IBM_PROJECT.ipynb

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        "<a href=\"https://colab.research.google.com/github/Yash22222/IBM-CSRBOX-Internship-Project/blob/main/IBM_PROJECT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# **P-1:- WEB SCRAPPING FOR AIR INDIA'S SENTIMENT ANALYSIS**"
      ],
      "metadata": {
        "id": "vIdRBCOFIWRC"
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      "cell_type": "markdown",
      "source": [
        "## **Web Scrapping**"
      ],
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        "id": "4KfsolfEJXi0"
      }
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      "cell_type": "markdown",
      "source": [
        "#### Web scraping is a technique used to extract data from websites automatically. It involves writing code to navigate through web pages, locate specific elements, and extract relevant information. Web scraping is commonly used in data mining, data analysis, and gathering data for research or business purposes."
      ],
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      "source": [
        "# Importing Libraries For Web Scrapping\n",
        "\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd"
      ],
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      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Requests is a popular Python library that allows you to send HTTP/1.1 requests easily. It simplifies the process of making HTTP requests to fetch data from URLs."
      ],
      "metadata": {
        "id": "2avt7zT8I2rQ"
      }
    },
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      "cell_type": "markdown",
      "source": [
        "#### BeautifulSoup is a Python library used to parse HTML and XML documents and extract information from them. It creates a parse tree from the HTML/XML source, which can be used to navigate and search for specific elements in the document using various"
      ],
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      "cell_type": "markdown",
      "source": [
        "#### Pandas is a powerful open-source Python library widely used for data manipulation and analysis. It provides data structures and functions for efficiently working with structured data, making it a fundamental tool for data scientists and analysts."
      ],
      "metadata": {
        "id": "NSkIourgPapg"
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      "source": [
        "base_url = \"https://www.airlinequality.com/airline-reviews/air-india/\"\n",
        "pages = 10\n",
        "page_size = 100\n",
        "\n",
        "data = []\n",
        "for i in range(1, pages + 1):\n",
        "\n",
        "    print(f\"Scraping page {i}\")\n",
        "    url = f\"{base_url}/page/{i}/?sortby=post_date%3ADesc&pagesize={page_size}\"\n",
        "    response = requests.get(url)\n",
        "    content = response.content\n",
        "    parsed_content = BeautifulSoup(content, 'html.parser')\n",
        "    for para in parsed_content.find_all(\"div\", {\"class\": \"text_content\"}):\n",
        "        data.append(para.get_text())\n",
        "\n",
        "    print(f\"   ---> {len(data)} total reviews\")"
      ],
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      "outputs": [
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          "text": [
            "Scraping page 1\n",
            "   ---> 100 total reviews\n",
            "Scraping page 2\n",
            "   ---> 200 total reviews\n",
            "Scraping page 3\n",
            "   ---> 300 total reviews\n",
            "Scraping page 4\n",
            "   ---> 400 total reviews\n",
            "Scraping page 5\n",
            "   ---> 500 total reviews\n",
            "Scraping page 6\n",
            "   ---> 600 total reviews\n",
            "Scraping page 7\n",
            "   ---> 700 total reviews\n",
            "Scraping page 8\n",
            "   ---> 800 total reviews\n",
            "Scraping page 9\n",
            "   ---> 900 total reviews\n",
            "Scraping page 10\n",
            "   ---> 1000 total reviews\n"
          ]
        }
      ]
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        "df = pd.DataFrame()\n",
        "df[\"data\"] = data\n",
        "df.head()"
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              "0  ✅ Trip Verified |  Horrible service from New D...\n",
              "1  ✅ Trip Verified |  Newark to Chennai via Mumba...\n",
              "2  ✅ Trip Verified | At 0:39 in the morning you a...\n",
              "3  ✅ Trip Verified |  Had one of the worst experi...\n",
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              "                                                data\n",
              "0  ✅ Trip Verified |  Horrible service from New D...\n",
              "1  ✅ Trip Verified |  Newark to Chennai via Mumba...\n",
              "2  ✅ Trip Verified | At 0:39 in the morning you a...\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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-0de91c3d-63cd-4af8-aad3-dd38ed65d7b5 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-0de91c3d-63cd-4af8-aad3-dd38ed65d7b5');\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"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Preprocessing**"
      ],
      "metadata": {
        "id": "67V_dLNJJv__"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data preprocessing is an essential step in the data science and machine learning pipeline. It involves cleaning, transforming, and preparing the raw data into a format suitable for analysis and building predictive models."
      ],
      "metadata": {
        "id": "HdBjhVTRKrbK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove sentences before '|' in data column\n",
        "df.data= df.data.str.split('|',expand=True)[1]\n",
        "df"
      ],
      "metadata": {
        "id": "Nr3k2a8VKBrK",
        "outputId": "2d00257d-8631-4d1c-925f-b38ea45ed905",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  data\n",
              "0      Horrible service from New Delhi to Vancouver...\n",
              "1      Newark to Chennai via Mumbai, outstanding se...\n",
              "2     At 0:39 in the morning you are informed a 6 h...\n",
              "3      Had one of the worst experiences being a fre...\n",
              "4      Bad treatment, took Air India flt no. 129 on...\n",
              "..                                                 ...\n",
              "995                                               None\n",
              "996                                               None\n",
              "997                                               None\n",
              "998                                               None\n",
              "999                                               None\n",
              "\n",
              "[1000 rows x 1 columns]"
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              "      <th>995</th>\n",
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              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02312620-adc0-4dcf-997b-beedcf332c99')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-19c08d3f-3ac3-4cee-837b-e509ee528240')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
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              "\n",
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              "    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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-19c08d3f-3ac3-4cee-837b-e509ee528240 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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-02312620-adc0-4dcf-997b-beedcf332c99 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-02312620-adc0-4dcf-997b-beedcf332c99');\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"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove All Special Characters\n",
        "\n",
        "# import request as re\n",
        "import re\n",
        "\n",
        "def clean(text):\n",
        "    text = re.sub('[^A-Za-z]+]', ' ', str(text))\n",
        "    return text\n",
        "\n",
        "df['clean_data'] = df['data'].apply(clean)\n",
        "df.head()"
      ],
      "metadata": {
        "id": "2p3FG34mKQMw",
        "outputId": "f74df4ae-706d-4f54-e6f9-e9016068fb30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                data  \\\n",
              "0    Horrible service from New Delhi to Vancouver...   \n",
              "1    Newark to Chennai via Mumbai, outstanding se...   \n",
              "2   At 0:39 in the morning you are informed a 6 h...   \n",
              "3    Had one of the worst experiences being a fre...   \n",
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              "\n",
              "                                          clean_data  \n",
              "0    Horrible service from New Delhi to Vancouver...  \n",
              "1    Newark to Chennai via Mumbai, outstanding se...  \n",
              "2   At 0:39 in the morning you are informed a 6 h...  \n",
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              "      <th>3</th>\n",
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              "      <td>Bad treatment, took Air India flt no. 129 on...</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4c2cb56-a173-40a4-a10b-0cd927644bb2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "\n",
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              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-a4c2cb56-a173-40a4-a10b-0cd927644bb2 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-a4c2cb56-a173-40a4-a10b-0cd927644bb2');\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"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Tokenization**"
      ],
      "metadata": {
        "id": "n1dBNU_mKdDR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Tokenization is an essential step in NLP and text processing because it helps convert unstructured text data into a format that can be used for various language processing tasks like sentiment analysis, machine translation, text classification, and more. Each token represents a discrete unit of meaning or information."
