Digital Commons Network^™

Predicting Order Status Using Xgboost, Kegan J. Penovich

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Invista, a Koch subsidiary, is a multinational producer of fibers, resins, and intermediaries, particularly nylon. To keep the company operating required them to take over 1.5 million orders over the course of - years, less than a third of which arrived on-time. Orders arriving other than when expected can cause many problems for any company. While arriving late is a clear problem, it also troublesome for them to arrive early. In the face of this, it becomes important to be able to tell a-priori if an order will arrive on-time or not.

To address this problem, we made use of …

Analyzing Suicidal Text Using Natural Language Processing, Cassandra Barton

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Using Natural Language Processing (NLP), we are able to analyze text from suicidal individuals. This can be done using a variety of methods. I analyzed a dataset of a girl named Victoria that died by suicide. I used a machine learning method to train a different dataset and tested it on her diary entries to classify her text into two categories: suicidal vs non-suicidal. I used topic modeling to find out unique topics in each subset. I also found a pattern in her diary entries. NLP allows us to help individuals that are suicidal and their family members and close …

Forecasting Stock Market Prices: A Machine Learning Approach, Abraham Alhomadi

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

There has been extensive literature written about the efficiency of the stock market. Practitioners and academicians have debated whether investors can exploit publicly available information to generate excess returns. Clearly predicting the stock market’s return with high accuracy has been enormously difficult, but we are interested in contributing to the continuous exploration of the efficiency of the stock market using machine learning techniques. We also want to examine the relationship between our dataset’s macroeconomic indicators and foreign nations’ stock markets with our target feature—the S&P 500. In this paper, we will be using supervised machine learning models, like Linear Regression, …

We Can Use Machine Learning To Determine Which Financial Ratios Are Best For Investors, Collin Butterfield

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

This study develops and tests the hypothesis that the machine learning algorithm, Random Forests, can be used to systematically pick financial ratios that would be best for indicating market trends and be used subsequently to perform comparable analysis to speculate whether a firm is over- or under-valued. Results show that financial ratio selection differs depending on the market sector to which a firm pertains. We examine the 11 financial sectors representing the key areas of the economy. We also look at four possible trading strategies that an investor could have: month-long, quarter-long, semi-annual, and annual to capture differing trading horizons.

Black-Scholes And Neural Networks, Gabriel Adams

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Neural networks have been proven to be universal approximators. We use neural networks to investigate the relationship between the quality of input data and the quality of outputted predictions from a neural network. We show that neural networks perform better on option pricing data with quality data and perform worse with lower quality data.

Rfviz: An Interactive Visualization Package For Random Forests In R, Christopher Beckett

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Random forests are very popular tools for predictive analysis and data science. They work for both classification (where there is a categorical response variable) and regression (where the response is continuous). Random forests provide proximities, and both local and global measures of variable importance. However, these quantities require special tools to be effectively used to interpret the forest. Rfviz is a sophisticated interactive visualization package and toolkit in R, specially designed for interpreting the results of a random forest in a user-friendly way. Rfviz uses a recently developed R package (loon) from the Comprehensive R Archive Network (CRAN) to create …

Demand Side Management In Smart Grid Using Big Data Analytics, Sidhant Chatterjee

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Smart Grids are the next generation electrical grid system that utilizes smart meter-ing devices and sensors to manage the grid operations. Grid management includes the prediction of load and and classification of the load patterns and consumer usage behav-iors. These predictions can be performed using machine learning methods which are often supervised. Supervised machine learning signifies that the algorithm trains the model to efficiently predict decisions based on the previously available data.

Smart grids are employed with numerous smart meters that send user statistics to a central server. The data can be accumulated and processed using data mining and machine …