Movie Recommendation System 🎬
The Movie Recommendation System is a Python-based web application that helps users discover movies similar to their favorite ones. Built using Streamlit, Pandas, and Pickle, this system utilizes a similarity matrix to recommend movies based on content similarity. Additionally, users can filter recommendations by genre for a more personalized experience.
🔹 Key Features:
✔️ Movie Similarity-Based Recommendations – Suggests movies based on content similarity.
✔️ Genre Filtering – Users can filter recommendations by selecting a specific genre.
✔️ Poster Fetching – Displays movie posters using the TMDb API.
✔️ Movie Ratings – Fetches and shows ratings for each recommended movie.
✔️ Interactive UI – Built with Streamlit for an easy-to-use experience.
🛠️ Technologies Used:
Python 🐍
Streamlit 🎨 (For the web interface)
Pandas 📊 (For data processing)
Pickle 📦 (For storing similarity matrices)
TMDb API 🎥 (For fetching posters and ratings)
📂 Data Used:
movie_dict.pkl – Stores movie IDs and titles.
new.pkl – Contains detailed movie data (genre, cast, etc.).
similarity.pkl – Precomputed similarity matrix for recommendations.
💡 Future Improvements:
🔹 Implement collaborative filtering for better recommendations.
🔹 Add sorting options (popularity, release year, etc.).
🔹 Enhance UI with animations and user profiles.
📂 File Structure
📁 movie-recommender │── t.py # Main Streamlit app |
│── new.pkl # Processed movie dataset |
│── movie_dict.pkl # Movie dictionary |
│── similarity.pkl # Similarity matrix |
│── README.md # Project documentation
📊 Dataset & Preprocessing
Movies are loaded from movie_dict.pkl and new.pkl.
A TF-IDF similarity matrix is loaded from similarity.pkl.
Genre filtering is applied to show relevant movies.
📜 License
This project is open-source and available under the MIT License.
🚀 Future Improvements
🔹 Implement collaborative filtering for better recommendations.
🔹 Improve recommendation accuracy with deep learning.
🔹 Add more filtering options like language and year.
Developer : Sumit Kumar Jaiswal email : sumit500123@gmail.com