The IBM HR Analytics Employee Attrition & Performance dataset from the Kaggle. I have first performed Exploratory Data Analysis on the data using various libraries like pandas,seaborn,matplotlib etc.. Then I have plotted used feature selection techniques like RFE to select the features. The data is then oversampled using the SMOTE technique in order to deal with the imbalanced classes. Also the data is then scaled for better performance. Lastly I have trained many ML models from the scikit-learn library for predictive modelling and compared the performance using Precision, Recall and other metrics.

Final Project of the MLOps Zoomcamp hosted by DataTalksClub.

Employee turnover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. In this post, we’ll use two cutting edge techniques.

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.

Predict employee attrition using a neural network in python/tensorflow

The employee turnover rate prediction ML model is a statistical tool that uses machine learning algorithms to forecast the likelihood of an employee leaving a company within a specific timeframe. This model is designed to analyze various factors that contribute to employee attrition, including job satisfaction, compensation, work-life balance, and

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A lightweight Flask application that predicts employee attrition using a machine learning model trained on HR analytics data. The app allows users to input key employee attributes and returns a prediction indicating the likelihood of the employee leaving the organization. Ideal for demonstrating ML integration with web development.

Healthcare Sector Employee Attrition Exploratory Data Analysis ## Introduction In this notebook we are going to apply an Exploratory Data Analysis (EDA) to the Watson Health Care employees dataset. The dataset contains employee and company data useful for supervised ML, unsupervised ML, and analytics. The main scope of the EDA is to analyse and find insights about the employees and the patterns or factors driving them quitting the job.

This project aims to provide insights into the factors influencing employee attrition and predict which employees are likely to leave the company.

A machine learning project to predict employee attrition using the Synthetic Employee Attrition Dataset from Kaggle. This repository includes data preprocessing, feature engineering, model training, evaluation, hyperparameter tuning, and model deployment using Flask.

Machine Learning model to predict employee attrition using Python and data analysis.

End-to-end MLOps project using Flask, DVC and machine learning

The "Attrition_ML" project is an educational team web application that leverages machine learning to predict employee attrition.

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人才流失预测 ML 实战项目 | HR Employee Attrition Prediction (Team Project, Est. 2026-03-22)

End-to-end ML pipeline to predict employee attrition. Unified preprocessing (feature pruning, engineered ratios, OneHotEncoder + StandardScaler persisted with joblib), 12 baseline models (XGBoost/LR/RF/… ), 80/20 split, batch evaluation (Accuracy/Precision/Recall/F1/AUC), rich logging and EDA plots.

We have data of about 1200 employees. We know following attributes of each employees. 'Gender', 'EducationBackground', 'MaritalStatus', 'EmpDepartment', 'EmpJobRole', 'BusinessTravelFrequency', 'OverTime', 'Attrition', 'Age', 'DistanceFromHome', 'EmpEducationLevel', 'EmpEnvironmentSatisfaction', 'EmpHourlyRate', 'EmpJobInvolvement', 'EmpJobLevel', 'EmpJobSatisfaction', 'NumCompaniesWorked', 'EmpLastSalaryHikePercent', 'EmpRelationshipSatisfaction', 'TotalWorkExperienceInYears', 'TrainingTimesLastYear', 'EmpWorkLifeBalance', 'ExperienceYearsAtThisCompany', 'ExperienceYearsInCurrentRole', 'YearsSinceLastPromotion', 'YearsWithCurrManager', 'PerformanceRating'. The goal is to train a ML model which can predict the employee performance based certain factors as inputs. This will be used to hire employees provide department wise performances and other department wise insights Top 3 Important Factors effecting employee performance Recommendations to improve the employee performance based on insights from analysis.

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Employee attrition using ml

Employee Attrition Prediction using ML

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MachineLearning

Predicting employee attrition using IBM HR Analytics dataset with machine learning.

This project utilizes machine learning algorithms to predict employee attrition, aiming to help organizations identify and mitigate risks associated with employee turnover. By analyzing various employee attributes and leveraging MLflow for experiment tracking, it offers valuable insights into optimizing human resource management practices.

ML.net application: Prediction of Employee Attrition using Linear regression model using the Stochastic Dual Coordinate Ascent (SDCA) method.

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A data science project predicting whether an employee will leave the company using HR dataset. Includes EDA, feature engineering, model training, and evaluation.