# How to implement a Backward elimination approach in a regression model?

320    Asked by ranjan_6399 in Data Science , Asked on Jan 15, 2020

# Multiple Linear Regression

# Importing the libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

# Importing the dataset

X = dataset.iloc[:, :-1].values

y = dataset.iloc[:, 4].values

# Encoding categorical data

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

labelencoder = LabelEncoder()

X[:, 3] = labelencoder.fit_transform(X[:, 3])

onehotencoder = OneHotEncoder(categorical_features = [3])

X = onehotencoder.fit_transform(X).toarray()

# Avoiding the Dummy Variable Trap

X = X[:, 1:]

# Splitting the dataset into the Training set and Test set

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)

sc_y = StandardScaler()

y_train = sc_y.fit_transform(y_train)"""

# Fitting Multiple Linear Regression to the Training set

from sklearn.linear_model import LinearRegression

regressor = LinearRegression()

regressor.fit(X_train, y_train)

# Predicting the Test set results

y_pred = regressor.predict(X_test)

# Building the optimal model using Backward Elimination

import statsmodels.formula.api as sm

X = np.append(arr = np.ones((50, 1)).astype(int), values = X, axis = 1)

X_opt = X[:, [0, 1, 2, 3, 4, 5]]

regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()

regressor_OLS.summary()

X_opt = X[:, [0, 1, 3, 4, 5]]

regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()

regressor_OLS.summary()

X_opt = X[:, [0, 3, 4, 5]]

regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()

regressor_OLS.summary()

X_opt = X[:, [0, 3, 5]]

regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()

regressor_OLS.summary()

X_opt = X[:, [0, 3]]

regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()

regressor_OLS.summary()