Netflix Data Analysis — Python EDA Project

Project Overview

An end-to-end Exploratory Data Analysis (EDA) project on Netflix Movies and TV Shows. The project cleans the dataset, analyzes content distribution, release trends, genres, countries, ratings and movie durations, and converts the findings into business-style insights.

Resume-ready description

Netflix Data Analysis | Python, Pandas, Matplotlib, Seaborn

• Performed exploratory data analysis on Netflix's content catalog to understand content distribution, ratings, genres, countries and release trends.
• Cleaned missing values, converted date fields, engineered year/month features and standardized duration fields.
• Created visualizations to identify content trends and translated findings into actionable insights.

Business Questions

1. How many Movies and TV Shows are available?
2. How has Netflix content changed over release years?
3. Which countries contribute the most titles?
4. What are the most common genres?
5. Which ratings dominate the catalog?
6. How do movie durations vary?
7. Which years saw the largest volume of content added to Netflix?
8. What practical insights can a content analyst derive?

Dataset

The notebook uses the commonly used netflix_titles.csv dataset. If the CSV is not present locally, the notebook downloads a public GitHub mirror at runtime.

Expected columns: show_id, type, title, director, cast, country, date_added, release_year, rating, duration, listed_in, description

Tools

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

Project Structure

netflix-data-analysis/
├── Netflix_Data_Analysis.ipynb
├── netflix_analysis.py
├── requirements.txt
├── README.md
└── data/
    └── netflix_titles.csv   # optional local copy

How to Run

pip install -r requirements.txt
jupyter notebook Netflix_Data_Analysis.ipynb

Or:

python netflix_analysis.py

Portfolio Tips

For GitHub, add 3–5 exported charts to an images/ folder and place them in the README. For LinkedIn, post the dashboard/charts plus 3 concise insights rather than only uploading the notebook.