classifyRisk

The classifyRisk dataset containing 6 features and 246 records. The target feature is risk, a factor with levels "good risk" and "bad loss" along with 5 predictors.

 datasets

Provides a collection of helper functions that make various techniques from data science more user-friendly for non-experts. In this way, our aim is to allow non-experts to become familiar with the techniques with only a minimal level of coding knowledge. Indeed, following an ancient Persian idiom, we refer to this as "eating the liver of data science" which could be interpreted as "getting intimately close with data science". Examples of procedures we include are: data partitioning for out-of-sample testing, computing Mean Squared Error (MSE) for quantifying prediction accuracy, and data transformation (z-score and min-max). Besides such helper functions, the package also includes several interesting datasets that are useful for multivariate analysis.

Abdolreza Mohammadi

liver

"Eating the Liver of Data Science"

Reza Mohammadi

Kevin Burke

classifyRisk function

The <code>classifyRisk</code> dataset, as a data frame, contains \(246\) rows (customers) and \(6\) columns (variables/features). The \(6\) variables are:<ul>
<li><code>mortgage</code>: A factor with levels "n" and "y".</li>
<li><code>nr_loans</code>: Number of loans that constomers have.</li>
<li><code>age</code>: age in years.</li>
<li><code>marital_status</code>: A factor with levels "single", "married", and "other".</li>
<li><code>income</code>: yearly income.</li>
<li><code>risk</code>: A factor with levels "good risk" and "bad loss".</li>
</ul>

classifyRisk: classifyRisk data set

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Examples