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Step 4: Data Sampler


               Stage 4   Modelling-  Modelling  in  the  AI  project  cycle  is  the  process  of  selecting  the  appropriate  machine
                         learning  algorithm,  training  it  with  the  data,  and  using  the  trained  model  to  make  predictions  or
                         classifications. It involves understanding the data, choosing the right type of model, and preparing it
                         for real-world use.
                         For this stage, we will train data in Orange Data Mining as the given step:



                               Step 5: Train Model

               Stage 5   Evaluation- After building and training your model, you need to assess its performance on new or
                         unseen data to ensure it is reliable and accurate. Evaluation helps in deciding whether to deploy the
                         model, adjust it, or try a different approach.
                         For this stage, we will evaluate the dataset in Orange Data Mining by following the given steps:


                               Step 6: Evaluate Model


                               Step 7: Predictions


               Stage 6   Deployment- It involves implementing the trained model into a real-world environment. It includes
                         integrating the AI system with existing infrastructure, ensuring scalability, and monitoring performance.
                         Continuous evaluation and retraining are necessary to maintain accuracy. Deployment also involves
                         handling security, compliance, and user feedback for improvements.
              Orange Data Mining Widgets

              Let us discuss about some commonly used widgets of Orange Data Mining.

              Data Widgets

              Data widgets are essential components that help you interact with, manipulate,
              and visualise your data. These widgets allow you to load datasets, explore data,
              preprocess it, and apply machine learning algorithms. Some of these Data Widgets
              are:
                 • File Widget: It is used to load and import data into the platform.
                 • CSV File Import Widget:  It  is  used  to  import  datasets  stored  in  Comma-
                Separated Values (CSV) files. CSV is a common data format used to store tabular
                data, making it an essential widget for users who work with this file type.
                 • Dataset Widget:  It  is  used  to  manage  and  view  datasets  within  Orange.  It
                provides a way to inspect the loaded data, check for inconsistencies, and perform basic data manipulations.
                 • Data Table Widget: It allows users to interact with their data in a structured and accessible way, displaying
                datasets in tabular form.
                 • Data Info Widget: It gives an overview of the dataset’s properties, helping users understand the data before
                applying machine learning algorithms or further data processing.






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