notebook.ipynb

{
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   "source": [
    "# NLP Data Science Technical Interview\n",
    "![NLP Data Science](NLP_data_science.jpg)\n",
    "\n",
    "\n",
    "## Problem\n",
    "A fictional B2B company sells products to online shops and wants to create a list of potential customers.\n",
    "They scrapped a large number of major German web sites and labelled some of the data with a flag denoting if the web site is an online shop or not.\n",
    "\n",
    "\n",
    "### Task 1\n",
    "Develop a classifier which is able to predict whether a web site is an online shop by looking at the HTML content of its main page.\n",
    "\n",
    "### Task 2\n",
    "Using this classifer, create predictions for each of the web sites which are unclassified so far (\"dataset 2\"). Provide the prediction as a CSV file containing the domain name and a flag that denotes if the respective web site is an online shop.\n",
    "\n",
    "### Task 3\n",
    "Explain your approach and its technical details to our team.\n",
    "\n",
    "### Task 4\n",
    "Alas, the VP Sales of the company does not trust black box models and thus wants to understand what the model learned and how it comes to its decisions. In order to convince him, illustrate which content a web page needs to contain or how it needs to be structured to be classified as an online shop by your model. The VP Sales is a non-technical person and does not have deep knowledge about data analysis, so keep this part of the presentation as easy to understand as possible.\n",
    "\n",
    "\n",
    "**NOTE**: We will combine task 3 into task 1, so that as we build the solution, it will also be explained.\n",
    "\n",
    "\n",
    "## Principles\n",
    "To solve this problem we will stick to a few basic principles.\n",
    "\n",
    "* The principle of finding the **lowest hanging fruit**. That is, we will find the the thing that brings the most value with the least effort.\n",
    "* One the core principles of **Agile**. We will solve the problem **incrementally** and **iteratively**. We don't want to get bogged down in perfecting one aspect and using up all of our time obsessing over one thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from utils.data_utils import DataUtils\n",
    "from utils.HTML_extractor import strip_tags\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First things first, let's have a look at our data! How about we start with the classified data csv (`dataset1.csv`) then the unclassified data set (`dataset2.csv`)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain</th>\n",
       "      <th>is_shop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>111.com</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12xl.de</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1a-buerotechnik.de</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1a-yachtcharter.de</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1blu.de</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>zeus-zukunft.de</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>zhaw.ch</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>zoeliakie-gourmet.at</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>zookauf-zwahr.de</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>zoologo.de</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>860 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   domain  is_shop\n",
       "0                 111.com        0\n",
       "1                 12xl.de        1\n",
       "2      1a-buerotechnik.de        1\n",
       "3      1a-yachtcharter.de        0\n",
       "4                 1blu.de        0\n",
       "..                    ...      ...\n",
       "855       zeus-zukunft.de        0\n",
       "856               zhaw.ch        0\n",
       "857  zoeliakie-gourmet.at        1\n",
       "858      zookauf-zwahr.de        1\n",
       "859            zoologo.de        1\n",
       "\n",
       "[860 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CLASSIFIED_CSV_PATH = 'data/dataset1.csv'\n",
    "\n",
    "classified_df = DataUtils.load_csv(CLASSIFIED_CSV_PATH)\n",
    "classified_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out of 860 entries, 50.0% are shops\n"
     ]
    }
   ],
   "source": [
    "number_of_entries = len(classified_df)\n",
    "number_of_shops = len(classified_df[classified_df[\"is_shop\"] == 1])\n",
    "percent_of_shops = (number_of_shops / number_of_entries)*100\n",
    "print(\n",
    "    f'out of {len(classified_df)} entries, {percent_of_shops}% are shops')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh I just love a balanced data set, but who doesn't? 🙂"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77records.de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>absperrtechnik24.de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ackermedia.de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>acris-ecommerce.at</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adepto-shop.de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>xeon-hosting.de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>xodox.de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>zaubermode.de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>zipf-immobilien24.de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>zunft.de</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   domain\n",
       "0            77records.de\n",
       "1     absperrtechnik24.de\n",
       "2           ackermedia.de\n",
       "3      acris-ecommerce.at\n",
       "4          adepto-shop.de\n",
       "..                    ...\n",
       "195       xeon-hosting.de\n",
       "196              xodox.de\n",
       "197         zaubermode.de\n",
       "198  zipf-immobilien24.de\n",
       "199              zunft.de\n",
       "\n",
       "[200 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UNCLASSIFIED_CSV_PATH = 'data/dataset2.csv'\n",
    "\n",
    "unclassified_df = DataUtils.load_csv(UNCLASSIFIED_CSV_PATH)\n",
    "unclassified_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay dokey, we have two csv files we loaded into pandas that contains the domain names and the flags denoting if the respective web site is an online shop. How about we check out an HTML file?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<!DOCTYPE html>\n",
       "<html dir=\"ltr\" lang=\"de\"><head>\n",
       "<!--\n",
       "\n",
       "\t\t=========================================================\n",
       "\t\tShopsoftware by Gambio GmbH (c) 2005-2017 [www.gambio.de]\n",
       "\t\t=========================================================\n",
       "\n",
       "\t\tGambio GmbH offers you highly scalable E-Commerce-Solutions and Services.\n",
       "\t\tThe Shopsoftware is redistributable under the GNU General Public License (Version 2) [http://www.gnu.org/licenses/gpl-2.0.html].\n",
       "\t\tbased on: E-Commerce Engine Copyright (c) 2006 xt:Commerce, created by Mario Zanier & Guido Winger and licensed under GNU/GPL.\n",
       "\t\tInformation and contribution at http://www.xt-commerce.com\n",
       "\n",
       "\t\t=========================================================\n",
       "\t\tPlease visit our website: www.gambio.de\n",
       "\t\t=========================================================\n",
       "\n",
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       "<noscript>\n",
       "\t\t\t\t&lt;div class=\"noscript_notice\"&gt;\n",
       "\t\t\t\t\tJavaScript ist in Ihrem Browser deaktiviert. Aktivieren Sie JavaScript, um alle Funktionen des Shops nutzen und alle Inhalte sehen zu können.\n",
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       "                  LOTUS™\n",
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       "</li>\n",
       "<li style=\"width:760px; height:330px;\">\n",
       "<a href=\"http://www.1a-buerotechnik.de/index.php?cat=c58_Schneidemaschinen-Hebelschneider--Rollenschneider--Stapelschneider.html\" target=\"_self\">\n",
       "<img alt=\"bei 1a-Bürotechnik günstig Schneidemaschinen einkaufen !\" class=\"slideImgControl\" id=\"slideImgControl1_6\" src=\"images/slider_images/slider_schneidemaschinen.jpg\" title=\"\"/>\n",
       "</a>\n",
       "</li>\n",
       "<li style=\"width:760px; height:330px;\">\n",
       "<a href=\"http://www.1a-buerotechnik.de/index.php?cat=c809_Luftreiniger-Luftreiniger--Plasma-True--AP--AeraMax.html\" target=\"_self\">\n",
       "<img alt=\"bei 1a-Bürotechnik günstig Luftreiniger einkaufen !\" class=\"slideImgControl\" id=\"slideImgControl1_7\" src=\"images/slider_images/sliderstartluftreiniger.jpg\" title=\"\"/>\n",
       "</a>\n",
       "</li>\n",
       "<li style=\"width:760px;height:330px;\">\n",
       "<a href=\"http://campaigns.fellowes.com/Sit-Stand-Free-Trial-DE\" target=\"_blank\">\n",
       "<img alt=\"Sie können die LOTUS™ Sitz-Steh-Workstation vor dem Kauf 45 Tage KOSTENLOS ausprobieren\" class=\"slideImgControl\" id=\"slideImgControl2_1\" src=\"images/slider_images/sitz-steh-kampagne.jpg\" title=\"LOTUS™\"/>\n",
       "</a>\n",
       "</li>\n",
       "<li style=\"width:760px;height:330px;\">\n",
       "<a href=\"http://www.1a-buerotechnik.de/index.php?cat=c228_Beamer--Dokumentenkameras--OHP-Beamer--OHP--Tageslichtprojektor--Overheadprojektor.html\" target=\"_self\">\n",
       "<img alt=\"bei 1a-Bürotechnik günstig Beamer einkaufen !\" class=\"slideImgControl\" id=\"slideImgControl2_2\" src=\"images/slider_images/sliderstartbeamer.jpg\" title=\"\"/>\n",
       "</a>\n",
       "</li>\n",
       "<li style=\"width:760px;height:330px;\">\n",
       "<a href=\"http://www.1a-buerotechnik.de/index.php?cat=c34_Aktenvernichter-aktenvernichter-partikelschnitt-shredder-streifenschnitt.html\" target=\"_self\">\n",
       "<img alt=\"bei 1a-Bürotechnik günstig Aktenvernichter einkaufen !\" class=\"slideImgControl\" id=\"slideImgControl2_3\" src=\"images/slider_images/sliderstartav_03.jpg\" title=\"\"/>\n",
       "</a>\n",
       "</li>\n",
       "<li style=\"width:760px;height:330px;\">\n",
       "<a href=\"http://www.1a-buerotechnik.de/index.php?cat=c107_Toner---Drum-Kits-Toner--Drum-Kits.html\" target=\"_blank\">\n",
       "<img alt=\"bei 1a-Bürotechnik günstig Toner einkaufen !\" class=\"slideImgControl\" id=\"slideImgControl2_4\" src=\"images/slider_images/slider_tonrec.jpg\" title=\"\"/>\n",
       "</a>\n",
       "</li>\n",
       "<li style=\"width:760px;height:330px;\">\n",
       "<a href=\"http://www.1a-buerotechnik.de/index.php?cat=c60_Kassen---Zubehoer-Registrierkasse----Kasse.html\" target=\"_self\">\n",
       "<img alt=\"bei 1a-Bürotechnik günstig Kassen einkaufen !\" class=\"slideImgControl\" id=\"slideImgControl2_5\" src=\"images/slider_images/kassenmiteuro.jpg\" title=\"\"/>\n",
       "</a>\n",
       "</li>\n",
       "<li style=\"width:760px;height:330px;\">\n",
       "<a href=\"http://www.1a-buerotechnik.de/index.php?cat=c58_Schneidemaschinen-Hebelschneider--Rollenschneider--Stapelschneider.html\" target=\"_self\">\n",
       "<img alt=\"bei 1a-Bürotechnik günstig Schneidemaschinen einkaufen !\" class=\"slideImgControl\" id=\"slideImgControl2_6\" src=\"images/slider_images/slider_schneidemaschinen.jpg\" title=\"\"/>\n",
       "</a>\n",
       "</li>\n",
       "<li style=\"width:760px;height:330px;\">\n",
       "<a href=\"http://www.1a-buerotechnik.de/index.php?cat=c809_Luftreiniger-Luftreiniger--Plasma-True--AP--AeraMax.html\" target=\"_self\">\n",
       "<img alt=\"bei 1a-Bürotechnik günstig Luftreiniger einkaufen !\" class=\"slideImgControl\" id=\"slideImgControl2_7\" src=\"images/slider_images/sliderstartluftreiniger.jpg\" title=\"\"/>\n",
       "</a>\n",
       "</li>\n",
       "<li style=\"width:760px; height:330px;\">\n",
       "<a href=\"http://campaigns.fellowes.com/Sit-Stand-Free-Trial-DE\" target=\"_blank\">\n",
       "<img alt=\"Sie können die LOTUS™ Sitz-Steh-Workstation vor dem Kauf 45 Tage KOSTENLOS ausprobieren\" class=\"slideImgControl\" id=\"slideImgControl1_1\" src=\"images/slider_images/sitz-steh-kampagne.jpg\" title=\"LOTUS™\"/>\n",
       "</a>\n",
       "</li><li style=\"width:760px; height:330px;\">\n",
       "<a href=\"http://www.1a-buerotechnik.de/index.php?cat=c228_Beamer--Dokumentenkameras--OHP-Beamer--OHP--Tageslichtprojektor--Overheadprojektor.html\" target=\"_self\">\n",
       "<img alt=\"bei 1a-Bürotechnik günstig Beamer einkaufen !\" class=\"slideImgControl\" id=\"slideImgControl1_2\" src=\"images/slider_images/sliderstartbeamer.jpg\" title=\"\"/>\n",
       "</a>\n",
       "</li></ul>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"first_page_content\">\n",
       "</div>\n",
       "<div class=\"future_products clearfix\">\n",
       "<h3>In Kürze bei uns verfügbar!</h3>\n",
       "<dl>\n",
       "<dt><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p3874_aktenvernichter-hsm-securio-b24-1-9x15mm-cross-cut--stufe-p5.html\">Aktenvernichter HSM Securio B24 1,9x15mm Cross-Cut, Stufe P5</a></dt>\n",
       "<dd>01.01.2018</dd>\n",
       "<dt><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p4707_aktenvernichter-hsm-securio-b32--0-78x11mm--stufe-p6--autom--oelung.html\">Aktenvernichter HSM Securio B32  0,78x11mm, Stufe P6, autom. Ölung</a></dt>\n",
       "<dd>01.01.2018</dd>\n",
       "</dl>\n",
       "</div>\n",
       "<h3>Unsere Empfehlungen</h3>\n",
       "<div class=\"article-list cols clearfix\">\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"new_products_8846\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8846_avervision-f17-8m-full-hd--aver-dokumentenkamera--praesentationskamera.html\"><img alt=\"Präsentationskamera AVerVision F15, Dokumentenkamera, Visualizer in Full HD\" src=\"images/product_images/thumbnail_images/f17-8m_01.jpg\" title=\"Präsentationskamera AVerVision F15, Dokumentenkamera, Visualizer in Full HD\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8846_avervision-f17-8m-full-hd--aver-dokumentenkamera--praesentationskamera.html\" title=\"Präsentationskamera AVerVision F15, Dokumentenkamera, Visualizer in Full HD\">AVerVision F17-8M Full HD, AVer...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8846_avervision-f17-8m-full-hd--aver-dokumentenkamera--praesentationskamera.html\"> 349,98 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"new_products_8770\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8770_led-deckenleuchte-maulstart--weiss--120-cm--35-w.html\"><img alt=\"Zur Deckenmontage oder als Hängeleuchte, inklusive Stahlseilabhängung\" src=\"images/product_images/thumbnail_images/11755_8273702.jpg\" title=\"Zur Deckenmontage oder als Hängeleuchte, inklusive Stahlseilabhängung\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8770_led-deckenleuchte-maulstart--weiss--120-cm--35-w.html\" title=\"Zur Deckenmontage oder als Hängeleuchte, inklusive Stahlseilabhängung\">LED-Deckenleuchte MAULstart, weiß, 120 cm, 35 W</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8770_led-deckenleuchte-maulstart--weiss--120-cm--35-w.html\"> 82,80 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"new_products_6364\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p6364_aktenvernichter-hsm-securio-af500--1-9x15mm--autom-blatteinzug.html\"><img alt=\"Aktenvernichter HSM Securio AFxxx, mit autom.Blatteinzug\" src=\"images/product_images/thumbnail_images/6364_0.jpg\" title=\"Aktenvernichter HSM Securio AFxxx, mit autom.Blatteinzug\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p6364_aktenvernichter-hsm-securio-af500--1-9x15mm--autom-blatteinzug.html\" title=\"Die einfache und komfortable Art der Datenvernichtung für Arbeitsgruppen. Der...\">Aktenvernichter HSM Securio AF500, 1,9x15mm,...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p6364_aktenvernichter-hsm-securio-af500--1-9x15mm--autom-blatteinzug.html\"> 749,98 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"new_products_8471\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8471_mobiles-whiteboard-maulstandard-emaille--drehbar--90x120cm.html\"><img alt=\"emaillebeschichtete Oberfläche\" src=\"images/product_images/thumbnail_images/6459284_8471_0.jpg\" title=\"emaillebeschichtete Oberfläche\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8471_mobiles-whiteboard-maulstandard-emaille--drehbar--90x120cm.html\" title=\"Mobiles Whiteboard MAULstandard Emaille, drehbar\">Mobiles Whiteboard MAULstandard Emaille,...