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.
/*
Program to implement univariate Linear Regression to fit a straight line using least squares.
Developed by: Karthick P
RegisterNumber: 212222100021
*/
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_pred = m*X+b
print (Y_pred)
print("X values : ",X)
print("Y values : ",Y)
dots=[150]
plt.figure(figsize=(10, 8))
plt.scatter(X,Y,color='green',s=dots)
plt.plot(X,Y_pred,color='red',linewidth=4)
plt.xlabel("X-axis",fontweight='bold',fontsize=20)
plt.ylabel("Y-axis",fontweight='bold',fontsize=20)
plt.show()
Thus the univariate Linear Regression was implemented to fit a straight line using least squares using python programming.