Hr Attrition Analytics Dashboard

I note that certain job roles may experience greater attrition than others.

I note that employees working overtime may show comparatively higher turnover.

I note that attrition may be concentrated within specific age groups.

I note that employees with lower job satisfaction may demonstrate higher attrition.

I note that employees with shorter organizational tenure may be more likely to leave.

I note that monthly income and job level may be associated with retention.

I note that work-life balance may influence employee stability.

I note that environment satisfaction may affect employee engagement.

I note that frequent business travel may be associated with higher turnover.

I note that specific departments may require targeted retention strategies.

I describe identifying workforce segments with higher attrition as a potential use of the dashboard.

I describe developing targeted employee-retention strategies as a potential use of the dashboard.

I describe monitoring department-level employee turnover as a potential use of the dashboard.

I describe evaluating overtime and workload patterns as a potential use of the dashboard.

I describe understanding employee satisfaction indicators as a potential use of the dashboard.

I describe supporting compensation and career-development planning as a potential use of the dashboard.

I describe reducing avoidable replacement and recruitment costs as a potential use of the dashboard.

I describe strengthening long-term workforce planning as a potential use of the dashboard.

I describe supporting evidence-based HR decisions as a potential use of the dashboard.

I describe clear presentation of HR KPIs as a strength of the project.

I describe dynamic filtering and cross-visual interaction as a strength of the project.

I describe a repeatable data-cleaning process using Power Query as a project strength.

I describe business calculations using DAX as a project strength.

I describe detailed demographic and turnover analysis as a project strength.

I describe employee wellness and satisfaction analysis as a project strength.

I describe management-friendly visual storytelling as a project strength.

I describe practical application of business intelligence in HR as a project strength.

I describe machine-learning-based attrition prediction as a possible future enhancement.

I describe employee attrition risk scoring as a possible future enhancement.

I describe department-specific prediction models as a possible future enhancement.

I describe Power BI Service deployment as a possible future enhancement.

I describe drill-through employee analysis as a possible future enhancement.

I describe historical attrition trend analysis as a possible future enhancement.

I describe what-if analysis for salary and overtime as a possible future enhancement.

I describe cloud-based data integration as a possible future enhancement.

I describe mobile-optimized report design as a possible future enhancement.

I describe real-time HR data connectivity as a possible future enhancement.

I state that the dataset contains a fixed number of employee records, without specifying the count here.

I state that business decisions require additional organizational and qualitative context.

I note that results may vary when the dashboard is applied to another dataset.

I state that employee analytics must be used responsibly.

I describe the report as providing structured analysis of employee turnover across demographics, job roles, departments, compensation, experience, overtime, satisfaction and employee wellness.

I use this Power BI dashboard as an educational, analytical, and portfolio project based on a publicly available sample dataset, not data from a specific company. It extends the project overview and dataset details already recorded on this page.

When I select a filter, the connected visualizations update automatically to display the selected employee segment.

I describe the dashboard's five analytical pages as a way to explore employee attrition from multiple perspectives.

I present the findings as associations within the selected dataset, not confirmed causes of employee attrition.

I state that the project uses a publicly available sample dataset that does not represent a specific company.

I say historical time-series attrition data is not available in the dataset.

I position the dashboard as support for HR decisions, not as the sole basis for employment-related decisions.

I state that sensitive demographic attributes such as age, gender and marital status should not be used to discriminate against employees.

I say dashboard findings should be evaluated alongside organizational policies, employee feedback, management judgment, legal requirements, fairness and privacy principles, and appropriate human review.

I describe the project's purpose as improving workforce understanding, not automating unfair employment decisions.

I describe this project as developed for educational, analytical and portfolio purposes.

I state that the dataset is publicly available and does not contain confidential or personally identifiable employee information from any real organization.

I state that all dashboard findings are based exclusively on the sample dataset and should not be treated as universal conclusions about employee behaviour.

I identify myself in the project attribution as Mousom Koley.

I list my qualification in the project attribution as B.Tech in Information Technology.

I list my areas as Data Analytics | AI/ML | Python Development.

I describe the dashboard as useful for exploring possible associations involving job role, overtime, age group, satisfaction, tenure, income, job level, work-life balance, environment satisfaction, business travel, and department. I frame workforce segmentation, retention planning, turnover monitoring, workload evaluation, compensation and career-development planning, allocation, recruitment costs, and long-term planning as potential uses—not measured outcomes. This is consistent with my broader HR analytics project description on my linkedin profile.

I list project strengths including interactive exploration, HR KPIs, cross-visual filtering, Power Query cleaning, DAX calculations, demographic and wellness analysis, management-friendly storytelling, and a scalable reporting structure. The listed skills span Power BI, Power Query, DAX, data preparation and modelling, KPI development, dashboard design, HR analytics, visualization, exploratory analysis, business problem solving, GitHub, and technical documentation.

git clone https://github.com/Mousam098/HR-Attrition-Analytics-Dashboard.git
cd HR-Attrition-Analytics-Dashboard

• I keep the repository files organized as README.md, HR_Dashboard.pbix, HR_Employee_Attrition.csv, and assets/hr-attrition-dashboard.png.

