Language_Detection_Model_Using_Natural_Language_Processing_&_Machine_Learning.ipynb

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    "colab": {
      "name": "Language Detection Model Using Natural Language Processing & Machine Learning.ipynb",
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        "id": "XJno4_-AALYN",
        "outputId": "2c4579fb-53ac-40ae-be3a-87c02e34a5fc"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3BoI5UVUndoZ"
      },
      "source": [
        "### 1. Importing all the essential libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbFw01U1ndoa"
      },
      "source": [
        "import string \n",
        "import re\n",
        "import codecs\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import feature_extraction\n",
        "from sklearn import linear_model\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import pipeline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "import itertools"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Kueyohfndoc"
      },
      "source": [
        "### 2. Loading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORuQ0qbQndod",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e037f890-66d5-46aa-b7d6-ac2968589da2"
      },
      "source": [
        "# Loading english raw data\n",
        "\n",
        "english_df = pd.read_csv(\"/content/drive/MyDrive/LangDetect Dataset/europarl-v7.bg-en.en\", \"ut-8\", nrows= 100000, header=None, names=[\"English\"])\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
          ]
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      "metadata": {
        "id": "uTWT4lLbndoe",
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          "height": 206
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        "outputId": "ec24e9aa-9d74-4e24-a8dc-8d8ae9dfd5bd"
      },
      "source": [
        "english_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>English</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>Membership of Parliament: see Minutes</td>\n",
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              "      <td>Verification of credentials: see Minutes</td>\n",
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              "      <th>4</th>\n",
              "      <td>Documents received: see Minutes</td>\n",
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              "                                             English\n",
              "0              Membership of Parliament: see Minutes\n",
              "1  Approval of Minutes of previous sitting: see M...\n",
              "2              Membership of Parliament: see Minutes\n",
              "3           Verification of credentials: see Minutes\n",
              "4                    Documents received: see Minutes"
            ]
          },
          "metadata": {},
          "execution_count": 4
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0bca0ebb-0e56-4e07-93fc-b2778dabe833"
      },
      "source": [
        "# Loading raw german data\n",
        "german_df = pd.read_csv(\"/content/drive/MyDrive/LangDetect Dataset/europarl-v7.de-en.de\", \"utf-8\", nrows= 100000, header=None, names=[\"German\"])\n",
        "german_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
          ]
        }
      ]
    },
    {
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      "metadata": {
        "id": "WKMKXswvndof",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef0ac789-9b3a-430e-d062-14195c191a8e"
      },
      "source": [
        "# Loading raw french data\n",
        "\n",
        "french_df = pd.read_csv(\"/content/drive/MyDrive/LangDetect Dataset/europarl-v7.fr-en.fr\", \"utf-8\", nrows= 100000, header=None, names=[\"French\"])\n",
        "french_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2f3r8Co3ndog",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8108c11-c2a5-4afd-ab40-93955fd18c27"
      },
      "source": [
        "# Loading raw spanish data\n",
        "\n",
        "spanish_df = pd.read_csv(\"/content/drive/MyDrive/LangDetect Dataset/europarl-v7.es-en.es\", \"utf-8\", nrows= 100000, header=None, names=[\"Spanish\"])\n",
        "spanish_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
          ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "u0sp8hoendoh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ddbe6e3-dba7-41c3-b31f-be718db157ff"
      },
      "source": [
        "#Loading raw bulgarian data\n",
        "\n",
        "bulgarian_df = pd.read_csv(\"/content/drive/MyDrive/LangDetect Dataset/europarl-v7.bg-en.bg\", \"utf-8\", nrows= 100000, header=None, names=['Bulgarian'])\n",
        "bulgarian_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
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            "Rows: 99976\n"
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e474418-9b89-4a92-e23b-3f7877c197c3"
      },
      "source": [
        "# Loading raw Czech data\n",
