University of Science and Technology

Faculty of Computer Science and Information Technology

Postgraduate Studies

Master of Information Systems Batch (4)

Thesis submitted in Partial Fulfillment of the Requirements for the Degree of Master of Information Systems

Implementation of Naïve Bayes algorithm for building churn prediction model for telecommunication company

Prepared By:

Lina Ahmed Mohammed Nour Ali

Supervised By:

Dr. Atif Ali

December 2014

I

م اي ( يك ل و ع ل ت م ي سوال منك م ر ا فيك ن سل ا ار م ا م م ك م ل ع ي و ة م ك الح و اب ت الكـ م ك م ل ع ي و م يك ك ز ي ا و ن ات ك

) )151( ون م ل ع وا ت ون ك ت م ل

صدق اهللا العظيم

151 سورة البقرة االية

II

Dedication

Having this opportunity of presenting this humble work, we

would like to dedicate it

To our parents …

To our brothers and sisters …

To our great teaching staff …

To all our friends …

III

Abstract

In telecommunication companies churn means customers’ decision to

move from one service provider to another. The competition environment

in telecom companies' makes their aim is to maintains their customers

who are likelihood to leave and earns their satisfaction, so to avoid the

problem of churn they need churn predictive models. Data mining

techniques can be used to build churn prediction model for

telecommunication companies to identify churner and non-churner

customers because it can extract the predictive information from large

databases. In this study the churn prediction model was built which is

contains of four steps, the used dataset contains of Customer behavior

data and some demographics data, and it has 9 attributes for each

customer and there is 300 instances, the model was implemented by

Naïve bayes algorithm using WEKA 3.7 software and the result which

obtained that we have 147 customers correctly predicted as non-churner

from 159 which is (92.5%) and 139 customers correctly predicted as

churner from 141 which is (98.5%) with high accuracy which was 95.3%.

IV

المستخلص

باالنتقال من مقدم خدمة الى مقدم العمیل في شركات االتصال فقدان العمالء یعني قرار

المحافظة على ھو الشركات ھدف ھذه جعلت في شركات االتصال بیئة المنافسة ان .خدمة اخر

مشكلة فقدان العمالء فإن من و للتخلص المتوقع ان یغادروا الشركة و اكتساب رضائھم ، العمالء

یمكن التنقیب عن البیانات ان عملیة .فقدان العمالءه الشركات تحتاج الى نماذج تنبؤ لھذ

لشركات االتصال لتعریف العمالء الذین قد نموذج التنبء بفقدان العمالء بناءاستخدامھا في

نموذج التنبؤ بفقدان العمالء النھا یمكن ان تفید كثیرا في الذین لن یغادروا یغادرون الشركة و

نموذج تم بناء ھذه الدراسة في. باستخالص المعلومات التنبوئیة من قواعد البیانات الكبیرة تقوم

توي علىتحالبیانات المستخدمة، مجموعة ، حتوي على اربع خطواتالذي إفقدان العمالء بتنبؤ

صفات 9 على تحتوي انھا كما شخصیة ،البیانات ال بعض في الشركة سلوك العمالء بیانات عن

