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<!DOCTYPE html>
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<title>2 ANÁLISIS EXPLORATORIO VARIABLES | Banco</title>
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<meta name="author" content="Carlos, Alberto y Juan Ramón" />
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<li class="chapter" data-level="1" data-path="introducción-y-objetivos.html"><a href="introducción-y-objetivos.html"><i class="fa fa-check"></i><b>1</b> Introducción y objetivos</a><ul>
<li class="chapter" data-level="1.1" data-path="introducción-y-objetivos.html"><a href="introducción-y-objetivos.html#introducción"><i class="fa fa-check"></i><b>1.1</b> Introducción</a></li>
<li class="chapter" data-level="1.2" data-path="introducción-y-objetivos.html"><a href="introducción-y-objetivos.html#objetivos"><i class="fa fa-check"></i><b>1.2</b> Objetivos</a></li>
</ul></li>
<li class="chapter" data-level="2" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html"><i class="fa fa-check"></i><b>2</b> ANÁLISIS EXPLORATORIO VARIABLES</a><ul>
<li class="chapter" data-level="2.1" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#importación-dataset"><i class="fa fa-check"></i><b>2.1</b> Importación dataset</a></li>
<li class="chapter" data-level="2.2" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#división-en-train-y-test"><i class="fa fa-check"></i><b>2.2</b> División en train y test</a></li>
<li class="chapter" data-level="2.3" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#estructura-y-resumen-de-train"><i class="fa fa-check"></i><b>2.3</b> Estructura y resumen de train</a></li>
<li class="chapter" data-level="2.4" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#exploratorio-univariante-de-las-variables-predictoras"><i class="fa fa-check"></i><b>2.4</b> Exploratorio univariante de las variables predictoras</a><ul>
<li class="chapter" data-level="2.4.1" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-age"><i class="fa fa-check"></i><b>2.4.1</b> Variable age</a></li>
<li class="chapter" data-level="2.4.2" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-balance"><i class="fa fa-check"></i><b>2.4.2</b> Variable balance</a></li>
<li class="chapter" data-level="2.4.3" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-job"><i class="fa fa-check"></i><b>2.4.3</b> Variable job</a></li>
<li class="chapter" data-level="2.4.4" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-marital"><i class="fa fa-check"></i><b>2.4.4</b> Variable marital</a></li>
<li class="chapter" data-level="2.4.5" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-education"><i class="fa fa-check"></i><b>2.4.5</b> Variable education</a></li>
<li class="chapter" data-level="2.4.6" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-default"><i class="fa fa-check"></i><b>2.4.6</b> Variable default</a></li>
<li class="chapter" data-level="2.4.7" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-housing"><i class="fa fa-check"></i><b>2.4.7</b> Variable housing</a></li>
<li class="chapter" data-level="2.4.8" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-loan"><i class="fa fa-check"></i><b>2.4.8</b> Variable loan</a></li>
<li class="chapter" data-level="2.4.9" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-contact"><i class="fa fa-check"></i><b>2.4.9</b> Variable contact</a></li>
<li class="chapter" data-level="2.4.10" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-mes"><i class="fa fa-check"></i><b>2.4.10</b> Variable mes</a></li>
<li class="chapter" data-level="2.4.11" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-day"><i class="fa fa-check"></i><b>2.4.11</b> Variable day</a></li>
<li class="chapter" data-level="2.4.12" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-duration"><i class="fa fa-check"></i><b>2.4.12</b> Variable duration</a></li>
<li class="chapter" data-level="2.4.13" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-campaign"><i class="fa fa-check"></i><b>2.4.13</b> Variable campaign</a></li>
<li class="chapter" data-level="2.4.14" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-pdays"><i class="fa fa-check"></i><b>2.4.14</b> Variable pdays</a></li>
<li class="chapter" data-level="2.4.15" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-poutcome"><i class="fa fa-check"></i><b>2.4.15</b> Variable poutcome</a></li>
<li class="chapter" data-level="2.4.16" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-previous"><i class="fa fa-check"></i><b>2.4.16</b> Variable previous</a></li>
<li class="chapter" data-level="2.4.17" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#variable-term_deposit-objetivo"><i class="fa fa-check"></i><b>2.4.17</b> Variable term_deposit (Objetivo)</a></li>
</ul></li>
<li class="chapter" data-level="2.5" data-path="análisis-exploratorio-variables.html"><a href="análisis-exploratorio-variables.html#exploratorio-multivariante"><i class="fa fa-check"></i><b>2.5</b> Exploratorio multivariante</a></li>
</ul></li>
<li class="chapter" data-level="3" data-path="etl.html"><a href="etl.html"><i class="fa fa-check"></i><b>3</b> ETL</a><ul>
<li class="chapter" data-level="3.1" data-path="etl.html"><a href="etl.html#tratamiento-de-datos-faltantes"><i class="fa fa-check"></i><b>3.1</b> Tratamiento de datos faltantes</a></li>
<li class="chapter" data-level="3.2" data-path="etl.html"><a href="etl.html#transformación-de-variables"><i class="fa fa-check"></i><b>3.2</b> Transformación de variables</a><ul>
<li class="chapter" data-level="3.2.1" data-path="etl.html"><a href="etl.html#train-poutcome"><i class="fa fa-check"></i><b>3.2.1</b> Train poutcome</a></li>
<li class="chapter" data-level="3.2.2" data-path="etl.html"><a href="etl.html#variable-previous-1"><i class="fa fa-check"></i><b>3.2.2</b> Variable previous</a></li>
<li class="chapter" data-level="3.2.3" data-path="etl.html"><a href="etl.html#variable-pdays-1"><i class="fa fa-check"></i><b>3.2.3</b> Variable pdays</a></li>
<li class="chapter" data-level="3.2.4" data-path="etl.html"><a href="etl.html#variable-campaign-1"><i class="fa fa-check"></i><b>3.2.4</b> Variable campaign</a></li>
<li class="chapter" data-level="3.2.5" data-path="etl.html"><a href="etl.html#variable-duration-1"><i class="fa fa-check"></i><b>3.2.5</b> Variable duration</a></li>
<li class="chapter" data-level="3.2.6" data-path="etl.html"><a href="etl.html#variable-month"><i class="fa fa-check"></i><b>3.2.6</b> Variable month</a></li>
<li class="chapter" data-level="3.2.7" data-path="etl.html"><a href="etl.html#variable-contact-1"><i class="fa fa-check"></i><b>3.2.7</b> Variable contact</a></li>
<li class="chapter" data-level="3.2.8" data-path="etl.html"><a href="etl.html#variable-balance-1"><i class="fa fa-check"></i><b>3.2.8</b> Variable balance</a></li>
<li class="chapter" data-level="3.2.9" data-path="etl.html"><a href="etl.html#variable-age-1"><i class="fa fa-check"></i><b>3.2.9</b> Variable age</a></li>
</ul></li>
<li class="chapter" data-level="3.3" data-path="etl.html"><a href="etl.html#selección-de-variables"><i class="fa fa-check"></i><b>3.3</b> Selección de variables</a></li>
</ul></li>
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<section class="normal" id="section-">
<div id="análisis-exploratorio-variables" class="section level1">
<h1><span class="header-section-number">2</span> ANÁLISIS EXPLORATORIO VARIABLES</h1>
<p>En el EDA de este proyecto se ha procedido a analizar cada variable individualmente. Se ha observado cada una de las variables por si sola, que representan, su distribución, estadísticos básicos, missing values y capacidad predictora en un enfoque univariante para un modelo de regresión logística. Todo ello para entender cada una de las variables y el como procesarlas de la mejor forma posible de cara a utilizarlas en un modelo de aprendizaje automático. Por ello, también se ha adelantado en esta fase algunas posibles transformaciones que puedan ser de utilidad en la ETL.</p>
<div id="importación-dataset" class="section level2">
<h2><span class="header-section-number">2.1</span> Importación dataset</h2>
<p>Fijamos semilla para asegurar la replicación de los resultados de este proyecto.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="análisis-exploratorio-variables.html#cb1-1"></a><span class="kw">set.seed</span>(<span class="dv">42</span>)</span></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="análisis-exploratorio-variables.html#cb2-1"></a>data =<span class="st"> </span><span class="kw">read_csv</span>(<span class="st">"BankCustomerdata.csv"</span>)</span></code></pre></div>
<pre><code>##
