OMG_PGFM

Project Overview

This project aims to create a machine learning foundation model for processing WhatsApp chat data. The goal is to preprocess, analyze, and extract features from the data to build predictive models.

Project Structure

• data: Directory containing raw and processed data files.
• notebooks: Jupyter notebooks for data preprocessing and analysis.
• scripts: Python scripts for feature extraction and model building.
• docs: Project documentation files.
• models: Trained machine learning models.

Setup Instructions

1. Clone the repository.
2. Create a virtual environment and activate it.
3. Install the required packages:

   conda install --file requirements.txt

4. Create the necessary directories and placeholder files:

   mkdir -p ./data
   echo "Sample content for ProtocollChat2_15_5_24.txt" > ./data/ProtocollChat2_15_5_24.txt
   echo "Sample content for activitys_12_3_24.txt" > ./data/activitys_12_3_24.txt
   echo "feature1,feature2,feature3\n1,2,3" > ./data/features.csv
   echo "Sample content for preprocessed_chat.csv" > ./data/preprocessed_chat.csv
   echo "Sample content for scores_10_3_24_.txt" > ./data/scores_10_3_24_.txt

Usage

1. Run the data preprocessing script:

   python scripts/preprocess_data.py

2. Run the feature extraction script:

   python scripts/feature_extraction.py

3. Train the machine learning model:

   python scripts/train_model.py

Contributing

Please read the CONTRIBUTING.md file for guidelines on contributing to this project.

License

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

Current Project Status

Progress and Achievements:

• Setup:
  • Installed necessary tools and created project structure.
• Development:
  • Implemented data preprocessing, feature extraction, and model building scripts.
  • Configured CI/CD with GitHub Actions.
• Testing and Documentation:
  • Automated tests set up and running.
  • README.md and other documentation updated regularly.

Next Steps:

• Monitor and address any issues in GitHub Actions.
• Continue developing and refining the model.
• Regularly update documentation.

Current Project Status

Progress and Achievements:

• Setup:
  • Installed necessary tools and created project structure.
• Development:
  • Implemented data preprocessing, feature extraction, and model building scripts.
  • Configured CI/CD with GitHub Actions.
• Testing and Documentation:
  • Automated tests set up and running.
  • README.md and other documentation updated regularly.

Next Steps:

• Monitor and address any issues in GitHub Actions.
• Continue developing and refining the model.
• Regularly update documentation.

Current Project Status

Progress and Achievements:

• Setup:
  • Installed necessary tools and created project structure.
• Development:
  • Implemented data preprocessing, feature extraction, and model building scripts.
  • Configured CI/CD with GitHub Actions.
• Testing and Documentation:
  • Automated tests set up and running.
  • README.md and other documentation updated regularly.

Next Steps:

• Monitor and address any issues in GitHub Actions.
• Continue developing and refining the model.
• Regularly update documentation.