ML Project
California Housing Prices:
Machine Learning!
Description
In my recent machine learning capstone project, I navigated the complexities of predicting California housing prices with a comprehensive approach. Integrating MySQL for efficient ETL processes, Python for data analysis, and Tableau for data visualization, the project showcased a seamless journey from data extraction to impactful visualizations. Leveraging key Python libraries such as pandas, numpy, and scikit-learn, I explored and fine-tuned various models, including Linear Regression, Random Forest, and Neural Networks. Through a meticulous process of training, validating, and testing on distinct datasets, Gradient Boosting emerged as the optimal model, delivering the lowest Root Mean Squared Error (RMSE) of 49,343.55 on the test data. The project’s success lies in the holistic integration of diverse tools and techniques, underscoring the importance of a multidisciplinary approach in the dynamic field of machine learning.
Conclusion
In conclusion, my machine learning capstone project successfully tackled the challenge of predicting California housing prices through a comprehensive and multidisciplinary approach. Integrating MySQL for efficient ETL processes, Python for robust data analysis, and Tableau for compelling visualizations, the project showcased a seamless workflow from data extraction to presentation. The exploration and fine-tuning of various models, such as Linear Regression, Random Forest, and Neural Networks, revealed Gradient Boosting as the optimal choice, delivering the lowest Root Mean Squared Error (RMSE) of 49,343.55 on the test data.