        "\n",
        "czech_df = pd.read_csv(\"/content/drive/MyDrive/LangDetect Dataset/europarl-v7.cs-en.cs\", \"utf-8\", nrows= 100000, header=None, names=['Czech'])\n",
        "czech_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "oszgaQaGndoi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f10419a8-fd17-43e4-daeb-16bd19569f48"
      },
      "source": [
        "# Loading raw Danish data\n",
        "\n",
        "danish_df = pd.read_csv('/content/drive/MyDrive/LangDetect Dataset/europarl-v7.da-en.da', \"utf-8\", nrows= 100000, header=None, names=['Danish'])\n",
        "danish_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "IO7HL0d4ndoi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f093f32f-17b5-43a5-988b-48f4d7c701a3"
      },
      "source": [
        "# Loading raw Finnish data\n",
        "\n",
        "finnish_df = pd.read_csv('/content/drive/MyDrive/LangDetect Dataset/europarl-v7.fi-en.fi', \"utf-8\", nrows= 100000, header=None, names=['Finnish'])\n",
        "finnish_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
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        "id": "MSJorZh6ndoj",
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        "outputId": "572eacfb-343c-445e-879c-515374cc2d22"
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      "source": [
        "# Loading raw Hungarian data\n",
        "\n",
        "hungarian_df = pd.read_csv('/content/drive/MyDrive/LangDetect Dataset/europarl-v7.hu-en.hu', 'utf-8', nrows= 100000, header=None, names=['Hungarian'])\n",
        "hungarian_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
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      "cell_type": "code",
      "metadata": {
        "id": "459x1iDNndok",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c25768ba-ea32-450c-8500-2bbcad99bf73"
      },
      "source": [
        "# Loading raw Latvian data\n",
        "\n",
        "latvian_df = pd.read_csv('/content/drive/MyDrive/LangDetect Dataset/europarl-v7.lv-en.lv', 'utf-8', nrows= 100000, header=None, names=['Latvian'])\n",
        "latvian_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r627RluJndok",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4c5f52c-1f85-4da0-8d6d-a006c1bf2396"
      },
      "source": [
        "# Loading raw Dutch data\n",
        "\n",
        "dutch_df = pd.read_csv('/content/drive/MyDrive/LangDetect Dataset/europarl-v7.nl-en.nl', 'utf-8', nrows= 100000, header=None, names=['Dutch'])\n",
        "dutch_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksJaCHJrndok",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06a256de-bcdc-4337-a7be-e1d1036fd12c"
      },
      "source": [
        "# Loading raw Polish data\n",
        "\n",
        "polish_df = pd.read_csv('/content/drive/MyDrive/LangDetect Dataset/europarl-v7.pl-en.pl', 'utf-8', nrows= 100000, header=None, names=['Polish'])\n",
        "polish_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9vYqB5Yndok",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edf7f80e-fc21-4175-c59c-2ddb2aa5a1c5"
      },
      "source": [
        "# Loading raw Portuguese data\n",
        "\n",
        "portuguese_data = pd.read_csv('/content/drive/MyDrive/LangDetect Dataset/europarl-v7.pt-en.pt', 'utf-8', nrows= 100000, header=None, names=['Portuguese'])\n",
        "portuguese_data.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Im2NTox6ndol",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b403dd02-3fc4-4974-eb81-783dcfe24ac4"
      },
      "source": [
        "# Loading raw Romanian data\n",
        "\n",
        "romanian_df = pd.read_csv('/content/drive/MyDrive/LangDetect Dataset/europarl-v7.ro-en.ro', 'utf-8', nrows= 100000, header=None, names=['Romanian'])\n",
        "romanian_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pu4klJtfndom",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "978fc30a-14be-4a89-978b-d1b77d13f916"
      },
      "source": [
        "# Loading raw Slovenian data\n",
        "\n",
        "slovenian_df = pd.read_csv('/content/drive/MyDrive/LangDetect Dataset/europarl-v7.sl-en.sl', 'utf-8', nrows= 100000, header=None, names=['Slovenian'])\n",
        "slovenian_df.head()\n",
        "\n",
        "df = pd.DataFrame(english_df)\n",
        "rows = df.shape[0]\n",
        "print(\"Rows: \" + str(rows))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 99976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_3HVfxQndom"
      },
      "source": [
        "### 3. Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "sG28AutMndom",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5ce211e-0f51-4c01-951b-13f7dcd92c20"
      },
      "source": [
        "for char in string.punctuation:\n",
        "    print(char, end = ' ')\n",
        "translate_table = dict((ord(char), None) for char in string.punctuation)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \\ ] ^ _ ` { | } ~ "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwIePQ3fndon"
