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Beverage type
string
Category
string
Added sugar (g)
float64
Calories
float64
Volume
string
Energy rating (1-5)
int64
Is high sugar?
int64
Starbucks Iced Caffe Latte (unsweetened, milk only)
Coffee
0
130
355 mL
5
0
Starbucks Caramel Frappuccino (Tall, with whip)
Coffee
30
260
355 mL
5
1
Starbucks Pumpkin Spice Latte (Tall, with whip)
Coffee
25
340
355 mL
4
1
Starbucks Chai Tea Latte (Tall)
Tea
23
190
355 mL
4
1
Starbucks Caffè Mocha (Tall, with whip)
Coffee
15
290
355 mL
4
0
Starbucks Caramel Macchiato (Tall)
Coffee
13
190
355 mL
4
0
Starbucks Matcha Green Tea Latte (Tall)
Tea
10
170
355 mL
5
0
Pepsi (regular)
Soft drink
34
130
300 mL
3
1
Diet Pepsi
Soft drink
0
0
300 mL
1
0
Coca Cola Classic
Soft drink
33
120
300 mL
3
1
Diet Coke
Soft drink
0
0
300 mL
2
0
Mountain Dew
Soft drink
39
140
300 mL
2
1
Diet Mountain Dew
Soft drink
0
0
300 mL
1
0
Sprite (Lemon lime)
Soft drink
34
130
300 mL
1
1
Sprite Zero
Soft drink
0
0
300 mL
1
0
Dr Pepper
Soft drink
34
130
300 mL
1
1
Gatorade Thirst Quencher (Lemon Lime)
Sport drink
19
70
300 mL
1
1
Gatorade Zero (no sugar electrolyte drink)
Sport drink
0
1
300 mL
2
0
Powerade (Fruit Punch)
Sport drink
19
75
300 mL
1
1
Powerade Zero
Sport drink
0
0
300 mL
2
0
Red Bull (original)
Energy drink
32
130
300 mL
4
1
Red Bull Sugarfree
Energy drink
0
10
300 mL
3
0
Monster Energy (original)
Energy drink
34
150
300 mL
2
1
Monster Zero Ultra (zero sugar)
Energy drink
0
0
300 mL
3
0
Snapple Lemon Iced Tea
Tea
23
95
300 mL
2
1
Snapple Zero Sugar Iced Tea
Tea
0
10
300 mL
5
0
Vitaminwater (Power C Dragonfruit)
Sport drink
14
50
300 mL
1
0
Vitaminwater Zero
Sport drink
0
0
300 mL
2
0
Arizona Green Tea w/ Ginseng & Honey
Tea
21
90
300 mL
2
1
Minute Maid Lemonade
Soft drink
34
130
300 mL
4
1

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_sugar was 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 rating and is_high_sugar are 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}
}
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