How to create a dataset for machine learning

Machine learning (ML) is a part of the artificial intelligence field. It is a powerful tool for data analysis, but its work and output is only as good as the initial dataset that drives it. Data-driven culture these days is one of the core parts of machine learning projects, and having enough data for specific ML purposes is arguably the most crucial aspect in this field.

In this article, we will provide a brief overview on how to create a dataset for ML purposes and make it useful for particular ML tasks. By the end, you will have a high-level understanding of what goes into generating the right data that drives every ML algorithm there is. Let's get into it.

What are machine learning datasets?

A machine learning dataset is a collection of data that trains and evaluates an ML model. Creating a good dataset for machine learning is a critical step in the process of training and evaluating ML models. In order to create an effective dataset, it is important to understand how to generate data for machine learning and what data is needed.

The quality and size of the dataset play a crucial role in determining the accuracy and performance of the model. In general, the more data the model has access to, the better it will perform. However, it is important to strike a balance between the amount of data stored for processing and the computational resources required to process it.

ML is a branch of artificial intelligence that lets computer systems learn and make predictions based on data without being explicitly programed to do so. This methodology can easily solve a wide range of complex problems, such as image and speech recognition, natural language processing, predictive maintenance, fraud detection, and powering recommendation systems. ML algorithms analyze vast amounts of data to identify patterns and make predictions with a high degree of accuracy.

For all those operational advantages, machine learning still requires good data up front in order to work as effectively as possible. Furthermore, that data then needs to be organized in a way that an ML algorithm can understand in order to complete its tasks.

What are the steps?

Training data collection

Creating a dataset for machine learning (ML) is an important step in the ML development process because it drives everything the algorithm outputs. Data sets load up an algorithm with the critical mass of clean and processed information it needs in order to work.

The larger and cleaner a dataset is, the more effective the ML algorithm can become. Thus, gathering as much data as possible while keeping it relevant and balancing it all with your hardware capabilities is an ongoing thing in the machine learning process.

So step one is to gather data properly from the beginning. If you already have data in paper ledgers or in .xlsx or .csv files, you may face challenges with digital data preparation. On the other hand, if you have a small dataset that is already friendly to ML, you're in a better position.

Data storage

Data can be stored several different ways, from on physical hard drives to in the cloud. There's even a popular big data storage solution known as the "data lake," a repository of vast amounts of unstructured data. Data lakes can be built on top of commercial versions of Apache Hadoop, third-party cloud solutions, or ready-made products purchased from specialized vendors.

Existing dataset, synthetic data, or data collection?

If you're just starting out and don't have data, there are large open source datasets available to you that make a good starting place. Public datasets are a valuable resource for anyone interested in machine learning and data analysis — they come from businesses and organizations that share their own open data with the public. These datasets can contain a wide range of information from various aspects of life, from healthcare records to weather patterns and transportation metrics to hardware utilization data.

Sometimes companies employ synthetic data generation, which implies artificially generated rather than real life datasets produced by real events. However, at the step of initial model training, synthetic data is not the best option.

To properly train a model, the data should represent the real world, and a synthetic dataset can be prone to distortion. Synthetic datasets should rather be used to validate machine learning models' results later.

Whatever your specific niche is, there is probably a useful public dataset out there for you. While these data sets may not provide loads of information about your specific business or its operations, they can still offer valuable insights into your industry and its niche, as well as your customer segments.

However, the real value in machine learning comes from collecting your own robust datasets that are specific to your business needs and activities, and then using that to drive your algorithm. There's nothing quite as good as a purpose-built solution to a problem, so the data set built in-house for the purposes of your machine learning project will mostly always be better than a public data set available to anyone.

When deciding between using a ready-made dataset or collecting your own data, it's important to consider your goals and the quality of available datasets. If your goals require unique, specific data that's not already out there, creating datasets is likely the best way. But using a ready-made dataset can also save time and resources.

You should also consider the quality of the data, as collecting on your own might require additional effort to clean and process the data before it gets used.

Next, depending on the ML approach you're using, you need to decide whether to label your data, and how exactly you want to do it. See our blog post to learn more about data labeling.

