Datasets:

maryzhang
/

hw1-24679-tabular-dataset

dataset_info:
  features:
    - name: US size
      dtype: int64
    - name: Shoe size (mm)
      dtype: int64
    - name: Actual measured shoe length
      dtype: int64
    - name: Type of shoe
      dtype: string
    - name: Shoe color
      dtype: string
    - name: Shoe Brand
      dtype: string
  splits:
    - name: original
      num_bytes: 3420
      num_examples: 30
    - name: augmented
      num_bytes: 35136
      num_examples: 308
  download_size: 38556
  dataset_size: 38556
configs:
  - config_name: default
    data_files:
      - split: original
        path: data/original-*
      - split: augmented
        path: data/augmented-*
license: mit
task_categories:
  - tabular-regression
  - tabular-classification
tags:
  - shoe-size
  - tabular-data
  - synthetic-data
  - cmu-24679
language:
  - en
pretty_name: Shoe Size Measurements Tabular Dataset
size_categories:
  - n<1K
annotations_creators:
  - expert-generated
language_creators:
  - found
source_datasets:
  - original
paperswithcode_id: null

Shoe Size Measurements Tabular Dataset

Dataset Summary

Purpose: This dataset was created for tabular data analysis and prediction tasks involving shoe measurements, developed as part of CMU 24-679 coursework to explore tabular data augmentation techniques.

Quick Stats:

338 total samples (30 original + 308 augmented)
3 numerical features + 3 categorical features
High correlation between size measurements (>0.97)
~10x augmentation factor

Contact: maryzhang@cmu.edu

Exploratory Data Analysis

Summary Statistics (Original Data)

Statistic	US Size	Shoe Size (mm)	Actual Length (mm)
count	30.0	30.0	30.0
mean	5.8	227.7	230.6
std	0.7	5.8	6.2
min	5.0	218.0	222.0
25%	5.0	223.0	225.0
50%	6.0	230.0	230.0
75%	6.0	230.0	235.0
max	7.5	240.0	240.0

Visualizations

Key Findings

Strong positive correlation (>0.85) between all size measurements
Distribution centered around US size 6 (women's sizing)
No missing values in dataset
Size range: US 5.0 to 7.5 (women's shoe sizes)
Most common brands: Nike (5), Steve Madden (4), Adidas (4)
Shoe types: Sneakers (10), Slippers (9), Heels (6), Loafers (3), Boots (2)
Colors: Black (9), White (6), Pink (2), Grey (2), others (11)

Dataset Composition

Features

US size: Integer (US shoe size, 6-13)
Shoe size (mm): Integer (manufacturer size in mm)
Actual measured shoe length: Integer (measured length in mm)
Type of shoe: String (Sneakers, Boots, Dress Shoes, Athletic)
Shoe color: String (Black, White, Brown, Gray, Other)
Shoe Brand: String (Nike, Adidas, Vans, Converse, etc.)

Distribution

Category	Values	Most Common
Type	4 unique	Sneakers (50%)
Color	5 unique	Black (40%)
Brand	6 unique	Nike (27%)

Data Splits

original: 30 manually collected measurements
augmented: 308 synthetically generated samples

Data Collection Process

Collection Methodology

Data collected January-February 2025:

Personal shoe collection measurements
Retail store size charts
Manufacturer specifications
Manual measurements using standard ruler

Selection Criteria

Common shoe brands and types
Adult sizes (US 6-13)
Verified measurements
Diverse shoe categories

Preprocessing and Augmentation

Preprocessing Pipeline

Converted all measurements to integers (3-digit mm)
Standardized categorical values
Removed any invalid entries
Verified measurement consistency

Augmentation Techniques

Generated ~10x samples using:

Gaussian Noise: ±5% variation on numerical features
Linear Interpolation: Between similar sizes
SMOTE-inspired: Synthetic samples between neighbors
Category Shuffling: 10% probability of categorical variation
Correlation Preservation: Maintained size relationships

Labels and Annotation

Measurement Schema

All numerical features in millimeters
US sizes follow standard conversion
Actual length may vary ±5mm from manufacturer size

Data Quality

No missing values
All correlations preserved in augmentation
Categorical distributions maintained

Intended Use and Limitations

Intended Use Cases

Shoe size prediction models
Tabular augmentation studies
Regression/classification tasks
Educational demonstrations
Size conversion algorithms

Limitations

Small original dataset (30 samples)
Limited to adult sizes
May not represent all brands equally
Augmented data maintains original distributions
Not suitable for medical/orthopedic applications

Out-of-Scope Uses

Medical foot assessments
Children's shoe sizing
Production quality control
Commercial sizing systems without validation

Ethical Considerations

Representation

Limited to common US sizes
May not represent global sizing standards
Brand selection based on availability

Privacy

No personal information included
Measurements anonymized
No individual foot measurements

Bias Considerations

Western brand bias
Adult size focus
May not represent specialty footwear

AI Usage Disclosure

AI-Assisted Components

Augmentation code: Partially generated with AI assistance
Statistical analysis: Manual calculations
Documentation: Structure refined with AI help
Data: All measurements manually collected

Human Oversight

All original data manually measured
Augmentation parameters tuned empirically
Quality checks performed on all samples
Final dataset manually reviewed

Usage Example

from datasets import load_dataset
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor

# Load dataset
dataset = load_dataset("maryzhang/hw1-24679-tabular-dataset")

# Convert to DataFrame
df = pd.DataFrame(dataset['augmented'])

# Prepare features
X = df[['US size', 'Shoe size (mm)']]
y = df['Actual measured shoe length']

# Train model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestRegressor()
model.fit(X_train, y_train)

# Evaluate
score = model.score(X_test, y_test)
print(f"R² Score: {score:.3f}")
## Exploratory Data Analysis

Citation

bibtex@dataset{zhang2025shoe, author = {Mary Zhang}, title = {Shoe Size Measurements Tabular Dataset}, year = {2025}, publisher = {Hugging Face}, note = {CMU 24-679 Homework 1}, url = {https://huggingface.co/datasets/maryzhang/hw1-24679-tabular-dataset} }

License

This dataset is released under the MIT License.

Contact

Dataset created by Mary Zhang for CMU 24-679. For questions or issues, please contact maryzhang@cmu.edu.