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Let's do this data analytics project

Imports & Settings: Loading up pandas, matplotlib, and seaborn—because clean code makes clean charts.

Load the Data: CSV → DataFrame, parse those dates, and verify with a quick df.head().

Inspect, Convert & Clean: Hunting missing values, converting date_added to datetime, dropping bad rows, and pulling out year_added. No dirty data allowed.

Titles Added per Year: Group by year_added and plot a bar chart—spot the biggest years for Netflix growth.

Top Genres: Break apart that listed_in column, count each genre, and shine a light on the top 10.

Top Producing Countries: Explode the country field, count it up, and see which nations are churning out the most Netflix titles.

Quick Summary (Optional): Final stats—total titles and year range—to give context to the whole analysis.

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