Targeting customers is a major task of bank telemarketing to send their service to customers. Now banks are using a number of data mining techniques to predict the success rate. The Decision Tree is a successful data mining technique for predicting bank telemarketing success.
The Decision Tree is a well known classifier and is simple and easy to apply. The performance of decision trees can be improved with appropriate attribute selection. In this research, ID3 decision tree technique of data mining is applied on widely used benchmark data set. The main focus of this research was on designing and implementation of a model that predicts the success of bank telemarketing using decision tree technique of data mining.
Table of Contents
INTRODUCTION
1.1 ID3 Algorithm:
1.2 Objective:
1.3 Problem Statement:
1.4 Project Scope:
1.5 Report Organization:
LITERATURE REVIEW SYSTEM DESIGN
3.1 Introduction:
3.2 Proposed Model Design:
3.2.1 Logical Design (Flow Diagram):
3.2.2 Use Case Diagram:
3.2.3 Time Sequence Diagram
3.2.4 Structural diagram:
IMPLEMENTATION
4.1 System Development Requirements:
4.1.1 Software Requirements:
4.1.2 Hardware Requirements:
4.2 Data Set Description:
4.2.1 Attribute Description:
4.3 Decision Tree:
4.3.1 Decision Tree Algorithm:
4.3.2 Pseudo code:
4.3.3 Root node:
4.3.4 For further nodes (i.e. Internal and Leaf nodes):
RESULTS
CONCLUSION
FUTURE WORK
REFERENCES
- Quote paper
- Salma Bibi (Author), Adeela Batool (Author), Fatima Mustafa (Author), 2015, Design and Implementation of a Model to Predict the Success of the Bank Telemarketing, Munich, GRIN Verlag, https://www.grin.com/document/310165
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Upload your own papers! Earn money and win an iPhone X. -
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