text_classification.ipynb

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      "source": [
        "from itertools import chain\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import nltk\n",
        "nltk.download('stopwords', quiet=True)\n",
        "# nltk.download('punkt', quiet=True)\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn import feature_extraction\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import model_selection as ms\n",
        "from sklearn import naive_bayes\n",
        "from sklearn import preprocessing\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "import string"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "78a3ed77",
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      },
      "source": [
        "Loading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
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          "height": 74
        },
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      "outputs": [
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
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              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
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              "\n",
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              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
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              " * process.\n",
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              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
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              "\n",
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              "\n",
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              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
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              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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              "    do {\n",
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              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
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              "</script> "
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving website_classification.csv to website_classification (3).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "text_df = pd.read_csv(io.BytesIO(uploaded['website_classification.csv']))"
      ],
      "metadata": {
        "id": "suJUeutJKDQL"
      },
      "id": "suJUeutJKDQL",
      "execution_count": 73,
      "outputs": []
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        {
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          "data": {
            "text/plain": [
              "   Unnamed: 0                                        website_url  \\\n",
              "0           0     https://www.booking.com/index.html?aid=1743217   \n",
              "1           1                   https://travelsites.com/expedia/   \n",
              "2           2               https://travelsites.com/tripadvisor/   \n",
              "3           3              https://www.momondo.in/?ispredir=true   \n",
              "4           4  https://www.ebookers.com/?AFFCID=EBOOKERS-UK.n...   \n",
              "\n",
              "                                cleaned_website_text Category  \n",
              "0  official site good hotel accommodation big sav...   Travel  \n",
              "1  expedia hotel book sites like use vacation wor...   Travel  \n",
              "2  tripadvisor hotel book sites like previously d...   Travel  \n",
              "3  cheap flights search compare flights momondo f...   Travel  \n",
              "4  bot create free account create free account si...   Travel  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>website_url</th>\n",
              "      <th>cleaned_website_text</th>\n",
              "      <th>Category</th>\n",
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              "      <td>https://www.booking.com/index.html?aid=1743217</td>\n",
              "      <td>official site good hotel accommodation big sav...</td>\n",
              "      <td>Travel</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>https://travelsites.com/expedia/</td>\n",
              "      <td>expedia hotel book sites like use vacation wor...</td>\n",
              "      <td>Travel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>https://travelsites.com/tripadvisor/</td>\n",
              "      <td>tripadvisor hotel book sites like previously d...</td>\n",
              "      <td>Travel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>https://www.momondo.in/?ispredir=true</td>\n",
              "      <td>cheap flights search compare flights momondo f...</td>\n",
              "      <td>Travel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>https://www.ebookers.com/?AFFCID=EBOOKERS-UK.n...</td>\n",
              "      <td>bot create free account create free account si...</td>\n",
              "      <td>Travel</td>\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "text_df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('The shape of our data is:', text_df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "huNxg7SrqbQF",
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      "id": "huNxg7SrqbQF",
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of our data is: (1408, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eb5a28e6",
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        "tags": [],
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      "source": [
        "Removing the redundant columns"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                cleaned_website_text Category\n",
              "0  official site good hotel accommodation big sav...   Travel\n",
              "1  expedia hotel book sites like use vacation wor...   Travel\n",
              "2  tripadvisor hotel book sites like previously d...   Travel\n",
              "3  cheap flights search compare flights momondo f...   Travel\n",
              "4  bot create free account create free account si...   Travel"
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "del text_df['Unnamed: 0'], text_df['website_url']\n",
        "text_df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T0XgrLIBq-nH",
        "outputId": "8ee21063-c3e1-4339-bc22-ec1ffde6713b"
      },
      "id": "T0XgrLIBq-nH",
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1408 entries, 0 to 1407\n",
            "Data columns (total 2 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   cleaned_website_text  1408 non-null   object\n",
            " 1   Category              1408 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 22.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "id": "cc7d1294",
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        "execution": {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Travel',\n",
              " 'Social Networking and Messaging',\n",
              " 'News',\n",
              " 'Streaming Services',\n",
              " 'Sports',\n",
              " 'Photography',\n",
              " 'Law and Government',\n",
              " 'Health and Fitness',\n",
              " 'Games',\n",
              " 'E-Commerce',\n",
              " 'Forums',\n",
              " 'Food',\n",
              " 'Education',\n",
              " 'Computers and Technology',\n",
              " 'Business/Corporate',\n",
              " 'Adult']"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "categories = list(text_df['Category'].unique())\n",
        "categories"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Cleaning"
      ],
      "metadata": {
        "id": "SvIvuB0prvou"
      },
      "id": "SvIvuB0prvou"
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate = text_df[text_df.duplicated()]\n",
        "duplicate.shape[0]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C2XfvRzvrY4l",
        "outputId": "f0d0c1c1-51a2-49a0-a345-d986f056bb80"
      },
      "id": "C2XfvRzvrY4l",
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Thus the dataset has duplicate entries and we need to remove these duplicate entries."
