Netflix Movies and TV Shows Data Analysis using SQL

Overview

This project involves a comprehensive analysis of Netflix's movies and TV shows data using SQL. The goal is to extract valuable insights and answer various business questions based on the dataset. The following README provides a detailed account of the project's objectives, findings, and conclusions.

Objectives

• Analyze the distribution of content types (movies vs TV shows).
• Identify the most common ratings for movies and TV shows.
• List and analyze content based on release years, countries, and durations.
• Explore and categorize content based on specific criteria and keywords.

Dataset

The data for this project is sourced from the Kaggle dataset:

• Dataset Link: Movies Dataset

Schema

DROP TABLE IF EXISTS netflix;
CREATE TABLE netflix
(
    show_id      VARCHAR(5),
    type         VARCHAR(10),
    title        VARCHAR(250),
    director     VARCHAR(550),
    casts        VARCHAR(1050),
    country      VARCHAR(550),
    date_added   VARCHAR(55),
    release_year INT,
    rating       VARCHAR(15),
    duration     VARCHAR(15),
    listed_in    VARCHAR(250),
    description  VARCHAR(550)
);

Tools & Skills Used

• PostgreSQL
• SQL (Joins, Aggregations, String Functions, Date Functions, Window Functions)
• Data Cleaning & Preprocessing
• Analytical Thinking
• Problem Solving

Key Insights

• TV Shows make up ~30% of the content, while Movies dominate the catalog.
• United States, India, and the UK host the largest volumes of content.
• Most content is rated TV-MA and TV-14.
• 100+ items were flagged under violent content using keyword logic.

Findings and Conclusion

• Content Distribution: The dataset showcases a rich mix of movies and TV shows, spanning various genres and rating levels.
• Common Ratings: Examining the most frequently assigned ratings offers insights into the platform's primary audience focus.
• Geographical Insights: Trends from top-producing countries, especially the consistent output from India, highlight regional content dynamics.
• Content Categorization: Sorting content using relevant keywords reveals deeper patterns in the type of entertainment available on Netflix This analysis offers a well-rounded understanding of Netflix’s content library and supports strategic planning for content curation and audience engagement.