40% OFF Movie Recommendation System Using Django and Machine Learning
The Movie Recommendation System Using Django and Machine Learning is a web application that recommends similar or related movies based on user search input. Developed using Python and Django, it includes a pre-trained machine learning model, movie search with dynamic suggestions, a responsive interface, and a Jupyter Notebook explaining the model training process.
Read more- Built with
- PythonDjangoMachine LearningBootstrapHTML/CSS
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- Source codeDatabase
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- Instant download after payment
Project Overview
The Movie Recommendation System Using Django and Machine Learning is a web-based application that helps users discover movies based on their search input. It integrates a pre-trained machine learning model with a Django backend to recommend similar or related movie titles. The project also includes dynamic movie search suggestions, a responsive frontend, and a Jupyter Notebook for understanding the model training process.
Available Features
- AI-Based Movie Recommendations: Suggests similar or related movies using a pre-trained machine learning model.
- Movie Search with Auto-Suggestions: Displays dynamic movie title suggestions as users type, using jQuery UI.
- Responsive Frontend: Uses HTML, CSS, and JavaScript to support different screen sizes.
- Video Background: Includes a video background asset for the website interface.
- Custom Branding: Includes a logo asset for application branding.
- Machine Learning Integration: Uses a model-related file in Parquet format for recommendation functionality.
- Training Notebook: Includes a Jupyter Notebook to help users understand the model training process.
- Organized Django Structure: Separates application configuration, recommendation logic, templates, static files, and dependencies.
Which Technologies Are Used in This Project?
- Python: Backend programming and recommendation logic.
- Django: Web application framework.
- SQLite3: Database storage.
- HTML and CSS: Structure and styling of web pages.
- JavaScript: Client-side interactions.
- jQuery UI: Dynamic movie search suggestions.
- Bootstrap: Responsive interface design.
- Jupyter Notebook: Documentation of the machine learning training process.
- Parquet: Data file format used by the supplied model-related resources.
More Details and Full Documentation
For project screenshots, requirements, and installation instructions, visit Movie Recommendation System Using Django and Machine Learning on UPDATEGADH.
- Django Project Configuration
- Movie Recommendation Application
- Movie Search
- Dynamic Search Suggestions
- Machine Learning Recommendation Model
- Recommendation Results
- SQLite Database
- Frontend Templates
- Static Resources and Branding
- Machine Learning Training Notebook
- Install Python and Visual Studio Code.
- Download and extract the project ZIP file.
- Open the extracted project folder in Visual Studio Code.
- Open the integrated terminal.
- Create a virtual environment:
python -m venv venv
- Activate the environment on Windows:
venv\Scripts\activate
- Install the project dependencies:
pip install -r requirements.txt
- Check the Django settings and confirm that the required Parquet model-related file is available at the expected location.
- Run database migrations if required:
python manage.py migrate
- Start the Django development server:
python manage.py runserver
- Open the application in your browser:
http://127.0.0.1:8000/
- Test movie searching, dynamic suggestions, and movie recommendations.
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