Movie_recommender_system

Based on your Jupyter Notebook (Movie_recommendation_system.ipynb) and Streamlit app (app.py), here is the optimized README file for your Movie Recommendation System project:

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🚀 Features

✅ Recommends 5 similar movies based on input
✅ Uses TF-IDF & Cosine Similarity for recommendations
✅ Fetches movie posters via TMDb API
✅ Interactive UI with Streamlit
✅ Fast & Efficient using precomputed similarity matrix

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📂 Project Structure

📂 Movie-Recommendation-System/
│-- 📜 app.py                   # Streamlit web app  
│-- 📜 Movie_recommendation_system.ipynb  # Jupyter Notebook for ML model  
│-- 📜 movie_dict.pkl            # Pickle file containing processed movie data  
│-- 📜 similarity.pkl            # Precomputed similarity matrix  
│-- 📂 dataset/                  # Raw and processed datasets  
│-- 📜 requirements.txt          # Dependencies  
│-- 📜 README.md                 # Project documentation  

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🛠️ Tech Stack

• Python
• Pandas & NumPy (for data processing)
• Scikit-Learn (for similarity computation)
• Streamlit (for UI)
• TMDb API (for movie posters)

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🔹 Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/yourusername/Movie-Recommendation-System.git
cd Movie-Recommendation-System

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Streamlit App

streamlit run app.py

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🖥️ How It Works?

🎭 1. Content-Based Filtering

• Uses TF-IDF Vectorization to analyze movie descriptions.
• Computes Cosine Similarity between movies to find the closest matches.

🔗 2. TMDb API Integration

• Fetches movie posters dynamically from TMDb API.
• Improves user experience with rich visuals.

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🔥 Future Enhancements

• ✅ Implement Collaborative Filtering for personalized recommendations
• ✅ Use Deep Learning (Neural Networks) for better accuracy
• ✅ Improve UI with more features

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