Historical battle simulation package for Python

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

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.

Turnover prediction, headcount forecasting and attrition-driver analysis for hourly retail workforces, with a ground-truth workforce simulator

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)…

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.

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.

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

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

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

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

Uncover the factors that lead to employee attrition at IBM

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:

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.

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.