Get high-quality training data for machine learning quickly and efficiently using a web scraping service.
Did you know that experts predict that the machine learning market will grow at close to a 40% rate over the next seven years?
If you're currently building Machine Learning (ML) models, you're in one of the so-called "future-proof" roles — or are you?
ML models are only as good as the data with which you train and test them. Without high quality training data, your machine learning model could actually end up being ineffective and harmful to your business. But don't worry — there's a solution.
With a web scraping service, you can amplify the quality of your training and test data overnight.
Curious to learn how?
Over the next seven minutes, you'll discover everything you need!
What is Web Scraping?
Web Scraping is an automated extraction technique for collecting data from websites.
Web scraping involves downloading a web page, parsing the content of that page, and extracting relevant data for storage or further processing. The purpose of web scraping is to quickly and efficiently gather large amounts of data from the internet.
And here's the exciting part: You can leverage web scraping to gather countless more training data points to feed your ML models.
Engineers can use web scraping to quickly collect data — structured (E.g., CSVs, Tables), semi-structured (E.g., HTML, JSON, XML) and unstructured (E.g., Log Files) — from multiple sources reliably; making web scraping an invaluable tool for ML training data.
Understanding the Basics of Web Scraping: A 101 Class
Let's take a 101 class on web scraping, ML, and how you can start integrating the two.
Manual vs. Automated Web Scraping
Generally, web scraping falls into two categories: Manual and Automated.
Manual Web Scraping is when you use web browsers, such as Chrome or Firefox, to manually copy data from a webpage. You're in good company if you think such manual copying and pasting sounds like torture.
Automated Web Scraping is when you use a software program that algorithmically reads, parses and extracts data from webpages. Such automated web scraping is orders of magnitude faster and more efficient than manual methods, as automated web scraping can extract data from hundreds or even thousands of web pages in a short period.
Why Web Scraping is Crucial for Machine Learning
Web scraping is an invaluable tool to efficiently gather large amounts of training and testing data from the web to build your machine learning models.
- Advantage #1: Quickly obtain large amounts of data from several web sources. Web scraping enables you to create training datasets from the web that are more representative, accurate, and reliable than manually-curated datasets.
- Advantage #2: Eliminate human error. Automated web scraping boosts the accuracy of your data, as you can algorithmically extract data directly from websites without manual intervention.
Advantage #3: Reduce the cost of acquiring training data. You can save time and money by automating web data extraction from multiple websites, in contrast to manually collecting data.
Web Scraping Tools to Start You on Your Journey
1. Requests
Requests is an open-source Python library for making HTTP requests to web pages simple and human-friendly.
Requests returns a Response object containing all the Response data, such as content, encoding and status.
The Requests library supports all of the standard HTTP methods: GET, POST, PUT, DELETE, HEAD and PATCH.
The Requests library is appropriate for simple web scraping requirements. Requests is not a suitable library for complex web scraping projects.
2. URLLib
URLLib is an open-source Python package that contains several modules for working with URLs.
Although the package is not very user-friendly, it does include capabilities to issue requests to URLs, handle request errors, parse URLs into parts (such as path and parameters) and read robots.txt files.
As with the Requests library, the URLLib package is appropriate for building simple web data extraction applications.
To build a complex web scraping engine with URLLib, you will need to handcraft several features; effectively "reinventing the wheel".
Therefore, URLLib is not suitable for complex web scraping requirements.
3. Beautiful Soup
Beautiful Soup is an open-source Python library that extracts data from HTML or XML documents, with a focus on making it easier to collect structured data from multiple web sources.
Beautiful Soup enables a simple and efficient method of navigating HTML documents, while enabling users to extract specific elements from web pages.
As with the Requests and URLLib libraries, Beautiful Soup is not suitable for complex projects.
4. Scrapy
Scrapy is another open-source framework written in Python, designed to facilitate the process of extracting data from websites quickly and efficiently.
Scrapy includes built-in modules to simplify and automate common web data extraction routines, such as:
- HTTP request and response handling
- Injecting custom headers
- Proxying
- Cleaning data
- Removing data duplication
- Storing data (E.g., to CSV, JSON, XML)
- Code debugging
Scrapy is a powerful framework to simplify and accelerate building complex and efficient web scraping spiders.
However, Scrapy has a steep learning curve and can be challenging to understand and use for beginner engineers.
5. WSaaS
WSaaS brings you all the power of web scraping with all the convenience of a software-as-a-service (SaaS).
WSaaS enables you to scrape data from any website and delivers training data in your chosen format, eliminating the need for building a web scraping engine yourself.