## -- Column specification --------------------------------------------------------
## cols(
## age = col_double(),
## job = col_character(),
## marital = col_character(),
## education = col_character(),
## default = col_character(),
## balance = col_double(),
## housing = col_character(),
## loan = col_character(),
## contact = col_character(),
## day = col_double(),
## month = col_character(),
## duration = col_double(),
## campaign = col_double(),
## pdays = col_double(),
## previous = col_double(),
## poutcome = col_character(),
## term_deposit = col_character()
## )</code></pre>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="análisis-exploratorio-variables.html#cb4-1"></a><span class="kw">nrow</span>(data)</span></code></pre></div>
<pre><code>## [1] 42639</code></pre>
</div>
<div id="división-en-train-y-test" class="section level2">
<h2><span class="header-section-number">2.2</span> División en train y test</h2>
<p>Dividimos el dataset original en el conjunto de <strong>train</strong> y <strong>test</strong>. Test se ha apartado hasta evaluación, tomando las decisiones de la ETL solo con train para evitar cualquier tipo de sobreajuste indirecto sobre este subconjunto.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="análisis-exploratorio-variables.html#cb6-1"></a>n =<span class="st"> </span><span class="kw">nrow</span>(data)</span>
<span id="cb6-2"><a href="análisis-exploratorio-variables.html#cb6-2"></a>trainIndex =<span class="st"> </span><span class="kw">sample</span>(<span class="dv">1</span><span class="op">:</span>n, <span class="dt">size =</span> <span class="kw">round</span>(<span class="fl">0.8</span><span class="op">*</span>n), <span class="dt">replace=</span><span class="ot">FALSE</span>)</span>
<span id="cb6-3"><a href="análisis-exploratorio-variables.html#cb6-3"></a>train =<span class="st"> </span>data[trainIndex ,]</span>
<span id="cb6-4"><a href="análisis-exploratorio-variables.html#cb6-4"></a>test =<span class="st"> </span>data[<span class="op">-</span>trainIndex ,]</span></code></pre></div>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="análisis-exploratorio-variables.html#cb7-1"></a><span class="kw">nrow</span>(train)</span></code></pre></div>
<pre><code>## [1] 34111</code></pre>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="análisis-exploratorio-variables.html#cb9-1"></a><span class="kw">nrow</span>(test)</span></code></pre></div>
<pre><code>## [1] 8528</code></pre>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="análisis-exploratorio-variables.html#cb11-1"></a><span class="kw">ftable</span>(train<span class="op">$</span>term_deposit)</span></code></pre></div>
<pre><code>## no yes
##
## 30938 3173</code></pre>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="análisis-exploratorio-variables.html#cb13-1"></a><span class="kw">ftable</span>(test<span class="op">$</span>term_deposit)</span></code></pre></div>
<pre><code>## no yes
##
## 7740 788</code></pre>
</div>
<div id="estructura-y-resumen-de-train" class="section level2">
<h2><span class="header-section-number">2.3</span> Estructura y resumen de train</h2>
<p>Observamos su estructura y un resumen de las principales variables</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="análisis-exploratorio-variables.html#cb15-1"></a><span class="kw">summary</span>(train)</span></code></pre></div>
<pre><code>## age job marital education
## Min. :18.00 Length:34111 Length:34111 Length:34111
## 1st Qu.:33.00 Class :character Class :character Class :character
## Median :39.00 Mode :character Mode :character Mode :character
## Mean :40.79
## 3rd Qu.:48.00
## Max. :95.00
## default balance housing loan
## Length:34111 Min. : -8019.0 Length:34111 Length:34111
## Class :character 1st Qu.: 63.5 Class :character Class :character
## Mode :character Median : 432.0 Mode :character Mode :character
## Mean : 1345.1
## 3rd Qu.: 1392.5
## Max. :102127.0
## contact day month duration
## Length:34111 Min. : 1.00 Length:34111 Min. : 0.0
## Class :character 1st Qu.: 8.00 Class :character 1st Qu.: 100.0
## Mode :character Median :16.00 Mode :character Median : 176.0
## Mean :15.84 Mean : 255.6
## 3rd Qu.:21.00 3rd Qu.: 315.0
## Max. :31.00 Max. :4918.0
## campaign pdays previous poutcome
## Min. : 1.00 Min. : -1.0 Min. : 0.0000 Length:34111
## 1st Qu.: 1.00 1st Qu.: -1.0 1st Qu.: 0.0000 Class :character
## Median : 2.00 Median : -1.0 Median : 0.0000 Mode :character
## Mean : 2.81 Mean : 34.1 Mean : 0.4633
## 3rd Qu.: 3.00 3rd Qu.: -1.0 3rd Qu.: 0.0000
## Max. :63.00 Max. :536.0 Max. :275.0000
## term_deposit
## Length:34111
## Class :character
## Mode :character
##
##
## </code></pre>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="análisis-exploratorio-variables.html#cb17-1"></a><span class="kw">describe</span>(train)</span></code></pre></div>
<pre><code>## train
##
## 17 Variables 34111 Observations
## --------------------------------------------------------------------------------
## age
## n missing distinct Info Mean Gmd .05 .10
## 34111 0 74 0.999 40.79 11.47 27 29
## .25 .50 .75 .90 .95
## 33 39 48 56 58
##
## lowest : 18 19 20 21 22, highest: 87 89 90 92 95
## --------------------------------------------------------------------------------
## job
## n missing distinct
## 34111 0 12
##
## lowest : admin. blue-collar entrepreneur housemaid management
## highest: services student technician unemployed unknown
##
## admin. (3892, 0.114), blue-collar (7608, 0.223), entrepreneur (1137, 0.033),
## housemaid (937, 0.027), management (7130, 0.209), retired (1503, 0.044),
## self-employed (1183, 0.035), services (3215, 0.094), student (581, 0.017),
## technician (5763, 0.169), unemployed (955, 0.028), unknown (207, 0.006)
## --------------------------------------------------------------------------------
## marital
## n missing distinct
## 34111 0 3
##
## Value divorced married single
## Frequency 3982 20680 9449
## Proportion 0.117 0.606 0.277
## --------------------------------------------------------------------------------
## education
## n missing distinct
## 34111 0 4
##
## Value primary secondary tertiary unknown
## Frequency 5272 17645 9847 1347
## Proportion 0.155 0.517 0.289 0.039
## --------------------------------------------------------------------------------
## default
## n missing distinct
## 34111 0 2
##
## Value no yes
## Frequency 33463 648
## Proportion 0.981 0.019
## --------------------------------------------------------------------------------
## balance
## n missing distinct Info Mean Gmd .05 .10
## 34111 0 6531 0.999 1345 2045 -191.0 0.0
## .25 .50 .75 .90 .95
## 63.5 432.0 1392.5 3570.0 5771.0
##
## lowest : -8019 -6847 -4057 -3372 -3313, highest: 64343 66721 71188 81204 102127
## --------------------------------------------------------------------------------
## housing
## n missing distinct
## 34111 0 2
##
## Value no yes
## Frequency 14509 19602
## Proportion 0.425 0.575
## --------------------------------------------------------------------------------
## loan
## n missing distinct
## 34111 0 2
##
## Value no yes
## Frequency 28463 5648
## Proportion 0.834 0.166
## --------------------------------------------------------------------------------
## contact
## n missing distinct
## 34111 0 3
##
## Value cellular telephone unknown
## Frequency 21769 2097 10245
## Proportion 0.638 0.061 0.300
## --------------------------------------------------------------------------------
## day
## n missing distinct Info Mean Gmd .05 .10
## 34111 0 31 0.998 15.84 9.544 3 5
## .25 .50 .75 .90 .95
## 8 16 21 28 30
##
## lowest : 1 2 3 4 5, highest: 27 28 29 30 31
## --------------------------------------------------------------------------------
## month
## n missing distinct
## 34111 0 12
##
## lowest : apr aug dec feb jan, highest: mar may nov oct sep
##
## Value apr aug dec feb jan jul jun mar may nov oct
## Frequency 2142 4813 171 1823 969 5244 4125 214 10852 3113 419
## Proportion 0.063 0.141 0.005 0.053 0.028 0.154 0.121 0.006 0.318 0.091 0.012
##
## Value sep
## Frequency 226
## Proportion 0.007
## --------------------------------------------------------------------------------
## duration
## n missing distinct Info Mean Gmd .05 .10
## 34111 0 1485 1 255.6 235 35 58
## .25 .50 .75 .90 .95
## 100 176 315 544 749
##
## lowest : 0 2 3 4 5, highest: 3284 3322 3366 3881 4918
## --------------------------------------------------------------------------------
## campaign
## n missing distinct Info Mean Gmd .05 .10
## 34111 0 45 0.921 2.81 2.435 1 1
## .25 .50 .75 .90 .95
## 1 2 3 5 8
##
## lowest : 1 2 3 4 5, highest: 43 44 55 58 63
## --------------------------------------------------------------------------------
## pdays
## n missing distinct Info Mean Gmd .05 .10
## 34111 0 409 0.393 34.1 62.25 -1 -1
## .25 .50 .75 .90 .95
## -1 -1 -1 175 301
##
## lowest : -1 1 2 4 5, highest: 495 515 518 520 536
## --------------------------------------------------------------------------------
## previous
## n missing distinct Info Mean Gmd .05 .10
## 34111 0 36 0.393 0.4633 0.8488 0 0
## .25 .50 .75 .90 .95
## 0 0 0 1 3
##
## lowest : 0 1 2 3 4, highest: 37 38 51 58 275
## --------------------------------------------------------------------------------
## poutcome
## n missing distinct
## 34111 0 4
##
## Value failure other success unknown
## Frequency 3401 1201 625 28884
## Proportion 0.100 0.035 0.018 0.847
## --------------------------------------------------------------------------------
## term_deposit
## n missing distinct
## 34111 0 2
##
## Value no yes
## Frequency 30938 3173
## Proportion 0.907 0.093
## --------------------------------------------------------------------------------</code></pre>
</div>
