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This project used pandas, numpy, matplotlib.pyplot, seaborn, nltk and WordCloud python libraries to analyze "What is the special feature of popular videos?"

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hej6853/Rowdy_Rooster_Project1-YouTube-Video-Trending-Analysis

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YouTube-Video-Trending-Analysis

The project topic began with the question "What is the special feature of popular videos?"

Background

As of 2020, there are 2+ billion logged-in monthly users out there. The number of channels grew more than 23%, and the number of channels that earn $10K USD per year or more grew by 50%. 70% of viewers bought from a brand after seeing it on YouTube. With the continued increase in revenue earned by channels through Youtube, identifying hot topics and key variables that contribute to a video’s success is very important.

We Analyzed trending video data over the previous two years to discover possible variations between variables across separate English-speaking countries/ regions.

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Build with

● Python with Jupyter Notebook

Libraries used

● pandas ● numpy ● matplotlib.pyplot ● seaborn ● nltk ● NLP ● WordCloud

Dataset Summary

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Data Proprocessing

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Data Analysis

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Conclusion

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If you want to improve your YouTube channel’s performance, you need to understand your data. The plus side of all of this data is you can dive deep into your videos’ performance and understand what’s happening on a granular level.

65% of people are visual learners. Understanding these statistics not only helps individual creators to create better content but can also benefit businesses to target the main audience or target content. According to Forbes, correctly targeted visual contents increase the amount spent by a customer by 31%, allows the business to raise their prices by 11%, and improves the level of trust with 92% of consumers who research the business.

License

© hej6853

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This project used pandas, numpy, matplotlib.pyplot, seaborn, nltk and WordCloud python libraries to analyze "What is the special feature of popular videos?"

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