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Essential Machine Learning Datasets for Every Developer
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28 Essential Machine Learning Datasets for Every Developer

11 minute read | June 22, 2023
Sakshi Gupta

Written by:
Sakshi Gupta

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The field of machine learning is highly dependent upon datasets, as they’re needed to train machine learning models and verify the accuracy of your results. Finding datasets, therefore, often becomes a time-consuming task for those working in this subset of software development.

That’s why we’ve put together a list of some of the best machine learning datasets out there. These are public datasets that cover a wide range of fields and data types. You can pick up data from any of these datasets and use them for a variety of machine learning projects.

What Is a Machine Learning Dataset?

A machine learning dataset is a pre-existing repository of thematically homogenous data that can be used to train and test machine learning models. The data in these datasets are already cleaned and warehoused so that they can easily be used for the purposes of machine learning tasks.

How Are Machine Learning Datasets Used?

Datasets for machine learning are primarily used to train models so that they can learn from existing data. They are also used to test machine learning algorithms and determine whether they’re functioning as they’re supposed to.

28 Machine Learning Datasets and Sources

Here’s a look at some of the most popular datasets available online and how you can use them in machine learning.

Dataset Aggregators and Providers

This is a list of websites that aggregate datasets. You can check back on them every now and then for new datasets across a variety of areas.

Kaggle

Kaggle is among the most well-known names when it comes to data science and machine learning. Their datasets page is a large repository containing data from across education, games, news, and more. You can dive into these datasets and find the actual data, along with reviews for training datasets, in the comments.

UCI Machine Learning Repository

This is a machine learning dataset repository created and maintained by the University of California at Irvine. You can easily filter the datasets by data types, area, attribute type, and more. Some of the most popular datasets are from areas as diverse as wine, car evaluations, and bank marketing campaigns.

Microsoft Azure Open Dataset

Microsoft Azure is a popular cloud platform, and this collection of datasets comes from that stable. The data available here covers five major categories: transportation, health, economics, population, and supplemental datasets.

data.gov

Data.gov is where all of the US government’s publicly available datasets live. The data available here is a treasure for anyone interested in matters pertaining to public life and municipal issues.

IMF Data

The International Monetary Fund has, as you’d expect, a large-scale dataset pertaining to economics and finance. But along with that, they also offer collections that cover areas such as climate change and sustainability initiatives.

World Bank Open Data

The World Bank’s public data collections are well-curated and easy to browse through. The main datasets are around global development data. In addition to that, you can also access a visualization tool for time series data and a microdata library.

Re3data.org

Re3 stands for Registry of Research Data Repositories. The organization provides various datasets that can be used for research. You can browse these datasets by country, content type, and subject. They also provide an API that you can use to programmatically retrieve the content in the datasets.

Google Dataset Search

Google Dataset Search, as the name suggests, is a search engine for publicly available datasets. So this isn’t data that Google itself is providing; rather, it’s a tool that you can use to find repositories out there using relevant keywords. This is a great search engine to use if you want to explore datasets that are out there in a specific area.

Earth Data

This is a fascinating dataset to play around with for anyone interested in earth science and astronomy. It comes to us as part of NASA’s  Earth Science Data Systems (ESDS) initiative, which provides open access to a lot of the data collected from the organization’s various projects.

BuzzFeedNews

BuzzFeed has created a collection of a lot of different datasets as part of the investigative journalism that it does. It’s a GitHub repository that contains data on everything from visa programs to voter demographics to nursing homes. The great part about this particular repository is that you can, in a lot of cases, also read the articles that were produced using the data. So you get an idea of how raw numbers are transformed into data storytelling pieces.

Natural Language Processing (NLP) Datasets

Natural language processing (NLP) is one of the most exciting subsets of machine learning. Let’s take a look at some NLP datasets.

Yelp

This dataset from Yelp offers a large amount of data that it has on local businesses and their reviews. It covers eleven metropolitan areas, with almost seven million reviews, and has more than 200,000 images.

Potential Applications

This dataset is perfect for anyone interested in analyzing the performance of a local business or using NLP to run text-mining projects on reviews.

WikiQA Dataset

This is a dataset from Microsoft that provides data on questions asked on Bing. It covers over 3,000 questions and almost 30,000 sentences that were produced as part of the answers.

