Hi, I'm Eric!

 

» About me: Passionate data scientist with a twist of exceptional soft skills. An experienced veteran at data-storytelling to non-technical audiences. 

» Special trade: Incorporate math, science, and philosophy to deliver both quantitative and qualitative predictions through machine learning.

 

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What are the benefits to online dating? Can't we just meet people in school, at work, or in public? Naturally, we don't all have the finesse to do that. Perhaps we are shy, not into office drama, or just not social enough. 

 

The benefit to online dating is that we can present ourselves in a wall of text without ever leaving our busy lives. How convenient! Also, we can filter out other users that don't meet our standards rather than asking uncomfortable questions in person. However, with online dating, there are downfalls to the benefits. What if people aren't honest and how do people get quality matches?

 

This analysis will try to answer and give suggestions to improve the online dating experience. The full paper and code can be found on my GitHub. (I highly recommend you take a look at this if you are interested in the science behind the data!)

Predicting Traffic Signs with Neural Networks

There's nothing more important than safety when you are driving a vehicle. Have you ever commuted to work, school, or a frequent place only to arrive with no recollection of what happened during your drive? Do you feel safe? I trust myself, so yes. But not always. The ability that we have to do such things comes from our brain being able to use past events to guide us to drive while continuing to learn as are aware of our surroundings. What we get used to are things like weather, traffic signs, motion, and other objects that we react to. (Hopefully) Our brain uses it to predict what we are likely to do, which could be simply driving to work while avoiding obstacles. However, what if one day, we weren't able to be entirely safe due to fatigue or another factor that our brain cannot simply react fast enough to?

Handwritten Digit Recognition with Convolutional Neural Networks

There's nothing more frustrating that waiting in front of an ATM during your lunch break to deposit a check. Many bank patrons today use a feature on their phones to deposit checks which uses their phone cameras. But just how accurate is it to be able to trust? Sadly, we don't have that information available but I would consider anything under 1% error to be excellent. Luckily to us, we can make a neural network to mimic what these apps use. In this project, I will develop a deep learning model to achieve a near state-of-the-art performance on the MNIST handwritten dataset. I'm going to use Keras with TensorFlow.

Recurrent Neural Network to Predict Stock Prices

We can use a neural network to use 5 years of stock data from Yahoo Finance to predict our current year's stock prices. This study is based on a paper from Stanford University.

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