🎬 Movie Recommendation System

This repository contains the development of a recommendation system using a movie dataset. It stores information in PostgreSQL and utilizes Python to build a machine learning model that suggests movies based on user preferences, leveraging PostgreSQL, Pandas, and Scikit-learn.

🎯 Project Goals

• Develop an efficient recommendation system for movies based on user preferences.

• Utilize collaborative and content-based filtering techniques.

• Store and process data using PostgreSQL for scalability.

• Perform data analysis and visualization to understand rating trends.

📂 Folder Structure

├── sources/
│   ├── queries/          # SQL queries for fetching data
│   ├── models/           # Machine learning models
│   ├── data/             # Raw and processed data
├── notebooks/            # Jupyter notebooks for EDA & model training
├── images/               # Visual assets (plots, schema diagrams, etc.)
├── scripts/              # Automation scripts
├── README.md             # Documentation

🚀 Installation & Setup

Prerequisites

Ensure you have the following installed:

• PostgreSQL (Download)
• Python 3.x (Download)
• Required Python Libraries:

  pip install pandas scikit-learn psycopg2 faker matplotlib seaborn

• DBeaver (optional, for database visualization: Download)

🗄️ Database Schema

Connection

1. In this case, we are going to use DBeaver, so the first thing we need to do is create a new connection.

• As shown in the picture, we need to configure the following settings:
  • Host: localhost
  • Database: postgres
  • Port: 5432
  • Username: postgres
  • Password: YOUR PASSWORD

2. After setting the parameters, we should test the connection to ensure it works before applying the configuration.

📌 Schema Diagram

movies

• movie_id (INT, PRIMARY KEY, AUTO_INCREMENT) - Unique identifier for each movie.
• title (VARCHAR(255)) - Title of the movie.
• release_year (INT) - Year the movie was released.
• genre (VARCHAR(100)) - Genre of the movie.
• description (TEXT) - Brief description of the movie.
• director (VARCHAR(100)) - Director of the movie.
• duration (INT) - Duration in minutes.

users

• user_id (INT, PRIMARY KEY, AUTO_INCREMENT) - Unique identifier for each user.
• username (VARCHAR(50)) - Username.
• email (VARCHAR(100)) - User's email address.
• password (VARCHAR(255)) - Encrypted password.
• join_date (DATETIME) - Date the user registered.

ratings

• rating_id (INT, PRIMARY KEY, AUTO_INCREMENT) - Unique identifier for each rating.
• user_id (INT, FOREIGN KEY) - Reference to the user who rated the movie.
• movie_id (INT, FOREIGN KEY) - Reference to the rated movie.
• rating (DECIMAL(3, 2)) - Rating given to the movie (e.g., from 1.0 to 5.0).
• rating_date (DATETIME) - Date the rating was made.

🔗 Relationships Between Tables

1. movies and ratings are related through movie_id, allowing storage of ratings given to each movie.
2. users and ratings are related through user_id, tracking which user gave a rating.

🤖 Use in a Recommendation System

• Collaborative Filtering: Uses ratings data to identify user preferences.
• User-Based vs Item-Based Filtering:
  • User-based: Finds similar users based on ratings.
  • Item-based: Suggests similar movies based on ratings.
• Hybrid Approach: Combines genre, director, and rating info.

🛠 Example Query for Recommendations

SELECT m.title, AVG(r.rating) AS avg_rating
FROM ratings r
JOIN movies m ON r.movie_id = m.movie_id
WHERE r.user_id IN (
    SELECT DISTINCT r1.user_id
    FROM ratings r1
    WHERE r1.movie_id = <USER_LAST_WATCHED_MOVIE>
)
GROUP BY m.title
ORDER BY avg_rating DESC
LIMIT 10;

📊 Model Evaluation Metrics

• RMSE (Root Mean Squared Error) - Measures prediction accuracy.

• Precision@K, Recall@K - Evaluate recommendation relevance.

• Confusion Matrix - Analyzes classification errors.

📊 Data Analysis

Average Rating by genre (table)

Average Rating by genre (graphic)

Feature Correlation Heatmap (graphic)

Predictions vs Actual Values (graphic)

Decision Tree for High Rating Prediction (table)

🛡️ Security & Privacy

• User passwords are stored securely using hashing (e.g., bcrypt).

• Database access is restricted to authorized users.

• Sensitive user information is never exposed in raw format.

📌 Future Improvements

• Implement deep learning for better recommendations.

• Improve handling of cold-start problems for new users/movies.

• Integrate real-time recommendations using streaming data.

📚 Documentation

• Postgres Documentation
• Faker Documentation
• Random Documentation
• Psycopg Documentation
• Pandas Documentation
• ReportLab Documentation

🔗 Sources

• DBeaver Community
• Install Docker Desktop on Linux
• Visual Studio Code
• Alacritty

🤝 Contributions

Contributions are welcome! 🎉
Feel free to submit issues or pull requests to enhance the functionality.

📝 Contribution Guidelines

1. Fork the repository.
2. Create a feature branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m "Added new feature").
4. Push to the branch (git push origin feature-branch).
5. Open a Pull Request.
6. For questions or suggestions, open an issue or contact me via email. 🚀