🏏 CodeAlpha_IPL_DataVisualization
Task 3 — Data Visualization
CodeAlpha Data Analytics Internship
Author: Limithra Shanmugam · AVS Engineering College
Overview
Comprehensive data visualization of IPL (Indian Premier League) match data from 2008 to 2023, transforming raw cricket statistics into compelling visual stories using Python.
Key Insights
#InsightFinding1Most wins all-timeMumbai Indians & CSK joint leaders2Toss decision65% captains choose to field first3Win type55% by wickets vs ~45% by runs4Toss advantageToss winner wins only ~52% — minimal edge5Top POTMV Kohli leads Player of the Match awards6Biggest host cityMumbai hosts the most IPL matches7Season growthMatches grew from ~45 (2008) to 60+ (2011+)
Folder Structure
CodeAlpha_IPL_DataVisualization/
│
├── data/
│ └── IPL_matches.csv # IPL matches dataset (2008–2023)
│
├── notebook.ipynb # Full visualization notebook
├── report.pdf # Professional visual report
└── README.md # This file
Visualizations Created
Horizontal bar chart — All-time wins by team (team colors) Multi-line chart — Season-wise win trends for top 4 teams Pie charts — Toss decision & toss-vs-match outcome Pie chart — Win type (by runs vs by wickets) Horizontal bar — Top Player of the Match award winners Bar chart — Matches hosted by venue Histogram — Win margin distribution (runs & wickets) Area + line chart — Season growth over 16 years Bar chart — Matches hosted by city
Skills Used
Python — core language Pandas — data loading, groupby, aggregation Matplotlib — bar, line, pie, area, histogram charts Seaborn — statistical visualizations ReportLab — professional PDF report generation
Quick Start bashpip install pandas numpy matplotlib seaborn
jupyter notebook notebook.ipynb Dataset IPL Matches data covering 16 seasons (2008–2023), 915 matches, 10 teams with features including match results, toss decisions, venues, cities, player of the match, and win margins.
CodeAlpha Data Analytics Internship — Task 3