The goal of these examples is to analyse the given datasets to determine whether some models can be established for purposes of prediction, to assess how stepwise prediction behaves with respect to a personally chosen model and determine an unknown trend in the cereal dataset:
- The first two examples harness simple tools for numerical and class predictions, by determining which relationship influences Canadian house prices for the first one and which influences the possibility of the bank's subscribing to the term deposit in the second one.
- The third one compares the results achieved through a stepwise regression with biderectional elimination against those of a personally chosen effective logistic regression on the bank data. In both cases, the aim is to determine the relationship that best predicts whether or not a user has subscribed to the term deposit.
- The fourth one looks for possible trend/patterns in the given cereal data. In particular, the variable that seems to be most interesting to predict is the amount of calories based on the other attributes.
The examples require R and RStudio to run, and a series of packages that will be suggested by RStudio when the rmd files are first opened.
The outputs are produced as html files, which are already included in the repo. If you need to run the markdowns select the Knit to HTML option (or the more suitable for you). To view the html files produced in this repository, you can download them (or produce them) and open them in a web browser, or use the following links directly to view those included in the repository
Any contribution with further examples or improvements are welcome.
MIT License
Copyright (c) 2023 Jurgen Gurakuqi
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