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            "Collecting sweetviz\n",
            "  Downloading sweetviz-2.1.3-py3-none-any.whl (15.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 15.1 MB 19.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-resources>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from sweetviz) (5.6.0)\n",
            "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3 in /usr/local/lib/python3.7/dist-packages (from sweetviz) (1.3.5)\n",
            "Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from sweetviz) (2.11.3)\n",
            "Requirement already satisfied: matplotlib>=3.1.3 in /usr/local/lib/python3.7/dist-packages (from sweetviz) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from sweetviz) (1.21.5)\n",
            "Requirement already satisfied: tqdm>=4.43.0 in /usr/local/lib/python3.7/dist-packages (from sweetviz) (4.64.0)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.7/dist-packages (from sweetviz) (1.4.1)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources>=1.2.0->sweetviz) (3.8.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2>=2.11.1->sweetviz) (2.0.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.3->sweetviz) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.3->sweetviz) (1.4.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.3->sweetviz) (3.0.8)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.3->sweetviz) (0.11.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.1.3->sweetviz) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=3.1.3->sweetviz) (1.15.0)\n",
            "Installing collected packages: sweetviz\n",
            "Successfully installed sweetviz-2.1.3\n"
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      "source": [
        "# Importing required libraries.\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns #visualisation\n",
        "import matplotlib.pyplot as plt #visualisation\n",
        "%matplotlib inline \n",
        "sns.set(color_codes=True)\n",
        "\n",
        "!pip install sweetviz\n",
        "import sweetviz as sv"
      ]
    },
    {
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      "source": [
        "# Loading Dataset\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/niranjana1997/ISTE-782-Vast-Challenge-2019/main/mc1-reports-data.csv', header=[0], sep=',')"
      ],
      "metadata": {
        "id": "K2T_1FhNC-Dj"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "# To display the top 5 rows\n",
        "df.head(5)"
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        "id": "KO5ZXJZeDBsE",
        "outputId": "2498b4b8-5163-48e4-e189-ce76005587ee"
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              "               time  sewer_and_water  power  roads_and_bridges  medical  \\\n",
              "0  08-04-2020 17:50             10.0      6                 10      3.0   \n",
              "1  09-04-2020 13:50              2.0     10                  0      8.0   \n",
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              "3  08-04-2020 17:25              1.0      1                  2     10.0   \n",
              "4  08-04-2020 02:50              9.0      7                  1      6.0   \n",
              "\n",
              "   buildings  shake_intensity  location  \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-52685e7a-7178-43f4-a838-1f5298ef98d0 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-52685e7a-7178-43f4-a838-1f5298ef98d0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Basic Data Exploration**"
      ],
      "metadata": {
        "id": "aXQkMWSd2lH2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary Statistics\n",
        "print(\"Summary Statistics: \")\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "vD096uTfytSG",
        "outputId": "0ceae2b4-ae5b-40b3-b425-f440c090c48c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary Statistics: \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sewer_and_water         power  roads_and_bridges       medical  \\\n",
              "count     82899.000000  83070.000000       83070.000000  35629.000000   \n",
              "mean          5.649139      6.045371           5.743289      5.322687   \n",
              "std           2.787791      2.851951           2.506399      2.527679   \n",
              "min           0.000000      0.000000           0.000000      0.000000   \n",
              "25%           3.000000      4.000000           4.000000      4.000000   \n",
              "50%           6.000000      7.000000           6.000000      6.000000   \n",
              "75%           8.000000      8.000000           7.000000      7.000000   \n",
              "max          10.000000     10.000000          10.000000     10.000000   \n",
              "\n",
              "          buildings  shake_intensity      location  \n",
              "count  82900.000000     70926.000000  83070.000000  \n",
              "mean       4.744005         2.682641      8.978488  \n",
