skiearn-kdd 1.0.4

Interactive Knowledge Discovery & Data Mining (KDD) study guide with comprehensive documentation

SKIEARN - Knowledge Discovery & Data Mining Study Guide

SKIEARN is an interactive Python package providing comprehensive documentation for Knowledge Discovery in Databases (KDD) and Data Mining concepts. Perfect for university students preparing for KDD/Data Mining exams!

🚀 Installation

pip install skiearn-kdd

📖 Usage

Simply import and run:

import skiearn

skiearn.print()

This will launch an interactive menu where you can browse through 13 comprehensive documentation files covering all KDD topics.

📚 What's Included

Main Documentation (13 Files)

1. Data Preparation & Formalism - Variable types, IID, splits, data leakage, curse of dimensionality
2. Statistics & Distributions - Descriptive statistics, normality tests, CLT
3. Hypothesis Testing - Z-tests, t-tests, ANOVA, Chi-square, bootstrap, permutation tests
4. Causality & Feature Selection - Causality concepts, Simpson's paradox, feature selection methods
5. Outliers & Robust Statistics - Detection methods (Z-score, IQR, MAD, LOF, Isolation Forest)
6. Supervised Learning - All major ML algorithms with formal explanations
7. Model Evaluation & Comparison - Metrics, cross-validation, statistical model comparison
8. Imbalanced & Missing Data - SMOTE, sampling techniques, imputation methods
9. Explainability & Visualization - SHAP, LIME, all visualization techniques
10. Dimensionality & Clustering - PCA, t-SNE, K-Means, DBSCAN, cluster validation
11. Advanced Topics - Time series, association rules, information theory, probability theory
12. Encoding & Validation - All encoding techniques, validation strategies
13. Exam Traps & Pitfalls ⚠️ - Common mistakes and how to avoid them

Additional Resources

• README - Study guide overview and organization
• Study Guide - Recommended week-by-week study path
• Verification - Complete section mapping (all 74 topics covered)

💡 Features

• ✅ 74 comprehensive topics covering all KDD/Data Mining concepts
• ✅ Executable Python code examples for every concept
• ✅ Statistical tests with proper interpretation
• ✅ Exam traps highlighted - avoid common mistakes
• ✅ Interactive menu - easy navigation
• ✅ No external dependencies - pure Python

🎯 Perfect For

• University students taking KDD/Data Mining courses
• Exam preparation and quick reference
• Understanding statistical foundations of ML
• Learning proper data preprocessing
• Avoiding common data science pitfalls

📋 Example Session

>>> import skiearn
>>> skiearn.print()

================================================================================
                    SKIEARN DOCUMENTATION VIEWER
               Knowledge Discovery & Data Mining Study Guide
================================================================================

📚 MAIN DOCUMENTATION:
  [ 1] Data Preparation & Formalism
      └─ Variable types, IID, splits, leakage, dimensionality, bias-variance

  [ 2] Statistics & Distributions
      └─ Mean/median/variance, skewness/kurtosis, normality tests

  ...

📖 Enter your choice: 3

📦 Package Contents

skiearn/
├── __init__.py          # Package initialization
├── viewer.py            # Interactive documentation viewer
└── docs/                # All documentation files
    ├── 01_data_preparation.txt
    ├── 02_statistics_distributions.txt
    ├── ... (13 main files + 3 additional resources)

🔧 Requirements

• Python 3.7 or higher
• No external dependencies!

📝 License

MIT License - feel free to use for educational purposes.

🤝 Contributing

Contributions welcome! If you find errors or want to add topics:

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

⭐ Support

If this package helped you ace your KDD exam, consider:

• Giving it a star on GitHub
• Sharing with classmates
• Reporting issues or suggestions

📧 Contact

For questions or suggestions, please open an issue on GitHub.

---

Good luck with your KDD exam! 🎓

Quick Start Example

import skiearn

# Launch interactive viewer
skiearn.print()

# Navigate using:
# - Numbers 1-13: View specific documentation
# - R: README
# - S: Study Guide
# - A: View all files
# - Q: Quit

Topics Covered

• Data preparation and preprocessing
• Statistical hypothesis testing
• Causality and feature selection
• Machine learning algorithms
• Model evaluation metrics
• Handling imbalanced and missing data
• Explainability (SHAP, LIME)
• Clustering and dimensionality reduction
• Time series analysis
• Information theory
• And much more!

All with executable Python code and detailed explanations!