2. Feature Engineering

When dealing with large datasets with many columns and variables, feature extracting is used to divide and reduce existing data into a manageable group.

But for image processing, machines can't extract features such as edges, shapes, or even size in this way

The process of reduction in the number of dimensions (or feature variables) in datasets is known as Dimensionality Reduction.

If a cube has 1000 points, we can reduce its dimensionality by simply taking the 3D data and viewing it as a 2D model. We can also remove feature variables...

• Nominal: used for labelling variables(m- male and f- female)
• Ordinal: used for measuring non-numeric with an order of the values(1-unhappy, 2-ok, 3- happy)
• Data Cleaning: In this data set, there are 2051 rows with 80 colum...