Praneet002 / Univariate-Linear-Regression

Implementation of Univariate Linear Regression

Implementation of Univariate Linear Regression

Aim:

To implement univariate Linear Regression to fit a straight line using least squares.

Equipment’s required:

1. Hardware – PCs
2. Anaconda – Python 3.7 Installation / Moodle-Code Runner

Algorithm:

1. Get the independent variable X and dependent variable Y.
2. Calculate the mean of the X -values and the mean of the Y -values.
3. Find the slope m of the line of best fit using the formula.
4. Compute the y -intercept of the line by using the formula:
5. Use the slope m and the y -intercept to form the equation of the line.
6. Obtain the straight line equation Y=mX+b and plot the scatterplot.

Program

Program for Univariate linear regression using the least squares method.
Developed by: Praneet S
RegisterNumber: 212221230078
'''
import numpy as np

# Preprocessing Input data

X = np.array(eval(input()))
Y = np.array(eval(input()))


# Mean 
X_mean =np.mean(X)
Y_mean =np.mean(Y)
num=0 #for slope
denom=0 #for slope

#to find sum of (xi-x') &(yi-y') &(xi-x')^2

for i in range(len(X)):
    num+=(X[i]-X_mean)*(Y[i]-Y_mean)
    denom+=(X[i]-X_mean)**2
    
#calculate slope

m=num/denom

#calculate intercept
#(xi-x') & (yi-y')

b=Y_mean-m*X_mean
print(m,b)


#line equation

y=m*X+b

print(y)

Sample Input and Output

Result

Thus the univariate Linear Regression was implemented to fit a straight line using least squares.