Multiple: y=b0+b1X1+b2X2+b3X3....bnXn

Polynomial: y=b0+b1X+b2(X)^2+....bn(X)^n

Step1: Importing package

import pandas as pd import numpy as np import sklearn import matplotlib.pyplot as plt import statsmodels

Step2; Read data using pandas

dataset = pd.read_csv('ordor.csv',delimiter='\t') X=dataset.iloc[:,1:4].values y=dataset.iloc[:,0].values

Step 3: Preprocessing of data: Not Required as there is no categorical data in this dataset

Step 4: Splitting of data into train and test set: Not required as total populationsize is very small (15,4)

step 5: Multiple Linear Regression Classifier.

from sklearn.linear_model import LinearRegression l=LinearRegression() l.fit(X,y) y_pred=l.predict(X)

Step 6: Polynomial Linear Regression Classifier

from sklearn.preprocessing import PolynomialFeatures P=PolynomialFeatures(degree=2) X_poly=P.fit_transform(X) Pl=LinearRegression() Pl.fit(X_poly,y) py_pred=Pl.predict(X_poly)

Visualize the Multiple Regression and Polynomial Regression

plt.scatter(X,y,color='green') plt.plot(X,y_pred,color='blue') plt.title('Multple LR') plt.xlabel('Temp,Ratio,Height') plt.ylabel('Odor') plt.show()

plt.scatter(X,y, color='red') plt.plot(X,py_pred,color='blue') plt.title('Polynomial LR') plt.xlabel('Temp,Ratio,Height') plt.ylabel('Odor') plt.show()


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.