PROJECT: 1) By using one arff dataset you should apply three different classification algorithm and you should compare the performances of those classification algorithms by using different parameters of the corresponding algorithms. While you are classifying the dataset use percentage split and k-fold cross validation to compare the results. You should explain all the attributes in detail and give brief information about the classifiers you are working with. Lastly use some items with unknown class labels and determine their classes with Weka. 2) Also apply the Appriori algorithm on another dataset that you choose. Explain all the rules and make some comments about the rules that you will find.

Find out how and when to use supertype/subtype connections in your data models.

Create an ER diagram that correctly describes the database for exams at Campus in Bäcksvängen in accordance with the following description: (Own assumptions must be clearly described.) Staff for various functions are involved in the exams. All staff have a full name and a unique staff code. Some people are examiners on one or more courses. A course has one name and one unique ladok code. For a course, you can add up its points based on the associated elements. One part is part of a course (for example an exam, a laboratory or project). A course can have several different parts. An element has a name, a description, a torque code, one point and two or more grades (eg: U, G, VG). An element can only belong to one course. Since the database is to process the exams, it is obvious that a part like described as an examination has an examination opportunity that is identified, among other things, by the date, start time, end time and that it is possible to calculate total writing time. A…

To begin with, why is it necessary to divide the data into training and validation sets? What is the training set going to be utilized for? The function of the validation set is still a mystery.

Figuring out whether it's suitable to use supertypes and subtypes in data modeling is an important skill?

You can only evaluate a model based on how well it performs on real-world data. what do you want to discuss in depth, expound on? Explain?

Please! can you help me doing Conceptual Analysis for this scenario. I have attached another screenshot to followup with data

Predictive modeling: Task: Ambulance Demand Data Generating Process: for New York City   Dates of Coverage {Please identify the specific dates that will be used for this dataset in your modeling effort other than the City Health Department's Emergency Medical Services Division.   }   Frequency of data collection {how often is the data collected?  After every incident? Daily? Yearly?}   Agency / Organization collecting the data {who specifically is collecting the data?  Please avoid using general references like “government” or “police}   Original Unit of Analysis {What is the original unit of analysis for the data as provided?  Calls for service?  Census tracts? Cities?}   Transformed Unit of Analysis{i.e. are you modifying the call data to support your model? Hint: if you are doing “demand” model you will be aggregating the data.}   Data Generation Description{here, I want you in your own words to describe how you think the data was generated.  Think 2-3 sentences.}   Data…