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 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.

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.

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.

Employee Retention

This project aims to address the issue of increasing employee resignations using descriptive statistical approaches and machine learning with the help of Python.

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

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

Figuring Out Which Employees May Quit

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…

This repo contains machine learning projects for beginners.

Improving Employee Retention by Predicting Employee Attrition Using Machine Learning

Employee turn-over (also known as "employee churn") is a costly problem for companies. The true cost of replacing an employee can often be quite large.

HR Analytics: Job Change Prediction for Data Scientists is an end-to-end solution that predicts employee retention risks and provides AI-powered recommendations. Features include machine learning predictions, GPT-2 strategy generation, and role-based access control via Streamlit dashboard.