One of the biggest challenges for companies that decide to deploy machine learning is preparing data for the machine learning model. Some business owners wrongly assume that an ML model would use any data for training purposes and this misconception can cost you quite a lot.
In order for the machine learning model to efficiently utilize your data and deliver expected results, you need to properly collect the data and prepare it in advance. There are several steps in the process and each is crucial for further data analysis.
Steps to Prepare Data for Machine Learning
Collect the data
It may come as no surprise that data preparation starts with proper data collection. The thing is, you cannot just use any data that you have available – you need specific data to help you answer a specific question.
The first thing you’ll need to do is define your question to answer. This will help you choose the suitable ML model: classification, clustering, regression, or ranking. And for each, you will need different data in order for the algorithm to correctly respond to your request. You can use either the data from open sources or the one that you already have – in the latter case, make sure that your data is a) enough and b) can help answer the question.
Your next step will be choosing the data source to use. Here are the options available:
- Open-source databases. The biggest advantage is their instant availability and amount of data. However, this data may not be suitable for your specific needs.
- Web scraping. This process implies using automated bots to obtain the data from a website by extracting the underlying HTML code.
- Synthetic databases. You can opt for building a synthetic database if there is not enough real data available. While this method is considered quite efficient, there are also certain risks associated with it (such as biased data).
- Internally sourced data. While it’s a great option, there are certain concerns about data privacy and its complexity.
Each option listed above comes with its pros and cons so it’s important you carefully consider all of them before choosing your preferred option.
Clean and preprocess the data
The next step after collecting the data is making it suitable for the machine learning model to process. The thing is, raw (unprocessed) data often has the following issues:
- The wrong format or different formats in a single dataset;
- Invalid or missed values;
- Excessive data.
Hence, in order to resolve these issues, you’ll need to perform data formatting, cleaning, and sampling. Let’s start with formatting. Data formatting means transferring all the data you are going to use into a single unified format. The format consistency is crucial for the ML model to properly train and learn.
Next is data cleaning. As the name implies, it means getting rid of incorrect values and missing data. As well, data cleaning often means eliminating sensitive data from some of the attributes.
Finally, data sampling is a process of breaking down your data into smaller samples. The main idea behind this is that a huge dataset takes too much time to be processed and analyzed. Thus, you can use a smaller data sample for quick prototyping before working with the whole dataset.
Reduce the data
Let’s elaborate on data sampling a bit more. Once you have your data collected and processed, it’s tempting to use it all in order to obtain better insights. However, not all of your data is equally valuable. Thus, you’ll need to do either attribute or record sampling to reduce the data.
The main idea behind the attribute sampling method is to identify your target attribute (the value that needs to be predicted) and then identify the values that will be critical for the target attribute. For example, if you want to predict what customers will be more likely to buy a certain product, you will most likely need such information as their gender and age and not their servicing bank. Also, note that domain expertise will help a lot in this approach as data scientists may not be fully aware of certain dependencies between different attributes. Say, if you need to predict possible pneumonia complications with patients, you’ll need to include asthma as one of the factors that cause it. And we can all agree that the knowledge of this dependency is quite specific and data scientists may simply not be aware of it, hence, they may not include asthma in the list of attributes.
Another method of data reduction is record sampling. It is a bit more general than attribute sampling and means simply eliminating erroneous or missing values to get accurate results. Record sampling is mostly used for yes/no and pass/fail results.
Feature engineering involves several steps and is aimed at making the data even more suitable and relevant. The more uniform and relevant your attributes are, the higher are the chances for the ML model to deliver accurate results.
First, you’d want to scale your data or normalize it. Scaling means bringing the data under a specific scale, such as a 0-100 range or a 0-1 range. Scaling is important so that machine learning algorithms correctly understand the hierarchy of the attributes and their dependencies.
Next, there is data decomposition. This means breaking a certain complex attribute into different separate attributes: i.e. breaking down a date into a day (Tuesday) and a certain time (1 PM). This is done in order to extract attributes that may have a significant impact on the algorithms’ behavior and to detect specific patterns and relationships between values. While data decomposition may sound similar to data reduction, this is not the same since in data decomposition, you add new attributes based on the ones you already have.
One more way to engineer features is data aggregation. In contrast to data decomposition, data aggregation means uniting certain features into a single one with an aim to make this single feature more valuable and meaningful.
Prepare Data for Machine Learning- What’s Next?
Once you process your data and ensure it’s suitable for the machine learning model, you will need to break it down into training, validation, and test sets. This is needed to guarantee unbiased results with the final model and its accuracy.
It also goes without saying that in order to correctly acquire and prepare the data, you’ll need to work in close collaboration with data scientists. By providing them with clear objectives and domain expertise, you will significantly speed up and facilitate the data processing while they will apply their skills and knowledge to come up with the most suitable machine learning model.