The direction of arrival (DOA) estimation is a well-known research problem. It is conditional to different microphone array geometry and acoustic room conditions. It also becomes more challenging in the presence of noise and reverberation. Many traditional signal processing approaches such as least square (LS) based rely on time difference of arrival estimation which is not robust to adverse acoustic conditions and hampers the DOA estimation. This problem can be solved using learningbased algorithms, which uses a large amount of data simulated on similar acoustic conditions. Though much of the work in learning algorithms until now leverages augmentation techniques and deep neural network (DNN) architecture for achieving robustness in DOA estimation, very less attention is given to the feature representation. Robust feature representation can be achieved using certain geometry of microphone array. In this work, a framework comprising of a learning-based DOA estimation along with a circular co-prime microphone array(CCMA) arrangement is proposed. Experiment results show that a robust feature representation is indeed essential in estimating the DOA accurately and gives a significant improvement in terms of root mean squared error(RMSE) and mean-absolute error(MAE) scores when compared to other state-of-the-art DNN and signal processing approaches.