Discussion on Covid-19 using machine learning

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Write some parts of the report which are highlighted by red color in the file belowThe topic : Covid-19 using machine learning

 

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EFFAT UNIVERSITY

COLLEGE OF ENGINEERING

DEPARTMENT OF INFORMATION SYSTEMS

 

 

 

 

Table of Contents

ABSTRACT.. 4

  1. INTRODUCTION/ BACKGROUND AND SIGNIFICANCE.. 5

1.1.         Introduction/background/overview/significance. 5

1.2.         Related research/Literature Review.. 5

1.3.         Motivation.. 5

1.4.         Referencing.. 5

  1. PROBLEM STATEMENT.. 6

2.1.         Overview.. 6

2.2.         Research Question/Hypothesis. 6

  1. AIMS AND OBJECTIVES. 7

3.1.         Aim.. 7

3.2.         Objectives. 7

  1. EXPECTED OUTCOMES/DELIVERABLES. 8
  2. STEPS TO BE TAKEN IN THE INVESTIGATION… 9

Phase 1: Starting the project. 9

Phase 2: Development of an analytical framework for efficient bank marketing.. 9

Phase 3: Development of the efficient classifier. 10

Phase 4: Verification and optimization of the model. 10

  1. RESEARCH DESIGN AND METHODS. 11

6.1.         Overview.. 11

6.2.         Population and Study Sample. 11

6.3.         Sample Size and Selection of Sample. 11

6.4.         Sources of Data. 11

6.5.         Collection of Data. 11

6.6.         Exposure Assessment. 11

6.7.         Data Management. 11

6.8.         Data Analysis Strategies. 11

6.9.         Research Design and Prototype. 11

6.10.      Clients/Stakeholders. 11

6.11.       Methods. 11

  1. RESULTS. 12
  2. DISCUSSION… 12
  3. CONCLUSION… 12
  4. ETHICS AND HUMAN SUBJECTS ISSUES. 12
  5. TIMEFRAMES/PROJECT PLAN/MILESTONES. 12

11.1.       Time Schedule (Ghantt Chart) 13

11.2.       Activity Sequencing.. 14

  1. STRENGTHS AND WEAKNESSES OF THE STUDY.. 15

12.1.      Strengths. 15

12.2.      Weaknesses. 15

  1. BUDGET.. 16

13.1.      Resource requirements. 16

13.2.      Budget/Funding.. 17

  1. REFERENCES. 18
  2. APPENDICES. 19

Appendix 1: Questionnaire. 19

Appendix 2: 20

 

 Related research/Literature Review

 

1.      STEPS TO BE TAKEN IN THE INVESTIGATION

This project focuses on design and development of an intelligent bank marketing system for a better bank marketing. The project will be carried out in four phases with significant milestone at the end of each phase. In the first phase, we will download and prepare the bank marketing data. Then we will search for information to have clear knowledge about the bank marketing problem and its solutions.

The second phase of the project would concentrate on an analytical framework to analyze the performance of different data mining algorithms that would be developed identifying the main components of the bank marketing.

The third phase would concentrate on the design and the implementation of efficient, reliable and robust data mining tool that achieves a better classification accuracy. Moreover, an ensemble of classifiers would be developed to increase classification performance of an efficient bank marketing.

The fourth and final phase would concentrate on preparing and submitting a conference paper. More details on the different phases are as follows:

Phase 1: Starting the project

First of all, we will download and prepare the bank marketing data for the implementation with WEKA data mining tool. Since the data is unbalanced we try to balance the by using SMOTE technique to have an efficient and accurate classification. Then we will check previous studies done on the same field to have clear knowledge about the bank marketing problem and its solutions and applied methods before.

Phase 2: Development of an analytical framework for efficient bank marketing

In this phase, efficient, intelligent and robust bank marketing system will be developed. The system will employ the bank marketing data with robust attribute selection. The extracted attributes will be employed by means of the Data mining techniques for intelligent and more efficient bank marketing environment. This platform will serve to design and develop application oriented bank marketing system. The downloaded data can be processed by employing a variety of feature extraction and attribute selection techniques. Then comparison of different feature extraction, attribute selection techniques, and data mining algorithms for bank marketing will be made. The employed feature extraction and attribute selection techniques should be robust and intelligent, so that a reliable bank marketing can be realized. The proposed platform will be used to understand and implement the intelligent system for a Bank Marketing. The finding of robust features and attributes of bank marketing data would be very meaningful for determining important insights into the effects of various parameters on the performance of bank marketing system to enlighten which feature extraction and attribute selection a is the most effective and less time consuming.

A classification problem is referred to as imbalanced when the instances in one or several classes, known as the majority classes, out number the instances of the other classes, called the minority classes. The synthetic minority over-sampling technique (SMOTE) (Chawla et al., 2002) is a well acknowledged over-sampling method. In the SMOTE, instead of mere data oriented duplicating, the positive classis over-sampled by creating synthetic instances in the feature space formed by the positive instances and their K-nearest neighbours.

