Market Response Project

Data: kaggle Github: github

• pandas, numpy, matplotlib, seaborn, plotly, sklearn, xgboost

This is a sample gallery to go along with this case study.

Market Response Model

• Building the uplift formula
• Exploratory Data Analysis (EDA) & Feature Engineering
• Scoring the conversion probabilities
• Observing the results on the test set

Uplift Modeling

1. Predict the probabilities of being in each group for all customers: build a multi-classification model
2. Calculate the uplift score. (US = TR + CN – TN – CR)
   • TR(Treatment Responders): Customers that will purchase only if they receive an offer
   • TN(Treatment Non-Responders): Customer that won’t purchase in any case
   • CR(Control Responders): Customers that will purchase without an offer
   • CN(Control Non-Responders): Customers that will not purchase if they don’t receive an offer