Potential Applications

The WikiQA dataset can be used to work on a lot of interesting natural language processing projects. It could also be used to assess behaviors on search engines and the effectiveness of crowdsourced responses.

WordNet

WordNet is like a thesaurus, except that the dataset establishes relationships between them in terms of semantic and lexical connections. The resulting dataset contains over 117,000 unordered sets of these related words and their conceptual relations.

Potential Applications

This dataset is a goldmine for anyone interested in linguistics and semantics. It can be used to create applications such as predictive keyboards, which make their predictions partly based on relationships between words in the English language.

The Wikipedia Corpus

This is basically the entire corpus of Wikipedia and the 4.4 million articles that are currently available on the platform. However, the difference with the dataset is that the search is a lot more powerful, and you can look things up by part of speech, synonyms, and phrases.

Potential Applications

The Wikipedia dataset can be used to run large-scale natural language processing machine learning projects since it contains a whopping 1.9 billion words.

Brazilian E-Commerce Public Dataset by Olist

Olist is a Brazilian company that provides an e-commerce platform for merchants in the country. This dataset has information from over 100,000 orders made across various marketplaces in Brazil.

Potential Applications

The e-commerce data available in this dataset can be used to conduct a lot of very interesting analyses. For example, you could create a natural language processing tool that assesses reviews for each product and use that for sales prediction.

Time Series Data, Analysis, and Forecasting Datasets

If you’re looking to train a machine learning model for time series data, analysis, or forecasting datasets, then consider one of the following.

Exoplanet Hunting in Deep Space

This is a dataset that allows you to nerd out on specific characteristics of thousands of stars in deep space. The observations available in the dataset are sourced from NASA’s Kepler Space Telescope.

Potential Applications

This dataset is perfect for citizen science projects. It can be used to introduce how machine learning is used in data science to analyze real-world data.

New York Stock Exchange

The New York Stock Exchange generates troves of data, and some of that is available via this dataset. The data available here covers the prices of different stocks over time, securities descriptions, and metrics from SEC filings.

Potential Applications

Quantitative analysis is increasingly becoming a key focus in applied machine learning. You can use this data to practice and build portfolio projects if you aspire to a career in financial analysis.

Web Traffic Time Series Forecasting

The Web Traffic Time Series Forecasting dataset provides information on different Wikipedia articles and how often they’re viewed daily. For each time series, you’re given the name of the article and the different types of devices from which it was viewed.

Potential Applications

You can use part of the dataset to build a time series forecasting application. The results from your forecast can be compared with actual data from a subsequent year to determine whether you were able to come up with the correct answers.

Computer Vision Datasets

Computer vision is a subset of machine learning that focuses on training computers to identify objects or people in an image. Try using one of these datasets to train your computer vision model.

Cityscapes Dataset

This dataset from Cityscapes contains a massive collection of video and still frames collected from street scenes from a wide range of cities across the world. The results are color images with pixel-level annotations, making it perhaps the largest dataset of its kind for city data.

Potential Applications

The Cityscapes dataset can be used to train computer vision algorithms using the available data as testing images. The resulting model may identify object categories or object instances within images. It may also be used to work on neural network projects studying how large amounts of annotated data can be processed usefully.

Open Images Dataset by Google

Google provides access to a large number of public images, but this particular dataset takes things a step further by annotating over 9 million images and using 6,000 category labels.

Potential Applications

The images in this dataset can be used as training images to automatically caption images. The annotations that are available with the images can be used to test the accuracy of the generated captions.

YouTube 8M Dataset

The YouTube 8M dataset contains data on annotated videos from the platform. The generated labels, which are verified by humans, cover 100 classes and over 237,000 segments in the captioning dataset.

Potential Applications

This dataset can be used to train a wide range of models that deal with complex audio-visual data. The segment-level data can be used to come up with classifier predictions that pertain to frame-level features within the dataset.

Indian Movie Face Database Dataset

The Indian Movie Face Database Dataset contains over 34,000 images of faces collected from 100 videos featuring Indian actors. The images are annotated on characteristics such as expression, pose, age, makeup, gender, and illumination.

Potential Applications

This dataset can be used to build machine learning applications that study a variety of aspects of the faces that appear on screen in Indian cinema. For example, computer vision models might be built to automatically measure things like age diversity or gender inclusion using the available images.

