Data preprocessing using mean removal

In the real world, we usually have to deal with a lot of raw data. This raw data is not readily ingestible by machine learning algorithms. To prepare data for machine learning, we have to preprocess it before we feed it into various algorithms. This is an intensive process that takes plenty of time, almost 80 percent of the entire data analysis process, in some scenarios. However, it is vital for the rest of the data analysis workflow, so it is necessary to learn the best practices of these techniques. Before sending our data to any machine learning algorithm, we need to cross check the quality and accuracy of the data. If we are unable to reach the data stored in Python correctly, or if we can't switch from raw data to something that can be analyzed, we cannot go ahead. Data can be preprocessed in many ways—standardization, scaling, normalization, binarization, and one-hot encoding are some examples of preprocessing techniques. We will address them through simple examples.