Netflix Analysis with Python

Uncover trends in global streaming content with data. Netflix has reshaped the entertainment industry, producing and distributing thousands of shows and movies worldwide. In this project, you’ll analyze a Netflix dataset that includes titles, genres, release years, countries, and more. Using Pandas for manipulation and Seaborn for visualization, you’ll explore how Netflix’s content library has evolved over time and across regions. You’ll begin by loading and cleaning the dataset—checking for missing values, standardizing date formats, and organizing categorical variables like genre and country. With Pandas, you’ll filter and subset the data to answer specific questions, such as the growth of Netflix content over the years or the balance between movies and TV shows. Next, you’ll create Seaborn visualizations to highlight trends and comparisons. Bar plots will show the distribution of genres, line plots will illustrate growth over time, and heatmaps can capture country-level contributions. You’ll investigate questions such as: How has the volume of Netflix content changed year by year? What genres dominate the platform globally? Which countries contribute the most to Netflix’s catalog? How do movies and TV shows compare in distribution? By the end of this project, you will be able to: Clean and organize a media dataset with Pandas. Summarize and compare content by type, genre, and country. Create Seaborn plots that highlight temporal and categorical patterns. Interpret results to understand Netflix’s global content strategy. This project gives you a practical opportunity to connect data analysis with real-world media trends. By examining Netflix data, you’ll not only strengthen your technical skills but also gain insights into the dynamics of streaming services in the digital age.