Data-driven analysis of employee attrition using HR analytics techniques to support retention strategies and workforce planning.

A collection of SQL-based data analytics projects demonstrating techniques for data extraction, transformation, analysis, and visualization. These projects showcase practical SQL queries and insights across various domains, including sales, customer segmentation, and employee attrition analysis.

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%

Developed an interactive HR attrition analysis dashboard using Power BI to gain insights into employee turnover and identify key attrition reasons within the organization. By analyzing attrition trends and visualizing relevant metrics, the dashboard helps HR professionals understand the underlying causes of attrition.

Exploratory data analysis project on Employee Attrition dataset. EMPLOYEE ATTRITION RATE : Employee Attrition Rate is calculated as the percentage of employees who left the company in a given period to the total average number of employees within that period.

Healthcare Sector Employee Attrition Exploratory Data Analysis ## Introduction In this notebook we are going to apply an Exploratory Data Analysis (EDA) to the Watson Health Care employees dataset. The dataset contains employee and company data useful for supervised ML, unsupervised ML, and analytics. The main scope of the EDA is to analyse and find insights about the employees and the patterns or factors driving them quitting the job.

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Exploratory Data Analysis of HR Analytics dataset to understand employee engagement, attrition patterns, and workforce insights using Python, Pandas, Seaborn, and Matplotlib.

Embarking on a journey of workforce insights, I conducted a comprehensive attrition analysis utilizing the robust capabilities of Power BI. This dynamic dashboard translates complex data into actionable insights, shedding light on employee attrition patterns within the organization.

The analysis that considers 1,470 employees from various departments and handling different job roles, mainly highlights the range of ‘Job Satisfaction’ and ‘Attrition’, based on the important metrics.

In this project I performed analysis on a company's analysis like what is the attrition rate, what is the average age of the active employees, etc and many more based on the specific year.

EDA on Employee Attrition Dataset: This repository includes data cleaning, feature engineering, visualizations, and analysis of key factors influencing employee turnover, with raw and cleaned datasets, a Jupyter notebook, and Python scripts. Tools used: Python, Pandas, Matplotlib, Seaborn.

We have data of about 1200 employees. We know following attributes of each employees. 'Gender', 'EducationBackground', 'MaritalStatus', 'EmpDepartment', 'EmpJobRole', 'BusinessTravelFrequency', 'OverTime', 'Attrition', 'Age', 'DistanceFromHome', 'EmpEducationLevel', 'EmpEnvironmentSatisfaction', 'EmpHourlyRate', 'EmpJobInvolvement', 'EmpJobLevel', 'EmpJobSatisfaction', 'NumCompaniesWorked', 'EmpLastSalaryHikePercent', 'EmpRelationshipSatisfaction', 'TotalWorkExperienceInYears', 'TrainingTimesLastYear', 'EmpWorkLifeBalance', 'ExperienceYearsAtThisCompany', 'ExperienceYearsInCurrentRole', 'YearsSinceLastPromotion', 'YearsWithCurrManager', 'PerformanceRating'. The goal is to train a ML model which can predict the employee performance based certain factors as inputs. This will be used to hire employees provide department wise performances and other department wise insights Top 3 Important Factors effecting employee performance Recommendations to improve the employee performance based on insights from analysis.

This project presents an interactive analysis of employee attrition using Power BI. The goal is to uncover key patterns and trends behind employee turnover across departments, job roles, age groups, and other demographics. The dashboard helps stakeholders make data-driven HR decisions to reduce attrition and improve retention strategies.

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This research explores the factors that contribute to employee turnover within a company. By examining a large dataset that includes demographic information, job tenure, department, and reasons for leaving, the study discovers patterns and connections between these factors and employee departures.

Analysis of employee attrition rate

Data Analytics Project

Analysis of Employee Attrition Rate and Performance

The insept analysis of employee attrition

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Predictive analysis of employee attrition using machine learning on the IBM HR dataset to identify patterns and enhance employee retention strategies.

This project uses HR data to analyze employee attrition, focusing on demographics, work history, compensation, performance, and job satisfaction. Through EDA and machine learning, we identify attrition patterns and predict flight risk using Logistic Regression and Gradient Boosting, providing insights for better HR decision-making.

An analysis of attrition among organization employee

Exploratory Data Analysis and Prediction of Employee Attrition

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

Data Visualization of Employee Attrition Analysis using Tableau

Employee Attrition Prediction | Comparative Analysis of classification ML techniques

Statistical analysis of employee attrition using SPSS and Excel

Analysis & Prediction of Employee Attrition using Machine Learning Algorithms