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Industry Oriented Projects
Project
Movie Recommendation project developed a content-based movie recommendation system using Python, employing a dataset of 10,000 movies and leveraging Cosine Similarity for personalized recommendations. Pre-processing steps such as stemming & lemmatization, tokenization, and handling stop words were implemented to refine text-based data.
The system utilizes NLTK for natural language processing, alongside statistical models for cosine similarity calculations. A user-friendly web app was built using Streamlit, enabling interactive exploration and providing the top five movie recommendations based on user preferences and movie attributes.
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