Almost 130 years later, Americans still donate hundreds of billions of dollars to charities each year — $450 billion in

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!

You can explicitly make a list of the name of your favorite colors. Now we are ready to do the visualization. We will loop through each category and plot them one by one to make a total plot.

The presentation is about the presenter and the message that is conveyed. An amazing presentation is most often the result of a very engaging and convincing delivery of a message by a person, rather than a bunch of well-formatted, catchy looking slides.

Make sure that your slide deck is serving as a tool to help emphasize points you are looking to make to your audience. Your presentation relies on your marketing and pitching skills and not on the words and images you have on your slide deck.

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!

This is the most common misconception that people have. Your slide deck or presentation material is only a tool meant to aid and enhance your presentation, and it is not the presentation itself.

def encircle(x,y, ax=None, **kw): if not ax: ax=plt.gca() p = np.c_[x,y] hull = ConvexHull(p) poly = plt.Polygon(p[hull.vertices,:], **kw) ax.add_patch(poly)

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.

You can add one more variable in this dataset that will control the size of the dots. For this, I will include the body mass index(BMXBMI). I will make a separate column names ‘dot_size’ that will be body_mass index multiplied by 10.

[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765, 1.0), (1.0, 0.4980392156862745, 0.054901960784313725, 1.0), (0.17254901960784313, 0.6274509803921569, 0.17254901960784313, 1.0), (0.5803921568627451, 0.403921568627451, 0.7411764705882353, 1.0), (0.5490196078431373, 0.33725490196078434, 0.29411764705882354, 1.0), (0.4980392156862745, 0.4980392156862745, 0.4980392156862745, 1.0), (0.7372549019607844, 0.7411764705882353, 0.13333333333333333, 1.0), (0.09019607843137255, 0.7450980392156863, 0.8117647058823529, 1.0)]

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.

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.

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bers the exhaustion and mania that comes with having a small child — and also having to deal with tech snafus involving massive corporations — I can laugh at that because I know how she feels. I’ve never done anything like that, but I can see how someone would feel pushed just far enough. That’s what a viral video like this one does: It exposes the emotions that burble around just below the surface of us all.

In the next plot, I will take the first 500 data from the dataset to plot, assuming that the whole dataset is organized randomly. I will add one more twist to it. I will add another variable. That is age. Because age can have an effect on blood pressure. Here I will encircle the data where age is more than 40. Here is the code

With respect to doing PCA, when you do SVD it will produce n-singular values / n-singular vectors equal to n-features. So if you have 13 features in your data it will produce 13 singular values / vectors. We can select which singular values we want to use based on how much variance they explain.