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@Avik-Jain @KarandikarMihir
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Data PreProcessing

As shown in the infograph we will break down data preprocessing in 6 essential steps. Get the dataset from here that is used in this example

Step 1: Importing the libraries

import numpy as np
import pandas as pd

Step 2: Importing dataset

dataset = pd.read_csv('Data.csv')
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 3].values

Step 3: Handling the 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])

Step 4: Encoding categorical data

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])

Creating a dummy variable

onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y =  labelencoder_Y.fit_transform(Y)

Step 5: Splitting the datasets into training sets and Test sets

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)

Step 6: 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)

Done 😄

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