building regularized regression model to understand the most important variables to predict the house prices in Australia
A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below.
The company is looking at prospective properties to buy to enter the market. You are required to build a regression model using regularisation in order to predict the actual value of the prospective properties and decide whether to invest in them or not.
The company wants to know:
Which variables are significant in predicting the price of a house, and
How well those variables describe the price of a house.
Also, determine the optimal value of lambda for ridge and lasso regression.
GrLivArea,Neighborhood_Crawfor, Exterior1st_BrkFace, OverallQual, Functional_Typ, TotalBsmtSF,Neighborhood_Somerst , Condition1_Norm, OverallCond and Neighborhood_NridgHt are the variables significant in predicting the house price
Optimal value of lambda for Ridge Regression = 10 and Optimal value of lambda for Lasso = 0.001
How well those variables describe the price of a house :
GrLivArea - an increase of 1 sq.feet of the house area will increase the price by 1.105 to 1.111 times
Neighborhood_Crawfor - an increase in value will increase the price by 1.076 to 1.08 times
Exterior1st_BrkFace - if the value is Brick face in value will increase price by 0.069 to 1.072
OverallQual - if overall Quality of the house is high, 0.069 to 1.071 times
Functional_Typ - increase in this value will increase house price by 0.062 to 1.064 times
TotalBsmtSF - increaase will have increase in price by a factor of 0.045 to 1.04 times
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Give credit here.
- This project was inspired by Upgrad COurse
- This project was based on Bike Sharing Assignment as part of PG course in AI and ML
Created by [@Zelphire] - feel free to contact me!