<div id="exploratorio-univariante-de-las-variables-predictoras" class="section level2">
<h2><span class="header-section-number">2.4</span> Exploratorio univariante de las variables predictoras</h2>
<div id="variable-age" class="section level3">
<h3><span class="header-section-number">2.4.1</span> Variable age</h3>
<p>La variable age es una variable cuantitativa situada en la escala de proporción. Dicha variable indica la edad del cliente.</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="análisis-exploratorio-variables.html#cb19-1"></a><span class="kw">describe</span>(train<span class="op">$</span>age)</span></code></pre></div>
<pre><code>## train$age
## n missing distinct Info Mean Gmd .05 .10
## 34111 0 74 0.999 40.79 11.47 27 29
## .25 .50 .75 .90 .95
## 33 39 48 56 58
##
## lowest : 18 19 20 21 22, highest: 87 89 90 92 95</code></pre>
<p>Debes ser mayor de edad para contratar el depósito.
La media de edad de nuestros individuos se encuentra en torno a los 41 años.</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="análisis-exploratorio-variables.html#cb21-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(<span class="dt">x =</span> age)) <span class="op">+</span></span>
<span id="cb21-2"><a href="análisis-exploratorio-variables.html#cb21-2"></a><span class="st"> </span><span class="kw">geom_histogram</span>(<span class="kw">aes</span>(<span class="dt">y =</span> ..density..), <span class="dt">fill=</span><span class="st">"skyblue2"</span>, <span class="dt">colour=</span><span class="st">"white"</span>) <span class="op">+</span></span>
<span id="cb21-3"><a href="análisis-exploratorio-variables.html#cb21-3"></a><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">'Edad del cliente'</span>)</span></code></pre></div>
<pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
<p><img src="bookDown_files/figure-html/unnamed-chunk-11-1.png" width="672" /></p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="análisis-exploratorio-variables.html#cb23-1"></a><span class="kw">ggplot</span>(train,<span class="kw">aes</span>(<span class="dt">x=</span>age, <span class="dt">fill =</span> term_deposit)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_histogram</span>(<span class="kw">aes</span>(<span class="dt">y =</span> ..density..)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">alpha =</span> <span class="dv">0</span>) <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span>term_deposit) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Edad del cliente"</span>)</span></code></pre></div>
<pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
<p><img src="bookDown_files/figure-html/unnamed-chunk-11-2.png" width="672" /></p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="análisis-exploratorio-variables.html#cb25-1"></a><span class="kw">ggplot</span>(train ,<span class="kw">aes</span>(<span class="dt">x=</span>age, <span class="dt">fill=</span>term_deposit)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">alpha =</span> <span class="fl">.5</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Edad del cliente"</span>)</span></code></pre></div>
<p><img src="bookDown_files/figure-html/unnamed-chunk-11-3.png" width="672" /></p>
<p>Podemos observar como son los jóvenes y los mayores los que mayor probabilidad de contratar el depósito tienen.</p>
<p>Se realiza un logit con la variable edad en forma continua para ver sus resultados.</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="análisis-exploratorio-variables.html#cb26-1"></a>logit_edad =<span class="st"> </span><span class="kw">glm</span>(<span class="kw">as.factor</span>(term_deposit) <span class="op">~</span><span class="st"> </span>age, <span class="dt">family=</span>(<span class="kw">binomial</span>(<span class="dt">link=</span>logit)), <span class="dt">data =</span> train)</span>
<span id="cb26-2"><a href="análisis-exploratorio-variables.html#cb26-2"></a><span class="kw">summary</span>(logit_edad)</span></code></pre></div>
<pre><code>##
## Call:
## glm(formula = as.factor(term_deposit) ~ age, family = (binomial(link = logit)),
## data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.4859 -0.4466 -0.4396 -0.4342 2.2147
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.429109 0.076792 -31.632 <2e-16 ***
## age 0.003707 0.001812 2.046 0.0408 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 21113 on 34110 degrees of freedom
## Residual deviance: 21108 on 34109 degrees of freedom
## AIC: 21112
##
## Number of Fisher Scoring iterations: 5</code></pre>
<p>Se comprueba como la variable edad en forma continua no es tan significativa ya que el peso de los ancianos se contrarresta con el peso de los jóvenes. Por ello aumentar la variable edad en una unidad no tiene un efecto tan significativo en nuestra variable objetivo.</p>
<p>Se puede apreciar gráficamente como los más jóvenes y los más mayores tienen mayor probabilidad de contratar el depósito. Queremos ver si estas particularidades tienen efecto que con en una variable continua se podrían obviar.</p>
<p>Para ello convertimos nuestra variable continua en una variable categórica.</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="análisis-exploratorio-variables.html#cb28-1"></a>train =<span class="st"> </span>train <span class="op">%>%</span><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">age_categorica =</span> <span class="kw">cut</span>(age, <span class="dt">breaks =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">29</span>, <span class="dv">44</span>, <span class="dv">59</span>, <span class="dv">100</span>), <span class="dt">right =</span> <span class="ot">TRUE</span>, <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">'Joven'</span>,<span class="st">'MedianaEdad'</span>,<span class="st">'Mayores'</span>,<span class="st">'Ancianos'</span>)))</span></code></pre></div>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="análisis-exploratorio-variables.html#cb29-1"></a><span class="kw">ftable</span>(train<span class="op">$</span>age_categorica)</span></code></pre></div>
<pre><code>## Joven MedianaEdad Mayores Ancianos
##
## 3792 18612 10695 1012</code></pre>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="análisis-exploratorio-variables.html#cb31-1"></a><span class="kw">describe</span>(train<span class="op">$</span>age_categorica)</span></code></pre></div>
<pre><code>## train$age_categorica
## n missing distinct
## 34111 0 4
##
## Value Joven MedianaEdad Mayores Ancianos
## Frequency 3792 18612 10695 1012
## Proportion 0.111 0.546 0.314 0.030</code></pre>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="análisis-exploratorio-variables.html#cb33-1"></a>train<span class="op">$</span>age_categorica =<span class="st"> </span><span class="kw">factor</span>(train<span class="op">$</span>age_categorica, <span class="dt">levels=</span>(<span class="kw">c</span>(<span class="st">'MedianaEdad'</span>, <span class="st">'Joven'</span>,<span class="st">'Mayores'</span>,<span class="st">'Ancianos'</span>)))</span></code></pre></div>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="análisis-exploratorio-variables.html#cb34-1"></a><span class="kw">levels</span>(train<span class="op">$</span>age_categorica)</span></code></pre></div>
<pre><code>## [1] "MedianaEdad" "Joven" "Mayores" "Ancianos"</code></pre>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="análisis-exploratorio-variables.html#cb36-1"></a><span class="kw">with</span>(train, <span class="kw">CrossTable</span>(age_categorica, term_deposit, <span class="dt">format =</span> <span class="st">'SPSS'</span>))</span></code></pre></div>
<pre><code>##