      ],
      "metadata": {
        "id": "Ufs0kmpJr2Hh"
      },
      "id": "Ufs0kmpJr2Hh"
    },
    {
      "cell_type": "code",
      "source": [
        "text_df = text_df.drop_duplicates()"
      ],
      "metadata": {
        "id": "ebUX-xSn15QH"
      },
      "id": "ebUX-xSn15QH",
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "id": "b3968664",
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        "id": "b3968664",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   cleaned_website_text Category\n",
              "0     official site good hotel accommodation big sav...   Travel\n",
              "1     expedia hotel book sites like use vacation wor...   Travel\n",
              "2     tripadvisor hotel book sites like previously d...   Travel\n",
              "3     cheap flights search compare flights momondo f...   Travel\n",
              "4     bot create free account create free account si...   Travel\n",
              "...                                                 ...      ...\n",
              "1403  old nude women porn mature granny sex horny ol...    Adult\n",
              "1404  bdsm cams bdsm chat bondage cams free bdsm vid...    Adult\n",
              "1405  porno dvd online european porn dvd cheap adult...    Adult\n",
              "1406  anal dream house anal dream house anal dream h...    Adult\n",
              "1407  world sex news daily sex news adult news eroti...    Adult\n",
              "\n",
              "[1375 rows x 2 columns]"
            ],
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              "\n",
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              "    </tr>\n",
              "  </tbody>\n",
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              "<p>1375 rows × 2 columns</p>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "text_df.drop_duplicates()"
      ]
    },
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Education                          114\n",
              "Business/Corporate                 108\n",
              "Travel                             106\n",
              "Streaming Services                 104\n",
              "E-Commerce                         101\n",
              "Sports                             100\n",
              "Games                               98\n",
              "News                                92\n",
              "Food                                92\n",
              "Computers and Technology            90\n",
              "Health and Fitness                  89\n",
              "Photography                         87\n",
              "Law and Government                  83\n",
              "Social Networking and Messaging     79\n",
              "Forums                              16\n",
              "Adult                               16\n",
              "Name: Category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "text_df['Category'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c1b22457",
      "metadata": {
        "papermill": {
          "duration": 0.032867,
          "end_time": "2021-11-29T07:49:42.275168",
          "exception": false,
          "start_time": "2021-11-29T07:49:42.242301",
          "status": "completed"
        },
        "tags": [],
        "id": "c1b22457"
      },
      "source": [
        "Number of unique rows by category"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "2adb9294",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:49:42.342949Z",
          "iopub.status.busy": "2021-11-29T07:49:42.341910Z",
          "iopub.status.idle": "2021-11-29T07:49:42.379435Z",
          "shell.execute_reply": "2021-11-29T07:49:42.380035Z",
          "shell.execute_reply.started": "2021-11-29T07:36:43.678413Z"
        },
        "papermill": {
          "duration": 0.073624,
          "end_time": "2021-11-29T07:49:42.380245",
          "exception": false,
          "start_time": "2021-11-29T07:49:42.306621",
          "status": "completed"
        },
        "tags": [],
        "id": "2adb9294"
      },
      "outputs": [],
      "source": [
        "cat_unique_val = {}\n",
        "\n",
        "for category in categories:\n",
        "    mask = text_df['Category'] == category\n",
        "    list_length = len(list(text_df[mask]['cleaned_website_text'].unique()))\n",
        "    cat_unique_val.update({category: list_length})\n",
        "\n",
        "    cat_unique_val"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "505a0208",
