This project is designed to leverage data analytics techniques to determine the optimal playing 11 for a cricket tournament. The process involves web scraping data from ESPN Cricinfo, transforming this data using Python and Pandas, and creating interactive dashboards in Power BI for insightful visualizations.

• Employed the third-party web scraper "Bright Data" to collect comprehensive information from ESPN Cricinfo, including match data, results, player batting statistics, and bowling statistics.
• Stored the collected data in JSON format for subsequent processing.

• Utilized Python and Pandas to convert the JSON data into CSV format.
• Ensured the data was prepared in a format compatible with Power BI, facilitating the process of table joins.

• Used Power Query in Power BI to further clean and transform the data for analysis.
• Developed dynamic dashboards featuring interactive charts and visualizations, highlighting key metrics such as power hitters, middle-order batsmen, and bowlers.
• These dashboards offered valuable insights into player performance, team strengths, and potential areas for improvement.

• Web Scraping: Bright Data (Third-party Web Scraper)
• Programming Language: Python
• Data Manipulation: Pandas
• Data Visualization: Power BI