23003324 / 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:

# Univariate Linear Regression
# Developed by: HARITHA RAMESH
# Register number: 23003324

import numpy as np
import matplotlib.pyplot as plt
x= np.array(eval(input()))
y=np.array(eval(input()))
X=np.mean(x)
Y=np.mean(y)
num,den=0,0
for i in range (len(x)):
    num +=((x[i]-X)*(y[i]-Y))
    den += ((x[i]-X)**2)
m=num/den
b=Y-m*X
print(m,b)
Ypred = m*x + b
print(Ypred)

plt.scatter(x,y)
plt.plot(x,Ypred,color='red')
plt.show()

Output:

Result:

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