P r o j e c t s

Comparison Of Unsupervised Machine Learning Algorithms on Clustering Task

In this study, we aimed to classify different kinds of beans using a comparison of K-Means Clustering and Principal Component Analysis. We then report the differences of results between our hand coded algorithm for K-Means, and that of the Sklearn package.

2021

Analysis of the Performance of Supervised Machine Learning Algorithms on Binary Classification Task

This project explores a small replication of the analysis done in the paper by Caruana & Niculescu-Mizil (CNM06), in order to evaluate different supervised learning algorithms and determine if any of them outperform the others at binary classification.

2021

Performing Machine Learning Analysis on Confusion EEG Brainwave Dataset

ML analysis was performed on an EEG dataset. The classification methods chosen are Logistic Regression, SVM, KNN, and Random Forest. For each model, we calculate MSE and RMSE to conduct cross validation, then we perform K-folds cross validation.

2021

Multivariate Linear Regression of Heart Disease Attributes to Blood Pressure

The UCI Statlog (Heart) data set was used to perform linear regression in order to predict blood pressure using 4 different models, each with a different number of attributes. Then, we use error metrics to report which model best predicted blood pressure.

2021

Exploratory Data Analysis on the Effects of U.S. Political Leanings on the Number of Covid-19 Cases

Using linear regression and multiple linear regression, we explore the relationship between these variables and find that there is a correlation between the political leaning and the safety policies of a US region and its overall amount of Covid cases.

2020