Polynomial Regression

What is linear regression: -

 Linear regression analysis is used to predict the value of a variable based on the value of another variable

to know more about Linear Regression checkout - Linear Regression

Multiple Linear Regression: -

Multiple linear regression is a regression model that estimates the relationship between a quantitative dependent variable and two or more independent variables using a straight line.

 

Polynomial Regression:

In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x.

As you could that the polynomial regression is essential when data is not in the linear form (i.e., the line here is curved, but in linear regression it is straight)

Practical

import pandas as pd

from sklearn.impute import SimpleImputer

import numpy as np

df=pd.read_csv(r'path')

# Numerical

num_var=df.select_dtypes(include=['int64','float64']).columns

print(df[num_var])

im=SimpleImputer(strategy='mean')

im.fit(df[num_var])

df[num_var]=im.transform(df[num_var])

print(df[num_var].isnull().sum())

# Categorical

cat_var=df.select_dtypes(include='O').columns

imp=SimpleImputer(strategy='most_frequent')

imp.fit(df[cat_var])

df[cat_var]=imp.transform(df[cat_var])

print(df.isnull().sum().sum())

################# DATA PREPROCESSING ###########################################

df2=df.drop(columns=df[cat_var])

# print(df2)

################## DATA SPlITING ################################################

X=df2.drop(columns='price', axis=1)

y=df2['price']

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2, random_state=69)

################## FEATURE SCALING ####################################################

from sklearn.preprocessing import StandardScaler

sc=StandardScaler()

sc.fit(X_train)

X_train=sc.transform(X_train)

X_test=sc.transform(X_test)

############### Training- Polynomial Regression #############################################

from sklearn.linear_model import LinearRegression

from sklearn.preprocessing import PolynomialFeatures

pl=PolynomialFeatures(degree=10)

pl.fit(X_train)

X_train_pl=pl.transform(X_train)

X_test_pl=pl.transform(X_test)

print(X_train.shape)

####### Linear Regression ##################################################

lr=LinearRegression()

lr.fit(X_train_pl, y_train)

print(lr.score(X_test_pl, y_test))

######### Prediction #############################################################

pre=lr.predict(X_test_pl)

print(pre)

print(y_test)

########## RMSE, MSE ################################################################

from sklearn.metrics import mean_squared_error

mse=mean_squared_error(y_test, pre)

rmse=np.sqrt(mse)

print(mse,rmse)

I would appreciate if you could make it better