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

Employee Retention

Figuring Out Which Employees May Quit

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…

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.

Improving Employee Retention by Predicting Employee Attrition Using Machine Learning

This repo contains machine learning projects for beginners.

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.

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.

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.

Predictive model on employee turnover using machine learning

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