      },
      "source": [
        "# Cleaning the data for english dataset\n",
        "\n",
        "data_eng = []\n",
        "lang_eng = []\n",
        "\n",
        "for i, line in english_df.iterrows():\n",
        "    line = line['English']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_eng.append(line)\n",
        "        lang_eng.append(\"English\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BlY4OWtndon"
      },
      "source": [
        "# Cleaning the data for german dataset\n",
        "\n",
        "data_ger = []\n",
        "lang_ger = []\n",
        "\n",
        "for i, line in german_df.iterrows():\n",
        "    line = line['German']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_ger.append(line)\n",
        "        lang_ger.append(\"German\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "284USTIUndon"
      },
      "source": [
        "# Cleaning the data for french dataset\n",
        "\n",
        "data_fre = []\n",
        "lang_fre = []\n",
        "\n",
        "for i, line in french_df.iterrows():\n",
        "    line = line['French']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_fre.append(line)\n",
        "        lang_fre.append(\"French\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EpnhAX8Yndon"
      },
      "source": [
        "# Cleaning the data for spanish dataset\n",
        "\n",
        "data_esp = []\n",
        "lang_esp = []\n",
        "\n",
        "for i, line in spanish_df.iterrows():\n",
        "    line = line['Spanish']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Spanish\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TM0JfV_Nndon"
      },
      "source": [
        "# Cleaning the data for Bulgarian dataset\n",
        "\n",
        "data_bul = []\n",
        "lang_bul = []\n",
        "\n",
        "for i, line in bulgarian_df.iterrows():\n",
        "    line = line['Bulgarian']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Bulgarian\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oedkwtrGndoo"
      },
      "source": [
        "# Cleaning the data for Czech dataset\n",
        "\n",
        "data_czech = []\n",
        "lang_czech = []\n",
        "\n",
        "for i, line in czech_df.iterrows():\n",
        "    line = line['Czech']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Czech\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrWK8Krfndoo"
      },
      "source": [
        "# Cleaning the data for Danish dataset\n",
        "\n",
        "data_dan = []\n",
        "lang_dan = []\n",
        "\n",
        "for i, line in danish_df.iterrows():\n",
        "    line = line['Danish']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Danish\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKkQtUSEndoo"
      },
      "source": [
        "# Cleaning the data for finnish dataset\n",
        "\n",
        "data_fin = []\n",
        "lang_fin = []\n",
        "\n",
        "for i, line in finnish_df.iterrows():\n",
        "    line = line['Finnish']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Finnish\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7OE1EB-ndoo"
      },
      "source": [
        "# Cleaning the data for Hugarian dataset\n",
        "\n",
        "data_hung = []\n",
        "lang_hung = []\n",
        "\n",
        "for i, line in hungarian_df.iterrows():\n",
        "    line = line['Hungarian']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Hungarian\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7tQkuxYndoo"
      },
      "source": [
        "# Cleaning the data for Latvian dataset\n",
        "\n",
        "data_lat = []\n",
        "lang_lat = []\n",
        "\n",
        "for i, line in latvian_df.iterrows():\n",
        "    line = line['Latvian']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Latvian\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVU3fkonndoo"
      },
      "source": [
        "# Cleaning the data for Dutch dataset\n",
        "\n",
        "data_dutch = []\n",
        "lang_dutch = []\n",
        "\n",
        "for i, line in dutch_df.iterrows():\n",
        "    line = line['Dutch']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Dutch\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HznqPerndop"
      },
      "source": [
        "# Cleaning the data for Polish dataset\n",
        "\n",
        "data_pol = []\n",
        "lang_pol = []\n",
        "\n",
        "for i, line in polish_df.iterrows():\n",
        "    line = line['Polish']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Polish\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qi8TKbeFndop"
      },
      "source": [
        "# Cleaning the data for Portuguese dataset\n",
        "\n",
        "data_port = []\n",
        "lang_port = []\n",
        "\n",
        "for i, line in portuguese_data.iterrows():\n",
