What is a Dataset: Types, Features, and Examples

Dataset is essentially the backbone for all operations, techniques or models used by developers to interpret them. Datasets involve a large amount of data points grouped into one table. Datasets are used in almost all industries today for various reasons. In this day and age, to train the younger generation to interact effectively with Datasets, many Universities publicly release their Datasets for example UCI and websites like Kaggle and even GitHub release datasets that developers can work with to get the necessary outputs.

What is a Dataset?

A Dataset is a set of data grouped into a collection with which developers can work to meet their goals. In a dataset, the rows represent the number of data points and the columns represent the features of the Dataset. They are mostly used in fields like machine learning, business, and government to gain insights, make informed decisions, or train algorithms. Datasets may vary in size and complexity and they mostly require cleaning and preprocessing to ensure data quality and suitability for analysis or modeling.

Let us see an example below:

This is the Iris dataset. Since this is a dataset with which we build models, there are input features and output features. Here:

• The input features are Sepal Length, Sepal Width, Petal Length, and Petal Width.
• Species is the output feature.

Datasets can be stored in multiple formats. The most common ones are CSV, Excel, JSON, and zip files for large datasets such as image datasets.

Why are datasets used?

Datasets are used to train and test AI models, analyze trends, and gain insights from data. They provide the raw material for computers to learn patterns and make predictions.

Types of Datasets

There are various types of datasets available out there. They are:

• Numerical Dataset: They include numerical data points that can be solved with equations. These include temperature, humidity, marks and so on.
• Categorical Dataset: These include categories such as colour, gender, occupation, games, sports and so on.
• Web Dataset: These include datasets created by calling APIs using HTTP requests and populating them with values for data analysis. These are mostly stored in JSON (JavaScript Object Notation) formats.
• Time series Dataset: These include datasets between a period, for example, changes in geographical terrain over time.
• Image Dataset: It includes a dataset consisting of images. This is mostly used to differentiate the types of diseases, heart conditions and so on.
• Ordered Dataset: These datasets contain data that are ordered in ranks, for example, customer reviews, movie ratings and so on.
• Partitioned Dataset: These datasets have data points segregated into different members or different partitions.
• File-Based Datasets: These datasets are stored in files, in Excel as .csv, or .xlsx files.
• Bivariate Dataset: In this dataset, 2 classes or features are directly correlated to each other. For example, height and weight in a dataset are directly related to each other.
• Multivariate Dataset: In these types of datasets, as the name suggests 2 or more classes are directly correlated to each other. For example, attendance, and assignment grades are directly correlated to a student’s overall grade.

Properties of Dataset

• Center of data: This refers to the "middle" value of the data, often measured by mean, median, or mode. It helps understand where most of the data points are concentrated.
• Skewness of data: This indicates how symmetrical the data distribution is. A perfectly symmetrical distribution (like a normal distribution) has a skewness of 0. Positive skewness means the data is clustered towards the left, while negative skewness means it's clustered towards the right.
• Spread among data members: This describes how much the data points vary from the center. Common measures include standard deviation or variance, which quantify how far individual points deviate from the average.
• Presence of outliers: These are data points that fall significantly outside the overall pattern. Identifying outliers can be important as they might influence analysis results and require further investigation.
• Correlation among the data: This refers to the strength and direction of relationships between different variables in the dataset. A positive correlation indicates values in one variable tend to increase as the other does, while a negative correlation suggests they move in opposite directions. No correlation means there's no linear relationship between the variables.
• Type of probability distribution that the data follows: Understanding the distribution (e.g., normal, uniform, binomial) helps us predict how likely it is to find certain values within the data and choose appropriate statistical methods for analysis.

Features of a Dataset

The features of a dataset may allude to the columns available in the dataset. The features of a dataset are the most critical aspect of the dataset, as based on the features of each available data point, will there be any possibility of deploying models to find the output to predict the features of any new data point that may be added to the dataset.

