Machine Learning in Python: Principal Component Analysis (PCA) for Handling High-Dimensional Data
[ad_1]
In this video, I will be showing you how to perform principal component analysis (PCA) in Python using the scikit-learn package. PCA represents a powerful learning approach that enables the analysis of high-dimensional data as well as reveal the contribution of descriptors in governing the distribution of data clusters. Particularly, we will be creating PCA scree plot, scores plot and loadings plot.
🌟 Buy me a coffee: https://www.buymeacoffee.com/dataprofessor
📎CODE: https://github.com/dataprofessor/code/blob/master/python/PCA_analysis.ipynb
⭕ Playlist:
Check out our other videos in the following playlists.
✅ Data Science 101: https://bit.ly/dataprofessor-ds101
✅ Data Science YouTuber Podcast: https://bit.ly/datascience-youtuber-podcast
✅ Data Science Virtual Internship: https://bit.ly/dataprofessor-internship
✅ Bioinformatics: http://bit.ly/dataprofessor-bioinformatics
✅ Data Science Toolbox: https://bit.ly/dataprofessor-datasciencetoolbox
✅ Streamlit (Web App in Python): https://bit.ly/dataprofessor-streamlit
✅ Shiny (Web App in R): https://bit.ly/dataprofessor-shiny
✅ Google Colab Tips and Tricks: https://bit.ly/dataprofessor-google-colab
✅ Pandas Tips and Tricks: https://bit.ly/dataprofessor-pandas
✅ Python Data Science Project: https://bit.ly/dataprofessor-python-ds
✅ R Data Science Project: https://bit.ly/dataprofessor-r-ds
⭕ Subscribe:
If you’re new here, it would mean the world to me if you would consider subscribing to this channel.
✅ Subscribe: https://www.youtube.com/dataprofessor?sub_confirmation=1
⭕ Recommended Tools:
Kite is a FREE AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I’ve been using Kite and I love it!
✅ Check out Kite: https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=dataprofessor&utm_content=description-only
⭕ Recommended Books:
✅ Hands-On Machine Learning with Scikit-Learn : https://amzn.to/3hTKuTt
✅ Data Science from Scratch : https://amzn.to/3fO0JiZ
✅ Python Data Science Handbook : https://amzn.to/37Tvf8n
✅ R for Data Science : https://amzn.to/2YCPcgW
✅ Artificial Intelligence: The Insights You Need from Harvard Business Review: https://amzn.to/33jTdcv
✅ AI Superpowers: China, Silicon Valley, and the New World Order: https://amzn.to/3nghGrd
⭕ Stock photos, graphics and videos used on this channel:
✅ https://1.envato.market/c/2346717/628379/4662
⭕ Follow us:
✅ Medium: http://bit.ly/chanin-medium
✅ FaceBook: http://facebook.com/dataprofessor/
✅ Website: http://dataprofessor.org/ (Under construction)
✅ Twitter: https://twitter.com/thedataprof/
✅ Instagram: https://www.instagram.com/data.professor/
✅ LinkedIn: https://www.linkedin.com/in/chanin-nantasenamat/
✅ GitHub 1: https://github.com/dataprofessor/
✅ GitHub 2: https://github.com/chaninlab/
⭕ Disclaimer:
Recommended books and tools are affiliate links that gives me a portion of sales at no cost to you, which will contribute to the improvement of this channel’s contents.
#dataprofessor #PCA #clustering #cluster #principalcomponentanalysis #scikit #scikitlearn #sklearn #prediction #jupyternotebook #jupyter #googlecolab #colaboratory #notebook #machinelearning #datascienceproject #randomforest #decisiontree #svm #neuralnet #neuralnetwork #supportvectormachine #python #learnpython #pythonprogramming #datascience #datamining #bigdata #datascienceworkshop #dataminingworkshop #dataminingtutorial #datasciencetutorial #ai #artificialintelligence #tutorial #dataanalytics #dataanalysis #factor #principalcomponent #principalcomponents #pc #machinelearningmodel
Source
[ad_2]
Anyone interested in a tutorial video on Bioinformatics (using Python)? Comments down below 👇
If you find value in this video, please give it a Like 👍 and Subscribe ❤️for more videos on Data Science. 😃
Thanks professor 😊
can we also make it to plot 2d scatter plots of different combinations instead of 3d with a for loop in plotly or we need to use matplotlib for it ? also what could be the usage of pca for bioinformatics ?
Incredible tutorial, congratulations! The 3d viz are awesome and help a lot to understand loadings x attributes relationship. I was wondering if isnt possible to access the scoring coeeficient matrix that is used internally by the Transform(X). Do you know how can I achieve that?
Hi sir, thank you very much for the video! I have experimental data set of time dependent signals 800(time)x49(signals) voltage values. I used PCA and reduced it to 800×2.How can I reduce further and extract information from these set for ML application and is there any other feature extraction method that you can advice for signal feature extraction ?
How do you transform your own dataset into sklearns format?
How could we know that the components need to be only 3 in the starting
pca = PCA(n_components=3)
hi, why not use the PCA function directly?
from sklearn.decomposition import PCA
pca = PCA(n_components=3)
pca.fit(X)
Interesting! I was just following an online course about PCA😄
Wow I loved those plots 😍
Very informative! Thanks for this important lesson 🙂 keep it up!