8300000.0      Secondary      Строгино               8.0  Moscow region   \n4   6450000.0      Secondary       Опалиха               6.0  Moscow region   \n\n   Number of rooms  Area  Living area  Kitchen area  Floor  Number of floors  \\\n0              1.0  30.6         11.1           8.5   25.0                25   \n1              1.0  49.2         20.0          10.0    6.0                15   \n2              1.0  44.7         16.2          13.1   10.0                25   \n3              1.0  35.1         16.0          11.0   12.0                33   \n4              1.0  37.7         15.2           4.0    5.0                 5   \n\n                  Renovation  \n0                   Cosmetic  \n1  European-style renovation  \n2                   Cosmetic  \n3  European-style renovation  \n4         Without renovation  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Price</th>\n      <th>Apartment type</th>\n      <th>Metro station</th>\n      <th>Minutes to metro</th>\n      <th>Region</th>\n      <th>Number of rooms</th>\n      <th>Area</th>\n      <th>Living area</th>\n      <th>Kitchen area</th>\n      <th>Floor</th>\n      <th>Number of floors</th>\n      <th>Renovation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6300000.0</td>\n      <td>Secondary</td>\n      <td>Опалиха</td>\n      <td>6.0</td>\n      <td>Moscow region</td>\n      <td>1.0</td>\n      <td>30.6</td>\n      <td>11.1</td>\n      <td>8.5</td>\n      <td>25.0</td>\n      <td>25</td>\n      <td>Cosmetic</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>9000000.0</td>\n      <td>Secondary</td>\n      <td>Павшино</td>\n      <td>2.0</td>\n      <td>Moscow region</td>\n      <td>1.0</td>\n      <td>49.2</td>\n      <td>20.0</td>\n      <td>10.0</td>\n      <td>6.0</td>\n      <td>15</td>\n      <td>European-style renovation</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>11090000.0</td>\n      <td>Secondary</td>\n      <td>Мякинино</td>\n      <td>14.0</td>\n      <td>Moscow region</td>\n      <td>1.0</td>\n      <td>44.7</td>\n      <td>16.2</td>\n      <td>13.1</td>\n      <td>10.0</td>\n      <td>25</td>\n      <td>Cosmetic</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8300000.0</td>\n      <td>Secondary</td>\n      <td>Строгино</td>\n      <td>8.0</td>\n      <td>Moscow region</td>\n      <td>1.0</td>\n      <td>35.1</td>\n      <td>16.0</td>\n      <td>11.0</td>\n      <td>12.0</td>\n      <td>33</td>\n      <td>European-style renovation</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6450000.0</td>\n      <td>Secondary</td>\n      <td>Опалиха</td>\n      <td>6.0</td>\n      <td>Moscow region</td>\n      <td>1.0</td>\n      <td>37.7</td>\n      <td>15.2</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>5</td>\n      <td>Without renovation</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y = df['Price']\ndf = df.drop('Price', axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:00.671114Z","iopub.execute_input":"2024-07-02T14:59:00.671454Z","iopub.status.idle":"2024-07-02T14:59:00.678383Z","shell.execute_reply.started":"2024-07-02T14:59:00.671426Z","shell.execute_reply":"2024-07-02T14:59:00.677261Z"},"trusted":true},"execution_count":159,"outputs":[]},{"cell_type":"code","source":"print('Missed values at all:',df.isna().sum().sum())","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:00.688485Z","iopub.execute_input":"2024-07-02T14:59:00.688858Z","iopub.status.idle":"2024-07-02T14:59:00.701020Z","shell.execute_reply.started":"2024-07-02T14:59:00.688824Z","shell.execute_reply":"2024-07-02T14:59:00.699792Z"},"trusted":true},"execution_count":160,"outputs":[{"name":"stdout","text":"Missed values at all: 0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Target - 'Price' columns.\n\nLets make EDA to other columns","metadata":{}},{"cell_type":"code","source":"bin_cols = ['Apartment type']\ndf['Apartment type'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:00.757968Z","iopub.execute_input":"2024-07-02T14:59:00.758844Z","iopub.status.idle":"2024-07-02T14:59:00.768232Z","shell.execute_reply.started":"2024-07-02T14:59:00.758808Z","shell.execute_reply":"2024-07-02T14:59:00.767203Z"},"trusted":true},"execution_count":161,"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"text/plain":"Apartment type\nSecondary       13152\nNew building     9524\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"We can see that 'Apartment type' column takes only two values, we can easy replace 'Secondary' and 'new building' as 0 and 1","metadata":{}},{"cell_type":"markdown","source":"Now lets try to encode 'metro stantion' column.","metadata":{}},{"cell_type":"code","source":"mean_target_cols = ['Metro station']\ndf['Metro station'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:00.782389Z","iopub.execute_input":"2024-07-02T14:59:00.782742Z","iopub.status.idle":"2024-07-02T14:59:00.793749Z","shell.execute_reply.started":"2024-07-02T14:59:00.782716Z","shell.execute_reply":"2024-07-02T14:59:00.792455Z"},"trusted":true},"execution_count":162,"outputs":[{"execution_count":162,"output_type":"execute_result","data":{"text/plain":"Metro