There are key concepts that lay the foundation in machine learning algorithms for understanding the theme. One of these concepts is a dataset, an assortment of cases that support machine learning techniques for various purposes. A dataset is sometimes known as a validation or training dataset that is often fed into the machine learning system to train models.
Before learning more about the data sources for machine learning, let’s discuss the standards terms or terminology used to define datasets. You will also learn how to get datasets for machine learning algorithms, acquire valuable intuition on the need of datasets, and know more about 30 popular datasets for your excursion through a machine learning theme.
What is a dataset?
A dataset is an array of data in which data is organized in some order. A dataset can comprise any data from a sequence of a collection to a database table.
A tabulation of datasets is considered a database table or matrix, with each column corresponding to a specific variable and each row corresponding to the dataset’s themes.
Types of data in datasets
Data in datasets can be categorized into numerical, categorical, and ordinal data.
- Numerical data includes temperature, prices among others.
- Categorical data are true or false, yes or no, blue or green, pass or fail.
- Ordinal data are closely associated with categorical data but often measured based on the appraisal.
Note: A factual dataset is enormous, which is often challenging to manage and process at the preliminary stage. Thereby, there is a need to use imitation datasets to practice machine learning algorithms.
Need of dataset
People need datasets for various purposes. These needs include working with machine learning initiatives, which require enormous data to train artificial intelligence models. The process of collecting and preparing the dataset is one of the most significant parts of complete an artificial intelligence or machine learning initiative.
The application of expertise behind any artificial intelligence or machine learning initiatives cannot function properly without effective preparation and pre-process of the dataset. Therefore, experts rely on the two sets of datasets, including training and test datasets, to develop machine learning initiatives.
How to get datasets for machine learning
As initially mentioned, identifying the right dataset for a machine learning algorithm is often difficult and challenging. However, the strategy to realizing success in machine learning algorithms is practicing with various kinds of datasets. Therefore, in this post, we will provide a detailed analysis of 30 popular datasets and their sources for your machine algorithms.
30 popular datasets that are easily accessible for your machine learning algorithm
Before learning more about datasets for machine learning, let’s highlight the source of these datasets.
We have listed 30 popular datasets that are freely accessible for everyone to gush the desire for machine learning algorithms. The added datasets carry a wide range of concepts, challenging stages, scopes, and characteristics.
The datasets are classified per their varying levels of difficulty to suit everyone. The datasets are capable of challenging your knowledge and achieve hands-on practice to stimulate your competence in various fields associated with artificial intelligence, including but not restricted to machine learning, deep learning, and data visualization and analysis.
We suggest you test your machine learning algorithms with all the different datasets listed.
1. Iris Flower dataset
The iris flower is a beginner dataset for machine learning algorithms. This dataset can help you build a simple initiative in machine learning algorithms.
The Iris flower dataset’s scope is small and does not require pre-processing data. Iris flower dataset has three multivariate classes: Virginica, Setosa, and Versicolor which each type of these classes has 50 instances.
2. Breast cancer Wisconsin dataset
Breast cancer Wisconsin is a Diagnostic Dataset that is popularly used to classify machine learning problems. The features include computed data from a digitized image of a fine needle aspirate of a breast mass that you can use characteristics of the cell nuclei present in the image.
Breast cancer Wisconsin dataset has three forms of attributes: ID, diagnosis, and 30 real-valued input features. This dataset also has 569 instances, including 357 benign and 212 malignant.
3. Spam SMS classifier dataset
Spam SMS classifier is a beginner-friendly and easy-to-understand dataset that can help you train your machine learning algorithm to predict spam messages.
This dataset has a set of SMS labeled messages collected for SMS Spam analysis. The dataset has the data in the CSV format.
4. Spam-Mails dataset
Spam-Mails is a beginner-friendly and easy to understand dataset used for machine learning purposes to build your algorithm. This dataset is vital in your model’s training to predict not legit emails.
It has a collection of 425 SMS spam messages that were manually extracted from the Grumbletext web site. However, you need to split your data in this dataset because it does not come with train and test division.
5. YouTube dataset
This dataset YouTube video dataset is based on YouTube information to help learn machine learning algorithms. The annotations generated from the machine are of high quality. Additionally, they emanate from visual entities and audio-visual features from segments of audio and frames that exist in billions. YouTube Dataset also has 6.1 million URLs, labeled with a vocabulary of 3,862 visual entities.
