import numpy as np from sklearn.linear_model import LinearRegression x = np.array([5,15,25,35,35,55]).reshape((-1,1)) y = np.array([5,20,14,32,22,38]) #print(x,'\n',y) # Model Selection model = LinearRegression() model.fit(x,y) model=LinearRegression().fit(x, y) print(model) # find the Coefficents r_sq = model.score(x,y) print("Coefficents of X,Y",r_sq) # find the interception print("Intercept:",model.intercept_) print("Slope:",model.coef_) # Passing the Data to model new_model = LinearRegression().fit(x,y.reshape(-1,1)) print("Intercepts:",new_model.intercept_) print('slope',new_model.coef_) # Find the Y values y_pred=model.predict(x) print('Predicted Response',y_pred) # OR y_pred=model.intercept_+model.coef_*x print("Predicted Response",y_pred,sep='\n') # New Input to Test x_new=np.arange(6).reshape((-1,1)) #print(x_new) y_new=model.predict(x_new) print(y_new) # Data Visulization import matplotlib.pyplot as plt plt.scatter(x,y) plt.plot(x_new,y_new) plt.show()