Practical Machine Learning in Python: Applying Artificial Intelligence to Classify Real World Data Sets by Gloyer Malcolm

Author:Gloyer, Malcolm [Gloyer, Malcolm]
Language: eng
Format: epub
Published: 2021-02-02T16:00:00+00:00

Appendix 1 – Predicting Customer Churn using Artificial Intelligence Classification Neural Networks

Malcolm Gloyer BSc, Chartered MCSI

February 2021

Introduction

Predicting Customer Churn – traditionally the domain of financial services and utility companies – is now increasingly the focus of technology companies engaged in telecommunication, broadband, TV, gaming and media.

Methodology

Multi-layer Perceptron (MLP) has the capacity to learn non-linear models as a supervised learning algorithm that learns a function f(⋅ ) : Rm → R0 by training on a dataset, where m is the number of dimensions for input and o is the number of dimensions for output. Given a set of features X= x1, x2, ..., xm and a target y, it can learn a non-linear function approximator for either classification or regression. It is different from logistic regression, in that between the input and the output layer, there can be one or more non-linear layers, called hidden layers. Figure 1 shows a one hidden layer MLP with scalar output.

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