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Teamwork Activity:

                    • Provide the brainstorming solutions for the problem statement.
                 Activity: Data Features

                    • Identifying the possible data features affecting the problem.
                 Activity: System Maps

                    • Creating system maps considering data features identified.




                    Data Acquisition


            Data acquisition means collecting raw facts, figures or statistics from relevant sources either for reference or
            for analysis needed for AI projects. In the process of making an AI project cycle, data acquisition is the second
            stage. It is a time-consuming process as different types of data are scattered everywhere and we need to focus
            on data relevant to our needs.


                    What is Data?

            Data is a piece of raw information or facts and statistics collected together for reference or analysis. They are
            raw facts that need to be processed to get meaningful information. Whenever we want the AI project to be able
            to predict an output, we need to train it with a data set first. Data plays an important part of an AI project as it
            creates the base on which the AI project is built.


            Types of Data

            There are two types of data:
               • Training Data:  It is data on which we train our AI project model. It is basically to fit the parameters of the
              project for the model. In training data, the output is available to the model.
               • Testing Data: It is used to check the performance of an AI model. In testing data, the data is not seen for which
              the predictions have to be made.

            For example, if we want to prepare an AI model to predict the school average of students in board examination,
            we will feed the marks obtained by students in board examination in the previous years, this will be treated as
            training data. Once the model is ready, it will predict the school average for the coming year. Now when we are
            testing it, we feed the different data set and that is the testing data.


            Data Features

            In the data acquisition stage, it is very important that the data we provide to an AI project is relevant. How do
            we know what data to be used in a problem scoping?
               • We need to visualize the factors that affect the problem statement, for which we need to extract the data
              features.
               • We need to find out the parameters that will affect the problem statement directly or indirectly.





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