Generating interesting/useful features from existing useless features I am new to ML and trying to work on a binary classification problem.
I came to know that one of the main factors for success of ML is feature engineering. I am here to seek some inspiration/help from experienced folks here on how to generate creative/interesting features based on existing features. My dataset is given below

In my dataset, I have already created some new features based on existing date columns (existing date columns are not shown in screenshot. Only derived date columns are shown here). Refer below
date_diff_1 - indicates the time taken by company to respond to customer in days.
date_diff_2 - indicates the time taken by customer to book his 1st PO after the deal is made.
date_diff_3 - indicates the time taken by customer to hit 1000$ revenue
Additionally, Division and category-Division variables are correlated. So, I plan to remove the highly cardinal column category-Division.
How does one approach feature engineering in scenarios like this? what are some of the usual/typical practice that data scientists follow to create interesting features to uncover hidden info (from the data)?

Some ideas:

*

*Year, month, hour etc. to capture time effects

*Details about the customer (country of residence, number of past purchases, etc.)

*Details about the product (product classification)

Process for feature engineering:

*

*Talk to subject matter experts

*Research similar models in the literature

*Creativity and reflection (easy, right? ;))

