WS04_wkshp.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sbogde/pandamonium/blob/main/WS04_wkshp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "c3390574-e5c8-45d7-bdf3-a439ecc36a96",
      "metadata": {
        "id": "c3390574-e5c8-45d7-bdf3-a439ecc36a96"
      },
      "outputs": [],
      "source": [
        "# !pip install folium"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "76929d0a-9189-4fc0-bd2e-b81e8c5fc264",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "76929d0a-9189-4fc0-bd2e-b81e8c5fc264",
        "outputId": "96796069-b8bf-40fa-bd96-70dcb11b4675"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x = [4, 3,4, 3, 4, 3, 11, 12 , 4, 11, 12]\n",
        "y = [21,20,22, 17, 17, 16, 23, 24, 16, 22, 21]\n",
        "\n",
        "plt.scatter(x, y)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "e30967cd-0e75-4982-a338-539c29a3b0d9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "e30967cd-0e75-4982-a338-539c29a3b0d9",
        "outputId": "540a3ac2-d3b6-4d27-f2a2-52b9752fb900"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "data = list(zip(x, y))\n",
        "results = []\n",
        "\n",
        "for i in range(1,11):\n",
        "    #init:The standard version of the k-means algorithm is implemented by setting init to \"random\" .\n",
        "    #Setting this to \"k-means++\" employs an advanced trick to speed up convergence\n",
        "    kmeans = KMeans(n_clusters=i, init=\"k-means++\", max_iter=300, random_state=42)\n",
        "    #random_state: This is setting a random seed. It is useful if we want to reproduce exact clusters over and over again.\n",
        "    #We can set it to any number we want.\n",
        "    kmeans.fit(data)#computes the cluster centers\n",
        "    results.append(kmeans.inertia_)\n",
        "\n",
        "plt.plot(range(1,11), results, marker='o')\n",
        "plt.title('Elbow method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('Inertia')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "5cb1ed2f-8a77-4403-bc90-ee81d6da66a1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "5cb1ed2f-8a77-4403-bc90-ee81d6da66a1",
        "outputId": "11ee9318-2d1d-4bd1-819a-be4de791c169"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "kmeans = KMeans(n_clusters=3)\n",
        "kmeans.fit(data) #computes the cluster centers\n",
        "\n",
        "plt.scatter(x, y, c=kmeans.labels_)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "8b942569-7e98-4379-ad70-dc3366906e98",
      "metadata": {
        "id": "8b942569-7e98-4379-ad70-dc3366906e98"
      },
      "outputs": [],
      "source": [
        "#Original\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import requests\n",
        "from io import StringIO\n",
        "import folium\n",
        "from folium.plugins import MarkerCluster\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# ---- Fetch Latest COVID-19 Data from GitHub ----\n",
        "#base_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/\"\n",
        "#latest_date = \"02-13-2020.csv\"  # Update with the latest date in MM-DD-YYYY format\n",
        "#url = base_url + latest_date\n",
        "#print(url)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ---- Step 1: Load the Dataset ----\n",
        "if 'google.colab' in str(get_ipython()):\n",
        "    from google.colab import drive, data_table\n",
        "    drive.mount('/content/drive', force_remount=True)\n",
        "    file_path = '/content/drive/My Drive/Colab Notebooks/data-mining/data/01-02-2023.csv'\n",
        "    data_table.enable_dataframe_formatter()\n",
        "else:\n",
        "    file_path = './data/01-02-2023.csv'  # Local path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YM7HaO0rOCW-",
        "outputId": "f74582be-07ad-4c0c-bb15-4f2b0b2417fa"
      },
      "id": "YM7HaO0rOCW-",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Load dataset\n",
        "df = pd.read_csv(file_path)\n",
        "print(df.describe())\n",
        "df.head(1)\n",
        "# ---- Data Preprocessing ----\n",
        "# Select relevant columns\n",
        "print(df.columns)  # Verify column names\n",
        "columns_to_use = [\"Country_Region\", \"Province_State\", \"Lat\", \"Long_\", \"Confirmed\", \"Deaths\", \"Recovered\", \"Active\"]\n",
        "df = df[columns_to_use]\n",
        "\n",
        "# Handle missing values\n",
        "#Replace NULL values with 0\n",
        "df.fillna({'Confirmed': 0, 'Deaths': 0, 'Recovered': 0, 'Active': 0}, inplace=True)\n",
        "df.dropna(subset=['Lat', 'Long_'], inplace=True)\n",
        "\n",
        "# Normalize data for clustering\n",
        "#StandardScaler, a popular preprocessing technique provided by scikit-learn, offers a simple yet effective method for standardizing feature values\n",
        "scaler = StandardScaler()\n",
        "df_scaled = scaler.fit_transform(df[[\"Confirmed\", \"Deaths\", \"Recovered\", \"Active\"]])\n",
        "\n",
        "# ---- Apply K-Means Clustering ----\n",
        "optimal_clusters = 5  # Change based on analysis\n",
        "kmeans = KMeans(n_clusters=optimal_clusters, init=\"k-means++\", max_iter=300, random_state=42)\n",
        "df['Cluster'] = kmeans.fit_predict(df_scaled)\n",
        "\n",
        "# ---- Visualizing Clusters on a World Map ----\n",
        "world_map = folium.Map(location=[20, 0], zoom_start=2)\n",
        "\n",
        "marker_cluster = MarkerCluster().add_to(world_map)\n",
        "\n",
        "for _, row in df.iterrows():\n",
        "    folium.Marker(\n",
        "        location=[row[\"Lat\"], row[\"Long_\"]],\n",
        "        popup=f\"Country: {row['Country_Region']}<br>Confirmed: {row['Confirmed']}<br>Deaths: {row['Deaths']}<br>Recovered: {row['Recovered']}\",\n",
        "        icon=folium.Icon(color=[\"red\", \"blue\", \"green\", \"purple\", \"orange\"][row[\"Cluster\"]])\n",
        "    ).add_to(marker_cluster)\n",
        "\n",
        "# Save map to an HTML file\n",
        "world_map.save(\"covid19_clusters_map.html\")\n",
        "print(\"✅ World Map with Clusters Saved as 'covid19_clusters_map.html'\")\n",
        "#print(\"✅ Map saved successfully!\")\n",
        "\n",
        "# Open the saved file in Jupyter Notebook\n",
        "import webbrowser\n",
        "webbrowser.open(\"covid19_clusters_map.html\")\n",
        "\n",
        "\n",
        "# ---- Clustering Insights ----\n",
        "print(\"\\n📌 **Cluster Analysis Summary:**\")\n",
