Welcome to the Movie Recommendation Model! This project aims to provide personalized movie recommendations using advanced machine learning techniques. Whether you're a movie enthusiast looking for your next watch or a developer interested in recommendation systems, this project offers a robust solution for suggesting movies based on user preferences and movie metadata.

• Personalized Recommendations: Get movie suggestions tailored to individual user preferences.
• Content-Based Filtering: Uses movie metadata to recommend similar movies.
• Scalable Architecture: Efficiently handles large datasets and multiple users.

1. Data Collection: Gather data on user ratings and movie metadata.
2. Data Preprocessing: Clean and preprocess the data for analysis.
3. Model Training: Train the recommendation model using content-based filtering.
4. Recommendation Generation: Generate personalized movie recommendations for users based on their preferences and movie attributes.

• Programming Language: Python
• Libraries and Frameworks: Pandas, NumPy, Scikit-learn, NLTK