        "    line = line['Portuguese']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Portuguese\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FL99cSaIndop"
      },
      "source": [
        "# Cleaning the data for Romanian dataset\n",
        "\n",
        "data_rom = []\n",
        "lang_rom = []\n",
        "\n",
        "for i, line in romanian_df.iterrows():\n",
        "    line = line['Romanian']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Romanian\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYeyN-Jtndop"
      },
      "source": [
        "# Cleaning the data for Romanian dataset\n",
        "\n",
        "data_slo = []\n",
        "lang_slo = []\n",
        "\n",
        "for i, line in slovenian_df.iterrows():\n",
        "    line = line['Slovenian']\n",
        "    if len(line) !=0:\n",
        "        line = line.lower()\n",
        "        line = re.sub(r\"\\d+\", \"\", line)\n",
        "        line = line.translate(translate_table)\n",
        "        data_esp.append(line)\n",
        "        lang_esp.append(\"Slovenian\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHTXpcUwndop"
      },
      "source": [
        "### 4. Transforming the data into a single dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAooljiendop",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b35d82cc-d647-436a-a972-9b1cddda5988"
      },
      "source": [
        "df = pd.DataFrame({\n",
        "    \"Text\" : data_eng+data_ger+data_fre+data_esp+data_bul+data_czech+data_dan+data_fin+data_hung+data_lat+data_dutch+data_port+data_pol+data_rom+data_slo,\n",
        "    \"Language\" : lang_eng+lang_ger+lang_fre+lang_esp+lang_bul+lang_czech+lang_dan+lang_fin+lang_hung+lang_lat+lang_dutch+lang_port+lang_pol+lang_rom+lang_slo\n",
        "})\n",
        "\n",
        "print(df.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1498648, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVyo88X-ndoq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "326824bd-bc18-4574-b56b-2a3edb2ba6ad"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c99b4d5d-16b8-4308-b256-2672dccda37a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Text</th>\n",
              "      <th>Language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>membership of parliament see minutes</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>approval of minutes of previous sitting see mi...</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>membership of parliament see minutes</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>verification of credentials see minutes</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>documents received see minutes</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c99b4d5d-16b8-4308-b256-2672dccda37a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c99b4d5d-16b8-4308-b256-2672dccda37a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c99b4d5d-16b8-4308-b256-2672dccda37a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                Text Language\n",
              "0               membership of parliament see minutes  English\n",
              "1  approval of minutes of previous sitting see mi...  English\n",
              "2               membership of parliament see minutes  English\n",
              "3            verification of credentials see minutes  English\n",
              "4                     documents received see minutes  English"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9huTtyfndoq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "966617ed-a341-445e-9b49-efd7ffc6c5bd"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ab7b85ff-8cef-4d48-be63-5e068f14ad07\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Text</th>\n",
              "      <th>Language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1498643</th>\n",
              "      <td>el gospod predsednik predlagam da se s spoštov...</td>\n",
              "      <td>Slovenian</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1498644</th>\n",
              "      <td>ponovno moramo opozoriti na vprašanje zaščite ...</td>\n",
              "      <td>Slovenian</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1498645</th>\n",
              "      <td>obravnavajmo brezposelnost podzaposlenost in n...</td>\n",
              "      <td>Slovenian</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1498646</th>\n",
              "      <td>te agencije za zaposlovanje ravnajo z njimi ko...</td>\n",
              "      <td>Slovenian</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1498647</th>\n",
              "      <td>kako ščitimo delavce</td>\n",
              "      <td>Slovenian</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab7b85ff-8cef-4d48-be63-5e068f14ad07')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ab7b85ff-8cef-4d48-be63-5e068f14ad07 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ab7b85ff-8cef-4d48-be63-5e068f14ad07');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                      Text   Language\n",
