Create README.md

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README.md

@@ -10,61 +10,7 @@ size_categories:
 
 # Watch Market Analysis Graph Neural Network Dataset
 
-## Link:
-
-- Github link to the code through which this dataset was generated from: [watch-market-gnn-code](https://github.com/calicartels/watch-market-gnn-code)
-- Link to interactive EDA that is hosted on a website : [Watch Market Analysis Report](https://incomparable-torrone-ccda90.netlify.app/)
-
-## Table of Contents
-[Summary](#summary)  
-[Dataset Description](#dataset-description)  
-[Technical Details](#technical-details)  
-[Exploratory Data Analysis](#exploratory-data-analysis)  
-[Ethics and Limitations](#ethics-and-limitations)  
-[Usage](#usage)  
-
-<details>
-<summary>Detailed Table of Contents</summary>
-
-* Summary
-  * Key Statistics
-  * Primary Use Cases
-* Dataset Description
-  * Data Structure
-  * Features
-  * Network Properties
-  * Processing Parameters
-* Technical Details
-  * Power Analysis
-  * Implementation Details
-  * Network Architecture
-  * Embedding Dimensions
-  * Network Parameters
-  * Condition Scoring
-* Exploratory Data Analysis
-  * Brand Distribution
-  * Feature Correlations
-  * Market Structure Visualizations
-  * UMAP Analysis
-  * t-SNE Visualization
-  * PCA Analysis
-  * Network Visualizations
-* Ethics and Limitations
-  * Data Collection and Privacy
-  * Known Biases
-  * Usage Guidelines
-  * License
-* Usage
-  * Required Files
-  * Loading the Dataset
-  * Code Examples
-
-</details>
-
----
-
-
-## Summary
+## Executive Summary
 
 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. 
 It addresses three key market characteristics that traditional recommendation systems often miss:
@@ -119,82 +65,30 @@ Key features include:
 - Edge Generation Batch: 32 watches
 - Network Architecture: Combined GCN and GAT layers with 4 attention heads
 
-## Technical Details
-
-### Power Analysis
-Minimum sample requirements based on statistical analysis:
-- Basic Network: 10,671 nodes (95% confidence, 3% margin)
-- GNN Requirements: 14,400 samples (feature space dimensionality)
-- Brand Coverage: 768 watches per brand
-- Price Segments: 4,320 watches per segment
-
-Current dataset (284,491 watches) exceeds requirements with:
-- 5,000+ samples per major brand
-- 50,000+ samples per price segment
-- Sufficient network density
-
-### Implementation Details
-
-#### Network Architecture
-- 3 GNN layers with residual connections
-- 64 hidden channels
-- 20% dropout rate
-- 4 attention heads
-- Learning rate: 0.001
-
-#### Embedding Dimensions
-- Brand: 128
-- Material: 64
-- Movement: 64
-- Temporal: 32
-
-#### Network Parameters
-- Connections per watch: 3-5
-- Similarity threshold: 70%
-- Batch size: 50 watches
-- Processing window: 1000 watches
-
-#### Condition Scoring
-- New: 1.0
-- Unworn: 0.95
-- Very Good: 0.8
-- Good: 0.7
-- Fair: 0.5
 
 ## Exploratory Data Analysis
 
-**NOTE:** 
-Only certain selected visualizations have been mentioned here, to see all the visualizations that have been explored in high-quality interactive graphs, please visit this site:
-[Watch Market Analysis Report](https://incomparable-torrone-ccda90.netlify.app/)
-
 ### Brand Distribution
-
-![Brand Distribution Treemap](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/2.png)
-
 The treemap visualization provides a hierarchical view of market presence:
 - Rolex dominates with the highest representation, reflecting its market leadership
-- Omega and Seiko follow as major players, indicating a strong market presence
+- Omega and Seiko follow as major players, indicating strong market presence
 - Distribution reveals clear tiers in the luxury watch market
 - Brand representation correlates with market positioning and availability
 
+[Treemap Image]
 
 ### Feature Correlations
-
-![Feature Correlation Matrix](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/3.png)
-
 The correlation matrix reveals important market dynamics:
 - **Size vs. Year**: Positive correlation indicating a trend toward larger case sizes in modern watches
 - **Price vs. Size**: Moderate correlation showing larger watches generally command higher prices
 - **Price vs. Year**: Notably low correlation, demonstrating that vintage watches maintain value
 - Each feature contributes unique information, validated by the lack of strong correlations across all variables
 
+[Correlation Matrix Image]
 
 ### Market Structure Visualizations
 
 #### UMAP Analysis
-
-![UMAP Visualization](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/4.png)
-
 The UMAP visualization unveils complex market positioning dynamics:
 - Rolex maintains a dominant central position around coordinates (0, -5), showing unparalleled brand cohesion
 - Omega and Breitling cluster in the left segment, indicating strategic market alignment
@@ -202,11 +96,9 @@ The UMAP visualization unveils complex market positioning dynamics:
 - Premium timepieces (yellower/greener hues) show tighter clustering, suggesting standardized luxury attributes
 - Smaller, specialized clusters indicate distinct horological collections and style categories
 
