🚀 BMI Category Predictor

A full-stack application built with Python, Flask, and machine learning to predict BMI categories based on user input.

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🧠 Technologies Used

• Python Libraries: scikit-learn, pandas, numpy, joblib, Flask
• Dataset: Kaggle’s "500 Person Gender-Height-Weight-Body Mass Index" dataset 📊
• Frontend: HTML, CSS, and JavaScript for a clean and responsive user interface 🎨

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🔍 Project Overview

This app processes user data to predict BMI categories using classification models trained on real-world data. It showcases skills in:

• Data preprocessing and feature engineering
• Building and training machine learning models
• Creating RESTful APIs with Flask
• Integrating frontend and backend for smooth user experience

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🎨 Design Preview

Design Files: See Designs folder in this repository.

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🎥 Preview Images

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🛠 Skills & Tools

Programming Languages & Frameworks

• Python
• Flask (Backend API development)
• JavaScript, HTML, CSS (Frontend design)

Machine Learning & Data Processing

• scikit-learn (Model building and evaluation)
• pandas & numpy (Data manipulation and preprocessing)
• joblib (Model serialization)

Tools & Platforms

• VS Code (Code editor)
• Kaggle (Dataset sourcing)
• Git & GitHub (Version control and project hosting)

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