      "metadata": {
        "papermill": {
          "duration": 0.030999,
          "end_time": "2021-11-29T07:49:42.444075",
          "exception": false,
          "start_time": "2021-11-29T07:49:42.413076",
          "status": "completed"
        },
        "tags": [],
        "id": "505a0208"
      },
      "source": [
        "Sort dictionary in descending order"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "id": "af4b3a57",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:49:42.510494Z",
          "iopub.status.busy": "2021-11-29T07:49:42.509449Z",
          "iopub.status.idle": "2021-11-29T07:49:42.516454Z",
          "shell.execute_reply": "2021-11-29T07:49:42.517037Z",
          "shell.execute_reply.started": "2021-11-29T07:36:43.721627Z"
        },
        "papermill": {
          "duration": 0.041971,
          "end_time": "2021-11-29T07:49:42.517215",
          "exception": false,
          "start_time": "2021-11-29T07:49:42.475244",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "af4b3a57",
        "outputId": "0edc36ec-9a79-472d-e54b-6f84502827d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Education': 114,\n",
              " 'Business/Corporate': 108,\n",
              " 'Travel': 106,\n",
              " 'Streaming Services': 104,\n",
              " 'E-Commerce': 101,\n",
              " 'Sports': 100,\n",
              " 'Games': 98,\n",
              " 'Food': 92,\n",
              " 'News': 92,\n",
              " 'Computers and Technology': 90,\n",
              " 'Health and Fitness': 89,\n",
              " 'Photography': 87,\n",
              " 'Law and Government': 83,\n",
              " 'Social Networking and Messaging': 79,\n",
              " 'Adult': 16,\n",
              " 'Forums': 16}"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "cat_unique_val_sorted = {}\n",
        "sorted_keys = sorted(cat_unique_val, key=cat_unique_val.get)\n",
        "\n",
        "for w in list(reversed(sorted_keys)):\n",
        "    cat_unique_val_sorted[w] = cat_unique_val[w]\n",
        "    \n",
        "# cat_unique_val_sorted\n",
        "cat_unique_val_sorted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "c6354e60",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:49:42.744811Z",
          "iopub.status.busy": "2021-11-29T07:49:42.743885Z",
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          "shell.execute_reply": "2021-11-29T07:49:42.747681Z",
          "shell.execute_reply.started": "2021-11-29T07:36:43.761509Z"
        },
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          "duration": 0.043015,
          "end_time": "2021-11-29T07:49:42.747868",
          "exception": false,
          "start_time": "2021-11-29T07:49:42.704853",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6354e60",
        "outputId": "3b202f50-1e39-4189-a815-c0f2f12a6b40"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1375, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "text_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0a34c54f",
      "metadata": {
        "papermill": {
          "duration": 0.032377,
          "end_time": "2021-11-29T07:49:42.812235",
          "exception": false,
          "start_time": "2021-11-29T07:49:42.779858",
          "status": "completed"
        },
        "tags": [],
        "id": "0a34c54f"
      },
      "source": [
        "<h3>Text cleaning</h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c9808a5",
      "metadata": {
        "papermill": {
          "duration": 0.03156,
          "end_time": "2021-11-29T07:49:42.876926",
          "exception": false,
          "start_time": "2021-11-29T07:49:42.845366",
          "status": "completed"
        },
        "tags": [],
        "id": "1c9808a5"
      },
      "source": [
        "<p>Removing stopwords and punctuation from <B><I>\"cleaned_website_text\" column</I></B></p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "id": "0c1747df",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:49:42.944425Z",
          "iopub.status.busy": "2021-11-29T07:49:42.943419Z",
          "iopub.status.idle": "2021-11-29T07:49:42.949975Z",
          "shell.execute_reply": "2021-11-29T07:49:42.950499Z",
          "shell.execute_reply.started": "2021-11-29T07:36:43.768723Z"
        },
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          "duration": 0.041931,
          "end_time": "2021-11-29T07:49:42.950693",
          "exception": false,