        "print(df.groupby(\"Cluster\")[[\"Confirmed\", \"Deaths\", \"Recovered\", \"Active\"]].mean())\n",
        "\n",
        "# ---- Elbow Method to Find Optimal Clusters ----\n",
        "wcss = []\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters=i, init=\"k-means++\", max_iter=300, random_state=42)\n",
        "    kmeans.fit(df_scaled)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "\n",
        "# Plot Elbow Curve\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(range(1, 11), wcss, marker='o', linestyle='--', color='blue')\n",
        "plt.title(\"Elbow Method for Optimal Clusters\")\n",
        "plt.xlabel(\"Number of Clusters\")\n",
        "plt.ylabel(\"WCSS\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EkNcEF6mN9t2",
        "outputId": "ae96d947-0c0b-4b9f-a699-b9ebe12a024a"
      },
      "id": "EkNcEF6mN9t2",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               FIPS          Lat        Long_     Confirmed         Deaths  \\\n",
            "count   3268.000000  3925.000000  3925.000000  4.016000e+03    4016.000000   \n",
            "mean   32405.943390    35.736183   -71.109728  1.646409e+05    1667.277141   \n",
            "std    18056.381177    13.441327    55.361480  1.046512e+06    8705.465768   \n",
            "min       60.000000   -71.949900  -178.116500  0.000000e+00       0.000000   \n",
            "25%    19048.500000    33.191535   -96.595639  3.737500e+03      46.000000   \n",
            "50%    30068.000000    37.895700   -86.717326  1.050600e+04     130.500000   \n",
            "75%    47041.500000    42.176955   -77.357900  4.577550e+04     465.250000   \n",
            "max    99999.000000    71.706900   178.065000  3.830609e+07  183348.000000   \n",
            "\n",
            "       Recovered  Active  Incident_Rate  Case_Fatality_Ratio  \n",
            "count        0.0     0.0    3922.000000          3974.000000  \n",
            "mean         NaN     NaN   27693.653663             3.396156  \n",
            "std          NaN     NaN   10388.048975            93.482133  \n",
            "min          NaN     NaN       0.000000             0.000000  \n",
            "25%          NaN     NaN   23345.450938             0.892777  \n",
            "50%          NaN     NaN   28611.368832             1.287045  \n",
            "75%          NaN     NaN   33164.946216             1.739872  \n",
            "max          NaN     NaN  218343.195266          5651.724138  \n",
            "Index(['FIPS', 'Admin2', 'Province_State', 'Country_Region', 'Last_Update',\n",
            "       'Lat', 'Long_', 'Confirmed', 'Deaths', 'Recovered', 'Active',\n",
            "       'Combined_Key', 'Incident_Rate', 'Case_Fatality_Ratio'],\n",
            "      dtype='object')\n",
            "✅ World Map with Clusters Saved as 'covid19_clusters_map.html'\n",
            "\n",
            "📌 **Cluster Analysis Summary:**\n",
            "            Confirmed         Deaths  Recovered  Active\n",
            "Cluster                                                \n",
            "0        7.083671e+04     762.828504        0.0     0.0\n",
            "1        6.401312e+06  134833.375000        0.0     0.0\n",
            "2        3.376334e+07   95469.000000        0.0     0.0\n",
            "3        2.813963e+06   26374.573171        0.0     0.0\n",
            "4        1.871753e+07  142420.000000        0.0     0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "69e103a9-bc4c-4c46-a1c8-deda89e39c97",
      "metadata": {
        "id": "69e103a9-bc4c-4c46-a1c8-deda89e39c97"
      },
      "outputs": [],
      "source": [
        "import pandas as pd #For working with data sets.\n",
        "import numpy as np\n",
        "import requests\n",
        "from io import StringIO\n",
        "import folium #It helps you create several types of maps\n",
        "from folium.plugins import MarkerCluster\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler #We will use this to normalize data for clustering\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns #data visualization library based on matplotlib\n",
        "\n",
        "# ---- Fetch Latest COVID-19 Data from GitHub ----\n",
        "#base_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/\"\n",
        "#latest_date = \"02-13-2020.csv\"  # Update with the latest date in MM-DD-YYYY format\n",
        "#url = base_url + latest_date\n",
        "#print(url)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset\n",
        "#https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data\n",
        "df = pd.read_csv(file_path)\n",
        "print(df.describe())\n",
        "df.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "j3uuW8u7OXhF",
        "outputId": "86e6a51a-79bd-45fb-d8f2-8102c09477b3"
      },
      "id": "j3uuW8u7OXhF",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               FIPS          Lat        Long_     Confirmed         Deaths  \\\n",
            "count   3268.000000  3925.000000  3925.000000  4.016000e+03    4016.000000   \n",
            "mean   32405.943390    35.736183   -71.109728  1.646409e+05    1667.277141   \n",
            "std    18056.381177    13.441327    55.361480  1.046512e+06    8705.465768   \n",
            "min       60.000000   -71.949900  -178.116500  0.000000e+00       0.000000   \n",
            "25%    19048.500000    33.191535   -96.595639  3.737500e+03      46.000000   \n",
            "50%    30068.000000    37.895700   -86.717326  1.050600e+04     130.500000   \n",
            "75%    47041.500000    42.176955   -77.357900  4.577550e+04     465.250000   \n",
            "max    99999.000000    71.706900   178.065000  3.830609e+07  183348.000000   \n",
            "\n",
            "       Recovered  Active  Incident_Rate  Case_Fatality_Ratio  \n",
            "count        0.0     0.0    3922.000000          3974.000000  \n",
            "mean         NaN     NaN   27693.653663             3.396156  \n",
            "std          NaN     NaN   10388.048975            93.482133  \n",
            "min          NaN     NaN       0.000000             0.000000  \n",
            "25%          NaN     NaN   23345.450938             0.892777  \n",
            "50%          NaN     NaN   28611.368832             1.287045  \n",
            "75%          NaN     NaN   33164.946216             1.739872  \n",
