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Building a Classification Model

                 The stages to build a classification model for Coral Bleaching are as follows:

                  Stage 1   Problem Scoping
                 Coral bleaching occurs when corals lose their vibrant colours and turn white. However, there is much more to this
                 phenomenon. The primary cause of coral bleaching is climate change.
                 Coral bleaching is significant because once corals die, reef ecosystems rarely recover. With fewer surviving corals,
                 reproduction becomes difficult, leading to the deterioration of entire reef ecosystems, which are vital for both people
                 and wildlife. Early detection of coral bleaching can help mitigate global disasters and protect marine ecosystems.
                 Do you believe that such projects help raise awareness about global issues and inspire you to think of innovative
                 solutions to address them?
                 Under which Sustainable Development Goal (SDG) would you categorise coral bleaching? Share your thoughts.


                  Stage 2   Data Acquisition
                 Relevant datasets are identified, collected, and prepared for analysis. For the coral bleaching project, the dataset
                 used is from the manuscript titled "Bag of Features (BoF) Based Deep Learning Framework for Bleached
                 Corals Detection." This dataset is intended for research and experimentation to develop AI models for detecting
                 bleached corals.
                 You can download the Coral Bleaching dataset from the following link:
                 https://orangewebsupport.co.in/AI/Coral_Bleaching.zip

                 OR
                 https://bit.ly/orange_computer_vision
                 Note, Image Analytics can be added by selecting Image option from Add-ins from Options tab.
                 The steps for acquiring training dataset are as follows:
                 Step 1     Open Orange Data Mining Tool and locate the Import Image widget.
                 Step 2     Drag and drop this widget onto your workflow canvas.

                 Step 3     Right-click on the Import Image widget on the canvas.
                 Step 4     Select the Rename option.
                 Step 5     Enter the new name: Training Data to make it easier to identify the widget's purpose.
                 Step 6     Double-click the renamed Training Data icon to open its configuration window.

                 Step 7     Click on the Browse button.
                            The Select Top Level Directory dialog box appears.
                 Step 8     Navigate the location containing your training dataset.

                 Step 9     Click on the Select Folder button.
                 Step 10    Click the Close button to close the Training Data dialog box.



                  Stage 3   Data Exploration

                 Data exploration involves analysing and visualizing the dataset to understand its structure, content, and features.
                 The goal is to ensure the data is ready for modelling. The steps for data exploration are as follows:
                 Step 11    Drag and drop the Image Viewer widget onto the canvas.

                 Step 12    Connect the Training Data to the Image Viewer.

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