          "start_time": "2021-11-29T07:49:42.908762",
          "status": "completed"
        },
        "tags": [],
        "id": "0c1747df"
      },
      "outputs": [],
      "source": [
        "stop_words = set(stopwords.words('english'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "id": "897f8734",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:49:43.021558Z",
          "iopub.status.busy": "2021-11-29T07:49:43.020438Z",
          "iopub.status.idle": "2021-11-29T07:49:43.022812Z",
          "shell.execute_reply": "2021-11-29T07:49:43.023284Z",
          "shell.execute_reply.started": "2021-11-29T07:36:43.784171Z"
        },
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          "end_time": "2021-11-29T07:49:43.023463",
          "exception": false,
          "start_time": "2021-11-29T07:49:42.984036",
          "status": "completed"
        },
        "tags": [],
        "id": "897f8734"
      },
      "outputs": [],
      "source": [
        "regular_punct = list(string.punctuation)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "797b5533",
      "metadata": {
        "papermill": {
          "duration": 0.031991,
          "end_time": "2021-11-29T07:49:43.087149",
          "exception": false,
          "start_time": "2021-11-29T07:49:43.055158",
          "status": "completed"
        },
        "tags": [],
        "id": "797b5533"
      },
      "source": [
        "<h3>According to title text in our dataframe column is cleared, but in any case we will aplly function to remove posssible stopwords and punctuation</h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "id": "43056066",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:49:43.160932Z",
          "iopub.status.busy": "2021-11-29T07:49:43.160054Z",
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        },
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          "exception": false,
          "start_time": "2021-11-29T07:49:43.120290",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43056066",
        "outputId": "c727afa0-9426-4ac2-9d50-c514cd08daaf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       official site good hotel accommodation big sav...\n",
              "1       expedia hotel book sites like use vacation wor...\n",
              "2       tripadvisor hotel book sites like previously d...\n",
              "3       cheap flights search compare flights momondo f...\n",
              "4       bot create free account create free account si...\n",
              "                              ...                        \n",
              "1403    old nude women porn mature granny sex horny ol...\n",
              "1404    bdsm cams bdsm chat bondage cams free bdsm vid...\n",
              "1405    porno dvd online european porn dvd cheap adult...\n",
              "1406    anal dream house anal dream house anal dream h...\n",
              "1407    world sex news daily sex news adult news eroti...\n",
              "Name: cleaned_website_text, Length: 1375, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "text_df['cleaned_website_text']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "id": "203c2cb2",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:49:43.234780Z",
          "iopub.status.busy": "2021-11-29T07:49:43.233580Z",
          "iopub.status.idle": "2021-11-29T07:49:43.239184Z",
          "shell.execute_reply": "2021-11-29T07:49:43.239678Z",
          "shell.execute_reply.started": "2021-11-29T07:36:43.805695Z"
        },
        "papermill": {
          "duration": 0.042874,
          "end_time": "2021-11-29T07:49:43.239888",
          "exception": false,
          "start_time": "2021-11-29T07:49:43.197014",
          "status": "completed"
        },
        "tags": [],
        "id": "203c2cb2"
      },
      "outputs": [],
      "source": [
        "def text_preprocessing(x):\n",
        "    filtered_sentence = []\n",
        "    word_tokens = word_tokenize(x)\n",
        "    \n",
        "    for w in word_tokens:\n",
        "        if w not in chain(stop_words, regular_punct):\n",
        "            # we make sure that all words are written in lowercase\n",
        "            filtered_sentence.append(w.lower())\n",
        "    \n",
        "    # Converting a list of strings back to a string\n",
        "    filtered_sentence = \" \".join(filtered_sentence)\n",
        "    return filtered_sentence"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ">>> import nltk\n",
