{"id":52038,"date":"2023-03-02T00:00:00","date_gmt":"2023-03-02T08:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/clustering-and-classifying-wine-using-machine-learning-r-and-griddb\/"},"modified":"2023-03-02T00:00:00","modified_gmt":"2023-03-02T08:00:00","slug":"clustering-and-classifying-wine-using-machine-learning-r-and-griddb","status":"publish","type":"post","link":"https:\/\/griddb.net\/en\/blog\/clustering-and-classifying-wine-using-machine-learning-r-and-griddb\/","title":{"rendered":"Clustering and Classifying Wine Using Machine Learning, R, and GridDB"},"content":{"rendered":"<div class=\"notebook\">\n    <div class=\"nbconvert\">\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>This article will cover the creation of wine clusters based on a <strong>Wine<\/strong> dataset's different attributes. <strong>R Programming<\/strong> will be used, which is very useful in creating a set of groups representing some of the differences and similarities found in the types of wines. In addition, <strong>GridDB<\/strong> will be our central database for this program as it is ideally suited to hold machine learning datasets. The article will outline the requirements to set up our database <strong>GridDB<\/strong>. Following that, we will briefly describe our dataset and model. To finish off, we will interpret the results and come up with our conclusion.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h2 id=\"Prerequisites\">Prerequisites<a class=\"anchor-link\" href=\"#Prerequisites\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>You will need:<\/p>\n<ul>\n<li>GridDB installation <a href=\"https:\/\/docs.griddb.net\/latest\/gettingstarted\/using-apt\/\">URL<\/a><\/li>\n<li>R version <strong>R version 4.2.2 Patched (2022-11-10 r83330)<\/strong> <a href=\"https:\/\/cran.rstudio.com\/\">Install R<\/a><\/li>\n<li>Jupyter Notebook found in <a href=\"https:\/\/www.anaconda.com\/\">Anaconda<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[6]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"nf\">sessionInfo<\/span><span class=\"p\">()<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>R version 4.2.2 Patched (2022-11-10 r83330)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 20.04.5 LTS\n\nMatrix products: default\nBLAS:   \/usr\/lib\/x86_64-linux-gnu\/openblas-pthread\/libblas.so.3\nLAPACK: \/usr\/lib\/x86_64-linux-gnu\/openblas-pthread\/liblapack.so.3\n\nlocale:\n [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n\nattached base packages:\n[1] stats     graphics  grDevices utils     datasets  methods   base     \n\nother attached packages:\n [1] factoextra_1.0.7 RJDBC_0.2-10     rJava_1.0-6      DBI_1.1.3       \n [5] forcats_1.0.0    stringr_1.4.1    dplyr_1.1.0      purrr_1.0.1     \n [9] readr_2.1.3      tidyr_1.3.0      tibble_3.1.8     ggplot2_3.4.0   \n[13] tidyverse_1.3.1 \n\nloaded via a namespace (and not attached):\n [1] ggrepel_0.9.3    Rcpp_1.0.10      lubridate_1.9.1  assertthat_0.2.1\n [5] digest_0.6.30    utf8_1.2.3       IRdisplay_1.1    R6_2.5.1        \n [9] cellranger_1.1.0 repr_1.1.6       backports_1.4.1  reprex_2.0.2    \n[13] evaluate_0.20    httr_1.4.4       pillar_1.8.1     rlang_1.0.6     \n[17] uuid_1.1-0       readxl_1.4.1     rstudioapi_0.14  munsell_0.5.0   \n[21] broom_1.0.3      compiler_4.2.2   modelr_0.1.10    pkgconfig_2.0.3 \n[25] base64enc_0.1-3  htmltools_0.5.4  tidyselect_1.2.0 fansi_1.0.4     \n[29] crayon_1.5.2     tzdb_0.3.0       dbplyr_2.3.0     withr_2.5.0     \n[33] grid_4.2.2       jsonlite_1.8.3   gtable_0.3.1     lifecycle_1.0.3 \n[37] magrittr_2.0.3   scales_1.2.1     cli_3.6.0        stringi_1.7.12  \n[41] fs_1.6.0         xml2_1.3.3       ellipsis_0.3.2   generics_0.1.3  \n[45] vctrs_0.5.2      IRkernel_1.3.2   tools_4.2.2      glue_1.6.2      \n[49] hms_1.1.2        fastmap_1.1.0    timechange_0.2.0 colorspace_2.1-0\n[53] rvest_1.0.3      pbdZMQ_0.3-9     haven_2.5.1     <\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h2 id=\"Requirements\">Requirements<a class=\"anchor-link\" href=\"#Requirements\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>The <strong>GridDB<\/strong> database storage system will store our dataset while building our clustering machine learning model. <strong>GridDB<\/strong> must be downloaded and configured in your operating system to be fully functional.