+[UMAP Image]
 
 #### t-SNE Visualization
-
-![t-SNE Analysis](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/5.png)
-
 T-SNE analysis reveals clear market stratification with logarithmic pricing from $50 to $3.2M:
 - **Entry-Level Segment ($50-$4,000)**
  - Anchored by Seiko in the left segment
@@ -217,13 +109,12 @@ T-SNE analysis reveals clear market stratification with logarithmic pricing from
  - Cartier demonstrates strategic positioning between luxury and mid-range
 - **Ultra-Luxury Segment ($35,000-$3.2M)**
  - Dominated by Patek Philippe and Audemars Piguet
- - Clear separation in the right segment
+ - Clear separation in right segment
  - Strong brand clustering indicating market alignment
 
-#### PCA Analysis
-
-![PCA Visualization](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/6.png)
+[t-SNE Image]
 
+#### PCA Analysis
 Principal Component Analysis provides robust market insights with 56.6% total explained variance:
 - **First Principal Component (31.3%)**
  - Predominantly captures price dynamics
@@ -234,33 +125,28 @@ Principal Component Analysis provides robust market insights with 56.6% total ex
 - **Brand Trajectory**
  - Natural progression from Seiko through Longines, Breitling, and Omega
  - Culminates in Rolex and Patek Philippe
- - Diagonal trend line serves as a market positioning indicator
+ - Diagonal trend line serves as market positioning indicator
 - **Market Implications**
  - Successful brands occupy optimal positions along both dimensions
  - Clear differentiation between adjacent competitors
  - Evidence of strategic market positioning
 
+[PCA Image]
 
 #### Network Visualizations
 
-
 **Force-Directed Graph**
-
-![Force-Directed Graph](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/7.png)
-
 The force-directed layout reveals natural market clustering:
 - Richard Mille's peripheral positioning highlights ultra-luxury strategy
 - Dense central clustering shows mainstream luxury brand interconnectivity
 - Edge patterns reveal shared market characteristics
 - Node proximity indicates competitive positioning
 
+[Force-Directed Graph Image]
 
 **Starburst Visualization**
-
-![Starburst Graph](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/8.png)
-
 Radial architecture provides a hierarchical market perspective:
-- Central node represents the overall market
+- Central node represents overall market
 - Green nodes show brand territories with strategic spacing
 - Blue peripheral nodes indicate individual timepieces
 - Node density reveals:
@@ -269,6 +155,8 @@ Radial architecture provides a hierarchical market perspective:
  - Segment diversification
 - Balanced spacing between brand nodes indicates market segmentation
 
+[Starburst Graph Image]
+
 
 ## Ethics and Limitations
 
@@ -339,6 +227,48 @@ While requiring:
 - Preserve attributions
 
 
+## Technical Details
+
+### Power Analysis
+Minimum sample requirements based on statistical analysis:
+- Basic Network: 10,671 nodes (95% confidence, 3% margin)
+- GNN Requirements: 14,400 samples (feature space dimensionality)
+- Brand Coverage: 768 watches per brand
+- Price Segments: 4,320 watches per segment
+
+Current dataset (284,491 watches) exceeds requirements with:
+- 5,000+ samples per major brand
+- 50,000+ samples per price segment
+- Sufficient network density
+
+### Implementation Details
+
+#### Network Architecture
+- 3 GNN layers with residual connections
+- 64 hidden channels
+- 20% dropout rate
+- 4 attention heads
+- Learning rate: 0.001
+
+#### Embedding Dimensions
+- Brand: 128
+- Material: 64
+- Movement: 64
+- Temporal: 32
+
+#### Network Parameters
+- Connections per watch: 3-5
+- Similarity threshold: 70%
+- Batch size: 50 watches
+- Processing window: 1000 watches
+
+#### Condition Scoring
+- New: 1.0
+- Unworn: 0.95
+- Very Good: 0.8
+- Good: 0.7
+- Fair: 0.5
+
 ## Usage
 
 ### Required Files

dataset_infos.json

@@ -1,18 +0,0 @@
-{
-    "default": {
-        "description": "Watch Market GNN Dataset",
-        "homepage": "https://huggingface.co/datasets/TMVishnu/watch-market-gnn",
-        "license": "apache-2.0",
-        "features": {
-            "watch_gnn_data": "torch_geometric",
-            "edges": "numpy",
-            "features": "numpy"
-        },
-        "task_templates": [
-            {
-                "task": "graph-ml",
-                "task_categories": ["graph-ml"]
-            }
-        ]
-    }
-}

final_embeddings.pt

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