In 2012, some scholars from Stanford published an article saying that they could use the Recurrent Neural Networks (RNN) to predict the stock price. I knew that RNN models could make powerful time-series predictions because of long short-term memory (LSTM). While I didn't expect that it could be used to predict the stock price, so I tried it myself, and I also got a very accurate model.
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I used to use Tableau during the data exploitation and reporting stages until I started using AWS QuickSight. AWS QuickSight is composed of Quick and Sight. After using it, I feel QuickSight is too quick and too insightful. Due to my Tableau background, I found out that QuickSight is very similar to Tableau and it also has some features that Tableau doesn't have. In this project, I'll show you my experience of using AWS QuickSight.
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Self-Organizing Map is a type of unsupervised deep learning model. The purpose of using SOMs is to reduce the dimension of our dataset and create visuals. In this project, I combined SOMs and the Artificial Neural Network to create a model for fraud detection. We don't want to see that our customers are cheated, and then we perform supervised learning to protect them in the future. Fraud has to be stopped before it happens.
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Building ETL pipelines should be data engineers' jobs. While as a data scientist, it's also necessary to know the fundamental ideas of data pipelines. In this project, I choose a public dataset from Deutsche Börse Public Dataset. By using AWS S3 and python, I'm able to extract the data from the original AWS S3 bucket and transform the data, then load the data to my AWS S3 bucket.
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This is an example of how I use classification models to identify malignant and benign for breast cancer. Usually, a 90% accuracy model is good enough, but it's not good enough for identifying cancer. Even though this is just an example and no one would use my models, I still treat this example seriously. That's what data scientists are about to do in the future, changing the world.
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