ipl-data-analysis
🏏 IPL Data Analysis Project
📌 Project Overview
This project analyzes IPL (Indian Premier League) ball-by-ball data using Python.
It focuses on:
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Player Performance Analysis
- Team Performance Analysis
- Match Trends
- Data Visualization
📸 Project Preview
Top Batsmen
Top Bowlers
Team Performance
Year-wise Matches
Run Distribution
🚀 Technologies Used
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Plotly
- Google Colab
📂 Dataset Information
- IPL Ball-by-Ball Dataset
- Contains match, player, and performance data
- Sample dataset included due to GitHub size limits
📊 Analysis Performed
Data Cleaning
- Removed unnecessary columns
- Handled missing values
- Converted date column
Exploratory Data Analysis
- Top batsmen (total runs)
- Strike rate analysis
- Top bowlers (wickets)
- Team performance
- Most winning teams
- Year-wise matches
- Run distribution
💡 Key Insights
- Top batsmen contribute majority of total runs
- Certain teams consistently dominate matches
- High strike rate players are crucial in scoring
- Wicket-taking bowlers influence match outcomes
- Matches have increased over the years
🧠 Skills Demonstrated
- Data Cleaning
- Exploratory Data Analysis
- Data Visualization
- Sports Analytics
- Insight Generation
- Python Programming
📁 Project Structure
ipl-data-analysis/
│
├── sample_ipl_data.csv
├── cleaned_ipl_data.csv (optional)
├── notebook.ipynb
├── insights.txt
├── README.md
│
└── images/
├── batsmen.png
├── bowlers.png
├── teams.png
├── yearly.png
└── distribution.png
▶️ How to Run
- Open notebook in Google Colab
- Upload dataset (or use sample file)
- Run all cells
- View outputs and graphs
📌 Future Improvements
- Add batting average
- Add bowler economy
- Build Streamlit dashboard
- Add ML model for prediction
👩💻 Author
Komal Margale Data Analyst | Python Enthusiast
⭐ Support
If you like this project, give it a ⭐ on GitHub .