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In this video, we're going to build a Streamlit web app in Python for analyzing YouTube channel data. Webscraping was performed using BrightData to obtain data pertaining to YouTube channel information that is exported in JSON format. 🔗 Learn more about BrightData and follow along https://get.brightdata.com/dataprofessor 🐙 Code for webscraping on BrightData https://github.com/dataprofessor/youtube-data-scripts 🐙 Code for app https://github.com/dataprofessor/youtube-data-app 🕹 Demo app https://youtube-data-channels.streamlit.app/ ⏰ Timeline: 0:00 Introduction 1:25 Designing the app 3:31 Preview of data app 6:11 BrightData for web scraping 8:49 Exploring the webscraped JSON data 9:36 Walkthrough of the code 12:30 Next steps 13:22 Conclusion 🖼️ Credit: Enter https://www.flaticon.com/free-icon/enter_1828381 Paste https://www.flaticon.com/free-icon/paste_3388655 Categories https://www.flaticon.com/free-icon/categories_4277485 Json https://www.flaticon.com/free-icon/json_2581811 Coding https://www.flaticon.com/free-icon/code_2920244 Plan https://www.flaticon.com/free-icon/plan_3050533 Data collection https://www.flaticon.com/free-icon/data-collection_7440290 Problem solving https://www.flaticon.com/free-icon/problem-solving_4133585 Data collection https://www.flaticon.com/free-icon/data-collection_9409659 Data https://www.flaticon.com/free-icon/data_1197409 Database https://www.flaticon.com/free-icon/database_5139710 IP https://www.flaticon.com/free-icon/ip_1674968 #datascience #webscraping #brightdata #streamlit #dataprofessor
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