Photo by Daniil Kuželev on Unsplash

Using a convolutional neural network to detect diabetic retinopathy.

For my capstone project at General Assembly, I choose to use the kaggle competition: APTOS 2019 Blindness Detection.

Diabetic Retinopathy is the leading cause of preventable blindness. According to the National Eye Institute, all forms of diabetic eye disease have the potential to cause severe vision loss and blindness.

The Asia Pacific Tele-Ophthalmology Society has sponsored a kaggle competition. The aim is to help people with diabetes in rural areas of India with the early detection of diabetic retinopathy. The people in rural areas are at higher risk since medical screening…


Photo by Ben Hershey on Unsplash

I love/hate fantasy football.

I love watching Red Zone on Sunday and watching my points go up. I love all the smack talk with my friends. I love all the rules and debating every single setting in my league. And obviously, I love analyzing the data.

There are only 2 things I hate about fantasy football: the amount of time I need to devote to it and most importantly, how I never win. After having my best regular season and ultimately losing in the playoffs, I decided to take a year off in 2017. …


Stop running neural nets on your puny laptop!

As data scientists, we all love Jupyter Notebook. But there comes a time when you’re working on a very large dataset and/or a complicated model and your computer just won’t cut it. The good news is that you can take your Jupyter Notebook file and import it into Kaggle. If you’re new to data science, Kaggle is a website that hosts data science competitions with cash prizes. Kaggle also has a wealth of information and a great community that is very willing to help you develop in your data science education.

Another Kaggle feature is that they have free, online…


Instagram ads aren’t as scary as you think (for now).

Let’s say you’re a young professional woman, aged 25–35, living in Washington, D.C. You occasionally like pictures of puppies and local brunch events. You and your friends are thinking about going to the Wiener 500 event this weekend, but haven’t decided yet (it’s a beer and Wiener dog racing event so it’s not really a hard decision). Now you pull up Instagram, and lo and behold, there is an ad for the Wiener 500! Instagram must be spying on you! How else would they know that you want to go?

There’s a lot of money to be made from data-driven…


Photo by Drew Graham on Unsplash

A crash course in making a classifier model in 13 lines of code!

So you want to build your first machine learning model? In data science, we use data to help us make predictions based on what we already know. We’ll try and use a few lines of code as possible: 13 total lines or 9 lines if you don’t count imports!

In this step-by-step tutorial you will:

  1. Import the data
  2. Create features and target variables
  3. Train/Test Split the data
  4. Fit a classification model
  5. Evaluate your model

Requirements

  1. Python 3.7
  2. NumPy 1.16
  3. SciPy 1.2
  4. scikit-learn 0.21

Note: I’m sure the older version of these packages will work but these are just the ones installed…

Andrew Picart

Data Science, yo!

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