I am very new to ML and currently, I am working on building a model that can predict recurring blood donors (a classification problem). I have a dataset which consists of 25 features (gender, height, age, previous donations, etc).
However, this data is not labelled. But, I was thinking of considering the ratio between the previous donation count of a donor and their age to label my data, and using a threshold value to classify whether the donor will come back to donate blood or not. For example, given a donor is 25 yrs old and has donated blood 20 times. So, the number of previous donations divided by the age of the donor equals to 0.8. So, if the threshold value is 0.55, then I would label this instance as a 1 (this is a recurring donor).
So, can I label my data using this technique? Or else, should I use some unsupervised learning model (like clustering)?
I have selected the important features from my dataset and I have cleaned up the data. I want to now train a model, but I am a bit undecided as to whether I should use unsupervised learning techniques or not. This is because it is sometimes difficult to derive meaning from the results of unsupervised learning models.