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Photo by Ian Schneider on Unsplash

Introduction

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:

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Photo by Austin Distel on Unsplash

Hacking, in this context, is when you are “hacking” a problem. In other words, you are:

  1. Identifying and defining the problem
  2. Assigning a diverse team to handle the problem
  3. Collaborating with team members
  4. Creating…

Learn how to implement dummy variables, scale features using StandardScaler, optimize parameters via GridSearchCV, and choose the best model.

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Photo by Alexander Schimmeck on Unsplash

Background

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). …


Use this phenomenon as a checks and balances system for your regression models.

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Photo by Christophe Hautier on Unsplash

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.

Logistic Regression Explained

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Photo by freestocks on Unsplash

Logistic Regression is a classification model that predicts a binary outcome. Binary means you have two choices — typically 0 and 1. …

About

Mo Kaiser

I dig finding patterns in data and behavior. | M.S. Analytics & Info Management | B.S. Psych | Connect with me: https://www.linkedin.com/in/kaisermorgan/

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