My current project may require me to build a model to predict the behavior of a certain group of people. the training data set contains only 6 variables (id is only for identification purposes):
id, age, income, gender, job category, monthly spend
monthly spend is the response variable. But the training dataset contains approximately 3 million rows, and the dataset (which contains
id, age, income, gender, job category but no response variable) to be predicted contains 1 million rows. My question is: is there any potential problems if I throw too many rows (3 million in this case) into a statistical model?I understand the computational expenses is one of the concern, are there any other concerns? Are there any books / papers that fully explain the data set size issue?