Data processing is transforming raw or unorganized data into a usable form. In machine learning and other AI subfields, it is transforming unstructured, structured, or semi-structured data into a functional form for ML and AI models.
Just how necessary is data processing to machine learning, though? Quite important. Data is the basic unit for all artificial intelligence subfields. AI has taken over industries because of how fast it can analyze mountains of data.
Let’s look at how influential data processing is to the machine learning process.
Understanding Data Processing for ML and AI
Artificial intelligence systems can’t help you if you don’t provide them with data. However, you can’t feed any data to a machine learning model.
Machine learning models work with four distinct data types:
- Numerical Data
Numerical data refers to raw numbers that are measurable. Measurable data includes height, distance, weight, or costs of different things.
- Categorical Data
Categorical data from the name itself is data that seeks to characterize objects. Attributes such as class, gender, ethnicity, race, and even the city of birth are categorical data. You can’t average this kind of data or classify it in chronological order.
- Text Data
Text data is data in the form of words. Sentences and paragraphs made of several words are all text data that function to offer your ML model context.
However, this kind of data is quite complex for machine learning models to understand. That’s why ML models analyze them with several methods, including sentiment analysis, word frequency, and text classification.
- Time-Series Data
This is akin to serialized data in that you can only collect it at particular periods. The serialization happens within specific time frames.
Time-series data has a definite starting and ending point making it easier to compare data over differing periods. For instance, you could have data ranging from one year to another year, weekly or monthly.
What Are the Steps in Data Processing for Machine Learning?
Data processing for machine learning involves several steps. These steps are:
1. Data Collection
This is the first step in the data processing process, where you must collect the data you want to use for your machine learning model. You can collect data from various sources such as databases, CSV files, or web scraping.
You also decide the amount of data you’ll need at this stage. Of course, every machine learning model has its complexities and will thus need particular amounts.
2. Data Cleaning/ Transformation
After wrapping up data collection, the next step is to clean the data. Data cleaning involves removing any irrelevant or duplicate data and handling missing values.
It also involves standardizing the data to ensure it is in a format your ML model will comprehend. That is essential since dirty data can lead to poor model performance.
3. Date Preprocessing/ Training
In this step, you’ll prepare the data for use in the ML model. Some of the exercises to carry out include:
- Data Scaling
- Encoding categorical variables
- Splitting the data into training and testing sets (It’s best to go with a 20 to 80 ratio)
- Model Evaluation/ Parameter Tuning
After training the model, you need to evaluate its performance on the test data. While evaluating performance, take care to tune the testing parameters well.
This will give you an idea of how well the model can predict unseen data. After all the above steps, you’ll sit back and wait for the results.
Where Do Data Engineers Source Data for Machine Learning?
There are many places where data engineers can source data for machine learning. Some common sources of data include:
1. Company Databases
Many organizations have large datasets stored in their databases. Engineers can use this data for machine learning.
One significant source is Amazon Web Services. AWS servers have many data amounts, some publicly available.
The fact that it isn’t that hard to get this data makes AWS one of the best sources of data. Some commercial providers also offer information but at a price.
2. Public data sources
The government can be a valuable source of data for ML programs. Many public data sources provide free datasets, such as the US Census Bureau, the World Bank, and Kaggle.
3. APIs
Many companies provide APIs (Application Programming Interfaces) that allow developers to access data from their platforms.
4. Web Scraping
Data engineers can use web scraping techniques to extract data from websites and online sources.
5. Crowdsourcing
It is possible to gather data from many people through crowdsourcing platforms, such as Amazon Mechanical Turk.
Data Processing is Key to The Performance of ML and AI Algorithms
Data processing is a critical step in the machine learning workflow because it directly affects the data quality that enables the training of machine learning models. Poor data processing will often lead to training data containing errors, outliers, and inconsistencies.
Such issues can negatively affect the performance of the ultimate model. In addition, machine learning algorithms typically require data to be in a specific format, such as numerical values, to work correctly.
Data processing helps ensure that your training data meets these requirements and is ready for machine learning.
Need some more light shed on data processing techniques? Book a call with an expert, and we’ll be happy to guide you through it.
