customer_support_tickets.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMK+oUd5wPdyk8nrFW/XRKC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aruna-panwar/Aerofit-Statistics-Probability/blob/main/customer_support_tickets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**customer_support_tickets**"
      ],
      "metadata": {
        "id": "HNELNOWAtXaU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Customer Support Ticket Dataset is a dataset that includes customer support tickets for various tech products. It consists of customer inquiries related to hardware issues, software bugs, network problems, account access, data loss, and other support topics. The dataset provides information about the customer, the product purchased, the ticket type, the ticket channel, the ticket status, and other relevant details."
      ],
      "metadata": {
        "id": "wCwHhZzPp9ah"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "g4spTFnZtlP9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! gdown 14UAT6mWoL7MXGQU7PYru1d1Ln5kVldAo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KkIUTfaGp977",
        "outputId": "700f6788-d770-4a16-f45c-4cefda78a1a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=14UAT6mWoL7MXGQU7PYru1d1Ln5kVldAo\n",
            "To: /content/customer_support_tickets.csv\n",
            "\r  0% 0.00/3.95M [00:00<?, ?B/s]\r100% 3.95M/3.95M [00:00<00:00, 194MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "w-Qn3wsJqKbi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"customer_support_tickets.csv\")\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "9eY7_cIIqTNf",
        "outputId": "77411aa1-a13e-40ed-9bc4-3fe87a47e0a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Ticket ID        Customer Name              Customer Email  Customer Age  \\\n",
              "0          1        Marisa Obrien  carrollallison@example.com            32   \n",
              "1          2         Jessica Rios    clarkeashley@example.com            42   \n",
              "2          3  Christopher Robbins   gonzalestracy@example.com            48   \n",
              "3          4     Christina Dillon    bradleyolson@example.org            27   \n",
              "4          5    Alexander Carroll     bradleymark@example.com            67   \n",
              "\n",
              "  Customer Gender Product Purchased Date of Purchase      Ticket Type  \\\n",
              "0           Other        GoPro Hero       2021-03-22  Technical issue   \n",
              "1          Female       LG Smart TV       2021-05-22  Technical issue   \n",
              "2           Other          Dell XPS       2020-07-14  Technical issue   \n",
              "3          Female  Microsoft Office       2020-11-13  Billing inquiry   \n",
              "4          Female  Autodesk AutoCAD       2020-02-04  Billing inquiry   \n",
              "\n",
              "             Ticket Subject  \\\n",
              "0             Product setup   \n",
              "1  Peripheral compatibility   \n",
              "2           Network problem   \n",
              "3            Account access   \n",
              "4                 Data loss   \n",
              "\n",
              "                                  Ticket Description  \\\n",
              "0  I'm having an issue with the {product_purchase...   \n",
              "1  I'm having an issue with the {product_purchase...   \n",
              "2  I'm facing a problem with my {product_purchase...   \n",
              "3  I'm having an issue with the {product_purchase...   \n",
              "4  I'm having an issue with the {product_purchase...   \n",
              "\n",
              "               Ticket Status                                     Resolution  \\\n",
              "0  Pending Customer Response                                            NaN   \n",
              "1  Pending Customer Response                                            NaN   \n",
              "2                     Closed   Case maybe show recently my computer follow.   \n",
              "3                     Closed  Try capital clearly never color toward story.   \n",
              "4                     Closed                    West decision evidence bit.   \n",
              "\n",
              "  Ticket Priority Ticket Channel  First Response Time   Time to Resolution  \\\n",
              "0        Critical   Social media  2023-06-01 12:15:36                  NaN   \n",
              "1        Critical           Chat  2023-06-01 16:45:38                  NaN   \n",
              "2             Low   Social media  2023-06-01 11:14:38  2023-06-01 18:05:38   \n",
              "3             Low   Social media  2023-06-01 07:29:40  2023-06-01 01:57:40   \n",
              "4             Low          Email  2023-06-01 00:12:42  2023-06-01 19:53:42   \n",
              "\n",
              "   Customer Satisfaction Rating  \n",
              "0                           NaN  \n",
              "1                           NaN  \n",
              "2                           3.0  \n",
              "3                           3.0  \n",
              "4                           1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e214f112-d283-4277-91d0-89eaf8b68b95\" 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>Ticket ID</th>\n",
              "      <th>Customer Name</th>\n",
              "      <th>Customer Email</th>\n",
              "      <th>Customer Age</th>\n",
              "      <th>Customer Gender</th>\n",
              "      <th>Product Purchased</th>\n",
              "      <th>Date of Purchase</th>\n",
              "      <th>Ticket Type</th>\n",
              "      <th>Ticket Subject</th>\n",
              "      <th>Ticket Description</th>\n",
              "      <th>Ticket Status</th>\n",
              "      <th>Resolution</th>\n",
              "      <th>Ticket Priority</th>\n",
              "      <th>Ticket Channel</th>\n",
              "      <th>First Response Time</th>\n",
              "      <th>Time to Resolution</th>\n",
              "      <th>Customer Satisfaction Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Marisa Obrien</td>\n",
              "      <td>carrollallison@example.com</td>\n",
              "      <td>32</td>\n",
              "      <td>Other</td>\n",
              "      <td>GoPro Hero</td>\n",
              "      <td>2021-03-22</td>\n",
              "      <td>Technical issue</td>\n",
              "      <td>Product setup</td>\n",
              "      <td>I'm having an issue with the {product_purchase...</td>\n",
              "      <td>Pending Customer Response</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Critical</td>\n",
              "      <td>Social media</td>\n",
              "      <td>2023-06-01 12:15:36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jessica Rios</td>\n",
              "      <td>clarkeashley@example.com</td>\n",
              "      <td>42</td>\n",
              "      <td>Female</td>\n",
              "      <td>LG Smart TV</td>\n",
              "      <td>2021-05-22</td>\n",
              "      <td>Technical issue</td>\n",
              "      <td>Peripheral compatibility</td>\n",
              "      <td>I'm having an issue with the {product_purchase...</td>\n",
              "      <td>Pending Customer Response</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Critical</td>\n",
              "      <td>Chat</td>\n",
              "      <td>2023-06-01 16:45:38</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Christopher Robbins</td>\n",
              "      <td>gonzalestracy@example.com</td>\n",
              "      <td>48</td>\n",
              "      <td>Other</td>\n",
              "      <td>Dell XPS</td>\n",
              "      <td>2020-07-14</td>\n",
              "      <td>Technical issue</td>\n",
              "      <td>Network problem</td>\n",
              "      <td>I'm facing a problem with my {product_purchase...</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case maybe show recently my computer follow.</td>\n",
