Done

solution.py

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+import numpy
+import pandas
+import sys
+from sklearn.linear_model import LinearRegression
+
+TRAIN_URL = "train/train.tsv"
+TEST_A = "test-A"
+DEV_0 = "dev-0"
+
+def get_trained_model(data):
+    x = numpy.array(data.get('x')).reshape(-1, 1)
+    y = numpy.array(data.get('y')).reshape(-1, 1)
+    m = LinearRegression()
+    m.fit(x, y)
+    return m
+
+
+def prepare_model():
+    csv = pandas.read_csv(TRAIN_URL, sep='\t', header=None, index_col=None)
+    data = {'x': csv[2].tolist(), 'y': csv[0].tolist()}
+    model = get_trained_model(data)
+    return model
+
+
+def calculate_prediction(path, prepared_model):
+    data_frame = pandas.read_csv(path + '/in.tsv', sep='\t', header=None, index_col=None)
+    input_array = numpy.array(data_frame[1].tolist()).reshape(-1, 1)
+    prediction = prepared_model.predict(input_array)
+    numpy.savetxt(path + "/out.tsv", prediction, fmt="%d", delimiter='\n')
+
+
+def predict_dev_zero(prepared_model):
+    calculate_prediction(DEV_0, prepared_model)
+
+
+def predict_test_a(prepared_model):
+    calculate_prediction(TEST_A, prepared_model)
+
+
+prepared_model = prepare_model()
+predict_dev_zero(prepared_model)
+predict_test_a(prepared_model)