🎬 Movie Recommendation System

Clean and intuitive movie selection interface showing Jurassic World recommendations

✨ Features

🎯 Smart Recommendations · Get 5 personalized movie suggestions based on content similarity
🖼️ Movie Posters · Visual display of recommended movies with poster images from TMDB API
🎨 Interactive UI · Clean and intuitive Streamlit web interface
📊 ML-Powered · Uses cosine similarity and TF-IDF vectorization for accurate recommendations
🔍 Content-Based Filtering · Analyzes movie genres, keywords, cast, crew, and plot overview

🛠️ Tech Stack

Python · Core programming language
Streamlit · Web application framework
Pandas · Data manipulation and analysis
Scikit-learn · Machine learning algorithms
NLTK · Natural language processing
TMDB API · Movie posters and metadata
Pickle · Model serialization

🚀 Quick Start

Prerequisites

Python 3.7+ · pip package manager

Installation

# Clone the repository
git clone https://github.com/yourusername/movie-recommendation-system.git
cd movie-recommendation-system

# Install dependencies
pip install streamlit pandas scikit-learn nltk requests pickle-mixin

# Download required data
python -c "import nltk; nltk.download('punkt')"

# Run the application
streamlit run app.py

Open your browser and navigate to http://localhost:8501

📁 Project Structure

movie-recommendation-system/
│
├── app.py                 # Main Streamlit application
├── movies_dict.pkl        # Preprocessed movie data
├── similarity.pkl         # Cosine similarity matrix
├── requirements.txt       # Python dependencies
├── README.md             # Project documentation
└── data/
    ├── tmdb_5000_movies.csv
    └── tmdb_5000_credits.csv

🔧 How It Works

Data Preprocessing · Merges movie and credits datasets · Extracts relevant features: genres, keywords, cast, crew, overview · Applies text preprocessing and stemming

Feature Engineering · Combines all text features into a single 'tags' column · Uses TF-IDF vectorization with 5000 max features · Removes English stop words

Similarity Calculation · Computes cosine similarity between all movies · Creates a similarity matrix for fast lookups

Recommendation Generation · Finds the selected movie's index · Retrieves top 5 most similar movies · Fetches movie posters from TMDB API

🎯 Algorithm Details

The recommendation system uses Content-Based Filtering

Vectorization · TF-IDF (Term Frequency-Inverse Document Frequency)
Similarity Metric · Cosine Similarity
Features Used · Movie overview, genres, keywords, cast, crew
Text Processing · Porter Stemming, stop word removal

📊 Dataset

Source · TMDB 5000 Movie Dataset
Movies · 4,800+ movies
Features · 20+ attributes per movie
Time Period · Various years of cinema

🔮 Future Enhancements

• Hybrid Filtering · Combine content-based with collaborative filtering
• User Profiles · Personalized recommendations based on viewing history
• Advanced NLP · Use word embeddings (Word2Vec, BERT) for better text understanding
• Real-time Data · Integration with live movie databases
• Rating System · Allow users to rate recommendations
• Genre Filtering · Filter recommendations by preferred genres
• Mobile App · React Native or Flutter mobile application

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

CampusX · for the comprehensive original tutorial that served as the foundation for this project
TMDB · for providing the movie database API
Kaggle · for the movie dataset
Streamlit · community for the amazing framework

📬 Contact

Your Name · shivamdali@gmail.com

Project Link: https://github.com/yourusername/movie-recommendation-system

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