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The Kaggle Project

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shruti tripathi

on 30 June 2014

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Transcript of The Kaggle Project

Regression Techniques
So far we have run logistic regression on the data in SAS and have achieved a concordance of 62%
Problem Description
Companies often offer discounts on products to attract customers.
This challenge asks the participants to predict those customers who will return to purchase the same product post an initial incentive.
The data provided includes a basket level pre-offer transaction history which dates to approximately a year prior to the date on which the offer was made is provided and a sample prediction of the post incentive behavior of a subset of the total number of customers is made.
Phases of the project
Understanding the data

Variable creation

Running regression on data

Improving prediction model
Understanding the variables
We are provided with four relational files:

transactions.csv - contains transaction history for all customers for a period of at least 1 year prior to their offered incentive

trainHistory.csv - contains the incentive offered to each customer and information about the behavioral response to the offer

testHistory.csv - contains the incentive offered to each customer but does not include their response (we are predicting the repeater column for each id in this file)

offers.csv - contains information about the offers

Challenge Name :
Acquire Valued Shoppers Challenge
Objective :
To predict which shoppers will become repeat buyers
The Kaggle Project
Variable Creation
We have analyzed all these files and used the existing variables to further create more derived variables using the RFM model and our own ideas
Improving prediction model
We are currently working on improving the prediction model and are testing techniques such as Random Forest for better variable creation and Poisson Distribution for more accurate prediction
Thank You!
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