🏏 IPL 2024 Data Analysis: SQL + Python Mini Project.

A beginner-friendly Oracle SQL & Python project using real IPL 2024 data.

Perfect for SQL learners, Python beginners, and portfolio builders!

📊 Project Overview This project loads IPL 2024 match, team, and player data into Oracle Database, runs 6 insightful analytical queries, and generates beautiful matplotlib visualizations to uncover trends like:

• 🏠 Do teams win more at home?
• 🎯 Does toss decision impact match outcome by venue?
• 🌟 Which teams & players dominate Player of the Match awards?
• 👑 How do captains perform — wins + personal impact?
• 💪 Who are the Top 5 All-Rounders (Batting + Bowling)?
• 🥇 Final Points Table based on wins

🗃️ File Structure

ipl-data-analysis/
│
├── data/
│   └── ipl_data_2024.xlsx          # Raw Excel data
│
├── sql/
│   ├── create_tables.sql           # DDL: Create TEAMS, PLAYERS, MATCHES
│   ├── insert_data.sql             # DML: Bulk insert using INSERT ALL
│   └── analysis_queries.sql        # 6 analytical queries with comments
│
├── python/
│   ├── 1_load_and_creation.py      # Reads Excel → creates Oracle tables
│   ├── 2_clean_load_to_oracle.py   # Cleans data (NaN → None) → bulk insert
│   ├── 3_fetch_and_visualize_1_home_advantage.py
│   ├── 3_fetch_and_visualize_2_toss_decision.py
│   ├── 3_fetch_and_visualize_3_potm_count.py
│   ├── 3_fetch_and_visualize_4_captain_perf.py
│   ├── 3_fetch_and_visualize_5_top_5_all.py
│   └── 3_fetch_and_visualize_6_points_table.py
│
├── plots/
│   ├── 1_Teams_performance_at_Home_Ground.png
│   ├── 2_Win_Percentage_by_Venue_and_Toss_Decision.png
│   ├── 3_Player_of_match_awards_by_Team.png
│   ├── 4_Captain_win_percentage.png
│   ├── 5_Top_5_AllRounders.png
│   └── 6_IPL_2024_Points_Table.png
│
└── README.md                       # You're here!

🚀 How to Run?

• Prerequisites
• ✅ Oracle Database (XE 21c or later recommended)
• ✅ Python 3.8+
• ✅ Libraries: oracledb, pandas, matplotlib, openpyxl

Steps

1. Set up Oracle DB

   • Ensure you can connect via: system/system@localhost:1521/xepdb1 (Modify connection string in Python files if needed)

2. Run Python Scripts (in order)

   • python/1_load_and_creation.py
   • python/2_clean_load_to_oracle.py
   • python/3_fetch_and_visualize_1_home_advantage.py
   • python/3_fetch_and_visualize_2_toss_decision.py
   • python/3_fetch_and_visualize_3_potm_count.py
   • python/3_fetch_and_visualize_4_captain_perf.py
   • python/3_fetch_and_visualize_5_top_5_all.py
   • python/3_fetch_and_visualize_6_points_table.py

3. View Queries

   • All queries saved in /sql folder

4. View Plots

   • All charts saved in /plots folder
   • Also displayed interactively during script run

🛠️ Skills Demonstrated

• ✅ SQL: Complex JOINs, CTEs, Window Functions (ROW_NUMBER, SUM OVER), Aggregations, Conditional Logic (CASE)
• ✅ Python: Data cleaning, Oracle DB connectivity (oracledb), Error handling
• ✅ Pandas: DataFrame manipulation, SQL result ingestion, data type conversion
• ✅ Matplotlib: Bar, Horizontal Bar, Grouped Bar, Lollipop charts — with colors, labels, grids
• ✅ Data Storytelling: Turning raw data into business insights with visualizations

🤝 Contributing Found a bug? Want to add Query 7?

• 👉 Fork this repo, make improvements, and submit a Pull Request!

🙌Acknowledgements

• IPL 2024 Official Data Sources
• Oracle Database Express Edition
• Matplotlib & Pandas communities

📬 Feedback

• 📩 Open an Issue or DM me on LinkedIn
• ❤️ I’d love to hear how this helped you!