I created some easy to use functions that include all the all the combined step-by-step code in my GitHub repo. Hopefull

To be honest this story is a bit half-baked as I realize Zig is just too big of a topic to cover in some grans sweeping story. There are so many other topics I could cover but which would simply make this story excessively long. Instead I will attempt to write multiple smaller articles focused on different aspects of Zig in the future.

One may ask, why not build these kinds of things into the language? If you have ever used Lua, you will know some of the advantages of instead giving you the building blocks to create an object-oriented system rather than hardwiring it.

Over the past ten years, the Toronto Raptors have been the best drafting team by a wide margin. This has contributed to seven straight post-season appearances, capped with an NBA Championship in 2019. Meanwhile, Cleveland ranked as the worst drafting team.

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only seven medieval cages left in Britain, the St. Osyth Cage had languished on the market for several months. The owner jumped at Mitchell’s offer of £147,000 ($191,765). “I could see myself living there and one day meeting someone and getting married,” she recalled. “Just like everyone else when they get a new start.”

To be honest this story is a bit half-baked as I realize Zig is just too big of a topic to cover in some grans sweeping story. There are so many other topics I could cover but which would simply make this story excessively long. Instead I will attempt to write multiple smaller articles focused on different aspects of Zig in the future.

Per figure 1 below, you might select 2 PC’s based on scree plot elbow with 58% variance explained or 4 PC’s on cumulative sum with 74% of variance explained. The main point to illustrate here is that from the original feature set of 13 features (Boston Housing data), it appears we can reduce down to a much smaller representation (2 - 4 PC’s) while retaining the valuable information using PCA.

There is no globally accepted selection threshold for a lower-dimensional space. Selection can be done by plotting the variance explained ratio by singular values (principal components) with a cumulative sum and/or skree plot (Figure 1 below). Once you visualize variance explained you can balance the trade off between the “elbow” in a scree plot and the cumulative sum of variance explained.

The results of this analysis also empirically validate the vital link between drafting success and winning. The relationship between team drafting scores and winning is strong and statistically significant — an increase of 0.1 in team draft score is associated with 3.7 more regular season wins in the last NBA season. Of the fourteen teams with an above average draft score, ten of them made the playoffs last year. Among the bottom five teams, none made the playoffs.

This roll-your-own approach can be very powerful. We have seen LISP programmers use this to build object-oriented programming systems in LISP, even creating multiple-dispatch systems akin to Julia.

In this analysis, I have created a metric that scores each draft pick by comparing them against the players taken after them (the full methodology is available at the end). After applying this over the last 10 years (the drafts from 2010 to 2019), we can now measure which teams have been successful in their drafting and what teams have not.

In Zig we use a similar approach described in more detail here by Nathan Michaels. Zig offers better features for doing this than C, so you see it use more to create generic iterators, allocators, readers, writers and many more things in Zig.

Drafting is one of the most crucial elements of a successful long-term franchise. Fueled by a series of great draft picks, the San Antonio Spurs went on to make the playoffs for a record-tying 22 seasons while winning five championships in the process. On the opposite end of the spectrum, the Sacramento Kings are currently riding a 14-year playoff drought — the second longest in NBA history. Unsurprisingly, this analysis ranks them among the teams least successful at drafting.

I created some easy to use functions that include all the all the combined step-by-step code in my GitHub repo. Hopefully, you now have a more intuitive understanding of PCA and can speak to it intelligently when asked in conversation or your next data science interview!

Well, that was a 5-minute introduction to PCA using SVD. We talked about some applications for PCA and then walked through the steps to do PCA using SVD. As you can see its not at all black box and really only a few steps to transform and reduce your data. The next time you’re working with a high dimensional dataset you can use these steps in your favor.

With Zig you could build C style object-oriented system, something more akin to Go or even object-oriented programming more similar to a more dynamic language like Objective-C.