Choosing input variables for Linear Regression for higher accuracy
I am using Linear Regression in trying to predict the Value using Site Code, Site Longitude and Site Latitude


Unfortunately none of these three variables can go directly into linear regression. 

Site Code looks like a numerical variable, but it is actually categorical. For example Site Code = 10 is probably orthogonal to Site Code = 5, and should not be interpreted as twice as significant. The correct way to handle this is to create a boolean dummy indicator variable for each possible site code. You can also use this method for some of your other object variables which also appear to be categorical.

Latitude and Longitude will not work well in a linear regression because their relationship is highly non-linear. For example two points can have the same latitude/longitude but be very far apart. One typical method is to convert (lat,lng) pairs into predefined zones, and treat the zone as a categorical variable.

The final type of variable you have is a date. You could convert this directly to a categorical variable, but it might be better to take only the month to reduce the number of categories and generalize seasonal effects better.