Role of Feature Engineering Method in the Machine Learning Process

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What is the need for features in engineering?

In all kinds of Machine Learning algorithms, there is a need for input data to bring out the outputs. For such algorithms, there are requirements for specialized features. 

Feature engineering is described as a process of collecting data that is appropriate for machine learning algorithms. Moreover, it is also used for testing the accuracy of models and trying to improve them further. In other words, feature engineering can easily transform raw data into features by representing the noted problem, which results in improved model accuracy. 

The prominence of Feature Engineering

Till now, it can easily understand the features and influence the outcomes of different analytical models while knowing the significance of feature engineering. 

  • Minimizing the complexity

Raw data acts as the input used for algorithms while building the predictive models. Hence, feature engineering offers assistance to such algorithms. 

  • Enhancing the accuracy

Feature engineering is ideal as an extensive process meant for transforming the data variable to some suitable format. 

Techniques include in Feature Engineering

There are certain techniques mentioned in Feature Engineering mentioned as – 

  • Imputation

Missing values occurred because of data restriction whenever someone is dealing with inadequate data. There are some other reasons as well behind this, which are human interruption, insufficient data sources, common error, and others. And, in all these, feature engineering can help you in that. 

  • Outliers

For the information, Outliers are considered to be deviated values or data points. In the process of handling the outliers, the technique is used for determining the outliers and cut them. 

  • Log transform

It has been seen that a skewed dataset can influence the overall performance of a model. Therefore, log transform can be helpful in stabilizing the skewness of a dataset. Feature engineering works intending to shape the dataset distribution. Actually, the ‘Log Transform’ reduces down the influence of outliers while resulting in a robust data model. 

  • Binning

In the machine learning process, overfitting is defined as a condition referring to different parameters. Binning is one of the techniques meant for normalizing the noisy data. The method includes the segmenting of various features into different bins.


Feature engineering is a kind of machine learning programmer completely suitable features from existing data and improving the performance of machine learning models. Now, it is easy to transform the data for generating better outcomes and making complicated models work better. 


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