✅ Custom Data Transformation with Data Quality Guarantees
WSaaS enables you to transform web data to create the exact features you require in your training data.
WSaaS implements proprietary data quality checks on your web data extracts, ensuring that your training data is complete and accurate.
✅ Robust Cloud Integration
Furthermore, WSaaS enables you to scrape on a recurring schedule and integrate your training data with cloud services on AWS, Google Cloud, Azure, Snowflake or Databricks.
WSaaS quickly and efficiently stores your data on cloud storage, such as Amazon S3, Google Cloud Storage (GCS) or Azure Blob Storage (ABS).
Additionally, WSaaS can integrate your data with cloud databases or data warehouses, such as Snowflake, Databricks, Amazon Redshift, Google BigQuery or Azure Synapse Analytics.
Finally, WSaaS enables you to quickly and efficiently integrate with cloud machine learning services, such on AWS (Such as SageMaker, Redshift ML, EMR or Glue), Google Cloud (Such as Vertex AI, BigQuery ML or Dataproc) or Azure (Such as Azure Machine Learning, SynapseML or HDInsight).
✅ End-to-End Training Data Extraction and Management
WSaaS enables you to quickly get comprehensive and high-quality training data you require to build your machine learning models.
The WSaaS service runs your end-to-end data management requirements from data extraction to feature engineering to data quality management, all the way to delivering your data in an easy-to-ingest format on the cloud for quick and easy integration with your ML pipelines.
Excited to try out WSaaS?
Get started today to experience the benefits of WSaaS for your machine learning training data!
Scraping on the Right Side of the Law
Web scraping has become increasingly popular in recent years but it is essential to be aware of pertinent legal and ethical considerations.
To ensure that your data extraction activities comply with applicable laws, avoid scraping copyrighted or sensitive information.
Also, steer clear of any data scraping that violates an individual's privacy rights.
For more information on the legality of web scraping, see Is Web Scraping Legal?
How to Create High-Quality Training Data With Web Scraping
We'll break this down into four steps:
- Identify the right websites and web pages.
- Scrape the data.
- Preprocess the scraped data into ready-to-use training data.
- Integrate into your existing training data.
1. Identify the right websites and web pages
Finding the websites or web pages with the desired training or test data is essential to successful web scraping. Several methods can assist you in determining which websites contain the data you want.
One such approach is to look for publicly-available APIs that include the data you are looking for, such as APIs provided by Google and Twitter. If such APIs do not provide the desired training data, identify which websites contain the information you want, using search engines or manually searching individual websites.
WSaaS can also help you identify the best web data sources and extract data from them to power your training data for machine learning.
2. Scrape the data
Build out and execute the web scraping application to extract the data you need from the websites and web pages you have identified.
As noted above, you can leverage Python packages/libraries/frameworks, such as Requests, URLLib, Beautiful Soup or Scrapy to build simple to complex web scraping engines.
Building and maintaining such engines can quickly get complex, particularly when you need to extract data on a recurring basis, perform extensive data transformations, enforce data quality or integrate your training data with the cloud.
You can skip struggling with scraping data from websites.
Instead go straight to building and fine-tuning your machine learning models. Use a professional service like WSaaS to scrape the training data you require to build your machine learning models.
3. Preprocess the scraped data into ready-to-use training data
After you have extracted data from the desired web pages, the next step is to format and clean the training data for use in your machine learning models. You'll have to transform the data into a suitable format and ensure all necessary information is present.
First, remove any HTML or structural elements from the extracted data to ensure the data is in a usable format.
Next, ensure that all necessary fields are present and appropriately fill in any missing values.
Additionally, you may need to transform numerical data into categorical data or vice versa.
Don't forget — sample bias can significantly degrade the quality of your training data. Sample bias is one of the common pitfalls in machine learning.
Causes of sample bias include insufficient sample size or unrepresentative sample data.
Therefore, (1) Ensure the size of your training data is sufficiently large; and (2) Use the following best practices to ensure that your scraped sample data is in fact representative of the population for which you are building ML models:
- When collecting data from multiple sources, keep track of where the data was sourced from and note differences in data quality across sources over time. When you identify a low-quality data source, stop using the data source or ensure you perform the requisite data transformation that shapes the data to comply with your data quality requirements.
- Use an appropriate sampling method (such as random sampling) to ensure that your training data is representative of the population.
- Monitor your data for potential bias by profiling and examining the distribution of data you collect over time.
