{"id":25620396,"url":"https://github.com/tejas-130704/dataanalysis-hr-manager","last_synced_at":"2026-03-01T13:32:30.647Z","repository":{"id":255881225,"uuid":"853790801","full_name":"tejas-130704/DataAnalysis-HR-Manager","owner":"tejas-130704","description":"Presence Insights of Employees This project provides insightful data analysis on employee attendance and presence, including work-from-home (WFH) data, sick leave records, and presence excluding holidays. 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The analysis spans a three-month period (April, May, June 2022) and is visualized using Power BI to help HR managers understand trends and optimize workforce management.\n\n## Table of Contents\n- [Project Overview](#project-overview)\n- [Data Source](#data-source)\n- [Project Structure](#project-structure)\n- [Power BI Visualization](#power-bi-visualization)\n- [Key Insights](#key-insights)\n- [Technologies Used](#technologies-used)\n- [How to Run](#how-to-run)\n- [Learning Experience](#learning-experience)\n\n## Project Overview\nThis project analyzes employee presence data over three months (April, May, June 2022), with a focus on:\n- Work-from-home (WFH) data\n- Sick leave (SL) records\n- Presence excluding holidays\n\nThe goal is to provide HR managers with valuable insights into employee attendance patterns across different days of the week, work types, and absences.\n\n## Data Source\nThe dataset for this project was sourced from the Codebasics YouTube channel, where I followed the steps and methodologies to perform this analysis.\n\n## Project Structure\nThe repository contains:\n- **Dataset**: The CSV file includes employee attendance, WFH, and sick leave data.\n- **Power BI Report**: A `.pbix` file with interactive visualizations for the data.\n- **README.md**: The project overview and instructions.\n\n## Power BI Visualization\nThe Power BI report provides detailed visualizations, including:\n- **Overall Attendance Metrics**: Showing the percentages for WFH, presence, and sick leave across all employees.\n- **Presence Percentage by Date**: Highlighting attendance trends over the months of April, May, and June 2022.\n- **Work-from-Home (WFH) Percentage by Date**: Displaying WFH patterns during the same period.\n- **Sick Leave Percentage by Date**: Visualizing the trends in sick leave.\n- **Attendance Breakdown by Day of the Week**: Offering insights into presence, WFH, and SL variations across different weekdays.\n\n![Screenshot 2024-09-07 074642](https://github.com/user-attachments/assets/144ee413-56c0-496a-a9a0-1931088486ab)\n\n\nThese dashboards allow HR managers to explore trends in employee presence and attendance, and they can easily filter data by date and department.\n\n## Key Insights\n1. **Presence Trends**: The presence percentage is consistently above 90%, with minor fluctuations during weekdays.\n2. **WFH Trends**: WFH shows a steady increase over the three months, particularly on Fridays.\n3. **Sick Leave**: Sick leave remains low but peaks slightly on Mondays and Tuesdays.\n4. **Weekly Breakdown**: Presence is slightly lower on Fridays, while WFH is highest on Fridays and Thursdays.\n\n## Technologies Used\n- **Power BI**: For creating interactive visualizations and dashboards.\n\n## How to Run\n1. Clone the repository:\n   ```bash\n   https://github.com/tejas-130704/DataAnalysit.git\n\n## Learning Experience\nDuring this project, I gained significant experience working with Power BI, enhancing my skills in creating insightful visualizations. I learned how to efficiently analyze attendance data and generate actionable reports for HR management.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftejas-130704%2Fdataanalysis-hr-manager","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftejas-130704%2Fdataanalysis-hr-manager","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftejas-130704%2Fdataanalysis-hr-manager/lists"}