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
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
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
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
A primer course on Data Science by Consulting & Analytics Club, IIT Guwahati
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:
HR Data를 활용한 퇴사 예측 모델 구현 프로젝트입니다 📊 dashboard
Using R to analyse the relationship between variables and attrition in Shanghai Ctrip call centre's WFH data.
Given the monthly information for a segment of employees for 2016 and 2017, predict whether a current employee will be leaving the organization in the upcoming two quarters (H1 2018)
Developed a comprehensive HR analytics dashboard to monitor employee attrition, performance, and engagement, utilizing metrics like job role, education, and work-life balance for data-driven decision-making.
This repository contains a comprehensive study on employee attrition analysis using data mining techniques. It includes data preprocessing, visualization, and predictive modeling (with algorithms such as Decision Tree, Random Forest, and Logistic Regression) to identify key factors influencing attrition, using the IBM HR dataset.
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