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Matthew Curry's Projects

census-income-classifier icon census-income-classifier

This is a classifier which takes Census Data and builds a logistic regression model to predict whether a particular individual makes above or below $50,000 a year. The script features data manipulation to deal with NaN values and to group overly granular features. It then features hyper-parameter tuning to determine the optimal type of regulization and the regularization constant before testing the best model on the set of test data (yielded 85% classification accuracy!!!!!!!)

coffee-shop-event-driven-simulation icon coffee-shop-event-driven-simulation

This is an event driven simulation of a coffee shop. The program takes a text file including the arrival times of customers to the shop, the revenue per customer, the cost per cashier, and the time to process a transaction and uses the principles of object oriented programming, event driven simulation, and various data structures to simulate the shop and calculate net profit for the day. This program was used to determine the optimum number of cashiers to use in the shop by changing this variable between runs and comparing net profit values.

community-center-calculus icon community-center-calculus

In a capstone Mathematics course at Lafayette College I was tasked with creating and presenting a model to optimize the Community Engagement Center's transportation schedule and to clearly present the model's results. To complete this project, I worked with a partner who was primarily responsible for developing the program, while my main task was to clearly write our results and define mathematically equations to describe how our program worked. This is the paper which I primarily wrote which I included to show my ability to convey technical information in writing.

data-structures icon data-structures

What I had remaining from CS 150 at Lafayette College. Implementation of various data structures in Java

keploy icon keploy

Unit Testing Toolkit that creates test-cases and data mocks from API calls.

nba_power icon nba_power

The following Jupyter Notebook seeks to compile data from 3 different Excel documents to ultimately relate Twitter engagements for a player with the player's salary and stats to see what player specific parameters are best at determining social media popularity. The approach to this question is to build a multiple regression model, test and verify its various assumptions, and ultimately examine the p-values of the different features.

re-region-api icon re-region-api

API to access demographic statistics and individual taxation estimates for US states and counties deployed on the AWS cloud.

re-region-etl icon re-region-etl

Dockerized ETL CLI tool to load source data for the re-region-api (https://github.com/Matthew-Curry/re-region-api) into a Postgres DB from the Census Bureau Data API and excel files produced by the Tax Foundation.

retireright icon retireright

Personal finance SPA that uses a Monte Carlo simulation to model user net worth based on various investment and lifestyle choices. Deployed using AWS serverless technologies.

sql_on_excel icon sql_on_excel

A simple command line tool to allow execution of SQL on Excel and CSV files by constructing temporary SQLite Databases. Outputs results of queries to an excel file.

stanford-machine-learning-week-1 icon stanford-machine-learning-week-1

This is my solution to the week 1 programming assignment for the CS 229 course on machine learning offered through Coursera. This assignment involved implementing linear regression through the gradient descent algorithm. To complete this assignment, I altered the warmUpExercise.m, plotData.m, gradientDescent.m, computeCost.m, gradientDescentMulti.m, computeCostMulti.m, featureNormalize.m, and normalEqn.m files

swagger-ui icon swagger-ui

Swagger UI is a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger-compliant API.

what-aspects-of-culture-impact-happiness icon what-aspects-of-culture-impact-happiness

This script compiles data from multiple excel files to create 3 separate regression models using cultural indices defined by Professor Geert Hofstede to explain national happiness levels from the 2018 world happiness report. The first model is a regression using just the cultural variables to explain happiness, while the later two models add features to limit omitted variable bias. Finally, the script includes several functions that analyze how statistical significance of the cultural indices changes between models to explain which omitted variables were responsible for the biased estimates and how they were responsible for such errors.

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