Skip to content

Latest commit

 

History

History
65 lines (46 loc) · 3.55 KB

README.md

File metadata and controls

65 lines (46 loc) · 3.55 KB
Raw

Tazama - Django Movie Recommendation Web App

Screenshot from 2023-11-15 17-39-46

Project Inspiration Blog. The Reel Story Behind Tazama

android-chrome-192x192 What's the reel story behind the tazama? Visit my blog post. Tazama: A Movie Recommendation web app

How tazama works?

Tazama is not just a movie recommendation app; it's a cinematic masterpiece! 🚀 Built with Django, HTML, CSS, Bootstrap, JS, and a dash of jQuery, it gives users personalized movie suggestions and also features a mini-blog with a social twist. Lights, camera, interaction! 🍿 It incorporates a powerful recommendation engine that utilizes content-based filtering with Natural Language Processing (NLP) and cosine similarity.

Watch the video

Why Django?

  1. The Object-Relational Mapping (ORM) system provided a smooth interaction with the database, simplifying data operations.
  2. Leveraging the Django REST Framework, I built a robust API, ensuring seamless communication between the front end and back end.
  3. Django's built-in authentication system secured user data, and the admin interface facilitated easy management of the movie dataset.
  4. The template engine, middleware support, and Django signals enhanced the app's functionality, while scalability features ensured efficient handling of a growing user base.
  5. The focus on security, extensive community support, and well-documented resources made Django the ideal framework for crafting Tazama's efficient and secure architecture.

Key Tazama Features:

  1. 🎥 Personalized movie recommendations based on similar movies.
  2. 🌐 Mobile responsive web app with playful UI.
  3. ✨ A mini-blog for a social touch. Users can register, log in, create profiles, and, update and delete blogs. Social features including likes, comments, and shares will be added to v2 of the app.

NLP Recommendation Engine

The movie recommender system is powered by a robust dataset containing over 9000 movies and one million tags. Leveraging the capabilities of Natural Language Processing (NLP) and employing cosine similarity, this engine provides users with personalized movie recommendations. By analyzing genres and tags, the system suggests movies that align with the user's taste, creating a tailored and enjoyable viewing experience. Recommendation Notebook

Installation

1. Create Virtual Environment

python -m venv venv
source venv/bin/activate

2. Install Dependencies

    pip install -r requirements.txt

Usage

1. Run Migrations

    python manage.py migrate

2. Start the Party

    python manage.py runserver

Deployment:

What's your next watch during Nextfilx and Chill? Visit tazama for a recommendation.

Tazama is deployed on a custom-configured Nginx and Gunicorn Linux server on Digital Ocean

Author:

Mithamo Beth