Data Cleaning

When doing online projects or learning from a course, the data used is often already in perfect form; there are no missing values or outliers, and all the features are accurate and useful. In reality, though, this is almost never the case. There are often rows and rows of data with inconsistencies that, if used as is, will provide us with flawed business insights, which could be disastrous if actually used to make business decisions.

For example, you're monitoring your shop's most and least active hours. This is done by tracking and storing information regarding your customers, especially what time they're coming into the shop. You have been storing the time in 24-hour clock format.

The next day, however, another employee takes over this responsibility and starts storing the time in 12-hour clock format. You suddenly have a column of data that has been stored in two different ways, and now 8:00 can mean both AM and PM. You don't notice this...