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)…

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

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

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

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 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:

NGO Fund Raising Attrition Churn Modelling

Analysis of employee attrition for the company and the variation by gender, department, job roles, level of education, field of education, age band, frequency of business travel, and marital status.

What makes an employee stay and what makes him leave?

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