This project uses synthetic HR data to predict employee attrition through data analysis and machine learning. It explores factors influencing employee turnover and helps HR departments make data-driven retention strategies.

A lightweight Flask application that predicts employee attrition using a machine learning model trained on HR analytics data. The app allows users to input key employee attributes and returns a prediction indicating the likelihood of the employee leaving the organization. Ideal for demonstrating ML integration with web development.

Employee Turnover is one of the key market challenges in Human Resource (HR) Analytics. Organizations usually invest a greater amount of money and time in the hiring of staff and nursing them in the hope to receive value addition. When an employee leaves the company, the reduction of opportunity costs is borne by the company. Turnover is especially prevalent in large-scale recruitment agencies. The risk of replacing workers remains important for most employers. This is due to the amount of time spent recruiting and selecting a successor, the sign-on incentives, and the lack of morale for several months as the new employee gets used to the new job. The tangible costs of workforce turnover will be the cost of recruiting new staff, the cost of recruitment and hiring, the time of transition, future product or service quality issues, the cost of temporary staff, the cost of training, the cost of lack of production, the cost of lost expertise and the cost of the job being empty before an acceptable replacement is found. We find that the attributes of workers such as Job Position, overtime, work level affect significantly attrition. Various classification methods are introduced such as logistic regression, linear discriminate analysis, ridge classification, lasso classification, decision trees, random forests to forecast and concurrently measure the likelihood of turnover of every new employee. Data from an HR department of the company available at Kaggle were used to estimate the employee turnover. The dataset includes 10 different attributes of 1470 personnel. Dataset specifies if the personnel is leaving or staying based on the attributes. Now, to construct a prediction model based on the previously mentioned machine learning algorithms with 90 percent of the total personnel's attributes and the rest for model testing. The best performing performance algorithm yields the best accuracy of Decision Tree Classifier is 93 percent and the worst accuracy of Logistic Regression is 0.18%

Here is a showcase of my capstone project about salifort motors.

Employee Attrition Prediction Project: A machine learning project predicting employee attrition using IBM HR Analytics data, providing insights into key factors behind employee turnover.

This project implements a machine learning pipeline for predicting employee attrition using HR data. The system analyzes various factors that contribute to employee turnover and provides predictions to help HR departments take preventive measures.

Employee Attrition Prediction with Machine Learning | Analyzing HR data to predict employee turnover using Random Forest and XGBoost. Includes EDA, feature engineering, model training, and evaluation. Achieved 92% accuracy.

Built a machine learning model to predict employee attrition using HR analytics data. Applied PCA for dimensionality reduction and trained a Random Forest classifier to identify key attrition drivers and improve prediction performance.

A lightweight HR Dashboard built with Streamlit for quick analysis and visualization of workforce attrition data. The application includes employee search, sentiment scraping using Selenium, interactive EDA, and an attrition prediction module using Scikit-learn, helping HR teams analyze retention trends efficiently.

machine learning based hr attrition prediction system built using python, scikit-learn and streamlit. the project analyzes employee data, predicts attrition risk, and provides interactive analytics dashboard with model insights and feature importance visualization.

The primary goal of this capstone project is to apply advanced data analytics techniques to real-world HR data. This project addresses employee attrition, satisfaction, performance, and turnover prediction using Python-based data analytics and machine learning methods.

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.

A machine learning project that predicts employee attrition using structured HR data. The model is trained using a Random Forest Classifier and deployed on AWS (S3 + SageMaker) for real-time prediction. This system helps organizations proactively identify at-risk employees and improve 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.

This repository contains an end-to-end HR Employee Attrition Prediction system, built using Machine Learning. The goal of this project is to predict whether an employee is likely to leave the organization and provide data-driven insights to HR teams through analysis and dashboards. This project is designed as a real-world

This repository includes my Summer 2025 AIML and Data Analyst internship projects. It covers chatbot development using Rasa & OpenRouter API, Customer Lifetime Value Prediction, and HR Analytics for employee attrition. Includes EDA, ML models, and full project documentation.

This Attrition Prediction App uses machine learning to predict employee turnover based on key features like age, income, and job role. With an intuitive interface, it helps HR teams identify at-risk employees and make data-driven decisions to improve retention and reduce turnover costs.