<\/p>\n<p>Make sure to run the following R statements to import the needed libraries helpful in running our <strong>Wine<\/strong> Cluster:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[7]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"nf\">install.packages<\/span><span class=\"p\">(<\/span><span class=\"w\"> <\/span><span class=\"s\">\"RJDBC\"<\/span><span class=\"p\">)<\/span>\n<span class=\"nf\">install.packages<\/span><span class=\"p\">(<\/span><span class=\"s\">\"factoextra\"<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"application\/vnd.jupyter.stderr\" tabindex=\"0\">\n<pre>Installing package into \u2018\/usr\/local\/lib\/R\/site-library\u2019\n(as \u2018lib\u2019 is unspecified)\n\nInstalling package into \u2018\/usr\/local\/lib\/R\/site-library\u2019\n(as \u2018lib\u2019 is unspecified)\n\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[8]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"nf\">library<\/span><span class=\"p\">(<\/span><span class=\"n\">tidyverse<\/span><span class=\"p\">)<\/span>\n<span class=\"nf\">library<\/span><span class=\"p\">(<\/span><span class=\"n\">RJDBC<\/span><span class=\"p\">)<\/span>\n<span class=\"nf\">library<\/span><span class=\"p\">(<\/span><span class=\"n\">factoextra<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h2 id=\"Setup-GridDB\">Setup GridDB<a class=\"anchor-link\" href=\"#Setup-GridDB\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>To set up the GridDB database, we will initialize the connection object by specifying the <strong>driverClass<\/strong> and <strong>classPath<\/strong> attributes.<\/p>\n<p>The following task can be as follows:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"n\">drv<\/span><span class=\"w\"> <\/span><span class=\"o\">&lt;-<\/span><span class=\"w\"> <\/span><span class=\"nf\">JDBC<\/span><span class=\"p\">(<\/span><span class=\"s\">\"com.toshiba.mwcloud.gs.sql.Driver\"<\/span><span class=\"p\">,<\/span>\n<span class=\"w\">            <\/span><span class=\"s\">\"\/usr\/share\/java\/gridstore-jdbc-5.0.0.jar\"<\/span><span class=\"p\">)<\/span>\n<span class=\"w\">             <\/span><span class=\"c1\">#identifier.quote = \"`\")<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>The second step is to set up the <strong>IP<\/strong> address and <strong>port<\/strong> with the credentials in mind. These values will depend on your configuration and credentials.<\/p>\n<p>The task can be done as follows:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"n\">conn<\/span><span class=\"w\"> <\/span><span class=\"o\">&lt;-<\/span><span class=\"w\"> <\/span><span class=\"nf\">dbConnect<\/span><span class=\"p\">(<\/span><span class=\"n\">drv<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"s\">\"jdbc:gs:\/\/127.0.0.1:20001\/myCluster\/public\"<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"s\">\"admin\"<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"s\">\"admin\"<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h2 id=\"Store-Data-in-GridDB\">Store Data in GridDB<a class=\"anchor-link\" href=\"#Store-Data-in-GridDB\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>In this section, we will store the data in our GridDB database. We will use the <code>CREATE<\/code> query to complete this task, creating a table representing your wine dataset.<\/p>\n<p>The following code was used to conduct the task explained in this section:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"n\">dbInsertTable<\/span><span class=\"w\"> <\/span><span class=\"o\">&lt;-<\/span><span class=\"w\"> <\/span><span class=\"kr\">function<\/span><span class=\"p\">(<\/span><span class=\"n\">conn<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"n\">name<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span>\n<span class=\"w\">                          <\/span><span class=\"n\">df<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"n\">append<\/span><span class=\"w\"> <\/span><span class=\"o\">=<\/span><span class=\"w\"> <\/span><span class=\"kc\">TRUE<\/span><span class=\"p\">){<\/span>\n<span class=\"w\">                          <\/span><span class=\"kr\">for<\/span><span class=\"w\"> <\/span><span class=\"p\">(<\/span><span class=\"n\">i<\/span><span class=\"w\"> <\/span><span class=\"kr\">in<\/span><span class=\"w\"> <\/span><span class=\"nf\">seq_len<\/span><span class=\"p\">(<\/span><span class=\"nf\">nrow<\/span><span class=\"p\">(<\/span><span class=\"n\">df<\/span><span class=\"p\">)))<\/span><span class=\"w\"> <\/span><span class=\"p\">{<\/span>\n<span class=\"w\">                            <\/span><span class=\"nf\">dbWriteTable<\/span><span class=\"p\">(<\/span><span class=\"n\">conn<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"n\">name<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"p\">],<\/span><span class=\"w\"> <\/span><span class=\"n\">append<\/span><span class=\"w\"> <\/span><span class=\"o\">=<\/span><span class=\"w\"> <\/span><span class=\"n\">append<\/span><span class=\"p\">)<\/span>\n<span class=\"w\">                            <\/span><span class=\"p\">}<\/span>\n<span class=\"w\">                          <\/span><span class=\"p\">}<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>One additional step is to use the function created to add the table to our GridDB database:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"w\"> <\/span><span class=\"n\">Alcohol<\/span><span class=\"w\">              <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">14.23<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">13.20<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">13.16<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">14.37<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">13.24<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">14.20<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">14.39<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Malic_Acid<\/span><span class=\"w\">           <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">1.71<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.78<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.36<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.95<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.59<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.76<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.87<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.15<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Ash<\/span><span class=\"w\">                  <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">2.43<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.14<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.67<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.50<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.87<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.45<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.45<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.61<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Ash_Alcanity<\/span><span class=\"w\">         <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">15.6<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">11.2<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">18.6<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">16.8<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">21.0<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">15.2<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">14.6<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">17.6<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Magnesium<\/span><span class=\"w\">            <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">127<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">100<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">101<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">113<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">118<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">112<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">96<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">121<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">97<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">98<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">10<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Total_Phenols<\/span><span class=\"w\">        <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">2.80<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.65<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.80<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">3.85<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.80<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">3.27<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.50<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.60<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Flavanoids<\/span><span class=\"w\">           <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">3.06<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.76<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">3.24<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">3.49<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.69<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">3.39<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.52<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.51<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Nonflavanoid_Phenols<\/span><span class=\"w\"> <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">0.28<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">0.26<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">0.30<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">0.24<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">0.39<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">0.34<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">0.30<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">0.31<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">0<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Proanthocyanins<\/span><span class=\"w\">      <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">2.29<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.28<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.81<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.18<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.82<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.97<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.98<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.25<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Color_Intensity<\/span><span