📊 IPL Data Analysis

This project performs Exploratory Data Analysis (EDA) on the Indian Premier League (IPL) cricket dataset to uncover insights about players, teams, and match trends.

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⚙️ Tools & Libraries

• Python 3.x
• Pandas
• Seaborn
• Matplotlib
• Jupyter Notebook (optional)

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🔍 Analysis Performed

Basic EDA

• Data cleaning and missing value treatment
• Summary statistics (matches, runs, wickets)
• Player performance overview (top batsmen, bowlers)
• Team-wise wins and losses

Advanced EDA

• Most Valuable Players (Player of the Match awards)
• Team performance across seasons
• Toss decision impact on match outcome
• Win distribution: batting first vs chasing
• Death overs (16-20) batsmen performance
• Powerplay (1-6) bowlers’ effectiveness

Team-Specific Analysis

• In-depth analysis of Royal Challengers Bangalore (RCB)
  • Season-wise wins
  • Favorite venues
  • Top batsmen and bowlers
  • Performance in death overs
  • Highest team totals

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📊 Visualizations

• Bar plots, pie charts, and line graphs for:
  • Player and team statistics
  • Seasonal trends
  • Match outcomes
  • Venue-wise performance