To implement univariate Linear Regression to fit a straight line using least squares.
- Hardware – PCs
- Anaconda – Python 3.7 Installation / Jupyter notebook
- Get the independent variable X and dependent variable Y.
- Calculate the mean of the X -values and the mean of the Y -values.
- Find the slope m of the line of best fit using the formula.
/*
Program to implement univariate Linear Regression to fit a straight line using least squares.
Developed by: praveen.v
RegisterNumber: 212222233004
*/
import numpy as np
import matplotlib.pyplot as plt
X = np.array(eval(input()))
Y = np.array(eval(input()))
X_mean = np.mean(X)
Y_mean = np.mean👍
num,denom=0,0
for i in range(len(X)):
num+= (X[i]-X_mean)*(Y[i]-Y_mean)
denom+= (X[i]-X_mean)**2
m = num/denom
b = Y_mean - m*X_mean
print(m,b)
y_predicted=m*X+b
print(y_predicted)
plt.scatter(X,Y)
plt.plot(X,y_predicted,color='red')
plt.show()
Thus the univariate Linear Regression was implemented to fit a straight line using least squares using python programming.