An accurate detection of spectrum opportunities is a key factor in governing the efficient spectrum usage in a cognitive radio (CR) system. Energy detection based spectrum sensing has been widely used due to its ease of implementation with lower computational complexity; however, its robustness and performance are highly affected by the noise uncertainty. In the present work, a real time hardware implementable spectrum sensor has been realized and tested for an unsupervised learning based K-means clustering approach, to detect the white spaces in the spectrum. A CR network with one primary transmitter and two secondary nodes has been considered for which the data is collected over an FM band using a software defined radio peripheral, i.e. USRP B210. The whole system has been implemented with the help of MATLAB Simulink & Xilinx System Generator. The decision accuracy of the proposed algorithm is verified at different values of the signal-to-noise ratios (SNRs) and found that the classification based sensing is quite accurate even at low SNR region.