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DeepFlixAI - AI-Powered Movie or OTT Content Recommendation System | Movie Recommendation System Get this Project : https://www.ieeexpert.com/python-projects/ai-powered-movie-ott-recommendations-using-script-understanding/ 🔍Implementation: Python with Flask. 🧠Algorithm / Model Used: NLP + CNN 🎯Web Framework: HTML,CSS,JS. ABSTRACT 🧠 Core Idea: Uses Natural Language Processing (NLP) to understand movie/series scripts for smart recommendations. 🔍 Problem Solved: Traditional OTT systems rely on surface-level data (ratings, genres, watch history), missing story depth. 📜 Our Approach: Analyze script, emotions, themes, and plot structures from scripts using advanced NLP. 🛠️ Tech Stack: Transformer Models (NLP & CNN) Sentiment Analysis Topic Modeling Content + Collaborative Filtering More Projects - https://www.ieeexpert.com/ieee-python-projects-2021-2022-machine_learning-project-titles/ Contact us on - +91 9363932473 Ieee Xpert, India. The Best Bulk Service Provider for IEEE Solutions Including Packages ======================= Base Paper Complete Source Code Complete Documentation Complete Presentation Slides Flow Diagram Database File Screenshots Execution Procedure Online Execution Support Addons Video Tutorials Supporting Softwares 🤝 Personalization: Matches extracted insights with user preferences to deliver context-aware suggestions. Unlock the secrets of OTT content recommendation systems using machine learning and Natural Language Processing (NLP)! In this project, we dive deep into analyzing movie/TV show scripts to build a smart recommendation engine from scratch. Perfect for final year CSE students, ML enthusiasts, and anyone curious about recommendation systems in Python! 🔗 Code: https://www.ieeexpert.com/ieee-python-projects-2021-2022-machine_learning-project-titles/ 🔍 What You’ll Learn: ✅ End-to-End Workflow: Data scraping, NLP preprocessing, model training & deployment. ✅ NLP Techniques: TF-IDF, sentiment analysis, and keyword extraction from scripts. ✅ KNN Algorithm: Build a content-based recommender using scikit-learn. ✅ OTT Industry Practices: How giants like Netflix/Amazon recommend content. ✅ Python Libraries: Pandas, NLTK, Scikit-learn, Streamlit (for UI). 🎯 Keywords Covered: OTT content recommendation, machine learning, recommendation engine, what is machine learning, OTT content recommendation machine learning, recommendation system machine learning, movie recommendation machine learning, movie recommendation system python machine learning project, movie recommendation system using machine learning and KNN, basic machine learning projects, machine learning projects for final year, final year CSE projects, end to end project ML, CSE project ideas, ML project ideas, how to get project ideas in CSE, identifying technical tasks in a CSE project. 📁 Project Structure: Data Collection: Scrape movie scripts & metadata. NLP Preprocessing: Clean text, extract features (genre, director, plot keywords). Model Building: Content-based filtering with KNN and cosine similarity. Deployment: Create a web app with Streamlit. Demo: Recommend shows based on script similarity! 👨‍💻 Perfect For: Final-year CSE students seeking industry-relevant projects. ML beginners exploring basic machine learning projects. Developers curious about recommendation engines or OTT platforms. 📚 Resources: Dataset: IMDb + Wikipedia + [Open Scripts Database] Tools: Python, Jupyter Notebook, Streamlit. Libraries: NLTK, Pandas, Scikit-learn. ⭐ Don’t forget to LIKE, SUBSCRIBE, and hit the BELL! 💬 Engage! Got questions? Comment below! Need custom CSE project ideas? Let’s brainstorm! 🔖 Hashtags: #MachineLearning #OTTRecommendation #CSEProjects #FinalYearProject #PythonProjects #RecommendationSystem #NLP #DataScience #LearnToCode #TechProject #CSE #MLProjects #KNN #PythonProgramming #projectideas 👉 Subscribe for more ML project walkthroughs, career tips, and Python tutorials! Turn on notifications so you never miss an update! 🔔 ✅ Why This Project Stands Out: Solves real-world OTT industry challenges. Covers full-stack ML skills (data → deployment). Ideal for your portfolio/resume! Timestamp 00:00:00 Introduction to the Project 00:00:19 Problem with Existing Systems 00:01:06 Proposed Solution 00:05:08 Comparison of Proposed vs. Existing Systems 00:09:57 Project Features and Methodology 00:17:06 Project Advantages 00:21:09 System scalability 00:21:24 Project Demo 00:35:41 User Personalization in Action 00:43:45 Final Call to Action