The Netflix Analysis with Jupyter Notebook is a comprehensive exploration of Netflix content, utilizing Python and Jupyter Notebook for data cleaning, preprocessing, and detailed analysis. This project delves into the Netflix dataset to extract meaningful insights and presents them through interactive visualizations.
Methodically navigating through the dataset, we executed data cleaning protocols, visualized null values, and handled them to ensure data integrity and precision. Further, we split and transformed columns related to movie durations and TV show seasons into distinct, insightful DataFrames.
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Top Actors & Directors: Utilized Dot and Dumbbell representation to uncover the most influential actors and directors within the dataset, offering insights into their prolific contributions.
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Most Watched Duration: Graphical representation to reveal viewer preferences in duration, highlighting patterns in the most watched durations across movies and TV shows.
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Top Movie Production Countries: Map representation showcasing the countries contributing significantly to movie production, providing a global perspective on film industry dynamics.
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Most Watched Genres: Bar chart representations elucidating the genres capturing maximum viewer attention.
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Viewing Patterns: Comparative visualizations highlighting distinctions in viewer engagement and consumption habits between TV shows and movies.
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Content Produced from 2001: Performed Over Time Analysis.
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Ratings Given: Line chart to show ratings given for TV shows and movies.
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Year with Most Releases: Content production in specific years.
To run this analysis on your local machine, follow these steps:
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Clone this repository:
git clone https://github.com/Nimmysp123/Data-Analysis-Projects
Special thanks to Kaggle for providing the Netflix dataset used in this analysis. The availability of such datasets contributes significantly to the exploration and understanding of diverse topics within the data science community.