#Data Preporcessing

#importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#imoprting the dataset
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:,:-1].values
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 the dataset into training 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,test_size=0.2,random_state=0)

#Feature Scalling
from sklearn.preprocessing import StandardScaler
sc_X=StandardScaler()
X_train=sc_X.fit_transform(X_train)
X_test=sc_X.transform(X_test)
ea58f1f1e3536c491d5d72a6ae5dc0b7eb6a5046
<mask token>

<mask token>
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:, :-1].values
Y = dataset.iloc[:, 3].values
<mask token>
imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
imputer = imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])
<mask token>
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)
<mask token>
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2,
    random_state=0)
<mask token>
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:, :-1].values
Y = dataset.iloc[:, 3].values
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])
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)
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2,
    random_state=0)
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

#Data Preporcessing

#importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#imoprting the dataset
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:,:-1].values
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 the dataset into training 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,test_size=0.2,random_state=0)

#Feature Scalling
from sklearn.preprocessing import StandardScaler
sc_X=StandardScaler()
X_train=sc_X.fit_transform(X_train)
X_test=sc_X.transform(X_test)