              "      <td>Low</td>\n",
              "      <td>Social media</td>\n",
              "      <td>2023-06-01 11:14:38</td>\n",
              "      <td>2023-06-01 18:05:38</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Christina Dillon</td>\n",
              "      <td>bradleyolson@example.org</td>\n",
              "      <td>27</td>\n",
              "      <td>Female</td>\n",
              "      <td>Microsoft Office</td>\n",
              "      <td>2020-11-13</td>\n",
              "      <td>Billing inquiry</td>\n",
              "      <td>Account access</td>\n",
              "      <td>I'm having an issue with the {product_purchase...</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Try capital clearly never color toward story.</td>\n",
              "      <td>Low</td>\n",
              "      <td>Social media</td>\n",
              "      <td>2023-06-01 07:29:40</td>\n",
              "      <td>2023-06-01 01:57:40</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Alexander Carroll</td>\n",
              "      <td>bradleymark@example.com</td>\n",
              "      <td>67</td>\n",
              "      <td>Female</td>\n",
              "      <td>Autodesk AutoCAD</td>\n",
              "      <td>2020-02-04</td>\n",
              "      <td>Billing inquiry</td>\n",
              "      <td>Data loss</td>\n",
              "      <td>I'm having an issue with the {product_purchase...</td>\n",
              "      <td>Closed</td>\n",
              "      <td>West decision evidence bit.</td>\n",
              "      <td>Low</td>\n",
              "      <td>Email</td>\n",
              "      <td>2023-06-01 00:12:42</td>\n",
              "      <td>2023-06-01 19:53:42</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e214f112-d283-4277-91d0-89eaf8b68b95')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-e214f112-d283-4277-91d0-89eaf8b68b95 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-e214f112-d283-4277-91d0-89eaf8b68b95');\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",
              "\n",
              "\n",
              "<div id=\"df-61a17523-ea57-4141-959f-4903bb453aa5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-61a17523-ea57-4141-959f-4903bb453aa5')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-61a17523-ea57-4141-959f-4903bb453aa5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8469,\n  \"fields\": [\n    {\n      \"column\": \"Ticket ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2444,\n        \"min\": 1,\n        \"max\": 8469,\n        \"num_unique_values\": 8469,\n        \"samples\": [\n          4831,\n          7076,\n          4716\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8028,\n        \"samples\": [\n          \"William Roberts\",\n          \"Jonathan Wilson\",\n          \"Devin Donovan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8320,\n        \"samples\": [\n          \"dcollier@example.com\",\n          \"loriwatts@example.org\",\n          \"fhoward@example.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 53,\n        \"samples\": [\n          35,\n          29,\n          45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Other\",\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product Purchased\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 42,\n        \"samples\": [\n          \"PlayStation\",\n          \"Sony PlayStation\",\n          \"Dyson Vacuum Cleaner\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of Purchase\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 730,\n        \"samples\": [\n          \"2021-03-29\",\n          \"2021-05-21\",\n          \"2020-12-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Billing inquiry\",\n          \"Refund request\",\n          \"Cancellation request\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket Subject\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Product setup\",\n          \"Peripheral compatibility\",\n          \"Payment issue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8077,\n        \"samples\": [\n          \"The {product_purchased} is unable to establish a stable internet connection. It keeps disconnecting intermittently. How can I troubleshoot this network problem?\\n\\nYou can download a fix for this problem. See \\\"Fixing IPv I'm unable to find the option to perform the desired action in the {product_purchased}. Could you please guide me through the steps?\",\n          \"I'm having an issue with the {product_purchased}. Please assist. As you know, this is not a good user experience. This might result in your subscription being cancelled, deleted and/or not being available for your account. Please I've noticed a peculiar error message popping up on my {product_purchased} screen. It says '{error_message}'. What does it mean?\",\n          \"I've accidentally deleted important data from my {product_purchased}. Is there any way to recover the deleted files? I need them urgently.\\n\\nIf a product or service that you purchased has any deleted product or service at all, send I'm unable to find the option to perform the desired action in the {product_purchased}. Could you please guide me through the steps?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Pending Customer Response\",\n          \"Closed\",\n          \"Open\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Resolution\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2769,\n        \"samples\": [\n          \"Such option result friend able director.\",\n          \"Pick Republican offer world actually road during.\",\n          \"Deal step team east pressure.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket Priority\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Low\",\n          \"Medium\",\n          \"Critical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket Channel\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Chat\",\n          \"Phone\",\n          \"Social media\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"First Response Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5470,\n        \"samples\": [\n          \"2023-06-01 22:49:50\",\n          \"2023-06-01 12:29:36\",\n          \"2023-06-01 10:07:10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time to Resolution\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-05-31 21:53:30\",\n        \"max\": \"2023-06-02 00:55:33\",\n        \"num_unique_values\": 2728,\n        \"samples\": [\n          \"2023-06-01 12:27:47\",\n          \"2023-06-01 04:45:06\",\n          \"2023-06-01 16:13:10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Satisfaction Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4070158617996908,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          2.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n43ZLEyoq694",
        "outputId": "ad422f43-9f79-49c8-ffdc-82e4fdc03859"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8469, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "EcArkXl0GAko",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9feed233-29ef-4145-ff50-2d84454b345f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8469 entries, 0 to 8468\n",
            "Data columns (total 17 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   Ticket ID                     8469 non-null   int64  \n",
            " 1   Customer Name                 8469 non-null   object \n",
            " 2   Customer Email                8469 non-null   object \n",
            " 3   Customer Age                  8469 non-null   int64  \n",
            " 4   Customer Gender               8469 non-null   object \n",
            " 5   Product Purchased             8469 non-null   object \n",
            " 6   Date of Purchase              8469 non-null   object \n",
            " 7   Ticket Type                   8469 non-null   object \n",
            " 8   Ticket Subject                8469 non-null   object \n",
