Knowledge discovery in data

To extract useful information from the given data, we initially collect the raw data that is to be used to learn the patterns. Next, we apply the data preprocessing techniques to remove the noise from the data. Further more, we extract the important features from the data, which are representative of the data, to develop the model. Feature extraction is the most crucial step for machine learning algorithms to work effectively. A good feature must be informative and discriminating for the machine learning algorithms. Feature selection techniques are used to remove the irrelevant, redundant, and noisy features. Further more, the prominent features are fed to the machine learning algorithms to learn the patterns in the data. Finally, we apply the evaluation measure to judge the performance of the developed model and use visualization techniques to visualize...