station\nКрасногвардейская            2697\nДепо                         1646\nБратиславская                1157\nКотельники                    981\nЖулебино                      731\n                             ... \n Каховская                      1\n Вешняки                        1\n Текстильщики                   1\n Марксистская                   1\n Улица Академика Королёва       1\nName: count, Length: 547, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"I would like to use the Mean target encoding, just cause the price of housing in Moscow strongly depends on the location","metadata":{}},{"cell_type":"code","source":"bin_cols.append('Region')\ndf['Region'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:00.821210Z","iopub.execute_input":"2024-07-02T14:59:00.821591Z","iopub.status.idle":"2024-07-02T14:59:00.832324Z","shell.execute_reply.started":"2024-07-02T14:59:00.821560Z","shell.execute_reply":"2024-07-02T14:59:00.830406Z"},"trusted":true},"execution_count":163,"outputs":[{"execution_count":163,"output_type":"execute_result","data":{"text/plain":"Region\nMoscow           16113\nMoscow region     6563\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"Region exactly takes binary values","metadata":{}},{"cell_type":"code","source":"one_hot_cols = ['Renovation']\none_hot_cols+= bin_cols\ndf['Renovation'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:00.845546Z","iopub.execute_input":"2024-07-02T14:59:00.845941Z","iopub.status.idle":"2024-07-02T14:59:00.856100Z","shell.execute_reply.started":"2024-07-02T14:59:00.845909Z","shell.execute_reply":"2024-07-02T14:59:00.854964Z"},"trusted":true},"execution_count":164,"outputs":[{"execution_count":164,"output_type":"execute_result","data":{"text/plain":"Renovation\nCosmetic                     12788\nEuropean-style renovation     3666\nWithout renovation            3198\nDesigner                      3024\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"Lest encode \"Renovation\" column by one-hot","metadata":{}},{"cell_type":"markdown","source":"Other columns is just numeric values, we will StandartScale it later","metadata":{}},{"cell_type":"code","source":"num_cols = df.select_dtypes(exclude=['object']).columns.to_list()\nnum_cols","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:00.871897Z","iopub.execute_input":"2024-07-02T14:59:00.872311Z","iopub.status.idle":"2024-07-02T14:59:00.880647Z","shell.execute_reply.started":"2024-07-02T14:59:00.872278Z","shell.execute_reply":"2024-07-02T14:59:00.879479Z"},"trusted":true},"execution_count":165,"outputs":[{"execution_count":165,"output_type":"execute_result","data":{"text/plain":"['Minutes to metro',\n 'Number of rooms',\n 'Area',\n 'Living area',\n 'Kitchen area',\n 'Floor',\n 'Number of floors']"},"metadata":{}}]},{"cell_type":"markdown","source":"Lets make MeanTargetTransformer for Metro station column\n\n(i lied chatgpt made it)\n\n((i lied twice we made it together (me 77%, him 33%)))","metadata":{}},{"cell_type":"markdown","source":"I want to make pipeline now","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler, Binarizer, OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression, ElasticNet\nfrom sklearn.pipeline import Pipeline\n\n\ncol_trans = ColumnTransformer([\n    ('scaler', StandardScaler(), num_cols),\n    ('onehot', OneHotEncoder(drop='first'), one_hot_cols),\n])\nmodel = Pipeline([\n    ('trans', col_trans),\n    ('model', ElasticNet())\n])\n\nX_train, X_test, y_train, y_test = train_test_split(df, y, test_size= 0.11,random_state = 21)\nif 'Price' not in df.columns.to_list():\n    means = pd.concat([X_train, y_train], axis = 1).groupby('Metro station')['Price'].mean()\ndf['Metro station'] = df['Metro station'].map(means)\n\ndf = pd.concat([X_train, y_train], axis = 1)\nprint(df.shape[0])\ntarget = 'Price'\nQ1 = df[target].quantile(0.25)\nQ3 = df[target].quantile(0.75)\nIQR = Q3 - Q1\n\n# Определение границ\nlower_bound = Q1 - 1.5 * IQR\nupper_bound = Q3 + 1.5 * IQR\n\ndf = df[(df[target] >= lower_bound) & (df[target] <= upper_bound)]\nprint(df.shape[0])\ny = df[target]\ndf = df.drop(target, axis = 1)\n\nX_train, X_test, y_train, y_test = train_test_split(df, y, test_size= 0.11,random_state = 21)\n\nmodel.