Hey all — it’s been some time since I’ve posted on here, so thought I’d make my comeback with a simple, straightforward machine learning tutorial.
Today, we’re going to:
Each traveler rating is mapped as Excellent (4), Very Good (3), Average (2), Poor (1), and Terrible (0). The average rating is used against each category per user.
I’m going to preface this article by saying I have zero preference for genre or subject matter. My idea of expanding the horizon means going for books you wouldn’t normally gravitate towards. For example, I choose books based on the following (not exhaustive) criteria:
Read more. It allows you to borrow someone else’s brain, and will make you more interesting at a dinner party.
― Matthew Dicks, Twenty-one Truths About…
Jealousy is an emotion that is avoided in conversation and disregarded when shared publicly. Society has painted it to be ugly and abnormal. If that were 100% true, why does everyone experience jealousy to a certain degree? The answer lies in the psychology.
Let’s first acknowledge that hacking has negative connotations surrounding it. Society has ingrained us to picture a person with a hoodie over their head sitting in a dark room, surrounded by computers and coding at an ungodly pace. That a hacker is someone who intends to perform malicious acts.
I’m here to tell you this is not entirely true. To me, hacking looks more like this:
Lending Club connects people who need money (borrowers) with people who have money (investors). Investors tend to give money to people who are less risky and more likely to pay their loans back. With that said, we are going to predict whether a borrower paid their loan back in full.
To do so, we are going to create a Random Forest model and a Support Vector model using the same train/test data. The final model will minimize the number of borrowers who were predicted they paid back their loan in full when they actually did not (our model selection criteria)…
In this article, we are going to put a spin on my previous Medium post where I used Logistic Regression to predict whether or not a patient had a positive breast cancer diagnosis. If you need a refresher, you can find the first post here and can see the dataset and full code here.
Breast cancer is the second most common cancer and has the highest cancer death rate among women in the United States. Breast cancer occurs as a result of abnormal growth of cells in the breast tissue, commonly referred to as a tumor. A tumor does not mean cancer — can be benign (no breast cancer) or malignant (breast cancer). Tests such as an MRI, mammogram, ultrasound, and biopsy are commonly used to diagnose breast cancer.
In this tutorial, we are going to create a model that will predict whether or not a patient has a positive breast cancer diagnosis based…
Here’s how to start differentiating between reality and “fake news”.
You’ve heard the age-old saying of “curiosity killed the cat but satisfaction brought it back”. Now you’re going to become well-versed in the data science version. As a general rule of thumb, a data scientist is equally part (1) computer scientist (2) machine learning specialist (3) mathematician (4) statistician (5) engineer (6) researcher (7) business expert and (8) storyteller. In other words, I’m pretty sure I just described a unicorn. And yeah, my eyes are rolling, too.
This is the easiest to discuss first — Don’t.
Data enthusiast, lifelong student, avid reader, aspiring polyglot | M.S. Analytics & Info Management | B.S. Psych