• Blog Link : https://towardsdatascience.com/build-a-movie-recommendation-api-using-scikit-learn-flask-and-heroku-bee239dc96e3

• API Link : https://bioscope-api.herokuapp.com/movie?title=insert-movie-name

An open movie recommendation API that has been built using Python and deployed on Heroku. This API is based on the Content-Based Filtering technique used in recommendation systems where we recommend items to a user based on the input item's attributes. We have more than 6000 movies present in our dataset and our recommendation algorithm works accordingly.

• TMDB 5000 Movies Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata
• The Indian Movie Database: https://www.kaggle.com/tmdb/tmdb-movie-metadata

Here, we have our logic written for the recommendation algorithm. Consists a total of 5 functions

• get_data() is used to fetch the data about the movies and return the dataset with it's attributes as the result for further preprocessing.

• combine_data() drops the columns not required for feature extraction and then combines the cast and genres column,finally returning the combine column as the result of this function.

• transform_data() takes the value returned by combine_data() and the plot column from get_data() and applies CountVectorizer and TfidfVectorizer respectively and calculates the Cosine values.

• recommend_movies() takes four parameters i.e movie_name and return values of get_data(), combine_data(), transform_data as input and returns the top 20 movies similar to our input movie.

• result() takes a movie's title as input and returns the top 20 recommendations using the recommend_movies() function.

• The Flask app where we use the recommendation.py as a module and return the results in JSON format.

• This has all the dependencies required to deploy our application on Heroku

• A Procfile is a file which describes how to run your application.

• Contains the dataset we have used to build our recommendation system.