Data preparation

Machine learning helps organizations make data-driven decisions and automate tasks that would otherwise require lots of manual effort. Its power lies in its ability to continuously learn and improve its performance over time, making it a highly valuable tool for solving complex problems.

Unfortunately, datasets are often flawed in various ways that can impact the accuracy and performance of machine learning models.

Some common flaws include:

• Imbalanced classes (one class of data points significantly outnumbers another class).

• Missing data values (which cause problems with the model's accuracy and generalization capabilities).

• Noisy data (irrelevant or incorrect information that negatively impacts a machine learning model's performance).

• Outliers (extremely high or low values that skew results).

To overcome these issues and more, data scientists need to clean and prepare the input that drives an ML algorithm's output. This ensures that the data model is reliable and will perform well.

Quality control

Evaluating the quality of your data is crucial in creating a dataset for machine learning that will yield accurate and meaningful results. Here are some good questions to ask for determining the viability of your dataset:

• Is your data appropriate for your task? For example, if you've been selling home appliances in the US, can you use the same data to predict stock and demand in Europe?

• Is your data balanced? If you have a large number of labeled data points for one class and only a few for another, your machine learning model may struggle to learn about the underrepresented class.

• Is your data trustworthy? Mistakes in data collection or labeling can impact the accuracy of your dataset, so quality control mechanisms need to be added to your collection and labeling pipelines. Multiple datasets contradicting each other might decrease the quality of model training.

• Have there been any technical issues when transferring data? For example, parts of the data might get duplicated or go missing due to things like server errors or a cyberattack.

• How many missing values does your data have? Such values can make it harder to use your dataset for machine learning.

The success of a machine learning algorithm depends heavily on the quality of the data that drives it. Make sure the data is appropriate, balanced, trustworthy, and free of errors or technical issues. By addressing these problems early on, your machine learning models will yield meaningful and accurate results.

Formatting, cleaning, and reducing data

There are three main steps that go into creating a quality dataset: formatting data, cleaning data, and reducing data.

Formatting data is about making sure that the data within a given attribute is expressed consistently. Are all the dates and addresses written in the same file format? Does every dollar amount come with a dollar sign ($) or not? Input formats must be the same across the entire dataset.

Data cleaning calls for removing any missing, erroneous, or less-representative values in the dataset to improve an ML algorithm's accuracy. There are several methods for cleaning training data, including substituting missing values with dummy values, using mean figures, or using the most frequent items. Some ML-as-a-service platforms can help automate this data cleaning process.

Reducing data is about shrinking the overall size of a dataset by removing any irrelevant or unnecessary information.

"Big data" has been a popular business term for several years now and is often seen as the goal for ML, but having petabytes of data on hand doesn't automatically lead to insights. In fact, a dataset that is large but not "clean" will often be more difficult for deriving valuable insights.

If you don't already have a data scientist on your team, this is probably the time to engage one. Domain expertise is important for determining which values should be included and which can be skipped. Appropriately reducing the size of the dataset improves the speed of computing time without sacrificing prediction accuracy.

By ensuring consistent data formatting, removing any missing or erroneous values, and shrinking the dataset size by only keeping relevant information, the end result is a dataset that's more useful in machine learning algorithms.

Define new connections between various types of data

It's important to capture specific relationships in your machine learning dataset. One way to do this is by "decomposing" complex values into multiple parts. This process is a bit like the opposite of reducing data — it involves adding new attributes based on the existing attributes.

For example, if your sales performance varies based on the day of the week, separating the day into a separate categorical value from the date can provide the algorithm with more relevant information.

You also might have different types of data gathered from different data sources. Joining transactional data with attribute data also enhances the predictive power of your ML analysis.

"Transactional data" refers to info about a specific moment, such as the price of a product at the time a user clicks the buy button. Attribute data is more static, however, and doesn't directly relate to specific events, such as a user's age or demographics. Both can be used as training data, depending on your goals.

Suppose you're tracking sensor readings to predict maintenance needs for industrial machinery. Transactional data, like log files, can be combined with attribute data, like the equipment model, batch, and location, in order to find dependencies between equipment behavior and attributes.

