Introduction
Machine learning is one of the most powerful technologies being used today. It is a very important branch of artificial intelligence used for making computers smarter – giving them the ability to learn without human intervention. This makes machine learning a vital tool for handling data. As data is used literally everywhere, from making business decisions to curating customer experiences, machine learning makes it easier to identify the patterns hidden within these huge sets of data.
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Most importantly, these datasets are a way to organize huge chunks of raw data. Using these datasets, programs are written to create applications that make business operations easier. In this article, we learn about the different datasets for machine learning.
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But before getting into that, let us first understand the basics of machine learning.
What is Machine Learning?
Machine learning is responsible for powering your most favorite platforms such as Netflix, Facebook, Twitter, YouTube, Spotify, Google, and Baidu. Even voice assistants such as Alexa and Siri select your favourite songs to use machine learning! All these platforms try to use the data associated with you. This includes your searches, clicks, your views, the pictures you share, comments, reacts, and posts. Learn more about the top machine learning applications.
Machine learning makes use of this data to get an idea about your preferences. For example, Netflix uses it to suggest a TV series you might enjoy watching, based on the ones you have watched. Even platforms such as Amazon uses machine learning to suggest your products, based on your previous purchase history.
The most prominent segment of the machine learning market is deep learning that may reach up to 1 billion by 2025.
Seems interesting? Let us get into the technicalities of the subject.
Categories of Machine Learning
Machine learning is broadly divided into three – supervised, unsupervised learning, and reinforcement learning.
Supervised learning
In this process, the computer will learn from a dataset called training data. It will take decisions and predict future outcomes based on this. You will learn about training datasets for machine learning later on. Here, the system is fed input-output pairs, and while working with these pairs, it learns how they are mapped together. It is like having a set of questions that have the correct answers tagged to them.
When the system or the algorithm learns the relation between the input-output pairs, it can predict the output when a new input is provided to it. Learn more about the types of supervised learning.
Unsupervised learning
Here, the computer looks into datasets for identifying hidden patterns without any assistance. It works on complicated tasks and discovers results on its own. Learn more about unsupervised learning.
Reinforcement learning
This machine learning process makes use of a trial and error method to determine the solution to a problem. So the output of the program will depend on the current input provided to it.
Now that you have a basic understanding of machine learning, let’s move on to the datasets.
What are datasets for machine learning?
A data set, as the name suggests, is a collection of data. It can be the data of a single database, where a variable is used for representing the columns. The rows of this table may be represented by a member of this particular dataset.
Preparing datasets for machine learning is important. This is because the algorithms cannot work properly on raw or unstructured data. A proper data set is required to solve the problems and arrive at decisions. For example, a weather application may not have the proper dataset containing the climate data of the past few days or weeks. So, it will not be able to deliver accurate weather forecasts for the upcoming week.
Thus, without proper datasets for machine learning, the machine learning project will not be successful even with trained data scientists.
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Datasets for machine learning are used for creating machine learning models. These models represent a real-world problem using a mathematical expression. To generate such a model, you have to provide it with a data set to learn and work.
The types of datasets that are used in machine learning are as follows:
1. Training data set
This is perhaps the most important among the datasets for machine learning. It is fed to a machine learning algorithm to create a model. The algorithm looks for data patterns to identify input variables. This will help it to reach its ultimate goal or the desired output. The output of this data set is a machine learning model that you can use for predicting results.
About 60% of the data set is taken up by a training data set.
2. Validation data set
A validation data set is used at the validation stage, while creating a machine learning project. This stage comes right after training. This data set is important for evaluating the machine learning model. Machine learning engineers use this set to tweak and adjust the hyperparameters of the model. These hyperparameters are parameters that have values set before the program starts learning.
Their values cannot be estimated from the data. For example, hyperparameters can include the depth of a tree or a number of undetected layers in a neural network.
According to famous writers Max Kuhn and Kjell Johnson, “a data model must be evaluated using samples that were not used for creating or adjusting it. This gives you an unbiased result of the model’s effectiveness. When working with a huge amount of data, it is best to set aside some samples of data for evaluation. The training set is the sample used for building the model, whereas the validation and testing samples are used for analyzing its performance.”
3. Test data set
The test datasets for machine learning are used for understanding how the machine learning model will work in the future. Using this data set, you will be able to understand how accurate your data model is. In simple terms, this data set will tell you how much your data model has learned from the training set.
These sets take up 20% of the data. The set will contain input variables along with verified outputs. However, in machine learning projects, we generally do not use a training data set in the testing stage. This is because the algorithm will be aware of the expected output, as it has learned from this data set previously.
After the testing phase, the data model is usually not adjusted anymore. This is because further adjustment can lead to overfitting. Overfitting occurs when a data model is trained with too much data. In this case, the model starts learning from the inaccurate data entries in the given data set. As a result, it does not work properly on new data sets. It is like trying to fit into oversized jeans when you can’t!
