updated readme with website hosted for viz

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by TMVishnu - opened
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  # Watch Market Analysis Graph Neural Network Dataset
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- ## Link:
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-
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- - Github link to the code through which this dataset was generated from: [watch-market-gnn-code](https://github.com/calicartels/watch-market-gnn-code)
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- - Link to interactive EDA that is hosted on a website : [Watch Market Analysis Report](https://incomparable-torrone-ccda90.netlify.app/)
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-
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- ## Table of Contents
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- [Summary](#summary)
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- [Dataset Description](#dataset-description)
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- [Technical Details](#technical-details)
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- [Exploratory Data Analysis](#exploratory-data-analysis)
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- [Ethics and Limitations](#ethics-and-limitations)
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- [Usage](#usage)
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-
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- <details>
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- <summary>Detailed Table of Contents</summary>
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-
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- * Summary
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- * Key Statistics
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- * Primary Use Cases
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- * Dataset Description
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- * Data Structure
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- * Features
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- * Network Properties
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- * Processing Parameters
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- * Technical Details
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- * Power Analysis
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- * Implementation Details
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- * Network Architecture
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- * Embedding Dimensions
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- * Network Parameters
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- * Condition Scoring
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- * Exploratory Data Analysis
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- * Brand Distribution
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- * Feature Correlations
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- * Market Structure Visualizations
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- * UMAP Analysis
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- * t-SNE Visualization
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- * PCA Analysis
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- * Network Visualizations
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- * Ethics and Limitations
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- * Data Collection and Privacy
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- * Known Biases
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- * Usage Guidelines
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- * License
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- * Usage
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- * Required Files
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- * Loading the Dataset
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- * Code Examples
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-
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- </details>
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-
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- ---
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-
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-
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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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  # Watch Market Analysis Graph Neural Network Dataset
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+ ## Executive 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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- {
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- "default": {
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- "description": "Watch Market GNN Dataset",
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- "homepage": "https://huggingface.co/datasets/TMVishnu/watch-market-gnn",
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- "license": "apache-2.0",
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- "features": {
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- "watch_gnn_data": "torch_geometric",
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- "edges": "numpy",
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- "features": "numpy"
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- },
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- "task_templates": [
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- {
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- "task": "graph-ml",
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- "task_categories": ["graph-ml"]
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- }
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- ]
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- }
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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