AI POWERED NUTRITION AND FITTNESS PLANNER

Project Code :TCMAPY1549

Objective

This project predicts body fat percentage using LSTM, Random Forest, and XGBoost based on body measurements and demographics. It includes preprocessing steps like missing value handling, outlier removal, and feature scaling. A web interface allows users to input data and receive predictions, personalized nutrition plans, and fitness routines for better health.

Abstract

This project predicts percent body fat using LSTM, Random Forest, and XGBoost based on various body measurements and demographics. The dataset includes parameters such as Density ,age, weight, height, neck, chest, abdomen, hip, thigh, knee, ankle, biceps, forearm, and wrist circumference, along with density from underwater weighing. The data preprocessing steps include handling missing values, outlier removal, feature scaling (Standard Scaler), and feature engineering to improve model performance. The system features a user-friendly web interface (HTML, CSS, JS) with an index page, about page, registration, and login. After logging in, users input their measurements for prediction. Based on the predicted body fat percentage, the system provides personalized nutrition plans and exercise routines to enhance health and fitness. This AI-powered system ensures a data-driven approach to health monitoring and decision-making. Keywords: Machine Learning, LSTM, Random Forest, XGBoost, Web-Based Prediction System.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Hardware Requirements   

  ·         Processor                                 - I3/Intel Processor

·         Hard Disk                                - 256 

  ·         Key Board                               - Standard Windows Keyboard

·         Mouse                                    - Two or Three Button Mouse 

  ·         Monitor                                   - SVGA

·         RAM                                       - 8GB

Software Requirements  

•      Operating System                    :  Windows 7/8/10

•      Server side Script                    :  HTML, CSS,  JS 

  •      Programming Language         :  Python 

  •      Libraries                                  : Flask, Pandas, MySQL. Connector, Scikit-learn 

•       IDE/Workbench                     :  VS Code 

  •      Technology                             :  Python 3.8+ 

  •      Server Deployment                 :  Xampp Server

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