34140.6

app.py

@@ -0,0 +1,37 @@
+import gzip
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.utils import shuffle
+from sklearn.metrics import accuracy_score
+
+
+def preprocess(x):
+    x = pd.concat([x, x['engineType'].str.get_dummies().astype(bool)], axis=1)
+    x = x.drop(['engineType', 'brand'], axis=1)
+    return x
+
+
+def makePrediction(path):
+    x_pred = pd.read_table(path + '/in.tsv', error_bad_lines=False, header=None,
+                           names=names[1:])
+    x_pred = preprocess(x_pred)
+
+    y_pred = model.predict(x_pred)
+    y_pred.tofile(path + '/out.tsv', sep='\n')
+
+
+names = ['price', 'mileage', 'year', 'brand', 'engineType', 'engineCap']
+
+train = pd.read_table('train/train.tsv', error_bad_lines=False, header=None,
+                      names=names)
+
+y_train = train['price']
+x_train = train.iloc[:, 1:]
+x_train = preprocess(x_train)
+
+model = LinearRegression()
+model.fit(x_train, y_train)
+
+makePrediction('dev-0')
+makePrediction('test-A')