Date manipulation
Time features can be of critical importance for some data science problems. In time series analysis, dates are obviously critical. Predicting that the S&P 500 is going to 3,000 means nothing if you don't attach a date to the prediction.
Dates without any processing might not provide much significance to most models and the values are going to be too unique to provide any predictive power. Why is 10/21/2019 different from 10/19/2019? If we use some of the domain knowledge, we might be able to greatly increase the information value of the feature. For example, converting the date to a categorical variable might help. If the target feature is that you are trying to determine when rent is going to get paid, convert the date to a binary value where the possible values are:
- Before the 5th of the month = 1
- After the 5th of the month = 0
If you are asked to predict foot traffic and sales at a restaurant, there might not be any...
