Assess four time-series statistical forecasting methods to determine the most accurate predictive model for daily temperature patterns in Seattle, Washington.

Investigate the relationship between the prices of LEGO® sets and the number of pieces in the set and its theme to gain a better understanding of where consumer's money is going and identify which specific franchises within LEGO® have the best price-to-piece ratio.

Analyze multiple data sources to profile a "fenceline community" in the US, the risks individuals in these communities are exposed to and the extent to which certain demographics are disproportionately exposed to those risks.

Conduct analysis on the transactional and customer data for a large supermarket brand to generate highly valued insights and provide recommendations to help the business make strategic decisions.

Use College Scorecard data to visualize the relationship between student enrollment and the distribution of degree areas based on the type and level of institution in Massachusetts.

Wrangle the Open Payment dataset in order to reveal findings about the financial relationships between drug and device companies and certain healthcare providers in Massachusetts.

A machine learning model leveraging multiple regression techniques to forecast house prices based on a range of predictor variables, with RMSLE used for accuracy evaluation.

This machine learning model utilizes Classification and Regression Trees (CART) to estimate house prices by analyzing several predictors, with RMSLE assessing the model’s precision.

An evaluation of 10-fold cross-validation accuracy estimates in predicting Kaggle competition scores using K-Nearest Neighbors, multiple regression, and CART models.

A machine learning model applying logistic regression to predict the likelihood of financial distress within two years, using factors like age and credit history, with accuracy gauged by AUC.

Employing LASSO regression, this machine learning model predicts house prices by focusing on key predictor variables, with RMSLE measuring performance.

A random forests machine learning algorithm is used here to predict house prices based on a diverse set of predictors, with RMSLE ensuring model accuracy.

A comprehensive relational database design that centralizes information, enabling more effective resource allocation, volunteer deployment, fundraising efforts, and community engagement for non-profit organizations.

This project models and replicates the ProPublica’s analysis that highlights the racial biases in the COMPAS recidivism algorithm.

A spell-checking program that stores its dictionary in a hash table checks whether a given word is spelled correctly and provides a method to suggest possible alternatives in the event of a misspelled word.

This project uses an ArrayDeque as a stack to compute the result of expressions written in both postfix notation and infix notation by implementing the Shunting-yard algorithm.

A program for solving mazes using recursion and animating the solution.

My own implementation of singly linked lists and doubly linked lists.

An animated card sorter that uses the Insertion Sort, Selection Sort, Merge Sort, and Quicksort algorithms on Linked Lists and compares their running times.

This project creates a DecisionTree class that inherits from the BinaryTree class, and utilizes it to develop a program that plays a "20 Questions" style guessing game.

A collection of mini projects including a recursive program that generates fractal patterns, an enhanced BST, algorithms for converting array to binary tree and tree to DLL, and an in-place heap sorting algorithm.