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.

Business Problem IBM HR Analytics Employee Attrition & Performance. Predict attrition of your valuable employees. Attrition is a problem that impacts all businesses, irrespective of geography, industry and size of the company. Employee attrition leads to significant costs for a business, including the cost of business disruption, hiring new staff and training new staff. As such, there is great business interest in understanding the drivers of, and minimizing staff attrition. In this context, the use of classification models to predict if an employee is likely to quit could greatly increase the HR’s ability to intervene on time and remedy the situation to prevent attrition. While this model can be routinely run to identify employees who are most likely to quit, the key driver of success would be the human element of reaching out the employee, understanding the current situation of the employee and taking action to remedy controllable factors that can prevent attrition of the employee. This data set presents an employee survey from IBM, indicating if there is attrition or not. The data set contains approximately 1500 entries. Given the limited size of the data set, the model should only be expected to provide modest improvement in indentification of attrition vs a random allocation of probability of attrition. While some level of attrition in a company is inevitable, minimizing it and being prepared for the cases that cannot be helped will significantly help improve the operations of most businesses. As a future development, with a sufficiently large data set, it would be used to run a segmentation on employees, to develop certain “at risk” categories of employees. This could generate new insights for the business on what drives attrition, insights that cannot be generated by merely informational interviews with employees. Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists. Education 1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor' EnvironmentSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' JobInvolvement 1 'Low' 2 'Medium' 3 'High' 4 'Very High' JobSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' PerformanceRating 1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding' RelationshipSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' WorkLifeBalance 1 'Bad' 2 'Good' 3 'Better' 4 'Best' IBM HR Analytics Employee Attrition & Performance Predict attrition of your valuable employees IBM HR Analytics Employee Attrition & Performance IBM HR Analytics Employee Attrition & Performance

HR analytics & machine learning project predicting employee attrition using the IBM HR dataset

Analyzing IBM HR Analytics dataset on Employee Attrition and other factors

Creating an analytic report based on IBM_HR-analytics-attrition-dataset

Projeto de ciência de dados para prever a saída (attrition) de funcionários, utilizando o dataset IBM HR Analytics.

Using the IBM HR Analytics Employee Attrition Dataset, this project predicts employee turnover with machine learning. It analyzes features like job satisfaction and income using Random Forest and XGBoost, addressing class imbalance with SMOTE. The study provides insights to enhance retention strategies.

Kaggle IBM Analytics https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset

Classification Model to predict attrition using IBM HR Analytics dataset - Attrition Analysis

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.

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

Employee Attrition Analysis flask app to Analyze and predict employee attrition using IBM HR Analytics Employee Attrition & Performance Dataset.

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

IBM HR Analytics Employee Attrition - EDA Exploratory Data Analysis (EDA) on IBM HR Analytics Employee Attrition dataset to uncover trends, key factors, and patterns affecting attrition. Includes data cleaning, visualization, and insights.

Analysed employee attrition patterns and identified important factors influencing attrition, using fictional data set on Kaggle : https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset

A Python-based causal inference project using the DoWhy library to perform causal analysis on the IBM HR Analytics Employee Attrition & Performance 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 dataset contains IBM HR analytics data aimed at predicting employee attrition. It includes demographic, job-related, and performance attributes to help analyse factors influencing workforce turnover and support data-driven HR decisions.

A comprehensive machine learning project to predict employee attrition using IBM HR Analytics dataset. This project demonstrates end-to-end data science workflow from exploratory analysis to model deployment.

Employee attrition is a major organizational challenge that increases costs and reduces productivity. Machine learning provides effective tools to analyze employee data and predict turnover, enabling data-driven HR decisions. This project applies classification techniques using the IBM HR Analytics dataset to predict employee attrition.

This repository offers a comprehensive analysis and prediction on employee attrition and performance using the IBM HR Analytics dataset. It includes data cleaning, visualization, and machine learning techniques to enhance hiring and retention strategies.

This project predicts employee attrition using the IBM HR Analytics Dataset. It involves data exploration, training classification models (Random Forest, Logistic Regression), and applying SHAP for model explainability. The goal is to provide actionable insights for HR strategies to improve employee retention.

This project focuses on analyzing employee attrition using IBM HR Analytics dataset. The goal is to uncover factors influencing attrition, perform exploratory data analysis (EDA), integrate SQL queries, and build machine learning models for prediction. A Streamlit dashboard was also developed for interactive exploration.

Dataset Analysis and Preprocessing: Download the IBM HR Analytics Employee Attrition & Performance dataset from a reputable source (e.g., Kaggle). Analyze the dataset to understand its structure and features. It contains various attributes related to employee demographics, job roles, satisfaction levels, performance ratings, etc., along with a targ

This dataset is from IBM HR Analytics Employee Attrition & Performance. It has 35 features describing each employee’s background and characteristics; and labelled with whether they are still in the company or whether they have gone to work somewhere else. Exploratory data analysis can help to understand and determine how these factors relate to workforce attrition.

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

Este repositório contém o projeto final do Bootcamp [RE]Start – Trilha de Engenharia de Dados, desenvolvido com base no dataset IBM HR Analytics Attrition & Performance. O objetivo é construir um pipeline de ETL automatizado que auxilie a empresa fictícia Data Girls S.A. em análise e tomada de decisões.

This study uses the IBM HR Analytics Attrition Dataset to build a machine learning model capable of predicting whether an employee is likely to leave the organization. An Extreme Gradient Boosting (XGBoost) classifier is used due to its strong predictive performance and ability to handle complex relationships between variables.

Como Cientista de Dados da Data Girls S.A., meu trabalho foi mergulhar nos dados de colaboradores para entender por que alguns decidem sair da empresa e, mais importante, como podemos prever e agir para reter os talentos. Utilizando o dataset "IBM HR Analytics Attrition & Performance", construí um modelo preditivo sobre a rotatividade.

Projeto final do Bootcamp Data Girls 2025 que analisa a rotatividade de funcionários usando Machine Learning. Com base no dataset IBM HR Analytics Attrition, o projeto identifica os principais fatores de risco e cria modelos preditivos (SVC e Random Forest) com até 89% de acurácia para antecipar saídas e apoiar decisões estratégicas de RH.