Flat_Classification.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sdsc-bw/Price_Intelligence/blob/main/Flat_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automatische Produktklassifizierung als Schlüsselelement zur Nachhaltigkeit im Umgang mit PIM Systemen."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2aNXoYlEBJ-"
      },
      "source": [
        "Große e-Commerce Konzerne bauen bei der Artikelverwaltung auf maßgeschneiderte Produktinformationssysteme (PIM-Systeme), die mit propritären Produkt- und Strukturketten Hand in Hand arbeiten. Neue Produkte werden i.d.R. über Lieferantenlisten in das hauseigene PIM-System eingepflegt. Dies kann entweder durch regelbasierte Zuweisungen und oder durch von Hand-Eintragung durch Angestellte geschehen. Um diesen Prozess zu optimieren, Arbeitende zu entlasten und die Effizenz der hauseigenen IT-Systeme zu optimieren, gibt es unterschiedliche Ansätze für eine automatische Zuweisung von Artikeln aus Lieferantenlisten in die PIM-Struktur. \n",
        "\n",
        "Ein einfacher und unkomplizierter Ansatz für die taxonomische Klassifizierung ist die flache Klassifizierung - Flat Classification. Bei dieser geht es nicht um übergeordnete Kategorien. Klassifiziert wird jeder Artikel nach seiner endgültigen Bezeichnung auf Blattebene im zugrundeliegenden Graphen, der die PIM-Struktur darstellt. Der offensichtliche Vorteil dieses Ansatzes ist seine Einfachheit, die direkt mit einem einsetzbaren Klassifikator-Algorithmus umgesetzt werden kann.\n",
        "\n",
        "Nachfolgend werden Gedankengänge für einen Vorreiter-Prozess eines Auto-Mappings auf Basis eines Flat-Classification Ansatzes dargestellt, der zum selbst ausprobieren verleiten sollen. Der Datensatz beinhaltet drei Spalten:\n",
        "- Beschreibung\n",
        "- Kurzbeschreibung\n",
        "- Produktgruppe\n",
        "\n",
        "Hier sollen die Beschreibungen in Verbindung mit NLP-Werkzeugen genutzt werden, um eine Klassifikation in die jeweilige Produktgruppe vorzunehmen.\n",
        "\n",
        "Bereigestellt wurden die Daten durch die Price Intelligence GmbH. Unter [diesem Link](https://github.com/sdsc-bw/Price_Intelligence) können die Daten heruntergeladen werden, respektive kann dieses Repository geklont werden.\n",
        "\n",
        "**Ablauf des Workshops:**\n",
        "- Bereitstellung der notwendigen Daten und Pakete\n",
        "- Visuelle Analyse\n",
        "- Strategien für eine erweiterte Textverarbeitung ableiten\n",
        "- Modellbildung\n",
        "- Bereistellung der Metriken\n",
        "- Anpassung der Strategien"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4yEX7xzxmb1"
      },
      "source": [
        "Zu Beginn werden alle benötigten Pakete importiert nebst Download weiterer benötigter Daten wie zum Beispiel Stopp-Wörter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "r_8YNt1cEAt8"
      },
      "outputs": [],
      "source": [
        "import nltk\n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "import re\n",
        "from nltk.corpus import stopwords \n",
        "from nltk.stem.wordnet import WordNetLemmatizer \n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QIs_iPrKQnY",
        "outputId": "51ddd4e6-811e-4323-91e5-78b224f75d33"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to\n",
            "[nltk_data]     C:\\Users\\Rainer\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Unzipping corpora\\stopwords.zip.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nkh2HrOrKhN5",
        "outputId": "eeb13d5b-280f-4acc-a8c7-fdf212506314"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to\n",
            "[nltk_data]     C:\\Users\\Rainer\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "nltk.download('wordnet')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2qAKkk_yy1D"
      },
      "source": [
        "Zunächst müssen die Daten in einem neuen Datenrahmen abgelegt und genauer betrachtet werden. Schließlich müssen grundlegende Entscheidungen über die Qualität von Beschreibung nebst Titel getroffen werden, um Folgeschritte besser planen zu können."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "jtiT3LxIIvx6"
      },
      "outputs": [],
      "source": [
        "df_products = pd.read_csv(r'https://github.com/sdsc-bw/Price_Intelligence/raw/main/Ressources/eCom_Produkte.csv', sep=',', encoding='utf-8-sig', compression='zip')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
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          "height": 361
        },
        "id": "UxgRy59jI7WR",
        "outputId": "dee6882d-f455-4513-80e3-e8b792f2dd08"
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              "      <td>Deckchair aus 100 % Polypropylen, PVC-frei und...</td>\n",
              "      <td>Stühle</td>\n",
              "      <td>Deckchair, blau</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Renato Santi-Umhängetasche aus Leder mit Softm...</td>\n",
              "      <td>Taschen und Geldbörsen</td>\n",
              "      <td>Umhängetasche, schwarz</td>\n",