              "std        2.256358         1.935366      5.123608  \n",
              "min        0.000000         0.000000      1.000000  \n",
              "25%        3.000000         1.000000      4.000000  \n",
              "50%        5.000000         2.000000      8.000000  \n",
              "75%        6.000000         4.000000     14.000000  \n",
              "max       10.000000         9.000000     19.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d4f36f15-d552-45e7-8b29-6317fc8490ea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sewer_and_water</th>\n",
              "      <th>power</th>\n",
              "      <th>roads_and_bridges</th>\n",
              "      <th>medical</th>\n",
              "      <th>buildings</th>\n",
              "      <th>shake_intensity</th>\n",
              "      <th>location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>82899.000000</td>\n",
              "      <td>83070.000000</td>\n",
              "      <td>83070.000000</td>\n",
              "      <td>35629.000000</td>\n",
              "      <td>82900.000000</td>\n",
              "      <td>70926.000000</td>\n",
              "      <td>83070.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.649139</td>\n",
              "      <td>6.045371</td>\n",
              "      <td>5.743289</td>\n",
              "      <td>5.322687</td>\n",
              "      <td>4.744005</td>\n",
              "      <td>2.682641</td>\n",
              "      <td>8.978488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.787791</td>\n",
              "      <td>2.851951</td>\n",
              "      <td>2.506399</td>\n",
              "      <td>2.527679</td>\n",
              "      <td>2.256358</td>\n",
              "      <td>1.935366</td>\n",
              "      <td>5.123608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>19.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d4f36f15-d552-45e7-8b29-6317fc8490ea')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d4f36f15-d552-45e7-8b29-6317fc8490ea 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-d4f36f15-d552-45e7-8b29-6317fc8490ea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the data type\n",
        "print(\"Data types: \")\n",
        "df.dtypes"
      ],
      "metadata": {
        "id": "_8OGQ2b0EpeN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64f60cef-7e26-4292-9fa9-7d5efa7b4dc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data types: \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "time                  object\n",
              "sewer_and_water      float64\n",
              "power                  int64\n",
              "roads_and_bridges      int64\n",
              "medical              float64\n",
              "buildings            float64\n",
              "shake_intensity      float64\n",
              "location               int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total number of rows and columns\n",
        "print(\"(Rows, Columns): \", df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdi8hUVoDD8c",
        "outputId": "093e01f8-2ce5-4b58-a2d7-364bb82b19b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(Rows, Columns):  (83070, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Rows containing duplicate data\n",
        "duplicate_rows_df = df[df.duplicated()]\n",
        "print(\"Number of duplicate rows: \", duplicate_rows_df.shape)\n",
        "\n",
        "print() \n",
        "\n",
        "# Dropping Duplicates\n",
        "df.drop_duplicates(inplace=True)\n",
        "\n",
        "# Used to count the number of rows after dropping duplicates\n",
        "print(\"Count of number of rows after dropping duplicates:\")\n",
        "print(df.count())"
      ],
      "metadata": {
        "id": "uYb-guvdDXR9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0db47604-9c5d-4b43-cfde-882a7d2f35cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows:  (18846, 8)\n",
            "\n",
            "Count of number of rows after dropping duplicates:\n",
            "time                 64224\n",
            "sewer_and_water      64068\n",
            "power                64224\n",
            "roads_and_bridges    64224\n",
            "medical              26104\n",
            "buildings            64069\n",
            "shake_intensity      52287\n",
            "location             64224\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the null values.\n",
        "print(\"Total Number of Null Values\")\n",
        "print(df.isnull().sum())\n",
        "\n",
        "print()\n",
        "\n",
        "#Check the percentages of missing value\n",
        "print(\"Percentage of missing values\")\n",
        "print(df.isnull().sum() / df.shape[0] * 100)\n",
        "\n",
        "print()\n",
        "\n",
        "# Checking for missing data using the seaborn's heatmap \n",
        "sns.heatmap(df.isnull()).set_title(\"Missing values\")\n",
        "\n",
        "# Inference:\n",
        "# 1. 'Medical' column - 59% of the data is missing\n",
