done

script.py

@@ -0,0 +1,40 @@
+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()