Employee-Managing-Scheduling-System

Employee Scheduler with Predictive Scheduling and Dynamic Availability This project implements a Django-based employee scheduler with features for predicting customer traffic and dynamically adjusting schedules based on real-time employee availability.

Functionality Overview:

• Employee Management: Manage employee data, including skills and availability. (Existing functionality)
• Shift Management: Create, view, and manage employee shifts. (Existing functionality)
• Predictive Scheduling:
  • Integrates with a machine learning model to predict customer traffic. (Requires further development)
  • Uses predicted traffic to estimate staffing needs.
• Dynamic Availability:
  • Enables employees to update their availability in real-time. (Requires further development)
  • Schedules employees based on both skills and current availability.

Installation:

1. Prerequisites:

   • Python 3.x (https://www.python.org/downloads/)
   • pip (usually comes with Python)
   • Git (https://git-scm.com/downloads) (for version control)

2. Clone the repository:

   git clone https://github.com/<your-username>/employee-scheduler.git

3. Install dependencies:

   cd employee_scheduler
   pip install -r requirements.txt

Building and Running:

1. Create a virtual environment (optional, but recommended):

   python -m venv venv
   source venv/bin/activate  # Windows/Linux
   source venv/bin/activate.bat  # macOS

2. Run Django migrations (to create database tables):

   python manage.py makemigrations
   python manage.py migrate

3. Start the development server:

   python manage.py runserver

   This will start the server at http://127.0.0.1:8000/ by default.

Current Development Status:

• The core functionalities of employee and shift management are implemented.

• The prediction and dynamic availability features are partially implemented and require further development.

• LinkedIn : https://www.linkedin.com/in/thekartikeyamishra/

Design Choices and Technical Challenges:

• Machine Learning Model Selection: Choosing the most suitable model for customer traffic prediction depends on the available data and desired accuracy. Explore options like Random Forest or LSTMs.
• Real-time Employee Availability: Implementing a mechanism for employees to update their availability in real-time requires additional development (e.g., a web interface or API). Security considerations should be addressed when integrating with external systems.
• Scheduling Algorithm: The current scheduling logic is a placeholder. You can implement a more sophisticated algorithm that considers factors like skills, experience, workload preferences, and fairness in assigning shifts.