Use These Example Datasets to Improve Machine Learning Outcomes

High-quality data is foundational to every machine learning project. Fortunately, thanks to example datasets, people do not have to source data from scratch to train their models. Although examining and cleaning the information before relying on it is necessary, users can find free or low-cost information for training purposes. Knowing about some reliable sources helps projects go faster and more smoothly, plus increases the chances the public will trust the results.

The FBI’s Crime Data Explorer

Many machine learning applications involve crime detection or prevention. Law enforcement decision-makers like the prospect of using advanced technologies to supplement their police officers, support staff and other professionals.

Anyone whose machine learning projects require example datasets about criminal behavior should check out the Crime Data Explorer offered by the FBI. This rich dashboard allows people to examine or download information for specific needs. 

It also has an officer accountability aspect because the data shows use-of-force details associated with specific instances. It’s easy to sort the information by state and year or look at country-wide specifics annually.

Another interesting aspect of this tool’s example datasets reveals the nature of contact between the public and law enforcement professionals. First, people can see how often an area’s residents got in touch with the police. They can also get statistics concerning police officers initiating communications with community members. Finally, information exists to show court or bailiff-related correspondence. 

Many parts of the Crime Data Explorer also feature relevant methodologies to explain potential discrepancies within some information. Becoming familiar with those is an important way to increase the reliability of machine learning algorithms’ performance and deepen people’s trust.

Additionally, a link on the Crime Data Explorer’s home page shows the latest updates. That list could be ideal for helping machine learning engineers get the newest information for highly specific projects or give them inspiration for the direction of future efforts. 

CDC WONDER

Health care has become an increasingly data-driven industry, especially as patient monitoring devices collect information on people during and outside hospital stays. Given the Centers for Disease Control and Prevention’s oversight of and emphasis on public health, it’s no surprise that the organization has relevant example datasets. After all, understanding broad and applicable trends could help people stay healthier and reduce their associated risks. 

Enter CDC Wide-ranging ONline Data for Epidemiologic Research (WONDER), which provides numerous datasets that support evidence-based public health conclusions and trends. The agency offers this information to the public and collaborating organizations, making this information a collectively valuable resource for equipping modern communities to deal with current or emerging threats while giving them information to handle those that arose in the past. 

The CDC also provides the data as ready for use in desktop applications and allows people to download material in several formats depending on what their machine learning projects require. The home screen breaks information into three main tabs for better usability and organization. 

The first displays all the systems containing different data types, along with categories such as births, deaths and cancer-related statistics. Next, people can see information broken down by topics, such as occupational deaths, flu cases and diabetes prevalence. The last tab is an alphabetical index, making it the easiest and most direct way to see if the WONDER database covers a particular area of interest. 

These example datasets also protect people’s identities in cases where it may otherwise be possible to determine who someone is based on what the tool shows. Users will appreciate the handy features that allow them to save queries or outputs for future reference, too. Keeping all the information together enables more efficient experiences, especially as machine learning data sources become more extensive. 

The National Center for Education Statistics’ DataLab

The National Center for Education Statistics details the extent and nature of people’s learning experiences, including how far they progress in their studies, the types of degrees earned and more. These example datasets could assist anyone interested in applying machine learning to improve the college experience, reduce learning obstacles or achieve other tailored aims. 

Getting acquainted with the data starts by using the website’s PowerStats search bar to narrow results based on specific needs. Clicking the upside-down triangle next to each header allows sorting the options based on populations and topics and choosing whether to examine data for multiple years. Additionally, people can search by typing keywords into the box.

Scrolling further down the page shows more details about the example datasets, such as the particular years represented in them or the information people can expect to find. Then, people can launch the chosen ones in web browsers for a closer look. 

Additionally, the Online Codebook feature allows site visitors to download the data at the micro level. Users can also click the star symbol associated with dataset groups to add those as favorites for quick reference later. 

The Registry of Open Data on AWS

Although many people know AWS as a cloud provider, it is less well-known as an example datasets source. However, there is a wealth of information to explore here, and there are handy tags to help people quickly find what they need. 

This dataset collection is not as nicely organized as some of the other possibilities covered here, but the information is so diverse that it is worth including. For example, as individuals dig through the content, they will find material spanning everything from human sleep statistics to the drainage potential of local soils. 

The search box at the top of the page is the easiest way to find information on particular topics. However, these example datasets also contain usage examples that could spark people’s inspiration or guide those newer to machine learning and its capabilities. Some of them walk through the various steps and applications that allowed agencies and other organizations to successfully use the information for projects. 

Let Example Datasets Shape Your Machine Learning Efforts 

All machine learning applications need trustworthy, accurate data to work. Gathering that information can be a time-intensive prospect, but collections such as those described above can significantly speed up this step. They are also excellent for helping people define their projects by exploring what others have done. 

Perhaps the best aspect of the example datasets mentioned here is that anyone can access them at no cost. The complimentary nature removes financial barriers that may otherwise restrict how and why people use machine learning.