Use U.S. state-level disease data on pneumonia and influenza in conjunction with data on state government enforced COVID regulations to look for an association between disease and disease mitigation efforts.

Objective is to build a count regression model to predict the number of cases of wine that will be sold given certain properties of the wine.

Our objective is to build multiple linear regression and binary logistic regression models to predict the probability that a person will crash their car and also the amount of money it will cost if the person does crash their car.

I will be using Extending the Linear Model with R: (Faraway), to explore solving a problem with multinomial data in chapter 7.

Looking at Residual Correlation

Building logistic regression models on the Boston housing training data to predict whether or not neighborhoods are at risk for high crime, and validate the model with the greatest predictive accuracy.

will be using Extending the Linear Model with R (Faraway), to explore solving a problem which has binary response data as well as discrete explanatory data. It can be confusing to navigate 1s and 0s or values that belong to a small range of values. Using more complex histograms, boxplots, and jitter plots as visualizations can make these type of datasets much more comprehensive.

I will be using Linear Models with R (Faraway) to practice solving a problem with the predictor using the SIMEX (simulation extrapolation) method.

Group work done with classification data

Doing practical exercises to study inferences and confidence intervals.

Collaborative work for predicting baseball team wins.

Multivariate calculus problems, some proposed in conceptual real world scenarios.

Random Univariate and Multivariate Calculus Problems for Data Science

Examples of Solving for Eigen Values and Eigen Vectors

3 x 3 Matrix Characteristic Polynomial

The goal of this project is to use a data set from a beverage manufacturing company consisting of 2,571 observations and 33 variables to train a model. The model will be used to predict pH in sample data provided separately.

This research paper is to explore the functionality of neural nets by applying its functionality to Credit Card Fraud Detection. Using data of continuous values that depict the attributes of credit card pictures, we will train two different neural net models to predict which credit cards are fraudulent.

This research article is about creating Spam Filters using different machine learning models for training and testing. We will see most of the challenges will come from both prepping the data and optimizing the performance of the training. Finally, we will assess the prediction results for highest performance based on accuracy and time efficiency.