Playing with puzzles is quite similar to data science – finding the most suitable pieces and then fitting them in perfectly to create a clear vision. Feature engineering is still one of the most crucial elements of this big picture. Even if you have come across this term before, let me explain it in a plain and basic manner. Feature engineering is what to do, why it is so important we consider it, and how it is decisive for the success of your data science project in this blog.

What Exactly Is Feature Engineering?

Let’s start from the basics. Just imagine you are sitting in front of a colossal data mountain. Perhaps it is sales figures, customers, or engagement metrics in any social media platform. But as it is, that raw data is more akin to a large ball of dough. This has potential for optimization, but it is still of little practical applicability. This is where feature engineering comes in, and it’s crucial when designing where data will be stored and how.

Feature extraction is all about converting data into “features” which are useful information that can be used to feed your machine learning model and make some decisions or predictions. Imagine it is a process as complex as molding that round ball of clay to its current form. It is all about selecting or developing proper features that would let the model perform at its best.

Why does Feature Engineering Matter?

Here’s the thing: if you’re going to boast a high machine learning algorithm, then it will not be effective if the features you give it are subpar. This is just how it is like with cooking – even if you have the best recipe in the world if the ingredients you are using are old or badly chosen, the dish is going to taste a lot worse.

In data science, the ‘ingredients’ are your features; note that depending on the problem domain, the terms features or independent variables might be interchangeable. They elaborate that proper engineering enables one to discover patterns while making precise predictions. If they lack it, your model may falter and hence perform dismally. Indeed, it would not be wrong to say that many veterans say that feature engineering is more than applying the model selected!

Feature Engineering in the Real World

Let’s make this a bit more accurate. Suppose you are involved in a plan to forecast the likelihood that a given customer will purchase a particular product given their history. These may be the ingredients of the raw data such as customer’s age, geographic location, past purchases, etc.

However, instead of using this raw data to feed into the model, you could transform this into features likely to hold more valuable information. For instance:

Recency of purchases: When was the last time they shopped with the business? It is easier to have a customer purchase a product again after he had purchased something from your store last week as compared to a customer who has not shopped in the last 6 months.
Average order value: Rather than being able to filter the amount of money the customer spends per item, there could be options where the average amount spent can be viewed. This might tell you something about their penchant for future larger purchases.
Customer lifecycle stage: Is he a new customer or a regular buyer, or maybe he is on the verge of abandoning the store? Forecasting a customer’s actions is possible by knowing where they are on the journey map.

These are some of them but I hope you get the whole picture. It’s simply a matter of making attributes that provide your model with additional information to process.

General Steps Involved in Feature Engineering

Fine, then how can you perform feature engineering? While there’s no one-size-fits-all approach, there are a few common steps that data scientists usually follow:

Basically, you should learn how to work with missing values – Well, let me tell you that when it comes to data, the first thing you have to do is address the issue of missing data at hand.

Scalability– Age and income are two examples of the kind of features that could, in fact, have very different scales. It is not uncommon to find features that can go up to 1,000 while others that are just only 1 up to 10. Scaling is recommended to make certain that the model gives all features equal chances.
Developing New Options – By far, the most appealing part of the process is where the key to success is found. While it is possible to generate a new feature from a simple combination or transformation of the others, better insights can be gained from the new feature, thus allowing the integration of those features to be proper. For instance, repeating the above formulation for ‘customer loyalty’ based on customer transactions' number and dollar value.
Encoding Categorical Variables – Rarely do your data come in forms such as “gender” or “size,” more specifically; male/female or small/medium/large. These have to be translated into numeric figures which are appreciable by this format of the model.

How Feature Engineering Improves Model’s Accuracy

We’ve talked a lot about the theory, but here’s the bottom line: Therefore, feature engineering may not only increase the accuracy of your model significantly. A well-constructed feature can reveal aspects in the data that your algorithm may even fail to notice or identify.

For instance, suppose the data set is to be used to estimate house prices. Rather than simply employing square footage you might develop a new dimension such as the ‘price per square foot’. This new feature could help the model to determine in which way the size of a house influences the price.

Feature engineering is essentially about identifying how different features depend on each other and then attempting to make them independent for the learning algorithm to make predictions effectively. It’s one of those steps that although takes some time usually significantly improves how well a certain data science project performs.

Conclusion: Why Skipping Feature Engineering Is A Big No-No

If there’s one takeaway from this blog, it’s this: do not take an undue focus away from feature engineering. It’s not an added activity on top of the process – it forms a critical component of effectively creating models for the real world. One can spend a good amount of time developing new algorithms or models, but in the end, feature engineering makes the difference between an average model and a great one.

Therefore, irrespective of whatever stage of data science you are at, pay the right amount of heed to feature engineering. And if you’re considering expanding your knowledge with a Data science course in Kolkata, this particular skill will set you apart.

Why Feature Engineering Is Vital For Data Science Projects