Project Name

Advance Regression Techniques Assignment - House Price Prediction

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements
• Contact

General Information

• 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 'train.csv' attached.

• The company is looking at prospective properties to buy to enter the market. The aim of the study is 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.

Conclusions

The best value for lambda for both Ridge and Lasso is:

• Ridge - 10
• Lasso - 0.0004

The Mean Squared error is:

• Ridge - 0.01548
• Lasso - 0.01566

The R2Score for train and test set are:

• Ridge

  • R2score for Train set: 0.9262
  • R2score for Test set: 0.8832

• Lasso

  • R2score for Train set: 0.9254
  • R2score for Test set: 0.8820

NOTE:

• The final model choosen is Lasso

• The most significant features that can be suggested to the Surprise Housing company are as follows.

  • SaleCondition (either Partial or Normal)
  • Neighborhood (specially Crawford)
  • Foundation (If it is Poured Concrete)
  • Exterior1st (If the exterior covering of the house has a brick face)
  • OverallQual
  • MSZoning (If in Floating Village Residential Zone)
  • OverallCond
  • Exterior2nd (If there is a second material used i.e.. Wood siding)
  • BsmtExposure (If it has good exposure)

Technologies Used

• Pandas
• Numpy
• Matplotlib
• Seaborn
• Sklearn

Acknowledgements

Give credit here.

• This project was inspired by a case study from the Executive PG Programm in Machine Learning by IIIT Bengaluru

Contact

Created by [@rahulkpareek] - feel free to contact me!