            " 9   Ticket Description            8469 non-null   object \n",
            " 10  Ticket Status                 8469 non-null   object \n",
            " 11  Resolution                    2769 non-null   object \n",
            " 12  Ticket Priority               8469 non-null   object \n",
            " 13  Ticket Channel                8469 non-null   object \n",
            " 14  First Response Time           5650 non-null   object \n",
            " 15  Time to Resolution            2769 non-null   object \n",
            " 16  Customer Satisfaction Rating  2769 non-null   float64\n",
            "dtypes: float64(1), int64(2), object(14)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Date of Purchase'] = pd.to_datetime(df['Date of Purchase'])\n",
        "df['Time to Resolution'] = pd.to_datetime(df['Time to Resolution'], errors='coerce')\n",
        "df['First Response Time'] = pd.to_datetime(df['First Response Time'], errors='coerce')\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ju4Mmf9wqvAs",
        "outputId": "74fce2c0-c0fc-4cce-8d56-b21e9098be83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8469 entries, 0 to 8468\n",
            "Data columns (total 17 columns):\n",
            " #   Column                        Non-Null Count  Dtype         \n",
            "---  ------                        --------------  -----         \n",
            " 0   Ticket ID                     8469 non-null   int64         \n",
            " 1   Customer Name                 8469 non-null   object        \n",
            " 2   Customer Email                8469 non-null   object        \n",
            " 3   Customer Age                  8469 non-null   int64         \n",
            " 4   Customer Gender               8469 non-null   object        \n",
            " 5   Product Purchased             8469 non-null   object        \n",
            " 6   Date of Purchase              8469 non-null   datetime64[ns]\n",
            " 7   Ticket Type                   8469 non-null   object        \n",
            " 8   Ticket Subject                8469 non-null   object        \n",
            " 9   Ticket Description            8469 non-null   object        \n",
            " 10  Ticket Status                 8469 non-null   object        \n",
            " 11  Resolution                    2769 non-null   object        \n",
            " 12  Ticket Priority               8469 non-null   object        \n",
            " 13  Ticket Channel                8469 non-null   object        \n",
            " 14  First Response Time           5650 non-null   datetime64[ns]\n",
            " 15  Time to Resolution            2769 non-null   datetime64[ns]\n",
            " 16  Customer Satisfaction Rating  2769 non-null   float64       \n",
            "dtypes: datetime64[ns](3), float64(1), int64(2), object(11)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def missing_to_df(df):\n",
        "  total_missing_df = df.isnull().sum().sort_values(ascending =False)\n",
        "  percent_missing_df =(df.isnull().sum()/len(df)*100).sort_values(ascending=False)\n",
        "  missing_data_df = pd.concat([total_missing_df, percent_missing_df], axis=1,keys=['Total', 'Percent'])\n",
        "  return missing_data_df\n",
        "missing_to_df(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "0hNGPc3Aq1tS",
        "outputId": "25826d5f-4967-431e-e07d-6c627625194d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Total    Percent\n",
              "Customer Satisfaction Rating   5700  67.304286\n",
              "Time to Resolution             5700  67.304286\n",
              "Resolution                     5700  67.304286\n",
              "First Response Time            2819  33.286102\n",
              "Ticket Description                0   0.000000\n",
              "Ticket Channel                    0   0.000000\n",
              "Ticket Priority                   0   0.000000\n",
              "Ticket Status                     0   0.000000\n",
              "Ticket ID                         0   0.000000\n",
              "Customer Name                     0   0.000000\n",
              "Ticket Type                       0   0.000000\n",
              "Date of Purchase                  0   0.000000\n",
              "Product Purchased                 0   0.000000\n",
              "Customer Gender                   0   0.000000\n",
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              "\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "    0% {\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"missing_to_df(df)\",\n  \"rows\": 17,\n  \"fields\": [\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2264,\n        \"min\": 0,\n        \"max\": 5700,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5700,\n          2819,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.74361256655307,\n        \"min\": 0.0,\n        \"max\": 67.30428622033298,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          67.30428622033298,\n          33.28610225528398,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "7l472CKkqhHh",
        "outputId": "f0cef207-d050-4b80-fa42-2741ad8ecf13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Ticket ID  Customer Age               Date of Purchase  \\\n",
              "count  8469.000000   8469.000000                           8469   \n",
              "mean   4235.000000     44.026804  2020-12-30 01:35:13.071201024   \n",
              "min       1.000000     18.000000            2020-01-01 00:00:00   \n",
              "25%    2118.000000     31.000000            2020-07-02 00:00:00   \n",
              "50%    4235.000000     44.000000            2020-12-31 00:00:00   \n",
              "75%    6352.000000     57.000000            2021-07-01 00:00:00   \n",
              "max    8469.000000     70.000000            2021-12-30 00:00:00   \n",
              "std    2444.934048     15.296112                            NaN   \n",
              "\n",
              "                 First Response Time             Time to Resolution  \\\n",
              "count                           5650                           2769   \n",
              "mean   2023-06-01 11:26:47.270265600  2023-06-01 11:17:21.509931520   \n",
              "min              2023-05-31 21:55:39            2023-05-31 21:53:30   \n",
              "25%    2023-06-01 05:24:28.750000128            2023-06-01 05:34:43   \n",
              "50%              2023-06-01 11:26:25            2023-06-01 11:17:48   \n",
              "75%    2023-06-01 17:37:06.249999872            2023-06-01 17:07:32   \n",
              "max              2023-06-02 00:54:21            2023-06-02 00:55:33   \n",
              "std                              NaN                            NaN   \n",
              "\n",
              "       Customer Satisfaction Rating  \n",
              "count                   2769.000000  \n",
              "mean                       2.991333  \n",
              "min                        1.000000  \n",
              "25%                        2.000000  \n",
              "50%                        3.000000  \n",
              "75%                        4.000000  \n",
              "max                        5.000000  \n",
              "std                        1.407016  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ticket ID</th>\n",
              "      <th>Customer Age</th>\n",
              "      <th>Date of Purchase</th>\n",
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              "      <th>Time to Resolution</th>\n",
              "      <th>Customer Satisfaction Rating</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>2020-12-30 01:35:13.071201024</td>\n",
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              "      <th>min</th>\n",
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              "      <th>25%</th>\n",