بإستخدام Naïve Bayes، تم تطبیق النموذج بخوارزمیة ال سجل 300 ھالكل من لكل عمیل

عمیل تم 159من 147 و النتائج التي تم الحصول علیھا ھو انھ یوجد WEKAبرنامج

تنبؤ صحیحة عبارة عن تصنیفھم تصنیف صحیح كعمالء لن تفقدھم الشركة و التي تمثل نسبة

عمیل تم تصنیفھم تصنیف صحیح كعمالء قد تفقدھم الشركة و التي 141من 139و % 92.5

. %95.3 عالیة عبارة عنوذلك بكفاءة % 98.5 صحیحة عبارة عن نسبة تنبؤتمثل

V

Contents

Content Page No

االیة

I

Dedication

II

Abstract

III

المستخلص

IV

Contents

V

List of Figures

VIII

List of Tables

IX

Chapter One Overview

1.1 Introduction

1

1.2 Research problem 1

1.3 Research objective 1

1.4 Research methodology 2

1.5 Research structure 3

Chapter Two Literature Riverview

2.1 Introduction 4

2.2 Churn

4

2.2.1 Customer churn

4

2.2.1.1 Voluntary churner 4

VI

2.2.1.2 Involuntary 4

2.3 Data mining 5

2.3.1 Unsupervised models 6

2.3.2 Supervised / Predictive Models 6

2.4 Data Mining Applications 6

2.5 Data Mining Techniques 7

2.5.1 Estimation 7

2.5.2 Regression Analysis 7

2.5.3 Clustering 8

2.5.4 Association 8

2.5.5 Visualization 8

2.5.6 Classification & Prediction 8

2.5.6.1 Building the Classifier or Model 9

2.5.6.2 Using classifier for classification 9

3.5.6.3 Classification Techniques 9

2.6 Related works 13

Chapter Three Methodology

3.1 Predictive Models 14

3.2 Research scenario 14

3.4 The Proposed model 14

3.4.1 Identify Problem domain 15

3.4.2 Data selection 16

VII

3.4.3 Data preparation 18

3.4.4 Data mining technique (Classification, Naïve Bayes algorithm)

20

Chapter Four Implementation

4.2 Model implementation tool 21

4.3 Results 24

Chapter Five Conclusion & Recommendation

5.1 Conclusion 25

5.2 Recommendation 26

References 27

VIII

List of Figures Figure

Page No

Fig 1.1 Research Methodology

2

Fig 1.2 Research structure

3

Fig 3.1 Proposed model 15

Fig 3.2 The data before modified 18

Fig 3.3 Total_all_M field after modified 19

Fig 3.4 Churn field after modified 19

Fig 4.1 Preprocessing in WEKA 21

Fig 4.2 data mining technique and algorithm 22

Fig 4.3 Algorithm's implementation 23

IX

List of Tables

Table Page No

Tab 3.1 Attributes table 17

Tab 4.1 Results table 24

Chapter One

Overview

1

1.1 Introduction:

Churn is a term used in many companies which is mean loss of customers of the

company for many rezones one of them the dissatisfaction of customer.

In telecommunication companies churn term refers to customer's decision to

leave the current service provider and move to other service provider, it can be

easily happen especially for prepaid customers because they have not any contract

same as to postpaid customers.

Churn occurs easily because of the strong and breeding competition

environment in services which are providing especially in telecommunication

sector, also churn can be happen for another rezones for examples customer's

dissatisfaction with services and high cost of these services which can be in another

service provider with best quality and lower cost. So churn become a concern issue

in telecom sector because retaining of existing customer is costly than acquiring

new one.

To identify churner and non-churner customers and understand the rezones of

this churn to reduce it, these companies can build churn prediction model which can

help them in churn churner issue to build this model they can use Data mining (DM)

this can be useful because DM can extracting a predictive information from large

databases, it's have many techniques which can for example Decision Tree (DT),

Support vector machine (SVM), Naïve Bayes… ect.

1.2 Research problem:

Churn is big issue for telecommunication companies so they need a churn

prediction model to help them to identify churner and non-churner customers.

1.3 Research objective:

The objective of this research is to build a churn prediction model which can

identify churner customers and non-churner customers and implementing this model

by using Naïve bayes algorithm.

2

1.4 Research methodology:

DM (Data Mining) is the process of extracting useful patterns from large

databases. The problem in this research is to build a churn prediction model to

identify subscriber as churner or non churner, so the DM classification is used to

build this model. The model contains four steps these steps in ordering are

identification of the problem, acquisition the data, preparation of data which was

acquisitioned, and finally implementing this model by classification technique and

Naïve bayes algorithm.

Figure 1.1 research methodology

Using data mining concept

Selecting classification technique for building

the model

Building the model

Implementing model by using Naïve bayes

algorithm

3

1.5 Research structure:

This research starting with defines its problem and its importance and then

explains research purpose its methodology.

The second chapter which is Literature Review about the churn and its categories,

then its defining Data Mining and its application and techniques, it's focused on

classification and its technique and explain Naïve algorithm after it we find in this

chapter the related works.

The third chapter contains predictive model and the steps which were followed

in this chapter to building the model.

Chapter four contains of implementation of this model by using classification

technique and Naïve bayes algorithm in WEKA 3.7 software.

Finally the last chapter which is the fifth contains of Conclusion and

recommendation.