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              "      <td>von TOGETHER Oberteil aus Spitze, blickdicht g...</td>\n",
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              "      <td>Jacke: RH, 3/4-Arm mit Schlitz, am Ausschnitt ...</td>\n",
              "      <td>Damen-Sets Strick-/Jacke und Pullover</td>\n",
              "      <td>Twinset, oliv-türkis</td>\n",
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              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Anhänger mit 1 weißen Süßwasserzuchtperle, ca....</td>\n",
              "      <td>Anhänger</td>\n",
              "      <td>Anhänger, GG 585 B</td>\n",
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              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>Pullover durch Zierknöpfe am Ausschnitt geschm...</td>\n",
              "      <td>Damen-Pullover</td>\n",
              "      <td>Pullover,blau</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>SUPERGA-Schnürer 2750 Cotu Classic aus Canvas....</td>\n",
              "      <td>Halbschuhe</td>\n",
              "      <td>Schnürschuh, rot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>Blousonshirt, Hemdkragen, Knopfleiste, 3 Knöpf...</td>\n",
              "      <td>Herren-Shirts</td>\n",
              "      <td>Blousonshirt,rosenholz</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>Damenring aus der Serie Firenze, mit mokkafarb...</td>\n",
              "      <td>Ringe</td>\n",
              "      <td>Damenring, Edelstahl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>WALDLÄUFER-Schnürschuh aus Leder. Mit schönem ...</td>\n",
              "      <td>Halbschuhe</td>\n",
              "      <td>Schnürschuh, weiß</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   Unnamed: 0                                       Beschreibung  \\\n",
              "0           0  Deckchair aus 100 % Polypropylen, PVC-frei und...   \n",
              "1           1  Renato Santi-Umhängetasche aus Leder mit Softm...   \n",
              "2           2  von TOGETHER Oberteil aus Spitze, blickdicht g...   \n",
              "3           3  Jacke: RH, 3/4-Arm mit Schlitz, am Ausschnitt ...   \n",
              "4           4  Anhänger mit 1 weißen Süßwasserzuchtperle, ca....   \n",
              "5           5  Pullover durch Zierknöpfe am Ausschnitt geschm...   \n",
              "6           6  SUPERGA-Schnürer 2750 Cotu Classic aus Canvas....   \n",
              "7           7  Blousonshirt, Hemdkragen, Knopfleiste, 3 Knöpf...   \n",
              "8           8  Damenring aus der Serie Firenze, mit mokkafarb...   \n",
              "9           9  WALDLÄUFER-Schnürschuh aus Leder. Mit schönem ...   \n",
              "\n",
              "                           Produktgruppe        Kurzbeschreibung  \n",
              "0                                 Stühle         Deckchair, blau  \n",
              "1                 Taschen und Geldbörsen  Umhängetasche, schwarz  \n",
              "2                          Damen-Kleider          Kleid, koralle  \n",
              "3  Damen-Sets Strick-/Jacke und Pullover    Twinset, oliv-türkis  \n",
              "4                               Anhänger      Anhänger, GG 585 B  \n",
              "5                         Damen-Pullover           Pullover,blau  \n",
              "6                             Halbschuhe        Schnürschuh, rot  \n",
              "7                          Herren-Shirts  Blousonshirt,rosenholz  \n",
              "8                                  Ringe    Damenring, Edelstahl  \n",
              "9                             Halbschuhe       Schnürschuh, weiß  "
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "df_products.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qeAODzAbzKta"
      },
      "source": [
        "In der Spalte namens Produktgruppe sind Wörter hinterlegt. Der Einfachheit halber wird jeder Kateogorie eine Ganzzahl zugewiesen, um die Klassifizierung des Vorhersagemodells durch eine **Produkt ID** zu vereinfachen."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "t7XxKRr6KFmF",
        "outputId": "a708a650-d4ca-4b9a-fd4f-54e6aa178ecf"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Beschreibung</th>\n",
              "      <th>Produktgruppe</th>\n",
              "      <th>Kurzbeschreibung</th>\n",
              "      <th>Produkt_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Deckchair aus 100 % Polypropylen, PVC-frei und...</td>\n",
              "      <td>Stühle</td>\n",
              "      <td>Deckchair, blau</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Renato Santi-Umhängetasche aus Leder mit Softm...</td>\n",
              "      <td>Taschen und Geldbörsen</td>\n",
              "      <td>Umhängetasche, schwarz</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                                        Beschreibung           Produktgruppe  \\\n",
              "0  Deckchair aus 100 % Polypropylen, PVC-frei und...                  Stühle   \n",
