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R Machine Learning Essentials by Michele Usuelli

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Predicting the output

The past marketing campaign targeted part of the customer base. Among other 1,000 clients, how do we identify the 100 that are keener to subscribe? We can build a model that learns from the data and estimates which clients are more similar to the ones that subscribed in the previous campaign. For each client, the model estimates a score that is higher if the client is more likely to subscribe. There are different machine learning models determining the scores and we use two well-performing techniques, as follows:

  • Logistic regression: This is a variation of the linear regression to predict a binary output
  • Random forest: This is an ensemble based on a decision tree that works well in presence of many features

In the end, we need ...

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