Figure 1.2 Research structure

Chapter 1: Overview

Chapter 2: Literature Review

Chapter 3: Methodology

Chapter 4: Implementation

Chapter 5: Conclusion & Recommendation

Chapter Two

Literature Review

4

2.1 Introduction Data mining is used in churn analysis to predict the churner customers when

churn is excepted to happen. In telecommunication companies when predicting the

customers who are likely to be churned, they can reduce churn rate by offering new

services or packages for customers to stay, also they can reduce the cost of services.

To predict the churner customers classification technique could be used which is

one of data mining techniques, by using classification technique the customers will

classifying into churner customers and non-churner customers.

2.2 Churn In today's competitive world in telecommunication companies, customer churn

is concern issue that for the retaining existing customers is higher cost than

acquiring new customers, so the company predict the customers who are with high

probability to churn and understand the reasons of this churn and try to solve it.

2.2.1 Customer Churn:

Should indicate when a customer has permanently stopped using his sim-card as

early as possible [1]. There are two main categories of churners which are:

2.2.1.1 Voluntary churner:

Voluntary churner is more difficult to determine; it occurs when a customer

makes a decision to terminate his/her service with the provider. When people think

about Telco churn it is usually the voluntary kind that comes to mind [2].

2.2.1.2 Involuntary:

These are the customers that Telco decides to remove from subscribers list.

Therefore’ this category includes people that are churned for fraud, non-payment

and customers who don’t use the phone [2]; it can be sub-divided into two main

categories:

5

1- Incidental churn:

Its indicate when something happened in customers lives. For example: change

in location churn, etc.

2- Deliberate churn:

It's happens for reasons of technology (customers wanting newer or better

technology), economics (price sensitivity), service quality factors, social or

psychological factors, and convenience reasons, and this churn is the problem that

most churn management solutions try to solve [2].

2.3 Data mining

Data mining is one of advanced type of analytical tools at this time

available; these tools can include statistical models, mathematical algorithms,

and machine learning methods (algorithms that increase their performance

automatically during experience, such as neural networks or decision trees) but

data analysis system that does not deal with large amounts of data [3].

Data mining become one of the most common areas of interest that can be

used in multiple disciplines. Disciplines such as telecommunication, banking,

health, education and Commercial markets commonly use data mining to gain

new customers, fight disease, reduce costs and increase benefits [4].

Data mining refers to discovering or “mining” knowledge from large

amounts of data known as Knowledge Discovery from Data (KDD) or is an

essential step in the process of knowledge discovery. The knowledge that

discovered from KDD process can be used for decisions support, knowledge

management and help query processing. Many terms may give the same or a

slight difference meaning of data mining, such as knowledge extraction,

knowledge mining from data, data/pattern analysis, data dredging and data

archaeology.

6

Data mining models consist of a set of rules, equations, or complex ‘‘transfer

functions’’ that can be used to identify useful data patterns, understand, and predict

behaviors. [5]

They can be grouped into two main classes according to their goal, as follows:

1- Supervised / Predictive Models.

2- Unsupervised Models.

2.3.1 UNSUPERVISED MODELS:

In unsupervised or undirected models there is no output field, just inputs.

The Pattern recognition is undirected; it is not guided by a specific target attribute.

The goal of such models is to uncover data patterns in the set of input fields. [5]

Unsupervised models include:

1. Cluster models.

2. Association and sequence models.

2.3.2 Supervised / Predictive Models:

In supervised, or predictive, directed, or targeted modeling, the goal is to predict

an event or estimate the values of a continuous numeric attribute. In these models

there are input fields or attributes and an output or target field. Input fields are also

called predictors because they are used by the model to identify a prediction

function for the output field.

We can think of predictors as the X part of the function and the target field as the Y

part the outcome, the model uses the input fields which are analyzed with respect to

their effect on the target field, pattern recognition is supervised by the target field,

relationships are established between input and output fields. An input–output

mapping ‘‘function’’ is generated by the model, which associates predictors with the

output and permits the prediction of the output values, given the values of the input

fields [5].

7

2.4 Data Mining Applications:

Data mining can implement on many applications such as:

1- Data mining for Financial Data Analysis

2- Data mining for Telecommunication Industries

3- Data mining for Retail Industries

4- Data mining for Healthcare and Biomedical Researches

5- Data mining in Science and Engineering

2.5 Data Mining Techniques:

The most popular data mining techniques are:

1- Estimation

2- Clustering

3- Association

4- Visualization

5- Regression analysis

6- Classification and prediction

2.5.1 Estimation:

These models are similar to classification models but with one major difference.