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:00.905455Z","iopub.execute_input":"2024-07-02T14:59:00.905835Z","iopub.status.idle":"2024-07-02T14:59:01.009129Z","shell.execute_reply.started":"2024-07-02T14:59:00.905804Z","shell.execute_reply":"2024-07-02T14:59:01.008094Z"},"trusted":true},"execution_count":166,"outputs":[{"name":"stderr","text":"INFO:sklearnex: sklearn.model_selection.train_test_split: fallback to original Scikit-learn\nINFO:sklearnex: sklearn.model_selection.train_test_split: running accelerated version on CPU\nINFO:sklearnex: sklearn.model_selection.train_test_split: fallback to original Scikit-learn\nINFO:sklearnex: sklearn.model_selection.train_test_split: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.fit: running accelerated version on CPU\n","output_type":"stream"},{"name":"stdout","text":"20181\n17198\n","output_type":"stream"},{"execution_count":166,"output_type":"execute_result","data":{"text/plain":"Pipeline(steps=[('trans',\n                 ColumnTransformer(transformers=[('scaler', StandardScaler(),\n                                                  ['Minutes to metro',\n                                                   'Number of rooms', 'Area',\n                                                   'Living area',\n                                                   'Kitchen area', 'Floor',\n                                                   'Number of floors']),\n                                                 ('onehot',\n                                                  OneHotEncoder(drop='first'),\n                                                  ['Renovation',\n                                                   'Apartment type',\n                                                   'Region'])])),\n                ('model', ElasticNet())])","text/html":"<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;trans&#x27;,\n                 ColumnTransformer(transformers=[(&#x27;scaler&#x27;, StandardScaler(),\n                                                  [&#x27;Minutes to metro&#x27;,\n                                                   &#x27;Number of rooms&#x27;, &#x27;Area&#x27;,\n                                                   &#x27;Living area&#x27;,\n                                                   &#x27;Kitchen area&#x27;, &#x27;Floor&#x27;,\n                                                   &#x27;Number of floors&#x27;]),\n                                                 (&#x27;onehot&#x27;,\n                                                  OneHotEncoder(drop=&#x27;first&#x27;),\n                                                  [&#x27;Renovation&#x27;,\n                                                   &#x27;Apartment type&#x27;,\n                                                   &#x27;Region&#x27;])])),\n                (&#x27;model&#x27;, ElasticNet())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;trans&#x27;,\n                 ColumnTransformer(transformers=[(&#x27;scaler&#x27;, StandardScaler(),\n                                                  [&#x27;Minutes to metro&#x27;,\n                                                   &#x27;Number of rooms&#x27;, &#x27;Area&#x27;,\n                                                   &#x27;Living area&#x27;,\n                                                   &#x27;Kitchen area&#x27;, &#x27;Floor&#x27;,\n                                                   &#x27;Number of floors&#x27;]),\n                                                 (&#x27;onehot&#x27;,\n                                                  OneHotEncoder(drop=&#x27;first&#x27;),\n                                                  [&#x27;Renovation&#x27;,\n                                                   &#x27;Apartment type&#x27;,\n                                                   &#x27;Region&#x27;])])),\n                (&#x27;model&#x27;, ElasticNet())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;scaler&#x27;, StandardScaler(),\n                                 [&#x27;Minutes to metro&#x27;, &#x27;Number of rooms&#x27;, &#x27;Area&#x27;,\n                                  &#x27;Living area&#x27;, &#x27;Kitchen area&#x27;, &#x27;Floor&#x27;,\n                                  &#x27;Number of floors&#x27;]),\n                                (&#x27;onehot&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n                                 [&#x27;Renovation&#x27;, &#x27;Apartment type&#x27;, &#x27;Region&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-62\" type=\"checkbox\" ><label for=\"sk-estimator-id-62\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Minutes to metro&#x27;, &#x27;Number of rooms&#x27;, &#x27;Area&#x27;, &#x27;Living area&#x27;, &#x27;Kitchen area&#x27;, &#x27;Floor&#x27;, &#x27;Number of floors&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-64\" type=\"checkbox\" ><label for=\"sk-estimator-id-64\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehot</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Renovation&#x27;, &#x27;Apartment type&#x27;, &#x27;Region&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet()</pre></div></div></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"preds = model.predict(X_test)\nfrom sklearn.metrics import mean_squared_error as MSE\nprint(MSE(y_test, preds) ** 0.5)","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:01.011283Z","iopub.execute_input":"2024-07-02T14:59:01.011688Z","iopub.status.idle":"2024-07-02T14:59:01.029398Z","shell.execute_reply.started":"2024-07-02T14:59:01.011651Z","shell.execute_reply":"2024-07-02T14:59:01.028170Z"},"trusted":true},"execution_count":167,"outputs":[{"name":"stderr","text":"INFO:sklearnex: