Churn Prediction in Banking System Using K-Means, Lof and Cblof

Thesis Info

Access Option

External Link

Author

Irfan Ullah

Institute

Virtual University of Pakistan

Institute Type

Public

City

Lahore

Province

Punjab

Country

Pakistan

Thesis Completing Year

2019

Thesis Completion Status

Completed

Subject

Software Engineering

Language

English

Link

http://vspace.vu.edu.pk/detail.aspx?id=336

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676721027357

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Customer churn prediction helps in identifying those customers who are probable to stop a subscription, product or service, and is therefore very essential for any business. Churn prediction can be very valuable for customer retention, as it helps in predicting customers that are at risk of leaving. It is more challenging to put forth churn prediction in banking sector, as there is no contractual agreement between a customer and the bank regarding the duration of services. Loss of customers can be very costly as it is very expensive to obtain new customers in this age of competition. There are many churn prediction techniques however; K-Means, Local Outlier Factors (LOF) and Cluster-Based Local Outlier Factors (CBLOF) have not been used so far for this purpose. In this research, I have applied these techniques for customer churn prediction. The results are evaluated and analyzed using Precision (Pr), Recall (Re) and F1 measure to justify the efficiency and effectiveness of this research.

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Title	Author	Supervisor	Degree	Institute
Churn Prediction in Banking System Using K-Means, Lof and Cblof	Irfan Ullah			Virtual University of Pakistan, Lahore, Pakistan
Customer Churn Prediction in Telecommunication Using Computational Intelligence	Idris, Adnan		PhD	Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan
Using K-Means Clustering for Program Comprehension	Saddam Hussain		Mphil	Quaid-i-Azam University, Islamabad, Pakistan
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Creating Optimized Clusters Using K-Means & Artificial Neural Network	Muhammad Wasif Zia, S. Umair Haider Hamdani	Umar Nauman	BS	COMSATS University Islamabad, Islamabad, Pakistan
Use of Artificial Neural Network Ann for Calculating K in K-Means Algorithms	Muhammad Wasif Zia	Manzoor Illahi Tamimy	RCS	COMSATS University Islamabad, Islamabad, Pakistan
Improved Version of the K-Means Clustering Algorithm	Muhammad Zahid Hussain	Imran Hassan	MS	Riphah International University, Lahore, Pakistan
K-Means Based Multiple Objects Tracking With Occlusion Reasoning	Shehzad, Muhammad Imran		PhD	COMSATS University Islamabad, Islamabad, Pakistan
Churn Predictor	Saria Fatima Asif	Adeel Anjum	BSE	COMSATS University Islamabad, Islamabad, Pakistan
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