Text Generation with Deep Learning (RNN)
Introduction
I have always heard that the best ideas just come to you without effort, and if you are lucky, at the exact time that you need them. I am someone with a lot of ideas and often love to explore them in the real world if possible. In this case, I wondered: “Could I train a computer to rap like a human?”. Fortunately I was in the position to actually try this out!
I was learning about Deep Learning, a method of Machine Learning using artificial neural networks, when I came across a video covering text generation with TensorFlow (link)…
In my opinion, finding ways to create visualizations during the EDA phase of a NLP project can become time consuming. Preprocessing is a lot different with text values than numerical data and finding the numbers to plot on a graph is possible, but usually requires some engineering. Luckily, there are some tools out there to help folks like myself in this stage!
In my last post, I went over a few quick ways to generate some visuals using a bag of words (BoW). …
Creating visuals for a NLP project
Introduction
In my opinion, every data science project could use some kind of visual, no matter how simple. This is normally not an issue, as there are plenty of ways to display numerical information in a meaningful way. However, when it comes to a projects involving Natural Language Processing (NLP) I find myself at a loss for where to start.
This is because often the dataset for these types of projects can be a “bag of words” (BoW), which is exactly what it sounds like and be quite challenging to make something like a scatterplot out of. …
How to avoid being blocked
In this post I will be going over one of the many ways one can save themselves from losing access to a specific website during web scraping. Specifically, I will detail the creation of a custom Python function I used to scrape a popular song lyric website for a Natural Language Processing project.
Since this post will be more about implementation rather than theory, this walkthrough may not work for your situation. If you are looking for more ideas on block-avoiding options I would recommend checking out this great blog post by Manthan Koolwal.
Getting Started
To create this function, there are…
NLP Sentiment Analysis with Deep Learning
This is the big leagues. Deep Learning with NLP. Something that before now I would imagine to be impossible with a standard laptop. But this project turned out to be a fun, an intriguing exploration of how a revolutionary technology can be deployed on a stock computer.
In this post I will explain how I took a data set of tweets from the internet and used deep learning via Keras to classify tweets according to their sentiment. …
Evaluating Classifiers with a Custom Function
In this post, I will go over how I created a function in Python that easily displays some performance metrics of a trained classification model. Since this function came about as part of a larger task, I will provide context along the way to help clarify as I share images and code of my process. Primarily I will be working with the Sci-Kit Learn and Matplotlib packages but have a special appearance from Pandas plotting method, Yellowbrick’s classifier module.
Background
I was given a simple project: use a premade data set to create a use case for a classification model, then…
Part 4: Predicting the vote
This post will cover the last component of my supervised machine learning project in which I used web scraping to create a data set on the candidates in United States Senate general elections starting in 1920 through 2016.
In part 3 of this series, I went over what kind of information I was able to glean from my data set using traditional Exploratory Data Analysis (EDA) techniques with the Pandas and Matplotlib Pyplot packages. …
Part 3: Exploring the data
In the previous two blogs, I detailed how I used the Requests, Beautiful Soup and Pandas packages to turn multiple Wikipedia pages covering United States Senate general elections into a data set for use in a machine learning model.
- Part 1 went over how to web scrape a single web page and store it’s corresponding HTML components locally
- Part 2 explained how I used that web page to access and scrape other pages using one function
This part will explore the final data set that I was able to put together using the methods detailed in the previous to posts…
Part 2: Scraping multiple pages at a time
In part 1, we were working through the beginning stages of creating a data set directly from information available on Wikipedia. In that post I covered how I used the Requests and Beautiful Soup packages to scrape a list of links from one page that would lead to other pages with the information needed for data set. In this post, I will continue to walk through the process I used to create a custom function to retrieve and store targeted information directly from web pages.
Where We Left Off…
At this point we have a list of all HTML elements on our root page…
Part 1: A web scraping introduction
2020 has been a year to say the least. One of the reasons I’ve felt so has to do with politics. I know a lot of folks do not necessarily like to speak about it (fine), but I wanted to do some (political) data science!
I wanted to make use of machine learning to predict the winners of a given election, but soon came to realize there were not many prepackaged datasets with this information available on the internet. This meant that, in order to do what I wanted, I would need to make my own data set through the…
Darius Fuller
Seattle, WA Aspiring Data Scientist
