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Accompanying Jupyter Notebook

Recently, I was challenged to make predictions about loan application requests, predicting their outcomes, using a dataset comprised of only 13 features. Prediction choices were limited to loan-request-approval or loan-request-denial, making it a classification problem. Would we build a model that performs any better than just picking the most frequent outcome for loan approvals?

Predicting approvals may be helpful to a loan officer, allowing them to spend more of their time vetting loans likely to be approved, ultimately increasing their loan closing volumes.

Step 1. Explore and Wrangle the Data

Let’s take a peek at the dataset…


This post scrutinizes some of the big-picture narratives, largely accepted as truth, that are often shared about the financial markets. It looks to determine if there are any relationships between these ideas and S&P 500 index levels; by no means was this pursuit meant to be an all inclusive or exhaustive assessment.

Anyone interested in checking out my data wrangling, the statistical analysis utilized, or how I created the visualizations used in this post, can find my notebook here.

Peter Lynch, head of the Magellan Fund at Fidelity Investments between 1977 and 1990, ran the best-performing mutual fund in the…

Frank Howd

Studying Data Science at Lambda School | UCONN Alumni

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