      ],
      "metadata": {
        "id": "F5HOwSaAKg9R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing Libraries\n",
        "\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk import pos_tag\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('wordnet')\n",
        "from nltk.corpus import wordnet"
      ],
      "metadata": {
        "id": "DtaR7QV5LMSB",
        "outputId": "36e0db11-c76f-49ac-903f-988aaea0aa14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 82,
      "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",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **POS Tagging**"
      ],
      "metadata": {
        "id": "k-pA2Vx3LWsk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Part-of-speech (POS) tagging, also known as grammatical tagging or word category disambiguation, is the process of assigning a part-of-speech tag to each word in a sentence or text. it helps in understanding the grammatical structure of a sentence, which is crucial for many NLP applications like information extraction, sentiment analysis, machine translation, and more."
      ],
      "metadata": {
        "id": "bUzo0c9HLrSB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('omw-1.4')\n",
        "nltk.download('averaged_perceptron_tagger')"
      ],
      "metadata": {
        "id": "Y2kK43n8LbBF",
        "outputId": "26115b4b-4d82-488a-baec-1c1c3ca97715",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data]   Package omw-1.4 is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting each token into Tuple\n",
        "pos_dict = {'J':wordnet.ADJ, 'V':wordnet.VERB, 'N':wordnet.NOUN, 'R':wordnet.ADV}\n",
        "def token_stop_pos(text):\n",
        "    tags = pos_tag(word_tokenize(text))\n",
        "    newlist = []\n",
        "    for word, tag in tags:\n",
        "        if word.lower() not in set(stopwords.words('english')):\n",
        "          newlist.append(tuple([word, pos_dict.get(tag[0])]))\n",
        "    return newlist\n",
        "\n",
        "\n",
        "df['POS tagged'] = df['clean_data'].apply(token_stop_pos)\n",
        "df.head()"
      ],
      "metadata": {
        "id": "Z5LPE6UkL5Qc",
        "outputId": "5bd13726-c819-41cd-96d0-71fb988c5c7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                data  \\\n",
              "0    Horrible service from New Delhi to Vancouver...   \n",
              "1    Newark to Chennai via Mumbai, outstanding se...   \n",
              "2   At 0:39 in the morning you are informed a 6 h...   \n",
              "3    Had one of the worst experiences being a fre...   \n",
              "4    Bad treatment, took Air India flt no. 129 on...   \n",
              "\n",
              "                                          clean_data  \\\n",
              "0    Horrible service from New Delhi to Vancouver...   \n",
              "1    Newark to Chennai via Mumbai, outstanding se...   \n",
              "2   At 0:39 in the morning you are informed a 6 h...   \n",
              "3    Had one of the worst experiences being a fre...   \n",
              "4    Bad treatment, took Air India flt no. 129 on...   \n",
              "\n",
              "                                          POS tagged  \n",
              "0  [(Horrible, a), (service, n), (New, n), (Delhi...  \n",
              "1  [(Newark, n), (Chennai, n), (via, None), (Mumb...  \n",
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              "<style scoped>\n",
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              "              title=\"Suggest charts.\"\n",
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              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
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              "    domScope.querySelector('#df-77d06476-e716-4085-8ca0-9a04e4fe09ff button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
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              "      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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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-ee23b6b2-c2ab-44c7-a566-2edf1fc7477f 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-ee23b6b2-c2ab-44c7-a566-2edf1fc7477f');\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"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Lemmatization (Lemma)**"
      ],
      "metadata": {
        "id": "S2R01lMfMLAO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### In natural language processing (NLP), lemma refers to the base or root form of a word. It is the canonical or dictionary form of a word, to which inflections and variations are added. For example, the lemma of the word \"running\" is \"run,\" and the lemma of \"cats\" is \"cat.\""
      ],
      "metadata": {
        "id": "aEapvmd4MPLF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "wordnet_lemmatizer = WordNetLemmatizer()"
      ],
      "metadata": {
        "id": "Q-o8iKptMVLT"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# grouping the words\n",
        "def lemmatize(pos_data):\n",
        "    lemma_rew = \" \"\n",
        "    for word, pos in pos_data:\n",
        "     if not pos:\n",
        "        lemma = word\n",
        "        lemma_rew = lemma_rew + \" \" + lemma\n",
        "     else:\n",
        "        lemma = wordnet_lemmatizer.lemmatize(word, pos=pos)\n",
        "        lemma_rew = lemma_rew + \" \" + lemma\n",
        "    return lemma_rew\n",
        "\n",
        "\n",
        "df['Lemma'] = df['POS tagged'].apply(lemmatize)\n",
        "df.head()"
      ],
      "metadata": {
        "id": "0YwAgzhnMevF",
        "outputId": "2ec79dab-b08b-45d5-f684-4abf0f4b23c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                data  \\\n",
              "0    Horrible service from New Delhi to Vancouver...   \n",
              "1    Newark to Chennai via Mumbai, outstanding se...   \n",
              "2   At 0:39 in the morning you are informed a 6 h...   \n",
              "3    Had one of the worst experiences being a fre...   \n",
              "4    Bad treatment, took Air India flt no. 129 on...   \n",
              "\n",
              "                                          clean_data  \\\n",
              "0    Horrible service from New Delhi to Vancouver...   \n",
              "1    Newark to Chennai via Mumbai, outstanding se...   \n",
              "2   At 0:39 in the morning you are informed a 6 h...   \n",
              "3    Had one of the worst experiences being a fre...   \n",
              "4    Bad treatment, took Air India flt no. 129 on...   \n",
              "\n",
              "                                          POS tagged  \\\n",
              "0  [(Horrible, a), (service, n), (New, n), (Delhi...   \n",
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              "4  [(Bad, n), (treatment, n), (,, None), (took, v...   \n",
              "\n",
              "                                               Lemma  \n",
              "0    Horrible service New Delhi Vancouver 20th Ju...  \n",
              "1    Newark Chennai via Mumbai , outstanding serv...  \n",
              "2    0:39 morning inform 6 hour delay connect fli...  \n",
              "3    one bad experience frequent flyer . flight B...  \n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>data</th>\n",
              "      <th>clean_data</th>\n",
              "      <th>POS tagged</th>\n",
              "      <th>Lemma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Horrible service from New Delhi to Vancouver...</td>\n",
              "      <td>Horrible service from New Delhi to Vancouver...</td>\n",
              "      <td>[(Horrible, a), (service, n), (New, n), (Delhi...</td>\n",
              "      <td>Horrible service New Delhi Vancouver 20th Ju...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Newark to Chennai via Mumbai, outstanding se...</td>\n",
              "      <td>Newark to Chennai via Mumbai, outstanding se...</td>\n",
              "      <td>[(Newark, n), (Chennai, n), (via, None), (Mumb...</td>\n",
              "      <td>Newark Chennai via Mumbai , outstanding serv...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>At 0:39 in the morning you are informed a 6 h...</td>\n",
              "      <td>At 0:39 in the morning you are informed a 6 h...</td>\n",
              "      <td>[(0:39, None), (morning, n), (informed, v), (6...</td>\n",
              "      <td>0:39 morning inform 6 hour delay connect fli...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Had one of the worst experiences being a fre...</td>\n",
              "      <td>Had one of the worst experiences being a fre...</td>\n",
              "      <td>[(one, None), (worst, a), (experiences, n), (f...</td>\n",
              "      <td>one bad experience frequent flyer . flight B...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bad treatment, took Air India flt no. 129 on...</td>\n",
              "      <td>Bad treatment, took Air India flt no. 129 on...</td>\n",
              "      <td>[(Bad, n), (treatment, n), (,, None), (took, v...</td>\n",
              "      <td>Bad treatment , take Air India flt . 129 15t...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-03818898-df38-4885-bcb1-7026dfdddd43')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-ec18235d-7858-4285-a4bf-e89212310e14\">\n",
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              "              title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-ec18235d-7858-4285-a4bf-e89212310e14 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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-03818898-df38-4885-bcb1-7026dfdddd43 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-03818898-df38-4885-bcb1-7026dfdddd43');\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"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['data','Lemma']]"
      ],
      "metadata": {
        "id": "7-rs9myKMmd0",
        "outputId": "5f802a41-04b3-4bc5-fbd9-a01df0522296",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  data  \\\n",
              "0      Horrible service from New Delhi to Vancouver...   \n",
              "1      Newark to Chennai via Mumbai, outstanding se...   \n",
              "2     At 0:39 in the morning you are informed a 6 h...   \n",
              "3      Had one of the worst experiences being a fre...   \n",
              "4      Bad treatment, took Air India flt no. 129 on...   \n",
              "..                                                 ...   \n",
              "995                                               None   \n",
              "996                                               None   \n",
              "997                                               None   \n",
              "998                                               None   \n",
              "999                                               None   \n",
              "\n",
              "                                                 Lemma  \n",
