Datasets:
File size: 2,383 Bytes
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license: mit
task_categories:
- tabular-classification
- text-classification
tags:
- fitness
- health
- synthetic-data
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: Age
dtype: int64
- name: Gender
dtype: string
- name: Weight_kg
dtype: int64
- name: Height_cm
dtype: int64
- name: Goal
dtype: string
- name: Equipment
dtype: string
- name: Experience
dtype: string
- name: Injury
dtype: string
- name: Recommended_Plan
dtype: string
splits:
- name: train
num_bytes: 1199740
num_examples: 10000
download_size: 73833
dataset_size: 1199740
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# 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.
The data is stored in **Parquet format** (Hugging Face native) for high efficiency and fast loading.
## Repository Contents
* **Copy of Final Project.ipynb (4)**: Colab's notebook
* **fitness_embeddings.npy (2)**: Pre-computed vector embeddings of the user profiles (generated via Sentence Transformer).
* **imp88.png**: Visualization of the Exploratory Data Analysis (EDA).
* **imp888.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.
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