Problem Statement
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:
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Which variables are significant in predicting the price of a house, and
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How well those variables describe the price of a house.
Also, determine the optimal value of lambda for ridge and lasso regression.
Business Goal
You are required to model the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for management to understand the pricing dynamics of a new market.
- The solution is divided into the following sections:
- Data Understanding and Exploration
- Data cleaning
- Data preparation
- Model building and evaluation
- Observation and inference
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The below mentioned variables are significant in predicting the price
- GrLivArea----------Above grade (ground) living area square feet
- TotRmsAbvGrd---Total rooms above grade (does not include bathrooms)
- Street_Pave-------Pave road access to property
- RoofMatl_Metal--Roof material_Metal
- LotArea------------- Lot size in square feet
- YearBuilt-------- ---Original construction date
- BsmtFinSF1-------Type 1 finished square feet
- TotalBsmtSF------Total square feet of basement area
- OverallQual--------Rates the overall material and finish of the house
- OverallCond-------Rates the overall condition of the house
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How well Ridge Regression is descibing the price of house?
- R2 Score of Ridge Regression on training dataset is 88%
- R2 Score of Ridge Regression on test dataset is 87%
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How well Lasso Regression is descibing the price of house?
- R2 Score of Lasso Regression on training dataset is 88%
- R2 Score of Lasso Regression on test dataset is 86%
- Pandas - version 1.3.5
- Seaborn - version 0.11.2
- Python - version 3.6.9
- Numpy - version 1.21.5
Give credit here.
- This project was inspired by Upgrad.
- This project was based on Upgrad's Tutorial.
Created by [@avs-abhishek123] - feel free to contact me!