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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?
272 Touchpad Artificial Intelligence (Ver. 3.0)-IX