              "1  Renato Santi-Umhängetasche aus Leder mit Softm...  Taschen und Geldbörsen   \n",
              "\n",
              "         Kurzbeschreibung  Produkt_ID  \n",
              "0         Deckchair, blau           0  \n",
              "1  Umhängetasche, schwarz           1  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_products['Produkt_ID'] = df_products['Produktgruppe'].factorize()[0]\n",
        "df_products.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4svV8sQBs-4"
      },
      "source": [
        "Zur Vergewisserung wie viele Daten enthalten sind, wird per shape Funktion die Gesamtzahl nebst verfügbarer Spalten plakativ dargestellt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4OKr8C_9jCj",
        "outputId": "1ac49d97-a99f-4f5b-fd78-6b314b0df67e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(101516, 4)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_products.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KgpQxrReCD7N"
      },
      "source": [
        "Die Gesamtzahl der Datenpunkte ist in wenigen Fälle ein guter Indikator für ein gutes Modell. Wichtig in diesem Beispiel ist, wie viel Daten (Artikel) pro Produktgruppe zur Verfügung stehen und ob die Verteilung der Artikel auf die Produktgruppen homogen ist. Daher finden wir zunächst heraus wie viel Produktgruppen wir überhaupt in den Daten finden:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g0g2uJBmCvSt",
        "outputId": "a59fdb36-9810-42b2-b800-dfbae9361932"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "441"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_products['Produktgruppe'].nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9w0_ZO5HS8jo"
      },
      "source": [
        "Die zuvor ermittelten Produktgruppen allesamt visuell darzustellen würde wenig Sinn machen. Interessanter wird die Darstellung, wenn die jeweils vorhandenen Artikel innerhalb der Produktgruppen durch Bereiche repräsentiert werden. Ebenso lassen sich gezielt die 30 Produktgruppen darstellen mit den höchsten Artikeln."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "amVkYvL8S9yZ",
        "outputId": "751ac8f9-2226-4e46-9501-01dbcc2fedbc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Damen-Shirts                  7959\n",
              "Damen-Blusen                  5402\n",
              "Damen-Hosen                   4570\n",
              "Damen-Pullover                3873\n",
              "Damen-Kleider                 3334\n",
              "Damen-Jacken                  2839\n",
              "BHs                           2257\n",
              "Ketten                        2127\n",
              "Damen-Jeans                   1895\n",
              "Lampen und Leuchten           1877\n",
              "Bettwäsche                    1809\n",
              "Taschen und Geldbörsen        1729\n",
              "Herren-Shirts                 1557\n",
              "Halbschuhe                    1547\n",
              "Damen-Strickjacken            1494\n",
              "Damen-Tops                    1483\n",
              "Polstermöbel                  1434\n",
              "Figuren und Skulpturen        1434\n",
              "Armschmuck und Fußkettchen    1353\n",
              "Damen-Blazer                  1319\n",
              "Tücher und Schals             1285\n",
              "Damen-Röcke                   1239\n",
              "Dekokissen                    1193\n",
              "Anhänger                      1192\n",
              "Unterhosen                    1189\n",
              "Teppiche, Läufer, Schmufa     1150\n",
              "Herren-Hosen                  1105\n",
              "Ohrringe                      1031\n",
              "Sandalen                      1026\n",
              "Herren-Hemden                  964\n",
              "Name: Produktgruppe, dtype: int64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_products_threshold = df_products['Produktgruppe'].value_counts()[:30]\n",
        "df_products_threshold"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3WOy2qJqdSI"
      },
      "source": [
        "Es zeigt sich, dass bereits innerhalb der ersten 30 Kategorien ein rapider Abfall der zur Verfügung stehenenden Artikel pro Kategorie stattfindet."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TkPgNnINrbP2",
        "outputId": "d159ebf3-3589-4cc7-886b-cb784ce426ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "411"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "threshold = 964 \n",
        "df_products_underrepresented = (df_products['Produktgruppe'].value_counts() < threshold)\n",
        "df_products_underrepresented.sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Wir benutzen daher nur die zuvor aufgezeigten 30 Produktgruppen."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "KvWDP7AeRnCt"
      },
      "outputs": [],
      "source": [
        "df_products_used = df_products[df_products.groupby('Produktgruppe')['Produktgruppe'].transform('size')>threshold].reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1KjBiXDUht2",