              "0      Horrible service New Delhi Vancouver 20th Ju...  \n",
              "1      Newark Chennai via Mumbai , outstanding serv...  \n",
              "2      0:39 morning inform 6 hour delay connect fli...  \n",
              "3      one bad experience frequent flyer . flight B...  \n",
              "4      Bad treatment , take Air India flt . 129 15t...  \n",
              "..                                                 ...  \n",
              "995                                               None  \n",
              "996                                               None  \n",
              "997                                               None  \n",
              "998                                               None  \n",
              "999                                               None  \n",
              "\n",
              "[1000 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-d7e8e2ca-566e-4bf2-ac24-069d66503e7f\">\n",
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              "      <th>1</th>\n",
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              "      <td>Newark Chennai via Mumbai , outstanding serv...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>At 0:39 in the morning you are informed a 6 h...</td>\n",
              "      <td>0:39 morning inform 6 hour delay connect fli...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Had one of the worst experiences being a fre...</td>\n",
              "      <td>one bad experience frequent flyer . flight B...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>Bad treatment , take Air India flt . 129 15t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <th>995</th>\n",
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              "      <th>996</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>None</td>\n",
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              "      <th>998</th>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 2 columns</p>\n",
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              "        displayQuickchartButton(document);\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "            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",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Sentiment Analysis**"
      ],
      "metadata": {
        "id": "oi19IU5MMusF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to determine the sentiment or emotional tone of a piece of text, such as a sentence, paragraph, or document. The goal of sentiment analysis is to identify whether the text expresses a positive, negative, or neutral sentiment towards a particular subject, product, service, or topic."
      ],
      "metadata": {
        "id": "l_MRL46GNOjA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### VADER - Valence Aware Dictionary for Sentiment Reasoning"
      ],
      "metadata": {
        "id": "QhW0zqWvMxBv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### VADER (Valence Aware Dictionary for Sentiment Reasoning) is a lexicon and rule-based sentiment analysis tool that is specifically designed for social media text. It is a part of the Natural Language Toolkit (NLTK) library in Python. The VADER model is widely used for sentiment analysis tasks, especially on short and informal text like tweets, Facebook posts, and online reviews."
      ],
      "metadata": {
        "id": "IJxTVGAoNP_Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing Libraries\n",
        "!pip install vaderSentiment\n",
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "analyzer = SentimentIntensityAnalyzer()"
      ],
      "metadata": {
        "id": "w_FrLrUaM2Aw",
        "outputId": "88f9dd04-5c5f-461c-8071-23be06c6f214",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: vaderSentiment in /usr/local/lib/python3.10/dist-packages (3.3.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from vaderSentiment) (2.27.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (2023.7.22)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (3.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# For Calculating Sentiment Analysis\n",
        "def vadersentimentanalysis(review):\n",
        "    vs = analyzer.polarity_scores(review)\n",
        "    return vs['compound']\n",
        "\n",
        "df['Sentiment'] = df['Lemma'].apply(vadersentimentanalysis)"
      ],
      "metadata": {
        "id": "HsTTUjJ0NEVh"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to Analyzing\n",
        "def vader_analysis(compound):\n",
        "    if compound >= 0.5:\n",
        "        return 'Positive'\n",
        "    elif compound < 0 :\n",
        "        return 'Negative'\n",
        "    else:\n",
        "        return 'Neutral'\n",
        "\n",
        "df['Analysis'] = df['Sentiment'].apply(vader_analysis)\n",
        "df.head()"
      ],
      "metadata": {
        "id": "StkrtuuiNb8T",
        "outputId": "bf54dfbe-7531-4b13-807b-6664a7d6f109",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                data  \\\n",
              "0    Horrible service from New Delhi to Vancouver...   \n",
              "1    Newark to Chennai via Mumbai, outstanding se...   \n",
              "2   At 0:39 in the morning you are informed a 6 h...   \n",
              "3    Had one of the worst experiences being a fre...   \n",
              "4    Bad treatment, took Air India flt no. 129 on...   \n",
              "\n",
              "                                          clean_data  \\\n",
              "0    Horrible service from New Delhi to Vancouver...   \n",
              "1    Newark to Chennai via Mumbai, outstanding se...   \n",
              "2   At 0:39 in the morning you are informed a 6 h...   \n",
              "3    Had one of the worst experiences being a fre...   \n",
              "4    Bad treatment, took Air India flt no. 129 on...   \n",
              "\n",
              "                                          POS tagged  \\\n",
              "0  [(Horrible, a), (service, n), (New, n), (Delhi...   \n",
              "1  [(Newark, n), (Chennai, n), (via, None), (Mumb...   \n",
              "2  [(0:39, None), (morning, n), (informed, v), (6...   \n",
              "3  [(one, None), (worst, a), (experiences, n), (f...   \n",
              "4  [(Bad, n), (treatment, n), (,, None), (took, v...   \n",
              "\n",
              "                                               Lemma  Sentiment  Analysis  \n",
              "0    Horrible service New Delhi Vancouver 20th Ju...    -0.8016  Negative  \n",
              "1    Newark Chennai via Mumbai , outstanding serv...     0.8863  Positive  \n",
              "2    0:39 morning inform 6 hour delay connect fli...     0.1416   Neutral  \n",
              "3    one bad experience frequent flyer . flight B...    -0.5095  Negative  \n",
              "4    Bad treatment , take Air India flt . 129 15t...    -0.8934  Negative  "
            ],
            "text/html": [
              "\n",
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>data</th>\n",
              "      <th>clean_data</th>\n",
              "      <th>POS tagged</th>\n",
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              "      <th>0</th>\n",
              "      <td>Horrible service from New Delhi to Vancouver...</td>\n",
              "      <td>Horrible service from New Delhi to Vancouver...</td>\n",
              "      <td>[(Horrible, a), (service, n), (New, n), (Delhi...</td>\n",
              "      <td>Horrible service New Delhi Vancouver 20th Ju...</td>\n",
              "      <td>-0.8016</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Newark to Chennai via Mumbai, outstanding se...</td>\n",
              "      <td>Newark to Chennai via Mumbai, outstanding se...</td>\n",
              "      <td>[(Newark, n), (Chennai, n), (via, None), (Mumb...</td>\n",
              "      <td>Newark Chennai via Mumbai , outstanding serv...</td>\n",
              "      <td>0.8863</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>At 0:39 in the morning you are informed a 6 h...</td>\n",
              "      <td>At 0:39 in the morning you are informed a 6 h...</td>\n",
              "      <td>[(0:39, None), (morning, n), (informed, v), (6...</td>\n",
              "      <td>0:39 morning inform 6 hour delay connect fli...</td>\n",
              "      <td>0.1416</td>\n",
              "      <td>Neutral</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Had one of the worst experiences being a fre...</td>\n",
              "      <td>Had one of the worst experiences being a fre...</td>\n",
              "      <td>[(one, None), (worst, a), (experiences, n), (f...</td>\n",
              "      <td>one bad experience frequent flyer . flight B...</td>\n",
              "      <td>-0.5095</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bad treatment, took Air India flt no. 129 on...</td>\n",
              "      <td>Bad treatment, took Air India flt no. 129 on...</td>\n",
              "      <td>[(Bad, n), (treatment, n), (,, None), (took, v...</td>\n",
              "      <td>Bad treatment , take Air India flt . 129 15t...</td>\n",
              "      <td>-0.8934</td>\n",
              "      <td>Negative</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5db1997-f853-4f5a-83a2-f184b80b4d5c')\"\n",
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              "    <div id=\"df-46efcbdb-0255-45e8-9649-260fd8f86f67\">\n",
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              "              title=\"Suggest charts.\"\n",
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              "    </g>\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-46efcbdb-0255-45e8-9649-260fd8f86f67 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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-e5db1997-f853-4f5a-83a2-f184b80b4d5c 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-e5db1997-f853-4f5a-83a2-f184b80b4d5c');\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"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Final Analysis\n",
        "vader_counts = df['Analysis'].value_counts()\n",
        "vader_counts"
      ],
      "metadata": {
        "id": "IoXuB9c7NhFN",
        "outputId": "c628acc6-dcc7-45fd-ea60-34f0c253d39d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Negative    387\n",
              "Neutral     317\n",
              "Positive    296\n",
              "Name: Analysis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Visualization Using Matplotlib**"
      ],
      "metadata": {
        "id": "DSqTSG9cNjVk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "zfD76j3ZNp0A"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Matplotlib is a popular data visualization library in Python. It provides a wide variety of functions and tools for creating high-quality plots and charts. With Matplotlib, you can create line plots, scatter plots, bar plots, histograms, pie charts, 3D plots, and more."
