I'm trying to create a prediction model for estimation of continuous variable based on about 35 Independent variables.My data set has circa 27k observartions. Here is the summary of the the targeted continuous variable:
Frequency Percent (0,5] 2706 10.053 (5,10] 5226 19.415 (10,25] 4397 16.335 (25,100] 7142 26.533 (100,1e+03] 6465 24.018 (1e+03,1e+05] 981 3.645 Total 26917 100.000
I tried (by using R) Random Forest (RandomForest package),Linear regression, Conditional Inference Trees (ctree function in party package) but all of them have results that have a significant overestimation. Here are the results of the prediction where I counted number of observations by thier distance from the actual values: Any idea how can i balance the results?
Here are some views on the data: The target variable is LTV for a user, I would like to predict LTV value after 180 days based on users behavior of the first 7 days. Here Is a summary fot the target variavle:
vars n mean sd median trimmed mad min max range skew kurtosis se 1 1 26917 178.35 622.29 33.49 66.63 39.28 0.03 22103.73 22103.71 14.1 325.08 3.79
UPDATE: Here are the distributions of the targeted variable (first)and the prediction (secound)results: