Project Name

This is a advance regression programming assignment with regularisation technique like Lasso and Ridge.

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

• A US-based housing company named Surprise Housing has decided to enter the Australian market
• The comapny wants to buy undervalued properties to resell for profit.
• They want to use Data analytics to predict property values
• Data: Australian house sale data (provided as CSV file)
• Task: Build a regression model to predict the actual value of prospective properties.

Conclusions

• The final Lasso regression model, after excluding the initial top predictors, identified 1stFlrSF, YearBuilt, YearRemodAdd, FullBath, and TotRmsAbvGrd as the most important predictors for determining house prices. This model provides a reliable framework for predicting house prices and making informed real estate decisions.

Technologies Used

• Python - version 3.10.9
• scikit-learn - version 1.2.1
• statsmodels - version 0.13.5
• Pandas - version 1.5.3
• Numpy - version 1.23.5
• Seaborn - version 0.12.2
• Matplotlib -version 3.7.0
• conda - version 23.3.1

Acknowledgements

• This project is based on Surprise Housing Assigment by upGrad

Contact

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