I am a novice data scientist and am currently working on a simple ML project
I have a dataset that looks like below
We derive the outcome label based on the rule that `if booked qty is more than 50% of target final qty, we consider it as positive. else, negative.
The ML model will only be using features from
Target final Qty and
Booked Qty are only used for deriving labels (and will not be used in model building)
Now I have two business objectives
a) let's say I would like to select all
negative items and follow up with the customers (steer them towards booking more order quantities). However, instead of reaching out to both
Req_id = 2 and
req_id=4, I would like to focus on
req_id that has more chance of becoming positive. I would like to have some likelihood or ranking measure based on input features that can help save our resources. What I have shown is just a sample. IRL, there might be thousands of records. Is there anyway to rank these rule based labels? is there any simple statistical or ML approach that you can suggest to rank the labels (even before the prediction for an unseen datapoint)?
b) I know we can get likelihood for new data points if I use logistic regression. But how can I rank the existing labels itself (with/without ML model). Is it even possible?
c) As you can see am generating labels based on a rule. In that case, do I even need ML for this problem? For ex: If a new/existing customer shares an requirement (
req_id) with us indicating the
target final qty of a product and that he will book/purchase on a certain date later, we would like to know whether he will meet his
target final Qty or not based on his input characteristics etc. Currently, we see most of the customers don't meet their target final qty. So, we would like to know whether they will meet(atleast 50% of) the target qty. I think the prediction will be useful in the time interval between
req stage and
order booking stage.
d) Or is there any other ML method or simple statistical method that you would suggest for this problem? Help please
Can help me with the above please?