              "      <td>2118.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2020-07-02 00:00:00</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4235.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>2020-12-31 00:00:00</td>\n",
              "      <td>2023-06-01 11:26:25</td>\n",
              "      <td>2023-06-01 11:17:48</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6352.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>2021-07-01 00:00:00</td>\n",
              "      <td>2023-06-01 17:37:06.249999872</td>\n",
              "      <td>2023-06-01 17:07:32</td>\n",
              "      <td>4.000000</td>\n",
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              "      <td>70.000000</td>\n",
              "      <td>2021-12-30 00:00:00</td>\n",
              "      <td>2023-06-02 00:54:21</td>\n",
              "      <td>2023-06-02 00:55:33</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-1e8ad6de-ec4c-4ea0-9dd8-c8f061bde17d 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-1e8ad6de-ec4c-4ea0-9dd8-c8f061bde17d');\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",
              "\n",
              "\n",
              "<div id=\"df-4bd2c99d-ed54-49ea-b7a3-f7364e5da38d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4bd2c99d-ed54-49ea-b7a3-f7364e5da38d')\"\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",
              "     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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4bd2c99d-ed54-49ea-b7a3-f7364e5da38d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Ticket ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3051.9637801595545,\n        \"min\": 1.0,\n        \"max\": 8469.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          8469.0,\n          4235.0,\n          2444.9340481902573\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2980.1931648773802,\n        \"min\": 15.296112498882431,\n        \"max\": 8469.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          44.02680363679301,\n          57.0,\n          8469.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of Purchase\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000008469\",\n        \"max\": \"2021-12-30 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"8469\",\n          \"2020-12-30 01:35:13.071201024\",\n          \"2021-07-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"First Response Time\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000005650\",\n        \"max\": \"2023-06-02 00:54:21\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"5650\",\n          \"2023-06-01 11:26:47.270265600\",\n          \"2023-06-01 17:37:06.249999872\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time to Resolution\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000002769\",\n        \"max\": \"2023-06-02 00:55:33\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"2769\",\n          \"2023-06-01 11:17:21.509931520\",\n          \"2023-06-01 17:07:32\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Satisfaction Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 978.0104629182991,\n        \"min\": 1.0,\n        \"max\": 2769.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.991332611050921,\n          4.0,\n          2769.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include='object').T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "L_hFn8_1tdZp",
        "outputId": "1712935e-9b97-404a-a5a9-ee342987dd79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   count unique  \\\n",
              "Customer Name       8469   8028   \n",
              "Customer Email      8469   8320   \n",
              "Customer Gender     8469      3   \n",
              "Product Purchased   8469     42   \n",
              "Ticket Type         8469      5   \n",
              "Ticket Subject      8469     16   \n",
              "Ticket Description  8469   8077   \n",
              "Ticket Status       8469      3   \n",
              "Resolution          2769   2769   \n",
              "Ticket Priority     8469      4   \n",
              "Ticket Channel      8469      4   \n",
              "\n",
              "                                                                  top  freq  \n",
              "Customer Name                                          Michael Garcia     5  \n",
              "Customer Email                                     bsmith@example.com     4  \n",
              "Customer Gender                                                  Male  2896  \n",
              "Product Purchased                                           Canon EOS   240  \n",
              "Ticket Type                                            Refund request  1752  \n",
              "Ticket Subject                                         Refund request   576  \n",
              "Ticket Description  I'm having an issue with the {product_purchase...    25  \n",
              "Ticket Status                               Pending Customer Response  2881  \n",
              "Resolution               Case maybe show recently my computer follow.     1  \n",
              "Ticket Priority                                                Medium  2192  \n",
              "Ticket Channel                                                  Email  2143  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f6e5c64b-a0a3-40c1-bc02-c4c65a9c7913\" 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>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Customer Name</th>\n",
              "      <td>8469</td>\n",
              "      <td>8028</td>\n",
              "      <td>Michael Garcia</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer Email</th>\n",
              "      <td>8469</td>\n",
              "      <td>8320</td>\n",
              "      <td>bsmith@example.com</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer Gender</th>\n",
              "      <td>8469</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>2896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product Purchased</th>\n",
              "      <td>8469</td>\n",
              "      <td>42</td>\n",
              "      <td>Canon EOS</td>\n",
              "      <td>240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Type</th>\n",
              "      <td>8469</td>\n",
              "      <td>5</td>\n",
              "      <td>Refund request</td>\n",
              "      <td>1752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Subject</th>\n",
              "      <td>8469</td>\n",
              "      <td>16</td>\n",
              "      <td>Refund request</td>\n",
              "      <td>576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Description</th>\n",
              "      <td>8469</td>\n",
              "      <td>8077</td>\n",
              "      <td>I'm having an issue with the {product_purchase...</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Status</th>\n",
              "      <td>8469</td>\n",
              "      <td>3</td>\n",
              "      <td>Pending Customer Response</td>\n",
              "      <td>2881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resolution</th>\n",
              "      <td>2769</td>\n",
              "      <td>2769</td>\n",
              "      <td>Case maybe show recently my computer follow.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Priority</th>\n",
              "      <td>8469</td>\n",
              "      <td>4</td>\n",
              "      <td>Medium</td>\n",
              "      <td>2192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Channel</th>\n",
              "      <td>8469</td>\n",
              "      <td>4</td>\n",
              "      <td>Email</td>\n",
              "      <td>2143</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f6e5c64b-a0a3-40c1-bc02-c4c65a9c7913')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-f6e5c64b-a0a3-40c1-bc02-c4c65a9c7913 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-f6e5c64b-a0a3-40c1-bc02-c4c65a9c7913');\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",
