Real Estate Investment Predictor
January 2024
PythonJupyter NotebooksPandasNumPyMatplotlibSeabornScikit-learnXGBoost
A machine learning-powered analysis of Chicago's real estate market using Zillow Home Value Index (ZHVI) data to identify high-potential investment opportunities across neighborhoods.
Motivation
Created to assist real estate investors in making data-driven decisions by forecasting property values and analyzing neighborhood trends in Chicago.
Key Learnings
- Performed extensive data cleaning and preprocessing on historical ZHVI and demographic data
- Developed ML models including XGBoost, Lasso, and Ridge to forecast home values
- Visualized trends in population density, property value, and neighborhood growth
- Achieved over 80% accuracy in price prediction
- Utilized cross-validation and grid search to tune hyperparameters