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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="author" content="Alvaro Pérez Niño" />
<title>Evaluación de la Oferta Inmobiliaria Urbana</title>
<style>
#navbar {
overflow: hidden;
background-color: #1E6BB8;
}
#navbar a {
float: left;
display: block;
color: #f2f2f2;
text-align: center;
padding: 14px 16px;
text-decoration: none;
font-size: 17px;
}
#navbar a:hover {
background-color: #04AA6D;
color: white;
}
#navbar a.active {
background-color: #04AA6D;
color: white;
}
.content {
padding: 16px;
}
.sticky {
position: fixed;
top: 0;
width: 100%;
}
.sticky + .content {
padding-top: 60px;
}
.active {
background-color: #04AA6D;
}
/* Style the tab */
.tab {
overflow: hidden;
border: 1px solid #ccc;
background-color: #04AA6D;
color: white;
display: flex;
align-items: center;
}
s
/* Style the buttons inside the tab */
.tab div {
background-color: inherit;
float: left;
border: none;
outline: none;
cursor: pointer;
padding: 14px 16px;
transition: 0.3s;
font-size: 17px;
}
/* Change background color of buttons on hover */
.tab div:hover {
background-color: #1E6BB8;
}
/* Create an active/current tablink class */
.tab div.active {
background-color: #1E6BB8
}
/* Style the tab content */
.tabcontent {
display: none;
padding: 6px 12px;
border: 1px solid #ccc;
border-top: none;
}
.tablinks{
padding: 1em;
cursor: pointer;
}
</style>
<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
var i, h, a;
for (i = 0; i < hs.length; i++) {
h = hs[i];
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
}
});
</script>
<style type="text/css">code{white-space: pre;}</style>
<style type="text/css" data-origin="pandoc">
pre > code.sourceCode { white-space: pre; position: relative; }
pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
pre > code.sourceCode > span:empty { height: 1.2em; }
.sourceCode { overflow: visible; }
code.sourceCode > span { color: inherit; text-decoration: inherit; }
div.sourceCode { margin: 1em 0; }
pre.sourceCode { margin: 0; }
@media screen {
div.sourceCode { overflow: auto; }
}
@media print {
pre > code.sourceCode { white-space: pre-wrap; }
pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
}
pre.numberSource code
{ counter-reset: source-line 0; }
pre.numberSource code > span
{ position: relative; left: -4em; counter-increment: source-line; }
pre.numberSource code > span > a:first-child::before
{ content: counter(source-line);
position: relative; left: -1em; text-align: right; vertical-align: baseline;
border: none; display: inline-block;
-webkit-touch-callout: none; -webkit-user-select: none;
-khtml-user-select: none; -moz-user-select: none;
-ms-user-select: none; user-select: none;
padding: 0 4px; width: 4em;
color: #aaaaaa;
}
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
div.sourceCode
{ }
@media screen {
pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
}
code span.al { color: #ff0000; font-weight: bold; }
code span.an { color: #60a0b0; font-weight: bold; font-style: italic; }
code span.at { color: #7d9029; }
code span.bn { color: #40a070; }
code span.bu { color: #008000; }
code span.cf { color: #007020; font-weight: bold; }
code span.ch { color: #4070a0; }
code span.cn { color: #880000; }
code span.co { color: #60a0b0; font-style: italic; }
code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; }
code span.do { color: #ba2121; font-style: italic; }
code span.dt { color: #902000; }
code span.dv { color: #40a070; }
code span.er { color: #ff0000; font-weight: bold; }
code span.ex { }
code span.fl { color: #40a070; }
code span.fu { color: #06287e; }
code span.im { color: #008000; font-weight: bold; }
code span.in { color: #60a0b0; font-weight: bold; font-style: italic; }
code span.kw { color: #007020; font-weight: bold; }
code span.op { color: #666666; }
code span.ot { color: #007020; }
code span.pp { color: #bc7a00; }
code span.sc { color: #4070a0; }
code span.ss { color: #bb6688; }
code span.st { color: #4070a0; }
code span.va { color: #19177c; }
code span.vs { color: #4070a0; }
code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; }
a.sourceLine {
pointer-events: auto;
}
</style>
<script>
// apply pandoc div.sourceCode style to pre.sourceCode instead
(function() {
var sheets = document.styleSheets;
for (var i = 0; i < sheets.length; i++) {
if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
try { var rules = sheets[i].cssRules; } catch (e) { continue; }
for (var j = 0; j < rules.length; j++) {
var rule = rules[j];
// check if there is a div.sourceCode rule
if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") continue;
var style = rule.style.cssText;
// check if color or background-color is set
if (rule.style.color === '' && rule.style.backgroundColor === '') continue;
// replace div.sourceCode by a pre.sourceCode rule
sheets[i].deleteRule(j);
sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
}
}
})();
</script>
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</head>
<body>
<section class="page-header">
<h1 class="title toc-ignore project-name">Evaluación de la Oferta
Inmobiliaria Urbana</h1>
<h4 class="author project-author">Alvaro Pérez Niño</h4>
<h4 class="date project-date">Febrero de 2024</h4>
</section>
<div id="navbar">
<a href="#problema" id="toc-introducción">Problematica</a>
<a href="#entendimiento-de-los-datos" id="toc-objetivos">Entendimiento de datos</a>
<a href="#preparación-de-los-datos" id="toc-métodos">Preparación de datos</a>
<a href="#análisis-de-componentes-principales" id="toc-resultados">Análisis PCA</a>
<a href="#análisis-de-conglomerados" id="toc-resultados">Análisis Conglo</a>
<a href="#análisis-de-correspondencia" id="toc-resultados">Análisis Corresp</a>
<a></a>
<a href="#anexos" id="toc-anexos">Anexos</a>
<a style="float:right" href="#autor" id="toc-autor" class="active">Autor</a>
</div>
<section class="main-content">
<div id="problema" class="section level1">
<h1>Problema</h1>
<p>Una empresa inmobiliaria líder en una gran ciudad está buscando
comprender en profundidad el mercado de viviendas urbanas para tomar
decisiones estratégicas más informadas. La empresa posee una base de
datos extensa que contiene información detallada sobre diversas
propiedades residenciales disponibles en el mercado. Se requiere
realizar un análisis holístico de estos datos para identificar patrones,
relaciones y segmentaciones relevantes que permitan mejorar la toma de
decisiones en cuanto a la compra, venta y valoración de propiedades.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(paqueteMODELOS)</span></code></pre></div>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>dataset_vivienda <span class="ot"><-</span> vivienda</span>
<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a><span class="fu">str</span>(dataset_vivienda)</span></code></pre></div>
<pre><code>## $ id : num [1:8322] 1147 1169 1350 5992 1212 ...
## $ zona : chr [1:8322] "Zona Oriente" "Zona Oriente" "Zona Oriente" "Zona Sur" ...
## $ piso : chr [1:8322] NA NA NA "02" ...
## $ estrato : num [1:8322] 3 3 3 4 5 5 4 5 5 5 ...
## $ preciom : num [1:8322] 250 320 350 400 260 240 220 310 320 780 ...
## $ areaconst : num [1:8322] 70 120 220 280 90 87 52 137 150 380 ...
## $ parqueaderos: num [1:8322] 1 1 2 3 1 1 2 2 2 2 ...
## $ banios : num [1:8322] 3 2 2 5 2 3 2 3 4 3 ...
## $ habitaciones: num [1:8322] 6 3 4 3 3 3 3 4 6 3 ...
## $ tipo : chr [1:8322] "Casa" "Casa" "Casa" "Casa" ...
## $ barrio : chr [1:8322] "20 de julio" "20 de julio" "20 de julio" "3 de julio" ...
## $ longitud : num [1:8322] -76.5 -76.5 -76.5 -76.5 -76.5 ...
## $ latitud : num [1:8322] 3.43 3.43 3.44 3.44 3.46 ...</code></pre>
<div id="retos" class="section level2">
<h2>Retos</h2>
<p>El reto principal consisten en realizar un análisis integral y
multidimensional de la base de datos para obtener una comprensión del
mercado inmobiliario urbano. Se requiere aplicar diversas técnicas de
análisis de datos, incluyendo:</p>
<ol style="list-style-type: decimal">
<li>Análisis de Componentes Principales</li>
<li>Análisis de Conglomerados</li>
<li>Análisis de Correspondencia</li>
<li>Visualización de resultados</li>
</ol>
</div>
</div>
<div id="solución" class="section level1">
<h1>Solución</h1>
<div id="cargue-de-librerias-requeridas" class="section level2">
<h2>0. Cargue de librerias requeridas</h2>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="co"># Gestionar las bibliotecas necesarias</span></span>
<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a><span class="fu">library</span>(gridExtra)</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">library</span>(mice)</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">library</span>(corrplot)</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">library</span>(psych)</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">library</span>(factoextra)</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">library</span>(GGally)</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">library</span>(knitr)</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">library</span>(cluster)</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">library</span>(FactoMineR)</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">library</span>(factoextra)</span>
</code></pre></div>
<div id="entendimiento-de-los-datos" class="section level2">
<h2>1. Entendimiento de los datos</h2>
<p><strong>1.1 Número de registros y atributos</strong></p>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" tabindex="-1"></a><span class="co"># Obtener el número de filas</span></span>
<span id="cb31-2"><a href="#cb31-2" tabindex="-1"></a>num_filas <span class="ot"><-</span> <span class="fu">nrow</span>(dataset_vivienda)</span>
<span id="cb31-3"><a href="#cb31-3" tabindex="-1"></a><span class="co"># Obtener el número de columnas</span></span>
<span id="cb31-4"><a href="#cb31-4" tabindex="-1"></a>num_columnas <span class="ot"><-</span> <span class="fu">ncol</span>(dataset_vivienda)</span>
<span id="cb31-5"><a href="#cb31-5" tabindex="-1"></a><span class="co"># Mostrar en pantalla resultado</span></span>
<span id="cb31-6"><a href="#cb31-6" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Registros: "</span>,num_filas,<span class="st">" y Atributos: "</span>,num_columnas)</span></code></pre></div>
<pre><code>## Registros: 8322 y Atributos: 13</code></pre>
<p><strong>1.2 Obtener los tipos de datos de los atribitos</strong></p>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" tabindex="-1"></a><span class="co"># Obtener los tipos de datos de los atribitos</span></span>
<span id="cb33-2"><a href="#cb33-2" tabindex="-1"></a>tipos_datos <span class="ot"><-</span> <span class="fu">sapply</span>(dataset_vivienda, typeof)</span>
<span id="cb33-3"><a href="#cb33-3" tabindex="-1"></a><span class="fu">print</span>(tipos_datos)</span></code></pre></div>
<pre><code>## id zona piso estrato preciom areaconst
