The purpose of our second project is to work as a team to apply concepts from the 2nd half of our Predictive Analytics course to a beverage data set. More specifically, to explore the data, determine whether we might use a linear regression, non-linear regression or tree-based model, to then build, compare, select our optimal model, and support why we made the selection that we did.

The purpose of this assignment was to explore Regression Trees and Rule-Based Model exercises from Applied Predictive Modeling.

This presentation was created to highlight the real world application of a Random Forest model. Big thanks to Manuel Tilgner for all of his work!

The purpose of this assignment was to explore Non-Linear Regression exercises from Applied Predictive Modeling (http://appliedpredictivemodeling.com/).

The purpose of this assignment was to explore Linear Regression exercises from Applied Predictive Modeling.

The purpose of our first project is to explore Time Series, Decomposition, Forecasting, Data Preprocessing / Overfitting, Exponential smoothing, and ARIMA. To see if / where we might apply the concepts we've covered upto this point, provided different data sets and asks.

The purpose of this assignment was to explore the ARIMA exercises from Forecasting: Principles and Practice.

The purpose of this assignment was to explore the Exponential Smoothing exercises from Forecasting: Principles and Practice.

The purpose of this assignment was to explore the Data Pre-processing exercises from Applied Predictive Modeling.

The purpose of this assignment was to explore the *Forecasting* exercises from Forecasting: Principles and Practice.

The purpose of this assignment was to explore the Time Series Decomposition exercises from Forecasting: Principles and Practice.

The purpose of this assignment was to explore the *Time Series Graphics* exercises from Forecasting: Principles and Practice.

The purpose of our Final Project was to explore the application of Neural Networks to loan approval data to then back compare model performance with a variety of Classification algorithms (ie. KNN, DT, RF, GBM).

With a high rate of avoidable deaths and chronic disease as well as an obesity rate at two times higher than the OECD average, the forecast for American health is gloomy. With that in mind, the purpose of this project was ultimately to answer three questions: 1) What United States counties are most favorable for an active, healthy lifestyle? 2) What are the differentiating characteristics that make them so? and 3) What might the best regression model be for modeling the relationship between our healthy lifestyle metric and these differentiating characteristics?

The purpose 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.

The purpose of this publication is to document the process of creating our dependent ‘healthy lifestyle’ metric as well as the pre-processing of our independent variables. It’s to answer Q1 and document the compilation of the dataset to be used for this Final Project.

The purpose of this assignment was to explore Clustering, Principal Component Analysis, and Support Vector Machines.

Build multiple linear regression and binary logistic regression models on training data to predict probability that a person will crash their car as well as the amount of money it will cost if the person crashes their car.

The purpose of the assignment is to build 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.

The purpose of this assignment was to explore classification via K-nearest neighbors, Decision Trees, Random Forests, and Gradient Boosting.

What counties in the state of New Jersey are most promising for a volleyball club? And how do you even measure such a thing?

The purpose of the assignment is to explore different model diagnostic metrics associated with classification models manually to then compare with built-in R packages that automate these calculations.

The purpose of the assignment was to explore linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and naive Bayes as applied to the Palmer penguin dataset.

The purpose of the assignment is to build a multiple linear regression model on the training data to predict the number of wins for the teams provided in a given baseball season.

The purpose of the assignment was to explore logistic and multinomial logistic regression.

The purpose of the assignment was to explore principles of data visualization with ggplot2.

The purpose of our Computational Mathematics Final Project is to explore / showcase (some of) what we’ve learned over the course of the semester.

An exploration of what the best Data Science companies are (to work for) and what characteristics make them so.

The purpose of the assignment was to explore Calculus: Functions of Several Variables.

What are the best Data Science companies to work for? and what are the characteristics that make them so?

The purpose of the assignment was to capture our exploration of Taylor Series expansion using an R markdown document.

The purpose of the assignment was to explore the fundamentals of Calculus using R.

Multiple regression model

The focus of this project is document classification.

The purpose of the assignment was to explore the properties of linear regression.

The purpose of this week's discussion topic is to build out a regression model and conduct residual analysis for any data set that interests us.

Extended Rachel's work by exploring the frequency UFO sightings within the US and applying plotCount(), removeGrid(), ggMarginal(), and rotateTextX() functions from ggExtra library.

The purpose of this assignment is to analyze an existing, interesting recommender system.

The purpose of the assignment was to explore linear regression.

The purpose of this week's discussion topic is to build out a simple linear regression model and test the assumptions using any data set of interest.

The purpose of this assignment is to familiarize ourselves with text mining and sentiment analysis.

The purpose of this assignment is to explore the application and properties of the Markov Chains and Random Walks.

To apply the principles of statistics and inference.

Create a programming sample “vignette” that demonstrates how to use one or more of the capabilities of the selected Tidyverse package with our selected dataset.

Read JSON data from NYT's API and convert to data frame in R.

Central limit theorem and moment generating functions.

Central Limit Theorem for continuous independent trials (sec'n 9.3 of course text).

What are the most valued data science skills?

To explore the application and properties of the Sum of Random Variables and the Law of Large Numbers.

Team Silver Fox's project planning document.

To familiarize ourselves with different file structures.

Important distribution functions.

The purpose of this assignment is to tidy and transform data. The dataset of interest describes subway ridership from 2013 to 2018 for Brooklyn, the Bronx, Manhattan and Queens.

The purpose of this assignment is to tidy and transform data. The dataset of interest describes the relationship between student exam performance v parental education levels.

The purpose of this assignment is to tidy and transform data. The dataset of interest describes Happiness v GDP for the United States v Finland.

Combinatorics and conditional probability.

Tidying and transforming data.

Probability distribution calculations.

The normal distribution.

Using R, we verify that SVD and Eigenvalues are related and write a function to compute the matrix inverse using co-factors.

We were to import and process a text file to generate a .CSV file with specified fields.

Matrix operations: rank, eigenvectors, and eigenvalues.

The goals for this lab are to (1) think about the effects of independent and dependent events, (2) learn how to simulate shooting streaks in R, and (3) to compare a simulation to actual data in order to determine if the hot hand phenomenon appears to be real.

Working with character strings and dates in R.

Presentation of problem 1.1 from Ch.1 of 'OpenIntro Statistics' (4th ed)

Handling and analyzing NYC flights data from 2013.

Collect, store (in a relational database), import, prepare, and analyze movie rating data.

Proof and demonstration of matrix transpose property and creation of matrix factorization (LU decomposition) function in R.

Perform vector and matrix operations in R.

To load and transform a (COVID concern levels) dataset using R.

Plot, analyze, and present data.

Read / write from .csv files and perform basic data wrangling.

Create a loop, vector and function.