Abhishek

Meera

Shilpa

Exploring Data

Building Count Dataset• Users that bought from Barsne&Noble

• Users that bought from Amazon

• Users that bought from both Amazon and BarnesAndNoble

• Users that bought only from Amazon and not from BarnesAndNoble

• Adding Demographic details to AmazonOnlyTable.For the data, since no one bought from BarnesAndNoble, total_books=0

• Creating Table for people who bought from BarnesAndNoble along with demographic details

• Combining the Bndemographic table with amazononlydemographic table to obtain the final table required for regression

Building Poisson Regression Model

Count of Quantity Quantity Actual Predicted

7639 0 1524.750499 0.4762498

753 1 150.2994012 0.3499975

362 2 72.25548902 0.1319051

175 3 34.93013972 0.0339198

126 4 25.1497006 0.0066854

82 5 16.36726547 0.0010758

74 6 14.77045908 0.00014705

30 7 5.988023952 1.7538E-05

48 8 9.580838323 1.86E-06

31 9 6.18762475 1.78E-07

20 10 3.992015968 1.69E-08

Building NBD Regression Model

Count of Quantity Quantity Actual Predicted

7639 0 0.808274 0.810374

753 1 0.079674 0.07003

362 2 0.038303 0.033905

175 3 0.018517 0.020919

126 4 0.013332 0.014299

82 5 0.008676 0.010348

74 6 0.00783 0.007766

30 7 0.003174 0.005978

48 8 0.005079 0.004688

31 9 0.00328 0.00373

20 10 0.002116 0.017963

Comparision

• NDB Regression fits the data better than the Poisson Regression Model and can be used for prediction.

• In comparison to Poisson Distribution is LL value 18819, NBD LL value is 8359. AIC and BIC values for NBD is better than Poisson Reg.

Tuning the NDB Model

Adding variables one by one and testing model

Added only Region. Added only Child

Added only Race.

Tuning the NDB Model

• Creation of New variable

DATE

Holidayvariable

Weekend variable

Holiday Shopping Tendency(0,1,2)

Week end Shopping

Tendency(0,1,2)

Customer:Percentage of

Holiday shopping

Customer: Percentage of

Weekend shopping

Tuning the NDB Model

• Creation of New variable

Log Likelihood

B8- Week end shopping TendencyB9-Holiday Shopping Tendency

Tuning the NDB Model

• Interaction Variable

Household size * child

Holiday shoppin trend * Weekend Shopping Trend income * age

Log Likelihood

Why Certain Customers Prefer Amazon Over BN?

Facts:• # of bookings for Amazon is 5 times more

• Missing values in education field was more than 60%

Logistic Regression:

• Dependent variable used is domain_type

• class variables: education and region

• New variable: total_books

• Replaced 99 by ‘.’ in education

Results

Inferences

• 56.7% of the values were being predicted well

• Education and region influence the results

• Region 1 and region 3 had relatively more probability as well

Our Learning and Takeaway

• Getting the data in the right format

• Some simple basic assumptions resulted in a final solution

• Creating count model from scratch

• Better understanding of Poisson and NBD model