text: # importing libraries
  
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # importing the dataset
  
  dataset = pd.read_csv('Data.csv')
  X = dataset.iloc[:,:-1].values
  y = dataset.iloc[:,-1].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)
  
  # Split into testing and training sets
  
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split (X,y,test_size = .25,random_state = 14124)
  
  # Feature Scaling
  
  from sklearn.preprocessing import StandardScaler
  sc_X = StandardScaler() 
  X_train = sc_X.fit_transform(X_train)
  X_test  = sc_X.fit_transform(X_test)
  
  
  
  
  
  
  
  