A DNA-binding protein (DNA-BP) is a protein that can bind and inter- act with a DNA. DNA-BPs regulate and effect various cellular processes like transcription, DNA replication, recombination, repair and modifica- tion. As such, these proteins can potentially be used for drug develop- ment in treating genetic diseases and cancers. This is why identification DNA-BPs is a very important task. As the experimental methods of this important task are expensive as well as time consuming, fast and accu- rate computational methods are sought for predicting whether a protein can bind with a DNA or not. In this paper, we focus on building a new computational model to identify DNA-binding proteins in an efficient and accurate way. Our model extracts meaningful information directly from the protein sequences, without any dependence on functional domain or structural information. After feature extraction, we have employed Random Forest (RF) model to rank the features. Afterwards, we have used Recur- sive Feature Elimination (RFE) method to extract an optimal set of fea- tures and trained a prediction model using Support Vector Machine (SVM) with linear kernel. Our proposed method, named as DNA-binding Protein Prediction model using Chou’s general PseAAC (DPP-PseAAC), demon- strates superior performance compared to the state-of-the-art predictors on standard benchmark dataset. DPP-PseAAC achieves accuracy values of 93.21%, 95.91% and 77.42% for 10-fold cross-validation test, jackknife test and independent test respectively. The source code of DPP-PseAAC, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/DNABinding.