AV - AmExpert 2019 - Hackathon

AmExpert 2019: AV Hackathon by American Express (Link)

Problem statement

Predict coupon redemption status for a credit card company (XYZ) to assist them in their discount marketing process using the power of machine learning.Based on previous transaction & performance data from the last 18 campaigns, predict the probability for the next 10 campaigns in the test set for each coupon and customer combination, whether the customer will redeem the coupon or not?

Data description

The data available in this problem contains the following information:

• User Demographic Details

• Campaign and coupon Details

• Product details

• Previous transactions

Dataset schema

Here is the schema for the different data tables available. The detailed data dictionary is provided next.

• train.csv: Train data

Variable	Definition
id	Unique id for coupon customer impression
campaign_id	Unique id for a discount campaign
coupon_id	Unique id for a discount coupon
customer_id	Unique id for a customer
redemption_status (target)	(0 - Coupon not redeemed, 1 - Coupon redeemed)

• campaign_data.csv: Campaign information for each of the 28 campaigns

Variable	Definition
campaign_id	Unique id for a discount campaign
campaign_type	Anonymised Campaign Type (X/Y)
start_date	Campaign Start Date
end_date	Campaign End Date

• coupon_item_mapping.csv: Mapping of coupon and items valid for discount under that coupon

Variable	Definition
coupon_id	Unique id for a discount coupon (no order)
item_id	Unique id for items for which given coupon is valid (no order)

• customer_demographics.csv: Customer demographic information for some customers

Variable	Definition
customer_id	Unique id for a customer
age_range	Age range of customer family in years
marital_status	Married/Single
rented	0 - not rented accommodation, 1 - rented accommodation
family_size	Number of family members
no_of_children	Number of children in the family
income_bracket	Label Encoded Income Bracket (Higher income corresponds to higher number)

• customer_transaction_data.csv: Transaction data for all customers for duration of campaigns in the train data

Variable	Definition
date	Date of Transaction
customer_id	Unique id for a customer
item_id	Unique id for item
quantity	quantity of item bought
selling_price	Sales value of the transaction
other_discount	Discount from other sources such as manufacturer coupon/loyalty card
coupon_discount	Discount availed from retailer coupon

• item_data.csv: Item information for each item sold by the retailer

Variable	Definition
item_id	Unique id for itemv
brand	Unique id for item brand
brand_type	Brand Type (local/Established)
category	Item Category

• test.csv: Contains the coupon customer combination for which redemption status is to be predicted

Variable	Definition
id	Unique id for coupon customer impression
campaign_id	Unique id for a discount campaign
coupon_id	Unique id for a discount coupon
customer_id	Unique id for a customer

• sample_submission.csv: This file contains the format in which you have to submit your predictions.

Evaluation Metric

Submissions are evaluated on area under the ROC curve (AUC).

Leaderboard

• Public LB score (AUC): 0.8701 # Pos: 307

• Private LB score (AUC): 0.86197 # Pos: 268

• Submitted solution: XGB/LGB ensemble

Best scoring model:

..to be upladed soon..

• Best scoring model (NOT submitted): AUC = 0.918 (to be uploaded soon..)