Project 4 Group 5 Google Colab Notebook
This project uses machine learning to predict housing prices based on features from the California Housing Prices dataset. Our primary goal is to explore patterns within the dataset and evaluate the performance of various regression models, such as Random Forest Regressor.
The dataset used may not fully reflect the current housing market and could be outdated. While it provides a good opportunity to practice machine learning techniques, the findings should not be generalized to the present housing market. This limitation is acknowledged to ensure proper interpretation of results and to avoid misleading conclusions.
- VSCode and Colab Compatibility Guide - Use this quick reference sheet if you prefer to run the Google Colab notebook through Visual Studio Code instead.
This project demonstrates iterative model optimization by comparing different algorithms, such as:
- RandomForestRegressor
- GradientBoostingRegressor
- LinearRegression
Performance metrics, such as R² score and Mean Squared Error, are tracked across models. Final performance results are printed at the end of the notebook to provide a clear evaluation of the best-performing model.
- Caroline M.
- Madison O.
- Weibin H.
- David K.