done

skrypt-dev-0.py

@@ -0,0 +1,41 @@
+import numpy as np
+import pandas as pd
+from scipy.sparse import data
+from sklearn import linear_model
+from sklearn import preprocessing
+from sklearn.pipeline import make_pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn import linear_model
+import csv
+import pandas as pd
+
+regression = linear_model.LinearRegression()  
+    
+train_file = pd.read_csv('train/train.tsv', delimiter='\t', names=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
+train_data_frame = pd.DataFrame(train_file, columns=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
+
+Y = train_data_frame[['price']]
+X = train_data_frame[['year', 'mileage', 'engineCapacity']]
+
+regression.fit(X, Y)
+
+in_file = pd.read_csv('dev-0/in.tsv', delimiter='\t', names=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
+in_data_frame = pd.DataFrame(in_file, columns=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
+
+reshape = in_data_frame[['year', 'mileage', 'engineCapacity']]
+
+y_predict = regression.predict(reshape)
+y_predict = np.concatenate(y_predict)
+
+
+labels = np.array2string(y_predict, separator='\n', suppress_small=True)
+        
+file_out = open("dev-0/out.tsv", 'w')
+file_out.write(labels[1:-1])
+
+with open("dev-0/out.tsv", 'r') as fix_space:
+    lines = fix_space.readlines()
+
+    lines = [line.replace(' ', '') for line in lines]
+with open("dev-0/out.tsv", 'w') as fix_space:
+    fix_space.writelines(lines)

skrypt-test-a.py

@@ -0,0 +1,41 @@
+import numpy as np
+import pandas as pd
+from scipy.sparse import data
+from sklearn import linear_model
+from sklearn import preprocessing
+from sklearn.pipeline import make_pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn import linear_model
+import csv
+import pandas as pd
+
+regression = linear_model.LinearRegression()  
+    
+train_file = pd.read_csv('train/train.tsv', delimiter='\t', names=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
+train_data_frame = pd.DataFrame(train_file, columns=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
+
+Y = train_data_frame[['price']]
+X = train_data_frame[['year', 'mileage', 'engineCapacity']]
+
+regression.fit(X, Y)
+
+in_file = pd.read_csv('test-A/in.tsv', delimiter='\t', names=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
+in_data_frame = pd.DataFrame(in_file, columns=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
+
+reshape = in_data_frame[['year', 'mileage', 'engineCapacity']]
+
+y_predict = regression.predict(reshape)
+y_predict = np.concatenate(y_predict)
+
+
+labels = np.array2string(y_predict, separator='\n', suppress_small=True)
+        
+file_out = open("test-A/out.tsv", 'w')
+file_out.write(labels[1:-1])
+
+with open("test-A/out.tsv", 'r') as fix_space:
+    lines = fix_space.readlines()
+
+    lines = [line.replace(' ', '') for line in lines]
+with open("test-A/out.tsv", 'w') as fix_space:
+    fix_space.writelines(lines)