A data-driven machine learning project designed to analyze, evaluate, and visualize employee performance using key performance indicators. The system helps organizations make informed HR decisions by transforming raw employee data into meaningful insights and predictive analysis.
This project was developed as part of an academic semester mini project in a team of four members. The application utilizes machine learning techniques and Streamlit-based visualization to present employee performance analytics through an interactive interface.
- Employee performance analysis
- Machine learning-based prediction system
- Data visualization and insights
- Streamlit-powered interactive interface
- Dataset training and model generation
- Performance evaluation using employee datasets
- Python
- Streamlit
- Pandas
- Scikit-learn
- Joblib
- Training the dataset using employee performance data
- Building and saving prediction models
- Running predictions through a Streamlit application
- Visualizing employee insights and analytics
- Assisted in project setup and local environment configuration
- Installed and configured Streamlit for application execution
- Participated in project testing and execution
- Contributed to project documentation and report preparation
- Collaborated in understanding the project workflow and implementation process
Academic Team Mini Project (4 Members)
The project was created to explore machine learning applications in human resource analytics and understand how predictive systems can support organizational decision-making.