Movie Recommendation System

Developed an intelligent Movie Recommendation System that suggests personalized movies based on user preferences using machine learning techniques. The system analyzes movie features such as genres, ratings, keywords, and user interactions to generate relevant recommendations.

Key Features:

Personalized movie recommendations.

Content-Based Filtering using movie metadata.

Data preprocessing and feature engineering.

Text vectorization with TF-IDF.

Similarity calculation using Cosine Similarity.

Fast movie search and recommendation generation.

User-friendly interface for entering movie titles and displaying recommendations.

Technologies Used:

Python

Pandas

NumPy

Scikit-learn

TF-IDF Vectorizer

Cosine Similarity

Streamlit / Flask (if applicable)

Jupyter Notebook

Outcome:

Built a recommendation engine capable of efficiently identifying similar movies, providing users with accurate and relevant suggestions while demonstrating practical applications of machine learning in recommendation systems.

The application includes an interactive web interface where users can search for a movie and instantly receive personalized recommendations with a simple and intuitive user experience.