Data Preprocessing: Make your data from messy to meaningful
Get your data ready for analysis with these 5 simple methods.
Well it’s the beginning of my final year and the good thing is I’m gradually gaining an interest in Data and Analytics, as it is one of my common papers this semester
So, yesterday I was studying Data Preprocessing from one of my notes given by my college
Intrigued by the concept, I conducted a small research on this topic and wanted to learn about it a little more.
Learning has always been fun for me as a student and it never stops.
Believe me, it’s an amazing topic to know about and I will explain it to you guys in the simplest way possible,
I swear.
Data Preprocessing
Data Preprocessing is the process of making your data ready for analysis by cleaning, transforming, and integrating it into a piece of meaningful information.
Imagine like you’re updating your wardrobe to stay stylish always, but instead of clothes, here you’re making sure that your information is accurate and relevant.
So, did I make yourself clear?
Let’s take this above-explained description as an example —
Imagine me as an analyst, I read and learned the technical definition of Data Preprocessing, and then explained it to you in a very convenient way by understanding the main point, also citing a real-life situation, so that you can relate to it better.
I’m sure, you’ve understood!
But wait! There’s more — It’s the methods of Data preprocessing
Preprocessing Methods
You might have thought this — “Huh! just cleaning, transforming and interpretations are required, that’s a pie” but let me clarify, you’re wrong my friend!
The deeper we go, the more we know.
Preprocessing is not just all about these 3 things, it consists of overall 5 methods which successfully complete the whole data analysis process.
Data Cleaning
Data cleaning is just as simple as its pronunciation sounds like — it’s all about “cleaning the data”, just think of it as tidying a messy room.
In real life, it can be used in E-commerce, correcting typos from the large datasets of customer addresses, so that our favourite delivery guys, don’t end up at the wrong door.
Data duplication
Simple as it sounds, removing identical data from the datasets which is not required.
Suppose a customer submits their form (like a survey form) twice, then this step merges these two forms into one, thus removing the confusion.
Data imputation
This step analyses those empty spaces in the data sets and fills them with meaningful information.
Suppose you have an incomplete customer profile, imputation can help to add relevant details to it.
Data standardization
Visually, all of us want things in a consistent and relevant format, we don’t want things that are just irregularly structured.
Data standardization serves as a helping hand in this scenario.
It ensures that your data set is in a viewable format that makes it easy to understand.
Data Reduction
Trimming down those old data from the system that you no longer need is called data reduction.
For example, if you have old customer records, data reduction will remove the outdated ones which on the contrary, saves your storage and also makes your analysis faster.
Here are 3 real-life examples where data preprocessing is used commonly
Healthcare
Many hospitals use data preprocessing methods to prepare cleaned data for analysis. Tasks such as removing duplicate records, collecting errors standardizing data format. It can help to identify trends and patterns in patient data.
Finance
Here, it is used to improve the accuracy of financial forecasting models, making it easier to identify various frauds and risks.
Personalization
Personified products and services according to customer preference can be done through data preprocessing. Includes identifying customer preferences, segmenting them into groups, and transforming the data into a format that can be used by ML algorithms
Conclusion
So, here we wrap up — what we learned through this article
Data preprocessing is the process of managing and evaluating data and giving it a proper look so that it gets ready for analysis.
There are 5 different methods of data preprocessing which include data cleaning, data duplication, data imputation, data standardization, and lastly data reduction.
Well, my plan is to bring more such informational articles over topics like data and analytics in a more conversational and friendly way.
I’m planning to write about the “problems faced during data preprocessing” next time
What do you think about it?
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Well I’m planning to add Data and Analytics also
