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.
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 analytics project using Python, Machine Learning and Streamlit to analyze employee attrition, predict retention risk and performance, and deliver interactive workforce insights.
HR Analytics project to predict employee attrition using ML models, feature engineering, and Power BI dashboards for insights
Exploratory Data Analysis (EDA) of the IBM HR Analytics Employee Attrition dataset to identify key factors influencing employee attrition. The project includes data cleaning, statistical analysis, feature exploration, visualization, and business insights to understand employee turnover patterns and support data-driven HR decision-making
This project demonstrates how to build machine learning models to predict employee attrition using human resources (HR) data.
Developed an employee attrition prediction model using HR Analytics data(74,498 rows). Implemented XGBoost with optimized settings, achieving high accuracy. Presented findings with insights on model performance, scalability, and business impact in a comprehensive Jupyter Notebook report.
🏢 ML web app to predict IBM employee attrition using Random Forest + SMOTE, with what-if simulation · Saturdays AI Bilbao
Employee Attrition Risk Prediction & Analytics Dashboard
Efficient employee attrition prediction using LazyPredict to evaluate and compare multiple classification models for HR analytics.
A Power BI dashboard analyzing employee attrition trends and HR insights using data visualization, DAX measures, and interactive filters.
This project aims to predict the likelihood of employee attrition in a company using IBM HR Dataset (link). By leveraging advanced classification techniques and feature engineering, the goal is to build a model that can accurately identify employees who are likely to leave the company.
This is a ML Project that predicts whether an employee is like to leave an organization using ML techniques.
An end-to-end HR predictive analytics pipeline in R. Features rigorous data engineering, SMOTE class balancing, and a threshold-optimized XGBoost model to accurately identify employee attrition risk. Translates complex ML outputs into actionable HR retention strategies.
Predicting employee attrition with machine learning and balanced Logistic Regression.
Built and deployed an employee attrition prediction application using Scikit-learn and Streamlit. Engineered HR-specific features, applied feature normalization, benchmarked multiple ML algorithms, optimized model hyperparameters and decision thresholds, and integrated the final Logistic Regression pipeline for real-time attrition risk prediction.
Predicting employee attrition using machine learning to identify key factors affecting workforce retention.
A machine learning project to analyze attrition drivers and predict at-risk employees for better HR decision-making.
Interactive HR Employee Attrition Dashboard using Power BI
A machine learning model to predict employee attrition in a company
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