This project employs logistic regression and advanced analytics to predict employee attrition, enhancing organizational productivity. Leveraging machine learning, it develops a robust model using features like age, job satisfaction, and work environment. Through EDA, feature engineering, and grid search model tuning, it optimizes performance metric

atrrition rate is a calculation of the number of individuals or items that vacate or move out of a larger, collective group that can be an organization over a specified time frame.

This Power BI dashboard analyzes employee attrition, visualizing key metrics such as overall attrition rate, hiring trends, and active employees by department and job role. It includes demographic insights, performance tracking, and detailed attrition analysis by various factors, helping stakeholders manage and reduce employee turnover effectively.

Application analyze and predict Employee TurnOver from the Company

Power BI dashboard analyzing employee attrition trends. Provides insights into workforce demographics, job roles, income, and overtime impact, helping HR teams make data-driven retention strategies.

This study uses machine learning to predict and understand employee attrition, aiming to provide actionable insights for proactive human resource strategies based on diverse employee attributes.

A Dashboard made in Excel showing the trend of how employees left the organization.

The goal of this project is to analyze employee retention data to uncover insights that can help improve retention strategies. By identifying key factors that influence employee attrition, we aim to provide actionable recommendations for enhancing employee satisfaction and retention rates.

Using Machine Learning and Data Analytics to predict the chances of Attrition of customers in a company.

Final Project Woz U Data Science Program

Explore the IBM HR Dataset on Kaggle

This is a personal project carried out during the Future Clan Bootcamp using the Microsoft Power BI