## "double" "character" "character" "double" "double" "double"
## parqueaderos banios habitaciones tipo barrio longitud
## "double" "double" "double" "character" "character" "double"
## latitud
## "double"</code></pre>
<p><strong>1.3 Cantidad de datos faltantes por atribito</strong></p>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" tabindex="-1"></a><span class="co"># Calcular la suma de valores faltantes por columna</span></span>
<span id="cb35-2"><a href="#cb35-2" tabindex="-1"></a>suma_valores_faltantes <span class="ot"><-</span> <span class="fu">colSums</span>(<span class="fu">is.na</span>(dataset_vivienda))</span>
<span id="cb35-3"><a href="#cb35-3" tabindex="-1"></a></span>
<span id="cb35-4"><a href="#cb35-4" tabindex="-1"></a><span class="co"># Mostrar el resultado</span></span>
<span id="cb35-5"><a href="#cb35-5" tabindex="-1"></a><span class="fu">print</span>(suma_valores_faltantes)</span></code></pre></div>
<pre><code>## id zona piso estrato preciom areaconst
## 3 3 2638 3 2 3
## parqueaderos banios habitaciones tipo barrio longitud
## 1605 3 3 3 3 3
## latitud
## 3</code></pre>
<p><strong>1.4 Descripción estadística General</strong></p>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" tabindex="-1"></a><span class="co"># Descripción estadística</span></span>
<span id="cb37-2"><a href="#cb37-2" tabindex="-1"></a><span class="fu">summary</span>(dataset_vivienda)</span></code></pre></div>
<pre><code>## id zona piso estrato
## Min. : 1 Length:8322 Length:8322 Min. :3.000
## 1st Qu.:2080 Class :character Class :character 1st Qu.:4.000
## Median :4160 Mode :character Mode :character Median :5.000
## Mean :4160 Mean :4.634
## 3rd Qu.:6240 3rd Qu.:5.000
## Max. :8319 Max. :6.000
## NA's :3 NA's :3
## preciom areaconst parqueaderos banios
## Min. : 58.0 Min. : 30.0 Min. : 1.000 Min. : 0.000
## 1st Qu.: 220.0 1st Qu.: 80.0 1st Qu.: 1.000 1st Qu.: 2.000
## Median : 330.0 Median : 123.0 Median : 2.000 Median : 3.000
## Mean : 433.9 Mean : 174.9 Mean : 1.835 Mean : 3.111
## 3rd Qu.: 540.0 3rd Qu.: 229.0 3rd Qu.: 2.000 3rd Qu.: 4.000
## Max. :1999.0 Max. :1745.0 Max. :10.000 Max. :10.000
## NA's :2 NA's :3 NA's :1605 NA's :3
## habitaciones tipo barrio longitud
## Min. : 0.000 Length:8322 Length:8322 Min. :-76.59
## 1st Qu.: 3.000 Class :character Class :character 1st Qu.:-76.54
## Median : 3.000 Mode :character Mode :character Median :-76.53
## Mean : 3.605 Mean :-76.53
## 3rd Qu.: 4.000 3rd Qu.:-76.52
## Max. :10.000 Max. :-76.46
## NA's :3 NA's :3
## latitud
## Min. :3.333
## 1st Qu.:3.381
## Median :3.416
## Mean :3.418
## 3rd Qu.:3.452
## Max. :3.498
## NA's :3</code></pre>
<p><strong>1.4.1 Gráfico de las Variables Cuantitativas</strong></p>
<p><img src="data:image/png;base64,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" 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" 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" 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oaEGDOC5g+PFSNoyxAIiqBBIzIFbff9QRE0JESmoO2+PyiChoTIErTlt19E0JAQCIqgQSOyBG35/UERNCREpqDtfj0ogoaEyBbUMjxWjKAtQyAoggaNyBa01e+LR9CQEJmCtvt98QgaEiJL0Ja/7RhBQ0IgKIIGjcgS1Pp98R4rRtCWITIF5ToogoaCyBbUMjxWjKAtQ2QJ2vJ71CNoSIgsQVv+KR8IGhIiS9DY4ho9gooddg3t8YDIHkFbfWcRBA0JkTmC2obHihG0ZQgERdCgEacEtV4hIajQYdfQHg+IbEEtP2rOY8UI2jIEgiJo0AgERdCgEQiKoEEjEBRBg0ZkCBr1gwv1CFo54pSgRcJjxQjaMgSCImjQCARF0KARCIqgQSMQFEGDRiAoggaNQFAEDRqBoAgaNAJBETRoBIIiaNAIBEXQoBEIiqBBIxAUQYNGICiCBo1AUAQNGoGgCBo0AkERNGgEgiJo0AgERdCgEbkE3fvocRwf3J25/uzkBkHlDruG9nhA5BF0d+adx/HR/cX46bsnNggqeNg1tMcDIoegj97+rRpBDz57rEfS4Q2CCh52De3xgMh9it/7+Fl88OmD4Y3a97KKcbnCYWOPdYJ9xqn6etPR2Xtmo28CfLiU/73bxNiYKOjudaPk8Cbd7/FXysYe64TyI+jW7Fx37dzK1uzV7sbFbnf5ov5P4rhlEmqIyC3oqBEUQQdj88pKd+vGHb3Zv3FH/cv8ROC4ZRJqiMgtKHPQPIJuL8x1O1PrqaCprhLHLZNQQ0RuQY/u306W74MbBD0Z2wvR1Lo5029EcwhaPiG3oFwHzSOonnyqOWh383L01gKCCiTkEnRSeKw4cEEHlOQUL5KAoK4E7V1ZYZFUPgFBRQXdXjCneG3mKpeZJBIQVFRQPfk8owbQThRdWNdT0uhc7gG0hvZ4QCCorKAlDruG9nhAICiCBo1AUAQNGoGgCBo0AkHLC2qP8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUBIKWyiiI8NBQTUEgaKmMgggPDdUUREiCms+82rxsPsBleyGKrmY8xoM9HhAlG6pwQg0RIQm6fG6l+/yT1qfWN1/J+BBBD/Z4QJRsqMIJNUQEJGjnBTWCDnzSemZ4sMcDolxDFU+oISIcQbc/+epY0JGfcOnBHg+IUg1VIqGGiHAEXbtqPok1+aT1ztSXUTSX8SgP9nhAlGqoEgk1RAQj6NZ76/1F0lsLcx21QuqcYQ46Piq3xwMiGEGX555/crU6xXem1tVCPmMI9WCPB0SZhiqTUENEKIJuzUY6jJK9KyvaVQSdFJXb4wERiqA6zBy0/0nr6hSf9UnWHuzxgCjZUIUTaogITdD+J63rC/UskiZE5fZ4QIQkaJ4ED/Z4QHhoqKYgELRURkGEh4ZqCgJBS2UURHhoqKYgELRURkGEh4ZqCkJEUI9h44J1gn1GQQRhHdWMoFZda50RLqLIUCKRUEMEglaBKNJTEgk1RCBoFYgiPSWRUEMEglaBKNJTEgk1RCBoFYgiPSWRUEMEglaBKNJTEgk1RCBoFYgiPSWRUEMEglaBKNJTEgk1RCBoFYjB6ERR8taBzTfMywtHvh3LqmWlMipHIGgViIHQb642r3zdmj1vzFzOehmsdctKZVSOQNAqECdCv0p7LXrNjKDmzdc5o3J7PCAQtArEiVibWu9+s25O8cmbr3NG5fZ4QCBoFYih2Lw8MAdN3nydMyq3xwMCQatAnIjk3Vda0P6br/NF5fZ4QCBoFYgTkdzoRws68ObrHFG5PR4QCFoF4kQ8F3Twzdc5onJ7PCAQtArEQOjT++ar612ug47Yi6AVIAZjLYqSK58ImrkXQStAFOkpiYQaIhC0CkSRnpJIqCECQatAFOkpiYQaIhC0CkSRnpJIqCECQatAFOkpiYQaIhC0FogiXSuSUTkCQWuBKNK1IhmVIxC0FogiXSuSUTkCQWuBKNK1IhmVIxC0FogiXSuSUTkCQWuBKNK1IhmVIxC0FogiXSuSUTkCQWuBKNK1IhmVIxC0FogiXSuSUTkCQWuBKNK1IhmVIxC0FogiXSuSUTkCQWuBGA7zmubthej8ir4viYqr43sgV18IJSCow4RwEUOxNatfgr980XwkX7eb/ZF81n0hlICgDhPCRQzGWvRa+pmRPfPuEP0J5nkDQe0K8NC1zUAMxjfmrfP6/33zblB9X5K8gaB2BXjo2mYghmNI0MwPhS7QF0IJCOowIVzEcAwJavEuUAS1LcBD1zYDMRxDgq5dHN/8eftCKAFBHSaEixiOzcFF0nLea0w6K968nN4uN2c0XdB+o0TZE3kPXdsMxOlW7V9myj8F1XfG3Vcr/rX8V6WaL6hulLgz8iTkoWubgRgOI+jWrL5Qn97XKUeYO+P20rE3dzRcUNMo8eiTkIeubQaiSNcOR3Jn3EM13nYsLks1XNC0UX458jfWQ9c2A1Gka4cjuTNuvL0wYraVERPmZwJF5UhwKmjSKPs3fjhqYu6ha5uBKNK1Q5HeGXd/9mruOaj5i+qY+VlWKJ0v2NisozpB00bpvXKnu/WzzEbx0LXNQBTp2qFI74xrMQdN/qI6Zn6WEVvK/w0roztRdHZsOS4FTRtF7x7RKB66tq2I4ejfGddC0OQvqmPmZxlx/PfXvAlq8NoZO6I7FLTfKAhaBSJDBrNIyn+K706Yn43MsFFaxfgElyNoWvHO83sIn4ogu7YZiOy+sLpQP2F+lhH6hVUbue9gnsbG2DWYB0Hj5/cQPhVBdm0zECO6e8y+7M7rWlxqNYukt2xevKIzqpuD9hGjdwXZtc1AWPfF6SggaHfSGTsjKpuDPkeM3hVk1zYDYd0Xp2PC/GxE9GxeXaWj0jmoQYzeFWTXNgNh3RenY8L8LCP0WLtqdZmpi6AtRVj3hUhCJ7K7UK/fTtUbO0BLCuq2o5pkT4iIEX5Y2FYkQw3Q58cO0AgKokpBJyYgKIhGCnpwd+b6MwRtEEJIt0AEPbq/GD99F0FbjSjim3VCQUEPPnsc7330GEFBOEUUFnTv42fxwacP1L9eVmGXSxD2YSno7vW+oDrKDOESCSCaiygo6PEIiqAgXCIKCpo5B/VTMYhWIQoKenT/9ulVvJ+KQbQKUVDQzOugfioG0SpEUUGHwmvFIFqFQFAQQSMQFETQCAQFETQCQUEEjUBQEEEjEBRE0AgEBRE0AkFBBI1AUBBBIxAURNAIBAURNEJE0PFh/YJ7+1fog2g8AkFBBI1AUBBBIxAURNAIh4ISRPlAUCLoQFAi6EBQIuhAUCLocCXo0Ls/88Xx++1zPv7WzMyiTcLuzMw7dojkbmkW8XTGlnF0f+btB5MfNkSwPG7VUHY12Sforsvb5Xu3Fp+nfDrx0B0JOnwXvFyxa9ko+uD2fmHRt7pJLGvSPli58Mjq0WnGru2vsl2CbqinNhkHdxftEkzX5e7yvVu6o42gu399e9KjHQk6fAeSPPHo7d/ajaC7ujFshbAepf/ir2wIR1/YDIY6dEPZRo5xZzDM7YpsMMP3N5ocSdfl7vK9j/7j3aQnjr74519N+j1wJKjtMZocS3li666KbUfQoy/+xeoUr85ytqffj//J7hQfWx+E9Qhq33m663JnqQercSVNmTjCOBJ0+C54+cJeUH0jHivCLUsXnt62m4PqKYfdKKonZLprLcL6t9J2PWBO8VYtpbsud5erBx/86pmZcN1OzoPjos4j6MFdOz9j285VR2G5SNJhNe0o0FC2U1b9W7NrNb9Xi6S/tPs1sxxBlZrqq1ofTl5SBjMHLbKKt3bH0p5kxWz7W2CFUGOJraCPLAsqcjaznBvv2c1B9exTTVvNmWNSazlbxd+2XjFbCmrtZ6GOshtBNeLoH60O45HtKd56IWY9gppJq1XnGefydrnpZ72Uf6qbdtI5vr7XQe0vCKoM2/VIgeuglgjVUPaX1+xi17YolWDXeXbXQU0/P33nP81v8qTD4S9JRNCBoETQgaBE0IGgRNCBoETQgaBE0IGgLuJwKdJxc/Bnq9HNnQtPKiqovoGgLuJw6Zr6ujNoaO+Nh6vDxhJ5AkFdRCJo8pUoFQjqIgYE7U2rc/21uPf6b6bNOV+f/S/F6c8ZUScGgrqI9BSvppz780rCjfMPe9PqG7U9XLoU6/97r9/TkmLopEBQF5Euks4/jP9PL4uUjcZFtd1RP4vVFzUlrbrIegSCuohkBN2f16v2HWXq2XvJiKkE1T/S36wmZ3piQiCoi0iXR2qk3J8/e8+MoCcF1f7qIZYYHwjqIo4FNULuDIygytd4JxXTTFCJsYGgLuJ4Fa+F7E0fC9pfJBlHk5GUGBcI6iLSRZK2VM01z/7D/M2+oHH/MlMyNa22zDoEghJBB4ISQQeCEkEHghJBB4ISQQeCEkEHghJBB4ISQQeCEkEHghJBx/8DaiWHvsmYIpYAAAAASUVORK5CYII=" 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" 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" /><!-- --><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAABjFBMVEUAAAAAADYAADoAAGIAAGYANokAOjoAOmYAOpAAYa4AZrYea7gzMzM2AAA2ADY2AGI2NgA2Nok2YWI2ia42idI6AAA6ADo6AGY6OgA6Ojo6OmY6ZmY6ZpA6ZrY6kLY6kNtNTU1NTW5NTY5NbqtNjshhAABhADZhAGJhrvRmAABmADpmOgBmOmZmZmZmkJBmkNtmtrZmtttmtv9uTU1uTW5uTY5ubo5ubqtuq8huq+SINgCINjaI0fSOTU2OTW6OTY6Obk2ObquOjo6OyP+QOgCQZgCQZjqQZmaQkDqQkGaQkLaQtpCQttuQ27aQ2/+rbk2rbm6rbo6rjk2ryKur5Mir5OSr5P+sYQCs8/S2ZgC2kDq2tpC2ttu225C229u22/+2/9u2///Ijk3IyI7I/+TI///PiTbPrq7P8/TbkDrbkGbbtmbbtpDb27bb29vb/7bb/9vb///kq27k///yrmLy0Yny867y89Ly8/T/tmb/yI7/25D/27b/29v/5Kv//7b//8j//9v//+T///+Sv3JaAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAZCUlEQVR4nO2djX8Ux32H17KIQTV2XCMnbZNyvAbFbVMLUwoR0FT0NSk4TStQU9uBViqq08YISb6TXL3tP96Z2T1pR7qX3Z2547vH8/3YrLQ3+7Cz+/Cb2ZF0SlJChJO87hMgZFAQlEgHQYl0EJRIB0GJdBCUSAdBiXQQlEgnWNBOMh/jPLLs/MmzU/sOHp592f1oJt5fRZqRYEGXk4jWrE0jKPESKujO7B9MPYpyJja9BD0Ogr6BCRV0bfrz2Yupk2ctmf4y/fbjJPnDL+0rX30/SaZ+ctTwq9nkHWeybfHOl64y/vr7WYuDvzVNPzF7kiS5mJG+cHXTKplVUHv4F07QE1wy2QkUdG9u5iBX6TuzydmXO7PGscTW1LXEpTtB7XT3d7otnI6uRf7RfFdQS/raF9TR3vl45hSXTHgCBbWPSO4xych10f5pCmH621mrlSmnZgKQD8rmhUfpV6aJafeJtezsS/PRjDVvxrT64cusqR3iu6SioHtzU79IvzUHnOSSSU+goMvGKFNFrUp2+rjjhvt8Kvm/v/672e4TVP7CkVrLU4+yI+zBO7Pf+bMv0u6B2X5f0Ozw/Ngil0x6wgTNRvTEaWWF6mTjrx3B85eOBJ33PuhYQbsKpsu25Q++7Aqa7S8Kmq1lubY+l0x6wgTNJ4Ru6PYF3ZtL/ugf//13c6UETX/7cab5cEFPcMmkJ0jQfIlyb87OKO2HRx7mHx3NFXsO8ceCmvzP3xgJjwV1kwc3ATge4rPpgMclk54gQTtJpl1BN/MwY55mpp8ZhV7aFaV8kd09JO04kY8fkrqCdpI/fpke/Jtpseae7jNBk5/Y56Wjh6RPss9OcMmkJ0jQ5XyNvpPki03dMf7i0SJSV6ROd3GosMx0PIh3m665ZaZjkl1Ycp8XPvO5ZMITImhhEWn6N/kXJHfMbPI7pkZm6/G/WD760tBXs3YtPm+RL9R3h3i3UP9D89nex8nM1znpv2eTH3x9tFBvZqnv/Ma2Pcklkx2+m4lIB0GJdBCUSAdBiXQQlEgHQYl0EJRIB0GJdBCUSAdBiXSCBG1XSKXGTSMJnlLTO4egMUmCp9T0zpUS9PDxYpru32tdeXVyg6CjASmShAVdby06Sdevntgg6IhAiiRdQXf/4q8W0/0HK+nurRV/M25BN5LkrYV2e/O83aRmk5x5seG+L/RCCLbh93BMJFlBDz/7V1Mvd2+/SvfvL/kb8+q7JoOqb8zsfPQo7Uw/25vLfiikc/TzHh2+LXTSM0DQ9Tt2QN++4pT0N3mLUf3r6ZWt6wub7z+1m/RJt2xuXbsRxGx4kRkTSbWCmmJ5OKCCjlvQ1TMvvrl7o71x5sXBTxeO9wUxG34Px0RSFXS9ZXNHYg5qZp92DvrNXTP3bO9d/66bkbatsEFp+D0cE0lV0DRbZjp8fCd7fC9uXkMF3Xj76da1C+3Vt5/uvLfQ3vrRU2OtGfKD0vB7OCaSuKAq66CFOWj2mRnhzwUyG34Px0QSFnRYRnVyvdJD0CdBa0wRzik+SJGEoENjhvf25gfmIckN8Z3ss/ApaNPv4ZhICDo8q0lirOwu1OefuSoalIbfwzGRELRiBEmCp9T0ziFoTJLgKTW9cwgakyR4Sk3vHILGJAmeUtM7pyvo7wUn/jmNDaRIQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGQlA/CCpGarCgI0m4oK+7ByRiqKAxIlj3mt45BK1yTmMDKZIQ1A+CipEQ1A+CipEmV9CNxP5qTfsbNpMb7dRszrw42tk/CCpGmlhBN99bsL+neOvajfbGWwt71y7YX/Ce7xxwGIKKkSZWUJut6wsbZ9xvzt7Jftl7vnPAIQgqRppoQVftqG6UvFYUNN/ZJwgqRppgQTfP59NNo+SeGelXzVz0eGefIKgYaYIFbefTzVXzp31I+vDujeOd/YKgYqSJFtQN6qu2ZKbtNnPQRpImXtBVVzBt4833n3Z39g+CipEmVlA7km9+8MIuLJnsGSufnOvuHHAYgoqRJlZQ80yUGB2fJDY30o3ELdRnOwcEQcVIkytovZNDUDESgvpBUDESgvpBUDESgvpBUDESgvpBUDHSZAgaTysEFSMhKIJKkxAUQaVJCIqg0iQERVBpEoIiqDQJQRFUmoSgCCpNQlAElSYhKIJKkxAUQaVJCIqg0iQERVBpEoIiqDQJQRFUmoSgCCpNQlAElSYhKIJKkxAUQaVJCIqg0iQERVBpEoIiqDQJQRFUmoSgCCpNQlAElSbJCrrdal1eSdP9e60rr05uEHQ0IEWSqqC7t1bS9avp4ePF0xsEHRFIkaQqaC7p/oOV0xsEHRFIkaQsqCmWu7dfpfv3l/yNeeldk0LLcK3ik8gEZJCguzcvLaXbV5yS/iZvUBA+XKv4pKj/oscDUiQpV9BTpfO4giLoKECKJGVB0+eLzEHHCVIkqQqaj+aHj+9kj+/FDYKOCKRIUhU0XW+1zByUddBxghRJsoIOT4GHoBNLQlAElSYhKIJKkxAUQaVJCIqg0iQERVBpEoIiqDQJQRFUmoSgCCpNQlAElSYhKIJKkxAUQaVJCIqg0iQERVBpEoIiqDQJQRFUmoSgCCpNQlAElSYhKIJKkxAUQaVJCIqg0iQERVBpEoIiqDQJQRFUmoSgCCpNQlAElSYhKIJKkxAUQaVJCIqg0iQERVBpEoIiqDQJQRFUmoSgCCpNQlAElSYhKIJKkxAUQaVJCIqg0iQERVBpEoIiqDQJQRFUmoSgCCpNarCghYRrFZ9EJiBU0BgRrHtN7xyCVrxg4wEpkhAUQaVJCIqg0iQERVBpEoIiqDQJQRFUmoSgCCpNQlAElSYhKIJKkxAUQaVJCIqg0iQERVBpEoIiqDQJQRFUmoSgCCpNQlAElSYhKIJKkxAUQaVJQoLuzCY2088QtFoEtWp653oJevDw4sHD+b25eSpoxQhq1fTO9RLUqrl8Me2cfYmg1SKoVdM710/QtZm0wxBfNYJaNb1zvQRNl52da1TQqhHUqumd6ymomYSmy8nUo5J+Imh0kCJJSNCqKfAQdGJJCIqg0iQRQc0T0t5cwjporQhq1fTOUUErXrDxgBRJCIqg0iQhQe1T/MHDGQStGkGtmt65noIuzzhLSxta4CHoxJJ0BM2/Cs9XkipHUKumdw5BK16w8YAUSTqCpmtWzb25iyX9RNDoIEWSkKBpxy6DlvYTQaODFElKglZMgYegE0tCUASVJgkJyo981IygVk3vXC9Bq6zRI+hIQIokHUEr/DQSgo4GpEjSEfTgIYLWi6BWTe9cL0ErLNEj6GhAiiQdQfl+0LoR1KrpnetZQf3s3my1FtN0/17ryquTGwQdDUiRpCro/v2ldPfTpcPHi+n61dTfIOiIQIokJUHXkmS++1PH29bD54v7D1bS3Vsr/gZBRwRSJAkJunz2d3PzxdVQU0V3b786vTEvvWtSODRcq/gkMgE5sQ5ql0KPn+UPH99Jt684Jf1N/npB+HCt4pOi/oseD0iRpFNBTwq6f++OeVTqU0ERdBQgRZKOoOmaHeKPvh909+aitZQ56BhBiiQhQb3vB838dMO8e3wvbhB0RCBFkpKgxay3bBZZBx0nSJGkKmiJFHgIOrEkHUH5UmfdCGrV9M71r6B7P+btF6tGUKumd66/oLwFePUIatX0zg0SlCG+agS1anrnBgi6TAWtGkGtmt65XoLmD0m8BXjlCGrV9M4NqKDlU+Ah6MSSEBRBpUlCgvL+oDUjqFXTO9dTUN4ftGYEtWp653oJytsv1o2gVk3vHIJWvGDjASmSdATl/UHrRlCrpneup6C8P2jNCGrV9M71FrRiCjwEnVgSgiKoNElJ0OLPxSNohasZC6RIEhL01M/FI+iYQYokHUFP/Vw8go4bpEhCUASVJukIeuLn4hF0/CBFkpCgrIPWjKBWTe9cb0ErpsBD0Ikl6QjKe9TXjaBWTe9cL0H5LR91I6hV0zvXS9C0who9go4EpEjSEZR3FqkbQa2a3rmeFbRqCjwEnVgSgiKoNElE0MpPSAg6ApAiSUnQir9qrsBD0IklISiCSpMQFEGlSQiKoNIkBEVQaZKMoEk3LNRXjaBWTe/cKUHrpMBD0IklISiCSpMQFEGlSQiKoNKkBgtaSLhW8UlkAkIFjRHButf0ziFoxQs2HpAiCUERVJqEoAgqTUJQBJUmISiCSpMQFEGlSQiKoNIkBEVQaRKCIqg0CUERVJqEoAgqTUJQBJUmISiCSpMQFEGlSQiKoNIkBEVQaRKCIqg0CUERVJqEoAgqTUJQBJUmISiCSpMQFEGlSQiKoNIkBEVQaRKCIqg0CUERVJqEoAgqTULQ8Qq6+f7Tdvubu8nbT9vpRpK8tWB2nU/OvKh0qU9ezZCD1UkIOlZBt64ZM9tPztn/dt5baG+8/XTr2oX26rlq1/rE1Qw5WJ2EoOMUdDX5fVNBt64v2EpqO2A+3Mz31I+gVgjaTEH/64XVMVfSdmD1zAsEHQ8IQUsIms1BjwTdPG/moFvXbpjKeqPaxfavZsCx8iQEfY2Ctu0c1D4kfXgXQUcNQtA6guZjO0P86EEIWlpQ/yGpu7d+BLVC0CYLmi8zdezw/sEL6+gTlplGDkLQ8oJuXXML9auJ3bQ3EhbqxwBC0FKC1rxg4wEpkhAUQaVJCIqg0iQERVBpEoIiqDQJQUckaDiotOqCWr0Rgu7eWknT/XutK69ObhC09pVvHElX0O3W5ZX08PFiun71xAZB61/5xpFkBX1+6Vemgu4/WLGV1N8gaP0r3ziSrKDZEL97+1W6f3/J35jX3jUpNA2XITopHMRvnn/9GSro9hWnpL/JXy8IHy5DdFI4iAoaFTTeCoqgNa9840jigjIHjXzlG0cSF/Tw8Z3s8b24QdD6V75xJHFBWQeNfOUbRxIWdFgKvHgyRCOFgxA0KghBfVI4CEGjghDUJ4WDEDQqCEF9UjgIQaOCENQnhYMQNCoIQX1SOAhBo4IQ1CeFgxA0KghBfVI4CEGjghDUJ4WDEDQqCEF9UjgIQaOCENQnhYMQNCoIQX1SOAhBo4IQ1CeFgxA0KghBfVI4CEGjghDUJ4WDEDQqCEF9UjgIQaOCENQnhYMQNCoIQX1SOAhBo4IQ1CeFgxA0KghBfVI4CEGjghDUJ4WDEDQqCEF9UjgIQaOCENQnhYMQNCoIQX1SOAhBo4IQ1CeFgxA0KghBfVI4CEGjghDUJ4WDEDQqCEF9UjgIQaOCENQnhYMQNCoIQX1SOAhBo4IQ1CeFgxA0KghBfVI4CEGjghDUJ4WDEDQqCEF9UjgIQaOCENQnhYMQNCoIQX1SOAhBo4IQ1CeFgxA0KghBfVI4CEGjghDUJ4WDEDQqCEF9UjgIQaOCENQnhYMQNCpo/IIWEi5DdFI4iF/H/fpDBS1BiloaGkdqcAUt8OLJEI0UDkLQqCAE9UnhIASNCkJQnxQOQtCoIAT1SeEgBI0KQlCfFA5C0KggBPVJ4SAEjQpCUJ8UDkLQqCAE9UnhIASNCkJQnxQOQtCoIAT1SeEgBI0KQlCfFA5C0KggBPVJ4SAEjQpCUJ8UDkLQqCAE9UnhIASNCkJQnxQOQtCoIAT1SeEgBI0KQlCfFA5C0KggBPVJ4SAEjQpCUJ8UDkLQqCAE9UnhIASNCkJQnxQOQtCoIAT1SeEgBI0KQlCfFA4qIejm+eTMi3YnsblQ4fr3vS8RGJFJCOrLEI0UDhou6Na1C+3Vc+7Kb7z9tML173tfIjAikxDUlyEaKRw0XNDN95+2t64vpFbVGxUuf//7EgMSl4SgvgzRSOGgKoKumpE+QhC02xZBy5P6x9bN1eRG2v7mbpQCiqBHbRG0PGlAzEPSh3eNoLaUxgiCdtsiaHnS4Lgh3jwpRQmCdtsiaHnS4JjimbafBK8xmVqc3GindhNlOougCOqqZ/vJuXYaPgW1s9mNtxbSjUilGEER1GbD1bvUihqWDYMxmqfhpdgmWh02/ZsK7VotEoJWIA2/muUv/KCYKnrw0yg6bJyLc06b7y20O1G+AlGVhKAVSMOvZunrPjCrZ17sXf9u8la4o6YORxuZ94JHhzokBK1AGn41S1/3QVk1BWbHFJqtHz0NJH1j6nA0QdfifAmiIglBK5CGX83S131AVm3ltKTwCe2WqcORpo6b5yOBKpIQdKykEndk1U3Q4ghqJ3x7wXU4T5w5aFUSgo6VNPyGWKfye7j5QYwxNdrUkTmofw+jkcJB4xT0ifuW0hvpapLEqVgIiqCVQSXuS+mWg2K/N3UnRh2OBqpMQtCxksrfl9ItB8bU4emnUqCqJAQdK6n8fSl/C8dF4kud/j2MRgoHIWhU0FgE3b/XuvIKQWuCGql6owQ9fLyYrl9F0JogBC3ftp6g+w9W0t1bKwhaD0TnypHqC7p7+1W6f3/JfPSuSbVjCameioJuX+kKalOhYivO/ps+Co6J1Kgh/riCIugoQIqkRgnadw4a9eQaRxI8paZ3rqagh4/v9H6Kj3pyjSMJnlLTO1dT0L7roFFPrnEkwVNqeufqCuplVCfXOJLgKTW9cwgakyR4Sk3vHILGJAmeUtM7h6AxSYKn1PTOIWhMkuApNb1zCBqTJHhKTe8cgsYkCZ5S0zuHoDFJgqfU9M4haEyS4Ck1vXMIGpMkeEpN7xyCxiQJnlLTO4egMUmCp9T0zkURtEriffu9IEnwlCalcwgqBVIkIWjzSYKnNCmdQ1ApkCLpDRGUkDpBUCIdBCXSQVAiHQQl0hmLoO4n6fOfB/V+LLTukdut1uVKyL4HHD5uXVqqQOrbfvdmhVMa0Nj12LzeWiwDG9bavb7ecr0fSBvWsPsXVbvsR91d7CKO3/WjZMYh6LbtVv6+eP7b49U80nb1eE8JZP8Dni/at/MpT+rXfv+e+bAsaEBj12N7F3c/XRoOG9bavW7POU0HX61hDY/+otJ9LGb3pqU7Qbf/+k6pQ44yYkHtv53nl35lTi1/TxL/rUmGZNCRx3tKInseYD9M0/In17d9/p5A5UD9G2c93rb3/fniUNiw1tnrh58tDevjsIbZ6+60H1S87K6/t/7jaibo4Wf/8rPy46fNKAU1w2E2Bux2e3d/yX9zp6Hpf6T5x1sN2fOA3dv/bIfs0qS+7f09Q0CDGnfveTnYsNb2dTMU2ynAYNqwhu71rIJWv5PmYFOc83uZlenSGaGg25eKvcvfF89/e7yh6Xfk7k0Dr4Lsc4Ct8OailSb1be9G7UtL5UCDGufK2fcYKgEb1tpJ8emSLY6DacMaFh4Gqt9JW29/9srNs+5kBb98GldBn7daroely1U3pw6wpEqFL+1T+9wpmQeIv/ysLKhX47xjmXL79+6c/Mv6Xp+erT1aaqcAwytoz4YFkBV4+/JKrQpq1DR/GiOyp9XyGcMcNB8fasxBBx5ZYo7mp9cB5h92+anjsPaV5mf9GuePy4tpqZnxsNYF7wbThjUsDGV15qB29mmmsVbqtNoYP7Zlpvx98fy3x6t5ZH6lyiP7H/DcDdnlSf3au/nZ1ZK9HNTYlSpnXBnYsNZdrw5/uTKYNqxhoYJWv5P5P6PLK+v2TKuN8c1cB11vVVu97H+A+bDS0l7f9tut4p4hoAGN3XDYarnHlXLroINad5c3h16tYQ2zNaKql714cLp++T9/6SYklZZC+UoSkQ6CEukgKJEOghLpICiRDoIS6SBo7awlF4OO//ZL//Odjx65bWf6WRB3soKgdXPw8E+DTOr6eCoIWgyC1k1n+vPZ+YDjEbRUELRuls9+/XDGePa9v0+mnx08TBLr1c5sknRHfrtv5ni789HPzYvzeZt5++dFd/Dn1lSjqzV2by6Z+gcDKnLe7CBozezNXUzXnEoz1kHzx9rZl3tzRsC1rALaffZzu7X/78yefWlfc5VzZ3bebt3BO8eCWuje3PSzIucND4LWTMfJOe/+z0ZlY9X/vUyPxm7vmcf84RpaD7+XeZcJOu8J6toaMYucNzwIWjPLeeF0Gq0lLmZM7pjNlBOrO5XsmMLZHcFd6+Vs5HeCdvflDdZcW2vwMecND4LWi5ks2uQjthPL7Z161K18fQV1B2cH9hO0yHnDg6D1kk0Q86lk2smLnZMx/+RoiLefdrpzz3ynmQ70ErQ7HShy3vAgaK24pyK3cXodPDRGGaGsUzuzmVjdh6Ojh6TcQyeheyCaz/bZJ6ODh1PZQ9KMe0gqct7wIGit7ORLoGtTP3c10S4lWZ/M/HLqn+ay104tM+XVsju9XE4yu92a0p//2FtmKnLe7CAokQ6CEukgKJEOghLpICiRDoIS6SAokQ6CEukgKJEOghLp/D+e087432RoLAAAAABJRU5ErkJggg==" /><!-- --></p>
<p><strong>1.4.2 Gráfico de las Variables Cualitativas</strong></p>
<p><img src="data:image/png;base64,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" /><!-- --><img src="data:image/png;base64,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" /><!-- --></p>
<p><strong>1.5 Análisis del dataset</strong></p>
<p>La base de datos de la empresa inmobiliaria contiene un total de
8,322 registros y 13 atributos. De estos, 3 atributos son categóricos
(zona, tipo y barrio), mientras que 10 atributos son numéricos (piso,
estrato, precio, área construida, parqueadero, baño, habitaciones,
longitud y latitud). Es importante destacar que el conjunto de datos
presenta pérdida de información, principalmente en los atributos de piso
(2638 registros) y parqueadero (1605 registros).</p>
<p>Debido a las inconsistencias mencionadas, se procede a normalizar el
conjunto de datos antes de aplicar diversas técnicas de análisis.</p>
</div>
<div id="preparación-de-los-datos" class="section level2">
<h2>2. Preparación de los datos</h2>
<p><strong>2.1 Eliminar el ID como dato irrelevante del
dataset</strong></p>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" tabindex="-1"></a><span class="co"># Eliminar el ID como dato irrelevante del dataset</span></span>
<span id="cb39-2"><a href="#cb39-2" tabindex="-1"></a>dataset_vivienda<span class="ot">=</span><span class="fu">select</span>(dataset_vivienda, <span class="sc">-</span>id)</span></code></pre></div>
<p><strong>2.2 Convertir el tipo de dato en el atributo “Piso” de
Caracteres a Numérico</strong></p>
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" tabindex="-1"></a><span class="co"># Convertir el tipo de dato en el atributo "Piso"</span></span>
<span id="cb40-2"><a href="#cb40-2" tabindex="-1"></a>dataset_vivienda<span class="sc">$</span>piso <span class="ot"><-</span> <span class="fu">as.numeric</span>(dataset_vivienda<span class="sc">$</span>piso)</span></code></pre></div>
<p><strong>2.3 Imputación de registros para completar los datos
faltantes</strong></p>
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" tabindex="-1"></a><span class="co"># Número de pisos</span></span>
<span id="cb41-2"><a href="#cb41-2" tabindex="-1"></a>media_piso <span class="ot"><-</span> <span class="fu">mean</span>(dataset_vivienda<span class="sc">$</span>piso[dataset_vivienda<span class="sc">$</span>piso <span class="sc">!=</span> <span class="dv">0</span> <span class="sc">&</span> </span>
<span id="cb41-3"><a href="#cb41-3" tabindex="-1"></a> <span class="sc">!</span><span class="fu">is.na</span>(dataset_vivienda<span class="sc">$</span>piso)], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb41-4"><a href="#cb41-4" tabindex="-1"></a>media_piso <span class="ot"><-</span> <span class="fu">round</span>(media_piso)</span>
<span id="cb41-5"><a href="#cb41-5" tabindex="-1"></a>dataset_vivienda <span class="ot"><-</span> dataset_vivienda <span class="sc">%>%</span> <span class="fu">mutate</span>(<span class="at">piso =</span> <span class="fu">ifelse</span>(piso <span class="sc">==</span> <span class="dv">0</span> <span class="sc">|</span> </span>
<span id="cb41-6"><a href="#cb41-6" tabindex="-1"></a> <span class="fu">is.na</span>(piso), media_piso, piso))</span>
<span id="cb41-7"><a href="#cb41-7" tabindex="-1"></a></span>
<span id="cb41-8"><a href="#cb41-8" tabindex="-1"></a><span class="co"># Estrato socioeconomico</span></span>
<span id="cb41-9"><a href="#cb41-9" tabindex="-1"></a>media_estrato <span class="ot"><-</span> <span class="fu">mean</span>(dataset_vivienda<span class="sc">$</span>estrato[dataset_vivienda<span class="sc">$</span>estrato <span class="sc">!=</span> <span class="dv">0</span> <span class="sc">&</span> </span>
<span id="cb41-10"><a href="#cb41-10" tabindex="-1"></a> <span class="sc">!</span><span class="fu">is.na</span>(dataset_vivienda<span class="sc">$</span>estrato)], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb41-11"><a href="#cb41-11" tabindex="-1"></a>media_estrato <span class="ot"><-</span> <span class="fu">round</span>(media_estrato)</span>
<span id="cb41-12"><a href="#cb41-12" tabindex="-1"></a>dataset_vivienda <span class="ot"><-</span> dataset_vivienda <span class="sc">%>%</span> <span class="fu">mutate</span>(<span class="at">estrato =</span> <span class="fu">ifelse</span>(estrato <span class="sc">==</span> <span class="dv">0</span> <span class="sc">|</span> </span>
<span id="cb41-13"><a href="#cb41-13" tabindex="-1"></a> <span class="fu">is.na</span>(estrato), media_estrato, estrato))</span>
<span id="cb41-14"><a href="#cb41-14" tabindex="-1"></a></span>
<span id="cb41-15"><a href="#cb41-15" tabindex="-1"></a><span class="co"># Número de parqueaderos</span></span>
<span id="cb41-16"><a href="#cb41-16" tabindex="-1"></a>media_parquea <span class="ot"><-</span> <span class="fu">mean</span>(dataset_vivienda<span class="sc">$</span>parqueaderos[dataset_vivienda<span class="sc">$</span>parqueaderos <span class="sc">!=</span> <span class="dv">0</span> </span>
<span id="cb41-17"><a href="#cb41-17" tabindex="-1"></a> <span class="sc">&</span> <span class="sc">!</span><span class="fu">is.na</span>(dataset_vivienda<span class="sc">$</span>parqueaderos)], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb41-18"><a href="#cb41-18" tabindex="-1"></a>media_parquea <span class="ot"><-</span> <span class="fu">round</span>(media_parquea)</span>
