How to Prepare Data for Machine Learning?
Machine learning has already changed the world around us, and it doesn’t seem to be slowing down anytime soon. Through the introduction of artificial intelligence, many processes that used to take up a lot of time have been streamlined to near perfection and now take no time.
While this is just the beginning, machine learning still has a long way to go before it’s perfect. It requires a lot of things to operate without any issues, one of which is data refinement.
When data is collected, it’s collected in a raw form via web crawlers and other means, and while raw data is a good start, it’s not nearly refined enough for AI.
AI doesn’t like raw data, as the machine learning process could teach the AI the wrong things. The best way to make sure that the AI has access to good data is to refine it and prepare it for further integration.
In this article, we’ll explore data preparation, define why it’s integral to the machine learning process, and give you some basic steps on how you can prepare your data.
What Is Data Preparation?
Data preparation is an essential process in which raw data is transformed into viable data suitable for integration with the machine learning process. When data is collected, it’s in raw form. Raw data includes all situational data, mistakes, and redundancies – making it unsuitable for further machine learning.
Machine learning teaches Artificial Intelligence – but it does so with the use of databases, sheets, and data centers. If the data is irrelevant, wrong, or raw, the AI could learn the wrong things, making the machine learning process redundant.
Data preparation sets the grounds for further analysis and uses in the machine learning process, and it’s an integral part of the process as a whole. The more data, the more extensive the refinement needs. That is why data-driven companies that use machine learning often tend to work with data centers rather than releasing their AI into the open internet.
Why is Data Preparation Necessary?
Think of AI as a child. If the child has access to the wrong information, incorrect ideas, and invalid data, it’ll soak up all of them. The best example of this is Tay, Microsoft’s artificial intelligence chatterbot, which was released to the open internet and quickly fell victim to trolling.
That is the probable future for all AI that’s released to the open internet for machine learning, and that’s why companies work with localized data sheets rather than the World Wide Web. The way the data is prepared and presented dictates the machine learning process’s success rate in layman’s terms, leading to a better, smarter, and fully-operational AI.
3 Steps to Prepare Data for Machine Learning
Many things go into preparing the data for machine learning, and they range far and wide in complexity. Data preparation can be boiled down to three main things, which are:
- Collecting the data
- Structuring the data
- Sectioning the data
To streamline the process, we’ve decided to split it up into four easy-to-follow steps.
Utilize Proper Data Collection Methods
Data collecting is one of the most critical parts of data preparation. Where and how you collect your data will dictate it’s quantity, quality, and the level of refinement it requires for further application in the machine learning process.
Many companies utilize web crawlers and data scraping agents to collect their data into huge datasheets, and while this is a fantastic method to do so, most bots won’t scrape the correct data most of the time. Advanced data harvesting agents can be modified and customized to collect data from given sources, which yields higher quality data that requires less refinement.
When it comes to data for machine learning applications, quality always beats quantity – which is why you should focus on generating high-quality datasheets from reliable sources.
Profiling and Formatting Data
After the data collection step is over, you’ll have to profile your data. Profiling means that you’ll have to examine the data collected and take statistics and summaries from it. While this step isn’t necessary per se, it’s still an integral part of the data preparation process.
Through data profiling, you can get an accurate picture and streamline further processes such as sectioning, defining key points, and deployment.
Formatting is a crucial step in the data preparation process. It organizes the data following preset specifications to be refined and analyzed with ease later on during the data preparation process.
Defining Data Key Points
Defining key data points will allow you to ensure that every piece or group of data is adequately defined. This is an advanced version of data formatting. Defining key points is an integral step in the data refinement process. Aside from defining data, this also lets you set the importance of each piece of data, making it more or less crucial to the machine learning process.
Setting the metrics through defining key data points allows you to set the grounds for further data preparation in the machine learning process.
Sectioning the Data
The last and most important step of data refinement and preparation is sectioning the data. Sectioning the data based on predetermined metrics and key points will allow for faster, simpler, and more streamlined deployment, which speeds up the whole machine learning process.
Machine learning, while exciting, is still in its infancy. There is a long way to go before machine learning becomes simple, efficient, and streamlined – but an excellent way to get there is through proper data preparation. On the other hand, data deployment is far more straightforward, as there are many platforms that deal with it. One of the most popular services in this industry is Pandio, which is an Apache Pulsar based distributed messaging system that’s specifically built for AI and machine learning. It offers seamless data integration, fantastic data retention, and recall and makes deployment a breeze.