Whatsapp Chat Data Analysis

Abstract

1. Introduction

Whatsapp has quickly become the world’s most popular text and voice messaging application. Specializing in cross-platform messaging with over 1.5 billion monthly active users, this makes it the most popular mobile messenger app worldwide.

I’m defining this function to maintain consistent colors for each person across all plots. Since the order will vary depending on the plot, this is passed to the function which will reorder colors in a particular order so that the color of a certain person remains the same no matter the plot. This will help maintain consistency and readability amongst the many graphs I will be plotting.

Next, I made a dictionary where each key is the name and the value for each would be their assigned color. I create a function that reorders colors given a list of names to match the ordering of the plot.

2. Objective

An Exhaustive WhatsApp Chat Data Analysis.

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

4. Technology Stack

• Regex (re) to extract and manipulate strings based on specific patterns.
• References:
• Regex - Python Docs
• Regex cheatsheet
• Regex Test - live
• Datetime Format
• pandas for analysis.
• matlotlib and seaborn for visualization.

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/tusharnankani/whatsapp-chat-data-analysis.git
cd whatsapp-chat-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 tusharnankani 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.