I have a data set describing water levels of rivers. It has following attributes:
Value float64
Variable Name object
Variable Unit Code object
Date Pulled object
Site Code int64
Site Latitude float64
Site Longitude float64
Site Name object
I am using Linear Regression in trying to predict the Value
using Site Code
, Site Longitude
and Site Latitude
but getting results which are no way near the actual values. I am wondering if it's my choice of independent variables that is creating this problem or should I try other types of Regression? If it's the first case, which approaches are recommended to get higher accuracy rate or more precisely, what should one do if there are not many relevant independent variables?
Value
? Are they collinear (a linear combination of each other)?. It is impossible to answer your question without knowing the relationships between the independent variables, and between the former and the independent variable. $\endgroup$