They are used to predict the value of a continuous field based on the observed

values of the input attributes [5].

2.5.2 Regression Analysis:

Regression analysis is a statistical tool for the investigation of relationships

between variables. Usually, the investigator seeks to ascertain the causal effect of

one variable upon another the effect of a price increase upon demand, for example,

8

or the effect of changes in the money supply upon the inflation rate. To explore such

issues, the investigator assembles data on the underlying variables of interest and

employs regression to estimate the quantitative effect of the causal variables upon

the variable that they influence. The investigator also typically assesses the

“statistical significance” of the estimated relationships, that is, the degree of

confidence that the true relationship is close to the estimated relationship [6].

2.5.3 Clustering:

Clustering is the method by which similar type of records are grouped together.

Usually, clustering is done to give the end user a high-level view of what is going on

in the database. Clustering is useful for coming up with a birds-eye view of the

business [7].

2.5.4 Association:

It is a popular method for discovering interesting relations between variables in

large databases. Introduced association rules for discovering regularities between

products in large scale transaction data recorded by point-of-sale (POS) systems in

supermarkets [7].

2.5.5 Visualization:

Visualization refers to the presentation so that users can view complex patterns.

The main goal of data visualization is to communicate information clearly and

effectively through graphical means. It is used with other data mining models to

provide a better and clearer understanding of the discovered patterns or relationships

[7].

2.5.6 Classification & Prediction

Classification and prediction are two forms of data analysis that can be used to

extract models describing important data classes or to predict future data trends. It

aims at building a model to predict future customer behaviors through classifying

databases records into a number of predefined classes based on certain criteria.

Classification predicts categorical (unordered) labels, prediction models continuous

9

valued functions. Basic techniques for data classification are decision tree classifier,

Bayesian classifier, Bayesian belief networks, rule- based classifiers, and support

vector machines. Methods for prediction include linear regression, non-linear

regression, etc [7].

Classification models are tested by comparing the predicted values to known

target values in a set of test data. The historical data for a classification project is

typically divided into two data sets: one for building the model; the other for testing

the model [8]. So Data Classification process includes the two steps:

1-Building the Classifier or Model

2-Using Classifier for Classification

2.5.6.1 BUILDING the Classifier or Model

All the classification models are built in 4 steps:

1-The first step is identifying a set of subjects with a known behavior. All the inputs

are known, as well as the target classes.

2-The second step is data preparation – it includes data cleaning, feature selections,

data transformation

3-The third step is training the model. This process uses about 80% of all the

subjects identified in the first step to identify the relationships between the inputs

and the target data. The techniques for finding these relationships depend on the

classification algorithm used.

4-The fourth step is testing the model. This step uses the remaining 20% of subjects

to test the relationships identified in the previous step. This is where the accuracy of

the model is checked.

2.5.6.2 USING CLASSIFIER FOR CLASSIFICATION

In this step the classifier is used for classification. Here the test data is used to

estimate the accuracy of classification rules. The classification rules can be applied

to the new data tuples if the accuracy is considered acceptable.

10

3.5.6.3 Classification Techniques:

The following modeling techniques are included in the class of classification

models:

1- Logistic regression:

This is a powerful and well-established statistical technique that estimates the

probabilities of the target categories. It is analogous to simple linear regression but

for categorical outcomes. It uses the generalized linear model and calculates

regression coefficients that represent the effect of predictors on the probabilities of

the categories of the target field [9].

2- Support vector machine (SVM):

SVM is a classification algorithm that can model highly nonlinear, complex data

patterns and avoid over fitting, that is, the situation in which a model memorizes

patterns only relevant to the specific cases analyzed [9].

3- Neural networks:

Neural networks are powerful machine learning algorithms that use complex,

nonlinear mapping functions for estimation and classification. They consist of

neurons organized in layers. The input layer contains the predictors or input

neurons. The output layer includes the target field. These models estimate weights

that connect predictors (input layer) to the output. Models with more complex

topologies may also include intermediate, hidden layers, and neurons. The training

procedure is an iterative process. Input records, with known outcomes, are presented

to the network and model prediction is evaluated with respect to the observed results

[9].

