Types of data sets:

 

 

Data sets can exist in different types, some of which are as follows:

 

Numerical data set:

 

This type of data set contains numerical numbers that are usually displayed as a table or matrix. Examples of this type of data set include statistical, economic, scientific and financial data sets.

 

 

Text datasets:

 

 

This type of datasets includes texts and documents and may include collections of articles, books, legal documents, user comments, and the like. This type of data is usually used in the field of natural language processing and text mining.

 

Image datasets:

 

This type of datasets includes images and machine images such as medical image datasets, machine image datasets and facial recognition datasets.

Audio dataset: This type of dataset includes audio files and audio signals such as speech datasets and audio signal recognition datasets.

Also, datasets can be collected from various sources such as public databases, online systems, web services, government sources, and other sources on the Internet and the real world.

 

How to create a dataset for intelligent models?

 

 

Building a dataset depends on the type of project and the intelligent model. However, some common methods for constructing datasets are as follows:

 

Manual creation:

 

You can create data manually. This method is usually used for small or experimental data sets. For example, you can create a text file and enter your data manually.

Aggregate existing data: You can aggregate existing data from various sources. These resources can be text files, databases, websites, APIs, etc. To collect data, you may need to use special tools such as web scraping or use related libraries and tools.

Generate Synthetic Data:

 

In some cases, you may need to generate synthetic data. For example, if you want to train a machine learning model to recognize images, you can create synthetic images using image generation tools such as OpenCV or PIL.

Use of public datasets:

 

In many domains, public datasets are available that include data collected from public sources. Some popular public datasets include MNIST for handwritten number recognition, CIFAR-10 and ImageNet for image recognition, and IMDb for movie and TV series datasets. 

 

How can we use web scraping to collect data?

 

 

 

To use web scraping to collect data, you can use a variety of languages ​​and tools such as Python and its libraries such as BeautifulSoup and Scrapy. The general steps of web scraping are as follows:

 

Installing required libraries:

 

First, you need to install the required libraries for web scraping. For Python, the BeautifulSoup library and the requests library are used to access web pages. You can install these libraries using a Python package manager like pip.

Choosing the target web page: It is very important to choose the web page from which you want to collect information. Make sure the target web page contains the desired information and has scraping permission.

Get web page content: Using the requests library, you can get web page content. Typically, this is done by sending a GET request to the URL of the web page.

Web Page HTML Inspection: Using the BeautifulSoup library, you can parse the HTML of a web page and access the desired elements. This library provides facilities so that you can access the desired elements and extract information using CSS selectors or XPath.

Data extraction and storage: After accessing the desired elements, you can extract the information and save it in the desired format. You may store this information in a text file, database, or other format.

Browse more pages (if needed): If the information you are looking for is on several different web pages, you can access more pages and gather information using web scraping. For this, you can use techniques such as pagination or to use web scraping to collect data, you can use various languages ​​and tools such as Python and its libraries such as BeautifulSoup and Scrapy.

 

 

A practical example of how to build a dataset

As a practical example, suppose you want to create a dataset for recognizing dog and cat images. The following steps show how to build this dataset:

 

Collect images:

 

First, you need to collect many images of dogs and cats. You can use the Internet, public image databases, and other sources such as different websites. It is better to choose images with the same size and quality.

Labeling images:

 

For each image, you must add a label indicating its type (dog or cat). You can do this by hand or use automatic labeling tools like BBox Label Tool or LabelImg.

Data division:

 

The data set is usually divided into two parts: Training Set and Test Set. The training part is used to train the model and the test part is used to evaluate the performance of the model. You can randomly divide the images between these two sections, typically 80% of the images will be assigned as the training section and 20% as the test section.

Data Preprocessing:

 

If needed, you may need to preprocess the images. This process includes resizing images, changing colors, applying data enhancement techniques (such as rotation, sharpening, cropping, etc.) and other transformations that are needed to improve the performance of the model.

Save data:

 

Finally, save the images and associated tags in a uniform format (such as CSV or JSON). This dataset is ready to be used in machine learning algorithms and neural networks.

By following these steps, you have created a dataset that contains images of dogs and cats, with each image having a corresponding label. You can use this dataset to train the dog and cat image recognition model.

 

 

 An example of how to build a dataset

 

 

As a practical example in the Python programming language, you can use OpenCV and Pandas library to create an image dataset of dogs and cats and save it to a CSV file. In the following, we will examine the code fragment for creating the image data set.

 

import cv2

 

import os

 

import pandas as pd

 

# Path to the folder containing images of dogs

 

dogs_dir = '/path/to/dogs'

 

# Path to the folder containing pictures of cats

 

cats_dir = '/path/to/cats'

 

# Create a list to store image information

 

data = []

 

# Reading images of dogs and adding information to the list

 

for filename in os.listdir(dogs_dir):

 

 if filename.endswith('.jpg'):

 

 img_path = os.path.join(dogs_dir, filename)

 

 img = cv2.imread(img_path)

 

 if img is not None:

 

 img = cv2.resize(img, (224, 224)) # Resize images to specific dimensions (for example, 224x224)

 

 data.append({'image': img, 'label': 'dog'})

 

# Reading pictures of cats and adding information to the list

 

for filename in os.listdir(cats_dir):

 

 if filename.endswith('.jpg'):

 

 img_path = os.path.join(cats_dir, filename)

 

 img = cv2.imread(img_path)

 

 if img is not None:

 

 img = cv2.resize(img, (224, 224)) # Resize images to specific dimensions (for example, 224x224)

 

 data.append({'image': img, 'label': 'cat'})

 

# Convert list to Pandas dataframe

 

df = pd.DataFrame(data)

 

# Save dataframe to CSV file

 

csv_file = '/path/to/dataset.csv'

 

df.to_csv(csv_file, index=False)

 

In this code, we first specify the path of the folders containing images of dogs and cats. Then we read the images, resize them and add them to the data list. Each image in the list has two fields: image that stores its image and label that stores the label of that image (dog or cat).

