🏏 IPL 2025 Data Analysis (Python)

📌 Project Overview

This project performs an in-depth analysis of the Indian Premier League (IPL) 2025 dataset using Python.
It explores matches, ball-by-ball data, players, venues, and performance trends to extract insights, visualize trends, and build baseline predictive models (e.g., match outcome prediction).

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📂 Project Structure

ipl-2025-analysis/ ├── data/ # Raw IPL 2025 datasets (matches.csv, ball_by_ball.csv, players.csv) ├── notebooks/ │ ├── 01-data-cleaning.ipynb │ ├── 02-eda.ipynb │ └── 03-modeling.ipynb ├── src/ # Utility scripts (data loaders, functions) ├── reports/ │ └── figures/ # Saved visualizations ├── requirements.txt └── README.md

⚠️ Note: Raw data files are large and may not be included. Download from the sources below and place them inside the data/ folder.

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📊 Data Sources

• IPL 2025 Records – Kaggle
• IPL Dataset 2008–2025 – Kaggle
• GitHub IPL Dataset
• ESPNcricinfo (for validation)

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⚙️ Installation & Setup

# Clone the repository
git clone https://github.com/yourusername/ipl-2025-analysis.git
cd ipl-2025-analysis

# Create a virtual environment
python -m venv venv
source venv/bin/activate   # Linux/Mac
venv\Scripts\activate      # Windows

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter Lab
jupyter lab

##📈 Analysis Highlights

✅ Season-wise Summary: Matches, winners, average runs/wickets

✅ Top Players: Batsmen (runs, strike rates), Bowlers (wickets, economy rates)

✅ Venue Analysis: Average scores, win percentages

✅ Toss Impact: Toss vs Match Outcome analysis

✅ Overs Breakdown: Powerplay, middle overs, death overs

✅ Predictive Modeling: Baseline match outcome prediction using ML

✅ Visualizations: Matplotlib, Seaborn, and Plotly

##🚀 Future Improvements

Build a live win probability model using ball-by-ball data

Deploy interactive dashboards with Streamlit or Power BI

Extend analysis across multiple IPL seasons (2008–2025)

##🏆 Credits

Data Sources: Kaggle, GitHub IPL Datasets, ESPNcricinfo

Purpose: Data Analysis portfolio project using Python