sklearn.linear_model.ElasticNet.predict: running accelerated version on CPU\n","output_type":"stream"},{"name":"stdout","text":"5892223.693092426\n","output_type":"stream"}]},{"cell_type":"code","source":"param_grid = {\n    'model__alpha': [0.00031622776601683794],  # значения alpha от 0.0001 до 10\n    'model__l1_ratio': [0.21]  # значения l1_ratio от 0 до 1\n}","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:01.030598Z","iopub.execute_input":"2024-07-02T14:59:01.030914Z","iopub.status.idle":"2024-07-02T14:59:01.035741Z","shell.execute_reply.started":"2024-07-02T14:59:01.030886Z","shell.execute_reply":"2024-07-02T14:59:01.034575Z"},"trusted":true},"execution_count":168,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import GridSearchCV\ngrid = GridSearchCV(model, param_grid, scoring='neg_mean_squared_error', verbose=10)\ngrid.fit(X_train, y_train)","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2024-07-02T14:59:01.037760Z","iopub.execute_input":"2024-07-02T14:59:01.038108Z","iopub.status.idle":"2024-07-02T14:59:01.389257Z","shell.execute_reply.started":"2024-07-02T14:59:01.038080Z","shell.execute_reply":"2024-07-02T14:59:01.388061Z"},"trusted":true},"execution_count":169,"outputs":[{"name":"stderr","text":"INFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.fit: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.predict: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.fit: running accelerated version on CPU\n","output_type":"stream"},{"name":"stdout","text":"Fitting 5 folds for each of 1 candidates, totalling 5 fits\n[CV 1/5; 1/1] START model__alpha=0.00031622776601683794, model__l1_ratio=0.21...\n[CV 1/5; 1/1] END model__alpha=0.00031622776601683794, model__l1_ratio=0.21;, score=-22358433778639.453 total time=   0.1s\n[CV 2/5; 1/1] START model__alpha=0.00031622776601683794, model__l1_ratio=0.21...\n","output_type":"stream"},{"name":"stderr","text":"INFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.predict: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.fit: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.predict: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\n","output_type":"stream"},{"name":"stdout","text":"[CV 2/5; 1/1] END model__alpha=0.00031622776601683794, model__l1_ratio=0.21;, score=-21132720512843.598 total time=   0.0s\n[CV 3/5; 1/1] START model__alpha=0.00031622776601683794, model__l1_ratio=0.21...\n[CV 3/5; 1/1] END model__alpha=0.00031622776601683794, model__l1_ratio=0.21;, score=-22258190200736.742 total time=   0.1s\n[CV 4/5; 1/1] START model__alpha=0.00031622776601683794, model__l1_ratio=0.21...\n","output_type":"stream"},{"name":"stderr","text":"INFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.fit: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.predict: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.fit: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.predict: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.fit: running accelerated version on CPU\n","output_type":"stream"},{"name":"stdout","text":"[CV 4/5; 1/1] END model__alpha=0.00031622776601683794, model__l1_ratio=0.21;, score=-22109588952401.984 total time=   0.1s\n[CV 5/5; 1/1] START model__alpha=0.00031622776601683794, model__l1_ratio=0.21...\n[CV 5/5; 1/1] END model__alpha=0.00031622776601683794, model__l1_ratio=0.21;, score=-21875071062940.387 total time=   0.0s\n","output_type":"stream"},{"execution_count":169,"output_type":"execute_result","data":{"text/plain":"GridSearchCV(estimator=Pipeline(steps=[('trans',\n                                        ColumnTransformer(transformers=[('scaler',\n                                                                         StandardScaler(),\n                                                                         ['Minutes '\n                                                                          'to '\n                                                                          'metro',\n                                                                          'Number '\n                                                                          'of '\n                                                                          'rooms',\n                                                                          'Area',\n                                                                          'Living '\n                                                                          'area',\n                                                                          'Kitchen '\n                                                                          'area',\n                                                                          'Floor',\n                                                                          'Number '\n                                                                          'of '\n                                                                          