May 23, 2023 · We explore the database design, query optimization, and data integration techniques utilized by the franchise to consolidate and analyze player statistics, match results, and other relevant...

Analyzed IPL cricket data using PySpark on Databricks with datasets stored on AWS S3; performed Spark SQL-based EDA on batting, bowling, and match outcomes; built Random Forest (R²: 0.85, …

5 days ago · Explore and run machine learning code with Kaggle Notebooks | Using data from IPL Complete Dataset (2008-2024)

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Problem Statements: Indian Premier League (IPL) Data Analysis Description: The Indian Premier League (IPL) is a professional Twenty20 cricket league in India contested by eight teams. The …

The CASE statement in SQL enhances batting performance analysis by transforming numerical run data into descriptive categories. For example, mapping one run as 'Single', four runs as 'Boundary', six …

Jul 10, 2025 · Whether you’re analyzing your favorite IPL team’s performance, comparing player stats, or just exploring historical trends, Python offers powerful tools to transform raw cricket data into...

Jan 6, 2024 · The file named deliveries.csv is the ball-by-ball data of all the IPL matches including data of the batting team, batsman, bowler, non-striker, runs scored, etc. So there are 636 match data...

Maximum runs are being scored in the last 5 overs of the match. MI and RCB have shown a incresing trend in the runs scored throughout the match.

May 4, 2024 · We analyze ball-by-ball data from IPL seasons 1 to 10 (2008-2017). The key regression algorithms we employ are Decision Tree, Linear Regression, Random Forest, Lasso Regression, …