      ],
      "metadata": {
        "id": "caDnBqc2Ny3a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.title('Reviews Data Analysis')\n",
        "plt.pie(vader_counts.values, labels = vader_counts.index, explode = (0, 0, 0.25), autopct='%1.1f%%', shadow=False)"
      ],
      "metadata": {
        "id": "OyUtIFEaN032",
        "outputId": "07ee236c-3a46-4ae1-b93c-b7e800c5e944",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        }
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<matplotlib.patches.Wedge at 0x7f241e4ee200>,\n",
              "  <matplotlib.patches.Wedge at 0x7f24204d5660>,\n",
              "  <matplotlib.patches.Wedge at 0x7f241e4efd90>],\n",
              " [Text(0.382349388436594, 1.031411142640103, 'Negative'),\n",
              "  Text(-1.0553537281010803, -0.310207202661626, 'Neutral'),\n",
              "  Text(0.8071717141593353, -1.0821154392490113, 'Positive')],\n",
              " [Text(0.20855421187450582, 0.5625878959855106, '38.7%'),\n",
              "  Text(-0.5756474880551347, -0.16920392872452325, '31.7%'),\n",
              "  Text(0.5082192274336556, -0.6813319432308589, '29.6%')])"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Coverting To CSV File**"
      ],
      "metadata": {
        "id": "orP_cVhgOScz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(\"Air_India_Data.csv\")"
      ],
      "metadata": {
        "id": "CwSrxHxVOWMI"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Viz Using WordCloud**"
      ],
      "metadata": {
        "id": "jM4AMt-UOhfW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### WordCloud is a popular Python library used for visualizing text data in the form of word clouds. A word cloud is a graphical representation of the most frequent words in a given text corpus, where the size of each word is proportional to its frequency in the text."
      ],
      "metadata": {
        "id": "GpHigcZ1PMsH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud, STOPWORDS\n",
        "stopwords = set(STOPWORDS)"
      ],
      "metadata": {
        "id": "5paP3Q4tOoC4"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import wordcloud\n",
        "def show_wordcloud(data):\n",
        "  wordcloud = WordCloud(\n",
        "      background_color = 'black',\n",
        "      stopwords=stopwords,\n",
        "      max_words = 200,\n",
        "      max_font_size = 40,\n",
        "      scale = 3,\n",
        "      random_state = 1)\n",
        "\n",
        "  wordcloud = wordcloud.generate(str(data))\n",
        "\n",
        "  fig = plt.figure(1, figsize = (12, 32))\n",
        "  plt.axis('off')\n",
        "\n",
        "  plt.imshow(wordcloud, interpolation = 'Bilinear')\n",
        "  plt.show()\n",
        "\n",
        "show_wordcloud(df.Lemma)"
      ],
      "metadata": {
        "id": "UnVvvOTcOuus",
        "outputId": "6c3ec895-f640-40fc-cb24-dab928d136e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        }
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x3200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **P2:- PREDICTIVE MODELING ON CUSTOMERS DATA**"
      ],
      "metadata": {
        "id": "2fBFtkRY_lYa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The predictive model is the quality of the data you use to train the machine learning algorithms. you must manipulate and prepare the provided customers' overall data so that you can build a high-quality predictive model. With the predictive model, it is important to interpret the results in order to understand how “predictive” the data really was and can feasibly use it to predict the target outcome."
      ],
      "metadata": {
        "id": "HuTwmxwcBde7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " ### Here We started From Exploratory Data Analysis\n",
        "\n",
        " #### Exploratory Data Analysis is a crucial step in the data analysis process, where the primary goal is to understand the data, gain insights, and identify patterns or relationships between variables."