        "# 2. 'Shake Intensity' column - 18% of the data is missing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 777
        },
        "id": "XAwxY1WluwMG",
        "outputId": "ddec571d-77de-434c-a11c-b6f6d8dbaf90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Number of Null Values\n",
            "time                     0\n",
            "sewer_and_water        156\n",
            "power                    0\n",
            "roads_and_bridges        0\n",
            "medical              38120\n",
            "buildings              155\n",
            "shake_intensity      11937\n",
            "location                 0\n",
            "dtype: int64\n",
            "\n",
            "Percentage of missing values\n",
            "time                  0.000000\n",
            "sewer_and_water       0.242900\n",
            "power                 0.000000\n",
            "roads_and_bridges     0.000000\n",
            "medical              59.354758\n",
            "buildings             0.241343\n",
            "shake_intensity      18.586510\n",
            "location              0.000000\n",
            "dtype: float64\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Missing values')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Boxplots"
      ],
      "metadata": {
        "id": "nqX8xRqZ6LUh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Subplots of boxplots of each category\n",
        "fig, axes = plt.subplots(2, 3, figsize=(15, 5), sharey=True)\n",
        "fig.suptitle('Boxplots')\n",
        "\n",
        "# Sewer and Water\n",
        "sns.boxplot(ax=axes[0,0], x=df['sewer_and_water'])\n",
        "axes[0,0].set_title(\"Sewer and Water\")\n",
        "\n",
        "# Power\n",
        "sns.boxplot(ax = axes[0,1], x=df['power'])\n",
        "axes[0,1].set_title(\"Power\")\n",
        "\n",
        "# Roads and Bridges\n",
        "sns.boxplot(ax = axes[0,2], x=df['roads_and_bridges'])\n",
        "axes[0,2].set_title(\"roads_and_bridges\")\n",
        "\n",
        "# Medical\n",
        "sns.boxplot(ax = axes[1,0], x=df['medical'])\n",
        "axes[1,0].set_title(\"medical\")\n",
        "\n",
        "# Buildings\n",
        "sns.boxplot(ax = axes[1,1], x=df['buildings'])\n",
        "axes[1,1].set_title(\"buildings\")\n",
        "\n",
        "# Shake Intensity\n",
        "sns.boxplot(ax = axes[1,2], x=df['shake_intensity'])\n",
        "axes[1,2].set_title(\"shake_intensity\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "ZXFY2B7Su7F9",
        "outputId": "1a13d06f-e3d2-42cd-8ff2-bca7152ff6a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'shake_intensity')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting a Histogram - Count of Reports with time\n",
        "df.time.value_counts().nlargest(40).plot(kind='bar', figsize=(10,5), color='orange')\n",
        "plt.title('Number of reports by time')\n",
        "plt.ylabel('Number of reports')\n",
        "plt.xlabel('Time');\n",
        "\n",
        "# Inference: \n",
        "# 1. Most number of reports were on 9th April, 2020 at 01:00\n",
        "# 2. 8th April, 9th April and 10th April were the days that reported the most."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "JKnVSB5pvUpL",
        "outputId": "5e2ecb96-811d-445c-cc24-176d61549a54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting a Histogram - Count of Reports with location\n",
        "df.location.value_counts().nlargest(40).plot(kind='bar', figsize=(10,5), color='black')\n",
        "plt.title('Number of reports by location')\n",
        "plt.ylabel('Number of reports')\n",
        "plt.xlabel('Location');\n",
        "\n",
        "# Inference\n",
        "# 1. Location 8 and Location 3 has most number of reports"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "gyyLNRZDz3zf",
        "outputId": "701dbf66-8548-40ab-bb99-cdc18ef9c8f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the relations between the variables.\n",
        "plt.figure(figsize=(20,10))\n",
        "correlation = df.corr()\n",
        "sns.heatmap(correlation, cmap=\"BrBG\", annot=True)\n",
        "correlation\n",
        "\n",
        "# Inference:\n",
        "# 1. 'Roads and Bridges' and 'Sewer and Water' has the highest correlation (0.62)\n",
        "# 2. 'Medical' and 'Location' has the least correlation (-0.37)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "AoTpRiQkv16Y",
        "outputId": "90a34343-fd18-4f2f-cf32-96852e122afd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   sewer_and_water     power  roads_and_bridges   medical  \\\n",
              "sewer_and_water           1.000000  0.412242           0.517856  0.404050   \n",
              "power                     0.412242  1.000000           0.504050  0.473640   \n",
              "roads_and_bridges         0.517856  0.504050           1.000000  0.474626   \n",
              "medical                   0.404050  0.473640           0.474626  1.000000   \n",
              "buildings                 0.488662  0.413643           0.421836  0.426151   \n",