## Cell Contents
## |-------------------------|
## | Count |
## | Chi-square contribution |
## | Row Percent |
## | Column Percent |
## | Total Percent |
## |-------------------------|
##
## Total Observations in Table: 34111
##
## | term_deposit
## age_categorica | no | yes | Row Total |
## ---------------|-----------|-----------|-----------|
## MedianaEdad | 17035 | 1577 | 18612 |
## | 1.410 | 13.749 | |
## | 91.527% | 8.473% | 54.563% |
## | 55.062% | 49.701% | |
## | 49.940% | 4.623% | |
## ---------------|-----------|-----------|-----------|
## Joven | 3286 | 506 | 3792 |
## | 6.830 | 66.598 | |
## | 86.656% | 13.344% | 11.117% |
## | 10.621% | 15.947% | |
## | 9.633% | 1.483% | |
## ---------------|-----------|-----------|-----------|
## Mayores | 9869 | 826 | 10695 |
## | 2.939 | 28.657 | |
## | 92.277% | 7.723% | 31.354% |
## | 31.899% | 26.032% | |
## | 28.932% | 2.422% | |
## ---------------|-----------|-----------|-----------|
## Ancianos | 748 | 264 | 1012 |
## | 31.436 | 306.511 | |
## | 73.913% | 26.087% | 2.967% |
## | 2.418% | 8.320% | |
## | 2.193% | 0.774% | |
## ---------------|-----------|-----------|-----------|
## Column Total | 30938 | 3173 | 34111 |
## | 90.698% | 9.302% | |
## ---------------|-----------|-----------|-----------|
##
## </code></pre>
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="análisis-exploratorio-variables.html#cb38-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(<span class="dt">x =</span> age_categorica, <span class="dt">fill =</span> term_deposit)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">position=</span> <span class="kw">position_fill</span>(<span class="dt">reverse =</span> <span class="ot">TRUE</span>)) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">'Edad del cliente'</span>, <span class="dt">y =</span> <span class="st">'prop'</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Edad del cliente"</span>)</span></code></pre></div>
<p><img src="bookDown_files/figure-html/unnamed-chunk-18-1.png" width="672" /></p>
<p>Se puede comprobar como las personas de mayor edad y los más jóvenes son los más propensos a contratar el depósito que las personas de mediana edad y que las personas mayores.</p>
<p>Realizamos un logit con esta variable categórica donde la categoría de referencia se encuentra en mediana edad. Aquellas personas entre 30 y 44 años.</p>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="análisis-exploratorio-variables.html#cb39-1"></a>logit_edadcat =<span class="st"> </span><span class="kw">glm</span>(<span class="kw">as.factor</span>(term_deposit) <span class="op">~</span><span class="st"> </span>age_categorica, <span class="dt">family=</span>(<span class="kw">binomial</span>(<span class="dt">link=</span>logit)), <span class="dt">data =</span> train)</span>
<span id="cb39-2"><a href="análisis-exploratorio-variables.html#cb39-2"></a><span class="kw">summary</span>(logit_edadcat)</span></code></pre></div>
<pre><code>##
## Call:
## glm(formula = as.factor(term_deposit) ~ age_categorica, family = (binomial(link = logit)),
## data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7775 -0.4208 -0.4208 -0.4009 2.2632
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.37975 0.02632 -90.411 <2e-16 ***
## age_categoricaJoven 0.50886 0.05453 9.332 <2e-16 ***
## age_categoricaMayores -0.10081 0.04477 -2.252 0.0244 *
## age_categoricaAncianos 1.33829 0.07627 17.546 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 21113 on 34110 degrees of freedom
## Residual deviance: 20760 on 34107 degrees of freedom
## AIC: 20768
##
## Number of Fisher Scoring iterations: 5</code></pre>
<p>Posteriormente en la selección de variables se realizará una modificación en las categorías de esta variable.</p>
</div>
<div id="variable-balance" class="section level3">
<h3><span class="header-section-number">2.4.2</span> Variable balance</h3>
<p>La variable balance es una variable cuantitativa situada en la escala de intervalo. Dicha variable indica el saldo del cliente.</p>
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="análisis-exploratorio-variables.html#cb41-1"></a><span class="kw">describe</span>(train<span class="op">$</span>balance)</span></code></pre></div>
<pre><code>## train$balance
## n missing distinct Info Mean Gmd .05 .10
## 34111 0 6531 0.999 1345 2045 -191.0 0.0
## .25 .50 .75 .90 .95
## 63.5 432.0 1392.5 3570.0 5771.0
##
## lowest : -8019 -6847 -4057 -3372 -3313, highest: 64343 66721 71188 81204 102127</code></pre>
<p>Variable tiene saldo negativo lo que podría limitar su transformación aunque existe otra variable en el modelo donde se indica si el cliente está o no en mora.</p>
<p>La distribución de la variable cuenta con una larga cola que hace complicado estudiar la distribución más allá de los valores iniciales.</p>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="análisis-exploratorio-variables.html#cb43-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(<span class="dt">x =</span> balance)) <span class="op">+</span></span>
<span id="cb43-2"><a href="análisis-exploratorio-variables.html#cb43-2"></a><span class="st"> </span><span class="kw">geom_histogram</span>(<span class="dt">fill=</span><span class="st">"skyblue2"</span>, <span class="dt">colour=</span><span class="st">"white"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">'Saldo del cliente'</span>)</span></code></pre></div>
<pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
<p><img src="bookDown_files/figure-html/unnamed-chunk-21-1.png" width="672" /></p>
<div class="sourceCode" id="cb45"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb45-1"><a href="análisis-exploratorio-variables.html#cb45-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(<span class="dt">x=</span>balance, <span class="dt">fill =</span> term_deposit)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_histogram</span>(<span class="kw">aes</span>(<span class="dt">y =</span> ..density..)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">alpha =</span> <span class="fl">.5</span>) <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span>term_deposit) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Saldo del cliente"</span>)</span></code></pre></div>
<pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
<p><img src="bookDown_files/figure-html/unnamed-chunk-21-2.png" width="672" /></p>
<div class="sourceCode" id="cb47"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb47-1"><a href="análisis-exploratorio-variables.html#cb47-1"></a><span class="kw">ggplot</span>(train ,<span class="kw">aes</span>(<span class="dt">x=</span>balance, <span class="dt">fill=</span>term_deposit)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">alpha =</span> <span class="fl">.3</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Saldo del cliente"</span>)</span></code></pre></div>
<p><img src="bookDown_files/figure-html/unnamed-chunk-21-3.png" width="672" /></p>
<p>Se comprueba como no se puede apreciar correctamente las funciones de densidad condicionadas a contratar o no el depósito. Por ello, debemos pensar en transformar nuestra variable.</p>
<p>Primero comprobamos como tener saldo negativo es distinto de estar en mora al menos con los datos que tenemos. Por ello, tendríamos que ir con más cuidado a la hora de realizar una transformación logaritmica de nuestra variable.</p>
<div class="sourceCode" id="cb48"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb48-1"><a href="análisis-exploratorio-variables.html#cb48-1"></a>balance_neg =<span class="st"> </span>train <span class="op">%>%</span><span class="st"> </span><span class="kw">filter</span>(balance <span class="op"><</span><span class="st"> </span><span class="dv">0</span>)</span>
<span id="cb48-2"><a href="análisis-exploratorio-variables.html#cb48-2"></a><span class="kw">describe</span>(balance_neg<span class="op">$</span>balance)</span></code></pre></div>
<pre><code>## balance_neg$balance
## n missing distinct Info Mean Gmd .05 .10
## 2964 0 844 1 -320 324 -938.0 -691.7
## .25 .50 .75 .90 .95
## -416.2 -232.0 -95.0 -28.0 -11.0
##
## lowest : -8019 -6847 -4057 -3372 -3313, highest: -5 -4 -3 -2 -1</code></pre>
<div class="sourceCode" id="cb50"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb50-1"><a href="análisis-exploratorio-variables.html#cb50-1"></a><span class="kw">describe</span>(balance_neg<span class="op">$</span>default)</span></code></pre></div>
<pre><code>## balance_neg$default
## n missing distinct
## 2964 0 2
##
## Value no yes
## Frequency 2610 354
## Proportion 0.881 0.119</code></pre>
<p>Las personas que tienen saldo negativo en nuestro data set son 2964 clientes.
