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

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

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.

SQL project exploring HR data for retention, salary trends, and performance metrics to guide workforce decisions.

SQL-based HR analytics project analyzing employee retention, performance, and compensation patterns to support data-driven business decisions.

People Analytics case study addressing post-layoff retention at Meta. Identified high-performer flight risk and delivered data-backed retention strategy to C-suite

HR Analytics dashboard built with Power BI to monitor employee performance, attrition, and organizational health.

Employee Retention

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.

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

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

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.

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.

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…

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.

ML model predicting employee attrition with 85.62% accuracy

HR data analysis exploring employee attrition trends for a mid-size tech company.

Employee attrition prediction system using Random Forest and XGBoost to identify turnover risk drivers and enable proactive HR intervention.

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