            "max          NaN     NaN  218343.195266          5651.724138  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   FIPS Admin2 Province_State Country_Region          Last_Update       Lat  \\\n",
              "0   NaN    NaN            NaN    Afghanistan  2023-01-03 04:21:07  33.93911   \n",
              "\n",
              "       Long_  Confirmed  Deaths  Recovered  Active Combined_Key  \\\n",
              "0  67.709953     207627    7849        NaN     NaN  Afghanistan   \n",
              "\n",
              "   Incident_Rate  Case_Fatality_Ratio  \n",
              "0     533.356919             3.780337  "
            ],
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8e5d5ee2-ebc9-468e-a4c3-ce00d7b45768 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-8e5d5ee2-ebc9-468e-a4c3-ce00d7b45768');\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",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4016,\n  \"fields\": [\n    {\n      \"column\": \"FIPS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18056.381176646817,\n        \"min\": 60.0,\n        \"max\": 99999.0,\n        \"num_unique_values\": 3268,\n        \"samples\": [\n          53017.0,\n          5033.0,\n          13017.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Admin2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1927,\n        \"samples\": [\n          \"Fajardo\",\n          \"Galveston\",\n          \"Whatcom\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Province_State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 598,\n        \"samples\": [\n          \"Yunnan\",\n          \"Sabah\",\n          \"Kiev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country_Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 201,\n        \"samples\": [\n          \"Kosovo\",\n          \"Barbados\",\n          \"Cabo Verde\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Last_Update\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"2021-10-10 23:21:42\",\n          \"2020-12-21 13:27:30\",\n          \"2022-09-12 23:21:04\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.441326582607672,\n        \"min\": -71.9499,\n        \"max\": 71.7069,\n        \"num_unique_values\": 3924,\n        \"samples\": [\n          37.937575,\n          34.485332,\n          39.96575964\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Long_\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.36147956878946,\n        \"min\": -178.1165,\n        \"max\": 178.065,\n        \"num_unique_values\": 3914,\n        \"samples\": [\n          -93.4116826,\n          -83.65468618,\n          103.3256\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Confirmed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1046511,\n        \"min\": 0,\n        \"max\": 38306086,\n        \"num_unique_values\": 3761,\n        \"samples\": [\n          4949,\n          67973,\n          6615514\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8705,\n        \"min\": 0,\n        \"max\": 183348,\n        \"num_unique_values\": 1309,\n        \"samples\": [\n          252,\n          319,\n          239\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recovered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Active\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Combined_Key\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4016,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Incident_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10388.048975072717,\n        \"min\": 0.0,\n        \"max\": 218343.1952662722,\n        \"num_unique_values\": 3920,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Case_Fatality_Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 93.48213271816917,\n        \"min\": 0.0,\n        \"max\": 5651.724137931034,\n        \"num_unique_values\": 3796,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            },
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/e523c247d1e24a05/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n\"Afghanistan\",\n\"2023-01-03 04:21:07\",\n{\n            'v': 33.93911,\n            'f': \"33.93911\",\n        },\n{\n            'v': 67.709953,\n            'f': \"67.709953\",\n        },\n{\n            'v': 207627,\n            'f': \"207627\",\n        },\n{\n            'v': 7849,\n            'f': \"7849\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n\"Afghanistan\",\n{\n            'v': 533.3569185493931,\n            'f': \"533.3569185493931\",\n        },\n{\n            'v': 3.7803368540700393,\n            'f': \"3.7803368540700393\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"number\", \"FIPS\"], [\"number\", \"Admin2\"], [\"number\", \"Province_State\"], [\"string\", \"Country_Region\"], [\"string\", \"Last_Update\"], [\"number\", \"Lat\"], [\"number\", \"Long_\"], [\"number\", \"Confirmed\"], [\"number\", \"Deaths\"], [\"number\", \"Recovered\"], [\"number\", \"Active\"], [\"string\", \"Combined_Key\"], [\"number\", \"Incident_Rate\"], [\"number\", \"Case_Fatality_Ratio\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let 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          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "d197e28c-ec1b-41d1-ae7f-d9b8b2efdca1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d197e28c-ec1b-41d1-ae7f-d9b8b2efdca1",
        "outputId": "21148e9b-1705-414b-a907-b9a0e9053fd4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['FIPS', 'Admin2', 'Province_State', 'Country_Region', 'Last_Update',\n",
            "       'Lat', 'Long_', 'Confirmed', 'Deaths', 'Recovered', 'Active',\n",
            "       'Combined_Key', 'Incident_Rate', 'Case_Fatality_Ratio'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# ---- Data Preprocessing ----\n",
        "# Select relevant columns\n",
        "print(df.columns)  # Verify column names\n",
        "columns_to_use = [\"Country_Region\", \"Province_State\", \"Lat\", \"Long_\", \"Confirmed\", \"Deaths\", \"Recovered\", \"Active\"]\n",
        "df = df[columns_to_use]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "61f5fd41-9e76-4341-a005-1ace346e7080",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61f5fd41-9e76-4341-a005-1ace346e7080",
        "outputId": "a474984a-07cf-4840-8d00-8c7de7d3734c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-d3c7bf3a957c>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.fillna({'Confirmed': 0, 'Deaths': 0, 'Recovered': 0, 'Active': 0}, inplace=True)\n",
