Employee Performance Prediction

Overview

Employee Performance Prediction is a project designed to analyze various data points related to employees' work performance and use machine learning algorithms, leveraging ML technology stack, to predict and evaluate their future performance. By incorporating factors such as past performance metrics, training data, feedback, and external factors, the system aims to provide insights that can aid in talent management, resource allocation, and workforce optimization strategies.

Structure

• Datasets: Contains the datasets used for training.
• Notebooks: Jupyter Notebooks for data exploration, preprocessing, and model training.
• Flask App: Code for deploying the predictive model via a web interface.
• IBM Files: Integration with IBM Watson for enhanced analysis.

Setup Instructions

1. Clone the repository.
2. Install dependencies listed in 'requirements.txt'.
3. Run Jupyter Notebooks for model training.
4. Deploy using Flask with 'app.py'.

Usage

• Use the trained model to predict employee performance by inputting relevant features.
• Access predictions via the Flask web interface.

Dependencies

• Python 3.x
• Flask
• Jupyter
• Scikit-learn

How to Contribute

1. Fork the repository.
2. Create a new branch for your feature.
3. Submit a pull request with detailed documentation.

For more information, visit the GitHub repository.