PrismDeck

This application is a movie recommendation app

Authors

• Michael Riley
• Josette Nelson
• Imir Ransom
• Corinne Kogut

Description

This is our group project from Zip Code Wilmington. PrismDeck is an application designed to recommend movies to users based on their preferences. Users can input their preferences such as movie genre (e.g., comedies), ratings between 3-5 stars, and specific release time frames. The application employs an algorithm or rules along with a trained model to match and rank movies according to user preferences. The model is trained on giving recommendations to the users for ratings using User_Based Callobrative Filtering.

Features

• Favorites:

  • Users will have a favorites list upon creation to save their favorite moviees that they enjoy. The application will give recommendations based on that as well

• Match & Rank

  • The application will pick out movies based off a algorithm or rules and a trained model

• Movie Link

  • When useres are recommended the list of movies, when the user clicks on the movie, iut will send them to the movie's website so that they may get further information on their recommneded movie

Dependencies

• Tools: The technologies that we will be using for this project are:
  • Python
  • Pandas
  • Jupyter Notebook
  • Apache Spark
  • Flask
  • HTML
  • CSS
  • CSV Files
  • MySQL
  • SQLAlchemy

Installing Requirements

Create a virtual environment

python3 -m venv /path/to/new/virtual/environment/name_of_environment

Then activate the virtual environment

source name_of_environment/bin/activate

After that, install the requirements to run the app

pip install -r requirements.txt

Now, you can run the app

export FLASK_APP=main

flask run

Dataset:

That the data set we will be using is from a folder of csv files gathered from Movie Lens - https://grouplens.org/datasets/movielens/latest/ . We will be using the csv files to help with the rules algorithm as well as using the data to help train our model

Rating Stars CSS/HTML

Modified to our needs from open-source code, permission granted under MIT License. See NOTICE.md

Model Training

With this being a recommendation system for users and such a large dataset, we leveraged Python with Apache Spark to use the Alternating Least Sqaured (ALS) algorithm. The data was split for 80% trainig of the model and 20% for testing the model. We then tested it's accuracy using the metric Root Mean Squared Error (RMSE). This metric as of now has given us back 0.8009857963477534 as our results with the metric. The model is also loaded into our Flask application in one of our various routes.