📊 WhatsApp Chat Analyser

Welcome to the WhatsApp Chat Analyser! This tool allows you to gain insights from your WhatsApp chat history by analyzing messages, activity trends, and various other metrics.

📋 Table of Contents

• Introduction
• Features
• Installation
• Usage
• Examples
• Technologies Used
• Contributing
• License
• Acknowledgements

🌟 Introduction

The WhatsApp Chat Analyser is designed to process exported WhatsApp chat data and provide meaningful statistics and visualizations. This tool helps you understand patterns in your conversations, such as your most active days, the most frequently used words, and much more.

✨ Features

• Message Analysis: Number of messages sent and received.
• Activity Trends: Daily, weekly, and monthly activity trends.
• Word Frequency: Most frequently used words in the chats.
• Response Times: Average response times.
• Participant Analysis: Analysis of contributions from each participant in group chats.

🛠 Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/whatsapp-chat-analyser.git
   cd whatsapp-chat-analyser

2. Create a virtual environment and activate it:

   python3 -m venv env
   source env/bin/activate  # On Windows, use `env\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

🚀 Usage

After setting up the environment, follow these steps to analyze your WhatsApp chats:

1. Export your WhatsApp chat to a text file and place it in the project directory.
2. Run the chat analyser:

   python analyse_chat.py --file chat.txt

3. View the generated analysis reports and visualizations in the output/ directory.

📊 Examples

Here are some examples of the analyses you can perform:

• Message Frequency Over Time: Identify your busiest chatting periods.
• Top Words: See which words you and your friends use the most.
• Response Times: Find out how quickly participants respond to messages.

💻 Technologies Used

• Python: For data processing and analysis.
• Pandas: For data manipulation and analysis.
• Matplotlib/Seaborn: For creating visualizations.
• NLP Libraries: For natural language processing tasks.

🤝 Contributing

We welcome contributions! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/your-feature-name

3. Make your changes and commit them:

   git commit -m "Add your commit message"

4. Push to the branch:

   git push origin feature/your-feature-name

5. Open a pull request.

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

🙏 Acknowledgements

Special thanks to the developers and contributors who made this project possible. Your support and feedback are invaluable.

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Feel free to explore, use, and contribute to this project. Happy analyzing! 📈