Datasets:
metadata
dataset_info:
features:
- name: Beverage type
dtype: string
- name: Category
dtype: string
- name: Added sugar (g)
dtype: float64
- name: Calories
dtype: float64
- name: Volume
dtype: string
- name: Energy rating (1-5)
dtype: int64
- name: Is high sugar?
dtype: int64
splits:
- name: original
num_bytes: 2590
num_examples: 30
- name: augmented
num_bytes: 25900
num_examples: 300
download_size: 13298
dataset_size: 28490
configs:
- config_name: default
data_files:
- split: original
path: data/original-*
- split: augmented
path: data/augmented-*
license: cc
language:
- en
task_categories:
- tabular-classification
tags:
- beverage
- energy
pretty_name: Beverage Energy Tracker
size_categories:
- 10K<n<100K
Dataset Card for Beverage Energy Tracker
This dataset card documents the Beverage Energy Tracker dataset.
It contains nutritional and energy-related features of 30 unique beverages, with an augmented split expanding to 300 rows.
Dataset Details
Dataset Description
- Curated by: Bareethul Kader (Carnegie Mellon University, EST&P program)
- Language(s): English (column labels, beverage names)
- License: CC BY 4.0
- Repository: bareethul/beverage-energy-tracker
Uses
Direct Use
- Educational practice in data collection, preprocessing, and augmentation.
- Demonstration of label-preserving jitter augmentation for tabular datasets.
- Binary classification task: predict whether a beverage is high sugar or not.
Out-of-Scope Use
- Not intended for clinical, nutritional, or health policy decision-making.
- Values are approximate and curated manually - not to be used for dietary guidance.
Dataset Structure
- Original split: 30 manually curated beverages.
- Augmented split: 300 rows, generated with Gaussian jitter (for continuous features) and ±1 step encoding (for ordinal features).
Features:
Beverage type(string)Added sugar (g)(float)Calories(float)Volume (mL)(integer)Energy rating (1–5)(ordinal)is_high_sugar(binary target:1 = sugar ≥ 20g,0 = sugar < 20g)
Dataset Creation
Curation Rationale
To study how nutritional features (sugar, calories, volume) can relate to sugar content classification (high vs low).
Data Collection and Processing
- Data manually collected from: Starbucks, ALDI USA, MarketDistrict, Amazon, Walmart product pages.
- Manual preprocessing and type formatting were applied (e.g., ensuring numeric columns, clipping noise to avoid negative values).
- Binary target
is_high_sugarwas derived using a threshold of 20g sugar.
Source Data Producers
- Original producers: beverage manufacturers and retailers (nutritional info posted publicly).
- Dataset curated by Bareethul Kader for educational purposes.
Annotations
- Annotation Process: Binary target derived from numeric sugar values.
- Annotators: Dataset creator.
Personal and Sensitive Information
- No personal or sensitive data included.
Bias, Risks, and Limitations
- Limited to 30 beverages (not representative of all products).
Energy ratingandis_high_sugarare simplified labels, not standardized nutrition metrics.- Augmentation may create unrealistic numeric combinations.
Recommendations
Users should be aware of the risks, biases, and limitations of the dataset.
It is intended only for educational demonstrations of dataset creation, preprocessing, and augmentation.
Do not generalize results to real nutrition/health applications.
Citation
BibTeX:
@dataset{bareethul_beverage_energy_tracker,
author = {Kader, Bareethul},
title = {Beverage Energy Tracker},
year = {2025},
publisher = {Hugging Face Datasets},
url = {https://huggingface.co/datasets/bareethul/beverage-energy-tracker}
}