Presentations about data science.
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Detecting_Implicit_Bias_in_Traffic_Stops by Mark Ferguson.
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Lemons: Predicting whether a Vehicle will be kicked back to the auction by Will Morgan.
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Predicting the success of cyber-related terrorist attacks by Rebecca Green.
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Breast cancer survivor models by Rich Gohram.
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Predicting Disruptive Children (Including visualization of PCA on binary variables) by Greg Condit
- Math lectures Part 1 Combine NLP with supervised and unsupervised learning to classify math lectures. By William Morgan.
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Cover to Cover: A (not so) Novel Approach to Book Reccommendations by Mark Espina. The saying goes "Don't Judge a book by it's cover" But Why? Anyone who shops at a local bookstore is definitely paying attention to the covers. And from personal experience, it is a key determinant on whether I end up purchasing a book. First, I will discuss the pros and cons of applying Convolutional Neural Nets to Image Classification, attempting to predict genre labels. In the second half, I will be exploring the application of feature extraction with similarity models as the basis for an Image Content-based retrieval system, Cover-to-Cover.
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Using machine learning to cluster and classify math lectures by Will Morgan. Using machine learning to cluster and classify math lectures.
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Capstone_2016_us_elections by Emile Badran. In this capstone project, I process tweets from the leading Democratic (Hillary Clinton) and Republican (Donald Trump) candidates and key 2016 US election hashtags. I apply Natural Language Processing and Network Analysis techniques to find the key topics, and the most influential actors that have guided the public debate.
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DNA Sequence detection with Genetically trained weights by Chistopher Sanchez
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