6. CIFAR -10 dataset
CIFAR 10 has several object images for classification. Over time, this dataset has been very popular in research related to machine learning. The dataset has ten varied classes with over 60,000 color images whose dimension is 32×32.
The diverse classes consist of cars, deer, dogs, horses, birds, frogs, trucks, airplanes, and ships.
7. IMDB dataset
IMDB dataset is often used for sentiment analysis purposes using a Machine learning algorithm. It has 25,000 highly polar moving reviews, which are maybe good or bad. IMDB dataset comes with a 50/50 split for training and testing purposes.
This dataset also achieved 88.89% accuracy.
8. Sentiment 140 dataset
Sentiment 140 dataset is a beginner-friendly dataset for machine learning purposes. It is built on Twitter data. With the aid of this dataset, text processing data is possible.
Subsequently, you can start building your algorithm on NLP.
Sentiment 140 dataset pre removed the emotions. It had six features altogether, including the tweet’s polarity, the tweet’s id, the tweet’s date, the query, the username of the tweeter, and the tweet’s text.
9. Facial image dataset
Facial images for both females and males form the foundation for the facial image dataset. It is essential when the intention is to determine either emotions or the gender of the victim.
Both algorithms use this dataset in deep learning as well as in machine learning.
The data variation it possesses is similar to that of background and scale, as well as expression variation.
10. RED Wine quality dataset
RED wine quality dataset is used for machine learning and deep learning enthusiast. Wine quality prediction is made possible because of this model. Also, note that the dataset consists of ordered classes that are not balanced.
11. The Wikipedia corpus dataset
The full-text collection on Wikipedia corpus is about 1.9 billion words. Therein is about 4 million articles. In the dataset, you can search depending on the word, the given phrase, or use a paragraph section. This is an essential use in machine learning.
12. Free Spoken digit dataset
The free-spoken digital dataset has English recordings of spoken digits that are either speech data or simply audio. It exists at 8kHz, with the file format being wav.
The trimming of the recordings is done in a way as to have close to minimal silence both at the end at the beginning. The task of spoken digits identification in audio has been a nightmare, which is the sole reason for this dataset’s existence.
Having this dataset as an opensource is a great advantage because anybody, including yourself, can contribute to the repository. This also allows growing more in the coming days.
13. Boston House price dataset
The United States Census on service about housing in the Boston mass region is the genesis of this dataset. By using the attributes available, this dataset can make predictions on the cost of housing. The dataset is also ideal to be handled by regression. The number of cases in the dataset is about five hundred and six.
14. Total cases dataset
This dataset is a comma-separated value whose 14 attributes in the database consist of elements like TAX, CRIM, and AGE, just to mention a few. The suitable machine learning algorithm for this kind of data is regression.
15. Pima Indian Diabetes dataset
It is an excellent choice model for the prediction of diabetes. The cases consist of female residents of Pima Indian heritage who are at minimum 21 years of age.
There are a total of nine columns in the dataset, including pregnancies, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, age, and outcome.
16. Diamonds dataset
The dataset contents comprise prices and other attributes of about 54,000 diamonds. These variables include the price in US dollars, carat, color, clarity X, cut, length in mm, Y or width in mm, Z or depth in mm, and table.
17. Mtcars dataset
The dataset comprises 10 concepts of designing automobiles, fuel consumption, and 32 automobiles performance. The US magazine Motor Trend of 1974 is the source of this data.
18. Titanic dataset
The data herein is summarized based on the status economically, age, sex, and survival of the oceanic liner victims’ victims tabbed Titanic.
It gives an explorative passengers’ fate for the voyage. Many books, documentaries, and movies have been done in the past, and some still in the pipeline about the famous sinking of the titanic.
19. Beavers dataset
Temperature regulation in the beaver’s body is the cornerstone of this dataset. Temperature-sensitive radio transmitters were implanted surgically on four female adult beavers. This happened after they were trapped alive.
After every 10 minutes, readings were taken from each of the beavers. During the recording, the beaver’s location has also noted, and the dichotomization of the beaver’s level of activity. The latter resonated with whether the beaver’s location was outside of the high-intensity region or whether it was in retreat.
20. Car-seats dataset
The contents are those of child sales car seats for about 400 varied stores. Essentially, the observation is made on eleven different variables whose data frame amounts to 400 observations.
The simulated dataset of 11 variables comprises sales, compPrice, income, advertising, population, price, shelveloc, age, education, urban, and the US.
