IPL Data Analysis

Abstract

1. Introduction

1. Take the win_by_wickets dataset and plot frequency distribution graph On x-axis - Win by Wickets on y-axis Win by Wickets margin 2. Relative frequency distribution graph using the same data set win_by_wickets Hint: use normalize = True argument for pandas.Series.value_counts Instead of showing frequency show percentage of values 3. Plot Cumulative relative frequency graph Use function pandas.Series.cumsum 4. Find out the probability of winning a match by 6 wickets or less? Find out the probability using the cumulative relative frequency graph Draw a vertical line from 6 on x-axis till it intercepts the curve and the draw the horizontal line till it intercepts y-axis Hint: Answer is 54% 5. Plot the normal distribution for win_by_wickets data. Calculate mean and standard deviation for win_by_wickets data Plot Histogram Plot Line Graph Plot Normal distribution between 1 and 10 using mean and standard deviation as calculated above 6. Calculate z-score if the team wins by 35 runs. Calculate mean and standard deviation for win_by_wickets data Use win_by_runs dataset 7. Calculate percentile using z-score. Use scipy.stats.norm.cdf function

2. Objective

1. Take the win_by_wickets dataset and plot frequency distribution graph On x-axis - Win by Wickets on y-axis Win by Wickets margin 2. Relative frequency distribution graph using the same data set win_by_wickets Hint: use normalize = True argument for pandas.Series.value_counts Instead of showing frequency show percentage of values 3. Plot Cumulative relative frequency graph Use function pandas.Series.cumsum 4. Find out the probability of winning a match by 6 wickets or less? Find out the probability using the cumulative relative frequency graph Draw a vertical line from 6 on x-axis till it intercepts the curve and the draw the horizontal line till it intercepts y-axis Hint: Answer is 54% 5. Plot the normal distribution for win_by_wickets data. Calculate mean and standard deviation for win_by_wickets data Plot Histogram Plot Line Graph Plot Normal distribution between 1 and 10 using mean and standard deviation as calculated above 6. Calculate z-score if the team wins by 35 runs. Calculate mean and standard deviation for win_by_wickets data Use win_by_runs dataset 7. Calculate percentile using z-score. Use scipy.stats.norm.cdf function

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/GD-Singh011/IPL-Data-Analysis.git
cd IPL-Data-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 GD-Singh011 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.