Explore the depths of IPL cricket through rigorous data analysis leveraging PySpark, Python, AWS S3, and Databricks. This project delves into extensive IPL datasets spanning seasons, teams, players, and matches to extract actionable insights and uncover hidden trends.
- Data Processing: Utilize PySpark for scalable data processing, ensuring efficient handling and transformation of large IPL datasets stored on AWS S3.
- Cloud Integration: Seamlessly integrate with AWS S3 for data storage and Databricks for scalable data exploration and visualization.
- Interactive Dashboards: Develop interactive dashboards on Databricks to visualize insights, trends, and performance metrics across IPL seasons. Three sample visualisation is shown using matplotlib. But the resulting dataset can be stored in a data warehouse and a BI tool like Tableau or PowerBI can be used for creating insightful dashboards.
- Scalable Data Handling: Leveraging PySpark and AWS S3 ensures robust scalability and performance in processing extensive IPL datasets.
- Predictive Modeling: Utilize advanced analytics to forecast player and team performances, enhancing strategic decision-making.
- Technical Learning: Ideal for data engineers, analysts, and researchers interested in sports analytics and cloud-based data solutions.
Future Sccope: Use Python for statistical analysis and machine learning models to predict player performance, match outcomes, and team strategy.