              "shake_intensity           0.016585  0.271468           0.026631  0.485277   \n",
              "location                 -0.163763 -0.106917          -0.153603 -0.247736   \n",
              "\n",
              "                   buildings  shake_intensity  location  \n",
              "sewer_and_water     0.488662         0.016585 -0.163763  \n",
              "power               0.413643         0.271468 -0.106917  \n",
              "roads_and_bridges   0.421836         0.026631 -0.153603  \n",
              "medical             0.426151         0.485277 -0.247736  \n",
              "buildings           1.000000         0.032014 -0.015340  \n",
              "shake_intensity     0.032014         1.000000 -0.010080  \n",
              "location           -0.015340        -0.010080  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ed2dfaf3-d739-4c5b-8fd5-483419aacece\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sewer_and_water</th>\n",
              "      <th>power</th>\n",
              "      <th>roads_and_bridges</th>\n",
              "      <th>medical</th>\n",
              "      <th>buildings</th>\n",
              "      <th>shake_intensity</th>\n",
              "      <th>location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sewer_and_water</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.412242</td>\n",
              "      <td>0.517856</td>\n",
              "      <td>0.404050</td>\n",
              "      <td>0.488662</td>\n",
              "      <td>0.016585</td>\n",
              "      <td>-0.163763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>power</th>\n",
              "      <td>0.412242</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.504050</td>\n",
              "      <td>0.473640</td>\n",
              "      <td>0.413643</td>\n",
              "      <td>0.271468</td>\n",
              "      <td>-0.106917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>roads_and_bridges</th>\n",
              "      <td>0.517856</td>\n",
              "      <td>0.504050</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.474626</td>\n",
              "      <td>0.421836</td>\n",
              "      <td>0.026631</td>\n",
              "      <td>-0.153603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>medical</th>\n",
              "      <td>0.404050</td>\n",
              "      <td>0.473640</td>\n",
              "      <td>0.474626</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.426151</td>\n",
              "      <td>0.485277</td>\n",
              "      <td>-0.247736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>buildings</th>\n",
              "      <td>0.488662</td>\n",
              "      <td>0.413643</td>\n",
              "      <td>0.421836</td>\n",
              "      <td>0.426151</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.032014</td>\n",
              "      <td>-0.015340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>shake_intensity</th>\n",
              "      <td>0.016585</td>\n",
              "      <td>0.271468</td>\n",
              "      <td>0.026631</td>\n",
              "      <td>0.485277</td>\n",
              "      <td>0.032014</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.010080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>location</th>\n",
              "      <td>-0.163763</td>\n",
              "      <td>-0.106917</td>\n",
              "      <td>-0.153603</td>\n",
              "      <td>-0.247736</td>\n",
              "      <td>-0.015340</td>\n",
              "      <td>-0.010080</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed2dfaf3-d739-4c5b-8fd5-483419aacece')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ed2dfaf3-d739-4c5b-8fd5-483419aacece 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-ed2dfaf3-d739-4c5b-8fd5-483419aacece');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_report = sv.analyze([df, \"Train\"])\n",
        "my_report.show_html('Report.html')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "3d9a0ce01a244c468b9c6ed5452eb340",
            "3f7c04d06002455d9a5e7ea277aab6cd",
            "6e423e50c03c40cea58b4139bca10f51",
            "e60b24a2006a400e9454fa1ab56d2310",
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            "f2cf7da2dee64b88987b40dee2963147",
            "ad9d5c1050fc42f6b7ebe53eb2cd8adb",
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            "ab7956ac994443b2b791e50b7c2e330b"
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        },
        "id": "Dau7G5eUgbCX",
        "outputId": "22942a97-4fd9-4c0e-eaa4-73bf0eddcceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                             |          | [  0%]   00:00 -> (? left)"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d9a0ce01a244c468b9c6ed5452eb340"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Report Report.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n"
          ]
        }
      ]
    }
  ]
}