Introduction

Data cleaning and preparation takes about 70% of the effort in the entire process of a machine learning project. This step is essential because the quality of the data determines the accuracy of the prediction model. A clean dataset should contain good samples of the scenarios that we want to predict, and this will give us good prediction results. Also, the data should be balanced, which means that every category we want to predict should have similar number of samples. For example, if we want to predict whether it will rain or not on any particular day, and if the sample data size is 100, the data could contain 40 samples for It will rain and 60 samples for It will not rain today, or vice versa. However, if the ratio is 20:80 or 30:70, it is an unbalanced dataset, and this will not yield good results for the minority class.

In the following section, we will look at the essential operations performed on data frames in R. These operations will help us to manipulate and analyze...