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3.  Since the data collected is big data, you can try to represent it graphically by using graphs, databases, flow
           charts, maps, etc. This makes it easier for you to interpret the patterns that the acquired data follows.

        4.  After exploring the pattern, the next step is to decide upon the type of model to build to achieve the suitable
           output.

        5.  Test the selected model and figure out the most efficient one.
        6.  Developing the algorithm on the selected model.

        7.  After the completion of the model, it needs to be now tested on some newly fetched data. The results will be
           helpful in evaluating the model and improving it.

        8.  Project cycle is now complete and ready to be deployed.


                 Setting Goals for an AI Project

        Problem scoping is the term used to define the process of selecting a problem which we might want to solve
        using AI knowledge. Identifying a problem and then having a vision to solve it is called problem scoping.

        Let us start scoping a problem. Look around, we are surrounded by problems, big or small. At times we don’t
        feel the problem, as we are so used to them. Look around and select a theme that interests you the most from
        the following diagram:




                        Environment          Travel        Entertainment      Education
                                            Tourism





                                    Cyber            Women             Social           Security
                                   security          Safety            Welfare






                           Digital        Agriculture        Research           Health
                           Literacy





                                 Infrastructure     Transport          Traffic         Disability






        You can select any one from the above diagram. Further you can also select from the 17 Sustainable Development
        Goals.








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