Machine Learning

Data Cleaning in a Nutshell

“Better data beats fancier algorithms.”

Garbage in, garbage out is the motto that needs to be followed to build an accurate machine learning model.

If the data under analysis is not accurate, then it is not useful. Irrespective of how accurate your model is, without data cleaning, it will deliver biased and inaccurate results.

Thus, data cleaning, also called data cleansing or data scrubbing, is one of the most crucial parts of machine learning.

What is data cleaning?

Data cleansing can be understood as a process of making the data ready for analysis.

Eliminating null records and unnecessary columns, fixing the outliers (junk values), restructuring the data to enhance its readability, etc. are some of the components of data cleaning.

Data cleaning also focuses on increasing the accuracy of the dataset by rectifying the existing information, instead of just removing chunks of useless data.

Steps involved in data cleaning

There is no particular procedure for data cleaning, it varies from one dataset to another. However, having a roadmap is essential to keep you on the right track.

Given below are the basic steps which can be followed to create a template for your data cleaning process.

Eliminating duplicates and irrelevant observations

  • Duplicate or redundant values affect the efficiency of the model to a large extent.  The data is repeated and may add towards either the correct side or incorrect side, thereby giving biased results. 
  • The irrelevant data do not add any value to the dataset, thus should be dropped or removed to save resources like memory and processing time.

Rectifying structural errors

  • Structural errors include inconsistencies in naming conventions, typos, and wrong capitalization. These typographical errors result in mislabeled classes or categories. 
  • For instance, the model might treat “NA” and “Not Applicable” as two different categories, though they represent the same value. These structural variations make the algorithms very inefficient resulting in unfaithful results.

Filter out the irrelevant outliers

  • Outliers are the values that do not fit in the dataset under observation. These values can be understood as the noise in the dataset.
  • Outliers arise due to manual errors or data entry mistakes. The Outliers are not always incorrect, so they should not be dropped until we have a valid reason.

Handling missing data

Handling missing values is the trickiest step in the data cleaning process. The missing values can’t be ignored or eliminated since they can represent something crucial. 

Following are a couple of the most common methods to deal with the missing data:

  • Removing the observations having missing values, but might result in losing some useful information.
  • Imputing the missing values based on the previous observations. Since it is based on assumptions and not actual observations, it does not add any value to the dataset and may result in losing the data integrity.

Some data cleansing tools

Data cleaning is the most important step in machine learning to get accuracy and efficiency.

Performing data cleansing on zillions of data manually is tedious and may result in errors.

This makes the data cleaning tools prominent since they help in keeping a large amount of data clean and consistent.

Openrefine, TIBCO Clarity, Trifacta Wrangler, IBM Infosphere, Cloudingo, Quality Stage, etc. are some of the most popular data cleaning tools.


Working with clean data comes with a lot of advantages like improved efficiency, reduced error margin, accuracy, consistency, better decision making, and many more.

Thus, the data should be cleansed before fitting any model with it.

If you want to invest in Data cleaning then you can learn by implementing it using Python or R.