The data can be easily visualized using the following block of code written in python 3. The below-mentioned code represents the directories of the downloaded data. dir_data = “dataset1/” dir_seg = dir_data + “/annotations_prepped_train/” dir_img = dir_data + “/images_prepped_tr...
Furthermore, all of the variables for predictive model were standardized to ensure them on the same scale by sklearn.preprocessing tools in Python (https://scikitlearn.org/stable/modules/preprocessing.html). Taking prediction at the lead time of 3 days as an example, values of elements in X1...
Throughout the experiment, a number of libraries were used: for data visualization, Matplotlib, Seaborn, and Ecg-plot; for data processing, NumPy and Pandas; and for modeling, TensorFlow and Keras. For the assessment of the models, Sklearn was used, along with other libraries like SciPy and...