class=\"w\">      <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">5.64<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">4.38<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">5.68<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">7.80<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">4.32<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">6.75<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">5.25<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">5.05<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">5<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Hue<\/span><span class=\"w\">                  <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">1.04<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.05<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.03<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">0.86<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.04<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.05<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.02<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1.06<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">OD280<\/span><span class=\"w\">                <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">3.92<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">3.40<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">3.17<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">3.45<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.93<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2.85<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">3.58<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">3.58<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">2<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Proline<\/span><span class=\"w\">              <\/span><span class=\"o\">&lt;<\/span><span class=\"n\">dbl<\/span><span class=\"o\">&gt;<\/span><span class=\"w\"> <\/span><span class=\"m\">1065<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1050<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1185<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1480<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">735<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1450<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1290<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">1295<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">10<\/span>\u2026\n<span class=\"o\">$<\/span><span class=\"w\"> <\/span><span class=\"n\">Customer_Segment<\/span><span class=\"w\">     <\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"nf\">dbSendUpdate<\/span><span class=\"p\">(<\/span><span class=\"n\">conn<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"nf\">paste<\/span><span class=\"p\">(<\/span>\n<span class=\"w\">  <\/span><span class=\"s\">\"CREATE TABLE IF NOT EXISTS wine_clusters\"<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span>\n<span class=\"w\">  <\/span><span class=\"s\">\"(  Alcohol FLOAT, <\/span>\n<span class=\"s\">      Malic_Acid FLOAT, <\/span>\n<span class=\"s\">      Ash FLOAT, <\/span>\n<span class=\"s\">      Ash_Alcanity FLOAT, <\/span>\n<span class=\"s\">      Magnesium FLOAT, <\/span>\n<span class=\"s\">      Total_Phenols FLOAT,<\/span>\n<span class=\"s\">      Flavanoids FLOAT,<\/span>\n<span class=\"s\">      Nonflavanoid_Phenols FLOAT,<\/span>\n<span class=\"s\">      Proanthocyanins FLOAT,<\/span>\n<span class=\"s\">      Color_Intensity FLOAT,<\/span>\n<span class=\"s\">      Hue FLOAT,<\/span>\n<span class=\"s\">      OD280 FLOAT,<\/span>\n<span class=\"s\">      Proline FLOAT,<\/span>\n<span class=\"s\">      Customer_Segment FLOAT);\"<\/span><span class=\"p\">))<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>To implement the clustering algorithm, we will use the <strong>wine<\/strong> dataset to create wine clusters. The dataset contains <strong>14<\/strong> attributes and <strong>178<\/strong> instances. The original dataset is provided by <strong>UCI Machine Learning Repositor<\/strong> in the following link:\n<a href=\"http:\/\/archive.ics.uci.edu\/ml\/datasets\/Wine+Quality\">http:\/\/archive.ics.uci.edu\/ml\/datasets\/Wine+Quality<\/a><\/p>\n<p>The last step is to populate the table using the wine dataset:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"nf\">dbInsertTable<\/span><span class=\"p\">(<\/span><span class=\"n\">conn<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"s\">\"wine_clusters\"<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"nf\">read_csv<\/span><span class=\"p\">(<\/span><span class=\"s\">\"data.csv\"<\/span><span class=\"p\">))<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h1 id=\"Retrieve-the-Data-from-GridDB\">Retrieve the Data from GridDB<a class=\"anchor-link\" href=\"#Retrieve-the-Data-from-GridDB\">\u00b6<\/a><\/h1>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>In this section, we will retrieve the data from our database. We will use the <code>SELECT<\/code> query to complete this task, which returns all database values.