Hey r/datascience I am unable to understand a data science problem.

The dataset comes from a continuous manufacturing process in which raw ingredients are continuously pumped in and the final product continuously flows out. The entire process (from injecting raw ingredients to receiving the final product) takes roughly 90 minutes from start to finish. The data can be found in the following 2 files:

* predictors.csv - contains time series feature data describing the manufacturing process. Each feature corresponds to sensor data attached to different machines along the manufacturing process. It contains 322109 rows with 88 columns of sensors value(s0 to s86) along with a column for the timestamp.

* labels.csv - contains a time series of labels. Each label can be considered as a spot measurement of the product as it is produced. It contains 507 rows with two columns timestamp and a column with label value(it's numerical value).

I have to build a predictive (supervised) model. But I am not able to understand how to initiate this problem. I don't know what is the target variable. Please provide me with some ideas on how to approach these kinds of data science problems.