forked from kubapok/auta-public
41 lines
1.5 KiB
Python
41 lines
1.5 KiB
Python
import pandas as pd
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from sklearn.linear_model import LinearRegression
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def proces(data1):
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data1 = pd.concat([data1, data1['engineType'].str.get_dummies().astype(bool)], axis=1)
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data1 = data1.drop(['engineType', 'brand'], axis=1)
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return data1
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def dev():
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data1_dev = pd.read_table('C:/Users/Ufnow/Desktop/Projekt/auta/auta-public/dev-0/in.tsv', error_bad_lines=False, header=None,
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names=['mileage', 'year', 'brand', 'engineType', 'engineCap'])
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data1_dev = proces(data1_dev)
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data2_pred = model.predict(data1_dev)
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data2_pred.tofile('C:/Users/Ufnow/Desktop/Projekt/auta/auta-public/dev-0/out.tsv', sep='\n')
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def testA():
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data1_test_A = pd.read_table('C:/Users/Ufnow/Desktop/Projekt/auta/auta-public/test-A/in.tsv', error_bad_lines=False, header=None,
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names=['mileage', 'year', 'brand', 'engineType', 'engineCap'])
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data1_test_A = proces(data1_test_A)
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data2_pred_A = model.predict(data1_test_A)
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data2_pred_A.tofile('C:/Users/Ufnow/Desktop/Projekt/auta/auta-public/test-A/out.tsv', sep='\n')
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data = pd.read_table('C:/Users/Ufnow/Desktop/Projekt/auta/auta-public/train/train.tsv', error_bad_lines=False, header=None,
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names=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCap'])
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data2_train = data['price']
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data1_train = data.iloc[:, 1:]
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data1_train = proces(data1_train)
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model = LinearRegression()
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model.fit(data1_train, data2_train)
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def main():
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dev()
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testA()
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if __name__ == '__main__':
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main()
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