<\/p>\n<p>The following code was used to conduct the task explained in this section:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"n\">queryString<\/span><span class=\"w\"> <\/span><span class=\"o\">&lt;-<\/span><span class=\"w\"> <\/span><span class=\"s\">\"select Alcohol, Malic_Acid, Ash, Ash_Alcanity, Magnesium, Total_Phenols,<\/span>\n<span class=\"s\">                Flavanoids, Nonflavanoid_Phenols, Proanthocyanins, Color_Intensity,<\/span>\n<span class=\"s\">                Hue, OD280, Proline, Customer_Segment from wine_clusters\"<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>To retrieve the data from our GridDB  database, we can use the <strong>dbGetQuery<\/strong> method to do so:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"n\">data<\/span><span class=\"w\"> <\/span><span class=\"o\">&lt;-<\/span><span class=\"w\"> <\/span><span class=\"nf\">dbGetQuery<\/span><span class=\"p\">(<\/span><span class=\"n\">conn<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"n\">queryString<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h2 id=\"The-Dataset\">The Dataset<a class=\"anchor-link\" href=\"#The-Dataset\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>To take a quick look at our dataset, we will run the following code:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"nf\">glimpse<\/span><span class=\"p\">(<\/span><span class=\"n\">data<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>Rows: 178\nColumns: 14\n$ Alcohol              <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 14.23, 13.20, 13.16, 14.37, 13.24, 14.20, 14.39, \u2026\n$ Malic_Acid           <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 1.71, 1.78, 2.36, 1.95, 2.59, 1.76, 1.87, 2.15, 1\u2026\n$ Ash                  <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 2.43, 2.14, 2.67, 2.50, 2.87, 2.45, 2.45, 2.61, 2\u2026\n$ Ash_Alcanity         <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 15.6, 11.2, 18.6, 16.8, 21.0, 15.2, 14.6, 17.6, 1\u2026\n$ Magnesium            <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 127, 100, 101, 113, 118, 112, 96, 121, 97, 98, 10\u2026\n$ Total_Phenols        <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 2.80, 2.65, 2.80, 3.85, 2.80, 3.27, 2.50, 2.60, 2\u2026\n$ Flavanoids           <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 3.06, 2.76, 3.24, 3.49, 2.69, 3.39, 2.52, 2.51, 2\u2026\n$ Nonflavanoid_Phenols <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 0.28, 0.26, 0.30, 0.24, 0.39, 0.34, 0.30, 0.31, 0\u2026\n$ Proanthocyanins      <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 2.29, 1.28, 2.81, 2.18, 1.82, 1.97, 1.98, 1.25, 1\u2026\n$ Color_Intensity      <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 5.64, 4.38, 5.68, 7.80, 4.32, 6.75, 5.25, 5.05, 5\u2026\n$ Hue                  <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 1.04, 1.05, 1.03, 0.86, 1.04, 1.05, 1.02, 1.06, 1\u2026\n$ OD280                <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 3.92, 3.40, 3.17, 3.45, 2.93, 2.85, 3.58, 3.58, 2\u2026\n$ Proline              <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 1065, 1050, 1185, 1480, 735, 1450, 1290, 1295, 10\u2026\n$ Customer_Segment     <span class=\"ansi-black-intense-fg\">&lt;dbl&gt;<\/span> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1\u2026\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>The attributes covered in this dataset are as follows:<\/p>\n<ul>\n<li><strong>Alcohol<\/strong>  this attribute is a numeric value.<\/li>\n<li><strong>Malic Acid<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>Ash<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>Ash Alcanity<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>Magnesium<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>Total Phenols<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>Flavanoids<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>Nonflavanoid Phenols<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>Proanthocyanins<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>Color Intensity<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>Hue<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>OD280<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>Proline<\/strong> this attribute is a numeric value.<\/li>\n<li><strong>Customer Segment<\/strong> this attribute is a numeric value that takes <strong>three<\/strong> categories of segments <strong>1<\/strong>, <strong>2<\/strong>, and <strong>3<\/strong>.