<span id="cb41-19"><a href="#cb41-19" tabindex="-1"></a>dataset_vivienda <span class="ot"><-</span> dataset_vivienda <span class="sc">%>%</span> <span class="fu">mutate</span>(<span class="at">parqueaderos =</span> <span class="fu">ifelse</span>(parqueaderos <span class="sc">==</span> <span class="dv">0</span> <span class="sc">|</span> </span>
<span id="cb41-20"><a href="#cb41-20" tabindex="-1"></a> <span class="fu">is.na</span>(parqueaderos), media_parquea, parqueaderos))</span>
<span id="cb41-21"><a href="#cb41-21" tabindex="-1"></a></span>
<span id="cb41-22"><a href="#cb41-22" tabindex="-1"></a><span class="co"># Número de baños</span></span>
<span id="cb41-23"><a href="#cb41-23" tabindex="-1"></a>media_banios <span class="ot"><-</span> <span class="fu">mean</span>(dataset_vivienda<span class="sc">$</span>banios[dataset_vivienda<span class="sc">$</span>banios <span class="sc">!=</span> <span class="dv">0</span> <span class="sc">&</span> </span>
<span id="cb41-24"><a href="#cb41-24" tabindex="-1"></a> <span class="sc">!</span><span class="fu">is.na</span>(dataset_vivienda<span class="sc">$</span>banios)], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb41-25"><a href="#cb41-25" tabindex="-1"></a>media_banios <span class="ot"><-</span> <span class="fu">round</span>(media_banios)</span>
<span id="cb41-26"><a href="#cb41-26" tabindex="-1"></a>dataset_vivienda <span class="ot"><-</span> dataset_vivienda <span class="sc">%>%</span> <span class="fu">mutate</span>(<span class="at">banios =</span> <span class="fu">ifelse</span>(banios <span class="sc">==</span> <span class="dv">0</span> <span class="sc">|</span> </span>
<span id="cb41-27"><a href="#cb41-27" tabindex="-1"></a> <span class="fu">is.na</span>(banios), media_banios, banios))</span>
<span id="cb41-28"><a href="#cb41-28" tabindex="-1"></a></span>
<span id="cb41-29"><a href="#cb41-29" tabindex="-1"></a><span class="co"># Número de habitaciones</span></span>
<span id="cb41-30"><a href="#cb41-30" tabindex="-1"></a>media_habitac <span class="ot"><-</span> <span class="fu">mean</span>(dataset_vivienda<span class="sc">$</span>habitaciones[dataset_vivienda<span class="sc">$</span>habitaciones <span class="sc">!=</span> <span class="dv">0</span> <span class="sc">&</span></span>
<span id="cb41-31"><a href="#cb41-31" tabindex="-1"></a> <span class="sc">!</span><span class="fu">is.na</span>(dataset_vivienda<span class="sc">$</span>habitaciones)], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb41-32"><a href="#cb41-32" tabindex="-1"></a>media_habitac <span class="ot"><-</span> <span class="fu">round</span>(media_habitac)</span>
<span id="cb41-33"><a href="#cb41-33" tabindex="-1"></a>dataset_vivienda <span class="ot"><-</span> dataset_vivienda <span class="sc">%>%</span> <span class="fu">mutate</span>(<span class="at">habitaciones =</span> <span class="fu">ifelse</span>(habitaciones <span class="sc">==</span> <span class="dv">0</span> <span class="sc">|</span> </span>
<span id="cb41-34"><a href="#cb41-34" tabindex="-1"></a> <span class="fu">is.na</span>(habitaciones), media_habitac, habitaciones))</span></code></pre></div>
<p><strong>2.4 Eliminar los registros totalmente
incompletos</strong></p>
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" tabindex="-1"></a><span class="co"># Eliminar los registros incompletos</span></span>
<span id="cb42-2"><a href="#cb42-2" tabindex="-1"></a>dataset_vivienda <span class="ot"><-</span> dataset_vivienda[<span class="fu">complete.cases</span>(dataset_vivienda), ]</span></code></pre></div>
<p><strong>2.5 Verificación de la normalización del dataset</strong></p>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" tabindex="-1"></a><span class="co"># Verificar los datos faltantes</span></span>
<span id="cb43-2"><a href="#cb43-2" tabindex="-1"></a><span class="fu">md.pattern</span>(dataset_vivienda, <span class="at">rotate.names =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
<pre><code>## /\ /\
## { `---' }
## { O O }
## ==> V <== No need for mice. This data set is completely observed.
## \ \|/ /
## `-----'</code></pre>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<pre><code>## zona piso estrato preciom areaconst parqueaderos banios habitaciones tipo
## 8319 1 1 1 1 1 1 1 1 1
## 0 0 0 0 0 0 0 0 0
## barrio longitud latitud rango_precio rango_area
## 8319 1 1 1 1 1 0
## 0 0 0 0 0 0</code></pre>
<p><strong>2.6 Transformar los atributos categóricos en
numéricos</strong></p>
<div class="sourceCode" id="cb46"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" tabindex="-1"></a><span class="co"># Transformación de atributos</span></span>
<span id="cb46-2"><a href="#cb46-2" tabindex="-1"></a><span class="co"># Ubicacion por Zona</span></span>
<span id="cb46-3"><a href="#cb46-3" tabindex="-1"></a>dataset_vivienda<span class="sc">$</span>zona_numerica <span class="ot"><-</span> <span class="fu">as.numeric</span>(<span class="fu">factor</span>(dataset_vivienda<span class="sc">$</span>zona, </span>
<span id="cb46-4"><a href="#cb46-4" tabindex="-1"></a> <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">"Zona Centro"</span>, <span class="st">"Zona Norte"</span>, <span class="st">"Zona Oeste"</span>, <span class="st">"Zona Oriente"</span>, <span class="st">"Zona Sur"</span>)))</span>
<span id="cb46-5"><a href="#cb46-5" tabindex="-1"></a></span>
<span id="cb46-6"><a href="#cb46-6" tabindex="-1"></a><span class="co"># Tipo de inmueble</span></span>
<span id="cb46-7"><a href="#cb46-7" tabindex="-1"></a>dataset_vivienda<span class="sc">$</span>tipo_numerica <span class="ot"><-</span> <span class="fu">as.numeric</span>(<span class="fu">factor</span>(dataset_vivienda<span class="sc">$</span>tipo, </span>
<span id="cb46-8"><a href="#cb46-8" tabindex="-1"></a> <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">"Apartamento"</span>, <span class="st">"Casa"</span>)))</span></code></pre></div>
</div>
<div id="análisis-de-datos" class="section level2">
<h2>3. Análisis de datos</h2>
<div id="análisis-de-componentes-principales" class="section level3">
<h3>3.1 Análisis de Componentes Principales</h3>
<p>Para el análisis de componentes principales PCA, no se tendran
encuenta los atributos complementarios (barrio, longitud y latitud),
debido a que son unicamente utilizadas para una visualización espacial y
al igual de los atributos generados para el análisis estadístico.</p>
<p><strong>3.1.1 Adecuación preliminar del dataset</strong></p>
<div class="sourceCode" id="cb47"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" tabindex="-1"></a><span class="co"># Adecuación preliminar</span></span>
<span id="cb47-2"><a href="#cb47-2" tabindex="-1"></a>dataset_PCA<span class="ot">=</span><span class="fu">select</span>(dataset_vivienda, <span class="sc">-</span>zona, <span class="sc">-</span>tipo, <span class="sc">-</span>barrio, <span class="sc">-</span>longitud, <span class="sc">-</span>latitud,</span>
<span id="cb47-3"><a href="#cb47-3" tabindex="-1"></a> <span class="sc">-</span>rango_precio, <span class="sc">-</span>rango_area)</span></code></pre></div>
<p><strong>3.1.2 Correlación de los atributos del dataset</strong></p>
<div class="sourceCode" id="cb48"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1" tabindex="-1"></a><span class="co"># Correlación de los atributos del dataset</span></span>
<span id="cb48-2"><a href="#cb48-2" tabindex="-1"></a>matrizcor <span class="ot"><-</span> <span class="fu">cor</span>(dataset_PCA)</span>
<span id="cb48-3"><a href="#cb48-3" tabindex="-1"></a><span class="fu">corrplot</span> (matrizcor, <span class="at">method =</span> <span class="st">"number"</span>, <span class="at">number.cex =</span> <span class="fl">0.5</span>, <span class="at">addCoef.col =</span> <span class="st">"#034D94"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>El análisis de correlaciones evidencia una fuerte relacion entre los
atributos: precio, áera construida, parqueaderos y baños; donde se puede
inferir que el <strong>precio</strong> de la vivienda es directamente
proporcional al áera construida, parqueaderos y baños.</p>
<p>Con el fin de determinar la pertinencia del PCA para el dataset se
realiza la medición de KMO y prueba de Barlett:</p>
<div class="sourceCode" id="cb49"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" tabindex="-1"></a><span class="co"># Medición de KMO y prueba de Barlett</span></span>
<span id="cb49-2"><a href="#cb49-2" tabindex="-1"></a>resultado_prueba <span class="ot"><-</span> psych<span class="sc">::</span><span class="fu">corr.test</span>(matrizcor)</span>
<span id="cb49-3"><a href="#cb49-3" tabindex="-1"></a><span class="fu">print</span>(resultado_prueba)</span></code></pre></div>
<pre><code>## Call:psych::corr.test(x = matrizcor)
## Correlation matrix
## piso estrato preciom areaconst parqueaderos banios habitaciones
## piso 1.00 0.09 -0.37 -0.69 -0.46 -0.61 -0.66
## estrato 0.09 1.00 0.65 0.09 0.34 0.26 -0.45
## preciom -0.37 0.65 1.00 0.75 0.78 0.78 0.20
## areaconst -0.69 0.09 0.75 1.00 0.68 0.86 0.70
## parqueaderos -0.46 0.34 0.78 0.68 1.00 0.64 0.26
## banios -0.61 0.26 0.78 0.86 0.64 1.00 0.68
## habitaciones -0.66 -0.45 0.20 0.70 0.26 0.68 1.00
## zona_numerica -0.17 -0.11 -0.42 -0.42 -0.39 -0.39 -0.29
## tipo_numerica -0.82 -0.45 0.20 0.72 0.32 0.58 0.84
## zona_numerica tipo_numerica
## piso -0.17 -0.82
## estrato -0.11 -0.45
## preciom -0.42 0.20
## areaconst -0.42 0.72
## parqueaderos -0.39 0.32
## banios -0.39 0.58
## habitaciones -0.29 0.84
## zona_numerica 1.00 -0.18
## tipo_numerica -0.18 1.00
## Sample Size
## [1] 9
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## piso estrato preciom areaconst parqueaderos banios habitaciones
## piso 0.00 1.00 1.00 1.00 1.00 1.00 1.00
## estrato 0.81 0.00 1.00 1.00 1.00 1.00 1.00
## preciom 0.33 0.06 0.00 0.64 0.41 0.43 1.00
## areaconst 0.04 0.82 0.02 0.00 1.00 0.10 1.00
## parqueaderos 0.21 0.38 0.01 0.04 0.00 1.00 1.00
## banios 0.08 0.50 0.01 0.00 0.06 0.00 1.00
## habitaciones 0.05 0.23 0.60 0.04 0.49 0.05 0.00
## zona_numerica 0.66 0.77 0.26 0.26 0.30 0.31 0.44
## tipo_numerica 0.01 0.23 0.60 0.03 0.39 0.10 0.00
## zona_numerica tipo_numerica
## piso 1.00 0.23
## estrato 1.00 1.00
## preciom 1.00 1.00
## areaconst 1.00 0.89
## parqueaderos 1.00 1.00
## banios 1.00 1.00
## habitaciones 1.00 0.17
## zona_numerica 0.00 1.00
## tipo_numerica 0.64 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option</code></pre>
<div class="sourceCode" id="cb51"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1" tabindex="-1"></a><span class="fu">cortest.bartlett</span>(<span class="fu">cor</span>(dataset_PCA),<span class="at">n=</span><span class="dv">850</span>)</span></code></pre></div>
<pre><code>## $chisq
## [1] 3412.678
##
## $p.value
## [1] 0
##
## $df
## [1] 36</code></pre>
<div class="sourceCode" id="cb53"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1" tabindex="-1"></a><span class="fu">KMO</span>(dataset_PCA)</span></code></pre></div>
