?? Day 60 of 365: Principal Component Analysis (PCA) ??
Ajinkya Deokate
Data Scientist | Researcher | Author | Public Speaking Expert @PlanetSpark | Freelancer
Hey, Data Scientists!
Welcome to Day 60 of our #365DaysOfDataScience journey! ??
For Day 60, we’ll dive into Principal Component Analysis (PCA), an incredibly important technique for dimensionality reduction. It might sound complex, but by the end of today, you'll see how PCA can simplify large datasets and make your analysis more manageable. Let’s explore this together!
?? What We’ll Be Exploring Today:
- Understanding the core concepts of Principal Component Analysis (PCA):
??- How PCA works to reduce the dimensionality of data.
??- Connections between PCA, Singular Value Decomposition (SVD), and eigenvectors.
??- Why PCA is useful in Data Science for simplifying datasets while retaining important information.
?? Learning Resources:
- Watch: The wonderful StatQuest YouTube series on PCA, which breaks down the topic in a super digestible way.
??- [StatQuest: Principal Component Analysis](https://www.youtube.com/results?search_query=statquest+pca)
- Read: PCA chapter in "Hands-On Machine Learning with Scikit-Learn.
领英推荐
?? Today’s Task:
- Implement PCA using Python's Scikit-learn library:
??1. Choose a dataset, such as the Iris or Digits dataset.
??2. Apply PCA to reduce the dimensionality of the dataset.
??3. Visualize the reduced dataset and see how much information you’ve preserved.
Collaborate & Share
Once you've implemented PCA, share your findings with the group! We can compare how PCA transforms our datasets and discuss which dimensions are the most important. This will be an excellent chance to learn from each other’s perspectives and approaches. ??
This topic is a bit more math-heavy, but don’t worry—working through it together will make it easier. PCA is a powerful tool, and once you get the hang of it, it’ll make your analyses much more efficient. You’ve got this! ??
Extra Resources ??
Please take a look at this excellent video by Krish Naik
Happy Learning and See you Soon!
***