 

In this step, feature extraction, attribute selection and data mining algorithms will be implemented as off-line. The feature extraction and attribute selection methods are in the set of data processing tools which extract features from the bank marketing data. Feature extraction and attribute selection are also one of the most important steps in data classification.  It is highly effective technique in selection of attributes and is frequently applied to complex, high dimensional, multivariate data. When the features are not appropriate for the given classification problem, obtained performances will be unsatisfactory. In this case, even the classification algorithm is optimally determined for the problem, because of the improper features/attributes; the algorithm cannot generate high performance. Therefore, it is mandatory to find and extract suitable features from the raw data to be able to obtain good classification results. Many feature extraction and attribute selection techniques will be applied. These are CFS Subset Evaluator, and Infogain Attribute Evaluator etc. will be applied.

Phase 3: Development of the efficient classifier

The marketing data data is of multi-dimensional nature. Therefore, it is difficult to find a robust feature extraction, attribute selection and data mining algorithm for Bank marketing. Data mining algorithms have ability to distinguish different type of data. In this phase the focus will be to make a comparison of different data mining algorithms in the field of Bank marketing. The outcome will be the analysis and choice of most appropriate sets of features extraction, attribute selection and data mining techniques, for the Bank marketing. After applying different feature extraction/attribute selection algorithms, the existing data mining algorithms (such as ANN, k-NN, SVM, decision tree algorithms, etc.) capable of dealing with network data will be applied, implemented and tested. For this purpose, various classification schemes for a particular data classification task will be developed. The aim of using data mining techniques is to make a better Bank marketing. Even though one of the algorithms would produce the best overall performance, ensemble classifier approach, where the idea is to consider more than one classification scheme can give better classification accuracy. Classifier ensembles are multiple classifier systems trained on different data or feature subsets, will be used to get better performance and accuracy.

Phase 4: Verification and optimization of the model

The fourth and final phase would concentrate on preparing and submitting a conference paper. Here, the performance metrics such as Area under ROC curve, F-measure, kappa statistic and total classification accuracy would be quantified with the help of WEKA software. Obtained results from experimentation would then be used to verify the accuracy, reliability and robustness of the developed models and would provide feedback for improvement and fine-tuning the models in phase 1, phase 2 and phase 3. The obtained results of the ensemble classifiers would be benchmarked with classical single classifiers. Moreover efficient, intelligent and robust methods will be achieved for bank marketing.

2.     RESEARCH DESIGN AND METHODS

2.1.             Overview

Use headings 2 and 3 as appropriate, and use these headings if appropriate.

 

2.2.            Population and Study Sample

 

2.3.            Sample Size and Selection of Sample

 

2.4.            Sources of Data

 

2.5.            Collection of Data

 

2.6.            Exposure Assessment

 

2.7.            Data Management

 

2.8.            Data Analysis Strategies

2.9.            Research Design and Prototype

  • Use case Diagrams
  • Database Diagrams
  • Relation Diagrams

 

2.10.     Clients/Stakeholders

 

3.     RESULTS

  • Give the tables and results of your research project one by one
  • Give the explanation on the results.

4.     DISCUSSION

  • Give discussion on the results of your research project
  • Give comments on the results.

5.     CONCLUSION

  • Conclude and summarize the results of your research project.

6.     ETHICS AND HUMAN SUBJECTS ISSUES

7.     TIMEFRAMES/PROJECT PLAN/MILESTONES

7.1.             Time Schedule (Ghantt Chart)

Change the activities and relate  it to the topic which is covid-19

Proposed steps schedule is planned like in the table.

 

Activity Months
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
ANN Stock Market Prediction                                                                        
Literature Survey                                                                        
Literature Search                                                                        
Literature Review                                                                        
Completed Literature Review                                                                        
Get Stock Market Data                                                                        
Develop ANN                                                                        
Investigate and Evaluate ANN                                                                        
Design ANN                                                                        
Develop and Test ANN                                                                        
Evaluation                                                                        
Train ANN                                                                        
Use Stock Market Models                                                                        
Analysis                                                                        
Review Statistical Tests                                                                        
Analyse and Evaluate                                                                        
Complete Report                                                                        
Project Completed                                                                        

 

7.2.            Activity Sequencing

Activity-on-the-node diagram represents the tasks you are performing in your project as nodes connected by arrows (Dawson, 2005)

 

8.     STRENGTHS AND WEAKNESSES OF THE STUDY

8.1.          Strengths

8.2.          Weaknesses

 

9.             REFERENCES

 

 

 

 

 

 

 

 

 

10. APPENDICES

Appendix 1: Questionnaire

 

 

Appendix 2:

 

Check the related slides and Rubric for the project report details

 

 

 

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