  This study presents a general machine learning framework to estimate the
traffic-measurement-level experience rate at given throughput values in the
form of a Key Performance Indicator for the cells on base stations across
various cities, using busy-hour counter data, and several technical parameters
together with the network topology. Relying on feature engineering techniques,
scores of additional predictors are proposed to enhance the effects of raw
correlated counter values over the corresponding targets, and to represent the
underlying interactions among groups of cells within nearby spatial locations
effectively. An end-to-end regression modeling is applied on the transformed
data, with results presented on unseen cities of varying sizes.
