SmartFit AI

[Streamlit App] (https://smartfit-ai.streamlit.app/)

License: MIT](https://opensource.org/licenses/MIT)

SmartFit AI is a comprehensive machine-learning-powered system that analyzes workout patterns, dietary habits, and health indicators to deliver personalized insights, predictions, and recommendations. It combines supervised, unsupervised, and deep-learning techniques to model calorie burn, cluster fitness profiles, and suggest optimal workouts and diet plans._

##Dashboard Preview

Key Highlights

• Predict calories burned, BMI, and fat percentage
• Identify user fitness/diet archetypes using clustering
• Build neural-network models for health profiling
• Recommend personalized diet and workout routines
• Interactive Streamlit dashboard for live exploration

Table of Contents

• Overview
• Features
• Dashboard Sections
• Dataset
• Model Performance

Overview

SmartFit AI leverages a dataset of user fitness metrics to provide actionable insights. Using techniques like PCA for dimensionality reduction, K-Means clustering for profile segmentation, and neural networks for predictions, it helps users optimize their health journeys. The interactive Streamlit app visualizes data, predicts outcomes, and generates recommendations in real-time.

Features

• Predictions: Real-time calorie burn, BMI, body fat analysis, and workout impact projections.
• Clustering- Unsupervised learning to group users into 5 fitness archetypes (Elite Athletes, Strength Builders, Enthusiasts, Beginners, Health Focus).
• Recommendations: AI-driven workout schedules and diet plans tailored to user stats, goals, and equipment.
• Visualizations: Radar charts, pie charts, heatmaps, scatter plots, and more for intuitive data exploration.
• Data Analysis: Correlation heatmaps, PCA visualizations, and distribution analyses.

Dashboard Sections

The Streamlit app is organized into intuitive sections:

1. ** Dashboard**

   • System overview with key metrics.
   • Cluster distribution visualization.
   • Calorie burn by workout type.
   • BMI distribution analysis.

2. ** Predictions**

   • Calorie Burn Calculator: Real-time prediction based on intensity, duration, and user stats.
   • BMI & Body Fat Analyzer: With visual gauge charts.
   • Workout Impact Predictor: Project weight changes over time with timeline charts.

3. ** Fitness Profiles**

   • 5 Fitness archetypes (Elite Athletes, Strength Builders, Enthusiasts, Beginners, Health Focus).
   • Interactive profile matching based on user input.
   • Radar charts showing fitness attributes.
   • Detailed cluster characteristics.

4. ** Diet Planner**

   • Personalized macronutrient calculations.
   • Sample meal plans for different goals.
   • Macronutrient distribution pie charts.
   • Weekly shopping lists.

5. ** Workout Recommender**

   • AI-generated workout schedules.
   • Detailed strength, cardio, and recovery sessions.
   • Progress tracking metrics.
   • Customized based on experience and equipment.

6. ** Data Explorer**

   • Correlation heatmaps.
   • Interactive distribution visualizations.
   • 2D/3D scatter plots.
   • PCA cluster visualization with explained variance.

Dataset

• Shape: (20,000, 62) – 20,000 rows with 62 features.
• Key Columns and Datatypes:
  • age: float64
  • gender: object
  • weight_kg: float64
  • height_m: float64
  • max_bpm: float64
  • ... (additional features like pct_fats: float64, difficulty_level_enc: int32, cluster: int32, pca1: float64, pca2: float64)
• Missing Values (%): 0.0 across all columns (e.g., age: 0.0, protein_per_kg: 0.0, sets: 0.0, benefit: 0.0, sodium_mg: 0.0, cholesterol_mg: 0.0, serving_size_g: 0.0,0.0).
• The dataset is fully clean with no missing values, making it ideal for direct modeling.

Model Performance

• Clustering: K-Means applied on PCA-reduced features (pca1, pca2).
• Clustering: K-Means applied on PCA-reduced features (pca1, pca2) to identify 5 fitness archetypes. Explained variance from PCA can be visualized in the app.
• Supervised Models (e.g., Neural Networks for calorie burn, BMI, and body fat prediction)