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>To display the first <strong>5<\/strong> rows of our dataset, we can use the <strong>head<\/strong> method to do so:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"nf\">head<\/span><span class=\"p\">(<\/span><span class=\"n\">data<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"n\">n<\/span><span class=\"w\"> <\/span><span class=\"o\">=<\/span><span class=\"w\"> <\/span><span class=\"m\">5<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output\" data-mime-type=\"text\/html\" tabindex=\"0\">\n<table class=\"dataframe\">\n<caption>A tibble: 5 \u00d7 14<\/caption>\n<thead>\n<tr><th scope=\"col\">Alcohol<\/th><th scope=\"col\">Malic_Acid<\/th><th scope=\"col\">Ash<\/th><th scope=\"col\">Ash_Alcanity<\/th><th scope=\"col\">Magnesium<\/th><th scope=\"col\">Total_Phenols<\/th><th scope=\"col\">Flavanoids<\/th><th scope=\"col\">Nonflavanoid_Phenols<\/th><th scope=\"col\">Proanthocyanins<\/th><th scope=\"col\">Color_Intensity<\/th><th scope=\"col\">Hue<\/th><th scope=\"col\">OD280<\/th><th scope=\"col\">Proline<\/th><th scope=\"col\">Customer_Segment<\/th><\/tr>\n<tr><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;dbl&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n<tr><td>14.23<\/td><td>1.71<\/td><td>2.43<\/td><td>15.6<\/td><td>127<\/td><td>2.80<\/td><td>3.06<\/td><td>0.28<\/td><td>2.29<\/td><td>5.64<\/td><td>1.04<\/td><td>3.92<\/td><td>1065<\/td><td>1<\/td><\/tr>\n<tr><td>13.20<\/td><td>1.78<\/td><td>2.14<\/td><td>11.2<\/td><td>100<\/td><td>2.65<\/td><td>2.76<\/td><td>0.26<\/td><td>1.28<\/td><td>4.38<\/td><td>1.05<\/td><td>3.40<\/td><td>1050<\/td><td>1<\/td><\/tr>\n<tr><td>13.16<\/td><td>2.36<\/td><td>2.67<\/td><td>18.6<\/td><td>101<\/td><td>2.80<\/td><td>3.24<\/td><td>0.30<\/td><td>2.81<\/td><td>5.68<\/td><td>1.03<\/td><td>3.17<\/td><td>1185<\/td><td>1<\/td><\/tr>\n<tr><td>14.37<\/td><td>1.95<\/td><td>2.50<\/td><td>16.8<\/td><td>113<\/td><td>3.85<\/td><td>3.49<\/td><td>0.24<\/td><td>2.18<\/td><td>7.80<\/td><td>0.86<\/td><td>3.45<\/td><td>1480<\/td><td>1<\/td><\/tr>\n<tr><td>13.24<\/td><td>2.59<\/td><td>2.87<\/td><td>21.0<\/td><td>118<\/td><td>2.80<\/td><td>2.69<\/td><td>0.39<\/td><td>1.82<\/td><td>4.32<\/td><td>1.04<\/td><td>2.93<\/td><td> 735<\/td><td>1<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h2 id=\"Data-Analysis\">Data Analysis<a class=\"anchor-link\" href=\"#Data-Analysis\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>Before storing or running our machine learning cluster, we will have to analyze the data. As a start, we will inspect the datatypes of our columns.<\/p>\n<p>To conduct this task, we will run the following code:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"nf\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">data<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>spc_tbl_ [178 \u00d7 14] (S3: spec_tbl_df\/tbl_df\/tbl\/data.frame)\n $ Alcohol             : num [1:178] 14.2 13.2 13.2 14.4 13.2 ...\n $ Malic_Acid          : num [1:178] 1.71 1.78 2.36 1.95 2.59 1.76 1.87 2.15 1.64 1.35 ...\n $ Ash                 : num [1:178] 2.43 2.14 2.67 2.5 2.87 2.45 2.45 2.61 2.17 2.27 ...\n $ Ash_Alcanity        : num [1:178] 15.6 11.2 18.6 16.8 21 15.2 14.6 17.6 14 16 ...\n $ Magnesium           : num [1:178] 127 100 101 113 118 112 96 121 97 98 ...\n $ Total_Phenols       : num [1:178] 2.8 2.65 2.8 3.85 2.8 3.27 2.5 2.6 2.8 2.98 ...\n $ Flavanoids          : num [1:178] 3.06 2.76 3.24 3.49 2.69 3.39 2.52 2.51 2.98 3.15 ...\n $ Nonflavanoid_Phenols: num [1:178] 0.28 0.26 0.3 0.24 0.39 0.34 0.3 0.31 0.29 0.22 ...\n $ Proanthocyanins     : num [1:178] 2.29 1.28 2.81 2.18 1.82 1.97 1.98 1.25 1.98 1.85 ...\n $ Color_Intensity     : num [1:178] 5.64 4.38 5.68 7.8 4.32 6.75 5.25 5.05 5.2 7.22 ...\n $ Hue                 : num [1:178] 1.04 1.05 1.03 0.86 1.04 1.05 1.02 1.06 1.08 1.01 ...\n $ OD280               : num [1:178] 3.92 3.4 3.17 3.45 2.93 2.85 3.58 3.58 2.85 3.55 ...\n $ Proline             : num [1:178] 1065 1050 1185 1480 735 ...\n $ Customer_Segment    : num [1:178] 1 1 1 1 1 1 1 1 1 1 ...\n - attr(*, \"spec\")=\n  .. cols(\n  ..   Alcohol = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Malic_Acid = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Ash = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Ash_Alcanity = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Magnesium = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Total_Phenols = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Flavanoids = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Nonflavanoid_Phenols = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Proanthocyanins = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Color_Intensity = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Hue = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   OD280 = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Proline = <span class=\"ansi-green-fg\">col_double()<\/span>,\n  ..   