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 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…

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

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…

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.

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

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.

Uncover the factors that lead to employee attrition at IBM

Final presentation project for completing Rakamin Academy Data Science Bootcamp.

Predicting why employees are leaving organization & building a model to predict in future, who will leave the company.

HR Analytics in R Script: "Why Employees leave the company?"

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

Understanding and predicting employee's attrition

This project focuses on predicting the attrition rate of employees by using different ML algorithms. The dataset is a fictional data taken from Kaggle

Employee churn prediction using Gradient Boosting Classifier

Tingginya tingkat employee attrition dapat mempengaruhi kinerja perusahaan. Oleh karena itu, perlu dilakukan proses analisa mengenai faktor-faktor apa saja yang menyebabkan seorang karyawan memilih untuk resign sehingga team HR dapat memberikan treatment khusus kepada karyawan agar tidak meninggalkan perusahaan.

IT is about the employee attrition, employee performance,hiring employee

Analyzed employee attrition using Python and data science libraries. Explored factors such as job role, department, and demographics to understand patterns influencing attrition. Random Forest demonstrated superior performance with an accuracy rate of 94%.