K Means Clustering Python Optimization – V3

Опубликовано: 01 Апрель 2020
на канале: Yiannis Pitsillides

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Learn how to optimize and improve your K means model in Python using SKLearn. Learn when and how to use PCA in order to improve your Kmeans clustering results in Unsupervised Learning. Then, learn how to deploy your model using Power BI and how to analyse the traits of all your clusters and create valuable insights for the business. Real life example! Hope you enjoy this video!

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Data Analytics Course Link:
http://ipidata.teachable.com/

Tutorial Overview:
1. What is Machine Learning in a nutshell
2. What is Supervised Learning (Supervised Vs Unsupervised Learning)
3. Problem formulation - What are we trying to solve?
4. Explaining how the whole automated process will work (Excel - SQL - Python - SQL - Power BI)
5. Loading the Raw Data into Python
6. Cleaning the Raw Data
7. What is Kmeans clustering
8. How to run Kmeans clustering using SKLean
6. What is Principal Component Analysis (PCA)
7. Who to run Kmeans and PCA together in Python
8. Ways to improve Kmeans results
9. Running Kmeans with optimal parameters
12. Creating the front end PowerBI Dashboard
13. Creating Insights from Clusters
14. Creating NPS analytics per Cluster
15. Discussing how these results can be used in real life

Data Analytics Course Link: http://ipidata.teachable.com/

Part 1 Video:
   • K Means Clustering Algorithm Example ...
Part 2 Video:
   • Principal Component Analysis PCA Pyth...

Yiannis GitHub – files: https://github.com/Pitsillides91/Pyth...

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K Means Clustering Python Optimization – V3

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