Web Application Link: Pymovies

Github project link: Cortez-Dev/pymovies-project

Pymovies is a python driven web application designed to provide users with movie recommendations based on frequently watched movies. User would be able to access personilized recommendation by creating account. User history will be saved while they browse movies and would be recommended movies based on their history. Users will also be able to save movies into watchlist to save movies for later. We provide users with filter to filter movies based on genre. User can also search for movies by title. Movies are ordered according to rating in descending order.

Download or clone the project from git repository on local machine and install all requirements using the command

pip install -r requirements.txt

Navigate to the project directory

• Create a admin using command

  python manage.py createsuperuser

• Create migrations and migrate using

  python manage.py makemigrations
  python manage.py migrate

• Insert movie database in Movie model using the movies.json file by entering django shell

  • Entering the shell

        python manage.py shell

  • Inserting movies in Movie Model

        import json
        form movies.models import Movie
    
        with open('movies.json') as f:
            movies_list = json.load(f)
    
        for movie in movies_list:
            movie = Movie(title=movie['title'], img_url=movie['img_url'], info=movie['info'], rating=movie['rating'], genre=movie['genre'], story=movie['story'])
            movie.save()

• After all above steps run server on local machine using

      python manage.py runserver

Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.

Apriori uses a breadth-first search strategy to count the support of itemsets and uses a candidate generation function which exploits the downward closure property of support. In simple terms, it proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.

Support is an indication of how frequently the itemset appears in the dataset

Confidence is an indication of how often the rule has been found to be true.

It is defined as :

If the lift is > 1, that lets us know the degree to which those two occurrences are dependent on one another, and makes those rules potentially useful for predicting the consequent in future data sets.

If the lift is < 1, that lets us know the items are substitute to each other. This means that presence of one item has negative effect on presence of other item and vice versa.

MIT