Scenario - I have data that does not have labels but I can create a function to label the data based on behavior and deploy the model so I don't have to keep labeling the data. Is this considered machine learning ?
Objective: Classify accounts with Volume spikes based on
low labels to deploy on big data (trillions of lines of data) .
Data: The data I have includes the following attributes: Account, Time, Date, Volume amount.
Create a new feature column called
spikeand create a pandas function to ID a spike greater than
5. Is this feature engineering ?
Next I create my
labelcolumn and classify it as
Next I train a machine learning classifier and deploy it to label future accounts with similar patterns in big data.
Thoughts on this process ? Is this approach correct for machine learning ?