Welcome to IPL Insights Visualization — an interactive dashboard that brings IPL data to life with advanced analytics and beautiful visuals. Built using Apache Spark, Streamlit, and Plotly, this app provides insights into player performances, venues, win probabilities, and more.
- 📈 Interactive dashboards powered by Plotly
- 🧠 Machine Learning-based Win Probability Predictor
- 🏆 Top Performers Leaderboard (Runs, Wickets)
- 🏟️ Venue-based Statistics
- ⏱️ Dynamic Scoreboard Visualizer
- 🌙 Built-in Dark Mode
- Python 3.10+
- Apache Spark (PySpark)
- Pandas, Numpy, Scikit-learn
- Plotly, Matplotlib, Seaborn
- Streamlit
📦 IPL-Insights-Visualization ├── app.py # Main Streamlit application ├── spark_ipl_analysis.py # Spark-based data processing ├── visuals/ │ ├── scoreboard.py │ ├── venue_analysis.py │ └── win_probability.py ├── data/ # Match & delivery CSV files ├── requirements.txt └── README.md
yaml Copy Edit
git clone https://github.com/VarshithGaddam/IPL-Insights-Visualization.git cd IPL-Insights-Visualization 2. Install dependencies pip install -r requirements.txt 3. Run the application
streamlit run app.py
Note: Streamlit Cloud does not support Apache Spark (PySpark). Consider converting your data processing to use pandas instead.
Push this project to GitHub
Visit streamlit.io/cloud
Click New App → Connect your GitHub repository
Choose app.py as the main file
Click Deploy
Place your image inside .streamlit/images/
Use the following code to load it:
from PIL import Image import streamlit as st
st.image(Image.open(".streamlit/images/ipl-dashboard-preview.png"), use_column_width=True) 🧠 Future Work Deep learning model for advanced match prediction
Real-time data streaming using Kafka + Spark Streaming
Geo-mapped stadium visualizations
Mobile app packaging (PWA / React Native)
This project is licensed under the MIT License.
IPL Datasets: Kaggle
Streamlit Team for awesome deployment
Open-source contributors who made this possible
Made with ❤️ by Varshith Gaddam