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Polynomial Regression in Telugu | Machine Learning Algorithms

#polynomialregression #machinelearningalgorithms Data Pre-processing Step: importing libraries import numpy as nm import matplotlib.pyplot as mtp import pandas as pd #importing datasets data_set= pd.read_csv('Position_Salaries.csv') #Extracting Independent and dependent Variable x= data_set.iloc[:, 1:2].values y= data_set.iloc[:, 2].values Building the Linear regression model: #Fitting the Linear Regression to the dataset from sklearn.linear_model import LinearRegression lin_regs= LinearRegression() lin_regs.fit(x,y) Building the Polynomial regression model: #Fitting the Polynomial regression to the dataset from sklearn.preprocessing import PolynomialFeatures poly_regs= PolynomialFeatures(degree= 2) x_poly= poly_regs.fit_transform(x) lin_reg_2 =LinearRegression() lin_reg_2.fit(x_poly, y) Visualizing the result for Linear regression: #Visulaizing the result for Linear Regression model mtp.scatter(x,y,color="blue") mtp.plot(x,lin_regs.predict(x), color="red") mtp.title("Bluff detection model(Linear Regression)") mtp.xlabel("Position Levels") mtp.ylabel("Salary") mtp.show() Predicting the final result with the Linear Regression model: lin_pred = lin_regs.predict([[6.5]]) print(lin_pred) Predicting the final result with the Polynomial Regression model: poly_pred = lin_reg_2.predict(poly_regs.fit_transform([[6.5]])) print(poly_pred)