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Machine Learning Datasets for Every Industry

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In this blog on the machine learning tutorial, we will discuss machine learning datasets. We will also provide a list of open-source datasets for machine learning, such as the UCI machine learning datasets, along with their respective descriptions.

How Do We Get the Right Dataset for Machine Learning?

Data is the most important component of Machine Learning. In order to train models, we should have the ‘right data’ in the ‘right format.’ Now, you must be thinking how do we get the right data, right? Well, getting the right data means collecting or identifying the data that correlates with the outcomes which need to be predicted. In other words, data needs to be aligned with the problem we are trying to solve. Also, the data used to build the model should not be non-representative, error-ridden, and of low quality. So let’s see how to get the right datasets for Machine learning
How Do We Get the Right Data for Machine Learning
In this module, we will be discussing the following topics:

Without more delay, let’s get started.

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Gathering Datasets for Machine Learning

Data collection is considered as the foundation of the Machine Learning model building. Without data, the concept of building a Machine Learning model is futile. The more data we have the better predictive model we can build out of it. But remember, ‘more data’ does not mean a bunch of irrelevant data.
We cannot add any data just to increase the quantity. So, we can say that any effort that is directed toward ‘finding the right data’ is well invested—that way after putting the collected data through a cleansing process, we will have ‘more data’ to build the model with.

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Now, I am sure that you must be wondering how we can find dataset for machine learning operations. Dataset for machine learning can be found in two formats—structured and unstructured. Let us elaborate on what structured and unstructured dataset for machine learning are.

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Structured Dataset Vs. Unstructured Datasets for Machine Learning

Structured data is highly organized. It is comprised of clearly defined data types which are easy to digest. More importantly, structured data is easily searchable. Whereas, unstructured data, with no defined data types, is not easily searchable. The below image provides further differences between structured and unstructured data.
Structured Data Vs. Unstructured Data

Structured data

Structured data can be displayed in rows and columns and, usually, it resides in relational databases (RDMS). Data can be created by human or machine, as long as it is fit to reside in an RDMS, it can be searchable both by human-generated queries and by using algorithms using type of data and field names. Typical structured data includes dates, phone numbers, credit card numbers, customer names, addresses, product names and numbers, transaction details, etc.

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Unstructured data

Unstructured data can be textual or non-textual, human or machine generated; it may also be in non-relational databases like NoSQL. It does not fit in relational databases. Human-generated unstructured data includes email text files, social media data, location-based data, and media files such as MP3, digital photo, audio, and video files. Typical machine-generated data includes weather data, surveillance photos and videos, sensor-based traffic data, etc.
Structured data requires less storage space, which makes it easier to manage. But unstructured data requires more storage space.
According to Gartner, unstructured data makes up to 80 percent of the enterprise data. Unstructured data is growing in an insane manner. According to IDC, unstructured data grows at 26.8 percent annually compared to the structured data, which grows at 19.6 percent annually. Due to the sheer volume of the unstructured data, traditional data collecting techniques often leave out valuable information.
That is why the unstructured data management needs to be different. Today’s enterprises need a separate data management platform that’s built specifically to handle unstructured data.

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List of Open-source Datasets for Machine Learning

There is a plethora of open-source datasets available for us to exercise Machine Learning Algorithms on. Here, we are listing out some of those.

  • Boston Housing Dataset has data in 506 rows and 14 columns; it is collected from the real estate industry in Boston (US).

List of Open-source Datasets for Machine Learning

  • data.gov contains data that can range from government budgets to school performance scores collected from multiple US government agencies.

US government agencies

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  • Stanford Dogs Dataset contains 20,580 images and 120 different dog breed categories.

Stanford Dogs Dataset

  • Kaggle Datasets contain a bunch of real-life datasets of all shapes and sizes in many different formats.

Kaggle Datasets

  • Amazon Dataset contains data collected from different fields such as Public Transport, Ecological Resources, and Satellite Images, and they are stored in Amazon Web Services (AWS).

Amazon Dataset

  • UCI Machine Learning Datasets Repository is another repository of hundreds of datasets from the School of Information and Computer Science, University of California.

UCI Machine Learning Repository

  • Google’s Datasets Search Engine is another great initiative by Google to unify tens of thousands of different repositories of datasets that can be searched by name with the help of the below

Google’s Datasets Search Engine

What Did We Learn so Far?

This blog briefs about gathering different datasets for machine learning and describes how data exists in different forms, namely, structured and unstructured. Also, we listed out some of the sources, like uci machine learning datasets, to exercise our Machine Learning models on. In the next module, we will be discussing about different aspects related to datasets for Machine Learning. See you there.

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