      ],
      "metadata": {
        "id": "-Nc8jFRX8kLG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install warn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9nQnFoc9NOy",
        "outputId": "9a548d1c-76fb-4f28-e329-5c5451519e84"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: warn in /usr/local/lib/python3.10/dist-packages (0.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "2IdMOMx97nJD"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import chardet\n",
        "import re"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Again Web Scrapping From [Skytrax](https://skytraxratings.com/) Website for Building Predictive Model"
      ],
      "metadata": {
        "id": "_LOYFyQBubGQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def scrapper(search_result):\n",
        "    data = []\n",
        "\n",
        "    try:\n",
        "        # 1. Rating\n",
        "        rating_span = search_result.find('div', itemprop=\"reviewRating\").find('span', itemprop='ratingValue')\n",
        "        rating = rating_span.text.strip() if rating_span else None\n",
        "        data.append(rating)\n",
        "\n",
        "        # 2. Header\n",
        "        header_h2 = search_result.find('div', class_=\"body\").find('h2', class_='text_header')\n",
        "        header = header_h2.text.strip() if header_h2 else None\n",
        "        data.append(header)\n",
        "\n",
        "        # 3. Customer Name, Country, Date\n",
        "        customer_info_h3 = search_result.find('div', class_=\"body\").find('h3', class_='text_sub_header userStatusWrapper')\n",
        "        customer_name_span = customer_info_h3.find('span', itemprop='name')\n",
        "        customer_name = customer_name_span.text.strip() if customer_name_span else None\n",
        "        data.append(customer_name)\n",
        "\n",
        "        # Country (Australia) is in parentheses, so we need to extract it using regex\n",
        "        country_regex = r'\\((.*?)\\)'\n",
        "        country_match = re.search(country_regex, customer_info_h3.text)\n",
        "        country = country_match.group(1) if country_match else None\n",
        "        data.append(country)\n",
        "\n",
        "        date_published_time = customer_info_h3.find('time', itemprop='datePublished')\n",
        "        date = date_published_time.text.strip() if date_published_time else None\n",
        "        data.append(date)\n",
        "\n",
        "        # 4. Is Verified\n",
        "        review_body_div = search_result.find('div', class_='text_content')\n",
        "        is_verified_em = review_body_div.find('em')\n",
        "        is_verified = is_verified_em.text.strip() if is_verified_em else None\n",
        "        data.append(is_verified)\n",
        "\n",
        "        # 5. Review\n",
        "        review = review_body_div.text.strip() if review_body_div else None\n",
        "        data.append(review)\n",
        "\n",
        "        # 6. Type of Traveller\n",
        "        type_of_traveller_td = search_result.find('div', class_=\"body\").find('td', class_='review-rating-header type_of_traveller')\n",
        "        type_of_traveller = type_of_traveller_td.find_next('td', class_='review-value').text.strip() if type_of_traveller_td else None\n",
        "        data.append(type_of_traveller)\n",
        "\n",
        "        # 7. Seat Type\n",
        "        seat_type_td = search_result.find('div', class_=\"body\").find('td', class_='review-rating-header cabin_flown')\n",
        "        seat_type = seat_type_td.find_next('td', class_='review-value').text.strip() if seat_type_td else None\n",
        "        data.append(seat_type)\n",
        "\n",
        "        # 8. Route\n",
        "        route_td = search_result.find('div', class_=\"body\").find('td', class_='review-rating-header route')\n",
        "        route = route_td.find_next('td', class_='review-value').text.strip() if route_td else None\n",
        "        data.append(route)\n",
        "\n",
        "        # 9. Date Flown\n",
        "        date_flown_td = search_result.find('div', class_=\"body\").find('td', class_='review-rating-header date_flown')\n",
        "        date_flown = date_flown_td.find_next('td', class_='review-value').text.strip() if date_flown_td else None\n",
        "        data.append(date_flown)\n",
        "\n",
        "        # 10. Seat Comfort\n",
        "        seat_comfort_td = search_result.find('td', class_='review-rating-header seat_comfort')\n",
        "        seat_comfort_span = seat_comfort_td.find_next('td', class_=\"review-rating-stars stars\").find_all('span', class_='star fill') if seat_comfort_td else None\n",
        "        seat_comfort = max([int(span.text) for span in seat_comfort_span]) if seat_comfort_span else None\n",
        "        data.append(seat_comfort)\n",
        "\n",
        "        # 11. Cabin Staff Service\n",
        "        cabin_staff_service_td = search_result.find('td', class_=\"review-rating-header cabin_staff_service\")\n",
        "        cabin_staff_service_span = cabin_staff_service_td.find_next('td', class_=\"review-rating-stars stars\").find_all('span', class_='star fill') if cabin_staff_service_td else None\n",
        "        cabin_staff_service = max([int(span.text) for span in cabin_staff_service_span]) if cabin_staff_service_span else None\n",
        "        data.append(cabin_staff_service)\n",
        "\n",
        "        # 12. Food & Beverages\n",
        "        food_and_beverages_td = search_result.find('td', class_=\"review-rating-header food_and_beverages\")\n",
        "        food_and_beverages_span = food_and_beverages_td.find_next('td', class_=\"review-rating-stars stars\").find_all('span', class_='star fill') if food_and_beverages_td else None\n",
        "        food_and_beverages = max([int(span.text) for span in food_and_beverages_span]) if food_and_beverages_span else None\n",
        "        data.append(food_and_beverages)\n",
        "\n",
        "        # 13. Inflight Entertainment\n",
        "        inflight_entertainment_td = search_result.find('td', class_=\"review-rating-header inflight_entertainment\")\n",
        "        inflight_entertainment_span = inflight_entertainment_td.find_next('td', class_=\"review-rating-stars stars\").find_all('span', class_='star fill') if inflight_entertainment_td else None\n",
        "        inflight_entertainment = max([int(span.text) for span in inflight_entertainment_span]) if inflight_entertainment_span else None\n",
        "        data.append(inflight_entertainment)\n",
        "\n",
        "        # 14. Ground Service\n",
        "        ground_service_td = search_result.find('td', class_=\"review-rating-header ground_service\")\n",
        "        ground_service_span = ground_service_td.find_next('td', class_=\"review-rating-stars stars\").find_all('span', class_='star fill') if ground_service_td else None\n",
        "        ground_service = max([int(span.text) for span in ground_service_span]) if ground_service_span else None\n",
        "        data.append(ground_service)\n",
        "\n",
        "        # 15. Wifi & Connectivity\n",
        "        wifi_and_connectivity_td = search_result.find('td', class_=\"review-rating-header wifi_and_connectivity\")\n",
        "        wifi_and_connectivity_span = wifi_and_connectivity_td.find_next('td', class_=\"review-rating-stars stars\").find_all('span', class_='star fill') if wifi_and_connectivity_td else None\n",
        "        wifi_and_connectivity = max([int(span.text) for span in wifi_and_connectivity_span]) if wifi_and_connectivity_span else None\n",
        "        data.append(wifi_and_connectivity)\n",
        "\n",
        "        # 16. Value For Money\n",
        "        value_for_money_td = search_result.find('td', class_=\"review-rating-header value_for_money\")\n",
        "        value_for_money_span = value_for_money_td.find_next('td', class_=\"review-rating-stars stars\").find_all('span', class_='star fill') if value_for_money_td else None\n",
        "        value_for_money = max([int(span.text) for span in value_for_money_span]) if value_for_money_span else None\n",
        "        data.append(value_for_money)\n",
        "\n",
        "        # 17. Recommended\n",
        "        recommended_td = search_result.find('td', class_='review-rating-header recommended')\n",
        "        recommended = recommended_td.find_next('td', class_='review-value rating-no').text.strip() if recommended_td else None\n",
        "        data.append(recommended)\n",
        "\n",
        "        # print(f'Successfully Scrapped Review of {customer_name}')\n",
        "\n",
        "    except:\n",
        "        pass\n",
        "\n",
        "    return data"
      ],
      "metadata": {
        "id": "0szTueZ3t_NV"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_data_list = []\n",
        "NULL = \"None\"\n",
        "\n",
        "# Scrapping data from website\n",
        "for i in range(1, 13):\n",
        "    URL = f\"https://www.airlinequality.com/airline-reviews/air-india/page/{i}/?sortby=post_date%3ADesc&pagesize=100\"\n",
        "    response = requests.get(URL)\n",
        "    webpage = response.text\n",
        "    soup = BeautifulSoup(webpage, \"html.parser\")\n",
        "    search_results = soup.find_all(\"article\", itemprop=\"review\")\n",
        "\n",
        "    for result in search_results:\n",
        "        final_data_list.append(scrapper(result))"
      ],
      "metadata": {
        "id": "qVK0zexwuHl6"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Created Data Frme"
      ],
      "metadata": {
        "id": "B8Iy0KgnvTmY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert nested list to a DataFrame\n",
        "columns = ['rating', 'header', 'customer_name', 'country', 'date', 'is_verified', 'review', 'type_of_traveller',\n",
        "           'seat_type', 'route', 'date_flown', 'seat_comfort', 'cabin_staff_service', 'food_and_beverages',\n",
        "           'inflight_entertainment', 'ground_service', 'wifi_and_connectivity', 'value_for_money', 'recommended']\n",
        "df = pd.DataFrame(final_data_list, columns=columns)\n",
        "\n",
        "# Write DataFrame to CSV file\n",
        "df.to_csv('output_data.csv', index=False)"
      ],
      "metadata": {
        "id": "TtEPHmXjuTcG"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Following the data scraping process, we obtained the file \"output_data.csv.\"\n",
        "\n",
        "- Before commencing the development of predictive models, we performed data cleaning using MS Excel, resulting in the creation of a new file named 'cleaned_output_data.csv.'"
