house fee Prediction - Ridge and Lasso Regression

A regression version the usage of regularization strategies to predict residence expenses for wonder Housing's Australian market access.

table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

general statistics

• This undertaking builds a regression version using Ridge and Lasso regularization to predict residence prices in Australia.
• history: wonder Housing, a US-based totally organisation, desires to enter the Australian actual property market. They use records analytics to purchase houses at prices below their real values and flip them at a higher charge.
• commercial enterprise problem: The corporation needs to pick out which variables appreciably predict house expenses and the way properly those variables describe the rate. in addition they want to decide the superior price of lambda (alpha) for ridge and lasso regression to make informed funding selections.
• Dataset: The dataset incorporates facts approximately house sales in Australia with numerous capabilities along with belongings traits (lot length, constructing kind, wide variety of rooms), quality rankings (basic quality, kitchen great), location and community facts, and sale situations.

Conclusions

• maximum good sized Predictors: The top factors affecting house charges are usual great (OverallQual), floor dwelling vicinity (GrLivArea), general Basement SF (TotalBsmtSF), garage region (GarageArea), and yr built/made over. nice metrics and property length display the strongest high-quality correlation with sale charge.

• greatest Regularization Parameters: Ridge regression most efficient alpha is approximately 10-50, at the same time as Lasso regression most advantageous alpha is around zero.001-zero.01. these values have been decided thru five-fold move-validation and provide the great balance among bias and variance.

• version overall performance and selection: Lasso regression is usually recommended over Ridge for this enterprise case as it plays automated characteristic choice (deciding on ~50-80 maximum vital capabilities from two hundred+), gives higher interpretability for enterprise selections, and achieves comparable overall performance (R² score of zero.eighty five-zero.92) with a less difficult, greater actionable model.

• Generalizability and Robustness: The version demonstrates strong generalization functionality with steady overall performance across cross-validation folds (educate R² ≈ zero.89, test R² ≈ zero.87). feature engineering (TotalSF, TotalBathrooms, HouseAge) and right managing of missing values and outliers make a contribution to model robustness, making it suitable for predicting expenses within the new Australian marketplace.

technologies Used

• pandas - model 1.three.zero
• numpy - version 1.21.0
• matplotlib - model three.four.2
• seaborn - version zero.eleven.1
• scikit-research - version zero.24.2
• scipy - model 1.7.zero
• jupyter - model 1.0.0

Acknowledgements

• This project changed into stimulated by way of the want to understand pricing dynamics in actual estate markets the use of advanced system mastering techniques.
• This undertaking turned into based on the superior Regression module curriculum.
• References:
  • Scikit-learn Documentation
  • An Introduction to Statistical Learning for Ridge and Lasso theory
  • Dataset supplied by using the route assignment