Description
- Trained an 85-feature machine learning model on 500,000+ housing records to predict housing prices in Cook County, Illinois
- Achieved a Root Mean Squared Error (RMSE) of ~$103k across a testing set of 30,000+ housing records, leveraging techniques such as feature engineering and k-fold cross-validation to tune hyperparameters and optimize model performance.
- GitHub Repository for this project can be found here!
- NumPy
- Pandas
- Scikit-Learn
- Seaborn
- Regular Expressions
Skills
- Ordinary Least Squares Regression
- K-fold Cross Validation
- Data Visualization
- Data Cleaning
- Machine Learning
- Regularization
- Feature Engineering
