Creating a Prediction Model for Customer Acquisition

Creating a Prediction Model for Customer Acquisition

In this article, a data set containing personal, household, building information of representative German population and existing customer base of a mail order company is analyzed [1]. The goal is to create a model for predicting if a person would probably respond to customer acquisition campaign.

Given, a large real-life data set containing information on general population and existing customer base. A major challenge is to target the most promising section of the population for customer acquisition. In this article, a data set containing personal, household, building information of representative German population and existing customer base of a mail order company is analyzed [1]. The goal is to create a model for predicting if a person would probably respond to customer acquisition campaign.

The aim of the project is to create models for answering following questions:

  1. What are the demographic features of a typical mail order customer?
  2. What models can be used for identifying a typical customer based on the features provided in a data set?
  3. Which models perform the best?

The strategy to answer the above question is

  1. to study the data and nature of problem: Is it a binary classification problem? Is it a imbalanced data set? What metric should be chosen? Are there many missing data and what would be the best strategy to handle them? Are the features of continuous nature or categorical type?
  2. draw inferences from two big datasets without labeled response (i.e. general population and customers of mail order company) using unsupervised machine learning algorithms (like principal component analysis for dimension reduction and subsequent cluster analysis). The inferences can be on finding clusters where customers are over-represented or under-represented and hence giving a hint on important features for identifying a potential customer.
  3. create prediction model given labeled training and test dataset from a mail order campaign using popular new machine learning algorithms like xgboost and classical proven-in use algorithms_ gradient boosted machine._

unsupervised-learning xgboost data-science udacity machine-learning

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