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You are required to undertake the following tasks:1. Problem IdentificationDownload the dataset assigned to you from the module Blackboard site.Read the data description file to learn some basic characteristics of the dataset. Make sure you have understood the nature of the data.Perform simple data exploration to get to know: the total number of instances in the dataset, the number of attributes, the data type of each attribute, the basic statistics of each attribute (value range, skewness, and kurtosis), etc. Identify and understand the business problems concerned with regard to the data.Translate the business problem to a data mining problem, and identify the associated data mining tasks to be performed.3. Data Preparation Transform the dataset into the proper format to be used by SAS® in order to carry out the required data mining task.Choose appropriate methods for data pre-processing, including dealing with missing values, tackling noisy data, conducting proper data transformation and normalisation, etc.Divide the whole dataset into several subsets to be used for model training, test and validation.4. Model BuildingPerform the data mining task you have identified in the first task using the pre-processed dataset. Each task should be completed by applying at least two different algorithms. For classifier building, for example, you may choose decision trees and artificial network networks, or decision trees and nearest-neighbour based algorithm, etc.In order to build the most appropriate and accurate models different combinations of the relevant model parameters should be considered for each of the selected algorithms.5. Model EvaluationUse the test and validation datasets created in the second task to evaluate the performance of the model produced from the data mining process. Compare the performance of different models in terms of accuracy, generalisation ability, simplicity and cost etc.Discuss how the models created can be used to address the main business problems identified in the first task.Final reportYou final report should be well-formatted as a formal report containing Title page, Table of Contents, Abstract and References. The main content of the report must as a minimum include the following information: A brief discussion on the methodology adopted for the data mining process.A discussion on what pre-processing was carried out on the given dataset and why it should be conducted.A discussion on each of the algorithms that were chosen and applied for the data mining task, and an explanation of the settings for the relevant nodes employed in SAS® Enterprise Miner.A detailed analysis and sound interpretation of the models constructed, including the performance of each model, and their applicability to address the original business problems. A reflective commentary and evaluation on the coursework. Essential statistics, screen shots, and graphs.The report should be submitted in a hard copy as well as an electronic copy.
Principles of fire risk assessment: The following principles of FRAs apply in the UK as they do in the USA: justifying assumptions, stating data sources,
Variables and Quantifiers in First-Order Logic - Artificial intelligence: Now suppose that we wanted to say that there is a meal at the Red Lion which costs only three pounds.
A 3.6-cm inside diameter stainless steel pipe is being used to convey a liquid food. The inside convective heat-transfer coefficient is 10 J/s.m2. o C. The pipe is 0.5 cm thick and
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the percolation should be visualize , and the path of the sphere also. i wanna see the relationship between the number of sphere & the percolation . change the # of sphere and show
how does a cast iron cooling curve look,effect of heating temp, duration also cooling rate
plant location
How do we classify the principal planes as minor and major principal planes?
The resistance of the strain gauges is usually measured with a Wheatstone bridge. All bridges have balancing circuits which can be apex or parallel. Both methods are equivalent.
Series of Cash Flows Most engineering economic analysis involve more than a single return occurring after the investment is made. In such cases, the present worth or the futur
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