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Created a Power BI report to identify top 11 players for a T20 cricket team by scraping data from espncricinfo with a Brightdata website tool, cleaning and transforming the data with pandas, and evaluating various player performance metrics.

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AbhishekDataAnalyst/ICC-Men-s-T20-World-Cup-2022

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ICC-Men-s-T20-World-Cup-2022

Comprehensive sports data analytics project. In order to do an analysis of T20 World Cup cricket data, we will cover web scraping (from the ESPN Cricinfo website), Python, Pandas, and Power BI in this project.

We have used cricket T20 World Cup (2022) data to provide insights on the best 11-player team in this end-to-end data analytics project for novice and expert users. Using Bright Data web scraping, we collected data from the ESPN Cricinfo website using Bright Data, transformed and cleaned the data in Pandas, and then created Power BI dashboards.

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Created a Power BI report to identify top 11 players for a T20 cricket team by scraping data from espncricinfo with a Brightdata website tool, cleaning and transforming the data with pandas, and evaluating various player performance metrics.

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