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@@ -6,14 +6,14 @@ The **Geometry Preserving Categorical Encoder (GPCE)** is a novel categorical da

 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

@@ -37,7 +37,7 @@ Most machine learning models require numerical inputs, making categorical encodi

 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

@@ -81,7 +81,7 @@ For each categorical feature:

 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

@@ -141,7 +141,7 @@ While it does not always achieve the highest accuracy in every setting,
 it consistently performs better than most baseline encoders and avoids
 severe performance degradation.

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 ## Usage

@@ -202,7 +202,7 @@ X_train_enc, X_test_enc = gpc_encoder(
 # - 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