Dummy Variables in Machine Learning

Program 1

import pandas as pd        # Version  2.2.2
import numpy as np         # Version 1.24.3
import matplotlib.pyplot as plt # Version 3.7.1
import os
from sklearn import  linear_model # 1.5.11import
df=pd.read_csv("D://scikit_data\home/homeprices.csv")
print(df)
df_dummi=pd.get_dummies(df.town)
print(df_dummi)
new_df=pd.concat([df,df_dummi],axis='columns')
#new_df=new_df.drop(['town'],axis='columns')
new_df.drop(['town','Shalimar'],axis='columns',inplace=True)
print(new_df)
M=new_df.drop('price',axis='columns') # independed variables
print(M)
N=new_df.price
print(N)
model=linear_model.LinearRegression()
model.fit(M,N)
print(model)
x=int(input("Enter Area for Predication: "))
os.system('cls')
print("Predication Price of Shlimar Town Ship",model.predict([[x,False,False]]))
print("Predication Price of DbPride Town Ship",model.predict([[x,False,True]]))
print("Predication Price of Aamrpal Town Ship",model.predict([[x,True,False]]))