Movie Recommendation System

A content-based movie and TV show recommendation system built using TF-IDF vectorization and cosine similarity.

<img src="image.png" width="300"/>

📌 Features

• Recommend movies and TV shows based on a given title.
• Utilizes TfidfVectorizer for feature extraction.
• Calculates similarity using cosine similarity.
• API endpoint for fetching recommendations.
• Web interface for user interaction.

🛠️ Technologies Used

• Python
• Flask
• Scikit-learn
• Pandas
• NumPy
• HTML, CSS, JavaScript

📂 Dataset

The dataset used consists of movie and TV show metadata, including titles, genres, and descriptions.

🚀 How It Works

1. TF-IDF Vectorization: Transforms text data into numerical form.
2. Cosine Similarity Calculation: Measures similarity between different content items.
3. Recommendation Generation: Retrieves the most similar movies or TV shows.

🔧 Preparing the Dataset (Optional)

If you want to train the model:

Alternatively, you can use the existing data.csv file as the dataset without retraining.

For training and function references, check:

.ipynb_checkpoints\movie_recommendation-checkpoint.ipynb

🚀 How It Works

1. TF-IDF Vectorization: Transforms text data into numerical form.
2. Cosine Similarity Calculation: Measures similarity between different content items.
3. Recommendation Generation: Retrieves the most similar movies or TV shows.

📜 Installation & Setup

1. Clone the repository:

   git clone https://github.com/nky001/movie-recommender.git
   cd movie-recommender
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the Flask app:

   python app.py
   

4. Access the web interface at:

   http://127.0.0.1:5000
   

📡 API Endpoints

• POST /recommend
  • Request: { "title": "Movie Name" }
  • Response: { "movies": ["Movie 1", "Movie 2"], "tv_shows": ["Show 1", "Show 2"] }

🖥️ Web Interface

The front-end displays recommended movies and TV shows dynamically.

🛠️ Troubleshooting

• If recommendations return empty, ensure the dataset is correctly loaded.
• Check app.py for any missing dependencies.

🎯 Future Enhancements

• Integrate TMDb API for additional movie details.
• Add collaborative filtering for better recommendations.
• Improve UI/UX with better visualizations.

📜 License

This project is open-source and available under the MIT License.