ChatSense - WhatsApp Chat Analyzer and Behavior Predictor

Overview

This machine learning model aims to provide a simple but powerful tool for analyzing WhatsApp chat data. By utilizing some machine learning techniques, it not only provides insights into chat but in future i will make it to predict behaviors and moods based on the conversation history.

Features

• Chat Analysis: Visualizes various aspects of WhatsApp chats, including message frequency and word usage.
• Mood Prediction: Uses machine learning to predict the mood or behavior of participants based on chat history.
• Customizable Visualization: Offers a range of visualization options using Matplotlib, Seaborn, and WordCloud.
• Data Preprocessing: Extracts URLs, emojis, and cleans text using regular expressions.
• Easy Integration: Can be integrated into existing projects or used as a standalone tool.

Requirements

• Python 3.x
• Dependencies:
  • matplotlib
  • pandas
  • seaborn
  • urlextract
  • emoji
  • wordcloud

Usage

1. Install Dependencies: Ensure Python 3.x is installed. Then, install the required packages:

   pip install matplotlib pandas seaborn urlextract emoji wordcloud

2. Prepare Data: Export your WhatsApp chat history as a text file and place it in the data_chats folder. Update the file variable in app.py (line 7) with the relative path to your chat file.

3. Run the Analyzer: Execute the script to analyze your WhatsApp chat data:

   python app.py

4. Explore Results: Review the generated visualizations and insights. Customize them as needed.

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch: git checkout -b feature/new-feature.
3. Make your changes.
4. Commit your changes: git commit -am 'Add new feature'.
5. Push to the branch: git push origin feature/new-feature.
6. Create a Pull Request.

Contact

For inquiries or feedback, please contact Satyam Kumar or connect on GitHub or LinkedIn.