Avoid struggling through such tedious data cleansing and profiling. Rest easy with the convenience of preprocessed, high-quality data extracts delivered to you on schedule by using WSaaS.
You can automate your data preprocessing by using Python data engineering libraries, such as Pandas and Numpy, to ensure that your extracted training data is accurate and up-to-date.
To process large volumes of data, otherwise called big data engineering, consider using Apache Spark for massively-parallel processing (MPP), which distributes your processing across several compute nodes in a cluster, in order to speed up your data processing. Apache Spark is available on AWS (EMR or Glue), Google Cloud (Dataproc) and Azure (HDInsight).
Alternatively, you can use a managed Data Warehouse-as-a-Service (DWaaS) which can also perform distributed MPP. Examples of such DWaaS offerings are Databricks, Snowflake, Azure Synapse Analytics, Google BigQuery or Amazon Redshift.
Whether you use Pandas, Numpy, Spark or a cloud DWaaS for your data engineering, ensure you pick the right capability to address your specific requirements.
Leverage such data engineering capabilities to perform feature engineering by transforming your data as necessary, such as performing mapping, reduction/aggregation, projection, calculation, filtering, sorting, joining, merging, insertion, updating, deletion, restructuring or reformating.
Stop worrying about such data engineering which can get complex very quickly.
Conveniently receive the custom-engineered data you require on the cloud by using WSaaS.
4. Integrate scraped data into your existing training data
After preprocessing your training data, the final step is to integrate your new training data with existing training data.
The data engineering capabilities noted above enable you to create, process, integrate and store new columns, variables or entirely new datasets from your input training data with your existing data.
Accelerate Your ML Projects with WSaaS Today
Let's recap some of the key takeaways:
- Web scraping is the process of automating the extraction of data from a web page. You can use web scraping to algorithmically gather data from several websites quickly and efficiently.
- Web scraping can boost the speed, scale and quality of data extraction required to create machine learning training data. Furthermore, web scraping can reduce the cost of acquiring training data.
- There are several Python libraries/packages/frameworks you can use for web scraping, such as Requests, URLLib, Beautiful Soup and Scrapy.
- Building a web scraping engine for yourself can get complex and expensive very quickly. Therefore, use a professional service, such as WSaaS, instead of performing web scraping yourself.
- Comply with all laws, regulations and policies when web scraping. You should collect data only from websites that permit web scraping and in total compliance with the terms and policies of such websites, as well as the laws and regulations of the applicable jurisdictions.
- There are four steps to use web scraping to extract machine learning training data, namely: (1) Identify the right websites and web pages; (2) Scrape the data; (3) Preprocess the scraped data into ready-to-use training data; (4) Integrate into your existing training data.
- Engineers can use several Python packages/libraries/frameworks — such as Pandas, Numpy, Spark — and/or cloud services — from AWS, Google Cloud, Azure, Snowflake or Databricks — to engineer and integrate the necessary features and/or observations required to update and enhance training data for machine learning models.
- Use WSaaS to speed up the time-to-market of acquiring data you need to feed your Machine Learning models. By leveraging WSaaS, you can save money, while boosting the quality of your machine learning training data.
Hopefully, this article has provided you with a valuable overview of web scraping and how it can supercharge your acquisition of high-quality data to build your machine learning models!
Looking for a reliable way to extract training data from websites?
WSaaS is the perfect solution for acquiring the training data you require to build and enhance your machine learning models. As experts in web scraping, we leverage our AI-powered, cloud-based, industrial-grade web scraping service to extract the data you need quickly and efficiently.
Request a quote today and see why over 1,000 customers love our web scraping service!
Frequently Asked Questions (FAQs)
What is web scraping with Python?
Web scraping with Python is extracting large amounts of data from websites using Python packages/libraries/frameworks, such as Requests, URLLib, Beautiful Soup or Scrapy. You can leverage such extracted data to train your machine learning models.
Why use web scraping?
Web scraping is an efficient and cost-effective way to collect large amounts of data from websites.
You can use scraped data as the training data for your machine learning models. You can also integrate scraped data with existing training data to improve the accuracy of your machine learning models.
If you're confused about how to scrape data and integrate it with your machine learning workflows, we can help.
Speak to us today to get started scraping and integrating the training data you need to build and enhance your machine learning models!
How much training data do I require to build a machine learning model?
The amount of training data you need to build a machine learning model depends on the model algorithm, the complexity of the model and the nature of the required model inference.
You want to ensure you have a sufficient size of training data to avoid Overfitting. Overfitting is when you build a model that "hugs" the training data too closely. A small training dataset size is one of the primary causes of Overfitting.
