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{"title":"Getting started deep learning classification on credit risk area","description":"Using powerful Keras API to train a sequential neural network to classify default of loan car customer. This post is for beginner in deep learning, it's a simple approach of deep learning for binary classification.","authors":[{"author":"David Saada","authorURL":"#","affiliation":"David Saada","affiliationURL":"#"}],"publishedDate":"2018-12-20T00:00:00.000+01:00","citationText":"Saada, 2018"}
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<div class="d-title">
<h1>Getting started deep learning classification on credit risk area</h1>
<p>Using powerful Keras API to train a sequential neural network to classify default of loan car customer. This post is for beginner in deep learning, it’s a simple approach of deep learning for binary classification.<br /></p>
</div>
<div class="d-byline">
David Saada (David Saada)
<br/>2018-12-20
</div>
<div class="d-article">
<div class="layout-chunk" data-layout="l-body">
</div>
<p>A credit risk is the risk of default on a debt that may arise from a borrower failing to make required payments. In the first resort, the risk is that of the lender and includes lost principal and interest, disruption to cash flows, and increased collection costs. We’ll focuse on retail banking area.</p>
<p>The dataset from Banque de France is composed of 10 features and 99.302 customers.</p>
<p>We’ll procede first to load packages and functions, and work on light data processing. At the end we’ll implement deep learning model and check output model indicators.</p>
<h1 id="configuation-packages-functions">Configuation packages / functions</h1>
<p>Packages: we load 5 packages for data processing, train deep learning model and indicators output</p>
<div class="layout-chunk" data-layout="l-body">
<pre class="r"><code>
# packages
library(data.table) # for data processing
library(dummies) # for data processing
library(tensorflow) # for classification
library(keras) # for classification
library(ROCR) # to measure model performance</code></pre>
</div>
<p>Functions: functions for data processing & output model indicators</p>
<div class="layout-chunk" data-layout="l-body">
<pre class="r"><code>
# compute lift
whatsMyLift <- function(score,cible, perc = 0.1,target){
n = ceiling(perc*length(score))
percentile = cible[order(score, decreasing = T)[1:n]]
length(percentile[percentile==target])/length(cible[cible==target])
}
# compute gini + lift
IndicatorsModels<-function(base,proba,target){
pred <- prediction(proba, FactorToBinNum(base$target,target))
perf <- performance(pred, measure = "tpr", x.measure = "fpr")
auc <- performance(pred, measure = "auc")