Seven Projects from Math - 4800 course at New York City College of Technology. Some project are done in a R environment in a Jupyter Notebook and the others are r-script files.
Project 1 - Gives an introduction to R working with matricies. Also, calculating the average error between two functions
Project 2 - Going over sampling from a dataset and correlations.
Project 3 - Part A goes over making histograms and frequency plots. Part B calls for use to find the subset of the student exams scores in which they increse with every exam.
Project 4 - This project invovles an analysis of the association rules for the attributes of the dataset. We first find the most frequent 3 - value itemset with the maximum support value. Then, we generated implication rules for the most frequent itemset.
Project 5 - This is a decision tree project in which we try to fin the 3 most important factors in a car evaluation for car buying. This project includes a write up.
Project 6 - This project is actually four different project, in which part one is a normal linear regression. Part 2 is a multilinear regression with the intercept. Part 3 is a linear regression that includes sin, log, and cubic functions. Part 4 includes a linear regression with a linearized exponental function. All parts include a write up.
Project 7 - Lasso Regression. This project looks at crime rates for american cities based on a number of social and economic factors. We use a lasso regression to get what factors are most important for predicting crime rate.