I open HR_Dashboard.pbix in Power BI Desktop. If the dataset path is missing, I use Home → Transform Data → Data Source Settings → existing source → Change Source, browse to the repository dataset, confirm the path, and select Close & Apply. I explore the report with navigation buttons, filters, and slicers; selecting a filter updates connected visualizations for the selected employee segment.

I describe possible future enhancements rather than established deployments: machine-learning prediction, risk scoring, department-specific models, Power BI Service, scheduled refresh, row-level security, drill-through, historical trends, salary and overtime what-if analysis, SQL and cloud integration, mobile optimization, recommendations, retention simulation, and real-time HR connectivity.

I note that the sample has a fixed number of employee records, but this content does not state the count. It also provides no measured KPI values, attrition rates, or segment-level results. My conclusion describes analysis across demographics, roles, departments, compensation, experience, overtime, satisfaction, and wellness; the project’s actual findings should not be treated as universal.

I built the HR Attrition Analytics Dashboard as an interactive Microsoft Power BI project for exploring employee turnover and workforce patterns. I use it to examine attrition across demographics, roles, departments, compensation, overtime, satisfaction, work-life balance, and career progression. This project extends the HR dashboard work described on my linkedin profile.

The project is based on the publicly available IBM HR Analytics Employee Attrition and Performance dataset, which contains 1,470 employee records and 38 attributes.

I use this public sample dataset for educational, analytical, and portfolio purposes; it does not contain confidential employee information from a real organization.

The repository has 1 star.

The repository started 2026-07 and was last updated 2026-07.

The project is intended to make workforce patterns easier to explore through a centralized interactive dashboard. The README frames employee departures as potentially increasing recruitment and onboarding costs, reducing productivity, removing experienced employees, creating knowledge-transfer gaps, adding workload for existing employees, disrupting team performance, and complicating long-term workforce planning. The dashboard is intended to help HR teams and business stakeholders compare attrition across employee dimensions and support workforce planning and retention strategies.

• I use the dashboard to investigate overall attrition; differences by age, department, role, education field, gender, and marital status; and associations with overtime, income, satisfaction, environment satisfaction, work-life balance, and organizational tenure. These are questions for analysis, not reported findings.

I organize the report into five analytical pages: Overview for workforce totals and headline measures; Demographics for age, gender, marital status, education, and education field; Turnover Analysis I for department, role, job level, travel, overtime, and distance from home; Turnover Analysis II for income, experience, tenure, promotion, and prior employers; and Employee Wellness for satisfaction, work-life balance, involvement, performance, and training.

The dataset covers demographic, employment, compensation, employee-experience, and career/performance fields. Examples include Age, Gender, Marital Status, Department, Job Role, Job Level, Years at Company, Monthly Income, Job Satisfaction, Work-Life Balance, Performance Rating, Overtime, and the Attrition target. The README also lists filters or slicers for Department, Job Role, Gender, Age Group, Education Field, Marital Status, Overtime, Business Travel, Job Level, and Attrition Status.

My described workflow moves from the IBM HR dataset through CSV or Excel, Power BI import, Power Query cleaning, transformation, data modelling, DAX measures, dashboard development, interactive analysis, and HR insights. The development steps include examining the data structure, checking types and categories, checking missing values and duplicates, creating analytical grouping columns, designing report pages, adding slicers and visual interactions, and testing navigation and visual consistency.

The README describes preprocessing activities including checking data types, missing or null values, and duplicate employee records; standardizing categories; renaming fields; creating age-group and attrition-status categories; validating Yes/No values in Attrition; preparing income and experience fields for analysis; and optimizing the model for dashboard performance. It lists these as activities, but does not report specific validation results.

I use Microsoft Power BI Desktop for dashboard design, data modelling, and visualization.

I use Power Query for data cleaning, transformation, and preparation.

I use DAX for KPI measures, calculated columns, and analytical calculations.

Total Employees = COUNTROWS('HR_Dataset')

Attrition Count = CALCULATE(COUNTROWS('HR_Dataset'), 'HR_Dataset'[Attrition] = "Yes")

Active Employees = CALCULATE(COUNTROWS('HR_Dataset'), 'HR_Dataset'[Attrition] = "No")

Attrition Rate = DIVIDE([Attrition Count], [Total Employees], 0)

Average Employee Age = AVERAGE('HR_Dataset'[Age])

Average Monthly Income = AVERAGE('HR_Dataset'[MonthlyIncome])

Overtime Attrition = CALCULATE([Attrition Count], 'HR_Dataset'[OverTime] = "Yes")

The DAX examples use the table name HR_Dataset and advise replacing it with the actual Power BI table name when necessary. The README defines Total Employees as employee records, Active Employees as non-attrited employees, Attrition Count as employees who left, Attrition Rate as the percentage who left, and the average-age, average-income, overtime-attrition, and average-tenure measures.

The README instructs me to place an optional dashboard screenshot in the assets folder using the exact filename hr-attrition-dashboard.png.

The README does not provide calculated KPI values, an observed attrition rate, or findings answering its proposed business questions. It also does not specify the actual Power BI table name or report specific results for its missing-value, duplicate, category-standardization, or model-validation checks.

I use Microsoft Excel for initial dataset inspection and preprocessing.

I use CSV for raw employee dataset storage.

I use GitHub for project documentation and version control.