4- Bayesian networks:

Bayesian models are probability models that can be used in classification

problems to estimate the likelihood of occurrences. They are graphical models that

11

provide a visual representation of the attribute relationships, ensuring transparency,

and an explanation of the model’s rationale.

5- Decision Tree:

Decision trees are the most commonly used tool for predictions and

classification of future events. The development of such trees is done in two major

steps: building and pruning. During the first phase the data set is partitioned

recursively until most of the records in each partition contain identical value. The

second phase then removes some branches which contain noisy data (those with the

largest estimated error rate) [10].

6- Naïve bays:

Naive Bayes is a type of supervised-learning module that contains examples of

the input-target mapping the model tries to learn, its uses the mathematics of Bayes'

Theorem to make its predictions. Naïve Bayes learning generates a probabilistic

model of the observed data [10].

Suppose we have C= {C1 , C2} which are class and X is a data set contains of

attributes X1 and X2 so:

Naïve Bayes have three main equations:

1- Prior probability for class :

푃푟푖표푟푝푟표푏푎푏푖푙푖푡푦표푓퐶1 =푁푢푚푏푒푟표푓푖푡푒푚푠표푓C1푇표푡푎푙푁푢푚푏푒푟표푓푖푡푒푚푠

2- Likelihood of X given class C1:

퐿푖푘푒푙푖ℎ표표푑표푓푋푔푖푣푒푛퐶푙푎푠푠퐶 = 푁푢푚푏푒푟표푓퐶1푖푛푡ℎ푒푣푖푐푖푛푖푡푦표푓푋

푇표푡푎푙푁푢푚푏푒푟표푓퐶1

Posterior probability of X1 being C1:

PosteriorprobabilityofXbeingC1 =

12

푃푟푖표푟푝푟표푏푎푏푖푙푖푡푦표푓퐶 ∗ 퐿푖푘푒푙푖ℎ표표푑표푓푋푔푖푣푒푛퐶푙푎푠푠퐶1

That is, the Naïve Bayesian classifier predicts that tuple X belongs to the class C1 if

and only if

푃(퐶1|푋1) > 푃(퐶2|푋1)

How it's come?

Suppose we have two classes C1 and C2 and we have data set XX having the

attributes XX={X1 , X2} and tuple X and having m instance:

1- The prior probability (PP) of C1 and C2:

푃푃퐶1 =푡ℎ푒푛푢푚푏푒푟표푓퐶1푖푡푒푚푠

푚

푃푃퐶2 =푡ℎ푒푛푢푚푏푒푟표푓퐶2푖푡푒푚푠

푚

2- The Likelihood (LH) of X given class C1 and C2:

퐿퐻퐶1 =푁푢푚푏푒푟표푓퐶1푖푛푡ℎ푒푣푖푐푖푛푖푡푦표푓푋1

푇표푡푎푙푁푢푚푏푒표푓퐶1

퐿퐻퐶2 =푁푢푚푏푒푟표푓퐶2푖푛푡ℎ푒푣푖푐푖푛푖푡푦표푓푋2

푇표푡푎푙푛푢푚푏푒푟표푓퐶2

3- Posterior probability (PRP) of X being C1 and C2:

푃푅푃퐶1 = 푃푃퐶1 ∗ 퐿퐻퐶1

푃푅푃퐶2 = 푃푃퐶2 ∗ 퐿퐻퐶2

So:

퐼푓(푃푅푃퐶1) > (푃푅푃퐶2)푇ℎ푒푛

The Naïve bays classifier predicts C1 For X

13

2.6 Related works:

Shyam V.Nath [11] study developed a model with data set that contains of 2000

customers by using Naive Bayes classifier the model was used to predict the future

churn for the wireless customers when it tested it showed with 68% accuracy.

In Emilía Huong Xuan Nguyen [12] study churn identified in postpaid and

prepaid sectors by implementing C4.5 decision tree, alternating decision tree, Naïve

Bayes and logistic regression, all postpaid classifiers achieve above 60% overall

accuracy on a testing set of 30 thousand instances and prepaid classifiers achieve

above 70% overall accuracy on a testing set of 40 thousand instances.

