Real Estate Price Prediction using Machine Learning (Linear & Ridge Regression)

I developed a Real Estate Price Prediction Model using Python and Scikit-learn.

Key steps included:

Data preprocessing and feature selection (transaction date, house age, distance to MRT, convenience stores, latitude, longitude).

Implemented Linear Regression to predict property prices.

Enhanced the model with a Pipeline using Polynomial Features and Ridge Regression for better accuracy and generalization.

Evaluated performance with R² score to measure prediction accuracy.

Technologies used:

Python (Pandas, NumPy, Scikit-learn)

Machine Learning (Linear Regression, Ridge Regression, Polynomial Features)

This project demonstrates the application of regression techniques to predict housing prices, useful for real estate insights and investment decisions.