Customer_Segment = <span class=\"ansi-green-fg\">col_double()<\/span>\n  .. )\n - attr(*, \"problems\")=&lt;externalptr&gt; \n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>The next step is to provide summary statistics for every column we have in the wine dataset using the following code:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"nf\">summary<\/span><span class=\"p\">(<\/span><span class=\"n\">data<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\" tabindex=\"0\">\n<pre>    Alcohol        Malic_Acid         Ash         Ash_Alcanity  \n Min.   :11.03   Min.   :0.740   Min.   :1.360   Min.   :10.60  \n 1st Qu.:12.36   1st Qu.:1.603   1st Qu.:2.210   1st Qu.:17.20  \n Median :13.05   Median :1.865   Median :2.360   Median :19.50  \n Mean   :13.00   Mean   :2.336   Mean   :2.367   Mean   :19.49  \n 3rd Qu.:13.68   3rd Qu.:3.083   3rd Qu.:2.558   3rd Qu.:21.50  \n Max.   :14.83   Max.   :5.800   Max.   :3.230   Max.   :30.00  \n   Magnesium      Total_Phenols     Flavanoids    Nonflavanoid_Phenols\n Min.   : 70.00   Min.   :0.980   Min.   :0.340   Min.   :0.1300      \n 1st Qu.: 88.00   1st Qu.:1.742   1st Qu.:1.205   1st Qu.:0.2700      \n Median : 98.00   Median :2.355   Median :2.135   Median :0.3400      \n Mean   : 99.74   Mean   :2.295   Mean   :2.029   Mean   :0.3619      \n 3rd Qu.:107.00   3rd Qu.:2.800   3rd Qu.:2.875   3rd Qu.:0.4375      \n Max.   :162.00   Max.   :3.880   Max.   :5.080   Max.   :0.6600      \n Proanthocyanins Color_Intensity       Hue             OD280      \n Min.   :0.410   Min.   : 1.280   Min.   :0.4800   Min.   :1.270  \n 1st Qu.:1.250   1st Qu.: 3.220   1st Qu.:0.7825   1st Qu.:1.938  \n Median :1.555   Median : 4.690   Median :0.9650   Median :2.780  \n Mean   :1.591   Mean   : 5.058   Mean   :0.9574   Mean   :2.612  \n 3rd Qu.:1.950   3rd Qu.: 6.200   3rd Qu.:1.1200   3rd Qu.:3.170  \n Max.   :3.580   Max.   :13.000   Max.   :1.7100   Max.   :4.000  \n    Proline       Customer_Segment\n Min.   : 278.0   Min.   :1.000   \n 1st Qu.: 500.5   1st Qu.:1.000   \n Median : 673.5   Median :2.000   \n Mean   : 746.9   Mean   :1.938   \n 3rd Qu.: 985.0   3rd Qu.:3.000   \n Max.   :1680.0   Max.   :3.000   <\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>To understand the distribution of our dataset and determine if any patterns will enable us to create clusters, we will develop a set of histograms for every one of our columns.<\/p>\n<p>This task can be coded as follows:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"n\">data<\/span><span class=\"w\"> <\/span><span class=\"o\">%&gt;%<\/span><span class=\"w\"> <\/span><span class=\"nf\">keep<\/span><span class=\"p\">(<\/span><span class=\"n\">is.numeric<\/span><span class=\"p\">)<\/span><span class=\"w\"> <\/span><span class=\"o\">%&gt;%<\/span><span class=\"w\"> <\/span><span class=\"nf\">gather<\/span><span class=\"p\">()<\/span><span class=\"w\"> <\/span><span class=\"o\">%&gt;%<\/span><span class=\"w\"> <\/span><span class=\"nf\">ggplot<\/span><span class=\"p\">(<\/span><span class=\"nf\">aes<\/span><span class=\"p\">(<\/span><span class=\"n\">value<\/span><span class=\"p\">))<\/span><span class=\"w\"> <\/span><span class=\"o\">+<\/span><span class=\"w\"> <\/span>\n<span class=\"w\">  <\/span><span class=\"nf\">facet_wrap<\/span><span class=\"p\">(<\/span><span class=\"o\">~<\/span><span class=\"w\"> <\/span><span class=\"n\">key<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"n\">scales<\/span><span class=\"w\"> <\/span><span class=\"o\">=<\/span><span class=\"w\"> <\/span><span class=\"s\">\"free\"<\/span><span class=\"p\">)<\/span><span class=\"w\"> <\/span><span class=\"o\">+<\/span><span class=\"w\"> <\/span><span class=\"nf\">geom_histogram<\/span><span class=\"p\">(<\/span><span class=\"n\">bins<\/span><span class=\"w\"> <\/span><span class=\"o\">=<\/span><span class=\"w\"> <\/span><span class=\"m\">30<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedImage jp-OutputArea-output\" tabindex=\"0\">\n<img fetchpriority=\"high\" decoding=\"async\" alt=\"No description has been provided for this image\" class=\"\" height=\"420\" 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\" width=\"420\">\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>Another viewpoint to take while analyzing the data is determining the number of customers based on our three segments. Based on our analysis, these values are close from a numerical perspective. For the first segment, we have a total of <strong>59<\/strong> rows. The following customer, referred to as number <strong>2<\/strong>, is the high proportion of our dataset with <strong>79<\/strong> rows. Last but not least, the final segment is totaled to a total number of <strong>48<\/strong> rows. The ratio of customer segments is evenly matched without any of the segments being exceptionally high compared to the others.