Create Netflix_Recommendation_Notebook_Code

Netflix_Recommendation_Notebook_Code

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+
+#ran on Kaggle
+!pip install sentence-transformers
+!pip install torch
+import torch
+from sentence_transformers import SentenceTransformer
+import numpy as np
+import pandas as pd
+from tqdm import tqdm  # For tracking progress in batches
+
+# Check if GPU is available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+# Load dataset
+dataset = pd.read_csv('/kaggle/input/d/infamouscoder/dataset-netflix-shows/netflix_titles.csv')
+
+# Load model to GPU if available
+model = SentenceTransformer("all-MiniLM-L6-v2").to(device)
+
+# Combine fields for embeddings
+def combine_description_title_and_genre(description, listed_in, title):
+    return f"{description} Genre: {listed_in} Title: {title}"
+
+# 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
+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
+
+# Convert list to numpy array
+embeddings = np.array(embeddings)
+
+# 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)