Diabetes Prediction

SQL Server & Power BI This project involves analyzing a 100,000-row dataset on diabetic patients using SQL Server and Power BI, focusing on data processing, optimization, and exploratory data analysis (EDA). 1. Data Processing & Optimization Validation & Cleaning: Ensuring age validity, verifying categorical values (0/1), and standardizing names. Duplicate Handling: Identifying and investigating duplicate employee records for inconsistencies. Performance Optimization: Implementing indexes and stored procedures for efficient querying. 2. Exploratory Data Analysis (EDA) General Statistics: Age distribution, BMI trends, and HbA1c levels for diabetes risk assessment. Health Condition Prevalence: Diabetes, Hypertension, and Heart Disease breakdown. Heart Disease: 3% affected, 97% unaffected. Correlation Analysis: Hypertension & Diabetes: Weak correlation (0.19). Heart Disease & Diabetes: Very weak correlation (0.17). Health Risks by Gender & Age: Diabetes & Hypertension: Slightly higher in women. Heart Disease: More prevalent in men. Diabetes Prevalence: Higher in older age groups. This structured analysis provides insights into health risks, supporting data-driven decision-making in healthcare.