Netflix Content Analysis: Tableau Dashboard

Overview

This project examines Netflix’s global content from 2010 to mid-2025, focusing on genre performance, country output, and content strategy over time. The aim was to identify key patterns and shifts in these areas to better understand Netflix’s content trends.

Key Questions:

1. What genres perform best globally?
2. Which countries produce the highest-rated content?
3. How has Netflix’s mix of movies vs. TV shows evolved over time?
4. What are the top-performing titles within each top genre?

Dataset

Source: Netflix Movies and TV Shows till 2025 This dataset contains two CSV files:

1. netflix_movies_detailed_up_to_2025.csv - Includes detailed information on Netflix Movies such as:

   • show_id - Unique identifier of the Movie
   • type - Content type (Movie)
   • title - Name of the Movie
   • director - Director(s) of the Movie (if available)
   • cast - Main actors/actresses featured in the Movie
   • date_added - Date the Movie was added to Netflix
   • release_year - Year the Movie was originally released
   • rating - Audience rating score (0–10)
   • duration - Runtime of the Movie (was NULL in the Movie)
   • genres - Genre(s) of the Movie
   • language - Primary language(s)
   • description - Short plot or summary
   • popularity - Popularity score based on audience engagement
   • vote_count - Number of audience ratings submitted
   • vote_average - Average rating based on audience votes
   • budget - Production budget (in USD, if available)
   • revenue - Box office or total revenue

2. netflix_tv_shows_detailed_up_to_2025.csv - Includes detailed information on Netflix TV shows, such as:

   • show_id - Unique identifier for the TV Show
   • type - Content type (TV Show)
   • title - Name of the TV Show
   • director - Director(s) of the TV Show (if available)
   • cast - Main actors/actresses featured in the TV Show
   • date_added - Date the TV Show was added to Netflix
   • release_year - Year the TV Show was originally released
   • rating - Audience rating score (0–10)
   • duration - Runtime of the TV Show (in seasons)
   • genres - Genre(s) of the TV Show
   • language - Primary language(s)
   • description - Short plot or summary
   • popularity - Popularity score based on audience engagement
   • vote_count - Number of audience ratings submitted
   • vote_average - Average rating based on audience votes

For this analysis and for consistency, I focused on title, type, release_year, rating, vote_count, country, genres from both files, as these fields directly relate to the questions I aimed to answer.

Schema:

Process

1. Data Cleaning and Structuring:

• Standardized country names and genre labels.
• Split multiple genres per title into individual records for accurate aggregation.
• Removed entries with missing or invalid rating or release_year.
• Standardized column names & formats

2. Using SQL, I aggregated and filtered the data to answer the key questions for analysis:

• Top genres globally using vote-weighted ratings to reduce bias from low-vote titles.
• Country-level performance, comparing weighted ratings and production counts.
• Movies vs TV Shows trends by year and share percentage.
• Top titles in top-performing genres for each type.

3. The processed data was visualized in Tableau through an interactive dashboard, featuring:

• Genre Trends Over Time – Showing shifts in popularity from 2010 to mid-2025.
• Top Producing Countries – Ranking countries by number of titles.
• Movies vs TV Shows – Tracking proportional changes across years.
• Top Genres & Titles – Highlighting the best-performing genres and their standout titles.

Dashboard Visualization