
Developed a comprehensive analytics notebook for the CricketIQ_Infosys_Internship_Feb2025 repository, focusing on match data visualization and performance evaluation in cricket. The work centered on building a reproducible data analysis workflow within Jupyter Notebook, leveraging Python, pandas, numpy, matplotlib, and seaborn to process, clean, and visualize match statistics, player performance, and bowling data. Clear documentation was included to support reproducibility and collaboration. This feature enabled rapid generation of actionable cricket insights, laying the groundwork for future dashboards and data-driven decision-making. The approach emphasized code quality, modularity, and readiness for team collaboration, with a focus on robust data analysis practices.
February 2025 summary for CricketIQ project work (AabidMK/CricketIQ_Infosys_Internship_Feb2025). Focused on delivering a self-contained analytics notebook and establishing a reproducible data-analysis workflow to enable data-driven cricket insights and performance evaluation.
February 2025 summary for CricketIQ project work (AabidMK/CricketIQ_Infosys_Internship_Feb2025). Focused on delivering a self-contained analytics notebook and establishing a reproducible data-analysis workflow to enable data-driven cricket insights and performance evaluation.

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