1.
Greentoe Consulting Project
Romano Foti - Insight 2015

2.
Motivation
Offer the user a tool to assess the probability of success of an auction bid
Objective
Improve the accuracy of the current gauge

3.
Starting Simple: Algorithms Comparison
2-Category classification problem:
1) Offer acceptance
2) Offer rejection
Logistic Regression: 6-Fold Cross-Validation ROC
Random Forest
Gradient Boosting
Naive Bayes

4.
Incorporating counter-offers
3-Category classification problem:
1) Offer acceptance
2) Offer rejection
3) Counter-offer
Precision Recall
Expired 0.69 0.71
Accepted 0.56 0.50
Counter Offer 0.61 0.61
Avg/Tot 0.64 0.64
Precision Recall
Expired 0.64 0.65
Accepted 0.42 0.70
Counter Offer 0.58 0.41
Avg/Tot 0.58 0.56
Random Forest: Multi-Class ROCLogistic Regression: Multi-Class ROC

5.
Final Product
Logistic Regression Model
How is this going to help Greentoe
Greentoe will use my codebase to improve the accuracy of the model and
provide more refined customer feedback
More accurate gauge
More trust
More customers
Fast
Interpretable Flexible

6.
About Me
Bioluminescence
Vulnerability of water supply
to climate change
ConstructionsConstructions
PhD
Impact of climate and
humans on the Everglades Urban GI performance
PhD and Post-doc research work

7.
Extra Slides

8.
Extra Slide 1 - Dataset
Product
Name
Product
Category
- -
Average
Retail Price
Minimum
Sale Price
Offer Price Offer Status
Lenses Photography - - $1500 $1200 $1150 Accepted
Guitar Music - - $625 $600 $400 Disregarded
Percentage of Accepted Offers
Musical
Instruments
Appliances Photography
TV & Home
Theater
Countered vs Non-countered Offers
- Provided dataset: ~13,000 rows (offers history) for ~1000 products
- Greentoe wanted to include subcategories and brands
- Greentoe wanted to incorporate information from counter offers
Musical
Instruments
Appliances Photography
TV & Home
Theater

9.
Extra Slide 2 – Input Screenshot

10.
Extra Slide 3 – Output 1 Screenshot

11.
Extra Slide 4 – Output 2 Screenshot