Mahak Gupta(10103496)

Mentor – Ms. Adwitiya Sinha

INTRODUCTION

Online shopping has emerged as the newest big thing andwhy not?It’s easy. It’s safe, and the best of all it saves TIME!Providing personalized product recommendations forshoppers on ecommerce sites has been proven to boostorder values, increase customer loyalty and enhance theonline shopping experience.

OBJECTIVE

The objective is to develop an application that will providethe online shopping customer a specific range of productscustomized according to their previous preferences andcharacteristic constraints through a website with highdatabase handling capability and also taking into accounttheir suggestions and reviews and giving them the assurancethat their opinion also matters.

LITERATURE SURVEY

We researched several papers and studied them thoroughly to

understand our topic and decide the path to implement it.

There were main 8 papers we selected to draw our work from.

They were in 2 broad categories namely,

online shopping with its applications and processes

Data mining algorithms to analyze the data and extract

results

Online Shopping Portal Processes

Data Mining Algorithm Operations

OPEN PROBLEMS AND ISSUES

Fickle-mindedness of the customer

Customer input

Real time nature of searches

Accuracy of the characteristic data

Multiple entries of same product varying on some

characteristic

Segmenting feedback based on phrases

NOVELTY AND BENEFITS

It acts as a personal shopper. It takes into account both previous

searches and current preferences for current real time

recommendations simultaneously.

Since it gives you the user ratings rather than the reviewer or

company ratings so that a realistic idea can be determined not the

hyped up image set by the manufacturers.

Segregation of feedback text is done on the basis of the complete

meaning of the phrase and not just individual words. Eg. ”Not Bad”

It is important to take into account the customer feedback as the

product is only worth what the customer sees it as.

PROPOSED ALGORITHM

As our program is about finding the right product to recommendto the customer based on their previous searches and preferences,we realized that one particular algorithm or method would not bethe right approach for us. So we decided to take some of thepopular algorithms of data mining and streamline them accordingto our requirements.

Naïve-bayes algorithmApriori algorithmK-means algorithmSoundex AlgorithmEtc.

TOOLS AND TECHNOLOGY

Microsoft Visual Studio 2012 Ultimate

C# with .NET Framework

MS Access for database

Characteristic Jabong Flipkart Myntra Snapdeal US

View without

registration

yes Yes yes no Yes

Shopping Cart Yes Yes Yes yes No

Recently

viewed

Yes No No No Yes

Constraint as

per categories

yes Yes no No Yes

Comparison of Other Existing Approaches/

Solution to the Problem Framed

IMPLEMENTATION

LAYOUTS

Forms for user interactions were made in visual studio using c# for

login, registration, password change, product display and selection, and

feedback gathering.

DATABASES

Synthetic database for products with their characteristics and each

user’s previous visits as well as their choices was created.

Also ratings and quality of manufacture is also added in the tables per

product. Table is also created for user password and login and also for

customer details.

IMPLEMENTATION

EXTRACTION CODE

For feedback mining real feedback data is extracted from xml file into

MS Access database for further analysis using an extraction code in C#

ALGORITHMS

Naïve-Bayes algorithm and K-means are data mining algorithms used

for classification and clustering of data. We have implemented it on our

synthetic database of online products to classify them as

recommendable or not as per each users preferences. We have also

implemented Soundex for feedback mining.

TEST PLAN

The purpose of testing is quality assurance, verification and

validation, or reliability estimation.

Unit Testing

Component testing

Integration testing

Validation Testing

System Testing