AN EFFICIENT CHURN MINING USING PARTICLE
SWARM BASED BOOSTED TREE
Sarbinder Pal Singh, Kiranbir Kaur, Sandeep Sharma Department of computer engineering and technology Guru Nanak Dev University, Amritsar, Punjab, India.
ABSTRACT
Churn Prediction has been major research problem with the growth of market development as customers asset more valuable persons for growth of company. The occurrence of churn customers is one of the crucial problems for the growth of a company, as it acquires higher costs. The task of churn prediction is to identify the customers who are pretending to shift from one company to another. As in the competitive environment, it becomes necessary to focus on retaining churn customers as well as attracting new customers. Various algorithms of Data Mining have been used for making distinguish between customers into loyal and churn, so that appropriate steps can be taken into consideration in order to retain the churn customers to the company as customers are more valuable to the survival and development of the company. The proposed Hybrid approach is an integration of two techniques named J48 and LogitBoost that have feature of PSO (Particle Swarm Optimization), provides better and accurate results in the prediction of churn customers. PSO is used to search the best solution with two best values named pbst and gbst by using iteration with initial velocity and positions. The experiment results reveal good distinction of churn and loyal customers from the
As demands keep on changing and new technologies arrive, the knowledge of vast numbers of platforms and programming languages is also required. With adequate knowledge I will be able to give new ideas on which programming platform and programming language to use so as to provide improved IT systems and solutions.
College of Information Systems & Technology IT/210 Version 5 Fundamentals of Programming With Algorithms and Logic
Many marketers agree that by reducing customer’s to competitors defection by only 5 per cent, companies can improve profits by anywhere from 25 per cent to 95 per cent. There is no question this will be a great advantage and could benefit any retailer. It is for this very reason why consumer’s relationship marketing and using tools such as loyalty scheme is
Having data is not valuable but using data is. Analytic insights are changing the way corporates strategize and also redefining customer expectations. Analytics is the new differentiator between success and failure in the cut throat e-commerce and internet services based industry. The huge proportions of data generated from the increasing number of smart phones, the social networks and the ever more penetrating internet are automating customer centric marketing and other services. The idea is to predict what a customer may want to buy even before the customer realizes what they need. The techniques to achieve these results are broadly classified as Predictive Analytics.
Kudler is looking for ways to increase sales and customer satisfaction. To achieve this goal Kudler will use data mining tools to predict future trends and behaviors to allow them to make proactive, knowledge-driven decisions. Kudler’s marketing director has access to information about all of its customers: their age, ethnicity, demographics, and shopping habits. The starting point will be a data warehouse containing a combination of internal data tracking all customers contact coupled with external market data
Customer retention/attrition is estimated by the Company to arrive at the adjusted revenue, however, no support is provided for the 75% of probability of retention.(Exhibit XI) Retention rate can be expected by conducting statistical analysis of historical customer turnover and revenue growth rates. When historical customer data of sufficient quality is not available, it may be necessary to rely on management estimates or industry data. Probability of retention is
Am indebted to Oxford university course on computing for the following, which I reproduce without a full understanding.
There are many ways that organizations are able to leverage the power of predictive analytics. One of the more popular ways is through their current SAP software. SAP has been one of the premier enterprise software tools and now has made its way into the predictive analytics side. The ability of the software to work with the current data environment is able to save both time and money. The current software has the capability to perform five forms of analysis including: Time series analysis, classification analysis, cluster analysis, association analysis, and outlier analysis. In the time series, analysis of historical patterns are used in the known data to make predictions about future values (MacGregor). Classification analysis tries to predict a variable using the data of other variables that you believe affect the values of the variable that we are trying to predict. Cluster Analysis groups the data into clusters or segments that have similar attributes which is often useful when trying to understand subsets of larger data. Association analysis looks at the association of different things, with an example being if a customer purchases this product what other product are they likely to buy. Finally an
In the field of computer information, there is a vast amount of information that is used for operations. This information must be stored somewhere in order to be used in the future, and for programs to use
Makerere University, Faculty of Computing and Information Technology, P.O. Box 7062, Kampala, Uganda, East Africa jlubeg@cit.mak.ac.ug 2 Department of Computer Science, University of Reading, P.O. Box 225, Whiteknights, Reading, Berkshire, RG6 6AY, United Kingdom shirley.williams@reading.ac.uk
Through using R, we have created a set of models that help to predict the likelihood of a customer churning. By working together in the analysis stage we were able to ‘bounce’ ideas off one another and grasp a further understanding of the data and customer churn. We created two different models: modelling for predictive accuracy and modelling for explanatory power.
It is designed with cryptographic protocol programming language that makes it work efficiently with cryptographic protocols network [7].
The Ultimate Guide: - Sumitabha Das 3. PC Software: - V.K. Jain “O Level” Computer Organization & Architecture Code: COMP-715 Credits: 4(3+1+0) UNIT-1 Introduction: Types of computers: Analog, Digital and Hybrid Computers, Modern Digital Computer, Number systems-
R.Vikram, Asst. Professor in CSE, GNITC, Hyderabad, A.P., India, captureratan@gmail.com K.Vikram, Asst. Professor in CSE, GNITC, Hyderabad, A.P., India, vikramkalvala84@gmail.com Patil ManikRao, Asst. Professor in CSE, GNITC, Hyderabad, A.P., India, manikvpatil@gmail.com
3. Presents the software description. It explains the implementation of the project using PIC C Compiler software.