employee-attrition

CSE343, Machine Learning Course Project, IIIT Delhi, Monsoon 2021

This repository demonstrates how data science can help to identify the employee attrition which is part of Human Resource Management

This project involves Employee Attrition Prediction using various data visualisation techniques & machine learning models. The repository consists of the .ipynb file and files used for deploying the ML model on 'Heroku' using the Flask framework.

This project is a machine learning classification problem. The objective of this project was to predict the rate of employee attrition in the current scenario based on different features. It was the classification problem. I tried three algorithms (Logistics, Decision Tree & Random Forest). But I got high accuracy score about 0.97 using random F…

Bill Gates was once quoted as saying, "You take away our top 20 employees and we [Microsoft] become a mediocre company". This statement by Bill Gates took our attention to one of the major problems of employee attrition at workplaces. Employee attrition (turnover) causes a significant cost to any organization which may later on effect its overal…

This app allows users to explore key factors for employee attrition. Survey data can be filtered by gender, age, and department.

Uncover the factors that lead to employee attrition using IBM Employee Data

Analyze employee attrition trends using SQL and Power BI. Gain insights to reduce turnover and enhance satisfaction. 📊🔍

Interactive Power BI dashboard for workforce analytics, employee attrition analysis, compensation insights, and HR performance monitoring.

End-to-end Employee Attrition Prediction system with data analysis, feature engineering, model comparison, explainable AI (SHAP), FastAPI deployment, and Docker.

In this project, the team strives to use machine learning principles to predict employee attrition, provide managerial insights to prevent attrition, and finally rule out and present the factors that lead to attrition.

End-to-end HR Analytics and Employee Turnover Prediction system using Machine Learning, SMOTE, KMeans clustering, cross-validation, and risk-based retention strategies.

The goal of this project is to analyze employee retention data to uncover insights that can help improve retention strategies. By identifying key factors that influence employee attrition, we aim to provide actionable recommendations for enhancing employee satisfaction and retention rates.

End-to-end HR attrition analysis on a 311-employee dataset — Python cleaning pipeline, 13 SQL business queries, a 5-page interactive Power BI dashboard, and a written insights report diagnosing where and why employees leave.

In this project I did Complete EDA, and Build a ML model that can accurately predict whether an Employee will be leave a company or not based on different factors.

End-to-End Data Analyst project on employee attrition using Python, MySQL, and Logistic Regression. Covers full EDA, SQL analytics, and predictive modeling for HR decision-making, risk segmentation, business KPIs, and actionable retention strategy.

Interactive Power BI dashboard analyzing employee attrition trends and workforce insights for data-driven HR decisions.

A Power BI Dashboard analyzing employee attrition to explore key factors behind employee turnover.

Clustering employee performances to predict resignation likelihood and develop strategies for employee retention

An HR Analytics Dashboard built using Tableau to analyze employee attrition trends and provide actionable workforce insights.