📊 What's Inside

Analysis Highlights

• Top performing IPL teams and players
• Impact of toss on match outcomes
• Popular venues
• Toss decision trends (bat vs chase)
• Match distribution by season
• Season-wise most successful teams
• PowerBI Analysis

All visuals are saved inside the outputs/ folder and explained using markdown in the Jupyter notebook.

---

Python Analysis

PowerBI

🛠️ Tools Used

• Python (Pandas, Matplotlib, Seaborn)
• Jupyter Notebook
• Markdown for storytelling
• PowerBI

---

Repo File Structure

.
├── Analysis
│   ├── powerbi
│   │   └── IPL_Analysis.pbix           # PowerBI Dashboard
│   └── python
│       ├── data_analysis.py
│       ├── dataset_cleaning.py
│       ├── IPL_Analysis_01.ipynb       # Jupyter Dashboard
│       └── IPL_Analysis_02.ipynb
├── Asserts
│   ├── dataset.png                     # PowerBI Screenshots
│   └── model_view.png
├── Datasets
│   ├── Cleaned Datasets
│   │   ├── cleaned_deliveries.csv
│   │   └── cleaned_matches.csv
│   ├── IPL-dataset.zip
│   └── Raw Datasets
│       ├── deliveries.csv
│       └── matches.csv
├── outputs                            # Python Analysis Screenshots
│   ├── matches_per_year.png
│   ├── result_type.png
│   ├── top5_teams.png
│   ├── top_players.png
│   ├── toss_results.png
│   └── venue_counts.png
└── README.md

9 directories, 19 files

📌 How to Run

1. Open the project in Jupyter Notebook.
2. Navigate to Analysis/ folder.
3. Run the notebook after installing required packages:

   pip install pandas matplotlib seaborn

---

📎 Notes

This project is for learning purposes. All data used is publicly available and cleaned for educational analysis.