        "outputId": "68113e63-15b4-4dbc-b47b-a77e4784f5b2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(61702, 4)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_products_used.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GP7iwb-kewrD",
        "outputId": "15df5c62-bb33-4778-8e0d-46162e311028"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Damen-Shirts                  7959\n",
              "Damen-Blusen                  5402\n",
              "Damen-Hosen                   4570\n",
              "Damen-Pullover                3873\n",
              "Damen-Kleider                 3334\n",
              "Damen-Jacken                  2839\n",
              "BHs                           2257\n",
              "Ketten                        2127\n",
              "Damen-Jeans                   1895\n",
              "Lampen und Leuchten           1877\n",
              "Bettwäsche                    1809\n",
              "Taschen und Geldbörsen        1729\n",
              "Herren-Shirts                 1557\n",
              "Halbschuhe                    1547\n",
              "Damen-Strickjacken            1494\n",
              "Damen-Tops                    1483\n",
              "Polstermöbel                  1434\n",
              "Figuren und Skulpturen        1434\n",
              "Armschmuck und Fußkettchen    1353\n",
              "Damen-Blazer                  1319\n",
              "Tücher und Schals             1285\n",
              "Damen-Röcke                   1239\n",
              "Dekokissen                    1193\n",
              "Anhänger                      1192\n",
              "Unterhosen                    1189\n",
              "Teppiche, Läufer, Schmufa     1150\n",
              "Herren-Hosen                  1105\n",
              "Ohrringe                      1031\n",
              "Sandalen                      1026\n",
              "Name: Produktgruppe, dtype: int64"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_products_used['Produktgruppe'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJ5EojAqr2hl"
      },
      "source": [
        "### Weitere Strategien\n",
        "Die Annahme ist bestätigt, dass 411 Produktgruppen merklich weniger Artikel für das später folgende Modelltraining beinhalten, Sie können der Stelle andere Strategien verfolgen und entweder einzelne und oder andere Gruppierungen von Produktgruppen vornehmen."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EjaBoVbF4Mpf"
      },
      "source": [
        "Nichtsdestotrotz ist es nun an der Zeit gezielt eine Artikelbeschriebung zu betrachten, um Strategien für die Verarbeitung abzuleiten. Nachfolgend wird wahllos eine Beschreibung dargestellt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "mCivCrha4V32",
        "outputId": "e7f47adf-1c29-4cbb-ea76-461ec444d226"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'Jomos-Schnürschuh aus genarbtem Nappaleder. Schaftrand- und Laschenpolsterung. Hoher Tragekomfort durch Lederfutter und auswechselbare, leicht gepolsterte Lederdecksohle. Flexibellaufsohle. Bequemweite H. in 4 Farben herren'"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_products_used['Beschreibung'][9]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QSFJbk3ss6oI"
      },
      "source": [
        "Es lässt sich ableiten, dass die Artikelbeschreibungen keiner bestimmten Norm folgen, die eine einheitliche Vorverarbeitung merklich vereinfachen würden. Nachfolgend wollen wir die jeweiligen Beschreibungstexte genauer unter die Lupe nehmen und erstellen eine neue Spalte, die jeweils die Zahl der genutzten Zeichen der Artikelbschreibung beinhaltet. Wir konvertieren hierzu die Beschreibungen in Strings und zählen die Buchstaben jeder Beschreibung, damit die Summe der Buchstaben in einer neuen Spalte nahmens Zeichenanzahl hinterlegt wird."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "8QgqDFVChW-b",
        "outputId": "9c6e731a-4811-40f7-e4c7-363769bda94f"
      },
      "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>Beschreibung</th>\n",
              "      <th>Produktgruppe</th>\n",
              "      <th>Kurzbeschreibung</th>\n",
              "      <th>Produkt_ID</th>\n",
              "      <th>Zeichenanzahl</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>40791</th>\n",
              "      <td>Ecksofa 'Genova' B293xT160xH88 cm, ca. 94,5kg ...</td>\n",
              "      <td>Polstermöbel</td>\n",
              "      <td>Ecksofa rechts schwarz</td>\n",
              "      <td>56</td>\n",
              "      <td>1826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40787</th>\n",
              "      <td>Ecksofa 'Genova' B293xT160xH88 cm, ca. 94,5kg ...</td>\n",
              "      <td>Polstermöbel</td>\n",
              "      <td>Ecksofa links schwarz</td>\n",
              "      <td>56</td>\n",
              "      <td>1826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50853</th>\n",
