Update Netflix_Recommendation_Notebook_Code

Netflix_Recommendation_Notebook_Code

@@ -8,35 +8,35 @@ import numpy as np
 import pandas as pd
 from tqdm import tqdm  # For tracking progress in batches
 
-# Check if GPU is available
+# check if GPU is available
 device = "cuda" if torch.cuda.is_available() else "cpu"
 print(f"Using device: {device}")
 
-# Load dataset
+# load dataset
 dataset = pd.read_csv('/kaggle/input/d/infamouscoder/dataset-netflix-shows/netflix_titles.csv')
 
-# Load model to GPU if available
+# load model to GPU if available
 model = SentenceTransformer("all-MiniLM-L6-v2").to(device)
 
-# Combine fields for embeddings
+# combine fields (title, genre, description) for embeddings
 def combine_description_title_and_genre(description, listed_in, title):
     return f"{description} Genre: {listed_in} Title: {title}"
 
-# Create combined text column
+# create combined text column
 dataset['combined_text'] = dataset.apply(lambda row: combine_description_title_and_genre(row['description'], row['listed_in'], row['title']), axis=1)
 
-# Generate embeddings in batches to save memory
+# generate embeddings in batches to save memory
 batch_size = 32
 embeddings = []
 
 for i in tqdm(range(0, len(dataset), batch_size), desc="Generating Embeddings"):
     batch_texts = dataset['combined_text'][i:i+batch_size].tolist()
     batch_embeddings = model.encode(batch_texts, convert_to_tensor=True, device=device)
-    embeddings.extend(batch_embeddings.cpu().numpy())  # Move to CPU to save memory
+    embeddings.extend(batch_embeddings.cpu().numpy())  # move to CPU to save memory
 
-# Convert list to numpy array
+# convert list to numpy array
 embeddings = np.array(embeddings)
 
-# Save embeddings and metadata
+# save embeddings and metadata
 np.save("/kaggle/working/netflix_embeddings.npy", embeddings)
 dataset[['show_id', 'title', 'description', 'listed_in']].to_csv("/kaggle/working/netflix_metadata.csv", index=False)