Interpreting transactional data to define attributes can also be useful. If you manually analyzed website session logs of individual visitors, you might assign attributes to them like "window shopper" or "instant buyer." That new attribute data can help optimize retargeting campaigns or predict a customer's lifetime value.

Rescaling

Data rescaling is the process of improving a dataset by reducing its dimensions and avoiding situations where some values outweigh others. It helps make ML-driven predictions more accurate.

Suppose you have a dataset with attributes such as car model, body style, years of use, and price. The price attribute will have larger numbers associated with it, and will "weigh" more than the other attributes.

Rescaling this dataset would call for evening out the weight of the price attribute. You can use a technique called "min-max normalization" to transform numerical values into a range from 0.0 to 1.0, where 0.0 represents the minimum and 1.0 the maximum values.

A simpler rescaling approach is called "decimal scaling," which involves changing data size by moving a decimal point in either direction.

Discreticizing

Discretizing data involves converting numerical values into categorical values, which can simplify the work for an algorithm and make predictions more relevant.

For example, if you're tracking customer ages, you won't be particularly concerned with the difference between a 14-year-old's purchases and a 15-year-old's purchases — they can be safely lumped together in a category that includes all teenagers, for example. Discretizing is about turning numbers into qualitative categories.

Rescaling and discretizing your data helps improve a dataset so that an ML algorithm can make more accurate predictions.

What a strong ML team looks like

For being such a computer-based pursuit, machine learning actually calls for quite a bit of human involvement up front. We've already mentioned the importance of having a good data scientist on board for your machine learning purposes, but that shouldn't be the only professional involved in creating your dataset.

Let's run through some of the important human roles that go into finalizing a dataset:

• Data engineer: designs and maintains the dataset's architecture, ensures data is stored securely and efficiently.

• Data collection/entry operator: collects and enters data into databases, ensures data is entered accurately following established procedures and standards.

• Quality assurance/control specialist: ensures accuracy, completeness, and consistency of data, develops and implements data quality checks, regularly audits data integrity.

• Data analyst: prepares, cleans, and organizes data for analysis, performs exploratory analysis looking for patterns and relationships in the data, communicates those findings.

• Data scientist: analyzes, processes, and models data for the purpose of gaining insight and making predictions, develops and implements machine learning algorithms and statistical models that solve complex business problems.

• Machine learning engineer: develops and deploys machine learning models into production environments, works closely with data scientists to understand their needs.

• Subject matter expert: provides domain knowledge and understanding to the data science team, frames the problem, identifies important variables, and validates data analysis results.

• Data annotator: more often than not, it's not enough just to collect data. Training an ML model requires a labeled dataset. A data annotator is a person who manually adds labels to data, to help train the model and monitor the quality of its output.

This list may run quite a bit longer, depending on the team. Other job titles worth mentioning here include statisticians, data visualization specialists, project managers, technical writers, and even an ethics review board.

Does it always have to be a big team?

Some of these roles can be carried out by the same person. Same as in many other fields, in data science the differences between roles can be blurry.

Sometimes an ML engineer who makes algorithm training possible, a software engineer who deploys the model, and even a data annotator who manually labels data are the same person, especially in smaller companies and startups. This situation in particular provides specialists with great experience in various fields, and gives them the opportunity to use their expertise from different fields to achieve the best results.

From creating stable data streams, to data preprocessing, data augmentation, validation, labeling, and quality assurance, all these roles are important. Working together, these are the humans that build intelligent software that can learn and improve itself over time. Be it a large team, or only a few people, they can come up with the most effective ways on how to create a dataset for machine learning.

TL;DR

Creating a machine learning dataset is a vital step in the larger ML process. That data directly impacts the accuracy and performance of the model, so it's important to collect raw data properly and store it suitably.

The decision to use an existing dataset or to dive into dataset creation on your own depends on your specific business goals and the quality of existing datasets already out there.

Data preparation and quality control are also important here. These practices ensure that only clean and accurate data goes into the model, so that it's trained in the best conditions possible to provide relevant results.

With a well-prepared dataset, machine learning algorithms can analyze vast amounts of data to identify patterns and make accurate predictions.