            "<ipython-input-26-d3c7bf3a957c>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.dropna(subset=['Lat', 'Long_'], inplace=True)\n"
          ]
        }
      ],
      "source": [
        "#Handle missing values\n",
        "#Replace NULL values with 0\n",
        "df.fillna({'Confirmed': 0, 'Deaths': 0, 'Recovered': 0, 'Active': 0}, inplace=True)\n",
        "df.dropna(subset=['Lat', 'Long_'], inplace=True)\n",
        "\n",
        "# Normalize data for clustering\n",
        "#StandardScaler, a popular preprocessing technique provided by scikit-learn, offers a simple yet effective method for standardizing feature values\n",
        "scaler = StandardScaler()\n",
        "df_scaled = scaler.fit_transform(df[[\"Confirmed\", \"Deaths\", \"Recovered\", \"Active\"]])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "cae1a4e4-e649-4997-a101-066a524c5434",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "cae1a4e4-e649-4997-a101-066a524c5434",
        "outputId": "ac860bbc-7143-47db-dd7f-866a84754cca"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ---- Elbow Method to Find Optimal Clusters ----\n",
        "wcss = []\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters=i, init=\"k-means++\", max_iter=300, random_state=42)\n",
        "    kmeans.fit(df_scaled) #computes the cluster centers\n",
        "    wcss.append(kmeans.inertia_)\n",
        "\n",
        "# Plot Elbow Curve\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(range(1, 11), wcss, marker='o', linestyle='--', color='blue')\n",
        "plt.title(\"Elbow Method for Optimal Clusters\")\n",
        "plt.xlabel(\"Number of Clusters\")\n",
        "plt.ylabel(\"WCSS\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "ef2e3be9-0bf1-4f31-a06b-59f7b800fdad",
      "metadata": {
        "id": "ef2e3be9-0bf1-4f31-a06b-59f7b800fdad"
      },
      "outputs": [],
      "source": [
        "# ---- Apply K-Means Clustering ----\n",
        "optimal_clusters = 5  # Change based on analysis\n",
        "kmeans = KMeans(n_clusters=optimal_clusters, init=\"k-means++\", max_iter=300, random_state=42)\n",
        "df['Cluster'] = kmeans.fit_predict(df_scaled) #computes the cluster centers also returns the cluster labels for each data point\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "6f6a35ee-11a1-4c37-89d0-163b12da6b0e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6f6a35ee-11a1-4c37-89d0-163b12da6b0e",
        "outputId": "7a3c8cf4-bbc1-4952-81f7-8e687d98454b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ World Map with Clusters Saved as 'covid19_clusters_map.html'\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# ---- Visualizing Clusters on a World Map ----\n",
        "world_map = folium.Map(location=[20, 0], zoom_start=2)\n",
        "\n",
        "marker_cluster = MarkerCluster().add_to(world_map)\n",
        "\n",
        "for _, row in df.iterrows():\n",
        "    folium.Marker(\n",
        "        location=[row[\"Lat\"], row[\"Long_\"]],\n",
        "        popup=f\"Country: {row['Country_Region']}<br>Confirmed: {row['Confirmed']}<br>Deaths: {row['Deaths']}<br>Recovered: {row['Recovered']}\",\n",
        "        icon=folium.Icon(color=[\"red\", \"blue\", \"green\", \"purple\", \"orange\"][row[\"Cluster\"]])\n",
        "    ).add_to(marker_cluster)\n",
        "\n",
        "# Save map to an HTML file\n",
        "world_map.save(\"covid19_clusters_map.html\")\n",
        "print(\"✅ World Map with Clusters Saved as 'covid19_clusters_map.html'\")\n",
        "#print(\"✅ Map saved successfully!\")\n",
        "\n",
        "# Open the saved file in Jupyter Notebook\n",
        "import webbrowser\n",
        "webbrowser.open(\"covid19_clusters_map.html\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "e383567a-5b8b-4205-b636-3944bbe17b9b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e383567a-5b8b-4205-b636-3944bbe17b9b",
        "outputId": "c2bdab6f-4226-40ae-a36d-0c6237147f8d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "📌 **Cluster Analysis Summary:**\n",
            "            Confirmed         Deaths  Recovered  Active\n",
            "Cluster                                                \n",
            "0        7.083671e+04     762.828504        0.0     0.0\n",
            "1        6.401312e+06  134833.375000        0.0     0.0\n",
            "2        3.376334e+07   95469.000000        0.0     0.0\n",
            "3        2.813963e+06   26374.573171        0.0     0.0\n",
            "4        1.871753e+07  142420.000000        0.0     0.0\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# ---- Clustering Insights ----\n",
        "print(\"\\n📌 **Cluster Analysis Summary:**\")\n",
        "print(df.groupby(\"Cluster\")[[\"Confirmed\", \"Deaths\", \"Recovered\", \"Active\"]].mean())\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ---- Step 1: Load the Dataset ----\n",
        "if 'google.colab' in str(get_ipython()):\n",
        "    from google.colab import drive, data_table\n",
        "    drive.mount('/content/drive', force_remount=True)\n",
        "    file_path2 = '/content/drive/My Drive/Colab Notebooks/data-mining/data/country_wise_latest.csv'\n",
        "    data_table.enable_dataframe_formatter()\n",
        "else:\n",
        "    file_path2 = './data/country_wise_latest.csv'  # Local path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTO2HctmPzlu",
        "outputId": "b11891ee-c7aa-4886-bdd2-012f40831f9d"
      },
      "id": "kTO2HctmPzlu",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "b17be4cf-869b-4e0a-89ef-46141eecb19a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b17be4cf-869b-4e0a-89ef-46141eecb19a",
        "outputId": "cdf30c57-483a-48e7-f262-1a088989c16a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          Confirmed         Deaths     Recovered        Active     New cases  \\\n",
            "count  1.870000e+02     187.000000  1.870000e+02  1.870000e+02    187.000000   \n",
            "mean   8.813094e+04    3497.518717  5.063148e+04  3.400194e+04   1222.957219   \n",
            "std    3.833187e+05   14100.002482  1.901882e+05  2.133262e+05   5710.374790   \n",
            "min    1.000000e+01       0.000000  0.000000e+00  0.000000e+00      0.000000   \n",
            "25%    1.114000e+03      18.500000  6.265000e+02  1.415000e+02      4.000000   \n",
            "50%    5.059000e+03     108.000000  2.815000e+03  1.600000e+03     49.000000   \n",