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8471_mobiles-whiteboard-maulstandard-emaille--drehbar--90x120cm.html\"> 318,73 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"new_products_4659\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p4659_aktenvernichter-fellowes-powershred-m-7c-partikelschnitt-4x46mm-black.html\"><img alt=\"Kompaktes Einstiegsgerät für den Datenschutz am Schreibtisch.\" src=\"images/product_images/thumbnail_images/4659_0.jpg\" title=\"Kompaktes Einstiegsgerät für den Datenschutz am Schreibtisch.\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p4659_aktenvernichter-fellowes-powershred-m-7c-partikelschnitt-4x46mm-black.html\" title=\"Kompaktes Einstiegsgerät für den Datenschutz am Schreibtisch. Der Powershred...\">Aktenvernichter Fellowes Powershred M-7C...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p4659_aktenvernichter-fellowes-powershred-m-7c-partikelschnitt-4x46mm-black.html\"> 49,73 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"new_products_7121\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p7121_tonrec-toner-31010l-ersetzt-lexmark-t640--schwarz--21-000-seiten.html\"><img alt=\"Toner-Modul kompatibel zu Lexmark T640, schwarz, 21.000 Seiten\" src=\"images/product_images/thumbnail_images/31010l.jpg\" title=\"Toner-Modul kompatibel zu Lexmark T640, schwarz, 21.000 Seiten\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p7121_tonrec-toner-31010l-ersetzt-lexmark-t640--schwarz--21-000-seiten.html\" title=\"Toner-Modul kompatibel zu Lexmark T640, schwarz, 21.000 Seiten\">Tonrec Toner 31010L ersetzt Lexmark T640,...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p7121_tonrec-toner-31010l-ersetzt-lexmark-t640--schwarz--21-000-seiten.html\"> 64,28 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"new_products_8375\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8375_olympia-registrierkasse-touch-200-weiss--handel-restaur---gdpdu.html\"><img alt=\"Anzahl Warengruppen: 99, Anzahl PLUs: 20.000, Anzahl Bediener: 80, 150 Zusatztexte\" src=\"images/product_images/thumbnail_images/touch_200_white_seite_rechts.jpg\" title=\"Anzahl Warengruppen: 99, Anzahl PLUs: 20.000, Anzahl Bediener: 80, 150 Zusatztexte\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8375_olympia-registrierkasse-touch-200-weiss--handel-restaur---gdpdu.html\" title=\"Touch 200 elektronische Registrierkasse mit Flachtastatur für die Gastronomie\">OLYMPIA Registrierkasse Touch 200 weiß,...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8375_olympia-registrierkasse-touch-200-weiss--handel-restaur---gdpdu.html\"> 859,00 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"new_products_4029\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p4029_aktenvernichter-dahle-116air--41606-cleantec---streifen-5-8mm-45-blatt.html\"><img alt=\"Aktenvernichter DAHLE 116air (41606 CleanTEC), Streifen 5,8mm,45 Blatt\" src=\"images/product_images/thumbnail_images/116_4029_0.jpg\" title=\"Aktenvernichter DAHLE 116air (41606 CleanTEC), Streifen 5,8mm,45 Blatt\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p4029_aktenvernichter-dahle-116air--41606-cleantec---streifen-5-8mm-45-blatt.html\" title=\"Innovativer Großvolumen-Aktenvernichter mit Feinstaubfiltersystem DAHLE CleanTEC\">Aktenvernichter DAHLE 116air (41606 CleanTEC),...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p4029_aktenvernichter-dahle-116air--41606-cleantec---streifen-5-8mm-45-blatt.html\"> 1.655,35 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"new_products_1488\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p1488_stapelschneider-dahle-848--tischmodell--schnittlaenge-475mm.html\"><img alt=\"Stapelschneider DAHLE 848, Tischmodell, Schnittlänge 475mm\" src=\"images/product_images/thumbnail_images/848_tischmodell.jpg\" title=\"Stapelschneider DAHLE 848, Tischmodell, Schnittlänge 475mm\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p1488_stapelschneider-dahle-848--tischmodell--schnittlaenge-475mm.html\" title=\"Stapelschneider, die blauen von Dahle\">Stapelschneider DAHLE 848, Tischmodell,...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p1488_stapelschneider-dahle-848--tischmodell--schnittlaenge-475mm.html\"> 1.214,55 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"new_products_6709\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p6709_luftreiniger--ideal-ap100-med-edition--luftwaescher-bis-100m--.html\"><img alt=\"IDEAL AP100 Med Edition, Profi-Luftreiniger für Räume bis 100m²\" src=\"images/product_images/thumbnail_images/ap100_ge.jpg\" title=\"IDEAL AP100 Med Edition, Profi-Luftreiniger für Räume bis 100m²\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p6709_luftreiniger--ideal-ap100-med-edition--luftwaescher-bis-100m--.html\" title=\"IDEAL AP100 Med Edition, Profi-Luftreiniger für Räume bis 100m²\">Luftreiniger, IDEAL AP100 MED Edition,...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p6709_luftreiniger--ideal-ap100-med-edition--luftwaescher-bis-100m--.html\"> 1.606,50 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"first_page_content\">\n",
       "</div>\n",
       "<h3>Angebote</h3>\n",
       "<div class=\"article-list cols clearfix\">\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"specials_5017\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p5017_aktenvernichter-fellowes-powershred-60cs-cross-cut-4x50mm--10-blatt.html\"><img alt=\"Leistungsstarker Aktenvernichter schneidet bis 10 Blatt gleichzeitig in Partikel\" src=\"images/product_images/thumbnail_images/fellowes_60cs_01.jpg\" title=\"Leistungsstarker Aktenvernichter schneidet bis 10 Blatt gleichzeitig in Partikel\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p5017_aktenvernichter-fellowes-powershred-60cs-cross-cut-4x50mm--10-blatt.html\" title=\"Schreibtisch-Aktenvernichter Fellowes Powershred 60Cs\">Aktenvernichter Fellowes Powershred 60Cs...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p5017_aktenvernichter-fellowes-powershred-60cs-cross-cut-4x50mm--10-blatt.html\"><span class=\"productOldPrice\">UVP  85,58 EUR</span><br/> Nur  85,80 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"specials_1384\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p1384_notebook-staender-fellowes-office-suites--fuer-laptops-bis-17-.html\"><img alt=\"Stellen Sie Ihren Laptop-Bildschirm auf eine bequeme Höhen, um Nackenverspannungen zu vermeiden\" src=\"images/product_images/thumbnail_images/1384_0.jpg\" title=\"Stellen Sie Ihren Laptop-Bildschirm auf eine bequeme Höhen, um Nackenverspannungen zu vermeiden\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p1384_notebook-staender-fellowes-office-suites--fuer-laptops-bis-17-.html\" title='Notebook-Ständer FELLOWES Office Suites, für Laptops bis 17\"'>Notebook-Ständer FELLOWES Office Suites, für...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p1384_notebook-staender-fellowes-office-suites--fuer-laptops-bis-17-.html\"><span class=\"productOldPrice\">UVP  53,34 EUR</span><br/> Nur  24,98 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"article-list-item\">\n",
       "<div class=\"article-list-item-inside\">\n",
       "<div class=\"article-list-item-image\">\n",
       "<span class=\"flyover_item\" id=\"specials_7046\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p7046_4in1-laminierset--laminiergeraet-a230plus--cutter--folien--eckenrunder.html\"><img alt=\"4 in 1 - Set\" src=\"images/product_images/thumbnail_images/a_230_plus_4in1_set.jpg\" title=\"4 in 1 - Set\"/></a></span>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<p class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p7046_4in1-laminierset--laminiergeraet-a230plus--cutter--folien--eckenrunder.html\" title=\"4in1 OLYMPIA Laminierset mit Laminiergerät A230\">4in1-Laminierset, Laminiergerät A230plus,...</a></p>\n",
       "<div class=\"article-list-item-price\">\n",
       "<div class=\"article-list-item-price-inline\">\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p7046_4in1-laminierset--laminiergeraet-a230plus--cutter--folien--eckenrunder.html\"><span class=\"productOldPrice\">UVP  20,75 EUR</span><br/> Nur  18,88 EUR</a></span>\n",
       "<br/>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
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       "<li class=\"\" id=\"menu_cat_id_619\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c619_Bueroausstattung---Schreibtisch-Arbeitsplatz-Einrichtungen-mit-FELLOWES--NOVUS--MAUL.html\" title=\"Mit diesen Produkten gestalten Sie Ihren Arbeitsplatz nach ergonomischen...\">Büroausstattung &amp; Schreibtisch</a>\n",
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       "<li class=\"\" id=\"menu_cat_id_946\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c946_Buerogeraete---Zubehoer-Buerogeraete--Bueromaschinen.html\" title=\"Zu unseren Bürogeräten finden Sie hier auch das passende Zubehör.\">Bürogeräte &amp; Zubehör</a>\n",
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       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"\" href=\"http://www.1a-buerotechnik.de/?cat=c1391_Geldzaehl----Pruefgeraete-Geldzaehlgeraete--Geldzaehlmaschinen--Geldpruefgeraete.html\" title=\"Geldzählgeräte und Geldprüfgeräte\">Geldzähl- &amp; Prüfgeräte</a>\n",
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       "<li class=\"\" id=\"menu_cat_id_60\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c60_Kassen-Registrierkassen--Kassen--GDPDU--Casio--Olympia.html\" title=\"Alle Kassen sind GDPdU / gobd zertifiziert und entsprechen den gesetzlichen...\">Kassen</a>\n",
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       "<li class=\"\" id=\"menu_cat_id_845\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c845_Lampen---Leuchten-Leuchten--Energiesparleuchten--LED-Leuchten--Schreibtischleuchten.html\" title=\"Leuchten - Spitzenqualität und Top-Design von MAUL und NOVUS\">Lampen &amp; Leuchten</a>\n",
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       "<li class=\"\" id=\"menu_cat_id_809\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c809_Luftreiniger-Luftreiniger--Plasma-True--AP--AeraMax.html\" title=\"Die Raumluft in Ihrem Büro oder Homeoffice kann bis zu 5 Mal mehr verschmutzt...\">Luftreiniger</a>\n",
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       "<h4>\n",
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       "</li>\n",
       "<li class=\"\" id=\"menu_cat_id_58\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c58_Schneidemaschinen-Hebelschneider--Rollenschneider--Stapelschneider.html\" title=\"Schneidemaschinen für Formate bis 1830 mm Schnittlänge für Schule, Büro und...\">Schneidemaschinen</a>\n",
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       "</li>\n",
       "<li class=\"\" id=\"menu_cat_id_1407\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c1407_Sicherheit---Alarmanlagen-Sicherheit-Alarmanlagen.html\" title=\"Alarmanlagen günstig hier kaufen\">Sicherheit &amp; Alarmanlagen</a>\n",
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       "<li class=\"\" id=\"menu_cat_id_319\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c319_Telefon---Kommunikation-Telefone-Diktiergeraete-Handys.html\" title=\"Telefone, Diktiergeräte und Handys\">Telefon &amp; Kommunikation</a>\n",
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       "</li>\n",
       "<li class=\"\" id=\"menu_cat_id_518\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c518_Tischrechner-druckende-Tischrechner--Taschenrechner--Grafikrechner.html\" title=\"druckende Tischrechner, Taschenrechner, sowie Grafikrechner für Schule und Uni\">Tischrechner</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li class=\"\" id=\"menu_cat_id_107\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c107_Toner---Trommeln-Toner--Drum-Kits--Drum-Units--Trommeln.html\" title=\"Tonerkassetten mit Plus-Qualität. 100% kompatibel.\">Toner &amp; Trommeln</a>\n",
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       "</li>\n",
       "<li class=\"\" id=\"menu_cat_id_1411\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c1411_Upcycling--Lampen---Schmuck-Globus-Lampen--Upcycling--Recycling.html\" title=\"Diese Upcycling-Lampen werden von uns aus gebrauchten Globen gebaut und mit...\">Upcycling, Lampen &amp; Schmuck</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li class=\"\" id=\"menu_cat_id_1323\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c1323_Restposten-Restbestand--Neuware.html\" title=\"Restbestand, Neuware\">Restposten</a>\n",
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       "<li class=\"last\" id=\"menu_cat_id_1202\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" width=\"5\"/><a class=\"\" href=\"http://www.1a-buerotechnik.de/?cat=c1202_Vorfuehrgeraete-Vorfuehrmodelle--Vorfuehrgeraete-1202.html\" title=\"Für dise Geräte betragen die Gewährleistungshaftung und die Werksgarantie 2...\">Vorführgeräte</a>\n",
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       "<div class=\"content-box-header-inner\">\n",
       "<div class=\"menubox_heading\">Neue Artikel</div>\n",
       "<a href=\"http://www.1a-buerotechnik.de/products_new.php\">\n",
       "<span class=\"png-fix circle_arrow_right\" title=\"Neue Artikel\">\n",
       "\t\t\t\t\t \n",
       "\t\t\t\t</span>\n",
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       "<div class=\"content-box-main-inner clearfix article-list offer\">\n",
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       "<div class=\"article-list-item-inside\">\n",
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       "<a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8879_abfallbeutel-fuer-hsm-386-1--386-2--225-2--b34--2700--3800--100-st--.html\"><img alt=\"Abfallbeutel für HSM 386.1, 386.2, 225.2, B34, 2700, 3800 (100 St.)\" src=\"images/product_images/thumbnail_images/beutel_1141_0_8879_0.jpg\" title=\"Abfallbeutel für HSM 386.1, 386.2, 225.2, B34, 2700, 3800 (100 St.)\"/></a>\n",
       "</div>\n",
       "<div class=\"article-list-item-text\">\n",
       "<br/>\n",
       "<span class=\"title\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8879_abfallbeutel-fuer-hsm-386-1--386-2--225-2--b34--2700--3800--100-st--.html\" title=\"Abfallbeutel für HSM 125.1, 225.1 (100 St.)\">Abfallbeutel für HSM 386.1, 386.2, 225.2, B34, 2700, 3800 (100 St.)</a></span><br/>\n",
       "<br/>\n",
       "<span class=\"price\"><a href=\"http://www.1a-buerotechnik.de/product_info.php?info=p8879_abfallbeutel-fuer-hsm-386-1--386-2--225-2--b34--2700--3800--100-st--.html\"><span class=\"productOldPrice\">Unser Normalpreis  104,25 EUR</span><br/> Ihr Preis  88,14 EUR</a></span><br/>\n",
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       "</div>\n",
       "</div>\n",
       "</div>\n",
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       "\t\t\tNews\n",
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       "<strong>Unsere Partner:</strong>\n",
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       "<hr/> <a href=\"https://www.guenstiger.de/\" jquery1282667420687=\"81\" style=\"text-decoration: none;\" target=\"_blank\"><img alt=\"Preisvergleich guenstiger.de\" border=\"0\" height=\"33\" src=\"images/Linkpartner/guenstiger_115.jpg\" width=\"115\"/></a>\n",
       "<hr/> <strong> </strong><a href=\"http://www.idealo.de/preisvergleich/Shop/272618.html\"><img src=\"https://img.idealo.com/folder/Shop/272/2726/272618/s1_rating_extern2.png\" style=\"border: 0px solid currentColor; border-image: none;\"/></a>\n",
       "</div>\n",
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       "<a href=\"http://www.preissuchmaschine.de/\"><img alt=\"\" border=\"0\" height=\"46\" src=\"http://www.1a-buerotechnik.de/images/Linkpartner/PSMLogoMid1.jpg\" width=\"100\"/></a>\n",
       "</div>\n",
       "<hr/>\n",
       "</div>\n",
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       "<div class=\"content-box-header-inner\">\n",
       "\t\t\tsicher einkaufen\n",
       "\t\t</div>\n",
       "</div>\n",
       "<div class=\"content-box-main clearfix\" id=\"menubox_extrabox5_body\">\n",
       "<div class=\"content-box-main-inner clearfix\">\n",
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       "<img alt=\"Käufersiegel Kundenbewertung\" height=\"142\" src=\"http://www.kaeufersiegel.de/bewertung/widgets/3a0772443a0739141292a5429b952fe6/1.png?1513869091995\" style=\"border: none;\" width=\"180\"/>\n",
       "</a></div>\n",
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       " \n",
       "\t\t</div>\n",
       "</div>\n",
