- This project analyzes the 2022 T20 World Cup data to determine the top 11 players based on their performance. I used ParseHub to collect data from the ESPNcricinfo website, then cleaned and transformed it with NumPy and pandas, Python libraries, and created dashboards in Power BI, utilizing DAX commands for advanced data analysis and calculations.
Overview:
- I web scraped the dataset from the ESPNcricinfo website, focusing on Bowlers, Batsmen, and match summaries from the T20 World Cup, and supplemented it with several datasets from Kaggle for a comprehensive analysis.
Tools Used:
- Parse Hub, Microsoft Excel, Microsoft Power BI.
Language Used:
- Python Programming
Steps in the Analysis:
- Web Scraping: I used ParseHub to fetch data from the ESPNcricinfo website and converted it into tabular form in Excel. This process was repeated for Batsmen, Bowlers, and match summaries.
- Data Cleaning: I cleaned and processed the data using Python, utilizing the NumPy and pandas libraries to ensure the datasets were ready for analysis.
- Data Visualization: I used Power BI to visualize the datasets, highlighting player performances and match summaries.
Conclusion:
- Overall, it was a valuable dataset for analyzing and gaining insights using Python, DAX commands, and Power BI.