            "75%    4.046050e+04     734.000000  2.260600e+04  9.149000e+03    419.500000   \n",
            "max    4.290259e+06  148011.000000  1.846641e+06  2.816444e+06  56336.000000   \n",
            "\n",
            "        New deaths  New recovered  Deaths / 100 Cases  Recovered / 100 Cases  \\\n",
            "count   187.000000     187.000000          187.000000             187.000000   \n",
            "mean     28.957219     933.812834            3.019519              64.820535   \n",
            "std     120.037173    4197.719635            3.454302              26.287694   \n",
            "min       0.000000       0.000000            0.000000               0.000000   \n",
            "25%       0.000000       0.000000            0.945000              48.770000   \n",
            "50%       1.000000      22.000000            2.150000              71.320000   \n",
            "75%       6.000000     221.000000            3.875000              86.885000   \n",
            "max    1076.000000   33728.000000           28.560000             100.000000   \n",
            "\n",
            "       Deaths / 100 Recovered  Confirmed last week  1 week change  \\\n",
            "count                  187.00         1.870000e+02     187.000000   \n",
            "mean                      inf         7.868248e+04    9448.459893   \n",
            "std                       NaN         3.382737e+05   47491.127684   \n",
            "min                      0.00         1.000000e+01     -47.000000   \n",
            "25%                      1.45         1.051500e+03      49.000000   \n",
            "50%                      3.62         5.020000e+03     432.000000   \n",
            "75%                      6.44         3.708050e+04    3172.000000   \n",
            "max                       inf         3.834677e+06  455582.000000   \n",
            "\n",
            "       1 week % increase  \n",
            "count         187.000000  \n",
            "mean           13.606203  \n",
            "std            24.509838  \n",
            "min            -3.840000  \n",
            "25%             2.775000  \n",
            "50%             6.890000  \n",
            "75%            16.855000  \n",
            "max           226.320000  \n",
            "Index(['Country/Region', 'Confirmed', 'Deaths', 'Recovered', 'Active',\n",
            "       'New cases', 'New deaths', 'New recovered', 'Deaths / 100 Cases',\n",
            "       'Recovered / 100 Cases', 'Deaths / 100 Recovered',\n",
            "       'Confirmed last week', '1 week change', '1 week % increase',\n",
            "       'WHO Region'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv(file_path2)\n",
        "print(df.describe())\n",
        "df.head(1)\n",
        "# ---- Data Preprocessing ----\n",
        "# Select relevant columns\n",
        "print(df.columns)  # Verify column names\n",
        "\n",
        "columns_to_use = [\"Country/Region\", \"Confirmed\", \"Deaths\", \"Recovered\", \"Active\", \"New cases\",\"New deaths\", \"New recovered\"]\n",
        "df = df[columns_to_use]\n",
        "\n",
        "#Handle missing values\n",
        "#Replace NULL values with 0\n",
        "df.fillna({'Confirmed': 0, 'Deaths': 0, 'Recovered': 0, 'Active': 0, 'New cases':0, 'New deaths':0, 'New recovered':0 }, inplace=True)\n",
        "df.dropna(subset=['Country/Region'], inplace=True)\n",
        "\n",
        "# Normalize data for clustering\n",
        "#StandardScaler, a popular preprocessing technique provided by scikit-learn, offers a simple yet effective method for standardizing feature values\n",
        "#scaler = StandardScaler()\n",
        "#df_scaled = scaler.fit_transform(df[[\"Confirmed\", \"Deaths\", \"Recovered\", \"Active\", \"New cases\",\"New deaths\", \"New recovered\"]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "2da3e55d-1919-4e7b-8ffb-f283f54ff703",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "2da3e55d-1919-4e7b-8ffb-f283f54ff703",
        "outputId": "cbf4c339-d812-4489-873b-61b1196745cf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Normalize data for clustering\n",
        "#StandardScaler, a popular preprocessing technique provided by scikit-learn, offers a simple yet effective method for standardizing feature values\n",
        "scaler = StandardScaler()\n",
        "df_scaled = scaler.fit_transform(df[[\"New cases\",\"New deaths\", \"New recovered\"]])\n",
        "\n",
        "# ---- Elbow Method to Find Optimal Clusters ----\n",
        "wcss = []\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters=i, init=\"k-means++\", max_iter=300, random_state=42)\n",
        "    kmeans.fit(df_scaled)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "\n",
        "# Plot Elbow Curve\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(range(1, 11), wcss, marker='o', linestyle='--', color='blue')\n",
        "plt.title(\"Elbow Method for Optimal Clusters\")\n",
        "plt.xlabel(\"Number of Clusters\")\n",
        "plt.ylabel(\"WCSS\")\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "c1a9e88e-de5e-43db-a854-11c6c6471214",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1a9e88e-de5e-43db-a854-11c6c6471214",
        "outputId": "b785e348-5f12-409f-83a0-0c48fff35ab0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "📌 **Cluster Analysis Summary:**\n",
            "            New cases   New deaths  New recovered\n",
            "Cluster                                          \n",
            "0         2683.500000    79.300000    2068.400000\n",
            "1        56336.000000  1076.000000   27941.000000\n",
            "2        33870.500000   625.500000   33663.000000\n",
            "3        15031.000000   541.500000    8095.500000\n",
            "4         6034.500000   320.000000    9218.000000\n",
            "5          209.705882     3.364706     141.441176\n"
          ]
        }
      ],
      "source": [
        "# ---- Apply K-Means Clustering ----\n",
        "optimal_clusters = 6  # Change based on analysis\n",
        "kmeans = KMeans(n_clusters=optimal_clusters, init=\"k-means++\", max_iter=300, random_state=42)\n",
        "df['Cluster'] = kmeans.fit_predict(df_scaled)\n",
        "\n",
        "\n",
        "# ---- Clustering Insights ----\n",
        "print(\"\\n📌 **Cluster Analysis Summary:**\")\n",
        "print(df.groupby(\"Cluster\")[[\"New cases\",\"New deaths\", \"New recovered\"]].mean())\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "b89c1652-b50c-4b87-9185-275e8585c145",
