Add linear regression model and results

.gitignore

@@ -1,3 +1,4 @@
 *~
 *.swp
 *.pyc
+env

linear_regression.py

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+from sklearn.linear_model import LinearRegression
+import pandas as pd
+
+def prepare_data(file, type):
+    data = pd.read_csv(file, header=None, sep="\t")
+    for c in data.select_dtypes(include=object).columns.values:
+        data[c] = data[c].astype("category").cat.codes
+    if type == 'train':
+        data = pd.get_dummies(data, columns=[4])
+    else:
+        data = pd.get_dummies(data, columns=[3])
+    return data
+
+data = prepare_data("./train/train.tsv", "train")
+
+data = data.loc[(data[0] > 1000)]
+
+price = data.iloc[:,0]
+training_data = data.iloc[:,1:]
+
+clf = LinearRegression().fit(training_data, price)
+
+with open('dev-0/out.tsv', 'w') as writer:
+    dev_data = prepare_data('dev-0/in.tsv', "dev")
+    for result in clf.predict(dev_data.iloc[:,0:]):
+        writer.write(str(int(result)) + '\n')
+
+with open('test-A/out.tsv', 'w') as writer:
+    test_data = prepare_data('test-A/in.tsv', "test")
+    for result in clf.predict(test_data.iloc[:,0:]):
+        writer.write(str(int(result)) + '\n')

requirements.txt

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+joblib==1.0.1
+numpy==1.20.3
+pandas==1.2.4
+python-dateutil==2.8.1
+pytz==2021.1
+scikit-learn==0.24.2
+scipy==1.6.3
+six==1.16.0
+sklearn==0.0
+threadpoolctl==2.1.0