Metadata-Version: 2.4
Name: ibrahimpy
Version: 0.1.0
Summary: Intelligent Benchmark Relational Analysis for Heuristic Index Modeling
Home-page: https://github.com/AkmdAshraf/IBRAHIM
Author: AkmdAshraf
Author-email: AkmdAshraf <your-email@example.com>
Maintainer-email: AkmdAshraf <your-email@example.com>
License: MIT
Project-URL: Homepage, https://github.com/AkmdAshraf/IBRAHIM
Project-URL: Repository, https://github.com/AkmdAshraf/IBRAHIM
Project-URL: Documentation, https://github.com/AkmdAshraf/IBRAHIM#readme
Project-URL: Issues, https://github.com/AkmdAshraf/IBRAHIM/issues
Keywords: benchmark,ranking,multi-criteria,decision-analysis,mcdm
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
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Requires-Dist: pandas>=2.0.0
Requires-Dist: scikit-learn>=1.3.0
Requires-Dist: matplotlib>=3.7.0
Requires-Dist: seaborn>=0.12.0
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# IBRAHIM (Intelligent Benchmark Relational Analysis for Heuristic Index Modeling)

[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue)](https://www.python.org/)
[![License](https://img.shields.io/badge/license-MIT-green)](LICENSE)
[![PyPI version](https://badge.fury.io/py/ibrahim.svg)](https://badge.fury.io/py/ibrahim)

A novel machine learning framework for multi-criteria decision analysis and benchmark-based ranking.

## 📊 Overview

IBRAHIM is an intelligent analytical model that evaluates multiple records based on many variables, determines the highest benchmark, and computes ranking scores. It combines elements of multi-criteria decision analysis (MCDA) with machine learning for robust, automated benchmarking.

### Key Features

- **Automatic Feature Direction Detection**: Determines whether variables should be maximized or minimized
- **Intelligent Benchmark Generation**: Creates ideal benchmark records based on feature directions
- **Flexible Weight Assignment**: Supports both automatic (ML-based) and manual weight specification
- **0-100 Scoring Scale**: Intuitive, normalized scores for easy interpretation
- **New Record Evaluation**: Compare any new record against the established benchmark
- **Visualization Tools**: Built-in plotting for ranking analysis

## 🚀 Installation

### From PyPI (once published)
```bash
pip install ibrahim
