employee-attrition
There are 37 repositories under employee-attrition topic.
aastha985/Employee_Attrition_Prediction
CSE343, Machine Learning Course Project, IIIT Delhi, Monsoon 2021
netsatsawat/HR-Analytics
This repository demonstrates how data science can help to identify the employee attrition which is part of Human Resource Management
sanatladkat/Employee-Attrition-Prediction
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.
m0h1t98/Employee-Attrition
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.
sandeepyadav10011995/Employee-Attrition-Prediction-Model
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.
UBC-MDS/532_Dashboard_Project_Group_14
This app allows users to explore key factors for employee attrition. Survey data can be filtered by gender, age, and department.
AbhinavS99/Predicting-Employee-Attrition-for-Augmenting-Institutional-Yield
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.
Niranjankumar-c/HRAnalyticsEmployeeAttrition
Uncover the factors that lead to employee attrition using IBM Employee Data
PritamDeb68/Employee-Attrition-Prediction
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.
roniantoniius/Employees-Performance-Clustering-and-Resignation-Prediction
Clustering employee performances to predict resignation likelihood and develop strategies for employee retention
alankarpatra/IBM_HR_Attrition
Uncover the factors that lead to employee attrition at IBM
DataDaneHQ/Salifort_Motors_Attrition_Analysis
This project analyzes employee attrition at Salifort Motors using machine learning and data analytics to identify key turnover drivers. The analysis spans data cleaning, exploratory data analysis (EDA), predictive modeling (logistic regression, decision trees, random forest, and XGBoost), and actionable HR recommendations.
mandar196/Employee_Attrition-HR-Analytics
Predicting why employees are leaving organization & building a model to predict in future, who will leave the company.
mchosasih99/Employee-Attrition-Prediction
Final presentation project for completing Rakamin Academy Data Science Bootcamp.
RibaKhan/Employee-Attrition-Prediction
This project focuses on predicting the attrition rate of employees by using different ML algorithms. The dataset is a fictional data taken from Kaggle
trajceskijovan/HR-Analytics
HR Analytics in R Script: "Why Employees leave the company?"
yanbin43/Classification-EmployeeAttrition
Understanding and predicting employee's attrition
Edmond21/IBM_Exploratory_Data_Analysis
Using data from the Human Resources department at IBM, I created an EDA on reasons why attrition occurred.
jasontanx/machine-learning-employee-attrition
An employee attrition prediction (machine learning) project
Luismunozp/Cv
Detalle de la experiencia como Senior Tech & Digital Acquisition
luuisotorres/EDA-and-Employee-Attrition-Prediction
Exploratory data analysis and machine learning classification models to predict employee attrition.
mayankpagaria/Employee-Attrition
IT is about the employee attrition, employee performance,hiring employee
Naveen-Sharma220722/Employee_Attrition_Analysis
A Dashboard made in Excel showing the trend of how employees left the organization.
omarnasser7/attrition-prediction
Efficient employee attrition prediction using LazyPredict to evaluate and compare multiple classification models for HR analytics.
rajtulluri/Analyzing-Employee-Exit-Survey
Analyzing Employee Exit Surveys of two Institutes - Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute
rgcollar/employee-churn-prediction
Employee churn prediction using Gradient Boosting Classifier
sudiptabhatta/IBM-HR-Dataset-Employee-Attrition-Prediction
CSE445 - Machine Learning Project
swarnima000/Intervie-Tech-Final-Project-Employee-Attrition
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%.
thetishadas/Salary_prediction
The Salary Prediction App forecasts employee salaries based on years at the company, satisfaction level, and monthly hours worked. It uses a cleaned dataset and applies machine learning models like Linear Regression, Support Vector Regression, and Random Forest Regressor, with hyperparameter tuning done via GridSearchCV for improved accuracy.
viandwip/Employee-Attrition-Prediction-by-Using-Machine-Learning-main
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.
Ansu-John/IBM-HR-Analytics-Employee-Attrition
Predict employee attrition using LogisticRegression and RandomForestClassifier.
EnriqueBarreiro/Portafolio
🚀 AI & ML projects tackling real-world challenges—predicting trends, optimizing decisions, and extracting insights. Featuring credit risk analysis, recommendation systems, ad campaign optimization, churn prediction, real estate pricing, and medical diagnosis models.
Jimoh1993/Human-Resources-Data-Analytics-Project
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.
RAHULPATEL2002/employee-attrition
This repository contains an analysis of employee attrition trends at Green Destinations. It includes data preprocessing, exploratory data analysis (EDA), and a predictive model using Logistic Regression to determine attrition likelihood based on employee attributes.
rasmodev/Employee-Attrition-Prediction
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.
vinit714/IBM-HR-Employee-Attrition
The data has been taken from IBM Employee HR Attrition Kaggle The main Business problem that is being solved here is how a system can be created to help big companies control their attrition by understanding which employee could leave to provide him/her some incentives to stay back.