Page 273 - Ai_417_V3.0_C9_Flipbook
P. 273

In the example given below, we are comparing data gathered for measuring the weight of 12 eggs in a box in
                    grams.


















                              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.



                                                                                                Data Literacy   271
   268   269   270   271   272   273   274   275   276   277   278