Update README.md

README.md

@@ -51,16 +51,16 @@ The data is stored in **Parquet format** (Hugging Face native) for high efficien
 
 ## Repository Contents
 
-* **fitness_embeddings.npy (1)**: Pre-computed vector embeddings of the user profiles (generated via Sentence Transformer).
+* **fitness_embeddings.npy (2)**: Pre-computed vector embeddings of the user profiles (generated via Sentence Transformer).
 * **imp88.png**: Visualization of the Exploratory Data Analysis (EDA).
 * **imp888.png**: Visualization of the Embeddings Analysis (PCA Clustering).
 
 ## Exploratory Data Analysis (EDA)
 The visualization below demonstrates the distribution and characteristics of the synthetic data. This analysis ensures the dataset is balanced across different categories and logically consistent regarding injuries and recommended plans.
 
-![Exploratory Data Analysis](imp88.png)
+![Exploratory Data Analysis](img8.png)
 
 ## Embeddings and User Segmentation
 To analyze user similarity and validate the data quality, we utilized a Sentence Transformer model to convert textual user profiles into high-dimensional vectors. The visualization below displays these embeddings reduced to 2D space using PCA. The distinct clusters indicate that the system successfully differentiates between different user types and their corresponding workout requirements.
 
-![Embeddings Analysis](imp888.png)
+![Embeddings Analysis](img88.png)