<pre><code>## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = dataset_PCA)
## Overall MSA = 0.79
## MSA for each item =
## piso estrato preciom areaconst parqueaderos
## 0.70 0.66 0.76 0.85 0.90
## banios habitaciones zona_numerica tipo_numerica
## 0.81 0.73 0.42 0.79</code></pre>
<p><strong>NOTA:</strong> El indice de KMO se encuentra por encima de
0.65 y el resultado del test de Barllet es mayor a 60. Por lo cual se
considera pertinencia para realizar análisis de componentes principales
PCA.</p>
<p><strong>3.1.3 Estandarización de los atributos</strong></p>
<p>Con el fin de evitar que las variables que tiene una escala con
valores más grandes afecten las estimaciones realizadas (sesgos) se
realiza la estandarización de las variables antes de proceder a realizar
el proceso de estimación de los componentes principales.</p>
<div class="sourceCode" id="cb55"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb55-1"><a href="#cb55-1" tabindex="-1"></a><span class="co"># Estandarización de los atributos</span></span>
<span id="cb55-2"><a href="#cb55-2" tabindex="-1"></a>dataset_PCA_scale <span class="ot"><-</span> <span class="fu">scale</span>(dataset_PCA)</span>
<span id="cb55-3"><a href="#cb55-3" tabindex="-1"></a><span class="fu">head</span> (dataset_PCA_scale)</span></code></pre></div>
<pre><code>## piso estrato preciom areaconst parqueaderos banios
## [1,] 0.07232598 -1.5872276 -0.5595498 -0.7339949 -0.8559050 -0.09047352
## [2,] 0.07232598 -1.5872276 -0.3465670 -0.3842568 -0.8559050 -0.79985072
## [3,] 0.07232598 -1.5872276 -0.2552886 0.3152194 0.1313764 -0.79985072
## [4,] -0.85191340 -0.6156201 -0.1031580 0.7349051 1.1186578 1.32828088
## [5,] -1.31403309 0.3559875 -0.5291236 -0.5940997 -0.8559050 -0.79985072
## [6,] -1.31403309 0.3559875 -0.5899759 -0.6150839 -0.8559050 -0.09047352
## habitaciones zona_numerica tipo_numerica
## [1,] 1.6595223 0.06192582 1.2586312
## [2,] -0.4474471 0.06192582 1.2586312
## [3,] 0.2548760 0.06192582 1.2586312
## [4,] -0.4474471 0.81508501 1.2586312
## [5,] -0.4474471 -1.44439256 -0.7944184
## [6,] -0.4474471 -1.44439256 -0.7944184</code></pre>
<p><strong>3.1.3 Elección del número de componentes
principales</strong></p>
<div class="sourceCode" id="cb57"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb57-1"><a href="#cb57-1" tabindex="-1"></a><span class="co"># Elección del número de componentes principales</span></span>
<span id="cb57-2"><a href="#cb57-2" tabindex="-1"></a>dataset_vivienda_PCA_scale <span class="ot"><-</span> (<span class="fu">prcomp</span>(dataset_PCA_scale))</span>
<span id="cb57-3"><a href="#cb57-3" tabindex="-1"></a><span class="fu">fviz_eig</span>(dataset_vivienda_PCA_scale, <span class="at">addlabels =</span> <span class="cn">TRUE</span>, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">80</span>),</span>
<span id="cb57-4"><a href="#cb57-4" tabindex="-1"></a> <span class="at">main=</span><span class="st">"Porcentaje de varianza por dimensión"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p><strong>NOTA:</strong> El primer componente retiene la mayor parte de
la varianza en los datos (41.2%), seguido del segundo componente con un
(18.7%), el tercer componente con un (11.6%), y el cuarto componente con
un (9.4%). En conjunto, estos cuatro primeros componentes abarcan el
(81%) de la variabilidad total de los datos. Basándonos en esta
información, se opta por seleccionar los primeros 7 componentes de los 9
disponibles, lo que proporciona una explicación del 95% de la
variabilidad en los datos:</p>
<div class="sourceCode" id="cb58"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb58-1"><a href="#cb58-1" tabindex="-1"></a><span class="co"># Elección del número de componentes principales</span></span>
<span id="cb58-2"><a href="#cb58-2" tabindex="-1"></a><span class="fu">get_eigenvalue</span>(dataset_vivienda_PCA_scale)</span></code></pre></div>
<pre><code>## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 3.7051221 41.168024 41.16802
## Dim.2 1.6787898 18.653220 59.82124
## Dim.3 1.0422569 11.580632 71.40188
## Dim.4 0.8433108 9.370120 80.77200
## Dim.5 0.5837778 6.486419 87.25841
## Dim.6 0.4116162 4.573513 91.83193
## Dim.7 0.3261995 3.624439 95.45637
## Dim.8 0.2300281 2.555868 98.01224
## Dim.9 0.1788988 1.987764 100.00000</code></pre>
<p><strong>3.1.4 Contribución de los Atributos del dataset y Componentes
Principales</strong></p>
<p>En la siguiente tabla se observa la contribución de cada atributo en
cada uno de los componentes seleccionados:</p>
<div class="sourceCode" id="cb60"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb60-1"><a href="#cb60-1" tabindex="-1"></a><span class="co"># Selección de componentes principales</span></span>
<span id="cb60-2"><a href="#cb60-2" tabindex="-1"></a><span class="fu">get_pca_var</span>(dataset_vivienda_PCA_scale)<span class="sc">$</span>contrib[,<span class="dv">1</span><span class="sc">:</span><span class="dv">7</span>]</span></code></pre></div>
<pre><code>## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## piso 1.1975944 17.46070205 10.362458692 58.74963270 5.81316264
## estrato 5.2423143 32.41017970 2.900545219 2.55123037 19.23961239
## preciom 18.2393027 10.10174414 0.134098876 1.15470257 0.06115514
## areaconst 20.3749305 0.30980302 0.708490246 0.08603833 0.04846937
## parqueaderos 13.0552130 2.77884594 0.308195450 6.37132703 60.21313582
## banios 20.3100773 0.24153885 0.006921985 3.84170337 7.23768307
## habitaciones 11.1713389 14.93073421 1.479758463 15.87998238 3.44004740
## zona_numerica 0.1379101 0.05489933 84.005252188 11.26170948 3.41184591
## tipo_numerica 10.2713189 21.71155275 0.094278880 0.10367377 0.53488825
## Dim.6 Dim.7
## piso 5.067356881 1.20500619
## estrato 0.351007003 11.85744822
## preciom 2.086033498 6.58918809
## areaconst 12.075515383 41.51400666
## parqueaderos 9.796287542 4.54579591
## banios 7.158257803 4.62800223
## habitaciones 25.898769682 0.05649899
## zona_numerica 0.001388144 1.02124719
## tipo_numerica 37.565384064 28.58280652</code></pre>
<p><strong>3.1.5 Gráfica de los Componentes Principales -
General</strong></p>
<div class="sourceCode" id="cb62"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb62-1"><a href="#cb62-1" tabindex="-1"></a><span class="co"># Gráfica de los Componentes Principales</span></span>
<span id="cb62-2"><a href="#cb62-2" tabindex="-1"></a>grafico_pca <span class="ot"><-</span> <span class="fu">fviz_pca_var</span>(dataset_vivienda_PCA_scale, <span class="at">col.var =</span> <span class="st">"contrib"</span>, </span>
<span id="cb62-3"><a href="#cb62-3" tabindex="-1"></a> <span class="at">gradient.cols =</span> <span class="fu">c</span>(<span class="st">"#158E61"</span>, <span class="st">"#034D94"</span>), <span class="at">repel =</span> <span class="cn">TRUE</span>) <span class="sc">+</span></span>
<span id="cb62-4"><a href="#cb62-4" tabindex="-1"></a> <span class="fu">theme_gray</span>(<span class="at">base_size =</span> <span class="dv">15</span>, <span class="at">base_family =</span> <span class="st">""</span>) <span class="sc">+</span></span>
<span id="cb62-5"><a href="#cb62-5" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">panel.background =</span> <span class="fu">element_rect</span>(<span class="at">fill =</span> <span class="st">"#F7F7F7"</span>),</span>
<span id="cb62-6"><a href="#cb62-6" tabindex="-1"></a> <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="fl">0.5</span>)) <span class="sc">+</span></span>
<span id="cb62-7"><a href="#cb62-7" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">"Gráfica de los Componentes Principales - General"</span>)</span>
<span id="cb62-8"><a href="#cb62-8" tabindex="-1"></a><span class="fu">print</span>(grafico_pca)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p><strong>3.1.6 Gráfica de los Componentes Principales -
Individual</strong></p>
<div class="sourceCode" id="cb63"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb63-1"><a href="#cb63-1" tabindex="-1"></a><span class="co"># Gráfica de los Componentes Principales - Individual</span></span>
<span id="cb63-2"><a href="#cb63-2" tabindex="-1"></a>grafico_pca_ind <span class="ot"><-</span> <span class="fu">fviz_pca_biplot</span>(dataset_vivienda_PCA_scale, <span class="at">repel=</span><span class="cn">FALSE</span>, <span class="at">col.var=</span><span class="st">'#063C6F'</span>, </span>
<span id="cb63-3"><a href="#cb63-3" tabindex="-1"></a> <span class="at">col.ind=</span><span class="st">'#158E61'</span>) <span class="sc">+</span></span>
<span id="cb63-4"><a href="#cb63-4" tabindex="-1"></a> <span class="fu">theme_gray</span>(<span class="at">base_size =</span> <span class="dv">15</span>, <span class="at">base_family =</span> <span class="st">""</span>) <span class="sc">+</span></span>
<span id="cb63-5"><a href="#cb63-5" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">panel.background =</span> <span class="fu">element_rect</span>(<span class="at">fill =</span> <span class="st">"#F7F7F7"</span>),</span>
<span id="cb63-6"><a href="#cb63-6" tabindex="-1"></a> <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="fl">0.5</span>)) <span class="sc">+</span></span>
<span id="cb63-7"><a href="#cb63-7" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">"Gráfica de los Componentes Principales - Individual"</span>)</span>
<span id="cb63-8"><a href="#cb63-8" tabindex="-1"></a><span class="fu">print</span>(grafico_pca_ind)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p><strong>3.1.6 Análisis de los Componentes Principales</strong></p>
<p>Después de llevar a cabo un análisis de los componentes principales
(PCA) en la base de datos del sector inmobiliario, se han identificado
patrones significativos que ofrecen una mejor comprensión de la
estructura de los datos. Se ha observado que las variables relacionadas
con características específicas de las propiedades, como el número de
pisos, estrato, precio, área de construcción, cantidad de parqueaderos,
número de baños y habitaciones, se distribuyen en varios factores o
instancias.</p>
<p>En primer lugar, se destaca que los factores más determinantes en la
oferta inmobiliaria se presentan de la siguiente manera: las propiedades
con precios más altos suelen tener un área de construcción más amplia,
con un mayor número de baños y parqueaderos. En segundo lugar, se
encuentran el estrato, el tipo de propiedad, el número de pisos y el
número de habitaciones como factores influyentes en el proceso de
compra-venta de inmuebles.</p>
<p>Estos factores permiten comprender las preferencias y características
más relevantes para los clientes, lo cual proporciona una base sólida
para asesorar a los clientes y tomar decisiones estratégicas en el
sector frente a los competidores.</p>
</div>
<div id="análisis-de-conglomerados" class="section level3">
<h3>3.2 Análisis de Conglomerados</h3>
<p><strong>3.2.1 Adecuación del dataset para el análisis de
conglomerados</strong></p>
