TaskXpert is a a machine learning-based system engineered to optimize workforce allocation on an Electric Vehicle (EV) battery assembly line. It uses a predictive model to forecast a worker's performance rating and intelligently recommend the most suitable task for them. This project serves as a comprehensive demonstration of how AI can be leveraged to enhance operational efficiency and productivity in a manufacturing environment.
- Predictive Analytics: Utilizes a robust
XGBoostmodel to predict a worker's future performance rating based on their skills, experience, and certifications. - Intelligent Task Recommendation: Recommends the optimal task from the assembly line that best matches a worker's profile, ensuring maximum productivity and a higher performance rating.
- Full-Stack Application: A complete web application built with Python's Flask framework, providing a clean and intuitive user interface for managers to input worker details and view results.
- Educational Resources: Includes a dedicated page with videos about the EV assembly and battery manufacturing process to provide context and background information.
- Efficient Deployment: The trained machine learning models and preprocessing objects are saved using
joblibto facilitate fast and seamless deployment in the web application.
- Backend Framework: Flask (Python)
- Machine Learning:
XGBoost,scikit-learn - Data Handling:
pandas,joblib - Frontend: HTML, CSS, JavaScript, Bootstrap
- Environment Variables:
python-dotenv - Data Visualization:
matplotlib,seaborn(for model evaluation)
Follow these steps to get the EV Workforce Allocation application up and running on your local machine.
- Python 3.x
- pip (Python package installer)
This project uses synthetic datasets to simulate worker and task allocation on an EV assembly line. The original datasets are available for download from this Google Drive link:
It is highly recommended to use a virtual environment to avoid conflicts.
# Create a virtual environment
python -m venv venv
# Activate the virtual environment
# On macOS/Linux
source venv/bin/activate
# On Windows
.\venv\Scripts\activate
# Install the required Python packages
pip install Flask pandas scikit-learn xgboost joblibOnce you have the dependencies installed, you can launch the Flask application from the project's root directory.
python app.pyThe application will start, and you can access it by opening your web browser and navigating to http://127.0.0.1:5000/.
- Explore the Home Page: Navigate to the home page to learn about the EV assembly and battery manufacturing processes.
- Watch Videos: Click on the "Watch Videos" button to access educational content related to EV battery production.
- Access the Allocator: Go to the "Task Allocator" page to input worker details.
- Input Worker Data: Fill out the form with the worker's skill category, skill level, years of experience, shift availability, certifications, and current task assigned.
- Get Recommendations: Click the "Recommended" button to receive a predicted performance rating and a task recommendation for the worker.
This application is provided for informational and demonstration purposes only. The predictions and recommendations are based on a synthetic dataset and should not be used as a substitute for professional operational management or decision-making.