This work embarked on a comprehensive exploratory data analysis (EDA) of a Netflix titles dataset, aiming to uncover insights and patterns within Netflix's vast content catalog.

The dataset, netflix_titles.csv, includes various details about movies and TV shows available on Netflix, such as:

• show_id: Unique identifier for each title
• type: Distinguishes between Movies and TV Shows
• title: Name of the title
• director: Director(s) of the title
• cast: Cast members involved
• country: Country of production
• date_added: When the title was added to Netflix
• release_year: Original release year
• rating: Content rating
• duration: Duration of the title
• listed_in: Genre(s) of the title
• description: Brief description of the title

1. Distribution Analysis: Explored the distribution of movies vs. TV shows, the number of titles added per year, and the distribution of show ratings.
2. Trend Analysis: Examined trends in release years and the country-wise distribution of titles.
3. Genre Analysis: Identified the most prevalent genres within the catalog.
4. Text Analysis: Conducted keyword extraction and sentiment analysis on titles and descriptions to uncover thematic elements and the emotional tone.

• Python: For data manipulation and analysis.
• Pandas: For data processing and analysis.
• Matplotlib and Seaborn: For data visualization.
• Scikit-learn: For applying machine learning techniques like TF-IDF.
• TextBlob: For performing sentiment analysis.