      "metadata": {
        "id": "b89c1652-b50c-4b87-9185-275e8585c145"
      },
      "outputs": [],
      "source": [
        "def CountFrequency(my_list):\n",
        "    freq = {}\n",
        "    for item in my_list:\n",
        "        if (item in freq):\n",
        "            freq[item] += 1\n",
        "        else:\n",
        "            freq[item] = 1\n",
        "\n",
        "    for key, value in freq.items():\n",
        "        print (\"% d : % d\"%(key, value))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "f1ed8be5-7620-4002-83f7-dc98b543c1dc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1ed8be5-7620-4002-83f7-dc98b543c1dc",
        "outputId": "4d5f4d5a-eff1-4f6f-fd34-10f820ae523c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " 5 :  170\n",
            " 0 :  10\n",
            " 2 :  2\n",
            " 3 :  2\n",
            " 4 :  2\n",
            " 1 :  1\n"
          ]
        }
      ],
      "source": [
        "CountFrequency(kmeans.labels_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "140ce972-00e6-4ebe-baac-410f84b50d43",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "140ce972-00e6-4ebe-baac-410f84b50d43",
        "outputId": "2f90e7ff-9378-4a6b-f22b-05ebf877ae68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ],
      "source": [
        "for index, row in df.iterrows():\n",
        "    if row['Country/Region']==\"Peru\":\n",
        "        cluster_i=row['Cluster']\n",
        "        break\n",
        "print(cluster_i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "7b072e54-2f16-4f2f-a6a0-b3570f2c2af0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7b072e54-2f16-4f2f-a6a0-b3570f2c2af0",
        "outputId": "19380ceb-24a9-42be-893d-075e2fa9e5dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Argentina\n",
            "Bangladesh\n",
            "Bolivia\n",
            "Chile\n",
            "Indonesia\n",
            "Iran\n",
            "Iraq\n",
            "Pakistan\n",
            "Russia\n",
            "Saudi Arabia\n"
          ]
        }
      ],
      "source": [
        "for index, row in df.iterrows():\n",
        "    if row['Cluster']==0:\n",
        "        print(row['Country/Region'])\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "794c54ed-02cf-4eaf-b13a-5f9d09fc1626",
      "metadata": {
        "id": "794c54ed-02cf-4eaf-b13a-5f9d09fc1626"
      },
      "outputs": [],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "18e97020-20ca-4638-8065-4f0fecddb789",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18e97020-20ca-4638-8065-4f0fecddb789",
        "outputId": "abebf26a-4c41-4c65-922a-383b369514ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: geopy in /usr/local/lib/python3.11/dist-packages (2.4.1)\n",
            "Requirement already satisfied: geographiclib<3,>=1.52 in /usr/local/lib/python3.11/dist-packages (from geopy) (2.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install geopy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "ba5cd1eb-9e35-40e3-ad37-732c561d9648",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ba5cd1eb-9e35-40e3-ad37-732c561d9648",
        "outputId": "56c5c688-a78c-438b-a80c-4eb5bb3ac5c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 : This is:    Afghanistan\n",
            "33.7680065 66.2385139 Afghanistan\n",
            "1 : This is:    Albania\n",
            "5.7587654 -73.9151617 Albania\n",
            "2 : This is:    Algeria\n",
            "28.0000272 2.9999825 Algeria\n",
            "3 : This is:    Andorra\n",
            "42.5407167 1.5732033 Andorra\n",
            "4 : This is:    Angola\n",
            "-11.8775768 17.5691241 Angola\n",
            "5 : This is:    Antigua and Barbuda\n",
            "17.2234721 -61.9554608 Antigua and Barbuda\n",
            "6 : This is:    Argentina\n",
            "-34.9964963 -64.9672817 Argentina\n",
            "7 : This is:    Armenia\n",
            "4.536307 -75.6723751 Armenia\n",
            "8 : This is:    Australia\n",
            "-24.7761086 134.755 Australia\n",
            "9 : This is:    Austria\n",
            "47.59397 14.12456 Austria\n",
            "10 : This is:    Azerbaijan\n",
            "40.3936294 47.7872508 Azerbaijan\n",
            "11 : This is:    Bahamas\n",
            "24.7736546 -78.0000547 Bahamas\n",
            "12 : This is:    Bahrain\n",
            "26.030093 50.553336789123236 Bahrain\n",
            "13 : This is:    Bangladesh\n",
            "24.4769288 90.2934413 Bangladesh\n",
            "14 : This is:    Barbados\n",
            "13.1500331 -59.5250305 Barbados\n",
            "15 : This is:    Belarus\n",
            "53.4250605 27.6971358 Belarus\n",
            "16 : This is:    Belgium\n",
            "50.6402809 4.6667145 Belgium\n",
            "17 : This is:    Belize\n",
            "17.1204943 -88.6859028 Belize\n",
            "18 : This is:    Benin\n",
            "9.5293472 2.2584408 Benin\n",
            "19 : This is:    Bhutan\n",
            "27.549511 90.5119273 Bhutan\n",
            "20 : This is:    Bolivia\n",
            "-17.0568696 -64.9912286 Bolivia\n",
            "21 : This is:    Bosnia and Herzegovina\n",
            "44.3053476 17.5961467 Bosnia and Herzegovina\n",
            "22 : This is:    Botswana\n",
            "-23.1681782 24.5928742 Botswana\n",
            "23 : This is:    Brazil\n",
            "-10.3333333 -53.2 Brazil\n",
            "24 : This is:    Brunei\n",
            "4.4137155 114.5653908 Brunei\n",
            "25 : This is:    Bulgaria\n",
            "42.6073975 25.4856617 Bulgaria\n",
            "26 : This is:    Burkina Faso\n",
            "12.0753083 -1.6880314 Burkina Faso\n",
            "27 : This is:    Burma\n",
            "17.1750495 95.9999652 Burma\n",
            "28 : This is:    Burundi\n",
            "-3.426449 29.9324519 Burundi\n",
            "29 : This is:    Cabo Verde\n",
            "16.0000552 -24.0083947 Cabo Verde\n",
            "30 : This is:    Cambodia\n",
            "12.5433216 104.8144914 Cambodia\n",
            "31 : This is:    Cameroon\n",
            "4.6125522 13.1535811 Cameroon\n",
            "32 : This is:    Canada\n",
            "61.0666922 -107.991707 Canada\n",
            "33 : This is:    Central African Republic\n",
            "7.0323598 19.9981227 Central African Republic\n",
            "34 : This is:    Chad\n",
