NYPD Crime #19 – Clustering To Explore Neighbourhoods (Part IV – Continued Because Spark Hates Me)

Review I ran into a major dead end in the last post. The problem? Data pre-processing… You don’t often think that data processing would be the activity that prevents you from moving forward eh? As a novice data scientist, you’re so infatuated with the high level objective, the meat of the analysis, the sexy chart […]

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All-NBA Predict #15 – Exploration of Historical NBA Players (Part VI, PCA to Explore Historical Lineups)

Last time, I explored 9 regions of our PCA bi-plot. I’ll review what I thought the 9 regions represented here: Region 1: All-Star Guards Region 2: All-Star Scorers Region 3: All-Star Big Men Region 4: Great Guards Region 5: Great All-Around / Two-Way Players Region 6: Great Big Men Region 7: Spot Up Shooters Region […]

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All-NBA Predict #14 – Exploration of Historical NBA Players (Part V, PCA to Cluster Playing Styles)

In our last post, we harnessed the power of PCA even further to look at different ways to break up the PCA bi-plot. With ggbiplot’s “ellipse” argument, we were able to check out where different players lie on the plot. To review, my 3 main questions were: How closely can I map different “types” of […]

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All-NBA Predict #13 – Exploration of Historical NBA Players (Part IV, PCA on Advanced Metrics & By Era)

Advanced Metrics Okay, so at this point, I’ve seen some of the benefits of data exploration methods. I really liked the PCA bi-plot for reasons I rambled about in the last post, namely the balance between interpretability and the value of information provided. Here, I’m just going to throw it at some of the advanced […]

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All-NBA Predict #11 – Exploration of Historical NBA Players (Part III, Principal Components Analysis)

From my last post: In the next post, I’ll try to throw Principal Components Analysis at the problem because… well basically more or less because I want to learn it regardless of whether it applies here or not! Do I need more of an intro? Let’s go! (This has more or less become my favourite […]

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