In my final week of bootcamp, we were tasked with presenting our capstones. Our capstones were to be the culmination of everything we have learned in class. From week one’s python challenges all the way to learning about object-oriented programming (OOP), we were finally down to our last project. Our capstones could be anything we wanted to make it (in terms of data science). I chose to center my project around Natural Language Processing (NLP). The main topic of my capstone was music, more specifically, songs and their genres, my goal was to model two classifiers that used audio attributes…


As most people involved in data science know, there are a multitude of areas one can focus their efforts on learning. This ranges from Natural Language Processing (NLP) to Support Vector Machines (SVMs), all the way to Deep Learning -which is what we are focusing on today. What is deep learning you may ask? I will do my best to explain it to you.

image from Premium Vector | Deep learning connect (freepik.com)

Deep learning is a class of machine learning. It works using neural networks to help it learn and “think”. These neural networks consist of many layers, thus the algorithms in deep learning can have raw data…


For this project, the goal was to collect data from two different subreddit forums (both with certain similarities) and identify whether or not a post came from that particular forum. To do this, I used Natural Language Processing (NLP) techniques as well as the NLTK toolkit. I will go into detail about he project and it’s challenges within this post.

To start, I chose 2 subreddit forums — rollerblading and roller-skating — and I used these as inputs to my web scraping function. I pulled the created_utc, which is a timestamp of the latest post at that time, and I…


Yetti Obasade

It’s week 6 of the course and we are halfway through. A lot has happened in just a few weeks. It doesn’t even seem like that much time has passed! Nonetheless, I have been enjoying this program so far. A few of the topics we have learned about over the last month includes, linear regression, over fitting and under fitting, bias vs variance, classification models, logistic regression, train vs test split, confusion matrices, scaling data, knn models, adaboost, bootstrapping, and a plethora of other data science subjects.

I will explain some of the topics above just to give…


By Yetunde Obasade

There is a lot of things that are interesting about data science, but recently I have come across the topic of racial and gender bias in machine learning. How does this happen? Well, one example is through algorithms that select candidate resumes for hiring. They usually train off what the hiring staff chooses. From there, these algorithms fine tune their selections. These models were found to select names that sounded “black” or ethnic far less than traditional eurocentric names. Despite candidates having similar or better credentials, these biases still existed.

Carrying over into this bias is facial…


I have not written many blog posts before, but this will be my first attempt at writing a decent one. To introduce myself, my name is Yetti, and I recently quit my job to explore my interest in software and data science. This decision was not easy for me at all, but I realized that I wasn’t satisfied with my previous position and the amount of time it would’ve taken me to move up in the AEC industry.

What is the AEC industry, you might ask? It stands for “Architecture, Engineering and Construction” and it is where majority of my…

Yetti Obasade

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