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       "<div class=\"content-box-header-inner\">\n",
       "\t\t\tInformationen\n",
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       "<div class=\"content-box-main clearfix\" id=\"menubox_information_body\">\n",
       "<div class=\"content-box-main-inner clearfix\">\n",
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       "<img alt=\"\" src=\"templates/EyeCandy/img/icon_arrow.gif\"/> <a href=\"http://www.1a-buerotechnik.de/shop_content.php?coID=100\">Sicherheitsstufen DIN 66399</a><br/>\n",
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       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c802_OLYMPIA-Kassen-Registrierkassen-fuer-Handel-und-Gastronomie.html\">OLYMPIA Kassen</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li class=\"last\" id=\"menu_cat_id_1454\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1454_SHARP-Kassen-SHARP--Registrierkassen-fuer-Handel-und-Gastronomie-1454.html\">SHARP Kassen</a>\n",
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       "<div class=\"submenu_container\" style=\"display:none;\">\n",
       "<div class=\"submenu png-fix clearfix\" id=\"submenu_box_id_845\">\n",
       "<div>\n",
       "<ul>\n",
       "<li id=\"menu_cat_id_1530\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1530_Ambienteleuchten-Tischleuchten--Tischlampen.html\">Ambienteleuchten</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1457\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1457_Arbeitsplatzleuchten-Arbeitsplatzleuchten--Lampen.html\">Arbeitsplatzleuchten</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1554\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1554_Deckenleuchten-Deckenleuchten--Deckenlampen.html\">Deckenleuchten</a>\n",
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       "</li>\n",
       "<li id=\"menu_cat_id_830\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c830_Energiesparleuchten-Energiespar--Energiespar-Leuchten.html\">Energiesparleuchten</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_825\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c825_Halogenleuchten-Schreibtischleuchten--Tischleuchten--Halogen-Leuchten.html\">Halogenleuchten</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1432\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1432_LED-Tischleuchten-LED-Tischleuchten.html\">LED-Tischleuchten</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1456\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1456_Lupenleuchten-Lupenleuchten.html\">Lupenleuchten</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_826\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c826_Standleuchten-Standleuchten--Arbeitsplatzleuchten--Deckenfluter--Stehlampen.html\">Standleuchten</a>\n",
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       "</li>\n",
       "<li id=\"menu_cat_id_1531\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1531_Upcycling-Leuchten-Deckenleuchten--Stehleuchten--upcycling.html\">Upcycling-Leuchten</a>\n",
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       "<li class=\"last\" id=\"menu_cat_id_1566\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1566_LED-Lampen--Leuchtmittel-LED-Lampen--Leuchtmittel.html\">LED-Lampen, Leuchtmittel</a>\n",
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       "<li id=\"menu_cat_id_1380\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1380_Luftreiniger-IDEAL-Luftreiniger-IDEAL.html\">Luftreiniger IDEAL</a>\n",
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       "<li id=\"menu_cat_id_1381\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1381_Luftreiniger-Fellowes-Luftreiniger-Fellowes.html\">Luftreiniger Fellowes</a>\n",
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       "<li id=\"menu_cat_id_1382\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1382_Zubehoer-IDEAL-True-HEPA--und-Kohle-Ersatzfilter.html\">Zubehör IDEAL</a>\n",
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       "</li>\n",
       "<li class=\"last\" id=\"menu_cat_id_1383\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1383_Zubehoer-Fellowes-True-HEPA--und-Kohle-Ersatzfilter-1383.html\">Zubehör Fellowes</a>\n",
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       "<div>\n",
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       "<li id=\"menu_cat_id_228\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c228_Beamer--Videoprojektoren-Beamer--OHP--Tageslichtprojektor--Overheadprojektor.html\">Beamer, Videoprojektoren</a>\n",
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       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c964_Dokumentenkameras--Visualizer-Visualizer--Presenter--Dokumentenkameras.html\">Dokumentenkameras, Visualizer</a>\n",
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       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c1507_Flipcharts-Flipchart--Flipcharts--Flip-Charts-1507.html\">Flipcharts</a>\n",
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       "<li id=\"menu_cat_id_1364\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1364_Halterungen-fuer-Beamer-Deckenhalterungen-fuer-Beamer.html\">Halterungen für Beamer</a>\n",
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       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c837_Jahresplaner-Planer--planen--Jahresplaner.html\">Jahresplaner</a>\n",
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       "<li id=\"menu_cat_id_846\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c846_Moderationstafeln---Zubehoer-Moderationswaende--Moderationstafeln.html\">Moderationstafeln &amp; Zubehör</a>\n",
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       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c271_Overhead---Tageslichtprojektoren-Tageslichtprojektoren--Overheadprojektoren.html\">Overhead-/ Tageslichtprojektoren</a>\n",
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       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c1070_Pinnboards--Pinnwaende-Pinnboards--Pinnwand.html\">Pinnboards, Pinnwände</a>\n",
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       "<li id=\"menu_cat_id_1203\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c1203_Plakatstaender--Kundenstopper-Kundenstopper--Prospektstaender--Prospekthalter--Plakatstaender--Aufsteller--Werbestaender.html\">Plakatständer, Kundenstopper</a>\n",
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       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c967_Schaukaesten-Schaukasten--Schaukaesten.html\">Schaukästen</a>\n",
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       "<li id=\"menu_cat_id_652\">\n",
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       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c652_Sichttafelhalter-NOVUS-Sichttafelhalter-Viewmaster.html\">Sichttafelhalter</a>\n",
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       "<li id=\"menu_cat_id_835\">\n",
       "<h4>\n",
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       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c995_Stellwaende-Stellwaende.html\">Stellwände</a>\n",
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       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1501_Videokonferenzsysteme-Videokonferenzsysteme.html\">Videokonferenzsysteme</a>\n",
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       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c203_Hebelschneider-Hebelschneider-203.html\">Hebelschneider</a>\n",
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       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a class=\"parent\" href=\"http://www.1a-buerotechnik.de/?cat=c307_Rollenschneider-Rollenschneider-307.html\">Rollenschneider</a>\n",
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       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_759\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c759_Tischrechner-druckend-Tischrechner--Taschrechner--druckende-Tischrechner.html\">Tischrechner druckend</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_519\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c519_Wissenschaftliche-Rechner-Grafikrechner--wissenschaftliche---technische-Rechner.html\">Wissenschaftliche Rechner</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li class=\"last\" id=\"menu_cat_id_1395\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1395_Verbrauchsmaterial-Verbrauchsmaterial-fuer-Tischrechner--Papierrolle--Farbrolle.html\">Verbrauchsmaterial</a>\n",
       "</h4>\n",
       "</li>\n",
       "</ul>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"submenu_container\" style=\"display:none;\">\n",
       "<div class=\"submenu png-fix clearfix\" id=\"submenu_box_id_107\">\n",
       "<div>\n",
       "<ul>\n",
       "<li id=\"menu_cat_id_1433\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1433_Brother-Brother--Toner--Drum--Drum-Units--Drum-Kits.html\">Brother</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1441\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1441_Canon-kompatible-Toner-Module-Canon-Laser-Drucker.html\">Canon</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1442\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1442_Dell-Dell.html\">Dell</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1443\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1443_Epson-Epson.html\">Epson</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1444\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1444_HP--Hewlett-Packard--HP--Hewlett-Packard--Toner--Drum-Kits--Drum-Units.html\">HP (Hewlett Packard)</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1445\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1445_IBM-IBM.html\">IBM</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1446\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1446_Kyocera-Mita-Kyocera-Mita.html\">Kyocera-Mita</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1434\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1434_Lexmark-Toner-Module--Kassettten-Drum-Units--Drum-Kits.html\">Lexmark</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1447\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1447_OKI-Toner--OKI.html\">OKI</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_111\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c111_Omega-Original-Toner-Module-von-Olympia.html\">Omega</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1448\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1448_Panasonic-Panasonic--Toner.html\">Panasonic</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1449\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1449_Ricoh-Toner--Ricoh.html\">Ricoh</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1450\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1450_Samsung-Samsung-Toner.html\">Samsung</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li class=\"last\" id=\"menu_cat_id_1451\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1451_Xerox-Xerox--Toner.html\">Xerox</a>\n",
       "</h4>\n",
       "</li>\n",
       "</ul>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"submenu_container\" style=\"display:none;\">\n",
       "<div class=\"submenu png-fix clearfix\" id=\"submenu_box_id_1411\">\n",
       "<div>\n",
       "<ul>\n",
       "<li id=\"menu_cat_id_1421\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1421_Dekoration-Upcycling--Recycling--Dekoration--Nespresso.html\">Dekoration</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1422\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1422_Hoerner-Hoerner.html\">Hörner</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li class=\"last\" id=\"menu_cat_id_1453\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1453_Schmuck-Upcycling--Recycling--Dekoration--Nespresso-1453.html\">Schmuck</a>\n",
       "</h4>\n",
       "</li>\n",
       "</ul>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"submenu_container\" style=\"display:none;\">\n",
       "<div class=\"submenu png-fix clearfix\" id=\"submenu_box_id_1323\">\n",
       "<div>\n",
       "<ul>\n",
       "<li id=\"menu_cat_id_1437\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1437_Drahtkaemme-Drahtkaemme.html\">Drahtkämme</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li id=\"menu_cat_id_1438\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1438_Plastikbinderuecken-Plastikbinderuecken.html\">Plastikbinderücken</a>\n",
       "</h4>\n",
       "</li>\n",
       "<li class=\"last\" id=\"menu_cat_id_1439\">\n",
       "<h4>\n",
       "<img alt=\"\" height=\"12\" src=\"images/logos/item_ltr.gif\" style=\"float: left; margin-right: 10px;\" width=\"5\"/><a href=\"http://www.1a-buerotechnik.de/?cat=c1439_Plastikspiralen-Plastikspiralen.html\">Plastikspiralen</a>\n",
       "</h4>\n",
       "</li>\n",
       "</ul>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<script type=\"text/javascript\">parentsIds = [];</script>\n",
       "<div id=\"dropdown_shopping_cart\" style=\"display: none;\">\n",
       "<div class=\"clearfix\" id=\"dropdown_shopping_cart_inner\">\n",
       "<div class=\"cart-item clearfix\">\n",
       "<div class=\"cart-item-inner clearfix\">\n",
       "<p>Sie haben noch keine Artikel in Ihrem Warenkorb.</p>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div id=\"customer_login\" style=\"display:none;\">\n",
       "<div class=\"customer_login_container png-fix\">\n",
       "<div class=\"customer_login_inside\">\n",
       "<form action=\"https://www.1a-buerotechnik.de/login.php?action=process\" method=\"post\">\n",
       "<label>E-Mail</label><br/>\n",
       "<input class=\"input-text\" name=\"email_address\" type=\"text\" value=\"\"/><br/>\n",
       "<label>Passwort</label><br/>\n",
       "<input class=\"input-text\" name=\"password\" type=\"password\" value=\"\"/><br/>\n",
       "<div class=\"submit-container\"><a class=\"button_blue button_set action_submit\" href=\"/#\"><span class=\"button-outer\"><span class=\"button-inner\">Anmelden</span></span></a></div>\n",
       "<div class=\"customer_login_links\">\n",
       "<a href=\"https://www.1a-buerotechnik.de/login.php\">\n",
       "\t\t\t\t\t\tKonto erstellen\n",
       "\t\t\t\t\t</a><br/>\n",
       "<a href=\"https://www.1a-buerotechnik.de/password_double_opt.php\">\n",
       "\t\t\t\t\t\tPasswort vergessen?\n",
       "\t\t\t\t\t</a>\n",
       "</div>\n",
       "</form>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div id=\"infobox\" style=\"display:none;\">\n",
       "<div class=\"infobox_container png-fix\">\n",
       "<div class=\"infobox_content\">\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<script src=\"gm_javascript.js.php?page=Index&amp;current_template=EyeCandy\" type=\"text/javascript\"></script><script type=\"text/javascript\">\n",
       "\n",
       "  var _gaq = _gaq || [];\n",
       "  _gaq.push(['_setAccount', 'UA-24578257-1']);\n",
       "  _gaq.push(['_gat._anonymizeIp']);\n",
       "  _gaq.push(['_trackPageview']);\n",
       "  _gaq.push(['_trackPageLoadTime']);\n",
       "  setTimeout('_gaq.push([\"_trackEvent\", \"NoBounce\", \"Over 10 seconds\"])',10000);\n",
       "  (function() {\n",
       "    var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;\n",
       "    ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';\n",
       "    var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);\n",
       "  })();\n",
       "\n",
       "</script><script async=\"\" src=\"/gm/javascript/GMCounter.js\" type=\"text/javascript\"></script>\n",
       "</body></html>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EXAMPLE_HTML_PATH = 'data/scraped_html/1a-buerotechnik.de.html'\n",
    "\n",
    "html = DataUtils.load_html(EXAMPLE_HTML_PATH)\n",
    "html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yep, that looks like HTML alright. But it isn't so useful for our purpose of classifying web sites as online shops when taking a strictly NLP approach (It is called Natural *Language* Processing after all, and HTML is not strictly Natural Language). We will need to somehow turn this HTML into something useful for our classifier.\n",
    "\n",
    "**NOTE**: It is interesting to see in the comments of this specific example it mentions `Shopsoftware by Gambio GmbH`, it might be interesting to see if many of them contain such a blatent reference to being a shop. However, we will stick for right now to the task of stripping down the HTML and classifying it as an online shop via the text of the website itself.\n",