But for the machine learning model to work successfully, you need to provide it with a good data set. Without datasets for machine learning, the algorithm will not be able to learn and solve the problems. For example, when you do not have the right books and resources, you cannot ace the test you want to.
Preparing datasets for machine learning
Let’s find out the steps needed to create datasets for machine learning.
Data collection
The first step is to collect all the relevant data that you may need for your machine learning model. The amount of data will depend upon the complexity of the machine learning project. A simple project will require less data than a complicated one. So, you need to determine all that you actually need to solve the problem at hand.
Data can be collected easily by answering the following questions:
- What type of data is available to you for the project?
- What data is not available that you need for the project? – This may include certain databases or data stored in cloud systems. You may need to derive this data.
- What data can you remove from the existing data? This means clearing out the unwanted data that is irrelevant to your project.
When you have the answers to all these questions, you can start collecting data from various sources. These can be text files, .csv files, looking at nested data structures in JSON and XML files and data repositories.
Now you can move on to the next step in creating datasets for machine learning.
Data preprocessing
Now that you have all the data that you need, you have to process it properly for your model. The preprocessing method is converting raw datasets into meaningful sets that are usable. The process consist of the three steps below:
Formatting
The raw data that you have collected many not be in a format that is suitable for your machine learning model. It may be in a JSON file or a relational database. You need to convert this data into a text file or a .csv file as per your convenience.
Cleaning
This is the process where you fix and remove missing and unwanted data from your data set. These instances of data may not help to solve the problem. Additionally, there may be sensitive information within some of the attributes that you may need to hide or remove completely. This makes your datasets for machine learning more meaningful.
Sampling
You may have collected a lot more data than you actually need for the project. Large data sets consume a lot of memory space. They also cause longer runtimes and much more computation when fed to a machine learning algorithm. To avoid these problems, you have to make smaller samples of the selected data that your model can use easily. This process is called sampling.
Feature engineering
Here, the data set is analyzed to determine the best features and patterns that will help in solving the problem and making predictions. So, in this process, some of the data may be removed from a large data set. The focus is on the most important features that suit the model.
Data can be decomposed into small parts to identify the crucial features. For example, sales data of a particular year can be broken down into months and days of the week. This way analysis of the sales performance is easier and faster. This also helps the machine learning algorithm compute faster.
Splitting the data
Now the data has to be split into three sets – training, testing, and validation. You need to split it into 70%, 20%, and 10% respectively for the sets. For proper testing, ensure that you select only non-overlapping data subsets. Splitting data sets properly to allow the machine learning model to reach the desired output faster. You can refine the data model later on.
Well, you have now learned how to curate a data set for a machine learning algorithm. But what if you have a project coming up and don’t have the time to build your own data set? Thanks to the internet, there are many ready-to-use data sets available for you to choose from.
Machine learning datasets online
Here are the most useful datasets for machine learning on the web:
- The Boston Housing Dataset
A popular choice among the datasets for machine learning. It is used for pattern recognition. It consists of information about the various Boston houses including data such as the number of rooms, tax rate and crime rate in the area. Consisting of 506 rows and 14 variables in the data columns, the data set is good for predicting housing prices.
- Parkinson data set
This data set consists of 195 patient records, along with 23 different attributes that have biomedical measurements. You can use the data set to separate healthy patients from the ones having Parkinson’s disease.
- IMDB
A data set consisting of 25,000 movie reviews. This is used for binary sentiment classification.
- MIMIC-III
This is an openly available data set that was created by the MIT Lab for Computational Physiology. It consists of health data of around 40,000 critical care patients. Information such as medication, lab tests, vital signs, and demographics are included here.
- Berkeley DeepDrive BDD100k
The Berkeley DeepDrive BDD100k is currently the largest data set used for developing machine learning programs for self-driving cars. It contains more than 100,000 videos driving at various times of the day in different climatic conditions. The data is based on the cities of New York and San Francisco.
- Uber Pickups Dataset
This data set has information about Uber customer pickups from April to September 2014 in New York. There are around 4.5 million customer data of this type and 14 million more from January to June 2015. You can perform data analysis using this data set to gather more information about customers. This can help companies enhance their business significantly.
- Mall Customers Data Set
This contains information about people visiting malls. The data set contains details such as gender, age, customer ID, spending score and much more. This can be very useful in target marketing. Based on data such as age and spending score, businesses can segment customers into groups. They can create unique customer experiences for these groups.
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Conclusion
Just like proper words and phrases make a poem stay with you for a long time, the right dataset is needed for a successful project. This is why many of the best companies recruit data engineers for the task of creating the best data set for a particular machine learning system. So take your time while preparing your datasets for machine learning.
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