              "1498643  el gospod predsednik predlagam da se s spoštov...  Slovenian\n",
              "1498644  ponovno moramo opozoriti na vprašanje zaščite ...  Slovenian\n",
              "1498645  obravnavajmo brezposelnost podzaposlenost in n...  Slovenian\n",
              "1498646  te agencije za zaposlovanje ravnajo z njimi ko...  Slovenian\n",
              "1498647                               kako ščitimo delavce  Slovenian"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JusiyBG0ndoq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26afa286-eb8f-43c8-da61-edb18f37a1a3"
      },
      "source": [
        "df['Language'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Latvian       99997\n",
              "Bulgarian     99996\n",
              "Slovenian     99990\n",
              "Hungarian     99982\n",
              "Romanian      99978\n",
              "English       99976\n",
              "Czech         99975\n",
              "Polish        99958\n",
              "Dutch         99879\n",
              "Finnish       99862\n",
              "Danish        99842\n",
              "German        99838\n",
              "French        99831\n",
              "Portuguese    99795\n",
              "Spanish       99749\n",
              "Name: Language, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lozX4v5nndoq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "19a06ce0-1f7a-4824-e328-c6761a57c85a"
      },
      "source": [
        "plt.figure(figsize=(20,20))\n",
        "sns.countplot(x = df['Language'], data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc43fe8d290>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kLNScrnndoq"
      },
      "source": [
        "### 5. Splitting the dataset\n",
        "\n",
        "* Splitting the dataset into Independent and Dependent variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1dLRVBQndor"
      },
      "source": [
        "x = df.iloc[:,0] # Independent Variable\n",
        "y = df.iloc[:,1] # Dependent Variable"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-oObIMrfndor",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f386a0ac-db27-4759-d465-81fb9afa95d6"
      },
      "source": [
        "y.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    English\n",
              "1    English\n",
              "2    English\n",
              "3    English\n",
              "4    English\n",
              "Name: Language, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQpHnwKUndor",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6806a36-503f-4f99-b189-7e875165b9a6"
      },
      "source": [
        "x.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                 membership of parliament see minutes\n",
              "1    approval of minutes of previous sitting see mi...\n",
              "2                 membership of parliament see minutes\n",
              "3              verification of credentials see minutes\n",
              "4                       documents received see minutes\n",
              "Name: Text, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sq44e9zbndor"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4Vrwsn_ndor"
      },
      "source": [
        "## Applying TF-IDF Vectorizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFlBcfvJndor"
      },
      "source": [
        "* `ngram_range` : It collets one , one two, one two three words \n",
        "* `analyzer` : We are not going word by word here we are going character by character that why we have used **char**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUBYVaVgndor"
      },
      "source": [
        "vectorizer = feature_extraction.text.TfidfVectorizer(ngram_range=(1,3), analyzer='char')\n",
        "# x = vectorizer.fit_transform(df['Text'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEI5Vtlyndos"
      },
      "source": [
        "pipe_mnb = pipeline.Pipeline([\n",
        "    ('vectorizer', vectorizer),\n",
        "    ('clf', MultinomialNB())\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIWi1-vEndos",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85c5e80b-501c-435b-df1b-ca764b51f66f"
      },
      "source": [
        "pipe_mnb.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('vectorizer',\n",
              "                 TfidfVectorizer(analyzer='char', ngram_range=(1, 3))),\n",
              "                ('clf', MultinomialNB())])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uV6QSLIWndos"
      },
      "source": [
        "pipe_lr = pipeline.Pipeline([\n",
        "    ('vectorizer', vectorizer),\n",
        "    ('lr_clf', LogisticRegression())\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsDlDCKLndos",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c89fb2c2-fb1e-43b1-8b1f-cf4d8f3e7fc2"
