Dataset Profiling (understand the data first)

Never choose a model, metric, or split before inspecting the actual data. The profile drives every later decision.

Where is the data? Pick the access path.

/kaggle/input exists only inside a Kaggle kernel. During a Commit the notebook code reads it; the assistant running on your machine cannot.

Kaggle option A (recommended, no download): run a profiling cell, paste the output back

Emit this cell for the user to run on Kaggle, then use the printed summary to decide method, metric, and split:

import glob, os, pandas as pd
base = '/kaggle/input'
print('TREE (top levels):')
for r, d, f in os.walk(base):
    depth = r[len(base):].count(os.sep)
    if depth <= 2:
        print('  ' * depth, os.path.basename(r) or r, f'[{len(f)} files]')
for c in glob.glob(base + '/**/*.csv', recursive=True)[:5]:
    df = pd.read_csv(c, nrows=5)
    print('\nCSV', c, df.shape, list(df.columns))
    print(df.head(3).to_string())
imgs = glob.glob(base + '/**/*.jp*g', recursive=True) + glob.glob(base + '/**/*.png', recursive=True)
print('\nimage files:', len(imgs))

Ask the user to paste the output, then extend the cell as needed (class counts, image sizes, label joins).

Kaggle option B: download locally with the Kaggle API

If the user has a kaggle.json token: kaggle datasets download -d <owner/dataset> (or kaggle competitions download -c <name>), unzip, then profile the local folder directly.

What to profile (by modality)

• Tabular: shape, column dtypes, missing percentage, target distribution, high-cardinality columns, and columns that could leak the label.
• Images: count per class, image sizes and aspect ratios, color vs grayscale, corrupt files, folder layout, and a few sample views.
• Text: length distribution, label balance, duplicates, and language.
• Time-series: sampling frequency, gaps, time range, and count per series.
• Audio: sample rate, clip duration, and count per class.

Always determine (these decide correctness)

• The ENTITY for a group split (patient, user, scene, document) so data-rigor-and-leakage can prevent leakage.
• Any OFFICIAL train and test split (often encoded in file names or a provided split file).
• Class balance and dataset size, which drive model capacity and augmentation.

Output

A short data profile: modality, size, schema or class counts, the split entity, the official split if any, and quality issues. Hand it to data-rigor-and-leakage for the split, then to the matching domain skill for the method.