updated readme with website hosted for viz
#4
by
TMVishnu
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- README.md +2 -56
- dataset_infos.json +0 -18
- data/edges.npz → edges.npz +0 -0
- data/features.npy → features.npy +0 -0
- data/watch_gnn_data.pt → watch_gnn_data.pt +0 -0
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README.md
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# Watch Market Analysis Graph Neural Network Dataset
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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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## 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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<details>
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<summary>Detailed Table of Contents</summary>
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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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</details>
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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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dataset_infos.json
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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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data/edges.npz → edges.npz
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data/features.npy → features.npy
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data/watch_gnn_data.pt → watch_gnn_data.pt
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