There are 0 repository under employee-attrition topic.
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 Forest.
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.
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 overall efficiency. As per CompData Surveys, over the past five years, total turnover has increased from 15.1 percent to 18.5 percent. For any organization, finding a well trained and experienced employee is a complex task, but it’s even more complex to replace such employees. This not only increases the significant Human Resource (HR) cost, but also impacts the market value of an organization. Despite these facts and ground reality, there is little attention to the literature, which has been seeded to many misconceptions between HR and Employees. Therefore, the aim of this paper is to provide a framework for predicting the employee churn by analyzing the employee’s precise behaviors and attributes using classification techniques.
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.
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
Uncover the factors that lead to employee attrition at IBM
This is a project based on the Employee Attrition analysis and then predicting it. Also analysing what are the major factors for attrition.
Predicting why employees are leaving organization & building a model to predict in future, who will leave the company.
Final presentation project for completing Rakamin Academy Data Science Bootcamp.
This project focuses on predicting the attrition rate of employees by using different ML algorithms. The dataset is a fictional data taken from Kaggle
HR Analytics in R Script: "Why Employees leave the company?"
Understanding and predicting employee's attrition
Using data from the Human Resources department at IBM, I created an EDA on reasons why attrition occurred.
An employee attrition prediction (machine learning) project
Exploratory data analysis and machine learning classification models to predict employee attrition.
IT is about the employee attrition, employee performance,hiring employee
Analyzing Employee Exit Surveys of two Institutes - Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute
PREDICTIVE ANALYTICS - LOGISTIC REGRESSION . Predicting employee attrition using HR data
CSE445 - Machine Learning Project
Predict employee attrition using LogisticRegression and RandomForestClassifier.
OBJECTIVE: Presenting an EDA / ML Models (DTR & RF) solution to help company X trying to control attrition by answering the following questions: What type of employees are leaving ? Which employees are prone to leave next ? Predict the future (DT & RF) employee who would tend to leave the company X in the future ? Recommendation for company decision making.
This is a Project done for J Component for the course Predictive Analytics
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.