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The independent features would include:
                 • Study hours: The number of hours a student spends studying.
                 • Attendance: Whether the student attended classes regularly or not.
                 • Previous grades: The grades the student received in previous exams.

                 • Extracurricular activities: Participation in extracurricular activities, such as sports or clubs.
              The dependent feature, in this case, would be:
                 • The final exam grade—the outcome or prediction that the model gives us.

              Together, they help us understand and improve student outcomes using AI-driven predictions.

                                       Input                 Processing                Output



                                     2 hr, 80%,
                                    B Badminton                 AI                        A

                                                           Model
                                     3 hr, 90%,
                                      A None                                             A+





                 • The independent variable is the cause. Its value is independent of other variables in your study.

                 • The dependent variable is the effect. Its value depends on changes in the independent variable.


              Data Preprocessing
              Data preprocessing is an essential phase in the machine learning process that prepares datasets for effective
              machine learning applications. It is the process of detecting and correcting (or removing) corrupt or inaccurate
              records from a dataset. It includes multiple processes to clean, transform, reduce, integrate, and normalise data.

                                                                                   Data
                                               Data              Data                                Feature
                         Data Cleaning                                          Integration &
                                           Transformation      Reduction                             Selection
                                                                               Normalisation
                       Data Processing and Data Interpretation


              Data processing means preparing and analysing raw information to train models or
              predict outcomes, including tasks like cleaning and training. Data interpretation in
              AI involves analysing model outputs to understand patterns, refine models, and make
              informed decisions.

              Observe and answer the following:
              ●  How many big  lollipops are there in the given picture?
              ●   If each large lollipop represents 5 units of sweetness, how much total sweetness do
                  the three lollipops represent?

              ●  Among the small round candies, which colour appears most frequently?
              ●  What is the ratio of pink round candies to blue round candies?


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