            "15.6134137 19.0156172 Chad\n",
            "35 : This is:    Chile\n",
            "-31.7613365 -71.3187697 Chile\n",
            "36 : This is:    China\n",
            "35.0000663 104.999955 China\n",
            "37 : This is:    Colombia\n",
            "4.099917 -72.9088133 Colombia\n",
            "38 : This is:    Comoros\n",
            "-12.2045176 44.2832964 Comoros\n",
            "39 : This is:    Congo (Brazzaville)\n",
            "-0.7264327 15.6419155 Congo (Brazzaville)\n",
            "40 : This is:    Congo (Kinshasa)\n",
            "-2.9814344 23.8222636 Congo (Kinshasa)\n",
            "41 : This is:    Costa Rica\n",
            "10.2735633 -84.0739102 Costa Rica\n",
            "42 : This is:    Cote d'Ivoire\n",
            "7.9897371 -5.5679458 Cote d'Ivoire\n",
            "43 : This is:    Croatia\n",
            "45.3658443 15.6575209 Croatia\n",
            "44 : This is:    Cuba\n",
            "23.0131338 -80.8328748 Cuba\n",
            "45 : This is:    Cyprus\n",
            "34.9174159 32.889902651331866 Cyprus\n",
            "46 : This is:    Czechia\n",
            "49.7439047 15.3381061 Czechia\n",
            "47 : This is:    Denmark\n",
            "55.670249 10.3333283 Denmark\n",
            "48 : This is:    Djibouti\n",
            "11.8145966 42.8453061 Djibouti\n",
            "49 : This is:    Dominica\n",
            "15.4113138 -61.3653618 Dominica\n",
            "50 : This is:    Dominican Republic\n",
            "19.0974031 -70.3028026 Dominican Republic\n",
            "51 : This is:    Ecuador\n",
            "-1.3397668 -79.3666965 Ecuador\n",
            "52 : This is:    Egypt\n",
            "26.2540493 29.2675469 Egypt\n",
            "53 : This is:    El Salvador\n",
            "13.8000382 -88.9140683 El Salvador\n",
            "54 : This is:    Equatorial Guinea\n",
            "1.613172 10.5170357 Equatorial Guinea\n",
            "55 : This is:    Eritrea\n",
            "15.9500319 37.9999668 Eritrea\n",
            "56 : This is:    Estonia\n",
            "58.7523778 25.3319078 Estonia\n",
            "57 : This is:    Eswatini\n",
            "-26.5624806 31.3991317 Eswatini\n",
            "58 : This is:    Ethiopia\n",
            "10.2116702 38.6521203 Ethiopia\n",
            "59 : This is:    Fiji\n",
            "-18.1239696 179.0122737 Fiji\n",
            "60 : This is:    Finland\n",
            "63.2467777 25.9209164 Finland\n",
            "61 : This is:    France\n",
            "46.603354 1.8883335 France\n",
            "62 : This is:    Gabon\n",
            "-0.8999695 11.6899699 Gabon\n",
            "63 : This is:    Gambia\n",
            "13.470062 -15.4900464 Gambia\n",
            "64 : This is:    Georgia\n",
            "32.3293809 -83.1137366 Georgia\n",
            "65 : This is:    Germany\n",
            "51.1638175 10.4478313 Germany\n",
            "66 : This is:    Ghana\n",
            "8.0300284 -1.0800271 Ghana\n",
            "67 : This is:    Greece\n",
            "38.9953683 21.9877132 Greece\n",
            "68 : This is:    Greenland\n",
            "43.0361995 -70.8328322 Greenland\n",
            "69 : This is:    Grenada\n",
            "12.1360374 -61.6904045 Grenada\n",
            "70 : This is:    Guatemala\n",
            "15.5855545 -90.345759 Guatemala\n",
            "71 : This is:    Guinea\n",
            "10.7226226 -10.7083587 Guinea\n",
            "72 : This is:    Guinea-Bissau\n",
            "11.815215 -15.2351044 Guinea-Bissau\n",
            "73 : This is:    Guyana\n",
            "4.8417097 -58.6416891 Guyana\n",
            "74 : This is:    Haiti\n",
            "19.1399952 -72.3570972 Haiti\n",
            "75 : This is:    Holy See\n",
            "30.034098800664776 -95.81166865095464 Holy See\n",
            "76 : This is:    Honduras\n",
            "15.2572432 -86.0755145 Honduras\n",
            "77 : This is:    Hungary\n",
            "47.1817585 19.5060937 Hungary\n",
            "78 : This is:    Iceland\n",
            "64.9841821 -18.1059013 Iceland\n",
            "79 : This is:    India\n",
            "22.3511148 78.6677428 India\n",
            "80 : This is:    Indonesia\n",
            "-2.4833826 117.8902853 Indonesia\n",
            "81 : This is:    Iran\n",
            "32.6475314 54.5643516 Iran\n",
            "82 : This is:    Iraq\n",
            "33.0955793 44.1749775 Iraq\n",
            "83 : This is:    Ireland\n",
            "52.865196 -7.9794599 Ireland\n",
            "84 : This is:    Israel\n",
            "30.8124247 34.8594762 Israel\n",
            "85 : This is:    Italy\n",
            "42.6384261 12.674297 Italy\n",
            "86 : This is:    Jamaica\n",
            "18.1850507 -77.3947693 Jamaica\n",
            "87 : This is:    Japan\n",
            "36.5748441 139.2394179 Japan\n",
            "88 : This is:    Jordan\n",
            "44.6663146 -93.6261918 Jordan\n",
            "89 : This is:    Kazakhstan\n",
            "48.1012954 66.7780818 Kazakhstan\n",
            "90 : This is:    Kenya\n",
            "1.4419683 38.4313975 Kenya\n",
            "91 : This is:    Kosovo\n",
            "42.5869578 20.9021231 Kosovo\n",
            "92 : This is:    Kuwait\n",
            "29.3796532 47.9734174 Kuwait\n",
            "93 : This is:    Kyrgyzstan\n",
            "41.5089324 74.724091 Kyrgyzstan\n",
            "94 : This is:    Laos\n",
            "20.0171109 103.378253 Laos\n",
            "95 : This is:    Latvia\n",
            "56.8406494 24.7537645 Latvia\n",
            "96 : This is:    Lebanon\n",
            "40.375713 -76.4626118 Lebanon\n",
            "97 : This is:    Lesotho\n",
            "-29.6039267 28.3350193 Lesotho\n",
            "98 : This is:    Liberia\n",
            "5.7499721 -9.3658524 Liberia\n",
            "99 : This is:    Libya\n",
            "26.8234472 18.1236723 Libya\n",
            "100 : This is:    Liechtenstein\n",
            "47.1416307 9.5531527 Liechtenstein\n",
            "101 : This is:    Lithuania\n",
            "55.3500003 23.7499997 Lithuania\n",
            "102 : This is:    Luxembourg\n",
            "49.6112768 6.129799 Luxembourg\n",
            "103 : This is:    Madagascar\n",
            "-18.9249604 46.4416422 Madagascar\n",
            "104 : This is:    Malawi\n",
            "-13.2687204 33.9301963 Malawi\n",
            "105 : This is:    Malaysia\n",
            "4.5693754 102.2656823 Malaysia\n",
