updated readme with website hosted for viz

.gitattributes

@@ -1,3 +0,0 @@
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text

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:
@@ -404,4 +350,4 @@ def get_watch_features(watch_id):
 ## Note
 - The dataset is optimized for PyTorch Geometric operations
 - Recommended to use GPU for large-scale operations
-- Consider batch processing for memory efficiency
+- Consider batch processing for memory efficiency

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"]
-            }
-        ]
-    }
-}