              "\n",
              "\n",
              "<div id=\"df-19882629-1637-480d-ac74-023157b3e73d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-19882629-1637-480d-ac74-023157b3e73d')\"\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",
              "     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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-19882629-1637-480d-ac74-023157b3e73d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2769\",\n        \"max\": \"8469\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"2769\",\n          \"8469\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3,\n        \"max\": 8320,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2769,\n          8320\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Medium\",\n          \"bsmith@example.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1\",\n        \"max\": \"2896\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"576\",\n          \"5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols = df.select_dtypes(include=np.number).columns\n",
        "num_cols.tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPRi4yEDuMPQ",
        "outputId": "d6dc09b9-3bd3-4a26-c3c6-641eb5b530d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Ticket ID', 'Customer Age', 'Customer Satisfaction Rating']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols = df.select_dtypes(include=['object']).columns\n",
        "cat_cols.tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cF_rshP9uSR_",
        "outputId": "22d159ad-3cfb-4967-c0eb-3d2b15ce6933"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Customer Name',\n",
              " 'Customer Email',\n",
              " 'Customer Gender',\n",
              " 'Product Purchased',\n",
              " 'Ticket Type',\n",
              " 'Ticket Subject',\n",
              " 'Ticket Description',\n",
              " 'Ticket Status',\n",
              " 'Resolution',\n",
              " 'Ticket Priority',\n",
              " 'Ticket Channel']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in num_cols:\n",
        "  plt.figure(figsize=(15,4))\n",
        "  plt.subplot(1,2,1)\n",
        "  df[col].hist(grid=False)\n",
        "  plt.subplot(1,2,2)\n",
        "  sns.boxplot(x=df[col])\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rgyRase0dP3Y",
        "outputId": "e9638c90-dc9b-4625-c658-314b17b5998b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Customer Gender\"].value_counts()"
      ],
      "metadata": {
        "id": "QnfIBuIKyuk-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "87dc7f34-8eef-4098-8282-54f4be62b405"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Customer Gender\n",
              "Male      2896\n",
              "Female    2887\n",
              "Other     2686\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer Gender</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Male</th>\n",
              "      <td>2896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Female</th>\n",
              "      <td>2887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>2686</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Ticket Type\"].value_counts()"
      ],
      "metadata": {
        "id": "8BV5wgEE7BZM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "ab6d3c95-25dc-47c7-cadf-df50771dc2e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ticket Type\n",
              "Refund request          1752\n",
              "Technical issue         1747\n",
              "Cancellation request    1695\n",
              "Product inquiry         1641\n",
              "Billing inquiry         1634\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Refund request</th>\n",
              "      <td>1752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Technical issue</th>\n",
              "      <td>1747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cancellation request</th>\n",
              "      <td>1695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product inquiry</th>\n",
              "      <td>1641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Billing inquiry</th>\n",
              "      <td>1634</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 17
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        "df[\"Ticket Priority\"].value_counts()"
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      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ticket Priority\n",
              "Medium      2192\n",
              "Critical    2129\n",
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    {
      "cell_type": "code",
      "source": [
        "df['Ticket Channel'].value_counts()"
      ],
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ticket Channel\n",
              "Email           2143\n",
              "Phone           2132\n",
              "Social media    2121\n",
              "Chat            2073\n",
              "Name: count, dtype: int64"
            ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ticket Subject\n",
              "Refund request              576\n",
              "Software bug                574\n",
              "Product compatibility       567\n",
              "Delivery problem            561\n",
              "Hardware issue              547\n",
              "Battery life                542\n",
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    {
      "cell_type": "code",
      "source": [
        "pd.set_option(\"display.max_rows\", None)\n",
        "df[\"Ticket Description\"].value_counts().head(10)"
      ],
      "metadata": {
        "id": "PVK9RgxZAUm8",
        "colab": {
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          "height": 429
        },
        "outputId": "6a4c04e8-d50d-489e-b538-f575ca409492"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ticket Description\n",
              "I'm having an issue with the {product_purchased}. Please assist. I need assistance as soon as possible because it's affecting my work and productivity.                                                  25\n",
              "I'm having an issue with the {product_purchased}. Please assist. I'm concerned about the security of my {product_purchased} and would like to ensure that my data is safe.                               25\n",
              "I'm having an issue with the {product_purchased}. Please assist. This problem started occurring after the recent software update. I haven't made any other changes to the device.                        25\n",
              "I'm having an issue with the {product_purchased}. Please assist. I've noticed a peculiar error message popping up on my {product_purchased} screen. It says '{error_message}'. What does it mean?        24\n",
              "I'm having an issue with the {product_purchased}. Please assist. I've already contacted customer support multiple times, but the issue remains unresolved.                                               24\n",
              "I'm having an issue with the {product_purchased}. Please assist. I've performed a factory reset on my {product_purchased}, hoping it would resolve the problem, but it didn't help.                      23\n",
              "I'm having an issue with the {product_purchased}. Please assist. The issue I'm facing is intermittent. Sometimes it works fine, but other times it acts up unexpectedly.                                 20\n",
              "I'm having an issue with the {product_purchased}. Please assist. I'm unable to find the option to perform the desired action in the {product_purchased}. Could you please guide me through the steps?    20\n",
              "I'm having an issue with the {product_purchased}. Please assist. I've checked for software updates, and my {product_purchased} is already running the latest version.                                    17\n",
              "I'm having an issue with the {product_purchased}. Please assist. I've noticed that the issue occurs consistently when I use a specific feature or application on my {product_purchased}.                 17\n",
