Recommender System

Project Overview

This Django-based movie recommendation system leverages the IMDB dataset to generate personalized movie suggestions. The application employs Natural Language Processing (NLP) techniques to analyze user preferences and movie metadata.

Technologies Used

• web development: Django, Python, HTML/CSS
• Database: SQLite
• Data Processing: Natural Language Processing (NLP)
• Machine Learning Techniques: Scikit-Learn, TF-IDF (Term Frequency-Inverse Document Frequency), Cosine Similarity

Getting Started

Prerequisites

Ensure you have Python and Django installed. Then, install the necessary packages:

pip install -r requirements.txt

Running the Application

Navigate to the recomsystem directory and execute the following commands:

1. Import CSV Data:

   python manage.py import_csv_data

   Imports movies, ratings, and tags from CSV files into the database.

2. Train TF-IDF Model:

   python manage.py train_tfidf

   Trains the TF-IDF model on movie descriptions for content-based filtering.

3. Generate Recommendations:

   python manage.py generate_recommendations

   Outputs movie recommendations based on user preferences.

Usage

The system uses user genre preferences along with collaborative filtering to suggest relevant movies.

Additional Notes

• Ensure the virtual environment is active when running commands.

• Database migrations may be required when models are updated.

  python manage.py migrate

Start the Development Server: Run the development server

  python manage.py runserver

Access the Application: Open your web browser and go to http://127.0.0.1:8000/ to see your application running.

Be sure to create an account and enjoy your experience! :)