<div class="sourceCode" id="cb64"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb64-1"><a href="#cb64-1" tabindex="-1"></a><span class="co"># Selección de variables</span></span>
<span id="cb64-2"><a href="#cb64-2" tabindex="-1"></a>dataset_Cong<span class="ot">=</span><span class="fu">select</span>(dataset_vivienda, <span class="sc">-</span>zona, <span class="sc">-</span>tipo, <span class="sc">-</span>barrio, <span class="sc">-</span>longitud, <span class="sc">-</span>latitud,</span>
<span id="cb64-3"><a href="#cb64-3" tabindex="-1"></a> <span class="sc">-</span>rango_precio, <span class="sc">-</span>rango_area)</span>
<span id="cb64-4"><a href="#cb64-4" tabindex="-1"></a></span>
<span id="cb64-5"><a href="#cb64-5" tabindex="-1"></a><span class="co"># Escalar las variables</span></span>
<span id="cb64-6"><a href="#cb64-6" tabindex="-1"></a>dataset_Cong_scale <span class="ot"><-</span> <span class="fu">scale</span>(dataset_Cong)</span>
<span id="cb64-7"><a href="#cb64-7" tabindex="-1"></a><span class="fu">head</span> (dataset_Cong_scale)</span></code></pre></div>
<pre><code>## piso estrato preciom areaconst parqueaderos banios
## [1,] 0.07232598 -1.5872276 -0.5595498 -0.7339949 -0.8559050 -0.09047352
## [2,] 0.07232598 -1.5872276 -0.3465670 -0.3842568 -0.8559050 -0.79985072
## [3,] 0.07232598 -1.5872276 -0.2552886 0.3152194 0.1313764 -0.79985072
## [4,] -0.85191340 -0.6156201 -0.1031580 0.7349051 1.1186578 1.32828088
## [5,] -1.31403309 0.3559875 -0.5291236 -0.5940997 -0.8559050 -0.79985072
## [6,] -1.31403309 0.3559875 -0.5899759 -0.6150839 -0.8559050 -0.09047352
## habitaciones zona_numerica tipo_numerica
## [1,] 1.6595223 0.06192582 1.2586312
## [2,] -0.4474471 0.06192582 1.2586312
## [3,] 0.2548760 0.06192582 1.2586312
## [4,] -0.4474471 0.81508501 1.2586312
## [5,] -0.4474471 -1.44439256 -0.7944184
## [6,] -0.4474471 -1.44439256 -0.7944184</code></pre>
<p><strong>3.2.2 Calcular las distancias Euclidianas</strong></p>
<div class="sourceCode" id="cb66"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb66-1"><a href="#cb66-1" tabindex="-1"></a><span class="co"># Paso 3: Distancias euclidianas</span></span>
<span id="cb66-2"><a href="#cb66-2" tabindex="-1"></a>dis_euc <span class="ot"><-</span> <span class="fu">dist</span>(dataset_Cong_scale, <span class="at">method =</span> <span class="st">"euclidean"</span>)</span>
<span id="cb66-3"><a href="#cb66-3" tabindex="-1"></a><span class="fu">head</span>(dis_euc, <span class="dv">5</span>)</span></code></pre></div>
<pre><code>## [1] 2.260578 2.155086 3.883624 4.140666 4.078783</code></pre>
<p><strong>3.2.3 Calcular el agrupamiento para diferentes números de
clústeres</strong></p>
<div class="sourceCode" id="cb68"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb68-1"><a href="#cb68-1" tabindex="-1"></a><span class="co"># Calcular el agrupamiento para diferentes números de clústeres</span></span>
<span id="cb68-2"><a href="#cb68-2" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb68-3"><a href="#cb68-3" tabindex="-1"></a>kmeans_results <span class="ot"><-</span> <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>, <span class="cf">function</span>(k) <span class="fu">kmeans</span>(dataset_Cong_scale, <span class="at">centers =</span> k))</span>
<span id="cb68-4"><a href="#cb68-4" tabindex="-1"></a><span class="co"># Calcular la inercia para cada número de clústeres</span></span>
<span id="cb68-5"><a href="#cb68-5" tabindex="-1"></a>inertia <span class="ot"><-</span> <span class="fu">sapply</span>(kmeans_results, <span class="cf">function</span>(x) x<span class="sc">$</span>tot.withinss)</span>
<span id="cb68-6"><a href="#cb68-6" tabindex="-1"></a><span class="co"># Graficar la curva del codo</span></span>
<span id="cb68-7"><a href="#cb68-7" tabindex="-1"></a><span class="fu">plot</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>, inertia, <span class="at">type =</span> <span class="st">"b"</span>, <span class="at">pch =</span> <span class="dv">19</span>, <span class="at">frame =</span> <span class="cn">FALSE</span>, </span>
<span id="cb68-8"><a href="#cb68-8" tabindex="-1"></a> <span class="at">xlab =</span> <span class="st">"Número de Clústeres"</span>,</span>
<span id="cb68-9"><a href="#cb68-9" tabindex="-1"></a> <span class="at">ylab =</span> <span class="st">"Inercia"</span>,</span>
<span id="cb68-10"><a href="#cb68-10" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"Método del Codo"</span>, <span class="at">col =</span> <span class="st">"#034D94"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p><strong>NOTA:</strong> Para este ejercicio, se determinó que el
número apropiado de clústeres es 4.</p>
<p><strong>3.2.4 Aplicar el método de agrupamiento</strong></p>
<p><strong>NOTA:</strong> Como método de agrupamiento se seleccionó el
de <strong>kmeans</strong></p>
<div class="sourceCode" id="cb69"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb69-1"><a href="#cb69-1" tabindex="-1"></a><span class="co"># Aplicación del método de agrupamiento</span></span>
<span id="cb69-2"><a href="#cb69-2" tabindex="-1"></a>num_clusters <span class="ot"><-</span> <span class="dv">4</span> </span>
<span id="cb69-3"><a href="#cb69-3" tabindex="-1"></a>resultado_kmeans <span class="ot"><-</span> <span class="fu">kmeans</span>(dataset_Cong_scale, <span class="at">centers =</span> num_clusters)</span>
<span id="cb69-4"><a href="#cb69-4" tabindex="-1"></a></span>
<span id="cb69-5"><a href="#cb69-5" tabindex="-1"></a><span class="co"># Asignación del Cluser a cada registro del dataset scalado</span></span>
<span id="cb69-6"><a href="#cb69-6" tabindex="-1"></a>dataset_Cong_scale<span class="sc">$</span>cluster <span class="ot"><-</span> resultado_kmeans<span class="sc">$</span>cluster</span></code></pre></div>
<pre><code>## Warning in dataset_Cong_scale$cluster <- resultado_kmeans$cluster: Realizando
## coercion de LHD a una lista</code></pre>
<p><strong>3.2.5 Interpretación de resultados</strong></p>
<div class="sourceCode" id="cb71"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb71-1"><a href="#cb71-1" tabindex="-1"></a><span class="co"># Crear tabla de frecuencias por clúster</span></span>
<span id="cb71-2"><a href="#cb71-2" tabindex="-1"></a>tabla_frecuencia <span class="ot"><-</span> <span class="fu">table</span>(dataset_Cong_scale<span class="sc">$</span>cluster)</span>
<span id="cb71-3"><a href="#cb71-3" tabindex="-1"></a>col_nombres <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"Cluster"</span>, <span class="st">"Frecuencia"</span>)</span>
<span id="cb71-4"><a href="#cb71-4" tabindex="-1"></a><span class="fu">kable</span>(tabla_frecuencia, <span class="at">format =</span> <span class="st">"markdown"</span>, <span class="at">col.names =</span> col_nombres)</span></code></pre></div>
<table>
<thead>
<tr class="header">
<th align="left">Cluster</th>
<th align="right">Frecuencia</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="right">937</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="right">1710</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="right">3399</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="right">2273</td>
</tr>
</tbody>
</table>
<p><strong>3.2.6 Visualización de los resultados</strong></p>
<div class="sourceCode" id="cb72"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb72-1"><a href="#cb72-1" tabindex="-1"></a>dataset_vivienda<span class="sc">$</span>cluster <span class="ot"><-</span> resultado_kmeans<span class="sc">$</span>cluster</span>
<span id="cb72-2"><a href="#cb72-2" tabindex="-1"></a></span>
<span id="cb72-3"><a href="#cb72-3" tabindex="-1"></a><span class="fu">fviz_cluster</span>(</span>
<span id="cb72-4"><a href="#cb72-4" tabindex="-1"></a> <span class="fu">list</span>(</span>
<span id="cb72-5"><a href="#cb72-5" tabindex="-1"></a> <span class="at">data =</span> <span class="fu">select</span>(dataset_vivienda, <span class="sc">-</span>zona, <span class="sc">-</span>tipo, <span class="sc">-</span>barrio, <span class="sc">-</span>longitud, <span class="sc">-</span>latitud,</span>
<span id="cb72-6"><a href="#cb72-6" tabindex="-1"></a> <span class="sc">-</span>rango_precio, <span class="sc">-</span>rango_area), <span class="at">cluster =</span> dataset_vivienda<span class="sc">$</span>cluster</span>
<span id="cb72-7"><a href="#cb72-7" tabindex="-1"></a> ),</span>
<span id="cb72-8"><a href="#cb72-8" tabindex="-1"></a> <span class="at">palette =</span> <span class="fu">c</span>(<span class="st">"#158E61"</span>, <span class="st">"#034D94"</span>, <span class="st">"#E5D352"</span>, <span class="st">"#A23E48"</span>, <span class="st">"#E06D06"</span>),</span>
<span id="cb72-9"><a href="#cb72-9" tabindex="-1"></a> <span class="at">ellipse.type =</span> <span class="st">"convex"</span>,</span>
<span id="cb72-10"><a href="#cb72-10" tabindex="-1"></a> <span class="at">repel =</span> <span class="cn">FALSE</span>, <span class="co"># repel is a logical value, no need for quotes around FALSE</span></span>
<span id="cb72-11"><a href="#cb72-11" tabindex="-1"></a> <span class="at">show.clust.cent =</span> <span class="cn">TRUE</span>,</span>
<span id="cb72-12"><a href="#cb72-12" tabindex="-1"></a> <span class="at">ggtheme =</span> <span class="fu">theme_minimal</span>()</span>
<span id="cb72-13"><a href="#cb72-13" tabindex="-1"></a>) <span class="sc">+</span> <span class="fu">ggtitle</span>(<span class="st">"Visualización de clustering inmobiliario"</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p><strong>3.2.7 Dendograma</strong></p>
<div class="sourceCode" id="cb73"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb73-1"><a href="#cb73-1" tabindex="-1"></a><span class="co"># Dendograma</span></span>
<span id="cb73-2"><a href="#cb73-2" tabindex="-1"></a>hierarchical_clustering <span class="ot"><-</span> <span class="fu">hclust</span>(dis_euc, <span class="at">method =</span> <span class="st">"complete"</span>)</span>
<span id="cb73-3"><a href="#cb73-3" tabindex="-1"></a><span class="co"># Graficar dendograma</span></span>
<span id="cb73-4"><a href="#cb73-4" tabindex="-1"></a><span class="fu">plot</span>(hierarchical_clustering, <span class="at">cex =</span> <span class="fl">0.6</span>, <span class="at">main =</span> <span class="st">"Dendrograma de Viviendas"</span>, <span class="at">las =</span> <span class="dv">2</span>,</span>
<span id="cb73-5"><a href="#cb73-5" tabindex="-1"></a> <span class="at">ylab =</span> <span class="st">"Distancia euclidiana"</span>, <span class="at">xlab =</span> <span class="st">"Viviendas"</span>, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">50</span>))</span>
<span id="cb73-6"><a href="#cb73-6" tabindex="-1"></a><span class="co"># Agregar rectángulos para los clústeres</span></span>
<span id="cb73-7"><a href="#cb73-7" tabindex="-1"></a><span class="fu">rect.hclust</span>(hierarchical_clustering, <span class="at">k =</span> num_clusters, <span class="at">border =</span> <span class="dv">2</span><span class="sc">:</span><span class="dv">5</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p><strong>NOTA:</strong> Con el fin de analizar cada cluster por