      ],
      "metadata": {
        "id": "oILREgKsvY10"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = 'cleaned_output_data.csv'"
      ],
      "metadata": {
        "id": "STVX0Lyc9Lbh"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('cleaned_output_data.csv', 'rb') as rawdata:\n",
        "  result = chardet.detect(rawdata.read(100000))\n",
        "\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzBgRm_W9_v9",
        "outputId": "0b79cabe-f165-48b4-eb95-6c885abda6b4"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'encoding': 'utf-8', 'confidence': 0.99, 'language': ''}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(data, encoding= 'utf-8')"
      ],
      "metadata": {
        "id": "p_FZ-voD_I5T"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "BR8PY7v8_fuh",
        "outputId": "fa0e2800-021a-49ad-a9e0-20207e1fd56b"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   rating                             header      customer_name  \\\n",
              "0       1         \"was given a regular meal\"       Simon Taylor   \n",
              "1       1        \"both times it was torture\"      Rohit Kaushal   \n",
              "2       1  \"flight was disgusting and dirty\"  Ann Benny Alackal   \n",
              "3       1       \"gotten progressively worse\"            R Singh   \n",
              "4       2                \"most disappointed\"            S Sethi   \n",
              "\n",
              "          country            date    is_verified  \\\n",
              "0  United Kingdom  22nd July 2023  Trip Verified   \n",
              "1       Australia  20th July 2023   Not Verified   \n",
              "2   United States  18th July 2023   Not Verified   \n",
              "3   United States  18th July 2023  Trip Verified   \n",
              "4           India  17th July 2023   Not Verified   \n",
              "\n",
              "                                              review type_of_traveller  \\\n",
              "0  ✅ Trip Verified |  Requested a gluten free mea...      Solo Leisure   \n",
              "1  Not Verified | Worst planes I have ever been i...          Business   \n",
              "2  Not Verified | I have been flying for the past...    Couple Leisure   \n",
              "3  ✅ Trip Verified | Sometime around 2017 I got t...      Solo Leisure   \n",
              "4  Not Verified |  My flight was supposed to depa...      Solo Leisure   \n",
              "\n",
              "       seat_type                        route date_flown  seat_comfort  \\\n",
              "0  Economy Class               Gatwick to Goa     May-23           3.0   \n",
              "1  Economy Class           Melbourne to Delhi     May-23           1.0   \n",
              "2  Economy Class                          NaN     Jul-23           1.0   \n",
              "3  Economy Class  Kolkata to Newark via Delhi     Jun-23           1.0   \n",
              "4  Economy Class           Delhi to Singapore     Jul-23           2.0   \n",
              "\n",
              "   cabin_staff_service  food_and_beverages  inflight_entertainment  \\\n",
              "0                  2.0                 1.0                     2.0   \n",
              "1                  1.0                 3.0                     NaN   \n",
              "2                  5.0                 1.0                     NaN   \n",
              "3                  1.0                 1.0                     1.0   \n",
              "4                  2.0                 1.0                     1.0   \n",
              "\n",
              "   ground_service  wifi_and_connectivity  value_for_money recommended  \n",
              "0             3.0                    NaN                1          no  \n",
              "1             1.0                    NaN                1          no  \n",
              "2             1.0                    NaN                1          no  \n",
              "3             1.0                    1.0                1          no  \n",
              "4             2.0                    NaN                2          no  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-60406347-87f3-4673-87e1-5014953120ab\">\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>rating</th>\n",
              "      <th>header</th>\n",
              "      <th>customer_name</th>\n",
              "      <th>country</th>\n",
              "      <th>date</th>\n",
              "      <th>is_verified</th>\n",
              "      <th>review</th>\n",
              "      <th>type_of_traveller</th>\n",
              "      <th>seat_type</th>\n",
              "      <th>route</th>\n",
              "      <th>date_flown</th>\n",
              "      <th>seat_comfort</th>\n",
              "      <th>cabin_staff_service</th>\n",
              "      <th>food_and_beverages</th>\n",
              "      <th>inflight_entertainment</th>\n",
              "      <th>ground_service</th>\n",
              "      <th>wifi_and_connectivity</th>\n",
              "      <th>value_for_money</th>\n",
              "      <th>recommended</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>\"was given a regular meal\"</td>\n",
              "      <td>Simon Taylor</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>22nd July 2023</td>\n",
              "      <td>Trip Verified</td>\n",
              "      <td>✅ Trip Verified |  Requested a gluten free mea...</td>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>Gatwick to Goa</td>\n",
              "      <td>May-23</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>\"both times it was torture\"</td>\n",
              "      <td>Rohit Kaushal</td>\n",
              "      <td>Australia</td>\n",
              "      <td>20th July 2023</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Not Verified | Worst planes I have ever been i...</td>\n",
              "      <td>Business</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>Melbourne to Delhi</td>\n",
              "      <td>May-23</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>\"flight was disgusting and dirty\"</td>\n",
              "      <td>Ann Benny Alackal</td>\n",
              "      <td>United States</td>\n",
              "      <td>18th July 2023</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Not Verified | I have been flying for the past...</td>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jul-23</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>\"gotten progressively worse\"</td>\n",
              "      <td>R Singh</td>\n",
              "      <td>United States</td>\n",
              "      <td>18th July 2023</td>\n",
              "      <td>Trip Verified</td>\n",
              "      <td>✅ Trip Verified | Sometime around 2017 I got t...</td>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>Kolkata to Newark via Delhi</td>\n",
              "      <td>Jun-23</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>\"most disappointed\"</td>\n",
              "      <td>S Sethi</td>\n",
              "      <td>India</td>\n",
              "      <td>17th July 2023</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Not Verified |  My flight was supposed to depa...</td>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>Delhi to Singapore</td>\n",
              "      <td>Jul-23</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60406347-87f3-4673-87e1-5014953120ab')\"\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5a190d52-b3e3-4bf8-9d5c-982a431c40a5')\"\n",
              "              title=\"Suggest charts.\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-5a190d52-b3e3-4bf8-9d5c-982a431c40a5 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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-60406347-87f3-4673-87e1-5014953120ab 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-60406347-87f3-4673-87e1-5014953120ab');\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"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ukh4BlVc_hPJ",
        "outputId": "c4041faf-4473-4041-9cd5-ee220bb649e1"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rating                      int64\n",
              "header                     object\n",
              "customer_name              object\n",
              "country                    object\n",
              "date                       object\n",
              "is_verified                object\n",
              "review                     object\n",
              "type_of_traveller          object\n",
              "seat_type                  object\n",
              "route                      object\n",
              "date_flown                 object\n",
              "seat_comfort              float64\n",
              "cabin_staff_service       float64\n",
              "food_and_beverages        float64\n",
              "inflight_entertainment    float64\n",
              "ground_service            float64\n",
              "wifi_and_connectivity     float64\n",
              "value_for_money             int64\n",
              "recommended                object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnBEh-VS_mpe",
        "outputId": "7d03cc10-ec58-4c93-f593-305685e8e4a8"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(876, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lsGYSmw_tiO",
        "outputId": "a3c249dd-95a4-4544-fcd0-24cb8a6ccdd4"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rating                      0\n",
              "header                      0\n",
              "customer_name               0\n",
              "country                     0\n",
              "date                        0\n",
              "is_verified               224\n",
              "review                      0\n",
              "type_of_traveller           0\n",
              "seat_type                   0\n",
              "route                       2\n",
              "date_flown                  1\n",
              "seat_comfort               54\n",
              "cabin_staff_service        57\n",
              "food_and_beverages        119\n",
              "inflight_entertainment    282\n",
              "ground_service             32\n",
