Datasets:
license: mit
task_categories:
- tabular-classification
- text-classification
tags:
- fitness
- health
- synthetic-data
size_categories:
- 10K<n<100K
SmartFit AI - Synthetic Fitness Dataset
Dataset Overview
This repository contains a synthetic dataset generated for the SmartFit AI project. The dataset consists of 10,000 user profiles designed to train a machine learning model for personalized workout recommendations. It simulates various biological and preference-based factors including age, gender, weight, height, fitness goals, equipment availability, and injuries.
Repository Contents
- smart_fit_dataset.csv: The primary dataset containing the raw tabular data used for training the model.
- fitness_embeddings.npy: Pre-computed vector embeddings of the user profiles.
- Copy_of_Final_Project.ipynb: The complete Jupyter Notebook containing data generation, training, and evaluation logic.
- imp1.png: Visualization of the Exploratory Data Analysis (EDA).
- imp3.png: Visualization of the Embeddings Analysis (PCA Clustering).
Exploratory Data Analysis (EDA)
The visualization below demonstrates the distribution and characteristics of the synthetic data. This analysis ensures the dataset is balanced across different categories and logically consistent regarding injuries and recommended plans.
Embeddings and User Segmentation
To analyze user similarity and validate the data quality, we utilized a Sentence Transformer model to convert textual user profiles into high-dimensional vectors. The visualization below displays these embeddings reduced to 2D space using PCA. The distinct clusters indicate that the system successfully differentiates between different user types and their corresponding workout requirements.
