49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
from matplotlib import pyplot as plt
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from sklearn import metrics
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from sklearn.linear_model import LinearRegression
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.pipeline import Pipeline
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import pandas as pd
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def start():
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data = pd.read_csv('sberbank_data.csv', index_col='id')
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x = data[['timestamp', 'full_sq', 'floor', 'max_floor', 'build_year', 'num_room', 'material', 'kremlin_km']]
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y = data[['price_doc']]
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x = x.replace('NA', 0)
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x.fillna(0, inplace=True)
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col_date = []
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for val in x['timestamp']:
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col_date.append(val.split('-', 1)[0])
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x = x.drop(columns='timestamp')
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x['timestamp'] = col_date
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.01, random_state=42)
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poly = Pipeline([('poly', PolynomialFeatures(degree=3)),
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('linear', LinearRegression())])
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poly.fit(x_train, y_train)
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y_mean = y['price_doc'].mean()
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y_predicted = poly.predict(x_test)
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for i, n in enumerate(y_predicted):
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if n < 10000:
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y_predicted[i] = y_mean
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print('Оценка обучения:')
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print(metrics.r2_score(y_test, y_predicted))
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plt.figure(1, figsize=(16, 9))
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plt.title('Сравнение результатов обучения')
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plt.scatter(x=[i for i in range(len(y_test))], y=y_test, c='g', s=5)
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plt.scatter(x=[i for i in range(len(y_test))], y=y_predicted, c='r', s=5)
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plt.show()
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start()
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