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README.md
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# Watch Market Analysis Graph Neural Network Dataset
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This dataset transforms traditional watch market data into a Graph Neural Network (GNN) structure, specifically designed to capture the complex dynamics of the pre-owned luxury watch market.
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It addresses three key market characteristics that traditional recommendation systems often miss:
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## Note
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- The dataset is optimized for PyTorch Geometric operations
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- Recommended to use GPU for large-scale operations
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- Consider batch processing for memory efficiency
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# Watch Market Analysis Graph Neural Network Dataset
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## Table of Contents
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1. [Summary](#summary)
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- [Key Statistics](#key-statistics)
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- [Primary Use Cases](#primary-use-cases)
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2. [Dataset Description](#dataset-description)
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- [Data Structure](#data-structure)
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- [Features](#features)
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- [Network Properties](#network-properties)
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- [Processing Parameters](#processing-parameters)
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3. [Technical Details](#technical-details)
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- [Power Analysis](#power-analysis)
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- [Implementation Details](#implementation-details)
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- [Network Architecture](#network-architecture)
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- [Embedding Dimensions](#embedding-dimensions)
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- [Network Parameters](#network-parameters)
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- [Condition Scoring](#condition-scoring)
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4. [Exploratory Data Analysis](#exploratory-data-analysis)
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- [Brand Distribution](#brand-distribution)
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- [Feature Correlations](#feature-correlations)
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- [Market Structure Visualizations](#market-structure-visualizations)
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- [UMAP Analysis](#umap-analysis)
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- [t-SNE Visualization](#t-sne-visualization)
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- [PCA Analysis](#pca-analysis)
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- [Network Visualizations](#network-visualizations)
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5. [Ethics and Limitations](#ethics-and-limitations)
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- [Data Collection and Privacy](#data-collection-and-privacy)
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- [Known Biases](#known-biases)
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- [Usage Guidelines](#usage-guidelines)
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- [License](#license)
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6. [Usage](#usage)
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- [Required Files](#required-files)
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- [Loading the Dataset](#loading-the-dataset)
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- [Code Examples](#loading-the-dataset)
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## Summary
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This dataset transforms traditional watch market data into a Graph Neural Network (GNN) structure, specifically designed to capture the complex dynamics of the pre-owned luxury watch market.
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It addresses three key market characteristics that traditional recommendation systems often miss:
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## Note
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- The dataset is optimized for PyTorch Geometric operations
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- Recommended to use GPU for large-scale operations
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- Consider batch processing for memory efficiency
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