By Damian Chan, Technical Success Manager, Matillion
Industry experts, competitors, and even your customers are talking about machine learning. Machine learning is the process of building and training models to process data. In this capacity, your models are learning from your data to make better predictions. In this way, machine learning allows computer systems to learn from data and make decisions without being explicitly programmed to do so.
Based on growing modern workloads, machine learning is
understood to be a form of artificial intelligence and mainly refers to
computers that can learn and improve their analysis on data over time without
reprogramming their core logic. Related to machine learning, deep learning is a
subset of machine learning involving artificial neural networks, inspired by
the function and structure of a brain.
Acting as the brain of your business, machine learning needs data and information to process and learn from. In this way, the machine is designed to learn the instructions from a given data set. But machines learn better from good data.
“Garbage in, garbage out”
When it comes to machine learning, you need to feed your
models good data to get good insights. Data in the real world can be really
messy and in most cases, some sort of data cleansing needs to be performed
prior to any data analysis. However, this can be a daunting task. Without the
right technology stack in place, data transformation is time-consuming and
tedious. Nevertheless, this is a critical step that ensures maximum data
quality, which increases the accuracy of predictions.
How data transformation can improve machine learning
Based on our customers’ experiences, there are some
common data transformations that you can perform so your data can be processed
within machine learning models.
Remove Unused and Repeated Columns
Hand picking the data that you specifically need will not only improve the speed at which your model trains but also helps when you come to analyze it.
Change Data Types
Using the correct data types helps to save memory usage. It can also be a requirement, such as making numerical data an integer, in order for calculations to be performed against it.
Handle Missing Data
At some point you’ll come across incomplete data and resolving it can vary depending on the data set. For example, if the missing value doesn’t render its associated data useless then you may want to consider imputation. Imputation is the process of replacing the missing value with a simple placeholder, or another value, based on some kind of assumption. Otherwise, if your data set is large enough then there is a likelihood that you can remove the data without incurring any substantial loss to your statistical power. However, proceed with caution as you may inadvertently create a bias in your model. On the other hand, not treating the missing data can also skew your results.
Remove String Formatting and Non-Alphanumeric Characters
This involves removing characters like line breaks, carriage returns, white spaces at the beginning and the end of values, currency symbols, etc. In addition, you may also want to consider word-stemming as part of this process. Although removing formatting and other characters makes the sentence less readable for humans, this approach helps the algorithm to better digest the data
Convert Categorical Data to Numerical
This step isn’t always necessary, but a lot of machine learning models require categorical data to be in a numerical format. This means converting values such as yes and no into 1 and 0. However, be cautious not to accidentally create order to unordered categories such as converting mr, miss and mrs into 1, 2 and 3.
You may encounter timestamps in all types of formats. In this case it’s a good idea to define a specific date/time format and convert all timestamps to the defined format.
Data transformation needs to keep up with big data
Machine learning can help your business process and
understand data insights faster – empowering data-driven decisions to be made
across your organization. But transforming data for analysis can be challenging
based on the growing volume, variety, and velocity of big data. To overcome
this challenge and unlock the potential of your data, you need ETL software
that is purpose-built for the cloud. Cloud-native tools that use an ELT
approach (extract and load into the cloud, and then transform) take advantage
of the power and scale of your cloud data warehouse and can mobilize your
business to move faster and outpace competitors.
Learn more about how Machine Learning can help your business gain a competitive advantage in our User Guide to Machine Learning.