In this blog plot we will explore the technical parts to my pervious blog post where I gave a brief introduction to the importance of Text analytics and some preliminary analysis. I will cover my preprocessing steps that were used to develop a text classification model.

To gain insights within you text data there are methods used to understand the properties of each document found within it. One common task that is routinely performed in Natural Language processing is Tokenization. Tokens are meant to separate a piece of text into smaller segments that can either be a word, combination of…


We live in an era where we are constantly communicating with each other through different mediums. Whether it be in text messages, audio, or video chats we can stay connected with one another without the boundaries of distance with the use of modern technology. We are also constantly participating in online activities such as reading articles, websites, blog posts, social media posts that keep our attention in content that we are interested in. All these interactions generate data at incredible speeds. Therefore, the ability to filter through these unstructured textual data is providing businesses the upper hand to lead in…


Through out my journey as a Data Scientist you learn quickly that your role does not only include performing the latest trending algorithm but understanding how to apply these techniques into the respective business you are in. One of the best ways to communicate these models and insights are through powerful visualizations. This blog post will consist of an introduction to two useful tools every data scientist should explore and add to their analytical toolbox. You will understand the differences for both Tableau and Power BI followed by a use case using Tableau.

Tableau is a powerful data visualization tool…


Welcome back! This will be the last part to this four-part series. If you missed the last couple of articles, I recommend going back and catching up.

Here are some quick links to the article:

1. Web-Scraping Techniques with Selenium

2. Sentiment Analysis of News Headlines: Ethereum

3. Ethereum Close Price Prediction Model: Random Forest & XGBoost

Just a recap, in the last article we spent a few minutes creating a classification model where we evaluated two different well-known methods in creating classification models: Random forest & XGBoost

In this exercise I was following a CRISP-DM methodology: A common methodology…


Let us continue from where we left off on my last article, where I conducted a sentiment analysis on news headlines for the cryptocurrency Ethereum. In this article the goal is to try and determine if our sentiment analysis will produce a successful model that can predict if the market will close higher or lower than the day before. Let us start off with looking at the dataset I created.


It is no surprise to us that many news headlines are worded in a way to lure you into clicking onto them. This year has brought about a flood of new retail investors into the crypto market space and if you’re one of them I can bet you have been trying to keep up with every news and information regarding it. One website you might be familiar with is Coinmarketcap.com. We will be performing a sentiment analysis on news headlines from Coinmarketcap.com …


One of the most important process in the realm of Data Science is the process of acquiring reliable datasets to apply these powerful, game changing techniques such as Natural Language Processing, Recommendation systems, classification algorithms and many more. There are a few methods in doing this but in my opinion the most refreshing and challenging methods is creating your own dataset from scratch. In this case you are faced with many obstacles, one being your ability to access data from a reputable source. This step is probably the hardest to do independently so here are a few questions to ask…

Eric Gustavo Romano

Hi! My name is Eric Gustavo Romano. I am a data science enthusiast and practitioner located in Jersey.

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