Diabetes Data Analytics Project – Power BI End-to-End Dashboard Project Overview This end-to-end data analytics project leverages Power BI to explore, clean, analyze, and visualize diabetes data. The goal is to create an interactive, insight-rich dashboard that can help healthcare professionals, analysts, and stakeholders understand the key drivers of diabetes and support data-driven decisions.
Dataset Information Attributes: Glucose BloodPressure Insulin BMI Age
Objectives Clean and transform raw diabetes data for visualization Build an interactive Power BI dashboard with key metrics Identify and highlight potential risk factors for diabetes Enable stakeholder exploration through filters and slicers
Tools & Technologies Power BI: Data modeling, dashboard development Power Bi : Power query
Workflow Overview
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Data Import Load the CSV dataset into Power BI using Power Query Editor.
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Data Cleaning Create calculated columns: Age Group (e.g., 20–30, 30–40, etc.) BMI Category (e.g., Underweight, Normal, Overweight, Obese) Risk Label (based on threshold combinations)
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Data Modeling Ensure a clean data model with meaningful relationships (if using additional tables). Define measures using DAX: Diabetes rate Average Glucose, BMI, Age by Outcome Percentage of diabetic patients in each segment
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Dashboard Design Create a multi-page Power BI dashboard with: Introduction Demographics Analysis Visualizations for Glucose, Insulin, Blood Pressure Risk classification by BMI and Glucose ranges Filters & Slicers Age group BMI category Outcome (Diabetic/Non-Diabetic) Diabetic count
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Insights & Recommendations Highlight trends such as: Higher glucose levels correlate with diabetes outcome Most diabetic cases occur in older and obese individuals Certain BMI and glucose thresholds show high-risk groups