Data engineering capstone project github

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Find Free Public Data Sets for Your Data Science Project | Springboard Blog

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Become a Machine Learning Engineer

This capstone project course will give you a taste of what data scientists go through in real life when working with data. You will learn about location data and different location data providers, such as Foursquare. You will also learn how to be creative in situations where data are not readily available by scraping web data and parsing HTML code. You will utilize Python and its pandas library to manipulate data, which will help you refine your skills for exploring and analyzing data.
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In Part 1 of my capstone project blog, I provided an overview of my capstone project, based on the Genetic Engineering Attribution Challenge, hosted by DrivenData for altLabs. I gave a brief primer on DNA and plasmids, and outlined the problem that altLabs is seeking to address the ability to identify the lab-of-origin just by the DNA sequence and some binary features of the plasmids. I followed this up with data exploration, feature engineering, and initial modeling using Random Forests, and commented on the performance of the Random Forest models. Finally, I laid out a thought process for evaluating which additional modeling approaches might be useful given the unique characteristics of DNA sequences relative to language or image processing, providing a rationale for using a 1D Convolutional Neural Network CNN for the next phase of modeling. For my Flatiron data science capstone, I chose a unique, engaging, and challenging— very challenging, as it turns out—problem posed by a current data science competition the Genetic Engineering Attribution Challenge, sponsored by altLabs and hosted by DrivenData to develop new algorithms to predict the labs-of-origin for DNA constructs called plasmids.
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