It is only possible to determine the standard features from some datasets since their functionalities and data would be completely different when compared to other datasets. Some possible features of a dataset are:

• Numerical Features: These may include numerical values such as height, weight, and so on. These may be continuous over an interval, or discrete variables.
• Categorical Features: These include multiple classes/ categories, such as gender, colour, and so on.
• Metadata: Includes a general description of a dataset. Generally in very large datasets, having an idea/ description of the dataset when it’s transferred to a new developer will save a lot of time and improve efficiency.
• Size of the Data: It refers to the number of entries and features it contains in the file containing the Dataset.
• Formatting of Data: The datasets available online are available in several formats. Some of them are JSON (JavaScript Object Notation), CSV (Comma Separated Value), XML (eXtensible Markup Language), DataFrame, and Excel Files (xlsx or xlsm). For particularly large datasets, especially involving images for disease detection, while downloading the files from the internet, it comes in zip files which will be needed to extract in the system to individual components.
• Target Variable: It is the feature whose values/attributes are referred to to get outputs from the other features with machine learning techniques.
• Data Entries: These refer to the individual values of data present in the Dataset. They play a huge role in data analysis.

Examples

There is an abundance of datasets available for different flavours on the internet. To download the datasets, you can go to websites like Kaggle, UCI Machine Learning Repository, and many other websites to download the datasets.

Let us look at some examples below:

Example 1:

This Dataset is available in Kaggle as “Cities and Towns in Tamil Nadu - Population statistics” in CSV file format. This dataset shows the population density distribution in Tamil Nadu, India in different locations/areas. This dataset is referred to from another website. From this, it is possible to create population density maps.

These types of datasets are used to perform visualizations on the map.

Example 2:

Another popular example is the “Iris” dataset which is also in CSV format.

This is a sample dataset to test classification algorithm (supervised) models on and is specifically created as a gateway to machine learning.

Example 3:

Another example of work on unsupervised models is the German Credit Risk dataset:

This dataset is used to cluster people in Germany based on some features as those with good credit scores or poor credit scores.

In this way, data can be clustered into different types. In this case, this dataset has been worked on with Tableau.

How to Create a Dataset

There are many ways in which you can create a dataset. One is by writing Python code to fill in random values till your preferred size and use it as test data for analysis.

The other way is to create tables/data by Prompting AI tools such as ChatGPT, Perplexity AI, or Bard to generate datasets. This is more commonly done to generate a huge number of sentences to be deployed in Large Language Models (LLM). These are the basis of Generative AI models such as ChatGPT.

Method 1: Using Python Code

To create a dataset, by running a Python script we can define the values, and features preemptively, and then fill these values within a certain range with random values as shown below:

import pandas as pd
import numpy as np
import random as rd

#Bussiness_type = ['Office_space','Restaurants','Textile_shop','Showrooms','grocery_shop']
Bussiness_type = [1, 2, 3, 4, 5]
#Demographics = ['Kids', 'Youth', 'Midde_aged', 'Senior']
Demographics = [1, 2, 3, 4]
#Accessibility = ['Bad', 'Fair', 'Good', 'Excellent']
Accessibility = [1, 2, 3, 4]
#Competition = ['low', 'medium', 'high']
Competition = [1, 2, 3]
Area = [250, 500, 750, 1000, 1500]
Rent_per_month = ['5000', '75000', '95000', '10000', '13000', '17000', '20000']
Gross_tax = [2.2, 3.4, 4.5, 5.6, 7.2, 10.2, 6.8, 9.3, 11, 13.4]
labour_cost = [3500, 5000, 6500, 7500, 9000, 11000, 16000, 25000, 15000, 12500]
location = ['San Diego', 'Miami', 'Seattle', 'LosAngeles', 'LasVegas', 'Idaho', 'Phoenix', 'New Orleans',
            'WashingtionDC', 'Chicago', 'Boston', 'Philadelphia', 'New York', 'San Jose', 'Detroit', 'Dallas']
#location = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]

buss_type = []
demo = []
access = []
comp = []
area = []
rpm = []
gtax = []
labour_cst = []
loc = []

