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.

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)…

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 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.

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.

"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.

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

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:

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

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.

Uncover the factors that lead to employee attrition at IBM

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

Interactive Power BI HR Analytics Dashboard tracking 1,470 employees — 16.12% attrition rate, department-wise breakdown, job satisfaction ratings & workforce KPIs.

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.