Linear Regression in Machine Learning | Linear Regression Example | (Data Science ss : 10.4)

Linear Regression in Machine Learning

So now we saw all the basic components of the Linear regression it’s time to take some real life examples & code it in our jupyter notebook.

First of all go to my github page & download this 2 csv files i.e House1 , House_2 .

https://github.com/Vegadhardik7/ALL_CSV

Save it in a suitable location & open your jupyter notebook & let’s get started.

press Shit+Enter to execute every step.

Q.1)Home Price in NewYork. Given these Home Prices find out prices of homes whose Area is 3300sqft.

Step1:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

#For execution -> (Press : Shift + Enter)

Step2:

df=pd.read_csv(r"E:\MACHINE_LEARNING\House1.csv")     #give path of where you saved the file

df.head(3)

output:

Step3:

#plot a graph

plt.xlabel("Area in sqft")

plt.ylabel("Price in USD")

plt.scatter(df.Area,df.Price)

output:

Step4:

from sklearn.linear_model import LinearRegression

reg=LinearRegression()

reg.fit(df[['Area']],df.Price)   #df[['Area']]   == because dependent variable show be in 2D array

#.fit means we are training our model using data points

Step5:

#It is 'x' in y=m*x+c i.e x=3300

x=reg.predict([[3300]])

x

output:

array([815538.27751196])

#Now notice the output. We got the house price.

To get the output just like that we can use the formula.

y=m*x+c

m=reg.coef_   #Its the ‘m’ in y=m*x+c.

m

output:

array([159.92822967])

c=reg.intercept_   #Its the ‘c’ in y=m*x+c.

c

output:

287775.1196172247

#Now to find y which is our dependent variable (y=m*x+c)

y=m*3300+c

y

output:

array([815538.27751196]) #Its is exactly same as reg.predict.

Now compare both the output of x & y you can see the output values we got is same.

The math we did of y=m*x+b is done in LinearRegression model that why the predicted value is same as calculated value.

As we predicted the price of 1 house

Now we will take house area of multiple houses & predict it's price based on our model.

code:

d=pd.read_csv(r"E:\MACHINE_LEARNING\Areas.csv")

d.head()

output:

p=reg.predict(d)

d['Price']=p  #help us to create new column in Area.csv file

d

output:

d.to_csv("E:\MACHINE_LEARNING\Predictions_of_prices.csv",index=False)

use to create new CSV file named : Predictions_of_prices.csv

Now go & see prediction_of_prices.csv file all the price will be predicted their

pp=pd.read_csv(r"E:\MACHINE_LEARNING\Predictions_of_prices.csv")

pp.head()

output:

plt.xlabel("Area in sqft")

plt.ylabel("Price in USD")

plt.scatter(df.Area,df.Price)

plt.plot(df.Area,reg.predict(df[['Area']]),color="red")

output:

Go check my jupyter notebook for better understanding.

https://github.com/Vegadhardik7/ALL_CSV/blob/master/Linear_Regression_House1.ipynb

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