Complete Guide For Feature Engineering

To develop an ML project first we find dataset relavent to the problem statement. Next we perform Exploratory Data Analysis(EDA) to find insights, then we perform Feature Engineering. Before knowing further details, for those who come from non technical or who came to just explore this blog you might not know what is a feature? It is nothing but a column in a dataset. Now ask ourself a very fundamental question....

Why Feature Engineering?


"Imagine our home's front door -it's designed as a vertical rectangle, allowing ur to enter without bending. Now, consider if it was shaped as a horizontal rectangle instead. Attempting to enter without bending would be impossible, right? That's because the door's shape is optimized for its purpose.

In a similar manner, feature engineering serves as the 'shaping' process for data in machine learning. Raw data is like the intial door shape - not perfectly suited for the algorithms to work efficiently. Feature engineering transforms these raw features into more suitable forms, just like the door's shape being optimized for easy entry. By performing feature engineering, we tailor the data to match the algorithm's requirements, enabling them to perform effectively and produce meaningful results."


What is Feature Engineering?

Simply it is the process of taking raw data and transforming it into suitable format so that we can enhance the model performance and accuracy.

Now let's findout what are types of feature Engineering?



1. Feature Transformation:

The what? : It is simply transforming or changing the given feature into a suitable form( I think you might have understood what is suitable form by now? if 'yes' your are following)

We have 4 possible techniques to transform a given column:

  1. Missing Value Imputation
  2. Handling Categorical Values
  3. Outlier Detection
  4. Feature Scaling




Comments

  1. sir jo aapne github pe youtube automation ki repo daali hai usko kese use kare google colab mai uske uper ek video bana do

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