    "\n",
    "**Did you know?** One of the most time consuming steps in NLP is the cleaning of text data scraped from websites? 😉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" Lastschrift, Kreditkarte und Rechnungskauf ohne Paypal-Konto - 1a-Bürotechnik Ihr Discount-Versand, schnell-kompetent-preiswert JavaScript ist in Ihrem Browser deaktiviert. Aktivieren Sie JavaScript, um alle Funktionen des Shops nutzen und alle Inhalte sehen zu können. Kundenlogin Kundengruppe: Gast Merkzettel Warenkorb Suchen Über uns ►►► Rechnungskauf für Behörden Go Startseite Ihr Warenkorb keine Produkte LOTUS™ In Kürze bei uns verfügbar! Aktenvernichter HSM Securio B24 1,9x15mm Cross-Cut, Stufe P5 01.01.2018 Aktenvernichter HSM Securio B32 0,78x11mm, Stufe P6, autom. Ölung 01.01.2018 Unsere Empfehlungen AVerVision F17-8M Full HD, AVer... 349,98 EUR LED-Deckenleuchte MAULstart, weiß, 120 cm, 35 W 82,80 EUR Aktenvernichter HSM Securio AF500, 1,9x15mm,... 749,98 EUR Mobiles Whiteboard MAULstandard Emaille,... 318,73 EUR Aktenvernichter Fellowes Powershred M-7C... 49,73 EUR Tonrec Toner 31010L ersetzt Lexmark T640,... 64,28 EUR OLYMPIA Registrierkasse Touch 200 weiß,... 859,00 EUR Aktenvernichter DAHLE 116air (41606 CleanTEC),... 1.655,35 EUR Stapelschneider DAHLE 848, Tischmodell,... 1.214,55 EUR Luftreiniger, IDEAL AP100 MED Edition,... 1.606,50 EUR Angebote Aktenvernichter Fellowes Powershred 60Cs... UVP 85,58 EUR Nur 85,80 EUR Notebook-Ständer FELLOWES Office Suites, für... UVP 53,34 EUR Nur 24,98 EUR 4in1-Laminierset, Laminiergerät A230plus,... UVP 20,75 EUR Nur 18,88 EUR Aktenvernichter & Medienshredder Büroausstattung & Schreibtisch Bürogeräte & Zubehör Geldzähl- & Prüfgeräte Kassen Lampen & Leuchten Luftreiniger Präsentationstechnik Schneidemaschinen Sicherheit & Alarmanlagen Telefon & Kommunikation Tischrechner Toner & Trommeln Upcycling, Lampen & Schmuck Restposten Vorführgeräte Hersteller Bitte wählenALBAAVerBock'n'RollBrotherCanonCasioCITIZENDahleDELLEBAEIKIFellowesHP (Hewlett-Packard)HSMIDEALKyoceraLexmarkLiesegangMAULNOVUSOKIOlympiaRENZRICOHSamsungSERDSHARPTonrec SwissVEGAXEROX Neue Artikel Abfallbeutel für HSM 386.1, 386.2, 225.2, B34, 2700, 3800 (100 St.) Unser Normalpreis 104,25 EUR Ihr Preis 88,14 EUR News Unsere Partner: sicher einkaufen Informationen Sitemap Sicherheitsstufen DIN 66399 Hersteller-Garantiebedingungen / Gewährleistung Mehr über... Impressum Kontakt Umwelt & Entsorgung Partner Zahlung & Versand Widerrufsrecht & Muster-Widerrufsformular Allgemeine Geschäftsbedingungen Hinweise zur Batterieentsorgung Datenschutzerklärung Shopsystem by Gambio.de © 2017 DAHLE Aktenvernichter EBA Aktenvernichter FELLOWES Aktenvernichter HSM Aktenvernichter IDEAL Aktenvernichter Olympia Aktenvernichter Auto-Feed, Auto-Einzug Festplatten-Shredder & Medienvernichter Karton-Shredder-Perforatoren Shredder-Pressen-Kombinationen Ablagen & Schalen Bilderhalter Blickschutzfilter PrivaScreen Computer- & Notebookständer Druckluft Fuß- & Rückenstützen Garderoben Geldkassetten Kabelorganisation Konzepthalter Mauspads MehrPlatzSets NOVUS Monitorarme- u. halterungen Notebook-Zubehör Organisationswände & Accessoires Planhalter & Infoleisten Schlüsselkästen Sichttafelhalter Tablet-Zubebör Tastaturschubladen Telefonarme Wanduhren Bindegeräte & Zubehör Brieföffner Falzmaschinen, Booklet-Maker Heftgeräte & Klammern Laminiergeräte & Folien Locher Schreibmaschinen Waagen CASIO Kassen OLYMPIA Kassen SHARP Kassen Ambienteleuchten Arbeitsplatzleuchten Deckenleuchten Energiesparleuchten Halogenleuchten LED-Tischleuchten Lupenleuchten Standleuchten Upcycling-Leuchten LED-Lampen, Leuchtmittel Luftreiniger IDEAL Luftreiniger Fellowes Zubehör IDEAL Zubehör Fellowes Beamer, Videoprojektoren Dokumentenkameras, Visualizer Flipcharts Halterungen für Beamer Jahresplaner Moderationstafeln & Zubehör Overhead-/ Tageslichtprojektoren Pinnboards, Pinnwände Plakatständer, Kundenstopper Planhalter & Infoleisten Projektortische Rednerpulte Schaukästen Sichttafelhalter Stehtische Stellwände Videokonferenzsysteme Whiteboards Hebelschneider Rollenschneider Stapelschneider Zubehör, Messer, Schnittleisten Vorführgeräte Alarmsysteme Zubehör für Alarmsysteme Babyphone Diktiergeräte Festnetz-Telefon Handys, Mobiltelefone Walkie Talkie Grafikrechner Tischrechner Tischrechner druckend Wissenschaftliche Rechner Verbrauchsmaterial Brother Canon Dell Epson HP (Hewlett Packard) IBM Kyocera-Mita Lexmark OKI Omega Panasonic Ricoh Samsung Xerox Dekoration Hörner Schmuck Drahtkämme Plastikbinderücken Plastikspiralen Sie haben noch keine Artikel in Ihrem Warenkorb. E-Mail Passwort Anmelden Konto erstellen Passwort vergessen? \""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_text = strip_tags(html)\n",
    "extracted_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at a few other examples (not shown), it looks pretty good for our purpose. Data can always be cleaner, but let's not get stuck cleaning this data further at this time and say it is good enough for now. We will see if we need to do some additional cleaning further down the road. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a way to read and clean up the HTML into normal text strings, we should probably apply that to all of the HTML data and while we are at it, merge it to the classified and unclassified csvs.\n",
    "\n",
    "To do this we will:\n",
    "* Get a file list from the HTML files in the directory\n",
    "* Strip the \".html\" extension from the file names\n",
    "* Match those stripped file names with the domain names in the csvs\n",
    "* Merge the parsed and stripped HTML to the csvs into a new column called `text`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting the file list is pretty straight forward. We will write a function that does this for us and throw it into the the `data_utils.py` file. Here's the output (for the first 10):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['toskanaferien.de.html', 'pc-ostsee.de.html', 'hammerkauf.de.html', 'mobile-laden.com.html', 'lutter.net.html', 'meinemarkenmode.de.html', 'sonnenhotels.de.html', 'gelesi.de.html', 'perfectmallorca.de.html', 'bellanea.de.html']\n"
     ]
    }
   ],
   "source": [
    "SCRAPED_DATA_DIR = 'data/scraped_html/'\n",
    "\n",
    "file_list = DataUtils.get_file_list(SCRAPED_DATA_DIR)\n",
    "print(file_list[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will need to write a function that will go through the list of files in the directory and returns a dataframe with the domains and text.\n",
    "\n",
    "**NOTE**: In the original files, there was a `.ipynb_checkpoints` directory in the `data/scraped_html` directory that we will not be using, so I 🗑️ tossed it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toskanaferien.de</td>\n",
       "      <td>Home - Toskanaferien HomeUrlaubsort Lari Dolc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pc-ostsee.de</td>\n",
       "      <td>PCO - Privat Charter Ostsee - Yachtcharter Di...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hammerkauf.de</td>\n",
       "      <td>Hammerkauf Online-Shop - Baumarkt 02251 650 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mobile-laden.com</td>\n",
       "      <td>Mobile-Laden.com - Handyzubehör zu Top Preise...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lutter.net</td>\n",
       "      <td>Immobilien Makler Rostock Häuser Wohnungen Ka...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>nakur.de</td>\n",
       "      <td>Nakur Warenkorb 0 Artikel Ihr Konto Kasse Anm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>babyviduals.de</td>\n",
       "      <td>Babynahrung tiefgekühlt von Babyviduals, Bio-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>taschenkaufhaus.de</td>\n",
       "      <td>Taschen, Rucksäcke und Reisegepäck: Taschenka...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>gastroimmobilien.net</td>\n",
       "      <td>gastroimmobilien.net - Gastro Immobilien Das ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1059</th>\n",
       "      <td>happycharter.com</td>\n",
       "      <td>Yachtcharter, Bootscharter und Bootsverleih x...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1060 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    domain                                               text\n",
       "0         toskanaferien.de   Home - Toskanaferien HomeUrlaubsort Lari Dolc...\n",
       "1             pc-ostsee.de   PCO - Privat Charter Ostsee - Yachtcharter Di...\n",
       "2            hammerkauf.de   Hammerkauf Online-Shop - Baumarkt 02251 650 1...\n",
       "3         mobile-laden.com   Mobile-Laden.com - Handyzubehör zu Top Preise...\n",
       "4               lutter.net   Immobilien Makler Rostock Häuser Wohnungen Ka...\n",
       "...                    ...                                                ...\n",
       "1055              nakur.de   Nakur Warenkorb 0 Artikel Ihr Konto Kasse Anm...\n",
       "1056        babyviduals.de   Babynahrung tiefgekühlt von Babyviduals, Bio-...\n",
       "1057    taschenkaufhaus.de   Taschen, Rucksäcke und Reisegepäck: Taschenka...\n",
       "1058  gastroimmobilien.net   gastroimmobilien.net - Gastro Immobilien Das ...\n",
       "1059      happycharter.com   Yachtcharter, Bootscharter und Bootsverleih x...\n",
       "\n",
       "[1060 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_df = DataUtils.process_html_files(SCRAPED_DATA_DIR)\n",
    "text_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe looks pretty good. Next we will merge this dataframe with the training and unclassified dataframes by the `domain` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "classified_text_df = pd.merge(classified_df, text_df, on=\"domain\")\n",
    "unclassified_text_df = pd.merge(unclassified_df, text_df, on=\"domain\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain</th>\n",
       "      <th>is_shop</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>111.com</td>\n",
       "      <td>0</td>\n",
       "      <td>dns-net.ch | DNS-NET Services GmbH Our domain...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12xl.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Bekleidung in Übergrößen für Herren | Herrenm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1a-buerotechnik.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Lastschrift, Kreditkarte und Rechnungskauf oh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1a-yachtcharter.de</td>\n",
       "      <td>0</td>\n",
       "      <td>Yachtcharter - 9196 Yachten online chartern M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1blu.de</td>\n",
       "      <td>0</td>\n",
       "      <td>Neue Internetpräsenz. hosted by www.1blu.de Hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>zeus-zukunft.de</td>\n",
       "      <td>0</td>\n",
       "      <td>Zeus Zukunft - Ihr Spezialist für Einkommenss...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>zhaw.ch</td>\n",
       "      <td>0</td>\n",
       "      <td>Willkommen an der ZHAW | ZHAW Zürcher Hochsch...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>zoeliakie-gourmet.at</td>\n",
       "      <td>1</td>\n",
       "      <td>Vital Gourmet 03177 25 295 / 0676 60 45 9 45 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>zookauf-zwahr.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Startseite 03581 / 405152 Login Kontakt Waren...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>zoologo.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Tierbedarf und Tierfutter günstig online kauf...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>860 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   domain  is_shop  \\\n",
       "0                 111.com        0   \n",
       "1                 12xl.de        1   \n",
       "2      1a-buerotechnik.de        1   \n",
       "3      1a-yachtcharter.de        0   \n",
       "4                 1blu.de        0   \n",
       "..                    ...      ...   \n",
       "855       zeus-zukunft.de        0   \n",
       "856               zhaw.ch        0   \n",
       "857  zoeliakie-gourmet.at        1   \n",
       "858      zookauf-zwahr.de        1   \n",
       "859            zoologo.de        1   \n",
       "\n",
       "                                                  text  \n",
       "0     dns-net.ch | DNS-NET Services GmbH Our domain...  \n",
       "1     Bekleidung in Übergrößen für Herren | Herrenm...  \n",
       "2     Lastschrift, Kreditkarte und Rechnungskauf oh...  \n",
       "3     Yachtcharter - 9196 Yachten online chartern M...  \n",
       "4    Neue Internetpräsenz. hosted by www.1blu.de Hi...  \n",
       "..                                                 ...  \n",
       "855   Zeus Zukunft - Ihr Spezialist für Einkommenss...  \n",
       "856   Willkommen an der ZHAW | ZHAW Zürcher Hochsch...  \n",
       "857   Vital Gourmet 03177 25 295 / 0676 60 45 9 45 ...  \n",
       "858   Startseite 03581 / 405152 Login Kontakt Waren...  \n",
       "859   Tierbedarf und Tierfutter günstig online kauf...  \n",
       "\n",
       "[860 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classified_text_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classified_text_df.to_csv('data/classified_text_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77records.de</td>\n",
       "      <td>DJ Equipment | DJ Zubehör ★ 77records.de Um 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>absperrtechnik24.de</td>\n",
       "      <td>Absperrpfosten, Schilder, Fahrradständer und ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ackermedia.de</td>\n",
       "      <td>Hosting - Avernis Domain bestellen / Domains ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>acris-ecommerce.at</td>\n",
       "      <td>E-Commerce &amp; Shopware Agentur aus Linz OÖ ► A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adepto-shop.de</td>\n",
       "      <td>ADEPTO - Reinigungsfachhandel - Versandkosten...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>xeon-hosting.de</td>\n",
       "      <td>Xeon-Hosting.de TeamSpeak3 Live Chat Warenkor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>xodox.de</td>\n",
       "      <td>XODOX - Webdesign, Server, E-Mail &amp; SEO aus Fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>zaubermode.de</td>\n",
       "      <td>kunterbunte Kindermode und Babykleidung - gün...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>zipf-immobilien24.de</td>\n",
       "      <td>Zipf und Partner Immobilien GmbH - Immobilien...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>zunft.de</td>\n",
       "      <td>Zunftkleidung online günstig kaufen - Zunft.de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   domain                                               text\n",
       "0            77records.de   DJ Equipment | DJ Zubehör ★ 77records.de Um 7...\n",
       "1     absperrtechnik24.de   Absperrpfosten, Schilder, Fahrradständer und ...\n",
       "2           ackermedia.de   Hosting - Avernis Domain bestellen / Domains ...\n",
       "3      acris-ecommerce.at   E-Commerce & Shopware Agentur aus Linz OÖ ► A...\n",
       "4          adepto-shop.de   ADEPTO - Reinigungsfachhandel - Versandkosten...\n",
       "..                    ...                                                ...\n",
       "195       xeon-hosting.de   Xeon-Hosting.de TeamSpeak3 Live Chat Warenkor...\n",
       "196              xodox.de  XODOX - Webdesign, Server, E-Mail & SEO aus Fo...\n",
       "197         zaubermode.de   kunterbunte Kindermode und Babykleidung - gün...\n",
       "198  zipf-immobilien24.de   Zipf und Partner Immobilien GmbH - Immobilien...\n",
       "199              zunft.de  Zunftkleidung online günstig kaufen - Zunft.de...\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unclassified_text_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unclassified_text_df.to_csv('data/unclassified_text_df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uh huh, looking pretty good with these dataframes. But I am sure we will get better results if we cleaned the data more. But I'd say we are ready for the next major step..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 1\n",
    "(with Task 3)\n",
    "\n",
    "Let's do some machine learning stuff! Well, almost.. There is the matter of figuring out our approach and of course doing the pre-processing that needs to be done.\n",
