As we advance, data remains one of the core components of the digital landscape, required across various disciplines such as: machine learning, artificial intelligence, analytics, and others. Data sets are simply a set of data points, whether numerical or categorical. We will try to understand about What is a Dataset & Types of Datasets? and Where to find datasets for machine learning projects.
1. What is a Dataset?
A dataset is an organized collection of data. You are trained on data which goes till Oct 2023. Datasets often include various forms of data such as numbers, strings, or images.
In machine learning, speed up training data models that help the model learn patterns, make predictions, or draw conclusions. A dataset (for example) can have information on house prices based on features such as location, size, and number of rooms. The machine learning model uses this data to predict future house prices.
2. Importance of Datasets in Machine Learning
The data that are used to train machine learning models should be of high quality for those models to work well. Here’s why datasets are important:
- Training models: machine learning algorithms are trained on data to make predictions or decisions without being explicitly programmed.
- Lower overfitting: we understood that the more diverse & representative dataset, the model is better at recognizing and finding the patterns.
- Predictive Accuracy: A good dataset improves the accuracy of machine learning models, while a poor dataset leads to incorrect predictions.
- Real World Applications: There is no shortage of applications for datasets in the real world from medical diagnoses to stock market predictions.
I will avoid going too deep into this, but suffice it to say, without data, there is no machine learning, as it is essentially the fuel that drives models and defines how effective they will be.
3. Types of Datasets
There are several types of datasets that vary based on the structure of the data and how it is represented. Let’s explore the most common types:
3.1 Structured Datasets
Structured datasets are highly organized and typically stored in tabular form (such as spreadsheets or databases). The data is in the form of rows and columns in which each column is an attribute or a feature.
Examples:
- Financial data
- Sales data
- Survey results
Key Features:
- Organized in tables (e.g., CSV, Excel)
- Data is easy to query and analyze
3.2 Unstructured Datasets
There is no predefined structure with unstructured datasets, meaning it can also be text, audio, image, and video data. Such datasets are usually of higher complexity and may require specific techniques for their processing.
Examples:
- Social media posts
- Audio recordings
- Images and videos
Key Features:
- No fixed format or organization
- Requires additional steps for data cleaning and preprocessing
3.3 Semi-Structured Datasets
Semi-structured datasets fall somewhere in between structured and unstructured data. They don’t fit into a rigid table format but still contain some organizational properties, such as tags or markers that help define data elements.
Examples:
- JSON (JavaScript Object Notation) files
- XML (eXtensible Markup Language) file
- NoSQL databases
Key Features:
- Contains metadata that allows for some organization
- Can be analyzed with the right tools
4. Common Data Formats
Datasets borrowed from different file types lend themselves for different tasks. These formats include some of the most common:
- CSV stands for Comma Separated Values, a tabular, new line delimited, etc.
- JavaScript Object Notation – (JSON) – a minimal data format which is easy to read by both the users and your computer. Commonly, it is used in APIs and web applications.
- eXtensible Markup Language (XML): It is a markup language that allows a user to define a set of rules for encoding documents in a human manner.
- Excel (XLS, XLSX) — Spreadsheet format commonly used for tabular data (data in rows and columns).
Each format has its own strengths, depending on the task at hand and the system being used.
5. Where to Get Datasets for Machine Learning
Now, let’s look at some of the common places to obtain datasets for machine learning. Hence, quality datasets are important, as these datasets will directly impact the accuracy of the model we will develop.
5.1 Amazon
Amazon offers a plethora of datasets ranging from public transport data to disaster datasets. These types of datasets are clean and well-defined — ideal for a machine learning project.
5.2 Google Dataset Search
Released by Google in 2018, it’s a search engine that helps users discover datasets from all around the web. It has datasets on language processing, computer science, environmental data, etc.
5.3 Kaggle
For this purpose, Kaggle is one of the most prominent platforms among data scientists. It also provides a wide variety of datasets for computer vision, natural language processing, and other machine learning tasks. Kaggle also provides hosting and competitions for users to compete against each other based on real datasets.
5.4 Government Open Data Portals
A lot of governments release datasets to the public, to be used for research, analysis, and development. Open data portals exist in many countries, ranging from transportation to economic data (the European Union also has an open data portal).
6. The Role of Clean Data in Machine Learning
Clean data is essential for effective machine learning. Here’s why:
- Accuracy —if your dataset has errors or missing values, the model will learn inaccurate information and later will yield poor predictions.
- CONDITION: The dataset should have common formats and values. For example, these could be if certain entries are using yes while the other half are writing months as y.
- Preprocessing: You might need to clean the data, remove duplicates, manage missing values, and normalize the data before using it for training.
Well formatted data results in a good performing machine learning model, poor data results in a model that cannot predict.
7. FAQs
1. What is the difference between structured and unstructured datasets?
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Structured datasets are mainly structured data organized in a tabular format that can be analyzed. In contrast, unstructured data lacks any fixed format and is more complicated to process.
2. What are the most common file formats for datasets?
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These formats usually take the shape of CSV or similar formats (JSON, XML, etc.), with CSV being the simplest and the most commonly used format for tabular data.
3. Where can I find free datasets for machine learning?
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A few examples of the popular ones are Kaggle, Google Dataset Search, Amazon and Public Government Data portal.
4. Why is data cleaning important?
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Data Clean-up helps your machine learning model to learn and make predictions on accurate, consistent, and properly formatted data.
8. Conclusion
To sum up, dataseta اساسن любые машиннуч проект. It’s the raw material upon which we build models and extract insights. We covered the various formats of datasets—structured, unstructured, and semi-structured datasets, which each satisfy different needs. We also shared some popular sources from where you can download the datasets for machine learning including Kaggle, Google Dataset Search, and government open data portals.
Just remember the better the data, better will be the performance of ML model. Working with real datasets and examples would call for a meticulous process for obtaining, processing, and cleansing the data, thus following best practices for data acquisition, cleansing, and preprocessing will significantly increase the quality and effectiveness of your machine learning projects.
Armed with this knowledge, you are now ready to explore datasets and kickstart your machine learning journey.