Cleaning and tidying up the data

Data cleaning, or rather tidying up the data, is the process of transforming the raw data into a specific form of consistent data that includes a simpler form of analysis. Cleaning the attributes of the bank dataset is considered quite critical and should be performed carefully. The R workspace includes a set of comprehensive tools that are specifically designed to clean the data in an effective manner. The following steps are implemented to this end:

1. Initial explanatory analysis
2. Data visualization
3. Error cleaning

Here, we will focus on various aspects of understanding the data summary and also getting a feel for the data. We will also implement the libraries required to clean and tidy up the data by observing the following steps:

1. Include the requisite libraries (as discussed in Chapter 3, Examining, Cleaning, and Filtering) that assist in cleaning...