Artembielov

tasks/artembielov_task1/House price/artembielov_house_price.py

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+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.neural_network import MLPRegressor
+from sklearn.linear_model import Ridge
+from sklearn.linear_model import LinearRegression
+from mlxtend.regressor import StackingRegressor
+
+
+path = '...'
+test = pd.read_csv(path + 'test.csv')
+train = pd.read_csv(path + 'train.csv')
+
+target = np.log(train.SalePrice)
+
+
+all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
+
+all_data = all_data.drop(['Utilities'], axis=1)
+
+useless = ['PoolQC', 'MiscFeature', 'Alley', 'Fence']
+all_data.drop(useless, axis=1)
+
+
+def encode(x):
+    return 1 if x == 'Partial' else 0
+
+
+all_data['enc_street'] = pd.get_dummies(all_data.Street, drop_first=True)
+all_data['enc_condition'] = all_data.SaleCondition.apply(encode)
+
+data_train = all_data[:train.shape[0]].select_dtypes(include=[np.number]).interpolate().dropna()
+data_test = all_data[train.shape[0]:].select_dtypes(include=[np.number]).interpolate().dropna()
+
+X_train = data_train
+X_test = data_test
+y = target
+
+lr = LinearRegression(n_jobs=-1)
+rd = Ridge(alpha=4.84)
+rf = RandomForestRegressor(n_estimators=12, max_depth=3, n_jobs=-1)
+gb = GradientBoostingRegressor(n_estimators=40, max_depth=2)
+nn = MLPRegressor(hidden_layer_sizes=(90, 90), alpha=2.75)
+
+model = StackingRegressor(regressors=[rf, gb, nn, rd], meta_regressor=lr)
+
+model.fit(X_train, y)
+
+y_pred = model.predict(X_train)
+
+Y_pred = model.predict(X_test)
+
+final_predictions = np.exp(Y_pred)
+submission = pd.DataFrame()
+submission['Id'] = test['Id']
+submission['SalePrice'] = final_predictions
+submission.to_csv('submission_v3.csv', index=False)