Built a content-based movie recommender leveraging NLP to extract semantic similarity between movie plots and user preferences.
Highlights:
- Utilized TF–IDF vectorization and cosine similarity to match user profiles with film metadata.
- Implemented in Python using Pandas, Scikit-learn, and NLTK.
- Provided dynamic recommendations based on user history.
Impact:
Enhanced engagement and discovery through personalized movie suggestions.
