This repository offers a comprehensive analysis and prediction on employee attrition and performance using the IBM HR Analytics dataset. It includes data cleaning, visualization, and machine learning techniques to enhance hiring and retention strategies.

This project focuses on analyzing employee attrition using IBM HR Analytics dataset. The goal is to uncover factors influencing attrition, perform exploratory data analysis (EDA), integrate SQL queries, and build machine learning models for prediction. A Streamlit dashboard was also developed for interactive exploration.

Employee Attrition Prediction Using Machine Learning (IBM HR Dataset)

Employee attrition analysis and prediction using the IBM HR Analytics dataset with Python and machine learning.

Employee Attrition Prediction Project Question: Develop a machine learning model to predict if an employee is likely to leave the company using IBM HR Analytics data. Focus on classification techniques and visualize feature importance. Dataset Link: https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset

📊 An interactive Streamlit dashboard for Employee Attrition Analysis & Prediction using the IBM HR dataset. It includes data cleaning, exploratory data analysis (EDA), machine learning model for attrition prediction, and real-time integration with Google Sheets for live updates.

The "Employee Attrition Prediction" repository contains a machine learning project that aims to predict employee attrition using various algorithms. It utilizes the IBM HR Analytics Attrition Dataset and implements algorithms such as Logistic Regression, XGBoost, Decision Tree, and Random Forest to analyze factors like age, monthly income, and depa