Aunque los clientes que tienen un crédito en mora tan solo son 354. Representa tan solo el 12% del total con saldo negativo.</p>
<div class="sourceCode" id="cb52"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb52-1"><a href="análisis-exploratorio-variables.html#cb52-1"></a>balance_cero =<span class="st"> </span>train <span class="op">%>%</span><span class="st"> </span><span class="kw">filter</span>(balance <span class="op">==</span><span class="st"> </span><span class="dv">0</span>)</span>
<span id="cb52-2"><a href="análisis-exploratorio-variables.html#cb52-2"></a><span class="kw">describe</span>(balance_cero<span class="op">$</span>balance)</span></code></pre></div>
<pre><code>## balance_cero$balance
## n missing distinct Info Mean Gmd
## 2716 0 1 0 0 0
##
## Value 0
## Frequency 2716
## Proportion 1</code></pre>
<div class="sourceCode" id="cb54"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb54-1"><a href="análisis-exploratorio-variables.html#cb54-1"></a><span class="kw">describe</span>(balance_cero<span class="op">$</span>default)</span></code></pre></div>
<pre><code>## balance_cero$default
## n missing distinct
## 2716 0 2
##
## Value no yes
## Frequency 2622 94
## Proportion 0.965 0.035</code></pre>
<p>Se comprueba como aquellos que tienen saldo 0 son 2716 clientes sumándoles aquellos con saldo negativo suponen 5680 observaciones mientras que las personas con mora pertenecientes a esos grupo únicamente suman 448 observaciones.</p>
<p>Por tanto, la variable default no podría explicar aquellas observaciones perdidas al realizar el logaritmo.</p>
<div class="sourceCode" id="cb56"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb56-1"><a href="análisis-exploratorio-variables.html#cb56-1"></a>balance_pos =<span class="st"> </span>train <span class="op">%>%</span><span class="st"> </span><span class="kw">filter</span>(balance <span class="op">></span><span class="st"> </span><span class="dv">0</span>)</span>
<span id="cb56-2"><a href="análisis-exploratorio-variables.html#cb56-2"></a><span class="kw">describe</span>(balance_pos<span class="op">$</span>balance)</span></code></pre></div>
<pre><code>## balance_pos$balance
## n missing distinct Info Mean Gmd .05 .10
## 28431 0 5686 1 1647 2210 24 63
## .25 .50 .75 .90 .95
## 221 635 1734 4089 6429
##
## lowest : 1 2 3 4 5, highest: 64343 66721 71188 81204 102127</code></pre>
<div class="sourceCode" id="cb58"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb58-1"><a href="análisis-exploratorio-variables.html#cb58-1"></a>train =<span class="st"> </span>train <span class="op">%>%</span><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">log_balance =</span> <span class="kw">log10</span>(balance))</span></code></pre></div>
<pre><code>## Warning: Problem with `mutate()` input `log_balance`.
## i Se han producido NaNs
## i Input `log_balance` is `log10(balance)`.</code></pre>
<pre><code>## Warning in mask$eval_all_mutate(dots[[i]]): Se han producido NaNs</code></pre>
<div class="sourceCode" id="cb61"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb61-1"><a href="análisis-exploratorio-variables.html#cb61-1"></a><span class="kw">describe</span>(train<span class="op">$</span>log_balance)</span></code></pre></div>
<pre><code>## Warning in w * sort(x - mean(x)): longitud de objeto mayor no es múltiplo de la
## longitud de uno menor</code></pre>
<pre><code>## train$log_balance
## n missing distinct Info Mean Gmd .05 .10
## 31147 2964 5687 0.999 -Inf NaN -Inf 0.6021
## .25 .50 .75 .90 .95
## 2.1430 2.7243 3.1926 3.5812 3.7852
##
## lowest : -Inf 0.0000000 0.3010300 0.4771213 0.6020600
## highest: 4.8085013 4.8242625 4.8524068 4.9095774 5.0091406</code></pre>
<p>Se transforma la variable mediante el logaritmo para suavizar el crecimiento al principio y acrecentarlo al final.</p>
<p>Al realizar esta transformación se producen valores NaNs. En la variable balance existen 5680 valores en los cuales el cliente no tiene saldo o su saldo es negativo.</p>
<div class="sourceCode" id="cb64"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb64-1"><a href="análisis-exploratorio-variables.html#cb64-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(<span class="dt">x =</span> log_balance)) <span class="op">+</span></span>
<span id="cb64-2"><a href="análisis-exploratorio-variables.html#cb64-2"></a><span class="st"> </span><span class="kw">geom_histogram</span>(<span class="dt">fill=</span><span class="st">"skyblue2"</span>, <span class="dt">colour=</span><span class="st">"white"</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">'Log saldo del cliente'</span>)</span></code></pre></div>
<pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
<pre><code>## Warning: Removed 5680 rows containing non-finite values (stat_bin).</code></pre>
<p><img src="bookDown_files/figure-html/unnamed-chunk-25-1.png" width="672" /></p>
<div class="sourceCode" id="cb67"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb67-1"><a href="análisis-exploratorio-variables.html#cb67-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(<span class="dt">x=</span>log_balance, <span class="dt">fill =</span> term_deposit)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_histogram</span>(<span class="kw">aes</span>(<span class="dt">y =</span> ..density..)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">alpha =</span> <span class="fl">.5</span>) <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span>term_deposit) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Log saldo del cliente"</span>)</span></code></pre></div>
<pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
<pre><code>## Warning: Removed 5680 rows containing non-finite values (stat_bin).</code></pre>
<pre><code>## Warning: Removed 5680 rows containing non-finite values (stat_density).</code></pre>
<p><img src="bookDown_files/figure-html/unnamed-chunk-25-2.png" width="672" /></p>
<div class="sourceCode" id="cb71"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb71-1"><a href="análisis-exploratorio-variables.html#cb71-1"></a><span class="kw">ggplot</span>(train ,<span class="kw">aes</span>(log_balance, <span class="dt">fill=</span>term_deposit)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">alpha =</span> <span class="fl">.5</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Log saldo del cliente"</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 5680 rows containing non-finite values (stat_density).</code></pre>
<p><img src="bookDown_files/figure-html/unnamed-chunk-25-3.png" width="672" /></p>
<p>Con esta visualización únicamente podemos intuir que aquellas personas con mayor saldo en cuenta tienen mayor probabilidad de contratar un depósito.</p>
<p>Por ello, otra opción que se podría realizar en vez de realizar el logaritmo y perder información se podría categorizar la variable respecto al saldo en cuenta.</p>
<div class="sourceCode" id="cb73"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb73-1"><a href="análisis-exploratorio-variables.html#cb73-1"></a>bal =<span class="st"> </span>train <span class="op">%>%</span><span class="st"> </span><span class="kw">filter</span>(balance <span class="op">></span><span class="st"> </span><span class="dv">0</span>)</span>
<span id="cb73-2"><a href="análisis-exploratorio-variables.html#cb73-2"></a><span class="kw">describe</span>(bal<span class="op">$</span>balance)</span></code></pre></div>
<pre><code>## bal$balance
## n missing distinct Info Mean Gmd .05 .10
## 28431 0 5686 1 1647 2210 24 63
## .25 .50 .75 .90 .95
## 221 635 1734 4089 6429
##
## lowest : 1 2 3 4 5, highest: 64343 66721 71188 81204 102127</code></pre>
<div class="sourceCode" id="cb75"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb75-1"><a href="análisis-exploratorio-variables.html#cb75-1"></a><span class="kw">sd</span>(bal<span class="op">$</span>balance)</span></code></pre></div>
<pre><code>## [1] 3225.537</code></pre>
<div class="sourceCode" id="cb77"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb77-1"><a href="análisis-exploratorio-variables.html#cb77-1"></a><span class="kw">IQR</span>(bal<span class="op">$</span>balance)</span></code></pre></div>
<pre><code>## [1] 1513.5</code></pre>
