Ipl Data Analysis

Abstract

1. Introduction

The Indian Premier League is a professional Twenty20 cricket league in India contested during March or April and May of every year by eight teams representing eight different cities in India. The league was founded by the Board of Control for Cricket in India in 2008.    

2. Objective

In this project, we will scrape the data from ipl website, and analyze the data and deploy the data visualization dashboard on Heroku.

This project demonstrates how Jupyter Notebook, Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/bprasad26/ipl_data_analysis.git
cd ipl_data_analysis

$ git clone https://github.com/bprasad26/ipl_data_analysis.git
$ cd ipl_data_analysis

# create virtual environment
$ python3 -m venv ipl_venv
# Activate the virtual environment
$ source ipl_venv/bin/activate

# install packages from requirements.txt file
$ pip install -r requirements.txt

# activate venv for jupyter notebook
$ python -m ipykernel install --user --name=ipl_venv
# start jupyter notebook
$ jupyter notebook
# Note - at the top change the kernal to ipl_venv from the kernal dropdown if not done automatically.

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by bprasad26 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.