To implement univariate Linear Regression to fit a straight line using least squares.
- Hardware โ PCs
- Anaconda โ 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.
- Compute the y -intercept of the line by using the formula:
- Use the slope m and the y -intercept to form the equation of the line.
- Obtain the straight line equation Y=mX+b and plot the scatterplot.
/*
Program to implement univariate Linear Regression to fit a straight line using least squares.
Developed by: YOGARAJ S
RegisterNumber: 212223040248
*/
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(Y)
num=0
denom=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='green')
plt.show()
Thus the univariate Linear Regression was implemented to fit a straight line using least squares using python programming.