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Predictive analysis of employee attrition using machine learning on the IBM HR dataset to identify patterns and enhance employee retention strategies.

A data-driven HR analytics project that identifies patterns and predictors of employee attrition using exploratory data analysis and machine learning to support strategic retention planning.

This project aims to build a machine learning model to predict employee attrition using a dataset from Kaggle. The project includes data preprocessing, exploratory data analysis (EDA), model selection, model optimization using GridSearchCV, and deployment of the final model using Gradio for interactive predictions.

This repository contains an end-to-end HR Employee Attrition Prediction system, built using Machine Learning. The goal of this project is to predict whether an employee is likely to leave the organization and provide data-driven insights to HR teams through analysis and dashboards. This project is designed as a real-world

End-to-end Machine Learning project for predicting employee attrition using the IBM HR Analytics dataset. The project includes exploratory data analysis, feature engineering, training of multiple models (Logistic Regression, Random Forest, Gradient Boosting, SVM), and a Voting Ensemble achieving 0.93+ AUC, deployed through an interactive Streamlit

Employee Attrition Prediction using Machine Learning analyzes employee data to identify attrition risk. A Random Forest model is used to predict outcomes and highlight key factors influencing employee turnover, supporting data-driven HR decision-making.

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Predictive analysis of employee attrition using machine learning. Includes data preprocessing, exploratory analysis, feature engineering, and model building to identify key factors influencing employee turnover.

This project focuses on analyzing employee attrition using exploratory data analysis (EDA) and building a predictive machine learning model to identify key drivers of employee turnover.

This project focuses on analyzing employee attrition using exploratory data analysis (EDA) and machine learning techniques. It includes data cleaning, visualization, feature engineering, and the implementation of classification models to predict employee attrition.

The project is predictive analysis of HR Attrition dataset with python using machine learning Algorithms . we need to predict whether a give employee will leave the organization or not

Employee Attrition & HR Analytics This project analyzes employee data to predict attrition and understand workforce trends using Python, Pandas, and machine learning. It includes data preprocessing, exploratory data analysis (EDA), visualization of key HR metrics, and building predictive models to identify factors influencing employee turnover.

📉 Predictive analysis of IBM HR employee attrition data (1,470 employees) using Python. Identifies turnover drivers via machine learning (Logistic Regression, Random Forest) and clustering. Provides actionable strategies to reduce attrition. Tools: Python, pandas, scikit-learn, matplotlib, seaborn

End-to-end Employee Attrition Analysis & Prediction project using Python, EDA, and Machine Learning. Identifies key drivers of attrition and deploys a Streamlit app to predict employee churn, helping HR teams make data-driven retention decisions.

The purpose of this project is to analyze employee attrition patterns using HR data to identify key drivers of employee turnover. It combines data analysis, visualization, SQL-based KPIs, and machine learning to provide actionable insights and predict attrition risk for better workforce retention decisions.

(Python) Lots of exploratory data analysis done on employee attrition data, then performance between 3 machine learning models (SKL Logistic Regression, SKL Random Forest, TensorFlow ANN) are used to predict attrition and results are compared.

A predictive machine learning project that identifies employees at high risk of leaving a company using the IBM HR Analytics Employee Attrition dataset. Includes full data science workflow: cleaning, feature engineering, model training, evaluation, and feature importance analysis for HR insights.

This analysis aims to predict employee attrition in a hospital setting using machine learning classifiers. The goal is to identify key factors influencing attrition and evaluate the performance of three models: Logistic Regression, Decision Tree, and Random Forest.

The intent of this analysis is to predict employee attrition at Canterra, Inc. using two Machine Learning models, KNN (K-Nearest Neighbor) and SVM (Support Vector Machine), and provide valuable recommendations to Canterra’s management.

This project focuses on predicting employee attrition, using machine learning techniques to understand and forecast why employees leave a company. The project involves data cleaning, exploratory data analysis (EDA), feature engineering, and the application of various machine learning models, including Logistic Regression, Random Forest Classifier.

This project addresses the challenge of employee attrition at an Edutech company by leveraging data analysis and machine learning. The analysis identifies key demographic and job-related factors driving turnover and develops a predictive model using an ExtraTreesClassifier optimized with Optuna.

HR Analytics project to analyze and predict employee attrition using machine learning models (Logistic Regression, Decision Tree, Random Forest, XGBoost). Includes exploratory data analysis, model evaluation, SMOTE for imbalance handling, and Power BI dashboard for visualization of key HR metrics.

This project tackles the critical business problem of employee turnover by building a machine learning model to predict attrition. Using an HR dataset, we perform exploratory data analysis (EDA), feature engineering, and model training to identify key factors that influence an employee's decision to leave.

A machine learning model to predict employee churn using HR data. Helps HR teams identify employees at risk of leaving and take proactive retention measures. Features include attrition analysis, salary impact, and work-life balance insights. Built with Python, Pandas, and Scikit-Learn. Enables data-driven HR decisions to reduce turnover.