An example of using real data from SupplyKick as an ML experiment
So, what exactly is Machine Learning?
Machine learning is a growing concept in computer science, where scientists attempt to teach computers to learn abstract concepts just as a human would. This is a major paradigm shift from traditional programming, in which programs operate using a set of logical rules that are explicitly stated by programmers. In machine learning, the programmers instead develop a “model,” a structure that is able to learn the data that the user feeds into it.
The first thing we need to do was get an actual dataset to manipulate. We did so using Amazon’s Marketplace Web Services API. We pulled in the responses from this API and stored them as CSV files that are easy for our program to read. We then clean the data by filtering out repeat entries or records that don’t contain any data at all. Because the goal is to predict future sales, we ordered the rows by date and totaled all orders on the same day. Finally, through experimentation, we found that the model fits best to data for individual products; rather than for all products, so we filtered down to only rows containing a specific product. The result of all of this cleaning is plotted in blue.
Let’s try getting some predictions! After no training, here’s how well the model fits to the training data.
It’s pretty terrible, but this is what we expect without any training. This would be like walking into a calculus test without even having heard what “math” is. After training on the data 100 times we are given the following result.
It seems to be picking up the general trends in the data but doesn’t quite hit the mark. Lets try doing another 900 iterations through the data, for a total of 1000 training runs.
Now the prediction is clearly fitting to the actual sales trend. Also, keep in mind that although 1000 iterations sound like a lot, the entire training was completed in about 15 seconds. However, the real challenge is fitting the model to data that the model has never seen before. Below are the predictions on the “test” dataset.
Though these predictions aren’t quite as good as on the training set, this is expected, and they are still very accurate overall and likely more precise than a human’s predictions.
What this could look like for you
- 3Blue1Brown- YouTube channel with amazing visual representations of abstract mathematical concepts
- Two Minute Papers- YouTube channel that distills recent machine learning research papers down to 2-minute videos
- Siraj Rival- YouTube channel with tutorials on lots of machine learning concepts
- Google Machine Learning Crash Course- Web tutorial series on machine learning (has more of a business/real-world focus)
- Machine Learning Mastery- Website with tons of machine learning tutorials and resources
- Colah's Blog- In depth explanation of ML concepts by Google Brain Scientist
- Distill- Explains abstract concepts through interactive machine learning widgets
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