<p>Se dividiría la variable en cuatro categorías.</p>
<p>Saldos negativos. Siendo la cantidad inferior a 0.</p>
<p>Saldos bajos. De acuerdo a nuestros datos, la media de saldos positivos se encuentra en torno a 1500 euros. Aunque está sesgada debido a la gran cantidad de saldos altos y a una gran dispersión en nuestros datos. Por ello, se aumenta el límite de esta franja hasta los 2000€. La mediana de los datos se encuentra en torno a 635 y el IQR en torno a 1500.</p>
<p>Saldos medios. Se encuentra cercanos desde la media hasta los 10000 euros.</p>
<p>Saldos altos. A partir de dicha cantidad teniendo en cuenta la distribución de nuestros datos.</p>
<div class="sourceCode" id="cb79"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb79-1"><a href="análisis-exploratorio-variables.html#cb79-1"></a>train =<span class="st"> </span>train <span class="op">%>%</span><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">balance_categorica =</span> <span class="kw">cut</span>(balance, <span class="dt">breaks =</span> <span class="kw">c</span>(<span class="op">-</span><span class="dv">10000</span>, <span class="dv">0</span>, <span class="dv">2000</span>, <span class="dv">10000</span>, <span class="dv">105000</span>), <span class="dt">right =</span> <span class="ot">FALSE</span>, <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">'Negativo'</span>,<span class="st">'Bajo'</span>,<span class="st">'Medio'</span>, <span class="st">'Alto'</span>)))</span></code></pre></div>
<div class="sourceCode" id="cb80"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb80-1"><a href="análisis-exploratorio-variables.html#cb80-1"></a><span class="kw">ftable</span>(train<span class="op">$</span>balance_categorica)</span></code></pre></div>
<pre><code>## Negativo Bajo Medio Alto
##
## 2964 24873 5649 625</code></pre>
<div class="sourceCode" id="cb82"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb82-1"><a href="análisis-exploratorio-variables.html#cb82-1"></a><span class="kw">describe</span>(train<span class="op">$</span>balance_categorica)</span></code></pre></div>
<pre><code>## train$balance_categorica
## n missing distinct
## 34111 0 4
##
## Value Negativo Bajo Medio Alto
## Frequency 2964 24873 5649 625
## Proportion 0.087 0.729 0.166 0.018</code></pre>
<div class="sourceCode" id="cb84"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb84-1"><a href="análisis-exploratorio-variables.html#cb84-1"></a><span class="kw">with</span>(train, <span class="kw">CrossTable</span>(balance_categorica, term_deposit, <span class="dt">format =</span> <span class="st">'SPSS'</span>))</span></code></pre></div>
<pre><code>##
## Cell Contents
## |-------------------------|
## | Count |
## | Chi-square contribution |
## | Row Percent |
## | Column Percent |
## | Total Percent |
## |-------------------------|
##
## Total Observations in Table: 34111
##
## | term_deposit
## balance_categorica | no | yes | Row Total |
## -------------------|-----------|-----------|-----------|
## Negativo | 2803 | 161 | 2964 |
## | 4.895 | 47.726 | |
## | 94.568% | 5.432% | 8.689% |
## | 9.060% | 5.074% | |
## | 8.217% | 0.472% | |
## -------------------|-----------|-----------|-----------|
## Bajo | 22684 | 2189 | 24873 |
## | 0.689 | 6.719 | |
## | 91.199% | 8.801% | 72.918% |
## | 73.321% | 68.988% | |
## | 66.501% | 6.417% | |
## -------------------|-----------|-----------|-----------|
## Medio | 4911 | 738 | 5649 |
## | 8.816 | 85.960 | |
## | 86.936% | 13.064% | 16.561% |
## | 15.874% | 23.259% | |
## | 14.397% | 2.164% | |
## -------------------|-----------|-----------|-----------|
## Alto | 540 | 85 | 625 |
## | 1.273 | 12.412 | |
## | 86.400% | 13.600% | 1.832% |
## | 1.745% | 2.679% | |
## | 1.583% | 0.249% | |
## -------------------|-----------|-----------|-----------|
## Column Total | 30938 | 3173 | 34111 |
## | 90.698% | 9.302% | |
## -------------------|-----------|-----------|-----------|
##
## </code></pre>
<p>Al establecer las categorías en base a nuestros datos de entrenamiento se podría caer también en overfitting.</p>
<div class="sourceCode" id="cb86"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb86-1"><a href="análisis-exploratorio-variables.html#cb86-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(<span class="dt">x =</span> balance_categorica, <span class="dt">fill =</span> term_deposit)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">position=</span> <span class="kw">position_fill</span>(<span class="dt">reverse =</span> <span class="ot">TRUE</span>)) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">'Saldo en cuenta cliente'</span>, <span class="dt">y =</span> <span class="st">'prop'</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Saldos del cliente"</span>)</span></code></pre></div>
<p><img src="bookDown_files/figure-html/unnamed-chunk-30-1.png" width="672" /></p>
<p>Se aprecia como a medida que crecen los saldos, la probabilidad de contratar un depósito aumenta.</p>
</div>
<div id="variable-job" class="section level3">
<h3><span class="header-section-number">2.4.3</span> Variable job</h3>
<p>Variable categórica en escala nominal que muestra el tipo de puesto laboral (management, technician, entrepeneur, administrative, blue-collar, services, retired, unknown)</p>
<div class="sourceCode" id="cb87"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb87-1"><a href="análisis-exploratorio-variables.html#cb87-1"></a>train<span class="op">$</span>job=<span class="st"> </span><span class="kw">factor</span>(train<span class="op">$</span>job)</span>
<span id="cb87-2"><a href="análisis-exploratorio-variables.html#cb87-2"></a><span class="kw">levels</span>(train<span class="op">$</span>job)</span></code></pre></div>
<pre><code>## [1] "admin." "blue-collar" "entrepreneur" "housemaid"
## [5] "management" "retired" "self-employed" "services"
## [9] "student" "technician" "unemployed" "unknown"</code></pre>
<p>Mostramos la tabla de frecuencias absolutas y relativas:</p>
<div class="sourceCode" id="cb89"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb89-1"><a href="análisis-exploratorio-variables.html#cb89-1"></a><span class="kw">ftable</span>(train<span class="op">$</span>job)</span></code></pre></div>
<pre><code>## admin. blue-collar entrepreneur housemaid management retired self-employed services student technician unemployed unknown
##
## 3892 7608 1137 937 7130 1503 1183 3215 581 5763 955 207</code></pre>
<div class="sourceCode" id="cb91"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb91-1"><a href="análisis-exploratorio-variables.html#cb91-1"></a><span class="kw">prop.table</span>(<span class="kw">ftable</span>(train<span class="op">$</span>job))</span></code></pre></div>
<pre><code>## admin. blue-collar entrepreneur housemaid management retired self-employed services student technician unemployed unknown
##
## 0.114098092 0.223036557 0.033332356 0.027469145 0.209023482 0.044062033 0.034680895 0.094251121 0.017032629 0.168948433 0.027996834 0.006068424</code></pre>
<p>La categoría más frecuente es la de <strong>unemployed</strong>, seguido de <strong>blue-collar</strong> y <strong>management</strong>.</p>
<div class="sourceCode" id="cb93"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb93-1"><a href="análisis-exploratorio-variables.html#cb93-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(job)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="kw">aes</span>(<span class="dt">y =</span> ..prop.., <span class="dt">group =</span> <span class="dv">1</span>),<span class="dt">position=</span><span class="st">'dodge'</span>, <span class="dt">fill =</span> <span class="st">'skyblue2'</span>) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>() <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Profesión del cliente"</span>)</span></code></pre></div>
<p><img src="bookDown_files/figure-html/unnamed-chunk-34-1.png" width="672" /></p>
<p>La cruzamos con la variable objetivo:</p>
<div class="sourceCode" id="cb94"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb94-1"><a href="análisis-exploratorio-variables.html#cb94-1"></a><span class="kw">with</span>(train, <span class="kw">CrossTable</span>(job, term_deposit, <span class="dt">format =</span> <span class="st">'SPSS'</span>))</span></code></pre></div>