<\/p>\n<p>The code that performs this task in R is as follows:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"n\">data<\/span><span class=\"w\"> <\/span><span class=\"o\">%&gt;%<\/span>\n<span class=\"w\">  <\/span><span class=\"nf\">group_by<\/span><span class=\"p\">(<\/span><span class=\"n\">Customer_Segment<\/span><span class=\"p\">)<\/span><span class=\"w\"> <\/span><span class=\"o\">%&gt;%<\/span>\n<span class=\"w\">  <\/span><span class=\"nf\">summarise<\/span><span class=\"p\">(<\/span><span class=\"n\">count<\/span><span class=\"w\"> <\/span><span class=\"o\">=<\/span><span class=\"w\"> <\/span><span class=\"nf\">n<\/span><span class=\"p\">())<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output\" data-mime-type=\"text\/html\" tabindex=\"0\">\n<table class=\"dataframe\">\n<caption>A tibble: 3 \u00d7 2<\/caption>\n<thead>\n<tr><th scope=\"col\">Customer_Segment<\/th><th scope=\"col\">count<\/th><\/tr>\n<tr><th scope=\"col\">&lt;dbl&gt;<\/th><th scope=\"col\">&lt;int&gt;<\/th><\/tr>\n<\/thead>\n<tbody>\n<tr><td>1<\/td><td>59<\/td><\/tr>\n<tr><td>2<\/td><td>71<\/td><\/tr>\n<tr><td>3<\/td><td>48<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h2 id=\"Implementing-a-K-means-Clustering-in-R\">Implementing a K-means Clustering in R<a class=\"anchor-link\" href=\"#Implementing-a-K-means-Clustering-in-R\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>This article will employ a k-means cluster. To explain, this unsupervised machine learning model creates groups of attributes based on similarities in a dataset. To simplify, our model will create wine groups based on acidity, flavor, and other chemical alcohol features.<\/p>\n<p>To run a K-means model using R programming, we will have first to normalize our data frame using the <code>scale<\/code> method as follows:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"n\">df_normalize<\/span><span class=\"w\"> <\/span><span class=\"o\">&lt;-<\/span><span class=\"w\"> <\/span><span class=\"nf\">as.data.frame<\/span><span class=\"p\">(<\/span><span class=\"nf\">scale<\/span><span class=\"p\">(<\/span><span class=\"n\">data<\/span><span class=\"p\">))<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h1 id=\"Build-the-K-means-Clustering\">Build the K-means Clustering<a class=\"anchor-link\" href=\"#Build-the-K-means-Clustering\">\u00b6<\/a><\/h1>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>The K-means Clustering model will output the different wine groups based on their chemical similarities in our R program. Our code will first initialize the K-means Cluster object. The K-means Clustering algorithm will then be set up using the standard parameters.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"n\">km.res<\/span><span class=\"w\"> <\/span><span class=\"o\">&lt;-<\/span><span class=\"w\"> <\/span><span class=\"nf\">kmeans<\/span><span class=\"p\">(<\/span><span class=\"n\">df_normalize<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"m\">3<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"n\">nstart<\/span><span class=\"w\"> <\/span><span class=\"o\">=<\/span><span class=\"w\"> <\/span><span class=\"m\">25<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<h1 id=\"Conclusion-&amp;-Results\">Conclusion &amp; Results<a class=\"anchor-link\" href=\"#Conclusion-&amp;-Results\">\u00b6<\/a><\/h1>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\"><div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div><div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput\" data-mime-type=\"text\/markdown\">\n<p>The final section of our article is the understanding of our results. To easily digest how well our K-means Cluster successfully outputted the wine groups based on their chemical analysis. The main number we shall focus on in this section is the number of clusters. Which represents the total number of wine groups discovered using our K means algorithm. Our K-means Cluster outputted three groups. To explain, this is considered a great result as we can identify the edges of each group and their dimensions from the graph. For future development, our model will fully use the <strong>GridDB<\/strong> database as it provides an easy input-output data interface and an incredible speed of data retrieval.<\/p>\n<p>The following cluster plot is a representation of our results:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><div class=\"jp-Cell jp-CodeCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\" tabindex=\"0\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[\u00a0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"cm-editor cm-s-jupyter\">\n<div class=\"highlight hl-R\"><pre><span><\/span><span class=\"nf\">fviz_cluster<\/span><span class=\"p\">(<\/span><span class=\"n\">km.res<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"n\">data<\/span><span class=\"w\"> <\/span><span class=\"o\">=<\/span><span class=\"w\"> <\/span><span class=\"n\">df_normalize<\/span><span class=\"p\">,<\/span><span class=\"w\"> <\/span><span class=\"n\">ellipse.type<\/span><span class=\"w\"> <\/span><span class=\"o\">=<\/span><span class=\"w\"> <\/span><span class=\"s\">\"convex\"<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedImage jp-OutputArea-output\" tabindex=\"0\">\n<img decoding=\"async\" alt=\"No description has been provided for this image\" class=\"\" height=\"420\" 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