I am new to machine learning (I am currently following the Udemy course machine learning from A-Z). Basically, I would like to reproduce the following analysis (https://www.datasciencecentral.com/profiles/blogs/predicting-house-sales)
My aim is to predict if a house to going to be sold in the coming year. I have an apartment database with the following info:
- owner name
- a few socio-demographic features
- When the apartment was last sold (date, target prediction variable)
I am not sure how to take into account this time component of the variable I am trying to predict:
- should I consider this as a regression model trying to compute the predicted sales date?
- should I consider this more of a classification problem with the y value being 1 if the apartment is predicted to be sold in the coming year. If so, how do I have to modify my dataset structure in order to train the model (should I represent I represent 1 apartment for each year and the outcome?).
What model would you recommend considering I am trying to model rare events? (sale likelyhood is 5% per year on average).
People seem to be using different models to achieve this: Cox (survival analysis), Random forest...
I am looking forward to reading your answers.