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Photo by National Cancer Institute on Unsplash

My primary goal with this article is to highlight a practical application as a result of building a convolutional neural network (CNN) model. In general, CNN models have a wide variety of applications; in this case, it’s building a model that can accurately detect pneumonia in x-ray images. Sounds cool, right? But why would we need a convolutional neural network when we have medical experts that can perform the same task?

Why would we need a CNN to detect pneumonia?

Across the world, there is a general lack of radiologists, and this number continues to diminish which causes significant resources to be spent in order to determine the results of medical imaging. In many cases, a lack of a radiologist delays test results. This could also mean relying on medical professionals that don’t have expertise in radiology, leading to misinterpreted results. Getting accurate results within a short period of time can be a difference-maker and possibly a life-saver for certain patients. …


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Police reform is a huge topic these days, especially as we get closer to a presidential election with two candidates who seem to have very different ideologies on the matter. A topic that could potentially be reviewed when looking at police reform is Terry Stops. In 1968, the Supreme Court ruled that if an officer has reasonable suspicion that a person committed, is committing, or is about to commit a crime, the officer is not violating the Fourth Amendment’s prohibition on unreasonable searches and seizures if they choose to stop, investigate, and even frisk the suspect to determine if they are armed. As you can imagine, racial profiling could potentially be a huge issue, and has long been a topic of discussion. Rather than focusing on the subject of race, my goal was solely to predict the highest amount of arrests as a result of Terry Stops at the time of initial suspicion. …


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This project involed an iterative approach to building a multiple linear regression model with python, scikit-learn, and statsmodels to predict sale prices for houses in King County, WA, utilizing data of homes sold in 2014 and 2015 (data provided by Flatiron School and a similar dataset is available on Kaggle). The first portion of this project is spent cleaning the data, engineering a few new features, and then building models, tweaking each model until a final model is built. Rather than using log transformations to normalize data, I decided to keep the model as interpretable as possible, using alternative techniques to normalize residuals. As a result, I narrowed down the data to predict homes under 900k, favoring predictions for middle class families. …


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Photo by Jake Hills on Unsplash

This post covers some of the steps involved with basic exploratory data analysis in Python, primarily utilizing the Pandas, Matplotlib, and Seaborn libraries. We won’t be diving into machine learning, modeling deep neural networks or utilizing gradient descent to minimize the cost function for a logistic regression model, primarily because this is my first data science project, so deep neural networks are slightly out of the question (for now)! This post is for true beginners.

If there’s anything that I would want a beginner like me to know when starting out their data science journey, it’s that reading books, articles, blogs, and watching video tutorials are all great (and necessary) things to do to expand your knowledge, but completing data science projects is the #1 method to gain applied knowledge and instill concepts that you have learned. I’m sharing my experience with hopes that other aspiring data scientists can not only relate to the pains and frustrations, but also the overwhelming feeling of success after completing a project, no matter how elementary you think it might be. Personally, I had to knock down a ginormous wall of self-doubt and completing my first project was key to doing that (not saying all self-doubt is completely gone, but I’ve learned not to be so hard on myself). …


I grew up in a very religious home, including going to church every Sunday morning, Sunday night and Wednesday night. I went to a small Christian school K-12th grades, which eventually led me to go to one of the few Christian colleges that was highly encouraged in that environment. Truthfully, I had no idea what I wanted to do with my life, but I thought appeasing my teachers and my peers was the right thing to do at the time.

Despite realizing midway through my junior year of college that this was not the path for me, I ended up sticking it out and graduated with a B.A. in Biblical Studies. As you can imagine, the amount of job offers was incredibly overwhelming (i.e. zero). …

About

David Bartholomew

Aspiring Data Scientist, currently attending Flatiron School’s online data science program.

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