Netflix Data Analysis

Overview

This project explores the Netflix titles dataset to understand the type of content available on the platform and how it has changed over time.
It focuses on simple, clear data analysis — cleaning, visualizing, and drawing insights using Python, Pandas, and Matplotlib.

Objectives

• Clean and prepare the dataset by handling missing values.
• Analyze the number of Movies vs TV Shows.
• Identify the top content-producing countries.
• Study yearly trends in Netflix releases.
• Find the most common genres.
• Present insights visually and clearly.

Dataset

The dataset contains information about Netflix titles such as: type, title, country, date_added, release_year, duration, and listed_in.

Source: Kaggle Netflix Dataset

Tools Used

• Python
• Pandas
• Matplotlib
• Jupyter Notebook / VS Code

Files

• netflix_analysis.ipynb – main notebook with code, analysis, and visuals
• netflix_cleaned.csv – cleaned dataset after preprocessing
• Netflix_analysis.pdf – final PDF summary
• README.md – project documentation

Key Insights

• Movies are more common than TV Shows.
• The United States is the top content producer.
• Netflix’s content library grew rapidly after 2015.
• Drama and Comedy are the most frequent genres.

Author

Mubashir – Student learning AI and Data Science, aiming to build strong freelancing and academic skills.