How to get datasets for Machine Learning
In this page, We will learn about How to get datasets for Machine Learning?, What is a dataset?, Types of data in datasets?, Need of Dataset, Popular sources for Machine Learning datasets.
How to get datasets for Machine Learning?
The key to being a great data scientist or succeeding in the
field of machine learning is to practice with a variety of
datasets. Finding a proper dataset for each type of machine
learning project, on the other hand, is a difficult issue. So,
in this topic, we'll go over the various sources from which
you may quickly obtain the dataset you need for your project.
Let's talk about datasets before we get into the sources of the machine learning dataset.
What is a dataset?
A dataset is a collection of data that has been organized in some way. Any type of data, from a series of arrays to a database table, can be stored in a dataset. An example of the dataset is shown in the table below:
A tabular dataset is a database table or matrix with each column corresponding to a certain variable and each row according to the dataset's fields. "Comma Separated File," or CSV, is the most often used file type for tabular datasets. However, the JSON file can be used more effectively to hold "tree-like data."
Types of data in datasets
- Numerical data: Such as house price, temperature, etc.
- Categorical data: Such as Yes/No, True/False, Blue/green, etc.
- Ordinal data: These data are similar to categorical data but can be measured on the basis of comparison.
Note: A real-world dataset is enormous, making it challenging to manage and interpret at first. As a result, any dummy dataset can be used to develop machine learning algorithms.
Need of Dataset
We need a lot of data to work on machine learning projects
because ML/AI models can't be trained without data. One of the
most important aspects of building an ML/AI project is
gathering and preparing the dataset.
If the dataset is not correctly prepared and pre-processed, the technology used in ML projects will not operate well. The datasets are totally relied on by the developers during the development of the ML project. Datasets are separated into two sections while creating ML applications:
- Training dataset:
- Test Dataset
Note: Because the datasets are huge, you'll need a fast internet connection on your computer to download them.
Popular sources for Machine Learning datasets
The following is a list of datasets that are freely available for public use:
1. Kaggle Datasets
Kaggle is one of the top resources for Data Scientists and
Machine Learners looking for datasets. It makes it simple for
users to search, download, and publish datasets. It also gives
you the chance to collaborate with other machine learning
engineers and solve complex Data Science problems.
Kaggle offers a high-quality dataset in a variety of formats that is easy to locate and download. The Kaggle dataset can be found here: https://www.kaggle.com/datasets
2. UCI Machine Learning Repository
One of the best places to find machine learning datasets is
the UCI Machine Learning Repository. This repository houses
databases, domain theories, and data generators that the
machine learning community uses to analyze ML techniques.
It has been widely utilized as a key source of machine learning dataset by students, teachers, and researchers since 1987. It categorizes datasets according to machine learning challenges and tasks such as regression, classification, clustering, and so on. It also includes well-known datasets including the Iris dataset, Car Evaluation dataset, Poker Hand dataset, and so forth.
The UCI machine learning repository can be found here: https://archive.ics.uci.edu/ml/index.php
3. Datasets via AWS
We may use AWS resources to search, download, access, and
share datasets that are publically available. These datasets
are accessible through AWS resources, although they are given
and maintained by a variety of government agencies, research
organizations, enterprises, and people.
Utilizing AWS resources, anyone may study and construct numerous services using shared data. The cloud-based shared dataset allows users to spend more time on data analysis rather than data collecting.
This site details the many types of datasets available, as well as examples and applications. It also has a search bar, which we may use to find the dataset we need. Anyone can contribute any dataset or example to the Open Data on AWS Registry.
The link for the resource is https://registry.opendata.aws.
4. Google's Dataset Search Engine
The Google dataset search engine was launched on September 5,
2018, by Google. This resource assists researchers in locating
publicly available online datasets.
The Google dataset search engine can be found here: https://toolbox.google.com/datasetsearch
5. Microsoft Datasets
Microsoft has created the "Microsoft Research Open Data"
repository, which has a variety of free datasets in fields
like natural language processing, computer vision, and
We can download the datasets to use on our present device or use them directly on the cloud infrastructure using this resource.
This resource's dataset can be downloaded or used via the URL: https://msropendata.com
6. Awesome Public Dataset Collection
Awesome public dataset collection contains high-quality
datasets that are categorized in a list according to
categories like agriculture, biology, climate, complex
networks, and so on. The majority of the datasets are free to
download, but some may not be, so verify the license before
The dataset is available for download from the Awesome public dataset collection: https://github.com/awesomedata/awesome-public-datasets
7. Government Datasets
Data on the government can be obtained from a variety of
sources. Various countries make public government data that
they have obtained from various ministries.
The purpose of making these datasets available is to improve public awareness of government activity and to use the data in novel ways. Here are some government dataset links:
8. Computer Vision Datasets
Visual data contains a large number of excellent datasets
related to computer vision, such as Image Classification,
Video Classification, Image Segmentation, and so on. As a
result, whether you're working on a deep learning or image
processing project, this is a good place to start.
The dataset can be downloaded from this source using this https://www.visualdata.io
9. Scikit-learn dataset
For machine learning enthusiasts, Scikit-learn is a fantastic
resource. Both toy and real-world datasets are available from
this source. These datasets can be retrieved using the generic
dataset API and the sklearn.datasets package.
Rather than importing any file from external sources, the toy dataset accessible on scikit-learn may be imported using certain predefined functions such as load boston([return X y]), load iris([return X y]), and so on. These datasets, on the other hand, are unsuitable for real-world initiatives.
This source's datasets can be downloaded via this URL: https://scikit-learn.org/stable/datasets