 

Next, we convert the data list into a dataframe in Pandas. Finally, we save the dataframe to a CSV file that contains the corresponding images and labels.

 

It is necessary to explain that the code above is a simple example, and if you intend to use the above code, you must set the appropriate paths for the folders and CSV file based on the project name and your environment. Also, if needed, you can change the codes and adapt it to your specific needs. This code snippet is free to use for personal projects, but if you intend to use it for commercial purposes, please let me know!!

 

 

 

Can we use this code to build another dataset?

 

 

The answer is yes. You can use this code to build other datasets. The code presented in the example above was just an example for building an image dataset of dogs and cats. You can easily create additional datasets by rerouting folders and assigning appropriate labels to each dataset. However, you should pay attention to some important points.

 

Folder path:

 

In the code above, the data folder path for dogs and cats is specified using dogs_dir and cats_dir. You should replace these paths with the appropriate paths for your desired categories.

Labels:

 

In the code above, appropriate labels (eg dog and cat) have been used for each data category. You should set the tags based on your desired category. For example, if you want to create a dataset categorized by cars and motorcycles, you can use the tags car and motorcycle.

Data Preprocessing:

 

If needed, you may want to change the data preprocessing operation. Such as resizing images, changing colors, using data augmentation techniques, etc. You can apply these changes to the code.

According to these points, you will be able to create different data sets using this code and use them to train and test machine learning algorithms.

 

Can we use this code to build an audio dataset?

The answer is yes, you can use this code snippet to build audio datasets as well. By modifying the code appropriately, you can read audio files, preprocess them, and save them as a dataset. Below I will examine a code snippet for building an audio dataset using the librosa library in the Python programming language:

 

import os

 

import librosa

 

import pandas as pd

 

# Path to the folder containing audio files

 

audio_dir = '/path/to/audio_files'

 

# Sound preprocessing settings

 

sample_rate = 22050

 

duration = 5 # desired duration for each audio file (in seconds)

 

# Creating a list to store audio file information

 

data = []

 

# Reading audio files and adding information to the list

 

for filename in os.listdir(audio_dir):

 

 if filename.endswith('.wav'):

 

 file_path = os.path.join(audio_dir, filename)

 

 audio, sr = librosa.load(file_path, sr=sample_rate, duration=duration)

 

 if len(audio) == sample_rate * duration:

 

 data.append({'audio': audio, 'label': 'class_label'})

 

# Convert list to Pandas dataframe

 

df = pd.DataFrame(data)

 

# Save dataframe to CSV file

 

csv_file = '/path/to/dataset.csv'

 

df.to_csv(csv_file, index=False)

 

In this code, we first specify the path of the folder containing audio files. Then, using the librosa library, we read the audio files and preprocess them with the specified settings. Here, we cut the specified duration (eg 5 seconds) from each audio file.

 

Then, we add information about each audio file to the data list. Each item in the list has two fields: audio that stores audio samples and label that stores the label of that audio file.

 

Then, we convert the data list into a data frame in Pandas and finally save the data frame to a CSV file.

 

 

 

What are the characteristics of a good dataset?

If you intend to use or create a data set, you should know that a data set must have the following characteristics:

 

Diversity:

 

The dataset should include diverse and balanced samples of data. To be more precise, different samples of each data category should be present and the lack of samples in one of the categories should not occur. This diversity is very important in using datasets for training and evaluating machine learning algorithms.

Correct Labeling:

 

Each data sample must have a correct and accurate label indicating the category or attribute it belongs to. Labels must be correctly and uniquely defined.

Adequate size:

 

The dataset should be large enough to train machine learning algorithms well. The appropriate volume depends on the problem at hand and educational needs. In some cases, the data set needs to be large to train complex models, while in some cases the smallest data set can be suitable.

Accuracy and precision:

 

The data set must have high accuracy and precision, which means the absence of errors, labeling mistakes, outliers, noise and other problems in the data set. The accuracy of the dataset can have a direct impact on the performance and reliability of machine learning models. Also, the dataset should have a balance in the distribution of data between categories. This means that the number of samples in each category is balanced and one category has significantly more samples than other categories.

Metadata:

 

Along with the dataset, metadata about the data can also be useful. This metadata can include information such as description, source, date, description and other information about the data.

This rule also applies to different types of data sets such as audio data sets. For example, there should be a variety of audio samples with different differences in style, speed, articulation, pronunciation, etc. Also, the tags should be correctly and accurately determined with respect to the audio content, and the volume and quality of the data should be adequate.