Reference no: EM132245475
Assignment -
In supervised machine learning, "classification" means trying to a predict a class (a discrete value among a finite set of possible discrete values) from the given input data. For example, predicting the type (class label) of an Iris flower (Setosa, Virginica, or Versicolor) from the given data of its sepal length, sepal width, petal length, and petal width.
"Regression" means trying to predict a real-number (continuous) value from the given input data. For example, predicting a house's price from the given data like its location, size, age, no. of bedrooms, no. of bathrooms, etc.
Describe a problem/application in your environment or in your field of study for which either classification or regression could be used.
You may want to include some/all of the following ideas which are relevant to the problem/application of your choice (as well as any other ideas that you want to include) in your write-up.
- Some descriptions of the problem/application - both in general technical terms (if applicable) as well as in layman's terms - so that an average person in engineering/science field could understand it.
- How important is the problem/application?
- What might be the benefits of using machine learning (classification or regression)?
- What are the possible input data (features)? How can the data be collected?
- What is the predicted output (predicted class label or value)? What are the possible further decision(s)/action(s) that might be followed based on the predicted output?
- Some examples of the data (input features and output classes/values).
- What are the possible sample size(s) (number of samples/instances) that you will be able to collect under different scenarios?
- What are the existing research works that already used machine learning for that particular problem/application (if any)?
- If there are existing works, in which aspects do you want to try differently with the goal of achieving better results than the existing ones?
Length of write-up: Min 750 words; Max 3,000 words.
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