This project aims to analyze and predict employee performance using machine learning techniques. The goal is to identify key factors that influence employee performance and to create a model that can accurately predict performance ratings. The project addresses the issue of declining employee performance, which has been impacting customer satisfaction and overall business productivity.
Project Goal The goal of this project is to create an easy-to-use tool that enables HR departments to better understand the key factors driving employee performance. By leveraging this model, organizations can enhance their HR strategies, improve employee satisfaction, and drive organizational productivity through targeted interventions and a more informed decision-making process.
Key Features Data Analysis: Perform in-depth analysis of employee data to identify important correlations. Model Development: Train and tune a machine learning model to predict employee performance ratings. Model Deployment: Deploy the model using Streamlit, providing an interactive web application for HR use. Evaluation Metrics: Assess the model using precision, recall, F1-score, and support metrics for accurate performance measurement.
Impact This project aims to streamline HR processes by offering actionable insights into employee performance. The ability to predict performance allows organizations to focus on employee retention, satisfaction, and aligning team goals with business objectives.
Technologies Used Python Machine Learning (Scikit-learn) Data Visualization (Matplotlib, Seaborn) Streamlit (Model deployment) GitHub (Version Control)