      },
      "source": [
        "pipe_lr.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('vectorizer',\n",
              "                 TfidfVectorizer(analyzer='char', ngram_range=(1, 3))),\n",
              "                ('lr_clf', LogisticRegression())])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nB3CZfF-ndos"
      },
      "source": [
        "## Model Prediction for Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-kpVRIjndos"
      },
      "source": [
        "lr_predicted = pipe_lr.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZsY9WCO-ndos"
      },
      "source": [
        "## Model evaluation for Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5mqWalhndot",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e31bd90-4464-42c4-c36d-a8fd821b7e62"
      },
      "source": [
        "lr_acc = (metrics.accuracy_score(y_test, lr_predicted))*100\n",
        "print('The logistic regression has:',lr_acc,'% accuracy')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The logistic regression has: 99.13188536349381 % accuracy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zlte0V3Gndot"
      },
      "source": [
        "## Model Prediction for MultinomialNB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nF1bpBlndot"
      },
      "source": [
        "mnb_predicted = pipe_mnb.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kP1jnkDHndot"
      },
      "source": [
        "## Model Evaluation for MultinomialNB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUrzTdvEndot",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f7095a7-5652-4b34-ddc0-50bcc388db28"
      },
      "source": [
        "mnb_acc = (metrics.accuracy_score(y_test, mnb_predicted))*100\n",
        "print('The MultinomialNB has :',mnb_acc,'% accuracy')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The MultinomialNB has : 99.01978447269208 % accuracy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7frJQNcPndot"
      },
      "source": [
        "## Evaluation matrix for Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ijHBaVNnndot",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "964fbbe3-6547-4c3a-ac7e-b9f09b52a821"
      },
      "source": [
        "matrix = metrics.confusion_matrix(y_test, lr_predicted)\n",
        "print('Confusion matrix: \\n', matrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix: \n",
            " [[19599     3     0     2    33     0    24    17     0     2    12     3\n",
            "    313     9    19]\n",
            " [    8 19864     1     1    23     1    14     9     3     0     8     3\n",
            "    176    23    12]\n",
            " [    0     0 19977     5     8     2     1     3     3     2     3     2\n",
            "     23     4     2]\n",
            " [    0     0     1 19881    17     3     0     5     3     2     3     3\n",
            "     15     0     6]\n",
            " [    0     0     0     0 19410     1     5     1     0     1     2     5\n",
            "    260     4     5]\n",
            " [    0     0     1     0     1 19877     1     1     0     1     0     1\n",
            "     78     5     2]\n",
            " [    0     1     3     5     6     2 19681     3     6     0     3     2\n",
            "     27     3     6]\n",
            " [    0     0     4     5     4     4     2 20034     1     2     6     1\n",
            "     31     3     4]\n",
            " [    6     1     3     1    17     0     9     5 19739     1     5     2\n",
            "    160    12    15]\n",
            " [    6     2     0     0    14     0    12     4     1 19828     7     2\n",
            "    160    12    10]\n",
            " [    9     1     3     0    15     0     5     6     4     0 19908     4\n",
            "    170     6    12]\n",
            " [    0     4     2     1     4     0     2     3     2     0     0 19944\n",
            "     28     1    28]\n",
            " [   19     2     2     1    30     2    21     6     2     0     9     5\n",
            "  19794    13    15]\n",
            " [    4     0     1     0    15     3     7     5     2     1     9     5\n",
            "    193 20041    11]\n",
            " [    0     2     1     2     3     2     4     5     0     2     4    15\n",
            "     56     2 19551]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R58YBa3Dndou"
      },
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Actual labels',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    See full source and example: \n",
        "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
        "    \n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cg5ciK5cndou",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cf54b60f-980d-4d40-9619-e2538ee3f0ae"
      },
      "source": [
        "plt.figure(figsize=(20,20))\n",
