Historical battle simulation package for Python

This repository contains all the data related to the employee Attrition Prediction model

This repository contains a collection of Data Science and Machine learning projects.

Uncover the factors that lead to employee attrition using IBM Employee Data

This repository contains an R functions designed to estimate the Average Treatment Effect on the Treated (ITT) and Local Average Treatment Effect (LATE) using various methods, including Difference in Means and Difference in Differences. The function allows for adjustment for clustering and provides options for methods such as Lee Bounds and IPW

A large company named XYZ, employs, at any given point of time, around 4000 employees. However, every year, around 15% of its employees leave the company and need to be replaced with the talent pool available in the job market. The management believes that this level of attrition (employees leaving, either on their own or because they got fired) is bad for the company, because of the following reasons - The former employees’ projects get delayed, which makes it difficult to meet timelines, resulting in a reputation loss among consumers and partners A sizeable department has to be maintained, for the purposes of recruiting new talent More often than not, the new employees have to be trained for the job and/or given time to acclimatise themselves to the company Hence, the management has contracted an HR analytics firm to understand what factors they should focus on, in order to curb attrition. In other words, they want to know what changes they should make to their workplace, in order to get most of their employees to stay. Also, they want to know which of these variables is most important and needs to be addressed right away.

Leverage external data and non-traditional methods to accurately assess and shortlist candidates with the relevant skillsets, experience and psycho-emotional traits, and match them with relevant job openings to drive operational efficiency and improve accuracy in the matching process

I recently completed an interactive and insightful Power BI Project. Analyzed 1,480 employee records, providing insights on attrition, work-life balance, and performance metrics for leader ship. This dashboard is the result of combining advanced data analytics techniques with visual storytelling to help organization's make informed decisions.

A flexible and powerful class for surgical removal of aged files and folders. Includes desktop configuration builder/manager, and a console app for human-free operation. Class can be directly included in an application.

In this project I wanted to predict attrition based on employee data. The data is an artificial dataset from IBM data scientists. It contains data for 1470 employees. Te dataset contains the following information per employee:

Uncover the factors that lead to employee attrition at IBM

Employee Attrition Prediction with Machine Learning | Analyzing HR data to predict employee turnover using Random Forest and XGBoost. Includes EDA, feature engineering, model training, and evaluation. Achieved 92% accuracy.

A primer course on Data Science by Consulting & Analytics Club, IIT Guwahati

"HR attrition analysis: SQL + Power BI — identifying key drivers of employee turnover (IBM dataset)"

Built a model using XGBoost that predicts the chances of Attrition of an employee working at IBM with 84% Precision.

HR Data를 활용한 퇴사 예측 모델 구현 프로젝트입니다 📊 dashboard

An AI-powered management dashboard that predicts productivity, attrition risk, and optimal human–AI task allocation for hybrid work environments.

Attrition data analysis identified distinct employee risk segments with unique turnover drivers. Based on these insights, targeted HR solutions were designed—such as overtime limits, career pathing, salary progression, and tailored benefits. The project was completed within 6 weeks from analysis to solution design.

Interactive HR analytics dashboard built in Tableau to track headcount, hiring trends, and attrition patterns. Includes demographic breakdowns and drill-downs to help People teams identify trends, spot turnover risks, and guide data-driven decisions.

An Excel-based HR Analytics Dashboard that provides insights into workforce composition, attrition trends, job satisfaction, and demographic distribution. Features interactive charts, KPIs, and filters for gender, department, and education to support data-driven HR decision-making.

“Predicting employee attrition using machine learning — includes SHAP interpretability for HR teams.”

This project analyzes employee attrition at Green Destinations, a travel agency, to identify trends and factors influencing departures. The analysis focuses on age, years at the company, and income. The repository includes data, analysis notebooks, models, and results, providing actionable insights for improving employee retention.

It's not about how you feel

This GitHub repository hosts a comprehensive HR attrition analysis report, providing valuable insights into employee turnover trends within an organization. The report includes in-depth statistical analysis, data visualizations, and actionable recommendations to help HR professionals and business leaders make informed decisions to reduce attrition.

This is a personal project carried out during the Future Clan Bootcamp using the Microsoft Power BI

What makes an employee stay and what makes him leave?

In this project, attrition prediction model was builded with the artificial neural networks.

Production-Grade Attrition Risk Modeling with Signal Integrity and KPI Alignment

No description provided.

An PowerBI-based HR Analytics Dashboard that provides insights into workforce composition, attrition trends, job satisfaction, and demographic distribution. Features interactive charts, KPIs, and filters for gender, department, and education to support data-driven HR decision-making.