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
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.
HR Data를 활용한 퇴사 예측 모델 구현 프로젝트입니다 📊 dashboard
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, attrition prediction model was builded with the artificial neural networks.
This repository contains a collection of Data Science and Machine learning projects.
High turn over employee must be prevented. Every company need to analyze their human resource data to know better, which employee has higher probability to resign. This is the app prototype (made by Python streamlit) to answer that needs.
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
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)
Final Project Woz U Data Science Program
To know the main reasons for attrition of employees.
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
Clustering project for assessment of Unsupervised Learning lecture (Jacek Lewkowicz)
Historical battle simulation package for Python
A primer course on Data Science by Consulting & Analytics Club, IIT Guwahati
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