4 ways of collecting data for Machine Learning models
Learn how to collect data to make fantastic predictions
Machine learning and AI are growing by leaps and bounds.
Companies are realizing that to stay competitive and have an edge over competitors, they need to embrace Machine Learning
But developing and deploying Machine Learning models is not enough. You need to develop a data strategy plan as well to get the desired results.
Data collection is the first step toward building any machine learning model. The quality of data you collect makes a huge difference in the quality of output.
As said, Garbage in, Garbage out. Your state-of-the-art Machine Learning model wont be good enough if you feed it with bad data. With bad data, all you can expect is bad results
So, now that you are aware of how important data is, let us take a look at various ways you can collect data for analysis and decision making.
There are 4 ways of collecting data for your model
1. Web Scraping
2. Open Source Data Set
3. Synthetic Data Set
4. Manual Data Creation
1. Web Scraping:
Suppose you need to collect data from websites. What can you do? You can copy and paste the details from the website and make use of the data, right?
What if you need a large amount of data. It will be very tedious and expensive to copy and paste all the relevant information across the web.
That’s where Web Scraping comes into the picture. Web scraping leverages intelligence automation to extract and collect structured web data.
Some of the purposes of web scraping are lead generation, market research, competitor analysis, price, and news monitoring, brand monitoring
Scraping enables Data Scientists to collect a massive amount of publicly available web data to make optimal decisions.
Web Scraping can have two aspects: Crawling and Scraping
Crawling refers to an artificial intelligence algorithm that searches for particular information across the web. While scraping helps to extract information data from the website.
Web Scrapers are capable of extracting complete or specific data from websites. But, it will be better to target specific data, so that the process takes less time.
Web scrapers are specialized tools to extract information from websites. They vary in design and complexity
Steps to implement Web Scraping
- Identify the website
- Create a list of URLs that you would like to scrape
- Locate the data in the HTML format
- Save the data in a structured format
Although web scraping is something you should go for, you should also be mindful of the ethical and legal implications of using such tools.
Before embarking on the web scraping journey, please be aware of the legal aspects
Try not to break rules.
2. Open Source Datasets:
The easiest way to collect data is to use open-source datasets. There are hundreds and thousands of datasets available over the internet
Below are some of the websites where you can obtain datasets
- Government Datasets
- UCI Machine Learning Repository
- Google Dataset Search Engine
- Microsoft Datasets
3. Synthetic Datasets:
Gathering data from the real world might pose a challenge.
There are privacy concerns and legal entanglement that might dissuade Data scientists to leverage information from the real world.
Synthetic data refers to the dataset created artificially rather than gathered from real-world scenarios.
For any Machine Learning model to be successful, it needs a large quantity of high-quality data.
Privacy regulations like GDPR and CCPA prevent companies from obtaining the personal information of customers and penalize them, in case of any wrongful activity.
Also, at times collecting real-world data might prove to be expensive or elusive as well- Think of fraudulent transactions at banks. In such cases, we cannot rely on real-world data.
Gartner predicts by 2024, 60 percent of the data will come from synthetic data.
So, we do have a point of Synthetic data is a dominant way of collecting data.
Benefits of Using Synthetic Data
A. Real Data Constraints:
Real data may have constraints due to privacy rules and regulations.
Synthetic data can reproduce all the relevant datasets without exposing the real-world dataset, hence making it foolproof.
B. Create Imaginary Dataset:
To simulate situations where real-world data does not exist, synthetic data can come in handy and help Data Scientists with data to make forecasting and predictions
Challenges with Synthetic Data
A. Quality Dependant on Input data
The quality of the synthetic data depends upon the input data and the Machine Learning model. If the quality is not good, the synthetic data generation will be below par.
B. Lower Acceptance:
Synthetic data is a new concept. Users may be apprehensive to use synthetic data due to uncertainties.
4. Internal Data Collection:
In an inquest for searching data, you can also tap into the organization data lake to receive data for Machine learning models
If you are in the manufacturing domain, you can set up sensors to retrieve data for processing.
The new age Machine Learning models, unlike the old ones, do not need much training data to learn.
Take for example a smart factory, which applies Machine Learning for Quality control- To identify defects.
As they need reduced training data, they can quickly identify defects and reduce wastage and hazards.
Another way to gather data is by crowdsourcing.
In crowdsourcing, humans, in exchange for payment, gather bits of data to prepare a comprehensive dataset.
That said, extracting and formatting data all by yourself can provide to be tedious work. You may need to hire resources to outsource tasks or done with the help of automation.
I hope you found this article informative. Please let me know in the comments below if you have any queries! Thanks!