              "      <td>Impressionen living Kuschelsofa, taupe Materia...</td>\n",
              "      <td>Polstermöbel</td>\n",
              "      <td>Kuschelsofa, taupe</td>\n",
              "      <td>56</td>\n",
              "      <td>1811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50137</th>\n",
              "      <td>Palmen-Armband, Glanz, Länge ca. 15,5 cm, mit ...</td>\n",
              "      <td>Armschmuck und Fußkettchen</td>\n",
              "      <td>Palmen-Armband,925 ver</td>\n",
              "      <td>55</td>\n",
              "      <td>1771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50145</th>\n",
              "      <td>Palmen-Armband, Glanz, Länge ca. 15,5 cm, mit ...</td>\n",
              "      <td>Armschmuck und Fußkettchen</td>\n",
              "      <td>Palmen-Armband, 925</td>\n",
              "      <td>55</td>\n",
              "      <td>1756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50201</th>\n",
              "      <td>Herz-Collier, Glanz, Länge ca. 45 cm, Federrin...</td>\n",
              "      <td>Ketten</td>\n",
              "      <td>Herz-Collier, 925 verg</td>\n",
              "      <td>46</td>\n",
              "      <td>1745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50230</th>\n",
              "      <td>Stern-Collier, Glanz, Länge ca. 45 cm, Federri...</td>\n",
              "      <td>Ketten</td>\n",
              "      <td>Stern-Collier,925 verg</td>\n",
              "      <td>46</td>\n",
              "      <td>1735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40801</th>\n",
              "      <td>Ecksofa 'Genova' B293xT160xH88 cm, ca. 94,5kg ...</td>\n",
              "      <td>Polstermöbel</td>\n",
              "      <td>Ecksofa rechts weiß</td>\n",
              "      <td>56</td>\n",
              "      <td>1730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40802</th>\n",
              "      <td>Ecksofa 'Genova' B293xT160xH88 cm, ca. 94,5kg ...</td>\n",
              "      <td>Polstermöbel</td>\n",
              "      <td>Ecksofa links weiß</td>\n",
              "      <td>56</td>\n",
              "      <td>1730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50148</th>\n",
              "      <td>Flamingo-Armband, Glanz, Länge ca. 15,5 cm, mi...</td>\n",
              "      <td>Armschmuck und Fußkettchen</td>\n",
              "      <td>Flamingo-Armband,925 r</td>\n",
              "      <td>55</td>\n",
              "      <td>1724</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            Beschreibung  \\\n",
              "40791  Ecksofa 'Genova' B293xT160xH88 cm, ca. 94,5kg ...   \n",
              "40787  Ecksofa 'Genova' B293xT160xH88 cm, ca. 94,5kg ...   \n",
              "50853  Impressionen living Kuschelsofa, taupe Materia...   \n",
              "50137  Palmen-Armband, Glanz, Länge ca. 15,5 cm, mit ...   \n",
              "50145  Palmen-Armband, Glanz, Länge ca. 15,5 cm, mit ...   \n",
              "50201  Herz-Collier, Glanz, Länge ca. 45 cm, Federrin...   \n",
              "50230  Stern-Collier, Glanz, Länge ca. 45 cm, Federri...   \n",
              "40801  Ecksofa 'Genova' B293xT160xH88 cm, ca. 94,5kg ...   \n",
              "40802  Ecksofa 'Genova' B293xT160xH88 cm, ca. 94,5kg ...   \n",
              "50148  Flamingo-Armband, Glanz, Länge ca. 15,5 cm, mi...   \n",
              "\n",
              "                    Produktgruppe        Kurzbeschreibung  Produkt_ID  \\\n",
              "40791                Polstermöbel  Ecksofa rechts schwarz          56   \n",
              "40787                Polstermöbel   Ecksofa links schwarz          56   \n",
              "50853                Polstermöbel      Kuschelsofa, taupe          56   \n",
              "50137  Armschmuck und Fußkettchen  Palmen-Armband,925 ver          55   \n",
              "50145  Armschmuck und Fußkettchen     Palmen-Armband, 925          55   \n",
              "50201                      Ketten  Herz-Collier, 925 verg          46   \n",
              "50230                      Ketten  Stern-Collier,925 verg          46   \n",
              "40801                Polstermöbel     Ecksofa rechts weiß          56   \n",
              "40802                Polstermöbel      Ecksofa links weiß          56   \n",
              "50148  Armschmuck und Fußkettchen  Flamingo-Armband,925 r          55   \n",
              "\n",
              "       Zeichenanzahl  \n",
              "40791           1826  \n",
              "40787           1826  \n",
              "50853           1811  \n",
              "50137           1771  \n",
              "50145           1756  \n",
              "50201           1745  \n",
              "50230           1735  \n",
              "40801           1730  \n",
              "40802           1730  \n",
              "50148           1724  "
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_products_used['Beschreibung'] = df_products_used['Beschreibung'].astype(str)\n",
