For my assignment, I decided to interview my brother, William, who is in his 3rd year at Dartmouth College. He is an engineering major, and just finished a class in Applied Machine Learning. When I explained my project to him, he immediately identified it as a problem which is well suited to be solved using recurrent neural networks. He used python to create a recurrent neural network that uses the past 24 months to predict the next months ESNO value. He walked me through how to use this software and how recurrent neural networks work (which I will explain more in my next blog post). Although this current version only predicts 1 month in advance, it is possible to adjust it to predict further ahead. However, predicting further ahead will decrease accuracy, which is not ideal. The graph which I have attached below shows predictions for 250 past months. We decided to test past months just to see how close the predicted values were to the actual values. As you can see, our prediction was incredibly close and the graphs are almost identical. I asked William what other things this type of neural network could be used to examine. I was also extremely curious in machine learning, and asked what other types of machine learning algorithms exist. William told me that this type of recurring neural network is commonly used for stock market predictions, and other instances when you want to use past experiences to predict the future. He also shared with me that there are decision trees, support vector machines, and clustering algorithms which are all different types of machine learning that have their own advantages. I hope to be able to learn more about these in the future!
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