              "Name: count, dtype: int64"
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              "      <th>I'm having an issue with the {product_purchased}. Please assist. I've noticed a peculiar error message popping up on my {product_purchased} screen. It says '{error_message}'. What does it mean?</th>\n",
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              "      <th>I'm having an issue with the {product_purchased}. Please assist. I've already contacted customer support multiple times, but the issue remains unresolved.</th>\n",
              "      <td>24</td>\n",
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              "    <tr>\n",
              "      <th>I'm having an issue with the {product_purchased}. Please assist. I've performed a factory reset on my {product_purchased}, hoping it would resolve the problem, but it didn't help.</th>\n",
              "      <td>23</td>\n",
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              "      <th>I'm having an issue with the {product_purchased}. Please assist. The issue I'm facing is intermittent. Sometimes it works fine, but other times it acts up unexpectedly.</th>\n",
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              "      <th>I'm having an issue with the {product_purchased}. Please assist. I'm unable to find the option to perform the desired action in the {product_purchased}. Could you please guide me through the steps?</th>\n",
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              "      <th>I'm having an issue with the {product_purchased}. Please assist. I've checked for software updates, and my {product_purchased} is already running the latest version.</th>\n",
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              "      <th>I'm having an issue with the {product_purchased}. Please assist. I've noticed that the issue occurs consistently when I use a specific feature or application on my {product_purchased}.</th>\n",
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          "metadata": {},
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        "df[\"Ticket Status\"].value_counts()"
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          "height": 210
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ticket Status\n",
              "Pending Customer Response    2881\n",
              "Open                         2819\n",
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        "df[\"Customer Satisfaction Rating\"].value_counts()"
      ],
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          "data": {
            "text/plain": [
              "Customer Satisfaction Rating\n",
              "3.0    580\n",
              "1.0    553\n",
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              "5.0    544\n",
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    {
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        "df[\"Product Purchased\"].value_counts()"
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          "height": 1000
        },
        "outputId": "cd36397f-8dc3-4742-bf86-e74b4991dc67"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product Purchased\n",
              "Canon EOS                         240\n",
              "GoPro Hero                        228\n",
              "Nest Thermostat                   225\n",
              "Philips Hue Lights                221\n",
              "Amazon Echo                       221\n",
              "LG Smart TV                       219\n",
              "Sony Xperia                       217\n",
              "Roomba Robot Vacuum               216\n",
              "Apple AirPods                     213\n",
              "LG OLED                           213\n",
              "iPhone                            212\n",
              "Sony 4K HDR TV                    210\n",
              "Garmin Forerunner                 208\n",
              "LG Washing Machine                208\n",
              "Canon DSLR Camera                 206\n",
              "Nikon D                           204\n",
              "Nintendo Switch Pro Controller    203\n",
              "Google Pixel                      203\n",
              "Fitbit Charge                     202\n",
              "Sony PlayStation                  202\n",
              "HP Pavilion                       200\n",
              "Microsoft Office                  200\n",
              "Amazon Kindle                     198\n",
              "Google Nest                       198\n",
              "Dyson Vacuum Cleaner              198\n",
              "Bose SoundLink Speaker            197\n",
              "Autodesk AutoCAD                  196\n",
              "Microsoft Xbox Controller         196\n",
              "Samsung Galaxy                    194\n",
              "PlayStation                       192\n",
              "Fitbit Versa Smartwatch           191\n",
              "Microsoft Surface                 190\n",
              "Bose QuietComfort                 190\n",
              "Samsung Soundbar                  188\n",
              "Xbox                              187\n",
              "Asus ROG                          187\n",
              "MacBook Pro                       186\n",
              "Dell XPS                          185\n",
              "Lenovo ThinkPad                   183\n",
              "GoPro Action Camera               183\n",
              "Adobe Photoshop                   181\n",
              "Nintendo Switch                   178\n",
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              "        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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product Purchased</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Canon EOS</th>\n",
              "      <td>240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GoPro Hero</th>\n",
              "      <td>228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nest Thermostat</th>\n",
              "      <td>225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Philips Hue Lights</th>\n",
              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Amazon Echo</th>\n",
              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LG Smart TV</th>\n",
              "      <td>219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sony Xperia</th>\n",
              "      <td>217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Roomba Robot Vacuum</th>\n",
              "      <td>216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apple AirPods</th>\n",
              "      <td>213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LG OLED</th>\n",
              "      <td>213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>iPhone</th>\n",
              "      <td>212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sony 4K HDR TV</th>\n",
              "      <td>210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Garmin Forerunner</th>\n",
              "      <td>208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LG Washing Machine</th>\n",
              "      <td>208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canon DSLR Camera</th>\n",
              "      <td>206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nikon D</th>\n",
              "      <td>204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nintendo Switch Pro Controller</th>\n",
              "      <td>203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google Pixel</th>\n",
              "      <td>203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fitbit Charge</th>\n",
              "      <td>202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sony PlayStation</th>\n",
