.gitignore

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

linear_regression.py

@@ -1,31 +0,0 @@
-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

@@ -1,10 +0,0 @@
-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