In wireless networks, the physical medium is the cause of most of the errors and performance drops. Thus, an efficient predictive estimation of wireless networks performance w.r.t. medium status by the communication peers would be a leap ahead in the improvement of wireless communication. For that purpose, we designed a measurement bench that allows us to accurately control the noise level on an unidirectional WIFI communication link in the protected environment of an anechoic room. This way, we generated different medium conditions and collected several measurements for various PHY layer parameters on that link. Using the collected data, we analyzed the ability to predictively estimate the throughput performance of a noisy wireless link from measured physical medium parameters, using machine learning (ML) algorithms. For this purpose, we chose two different classes of ML algorithms, namely SVR (Support Vector Regression) [1] and k-NN (k-Nearest Neighbors) [2] to study the tradoff between complexity and estimation accuracy. Finally, we ranked the pertinence of the most common physical parameters for estimating or predicting the throughput that can be expected by users on top of the IP layer over a WIFI link.