This is a machine learning project with Python that aims to build a machine learning model to predict housing prices in California. The data used in this project was downloaded from Kaggle.
The following Python packages were used in this project:
- matplotlib
- numpy
- optuna
- pandas
- plotly
- scikit-learn
- seaborn
- statsmodels
The models were selected based on their initial performance, followed by cross-validation, and learning and validation curves.
The final model achieved an R-squared metric of about 85% after performing bayesian optimization.
This project was a part of a data science and machine learning training boot camp sponsored by GDSC.
Please refer to the project code for more detailed information on the implementation.