    "\n",
    "Sticking with our principles, we will start with the easiest solution and then we will benchmark it and see where it gets us. So instead of doing some crazy SOTA stuff, we will encode the the text into TF-IDF vectors and use that as features for a \"classic\" intent classifier. \n",
    "\n",
    "This is very similar to the task of classifying emails as `spam` or `ham` for another binary classification example, but it can also be considered similar to doing multi-class intent matching that could be used in NLU for tasks found in matching utterances to intents like in a voice assistant. \n",
    "\n",
    "### Why are we using TF-IDF?\n",
    "Well based on experience, we have found that TF-IDF is pretty good at being the features for an intent classifier in tasks like this, yet is super easy to do and computationally inexpensive. It is usually a good place to start.\n",
    "\n",
    "Our **hypothesis** here is that ranking the terms in the documents by their frequency while lowering the rank of terms that are found in a lot of documents, while disregarding the word order is a good way to get a sense of the overall importance of the terms and therefore makes good features. So this is better than a simple bag of words (BoWs) approach, which would just be getting the frequeny of each term. However, it might not be as good as other approaches that might use deeper context to get more features (such as word embeddings).\n",
    "\n",
    "Of course we could also use stop words with TF-IDF, but we will stick to just TF-IDF for now and see where that gets us. We just love those low hanging fruits!\n",
    "\n",
    "### Application\n",
    "![old school cool](old_school_cool.png)\n",
    "\n",
    "To do all of this, we will go with the easy to use `sklearn` library. It is what we like to call \"old school cool\".\n",
    "\n",
    "It is a great way to get started and explore the relationships of the features, train models, and evaluate them. Luckily, I happen to have written an NLU engine that is open source and uses the same library, so we will use parts of that. Please see this [Secret Sauce AI repo](https://github.com/secretsauceai/NLU-engine-prototype-benchmarks) for more information (watch out: this NLU project is still a major work in progress!). Because the project isn't done yet, there is no pipy package for it yet, so we will just grab the pieces we want and go from there.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the classified and unclassified dataframes from the previous step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "classified_text_df = DataUtils.load_csv('data/classified_text_df.csv')\n",
    "unclassified_text_df = DataUtils.load_csv('data/unclassified_text_df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing TF-IDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kicking this step off, we will first create a TF-IDF vectorizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nlu_engine import TfidfEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vectors, tfidf_vectorizer = TfidfEncoder.create_vectorizer(\n",
    "    classified_text_df['text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To have a better understanding, let's have a look at the top 100 TF-IDF terms in the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['dc', 'tattoos', '2018', 'auswandern', 'amp', '18', 'hotels',\n",
       "       'shopper', 'nächte', 'bidok', 'akku', 'leverkusen', 'teppiche',\n",
       "       'euronics', 'arrangement', 'owatrol', 'kettler', 'weber', 'fl',\n",
       "       'pickerd', 'name', 'alanya', 'ansehen', 'tee', 'teleclinic',\n",
       "       'nestlé', 'caparol', 'wandtattoo', 'personen', 'bio', 'teneriffa',\n",
       "       'thermomix', 'uvstars', 'horizon', 'teneriffa', 'spreewald',\n",
       "       'bali', 'backyard', '000', 'tui', 'samova', 'emsland', 'hotze',\n",
       "       'prix', 'doppler', 'stufenmatten', 'laminat', 'lidl', 'hairfor2',\n",
       "       'gundel', 'shoppen', 'duftholz', 'mli', 'produktgruppen', 'braun',\n",
       "       'hinzufügen', 'kochwerte', 'conow', 'sae', 'kw', 'sprachreisen',\n",
       "       'internetpräsenz', 'torsino', 'mille', 'produktgruppen', 'eur',\n",
       "       'ferienhaus', '2018sa', 'spalte', 'bioratio', 'anleitung',\n",
       "       'weingarten', 'kundenreservierung', 'heizstrahler', 'pharma',\n",
       "       'zhaw', 'ledlenser', 'azoren', 'cloud', 'unavailable', 'adessa',\n",
       "       'gastro', 'staubsaugerbeutel', 'winterthur', 'rundreise', 'büsum',\n",
       "       'itam', 'xxltravel', 'bonn', 'lego', 'schmiermaxe', 'server',\n",
       "       'brooncars', '173', 'simson', 'chefkoch', 'unterkünfte', '90',\n",
       "       'xxxxxxxxxxx', 'aida'], dtype='<U733')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_100_features = TfidfEncoder.get_top_n_features(\n",
    "    tfidf_vectorizer, tfidf_vectors, 100)\n",
    "top_100_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: As this is only a first version we haven't removed stopwords or further cleaning of the text. However, I would experiment more with cleaning the data and benchmarking that in the next version if this were a real project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics, machine learning or AI?\n",
    "Ohhhh yeah, we are ready to go! Well almost... Let's have a quick at a boring math lesson first.  \n",
    "\n",
    "![statistics, machine learning, AI](https://miro.medium.com/max/1400/1*x7P7gqjo8k2_bj2rTQWAfg.jpeg)\n",
    "\n",
    "One important thing in the classification of machine learning classifiers is the difference between `descrimintive` and `generative` classifiers. In my opinion, this is one of the most helpful ways to categorize the mathematics behind probabilistic machine learning classifiers.\n",
    "\n",
    "`Descriminative`: The classifier uses the conditional probability of the features to predict the class directly via the posterior probability of the class, `p(y|x)`. Simply put, it uses the direct inputs `x` to determine the labels, `y`.\n",
    "\n",
    "`Generative`: The classifier uses the joint probability of the inputs, `x` and labels `y` to calculate `p(x,y)` using Bayes rule to pick the most likely label. As the name implies, it is trying to generate the inputs `x` from the labels, `y`.\n",
    "\n",
    "Got it? Alright let's go!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The first classifier example\n",
    "We will load the modified NLU engine and train a model. We will use `Logistic Regression` for this first example. Why? Because it is a good baseline model that is easy to train and easy to evaluate.\n",
    "\n",
    "It is very fast to train and run inference on, the performance is pretty outstanding given how light the model is . This is actually why several NLU engines use this for probabilistic inference of intent, such as [Snips NLU](https://github.com/snipsco/snips-nlu).\n",
    "\n",
    "`Logistic Regression` itself is a discrimitive classifier. It is a supervised learning model that is used to predict the class of a given sample based on the features of that sample.\n",
    "\n",
    "Much as the name implies, it uses a logit (logarithmic) function to map linear combinations of features (ie the TFIDF terms here) to probabilities of belonging to a particular class. In our case it is a binary classification problem, meaning that there are only two classes.\n",
    "\n",
    "In short, we ❤️ `Logistic Regression`!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nlu_engine import NLUEngine\n",
    "from nlu_engine import IntentMatcher\n",
    "from nlu_engine import LR\n",
    "from nlu_engine import Analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training LogisticRegression(random_state=0, solver='liblinear')\n"
     ]
    }
   ],
   "source": [
    "LR_model = IntentMatcher.train_classifier(LR, tfidf_vectors, classified_text_df['is_shop'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can breifly check out the prediction of labels here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting labels\n"
     ]
    },
    {
     "data": {
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       "       1, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IntentMatcher.predict_labels(LR_model, tfidf_vectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analytics\n",
    "\n",
    "What good is machine learning if we can't benchmark and compare our results? Well, we can!\n",
    "\n",
    "Let's first demonstrate how to do this with `Logistic Regression` and go step-by-step, then we will put it all together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validating with LogisticRegression(random_state=0, solver='liblinear')\n",
      "Time it took to cross validate LogisticRegression(random_state=0, solver='liblinear'): 1.8179833889007568\n"
     ]
    }
   ],
   "source": [
    "predictions = Analytics.cross_validate_classifier(LR, tfidf_vectors, classified_text_df['is_shop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating report for LogisticRegression(random_state=0, solver='liblinear')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'0': {'precision': 0.9381898454746137,\n",
       "  'recall': 0.9883720930232558,\n",
       "  'f1-score': 0.9626274065685163,\n",
       "  'support': 430},\n",
       " '1': {'precision': 0.9877149877149877,\n",
       "  'recall': 0.9348837209302325,\n",
       "  'f1-score': 0.9605734767025089,\n",
       "  'support': 430},\n",
       " 'accuracy': 0.9616279069767442,\n",
       " 'macro avg': {'precision': 0.9629524165948007,\n",
       "  'recall': 0.9616279069767442,\n",
       "  'f1-score': 0.9616004416355126,\n",
       "  'support': 860},\n",
       " 'weighted avg': {'precision': 0.9629524165948008,\n",
       "  'recall': 0.9616279069767442,\n",
       "  'f1-score': 0.9616004416355125,\n",
       "  'support': 860}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = Analytics.generate_report(\n",
    "    LR, predictions, classified_text_df['is_shop'])\n",
    "report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_shop</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.988372</td>\n",
       "      <td>0.962627</td>\n",
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       "      <td>LogisticRegression</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>macro avg</td>\n",
       "      <td>0.962952</td>\n",
       "      <td>0.961628</td>\n",
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       "      <td>860.000000</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>TFIDF</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>weighted avg</td>\n",
       "      <td>0.962952</td>\n",
       "      <td>0.961628</td>\n",
       "      <td>0.961600</td>\n",
       "      <td>860.000000</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>TFIDF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        is_shop  precision    recall  f1-score     support  \\\n",
       "0             0   0.938190  0.988372  0.962627  430.000000   \n",
       "1             1   0.987715  0.934884  0.960573  430.000000   \n",
       "2      accuracy   0.961628  0.961628  0.961628    0.961628   \n",
       "3     macro avg   0.962952  0.961628  0.961600  860.000000   \n",
       "4  weighted avg   0.962952  0.961628  0.961600  860.000000   \n",
       "\n",
       "           classifier encoding  \n",
       "0  LogisticRegression    TFIDF  \n",
       "1  LogisticRegression    TFIDF  \n",
       "2  LogisticRegression    TFIDF  \n",
       "3  LogisticRegression    TFIDF  \n",
       "4  LogisticRegression    TFIDF  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ENCODING = 'TFIDF'\n",
    "report_df = Analytics.convert_report_to_df(classifier=LR, report=report, encoding=ENCODING)\n",
    "report_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have crammed quite a bit of information into this dataframe, so let's take a look at it and discuss it a bit before we go any further.\n",
    "\n",
    "The formating of the dataframe isn't perfect, but I personally prefer to keep all the information in one dataframe over having to look at multiple dataframes, and in my opinion it looks nicer than outputting a dictionary like we did above.\n",
    "\n",
    "We have here our two classes for `is_shop`, that is `0` and `1`. Notice the `accuracy`, `macro avg`, and `weighted avg` are in the same column as `is_shop`. This isn't correct, however as stated, it is just nice to have all of this in one dataframe, so I think we can live with that.\n",
    "\n",
    "##### What fraction of positive predictions are true predictions?\n",
    "The `precision` is the proportion of *true positives* over *true positives* and *false positives*.\n",
    "\n",
    "##### Out of all the true positives, what fraction of them did we identify?\n",
    "The `recall` is the proportion of *true positives* over *true positives* and *false negatives*.\n",
    "\n",
    "\n",
    "The `f1-score` is the harmonic mean of precision and recall (2 times the `precision` times the `recall` over the addition of the `precision` and the `recall`). If the `f1-score` is high, it means that the model is doing a good job at predicting the class of a given sample. This is the defacto standard metric for evaluating the performance of a model.\n",
    "\n",
    "The `support` is the number of samples of the given class that were used to compute the `precision`, `recall`, and `f1-score`. NOTE: the `accuracy` row does not contain the support, but the accuracy across the row.\n",
    "\n",
    "The `accuracy` is the proportion of samples that were correctly predicted. This is a great measure when you have a binary classification problem and one class isn't more important than another. It isn't so good when you would be dealing with life and death though!\n",
    "\n",
    "The `macro avg` for the `f1-score` is a metric of the f1 score that calculates independently of the classes, then averages them together.\n",
    "\n",
    "Whereas `weighted avg` for the `f1-score` is a metric of the f1 score that calculates the f1 score for each class taking into account their `support`, then averages them together. \n",
    "\n",
    "Since our classes are balanced, this isn't really interesting, and it can be easily seen that the `macro avg` is the same as the `weighted avg` for our data.\n",
    "\n",
    "\n",
    "For comparsions we will stick to `macro avg f1-score`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this all in mind, we can put it all together to see how well our model performs and get a dataframe of those results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validating with LogisticRegression(random_state=0, solver='liblinear')\n",
      "Time it took to cross validate LogisticRegression(random_state=0, solver='liblinear'): 1.249725103378296\n",
      "Generating report for LogisticRegression(random_state=0, solver='liblinear')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>is_shop</th>\n",
       "      <th>precision</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.987715</td>\n",
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       "      <td>0.960573</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.961628</td>\n",
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       "      <td>0.961628</td>\n",
       "      <td>0.961628</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>TFIDF</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>macro avg</td>\n",
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       "      <td>0.961628</td>\n",
       "      <td>0.961600</td>\n",
       "      <td>860.000000</td>\n",
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       "      <td>TFIDF</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>weighted avg</td>\n",
       "      <td>0.962952</td>\n",
       "      <td>0.961628</td>\n",
       "      <td>0.961600</td>\n",
       "      <td>860.000000</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>TFIDF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        is_shop  precision    recall  f1-score     support  \\\n",
       "0             0   0.938190  0.988372  0.962627  430.000000   \n",
       "1             1   0.987715  0.934884  0.960573  430.000000   \n",
       "2      accuracy   0.961628  0.961628  0.961628    0.961628   \n",
       "3     macro avg   0.962952  0.961628  0.961600  860.000000   \n",