Chapter Three

Methodology

14

3.1 Predictive Models:

Predictive modeling is focus on how the customer will behave in the future by

analyzing their past behavior. Predicting customer who is likely to churn is on

example of predictive modeling. Predictive modeling is used in analyzing Customer

Relationship Management (CRM) data and DM to produce customer-level models

that describe the likelihood that a customer will take a particular action. The actions

are usually sales, marketing and customer retention related [13].

The predictive models can be classified into two types of model:

1- Traditional model or techniques

2- Soft computing techniques

3.2 Research scenario: In this research the model which is built contains of four main steps these steps

containing problem identification which is customer churn in telecommunications

companies for prepaid customers and it's a concern issue for the most companies

then the data was selected to implement the model this data was containing of

customer behavior data which identifies the services that the customer uses and how

he/she use it and customer demographics data which is containing of some personal

data about the customer after selecting data these data under gone preprocessing

operations which are cleaning noise data and modifying the wrong values after

preprocessing the data mining techniques was implemented on this data this data

mining technique was classification which predicting a certain outcome based on a

given input (data set) in the classification Naïve bayes technique was selected and

implemented by using WEKA 3.7 software. All previous steps will be discussed in

this chapter and chapter 4.

3.4 The Proposed model:

The proposed model composed of four steps:

1- Identify problem domain

15

2- Data selection (from Telecommunication industry)

3- Data preparation

4- Data mining technique (Classification), this step produce two types of customers:

1- Churner Customers

2- Non-Churner Customers

Figure 3.1 Proposed model

3.4.1 Identify Problem domain:

Customer churn is a concern issue for the most companies among all this

companies telecommunication companies are in the top of the list, so in order to this

we must recognize the churner before they churn. Churn prediction is a phenomenon

which is used to identify the possible churners in advance before they leave the

network. This helps the CRM department to prevent subscribers who are likely to

churn in future by taking the required retention policies to attract the likely churners

Identify problem

Data

Selection

Data preparation

Data mining technique

(Classification, Naïve Bayes algorithm)

Churners

Non-Churners

16

and to retain them. Thereby, the potential loss of the company could be avoided.

This research utilizes data mining techniques to identify the churners.

3.4.2 Data selection:

Churn prediction models requires the past history or the usage behavior of

customers during a specific period of time to predict their behavior in the near

future. Data Acquisition is a difficult problem for the researchers to acquire the

actual dataset from the telecom industries. This is because the customer’s private

details may be misused. That for The data which is used to implement this model

was generated by www.Mockaroo.com website for generating data, this website

generating random values of data; the data which is generated is sample of

telecommunication data that containing the customer behavior data and customer

demographic data, the data was composed from 10 attributes and 300 instances.

17

Table 3.1 Attributes table

Attribute description Attribute type Attribute

Customer's phone number Numeric Phone

Customer's Name Nominal (text) Name

Customer's gender (Male / Female) Nominal (text) Gender

Mean of all in Minutes which is

customer receive

Numeric M_in_M

Mean of all out Minutes which is

customer make

Numeric M_out_M

Total of all Minutes (in and out)

Total_in_out = M_in_M + M_out_M

Numeric Total_all_M

Means of all customer's SMS which

is sent or received

Numeric M_SMS

Associated services rather than calls

or SMSs (Yes / No)

Nominal Asso_ser

Label class Nominal Churn

18

3.4.3 Data preparation:

Because of random values generation of data's values, the data was containing

many wrong values, so in this step these wrong values was corrected by changing

these wrong values, these wrong values was very obvious in value of Total_in_out

attribute which is dependent on summation of No_in_m and No_out_m, the values

of this attribute was corrected by using Excel formulas.

Figure 3.2 The data before modified

19

The formula of Total_in_out was:= 푆푈푀(퐷2: 퐸2)

Figure 3.3 Total_all_M field after modified

The formula of Churn was:

= 퐼퐹(퐻2 ≥ 300; non_churn; churn)

Figure 3.4 Churn field after modified

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3.4.4 Data mining technique (Classification, Naïve Bayes algorithm):

In this step the model was implemented by using Naïve bayes algorithm in

classification. Naïve bayes algorithm was implemented to predicts wither the

customer will churn or not. Naïve bayes is a simple probabilistic classifier which is

simple and easy to understand, its deal with any number of features or classes, and

it's strong so if there are a few noises in data it doesn't affects on the results,

depending on the nature of the probability of Naive Bayes classifier it can be trained

efficiently in supervised learning.

