# Machine Learning Model 2

Basically the Multiple Linear regression is same as the linear equation only difference is the having many independent variable and in linear regression only on independent variable is there.

Y = @ + # X       ( Linear regression )

Y is dependent Variable

X is independent variable

@ is slope

# is linear regression constant

Y = @ + #1 .X1 + #2 .X2 + #3X3 + #4 .X4

Y is dependent Variable

X1,X2 ,X3 ,X4 is independent variable

@ is slope

#1 , #2 , #3 , #4 is MULTIPLE linear regression constant

In multiple regression we compare many items that independent variable to dependent variable and predict the result

We Process the data firstly and then we work on the regression

data preprocessing

# template is heavnly need and also feature scaling is not need for mant time but if required we used

import numpy as np
import matplotlib.pyplot as plot
import pandas as pd

#import the data sets
X = dataset.iloc[: , :-1].values
Y = dataset.iloc[: ,:3].values

#split the set to traning and test sets

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# test size should be displayed

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

# scale dumay vriable

now we do an Multiple linear Rgression

# the multiple linear regression is having many component independent ,  Y = a+ax1+ax2....
# we need to remove the coloum which is noy useful \$ garbage in and out
# in = out ... ifthe variable which not impact on

# 5 method of building models 2,3 ,4 are step wise regression ...
#1 all in : -  if you know that important varaible or just gave you and make model
# 2  : -
# 3
# 4
# 5
import numpy as np
import matplotlib.pyplot as plot
import pandas as pd

# all expect the last is independ variable
# 4 is dependent variable which is the last coloum and
X = dataset.iloc[:, :-1].values
Y = dataset.iloc[:, :4].values
# categorial data
from sklearn.preprocessing import LabelEncoder ,OneHotEncoder
X = X.reshape(1,-3)
labelencoder_X = LabelEncoder()   # index gaves which has coloum which has diff data type than int...
X[:, 0]=labelencoder_X.fit_transform(X[:,0])

onehotencoder = OneHotEncoder(OneHotEncoder(categories='auto'sparse=False))
#dummy create  pasiing index
X = onehotencoder.fit_transform(X).toarray()

#split the set to traning and test sets

#dumay variable the type , this used to remove the trap
#removing tthe first index
X = X [: ,:-1]

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# test size should be displayed

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

# now we learn all the model the traing set now we have to test the model using some variables

Y_pred = regressor.predict(X_test)
print(Y_pred)