       "4  weighted avg   0.962952  0.961628  0.961600  860.000000   \n",
       "\n",
       "           classifier encoding  \n",
       "0  LogisticRegression    TFIDF  \n",
       "1  LogisticRegression    TFIDF  \n",
       "2  LogisticRegression    TFIDF  \n",
       "3  LogisticRegression    TFIDF  \n",
       "4  LogisticRegression    TFIDF  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report_df = NLUEngine.evaluate_intent_classifier(tfidf_vectors=tfidf_vectors, labels=classified_text_df['is_shop'], classifier=LR, encoding=ENCODING)\n",
    "report_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benchmark them all\n",
    "Now we can simply run this over a bunch of different classifiers and see which one performs best.\n",
    "\n",
    "For this I have selected the following classifiers:\n",
    "* `Gaussian Naive Bayes` is a generative classifier, I would guess that it won't perform as well as `Logistic Regression` for example, and perhaps will perform the worst. It can be good when you have really sparse data though!\n",
    "* `Logistic Regression` which has already been discussed.\n",
    "* `Decision Tree` is descriminative.\n",
    "* `Random Forest` is another personal favorite of mine. It is a descriminative classifier. As the name implies, it randomly bags up features to form descrion trees via permutation. It is known as an ensemble classifier because it takes the permutated trees and ensembles them together into a forest.\n",
    "* `K-Nearest Neighbors` is a descriminative classifier. As the name tells us, it uses the nearest neighbors to predict the class of a given sample.\n",
    "* `AdaBoost` is another tree based discriminative classifier.\n",
    "* `Support Vector Machine` is a descriminative classifier. It uses hyperplanes to draw boundries between classes based on the support, hench the name. It is a bit more computationally expensive than the other classifiers, but it is a great classifier. I would hypothesize that it could outperform the other classifiers by a small margin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nlu_engine import NB, DT, RF, KN, ADA, SVM\n",
    "\n",
    "classifiers = [LR, DT, RF, KN, ADA, SVM, NB]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validating with LogisticRegression(random_state=0, solver='liblinear')\n",
      "Time it took to cross validate LogisticRegression(random_state=0, solver='liblinear'): 1.089665412902832\n",
      "Generating report for LogisticRegression(random_state=0, solver='liblinear')\n",
      "Cross validating with DecisionTreeClassifier(random_state=42)\n",
      "Time it took to cross validate DecisionTreeClassifier(random_state=42): 3.7245254516601562\n",
      "Generating report for DecisionTreeClassifier(random_state=42)\n",
      "Cross validating with RandomForestClassifier(random_state=0)\n",
      "Time it took to cross validate RandomForestClassifier(random_state=0): 8.819599628448486\n",
      "Generating report for RandomForestClassifier(random_state=0)\n",
      "Cross validating with KNeighborsClassifier(n_neighbors=100)\n",
      "Time it took to cross validate KNeighborsClassifier(n_neighbors=100): 0.2633943557739258\n",
      "Generating report for KNeighborsClassifier(n_neighbors=100)\n",
      "Cross validating with AdaBoostClassifier(n_estimators=100)\n",
      "Time it took to cross validate AdaBoostClassifier(n_estimators=100): 81.84318375587463\n",
      "Generating report for AdaBoostClassifier(n_estimators=100)\n",
      "Cross validating with SVC()\n",
      "Time it took to cross validate SVC(): 12.692724466323853\n",
      "Generating report for SVC()\n",
      "Cross validating with GaussianNB()\n",
      "GaussianNB() has been detected, switching to a dense array.\n",
      "Time it took to cross validate GaussianNB(): 15.121998071670532\n",
      "Generating report for GaussianNB()\n"
     ]
    }
   ],
   "source": [
    "classifier_reports_df = NLUEngine.evaluate_all_classifiers(classifiers=classifiers, x_train=tfidf_vectors, y_train=classified_text_df['is_shop'], encoding=ENCODING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_reports_df.to_csv('data/classifier_reports_df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can skip running this and go straight to the reports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_reports_df = DataUtils.load_csv('data/classifier_reports_df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_shop</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "      <th>classifier</th>\n",
       "      <th>encoding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>macro avg</td>\n",
       "      <td>0.974788</td>\n",
       "      <td>0.974419</td>\n",
       "      <td>0.974414</td>\n",
       "      <td>860.0</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>TFIDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>macro avg</td>\n",
       "      <td>0.971239</td>\n",
       "      <td>0.970930</td>\n",
       "      <td>0.970925</td>\n",
       "      <td>860.0</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>TFIDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>macro avg</td>\n",
       "      <td>0.962952</td>\n",
       "      <td>0.961628</td>\n",
       "      <td>0.961600</td>\n",
       "      <td>860.0</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>TFIDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>macro avg</td>\n",
       "      <td>0.960860</td>\n",
       "      <td>0.959302</td>\n",
       "      <td>0.959268</td>\n",
       "      <td>860.0</td>\n",
       "      <td>SVC</td>\n",
       "      <td>TFIDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>macro avg</td>\n",
       "      <td>0.954392</td>\n",
       "      <td>0.952326</td>\n",
       "      <td>0.952271</td>\n",
       "      <td>860.0</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>TFIDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>macro avg</td>\n",
       "      <td>0.943083</td>\n",
       "      <td>0.943023</td>\n",
       "      <td>0.943021</td>\n",
       "      <td>860.0</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>TFIDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>macro avg</td>\n",
       "      <td>0.913920</td>\n",
       "      <td>0.911628</td>\n",
       "      <td>0.911505</td>\n",
       "      <td>860.0</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>TFIDF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     is_shop  precision    recall  f1-score  support              classifier  \\\n",
       "3  macro avg   0.974788  0.974419  0.974414    860.0  RandomForestClassifier   \n",
       "3  macro avg   0.971239  0.970930  0.970925    860.0      AdaBoostClassifier   \n",
       "3  macro avg   0.962952  0.961628  0.961600    860.0      LogisticRegression   \n",
       "3  macro avg   0.960860  0.959302  0.959268    860.0                     SVC   \n",
       "3  macro avg   0.954392  0.952326  0.952271    860.0    KNeighborsClassifier   \n",
       "3  macro avg   0.943083  0.943023  0.943021    860.0  DecisionTreeClassifier   \n",
       "3  macro avg   0.913920  0.911628  0.911505    860.0              GaussianNB   \n",
       "\n",
       "  encoding  \n",
       "3    TFIDF  \n",
       "3    TFIDF  \n",
       "3    TFIDF  \n",
       "3    TFIDF  \n",
       "3    TFIDF  \n",
       "3    TFIDF  \n",
       "3    TFIDF  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_reports_df[classifier_reports_df['is_shop'] == 'macro avg'].sort_values(by='f1-score', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whoa, `Random Forest` for the win! I was actually a bit suprised, as from my personal experience `Support Vector Machine` is often the best classifier for problems like this. It goes to show, you never can tell. However, it was true that the Gaussian Naive Bayes classifier performed the worst."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would be interesting to fine tune the parameters of `Random Forest` with a grid search and cross validation to see how well it can possibly perform. However this isn't a Kaggle competition and time is limited, so we won't do that here. I think the score is high enough for this example. \n",
    "\n",
    "If time would have allowed, I would have loved to try out using these methods with Word2Vec and I would have especially loved to compare this to more modern classification approaches such as can be found with 🤗 HuggingFace."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2\n",
    "\n",
    "And now it's time to use our `Random Forest` classifier to predict the class of the given unlabeled dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification of the unclassified data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case we skipped ahead, we can load most of the stuff we need for this task here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nlu_engine import TfidfEncoder\n",
    "from nlu_engine import NLUEngine\n",
    "from nlu_engine import IntentMatcher\n",
    "from nlu_engine import RF\n",
    "\n",
    "classified_text_df = DataUtils.load_csv('data/classified_text_df.csv')\n",
    "unclassified_text_df = DataUtils.load_csv('data/unclassified_text_df.csv')\n",
    "\n",
    "tfidf_vectors, tfidf_vectorizer = TfidfEncoder.create_vectorizer(\n",
    "    classified_text_df['text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay dokey, let's train the `Random Forest` classifier.\n",
    "\n",
    "NOTE: we are using all of the labeled data. Since we have really great cross validated benchmarks, we can skip doing any train/test splits and maximize the value of the data we have to make predictions on the unlabeled dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training RandomForestClassifier(random_state=0)\n"
     ]
    }
   ],
   "source": [
    "RF_model = IntentMatcher.train_classifier(RF, tfidf_vectors, classified_text_df['is_shop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "unclassified_text_vectors = TfidfEncoder.get_vectors(unclassified_text_df['text'], tfidf_vectorizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "unclassified_text_predicted_df = unclassified_text_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting labels\n"
     ]
    }
   ],
   "source": [
    "unclassified_text_predicted_df['is_shop_predicted'] = IntentMatcher.predict_labels(\n",
    "    RF_model, unclassified_text_vectors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "unclassified_text_predicted_df.to_csv('data/unclassified_text_predicted_df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of the predictions\n",
    "\n",
    "So how'd we do? Let's find out!\n",
    "\n",
    "We are going to use a widget called `IPySheet` to display the dataframe nicely and create a boolean column (also just called a button, LOL) for ground truthing our predictions. The column will be called `is_shop`. We will also add a column called `ambiguous`, so if we aren't sure about the prediction, we can mark it as ambiguous.\n",
    "\n",
    "We will also use `preview_text` instead of `text` for the text column, because the text field can be **HUGE**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipysheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b76e817b55c4bc08080ef5e869bfb34",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, numeric_format=None, row_end=199, row_start=0, squeeze_row=Fal…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "review_classifications_sheet = DataUtils.create_sheet(\n",
    "    unclassified_text_predicted_df)\n",
    "review_classifications_sheet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviewed_classifications_df = DataUtils.convert_sheet_to_dataframe(review_classifications_sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviewed_classifications_df.to_csv('data/reviewed_classifications_df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load the annotated dataframe here to skip everything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviewed_classifications_df = DataUtils.load_csv('data/reviewed_classifications_df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After going through and ground truthing our unclassified data, I was unsure of some of the sites.\n",
    "\n",
    "We will remove these from consideration. I would be curious to know how many of the sites were actually shops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain</th>\n",
       "      <th>is_shop_predicted</th>\n",
       "      <th>preview_text</th>\n",
       "      <th>is_shop</th>\n",
       "      <th>ambiguous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>allstars-direktvertrieb.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Allstars-Vertrieb Um Allstars-Vertrieb in vol...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>baender24.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Baender24.de Diese Seite benötigt Cookies. Si...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>bagel.de</td>\n",
       "      <td>0</td>\n",
       "      <td>Bagel Gruppe OkBagel verwendet Cookies. Mit d...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>conmoto.com</td>\n",
       "      <td>0</td>\n",
       "      <td>Conmoto - Home page JavaScript scheint in Ihr...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>creative-beads.com</td>\n",
       "      <td>1</td>\n",
       "      <td>| Creative Beads Perlen und Schmuckzubehör Ge...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>der-fellmann.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Hochwertige Lammfell &amp; Naturfellprodukte bei ...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>deutschesportonline.de</td>\n",
       "      <td>0</td>\n",
       "      <td>Startseite | Deutsche Sport Online GmbH Deuts...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>fischers-lagerhaus.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Startseite .fb_hidden{position:absolute;top:-...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>fuxtec.de</td>\n",
       "      <td>1</td>\n",
       "      <td>FUXTEC Gartengeräte Sehr gut4.69/5.00Zertifiz...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>hundeleben-exklusiv.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Exklusive Hundemode | Hundetaschen Luxus | Hu...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>infuu.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Halterungssysteme für Notebook, Kamera,Tablet...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>kp-networks.com</td>\n",
       "      <td>0</td>\n",
       "      <td>KP-Networks GbR Ich stimme zuDie Seite verwen...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>lederoase.de</td>\n",
       "      <td>1</td>\n",
       "      <td>www.lederoase.de // /** * global init */ var ...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>schacht-westerich.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Schreibgeräte und Papiere bei Schacht &amp; Weste...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         domain  is_shop_predicted  \\\n",
       "9    allstars-direktvertrieb.de                  1   \n",
       "12                 baender24.de                  1   \n",
       "14                     bagel.de                  0   \n",
       "31                  conmoto.com                  0   \n",
       "32           creative-beads.com                  1   \n",
       "37              der-fellmann.de                  1   \n",
       "39       deutschesportonline.de                  0   \n",
       "51        fischers-lagerhaus.de                  1   \n",
       "54                    fuxtec.de                  1   \n",
       "71       hundeleben-exklusiv.de                  1   \n",
       "79                     infuu.de                  1   \n",
       "87              kp-networks.com                  0   \n",
       "95                 lederoase.de                  1   \n",
       "142        schacht-westerich.de                  1   \n",
       "\n",
       "                                          preview_text  is_shop  ambiguous  \n",
       "9     Allstars-Vertrieb Um Allstars-Vertrieb in vol...        0          1  \n",
       "12    Baender24.de Diese Seite benötigt Cookies. Si...        0          1  \n",
       "14    Bagel Gruppe OkBagel verwendet Cookies. Mit d...        0          1  \n",
       "31    Conmoto - Home page JavaScript scheint in Ihr...        0          1  \n",
       "32    | Creative Beads Perlen und Schmuckzubehör Ge...        0          1  \n",