separado, se calculan medidas de tendencia central para las variables
contenidas en cada cluster generado:</p>
<div class="sourceCode" id="cb74"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb74-1"><a href="#cb74-1" tabindex="-1"></a><span class="co"># Análisis de clusters</span></span>
<span id="cb74-2"><a href="#cb74-2" tabindex="-1"></a>median_clus <span class="ot"><-</span> <span class="fu">aggregate</span>(<span class="fu">cbind</span>(piso, estrato, preciom, areaconst, parqueaderos, </span>
<span id="cb74-3"><a href="#cb74-3" tabindex="-1"></a> banios, habitaciones, zona_numerica, tipo_numerica)</span>
<span id="cb74-4"><a href="#cb74-4" tabindex="-1"></a> <span class="sc">~</span> cluster ,<span class="at">data =</span> dataset_vivienda, </span>
<span id="cb74-5"><a href="#cb74-5" tabindex="-1"></a> <span class="at">FUN =</span> <span class="cf">function</span>(x) <span class="fu">c</span>(<span class="at">Media =</span> <span class="fu">round</span>(<span class="fu">mean</span>(x), <span class="dv">0</span>)))</span>
<span id="cb74-6"><a href="#cb74-6" tabindex="-1"></a>col_nombres <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"Cluster"</span>, <span class="st">"Piso"</span>, <span class="st">"Estrato"</span>, <span class="st">"Precio"</span>, <span class="st">"Área construida"</span>, <span class="st">"Parqueaderos"</span>,</span>
<span id="cb74-7"><a href="#cb74-7" tabindex="-1"></a> <span class="st">"Baños"</span>, <span class="st">"Habitaciones"</span>, <span class="st">"Zona"</span>, <span class="st">"Tipo"</span>)</span>
<span id="cb74-8"><a href="#cb74-8" tabindex="-1"></a><span class="fu">kable</span>(median_clus, <span class="at">format =</span> <span class="st">"markdown"</span>, <span class="at">col.names =</span> col_nombres)</span></code></pre></div>
<table>
<colgroup>
<col width="9%" />
<col width="5%" />
<col width="9%" />
<col width="8%" />
<col width="18%" />
<col width="15%" />
<col width="6%" />
<col width="15%" />
<col width="5%" />
<col width="5%" />
</colgroup>
<thead>
<tr class="header">
<th align="right">Cluster</th>
<th align="right">Piso</th>
<th align="right">Estrato</th>
<th align="right">Precio</th>
<th align="right">Área construida</th>
<th align="right">Parqueaderos</th>
<th align="right">Baños</th>
<th align="right">Habitaciones</th>
<th align="right">Zona</th>
<th align="right">Tipo</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">1</td>
<td align="right">3</td>
<td align="right">6</td>
<td align="right">1025</td>
<td align="right">441</td>
<td align="right">4</td>
<td align="right">5</td>
<td align="right">5</td>
<td align="right">4</td>
<td align="right">2</td>
</tr>
<tr class="even">
<td align="right">2</td>
<td align="right">5</td>
<td align="right">6</td>
<td align="right">617</td>
<td align="right">166</td>
<td align="right">2</td>
<td align="right">4</td>
<td align="right">3</td>
<td align="right">3</td>
<td align="right">1</td>
</tr>
<tr class="odd">
<td align="right">3</td>
<td align="right">4</td>
<td align="right">4</td>
<td align="right">217</td>
<td align="right">80</td>
<td align="right">1</td>
<td align="right">2</td>
<td align="right">3</td>
<td align="right">4</td>
<td align="right">1</td>
</tr>
<tr class="even">
<td align="right">4</td>
<td align="right">3</td>
<td align="right">4</td>
<td align="right">377</td>
<td align="right">214</td>
<td align="right">2</td>
<td align="right">3</td>
<td align="right">5</td>
<td align="right">4</td>
<td align="right">2</td>
</tr>
</tbody>
</table>
<p><strong>3.2.8 Clusters por Precio de la vivienda vs Área
construida</strong></p>
<div class="sourceCode" id="cb75"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb75-1"><a href="#cb75-1" tabindex="-1"></a><span class="co"># Clusters por Precio de la vivienda vs Área construida</span></span>
<span id="cb75-2"><a href="#cb75-2" tabindex="-1"></a>variable1 <span class="ot"><-</span> dataset_vivienda<span class="sc">$</span>preciom</span>
<span id="cb75-3"><a href="#cb75-3" tabindex="-1"></a>variable2 <span class="ot"><-</span> dataset_vivienda<span class="sc">$</span>areaconst</span>
<span id="cb75-4"><a href="#cb75-4" tabindex="-1"></a></span>
<span id="cb75-5"><a href="#cb75-5" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> dataset_vivienda, <span class="fu">aes</span>(<span class="at">x =</span> variable1, <span class="at">y =</span> variable2, </span>
<span id="cb75-6"><a href="#cb75-6" tabindex="-1"></a> <span class="at">color =</span> <span class="fu">as.factor</span>(cluster))) <span class="sc">+</span> <span class="fu">geom_point</span>() <span class="sc">+</span></span>
<span id="cb75-7"><a href="#cb75-7" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Precio"</span>, <span class="at">y =</span> <span class="st">"Área construida"</span>, <span class="at">color =</span> <span class="st">"Clúster"</span>) <span class="sc">+</span></span>
<span id="cb75-8"><a href="#cb75-8" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">"Clusters por Precio de la vivienda vs Área construida"</span>) <span class="sc">+</span></span>
<span id="cb75-9"><a href="#cb75-9" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p><strong>3.2.9 Clusters por Zona vs Tipo de vivienda</strong></p>
<div class="sourceCode" id="cb76"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb76-1"><a href="#cb76-1" tabindex="-1"></a><span class="co"># Clusters por Zona vs Tipo de vivienda</span></span>
<span id="cb76-2"><a href="#cb76-2" tabindex="-1"></a>variable1 <span class="ot"><-</span> dataset_vivienda<span class="sc">$</span>zona</span>
<span id="cb76-3"><a href="#cb76-3" tabindex="-1"></a>variable2 <span class="ot"><-</span> dataset_vivienda<span class="sc">$</span>tipo</span>
<span id="cb76-4"><a href="#cb76-4" tabindex="-1"></a></span>
<span id="cb76-5"><a href="#cb76-5" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> dataset_vivienda, <span class="fu">aes</span>(<span class="at">x =</span> variable2, <span class="at">y =</span> variable1, <span class="at">color =</span> <span class="fu">as.factor</span>(cluster))) <span class="sc">+</span> </span>
<span id="cb76-6"><a href="#cb76-6" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span></span>
<span id="cb76-7"><a href="#cb76-7" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Tipo"</span>, <span class="at">y =</span> <span class="st">"Zona"</span>, <span class="at">color =</span> <span class="st">"Clúster"</span>) <span class="sc">+</span></span>
<span id="cb76-8"><a href="#cb76-8" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">"Clusters por Zona vs Tipo de vivienda"</span>) <span class="sc">+</span></span>
<span id="cb76-9"><a href="#cb76-9" tabindex="-1"></a> <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb76-10"><a href="#cb76-10" tabindex="-1"></a> <span class="fu">facet_wrap</span>(<span class="sc">~</span> cluster, <span class="at">ncol =</span> <span class="dv">2</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAABqlBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZrYAv8QZGUgZGXEZSJcZcboaGho6AAA6ADo6AGY6OgA6OmY6OpA6ZmY6ZpA6ZrY6kLY6kNtIGRlIGUhIGXFISHFISJdIl91NTU1NTW5NTY5Nbo5NbqtNjshmAABmADpmAGZmOgBmOmZmZmZmkJBmkNtmtrZmtttmtv9uTU1uTW5uTY5ubo5ubqtuq6tuq+RxGRlxGUhxSBlxuv98rgCOTU2OTW6OTY6Obk2ObquOjm6Ojo6OyP+QOgCQOjqQZgCQZjqQZmaQkDqQkGaQkLaQtpCQttuQ27aQ29uQ2/+XSBmXSEiXupeX3d2X3f+rbk2rbm6rbo6rjk2rq26rq46ryKur5Mir5P+2ZgC2kDq2kGa2tma2tpC2ttu229u22/+2/9u2//+6cRm6///HfP/Ijk3IyI7I5KvI///bkDrbkGbbtmbbtpDb25Db27bb29vb2//b/7bb/9vb///dl0jd///kq27k///r6+v4dm3/tmb/unH/yI7/25D/27b/3Zf/5Kv//7b//7r//8j//9v//93//+T///8BjUvFAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAbwElEQVR4nO2di3sbx3XFQdmikkawk8YO3bRGFJGu2qQmLSlsGsmSU7JNX2mVtqabNhHTplFli5GYpE0bKZJFExJo0tz/uTuPxd7F42IhDPYO5p7f94kgsIs9c7THsw/IB60MgIhpSQ8AAA4EFEQNAgqiBgEFUYOAgqhBQEHUIKAgahBQEDUNBvTk37/Yar30tvlt++zDUWt0/+jujBLbLc/6i2/kaK3YyJm740Za0VxmlpG389viNqOZ5gLabbu9/vr4XbV/BgEFVRoLaL7bX//fLPvvS3l25hZQz0FrYqwmUCOY5apBkoWAjqaxgO63Vuxjt332odn9LgJ2t5x8r9VaettNf/lKzy61Wl+6Y5ftt87c8UsdR2srB+3Wy7fM72a9l8v1SqmDtg16ufzsT7/YWvqmWfTL/CzD/ZYV/z0crS1nFQ1DEVDzOErT88v89Z/ZZBWDrm7YvL3yhLojo+pvpjI+kDUX0JPtYnb8tdvtZUD9cXndB9SdCizdyl9/qd06+5ttesg+Wvty2y3NY1hZr5zwum2bVrK8f9Tfrxz/u23zy4FXrpwWVAM6rOmwm3v50nJWDnpww2cfVp5Qd9VR2c1Uxwey5gJq5qk+1YB226+bHbfsZp6TbTOV/aptg7uSkaVuM63lhyc/admlb5s9arbkJ+diFZuTyvLlh/lv5j0muv2NuSHsnrlb1SgXFQEd1iyUvp8927YLikFXN1wa7T8h7vyo6GYq4wNZJAF96Rs/s6+bgHbbK/5XN+mWS91mbDhsqOwGd5dulZOz27abgAaX+wH8+qd/1W4VQ9nPo2xer2oUIyweR2lmTmLFC5WDrm7YboY+GXTn1IvNDI4PRBHQbNcc1l6743bwgb+CNsFywSiWks3k+9wdObOD/nqe3f65bnW5VfJ3EoqhmGgcmDxXNIoRFo+jNO1K9q12w+Wgqxv2R/XyyaA79+ZiM4PjAxLnoL947c5AQLNfXbL3dEYHtL/UMDGg+8XeHRXQ/GD95b/9z//r/7diluzaLVONYknx+AIBLTZsN0Of8AEdHB8QuIo/WvN3F3fJgTfL/ucv893kDvHFJQIJnl3q3u12a/UQXwb0wJzYWQaXu5OJ9axyjre/9Pf9NPQ1qHZ5iK9qeomVrDhKD1zX+A27zZAng+7cqKqbwTkoReA+6IrbObutb9orhTxUX8mnmZ/ke33fXt3mlwz5NUNxk7xc6rfyleGLpH5AD9r9XweXuyjk8X12qbzk77b/2KSyquHeXblIGtL0g3E3x5bJoKsbdpshTwbduXmz2MzQ+IDAJ0nFxHJQ3FzxN1zM5YS5zeSPgiv9OaZYajhae6ntD8bkNlJ/h+4WnwGZ7VSXkxtaLZpis6mqhltAAzqs6ehbKAc9uGFvof9kyN1yNvQ3gYASGvws/tn32pXP4n/Rbr32m/6N+tfzV44umSN095JbzYekv9SQHwjzkLjLmW55I35UQAeW2yjY++zf3y1PNv0pa0UjywYDOqzpyU9dX/4vexpdDLq6Yb8Z+qTqrn8O7jYzND6wYP+a6UjgAkJCExQgoFFqggIENEpNUICARqkJChYsoEAbCCiIGgQURA0CCqIGAQVRg4CCqEFAQdQgoCBqEFAQNQgoiBoEFEQNAgqiBgEFUYOAgqhBQEHUpB3Q51/9SHoIzfP8jfPnvyM9iGAkHdBPzn9eX0A//fb72fPfe196GKFIOaAffu6fFM6gn3wh//FhMlNoygFVeojP3CyaCAhognz2gzelhxAMBDQ9Pv3TdPKJgKbH8zeSOQHNEND0SCufCGhyPDpvSCakaQcULDwIKIgaBBREDQIKogYBBVGDgIKoWZiAfpysGIdO1xQEVFyMQ6drCgIqLsah0zUFARUX49DpmoKAiotx6HRNQUDFxTh0uqYgoOJiHDpdUxBQcTEOna4pCKi4GIdO1xQEVFyMQ6drCgIqLsah0zUFARUX49DpmoKAiotx6HRNQUDFxTh0uqYgoOJiHDpdUxBQcTEOna4pCKi4GIdO15QpA3rYMWyOXX58vdO58GDGMY1E567S6ZryAjNo753b4xYdX9/KsscXn8wyotE8ffo0/EajEOO4du1aomK1mT6gpzubdqbMJ8re1R92Onkkexv2If/lcj57Hr972zzmf3pXbgaaTp8+bTA0jYpxXLvWYGgaFavP9AG1E+TeZnZ48Ulvwz7kiXTRzMPrZs8ioBtbxbs+ng2bmRm30aRYGNc2MzNuo0mxIIkcYOqAHpo58fjGAzJRmpfNK3Zxp/O12/2AXg51OooZNC2x+kwbUHuWaQN4+kEZ0D16aZSfowYPKM5BExOrzbQB3Vs1P6szqAmty+KhXbq3FT6gSq9ndbqmTHubyV+h+3PQMofu0t7Or3lyzVnp4wsI6MzodE2ZLqCnO/Y+6GpxFe8P8Y87na/ftNdD5j5ofg5qXvnWDQR0ZnS6puCTJHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVPGB3RSV60pZ+j49rqhhoaQlQ0OnbtKp2sKP4MyXbW2hvH4+pgAL3ZAdZaHLWBAua5a17foW2rv53mk69nf3QuhaPBvT2f94iIGlOuqdTVipztbpqXWvJSv93g18+uVL3jClKw2g84C2wA0HVC2q9Y1LZZViya6x64vzBXe+RdCDRQz6NxZtBmU76p1ATUzaFES2jHFdiSg7oVQA8U56NxZtIDyXbXlOahf4iZLEtBws6dB5/WsTtcU5jYT31VrCmrtVXwxtw6t514INVCdu0qna8rYgE7sqrU3Srf6c6ZZr/z6hNMdexUf7givdFfpdE3BJ0niYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMmBnSaouWSxS6wjWdX6XRNqTWDTl+0HDygjRbO6Wy3a7ZKry51AlqraNmvYGZcX64csmG50cpOnf2gDZeR1qVOQGsVLftCZRNYU7QcuGE5QOlxo2JhXDfasBxATCig9YqWfaGyecgyUrkcZpiYQdMSq8/kgNYsWi4KlfODv2thDNuwjHPQtMRqMzmgNYuWyWTpemzRsBwAna4pk28z1Sxa9iuY1ctv+UDD8ozodE2ZFNDaRcu+Ydkf+9GwHAadrin4JElcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTteUOgGd0GHb2yjKm8hrKLANgk7XlLozKNNh29swNWJzDijKw9ISq03NgHIdtr3LP1+lHba9Kzc7HRTYBgD1i7UDynXY5j/3trKyLswc8lFgG8I1CmzrBpTtsDVVi+8VhYukohEFtrOCGbRmQPkOW9t2t+lLGcsVUGA7OzgHrRdQvsPW5fJHQzMoCmxnR6drSq3bTHyHrY2pvZSvrIAC2wDodE2pEdBJHbZuHnVNy8UKKLANg07XFHySJC7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLcSTier/Vai3dyrLuK/mP7KC1QpY9u8O+FQEVF+NIwvXJ9tmHJqTrLqAn2+tkocvseBBQcTGOJFzvmnzmCT37cEQaEdDoxThScH201p8x8zTaQJof3XZ+3F83P1fySbXVOnM367761+ahAgIqLsaRgmsyR5KAusf2unk82V52E2x7eejdCKi4GEcKrruv9idFGlD/qnl+YKbNfKLN8zr0bgRUXIwjBdejZ9Bst9Va9kvNRX7OyqjzUQRUXIwjBdfFOejRW7doQM2C/IzTBtRdRY28YEJAxcU4knDt87dv00gC6g7r5hC/5J4ioFGKcSThunIf9GjNXLQv3bLnnfb5ulshTykCGqUYRyKud8knSebG0p+8lU+b/rXd/FTU3GbKf0dAoxTj0OmagoCKi3HodE1BQMXFOHS6piCg4mIcOl1T2IBOqFZ2Kwy124RvVzbo3FU6XVMmzqBMtbKtu8n2BhO6+AFFu108TAooV61sGkDNGluVFczv96/cDNuw3GhA0Q8aEZMCylUrl911rkzZr2Cq78z3KlTa7cK0ADcDGpZfEIGAstXKh0XBty9TLosZbWG9e1eogWIGTUmsPnxAJ1QrX31SrGbLlKsB9e8KNVCcgyYlVhs+oHy1cv8c1JcppzKDRnQ9q9M1hb/NxFcrl1fxgyu4iRYNyzOj0zWFC+ikauXyPqgrU/YrnO5cuG8CurBX8RHtKp2uKfgkSVyMQ6drCgIqLsah0zUFARUX49DpmoKAiotxqHBN/rfPYRBQcTGOtFyPvr98MFTWQEFAxcU4knI9+hO63aW/wwwatRhHUq7HfYSMQ3zcYhxJuUZAF1OMIy3XY/6NAwIatxiHCtcIaNxiHCpcI6Bxi3GocI2Axi3GodM1BQEVF+PQ6ZpSCahtZW6xN/YBaBQa0JPtlZPtdVIpDoA0NKAmmrsr2YGvEwVAnsGA7i+7xnAAoqByDrpr07mPGRREQyWg+Ulottta4r9ZCYAGWZjbTEAnCCiIGtwHBVFTvQ86/E10AIgyeJsJgKiozqAIKIiMyjkobtGDxjEXPszEWD3Et3CRBObHqILHo7dGf4FXAW4zgaYYWZF7YC7Md8dPoQgoaIqxHc5mFh1HNaD2i7tXwg5Ljk/On//8R9KDkOCzH3xHegijGBdQ8wn7WCoB3Tdnn+braJPg+Vc/yh59QXoUEjw6H2VAx5SM84EbcR80pWt5E1J1PP/9P4szoCPpttmbm4kHVOMM+tk//nOch/iRTMhnyof4fCp543PvS4+heR69Gek56EjsZQ9zIzTli6ScT7+tLqHP/+C3ixTQSaR+m+nDdHZVTR6dN7wpPYxQJBzQT37ntxpn0Cza20wvRD+gR39YXBolc5GUzyUaz0FTDehay/9r0GQCChKABPQbbfe/yyGgIB5IQNfzi3jzfxwjoCAeKgHNTrZb6wgoiIhqQO13gvwHAgqiYTCgWbaLf7AM4mHEfdDyhlNM6GzK1OmasjA36nXuKp2uKQiouBiHTtcUBFRcjEOnawoCKi7GocD1Af+/ESOg4mIcabk+d+7c0GvmO2j2mcolBFRcjCMp1+fOjUpoxn9REgIqLsaRlOuxAcUMGrUYR1KuxwS02+Y6vRFQcTGOtFyPmUCnKG6ImLR2VV2UuE6h+kbJrhogfdfmW7kwg0YtxqHA9X6L/V4ZBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TZkyoIcdw+bY5cfXO50LD2Yc00h07iqdrikvMIP23rk9btHx9a0se3zxySwjGs24e7xzoVExjnv37iUqVpvpA3q6s2lnynyi7F39YaeTR7K3YR/yXy7ns+fxu7fNY/6nd+VmoOl07Me486BRMY579xoMTaNi9Zk+oHaC3NvMDi8+6W3YhzyRLpp5eN3sWQR0Y6t418ezYTMz4zaaFAvj2mZmxm00KRYkkQNMHdBDMyce33hAJkrzsnnFLu50vna7H9DLoU5HMYOmJVafaQNqzzJtAE8/KAO6Ry+N8nPU4AHFOWhiYrWZNqB7q+ZndQY1oXVZPLRL97bCB1Tp9axO15RpbzP5K3R/Dlrm0F3a2/k1T645K318AQGdGZ2uKdMF9HTH3gddLa7i/SH+cafz9Zv2esjcB83PQc0r37qBgM6MTtcUfJIkLsah0zUFARUX41Dhmv0aeARUXIwjLddj7hPs1/067qhJa1fVJSnXY+60dr90CQGNWYwjKdejA3ryF/+AQ3zUYhxJuR4d0P0VnIPGLcaRlutR+ez+7kMENG4xjvRdu+/qHP/1mwiouBiHCteYQeMW41DhGgGNW4xDp2sKAiouxqHTNQUBFRfj0OmagoCKi3HodE1BQMXFOHS6piCg4mIcOl1TEFBxMQ6drikIqLgYh07XFARUXIxDp2sKAiouxqHTNQUBFRfj0OmagoCKi3HodE1BQMXFOHS6piCg4mIcOl1Txgd0UletKWfo+Pa6oYaGkJUNDp27SqdrCj+DMl21tobx+PqYAAcPKMrD0hKrDRtQrqvW9S36ltr7eR7pevZ390IYUL+Yllh92IByXbWuRux0Z8u01JqX8vUer2Z+vfIFz4ztqiiwnTsLWGDLdtW6psWyatFE99j1hbnCO/9CmGFiBk1LrD5MQPmuWhdQM4MWJaEdU2xHAupeCDROnIOmJVYbJqB8V215DuqXuMmSBDTU7OnQeT2r0zWFuc3Ed9Waglp7FV/MrUPruRdCDVTnrtLpmjI2oBO7au2N0q3+nGnWK78+4XTHXsUHO8Jr3VU6XVPwSZK4GIdO1xQEVFyMQ6drCgIqLsah0zUFARUX49DpmoKAiotx6HRNQUDFxTh0uqYgoOJiHDpdUxBQcTEOna4pCKi4GIdO1xQEVFyMQ6drCgIqLsah0zUFARUX49DpmoKAiotx6HRNQUDFxTh0uqYgoOJiHDpdUxBQcTEOna4pCKi4GIdO1xQEVFyMQ6drCgIqLsah0zUFARUX49DpmoKAiotx6HRNQUDFxTh0uqYgoOJiHDpdUxBQcTEOna4pCKi4GIdO15SJAZ2maLkEDcth0OmaUmsGnb5oGQ3LQUC7Xa2A1ipa9iuYGdeXK6NheVbQD1ovoLWKln2hsgmsKVpGw3IA12hYrhXQekXLvlDZPGQZqVwOM0zMoGmJ1WdyQGsWLReFyvnB37UwomF5dnAOWiOgNYuWyWTpemzRsBwAna4pk28z1Sxa9iuY1ctv+UDD8ozodE2ZFNDaRcu+Ydkf+9GwHAadrin4JElcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTteUOgGd0GHb2yjKm8hrKLANgk7XlLozKNNh29swNWJzDqjO8rBGM7Oo5WEWrsO2d/nnq7TDtnflZqeDAtsgNBjQha1ftHAdtvnPva2srAszh3wU2IZw3SiLW2CbTeiwNVWL7xWFi6SiEQW2s4MZtFZA+Q5b23a36UsZyxVQYBsAnIPWCijfYety+aOhGRQFtrOj0zWl1m0mvsPWxtReyldWQIFtAHS6ptQI6KQOWzePuqblYgUU2IZBp2sKPkkSF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZqCgIqLceh0TUFAxcU4dLqmIKDiYhw6XVMQUHExDp2uKQiouBiHTtcUBFRcjEOnawoCKi7GodM1BQEVF+PQ6ZrCBnRCtbJbYajdJny7skHnrtLpmjJxBmWqlW3dTbY3mNC5BFRnu12jhXOL2W7HVSubBlCzxlZlBfP7/Ss30bA8O41Wdi5oPyhXrVx217kyZb+Cqb4z36tQabebsf5XZ8NygNLjRsUEAspWKx8WBd++TLksZrSF9e5dYYaJGTQtsfrwAZ1QrXz1SbGaLVOuBtS/K9A4cQ6allht+IDy1cr9c1BfpjzHGVTr9axO1xT+NhNfrVxexQ+u4CZaNCzPjE7XFC6gk6qVy/ugrkzZr3C6c+G+CWjIq3itu0qnawo+SRIX49DpmoKAiotx6HRNQUDFxTh0uqYgoOJiHDpdUxBQcTEOna4pCKi4GIdO1xQEVFyMQ6drCgIqLsah0zUFARUX49DpmoKAiotx6HRNQUDFxTh0uqYgoOJiHDpdUxBQcTEOna4pCKi4GIdO15SFCSjQCQIKogYBBVGDgIKoQUBB1CCgIGoQUBA1CCiIGgQURE0sAR3TQVKjyjFQ2+PIqtN5o9P1VMQS0MM/H9mT29iuGl11Om90up6KSAJ6+sGP33uSHd/4V9NQ4htIe1dudkxNmXt2+sG/dTqbh7bwebCOdPYq0rLq1LefHrqCtH4X6lzQ6Xo6Iglo7+qTPdODe/HJYf4X7xpITcmoKStzz053TAHpqp06ButIZ6+B6r/bq5nms1yi7EKdCzpdT0ckAX28adpGTXGeb2ws60b9M/O6+ZP/7Q3Vkc5epNevOvVqZGu+um8e6HQ9HXEE1NaUuUkk29vyDaTVPlK6q0bVkc5WRdqvOi3aT/OjnDns9rtQ54FO11MSR0Dt39TeltlV+V+5byCt9pHSXTVYRzr7XFJWnfbbT23deflsDuh0PSVxBPSxOSU/XD2+vtrvGX2HzBXmGdlVw32ls1eRFtezXs1srRxJIJdDmipdT0kUAT39F1fY/ON3v2sPMa6B1B7Cdi48cM/orhqsIw3xhSLFHUHffuoPcqQLNTg6XU9LFAEtCNgYvkDodF0XBFQcna7rElVAARgEAQVRg4CCqEFAQdQgoOE42W45lruv3JIeTCogoGFBNAODgIYFAQ0MAhoWF9D8Z/eVv2m1zj50B/5l6WEtLghoWMqAts/cPdlezoo/4MVAQMNCArpuHw/O3M0y+wO8CAhoWMghPv/laG39wBzmcWb6wiCgYUFAA4OAhmXgEP/q3YOlWzjEzwACGhZcJAUGAQ1L9TaTySVuM80EAjoncNoZBgR0TiCgYUBA5wQCGgYEFEQNAgqiBgEFUYOAgqhBQEHUIKAgahBQEDX/D6hnXCvSW3oyAAAAAElFTkSuQmCC" /><!-- --></p>
<p><strong>3.2.10 Clusters por Número de piso vs Estrato</strong></p>
<div class="sourceCode" id="cb77"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb77-1"><a href="#cb77-1" tabindex="-1"></a><span class="co"># Clusters por Número de piso vs Estrato</span></span>
<span id="cb77-2"><a href="#cb77-2" tabindex="-1"></a>variable1 <span class="ot"><-</span> dataset_vivienda<span class="sc">$</span>piso</span>
<span id="cb77-3"><a href="#cb77-3" tabindex="-1"></a>variable2 <span class="ot"><-</span> dataset_vivienda<span class="sc">$</span>estrato</span>
<span id="cb77-4"><a href="#cb77-4" tabindex="-1"></a></span>
<span id="cb77-5"><a href="#cb77-5" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> dataset_vivienda, <span class="fu">aes</span>(<span class="at">x =</span> variable1, <span class="at">y =</span> variable2, <span class="at">color =</span> <span class="fu">as.factor</span>(cluster))) <span class="sc">+</span> </span>
<span id="cb77-6"><a href="#cb77-6" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span></span>
<span id="cb77-7"><a href="#cb77-7" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Piso"</span>, <span class="at">y =</span> <span class="st">"Estrato"</span>, <span class="at">color =</span> <span class="st">"Clúster"</span>) <span class="sc">+</span></span>
<span id="cb77-8"><a href="#cb77-8" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">" Clusters por Número de piso vs Estrato"</span>) <span class="sc">+</span></span>
<span id="cb77-9"><a href="#cb77-9" tabindex="-1"></a> <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb77-10"><a href="#cb77-10" tabindex="-1"></a> <span class="fu">facet_wrap</span>(<span class="sc">~</span> cluster, <span class="at">ncol =</span> <span class="dv">2</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p><strong>3.2.11 Análisis de Conglomerados</strong></p>
<p>El análisis de conglomerados para el análisis ded la oferta
inmobiliaria ha proporcionado una visión detallada de la diversidad de
propiedades, dividiéndolas en cuatro clusters distintos. En el
<strong>primer cluster</strong>, se encuentran casas de alto precio y
amplias áreas, preferiblemente en pisos bajos, mientras que el
<strong>segundo cluster</strong> muestra propiedades también de alto
precio, pero con áreas más reducidas, inclinándose hacia apartamentos en
pisos altos. Por otro lado, el <strong>tercer cluster</strong> presenta
propiedades de precio más asequible y áreas más pequeñas, sin una
preferencia clara por los pisos. Similar al primero, el
<strong>cuarto</strong> cluster refleja casas de alto precio y amplias
áreas, nuevamente favoreciendo los pisos bajos. Además, al comparar el
primer cluster con el cuarto cluster, se evidencian diferencias notables
en términos de la cantidad de parqueaderos, baños y estrato de las
propiedades. Mientras que el <strong>primer cluster</strong> refleja
propiedades lujosas con varios parqueaderos y baños, ubicadas en
estratos altos, el <strong>cuarto cluster</strong> presenta propiedades
con menos comodidades, con un número reducido de parqueaderos y baños, y
ubicadas en estratos bajos. Estas discrepancias resaltan las distintas
preferencias y perfiles de los compradores en cada segmento, donde el
primer cluster podría atraer a quienes valoran el lujo y la comodidad,
mientras que el cuarto cluster sería más atractivo para aquellos que
buscan precios más accesibles.</p>
</div>
<div id="análisis-de-correspondencia" class="section level3">
<h3>3.3 Análisis de Correspondencia</h3>
<p><strong>3.3.1 Análisis de Correspondencia de zona y
estrato</strong></p>
<p>Tabla de frecuencia para los atributos zona y estrato.</p>
<div class="sourceCode" id="cb78"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb78-1"><a href="#cb78-1" tabindex="-1"></a><span class="co"># Tabla de frecuencia para los atributos zona y estrato.</span></span>
<span id="cb78-2"><a href="#cb78-2" tabindex="-1"></a>tabla_frecuencia <span class="ot"><-</span> <span class="fu">table</span>(dataset_vivienda<span class="sc">$</span>zona, dataset_vivienda<span class="sc">$</span>estrato)</span>
<span id="cb78-3"><a href="#cb78-3" tabindex="-1"></a>col_nombres <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"Zona"</span>, <span class="st">"Est 03"</span>, <span class="st">"Est 04"</span>, <span class="st">"Est 05"</span>, <span class="st">"Est 06"</span>)</span>
<span id="cb78-4"><a href="#cb78-4" tabindex="-1"></a><span class="fu">kable</span>(tabla_frecuencia, <span class="at">format =</span> <span class="st">"markdown"</span>, <span class="at">col.names =</span> col_nombres)</span></code></pre></div>
<table>
<thead>
<tr class="header">
<th align="left">Zona</th>
<th align="right">Est 03</th>
<th align="right">Est 04</th>
<th align="right">Est 05</th>
<th align="right">Est 06</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Zona Centro</td>
<td align="right">105</td>
<td align="right">14</td>
<td align="right">4</td>
<td align="right">1</td>
</tr>
<tr class="even">
<td align="left">Zona Norte</td>
<td align="right">572</td>
<td align="right">407</td>
<td align="right">769</td>
<td align="right">172</td>
</tr>
<tr class="odd">
<td align="left">Zona Oeste</td>
<td align="right">54</td>
<td align="right">84</td>
<td align="right">290</td>
<td align="right">770</td>
</tr>
<tr class="even">
<td align="left">Zona Oriente</td>
<td align="right">340</td>
<td align="right">8</td>
<td align="right">2</td>
<td align="right">1</td>
</tr>
<tr class="odd">
<td align="left">Zona Sur</td>
<td align="right">382</td>