<pre><code>##
## Cell Contents
## |-------------------------|
## | Count |
## | Chi-square contribution |
## | Row Percent |
## | Column Percent |
## | Total Percent |
## |-------------------------|
##
## Total Observations in Table: 34111
##
## | term_deposit
## job | no | yes | Row Total |
## --------------|-----------|-----------|-----------|
## admin. | 3509 | 383 | 3892 |
## | 0.125 | 1.214 | |
## | 90.159% | 9.841% | 11.410% |
## | 11.342% | 12.071% | |
## | 10.287% | 1.123% | |
## --------------|-----------|-----------|-----------|
## blue-collar | 7103 | 505 | 7608 |
## | 5.954 | 58.055 | |
## | 93.362% | 6.638% | 22.304% |
## | 22.959% | 15.916% | |
## | 20.823% | 1.480% | |
## --------------|-----------|-----------|-----------|
## entrepreneur | 1061 | 76 | 1137 |
## | 0.859 | 8.376 | |
## | 93.316% | 6.684% | 3.333% |
## | 3.429% | 2.395% | |
## | 3.110% | 0.223% | |
## --------------|-----------|-----------|-----------|
## housemaid | 873 | 64 | 937 |
## | 0.631 | 6.154 | |
## | 93.170% | 6.830% | 2.747% |
## | 2.822% | 2.017% | |
## | 2.559% | 0.188% | |
## --------------|-----------|-----------|-----------|
## management | 6345 | 785 | 7130 |
## | 2.293 | 22.357 | |
## | 88.990% | 11.010% | 20.902% |
## | 20.509% | 24.740% | |
## | 18.601% | 2.301% | |
## --------------|-----------|-----------|-----------|
## retired | 1250 | 253 | 1503 |
## | 9.399 | 91.641 | |
## | 83.167% | 16.833% | 4.406% |
## | 4.040% | 7.974% | |
## | 3.665% | 0.742% | |
## --------------|-----------|-----------|-----------|
## self-employed | 1073 | 110 | 1183 |
## | 0.000 | 0.000 | |
## | 90.702% | 9.298% | 3.468% |
## | 3.468% | 3.467% | |
## | 3.146% | 0.322% | |
## --------------|-----------|-----------|-----------|
## services | 2980 | 235 | 3215 |
## | 1.407 | 13.721 | |
## | 92.691% | 7.309% | 9.425% |
## | 9.632% | 7.406% | |
## | 8.736% | 0.689% | |
## --------------|-----------|-----------|-----------|
## student | 462 | 119 | 581 |
## | 8.007 | 78.069 | |
## | 79.518% | 20.482% | 1.703% |
## | 1.493% | 3.750% | |
## | 1.354% | 0.349% | |
## --------------|-----------|-----------|-----------|
## technician | 5244 | 519 | 5763 |
## | 0.056 | 0.544 | |
## | 90.994% | 9.006% | 16.895% |
## | 16.950% | 16.357% | |
## | 15.373% | 1.522% | |
## --------------|-----------|-----------|-----------|
## unemployed | 848 | 107 | 955 |
## | 0.381 | 3.715 | |
## | 88.796% | 11.204% | 2.800% |
## | 2.741% | 3.372% | |
## | 2.486% | 0.314% | |
## --------------|-----------|-----------|-----------|
## unknown | 190 | 17 | 207 |
## | 0.027 | 0.264 | |
## | 91.787% | 8.213% | 0.607% |
## | 0.614% | 0.536% | |
## | 0.557% | 0.050% | |
## --------------|-----------|-----------|-----------|
## Column Total | 30938 | 3173 | 34111 |
## | 90.698% | 9.302% | |
## --------------|-----------|-----------|-----------|
##
## </code></pre>
<p>Con la tabla de frecuencias se observa que el porcentaje de contratación del depósito varía según nivel de empleo.</p>
<div class="sourceCode" id="cb96"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb96-1"><a href="análisis-exploratorio-variables.html#cb96-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(<span class="dt">x =</span> job, <span class="dt">fill =</span> term_deposit)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">position =</span> <span class="st">'dodge'</span>) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>() <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span>term_deposit, <span class="dt">nrow =</span> <span class="dv">1</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Contratación de depósito según profesión del cliente"</span>)</span></code></pre></div>
<p><img src="bookDown_files/figure-html/unnamed-chunk-36-1.png" width="672" /></p>
<div class="sourceCode" id="cb97"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb97-1"><a href="análisis-exploratorio-variables.html#cb97-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(<span class="dt">x =</span> job, <span class="dt">fill =</span> term_deposit)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="kw">aes</span>(<span class="dt">y =</span> ..prop.., <span class="dt">group =</span> <span class="dv">1</span> ),<span class="dt">position =</span> <span class="st">'dodge'</span>) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>() <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span>term_deposit, <span class="dt">nrow =</span> <span class="dv">1</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Contratación de depósito según profesión del cliente"</span>)</span></code></pre></div>
<p><img src="bookDown_files/figure-html/unnamed-chunk-36-2.png" width="672" /></p>
<div class="sourceCode" id="cb98"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb98-1"><a href="análisis-exploratorio-variables.html#cb98-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(<span class="dt">x =</span> job, <span class="dt">fill =</span> term_deposit)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">position=</span> <span class="kw">position_fill</span>(<span class="dt">reverse =</span> <span class="ot">TRUE</span>)) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>() <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">'tipo de puesto laboral'</span>, <span class="dt">y =</span> <span class="st">'prop'</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Contratación de depósito según profesión del cliente"</span>)</span></code></pre></div>
<p><img src="bookDown_files/figure-html/unnamed-chunk-36-3.png" width="672" /></p>
<p>Estudiante es el estado profesional más propenso a contratar el producto, seguido de jubilado. Pero todas las profesiones parecen ser buenos discriminadores de la probabilidad de contratación. Por lo que esta variable parece que pueda ser una buena variable predictora.</p>
<p>Hallamos la regresión logística múltiple en función de la variable job mediante las categorías dumificadas. Calculamos los parámetros de la regresión logística múltiple</p>
<div class="sourceCode" id="cb99"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb99-1"><a href="análisis-exploratorio-variables.html#cb99-1"></a>train<span class="op">$</span>job[train<span class="op">$</span>job <span class="op">==</span><span class="st"> "blue-collar"</span>] <-<span class="st">"blue_collar"</span></span></code></pre></div>
<pre><code>## Warning in `[<-.factor`(`*tmp*`, train$job == "blue-collar", value =
## structure(c(1L, : invalid factor level, NA generated</code></pre>
<div class="sourceCode" id="cb101"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb101-1"><a href="análisis-exploratorio-variables.html#cb101-1"></a>train<span class="op">$</span>job[train<span class="op">$</span>job <span class="op">==</span><span class="st"> "self-employed"</span>] <-<span class="st">"self_employed"</span></span></code></pre></div>
<pre><code>## Warning in `[<-.factor`(`*tmp*`, train$job == "self-employed", value =
## structure(c(1L, : invalid factor level, NA generated</code></pre>
<div class="sourceCode" id="cb103"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb103-1"><a href="análisis-exploratorio-variables.html#cb103-1"></a>train <-<span class="st"> </span><span class="kw">dummy_cols</span> (train, <span class="dt">select_columns =</span> <span class="kw">c</span>(<span class="st">"job"</span>))</span>
<span id="cb103-2"><a href="análisis-exploratorio-variables.html#cb103-2"></a>modelo.log.m.job <-<span class="st"> </span><span class="kw">glm</span>(<span class="kw">as.factor</span>(term_deposit) <span class="op">~</span><span class="st"> </span>job, <span class="dt">family =</span> binomial, <span class="dt">data =</span> train )</span>
<span id="cb103-3"><a href="análisis-exploratorio-variables.html#cb103-3"></a><span class="kw">summary</span>(modelo.log.m.job)</span></code></pre></div>
<pre><code>##
## Call:
## glm(formula = as.factor(term_deposit) ~ job, family = binomial,
## data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6770 -0.4830 -0.4345 -0.3896 2.3261
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.21505 0.05381 -41.161 < 2e-16 ***
