Kaggle Prep

One-command CLI tool to download, profile, visualize, and generate production-ready starter code & notebooks for any Kaggle dataset.

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Table of Contents

• Why Kaggle Prep?
• Key Features
• Installation
• Kaggle Credentials Setup
• Quick Start in 30 Seconds
• CLI Command Reference
• Generated Outputs & Architecture
• Comprehensive Error & Troubleshooting Guide
• Development & Contributing
• License

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Why Kaggle Prep?

Setting up a new data science project or Kaggle competition usually takes 30–60 minutes of repetitive boilerplate:

1. Downloading and unzipping files.
2. Checking missing values, types, duplicates, and memory footprints.
3. Writing 10+ standard EDA visualization scripts (distributions, outliers, correlations, class balance).
4. Writing preprocessing pipelines (imputation, categorical encoding, scaling).
5. Setting up a starter Jupyter Notebook with baseline ML models.

kaggle-prep automates the entire workflow in a single terminal command.

kaggle-prep uciml/iris --all --target Species

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Key Features

1. Zero-Config Smart Download: Instantly download public Kaggle datasets without needing API keys upfront using built-in kagglehub integration.
2. Interactive Setup Wizard (--setup): Configure and validate your Kaggle API credentials interactively in seconds.
3. Instant Automated Profiling: Compute row/col counts, missing rates, data types, duplicate counts, IQR outliers, skewness, and cardinality.
4. Standalone HTML Reports: Generates responsive, self-contained HTML reports with zero external runtime dependencies.
5. 10+ Production EDA Visualizations:
   • Dataset overview & metric cards
   • Missing value matrix & percent heatmaps
   • Feature distribution histograms & KDE curves
   • Skewness ranking & Q-Q normality plots
   • Violin plots & IQR outlier summaries
   • Correlation heatmaps & top-correlated feature pairs
   • High-cardinality flags & categorical frequency bar charts
   • Datetime row count trends
   • Target-Aware Analysis: Class balance bar/pie charts and feature distributions segmented by target class.
6. Auto-Generated Preprocessing Scripts: Clean Python code with Scikit-Learn pipelines tailored to your dataset's column schema.
7. Complete Starter Jupyter Notebooks: Pre-configured with modular sections: imports, EDA, missing analysis, outlier detection, ML preprocessing, and baseline model training.
8. Multi-Format & Universal Python Support: Works natively on Python 3.9, 3.10, 3.11, 3.12, and 3.13+ across Windows, macOS, and Linux. Supports .csv, .tsv, .parquet, .xlsx, and .json.

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Installation

Standard Installation (via pip)

pip install kaggle-prep

Full Installation (with ML Baseline and Excel support)

pip install "kaggle-prep[all]"

Using pipx (Isolated CLI application)

pipx install kaggle-prep

Install from Source

git clone https://github.com/SumitGavali/CLI-DataAnalysis.git 
cd CLI-DataAnalysis
pip install -e .

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Kaggle Credentials Setup

Option 1: Interactive Wizard (Recommended)

Simply run the setup wizard:

kaggle-prep --setup

The wizard will guide you through entering your username and API key, and automatically creates a secure ~/.kaggle/kaggle.json file.

Option 2: Manual Setup

1. Log into your account at Kaggle.
2. Navigate to Account Settings -> kaggle.com/settings/api.
3. Click "Create New Token" to download kaggle.json.
4. Move kaggle.json to your home directory:
   • Windows: C:\Users\<YourUsername>\.kaggle\kaggle.json
   • Linux / macOS: ~/.kaggle/kaggle.json
5. On Linux/macOS, set secure permissions:

   chmod 600 ~/.kaggle/kaggle.json

Option 3: Zero-Config Mode (No Credentials Needed)

For public datasets, kaggle-prep automatically uses kagglehub to download data without requiring any API token or login!

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Quick Start in 30 Seconds

1. Full Automated Analysis (All-in-One)

kaggle-prep uciml/iris --all

2. Supervised Analysis with a Target Column

kaggle-prep uciml/iris --all --target Species

3. Kaggle Competitions

kaggle-prep titanic --competition --all --target Survived

4. Analyze Existing Local Data

# Point to an existing local dataset folder
kaggle-prep my-dataset --local --all

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CLI Command Reference

Usage: kaggle-prep [dataset] [OPTIONS]

Positional Arguments:
  dataset                     Kaggle dataset slug (e.g. 'uciml/iris') or competition name

Workflow & Pipeline Flags:
  -a, --all                   Run full pipeline (profile + report + visualize + preprocess + notebook)
  -p, --profile               Generate data profile JSON and console summary
  -r, --report                Generate a standalone HTML profile report
      --visualize             Generate 10+ EDA visualization charts
      --preprocess            Generate an automated preprocessing Python script
  -n, --notebook              Generate a complete starter Jupyter notebook (.ipynb)

