Machine Learning project that includes from the processing and data collection (Data Engineer) to the training and maintenance of the ML model of movie recommendation. For this I was provided with 4 datasets from different streaming platforms, amazon, disney, hulu, netflix and 8 datasets containing the rating/scoring made by certain users.

The function get_max_duration(year, platform, duration_type) returns only the movie name string. The movie (only movie, not series, etc) with the longest duration according to year, platform and duration type.

The function get_score_count(platform, scored, year) returns an int, with the total number of movies that meet the request. The number of movies (only movies, not series, etc) by platform, with a score greater than XX in a given year.

The function get_count_platform(platform) returns an int, with the total number of movies of that platform. Number of movies (only movies, not series, etc) by platform. Platforms should be named amazon, netflix, hulu, disney.

The function get_actor(platform, year) returns only the string with the name of the most repeated actor according to the given platform and year.

The function prod_per_county(type,country,year) returns the amount of contents/products according to the type of content (movie,series) by country and year in a dictionary with variables named 'country' (country name), 'year' (year), 'movie' (amount of contents/products).

The function get_contents(rating) returns the total number of contents with that audience rating (for which audience the movie was rated).

the function get_recommendation(title: str) returns a Python list with 5 values, each one being the string of the name of the movies with the highest score, in descending order.

The .gitignore file has the content that ignores the git push.

In the notebook ETL_EDA_ML.ipynb I perform the ETL, EDA and ML processes.

The file df_platform.csv is the dataset after the ETL and EDA to be consumed with the FastAPI.

The main.py file has the functions to be instantiated by FastAPI.

The requirements.txt file has the names of the libraries used for the deploy.

Visual Studio Code.

Git.

FastAPI.

Render.

Python.

Drive con los datasets: https://drive.google.com/drive/folders/1f-wDJLGXNbYHJF5DRXRzOcUn_MW3ohbj?usp=share_link

Video explicativo de la API: https://youtu.be/RbXuFjBTWpA

Deploy de la API en Render: https://entertainment-recommendation.onrender.com/Docs