In this article, we learn what is data pre-processing and why we need it?
Data is no less than an asset in today’s world. We see data all around us. There are many companies that produce a large amount of data that could be used for the different machine learning models. We see Amazon and Facebook or Google for that matter that produces data that is extremely large.
Each search in Google can result in lots of data being produced and with a large base of users added to that, we see that there is a huge generation of data. Therefore, the data that is available to us is abundant and we have to ensure that we make the best use of it.
In the machine learning model, Data play an important role. If we have more data, there is a higher chance for a machine-learning algorithm to understand it and give accurate predictions to the unseen data respectively.
Let us also understand the pre-processing data that we have so that we get a good understanding of the feature engineering techniques. So let's get started!
What is Data Pre-Processing?
Data preprocessing helps to enhance the quality of data and promotes the extraction of meaningful insights from the data. In simple words, data preprocessing in Machine Learning is a data mining technique that transforms raw data into an understandable and readable format.
When creating a machine learning project, it is not always a case that we come across clean and formatted data. And while doing any operation with data, it is mandatory to clean it and put it in a formatted way. So for this, we use the data preprocessing task.
Why do we need it?
When it comes to creating a Machine Learning model, data preprocessing is the first step marking the initiation of the process. Typically, real-world data is incomplete, inconsistent, inaccurate, and maybe in an unusable format that cannot be directly used for machine learning models.
This is where data preprocessing enters the scenario it helps to clean, format, and organize the raw data, thereby making it ready to go for Machine Learning models.