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How to create datasets for machine learning – Including free services [2022]!

machine learning

Various datasets are used for machine learning.

There are many datasets such as images, videos, voices, and texts, and datasets are indispensable for machine learning in order to improve the accuracy of machine learning.

So how do we create these datasets?

This article introduces how to create datasets, introduces datasets that can be used for free, and points to note when creating datasets.

Table of contents

  • The Need for Datasets in Machine Learning
  • Advantages of using high-quality datasets for machine learning
  • dataset type
    • Training set
    • Validation set
    • Test set
  • How to create a dataset to improve the accuracy of machine learning and its flow
    • Clarify and set model challenges
    • Data collection
    • Annotate data
  • Free data set
    • Image
    • Movie
    • Audio
    • Text
    • Economy and finance
  • 6 things to keep in mind when creating datasets
    • ① Create Excel as a csv file instead of Xlsx format
    • (2) Determine rules for file names
    • ③Determine rules for variable names
    • ④Empty cells unify meaning
    • (5) Use different sample names and feature names
    • (6) Do not merge cells
  • Summary

The Need for Datasets in Machine Learning

A dataset is a collection of data required for machine learning.

Machine learning consists of computers learning from large amounts of data. Furthermore, the quality and quantity of data also affect the accuracy of learning results, so it is necessary to emphasize the quality of the dataset.

You should also take advantage of the “negative samples” in your dataset. A negative sample is a sample that allows for unambiguous detection of objects and non-objects.

From the above, it can be said that the quantity and quality of datasets are very important in machine learning.

Advantages of using high-quality datasets for machine learning

Here are some specific benefits of using high-quality datasets.

What is a good dataset in the first place?

There is a large amount of data in the world, but not all data can be used. Choose data that is free from mislabeling, noise, and bias from a large amount of data.

Supervised learning , which learns the patterns and rules of correct data, is required to improve the accuracy of prediction and analysis, both in quantity and quality of AI training data. Since the performance of AI training data is directly linked to the quantity and quality of the dataset, it is an essential condition for realizing highly accurate predictions and analyses.

Also, by using a good quality dataset, annotation becomes possible. Annotation is to attach related information to certain data as annotations. In recent years, the demand for annotation has also increased.

Dataset type

Below are the types of datasets. It is mainly divided into the following three types.

  1. Training set
  2. Validation set
  3. Test set

Training set

Training in machine learning means “automatically adjusting” machine learning model parameters (such as network weights and biases in the case of neural networks) using machine learning algorithms. It is an important step in machine learning.

The training set is the first and largest dataset used. Model learning is based on this training set.

Validation set

The variation set is used to tune the hyperparameters after training on the training set.

Hyperparameters are classifiers that control the behavior of machine learning algorithms.

After training the hyperparameters on the training set, we use the validation set and adopt the one that performs best.

Test set

A test set is a data set used to check the accuracy of a model. It is often used only in the final stages of verifying data accuracy, and is used for performance testing.

By preparing a test dataset, you will be able to do a legitimate accuracy verification.

How to create a dataset to improve the accuracy of machine learning and its flow

We will introduce how to create a dataset to improve the accuracy of machine learning and the flow of how to create it. It is done in three easy steps.

  1. Clarify and set model challenges
  2. Data collection
  3. Annotate data

Clarify and set model challenges

First, clarify and set the model challenge.

There are various issues with models, such as what is the purpose of introducing machine learning and what problems should be solved. It would be nice to be specific. It is difficult to create an accurate data set with only a high-level problem. By clarifying the problem, it will be easier to determine the direction of the project.

Data collection

After setting up the model challenge, it’s time to collect the data.

Data quality and quantity are of utmost importance in machine learning. If the amount of data is insufficient during training, a phenomenon called “overfitting” may occur.

Overfitting refers to the creation of a model that fits the training data but fails to predict new data. To avoid overfitting, the amount of data should be increased in small increments.

Annotate data

Finally, annotate the data. Annotation is “a human teaching a machine about the characteristics of things”.

Annotation is used as a technique for memorizing data rules and patterns precisely.

Regarding copyright, when using for the purpose of AI development, it is possible to use copyrighted materials under certain conditions, so image data can be used.

Free data set

We will introduce several data sets that can be used free of charge, divided into images, videos, sounds, texts, and economics and finance.