Chapter Four

Implementation

21

4.1 Model implementation tool:

The model in this research was implemented by using WEKA 3.7 software

which is one of the data mining and machine learning tools its an open source

software under GNU General Public license, this tool developed by WEKA team, its

contain of inbuilt algorithms for data mining and machine learning, this tool is

easily to use for peoples who are haven't much knowledge about data Mining

Figure 4.1 Preprocessing in WEKA

Figure 4.1 describes preprocess of data in WEKA. In the left section it

displays the number of instance, number of attributes and attributes names, in right

section its showing the labeled class and its name, type and distinct. The default

partitioning of labeled class value was 156 customers of 300 are non-churner and

141 of customers are churner.

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Figure 4.2 data mining technique and algorithm

In figure 4.2 data mining technique was selected in (1) which is

Classification, in (2) classification technique was Naïve Bayes implemented by

Naïve bayes algorithm.

)1(

)2(

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Figure 4.3 Algorithm's implementation

In figure 4.3 the Naïve bayes algorithm was implemented on data set to build

Churn prediction model, in (1) the data was split by using cross validation option

into 70% for training and 30% for testing or validation the model. The results of

implementing Naïve bayes algorithm are represented in the in (2) by confusion

matrix.

Confusion matrix is useful tool for analyzing how well your classifier can

recognize tuples of different classes. True Positive (TP) and True Negative (TN) tell

us when the classifier is getting things right, while False Positive (FP) and False

Negative (FN) tell us when the classifier is getting things wrong, P' is the number of

)1(

)2(

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tuples that were labeled as positive (TP+FP) and N' is the number of tuples that

were labeled as negative (TN+FN) [7].

4.2 Results:

Table 4.1 Result

Classes Non-churn Churn Total

Non-churn 147 12 159

Churn 2 139 141

Total 149 151 300

From previous table which is describes confusion matrix there are two classes which

are Non-churn and Churn, so:

True Positive (TP) instance that classified correctly as Non-churn = 147

True Negative (TN) instance that classified correctly as Churn = 139

False Positive (FP) instance that classified incorrectly as Non-churn = 2

False Negative (FN) instance that classified incorrectly as Churn = 12

The number of tuples labeled as positive P' = TP + FP = 149

The number of tuples labeled as negative N' = TN + FN = 151

So from prediction we have 147 correctly predicted as Non-churner customer from

159 which is (92.5%) and 139 correctly predicted as Churner customer from 141

which is (98.5%).

퐴푐푐푢푟푎푐푦표푓푐푙푎푠푠푖푓푖푒푟 = 푇푃 + 푇푁

푇표푡푎푙푛푢푚푏푒푟표푓푖푛푠푡푎푛푐푒

25

So:

The accuracy of Naïve bayes classifier in given test = = 95.3%

which is a high accuracy.

Chapter Five

Conclusion

& Recommendations

26

5.1 Conclusion:

Customer churn is a big issue in telecom companies especially for prepaid

subscribers because it happen easily under light of strong competition in this

business area, so these companies need to build a churn prediction model t identify

churner and non-churner customer and avoid this churn.

In this study the churn prediction model was built, this model contains of four

steps which in ordering are: identify problem which is the churn problem in telecom

companies, data selection in this step the data which is selected was generated from

www.Mokaro.com website, data preparation, implementation of Naïve bayes

algorithm in the model by using WEKA 3.7 software.

The model was built by treading its four steps and using the data which

selected and Naïve bayes algorithm to implement it the result was there is from 147

customers correctly predicted as Non-churner customer from 159 which is (92.5%)

and 139 customers correctly predicted as Churner customer from 141 which is

(98.5%) with 95.3% of accuracy.

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5.2 Recommendation:

In this study the model was implemented by using Naïve bayes algorithm, in

the future it could implemented by using another algorithm for example

Support Victor Machine (SVM) algorithm which has higher accuracy by

comparing it with another algorithms which was implemented at many

another application, so if SVM implemented for churn prediction model it

may results higher accuracy than Naïve Bayes algorithm.

This study stops at identifying churner and non-churner customers, so the

future researches can develop by adding a new step to the model to evaluate

the churners by categorizing them into three categories which are (high ,

medium , low) by using clustering technique and implementing one of its

algorithm

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