Decision Appraisal Report 3
COLLEGE OF ENGINEERING
DEPARTMENT OF INFORMATION SYSTEMS
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:
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.
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.
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.
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.
Use headings 2 and 3 as appropriate, and use these headings if appropriate.
Change the activities and relate it to the topic which is covid-19
Proposed steps schedule is planned like in the table.
|ANN Stock Market Prediction|
|Completed Literature Review|
|Get Stock Market Data|
|Investigate and Evaluate ANN|
|Develop and Test ANN|
|Use Stock Market Models|
|Review Statistical Tests|
|Analyse and Evaluate|
Activity-on-the-node diagram represents the tasks you are performing in your project as nodes connected by arrows (Dawson, 2005)
Check the related slides and Rubric for the project report details
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