       "37    Hochwertige Lammfell & Naturfellprodukte bei ...        0          1  \n",
       "39    Startseite | Deutsche Sport Online GmbH Deuts...        0          1  \n",
       "51    Startseite .fb_hidden{position:absolute;top:-...        0          1  \n",
       "54    FUXTEC Gartengeräte Sehr gut4.69/5.00Zertifiz...        0          1  \n",
       "71    Exklusive Hundemode | Hundetaschen Luxus | Hu...        0          1  \n",
       "79    Halterungssysteme für Notebook, Kamera,Tablet...        0          1  \n",
       "87    KP-Networks GbR Ich stimme zuDie Seite verwen...        0          1  \n",
       "95    www.lederoase.de // /** * global init */ var ...        0          1  \n",
       "142   Schreibgeräte und Papiere bei Schacht & Weste...        0          1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviewed_classifications_df[reviewed_classifications_df['ambiguous'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviewed_classifications_df = reviewed_classifications_df[\n",
    "    reviewed_classifications_df['ambiguous'] == 0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So barring the ambiguous entries, how did we do?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain</th>\n",
       "      <th>is_shop_predicted</th>\n",
       "      <th>preview_text</th>\n",
       "      <th>is_shop</th>\n",
       "      <th>ambiguous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>baulando.com</td>\n",
       "      <td>1</td>\n",
       "      <td>BAULANDO - Arbeitsschutz, Baumaschinen, Diama...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>estugo.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Onlineshop Hosting für Gambio, Shopware, JTL ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>hollandrad.de</td>\n",
       "      <td>1</td>\n",
       "      <td>Hollandräder in toller Auswahl! | Hollandrad ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>kreativgewerbe.de</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;!--table,div,span,font,p{display:none} --&gt;Pl...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>kuechle.de</td>\n",
       "      <td>0</td>\n",
       "      <td>Küchle - Backen, Dekorieren, Oblaten, Esspapi...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>medherbs.de</td>\n",
       "      <td>0</td>\n",
       "      <td>MedHerbs - Bestes aus der Natur Bestes aus der...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>modellbau-amstetten.at</td>\n",
       "      <td>0</td>\n",
       "      <td>Startseite Angebot Kontakt Anfahrt Impressum ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>springerle.com</td>\n",
       "      <td>0</td>\n",
       "      <td>www.springerle.com - Herzlich willkommen! Wil...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     domain  is_shop_predicted  \\\n",
       "15             baulando.com                  1   \n",
       "48                estugo.de                  1   \n",
       "67            hollandrad.de                  1   \n",
       "88        kreativgewerbe.de                  0   \n",
       "92               kuechle.de                  0   \n",
       "105             medherbs.de                  0   \n",
       "111  modellbau-amstetten.at                  0   \n",
       "160          springerle.com                  0   \n",
       "\n",
       "                                          preview_text  is_shop  ambiguous  \n",
       "15    BAULANDO - Arbeitsschutz, Baumaschinen, Diama...        0          0  \n",
       "48    Onlineshop Hosting für Gambio, Shopware, JTL ...        0          0  \n",
       "67    Hollandräder in toller Auswahl! | Hollandrad ...        0          0  \n",
       "88    <!--table,div,span,font,p{display:none} -->Pl...        1          0  \n",
       "92    Küchle - Backen, Dekorieren, Oblaten, Esspapi...        1          0  \n",
       "105  MedHerbs - Bestes aus der Natur Bestes aus der...        1          0  \n",
       "111   Startseite Angebot Kontakt Anfahrt Impressum ...        1          0  \n",
       "160   www.springerle.com - Herzlich willkommen! Wil...        1          0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incorrectly_classified_df = reviewed_classifications_df[reviewed_classifications_df['is_shop_predicted'] != reviewed_classifications_df['is_shop']]\n",
    "incorrectly_classified_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From a total of 186 entries, 96.0% were correctly classified\n"
     ]
    }
   ],
   "source": [
    "number_of_entries = len(reviewed_classifications_df)\n",
    "percent_correct = round(((number_of_entries - len(\n",
    "    incorrectly_classified_df)) / number_of_entries), 2)*100\n",
    "\n",
    "print(\n",
    "    f'From a total of {len(reviewed_classifications_df)} entries, {percent_correct}% were correctly classified')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I don't want to dive any deeper into analysis of this data, as I don't feel like my ground truthing might be 100% as there were so many ambiguous sites for me. But I would say this percentage of correct classifications would fall within our expected range.\n",
    "\n",
    "I am very sure if we would further clean the data (general cleaning, stopwords, etc.) and fine tune the parameters, we could score much higher with this method. It would be interesting to see what would be the results then."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 4\n",
    "\n",
    "They say that a picture is worth a thousand words. But finding the right picture can be hard. We want to find a way to bring clear transparency to the features used in our classifier for someone who has no knowledge of machine learning. What do we want to explain?\n",
    "\n",
    "* We want to explain that the classes are binary: a website either has a shop or it doesn't.\n",
    "* We want to show the features used in the classifier, in this case, the terms.\n",
    "* Perhaps we should show how important the features are.\n",
    "\n",
    "When it comes to displaying the ranking of terms, people often use word clouds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cells with code wouldn't be shown to the stakeholders, but they are good to show how we do this. Ideally this visualization would be a part of a powerpoint presentation, not in a notebook. I have also provided an alternative or additional solution which was not implemented."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to calculate the features from the classifier and then use them in the diagram. As always, this won't really show 100% of what the classifier is doing, as the classifier is using more than one term to classify the text from the websites, but its a good way to show the features used in the classifier. And it would be important to express that this is just a simplification of the classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121734,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = RF_model.feature_importances_\n",
    "feature_importances.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121734"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = tfidf_vectorizer.get_feature_names()\n",
    "len(feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_ranks = zip(feature_names, feature_importances)\n",
    "feature_ranks_df = pd.DataFrame(feature_ranks)\n",
    "feature_ranks_df.columns = ['feature', 'rank']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's scale the rank so that it works better with a wordcloud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_ranks_df['rank'] = feature_ranks_df['rank'].apply(lambda x: x*10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68467</th>\n",
       "      <td>konto</td>\n",
       "      <td>183.086480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27609</th>\n",
       "      <td>artikel</td>\n",
       "      <td>150.112731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115160</th>\n",
       "      <td>warenkorb</td>\n",
       "      <td>137.972614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121059</th>\n",
       "      <td>zzgl</td>\n",
       "      <td>135.222411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112812</th>\n",
       "      <td>versand</td>\n",
       "      <td>131.022325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65962</th>\n",
       "      <td>kategorien</td>\n",
       "      <td>113.093964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00</td>\n",
       "      <td>98.003332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22249</th>\n",
       "      <td>95</td>\n",
       "      <td>88.167877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98274</th>\n",
       "      <td>shops</td>\n",
       "      <td>85.727102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24267</th>\n",
       "      <td>agb</td>\n",
       "      <td>80.277729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21502</th>\n",
       "      <td>90</td>\n",
       "      <td>65.310600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117143</th>\n",
       "      <td>widerrufsrecht</td>\n",
       "      <td>63.625396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35253</th>\n",
       "      <td>browser</td>\n",
       "      <td>62.256958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120311</th>\n",
       "      <td>zubehör</td>\n",
       "      <td>59.622756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100125</th>\n",
       "      <td>sortiment</td>\n",
       "      <td>54.179251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112862</th>\n",
       "      <td>versandkosten</td>\n",
       "      <td>54.096822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40077</th>\n",
       "      <td>datenschutz</td>\n",
       "      <td>53.907316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26365</th>\n",
       "      <td>anmelden</td>\n",
       "      <td>53.700982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96112</th>\n",
       "      <td>schwarz</td>\n",
       "      <td>51.945959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59688</th>\n",
       "      <td>hersteller</td>\n",
       "      <td>44.689705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87890</th>\n",
       "      <td>produkte</td>\n",
       "      <td>42.299242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63024</th>\n",
       "      <td>inkl</td>\n",
       "      <td>41.805235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97816</th>\n",
       "      <td>set</td>\n",
       "      <td>41.603625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79517</th>\n",
       "      <td>mwst</td>\n",
       "      <td>39.995687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70157</th>\n",
       "      <td>können</td>\n",
       "      <td>39.187305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76752</th>\n",
       "      <td>merkzettel</td>\n",
       "      <td>36.545250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115145</th>\n",
       "      <td>ware</td>\n",
       "      <td>36.258854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119337</th>\n",
       "      <td>zahlungsarten</td>\n",
       "      <td>34.411248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64345</th>\n",
       "      <td>javascript</td>\n",
       "      <td>31.416455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98303</th>\n",
       "      <td>shopzertifikat</td>\n",
       "      <td>31.361866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature        rank\n",
       "68467            konto  183.086480\n",
       "27609          artikel  150.112731\n",
       "115160       warenkorb  137.972614\n",
       "121059            zzgl  135.222411\n",
       "112812         versand  131.022325\n",
       "65962       kategorien  113.093964\n",
       "0                   00   98.003332\n",
       "22249               95   88.167877\n",
       "98274            shops   85.727102\n",
       "24267              agb   80.277729\n",
       "21502               90   65.310600\n",
       "117143  widerrufsrecht   63.625396\n",
       "35253          browser   62.256958\n",
       "120311         zubehör   59.622756\n",
       "100125       sortiment   54.179251\n",
       "112862   versandkosten   54.096822\n",
       "40077      datenschutz   53.907316\n",
       "26365         anmelden   53.700982\n",
       "96112          schwarz   51.945959\n",
       "59688       hersteller   44.689705\n",
       "87890         produkte   42.299242\n",
       "63024             inkl   41.805235\n",
       "97816              set   41.603625\n",
       "79517             mwst   39.995687\n",
       "70157           können   39.187305\n",
       "76752       merkzettel   36.545250\n",
       "115145            ware   36.258854\n",
       "119337   zahlungsarten   34.411248\n",
       "64345       javascript   31.416455\n",
       "98303   shopzertifikat   31.361866"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_ranks_df.sort_values(by='rank', ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take the top 30 features, those should be enough examples for our purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_rank_dict = feature_ranks_df.sort_values(by='rank', ascending=False).head(\n",
    "    30).set_index('feature').to_dict()['rank']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'konto': 183.08648018614716,\n",
       " 'artikel': 150.1127310401032,\n",
       " 'warenkorb': 137.97261423219385,\n",
       " 'zzgl': 135.22241131174442,\n",
       " 'versand': 131.0223252527508,\n",
       " 'kategorien': 113.09396393402804,\n",
       " '00': 98.0033321349261,\n",
       " '95': 88.16787731175798,\n",
       " 'shops': 85.72710181006958,\n",
       " 'agb': 80.27772873742923,\n",
       " '90': 65.31059979602215,\n",
       " 'widerrufsrecht': 63.62539627956463,\n",
       " 'browser': 62.256957690267285,\n",
       " 'zubehör': 59.622756345964376,\n",
       " 'sortiment': 54.1792510598168,\n",
       " 'versandkosten': 54.09682194700898,\n",
       " 'datenschutz': 53.90731564297089,\n",
       " 'anmelden': 53.700981663782855,\n",
       " 'schwarz': 51.94595899037167,\n",
       " 'hersteller': 44.689705014401255,\n",
       " 'produkte': 42.29924216652093,\n",
       " 'inkl': 41.8052350629152,\n",
       " 'set': 41.603624523048495,\n",
       " 'mwst': 39.99568659221729,\n",
       " 'können': 39.18730456648102,\n",
       " 'merkzettel': 36.54525023352332,\n",
       " 'ware': 36.25885351836665,\n",
       " 'zahlungsarten': 34.41124807081042,\n",
       " 'javascript': 31.41645481110216,\n",
       " 'shopzertifikat': 31.361866119184548}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_rank_dict\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stakeholder example\n",
    "One thing I love is to do presentations. I love combining this technology and explaining it to others, especially very visually! It is very much like teaching a class, and I do enjoy teaching. \n",
    "\n",
    "So with that in mind, we could make a cool looking example for the customers to really understand what is going on with all of this AI bla-bla technical stuff.\n",
    "\n",
    "**Setup**: Of course we would want to explain how well the classifier works, and we will need to explain it either detects a website as a shop or not (binary).\n",
    "\n",
    "Firstly, I would ask the customer to read the small website text and ask them: **What are the most important keywords that allowed you as a HUMAN to know this is a webshop?** It is better when there are several people in the room to really have them discuss this (make it fun!). After they mention a few, I would point out that some keywords they pick could also indicate that it isn't an online shop, but just a website for a shop that only exists in the real world, and other pitfalls like that (ie word order doesn't matter for this specific solution). \n",
    "\n",
    "\n",
    "**NOTE**: I would do this like a game to make it fun!\n",
    "\n",
    "\n",
