It is a binary classification task, where given a set of features we need to predict whether the employee is likely to leave or not

Predictive model on employee turnover using machine learning

This project analyzes employee retention using machine learning models and explores factors affecting it, such as workload, job satisfaction, and salary disparities. The goal is to provide actionable insights for HR and management, aiding in the development of effective retention strategies.

An interactive Employee Retention Dashboard that visualizes simulated data to analyze turnover trends and employee satisfaction.

An end-to-end data science project on HR attrition. Built a predictive model to identify at-risk employees and provided actionable, data-driven recommendations to improve retention.

Employee Retention

This project is a capstone part of the Google Advanced Data Analytics Professional Certificate on Coursera. This project involves data preparation and cleaning, exploratory data analysis (EDA), feature engineering, and model building and evaluation. Machine learning techniques are Logistic Regression, Decision Tree, Random Forest and XGBoost.

The main goal of this project is to accurately predict that the employee will resign or not based on predefined criteria. Various implementations and learning methods are used in this project to increase the efficiency of predicting that any employee will apply for resignation. A web-app is also made to facilitate the execution of the project. T…

Interactive Power BI Dashboard for HR Analytics. Visualizes employee attrition trends, demographic breakdowns, and key retention drivers using DAX and dynamic filtering.

RetenX is a Flask-based web app for predicting employee attrition using machine learning. It analyzes HR data, provides insights via interactive visualizations, and offers personalized retention strategies. Features include single/batch predictions, model comparisons, historical trend analysis.

This is a group project in the Data Science for Business I course where we took a data-driven approach to foster employee retention and enhance operational efficiency by building predictive models on Python.

Excel project analyzing employee churn to identify key factors and improve retention strategies.

ML model predicting employee attrition with 100% accuracy

"RetenX is a Machine Learning–based Employee Attrition Prediction System that helps HR professionals analyze workforce data, identify attrition risks, and take proactive measures for employee retention. The project features data preprocessing, visualization, multiple ML models, and a Flask-based web application."

This repo contains machine learning projects for beginners.

This project presents an interactive Tableau dashboard that analyzes employee attrition trends across various dimensions such as department, gender, age, and job satisfaction. The goal is to help HR teams identify patterns in workforce turnover and support data-driven decision-making to improve employee retention.

AI-powered HR Flight Risk Simulator. Uses Logistic Regression with Class Balancing to predict employee turnover and simulate retention strategies. Deployed on Streamlit.

This project demonstrates predictive modeling for employee turnover factors for an automotive manufacturer

We looked at employee retention over at Salifort Motors. Did a full analysis using that PACE method. Essentially, we implemented a Random Forest model. It managed a pretty good 76.2% F1-score. Finding those potential savings. Like $107 million worth. Predictions and some clever HR steps to hang onto folks. Turnover gets handled that way.

Figuring Out Which Employees May Quit