An overfitted model works well on the training data but does not perform well with data the model has not "seen". Overfitting causes a model not to generalize well from the training data to any data from the problem domain.
Therefore, ensure your model generalizes well by using training data that is large enough and that is representative of the domain population, in order to avoid overfitting.
Imbalanced Classes is another phenomenon you want to be aware of when building your machine learning models.
Imbalanced Classes is a machine learning classification problem in which the difference(s) between/across the proportions of each class is/are large. E.g., 4:1 representation of two classes in binary classification.
You can combat Imbalanced Classes by increasing the size of your training data. Additional techniques — which are variations of increasing the size of your training data — to combat Imbalanced Classes are to: (1) Resample your dataset: Use oversampling (of under-represented classes) or undersampling (of over-represented classes); (2) Generate synthetic samples by randomly sampling attributes from instances of the under-represented class; or (3) Decompose your over-represented class into more granular classes.
Generally speaking, more complex models require larger training datasets.
However, the volume of training data is not the be all and end all. Data Quality irrespective of the data volume is even more important.
Therefore, a key goal to keep in mind when acquiring, curating and evaluating your training data is to ensure that the training data profile and distribution sufficiently reflect the distribution of the target population for which you want to train data. You want to avoid skewed data, as such data will lead to biased machine learning models.
That said, collecting and processing large amounts of high-quality training data for your machine learning models is time-consuming and costly.
If you want to get started with high-quality training data quickly and cost-effectively, contact us today to kick off the extraction, cleansing and customization of the web data you need to build and enhance your machine learning models.
What are the advantages of using web scraping as opposed to APIs for extracting machine learning training data?
Web Scraping and APIs are two methods for extracting machine learning training data, each with its own advantages and disadvantages.
Web Scraping enables you to extract data from websites that do not have APIs. Furthermore, for websites that do have APIs, web scraping enables you to extract data that is not available in the APIs.
Web Scraping enables you to extract data from many more websites than when you rely on only APIs.
Therefore, web scraping empowers you with a richer set of training data that you can use to build even more powerful and accurate machine learning models.
APIs, on the other hand, can provide more structured and organized data than web scraping.
APIs also provide more control over the data being collected, as APIs typically return data in a well-defined format, making it easier for you to preprocess and clean the data.
Interested in the best of both worlds?
We can scrape data from any website you want and then deliver it to you in a well-structured and organized format.
Let us know how you want to shape and transform your web data then leave the rest to us!
Get started with us today to extract data from any website and transform it into your format and structure of choice.
What are the benefits of using a web scraping service to extract machine learning data versus building a web scraping engine myself?
Using a web scraping service to extract machine learning data has several benefits compared to building a web scraping engine yourself.
Firstly, web scraping services have the expertise and infrastructure to handle complex web scraping tasks, such as extracting data from websites that use dynamic content, AJAX or other uses of JavaScript.
Web scraping services can also handle complex, large-scale web scraping projects that can prove to be a massive distractions for machine learning engineers or data scientists.
Secondly, the best web scraping services provide higher quality data, with built-in data quality checks backed by custom data transformation.
The best web scraping services deliver preprocessed, high-quality data extracts to you on a regular schedule, without you having to worry about any data quality or data engineering work!
Finally, using a web scraping service can save you time and money. Building and maintaining a web scraping engine can require significant engineering effort, especially when you need to extract data on a recurring basis, perform extensive data transformations or integrate your training data with the cloud.
By leveraging a web scraping service, you can skip the time-consuming process of building a web scraping engine and instead focus on building and fine-tuning your machine learning models.
What limitations should I be aware of when I use web scraping to extract machine learning training data?
1. Legal and Ethical Considerations
Web scraping may not be legal or ethical in some cases, such as when it involves scraping copyrighted or sensitive information; or violating an individual's privacy rights. It is important to comply with all relevant laws and regulations when scraping data.
Read more on the legality of web scraping.
2. Data Quality
The quality of the training data you extract from a website may not be at the level you require to build your machine learning models. The data may contain errors, missing values or be unrepresentative of the target population.
Therefore, it is important to perform quality checks and preprocessing steps to ensure that the training data is of high quality.
It is best to work with a web scraping service that possesses robust web data engineering capabilities to transform the raw web data into and apply the business rules you require for your training data.
3. Scalability
Web scraping can be time and resource-intensive. Consider using a web scraping service that possesses robust cloud integration and extensive web big data engineering capabilities to extract, process and store large volumes of web data efficiently.