Practices for Feature Engineering in 2024: From ‘raw data’ to Insights


The age of (Internet of Things) IOT has ushered us into an era of a data-driven approach toward finding better solutions or providing direction in decision-making. According to “Domo,” we have been creating 2.5 quintillion bytes of data daily.  

Is all this data useful?

Can this data be used directly?

So, this is where feature engineering comes into play. 

this mage shows the Raw Data

What Is Feature Engineering?

Consider feature engineering as a puzzle, except not every piece fits. This means there is no objective solution to this. This means that from a plethora of features, the one that produces the most optimal features can be employed. So, a feature refers to the data relevant to the problem at hand and can contribute towards finding a better solution.

this image shows Feature Engineering

A data engineer would hardly come across real-life data that could be organized and structured to this extent. Before this raw data can be consumed by a machine learning algorithm, utilized in business intelligence reporting, or employed for any purpose, it must be converted into a structured format.

This process of transforming raw data into useful features is known as feature engineering.

This brings us to the question: why should we go about this if there is no correct solution? 

Further Reading: Feature Engineering Process: A Comprehensive Guide – Part A

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Necessity Of Feature Engineering

Since there is no correct solution, we need to find empirical evidence to show that the proposed solution will be the most optimal and relevant. The selected feature set and its mathematical transformations contribute immensely towards the reliability of the machine learning model. In fact, the situation where the incorrect feature is selected is known as “Garbage in-Garbage out,” which, as the name suggests, results in incorrect, unreliable, and outright incorrect results.  

There are a few techniques that can be utilized to avoid such situations. We will briefly skim over a few and then go into detail in a later article. The following are some of these techniques:  

  • Handling missing valuesNo data is perfect. There is almost always the predicament of missing values. To use a dataset, we need to remove or impute these missing values from the dataset. Averaging is one way to go. There are others with their applicability and shortcomings  
This image shows Handling missing values
  • Outlier Detection: Like missing values, datasets may also have outliers. This can be due to a malfunctioning device, environmental swings, or an extremely rare occurrence. Either way, such data may sway the results.
This image show's Outlier Detection
  • Data Transformation: Data transformations are another essential part of data engineering. As mentioned earlier, data will not necessarily be in the most optimal state by default and may need to be transformed. Log-based transformation often comes in handy to normalize the data, but there are other ways to transform it depending on the data and its intent.

Further Reading: Feature Engineering Process: A Comprehensive Guide – Part B

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In a nutshell, these efforts result in a ‘make-or-break’ difference for the solution to be built. Every valid transformation, every correct feature selected, and every rightly missing value imputed takes us one step closer to the optimal solution. Time-series prediction is quite sensitive to data imputation techniques. Incorrect feature engineering practices can lead to the introduction of bias as a result. An incorrect feature-engineering process sways us from a reliable solution and will lay waste to upcoming efforts.

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