Lab_ML(20/01/25)

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn import linear_model

header = ["Area", "Price"]
data=[[2600,550000],[3000,565000],[3200,610000],[3600,680000],[4000,725000]]
data = pd.DataFrame(data, columns=header)
data.to_csv('D://ARIJIT-6SEM/ML/House_price.csv',index=False )

Display_csv = pd.read_csv('D://ARIJIT-6SEM/ML/House_price.csv', usecols= ['Area','Price'])
Display_csv

plt.scatter(Display_csv['Area'],Display_csv['Price'],c='Blue',marker='*')
plt.xlabel("Area in sq. ft.")
plt.ylabel("Price in US$")
plt.show()

reg=linear_model.LinearRegression()
reg

Display_csv['Area']
Display_csv['Price']
reg.fit(Display_csv[['Area']], Display_csv.Price)

p=reg.predict([[6000]])
p

q = reg.coef_
q

r = reg.intercept_
r

y =q*6000 +r
y

plt.xlabel("Area in sq. ft.")
plt.ylabel("Price in US$")
plt.scatter(Display_csv['Area'],Display_csv['Price'],c='Blue',marker='*')
plt.plot(Display_csv['Area'], reg.predict(Display_csv[['Area']].values))
plt.show()

x=np.array(Display_csv['Area'])
y=np.array(Display_csv['Price'])
meanofx=sum(x)/len(x)
meanofy=sum(y)/len(y)
m1=0
m2=0
for i in range(len(x)):
    m1=m1+(x[i]-meanofx)*(y[i]-meanofy)
    m2=m2+(x[i]-meanofx)**2
b1=m1/m2
b0=meanofy-b1*meanofx
y=b0+b1*6000
y