        "df_products_used['Zeichenanzahl'] = df_products_used['Beschreibung'].apply(len)\n",
        "df_products_used.sort_values(by='Zeichenanzahl', ascending=False).head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5uxkRSnIlYvC"
      },
      "source": [
        "Wir können dem Modell positiv entgegenwirken, indem wir die Artikelbeschreibungen formatieren. Hierzu setzen wir eine neue Methode auf, die alle Wörter in Kleinschreibung konvertiert, Sonderzeichen löscht und Buchstaben löscht, die einzeln oder zu zweit vorhanden sind aber nicht als Stopp-Wörter gesehen werden.\n",
        "Nun kann es vorkommen, dass durch das Aufäumen des Texts einzelne Buchstaben oder zwei Buchstaben vorhanden sind, die nicht durch die Stoppwörter entfernt werden. Sie können hier noch einen weiteren Ausdruck einfügen, um diese zu entfernen."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "KkOrp2fMlZZc"
      },
      "outputs": [],
      "source": [
        "def vorverarbeitung(words):\n",
        "    words = re.sub(\"[^A-Za-zäÄöÖüÜß]\", \" \", words)\n",
        "    words = re.sub(\"ö\", \"oe\", words)\n",
        "    words = re.sub(\"ä\", \"ae\", words)\n",
        "    words = re.sub(\"ü\", \"ue\", words)\n",
        "    words = re.sub(\"Ö\", \"Oe\", words)\n",
        "    words = re.sub(\"Ä\", \"Ae\", words)\n",
        "    words = re.sub(\"Ü\", \"Ue\", words)\n",
        "    words = re.sub(\"ß\", \"ss\", words)\n",
        "    words = re.sub(r'\\b\\w{1,2}\\b', \" \", words)\n",
        "    text = words.lower().split()\n",
        "\n",
        "    return \" \".join(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "Vgq__Blvmh3v"
      },
      "outputs": [],
      "source": [
        "df_products_used['Beschreibung_Sauber'] = df_products_used['Beschreibung'].apply(vorverarbeitung)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "sLZfR2Spne-x",
        "outputId": "0e38a897-eb7d-46a6-d008-aff5db9caba9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'jomos schnuerschuh aus genarbtem nappaleder schaftrand und laschenpolsterung hoher tragekomfort durch lederfutter und auswechselbare leicht gepolsterte lederdecksohle flexibellaufsohle bequemweite farben herren'"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_products_used['Beschreibung_Sauber'][9]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jn7dC3un1Gjc"
      },
      "source": [
        "Jetzt können wir uns aus dem Standard-Repertoire an Werkzeugen bedienen. Zunächst können die Stoppwörter aus den Artikelbeschreibungen entfernt werden, um den rohen Informationsgehalt zu erhalten. Stoppwörter können prinzipiell ignoriert werden, da sie dem Text wenig Bedetung verleihen.\n",
        "\n",
        "Für diesen Fall müssen wir die deutschen Stoppwörter aus allen Beschreibungstexten filtern."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "fCJqeR5KLA7g"
      },
      "outputs": [],
      "source": [
        "german_stopwords = stopwords.words('german')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Wir können uns an der Stelle auch die Stoppwörter ansehen. Der Einfachheit halber werden nur die ersten 10 dargestellt. Sie können sich hier alle anzeigen lassen, indem die eckige Klammer entfernt wird."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-sHy-bBteAm",
        "outputId": "7f8a2293-2e1d-4a38-86d1-672524160d84"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['aber', 'alle', 'allem', 'allen', 'aller', 'alles', 'als', 'also', 'am', 'an']"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "german_stopwords[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "-FKCiVwMLeuk"
      },
      "outputs": [],
      "source": [
        "def delete_stopwords(text):\n",
        "    text = [word.lower() for word in text.split() if word.lower() not in german_stopwords]\n",
        "    return \" \".join(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "Fq5M6ZwSLurE"
      },
      "outputs": [],
      "source": [
        "df_products_used['Beschreibung_Sauber'] = df_products_used['Beschreibung_Sauber'].apply(delete_stopwords)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7j0j1oBw15SA"
      },
      "source": [
        "Nun wird die Lemmatisierung eingesetzt. Bei der Lemmatisierung werden die verschiedenen Formen eines Wortes gruppiert, damit sie als ein einziges Element analysiert werden können. Zwar ähnelt die Lemmatisierung dem Stemming, aber sie bringt die Wörter in einen Kontext. Sie verbindet also Wörter mit ähnlichen Bedeutungen zu einem Wort was uns bei den Artikelbeschreibungen einen deutlichen Vorteil bringen könnte."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "ZNJtHf0FMwZq"
      },
      "outputs": [],
      "source": [
        "lem = WordNetLemmatizer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "_ie_m63HMybp"
      },
      "outputs": [],
      "source": [
        "def building_lemmatization(text):\n",
        "    lemma_text = [lem.lemmatize(word) for word in text.split()]\n",
        "    return \" \".join(lemma_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "ZyTvXgAtM5WS"
