Object Oriented Programming & E-Commerce

8/3/2019 Object Oriented Programming & E-Commerce

1/40

O b j e c t O r i e n t e d P r o g r a m m i n g &O b j e c t O r i e n t e d P r o g r a m m i n g &

EE -- C o m m e r c e C o m m e r c e

Data MiningData Mining


2/40


3/40

Group Members

Amit S ingh

Art i Kumar i

Sar abj i t K aur

Bhar ti S i ngh

GROUP NO:-5

Anchor Faculty

Prof.Swati Rekha Mohanty


4/40

AGENDA What is data mining?

Examples of data mining.

Related terms of data mining.

What can data mining do?

How does data mining work?

Examples of data mining in todays scenario.

Five major elements.

Major components of data mining.

Data mining classification.

Functions.

Stages.

Popularity growing reason.

Future of data mining.

Organizational issues.

Solutions of industries.

Challenges.Applications.

Advantages.

Data mining models.

DM in banking : ICICI bank

DM in retail : wal- mart

Conclusions

Bliography


5/40

Wh a t i s D a t a M i n i n g ?

Data mining is defined as the process ofextracting previously unknown, valid, and

actionable information from large databases and

then using the information to make crucial

business decisions .

Data mining refers to extracting or miningknowledge from large amounts of data.


6/40

Examples Of Data Mining

Predicting whether a newly arrived customer willspend more than 100 $ at a department store.

The company uses data mining to find out what

advertising are effective for their competitors.


7/40

Some Related Terms Of Data Mining

D a t a : -

Data are any facts, numbers, or text that can be processed by acomputer.

I n f o r m a t i o n : -

The patterns, associations, or relationships among allthis data can provide information.

D a t a w a r e h o u s i n g : -

Data warehousing is defined as a process of centralized datamanagement and retrieval.


8/40

What can data mining do?

Data mining is primarily used today by companies.

It enables the companies to determine relationships

among Internal factor and External factor.

Some examples of Wall Mart.


9/40


10/40

Examples of Data mining in

today's scenario

The US government uses data mining to trackfraud.

A super market becomes uses an informationbroker.

Basketball teams use it to track game

strategy. Bank of America uses it for Sales forecasting.


11/40

Data mining consists of f ive major

elements:

Extract, transform, and load transaction data onto thedata warehouse system.

Store and manage the data in a multidimensionaldatabase system.

Provide data access to business analysts andinformation technology professionals.

Analyze the data by application software.

Present the data in a useful format, such as a graphor table.


12/40


13/40

Data mining tasks

can be classified as two types:

Descriptive:- It characterize the general

properties of the data in the database.

Predictive:- It perform inference on the currentdata in order to make predictions.


14/40


15/40

Functions of Data Mining

Identify your best prospects and then retain them as customers.

Predict cross-sell opportunities and make recommendations.

Learn parameters influencing trends in sales and margins.

Segment markets and personalize communications.

Identify which transactions are most likely to be fraudulent.

Identify which prospects should be included in a mailing list toobtain the highest response rate.

Predict what each individual accessing a web site is most likelyinterested in seeing.

Reveal the difference between typical customers this month and

last. Understand what products or services are commonly purchased

together; e.g., beer and diapers.


16/40

Stages of Data Mining

Explorations Model building and

validationsDeployment


17/40

Contd.Diagram of Stages

Selection Pre-processing

DATA

TRANSFORMEDDATA

MININGPATTERN KNOWLEDGE

TARGET DATA PROCESSEDDATA

Transformation Mining Evaluation


18/40

Reasons for growing popularity

of Data Mining

Growing Data Volume

Limitations of Human

Analysis

Low Cost of Machine

Learning


19/40

Future of Data Mining

Unfulfilled expectations

Common goals

Technology cycle


20/40

Organisational issues

There are manyorganisational issues such

asPolitical issues


21/40

Data Mining Solutions for Different

Industries

SaaS Analystics, analytics on-demand, analytics in the cloud. BI (Business Intelligence), Database and OLAP software

Bioinformatics and Pharmaceutical solutions

CRM (Customer Relationship Management)

Data Providers, Data Cleansing (Cleaning) Tools

E-Commerce solutions

Email analysis, response, and marketing

Fraud Detection solutions

Health Care and Human Resources solutions

Knowledge Management and News Marketing solutions, including real-time, email, web, and affiliate

marketing

Microarray data analysis, gene expression analysis


22/40

Contd.

Personalization solutions

Privacy software and solutions Real-Time Analytics and Decisioning solutions

Retail solutions

Risk Analysis and Credit Scoring

Sports and Entertainment

Stock and Investment Analysis and Prediction

Survey creation and analysis

Telecom

Travel sites and solutions

Twitter Analytics sites and solutions. Web Advertising

Web Mining,Web Content Mining


23/40

Challenges faced by Data Mining

Privacy issue

Security Issue

Misuse of information / Inaccurate information


24/40

Advantages of Data Mining

Marketing:helps in marketing companies to buildmodels based on historical data to predict who will respond to

new marketing campaign

Finance: helps financial institutions to provide

information about loan information and credit reporting

Manufacturing: by Applying DM in operationalengineering data, manufacturer can detect faulty equipment.

