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Day3_Multiple_Linear_Regression.md

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Multiple Linear Regression

Step 1: Data Preprocessing

Importing the libraries

import pandas as pd
import numpy as np

Importing the dataset

dataset = pd.read_csv('50_Startups.csv')
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 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)

Step 2: Fitting Multiple Linear Regression to the Training set

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, Y_train)

Step 3: Predicting the Test set results

y_pred = regressor.predict(X_test)