## jobentrepreneur -0.42118 0.13037 -3.231 0.001235 **
## jobhousemaid -0.39800 0.14024 -2.838 0.004539 **
## jobmanagement 0.12531 0.06578 1.905 0.056789 .
## jobretired 0.61754 0.08746 7.061 1.65e-12 ***
## jobservices -0.32504 0.08653 -3.757 0.000172 ***
## jobstudent 0.85861 0.11603 7.400 1.37e-13 ***
## jobtechnician -0.09788 0.07081 -1.382 0.166836
## jobunemployed 0.14500 0.11585 1.252 0.210706
## jobunknown -0.19876 0.25881 -0.768 0.442503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 16576 on 25319 degrees of freedom
## Residual deviance: 16382 on 25310 degrees of freedom
## (8791 observations deleted due to missingness)
## AIC: 16402
##
## Number of Fisher Scoring iterations: 5</code></pre>
<p>Obtenemos los intervaleos de confianza de los coeficientes parciales de correlación del modelo. Los intervalos que contengan el 0 indican que la variable dummy a la que están asociados no es significativa en el modelo.</p>
<div class="sourceCode" id="cb105"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb105-1"><a href="análisis-exploratorio-variables.html#cb105-1"></a><span class="kw">confint</span>(<span class="dt">object =</span> modelo.log.m.job , <span class="dt">level=</span><span class="fl">0.95</span>)</span></code></pre></div>
<pre><code>## Waiting for profiling to be done...</code></pre>
<pre><code>## 2.5 % 97.5 %
## (Intercept) -2.322046533 -2.11104003
## jobentrepreneur -0.683303370 -0.17156853
## jobhousemaid -0.681013991 -0.13040880
## jobmanagement -0.002893356 0.25504234
## jobretired 0.445369745 0.78832996
## jobservices -0.495677094 -0.15633666
## jobstudent 0.628183294 1.08338812
## jobtechnician -0.236311421 0.04132549
## jobunemployed -0.086062909 0.36848112
## jobunknown -0.742160419 0.27867447</code></pre>
<p>Los predictores estadísticamente significativos de la regresión son el intercept y las variables job_student y job_retired con pvalores menores que el nivel de significación impuesto (alfa = 0.05).</p>
<p>Los coeficientes positivos indican que aumenta la probabilidad de contrato del depósito para la categoría asociada y los negativos la decrementan. Los estudiantes tienen asociado un coeficiente positivo (1.027) y los retirados 0.702. A mayor valor del coeficiente, mayor probabilidad de contratación.</p>
</div>
<div id="variable-marital" class="section level3">
<h3><span class="header-section-number">2.4.4</span> Variable marital</h3>
<p>Variable categórica en escala nominal, muestra el estado civil (married, single, divorced).</p>
<div class="sourceCode" id="cb108"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb108-1"><a href="análisis-exploratorio-variables.html#cb108-1"></a>train<span class="op">$</span>marital =<span class="st"> </span><span class="kw">factor</span> (train<span class="op">$</span>marital)</span>
<span id="cb108-2"><a href="análisis-exploratorio-variables.html#cb108-2"></a><span class="kw">levels</span>(train<span class="op">$</span>marital)</span></code></pre></div>
<pre><code>## [1] "divorced" "married" "single"</code></pre>
<div class="sourceCode" id="cb110"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb110-1"><a href="análisis-exploratorio-variables.html#cb110-1"></a>reorder_marital =<span class="st"> </span><span class="kw">factor</span>(train<span class="op">$</span>marital, <span class="dt">levels =</span> (<span class="kw">c</span>(<span class="st">'single'</span>,<span class="st">'married'</span>,<span class="st">'divorce'</span>)))</span>
<span id="cb110-2"><a href="análisis-exploratorio-variables.html#cb110-2"></a><span class="kw">levels</span>(reorder_marital)</span></code></pre></div>
<pre><code>## [1] "single" "married" "divorce"</code></pre>
<p>Mostramos la tabla de frecuencias absolutas y relativas:</p>
<div class="sourceCode" id="cb112"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb112-1"><a href="análisis-exploratorio-variables.html#cb112-1"></a><span class="kw">ftable</span>(train<span class="op">$</span>marital)</span></code></pre></div>
<pre><code>## divorced married single
##
## 3982 20680 9449</code></pre>
<div class="sourceCode" id="cb114"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb114-1"><a href="análisis-exploratorio-variables.html#cb114-1"></a><span class="kw">prop.table</span>(<span class="kw">ftable</span>(train<span class="op">$</span>marital))</span></code></pre></div>
<pre><code>## divorced married single
##
## 0.1167365 0.6062560 0.2770074</code></pre>
<div class="sourceCode" id="cb116"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb116-1"><a href="análisis-exploratorio-variables.html#cb116-1"></a><span class="kw">ggplot</span>(train, <span class="kw">aes</span>(marital)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="kw">aes</span>(<span class="dt">y =</span> ..prop.., <span class="dt">group =</span> <span class="dv">1</span> ), <span class="dt">fill =</span> <span class="st">'skyblue2'</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Estado civil del cliente"</span>)</span></code></pre></div>
<p><img src="bookDown_files/figure-html/unnamed-chunk-42-1.png" width="672" /></p>
<p>El estado civil más numeroso es casado.</p>
<p>La cruzamos con la variable objetivo:</p>
<div class="sourceCode" id="cb117"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb117-1"><a href="análisis-exploratorio-variables.html#cb117-1"></a><span class="kw">with</span>(train, <span class="kw">CrossTable</span>(marital, term_deposit, <span class="dt">format =</span> <span class="st">'SPSS'</span>))</span></code></pre></div>
<pre><code>##
## Cell Contents
## |-------------------------|
## | Count |
## | Chi-square contribution |
## | Row Percent |
## | Column Percent |
## | Total Percent |
## |-------------------------|
##
## Total Observations in Table: 34111
##
## | term_deposit
## marital | no | yes | Row Total |
## -------------|-----------|-----------|-----------|
## divorced | 3575 | 407 | 3982 |
## | 0.371 | 3.615 | |
## | 89.779% | 10.221% | 11.674% |
## | 11.555% | 12.827% | |
## | 10.480% | 1.193% | |
## -------------|-----------|-----------|-----------|
## married | 19008 | 1672 | 20680 |
## | 3.376 | 32.921 | |