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Task #Creativity & Innovativeness
Open a website https://www.kaggle.com. Kaggle is like a playground for data enthusiasts!
It’s an online platform where people from all over the world come together to play with data,
learn new things, and compete in data science competitions.
Do this
The Titanic competition on Kaggle is a classic and beginner-friendly challenge that introduces you to the
basics of data analysis and machine learning. The goal is to predict whether a passenger survived the Titanic
shipwreck based on factors like age, gender, ticket class, and more.
Explore:
Kaggle provides tutorials and notebooks to help you get started with the Titanic competition. You can find
them under the "Notebooks" tab on the competition page.
Features of Data
Data features are also called the characteristics or properties of the data. They describe each piece of information
in a dataset. They define what each data point represents and help us make sense of the data. For example,
● In a table of student records, features could include things like the student’s name, age, or grade.
● In a photo dataset, features might include properties like the colour present in each image, the resolution,
brightness, or the presence of certain objects.
These features help us understand and analyse the data. In AI models, we need two types of features: Independent
and Dependent.
Independent
Independent variables (sometimes called predictor variables) are those that are used to generate predictions
about or to account for the variation in the dependent variable (the goal). These features are the input to the
model—they’re the information we provide to make predictions.
Dependent
The dependent variable is the variable about which predictions or explanations are being sought. These features
are the outputs or results of the model—they’re what we’re trying to predict. For example, imagine we’re
building an AI model to predict students’ final exam grades based on various factors.
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.
Data Literacy 167

