First post on StackExchange. I’m fairly new to ML, with about 1 year of experience so please pardon any ignorance or misuse of terms.
I have a multivariate time series dataset where I would like to predict the likelihood of an outcome of 1/0 (think of this as a Conversion) in the next 3 time periods for unique ObjectID’s. I have a table that takes snapshots of ObjectID over time, and I want to use current data to predict if the ObjectID will convert (1 or 0) in the coming 3 time periods. Once an ObjectID reaches conversion, it will stay as such.
There’s a twist: some of the X variables are static, they do not change with time. I call these attributes, there are 8 in my dataset. These are essentially characteristics ObjectID in question. I have 2 variables that change with time, Age (in months) and a categorical variable with 7 levels through which the ObjectID progresses. Here’s how the data looks:
ObjID Age Time Attr1 Attr2 Att3 CurrCat Conversion id1234 0 1/1/2019 ABC XYZ HIJ A 0 id1234 1 1/2/2019 ABC XYZ HIJ B 0 id1234 2 1/3/2019 ABC XYZ HIJ A 0 id1234 3 1/4/2019 ABC XYZ HIJ D 0 <-- current time id6789 0 1/1/2019 CBA ZYX JIH C 0 id6789 1 1/2/2019 CBA ZYX JIH C 0 id6789 2 1/3/2019 CBA ZYX JIH D 1 id6789 3 1/4/2019 CBA ZYX JIH A 1
How can I setup this dataset for a classification or decision tree model?
I'll be building the model in Python, so any suggested packages would be helpful too.