COVID-19 Power BI Dashboard

Power BI analysis of COVID-19 dataset

This project explores how different factors influenced COVID-19 cases and outcomes using a public dataset. The analysis was done in Power BI, covering administrative, clinical, comorbidity, and demographic aspects of patients.

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Table of Contents

• Objectives
• Dataset
• Dashboard Sections
• Tools Used
• Key Insights
• Repository Contents

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Objectives

• Clean and prepare raw COVID-19 data for analysis.
• Build an interactive Power BI dashboard.
• Highlight the effect of administrative, clinical, comorbidities, and demographic factors on COVID-19 outcomes.

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Dataset

• Source: COVID-19 Dataset (Kaggle)
• Records: 1M patients (Mexico)
• Key fields: Sex, Age, Diabetes, Hypertension, Obesity, ICU, Intubation, Date of Death, Patient Type, etc.

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Dashboard Sections

Overview | Administrative | Clinical Care

Comorbidities | Demographics

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Tools Used

• Power BI: Data modeling, DAX, dashboard design
• Excel: Data cleaning and preparation
• DAX: Calendar table and measures for time-based analysis

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Key Insights

• Hospital type and medical units influenced admission and outcomes.
• ICU and intubation patients had higher mortality compared to outpatients.
• Comorbidities like diabetes, hypertension, and obesity increased risk.
• Age and gender were major demographic factors affecting severity.

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Repository Contents

📁 images/ → Dashboard screenshots
📄 COVID-19 Patient Analysis.pbix → Power BI file
📄 README.md → Documentation

Report

View the full PDF report here.