Using an MLB training dataset of each team from 1871 to 2006 to predict the number of wins on the test set.

Building Machine Learning models to predict NBA player salaries using 2022-2023 NBA players dataset.

Group project using dataset to predict pH levels based on features in dataset.

Comparing the results of Decision Tree and Random Forest models with Support Vector Machine models.

Using Data Mining and Association tools to create a cluster analysis using a Grocery list dataset.

Exploring tree-based models such as Random Forest, Bagged Trees, and Gradient Boosting.

Creating and comparing nonlinear regression models.

Exploring and analyzing Decision Tree and Random Forest modeling techniques using a heart attack risk dataset.

Answering questions from the Kuhn and Johnson textbook, creating models and comparing their performances on train and test sets.

Creating a Time-Series analysis for two business situations.

Exploring ARIMA modeling using R.

Answering questions pertaining to Exponential Smoothing.

Exploring how to handle missing data in predictor variables.

Applying Time Series methods to various datasets and analyzing its results.

Using Box Cox Transformations, STL and X-11 decompositions on various datasets.

Exploring data using time series functions.

Final Project using Crab Age dataset and answering questions pertaining to Probability, Calculus, Linear Algebra, and Multiple Linear Regression modeling. Revised to include Kaggle submission score

Final Project using Crab Age dataset and answering questions pertaining to Probability, Calculus, Linear Algebra, and Multiple Linear Regression modeling.

Answering questions pertaining to what was covered during the semester.

Answering math questions using Taylor Series expansion.

Answering Calculus-related questions

Answering questions related to dataset involving life expectancy, using simple and multiple linear regression, as well as variable transformation.

Using Sleep Efficiency dataset to create a multiple linear regression model.

Creating a Simple Linear Regression Model using the cars dataset.

Answering Gambler's Ruin question.

Answering questions calculating the probability of independent random variables, and using moment generating functions to calculate the expected values and variances of binomial and exponential distributions, respectively.

Exploring annual salaries of Data Science jobs, examining the variation of salaries by role and by U.S. state.

Answering questions pertaining to Sum of Independent Random Variables and Law of Large Numbers.

Answering questions using Cumulative Distribution Function, Geometric, Exponential, Binomial, and Poisson Distributions.

Answering probability and combinatoric questions.

Answering probability questions related to Bayesian, Binomial, Poisson, Hypergeometric, and Geometric distribution methods.

Building and visualizing eigenimagery that accounts for 80% of the variability using shoe images.

Solving problems pertaining to finding the rank of a matrix, as well as finding the character polynomial, eigenvalues and eigenvectors of a matrix.

Showing how a square matrix multiplied by its transpose is equal to the transpose of the square matrix multiplied by itself. Creating a function to find the LU of a square matrix.

An analysis of the allocation of funds based on state/territory population and 2020 Presidential Election results.

Using a transformation matrix to create animations that shear, scale, rotate, and project the initials of my name.

Exploring NBA dataset obtained from Kaggle to determine factors that influence a player's WinShare.

Exploring NBA team and player clutch stats, extracting data using the hoopR library.

Exploring Multiple Linear Regression using dataset of grading scores of professors.

Extending assignment focused on NCAA women's basketball dataset.

Using datasets from OpenSecrets.org, I explored various areas of lobbying.

Exploring Linear Regression using the Human Freedom Index dataset.

Exploring political donations made by Americans sports owners during the 2016, 2018, and 2020 election cycles.

Final project proposal using NBA Draft data of each player from 1989 to 2021.

Exploring Nobel Prize data using JSON files extracted from Nobel Prize API.

Inference for numerical data.

Using The New York Times' API to access articles that were most viewed on the NYTimes website in the last 30 days.

Exploring inference for categorical data.

A brief, detailed description of the scope of our project.

Creating data tables of selected books using html, xml, and json file types.

A comprehensive look at confidence intervals and its impact on statistical analysis.

An in-depth analysis of sampling distributions.

Examining weekly gas price data from 2007 to 2023.

A breakdown of Covid-19 data in the five boroughs of New York City.

Using MTA ridership data, analyzing trends in subway ridership since March 2020.

Breakdown of fast food dataset.

A detailed look at MTA daily ridership, from March 1, 2020 until February 23, 2023.

Breaking down the Hot Hand Basketball theory using the statistics of Kobe Bryant.

Breaking down arrival on-time and delay performances of Alaska and Am West Airlines.

Creating a CSV file using chess data.

Break down of NYC flights dataset.

Using Regular Expressions to answer questions.

A breakdown of a movie ratings survey I conducted

Comparison of birth rates among boys and girls, using a baptism dataset created by Arbuthnot from 1629 to 1710, and another dataset of births in the U.S. from 1940 to 2002.

Exploring data on how Americans view the job President Trump and President Biden did on responding to COVID-19.

This file examines a dataset pertaining to whether Americans approve or disapprove of how President Trump and President Biden handled the response to the Coronavirus pandemic.