Body Type Predictor Model

This project aimed to classify body types (e.g., Hourglass, Pear, Rectangle) based on bust, waist, and hip measurements. Since no suitable dataset existed, I generated synthetic data in Python by defining realistic measurement ranges for each body type, creating 100 samples per category. Using this data, I trained a Logistic Regression model, achieving an accuracy of around 73%. I then built a simple web app using Streamlit that allows users to input their measurements and receive an immediate prediction of their body type. The project demonstrated the value of synthetic data generation and showed how a straightforward machine learning approach can provide accurate classification results. Future improvements could include additional features and experimenting with more advanced algorithms.