---
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
![EDA Plots](eda_plots.png)

### 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
1. Converted all measurements to integers (3-digit mm)
2. Standardized categorical values
3. Removed any invalid entries
4. 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
```python
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.