        "plot_confusion_matrix(matrix, classes = ['ENG', 'GER', 'FRA', 'ESP','BUL','CZ','DAN','FIN','HUN','LAT','DUTCH','POL','PRT','ROM','SLO'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix, without normalization\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bddLqP25G207wbtrdxcNpnefXoC3sdwKeXlUXDTXOIaQVLj8a2Lqqvtvv4v9TVR3cH/8P4OqqeuqQ4xxSWi26c4CPVtV+/dgngZOr6iP96zfSmlI9b+QBmsfSMmR3S6s5tzUtG+vOtK2rywOvBi4C3lVV1w022IH0LKIXV9VeE8dWA15Pm59DquoXSY4ANgee08tajEKSlwPXV9U7+5376/qF8g1pdbAOpy3uXwI8v6ouHnTAA+vb494MfI4WNP5xVb2zP/YU2gL/cuBDVTWmbOOn0l5TT6Ztdd6aFvB7V1WdmGRHWn3M/6yqI4cb6eyT5D3AFsCGVfWnJFPB0BdV1bnDjm5YSV4N7EY7n+1Bu3g+Hjizqn6VZK1+/KAxvd6m0wOihwAvnMqw7ccfRQt6nQccO6IbNvekncdeU1Vf68dWpwXcL62qfZJsSMuQfDSw8VjWAX0b/IrAZcDpVbV9P/5q2o61g4CVqurPaeVzpjK3f7mknzmfpXVRP4iWoFHA3Wnnu7vQEjgWAG+uqj9MfE/Gsostrdb6J2g3Yg6tqt9MPLYW7XV4OW1X5H2B547ltTYXrLv+hnXCad8aehiz0n3WWPG8qtpo6HHcEhbrHUCSzWi1id4NXFFV/9F3YL6CdoJ4ES2I9SxaHb9txpKJNSWtNuiDgd8AP06yCvDvwGemnlNVT0xyVpJjq2rbgYY6mL718g+0gPo2SZ5eVcfQTp47J3ku7YR6GS2zdiXaHf9R6Yt3aK+l5yb5Pa351IW0O9W/pGVhP4iWEXLpGAOi3QLgrkkOq6pXQMsY7Vmhb6fVfNq9qvZKcuex3M2fWKQ/gIXbuq6Hm2yL+19avad1gG3HHBCdmK+DadtQfwCcRav/uAFwfFV9Jq1MxaOA0bze0kp4vBp4Q1X9rs/Vp/rOkT2SUFUf6+fAddO60Y92K2HPqL1qag6qavck7wfOS7JhVR2a5Bparcdte6bRqEy83g6n1ef7Y1Vt0W+QHgL8OcnUjptXuhNioZ6Y8I+00jln9d001/f/np1W/uSFtLJNH6/WMXu++ystiPWN/r60Qn+v2hE4P8lZVfXvaXWOrxhLsLi7XVX9te+C+FqSf62qA4EHAk+kbQWv/h51ZlV9YcjBDimt1vxUF/Xz+rH30jJoNwWOol3vHpLkgKnX1ogCok8FHkG7cfwq4GVJ3llVv+nvP/8NPCLJI2jrzqvGuMtPWlYMii5j/S7jrrSg6BrAWkmOAn5Hu5u2Py0T6fPAG4ca55CS/D/aAuMS4J20wN79gDf17YVTz1upqjZOcp9hRjqcRTKNvkQLdu6cVr9oAQv/xh4IbAtsNqatTVPSaj69mZYB8oskj6PNzWeAjwM/r6pKq2N4p6r6xmCDHVDPnL26Z2TvT6uF+Q5gn2r+mORfgQ9NBWnGEhCFmyzSvwDs14Mx5/X386nA6AtoW5//pap+ONBQZ4WJ+Qoty2qVqvpKWr3nTwJ3SPJ62mvxkLG8N/WskBNoOz9O7NnFr0uryXc8LWt0tyQrVNUHk9xhLHOzOD0gejhwXJLjp+aiql7cL65PSbJ5VR2R5Cpa+ZhRmMymWiSI8D/Aekkuo5UUej4tw+gpwKlV9adlPthZqmeC/g9wJ9qOEWivQSYCx+fQgoS70kvuzFdTux9oO9UeREvIOAa4Oq1O/e+TfJQ+V1V1/oDDXebSynm9IMnFtBsMj6HdnHk6Lfng4bRkjk1oJRbOGGqss8SawNF9rbRiVV1dVS9N8j7adcsm/b9bANcOOdBlrV/DHQgcUFXfSPJiWpB4zyRHTmUWp5WneGRV7TngcLUEAcbXrnj+sqboMpRkI1qQag9aUOa/aW+Ka9C2zu1Ny+Z7f5J/HGqcQ+pZNO+mZWH9ldZt76T++dkTz3serev8SlV1+QBDHcyimUbAldXqXH2GlpV1dFVdWlUHVquztlFV/XzAIQ+iz9NrgX2r6j974O+nVfWSqnprVf2sB0R3BB5Kz/wbm7518ATg6CRvrKpLaLVXV+v/nbI5LeA+umzjCd+mZRVv3wOjN1TbOr8DLdvo5WMOiCZZO8nGSTZPsnq/wJ6ar9fSGr78c1U9jVYa5WdjCvpV1RXANrT6fOsC7wcuqKoreybkcbTA+3OS3H5Mc7OotO7Dv6EFi58MPCmtbiYAVfVSWiBii/71UVX1oyHGOpAFcGOmIwDVGnH8Gy3r6EJgr6o6vd9MfvHk1syxSyvP9BnaNt7vAqsufOhGy9HWWlNz+bthRrv09YDfx/r79Ma0DOMnJdkYoKr+2p+6Mm1tQM8iHYW+njwQ+CZt+/zLadfQj6UF/75XrQ77eVV1OK18zq+GGu9QFvmbuB/tvXuqi/pU08W9aLVq71mtXMWhVfW/y3akw0nb5fcKWrLG55Ks0jNAD6KVzdmhP++FwJtowVJJS5mZosvIRMbai6rqB2m16Y6ibd99f1W9pT9nXeAZwPeGG+0w0pqYvJtWM+XbE8fPoW19flvfrvNAYE9g56q6aoixDuVmMo32ot1xvZ4WgFhxIqt2NNkzUxaZp1P6PH2IVtD9a/0596ctPp4N7DCmRdmUvtDfj7bY/wmtgdLyPWP0LbSsyG8BX6TN044TF0ejU61O2Adona8PT3Iu7YbNM4FnjSwocxNZ2PDtJ7QAw0PSuqqf1+fsrbSLgC8C9AvH0anW8O56Wr3w/arq7WllBG6oViPzc8AxNbKGgZPS6hfuDVwJvIGWRbRdf+zUqvp9f+qVtFrHo9Jv8J2bZIOquiIL6xsvqKqf98DWI6rquJ51fE1V/WXocc8WSZ5MWw88v6rOT3Jn4BNJft13aU09bwdazf/3zOedEX0d8CbgY7RmXNvTuoP/Dy1z/b7AibRrk2fSGsGNaZvz1Hpyu6r6ct+ddijwmKr6bL92+Vpfd+/Xv21U1yYTVqBlGgN8BHh4ku2AL1fVtf296tp+w+FOtN1aY5urq2nntKuSrAS8KsmmwG9pdVefnlZmb11atvbo4gHSEAyKLgOLZKydn9Zl7o+07ThHAWskObCqTgROTHLESAMP69MaTUwGRA+hFeN+P/AB2sIM2oni+8t+iMPqF0DbAAck+RGtzuNxPcuIJMfT7mI/JckXqnWaH8XCddJi5ukw4IvVmwZ019DuZD+nRlj/cWKh/4yq+lLfSvgE4J19Z+butK1iuwO/BravkdU2XpwedHgbrS7WFrSatNuOeW76Oe51tHILp/djrwe+1IOln6M1VTq1P7ZcLazFOjpVdVIPzLwryfuq6g89i+baMW9vTm7cEv572rl+E1qzlwN7BtI2wB2TfJVWu3cD4G2DDXgg1WrO7Ql8M8ljqtV7vB0LdzucA+yVZK1qdenU9dfdu2ldr9dJcna1uv67Ae/tAcCraXWOd6dlts/ngOiiAb/70rJEf0HbpTV1/bItrQzDdmM7102sJw9JcnpVXZ7kWtq124Kq+mGSJwDHJjmMVmd1dOvunn39kiQXAN+tqi8kuYRWP3QB7WbfdUmeBdyTtq4co98DJ9MC6w+lrYs+DnwfeBrwI1p9+tfUyBosS0Oy+/xS1hccv6FlrH0xrZvch2lbn09Lci/gPbRtYAdV1a8nLgxGYerfm+RdwB+q6rX9+Fa0TL730brzvYVWwuPMMQZEJ/UgxAm0TKO3TmQaVZJVgeXGvPVyyuLmqVoThS1pC5NzasRNJ3rA6s3A82gLtG/Stl9+FvhJVf3TcKPTXDBxjtu2Z6atNJX5kVaf9rlVtXZaLbo7VtV2YzvHLUk/x72DlnF0xdDjGdpExuNy1cpSbE/bCv5Z2vvT5rTtmOvQbuq/fMxZNP3v5920Ejm/61n+1yZ5GC3z79U14nIei0orWfF5WuOkP9DOe1cCh/UbE48HNqQFKv5AK0V04UDDXWb6OuAQ2vvQlWkNur5WVUf1x29Py2xbucbd9G0r4AhaQOuetBvqV028by1frYTF6PQ19RuBj9Kyje9N24V0HS0B6CHAWrQ6q09l5F3U+3Xaw4H7AF+qqqv78Y8An6uqLw85Ps3MI9bfsE74qt3nF+feq8+97vMGRZeBiW2Fz6NlrJ1UVYdNLPzXpGU77FNVY71zRr/T+hraQv78njmTqromyX60u2efHXMQa1JaDah3AY+eyjQa64Ls5kzM08bVGgU8D3gp8Owxb3eesmjguB9blb5lvqxDp2n0c9xbgU2r6rd9G+HUIv8MWsbVpcBdq+qnAw511ulbC98AbETbkTrKRdnUlnDgUVX1qyT3pAVDv0PbWfMn4G09CHE34JqJbfSjtZjA6J60mn2b1ghriS9JWgfnFYG/VNWFfX25KbA1CwOjV07cpB9VNvtiAn7PrdZlfYFr7oWSbAGcAtyjv09N3gQc5c2+iRujU9nG96aVZDqqWhOh5WhlTp5DK8nwAzPY/6+eQfsa2q6sS4cej6b3iPU3rBMNii7WveZgUNRGS8tAVR1Pu2N2AfCVHhBd0AOiTwXuSqvVN9qAaHcW8A3g2UkeVVXX9oDoDrQ7i992cbZQVf0HsA9wdpI1DIgu3sQ8nZnkJbR6kM83INpU1Um07KvnJ7lTP/wsYBUW1oaSlqif415Fey9avW7aVOF3/Tl/NSD6f1XVl4DHVWvaNboL6in95suewGk90/FjwCerNVQ6kVZ/7s1J7lhVvzIg2lQru7QHcHparfqX0uobGxC9qScDBwN3Squzei3wFVojrzsAeydZbeo1OKaAKNz4d/QS2t/Pbj0gupJr7puqqlOBpwBfTXK3mqiHOdb371rYQPCtSe5QVT+jNTA7OMkRtPrQKwIfr