<filename>Part 1 - Data Preprocessing/Section 2 -------------------- Part 1 - Data Preprocessing --------------------/data_preprocessing.py
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 14 19:35:43 2018

@author: squirke
"""
##Importing libaries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

##Importing libariesImporting the datat
np.set_printoptions(threshold = np.nan)
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:,:-1].values
dfx = pd.DataFrame(X)
Y = dataset.iloc[:, 3].values

##taking care of 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 categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
onehotencoder = OneHotEncoder(categorical_features =[0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_y = LabelEncoder()
Y = labelencoder_y.fit_transform(Y)

## splitting data into training and test
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

# feature scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
<filename>Part 1 - Data Preprocessing/Section 2 -------------------- Part 1 - Data Preprocessing --------------------/data_preprocessing.py
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 14 19:35:43 2018

@author: squirke
"""
##Importing libaries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

##Importing libariesImporting the datat
np.set_printoptions(threshold = np.nan)
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:,:-1].values
dfx = pd.DataFrame(X)
Y = dataset.iloc[:, 3].values

##taking care of 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 categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
onehotencoder = OneHotEncoder(categorical_features =[0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_y = LabelEncoder()
Y = labelencoder_y.fit_transform(Y)

## splitting data into training and test
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

# feature scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
# -*- coding: utf-8 -*- Created on Sat Apr 14 19:35:43 2018
 
 @author: squirke ##Importing libaries ##Importing libariesImporting the datat ##taking care of missing data ##Encoding categorical data ## splitting data into training and test # feature scaling