Uncover how Netflix crafts content for global engagement — powered by Python, PowerBI, and binge-worthy data.
A complete data-driven breakdown of Netflix's 2023 content catalog — uncovering insights on what performed well, when content was released, and how language and type affected viewership. Built using Python, Pandas, and Seaborn, this project explores trends to support content strategy decisions with engaging visuals and data storytelling.
- 📖 About the Project
- 📂 Dataset Overview
- 📊 Visualizations & Insights
- ⚙️ Technologies Used
- 🧠 Key Findings
- 🛠️ How to Run
- 📁 Project Structure
- 🚀 Future Improvements
- 🙋♂️ Author & Contact
Netflix Content Strategy Analysis (2023) is a data analysis project that leverages the power of Python to uncover insights about Netflix’s global content lineup. This project is aimed at:
- Identifying high-performing titles by viewership
- Analyzing seasonality and release day patterns
- Highlighting language-based content performance
- Supporting strategic release planning and content investment
The dataset used is named netflix_content_2023.csv, containing the following key columns:
| Column Name | Description |
|---|---|
| Title | Name of the Netflix content |
| Content Type | Type of content (Film, Series, Documentary) |
| Language Indicator | Language in which the content was released |
| Hours Viewed | Total hours viewed globally (with commas) |
| Release Date | Date the content was released |
- Removed commas from
Hours Viewedand converted it to integer. - Converted
Release Dateto datetime and extracted:Release MonthRelease Day NameRelease Season(Winter, Spring, Summer, Fall)
- Insight: Distribution across Films, Series, and Documentaries helps understand Netflix's content format focus.
- Insight: Showcases the biggest content hits of 2023 and which titles drove massive engagement.
- Insight: Winter releases had the highest total viewership, hinting at strategic content drops during cold months.
- Insight: English dominates, but non-English content (Korean, Spanish, Hindi) attracts large audiences.
- Insight: Most content is released on Fridays, aligning with global weekend binge-watching behavior.
| Tool/Library | Purpose |
|---|---|
| Python 3.11+ | Core programming language |
| Pandas | Data loading, cleaning, EDA |
| NumPy | Numerical operations |
| Matplotlib | Basic plotting |
| Seaborn | Advanced, aesthetically pleasing charts |
| VS Code | Development Environment |
- Friday is the favorite release day, maximizing weekend engagement.
- Winter content outperforms other seasons in terms of total hours viewed.
- Films are more numerous, but Series tend to gather more total watch time per title.
- Non-English titles are emerging as strong performers, reflecting Netflix’s global content strategy.
- Top 10 titles account for a major share of total viewership — emphasizing the power of flagship content.
- Clone the Repository
git clone https://github.com/your-username/netflix-content-analysis.git cd netflix-content-analysis - 📥 Install Required Packages
pip install -r requirements.txt
- 🚀 Run the Analysis Script
python netflix_analysis.py
📦netflix-content-analysis
┣ 📁assets/
┃ ┣ plot1_content_type.png
┃ ┣ plot2_top10_titles.png
┃ ┣ plot3_seasonal_viewership.png
┃ ┣ plot4_languages.png
┃ ┗ plot5_release_day.png
┣ 📄 netflix_analysis.py
┣ 📄 netflix_content_2023.csv
┣ 📄 requirements.txt
┗ 📄 README.md
🌐 Convert to an interactive Streamlit Dashboard
📊 Add genre-based analysis
🤖 Use ML models to predict content success
📈 Build an interactive Power BI dashboard
📧 muskkaniyer@gmail.com 🔗 LinkedIn 🔗 GitHub
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