Metadata-Version: 2.4
Name: pysi-kdd
Version: 1.0.0
Summary: A comprehensive KDD and Data Mining code snippet reference package
Home-page: https://github.com/yourusername/pysi
Author: User
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: imbalanced-learn
Requires-Dist: statsmodels
Requires-Dist: xgboost
Requires-Dist: shap
Dynamic: author
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# PySI - Ultimate KDD Snippets 🚀

**PySI** is a "raw hardcore" code snippet reference package designed for KDD (Knowledge Discovery in Databases) and Data Mining exams. It provides instant access to over 100+ production-ready snippets for pandas, numpy, sklearn, and more.

## Installation

```bash
pip install pysi-kdd
```

## Usage

```python
from pysi import smenu, scode

# 1. Interactive Menu
smenu()

# 2. Get Code Snippets
scode('pandas_cleaning')  # Data manipulation
scode('clf_models')       # Classification models
scode('cheatsheet')       # Exam quick reference
```

## Features

*   **Data Manipulation**: Comprehensive Pandas/Numpy cheatsheet (cleaning, merging, transforming).
*   **Statistics**: Hypothesis tests, sample size, normality, homogeneity.
*   **Preprocessing**: Scaling, encoding, missing value imputation (MICE, KNN).
*   **Modeling**: Regression, Classification, Ensemble methods.
*   **Advanced**: Causal Inference, SHAP explainability.
*   **Reference**: Quick exam cheatsheet.

## Categories

*   Data Loading & Manipulation
*   Statistics & Tests
*   Preprocessing & Feature Engineering
*   Outlier Detection & Treatment
*   Balancing (SMOTE, ADASYN)
*   Regression & Classification Models
*   Evaluation Metrics & Validation