        ">>> nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zf00h5riLFE4",
        "outputId": "3b519d24-8b2f-402d-c708-c98029fa0e25"
      },
      "id": "zf00h5riLFE4",
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "id": "8df2bef6",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:49:43.311478Z",
          "iopub.status.busy": "2021-11-29T07:49:43.310398Z",
          "iopub.status.idle": "2021-11-29T07:50:00.856950Z",
          "shell.execute_reply": "2021-11-29T07:50:00.856287Z",
          "shell.execute_reply.started": "2021-11-29T07:36:43.812943Z"
        },
        "papermill": {
          "duration": 17.583159,
          "end_time": "2021-11-29T07:50:00.857120",
          "exception": false,
          "start_time": "2021-11-29T07:49:43.273961",
          "status": "completed"
        },
        "tags": [],
        "id": "8df2bef6"
      },
      "outputs": [],
      "source": [
        "text_df['cleaned_website_text'] = text_df['cleaned_website_text'].apply(text_preprocessing)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "585eded3",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:50:00.935352Z",
          "iopub.status.busy": "2021-11-29T07:50:00.934486Z",
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          "shell.execute_reply": "2021-11-29T07:50:00.938610Z",
          "shell.execute_reply.started": "2021-11-29T07:37:01.356413Z"
        },
        "papermill": {
          "duration": 0.046834,
          "end_time": "2021-11-29T07:50:00.938878",
          "exception": false,
          "start_time": "2021-11-29T07:50:00.892044",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "585eded3",
        "outputId": "be4d8ba1-00d1-4efb-fe84-c6cf8f1581db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       official site good hotel accommodation big sav...\n",
              "1       expedia hotel book sites like use vacation wor...\n",
              "2       tripadvisor hotel book sites like previously d...\n",
              "3       cheap flights search compare flights momondo f...\n",
              "4       bot create free account create free account si...\n",
              "                              ...                        \n",
              "1403    old nude women porn mature granny sex horny ol...\n",
              "1404    bdsm cams bdsm chat bondage cams free bdsm vid...\n",
              "1405    porno dvd online european porn dvd cheap adult...\n",
              "1406    anal dream house anal dream house anal dream h...\n",
              "1407    world sex news daily sex news adult news eroti...\n",
              "Name: cleaned_website_text, Length: 1375, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "text_df['cleaned_website_text']"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2c7b103d",
      "metadata": {
        "papermill": {
          "duration": 0.039486,
          "end_time": "2021-11-29T07:50:01.012853",
          "exception": false,
          "start_time": "2021-11-29T07:50:00.973367",
          "status": "completed"
        },
        "tags": [],
        "id": "2c7b103d"
      },
      "source": [
        "<h3>Preprocessing the data</h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "275de256",
      "metadata": {
        "papermill": {
          "duration": 0.04675,
          "end_time": "2021-11-29T07:50:01.101929",
          "exception": false,
          "start_time": "2021-11-29T07:50:01.055179",
          "status": "completed"
        },
        "tags": [],
        "id": "275de256"
      },
      "source": [
        "<h4>One of the simplest\n",
        "methods of encoding text data is by word count: For each phrase, you count the\n",
        "number of occurrences of each word within it. In scikit-learn, this is easily done using\n",
        "CountVectorizer:</h4>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "id": "9bd69000",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:50:01.312482Z",
          "iopub.status.busy": "2021-11-29T07:50:01.311673Z",
          "iopub.status.idle": "2021-11-29T07:50:02.711274Z",
          "shell.execute_reply": "2021-11-29T07:50:02.710486Z",
          "shell.execute_reply.started": "2021-11-29T07:37:01.366281Z"
        },
        "papermill": {
          "duration": 1.437218,
          "end_time": "2021-11-29T07:50:02.711434",
          "exception": false,
          "start_time": "2021-11-29T07:50:01.274216",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9bd69000",
