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:
- Initial explanatory analysis
- Data visualization
- 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 ...
