# Multiple Linear Regression Model with Normal Equation

Prerequisite: NumPy

Consider a data set,

#### let us consider,

Here area, rooms, age are features / independent variables and price is the target / dependent variable. As we know the hypothesis for multiple linear regression is given by:  where, NOTE: Here our target is to find the optimum value for the parameters θ. To find the optimum value for θ we can use the normal equation. So after finding the values for θ, our linear hypothesis or linear model will be ready to predict the price for new features or inputs.

Normal Equation is : Considering the above data set we can write,

X: an array of all independent features with size (n x m) where m is a total number of training samples and n is the total number of features including (x0 = 1)

XT: Transpose of array X

y: y is 1D array/column array/vector of target/dependent variable with size m where m is a total number of training samples.

So for the above example we can write :

X =   [[ 1, 23,  3,  8],

[ 1, 15,  2,  7],

[ 1, 24,  4,  9],

[ 1, 29,  5,  4],

[ 1, 31,  7,  6],

[ 1, 25,  3,  10]]

X T=  [[ 1,  1,  1,  1, 1, 1],

[23, 15, 24, 29, 31, 25],

[ 3,  2,  4,  5,  7,  3],

[ 8,  7,  9,  4,  6,  10]]

y=  [6562, 4569, 6897, 7562, 8234, 7485]

## Python

 import numpy as np     class LinearRegression:      def __init__(self):          pass        def __compute(self, x, y):          try:              '''              # multiline code              var = np.dot(x.T,x)              var = np.linalg.inv(var)              var = np.dot(var,x.T)              var = np.dot(var,y)              self.__thetas = var              '''             # one line code              self.__thetas = np.dot(np.dot(np.linalg.inv(np.dot(x.T,x)),x.T),y)          except Exception as e:              raise e                 def fit(self, x, y):          x = np.array(x)          ones_ = np.ones(x.shape)          x = np.c_[ones_,x]          y = np.array(y)          self.__compute(x,y)         @property     def coef_(self):          return self.__thetas         @property     def intercept_(self):          return self.__thetas[1:]         def predict(self, x):          try:              x = np.array(x)              ones_ = np.ones(x.shape)              x = np.c_[ones_,x]              result = np.dot(x,self.__thetas)              return result                      except Exception as e:              raise e     # testing of code...     # datasets  x_train = [[2,40],[5,15],[8,19],[7,25],[9,16]]  y_train = [194.4, 85.5, 107.1, 132.9, 94.8]  x_test = [[12,32],[2,40]]  y_test = []     # testing the model...  lr = LinearRegression()  lr.fit(x,y)  print(lr.coef_,lr.intercept_)  print(lr.predict(x_t))

#### Output :

Value of Intercept =  305.3333333334813

Coefficients are =  [236.85714286  -4.76190476 102.9047619 ]

Actual value of Test Data =  [8234, 7485]

Predicted value of Test Data =  [8232.         7241.52380952]