Capstone Project for Udacity’s Data Scientist Nanodegree

 Capstone Project for Udacity’s Data Scientist Nanodegree

The data for this case simulates how people make purchasing decisions and how those decisions are influenced by promotional offers.

Introduction

Project Overview

The data for this case simulates how people make purchasing decisions and how those decisions are influenced by promotional offers.

Each person in the simulation has some hidden traits that influence their purchasing patterns and are associated with their observable traits.

People produce various events, including receiving offers, opening offers, and making purchases.

As a simplification, there are no explicit products to track. Only the amounts of each transaction or offer are recorded.

There are three types of offers that can be sent: buy-one-get-one (BOGO), discount, and informational.

  1. BOGO: a user needs to spend a certain amount to get a reward equal to that threshold amount.
  2. Discount: a user gains a reward equal to a fraction of the amount spent.
  3. Informational: there is no reward, but neither is there a requisite amount that the user is expected to spend.

Offers can be delivered via multiple channels: Email, Mobile, Social, Web

Problem Statement

We will use the data to find a better promotion strategy by trying to answer the following questions:

a) Identify which groups of people are most responsive to each type of offer.

b) How best to present each type of offer?

c) How many people across different categories actually completed the transaction in the offer window?

d) Which individual attributes contributed the most during the offer window?

We will also try to train a model to predict the amount that can be spent by an individual given the individual’s traits and offer details. This will help us decide which promotional offer best suits the individual and respond to the target audience with better accuracy.

machine-learning udacity data-science data-visualization data-analysis data visualization

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