Before we have a clean dataset, let's take a look at an example JSON response we scraped from the website. This is a JSON response from the first theme.
When you click a theme in the Kuler website, it shows the theme's page where you can see an enlarged image of the theme, and other information. On the right side, you can see "Action" and "Info" frames. The latter has following information: Author of the theme ("Created By"): nominal Date created ("Created"): ordinal Number of views ("Viewed"): quantitative Rating: quantitative (shown in number of stars) Number of likes ("Appreciated By"): quantitative Tags: nominal
This is an introduction to a toy project that I worked in 2015 when I was applying for Insight Data Science Fellowship. This was my first data science project (still unfinished) using unsupervised learning for clustering popular color themes in Adobe Kuler. I will talk about important steps in the project in the following posts.
One of the projects I have been working on is to measure one's irrelevant memory. Wait, what? Yes, irrelevant memory. Let's say there is an object with color and orientation, like an ellipse with a color. I ask you to memorize orientation. Now, orientation is relevant and color is irrelevant. If I want to test whether color information has been automatically registered (or encoded) in your memory, what shall I do? There is one way to test this. Let's say we have 100 trials of an experiment. For the first 99 trials, I ask you to only memorize orientation from a display. At the end of each trial, I test your orientation memory. But at the 100th, last trial, I ask you to recall color. And yes, you did not see this coming. That is the most important part : you should not know about the last trial!