Beginner-Friendly Datasets

Just starting out with machine learning? Try one of these beginner-friendly models.

Car Evaluation Dataset

This car evaluation dataset comes to us from the UC Irvine Machine Learning Repository. It assesses cars based on a few simple parameters, including price, maintenance costs, comfort, and technical specifications.

Potential Applications

The car evaluation dataset can be used to build a machine learning decision model that connects car features to sales forecasts. One might also consider building a model that studies how different features contribute to variations in the prices of cars.

Mushroom Classification

Identifying edible and poisonous mushroom species is an important undertaking for mushroom hunters and humans in general. This dataset provides information on mushroom samples across species and their safety for consumption.

Potential Applications

There are currently no simple rules based on which we can determine the credibility of a mushroom. Combining this dataset with machine learning could help us identify characteristics that edible and poisonous mushroom species have in common.

Pima Indians Diabetes Database

This is a database originally sourced from the National Institute of Diabetes and Digestive and Kidney Diseases. It contains a variety of diagnostic measurements obtained from patients, including BMI, age, insulin levels, and so on.

Potential Applications

Predicting the occurrence of diseases in specific populations is an important task for machine learning engineers involved in the medical field. This dataset can be used to draw connections between a mix of predictor variables and the prevalence of diabetes.

Titanic – Machine Learning From Disaster

This is a dataset from Kaggle that puts you to the rather morbid task of determining which groups of people on the Titanic were most likely to survive the shipwreck. The dataset contains information on the age, gender, socio-economic status, and other demographics of the passengers on the ship.

Potential Applications

This is a dataset that tests your ability to build predictive models using machine learning, which is a skill that can come in handy in various areas. Similar techniques are used when companies want to forecast sales in a particular quarter or predict what kinds of discounts correlate most strongly with new customer acquisition.

Enterprise-Grade Datasets

Already working for the company of your dreams? Consider using one of these datasets for your company.

Credit Card Fraud Detection

Credit card fraud is a key area of research for banks and non-banking financial institutions. The given dataset provides information on credit card transactions made in a single month by European credit card users. The fraudulent transactions are labeled and constitute slightly more than 0.1% of all transactions.

Potential Applications

The given dataset can be used to study the characteristics of fraudulent transactions. You could build a simulator that takes the metrics of each transaction as its input and outputs the likelihood of a transaction being fraudulent.

Netflix Prize Data

The Netflix Prize was a competition that the streaming platform ran to find a team that could best predict the ratings that a film would garner from users. The training set contained a list of almost 18,000 movies along with information on customer IDs, ratings, and dates.

Potential Applications

Prediction models built in hackathons such as this one by Netflix often undergird recommendation engines. Once you can accurately predict how much a user will like a particular item, you can recommend more of those items to them within your application.

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Why Is Choosing the Right Dataset Important?

Datasets can make or break machine learning software. So it’s essential that you get the right data for training, validating, and testing your machine learning models.

Most of the data that are required for machine learning is training data. This is data that you can use to train your algorithms so that they can begin to identify patterns on their own. More than 50% of the data required for a machine learning project is for this purpose.

You also require data for the validation and testing steps of the process. The validation dataset is a dataset containing a representative sample of the training data. The testing dataset contains unlabeled data and is used to measure the effectiveness of a model or algorithm.

As you can see, you’ll need clean, structured data for all of these steps in the machine learning process. You could, of course, mine your own data and prepare it yourself. But that’s often a time-consuming task. And that’s exactly why the datasets mentioned in this article are so valuable.

Machine Learning Datasets FAQs

We’ve got the answers to your most frequently asked questions.

What Are the Best Places To Find Publicly Available Datasets for Machine Learning?

There is a wide range of sources from which you can obtain public datasets for your machine learning projects. A good place to start is with a dataset search engine such as the Google Dataset Search tool.

Can I Create My Own Dataset?

Yes, you can create your own dataset. It’s possible to find sources for data online and mine your own data. However, this can be a time-consuming process. So whenever possible, use publicly available datasets.

How Do You Create a Dataset?

The following are the steps to create your own dataset:
1. Data acquisition: Find sources for the training images and other real-world data you require for your project.
2. Data cleaning: Clean the data so that it doesn’t include any erroneous entries, outliers, duplicates, etc.
3. Data labeling: Label the collected data so that your machine learning algorithms have something to learn from.

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About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.