6oTDYjeVJI1k+wN7E/rlWFAVBqANUWXkbppDbEjF5OxNsqT6aS6aROTQ5L8JwubmPxjVV026ABnoWrNllYATk0y6kyjm9PnaQFwDLB+WafnJqrVVNsb+HqS99KaKu1arZuqNK3+GtuD1gBmKmttJ+AewOiamN0SNeI6opN6ltG1tG7g+1XVe/pDZ9IuqjehNfLQhP7aWw74Mu389p2hxzTbVNUhSa4C/hU4MMm3e6mB04AbaOe8lyY5ZKxrqKo6tWf9fzXJZjXC7ukzMbHuPsl1d1OtgeANwHlJTqIlXR1GS/rZhVby5BW0shS6qd8DP6Rl2hoQlQbi9vllrG9ROQR4L20rwW4GaG4qycq0BgpPBH5Oq21kXaybkWRVL6ynl2SVsgvvEvXMdQPHutUWOcftSAuuz/u6fLrtLFoaZuK47983w/m5qSQPoV3nXDxxbB/a2vKAqvpWP7YcLeD+X1U1+hs4lvSYGdfd/9dEeYE1p15L/fW1hqWYNN+4fX7J5uL2eYOiAzDwIGm28sJafyvPcfpb2YRKf4skqwEvpzUyObSq/mvisZcD/0hrgGqgZjEM+OnW6u/dh9HqGpttrHnLoOiSzcWgqDVFB1BVxwF38mJR0mxjQFR/K89x+lv1GoevopWGWS5Jhh6T5o5e+uVzwOXAHknWmXjscOAHtIZUWgwDorq1+nv3vrTyAsYZJM0J1hQdiIEHSdJ85TlOf6uq+lKSr4yt6Y1uvSRr0+oYLg+cCxxJayD0kiTvm9hKfzFgEyFpKfC9W2Ph7dr5w6CoJEmSZh0z1jRTvUnQAcBPgNWAtYGtgY8BzwVek+RjwBrADsBOAw1Vmvd875Y0lxgUlSRJkjQnJdkSeB2wT1Wd3o/tDxwPbAUcDDwf2JvWAfsFVfX9YUYrSZJmE4OikiRJkuacJGsAJwDbVtXpSVaqqquqav9ei/bLwIZVdWSSo2kd1a8ddNCSJGnWsACyJEmSpDmnqq4AtgHekuTOVXVVkhX7Y2+gNVtau399jQFRSZI0yUxRSZIkSXNSVR2f5Abg7CQbVdXvkizfA6BXAlcNPERJ0jwT7LQ0X5gpKkmSJGnOqqoTgT2Ac5OsXlXXJtkJuAfwq2FHJ0mSZiszRSVJkiTNaVV1YpI9gDOSvBfYEdilqgyKSpKkxTIoKkmSJGnO64HRBcAxwPpVddHQY5IkSbOXQVFJkiRJ80JVHZfkTlX1l6HHIkmSZjdrikqSJEmaNwyISpKkmTBTVJIkSZIkSZoJm8/PG2aKSpIkSZIkSRoVg6KSJEmSJEmSRsWgqCRJGqUk1ye5IMmFST6bZJW/4Wd9OMkz++cfTLLOzTx30ySPvRW/48dJ7jLT44s850+38Hftn+SVt3SMkiRJ0lxhUFSSJI3VX6tqvap6GHANsNvkg0luVe31qnphVV18M0/ZFLjFQVFJkiRJtx2DopIkSXAm8MCexXlmkmOBi5MsSPK2JOck+W6SFwOkeXeSS5KcCtxt6gcl+VqSjfrnWyY5P8l3knwlyf1pwdd9epbqJknumuTz/Xeck+Qf+vfeOckpSS5K8kFmUNY/yReTnNe/Z9dFHnt7P/6VJHftx9ZKclL/njOTPPi2mExJkqT5Kn4s9mMusvu8JEkatZ4RuhVwUj+0AfCwqrqsBxb/UFWPTLIi8I0kpwDrA38PrAPcHbgYOHqRn3tX4APA4/rPWqOqrkhyJPCnqjq0P++TwNur6utJ7gucDDwEeAPw9ap6U5KnALvM4J/zgv47VgbOSfL5qvotcHvg3KraJ8nr+8/eAzgK2K2qfpjk0cB7gc1vxTRKkiRJc4pBUUmSNFYrJ7mgf34m8G+0be1nV9Vl/fiTgHWn6oUCdwTWBh4HfKqqrgd+keS0xfz8jYEzpn5WVV2xhHFsAayT3HiP/Q5JVu2/4+n9e49P8rsZ/Jv2SvK0/vl9+lh/C9wA/Hs//nHgmP47Hgt8duJ3rziD3yFJkiTNeQZFJUnSWP21qtabPNCDg3+ePATsWVUnL/K8rW/DcSwHbFxVVy1mLDOWZFNagPUxVfWXJF8DVlrC06v/3t8vOgeSJEnSGFhTVJIkaclOBl6SZHmAJA9KcnvgDGD7XnN0TWCzxXzvWcDjkjygf+8a/fgfgdUmnncKsOfUF0mmgpRnAM/px7YCVp9mrHcEftcDog+mZapOWQ6YynZ9Dm1b/pXAZUme1X9Hkjximt8hSZIkzQsGRSVJkpbsg7R6oecnuRB4P22nzReAH/bHPgp8a9FvrKpfA7vStqp/h4Xb178MPG2q0RKwF7BRb+R0Ma0RE8AbaUHVi2jb6H86zVhPAm6X5PvAW2lB2Sl/Bh7V/w2bA2/qx58L7NLHdxGw3QzmRJIkSZrzUlVDj0GSJEmSJEma1dbbYMM65fSzpn/iCN39DiucV1UbDT2OW8JMUUmSJEmSJEmjYlBUkiRJkiRJ0qgYFJUkSZIkSZI0KgZFJUmSJEmSJI3K7YYegCRJkiRJkjQXhAw9BN1GzBSVJEmSJEmSNCoGRSVJkiRJkiSNikFRSZIkSZIkSaNiUFSSJEmSJEnSqBgUlSRJkiRJkjQqdp+XJEmSJEmSZsLm8/OGmaKSJEmSJEmSRsWgqCRJkiRJkqRRMSgqSZIkSZIkaVQMikqSJEmSJEkaFRstSZIkSZIkSTNgn6X5w0xRSZIkSZIkSaNiUFSSJEmSJEnSqBgUlSRJkiRJkjQqBkUlSZIkSZIkjYpBUUmSJEmSJEmjYvd5SZIkSZIkaQZi+/l5w0xRSZIkSZIkSaNiUFSSJEmSJEnSqBgUlSRJkiRJkjQqBkUlSZIkSZIkjYqNliRJkiRJkqRphWCnpfnCTFFJkiRJkiRJo2JQVJIkSZIkSdKoGBSVJEmSJEmSNCoGKeTxOAAABGNJREFURSVJkiRJkiSNikFRSZIkSZIkSaNi93lJkiRJkiRpGgFi8/l5w0xRSZIkSZIkSaNiUFSSJEmSJEnSqBgUlSRJkiRJkjQqBkUlSZIkSZIkjYpBUUmSJEmSJEmjYlBUkiRJkiRJ0qgYFJUkSZIkSZI0KgZFJUmSJEmSJI2KQVFJkiRJkiRJo2JQVJIkSZIkSdKo3G7oAUiSJEmSJElzQTL0CHRbMVNUkiRJkiRJ0qgYFJUkSZIkSZI0KgZFJUmSJEmSJI2KQVFJkiRJkiRJo2KjJUmSJEmSJGkGgp2W5gszRSVJkiRJkiSNikFRSZIkSZIkSaNiUFSSJEmSJEnSqBgUlSRJkiRJkjQqBkUlSZIkSZIkjYrd5yVJkiRJkqTpBGLz+XnDTFFJkiRJkiRJo2JQVJIkSZIkSdKoGBSVJEmSJEmSNCoGRSVJkiRJkiSNio2WJEmSJEmSpGmkf2h+MFNUkiRJkiRJ0qgYFJUkSZIkSZI0KgZFJUmSJEmSJI2KQVFJkiRJkiRJo2JQVJIkSZIkSdKo2H1ekiRJkiRJmgnbz88bZopKkiRJkiRJGhWDopIkSZIkSZJGxaCoJEmSJEmSpFExKCpJkiRJkiRpVGy0JEmSJEmSJM1A7LQ0b5gpKkmSJEmSJGlUDIpKkiRJkiRJGhWDopIkSZIkSZJGxaCoJEmSJEmSpFExKCpJkiRJkiRpVOw+L0mSJEmSJM1AbD4/b5gpKkmSJEmSJGlUDIpKkiRJkiRJGhWDopIkSZIkSZJGxaCoJEmSJEmSpFGx0ZIkSZIkSZI0A/ZZmj/MFJUkSZIkSZI0KgZFJUmSJEmSJI2KQVFJkiRJkiRJo2JQVJIkSZIkSdKoGBSVJEmSJEmSNCp2n5ckSZIkSZJmwvbz84aZopIkSZIkSZJGxaCoJEmSJEmSpFExKCpJkiRJkiRpVAyKSpIkSZIkSRoVGy1JkiRJkiRJMxA7Lc0bZopKkiRJkiRJGhWDopIkSZIkSZJGxaCoJEmSJEmSpFExKCpJkiRJkiRpVAyKSpIkSZIkSRoVu89LkiRJkiRJ0wgQm8/PG2aKSpIkSZIkSVpqkmyZ5JIklyZ5zdDjAYOikiRJkiRJkpaSJAuA9wBbAesAOyRZZ9hRGRSVJEmSJEmStPQ8Cri0qn5UVdcAnwa2G3hMBkUlSZIkSZIkLTX3Ai6f+Ppn/digbLQkSZIkSZIkTeP88887eeXlc5ehxzFLrZTk3Imvj6qqowYbzQwYFJUkSZIkSZKmUVVbDj2GOernwH0mvr53PzYot89LkiRJkiRJWlrOAdZO8oAkKwDPBo4deExmikqSJEmSJElaOqrquiR7ACcDC4Cjq+qigYdFqmroMUiSJEmSJEnSMuP2eUmSJEmSJEmjYlBUkiRJkiRJ0qgYFJUkSZIkSZI0KgZFJUmSJEmSJI2KQVFJkiRJkiRJo2JQVJIkSZIkSdKoGBSVJEmSJEmSNCoGRSVJkiRJkiSNyv8HnvMoFXhDrYkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKe5Y0QZndou"
      },
      "source": [
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VickW5dndou"
      },
      "source": [
        "lrfile = open('/content/drive/MyDrive/LangDetect/lrmodel.pckl', 'wb')\n",
        "pickle.dump(pipe_lr, lrfile)\n",
        "lrfile.close()\n",
        "\n",
        "mnbfile = open('/content/drive/MyDrive/LangDetect/mnbmodel.pckl', 'wb')\n",
        "pickle.dump(pipe_mnb, mnbfile)\n",
        "mnbfile.close()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}