Data Science Projects

  • Linear Regression Project

    This project features data from a NYC ecommerce company that sells clothing. The company wants to know where to focus their marketing efforts: mobile application or website. This projects uses Python to process, model and visualize the data

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  • Web Scraper for NewEgg Graphics Cards Information Code Example

    This project focuses on web scraping data regarding computer graphics cards on the website, NewEgg. This was written in Python and primarily used the Beautiful Soup library

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  • Logistic Regression Project Example

    This project features data from an advertising firm that hopes to predict engagement with a specific advertisement. This project uses Python to process, model and visualize the data

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  • Random Forest/Decision Tree Example

    This project features data from LendingClub.com. Using both a Random Forest model and a Decision Tree model, this report tries to create a profile for lenders to find potential borrowers who will be more likely to pay off their loans

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  • Natural Language Processing (NLP) Project Example

    This project features Yelp review data to predict whether a review will be 1 or 5 stars based on the text content of the review. This was written in Python and primarily uses the Sci Kit Learn library and The Natural Language Toolkit (NLTK) suite of libraries

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  • Twitter Sentiment Analysis

    This project uses the Twitter API and Python to gather tweets from the Human Rights Campaign in order to categorize the last 100 tweets into positive or negative categories

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  • K Nearest Neighbors vs Support Vector Machine Project Example

    This project features data from a Cleveland database that hopes to predict the presence of heart disease within a patience based on specific health and demographic predictors. This project uses both a K Nearest Neighbors model and a Support Vector Machines Model for the analysis

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  • Exploration of Modeling Techniques to Predict Loan Risk

    This project presentation focuses on creating a model that calculates risk of credit loan default. The model uses information about the borrower, loan information and credit history to predict the riskiness of the loan. An overview of a few modeling techniques is included in this presentation

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