Feature Engineering: Fundamentals In 4 Important Points

Ajay Ohri


Feature engineering itself is an art and the process of utilizing data domain knowledge to create features that facilitate algorithms’ work. Feature engineering, when carried out with precision, improves the predictive power of machine learning algorithms. Feature Engineering is a simple tool used to make your data better suited to the problem at hand. Keep reading for more on Feature Engineering for Machine Learning. This is also regarded as applied machine learning.

Features are one of the fundamental elements of datasets. Feature engineering involves levers – data mining techniques to improve the efficiency of machine learning algorithms. Characteristics, variables and attributes are all considered identical since they all influence the outcome of a process. Feature engineering includes many processes that will be explained in the following sub-sections. 

  1. What Is Feature Engineering?
  2. List Of Feature Engineering Techniques
  3. Feature Engineering Examples
  4. How To Do Feature Engineering?

1. What Is Feature Engineering?

Feature engineering is converting available data into a form that is easier to understand and interpret. Find out What is Feature Engineering? One with a data-related background needs to visualize the machine learning model to make it more digestible. It has clear procedures that are methodical, demonstrable and comprehensible. In addition, mastering/controlling feature engineering comes with practising and studying things during empirical learning. Successful machine learning in feature engineering depends on the user’s presentation of the data.

2. List Of Feature Engineering Techniques

What are some of the feature engineering techniques? – Here goes the list with a brief explanation:-

  • IMPUTATION – Data imputation refers to the replacement of missing data with statistical estimations. The aim is to provide a full data set in use for the machine learning process. 
  • BINNING- Binning may be applied to categorical and numeric data. The main aim is to make the model more robust and avoid overfilling.
  • LOG TRANSFORM- Log transform is used to handle ambiguous data, and after the application, the data becomes more approximative to normal.
  • TREATMENT OF OUTLIERS – Before understanding susceptibility to outliers, the best way to detect outliers is to demonstrate the available data visually. Any value having a distance to the average higher than x* SD can be assumed as an outlier.
  • ONE HOT ENCODING – One hot is used to distribute the values of a column across multiple column flags. This method makes it possible to convert your categorical data in numeric format.
  • GROUPING OPERATIONS- The data available with the user can be grouped using a dynamic cross-tabulation based on:
  • a) categorical and
  •  b) numeric base.
  • SCALING – In cases where the numerical dataset differs from each other, scaling is used to maintain symmetric dataset.

3. Feature Engineering Examples

This section will discuss some of the feature engineering examples, each of them having its own results.

  • Predict language – Suppose you are given a sentence in English, then there are a number of possible features that can be made by breaking sentences into single words.
  • Fourier transforms for speech transcription – this includes converting speech to written text. The additional amplitude of sound waves is measured at discrete points, referred to as sampling. Make additional models and signs from the spectrogram.
  • Redefine digital quantities- For better exposure to affected structures, quantities such as weight, time, and distance are where the global quantity can be saved in intervals.

4. How To Do Feature Engineering?

Now the question arises: How to do feature engineering? and what are some of the methods? In order to get the best results, the practitioner needs to create new features from the raw data available. This requires spending a great deal of time, reflecting on the underlying form of the problems. The design of functions and the selection of functions are not mutually exclusive. Both go hand in hand and are of equal importance. Unfortunately, we do not have an automatic build function to date. However, the machine learning process, in a wide sense, involves many activities.

At first, is the definition of the problem. The selection and preparation of the data, at the middle, is the preparation of the model, the evaluation and the adjustment, and at the end is the presentation of the results. This is an iterative process that interacts with data selection and model assessment over and over again until we run out of time. You need a well thought out and designed test harness to objectively estimate the model’s skill on invisible data. I hope your concepts have become more concrete.


In addition, Feature engineering has numerous algorithms, some of which require the use of similarities or distance measurements to discover the dense region of observations.

Some of these algorithms include:



Although feature engineering offers data scientists immense value in preparing data in an uncomfortable and fast way in its emerging stages, feature engineering is a vital data science process to make the most of the available data. Different techniques aim to obtain a coherent data set that is understandable and easy to use in order to achieve accurate and reliable results for the machine learning algorithms. Features influence machine learning algorithms’ output quality and characteristic techniques to improve the features involved in algorithms training.

There are no right or wrong ways of learning AI and ML technologies – the more, the better! These valuable resources can be the starting point for your journey on how to learn Artificial Intelligence and Machine Learning. Do pursuing AI and ML interest you? If you want to step into the world of emerging tech, you can accelerate your career with this Machine Learning And AI Courses by Jigsaw Academy.



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