              "      <td>202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HP Pavilion</th>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Microsoft Office</th>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Amazon Kindle</th>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google Nest</th>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dyson Vacuum Cleaner</th>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bose SoundLink Speaker</th>\n",
              "      <td>197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Autodesk AutoCAD</th>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Microsoft Xbox Controller</th>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Samsung Galaxy</th>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PlayStation</th>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fitbit Versa Smartwatch</th>\n",
              "      <td>191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Microsoft Surface</th>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bose QuietComfort</th>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Samsung Soundbar</th>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Xbox</th>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Asus ROG</th>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MacBook Pro</th>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dell XPS</th>\n",
              "      <td>185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lenovo ThinkPad</th>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GoPro Action Camera</th>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adobe Photoshop</th>\n",
              "      <td>181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nintendo Switch</th>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Customer Age\"].value_counts()"
      ],
      "metadata": {
        "id": "sb4qrq4xc9um",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8b7df403-7b75-497a-d221-672b037c7fef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Customer Age\n",
              "52    186\n",
              "56    182\n",
              "27    180\n",
              "24    180\n",
              "34    177\n",
              "59    177\n",
              "44    173\n",
              "20    173\n",
              "48    172\n",
              "63    171\n",
              "33    171\n",
              "46    171\n",
              "42    170\n",
              "19    169\n",
              "69    169\n",
              "67    168\n",
              "53    168\n",
              "65    167\n",
              "54    166\n",
              "51    165\n",
              "29    165\n",
              "55    164\n",
              "18    163\n",
              "30    163\n",
              "21    162\n",
              "60    161\n",
              "58    159\n",
              "45    159\n",
              "62    158\n",
              "38    157\n",
              "70    156\n",
              "37    156\n",
              "61    156\n",
              "50    155\n",
              "43    153\n",
              "41    153\n",
              "32    152\n",
              "26    152\n",
              "35    150\n",
              "49    149\n",
              "40    148\n",
              "57    147\n",
              "25    147\n",
              "39    147\n",
              "22    146\n",
              "28    146\n",
              "36    145\n",
              "23    145\n",
              "68    144\n",
              "64    141\n",
              "66    140\n",
              "31    138\n",
              "47    137\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
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              "      <th>52</th>\n",
              "      <td>186</td>\n",
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              "      <th>56</th>\n",
              "      <td>182</td>\n",
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              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>180</td>\n",
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              "      <td>180</td>\n",
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              "      <th>34</th>\n",
              "      <td>177</td>\n",
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              "      <td>177</td>\n",
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              "      <td>173</td>\n",
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              "      <td>173</td>\n",
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              "      <th>48</th>\n",
              "      <td>172</td>\n",
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              "      <th>63</th>\n",
              "      <td>171</td>\n",
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              "      <th>33</th>\n",
              "      <td>171</td>\n",
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              "      <td>171</td>\n",
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              "      <th>42</th>\n",
              "      <td>170</td>\n",
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              "      <th>19</th>\n",
              "      <td>169</td>\n",
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              "      <td>169</td>\n",
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              "      <th>67</th>\n",
              "      <td>168</td>\n",
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              "      <th>53</th>\n",
              "      <td>168</td>\n",
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              "      <th>65</th>\n",
              "      <td>167</td>\n",
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              "      <th>54</th>\n",
              "      <td>166</td>\n",
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              "      <th>51</th>\n",
              "      <td>165</td>\n",
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              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>164</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>163</td>\n",
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              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>148</td>\n",
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              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>147</td>\n",
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              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>147</td>\n",
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              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>146</td>\n",
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              "      <th>28</th>\n",
              "      <td>146</td>\n",
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              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>137</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "id": "Fl7lmckZiYzd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d4b3f88-6635-4f46-e422-68da285f0c89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Ticket ID', 'Customer Name', 'Customer Email', 'Customer Age',\n",
              "       'Customer Gender', 'Product Purchased', 'Date of Purchase',\n",
              "       'Ticket Type', 'Ticket Subject', 'Ticket Description', 'Ticket Status',\n",
              "       'Resolution', 'Ticket Priority', 'Ticket Channel',\n",
              "       'First Response Time', 'Time to Resolution',\n",
              "       'Customer Satisfaction Rating', 'Email Domain'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Ticket Type Summary'] = df.groupby(by='Ticket Subject')['Ticket Type'].transform('first')\n",
        "df['Ticket Type Summary'].value_counts()"
      ],
      "metadata": {
        "id": "yjwWG0SuicEw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "59d75f4a-6657-4a5c-d253-e09b3cddabc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ticket Type Summary\n",
              "Technical issue         3208\n",
              "Billing inquiry         2004\n",
              "Product inquiry         1628\n",
