Diabetes Patients Analysis

In this data-driven project, I analyzed a comprehensive dataset provided by the National Institute of Diabetes and Digestive and Kidney Diseases, facilitated by MeriSkill. The dataset's primary objective was to predict the diagnostic probability of diabetes among patients.My role involved intensive data analysis and interpretation, focusing on key metrics such as Diabetes Pedigree Function (DPF), skin thickness, insulin and glucose levels, BMI, and blood pressure.Key highlights of the project include:- Successfully handled and analyzed a dataset of 768 patients, out of which 268 were diagnosed with diabetes, reflecting my ability to manage large volumes of data effectively.- Devised a strategic patient segmentation based on age groups, i.e., Young Adults (21 to 39), Middle Age (40 to 59), and Old Adults (60 and above), which aided in more focused and age-specific analyses.- Determined that the average age of patients in the dataset was approximately 33 years, underscoring my ability to derive meaningful insights from complex data.The project involved extensive use of Power BI and Excel for data processing and visualization, displaying my proficiency in these vital analytical tools.

• This project reinforced my data analysis skills in the healthcare domain, and solidified my ability to derive actionable insights from vast datasets. The knowledge gained from this project could influence future diabetes diagnosis and management strategies.