Government:DM helps government agency byanalyzing records of financial transaction to build pattern thatcan detect money related fault and criminal activity


25/40

Application of Data Mining

Industry Application

Finance Credit card analysis

Insurance Claim fraud analysis

Telecommunication Call record analysis

Transport Logistic management

Consumer Goods Promotion analysis

Data Service Provider Value added data

Utilities Power usage analysis


26/40

Types of Data Mining Models used in

different business

Claims Fraud Models

Customer Clone Models

Response Models

Revenue and Profit Predictive Models

Cross-Sell and Up-Sell Models

Marketing Effectiveness Creative Models

Real Time Web Personalization with eNuggets


27/40

Data Mining: A competitive tool in

the banking

Example:ICICI bank

ICICI: Industrial credit & investment corporation of

India. It is one of the 2nd largest private bank of India

Established in 1994 CHANDA KOCHHAR is the CEO of ICICI Bank.

ICICI bank uses Data mining technology to handle its dataeffectively and efficiently


28/40


29/40

Software used by ICICI

ICICI bank and all other public and privatebanks are computerized and all sort oftransaction takes place through a software thatis TBC (total branch computerization) software

package.It is design to carry out day to day operation atthe branch level

This software packages help them to connectwith all the branch of bank as well as the RBIand head offices.


30/40

Data Mining solve the issue in the

following areas

ICICI bank uses DM for the following areas: Customer segmentation

Profitability

Credit scoring and approval

Predicting payment default Marketing

Detecting fraudulent transaction

Cash management

Forecasting operations

To identify their most profitable credit card customersor high risk loan applicant.


31/40

ICICI uses Data mining in the

following area

Marketing: ICICI uses database marketing to improve customersatisfaction and by consolidating five year of customer history records,thus bank is able to market and sell targeted services to customers.

Cross-selling: here a service provider make it attractive for acustomer to buy additional product or service with the same business.

Risk Management: Bank executives uses Data mining to knowwhether the customer dealing with is reliable or not.

Fraud detection: ICICI has two approaches to detect fraud-1st the bank taps the data warehouse of the 3rd parties and uses datamining programs to identify fraud pattern.

2nd by using hybrid approach

Customer acquisitions and retentions: Data Mininghelp bank to retain its present customer and as well gain newcustomer.


32/40

Data Mining a competitive tool in

Retail industry

Retailers have been collecting enormous amount of data throughout the year and now they have the tool to sort those data's andfind useful piece of information

Data mining helps in providing information's on:

Sales trends Customer buying habits and preference

Seasonal variations

Customers peak traffic

So this are some vital area where DM is used bu bigretail companies.


33/40

Wal-Mart Stores, Inc.

Wal-Mart Stores, Inc. branded asWalmart since 2008andWal-Mart before then, is anAmerican multinational retailer corporation that runschains of large discount department stores and

warehouse stores. The company is the world's 18thlargest public corporation, Wal-Mart has 8,500 stores in15 countries, under 55 different names. Its was foundedin 1962 by Walton family.

so it has to deal with large amount of data andinformation to run their efficiently and effectively


34/40


35/40

Use of Data Mining in following area

Marketing:Wal-Mart uses Market Basket analysis. It isthe method to find optimum location for promotion of product.

Under marketing there are some sub area where DM is used byWal-Mart:-

o Store operations

o Loss prevention

o Merchandising

o Inventory

o Workforce management

o Customer

Risk Management:Wal-Mart uses DM to understand

which product will be vulnerable to competitive offers or changingcustomer purchasing power


36/40

Contd..

Fraud detections: it was estimated that 38% ofretail shrink occur due to dishonest employees but byimplementing DM the loss has been decreases.

Wal-Mart uses exception based reporting which recordall the transaction done throughout the day by the

employee and it provide detail information about andthe fraud can be easily detected.

Customer acquisitions and retentions:bystudying past purchasing behavior of the customer,

they could plan their promotional strategies andincentives to retain their present customer as well asto gain their new customers .


37/40

Conclusion Data mining is a tool used to extract important information from

existing data and enable better decision-making throughout thebanking and retail industries. They use data warehousing tocombine various data from databases into an acceptable formatso that the data can be mined. The data is then analysed andthe information that is captured is used throughout theorganisation to support decision-making. It is universally

accepted that many industries (including banking, retail andtelecom) are using data mining effectively. Undoubtedly, datamining has many uses in industries. Its practical applications insuch areas as analysing medical outcomes, detecting creditcard fraud, predicting customer purchase behaviour, predicting

the personal interests of Web users, optimising manufacturingprocesses etc. have been very successful.


38/40

Bibliography

www.google.co.in

http://www.youtube.com/watch?v=_QH4oIOd9nc

www.dataminingtools.com

www.thesys-group.com

http://www.megaputer.com/dm/dm101.php3

http://www.statserv.com/datamining.html

http://www.caida.org/tools/measurement/skitter

http://www.pcc.qub.ac.uk/tec/courses/datamining/stu_notes/dm_book_2.html

http:

//www.anderson.ucla.edu:

8888/faculty/jason.frand/teacher/technologies/palace/iss

www.anderson.ucla.edu:888/faculty/jason.frand/teacher/technologies/palace/datamining.htm


39/40


40/40

Date post:	06-Apr-2018
Category:	Documents
Upload:	amit-singh
View:	223 times
Download:	0 times

Object Oriented Programming & E-Commerce

Documents