Beyond predicting overall volumes, the work extends to sub-categories, including ward allocation and patient complexity, providing more granular insights for hospital resource planning.

I experimented with a range of models, including SARIMAX for time-series forecasting, LSTM for sequence learning, and Random Forest for robust feature-driven predictions. Throughout the project, I gained hands-on experience in feature engineering, data cleaning and pre-processing, training and testing algorithms, alongside comparing algorithm performance to determine the most suitable approaches for different forecasting objectives.

Note: the project is contained in a private repository as it contains sensitive data.

The application was designed to let users record runs, wickets, overs, and other match statistics in real time, with a clean and intuitive interface tailored for mobile use.

This project not only improved my proficiency in Swift and Xcode, but also gave me practical experience in UI/UX design, data persistence, and application testing. It highlighted the end-to-end development process, from planning and design to implementation and debugging.

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The app allowed users to record runs, wickets, overs, and other match details, while maintaining a consistent and responsive interface across both iOS and Android devices.

This project gave me practical experience in Dart programming, Flutter widgets, and responsive UI design. I also explored best practices for building layouts that adapt seamlessly to different screen sizes and platforms, strengthening my understanding of cross-platform mobile development.

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By collecting real-world football data and applying predictive models, I gained hands-on experience in data-driven sports analytics.

Key aspects of the project includes web scraping, data analysis, machine learning, alongside combined data collection, analysis, and predictive modeling to gain practical experience in real-world football analytics.

This project strengthened my skills in Python programming, data wrangling, feature engineering, and model evaluation, while giving me a deeper understanding of how sports analytics and machine learning can be applied to predict outcomes.

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