shanexf/packaged-snack-nutrition

24-679 (Fall 2026): Packaged Snack Nutrition

shanexf/packaged-snack-nutrition

Nutrition Facts values for 30 packaged snack products, hand-collected from package labels, plus explicitly marked synthetic variants. The classroom classification task predicts the snack category (chips, crackers, cookies, candy, granola_bars) from eight per-serving nutrition numbers.

Source and task

Each original row is one product. The eight features were read directly from the product's Nutrition Facts panel: serving size (g), servings per container, calories, total fat (g), sodium (mg), total carbohydrate (g), sugar (g), and protein (g). The category is the type of snack the product is sold as; six products were collected for each of five categories. The preparation notebook reads Package food nutritional value.csv, keeps the collected sample_id as source_id, and checks that every value is finite and nonnegative, that serving fields are positive, and that sugar never exceeds total carbohydrate. These checks establish valid domains, not the accuracy of a label.

Course: 24-679, Fall 2026, Carnegie Mellon University. Repository maintainer: the account shown above.

Fields

Stored field / group	Meaning and modeling role
serving_size_g, servings_per_container	Continuous features from the label; strictly positive.
calories, total_fat_g, sodium_mg, carbs_g, sugar_g, protein_g	Continuous per-serving nutrition features; nonnegative.
category	Nominal classification target with five classes. Synthetic rows inherit it from their parents.
source_id, parent_id, second_parent_id	Unique example key and original source keys; provenance only.
augmentation, is_augmented, mix_weight	Method, synthetic flag, and primary-parent weight; exclude from predictors.

Numeric features are stored as floats in both splits. The machine-readable feature metadata at the top of this card preserves every exact column name and storage type.

Splits

These counts are computed from the packaged splits for this run.

Split	Original rows	Synthetic rows	Total rows
original	30	0	30
augmented	0	399	399
train	21	399	420
validation	4	0	4
test	5	0	5

Split	Category	Rows
original	candy	6
original	chips	6
original	cookies	6
original	crackers	6
original	granola_bars	6
augmented	candy	76
augmented	chips	74
augmented	cookies	76
augmented	crackers	96
augmented	granola_bars	77
train	candy	80
train	chips	78
train	cookies	80
train	crackers	101
train	granola_bars	81
validation	candy	1
validation	chips	1
validation	cookies	1
validation	granola_bars	1
test	candy	1
test	chips	1
test	cookies	1
test	crackers	1
test	granola_bars	1

original holds the 30 collected rows unchanged and augmented holds only synthetic rows; these two splits are the assignment deliverable. The remaining splits support model comparison: original rows were randomly partitioned before augmentation (holdout fraction 30%, stratified by category, seed 24679; test receives 50% of that holdout, seed 24680). train = training originals + every synthetic row; validation and test = untouched holdout originals. Every parent of every synthetic row is a training original, so no holdout product has a synthetic child anywhere in the dataset. augmented is exactly the synthetic portion of train. Keep these boundaries fixed for downstream comparisons; changing or reordering the source CSV changes them.

Augmentation and preprocessing

This run requests 5 copies per method and original training row before filtering (seed 24679). Unchanged rows and repeated feature combinations for a given primary parent are removed. Every synthetic value is rounded to the resolution printed on a Nutrition Facts label (1 g serving size, 0.5 servings, 5 kcal, 0.5 g fat/sugar/protein, 5 mg sodium, 1 g carbohydrate) and clipped to its domain.

• Additive numeric jitter: perturb every feature with Gaussian noise. Each standard deviation is max(5% of that column's IQR, one label step).
• Multiplicative numeric scaling: independently multiply each feature by a factor in 0.90–1.10.
• Within-class Mixup: select two distinct original parents from the same category. Apply the same primary weight in 0.60–0.90 to all eight features; copy the shared category; record both parents and the weight.
• Within-class nearest-neighbor interpolation (SMOTE-style): select one of the 3 nearest same-category products in standardized feature space and place the child at a uniform step along the segment between them; mix_weight stores the primary parent's share.

No method changes the category. Original and single-parent rows repeat the primary key in second_parent_id and use mix_weight=1.0. Categorical perturbation and CTGAN are not used.

Augmentation method	Stored rows
additive_numeric_jitter	105
multiplicative_numeric_scale	105
within_class_mixup	99
within_class_neighbor_interpolation	90

Intended use and limitations

Use for teaching tabular data contracts, provenance, augmentation, and small-sample classification. Thirty products from a small number of brands do not represent the packaged-food market, and categories overlap in nutrition space (a sweet granola bar can resemble a cookie). Synthetic rows add spread around real products but not new independent products; a domain-valid synthetic row may still describe a product nobody sells. Compare training on the original training rows alone against the train split, using the same validation and test rows; report accuracy and macro-F1 against a majority-class baseline. With four or five holdout rows per partition, scores will be noisy. Do not use this classroom sample for nutrition guidance or consequential decisions.

Privacy and licensing

The data describe products, not people; no personal information is included. Values were transcribed from public package labels. No license was specified in repository metadata when this card was first added; this card does not assign one.

Load

from datasets import load_dataset
ds = load_dataset("shanexf/packaged-snack-nutrition")
original, augmented = ds["original"], ds["augmented"]
# Train with ds["train"], choose settings with ds["validation"], then score ds["test"].

Regenerate this card with the preparation notebook after changing the data; its counts are calculated from the actual packaged splits. The YAML schema and split configuration are preserved from the upload.