script.py

@@ -1,40 +0,0 @@
-import pandas as pd
-from sklearn.linear_model import LinearRegression
-
-def proces(data1):
-    data1 = pd.concat([data1, data1['engineType'].str.get_dummies().astype(bool)], axis=1)
-    data1 = data1.drop(['engineType', 'brand'], axis=1)
-    return data1
-
-
-def dev():
-    data1_dev = pd.read_table('C:/Users/Ufnow/Desktop/Projekt/auta/auta-public/dev-0/in.tsv', error_bad_lines=False, header=None,
-                          names=['mileage', 'year', 'brand', 'engineType', 'engineCap'])
-    data1_dev = proces(data1_dev)
-    data2_pred = model.predict(data1_dev)
-    data2_pred.tofile('C:/Users/Ufnow/Desktop/Projekt/auta/auta-public/dev-0/out.tsv', sep='\n')
-
-
-def testA():
-    data1_test_A = pd.read_table('C:/Users/Ufnow/Desktop/Projekt/auta/auta-public/test-A/in.tsv', error_bad_lines=False, header=None,
-                            names=['mileage', 'year', 'brand', 'engineType', 'engineCap'])
-    data1_test_A = proces(data1_test_A)
-    data2_pred_A = model.predict(data1_test_A)
-    data2_pred_A.tofile('C:/Users/Ufnow/Desktop/Projekt/auta/auta-public/test-A/out.tsv', sep='\n')
-
-data = pd.read_table('C:/Users/Ufnow/Desktop/Projekt/auta/auta-public/train/train.tsv', error_bad_lines=False, header=None,
-                         names=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCap'])
-data2_train = data['price']
-data1_train = data.iloc[:, 1:]
-data1_train = proces(data1_train)
-
-model = LinearRegression()
-model.fit(data1_train, data2_train)
-
-def main():
-    dev()
-    testA()
-
-
-if __name__ == '__main__':
-        main()