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+# GPC Encoder (Geometry Preserving Category Encoder)
+
+## Introduction & Purpose
+
+In ML/DL preprocessing, categorical data is commonly encoded using **one-hot**, **label**, or **target mean encoding**, each of which has clear limitations:
+
+- **One-Hot Encoding**
+  - Causes a rapid increase in dimensionality for high-cardinality features
+  - Increases model complexity and memory usage
+
+- **Label Encoding**
+  - Introduces artificial ordinal relationships between categories
+  - Can mislead linear or distance-based models
+
+- **Target Mean Encoding**
+  - Prone to **data leakage**, as global target statistics influence encoding
+  - Unstable for rare categories or small datasets, leading to bias
+
+The purpose of **GPC Encoder** is to address all of these issues by designing an encoding method that:
+
+1. Does **not impose any ordering** on categories  
+2. Does **not drastically increase dimensionality**  
+3. **Avoids data leakage** by relying only on training data statistics  
+
+---
+
+## Requirements
+
+- **Python 3.11.4**
+- See `requirements.txt` for full dependency details
+