Diabetes Prediction System Using Machine Learning And Web-Based Interactive Tool

The prevalence of diabetes is rising globally, making early detection crucial for effective management and prevention of complications. This project aims to develop an end-to-end machine learning application to predict the likelihood of diabetes in patients based on diagnostic measurements. Using the Pima Indians Diabetes Database, we employed a Random Forest Classifier to build a predictive model. The model is deployed as a web application using Flask, allowing users to input medical details (e.g., Glucose, BMI, Age) and receive real-time predictions. The project includes data gathering, descriptive analysis, data visualizations, data preprocessing, model building, and model deployment on Heroku.