feature engineering

• You must determine the best way to represent raw dataset values as trainable values in the feature vector.
• Every value in a feature vector must be a floating-point value
• This process is called [[Feature Engineering]], and it is a vital part of machine learning.

The most common feature engineering techniques are:

1. [[Normalization]]: Converting numerical values into a standard range.
2. [[Binning]] (also referred to as bucketing): Converting numerical values into buckets of ranges.