marketing

The marketing dataset contains \(8\) features and \(40\) records as 40 days that report how much we spent, how many clicks, impressions and transactions we got, whether or not a display campaign was running, as well as our revenue, click-through-rate and conversion rate. The target feature is revenue and the remaining 7 variables are 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

marketing function

The <code>marketing</code> dataset, as a data frame, contains \(40\) rows and \(8\) columns (variables/features). The \(8\) variables are:<ul>
<li><code>spend</code>: daily send of money on PPC (apy-per-click).</li>
<li><code>clicks</code>: number of clicks on for that ad.</li>
<li><code>impressions</code>: amount of impressions per day.</li>
<li><code>display</code>: whether or not a display campaign was running.</li>
<li><code>transactions</code>: number of transactions per day.</li>
<li><code>click.rate</code>: click-through-rate.</li>
<li><code>conversion.rate</code>: conversion rate.</li>
<li><code>revenue</code>: daily revenue.</li>
</ul>

marketing: marketing data set

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Examples