Adding preprocessing step

Author: Nikronic

Part 1 - Data Preprocessing/Data.csv

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+Country,Age,Salary,Purchased
+France,44,72000,No
+Spain,27,48000,Yes
+Germany,30,54000,No
+Spain,38,61000,No
+Germany,40,,Yes
+France,35,58000,Yes
+Spain,,52000,No
+France,48,79000,Yes
+Germany,50,83000,No
+France,37,67000,Yes

Part 1 - Data Preprocessing/preprocessing.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jul 16 23:11:13 2018
+
+@author: Mohammad Doosti Lakhani
+"""
+
+# imporing libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# importing dataset
+dataset = pd.read_csv('Data.csv')
+x = dataset.iloc[:,:-1].values
+y = dataset.iloc[:,3].values
+
+# resolving missing data
+from sklearn.preprocessing import Imputer
+imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis= 0)
+imputer = imputer.fit(x[:,1:3])
+x[:,1:3] = imputer.transform(x[:,1:3])
+
+# encoding categorial data types to labelEncoder and onehotencoder
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder_x = LabelEncoder()
+labelencoder_x = labelencoder_x.fit(x[:,0])
+x[:,0] = labelencoder_x.transform(x[:,0])
+
+labelencoder_y = LabelEncoder()
+labelencoder_y = labelencoder_y.fit(y)
+y = labelencoder_y.transform(y)
+
+onehotencoder_x = OneHotEncoder(categorical_features=[0])
+onehotencoder_x = onehotencoder_x.fit(x)
+x = onehotencoder_x.transform(x).toarray()
+
+# splitting dataset into Train set and Test set
+from sklearn.model_selection import train_test_split
+x_train,x_test,y_train,y_test = train_test_split(x,y, train_size = 0.8 , random_state=0)
+
+# feature scaling
+from sklearn.preprocessing import StandardScaler
+standardscaler_x = StandardScaler()
+standardscaler_x = standardscaler_x.fit(x_train)
+x_train = standardscaler_x.transform(x_train)
+x_test = standardscaler_x.transform(x_test)
+