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Task 1: Descriptive Analysis Report In this task, you are required to read the following journal articles via APIC library ( ) and write a discussion report...

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Task 1: Descriptive Analysis Report In this task, you are required to read the following journal articles via APIC library ( ) and write a discussion report based on the points below: Wang, Y XXXXXXXXXXArtificial intelligence in educational leadership: a symbiotic role of human-artificial intelligence decision-making. Journal of Educational Administration, 59(3), 256–270. XXXXXXXXXXSilver, M. S XXXXXXXXXXDescriptive analysis for computer-based decision support. Operations Research, 36(6), 904–916. XXXXXXXXXXSeydel, J XXXXXXXXXXData envelopment analysis for decision support. Industrial Management & Data Systems, 106(1), 81–95. XXXXXXXXXX • Investigating and discuss the theoretic foundations of decision support systems (DSS). • Identify and discuss the major issue of descriptive analysis for DSS. • Identify and discuss the major process of descriptive analysis development with a means for describing and differentiating DSS. • Identify and discuss some of prescriptive decision support tools. • Support your response with proper examples and references from at least three journal papers. • The report follows a referencing style that complies with the APA style and the in-text citations. The recommended word length for this task is 1700 to 2000 words.
Answered 48 days After Mar 14, 2022


Deblina answered on Mar 17 2022
80 Votes
Table of Contents
Introduction    3
Predictive Analysis Methods for Data Mining    3
Association    3
Clustering    4
Classification    4
Numeric Prediction    5
Machine Learning Methods    5
Naïve Bayes    5
Logistic Regression    5
Support Vector Machine    6
Random Forest    6
KNN    7
Predictive Analysis Tools    7
Open-Source Software    7
Weka Tool    8
Conclusion    8
References    10
Predictive analytics focuses on identifying the optimum likelihood of the event based on a set of data. In a literary context, the predictive analysis uses different methods and tools of estimation and forecast. The process requires the use of large sets of data mathematical algorithms and learning technology which provides an effective evaluation of the structure and interpretation of the data. Predictive analysis in a more rigorous sense points out the basic aspects and characteristics of a given dataset for deriving effective predictions and direct decisions. These particular processes of analytics are contemplated by the use of software which is refe
ed to as data mining. This refers to the process of discovering effective patterns and trends in large data sets. On the other hand, predictive analytics refers to the process of extraction of viable information from the large data phase and making eventful predictions and estimates with efficient future outcomes by analyzing the historical data.
This report focuses on exploring different predictive analytics methods for data mining that are used in the particular article, " Heart Patients Data Set Analysis- using Weka tool" by Maryum. The data mining methods and the predictive analysis tools used in this article were used to make decision-making and visualization methods for the given sets of data. This particular article was focused on providing effective analysis and information to the health department. It also contemplates various algorithms that are effective to considered the historical patterns on trends of data affecting the decisions of the health department.
Predictive Analysis Methods for Data Mining
Predictive Analytics is a part of advanced analytics that is used for making predictions about unknown future events. It uses numerous techniques like data mining and predictive modeling that analysis the data for making effective predictions and business decisions. It captures the relationships with the various variables and associated factors that access the risk with a particular set of conditions (Bendre & Manthalkar, 2019). The predictive modeling focuses on statistical modeling techniques that are effectively used in the discussed article that relates to the datasets relating to the heart disease that will be effectively used for making decisions by the health care department.
Association methods of predictive analytics in data mining refer to the probability of relationships between the data items within the large data sets in the various databases. The association methods of data mining outline the applications and discovery of co
elations among the variables in the transactional data or medical data as the case may be. In the application of data mining methods association rule deliberately focuses on concu
ency and co
elations that help to explain the patterns in data with effective information regarding the relational databases (Doleck et al., 2019). In a more contemplative manner, it can be effectively mentioned that the association methods of data mining are effectively used in the medical field to consider when making diagnoses by the doctors and determine the condition of the probability of a persistent issue of the patients. In most cases, the association rules are implemented by considering the condition of the probability of a given illness and comparing them with the other associated variables in the data recorded in the past cases. The association methods of data mining involve the extensive use of machine learning models to identify the associative relationship between the various variables in the model by considering an antecedent and a consequent in the given data sets.
Cluster analysis is an algorithm-based data mining method that are focus on analyzing a group of data points that can be comprised together into a set of clusters in which the data points are classified on the basis of certain similar characteristics. In a given data set when the data is grouped into different groups by combining the elements on the basis of their similarity, it forms to be a cluster. In a literary sense, it refers to a collection of similar data that are grouped together such that the data is a
anged in an appropriate pattern that forms clusters. In this data mining method, the associated data are identified and the data are grouped accordingly on the basis of characteristics (Guryanova et al., 2019). This helps to classify the data into several subsets and is considered to be on supervised machine learning-based algorithm that comprises various data points into clusters. This is a popular data mining method that involves assigning different data points to different categories which are refe
ed to as clusters.
Classification is a method of data mining that has been extensively used for the purpose of predictive analytics in which the probability is calculated on the basis of the particular category of the data point. In this aspect, the data points are classified into two classes- binary classification and multiclass classification problems. This particular data mining...

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