A data analytics case study using Python, Pandas, Matplotlib, and Seaborn to analyze Netflix Movies and TV Shows and uncover trends in content growth, genre dominance, global production, ratings, and creator activity.
This project analyzes the Netflix Movies and TV Shows dataset to understand how the platform's catalog has evolved over time. The analysis explores content growth, Movies vs TV Shows distribution, genre popularity, country contribution, ratings, and director activity.
The goal is to transform raw entertainment metadata into clear insights that communicate Netflix's catalog strategy and global content patterns.
Streaming platforms generate large content catalogs, but raw title metadata does not immediately explain platform strategy. This project answers:
- How has Netflix expanded its content library over time?
- Are Movies or TV Shows more dominant in the catalog?
- Which genres appear most frequently?
- Which countries contribute the most Netflix content?
- Which directors have the highest number of titles?
- What does the ratings distribution suggest about audience targeting?
- Netflix content growth accelerated sharply after 2015, with the strongest expansion between 2016 and 2019.
- Movies make up a significantly larger share of the catalog than TV Shows.
- International Movies, Dramas, and Comedies are among the most represented genres.
- The United States contributes the highest number of titles, followed by India and the United Kingdom.
- Several directors appear multiple times, suggesting recurring creator partnerships.
- Ratings trends provide insight into how Netflix balances mainstream, family, teen, and mature content.
This analysis translates Netflix catalog metadata into decision-ready insights that can support:
- Content Strategy: Identifying when Netflix expanded most aggressively and which content types dominate the catalog.
- Market Expansion: Highlighting top contributing countries and where global production is most concentrated.
- Audience Targeting: Using ratings and genre patterns to understand how the catalog is positioned for viewer segments.
- Python
- Pandas
- Matplotlib
- Seaborn
- Jupyter Notebook
- Data Cleaning
- Exploratory Data Analysis
- Data Visualization
- Insight Communication
Dataset: Netflix Movies & TV Shows Dataset
Source: Kaggle - Netflix Shows Dataset
Approximate Size: 8,800+ titles
Key fields used in the analysis:
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_in
The dataset was cleaned and processed using Pandas before analysis. Main steps included:
- Removed or handled missing and inconsistent values
- Converted date fields for time-based analysis
- Aggregated titles by year to analyze growth trends
- Grouped genres and countries to identify dominant categories
- Counted Movies vs TV Shows distribution
- Analyzed director frequency and rating distribution
- Generated visualizations using Matplotlib and Seaborn
Netflix's catalog expanded rapidly after 2015 and peaked around 2019, suggesting a major platform expansion period.
Movies clearly dominate the Netflix catalog, showing that film content makes up the majority of available titles.
International Movies, Dramas, and Comedies appear most frequently, highlighting Netflix's focus on broad and globally appealing categories.
The United States leads content production by a wide margin, followed by India and the United Kingdom.
Several directors contribute multiple titles, showing recurring creator presence within the Netflix catalog.
Ratings analysis helps explain how Netflix content is distributed across different audience groups and maturity levels.
Dataset -> Data Cleaning -> Exploratory Data Analysis -> Visualization -> Insights
flowchart LR
A["Netflix CSV Dataset"] --> B["Data Cleaning / Preprocessing"]
B --> C["Exploratory Data Analysis"]
C --> D["Matplotlib & Seaborn Visualizations"]
D --> E["Business-Ready Insights"]
E --> F["Content Growth"]
E --> G["Genre Distribution"]
E --> H["Country Contribution"]
E --> I["Director Activity"]
E --> J["Ratings Distribution"]
This repository also includes a responsive project portfolio webpage:
index.htmlstyle.css
Direct project links:
The webpage presents the project as a recruiter-friendly case study with overview, key findings, visual analysis, workflow, architecture, and run instructions.
- Python 3.10+
- Jupyter Notebook
Install the required libraries:
pip install -r requirements.txtIf you prefer installing manually:
pip install pandas matplotlib seaborn notebookgit clone https://github.com/bindhusaahithi/Netflix-Analysis.git
cd Netflix-Analysisjupyter notebook Notebook/Netflix_Analysis.ipynbRun all cells to reproduce the analysis and visualizations.
Netflix-Analysis/
├── Data/
│ └── netflix_titles.csv
├── Notebook/
│ └── Netflix_Analysis.ipynb
├── visuals/
│ ├── content_growth.png
│ ├── movies_vs_tvshows.png
│ ├── top_countries.png
│ ├── top_directors.png
│ ├── top_genres.png
│ └── top_ratings.png
├── index.html
├── requirements.txt
├── style.css
└── README.md
- Build an interactive dashboard using Streamlit or Power BI
- Add deeper analysis by release year and country-genre combinations
- Perform text analysis on Netflix descriptions
- Train a recommendation or classification model using content metadata
- Compare Netflix content patterns with other streaming platforms
This analysis shows that Netflix's catalog expanded aggressively in the late 2010s, with Movies remaining the dominant content type. The dataset also reflects strong international breadth, with major contributions from the United States, India, and the United Kingdom. Overall, the project demonstrates how exploratory data analysis can reveal meaningful patterns in content strategy, audience targeting, and global production trends.
Bindhu Saahithi
Master's in Data Science
GitHub: bindhusaahithi