The IBM HR Analytics Employee Attrition & Performance dataset from the Kaggle. I have first performed Exploratory Data Analysis on the data using various libraries like pandas,seaborn,matplotlib etc.. Then I have plotted used feature selection techniques like RFE to select the features. The data is then oversampled using the SMOTE technique in order to deal with the imbalanced classes. Also the data is then scaled for better performance. Lastly I have trained many ML models from the scikit-learn library for predictive modelling and compared the performance using Precision, Recall and other metrics.

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.

Business Problem IBM HR Analytics Employee Attrition & Performance. Predict attrition of your valuable employees. Attrition is a problem that impacts all businesses, irrespective of geography, industry and size of the company. Employee attrition leads to significant costs for a business, including the cost of business disruption, hiring new staff and training new staff. As such, there is great business interest in understanding the drivers of, and minimizing staff attrition. In this context, the use of classification models to predict if an employee is likely to quit could greatly increase the HR’s ability to intervene on time and remedy the situation to prevent attrition. While this model can be routinely run to identify employees who are most likely to quit, the key driver of success would be the human element of reaching out the employee, understanding the current situation of the employee and taking action to remedy controllable factors that can prevent attrition of the employee. This data set presents an employee survey from IBM, indicating if there is attrition or not. The data set contains approximately 1500 entries. Given the limited size of the data set, the model should only be expected to provide modest improvement in indentification of attrition vs a random allocation of probability of attrition. While some level of attrition in a company is inevitable, minimizing it and being prepared for the cases that cannot be helped will significantly help improve the operations of most businesses. As a future development, with a sufficiently large data set, it would be used to run a segmentation on employees, to develop certain “at risk” categories of employees. This could generate new insights for the business on what drives attrition, insights that cannot be generated by merely informational interviews with employees. Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists. Education 1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor' EnvironmentSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' JobInvolvement 1 'Low' 2 'Medium' 3 'High' 4 'Very High' JobSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' PerformanceRating 1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding' RelationshipSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' WorkLifeBalance 1 'Bad' 2 'Good' 3 'Better' 4 'Best' IBM HR Analytics Employee Attrition & Performance Predict attrition of your valuable employees IBM HR Analytics Employee Attrition & Performance IBM HR Analytics Employee Attrition & Performance

IBM HR Analytics Employee Attrition & Performance Analysis

IBM HR Analytics Employee Attrition & Performance

Attrition is a critical issue and pretty high in the industry these days. It’s the major problem which highlights in all the organizations. Though the term ‘ATTRITION’ is common, many would be at a loss to define what actually Attrition is, “Attrition is said to be the gradual reduction in the number of employees through retirement, resignation or death. It can also be said as Employee Turnover or Employee Defection” Whenever a well-trained and well-adapted employee leaves the organization, it creates a vacuum. So, the organization loses key skills, knowledge and business relationships. Modern managers and personnel administrators are greatly interested in reducing Attrition in the organization, in such a way that it will contribute to the maximum effectiveness, growth, and progress of the organization. Retaining employees is a critical and ongoing effort. One of the biggest challenges in having managers in the place that understands it is their responsibility to create and sustain an environment that fosters retention. Staff requires reinforcement, direction and recognition to grow and remain satisfied in their positions. Managers must recognize this and understand that establishing such fundamentals demonstrates their objectives to support nature and motivate their employees. The main objectives of this study are to know the reasons, why attrition occurs, to identify the factors which make employees dissatisfy, to know the satisfactory level of employees towards their job and working conditions and to find the areas where companiesare lagging behind.

Rice University - IBM HR Analytics Employee - Attrition & Performance Analysis

Final project for Statistical Learning

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%

Project machine learning for school lab.

IBM HR Analytics Employee Attrition & Performance

HR Analytics Dashboard: Employee Attrition, Performance, and Satisfaction Insights

IBM HR Analytics Employee Attrition & Performance

Predicting employee attrition using data mining and machine learning techniques — includes data preprocessing, EDA, and model comparison.

Predict attrition of your valuable employees

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🚀 Employee Performance Analysis using Python – Analyzing key factors like training hours, overtime, and salary to understand their impact on employee performance using Pandas, Seaborn, and NumPy. 📊✨

Analyzing IBM HR Analytics Employee Attrition & Performance using Decision Tree and Keras Deep Learning

This project is a Power BI-based HR Analytics dashboard designed to provide insights into employee demographics, attrition trends, job satisfaction, performance, and other key HR metrics.

A Power BI-driven HR analytics project that visualizes employee attrition trends, performance metrics, and demographic insights. Includes data cleaning, modeling, and dashboard development using Power Query and DAX. Offers actionable recommendations to support HR strategies for employee retention, diversity, and satisfaction across departments.

• it is interactive HR analytics dashboard using Power BI to visualize and analyze HR-related metrics such as employee count, performance, attrition rate, age etc. • It Processed and verified HR data with SQL before loading into Power BI. Implemented dynamic visuals and interactivity to enable users to explore key HR insights.

A comprehensive HR Analytics dashboard built using Power BI, analyzing employee attrition, demographics, job satisfaction, performance, and compensation trends. This project helps organizations identify workforce patterns and make data-driven decisions to improve retention and engagement.

The HR Analytics Dashboard project is a visual exploration of key HR metrics and workforce trends using Power BI. Designed as part of an academic initiative, this dashboard aims to support data-driven decision-making within human resource management, highlighting employee demographics, attrition patterns, performance indicators, and hiring trends.

A Power BI and Tableau-based HR analytics dashboard that provides insights into employee attrition, salary distribution, job satisfaction, and performance trends. Built using CSV datasets, it enables HR teams to analyze key KPIs like employee tenure, attrition rates, and job roles, helping make data-driven decisions for workforce management.

IBM HR Analytics Employee Attrition and Performance

IBM HR Analytics Employee Attrition & Performance Project

IBM HR Analytics Employee Attrition & Performance. Predict attrition of your valuable employees

HR Analytics in employee attrition and performance helps organizations identify factors driving turnover, predict which employees are at risk, and evaluate productivity. By leveraging data on demographics, engagement, training, and performance, companies can design retention strategies, boost workforce efficiency, and support decision-making.

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