Creating an application
We created a classification model that is able to estimate the probability of any text being classified as spam or not spam, based on the most common spam words and characters from the Spambase dataset. However, if we never add that model to a tool where a person can input text, the likelihood is that the model will become useless. So, the solution is to deploy it, embedding the classifier in a web application. Let’s define our project next.
The project
The project for this last chapter is described in the following bullet points:
- Problem: Create an interactive application able to deploy a machine learning model to the web.
- Description: The tool will be able to receive textual input, transform the data to a data frame that will feed the machine learning random forest classifier. The model predicts the probability that a text message is spam or not.
- Tools: Shiny library and RStudio.
