Seiji Sasaki

Overview In this project I set out to create a classification model in order to predict key characteristics where soccer players are expected to show significant market value growth in the short to mid-term future. I relied on data available on Transfermarkt.com, a European soccer player market valuation database/website used by soccer clubs around the world.  Data Collection and Cleaning In order to collect the relevant data, I developed a scrapping program using Beautiful Soup on Python. After scraping all of the necessary data, I used python to remove any null values and standardized all data types. After collecting and cleaning the data through Python. I was finally ready to perform preliminary data exploration to find any key trends of themes in the dataset.  Exploratory Data Analysis Size = Change in market value, X = age, Y = Market Value From an initial review of the dataset, I found that there was most likely a strong relationship between the age of the player and exp...

For this project, I worked with a graduate student at the Duke Law School to perform exploratory analysis on loan backed fine art. As a topic that has minimal prior research, this project was particularly interesting as we had to create and collect out own datasets and provided original insight in the topic.  Through our analysis, we were able to find that collateralized works tend to sell for a higher hammer price and that the volume of art backed loans negatively correlates to the housing market. This project demonstrates the following technical skills: - Collecting and cleaning data - Advanced statistical modeling - STATA - Excel - Working with financial time-series data

In this project, I worked with a group of three other students with the goal to find arbitrage opportunities between the New York, London, and Paris art markets. We developed two models; a multivariate regression model to predict the hammer price and a logistic regression model to predict the likelihood of a sale.  This project demonstrates the following technical skills: - Collecting and cleaning data - Advanced statistical modeling - Testing statistical models - Machine Learning - R - STATA - Excel