attrition-analysis

An explanation-first HR analytics system that reconstructs why employee exit becomes rational. Instead of predicting attrition, it generates human-readable exit narratives by decomposing pressure and retention forces, adding peer context and counterfactual interventions to reveal how stability erodes over time.

Developed an interactive HR attrition analysis dashboard using Power BI to gain insights into employee turnover and identify key attrition reasons within the organization. By analyzing attrition trends and visualizing relevant metrics, the dashboard helps HR professionals understand the underlying causes of attrition.

End-to-End Data Analyst project on employee attrition using Python, MySQL, and Logistic Regression. Covers full EDA, SQL analytics, and predictive modeling for HR decision-making, risk segmentation, business KPIs, and actionable retention strategy.

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.

A Power BI-driven HR analytics project that visualizes employee attrition trends, performance metrics, and demographic insights. Includes data cleaning, modeling, and dashboard development using Power Query and DAX. Offers actionable recommendations to support HR strategies for employee retention, diversity, and satisfaction across departments.

HR attrition analysis on a 10,000-employee dataset — Python, Plotly, and statistical testing (Chi-Square, Spearman) to examine how department, role, income, and satisfaction relate to employee turnover.

Data analysis project using Python (Pandas,Random Forest,Confusion Matrix and train_test_split) on HR Attrition dataset

End-to-end Employee Lifecycle Analytics project using PostgreSQL, Python, PySpark and Streamlit to analyze 3,000 employee records across recruitment, engagement, training, attrition and early attrition. Includes SQL pipelines, predictive modeling, PySpark validation and a deployed interactive dashboard.

Predicting employee attrition entails gathering historical data on employees, identifying key features, training machine learning models, and deploying them for real-time predictions to aid in retention strategies and organizational stability. Regular monitoring and updates ensure ongoing effectiveness.

An interactive Workforce Intelligence Dashboard for analyzing employee attrition patterns, workforce health, retention risk, and cost impact using Python, Excel, and Chart.js — built for data-driven workforce planning.

This project uses HR analytics to identify key factors contributing to high employee attrition rates, helping organizations understand and mitigate turnover issues.

End-to-end People Analytics & Workforce Attrition Intelligence dashboard built in Power BI, DAX, and Power Query M.

Power BI dashboard analyzing IBM HR employee data — attrition drivers, salary distribution, work-life balance, and travel patterns across departments, with AI-powered decomposition tree analysis.

PowerBI visualization Portfolio

HR Attrition & Workforce Analysis using Excel and Tableau. Explores employee turnover patterns, demographics, and retention insights through interactive dashboards and pivot-based reporting.

HR People Analytics — 14K IBM-style employee records, attrition prediction with 87% recall, 5 workforce dashboards covering salary bands, tenure cohorts, overtime impact | Python | ML

This repository contains a Power Bi dashboard of IBM HR Analytics to answer some questions about the employee attrition and performance data. This repository can be used as a starting point for anyone who wants to learn how to use Power Bi to analyze data.

Built an interactive HR Attrition Dashboard in Power BI to help HR teams identify at-risk employee groups and make data-driven retention decisions across 1,470 employees.