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.

This project predicts employee attrition using IBM HR Analytics dataset. The R script implements 9 Machine Learning algorithms and a PowerBI dashboard is created for data visualization. ML algorithms are implemeted using CARET package in order to solve a binary classification problem. Algorithms' plots, results and resampling plots are exported as output files.

The project “HR Analytics – Employee Attrition Prediction” aims to predict employee attrition based on various work-related factors using the IBM HR Analytics Dataset.

Performed Analysis on ‘IBM HR Analytics Employee Attrition & Performance’ dataset to analyze the main causes of Attrition and Predict Employee Attrition by applying various models like Logistic Regression, Random Forest, Neural Network and Genetic Algorithm.

This repository explores the IBM HR Analytics Employee Attrition dataset, a widely used dataset for workforce analytics and predictive modeling. The project focuses on identifying the key drivers of employee turnover and retention, using statistical analysis, visualizations, and machine learning techniques.

A Machine Learning project that predicts employee attrition using Logistic Regression based on IBM HR Analytics dataset.

The Employee-Attrition-Predictor focuses on predicting employee attrition using machine learning techniques. It is likely inspired by the IBM HR Analytics Employee Attrition & Performance dataset, a widely-used dataset in the field of HR analytics and employee retention.

Machine Learning project on IBM HR Analytics Attrition Dataset: Predict employee attrition, forecast salaries, and estimate financial risk through a novel Expected Salary Loss metric.

Predicting employee attrition using six machine learning models on the IBM HR Analytics dataset, with a focus on model comparison, class imbalance, and actionable HR insights.

IBM HR Analytics Employee Attrition Dataset-This project predicts employee attrition using an Artificial Neural Network (ANN) trained on the IBM HR Analytics dataset. It includes data preprocessing, feature encoding, model training, evaluation, and performance analysis to identify key factors influencing employee turnover.

HR Analytics Project to predict Employee Attrition using Machine Learning models (Logistic Regression, Decision Tree). Includes data cleaning, feature engineering, model building, evaluation, and business insights based on IBM HR Analytics Employee Attrition dataset.

Employee Attrition Prediction Project Question: Develop a machine learning model to predict if an employee is likely to leave the company using IBM HR Analytics data. Focus on classification techniques and visualize feature importance. Dataset Link: https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset

This project predicts employee attrition (whether they will leave the company) using a Random Forest model trained on the IBM HR Analytics dataset.

R Shiny dashboard for predicting employee attrition using XGBoost, built on the IBM HR Analytics dataset. Includes EDA, ML pipeline (tidymodels), and an interactive prediction tool with actionable HR recommendations.

Predict employee attrition and segment workforce using HR analytics. This system applies machine learning on Kaggle’s IBM dataset to identify turnover risks and group employees based on key features for strategic HR insights.

This dataset analysis focuses on predicting employee attrition within a fictional company using the IBM HR Analytics Employee Attrition & Performance dataset. With a comprehensive exploration of various employee-related factors, including demographics, job role, performance, and satisfaction metrics.

This project uses machine learning to predict employee attrition based on the IBM HR Analytics Employee Attrition & Performance dataset. The goal is to help organizations identify employees at risk of leaving and take proactive measures to retain them.

This collection of Python scripts focuses on leveraging Decision Trees and Random Forests for classification tasks. The analysis is performed on the IBM HR Analytics Attrition Dataset, exploring employee attrition patterns and predicting outcomes using Decision Trees and Random Forests.

This project aims to predict employee attrition using a Logistic Regression model trained on the IBM HR Analytics Employee Attrition & Performance dataset. The dataset contains various features related to employee demographics, job satisfaction, work-life balance, and more. By analyzing these factors, the model identifies patterns and predicts whet

Performed HR analytics on IBM’s employee dataset to identify attrition drivers and predict turnover risk with 87% model accuracy using Logistic Regression. Delivered actionable insights via Power BI dashboard improving retention strategy.

This repository contains the code for my research on the IBM HR Analytics Employee Attrition & Performance dataset from Kaggle. The research explores factors influencing employee attrition, predicts turnover using machine learning models, and provides insights into improving employee retention strategies.

This project focuses on predicting employee attrition using machine learning. Utilizing the IBM HR Analytics Attrition Dataset, key steps include data pre-processing, visualization and training various models like RandomForest, Logistic Regression, and Gradient Boosting. You can access the web app on the given link.

End-to-end ML pipeline predicting employee attrition using XGBoost, SHAP interpretability, and SMOTE for class imbalance. Includes EDA, feature engineering, model comparison, and an interactive Streamlit app. Built on the IBM HR Analytics dataset.

This project focuses on analyzing and predicting employee churn (attrition) using the IBM HR Analytics Attrition dataset. The goal is to build and evaluate various machine learning models to identify the key factors contributing to employee turnover and to predict which employees are most likely to leave the company.

Predicts employee attrition using Gradient Boosting with SMOTENC class balancing and adaptive threshold optimisation. Achieves 87.41% accuracy and 81.76% recall on the IBM HR dataset. Deployed as a Streamlit dashboard with SHAP explainability, batch scoring, and analytics.

This repository contains analysis and predictive modeling based on the IBM HR Analytics Employee Attrition & Performance dataset, available on Kaggle. The main objective is to predict the probability of an employee leaving the company, using variables such as age, gender, marital status, job satisfaction, among others.

An end-to-end employee attrition prediction project built on the IBM HR Analytics dataset, where multiple machine learning models were trained, compared, and optimized to predict employee turnover. The project also uses SHAP for explainability and survival analysis to understand the key factors influencing attrition and employee retention.

Built machine learning models on the IBM HR Analytics dataset to predict employee attrition using techniques like SMOTE, feature engineering, and advanced classifiers. Gradient Boosting and XGBoost achieved >90% accuracy, uncovering key drivers such as overtime, commute distance, and promotion history.

Predicts voluntary employee attrition using logistic regression on the IBM HR Analytics dataset (1,470 employees). Identifies top drivers including overtime, role tenure, and income. Includes full Python notebook, feature importance analysis, flight risk scoring, and Power BI-ready output.

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 also used feature selection techniques like RFE (a wrapper method )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 .