# Net_profit is to be calculated

for i in range(1000):
    buss_type.append(rd.choice(Bussiness_type))
    demo.append(rd.choice(Demographics))
    access.append(rd.choice(Accessibility))
    comp.append(rd.choice(Competition))
    area.append(rd.choice(Area))
    rpm.append(rd.choice(Rent_per_month))
    gtax.append(rd.choice(Gross_tax))
    labour_cst.append(rd.choice(labour_cost))
    loc.append(rd.choice(location))


dic_data = {'Business_type': buss_type, 'Demographics': demo, 'Accessibility': access, 'Competition': comp,
            'Area(sq feet)': area, 'Rent_per_month': rpm, 'Gross_tax(%)': gtax, 'labour_cost(USD)': labour_cst, 'location': loc}
frame_data = pd.DataFrame(dic_data)
frame_data.to_csv('autogen_data.csv')

Output:

This creates a CSV file with 9 features (columns) and 1000 rows:

1. Business Type
2. Demographics
3. Accessibility
4. Competition
5. Area (square feet)
6. Rent Per Month
7. Gross Tax
8. Labour Cost
9. Location

Method 2: Using Generative AI Tools

The other way to create datasets is to generate data with the help of Generative AI tools such as ChatGPT etc.

Consider the example given below:

Output:

In this way, it is possible to generate a huge amount of data to create your dataset for models in these ways.

Methods Used in Datasets

Many methods are applied when it involves working with Datasets. It depends on the reason you work with your given dataset. Some of the common methods that are applied to datasets are:

1. Loading and Reading Datasets:

Set of methods that are used in loading and reading the datasets initially to execute the required tasks.

Eg - read_csv(), read_json(), read_excel() etc.

2. Exploratory Data Analysis:

To perform Data Analysis and visualize it, we use these functions on a dataset to work.

Eg - head(), tail(), groupby() etc

3. Data Preprocessing:

Before analyzing a dataset, it is preprocessed to remove erroneous values, and mislabeled data points by using specific methods.

Eg - drop(), fillna(), dropna(), copy() etc

4. Data Manipulation:

Data points in the dataset are arranged/ rearranged to manipulate the features. At some points, even features of the dataset are manipulated to decrease computational complexity and so on. This may involve methods or functions merging columns, adding new data points, and so on.

Eg - merge(), concat(), join() etc

5. Data Visualization:

Methods used to explain the dataset to people not in the technical field like - the use of bar graphs and charts to provide a pictorial representation of the dataset of the company/ business.

Eg - plot()

6. Data Indexing, Data Subsets:

Methods that are used to refer to a particular feature in a dataset, we use data indexing or create definitive subsets.

Eg - iloc()

7. Export Data:

Methods that are used in exporting the data you’ve worked on in different formats as required.

Eg - to_csv(), to_json() etc

Data vs. Datasets vs. Database

Data

It includes facts such as numerical data, categorical data, features, and so on. But data as a standalone, cannot be utilized properly. To perform analysis, a large amount of data collection is required.

Datasets

A dataset is a collection of data that contains data specific to its category and nothing else. This is used to develop Machine Learning models perform Data Analysis, Data and Feature Engineering. Datasets may be structured (Height, weight analysis) or unstructured (audio files, videos, images).

Database

A database contains multiple datasets. It is possible for a database to house several Datasets that may not be related to each other. Data in Databases can be queried to perform several applications.

There are several types of databases to house several types of data, structured or unstructured data. These are divided into SQL databases and NoSQL databases.

Conclusion

Datasets play a vital role in every facet of our lives. In this modern day, all devices are made to collect data and create datasets for advertisers/businesses to personalize their advertisements to consumers. The limitation is that as a result of over-reliance on datasets, the mining techniques of data have become ethically questionable with many social media applications and websites getting criticism for data privacy issues, data leaks, and so on. As a result, data is the currency and many companies mine user information without the user’s knowledge to create datasets.