    "**NOTE**: I might not use this example text, it was just the first one I picked, perhaps a smaller text or something more clear would be better? This is where I love hearing input from the team(s) or other stakeholders. Also, I would want to have the example text formatted a bit better, look cooler, etc. This is just a prototype of what I would do in an actual presentation...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' DJ Equipment | DJ Zubehör ★ 77records.de Um 77records.de / Dein Shop für DJ-, Studio- und Licht-Equipment in vollem Umfang nutzen zu können, empfehlen wir Ihnen Javascript in Ihrem Browser zu aktiveren. Versandkostenfrei ab 25.- in DE / Tiefpreisgarantie Tel 08241 40 90 10 Fax 08241 40 90 199 77records.de / Dein Shop für DJ-, Studio- und Licht-Equipment Mein Konto Merkzettel Service/Hilfe Deutsch Deutsch Deutsch English Schweiz Warenkorb Warenkorb 0,00 €* Positionen anzeigen Bonuspunkte: 0 Home DIGITAL-DJ DJ Controller Mediaplayer Digital Vinyl Audio Interfaces DJ Software Effektgeräte Cases & Bags Tablet Hüllen, Sleeves & Bags Kabel & Adapter Laptop Ständer DJ-Tische Staubschutzcover Zubehör Digital-DJ Set-Bundles PLATTENSPIELER DJ-Plattenspieler USB Plattenspieler Plattenspieler-Cases Tonabnehmer DJ-Plattenspieler-Sets Phono USB Adapter Phono Vorverstärker Plattenspieler Ersatzteile Plattenspieler Zubehör Schallplatten & CD-Möbel Vinyl DJ-Tische Slipmats Kabel & Adapter Plattenkoffer CD-PLAYER DJ Einzel-CD-Player Doppel CD-Player Mediaplayer CD Player Bags & Zubehör CD-Player Cases CD-Aufbewahrung CD-Cases CD-Player Ständer Kabel & Adapter CD-Player Set-Bundles MIXER BAGS & CASES KOPFHÖRER IN-EARS ZUBEHÖR KOPFHÖRER LICHT Discokugeln LED Effekte Flowereffekte LED-BAR-Leisten LED PAR Scheinwerfer LED Effekte m.ACCU LED-Möbel Flutstrahler Gobo, Logo & Effektprojektion Nebelmaschinen Nebelfluide & Nebel-Zubehör Schneemaschinen Seifenblasenmaschinen Seifenblasenkonzentrate Moving Heads Laser Leuchtmittel Outdoor Light Packs Panels Party & Deko Licht Kabel & Adapter Ständer & Stative Scanner Spots Stroboskope Zubehör Lichtschlauch / Rubberlight STUDIO Audio Interfaces Kompressoren Mischpulte STUDIO-SPAR-BUNDLES Drums Studio Software Midi Controller Midi Keyboards Recorder Mikrofone Sampler Studio Lautsprecher Ständer & Stative Synthesizer Vorverstärker Workstations Zubehör ...mehr Racks - Road Cases Instrumente DJ-Möbel Effektgeräte Home Hifi Accessories Kabel & Adapter Lautsprecher & Verstärker Mikrofone Ständer & Stative SPECIAL DEALS LOVECOLORS Marken 12345Slide leftSlide right NEU Pioneer DJ-Starter-Pack / WeGO4 inkl.... Starter steht drauf - 100% Pioneer ist drin... mit diesem Set ist der Start gemacht 519,00 € * Zum Produkt TIPP! NEU JB Systems BATTLE 4-USB / 4-Kanal Battlemixer 9 Eingänge verteilt auf 4 Kanäle, Line-In, Phono-In, Mikrofon-In und 2 bidirektionale USB-Ports! Battle-mode! 179,00 € * Zum Produkt Reloop TOUCH 7\"-Touchscreen DJ-Controller Weltweit erster DJ Controller mit Touchscreen inkl. VirtualDJ 8 PRO in der Vollversion 699,00 € * Zum Produkt OMNITRONIC WAMS-065BT / Mobil-PA mit... Superkompakte, leichte und leistungsfähige Aktivbox mit Bluetooth, Funkmikrofon, SD-Karten Slot, Mischpult, Effekt und EQ - Hier im Set-Angebot mit 12V / 2900 mAh... 115,00 € * Zum Produkt TIPP! NEU M-Audio UBER Mic - Professionelles USB Mikrofon M-Audio bietet mit dem UBER Mic ein professionelles USB-Mikrofon mit vier wechselbaren Richtcharakteristiken und Kopfhörerausgang an. 119,00 € * Zum Produkt TIPP! NEU Numark NS6II - Premium 4-Deck DJ Controller Professioneller 4-Deck DJ Controller mit gut ablesbaren 2? LCD Displays 799,00 € * Zum Produkt CAD AS32 - High End Reflection Filter / Mic... High End Akustik Schirm für Gesangs- und Instrumentenaufnahmen Statt: 129,00 € * 89,00 € * Zum Produkt % TIPP! 7even® Headphone white / Dj, Hifi, Sport... Hochwertiger und solider Dj Kopfhörer in weiß mit tauschbarem Kabel, 50mm Treiber im edlen Rubberfinish und Chromeframe Statt: 129,00 € * 55,00 € * Zum Produkt % TIPP! NEU Pioneer DJS-1000 / Stand-alone-DJ-Sampler Pioneer DJ DJS-1000 / DJ Pad Player, Sampler 1.279,00 € * Zum Produkt Unsere NeuheitenSlide leftSlide right Teenage Engineering PO-24 office... 69,00 € * Pioneer DJ-Starter-Pack / WeGO4... 519,00 € * EUROLITE LED Mini Strobe Bar SMD 48 59,90 € * JB Systems BATTLE 4-USB /... 179,00 € * Loading... DIGITAL-DJ DJ-Plattenspieler BAGS & CASES Discokugeln Tablet Hüllen, Sleeves & Bags LED-Möbel KOPFHÖRER Midi Controller Dj-Equipment Spar Bundle Sets:Slide leftSlide right Pioneer DDJ-SR Set: inklusive... 619,00 € * Pioneer DDJ-SR Set: inklusive... 609,00 € * Reloop Beatmix 2 MK2 Set Bundle... 289,00 € * Reloop Beatmix 4 Set Bundle:... 349,00 € * Reloop Terminal Mix 8 Set Bundle... 579,00 € * Reloop Neon Set Bundle inkl.... 159,00 € * Reloop Terminal Mix 8 Set Bundle... 579,00 € * Pioneer CDJ-850-K Set: 2 x... 1.899,00 € * 123Top MarkenSlide leftSlide right Slide leftSlide right Top SellerSeite 1 von 6Slide leftSlide right Pioneer PLX-500 / Plattenspieler... Statt: 369,00 € * 349,00 € * Pioneer PLX-500 weiss /... Statt: 369,00 € * 349,00 € * Love Colors EINHORN PULVER -... 12,90 € * Shure M44-7 inklusive Headshell... 85,00 € * Loading... Innovatives DJ Equipment und DJ Zubehör, das keine Wünsche offen lässt Lassen Sie die nächste Party unvergesslich werden und überraschen Sie Partygäste mit hochwertigem DJ Equipment und zahlreichen Special Effekten. DJs auf der Überholspur, Newcomer und Party Veranstalter aufgepasst, denn hier finden Sie vielseitiges Party und DJ Equipment für Ihre nächsten Events. Ob DJ Zubehör wie DJ Plattenspieler, Mediaplayer, Mixer, Mischpulte, Controller, Sampler, FX, Slipmats und Kopfhörer oder Party Equipment wie Discokugeln, Nebelmaschinen, Seifenblasen-, Schnee- oder Konfettimaschinen bis hin zur passenden Ausstattung wie LED Cubes, Möbel oder Vinylregale: Lassen Sie sich von unserem abwechslungsreichen Sortiment überraschen und finden Sie sämtliche Produkte, die auf keiner guten Party fehlen dürfen. Entdecken Sie unser professionelles DJ Equipment & vielseitiges DJ Zubehör Als aufstrebender oder erfolgreicher DJ sind Sie bei uns genau an der richtigen Adresse, denn bei uns erwartet Sie nicht nur eine hochwertige Auswahl an DJ Equiment für professionelle DJs, sondern auch funktionales DJ Zubehör für Einsteiger und Newcomer. Als Newcomer können Sie bei uns professionelle Technik finden, die wir Ihnen zu günstigen Preisen anbieten, so dass Sie nicht direkt zum Start ein teures DJ Equipment kaufen müssen. Unter unseren attraktiven Angeboten und Sets finden Sie echte Schnäppchen, die sich lohnen. DJ Zubehör wie ein DJ Mischpult erwartet Sie in unserem Shop selbstverständlich von führenden Anbieter auf dem Markt. Mit Pioneer, Ortofon, Allen & Heath, Shure, Denon, Akai, Reloop, Korg, Sennheiser, JBL, Gemini, Alesis, Numark, AKG, M-Audio, Presonus, RØDE, Synq, Ableton, Propellerhead, IK Multimedia, RME, Ultrasone, UDG, Magma, Zomo, Korg, Stanton, Eurolite, American Dj, Omnitronic und vielen mehr haben wir als DJ Zubehör Lieferant sämtliche bekannten Marken für Sie auf Lager. Mehr als Plattenspieler: Umfangreiches DJ Equipment für Ihre Ausrüstung DJ-Controller DJ Mixer DJ-Bags DJ Cases Kopfhörer Laptop Ständer Discokugeln DJ Equipment und DJ Zubehör: Profitieren Sie von attraktiven Angeboten Der DJ Plattenspieler bildet natürlich den Grundbaustein Ihres DJ Zubehörs, daher finden Sie bei uns sämtliche Plattenspieler, die keine Wünsche offen lassen, vor. USB Plattenspieler und Plattenspieler mit Direktantrieb warten schon auf den Einsatz bei Ihrem nächsten DJ Gig. In unserem DJ Zubehör Sortiment können Sie außerdem Controller bestellen, die besonders häufig von DJs verwendet werden. Tonabnehmer, Slipmats, Plattenbürsten und DJ Player sind in unserem Shop besonders preiswert zu bestellen und runden Ihr DJ Equipment erst richtig ab. Bei uns finden Sie vom 2-Kanal Battle Mixer über 3-Kanal bis hin zu USB-Mixern und Clubmischpulten alles, was Sie für einen erfolgreichen DJ Auftritt benötigen. Bei weiteren Fragen und Interesse an unseren Produkten, freuen wir uns darauf, Sie ausführlicher zu beraten. BonusartikelSlide leftSlide right UDG Digi Trolley To Go... für 850 Punkte Reloop Play - USB Audio... für 500 Punkte 7even® Color Tablet-Sleeve /... für 100 Punkte 7even® Professional Vinyl Deep... für 100 Punkte Loading... Newsletter 5€ Gutscheinsichern Telefonkontakt 08241 40 90 10 Mo. - Fr. 09:00 - 18:00 Livechat Service 77 DEAL - Die Tiefpreis und Deal Garantie EDU (SchulVersionen) Entsorgung Finanzierung & Leasing Kontakt Informationen Jobs Über uns Versand und Zahlungsbedingungen Angaben zur Lieferzeit Widerrufsrecht Datenschutz Allgemeine Geschäftsbedingungen Impressum Zahlung & Versand Top-Marken 7even LED-Cube Ableton ADJ AIAIAI Akai AKG Alesis Allen & Heath Audio Technica Beats by Dr. Dre Crane Crown Decksaver Denon Eurolite Fluid Audio Gemini Hercules JB Systems JBL Klotz Korg KRK M-Audio Magma Dj Bags Monster Nektar Nocs Novation Numark Odyssey Ortofon Pioneer DJ Prodector Rane Reloop Sennheiser Serato Shure Skullcandy SOL Republic Sony Stanton Technics UDG Ultrasone Vestax Zomo Zoom Top-Kategorien 7even® Headphone - Kopfhörer Audio Interfaces B-Waren und Abverkaufs Angebote Beats Shop Beyerdynamic Shop CD Koffer / CD Case CD Player Set Bundles Digital DJ Set Bundles Digital Vinyl Discokugel DJ CD Player DJ Controller DJ Kopfhörer DJ Mixer DJ Plattenspieler DJ und Studio Möbel DJ Zubehör DJ-Kopfhörer Tipps DJ-Tech Abverkauf Effektgeräte Hifi Kopfhörer Kopfhörer Ohrpolster Laptop Ständer LED Design Möbel LED Lichteffekte LED-Würfel u. Kugeln Love Colors HOLI Einhornpulver Mikrofone Nebelmaschinen Numark Shop Ortofon Shop Pioneer Controller Pioneer DDJ Controller Pioneer Mischpulte Plattenkoffer Plattenspieler Zubehör Pro Stands Prodector Shop Reloop Beatmix & Jockey Schallplatten-Zubehör Schneemaschine Seifenblasenmaschinen Sennheiser HD-25 Kopfhörer Shure Kopfhörer Shop Shure Kopfhörer Shop Slipmats Spiegelkugeln Studio Equipment Studio Kopfhörer Technics Ersatzteile Technics Plattenspieler Technics SL-1210 Tonabnehmer UDG Bags USB Plattenspieler Vestax DJ-Controller Xone Mixer Zomo DJ-Bags Zomo Flightbags Zomo Flightcases Links zu anderen interessanten Webseiten: Copyright © 2013 77records.de - Alle Rechte vorbehalten * Alle Preise inkl. gesetzl. Mehrwertsteuer zzgl. Versandkosten und ggf. Nachnahmegebühren, wenn nicht anders beschrieben. Alle verwendeten Bilder, Logos und aufgeführten Marken gehören ihren jeweiligen Inhabern und unterliegen den jeweiligen Bestimmungen Realisiert mit Shopware 15 Jahre 77records 77 DEAL - Die Tiefpreis und Deal Garantie Jobs Newsletter Über uns Kontakt Versand und Zahlungsbedingungen Angaben zur Lieferzeit Widerrufsrecht Datenschutz Allgemeine Geschäftsbedingungen Impressum '"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TEXT = unclassified_text_df['text'][0]\n",
    "\n",
    "text_encoded = TfidfEncoder.get_vectors_from_string(\n",
    "    TEXT, tfidf_vectorizer)\n",
    "\n",
    "TEXT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_in_text_ranked = {}\n",
    "for word in TEXT.split(' '):\n",
    "    if word in feature_ranks_df['feature'].values:\n",
    "        words_in_text_ranked[word] = feature_ranks_df[feature_ranks_df['feature']\n",
    "                                                      == word]['rank'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'für': 11.366318446951125,\n",
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       " 'in': 2.890936112593552,\n",
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       " 'gehören': 0.0,\n",
       " 'ihren': 3.3942389015250325,\n",
       " 'jeweiligen': 0.460316681819534,\n",
       " 'unterliegen': 0.0,\n",
       " '15': 0.5230400761080775}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_in_text_ranked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f72f35d9c90>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "text_example_wordcloud = WordCloud(background_color=\"rgba(255, 255, 255, 0)\", mode=\"RGBA\", relative_scaling=0,\n",
    "                                   normalize_plurals=False, prefer_horizontal=1, min_font_size=10).generate_from_frequencies(words_in_text_ranked)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.axis(\"off\")\n",
    "plt.title('Text example features', fontsize=24)\n",
    "\n",
    "plt.imshow(text_example_wordcloud.recolor(color_func=lambda **kwargs: \"black\"), interpolation=\"bilinear\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After our little fun game of guess the important keywords in the text, I would show this word cloud (of course it would have to look waaay cooler for the presentation version!). I might also remove German stopwords (but I might also do this for the classifier too and benchmark these results on the next pass of working on the solution).\n",
    "\n",
    "I would ask how this aligns with the words guessed by the customers. I assume without stopwords it would probably align pretty well, but we could briefly discuss surprises in there. \n",
    "\n",
    "Then I would explain, much like we spotted keywords and tried to figure out what was important, so does our \"AI\" for all texts.\n",
    "\n",
    "And then I would show them the general results (rankings) of the classifier, while reminding them these are just the highest ranking features, just like a person the AI uses all of the words in the text to figure out if it is a web shop or not (after all, I didn't give them a chopped up word list out of context, I gave them the actual text, and so it is only fair to do the same for the AI)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_feature_wordcloud = WordCloud(background_color=\"rgba(255, 255, 255, 0)\", mode=\"RGBA\", relative_scaling=0,\n",
    "                                 normalize_plurals=False, prefer_horizontal=1, min_font_size=10).generate_from_frequencies(feature_rank_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f72f35d9ad0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "plt.axis(\"off\")\n",
    "plt.title('Top 30 features', fontsize=24)\n",
    "plt.imshow(RF_feature_wordcloud.recolor(\n",
    "    color_func=lambda **kwargs: \"black\"), interpolation=\"bilinear\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally, the colors need a bit of work on these word clouds, but not bad for a first pass. I would wonder if this company has a specific color palette, I would try to use theirs as much as possible for consistency in the presentation. (I would make sure that they get that these are just the top 30 ranked single features, but the classifier uses the whole text of the website to make the prediction, sometimes facts like this slip through, even if you are sure you explained it!).\n",
    "\n",
    "Then I would show them a cleaned up version of the output of the classifier (I have hardcoded the domain in here).."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting labels\n",
      "The website 77records.de is classified as a web shop\n"
     ]
    }
   ],
   "source": [
    "website_prediction = IntentMatcher.predict_labels(\n",
    "    RF_model, text_encoded)\n",
    "\n",
    "website_prediction = website_prediction[0]\n",
    "\n",
    "domain = unclassified_text_df['domain'][0]\n",
    "\n",
    "if website_prediction == 1:\n",
    "    print(\n",
    "        f'The website {domain} is classified as a web shop')\n",
    "\n",
    "elif website_prediction == 0:\n",
    "    print(\n",
    "        f'The website {domain} is classified as a non-web shop')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then it can be seen that it predicts that the website is a shop (for the actual presentation, the output would probably be in German).\n",
    "\n",
    "\n",
    "NOTES:\n",
    "\n",
    "* I would love to discuss with the team and/or other stakeholders of the project if another example would be needed for the presentation (ie a non-web shop example).\n",
    "\n",
    "* One thing that might be too technical for our stake holder would be to use decision tree diagrams, like explained [here](https://towardsdatascience.com/4-ways-to-visualize-individual-decision-trees-in-a-random-forest-7a9beda1d1b7). I would want to discuss with the team and/or other stakeholders if this would be a viable way to dismystify the classifier and how exactly it makes the predictions. \n",
    "\n",
    "Of course, this would all be predicated on this being the chosen classifier, perhaps a better solution would present itself. Data science, just like engeerining is an iterative process of improvements through experimentation. I only wish I had more time to have tried out a lot of the solutions we have proposed here (stopwords, word2vec, classifier tuning, etc). But all in all, this was really a fun exercise to do! I hope you have enjoyed it as much as I did.\n",
    "\n",
    "\n",
    "-Bartmoss"
   ]
  }
 ],
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