        "outputId": "7f150bed-e279-4155-83db-b7086c6696d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1375, 59995)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "counts = feature_extraction.text.CountVectorizer()\n",
        "X = counts.fit_transform(text_df['cleaned_website_text'].values)\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "id": "8cf05efc",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:50:02.783544Z",
          "iopub.status.busy": "2021-11-29T07:50:02.782838Z",
          "iopub.status.idle": "2021-11-29T07:50:02.786065Z",
          "shell.execute_reply": "2021-11-29T07:50:02.785494Z",
          "shell.execute_reply.started": "2021-11-29T07:37:02.752597Z"
        },
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          "exception": false,
          "start_time": "2021-11-29T07:50:02.745031",
          "status": "completed"
        },
        "tags": [],
        "id": "8cf05efc"
      },
      "outputs": [],
      "source": [
        "y = text_df['Category'].values"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "314ad410",
      "metadata": {
        "papermill": {
          "duration": 0.035498,
          "end_time": "2021-11-29T07:50:02.855929",
          "exception": false,
          "start_time": "2021-11-29T07:50:02.820431",
          "status": "completed"
        },
        "tags": [],
        "id": "314ad410"
      },
      "source": [
        "Training the model using Naive Bayes Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "id": "1eca3165",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:50:02.928529Z",
          "iopub.status.busy": "2021-11-29T07:50:02.927794Z",
          "iopub.status.idle": "2021-11-29T07:50:02.937923Z",
          "shell.execute_reply": "2021-11-29T07:50:02.938511Z",
          "shell.execute_reply.started": "2021-11-29T07:37:02.758896Z"
        },
        "papermill": {
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          "end_time": "2021-11-29T07:50:02.938714",
          "exception": false,
          "start_time": "2021-11-29T07:50:02.889767",
          "status": "completed"
        },
        "tags": [],
        "id": "1eca3165"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = ms.train_test_split(X, y, test_size=0.2, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "id": "465d2bee",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:50:03.015082Z",
          "iopub.status.busy": "2021-11-29T07:50:03.014327Z",
          "iopub.status.idle": "2021-11-29T07:50:03.084958Z",
          "shell.execute_reply": "2021-11-29T07:50:03.085539Z",
          "shell.execute_reply.started": "2021-11-29T07:37:02.777901Z"
        },
        "papermill": {
          "duration": 0.112753,
          "end_time": "2021-11-29T07:50:03.085719",
          "exception": false,
          "start_time": "2021-11-29T07:50:02.972966",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "465d2bee",
        "outputId": "58596d60-15ff-44eb-a5e9-68c438dc0f9a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "model_naive = naive_bayes.MultinomialNB()\n",
        "model_naive.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "id": "e288ff94",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:50:03.159111Z",
          "iopub.status.busy": "2021-11-29T07:50:03.158341Z",
          "iopub.status.idle": "2021-11-29T07:50:03.175763Z",
          "shell.execute_reply": "2021-11-29T07:50:03.176335Z",
          "shell.execute_reply.started": "2021-11-29T07:37:02.852088Z"
        },
        "papermill": {
          "duration": 0.056138,
          "end_time": "2021-11-29T07:50:03.176514",
          "exception": false,
          "start_time": "2021-11-29T07:50:03.120376",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e288ff94",
        "outputId": "c29f74de-2888-4ff4-d8cf-37949bfd95ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9818181818181818"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "model_naive.score(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "id": "2f3feaa8",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:50:03.248121Z",
          "iopub.status.busy": "2021-11-29T07:50:03.247414Z",
          "iopub.status.idle": "2021-11-29T07:50:03.257812Z",
          "shell.execute_reply": "2021-11-29T07:50:03.258459Z",
          "shell.execute_reply.started": "2021-11-29T07:37:02.873472Z"
        },
        "papermill": {
          "duration": 0.048067,
          "end_time": "2021-11-29T07:50:03.258640",
          "exception": false,