21. msleep dataset
msleep dataset is not just updated, but it also expands the mammal’s sleep dataset. It is a dataset with 83 rows and 11 variables.
22. Cushing’s dataset
The dataset comprises twenty-seven rows and three columns. What is notable about the Cushings Dataset is the kind of observations made. The urinary excretion rates of two metabolite steroids are the subject of concern. The hypersensitive disorder syndrome is characterized by the secretion of cortisol in excess by the adrenal gland.
23. ToothGrowth dataset
In this dataset, 60 guinea pigs are used to establish tooth growth and how cells respond. In other words, the odontoblast’s length is the foundation. In the 60 observations for this data frame, 3 variables were used.
Also, note that every single animal received one out of the three levels of vitamin C doses. The methods of delivery engaged included ascorbic acid or orange juice.
24. Forecast pollution dataset
The contents of the dataset originate from the United States Embassy based in Beijing, and therein is meteorological data for the Capital International Airport of Beijing. The contents also include PM2.5.
Conclusively, it has about 43,824 rows and 13 columns. The forecasting of this set of data is in pollution, especially when you take advantage of the available air quality attributes.
Multivariate Time Series Forecasting is an additional take from this dataset.
25. Relate returns of Istanbul stock exchange dataset
The data includes Instanbul Stock Exchange returns with about 536 rows and columns adding up to 9. If you intend to understand how other stock market indices work, it is imperative to use this dataset to find a predictive kind of relationship.
The additional seven international indices included are DAX, NIKKEI, SP, BOVESPA, FTSE, MSCI_EM, and MSCE_EU.
26. Computer Vision datasets
The dataset is very particular to visual data that includes segmentation of images, classification of images, and classification of videos. It is all about computer vision.
27. Government datasets
While seeking transparency in the government’s dealings, most governments have put data-centric data in the public domain. Most of this has been collected from various units and departments in the government offices.
Besides building trust levels among the citizens, the data can also be used in a novel way to draw meaningful conclusions that can help address some of the issues the government aces.
28. Microsoft datasets
The Microsoft Research unit’s open data unit is a diverse collection of datasets on various fields, including but not limited to computer vision, natural language processing, and data on target science domains.
The cloud platform provides an opportunity to interact with the data directly from the cloud infrastructure or pull the data and use it on your local device. The data collected is available for you to access free of charge.
29. Datasets via AWS
AWS resource center enables users to access, download, search or even share datasets available for the public domain. Data stored here is diverse, and some are maintained by different groups, individuals, organizations, businesses, researchers, and government institutions.
You have no limitation in sharing, building, or even analyzing the different services available on the AWS resources. In fact, by centralizing the data more on the cloud, the time spent on data acquisition is greatly minimized. The additional time is invested into the analysis of the data.
30. Scikit-learn dataset
Machine learning enthusiasts find Scikit-learn an interesting resource. By using it, you get real-world datasets besides the toy ones. You can get these datasets using the general dataset API or datasets package for Scikit-learn.
Summary
This article elaborates on what datasets are and breaks down what we consider the top 30 datasets any machine learning scientist has to know. It stands out that datasets are an integral part of machine learning. It is also central to natural language processing.
Machine learning would be almost impossible without training of the respective datasets. By training datasets, they can carry out the classification of text, do product categorization, and facilitate text mining.
As mentioned in the article, we are mainly concerned with datasets that can answer the queries we have sufficiently. Also, seek datasets with excessive columns and rows since they are flexible and simple to work with. Not to forget, clean datasets will save you a ton of time that you would have otherwise wasted trying to get it clean and ready for use.
There are also repositories available that readily provide you with various datasets for practice purposes when learning machine learning. Some of these repositories like Kaggle have niched datasets with different master lists. Other repositories like the UCI Machine Learning provide you with an alternative to download datasets that interest you even without registration. You will find most of the datasets in the two repositories mentioned herein to be particularly interesting. Despite these datasets being contributed by individual users, they are relatively clean. We are hopeful that the same way it was useful to us, it would also beneficial to you.
Finally, do not forget to explore the available broader categories of datasets that fall on different domains. In particular, look at datasets for autonomous vehicles, natural language processing, sentiment analysis, datasets for images, finance, economics, and datasets for public governance.
We hope you find the article interesting. If you feel like deliberating on any issue or sharing for that matter, please do not hesitate to do so.
We will be more than glad to engage with you. Good luck!!
