Скачать или смотреть Identifying NBA Player Archetypes Using KMeans Clustering - Part One

#DFS #DailyFantasySports

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Hey everyone welcome back! Today we are going to be utilizing and expanding upon the basics of K-Means clustering to identify player groupings based on playstyle.

As usual, a written tutorial can be found on my blog at www.nicksniche.net along with downloadable files for both the excel dataset and the jupyter notebook file.

Chapters:
0:00 Introduction and Overview
2:30 Import Statements
4:07 Reviewing Dataset in Excel
7:00 Creating and Scaling Feature Dataset
11:50 Begin Principal Component Analysis
15:00 Review Explained Variance Ratios
16:21 Graph Explained Variance per Components
18:10 Calculate Rate of Change of Curve
19:10 Plotting Rates of Change
19:46 Identifying Optimal Number of Components

This is a pretty big topic so we are going to break into two parts for easier consumption. The overall process is going to be as follows:

1. Part One
1.1 Acquire Data -- Dataset is provided for repeatability purposes on my blog.
1.2 Import data into pandas dataframe
1.3 Create 'features' dataframe for data to use in ML processes.
1.4 Standardize data by feature for uniformity
1.5 Perform Principal Component Analysis (PCA)
1.5.1 Dimension Reduction for more efficient analysis
1.5.2 Define how many components to reduce to.

2. Part Two
2.1 Identify the optimal number of clusters
2.1.1 Use Silhouette method
2.2 Run K-Means algorithm to create clusters
2.3 Review Clusters
2.3.1 Rinse and repeat with varying data to get optimal results

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