The following is the first part in an ongoing project to analyze sentiment on the famous (or infamous?) subreddit, r/Wallstreetbets. The million dollar question is: can bullish or bearish sentiment on r/Wallstreetbets in combination with stock fundamentals be used to build a model predicting the direction of a particular stock price?

Disclaimer: This is not investment advice. If you make investment decisions based on this article or anything I write, you will likely lose money. Please do your own research and make your own investment decisions.

The Process

Even though this project is still in the research phase, there is…

According to Federal Reserve Economic Data, credit card delinquency rates have been increasing since 2016 (sharp decrease in Q1 2020 is due to COVID relief measures).

The bank performs a charge-off on delinquent credit cards and eats the losses. If only there was a way to predict which customers had the highest probability of defaulting so it may be prevented…


Can we reliably predict who has is likely to default? If so, the bank may be able to prevent the loss by providing the customer with alternative options (such as forbearance or debt consolidation, etc.). …

Github repo for this can found here

After the Gamestop fiasco with the subreddit r/wallstreetbets, I became very intrigued with the stock market. It promises the opportunity of extreme wealth. If you could only be right on one stock, you can change your life forever. So many people have pursued this dream, and yet so many people have had their dreams crushed like Tom Brady did to the Kansas City Chiefs.


The number of variables that go into the price of a stock are countless. The average human cannot possibly fully discern how all of this works. Processing tens…

I recently dove into a project to conduct a topic modeling analysis on President Donald Trump’s speeches. I web-scraped 8 speeches from October 2020 in the following states:

  1. Arizona
  2. Nebraska
  3. Wisconsin
  4. Michigan
  5. Pennsylvania


Step 1: Import data/text

Step 2: Clean and preprocess data/text

Step 3: Perform topic modeling analysis

Step 4: Recommend talking points based on extracted topics


Speech transcripts (text) can be found here


I built a function in python to automate this for any link from the website mentioned above:

import requests
from bs4 import BeautifulSoup
from IPython.core.display import display, HTML
def get_transcript(url):

The following is a breakdown of a project I completed at Metis Data Science Bootcamp

Goal: To web scrape sneaker data from and use linear regression to predict sneaker prices

Tools: Python, Pandas, Numpy, Selenium, BeautifulSoup, Scikit-Learn, Statsmodels, Matplotlib, Seaborn

Features: Age, # of Sales, Volatility, Price Premium, Brand Name

Target: Sale Price

Data Cleaning

The data came on very messy. All of the columns were the wrong type, there were missing values and NaNs, and there were unusual elements (such as ‘?’, ‘/’, etc.). So all of that had to be cleaned up. Furthermore, outliers and NaNs were removed.

Exploratory Data Analysis


Allow me to introduce myself. I am an aspiring data scientist in northern California. I come from a banking background and I plan on transitioning to AI in lending. Data science in lending is still in it’s infancy and I can’t wait to delve into this world and make interesting discoveries. I have joined a bootcamp to help me transition into the data science field.

Last week I started my first week at the Metis Data Science Bootcamp. It was one of the most rigorous weeks intellectually I have ever endured. Eight hours of programming, lectures, and group project work…

Marcos Dominguez

Data Scientist with a background in banking and finance

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store