app.py

@@ -1,37 +0,0 @@
-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')