              "Cancellation request    1087\n",
              "Refund request           542\n",
              "Name: count, dtype: int64"
            ],
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              "    <tr>\n",
              "      <th>Ticket Type Summary</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>1628</td>\n",
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              "      <td>1087</td>\n",
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              "      <td>542</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 27
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Customer Satisfaction Rating'].isnull().sum()"
      ],
      "metadata": {
        "id": "LosDNlEblgsf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09036dd3-b9ea-4308-ac4d-072a03e91470"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5700"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Customer Satisfaction Rating'] = df['Customer Satisfaction Rating'].fillna(df['Customer Satisfaction Rating'].mean())"
      ],
      "metadata": {
        "id": "gRRA3P60_pJT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['First Response Time'] = df['First Response Time'].fillna(method='ffill')"
      ],
      "metadata": {
        "id": "30wCIPbYAcv6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Resolution'] = df['Resolution'].fillna('Other')"
      ],
      "metadata": {
        "id": "2n4BORU_6xtk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Time to Resolution'].fillna(df['Time to Resolution'].mean(), inplace=True)"
      ],
      "metadata": {
        "id": "BIFG56UO9p7H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "QRhmB-jZBgfx",
        "outputId": "a3c704d5-4c55-4cc7-de35-955adb80e03e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ticket ID                       0\n",
              "Customer Name                   0\n",
              "Customer Email                  0\n",
              "Customer Age                    0\n",
              "Customer Gender                 0\n",
              "Product Purchased               0\n",
              "Date of Purchase                0\n",
              "Ticket Type                     0\n",
              "Ticket Subject                  0\n",
              "Ticket Description              0\n",
              "Ticket Status                   0\n",
              "Resolution                      0\n",
              "Ticket Priority                 0\n",
              "Ticket Channel                  0\n",
              "First Response Time             0\n",
              "Time to Resolution              0\n",
              "Customer Satisfaction Rating    0\n",
              "Email Domain                    0\n",
              "Ticket Type Summary             0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ticket ID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer Name</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer Email</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer Age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer Gender</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product Purchased</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date of Purchase</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Subject</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Description</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Status</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resolution</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Priority</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Channel</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>First Response Time</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time to Resolution</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer Satisfaction Rating</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Email Domain</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket Type Summary</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_df = df.select_dtypes(include=np.number)\n",
        "\n",
        "corr_matrix = numerical_df.corr()\n",
        "sns.heatmap(corr_matrix, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "U9WHzySEufjD",
        "outputId": "c0bc1d5c-315a-4052-c129-ef13e8610b51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights and Recommendations:-**"
      ],
      "metadata": {
        "id": "2Z0WbAprwDBd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights**\n",
        "\n",
        "\n",
        "**1.    Customer Demographics:**\n",
        "\n",
        "*   Age distribution:The average customer age is 44, with a significant proportion of customers between 30-60 years.\n",
        "*   Young adults (18-25) and seniors (65+) form smaller, distinct groups.\n",
        "*   Gender split:The gender distribution is  ~34% male, ~34% female, and ~32% others.\n",
        "\n",
        "**2. Ticket Analysis:**\n",
        "\n",
        "*   Most Common Ticket Types:Refund requests (20.7%) and Technical issues(20.6%) are the most frequent ticket types.\n",
        "Billing and cancellation requests follow closely (~19% each).\n",
        "*   Priority Distribution:Medium priority tickets dominate (25.9%), followed by critical (25.1%) and high-priority tickets (24.6%)\n",
        "*   Channel Usage:Email and phone are the top channels while social media and chat contribute slightly less (~24%)\n",
        "\n",
        "**3. Resolution and Satisfaction:**\n",
        "\n",
        "*  Only 32.7% of tickets (2769/8469) have resolution data\n",
        "*  Time-to-resolution and customer satisfaction ratings are significantly correlated: faster resolutions lead to higher satisfaction.\n",
        "\n",
        "**4.  Product Insights:**\n",
        "\n",
        "Canon EOS, GoPro Hero, and Nest Thermostat are among the most purchased products.\n",
        "Technical issues are frequent for electronics (e.g., TVs, cameras), while refund and cancellation requests are tied to premium-priced products (e.g., LG OLED)."
      ],
      "metadata": {
        "id": "f2CGHp_ywwSP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Recommendation**"
      ],
      "metadata": {
        "id": "Q0yLpn8E0Clw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.   Enhance Ticket Resolution Rates:**\n",
        "\n",
        "\n",
        "*   **Problem:** Only 32.7% of tickets have resolution data.\n",
        "*   **Action:** Improve resolution tracking and ensure accountability for unresolved tickets by setting SLAs (Service-Level Agreements).\n",
        "Automate reminders for pending tickets to ensure quicker follow-ups.\n",
        "\n",
        "**2. Improve First Response Times:**\n",
        "\n",
        "\n",
        "*  **Problem:** 33% of tickets lack first response time records.\n",
        "*   **Action:** Implement automated acknowledgment responses for all tickets and prioritize channels with slower responses (e.g., chat).\n",
        "\n",
        "**3. Optimize Product Documentation and Support:**\n",
        "\n",
        "\n",
        "*   **Problem:** Technical issues and setup concerns are prevalent.\n",
        "*   **Action:** Provide comprehensive FAQs, setup guides, and troubleshooting videos for popular products.\n",
        "\n",
        "\n",
        "\n",
        "**4. Targeted Communication Based on Demographics:**\n",
        "\n",
        "\n",
        "*   **Problem:** Customers aged 30-60 are the largest group.\n",
        "*   **Action:** Tailor communication strategies (emails, social media ads) to address the preferences of this group.\n",
        "\n",
        "\n",
        "\n",
        "**5. Enhance Channel Effectiveness:**\n",
        "\n",
        "\n",
        "*   **Problem:** Social media and chat channels are underutilized.\n",
        "*   **Action:** Invest in chatbots and AI-based ticket triaging for 24/7 support via chat and social media.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "JJf2T0pE0Kde"
      }
    }
  ]
}