▶ What is Feature Engineering?
Feature engineering is the process of transforming raw data into useful features that can be used to improve the performance of machine learning models. Here feature is column name in dataset like Name, Age, Height.
Imagine we have a dataset with lots of information, like a spreadsheet with columns for age, height, weight, etc. Feature engineering involves:

1. Selecting the most important columns (features).
2. Creating new columns from the existing ones (like calculating BMI from height and weight).
3. Transforming data to make it more useful (like converting text to numbers).

▶ Why It’s Important:
1.Improves Model Accuracy: Better features help the model make more accurate predictions.

1. Simplifies Data: Makes complex data easier to understand and use.
2. Highlights Key Information: Brings out the most important aspects of the data. Example: If you have data about houses, we might create a new feature called “price per square foot” by dividing the price by the size of the house. This new feature can help the model understand the value of the house better.

▶ Types of feature engineering

1. Feature Transformation: Feature Transformation is the process of transforming the features into a more suitable representation for the machine learning model. This is done to ensure that the model can effectively learn from the data.
2. Feature Creation : Feature Creation is the process of generating new features based on domain knowledge or by observing patterns in the data. It is a form of feature engineering that can significantly improve the performance of a machine-learning model.
3. Feature Selection: Feature Selection is the process of selecting a subset of relevant features from the dataset to be used in a machine-learning model. It is an important step in the feature engineering process as it can have a significant impact on the model’s performance.
4. Feature Extraction: Feature Extraction is the process of creating new features from existing ones to provide more relevant information to the machine learning model. This is done by transforming, combining, or aggregating existing features.
5. Feature Scaling: Feature Scaling is the process of transforming the features so that they have a similar scale. This is important in machine learning because the scale of the features can affect the performance of the model.

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