          "start_time": "2021-11-29T07:50:03.210573",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2f3feaa8",
        "outputId": "8b8b1c8d-8238-4182-c064-1804ad10e91e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9163636363636364"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "model_naive.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b54b3da3",
      "metadata": {
        "papermill": {
          "duration": 0.037236,
          "end_time": "2021-11-29T07:50:03.331278",
          "exception": false,
          "start_time": "2021-11-29T07:50:03.294042",
          "status": "completed"
        },
        "tags": [],
        "id": "b54b3da3"
      },
      "source": [
        "Therefore we have achieved an accuracy of 91% on our test dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "29a471b5",
      "metadata": {
        "papermill": {
          "duration": 0.035868,
          "end_time": "2021-11-29T07:50:03.404781",
          "exception": false,
          "start_time": "2021-11-29T07:50:03.368913",
          "status": "completed"
        },
        "tags": [],
        "id": "29a471b5"
      },
      "source": [
        "<h4>Confusion matrix</h4>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "id": "b41ec39f",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:50:03.483615Z",
          "iopub.status.busy": "2021-11-29T07:50:03.482856Z",
          "iopub.status.idle": "2021-11-29T07:50:03.495242Z",
          "shell.execute_reply": "2021-11-29T07:50:03.495776Z",
          "shell.execute_reply.started": "2021-11-29T07:37:02.888550Z"
        },
        "papermill": {
          "duration": 0.054143,
          "end_time": "2021-11-29T07:50:03.495986",
          "exception": false,
          "start_time": "2021-11-29T07:50:03.441843",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b41ec39f",
        "outputId": "2b0ec999-1b3c-4f5f-ab78-3c2991015d11"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 3,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0, 17,  0,  3,  0,  0,  0,  0,  0,  1,  3,  0,  0,  0,  0,  1],\n",
              "       [ 0,  0, 16,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0, 16,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  1,  0,  0, 20,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0, 14,  0,  0,  0,  0,  0,  0,  0,  0,  0,  2],\n",
              "       [ 0,  0,  2,  0,  0,  0,  0,  0,  0,  0,  2,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0, 18,  0,  0,  0,  0,  1,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0, 12,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0, 17,  1,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  1,  0,  0,  0,  0,  0,  0,  0, 24,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 23,  0,  0,  0,  0],\n",
              "       [ 0,  1,  2,  0,  1,  0,  0,  0,  0,  0,  0,  0,  7,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 23,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 15,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 27]])"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "confusion_matrix(y_test, model_naive.predict(X_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "id": "8256a851",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-29T07:50:03.576050Z",
          "iopub.status.busy": "2021-11-29T07:50:03.575333Z",
          "iopub.status.idle": "2021-11-29T07:50:05.105287Z",
          "shell.execute_reply": "2021-11-29T07:50:05.105862Z",
          "shell.execute_reply.started": "2021-11-29T07:37:02.902445Z"
        },
        "papermill": {
          "duration": 1.570715,
          "end_time": "2021-11-29T07:50:05.106055",
          "exception": false,
          "start_time": "2021-11-29T07:50:03.535340",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 873
        },
        "id": "8256a851",
        "outputId": "d9785915-0f6f-4e3a-94cd-f95398892eb7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "mat = confusion_matrix(y_test, model_naive.predict(X_test))\n",
        "plt.figure(figsize=(18,12))\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=categories,\n",
        "            yticklabels=categories\n",
        "           )\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label');"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z8EGCHr1uPyQ"
      },
      "id": "z8EGCHr1uPyQ",
      "execution_count": 100,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
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