Netflix Content Analysis
Exploratory data analysis on Netflix's 2023 content catalog. Uncovers genre distributions, regional content production patterns, release timing trends, and content type breakdowns (movies vs. series) — with Matplotlib and Seaborn visualizations throughout.
Problem
Netflix's content strategy is opaque. Without structured analysis, it's impossible to see which genres dominate, how regional production has shifted, or what release timing patterns exist across the global catalog.
Solution
Python EDA pipeline (Pandas, Matplotlib, Seaborn, Jupyter) cleaned and analyzed the full 2023 Netflix dataset. Built visualizations covering genre frequency, country-of-origin distributions, year-over-year content volume trends, and movie vs. series ratio breakdowns.
Architecture
Pandas pipeline handled missing values, type casting, multi-value genre columns (split and exploded), and date parsing for release year analysis.
Genre frequency analysis, country-of-origin heatmaps, content type (movie vs. series) ratio over time, and release month/year distribution patterns.
Matplotlib and Seaborn charts: bar charts for genre distribution, heatmaps for regional production, line charts for volume trends, and pie charts for content type splits.
Highlights
- Full EDA on Netflix's 2023 catalog with Pandas, Matplotlib, and Seaborn.
- Genre distribution analysis revealing top categories by content volume.
- Country-of-origin heatmaps showing regional production concentration.
- Year-over-year content volume trends and movie vs. series ratio breakdowns.