Major advances in this field can result from advances in learning (such as ), computer hardware, and, less-intuitively, the availability of high-quality training datasets. Datasets are an integral part of the field of machine learning. These are used for research and have been cited in academic journals.To load the German credit data, available as credit-g.arff in the Weka 3.8 distribution. I sincerely hope someone will come to my rescue. Weka Explorer Interface with the Iris dataset loaded. The resultant prediction is then evaluated against the original class labels of the test dataset to find the accuracy of the model.Open the data/iris.arff Dataset. Once we have the fitted model, we can apply the model to the test dataset to predict the values of our response variable. Then we train our model on the training dataset. We split the data into training and test set. In this step, we build our classification model. The resultant dataset with the reduced number of features is ready for use by the classification algorithms. This can be done by finding the correlation between various attributes. We can then safely remove one of the two attributes. We also need to check if there are any redundant information represented using two attributes. Such features should be removed from the dataset. The raw data we have may contain many features/independent variables, and there will be many features which will be quite useless from the viewpoint of predicting the response variable. For example, we may want to remove the outliers, remove or change imputations (missing values, and so on). The purpose of preprocessing is to make your raw data suitable for the data science algorithms. For this case study, we are using the German Credit Scoring Data Set in the numeric format which contains information about 21 attributes of 1000 loans. The first step is to get the dataset that we will use for building the model. These steps are explained below: Step 1 – Data Selection We will preform various steps in building our predictive model.
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