              "wifi_and_connectivity     574\n",
              "value_for_money             0\n",
              "recommended                 3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.value_for_money.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P647z29t_1rs",
        "outputId": "643a6ad5-6165-4092-8c73-a5557e176278"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    405\n",
              "5    147\n",
              "2    120\n",
              "4    108\n",
              "3     96\n",
              "Name: value_for_money, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Mutual Information (MI) score graphs are used to visualize the relevance of each feature in the dataset with respect to the target variable. Mutual information is a measure of the dependency between two variables, and in the context of feature selection"
      ],
      "metadata": {
        "id": "eNyA8lpS8XrA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('value_for_money', axis=1)\n",
        "y = df.value_for_money"
      ],
      "metadata": {
        "id": "wDtIoRRZABDf"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for colname in X.select_dtypes(\"object\"):\n",
        "    X[colname], _ = X[colname].factorize()\n",
        "\n",
        "X.dropna(inplace=True)\n",
        "\n",
        "X.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4w7CfycpHNu6",
        "outputId": "f15b23c9-48fe-4d95-d5c3-e510ce9ccb1a"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rating                      int64\n",
              "header                      int64\n",
              "customer_name               int64\n",
              "country                     int64\n",
              "date                        int64\n",
              "is_verified                 int64\n",
              "review                      int64\n",
              "type_of_traveller           int64\n",
              "seat_type                   int64\n",
              "route                       int64\n",
              "date_flown                  int64\n",
              "seat_comfort              float64\n",
              "cabin_staff_service       float64\n",
              "food_and_beverages        float64\n",
              "inflight_entertainment    float64\n",
              "ground_service            float64\n",
              "wifi_and_connectivity     float64\n",
              "recommended                 int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import mutual_info_classif\n",
        "\n",
        "# Check the shapes of X and y\n",
        "print(\"Shape of X:\", X.shape)\n",
        "print(\"Shape of y:\", y.shape)\n",
        "\n",
        "# Ensure X and y have the same number of samples\n",
        "if X.shape[0] != y.shape[0]:\n",
        "    # If the number of samples is not consistent, take the common samples between X and y\n",
        "    common_samples = X.index.intersection(y.index)\n",
        "    X = X.loc[common_samples]\n",
        "    y = y.loc[common_samples]\n",
        "    print(\"After correction - Shape of X:\", X.shape)\n",
        "    print(\"After correction - Shape of y:\", y.shape)\n",
        "\n",
        "# Calculate mutual information scores\n",
        "mi_score = mutual_info_classif(X, y)\n",
        "\n",
        "# Create a pandas Series with feature names as index\n",
        "mi_score = pd.Series(mi_score, name=\"MI Score\", index=X.columns)\n",
        "\n",
        "# Sort the features based on mutual information score in descending order\n",
        "mi_score = mi_score.sort_values(ascending=False)"
      ],
      "metadata": {
        "id": "0s5b1FmWHPYf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f72ada05-99f9-4498-d8f9-7687aa47f3e7"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X: (292, 18)\n",
            "Shape of y: (876,)\n",
            "After correction - Shape of X: (292, 18)\n",
            "After correction - Shape of y: (292,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mi_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWmcD6ftIDp-",
        "outputId": "e022b567-f636-4703-b266-a33d8241c883"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rating                    0.396720\n",
              "food_and_beverages        0.257183\n",
              "seat_comfort              0.245888\n",
              "ground_service            0.230483\n",
              "inflight_entertainment    0.186036\n",
              "wifi_and_connectivity     0.168668\n",
              "cabin_staff_service       0.159890\n",
              "date_flown                0.097315\n",
              "review                    0.081002\n",
              "date                      0.080203\n",
              "header                    0.075882\n",
              "customer_name             0.067251\n",
              "country                   0.059549\n",
              "route                     0.053797\n",
              "is_verified               0.027018\n",
              "seat_type                 0.016821\n",
              "type_of_traveller         0.000000\n",
              "recommended               0.000000\n",
              "Name: MI Score, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_mi_score(score, color='blue'):\n",
        "    score = score.sort_values(ascending=True)\n",
        "    width = np.arange(len(score))\n",
        "    ticks = list(score.index)\n",
        "    plt.barh(width, score, color=color)  # Set the color here\n",
        "    plt.yticks(width, ticks)\n",
        "    plt.title(\"Mutual Information Score Graph\")\n",
        "\n",
        "# Example usage with a different color (e.g., 'orange')\n",
        "plt.figure(dpi=100, figsize=(8, 5))\n",
        "plot_mi_score(mi_score, color='orange')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "Ct4agyQZJktW",
        "outputId": "69278d4f-2b2a-4166-f76c-400522eae5cd"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test and train\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "g2wFcpEVJ-VF"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Test and train split is a crucial step in the process of building and evaluating machine learning models. It involves dividing the available dataset into two separate sets: the training set and the test set."
      ],
      "metadata": {
        "id": "-CbCjkkRP5e4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "hca5umzGcXkz"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and temporary sets\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Now, split the temporary set into validation and final testing sets\n",
        "X_validation, X_test, y_validation, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)"
      ],
      "metadata": {
        "id": "yvFp85eqcxeZ"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The reason we train a machine learning model is to learn patterns and relationships within the data that can be used to make predictions or classifications on new, unseen data."
      ],
      "metadata": {
        "id": "g9NhMac3P9Jz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### The fit method of the MinMaxScaler is used to compute the minimum and maximum values for scaling, but we are returning the original data X instead of the scaled data."
      ],
      "metadata": {
        "id": "3beSNfEOdZJu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "def scale(X):\n",
        "    scaler = MinMaxScaler()\n",
        "    scaled_X = scaler.fit_transform(X)\n",
        "    return scaled_X\n"
      ],
      "metadata": {
        "id": "3HCI-OqgdSQu"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Min-Max Scaling (also known as normalization) is a preprocessing technique used to scale numerical features to a fixed range, typically between 0 and 1. It transforms the original feature values into a normalized range to ensure that all features have the same scale."
      ],
      "metadata": {
        "id": "M1l7L6oQQhE4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Training Random Forest Classifier With Top-6 Features**\n",
        "### The decision to use a Random Forest classifier with top 6 features depends on the specific problem, the dataset characteristics, and the desired trade-offs between performance, interpretability, and resource efficiency."
      ],
      "metadata": {
        "id": "8G6nykZ16ziy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# X:- Features (With all selected features as columns)\n",
        "# y:- Target Variable\n",
        "\n",
        "# Select the top 5 features based on mutual information score\n",
        "mi_score = mutual_info_classif(X, y)\n",
        "mi_score = pd.Series(mi_score, name=\"MI Score\", index=X.columns)\n",
        "selected_features = mi_score.sort_values(ascending=False).index[:5]\n",
        "\n",
        "#Convert the target variable to start from 0 (if needed)\n",
        "y = y - y.min()\n",
        "\n",
        "#Scale the features (optional but recommended for many machine learning algorithms)\n",
        "scaler = MinMaxScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "#Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train the Random Forest classifier using the selected features\n",
        "X_train_selected = X_train[:, [X.columns.get_loc(col) for col in selected_features]]\n",
        "X_test_selected = X_test[:, [X.columns.get_loc(col) for col in selected_features]]\n",
        "\n",
        "rf_classifier = RandomForestClassifier()\n",
        "rf_classifier.fit(X_train_selected, y_train)\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "accuracy = rf_classifier.score(X_test_selected, y_test)\n",
        "print(\"Accuracy SCore:\", accuracy*100)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcU--FObdd8a",
        "outputId": "1c1b4854-a817-4ab5-bb80-8889870ab6b2"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy SCore: 74.57627118644068\n",
            "Accuracy: 0.7457627118644068\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Training Random Forest Classifier With all Features**\n",
        "#### Using a Random Forest classifier with all features can be advantageous when dealing with complex datasets, as it allows the model to leverage the full information present in the data."
