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README.md

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 This repository contains the full implementation of GPCE, along with experimental code used to evaluate its performance against widely adopted categorical encoders.
 
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 ## Requirements
 
 - **Python 3.11.4**
 - See `requirements.txt` for full dependency details
 
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 ## Motivation
 
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 No single traditional method successfully resolves all of these issues at once. GPCE is designed to address this gap.
 
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 ## Key Idea Behind GPCE
 
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 This geometric symmetry ensures that categories are treated as **equally distinct entities**, faithfully reflecting their categorical nature.
 
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 ## Core Design Principles
 
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 3. **Leakage-Free Encoding**  
    The encoding process is completely independent of target labels and uses only training data.
 
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 ## Methodology
 
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 The resulting encoded features are concatenated with existing numerical features and passed directly to machine learning models.
 
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 ## Advantages of GPCE
 
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 While GPCE may not always achieve the single highest accuracy in every setting, it consistently performs near the top and rarely exhibits severe performance degradation.
 
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 ## Experimental Evaluation
 
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 Results demonstrate that GPCE achieves **high predictive stability and competitive accuracy** across diverse learning algorithms.
 
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 ## Experimental Results
 
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 it consistently performs better than most baseline encoders and avoids
 severe performance degradation.
 
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 ## Usage
 
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 # - Are directly usable in standard machine learning models
 ```
 
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 ## Sample Dataset
 
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 This dataset contains multiple high-cardinality categorical features and serves as a realistic benchmark for evaluating categorical encoding methods.
 
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 ## License