IPL-DATA-ANALYSIS

🏏 IPL Exploratory Data Analysis (EDA) Project

This project explores the rich dataset of the Indian Premier League (IPL) using Python libraries like pandas, matplotlib, and seaborn. The goal is to uncover hidden insights from both match-level and player-level data.

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📂 Dataset

• matches.csv: Contains match-level data from IPL 2008 to 2023
• deliveries.csv: Ball-by-ball delivery-level data for each match

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🧹 Data Cleaning

• Removed irrelevant columns (umpires, method, result_margin, super_over, etc.)
• Renamed match_id and standardized season (e.g., 2007/08 → 2008)
• Joined datasets using consistent IDs

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📊 Analysis Highlights

📅 Match-Level Analysis

• Number of matches per season
• Toss decision trends
• Teams with the most wins

🧤 Player-Level Insights

• Top run scorers, six-hitters, and fours
• Strike rate and death-over dominance
• Bowlers with most wickets, best economy, and dot balls

🔁 Matchups & Patterns

• Most runs by a batter against a specific team
• Most dismissals by a bowler to a batsman
• Batting averages vs top teams

🥧 Dismissal Analysis

• Distribution of dismissal types shown with pie chart
• Grouped rare dismissals under “Others” for clarity

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📈 Visualizations

• Bar charts (Top batsmen, bowlers, matches per season)
• Pie chart (Dismissal types)
• Line plots & comparisons
• Heatmaps (optional)

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📌 Tools Used

• Python
• pandas
• matplotlib
• seaborn
• Jupyter Notebook

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📁 Project Structure

• 📦 IPL-DATA-ANALYSIS
• 📊 cleaned_matches.csv
• 📊 cleaned_deliveries.csv
• 📓 IPL DATA.ipynb
• 📄 README.md