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This project uncovers match trends, player performances, venue stats, and strategic insights using Python, Pandas, Matplotlib, and Seaborn — with clear visual storytelling.\n\n## 📊 Project Highlights\n\n- ✅ Analyzed 74 IPL matches from 2022\n- 🏆 Identified top teams and players\n- 🎯 Explored toss decisions and match outcomes\n- 📈 Visualized scoring, bowling, and venue patterns\n- 📊 Included 12+ charts and plots for better understanding\n\n## 🧰 Tools \u0026 Technologies\n\n- Python\n- Pandas\n- NumPy\n- Matplotlib\n- Seaborn\n\n## 📂 Dataset\n\n- Format: CSV\n- Fields: match date, venue, teams, scores, toss decisions, winners, player of the match, top scorer, best bowling figures, etc.\n\n## 📌 Key Analyses \u0026 Visuals\n\n### 1. 🏆 Most Match Wins by Team\n- **Bar Chart** showing which teams won the most matches\n\n### 2. 🎲 Toss Decision Trends\n- **Count Plot** of toss decisions (Bat/Field)\n- **Count Plot** of toss winners by team\n\n### 3. 🎯 Toss Winner vs Match Winner\n- Percentage of matches where toss winner also won the match\n\n### 4. 🏁 Win Type Distribution\n- **Count Plot** showing wins by Runs vs Wickets\n\n### 5. 🌟 Top 'Player of the Match' Awards\n- **Bar Chart** of top 10 players with most awards\n\n### 6. 🏏 Top Scorers\n- **Horizontal Bar Chart** of top 5 batsmen by total runs\n\n### 7. 🎯 Best Bowling Figures\n- **Horizontal Bar Chart** of top 10 bowlers by total wickets\n\n### 8. 🏟️ Venue Analysis\n- **Bar Chart** showing number of matches played at each stadium\n\n### 9. 🚀 Highest Win Margin (Runs)\n- Match with the largest run margin\n\n### 10. 🔥 Highest Individual Score\n- Player with the highest single-match score\n\n### 11. 🎯 Best Bowling Performance\n- Bowler with the best single-match bowling figure\n\n\n## 📸 Sample Visuals  \nHere’s a glimpse of the visualizations included:\n\n### 🏆 Most Match Wins by Team  \n![Most Match Wins by Team](match_wins.png)\n\n### 🧢 Toss Decision Trends  \n![Toss Decision Trends](toss_decision.png)\n\n### 🏁 Win Type Distribution  \n![Win Type Distribution](win_type.png)\n\n### 🌟 Top Player of the Match Awards  \n![Top Player of the Match Awards](player_awards.png)\n\n### 🏏 Top Scorers  \n![Top Scorers](top_scorers.png)\n\n### 🎯 Best Bowling Figures  \n![Best Bowling Figures](best_bowling.png)\n\n### 🏟️ Venue Analysis  \n![Venue Analysis](venue_analysis.png)\n\n\n\n\u003e 📁 All plots are saved in the `/images` folder. You can regenerate them by running the script.\n\n## 🚀 How to Run\n\n```bash\n# Clone the repository\ngit clone https://github.com/amish5ingh/Cricket-Data-Analytics-IPL.git\n\n# Navigate to the project folder\ncd Cricket-Data-Analytics-IPL\n\n# Launch Jupyter Notebook\njupyter notebook\n\n# Open the notebook file\nipl_analysis.ipynb\n\n# Run all cells step-by-step to see data analysis and visualizations\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famish5ingh%2Fcricket-data-analytics-ipl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famish5ingh%2Fcricket-data-analytics-ipl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famish5ingh%2Fcricket-data-analytics-ipl/lists"}