Data & Target Options:
  -t, --target TARGET         Specify target column name for supervised EDA & balance analysis
  -o, --output-dir DIR        Directory to save downloaded data (default: data)
  -l, --local                 Use local data in output directory (skips downloading)
  -c, --competition           Download from Kaggle Competition instead of Dataset
  -s, --sample N              Sample N rows from dataset (ideal for multi-GB datasets)

Visualization Controls:
  -m, --max-cols N            Maximum number of columns to plot in distributions (default: 10)
  -d, --dpi DPI               Plot figure resolution DPI (default: 150)
  -f, --fig-format FORMAT     Plot file format: png, pdf, svg, jpg (default: png)

Utility Flags:
      --setup                 Launch interactive Kaggle credentials setup wizard
  -v, --version               Show program version and exit
      --verbose               Enable verbose diagnostic logs
  -h, --help                  Show help message and exit

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Generated Outputs & Architecture

When you run kaggle-prep <dataset> --all, the following structured directories are generated:

project_root/
├── data/
│   ├── dataset.csv                  # Downloaded raw dataset
│   └── dataset_preprocess.py        # Ready-to-run preprocessing pipeline
├── data_profiles/
│   └── dataset_profile.json         # JSON schema & metrics summary
├── reports/
│   └── dataset_report.html          # Interactive standalone HTML report
├── eda_plots_<dataset>/             # 10+ high-res EDA charts
│   ├── 01_overview.png
│   ├── 02_missing_values.png
│   ├── 03_distributions.png
│   ├── 04_skewness.png
│   ├── 05_qq_normality.png
│   ├── 06_violin_plots.png
│   ├── 07_outlier_summary.png
│   ├── 08_correlation.png
│   ├── 09_cardinality.png
│   ├── 10_categorical_bars.png
│   ├── 11_target_balance.png
│   └── 12_target_features.png
└── notebooks/
    └── dataset_analysis.ipynb       # Complete starter Jupyter notebook

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Comprehensive Error & Troubleshooting Guide

1. 401 Unauthorized / Authentication Error

• Cause: Your kaggle.json token is missing, expired, or corrupted.
• Resolution:

  kaggle-prep --setup

  Follow the prompt to re-enter your Kaggle username and API key.

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2. 403 Forbidden

• Cause: The dataset or competition requires accepting competition rules or terms of service on Kaggle before downloading.
• Resolution:
  1. Open your browser and visit: https://www.kaggle.com/datasets/<dataset_name> (or https://www.kaggle.com/competitions/<competition_name>).
  2. Click "Download" or "Join Competition / I Understand and Accept".
  3. Re-run kaggle-prep <dataset_name> --all.

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3. 404 Not Found

• Cause: Typo in the dataset identifier or attempting to download a competition without the --competition flag.
• Resolution:
  • Datasets must follow the owner/dataset-name format:

    kaggle-prep uciml/iris --all
    kaggle-prep debayank2024/netflix-movies-and-series --all

  • For competitions, supply the --competition (or -c) flag:

    kaggle-prep titanic --competition --all
    kaggle-prep house-prices-advanced-regression-techniques -c --all

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4. 429 Too Many Requests (Rate Limit)

• Cause: Kaggle API limits requests per hour for single users.
• Resolution:
  • Wait a short period (15–30 minutes) before initiating bulk downloads.
  • Use --local flag to analyze data already downloaded to your disk without hitting the API.

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5. ModuleNotFoundError or Python Dependency Issues

• Cause: Old Python environment or rigid numpy version constraints.
• Resolution:
  • Ensure you are on Python 3.9+ (python --version).
  • Upgrade pip and reinstall:

    python -m pip install --upgrade pip
    pip install --upgrade kaggle-prep

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6. Windows Terminal Character Encoding (UnicodeEncodeError)

• Cause: Legacy Windows cmd/powershell consoles using cp1252 encoding.
• Resolution: kaggle-prep 0.3.0+ includes automatic stream reconfiguration and ASCII fallbacks. If running inside custom scripts, set:

  $env:PYTHONIOENCODING = "utf-8"

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Development & Contributing

Contributions are welcome! Follow these steps to set up the development environment:

# 1. Clone the repository
git clone https://github.com/SumitGavali/CLI-DataAnalysis.git
cd kaggle-prep

# 2. Install editable version with test dependencies
pip install -e ".[dev]"

# 3. Run the automated test suite
pytest tests/ -v

Running Tests

python -m pytest tests/ -v

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License

Distributed under the MIT License. See LICENSE for more information.

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Built with ❤️ by Sumit Gavali