  • Label Me

Annotated dataset for semantic segmentation.

  • MegaFace

Face recognition mixed with noise data and a large dataset used in an open face recognition algorithm competition at the University of Washington.


A dataset of handwritten digit images that is said to be the first machine learning beginner to use.


  • YouTube-8M Dataset

A dataset of 8 million YouTube videos tagged with 4800 Knowledge Graph entities published by the Google research team. We collect human-validated labels on about 237K segments of 1000 classes.

  • YouTube-Bounding Boxes Dataset

Here is a large dataset with bounding box labeled videos. The dataset consists of approximately 380,000 15-20 video segments extracted from 240,000 different publicly available YouTube videos, automatically selecting objects in natural settings without editing or post-processing can do.

  • Kinetics

This is a video dataset published by Deep Mind, in which approximately 650,000 videos are labeled with interactions between humans and objects such as playing musical instruments, and actions such as handshakes.


  • The NES Music Database

A dataset for building an automatic music composition system, published by a postdoctoral fellow at Stanford University. A total of 5278 songs with 397 titles are included.

  • The Largest MIDI Collection on the Internet

A large dataset of MIDI data published on reddit.

  • NSynth Dataset

This dataset contains about 300,000 single notes from 1,006 instruments, published by the open source research project Magenta.


  • Resources for Natural Language Processing

The Kurohashi, Kawahara, and Murawaki Laboratory at Kyoto University publishes this information on tools and datasets for natural language processing.

  • Aozora Bunko

We publish data of works whose copyright has expired and works licensed by the author.

  • Aozora Bunko Morphological Analysis Data Collection

You can obtain CSV data that has undergone morphological analysis for the works of Aozora Bunko.

economy and finance

  • Quandl

A wide variety of financial and economic data sets can be retrieved. There are many articles on data acquisition in Python.

  • Bitcoin Historical Data

Bitcoin data at 1-minute intervals from January 2012 to August 2019 published on Kaggle.

  • World Bank Open Data

You can easily search and download World Bank data.

6 Things to keep in mind when creating datasets

There are also some things to keep in mind when creating datasets. Here, we will introduce six of them in an easy-to-understand manner.

  1. Create Excel as csv file instead of Xlsx format
  2. Set rules for file names
  3. Decide on rules for variable names
  4. Empty cells unify meaning
  5. Use different sample names and feature names
  6. Do not merge cells

① Create Excel as a csv file instead of Xlsx format

The first is to create Excel as a csv file instead of Xlsx format.

You will use Excel when assembling the dataset. When saving a file after consolidation, the default file format is xlsx format. However, when using this file format for data analysis and machine learning, extra information derived from Excel is included, making it difficult to handle.

The csv format is easy to handle and easy to check and modify the data set. Therefore, let’s create Excel as a csv file instead of Xlsx format.

(2) Determine rules for file names

The second thing is to decide the rule of the file name.

The number of files will increase as the data is created. At this time, if the file name is decided randomly, it becomes difficult to manage the data.

Therefore, setting a rule for the file name will reduce the time required for data extraction.

③Determine rules for variable names

The third is to decide the rules for variable names.

There are also rules for naming variables, and calculation errors may occur if the rules are not followed. It is also important to make the name as descriptive as possible.

There are sites that suggest names for variables when you type in Japanese. When in doubt, try using it.

④Empty cells unify meaning

The fourth is to unify the meaning of empty cells.

As you fill in your dataset, you will encounter blank cells where you have not entered anything.

There is no problem with blank cells themselves, but it is necessary to unify whether the item in the blank cell means that no measurement was performed or that the measurement result was zero. . Let’s set a single meaning for what a blank cell means.

(5) Use different sample names and feature names

The fifth is to use different sample names and feature names.

Having the same sample name for different samples, or the same feature name for different features can cause problems when parsing the dataset.

Let’s give all samples and features a different sample name and feature name respectively.

(6) Do not merge cells

The sixth is to keep each cell independent.

There is a function of merging cells in the function of Excel. If you use this when merging datasets, you will not be able to read the data.

A similar problem occurs when merging cells for sample names and feature names. Don’t merge all cells.


In this article, we introduced the benefits of using datasets in machine learning, the types of datasets, and the points to note when using datasets.



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