Approach Problem Statement: You are working as a data scientist with HR Department of a large insurance company focused on sales team attrition. Insurance sales teams help insurance companies generate new business by contacting potential customers and selling one or more types of insurance. The department generally sees high attrition and thus staffing becomes a crucial aspect. To aid staffing, you are provided with the monthly information for a segment of employees for 2016 and 2017 and tasked to predict whether a current employee will be leaving the organization in the upcoming two quarters (01 Jan 2018 - 01 July 2018) or not, given: 1.Demographics of the employee (city, age, gender etc.) 2.Tenure information (joining date, Last Date) 3.Historical data regarding the performance of the employee (Quarterly rating, Monthly business acquired, designation, salary) As the objective was to predict if an employee will leave the organization in the upcoming two quarters, the target variable was taken such that if an employee leaves the organization within 180 days of review it was taken was 1 and 0 otherwise i.e., if the last working day is 25-11-2017 and a review was conducted on 01-05-2017(208 days prior), target would be 0 and for the next review conducted on 01-06-2017(177 days prior), the target would be 1. The training data was taken only till 01-08-2017 as a full 180 days was required for prediction. The predictions had to be done at review level for each employee otherwise there would not be sufficient data and the changes in employee performance /behaviour might be difficult to catch if data was minimized to one row per employee. Data Pre-Processing/Feature Engineering: In the dataset, there are 13 features which are Emp_ID, Reporting Date, Age, Gender,City,Education,Salary,DateofJoining,LastWorkingDate,Joining_Designation, Designation, Total_Business_Value, Quarterly_Rating. First step in Building a Model is to understand the Data-Set, and after understanding I came to know that, there are ‘2200’ Duplicate values present in the ‘Emp_ID’ column (primary key). After that I’d Drop all the Duplicate values on the basis of last ‘Reporting Date’, and we get the Distinct ‘Emp_ID’ column. The Next step is that the target variable is not specifically mentioned in the train data. For constructing the target variable as shown in the definition, one should first look at the ‘LastWorkingDate’ column. Wherever the column has null values, that means the employee is continuing his/her work at the organization at least in the next year. Wherever any date record is appearing, that means the employee has left the organization on that particular date. So as per definition, we will put 0 where ‘LastWorkingDate’ column is null and 1 where ‘LastWorkingDate’ column has a date. Next, we take the age of that employee the last it was reported. Gender and City were taken from the dataset given. Education and Salary were also taken the last time it was reported. Joining Designation is taken as it is from the dataset. Designation is the designation of the employee at the last time it was reported. Total Business Value is the sum of the Total Business Value acquired by the employee. Quarterly_Rating is the rating the employee was given the last time it was reported. Model Building: Now, before building the model, the categorical feature ‘Gender’, ‘Education-Level’, ‘City’, ‘Quarterly Rating’ was One-hot encoded. All the numerical features were scaled using StandardScalar. Then search for the parameter values like ‘n-estimators’ and ‘max-depth’ which gives the best f1-score using GridSearchCV. Model Selection: Before finalizing on Decision Tree; few classification models like LogisticRegression, KNN, SVM, XGBoost and GradientBoost were also applied on the dataset. XGBoost led to overfitting the data. SVM, Gradient Boost and Random Forest performed well on the data. Since Decision Tree gave a good f1-score = 0.6966, this model was selected to predict the employee attrition.

Employee Attrition Prediction Using Machine Learning (IBM HR Dataset)

HR Attrition Prediction Dashboard built in Google sheets using dummy employee data (for HR practice)

Employee attrition prediction using IBM HR Analytics data with ML models and retention insights.

Employee attrition analysis and prediction using HR data, statistical methods, and machine learning in Python.

Machine learning–based prediction of employee attrition using supervised classification models and structured HR analytics data.

HR Employee Attrition Analysis and Prediction using Machine Learning (XGBoost & Random Forest) with Exploratory Data Analysis.

ML-powered HR Attrition Prediction App - Predicts employee resignation probability using company HR data. Built with Python and Scikit-learn.

Employee Attrition Prediction (HR Analytics) helps organizations analyze employee data, identify factors driving turnover, and predict attrition using machine learning and visual dashboards, enabling data-driven HR decisions and retention strategies.

Employee Attrition & Performance Prediction – A Streamlit app that analyzes HR data, explores attrition trends, and uses machine learning models to predict employee attrition risk and performance ratings.

Employee Attrition & Performance Prediction – A Streamlit app that analyzes HR data, explores attrition trends, and uses machine learning models to predict employee attrition risk and performance ratings.

Employee Attrition Prediction system using Machine Learning and Streamlit to identify at-risk employees and support data-driven HR retention strategies

📉 ML-based prediction of employee attrition using HR data with SMOTE and XGBoost. Business-driven insights included.