IPL Dataset Analysis

Abstract

1. Introduction

IPL is the biggest sports festival our India. It consist of all Great international crickets from different countries and domestic players of India. We have three csv dataset files which has the data of IPL matches per ball summary, venue details and total Match summary. This dataset consists of three separate CSV files : matches and deliveries. These files contain the information of each match summary and ball by ball details, respectively.

1) Python 2) Pyspark library 3) SQl 4) Databricks Notebook (PLatform to write and run our code)

We can get to know more about the dataset by applysin as many queries as we want but for our study purpose it is limited to 9 queries only.

2. Objective

Now that this year's IPL is over, let's not curb our cricket love and start analyzing the whole of IPL with this latest and complete Indian Premier League dataset. It contains the match descriptions, results, winners, player of the matches, ball by ball dataset and much more. So, stop thinking and start analyzing . Content This dataset consists of three separate CSV files : matches and deliveries. These files contain the information of each match summary and ball by ball details, respectively.

This project demonstrates how Jupyter Notebook 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
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/vikrant65-byte/IPL-dataset-Analysis.git
cd IPL-dataset-Analysis

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 vikrant65-byte and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.