updated readme with website hosted for viz (#4)

- updated readme with website hosted for viz (e6c8583cb795c27dd3d7f70de6a9e8b8d2fa4519)

README.md

@@ -65,9 +65,54 @@ 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)
@@ -240,48 +285,6 @@ 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