      ],
      "metadata": {
        "id": "98RJMW9s6cuz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import pandas as pd\n",
        "\n",
        "# X: Features\n",
        "# y: Target Variable\n",
        "\n",
        "# Remove rows with missing values from X and y\n",
        "X.dropna(inplace=True)\n",
        "y = y[X.index]  # Align the target variable y with the updated X\n",
        "\n",
        "# Scale the features (optional but recommended for many machine learning algorithms)\n",
        "scaler = MinMaxScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "#Train the Random Forest classifier using all features\n",
        "rf_classifier = RandomForestClassifier()\n",
        "rf_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "accuracy = rf_classifier.score(X_test, y_test)\n",
        "print(\"Accuracy:\", accuracy*100)\n",
        "print(\"Accuracy Score:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnT2lCGtizpQ",
        "outputId": "4715fae8-81b5-446b-d192-848bad642a66"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 71.1864406779661\n",
            "Accuracy Score: 0.711864406779661\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest is an ensemble learning method that combines multiple decision trees. By using all features, the model can capture complex relationships and interactions between different features, leading to a potentially more accurate and robust model."
      ],
      "metadata": {
        "id": "VlKH2KN6_Q8V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " ### **Training XGB Classifier With Top-6 Features**\n",
        "\n",
        " ## Training an XGBoost (Extreme Gradient Boosting)\n",
        "\n",
        " ### Training an XGBoost classifier with the top 6 features can be a pragmatic approach when computational resources are limited, interpretability is a priority, or when there is a concern about overfitting."
      ],
      "metadata": {
        "id": "Wd1GfHOe51-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# X:- Features (with all the selected features as columns)\n",
        "# y:- Target Variable\n",
        "\n",
        "# Select the top 6 features based on mutual information score\n",
        "selected_features = ['rating', 'food_and_beverages', 'ground_service', 'seat_comfort', 'inflight_entertainment']\n",
        "\n",
        "# Ensure the selected features are present in the DataFrame\n",
        "if all(feature in X.columns for feature in selected_features):\n",
        "    X = X[selected_features]\n",
        "else:\n",
        "    raise ValueError(\"One or more selected features are not present in the DataFrame.\")\n",
        "\n",
        "# Convert the target variable to start from 0 (if needed)\n",
        "y = y - y.min()\n",
        "\n",
        "# Scale the features (optional but recommended for many machine learning algorithms)\n",
        "scaler = MinMaxScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train the XGBoost classifier using the top 6 features\n",
        "xgb_classifier = XGBClassifier()\n",
        "xgb_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "accuracy = xgb_classifier.score(X_test, y_test)\n",
        "print(\"Accuracy Score:\", accuracy)\n",
        "print(\"Accuracy:\", accuracy*100)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrTF1XndlWZ2",
        "outputId": "c4dff309-3a24-446f-b76c-8ec8c6b52cb0"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score: 0.711864406779661\n",
            "Accuracy: 71.1864406779661\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Training XGB Classifier With All Features**\n",
        "\n",
        "### Training the XGBoost classifier with all features can be a reasonable approach when the dataset is not too large, and computational resources are sufficient."
      ],
      "metadata": {
        "id": "QyJzuTlh5hlO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# X:- Features\n",
        "# y:- Target variable\n",
        "\n",
        "# Select all features based on mutual information score\n",
        "mi_score = mutual_info_classif(X, y)\n",
        "mi_score = pd.Series(mi_score, name=\"MI Score\", index=X.columns)\n",
        "selected_features = mi_score.sort_values(ascending=False).index\n",
        "\n",
        "# Convert the target variable to start from 0 (if needed)\n",
        "y = y - y.min()\n",
        "\n",
        "# Scale the features (optional but recommended for many machine learning algorithms)\n",
        "scaler = MinMaxScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train the XGBoost classifier using all features\n",
        "xgb_classifier = XGBClassifier()\n",
        "xgb_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "accuracy = xgb_classifier.score(X_test, y_test)\n",
        "print(\"Accuracy Score:\", accuracy)\n",
        "print(\"Accuracy:\", accuracy*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RMLrrA_OmceJ",
        "outputId": "0e3fa7df-67a6-47b5-9652-92617e894666"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score: 0.711864406779661\n",
            "Accuracy: 71.1864406779661\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### XGBoost is a powerful machine learning algorithm known for its ability to capture complex relationships in the data. By using all available features, the model can potentially exploit intricate patterns and interactions among the features, leading to more accurate predictions."
      ],
      "metadata": {
        "id": "xlNDl0-U_Unp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Validating Model With Test Dataset**\n",
        "\n",
        "#### Validating the model on the test dataset is an essential step in the machine learning workflow to assess how well the model performs on unseen data."
      ],
      "metadata": {
        "id": "wS9xcj3Z4rNq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# X:- Features\n",
        "# y:- Target variable\n",
        "\n",
        "# Select all features based on mutual information score\n",
        "mi_score = mutual_info_classif(X, y)\n",
        "mi_score = pd.Series(mi_score, name=\"MI Score\", index=X.columns)\n",
        "selected_features = mi_score.sort_values(ascending=False).index\n",
        "\n",
        "# Convert the target variable to start from 0 (if needed)\n",
        "y = y - y.min()\n",
        "\n",
        "# Scale the features (optional but recommended for many machine learning algorithms)\n",
        "scaler = MinMaxScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train the Random Forest classifier using all features\n",
        "rf_classifier = RandomForestClassifier(random_state=1)\n",
        "rf_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Use the trained classifier to make predictions on the test set\n",
        "y_pred = rf_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "accuracy = rf_classifier.score(X_test, y_test)\n",
        "print(\"Accuracy:\", accuracy*100)\n",
        "print(\"Accuracy Score:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6xs2GfXSpiNL",
        "outputId": "d0ce853e-30aa-4449-b40b-77e4d79c14e6"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 71.1864406779661\n",
            "Accuracy Score: 0.711864406779661\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **WHAT WE ANALYZE HERE:-**\n",
        "\n",
        "**1. Data Preprocessing:-** We started by performing data preprocessing tasks such as handling missing values, encoding categorical variables, and converting the target variable to start from 0.\n",
        "\n",
        "**2. Feature Selection:-** We used mutual information scores to select the top 6 features that have the highest predictive power for the target variable. These features are 'rating', 'seat_comfort', 'ground_service', 'food_and_beverages', 'inflight_entertainment', and 'wifi_and_connectivity'.\n",
        "\n",
        "**3. Model Training:-** We trained two classifiers using the selected features:\n",
        "\n",
        "   - Random Forest Classifier: We used the Random Forest classifier with the top 6 features and achieved a good accuracy on the test set.\n",
        "   - XGBoost Classifier: We also trained an XGBoost classifier with the top 6 features and achieved good performance.\n",
        "\n",
        "**4. Model Evaluation:-** We evaluated both models using the test set and found that both classifiers have good accuracy and performance.\n",
        "\n",
        "## **OUR FINAL CONCLUSION IS:-**\n",
        "\n",
        "Since the Random Forest classifier showed slightly higher accuracy and Accuracy score compared to the XGBoost classifier, we chose the Random Forest classifier with the top 6 features as our final model.\n",
        "\n",
        "The final Random Forest classifier with the selected features can be used to predict customer satisfaction or any other target variable in the dataset. However, but the model's performance might vary depending on the quality and representativeness of the data used for training.\n"
      ],
      "metadata": {
        "id": "kgIdprak85U3"
      }
    }
  ]
}