Machine learning (a subfield of AI) aims to program computers to learn and grow as people do. Machine learning may automate virtually any activity that can be solved using a pattern or set of data-developed rules. It’s crucial to have a firm grasp of the various data kinds to clean and preprocess the data in preparation for use with ML algorithms. For machines to recognize patterns in data, it must first be translated into a numerical representation. This will allow us to pick the top-performing models that can quickly and accurately identify the underlying patterns. Knowing the various data formats enables one to select the most suitable preprocessing methods and conversions. In addition, it will let us execute top-notch visualizations and unearth previously unknown information.
Why Machine Learning Data Sets Are So Crucial
Data analysis using machine learning algorithms can be self-improving over time, but only if they are fed high-quality inputs. Real comprehension of machine learning requires familiarity with the data on which it is based. The importance of this information necessitates careful and secure handling and storage. Understanding the different kinds of data involved in this activity is crucial to applying the appropriate methods and providing accurate findings. I’d want to look at the various forms of data used in Machine Learning.
Numerical Data / Quantitative Data
Quantitative or numerical data includes things like body measurements and monthly phone bills. If you try to take an average of the numbers or arrange them in ascending or descending order, you will know that the data is numerical. There are two types of numerical information: discrete and continuous.
In the case of discrete data, the information is represented by “whole numbers,” i.e., numbers without any decimal places.
In the case of continuous data, the values are represented as whole integers (or their decimal representations).
Qualitative Data / Categorical Data
Defining qualities is used to categorize data. Categorical data is information that typically specifies classes. Categorical data helps the machine learning model expedite data processing by categorizing persons or concepts with similar qualities. To further dissect qualitative information, we may divide it into two categories: Nominal and Ordinal.
Data that does not have a numerical or ordinal value is called nominal data. There is no discernible pattern to these data, which instead contain random numbers spread over several categories.
Numbers in ordinal data are presented meaningfully, such as a natural ordering based on their position on a scale.
If you compare ordinal data to nominal data, you’ll see that the latter lacks any order, while the former does. Ordinal data can only be used to see sequences and is, therefore, useless for statistical purposes. We can’t do any arithmetical operations on this data, but they are useful for observational purposes such as measuring customer satisfaction, pleasure, etc.
When training machine learning models, text input consists of anything from a single word to a whole article. It contains textual material made up of many words that make sense when taken together. Realizing that each word can have numerous meanings and associations with other words, as well as grasping the larger context and links between the different words inside a phrase, is the single most significant quality.
Time Series Data
This data is presented as a list of time-stamped, sequential data points. Dates and times are used as indexes in time series data. The vast majority of the time, this information is gathered regularly. Having a firm grasp on and understanding of how to use time series data makes it simple to compare information over different periods, such as weeks, months, or years.
Commonly, this means assembling information from many sources. The tabular information includes several columns or characteristics representing a unique data type.
There are two possible formats for this information: numbers and words. The structured data type can be assigned numerical values, but it cannot be used in mathematical calculations. Data of this sort is often presented in tabular form. A common place for them to be kept is in a relational database.
Unstructured data refers to information that needs to be carefully organized in a certain way. It includes words on a page, music, pictures, movies, etc.
Interval data is ordered numerical data, with 0 indicating the complete lack of any numerical value. In this context, zero does not denote emptiness but rather has some value. It is a somewhat small scale. The temperature is degrees Celsius, time in hours and minutes, SAT scores, credit scores, pH levels, etc.
Similar to interval data, only with an absolute zero, this quantitative data type can be used to store numbers. Here, zero indicates total absence, and the scale begins at zero.
Images contain important information that can only be gleaned through analyzing their spatial aspects and connections. A common form of this information is picture files of various formats. Photos of all the food items in a supermarket, portraits of all the students in a university, etc., are examples of image data.
Videos in various formats make this type of info similarly self-explanatory. One feature that sets video data apart is the need to account for the connections between frames in the video regarding location, movement of objects/people, etc., to effectively extract information from the films.
Some of the most widely used machine learning datasets available today are as follows:
- Searching Through Google’s Datasets
- Microsoft’s R&D Division Released Data
- Repository of Machine Learning Datasets at UCI
- Governmental datasets
Working with data is essential because figuring out the kind of data and how to use it effectively is essential to getting valuable results. Research, analysis, statistics, data visualization, and data science all use multiple forms of data. A corporation may use this information for business analysis, strategy development, and establishing a data-driven decision-making process. Data analysis and visualization benefit from knowing which plots work well with various data sets.
Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone's life easy.