Group 7_DMBD Term Project

8/10/2019 Group 7_DMBD Term Project

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By Nancy (

Subhash Rajeev (1Vishnu Poduval (1

Vishal Wagh(Eswar Sunil Kumar (

Aaron Ernest (


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Objectives of the study

BusinessObjective

To gauge the buying behavior of customers from an ecommerce point of viewand thereby to identify the major issues that prevent different classes of usersfrom using the internet for making purchases

Factors UnderConsideration

General demographics of the users

Technology demographics of the users

Internet shopping habits

Web and Internet Usage habits

Source ofdatabase

GVUCs 8th WWW user survey( Run from October 10, 1997 to November 16,1997)

Special pointers provided to the by Yahoo, Netscape and WebTV


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Mining Main challenge faced - Branching

- Categorization of the dataGeneral

Technology

Internet Usage

Privacy

E-commerce

Gender

Connection

Speed &

Upgrades

Indispensable

Technologies

Cookie

Privacy

Reasons for Using the Web

for Personal Shopping

Primary

Language

Email

accounts Frequency of Use Internet Laws

Time Spent Searching

Personal Shopping

Registered to

Vote

Equipment

Owned

Navigation

Services

Content

Providers

have right to

resell user

information

Success Rate of Personal

Shopping

Imp Issue

Facing the

Internet

Frequency of

Switching

Browsers

Years on

Internet

Which arewho have/online? W

What are t

for people What is th

reasons wiclassificatgeography

Is there a spent on thabits?

How muchmatter in t

customers


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Dataset- Numeric, stores in ASCII file

No of datasets- 10044. Fields- 60 After Cleaning, no of datasets- 7290, no of training datasets- 1822, no of test datasets- 5

The training dto develop the

later tested onfields are highleach other.In most of suchthem have bee

Overview of Dataset

Variables(Demographic Segmentation)

Variables(Psychographic Segmentation)

Age Years on Internet

Country Major Occupation

Gender Who_pays_for_access

Education_Attainment Willingness_to_Pay_Fees

Race Most_important_Issue_Facing_the_Internet

Major Geographical Location Sexual Preference

Marital Status Primary_Place_of_WWW Access

Household Income

The entire recoded asindex to thin an addit

file


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MethodologyData cleaning

Sampling Training and test

Demographic segmentation models trained

C 5.0 with balancing

C 5.0 with balancing and boosting

C 5.0 with balancing, boosting and misclassification costs

Psychographic segmentation models trained

C 5.0 balanced

C 5.0 Boosted C 5.0 misclassified

Neural network

Boosted Neural network

Testing of models

Interpreting results of most accurate and suitable model

Target variWhether uonlineVariable tyDistributio78% Yes, 2

Factor of baMisclassific

demographNo. of trial

Other models were used as well, only these have been represented in the PPT for having the mostconfusion matrices and lift curves

Sampling mTraining Testing D

EvaluationGains chamatrices


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MethodologyModels usedDemographic Psychographi


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ResultsDemographic


C 5.0 with balancing, boostingand misclassification costs

C 5.0 with balancing and boosting

C 5.0 with balancing, boosting and misclassification costs has the highest model accuracy for te

It also has the highest number of true positives


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ResultsDemographic


C 5.0 with balancing, boostingand misclassification costs

C 5.0 with balancin

C 5.0 with balancing, boosting and misclassification costs has the highest amount of lif

l


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ResultsDemographic- Balanced C 5.0 model with boosting and misc

penalties


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ResultsPsychographic

Normal C 5.0 with balancing

C 5.0 with boosting

C 5.0 with misclassification penalties

Simple

Neural netw


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ResultsPsychographic

Normal C 5.0 with balancing

C 5.0 with boosting

C 5.0 with misclassification penalties

Simple neural network

Neural network with


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ResultsPsychographic Simple neural network

Time spent on the internet seems to be a crucial factor when itcomes to understanding what factors come into considerationwhen deciding whether to purchase online


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Interpretation and Concluding Rem Thus we can see that age, years spent on the internet

and primary place of internet access are the most

important characteristics that decide whether usersbuy online

Surprisingly, the purchasing power or monthly incomeand occupation had a much lower importance when itcame to predicting whether a user would purchaseonline

However, since this data is from 1997, at a time whenusers were new to the internet, it makes sense thatfamiliarity with the medium was necessary in order toreach consumers

E-commerce shoppers during this era were earlyadoptersin the product lifecycle and were technology

savvy and already used to the internet. They trusted itas well.

E- commerce websites ther

only advertise online since the cost of advertising as agsince their buyers were ma

familiar with th


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Group 7_DMBD Term Project

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