- 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 (Pandas, Matplotlib, Seaborn)
- Jupyter Notebook
- Markdown for storytelling
- PowerBI
.
├── 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
- Open the project in Jupyter Notebook.
- Navigate to
Analysis/folder. - Run the notebook after installing required packages:
pip install pandas matplotlib seaborn
This project is for learning purposes. All data used is publicly available and cleaned for educational analysis.