      },
      "outputs": [],
      "source": [
        "df_products_used['Beschreibung_Sauber'] = df_products_used['Beschreibung_Sauber'].apply(building_lemmatization)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wtsA8Qrqujb5"
      },
      "source": [
        "Mit der Funktion train_test_split sind wir in der Lage einen einzelnen Datensatz für zwei verschiedene Zwecke aufzuteilen: Einerseits Training, andererseits Test. Der Testdatensatz dient der Erstellung unseres Modells. Der Testdatensatz dient der Anwendung des Modells auf unbekannte Daten, um die Leistung unserers Modells zu bewerten."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "WYVpOVTgNrWl"
      },
      "outputs": [],
      "source": [
        "X_LinSVC = df_products_used['Beschreibung_Sauber']\n",
        "Y_LinSVC = df_products_used['Produkt_ID']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_LinSVC, Y_LinSVC, test_size=0.33, random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LkwMcLhFwBBM"
      },
      "source": [
        "Wir können uns hier eine Hilfe bedienen: Einer Pipeline! \n",
        "\n",
        "Eine Pipeline für maschinelles Lernen lässt sich erstellen, indem eine Abfolge von benötigten Schritten zusammengestellt wird, die beim Training eines Modells erforderlich sind. \n",
        "Wir erhalten somit eine Automatisierung eines Arbeitsablaufs für maschinelles Lernen. Nutzen lässt sich diese für Vorverarbeitung, Merkmalsauswahl, Klassifizierung/Regression und oder Nachverarbeitung. \n",
        "\n",
        "Je nach Komplexität der Applikation müssen möglicherweise weitere notwendige Schritte in eine Pipeline eingefügt werden."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "e-BVNlBkOQb9"
      },
      "outputs": [],
      "source": [
        "lin_svc_model = Pipeline([('vect', CountVectorizer(max_features=3500, ngram_range=(1,4))),\n",
        "                        ('tfidf', TfidfTransformer()),\n",
        "                        ('clf', LinearSVC())])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwQPL1SoOXco",
        "outputId": "9d6c385d-e54a-4a08-ff55-d514498421a1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Pipeline(steps=[('vect',\n",
              "                 CountVectorizer(max_features=3500, ngram_range=(1, 4))),\n",
              "                ('tfidf', TfidfTransformer()), ('clf', LinearSVC())])"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lin_svc_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "mUSs3hCwOvNt"
      },
      "outputs": [],
      "source": [
        "y_test_array = np.array(y_test)\n",
        "prediction = lin_svc_model.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Nachfolgend kommen zwei Werkzeuge zum Einsatz:\n",
        "- Konfusionsmatrix\n",
        "- Klassifikationsbericht"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DOHxLbjska52"
      },
      "source": [
        "## Konfusionsmatrix\n",
        "\n",
        "Bei einem Klassifizierungsproblem kann es sein, dass die Angabe einer einzigen Klassifizierungsgenauigkeit nicht das ganze Bild wiedergibt. Daher wird eine Konfusionsmatrix oder Fehlermatrix verwendet, um die Leistung eines Klassifizierungsalgorithmus zusammenzufassen. Richtige und falsche Vorhersagen werden in einer Tabelle mit ihren Werten zusammengefasst und nach den einzelnen Klassen aufgeschlüsselt. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Zunächst müssen wir noch die 30 ausgewählten Produktgruppen aufschlüsseln, damit wir die Legende der Konfusionsmatrix nebst Klassifikationsbericht interpretieren können."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                 Produktgruppe  Produkt_ID  Zeichenanzahl\n",
            "0       Taschen und Geldbörsen           1         492781\n",
            "1                Damen-Kleider           2         762547\n",
            "2                     Anhänger           4         314359\n",
            "3               Damen-Pullover           5         943946\n",
            "4                   Halbschuhe           6         550779\n",
            "5                Herren-Shirts           7         419841\n",
            "6                 Damen-Shirts           9        1912423\n",
            "7                  Damen-Hosen          10        1218024\n",
            "8                          BHs          12         464296\n",
            "9                     Ohrringe          13         201934\n",
            "10                  Damen-Tops          14         305123\n",
            "11                 Damen-Jeans          16         647676\n",
            "12          Damen-Strickjacken          19         368640\n",
            "13                  Unterhosen          21         137277\n",
            "14                Damen-Blusen          22        1382932\n",
            "15           Tücher und Schals          33         165257\n",
            "16                Herren-Hosen          35         322333\n",
            "17                    Sandalen          37         276095\n",
