[Project repo] On People Analytics for predicting employee turnover. (Employee Experience article included)

A Company uses this predictive analysis to measure how many employees they will need if the potential employees will leave their organization. A company also uses this predictive analysis to make the workplace better for employees by understanding the core reasons for the high turnover ratio.

A human capital analytics project to predict employee turnover and build retention strategies using logistic regression, decision trees, random forests, and K-means clustering.

Analysis and prediction of employee Turnover - People Analytics 101 Udemy Course

Decision trees and Random forests using scikit-learn and Python to build an employee churn prediction application with interactive controls

Using Random forest to predict employee turnover

A case study in Predictive Analytics to understand why employees leave a company and predict the next possible leaver

Predict whether an employee will stay or leave the company

tracking survival rate of new employees with a best fitted Cox Proportional Hazards model using 4 most significant personality traits

Analyzed employee turnover (Jan 2022 - Mar 2023) at my former organization, considering trends, departmental attrition, and tenure insights. Used predictive analytics from the 2022 Employee Engagement Survey to identify groups with flight risk. Incorporated Survival Analysis for temporal patterns, guiding decisions to improve retention.

Machine Learning project to predict employee turnover using Logistic Regression with L1 and L2 Regularization.

Kaggle HR Analytics competition. Logistic regression model built with Stata to predict employee job change.

This code consists of KNN implementation for the scratch

Analysis of employee attrition data using Python

A Streamlit-based web app that predicts employee attrition using machine learning. Upload employee data and get instant predictions on who is likely to leave.

Employee Turnover Analysis using Data Analysis in Excel: This project analyzes employee data using Excel to identify key factors contributing to turnover. By leveraging statistical analysis, pivot tables, and data visualization techniques, it provides actionable insights to reduce attrition and improve employee retention strategies.