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ML data cleaning guide or how to prepare a perfect dataset for GiniMachine

ML Data Cleaning Guide or How to Prepare a Perfect Dataset for GiniMachine

Data cleaning (or scrubbing), its benefits, and the tools used for this purpose are becoming extremely popular with the digital transformation of businesses in recent years.

Some of our clients wonder: what does data scrubbing do? In this article, the GiniMachine team decided to cover the basics of machine learning cleaning data in analytics, explain why data cleaning is important, and find out if it’s a must-have or just a nice-to-have in this data cleaning guide. 

With unprepared or “dirty” data important business decisions are at risk to be biased. Data cleaning can relieve potential headaches and create reliable advantages for data-driven decision-making processes. 

What Does Scrubbing Data Do?

Data cleaning, data cleansing, or data scrubbing are notions used for the same process: identifying bad data or any issues with the data, and then correcting it step-by-step. Unfixable data elements need to be removed. 

In machine learning, cleaning data is highly recommended. The results of the MonkeyLearn survey state that data scientists spend around 60% of their time cleaning data for machine learning. 

For sure, GiniMachine is created to work with raw unprocessed data. However, if the data is not valid from a rational point of view and does not match business needs and processes, it is hardly possible to build a reliable predictive model even after years of trying. 

This is why preliminary validation, scrubbing/cleansing, and putting the data together in a ready-to-use dataset is vital.

Why Data Cleaning Is Important?

Working with bad data is a tedious job and a massive time waste. Using unclean data may also be costly. According to eh Gartner’s research, bad data may cost businesses anywhere from $9.7 million to $14.2 million every year.

Bad or unclean data is usually a result of human error, data scraping, or merging data from different sources. The latter often happens with multichannel data, when the dataset consists of various parts provided separately from each other. 

Benefits of Data Cleaning

Let’s try to figure out why data cleaning is important and which benefits it brings. The key benefit of data cleaning for analytics is to obtain a template for the company’s data handling. Clean data means dataset verification for no duplication, mislabeling, missing values, or improper data in the fields. If the input data is hardly reliable, the output data is highly likely to be so. 

Data cleaning helps to:

  • Eliminate errors in the dataset
  • Create a better reporting system to understand how these errors appear
  • Spend less time and effort on building models due to higher quality data in the decision-making process
  • Establish good practices for collecting data the proper way.

Data Cleaning Checklist: 9 Steps to Polished Data

Let’s start with some bad news: data cleaning works case by case. It means each case and each dataset requires a specific method of data cleansing. 

The good news is that we have a data cleaning checklist with techniques to implement step-by-step:

1. Clear formatting

Heavily formatted data may be difficult to read for machine learning algorithms, while it may include hidden symbols and intervals. It can make the dataset confusing and the results incorrect. This step seems to be the easiest, while most tools have a standard button for that. 

2. Duplicates removal

Gathering data from various sources or human errors are highly likely to cause duplicated entries in the dataset. In addition to confusing the model results, duplicates can also decrease the quality of visualization. This is why a careful search for duplicates is crucial in data cleaning. 

3. Irrelevant data removal

Considering what’s relevant and what’s not may sound like a tedious task. However, filtering becomes easier when you analyze the purpose of cleaning data for machine learning. For example, if you are analyzing the creditworthiness of your customers, removing personal data (such as name or email address) would be reasonable. In addition to personally identifiable data, feel free to remove URLs and HTML tags, tracking codes, or extra blank spaces in the text.

4. Data type conversion

To help the system algorithms with mathematical equations, you need to pay attention to numbers. Cleaned datasets should not include numbers imputed as text values, so for data cleaning, it is important to show them as numerals. It is also true for dates: make sure you replace June 29th, 1990 with 06/29/1990. The same applies to currencies and measurements: for example, if you have a field with amounts in US dollars and Euros, convert them into the same currency. Also, countries: US, United States and the Unitied States of America. 

5. Standardized capitalization

A mixture of capitalization is likely to lead to additional unnecessary categories in the dataset. Also, it may cause difficulties in translation, while Bill and bill have two completely different meanings. 

6. Error elimination

By enabling a simple spell-check you can avoid typos, misspelled words, and punctuation mistakes. That may not be a problem for your AI/ML decision-making software, but just imagine that an extra period in the customer’s email address may lead to a broken communication channel with the customer. 

7. Translation from other languages

Working with monolingual data significantly improves the quality of the data analysis. Most NLP (natural language processing) models are also able to work with one language at once. 

8. Work with missing values

GiniMachine can flawlessly work with missing values, automatically ignoring them, but not all AI/ML platforms are capable of it. For such cases, we suggest either removing the entries containing that missing value or inputting the missing word or number manually. The choice depends on your goals and on the rational aspect of this issue. 

9. Review the results

Once the team is done with all of the above, it can start implementing the new data cleansing standards. To understand if the standards really work, you may use these three questions:

  • Does the data make sense now?
  • Does the data in each category or class comply with the rules for each?
  • Does the data analysis support or break your working theory? 

Periodic reviews and re-evaluation of the data governance practices need to become a part of the routing for data stewards and data governance employees. No matter what you call it: data scrubbing, data munging, data wrangling, or simply data cleaning – its goal is to process raw data to the format compliant with the use case requirements. 

Data Cleaning Tools

Data cleaning techniques include manual and automated: automated require reliable tools to speed up the process, change data formats, manage, analyze, and prepare data for further use. 

The GiniMachine team suggests the following list of tools depending on the company size, task complexity, and business needs: 

Wrapping it All Up

Cleaning data for machine learning be extremely time-consuming, but it will cost your business a lot of money later if you ignore its importance. 

GiniMachine AI/ML-based platform works with raw unprocessed data and can do most of the job for you. However, taking care of your dataset from the business relevance point of view can improve the output, draw more valuable insights and avoid bias caused by “dirty” data. 

Harness the power of artificial intelligence and machine learning for scoring, predictions, and suggestions – book a demo call and try GiniMachine in action.


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