            "18                Damen-Jacken          41         857561\n",
            "19                      Ketten          46         712283\n",
            "20                Damen-Blazer          47         420083\n",
            "21  Armschmuck und Fußkettchen          55         418235\n",
            "22                Polstermöbel          56         721606\n",
            "23         Lampen und Leuchten          61         595529\n",
            "24                 Damen-Röcke          70         262160\n",
            "25      Figuren und Skulpturen          78         213290\n",
            "26                  Dekokissen          82         301262\n",
            "27                  Bettwäsche         107         672309\n",
            "28   Teppiche, Läufer, Schmufa         129         425714\n"
          ]
        }
      ],
      "source": [
        "grouped_data = df_products_used.groupby(['Produktgruppe','Produkt_ID']).sum()\n",
        "grouped_data = grouped_data.sort_values(by=['Produkt_ID'], ascending=True).reset_index()\n",
        "print(grouped_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "6TlRvlQZKate"
      },
      "outputs": [],
      "source": [
        "for_plotting = confusion_matrix(prediction,y_test_array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "E06MbhEDb470"
      },
      "outputs": [],
      "source": [
        "cm_df = pd.DataFrame(for_plotting)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "F_eI903jdbdt",
        "outputId": "01116a42-2138-4a4a-a8a6-fcdf13905904"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize = (25,20))\n",
        "sns.heatmap(cm_df/np.sum(cm_df), annot=True, fmt='.2%', cmap='Blues')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wh8gbIsrO3ac",
        "outputId": "c78e8b9f-e176-44f7-c24a-1aef5955df9d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.90      0.83      0.86       566\n",
            "           2       0.74      0.85      0.79       965\n",
            "           4       0.95      0.96      0.95       406\n",
            "           5       0.85      0.85      0.85      1322\n",
            "           6       0.91      0.94      0.93       488\n",
            "           7       0.88      0.88      0.88       505\n",
            "           9       0.91      0.83      0.87      2946\n",
            "          10       0.90      0.82      0.86      1612\n",
            "          12       0.93      0.86      0.90       810\n",
            "          13       0.95      0.94      0.94       340\n",
            "          14       0.66      0.77      0.71       433\n",
            "          16       0.86      0.90      0.88       589\n",
            "          19       0.75      0.84      0.79       439\n",
            "          21       0.75      0.89      0.82       339\n",
            "          22       0.89      0.85      0.87      1822\n",
            "          33       0.92      0.94      0.93       427\n",
            "          35       0.78      0.88      0.83       314\n",
            "          37       0.89      0.91      0.90       333\n",
            "          41       0.75      0.79      0.77       839\n",
            "          46       0.93      0.90      0.91       773\n",
            "          47       0.74      0.83      0.78       381\n",
            "          55       0.79      0.87      0.83       409\n",
            "          56       0.99      0.99      0.99       483\n",
            "          61       0.95      0.93      0.94       611\n",
            "          70       0.79      0.89      0.84       367\n",
            "          78       0.86      0.90      0.88       471\n",
            "          82       0.98      0.96      0.97       421\n",
            "         107       0.98      0.99      0.99       566\n",
            "         129       0.97      0.99      0.98       385\n",
            "\n",
            "    accuracy                           0.87     20362\n",
            "   macro avg       0.87      0.89      0.88     20362\n",
            "weighted avg       0.88      0.87      0.87     20362\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(prediction, y_test_array))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LrjtlpB_Zwss"
      },
      "source": [
        "An dieser Stelle überlassen wir Ihnen das Feld und möchten Sie dazu animieren die **Parameter anzupassen** und die Beschreibungen weiter zu verarbeiten, um entweder in Richtung robustes oder akkurates Modell zu forschen.\n",
        "\n",
        "Nutzen Sie auch weitere **Modelle** und lassen Sie uns teilhaben, wie die Ergebnisse aussehen!\n",
        "\n",
        "**Wir wünschen Ihnen viel Spaß und freuen uns Ihre Ergebnisse zu sehen!**"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "KITag_03_Open.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "b3ba2566441a7c06988d0923437866b63cedc61552a5af99d1f4fb67d367b25f"
    },
    "kernelspec": {
      "display_name": "Python 3.8.5 64-bit ('base': conda)",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}