'floors']),\n                                                                        ('onehot',\n                                                                         OneHotEncoder(drop='first'),\n                                                                         ['Renovation',\n                                                                          'Apartment '\n                                                                          'type',\n                                                                          'Region'])])),\n                                       ('model', ElasticNet())]),\n             param_grid={'model__alpha': [0.00031622776601683794],\n                         'model__l1_ratio': [0.21]},\n             scoring='neg_mean_squared_error', verbose=10)","text/html":"<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;trans&#x27;,\n                                        ColumnTransformer(transformers=[(&#x27;scaler&#x27;,\n                                                                         StandardScaler(),\n                                                                         [&#x27;Minutes &#x27;\n                                                                          &#x27;to &#x27;\n                                                                          &#x27;metro&#x27;,\n                                                                          &#x27;Number &#x27;\n                                                                          &#x27;of &#x27;\n                                                                          &#x27;rooms&#x27;,\n                                                                          &#x27;Area&#x27;,\n                                                                          &#x27;Living &#x27;\n                                                                          &#x27;area&#x27;,\n                                                                          &#x27;Kitchen &#x27;\n                                                                          &#x27;area&#x27;,\n                                                                          &#x27;Floor&#x27;,\n                                                                          &#x27;Number &#x27;\n                                                                          &#x27;of &#x27;\n                                                                          &#x27;floors&#x27;]),\n                                                                        (&#x27;onehot&#x27;,\n                                                                         OneHotEncoder(drop=&#x27;first&#x27;),\n                                                                         [&#x27;Renovation&#x27;,\n                                                                          &#x27;Apartment &#x27;\n                                                                          &#x27;type&#x27;,\n                                                                          &#x27;Region&#x27;])])),\n                                       (&#x27;model&#x27;, ElasticNet())]),\n             param_grid={&#x27;model__alpha&#x27;: [0.00031622776601683794],\n                         &#x27;model__l1_ratio&#x27;: [0.21]},\n             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;trans&#x27;,\n                                        ColumnTransformer(transformers=[(&#x27;scaler&#x27;,\n                                                                         StandardScaler(),\n                                                                         [&#x27;Minutes &#x27;\n                                                                          &#x27;to &#x27;\n                                                                          &#x27;metro&#x27;,\n                                                                          &#x27;Number &#x27;\n                                                                          &#x27;of &#x27;\n                                                                          &#x27;rooms&#x27;,\n                                                                          &#x27;Area&#x27;,\n                                                                          &#x27;Living &#x27;\n                                                                          &#x27;area&#x27;,\n                                                                          &#x27;Kitchen &#x27;\n                                                                          &#x27;area&#x27;,\n                                                                          &#x27;Floor&#x27;,\n                                                                          &#x27;Number &#x27;\n                                                                          &#x27;of &#x27;\n                                                                          &#x27;floors&#x27;]),\n                                                                        (&#x27;onehot&#x27;,\n                                                                         OneHotEncoder(drop=&#x27;first&#x27;),\n                                                                         [&#x27;Renovation&#x27;,\n                                                                          &#x27;Apartment &#x27;\n                                                                          &#x27;type&#x27;,\n                                                                          &#x27;Region&#x27;])])),\n                                       (&#x27;model&#x27;, ElasticNet())]),\n             param_grid={&#x27;model__alpha&#x27;: [0.00031622776601683794],\n                         &#x27;model__l1_ratio&#x27;: [0.21]},\n             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=10)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;trans&#x27;,\n                 ColumnTransformer(transformers=[(&#x27;scaler&#x27;, StandardScaler(),\n                                                  [&#x27;Minutes to metro&#x27;,\n                                                   &#x27;Number of rooms&#x27;, &#x27;Area&#x27;,\n                                                   &#x27;Living area&#x27;,\n                                                   &#x27;Kitchen area&#x27;, &#x27;Floor&#x27;,\n                                                   &#x27;Number of floors&#x27;]),\n                                                 (&#x27;onehot&#x27;,\n                                                  OneHotEncoder(drop=&#x27;first&#x27;),\n                                                  [&#x27;Renovation&#x27;,\n                                                   &#x27;Apartment type&#x27;,\n                                                   &#x27;Region&#x27;])])),\n                (&#x27;model&#x27;, ElasticNet())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;scaler&#x27;, StandardScaler(),\n                                 [&#x27;Minutes to metro&#x27;, &#x27;Number of rooms&#x27;, &#x27;Area&#x27;,\n                                  &#x27;Living area&#x27;, &#x27;Kitchen area&#x27;, &#x27;Floor&#x27;,\n                                  &#x27;Number of floors&#x27;]),\n                                (&#x27;onehot&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n                                 [&#x27;Renovation&#x27;, &#x27;Apartment type&#x27;, &#x27;Region&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Minutes to metro&#x27;, &#x27;Number of rooms&#x27;, &#x27;Area&#x27;, &#x27;Living area&#x27;, &#x27;Kitchen area&#x27;, &#x27;Floor&#x27;, &#x27;Number of floors&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehot</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Renovation&#x27;, &#x27;Apartment type&#x27;, &#x27;Region&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-73\" type=\"checkbox\" ><label for=\"sk-estimator-id-73\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-74\" type=\"checkbox\" ><label for=\"sk-estimator-id-74\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"print((-grid.best_score_)**0.5)\nprint(grid.best_params_)\nprint(MSE(y_test, grid.predict(X_test)) ** 0.5)","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:01.390441Z","iopub.execute_input":"2024-07-02T14:59:01.390751Z","iopub.status.idle":"2024-07-02T14:59:01.407496Z","shell.execute_reply.started":"2024-07-02T14:59:01.390724Z","shell.execute_reply":"2024-07-02T14:59:01.406384Z"},"trusted":true},"execution_count":170,"outputs":[{"name":"stderr","text":"INFO:sklearnex: sklearn.linear_model.ElasticNet.predict: running accelerated version on CPU\n","output_type":"stream"},{"name":"stdout","text":"4684741.28437339\n{'model__alpha': 0.00031622776601683794, 'model__l1_ratio': 0.21}\n4831536.3669095235\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Первая версия: рмсе - 49кк\n\nВторая версия: рмсе - 4.7 кк (удалили выбросы в самом начале)\n","metadata":{}},{"cell_type":"code","source":"from sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\nfrom sklearn.svm import SVR\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\nfrom datetime import datetime as dt\n\nparam_grid = [\n    {\n        'model': [GradientBoostingRegressor()],\n        'model__n_estimators': [600], #600 is the best\n        'model__learning_rate': [0.2],\n        'model__max_depth': [5],\n        'model__min_samples_split': [5],\n        'model__min_samples_leaf': [2],\n        'model__subsample': [1.0]\n    }\n]\nparam_grid2 = [\n    {\n        'model': [RandomForestRegressor()],\n        'model__n_estimators': [400], #400\n        'model__max_features': ['log2'],\n        'model__max_depth': [30],\n        'model__min_samples_split': [2, 10],\n        'model__min_samples_leaf': [1, 2]\n    },\n    \n]\nstart = dt.now()\nprint('start time\\t',start)\ngrid = GridSearchCV(model, param_grid2, scoring='neg_mean_squared_error', verbose=10)\n#grid.fit(X_train, y_train)\nend = dt.now() - start\nprint('total time\\t', end)","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2024-07-02T14:59:01.408801Z","iopub.execute_input":"2024-07-02T14:59:01.409130Z","iopub.status.idle":"2024-07-02T14:59:01.418487Z","shell.execute_reply.started":"2024-07-02T14:59:01.409093Z","shell.execute_reply":"2024-07-02T14:59:01.417389Z"},"trusted":true},"execution_count":171,"outputs":[{"name":"stdout","text":"start time\t 2024-07-02 14:59:01.413806\ntotal time\t 0:00:00.000193\n","output_type":"stream"}]},{"cell_type":"markdown","source":"patched sklearn total time: 49 seconds\n\nUNpatched total time: 1 minute 27 seconds ","metadata":{}},{"cell_type":"code","source":"best_params_gradient = {\n    'model': GradientBoostingRegressor(learning_rate=0.2,\n                                       max_depth=5,\n                                       min_samples_leaf=2,\n                                       min_samples_split=5,\n                                       n_estimators=600),\n}\nbest_params_random = {\n    'model': RandomForestRegressor(max_depth=30, \n                                   max_features='log2', \n                                   n_estimators=400,\n                                   min_samples_leaf = 1,\n                                   min_samples_split = 2,)\n}\nbest_params_elastic = {\n    'model': ElasticNet(alpha = 0.00031622776601683794, l1_ratio = 0.21),\n}\n\nbest_params_array = [\n    best_params_gradient,\n    best_params_random,\n    best_params_elastic,\n]","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:01.421015Z","iopub.execute_input":"2024-07-02T14:59:01.421380Z","iopub.status.idle":"2024-07-02T14:59:01.433943Z","shell.execute_reply.started":"2024-07-02T14:59:01.421348Z","shell.execute_reply":"2024-07-02T14:59:01.432988Z"},"trusted":true},"execution_count":172,"outputs":[]},{"cell_type":"code","source":"#print('best params: \\t',grid.best_params_)\n#model.set_params(**grid.best_params_)\nmodel.set_params(**best_params_random)\nstart = dt.now()\nmodel.fit(X_train, y_train)\nend = dt.now() - start\nprint('total time\\t\\t', end)\npreds = model.predict(X_test)\nprint('test val score:\\t\\t', MSE(y_test, preds) ** 0.5)","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:01.435228Z","iopub.execute_input":"2024-07-02T14:59:01.435633Z","iopub.status.idle":"2024-07-02T14:59:06.515893Z","shell.execute_reply.started":"2024-07-02T14:59:01.435594Z","shell.execute_reply":"2024-07-02T14:59:06.514857Z"},"trusted":true},"execution_count":173,"outputs":[{"name":"stderr","text":"INFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.ensemble.RandomForestRegressor.fit: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\n","output_type":"stream"},{"name":"stdout","text":"total time\t\t 0:00:04.766275\n","output_type":"stream"},{"name":"stderr","text":"INFO:sklearnex: sklearn.ensemble.RandomForestRegressor.predict: running accelerated version on CPU\n","output_type":"stream"},{"name":"stdout","text":"test val score:\t\t 3309970.109684073\n","output_type":"stream"}]},{"cell_type":"code","source":"gr = pd.DataFrame({'test' : y_test.values, 'preds': preds})\ngr = gr.sort_values(['test'])\ngr","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:06.517304Z","iopub.execute_input":"2024-07-02T14:59:06.518369Z","iopub.status.idle":"2024-07-02T14:59:06.532395Z","shell.execute_reply.started":"2024-07-02T14:59:06.518330Z","shell.execute_reply":"2024-07-02T14:59:06.531237Z"},"trusted":true},"execution_count":174,"outputs":[{"execution_count":174,"output_type":"execute_result","data":{"text/plain":"            test         preds\n413    1150000.0  3.566032e+06\n1141   1941650.0  2.038039e+06\n407    2390000.0  3.968993e+06\n1524   2607300.0  2.649635e+06\n558    2607300.0  2.651585e+06\n...          ...           ...\n1400  50983800.0  3.067470e+07\n1300  51000000.0  4.808611e+07\n237   51000000.0  2.973121e+07\n327   51000000.0  3.853908e+07\n403   51064000.0  4.837989e+07\n\n[1892 rows x 2 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>test</th>\n      <th>preds</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>413</th>\n      <td>1150000.0</td>\n      <td>3.566032e+06</td>\n    </tr>\n    <tr>\n      <th>1141</th>\n      <td>1941650.0</td>\n      <td>2.038039e+06</td>\n    </tr>\n    <tr>\n      <th>407</th>\n      <td>2390000.0</td>\n      <td>3.968993e+06</td>\n    </tr>\n    <tr>\n      <th>1524</th>\n      <td>2607300.0</td>\n      <td>2.649635e+06</td>\n    </tr>\n    <tr>\n      <th>558</th>\n      <td>2607300.0</td>\n      <td>2.651585e+06</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1400</th>\n      <td>50983800.0</td>\n      <td>3.067470e+07</td>\n    </tr>\n    <tr>\n      <th>1300</th>\n      <td>51000000.0</td>\n      <td>4.808611e+07</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>51000000.0</td>\n      <td>2.973121e+07</td>\n    </tr>\n    <tr>\n      <th>327</th>\n      <td>51000000.0</td>\n      <td>3.853908e+07</td>\n    </tr>\n    <tr>\n      <th>403</th>\n      <td>51064000.0</td>\n      <td>4.837989e+07</td>\n    </tr>\n  </tbody>\n</table>\n<p>1892 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"preds = model.predict(df)\ngr = pd.DataFrame({'test' : y.values, 'preds': preds})\ngr","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:06.534035Z","iopub.execute_input":"2024-07-02T14:59:06.534528Z","iopub.status.idle":"2024-07-02T14:59:09.275246Z","shell.execute_reply.started":"2024-07-02T14:59:06.534489Z","shell.execute_reply":"2024-07-02T14:59:09.274140Z"},"trusted":true},"execution_count":175,"outputs":[{"name":"stderr","text":"INFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.ensemble.RandomForestRegressor.predict: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\n","output_type":"stream"},{"execution_count":175,"output_type":"execute_result","data":{"text/plain":"             test         preds\n0       3094710.0  3.126549e+06\n1      34990000.0  3.485634e+07\n2       3285243.0  3.216272e+06\n3      10879360.0  1.069714e+07\n4       9090240.0  9.015512e+06\n...           ...           ...\n17193   5582764.0  5.906047e+06\n17194  19500000.0  2.146293e+07\n17195  12450000.0  1.266569e+07\n17196   9800000.0  1.199682e+07\n17197   8218650.0  8.258569e+06\n\n[17198 rows x 2 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>test</th>\n      <th>preds</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3094710.0</td>\n      <td>3.126549e+06</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>34990000.0</td>\n      <td>3.485634e+07</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3285243.0</td>\n      <td>3.216272e+06</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10879360.0</td>\n      <td>1.069714e+07</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9090240.0</td>\n      <td>9.015512e+06</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>17193</th>\n      <td>5582764.0</td>\n      <td>5.906047e+06</td>\n    </tr>\n    <tr>\n      <th>17194</th>\n      <td>19500000.0</td>\n      <td>2.146293e+07</td>\n    </tr>\n    <tr>\n      <th>17195</th>\n      <td>12450000.0</td>\n      <td>1.266569e+07</td>\n    </tr>\n    <tr>\n      <th>17196</th>\n      <td>9800000.0</td>\n      <td>1.199682e+07</td>\n    </tr>\n    <tr>\n      <th>17197</th>\n      <td>8218650.0</td>\n      <td>8.258569e+06</td>\n    </tr>\n  </tbody>\n</table>\n<p>17198 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"def get_variable_name(variable, local_vars):\n    return [name for name, value in local_vars.items() if value is variable]","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:09.276630Z","iopub.execute_input":"2024-07-02T14:59:09.277522Z","iopub.status.idle":"2024-07-02T14:59:09.283142Z","shell.execute_reply.started":"2024-07-02T14:59:09.277489Z","shell.execute_reply":"2024-07-02T14:59:09.281848Z"},"trusted":true},"execution_count":176,"outputs":[]},{"cell_type":"code","source":"size = 12\nfig = plt.figure(figsize=(size, len(best_params_array) * size))\n\nfor k, i in enumerate(best_params_array, 1):\n    fig.add_subplot(len(best_params_array), 1, k)\n    \n    model.set_params(**i)\n    \n    model.fit(X_train, y_train)\n    preds = model.predict(df)\n    \n    gr = pd.DataFrame({'test' : y.values, 'preds': preds})\n    gr = gr.sort_values(['test'])\n    \n    plt.plot(np.arange(0, gr.shape[0]), gr.test.values)\n    plt.scatter(np.arange(0, gr.shape[0]), gr.preds.values, c='orange', s = 1)\n    plt.title(get_variable_name(i, locals())[0])\n    print(f'{k} is ploted')","metadata":{"execution":{"iopub.status.busy":"2024-07-02T14:59:09.284637Z","iopub.execute_input":"2024-07-02T14:59:09.284989Z","iopub.status.idle":"2024-07-02T14:59:35.054001Z","shell.execute_reply.started":"2024-07-02T14:59:09.284957Z","shell.execute_reply":"2024-07-02T14:59:35.052790Z"},"trusted":true},"execution_count":177,"outputs":[{"name":"stderr","text":"INFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.ensemble.RandomForestRegressor.fit: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\n","output_type":"stream"},{"name":"stdout","text":"1 is ploted\n","output_type":"stream"},{"name":"stderr","text":"INFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.ensemble.RandomForestRegressor.predict: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.fit: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.utils.validation._assert_all_finite: running accelerated version on CPU\nINFO:sklearnex: sklearn.linear_model.ElasticNet.predict: running accelerated version on CPU\n","output_type":"stream"},{"name":"stdout","text":"2 is ploted\n3 is ploted\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x3600 with 3 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"},"metadata":{}}]},{"cell_type":"markdown","source":"Best model test validation score:  3 290 488\n\n\nbest gradient boost score:  3 451 674\n\ntotal 'gradient' unpatched time \\ patched : ( 0:00:17.354130 \\  0:00:17.259335 )","metadata":{}}]}