In this project, we enlisted the numerical and categorical attributes present in the publicly available dataset. Missing values were dropped to give better insights in data analysis. ANOVA and Chi-Square tests were carried out during statistical analysis. Machine Learning algo's were applied to understand, manage, and mitigate employee attrition.

This project basically involves Human Resource Analysis and Employee Attrition Prediction using various data visualisation techniques & machine learning models

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%

This dataset contains IBM HR analytics data aimed at predicting employee attrition. It includes demographic, job-related, and performance attributes to help analyse factors influencing workforce turnover and support data-driven HR decisions.

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HR_Analytics_and_Employee_Churn+Prediction

Exploratory Data Analysis and Prediction of Employee Attrition

This project addresses two interrelated machine learning tasks in the field of HR analytics for the company "Care with Work"

Analysed employee attrition patterns and identified important factors influencing attrition, using fictional data set on Kaggle : https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset

IBM-HR-Analytics-Employee-Attrition-and-Performance-Prediction.

HR Analytics of Employee-Attrition-Analysis-and-Prediction

This project aims to analyze and predict employee attrition using HR analytics data. It involves data exploration, statistical testing, machine learning modeling, and visualization to uncover patterns in employee turnover and identify key factors influencing attrition.

Data analytics and Prediction model for IBM HR data

this is an example of solution for the kaggle "HR analytics Job Prediction" https://www.kaggle.com/datasets/mfaisalqureshi/hr-analytics-and-job-prediction?select=HR_comma_sep.csv

This repository contains mini projects like HR Analytics and . Healthcare Appointment No-Show Prediction

HR analytics: employee attrition prediction, performance analysis, workforce metrics, and talent retention using classification models

Empowering HR with smart salary prediction and analytics. Predict, manage, and analyze employee salaries effortlessly with PayPredict.

AI-Powered HR Analytics & Attrition Prediction System using Python, ML, n8n, Power BI and Gemini AI

Enterprise-grade HR Analytics Dashboard with Machine Learning - Power BI + Python integration for employee attrition prediction and workforce insights

Exploratory Data Analysis and prediction on test set of HR Analytics: Job Change of Data Scientists dataset on Kaggle

End-to-end HR Analytics and Employee Attrition Prediction app built with Python and Streamlit. Includes EDA, ML models, and interactive dashboards.

Machine learning-powered HR analytics app that predicts employee attrition risk. Includes 📊 EDA dashboard, 👤 individual prediction, and 📂 batch prediction. Helps HR teams uncover patterns, act early, and improve employee retention. Built with Python & Streamlit.

Cloud-native HR analytics platform using Terraform, Azure AI (NER), Azure SQL, FastAPI, ML salary prediction, Docker, CI/CD and OpenTelemetry observability.

Completed impactful projects like Sales Data Analysis, Diabetes Prediction Analysis and HR Analytics using tools like Python, Machine Learning, Pandas, NumPy, Matplotlib, Seaborn and Power BI for data-driven insights. Transformed data into actionable strategies, boosting revenue, healthcare and HR efficiency.

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.

This repository contains four data analytics projects including Big Data analysis on Netflix data, Titanic survival prediction using machine learning, an HR Analytics dashboard built in Power BI, and sentiment analysis on Amazon reviews using NLP techniques.

AI-powered HR analytics platform with 92.3% attrition prediction accuracy and 96% resume-job matching. Features predictive modeling, compensation equity analysis, and conversational AI assistant using OpenAI GPT-4o-mini and Streamlit.

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.