In this video, I will be showing you how to establish a device mastering design comparison web app in Python utilizing the Streamlit and lazypredict libraries. This app will enable you to swiftly assess the prediction general performance of additional than 30 device studying types in 1 step by simply just uploading an enter CSV file. Soon after uploading the CSV file the types will be built and the app will make it possible for you to down load the success of the design functionality (this incorporates the tabular CSV file and graphical plots in PDF structure.
👉 Code:
👉 Demo:
🌟 Subscribe to this YouTube channel
🌟 Be a part of the E-newsletter of Knowledge Professor
🌟 Purchase me a coffee
🌟 Download Kite for Totally free
⭕ Playlist:
Verify out our other movies in the next playlists.
✅ Details Science 101:
✅ Info Science YouTuber Podcast:
✅ Information Science Virtual Internship:
✅ Bioinformatics:
✅ Details Science Toolbox:
✅ Streamlit (Web Application in Python):
✅ Shiny (Internet Application in R):
✅ Google Colab Recommendations and Tips:
✅ Pandas Tips and Tips:
✅ Python Info Science Challenge:
✅ R Facts Science Undertaking:
✅ Weka (No Code Device Discovering):
⭕ Advised Textbooks:
🌟
✅ Hands-On Machine Learning with Scikit-Master :
✅ Info Science from Scratch :
✅ Python Knowledge Science Handbook :
✅ R for Information Science :
✅ Artificial Intelligence: The Insights You Need from Harvard Organization Evaluation:
✅ AI Superpowers: China, Silicon Valley, and the New Globe Order:
⭕ Stock pics, graphics and video clips used on this channel:
✅
⭕ Observe us:
✅ Medium:
✅ Facebook:
✅ Website: (Beneath building)
✅ Twitter:
✅ Instagram:
✅ LinkedIn:
✅ GitHub 1:
✅ GitHub 2:
⭕ Disclaimer:
Advised publications and instruments are affiliate hyperlinks that offers me a portion of sales at no price tag to you, which will contribute to the advancement of this channel’s contents.
#streamlit #machinelearning #python #lazypredict #bigdata #datascienceproject #randomforest #decisiontree #svm #neuralnet #neuralnetwork #supportvectormachine #learnpython #pythonprogramming #datascience #datamining #bigdata #datascienceworkshop #dataminingworkshop #dataminingtutorial #datasciencetutorial #ai #artificialintelligence #tutorial #dataanalytics #dataanalysis #machinelearningmodel #dataprofessor #artificialintelligence #ai
👉Watch Part 1 of the Streamlit tutorial series https://www.youtube.com/watch?v=ZZ4B0QUHuNc&list=PLtqF5YXg7GLmCvTswG32NqQypOuYkPRUE
** Support this Channel
If you like this video, please make sure to LIKE, SUBSCRIBE, COMMENT and SHARE!
🌟 Buy me a coffee https://www.buymeacoffee.com/dataprofessor
🌟 Download Kite for FREE https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=dataprofessor&utm_content=description-only
👉 Subscribe to this YouTube channel https://www.youtube.com/dataprofessor?sub_confirmation=1
👉 Join the Newsletter of Data Professor http://newsletter.dataprofessor.org
That's great.
Data Professor, Is better to do a sentiment analysis project as a beginner?
Need suggestion.
Really exciting when u your introducing a new things for us…love this channel so much❤️❤️❤️
What are the downsides of using such solutions? Do they work well? Just seem so easy then. Can you discuss in future.
Thank you so much ,very helpful content I appreciate your efforts
Nc video! As always.
That's again a very useful video…Thanks 🙂
Thank you!
Absolutely Lovely. Even I had completed making exactly a similar thing and had completed on 30th Jan 2021. But, I must say, your work is far more complete and concise and I would love to follow your application only. Thanks a lot for sharing.
Nice to see you again
Nice! Will you cover Bokeh+Panel on any of your videos?
Argh.
This app has gone over its resource limits. Please try again in a few minutes.
Awesome..
I am really enjoying your channel – it has a great mix of new libraries and simple explanations. Many thanks.
amazing content
Very useful video professor, keep posting more nd more videos 😀
This is absolutely incredible. fantastic work.
Can we please have data base tutorial sql/nosql with streamlit.
Amazing and inspiring!
Thank you Dr!
Great video. You should know that in order for the app to work the git repository should be cloned and not just download (wget) the requirements file.
love the video absolutely amazing
but i am getting an error after running the app
everything works fine till rendering the train daset
but after that test dataset doest load and the i get the following error
The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
can anyone please help
This is HUGE!! Thanks!
Just found your channel today and I have to say I’m already a fan. Great work!
This video is awesome!! I was also writing the code from scratch as per the logic taught in video, And I'm experiencing some issues.
Code section:
ax1 = sns.barplot(y=predictions_test.index, x="R-Squared", data=predictions_test)
Error:
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
Why is it being caused, and How do I fix it?
Please how did you create the app.py? Did you just run "streamlit app.py" ?
really insightful! thanks and keep em up!