Movie_analysis_with_postgresql

Company A is a US based Netflix competitor. Rather than charging a monthly fee, Company A charges a low annual fee of $19.99 and allows users to rent movies for a discounted rate compared to other services. Company A is focused on growing users in English speaking countries. Company A has two key initiatives for 2020:

1. Produce new Company A exclusive movies to differentiate the platform.
2. Personalize the Company A experience via Product & Marketing.

You’ve been brought into Company A to support these goals and you’ve been tasked with two separate items:

1. Find insights regarding customer preferences to determine the types of movies to produce.
2. Build out curated user data tables that enable personalization efforts.

GOALS

1. Finding the genre out of 5 genres (Family, Comedy, Crime, Action, and Thriller) has the highest probability of success based on revenue.
2. Finding the genre out of 5 genres (Family, Comedy, Crime, Action, and Thriller) recommended the team based on customer engagement.
3. Describing the data for marketing that can help to answer marketing teams' questions.

DATA DICTIONARY

The movies metadata dataset contains information of each movie such as movie id, genres, title, duration, budget, genres, revenue.

Movies_metadata
belongs_to_collection	string
budget	float
genres	string
id	float
original_language	string
original_title	string
overview	string
popularity	float
poster_path	string
production_companies	string
production_countries	string
release_date	string
revenue	float
runtime	float
spoken_languages	string
status	string
tagline	string
title	string

The movilens_rating dataset contains information about each user’s vote details.

Movilens_rating
Index	float
movie_id	float
rating	float
rating_date	string
user_id	float

In movies_metadata dataset genres column convert to JSON format (replace single quotes with double quotes) and parse each genre to the columns with Postgres Functions.