This repository contains a WhatsApp Chat Analyzer built using Machine Learning (ML) techniques. The goal of this project is to analyze WhatsApp chat logs to extract meaningful insights, generate visualizations, and uncover patterns in communication behavior.
- Features
- Installation
- Usage
- Examples
- Technologies Used
- Contributing
- Contact
- Message Frequency: Track message frequency over time to identify periods of high and low activity.
- Word Cloud: Generate word clouds to visualize the most commonly used words in the chat.
- User Statistics: Extract user-specific statistics, such as the number of messages sent, average message length, and active hours.
- Emoji Analysis: Analyze the usage of emojis to understand emotional expression.
- Activiy : Analysis of active members on group.
- Emoji Frequence: Track of most used emojis in the chat.
- Python 3.7 or higher
- pip (Python package installer)
- Clone the repository:
https://github.com/VaibhavVermaa16/Whatsapp-chat-analyser.gitcd whatsapp-chat-analyzer - Create a virtual environment (optional but recommended):
python -m venv venvsource venv/bin/activate # On Windows: venv\Scripts\activate - Install the required dependencies:
pip install -r requirements.txt
- Export your WhatsApp chat as a text file and save it in the main directory.
- Run the analysis script:
streamlit run main.py - Upload the chat file on the web page.
- Python: The primary programming language for the project.
- Pandas: For data manipulation and analysis.
- Numpy: For numerical computations.
- Matplotlib/Seaborn: For data visualization.
- Streamlit: For web application
- Wordcloud: For creating word cloud.
Contributions are welcome! Please follow these steps:
- Fork the repository.
- Create a new branch:
git checkout -b feature/your-feature - Make your changes and commit them.
git commit -m 'Add some feature' - Push to the branch:
git push origin feature/your-feature - Open a pull request.