CampusX Data Science Mentorship Program 2022-23 - Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization

In this video, we provide a code example and visualization to showcase how to implement PCA in Python. Follow along and see the power of PCA in action, simplifying data and enhancing visualization for better insights. Code used: https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day47-pca TASK PCA:  https://colab.research.google.com/drive/19AtHlobDsFzsOSXKl8K9qlsqNu2McXza?usp=sharing Task PCA Solution : https://colab.research.google.com/drive/1gsYahZavBH9lX6CjRJztV-lIHkwr2ajL?usp=sharing ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ⌚Time Stamps⌚ 00:00 - Practical Example on MNIST Dataset 01:30 - PCA Code Demo 21:50 - 2D Visualization 28:50 - Explained Variance 30:48 - Finding Optimum number of principle components required 34:34 - PCA Code Demo 39:35 - When does PCA not work? 43:00 - Outro