            "106 : This is:    Maldives\n",
            "3.7203503 73.2244152 Maldives\n",
            "107 : This is:    Mali\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:urllib3.connectionpool:Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError(\"HTTPSConnectionPool(host='nominatim.openstreetmap.org', port=443): Read timed out. (read timeout=1)\")': /search?q=Mali&format=json&limit=1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16.3700359 -2.2900239 Mali\n",
            "108 : This is:    Malta\n",
            "35.8885993 14.4476911 Malta\n",
            "109 : This is:    Mauritania\n",
            "20.2540382 -9.2399263 Mauritania\n",
            "110 : This is:    Mauritius\n",
            "-20.2759451 57.5703566 Mauritius\n",
            "111 : This is:    Mexico\n",
            "23.6585116 -102.0077097 Mexico\n",
            "112 : This is:    Moldova\n",
            "47.2879608 28.5670941 Moldova\n",
            "113 : This is:    Monaco\n",
            "43.7323492 7.4276832 Monaco\n",
            "114 : This is:    Mongolia\n",
            "46.8250388 103.8499736 Mongolia\n",
            "115 : This is:    Montenegro\n",
            "-29.6826112 -51.4687455 Montenegro\n",
            "116 : This is:    Morocco\n",
            "28.3347722 -10.371337908392647 Morocco\n",
            "117 : This is:    Mozambique\n",
            "-19.302233 34.9144977 Mozambique\n",
            "118 : This is:    Namibia\n",
            "-23.2335499 17.3231107 Namibia\n",
            "119 : This is:    Nepal\n",
            "28.3780464 83.9999901 Nepal\n",
            "120 : This is:    Netherlands\n",
            "52.2434979 5.6343227 Netherlands\n",
            "121 : This is:    New Zealand\n",
            "-41.5000831 172.8344077 New Zealand\n",
            "122 : This is:    Nicaragua\n",
            "12.6090157 -85.2936911 Nicaragua\n",
            "123 : This is:    Niger\n",
            "17.7356214 9.3238432 Niger\n",
            "124 : This is:    Nigeria\n",
            "9.6000359 7.9999721 Nigeria\n",
            "125 : This is:    North Macedonia\n",
            "41.6171214 21.7168387 North Macedonia\n",
            "126 : This is:    Norway\n",
            "64.5731537 11.52803643954819 Norway\n",
            "127 : This is:    Oman\n",
            "21.0000287 57.0036901 Oman\n",
            "128 : This is:    Pakistan\n",
            "30.3308401 71.247499 Pakistan\n",
            "129 : This is:    Panama\n",
            "8.559559 -81.1308434 Panama\n",
            "130 : This is:    Papua New Guinea\n",
            "-5.6816069 144.2489081 Papua New Guinea\n",
            "131 : This is:    Paraguay\n",
            "-23.3165935 -58.1693445 Paraguay\n",
            "132 : This is:    Peru\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:urllib3.connectionpool:Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError(\"HTTPSConnectionPool(host='nominatim.openstreetmap.org', port=443): Read timed out. (read timeout=1)\")': /search?q=Peru&format=json&limit=1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-6.8699697 -75.0458515 Peru\n",
            "133 : This is:    Philippines\n",
            "12.7503486 122.7312101 Philippines\n",
            "134 : This is:    Poland\n",
            "52.215933 19.134422 Poland\n",
            "135 : This is:    Portugal\n",
            "39.6621648 -8.1353519 Portugal\n",
            "136 : This is:    Qatar\n",
            "25.3336984 51.2295295 Qatar\n",
            "137 : This is:    Romania\n",
            "45.9852129 24.6859225 Romania\n",
            "138 : This is:    Russia\n",
            "64.6863136 97.7453061 Russia\n",
            "139 : This is:    Rwanda\n",
            "-1.9646631 30.0644358 Rwanda\n",
            "140 : This is:    Saint Kitts and Nevis\n",
            "17.250512 -62.6725973 Saint Kitts and Nevis\n",
            "141 : This is:    Saint Lucia\n",
            "13.8250489 -60.975036 Saint Lucia\n",
            "142 : This is:    Saint Vincent and the Grenadines\n",
            "12.90447 -61.2765569 Saint Vincent and the Grenadines\n",
            "143 : This is:    San Marino\n",
            "43.9458623 12.458306 San Marino\n",
            "144 : This is:    Sao Tome and Principe\n",
            "0.9713095 7.02255 Sao Tome and Principe\n",
            "145 : This is:    Saudi Arabia\n",
            "25.6242618 42.3528328 Saudi Arabia\n",
            "146 : This is:    Senegal\n",
            "14.4750607 -14.4529612 Senegal\n",
            "147 : This is:    Serbia\n",
            "44.024322850000004 21.07657433209902 Serbia\n",
            "148 : This is:    Seychelles\n",
            "-4.6574977 55.4540146 Seychelles\n",
            "149 : This is:    Sierra Leone\n",
            "8.6400349 -11.8400269 Sierra Leone\n",
            "150 : This is:    Singapore\n",
            "1.357107 103.8194992 Singapore\n",
            "151 : This is:    Slovakia\n",
            "48.7411522 19.4528646 Slovakia\n",
            "152 : This is:    Slovenia\n",
            "46.1199444 14.8153333 Slovenia\n",
            "153 : This is:    Somalia\n",
            "8.3676771 49.083416 Somalia\n",
            "154 : This is:    South Africa\n",
            "-28.8166236 24.991639 South Africa\n",
            "155 : This is:    South Korea\n",
            "36.638392 127.6961188 South Korea\n",
            "156 : This is:    South Sudan\n",
            "7.8699431 29.6667897 South Sudan\n",
            "157 : This is:    Spain\n",
            "39.3260685 -4.8379791 Spain\n",
            "158 : This is:    Sri Lanka\n",
            "7.5554942 80.7137847 Sri Lanka\n",
            "159 : This is:    Sudan\n",
            "34.0678644 -102.524362 Sudan\n",
            "160 : This is:    Suriname\n",
            "4.1413025 -56.0771187 Suriname\n",
            "161 : This is:    Sweden\n",
            "59.6749712 14.5208584 Sweden\n",
            "162 : This is:    Switzerland\n",
            "46.7985624 8.2319736 Switzerland\n",
            "163 : This is:    Syria\n",
            "34.8147886 38.42317811509885 Syria\n",
            "164 : This is:    Taiwan*\n",
            "23.5983227 120.83537694479215 Taiwan*\n",
            "165 : This is:    Tajikistan\n",
            "38.6281733 70.8156541 Tajikistan\n",
            "166 : This is:    Tanzania\n",
            "-6.5247123 35.7878438 Tanzania\n",
            "167 : This is:    Thailand\n",
            "14.8971921 100.83273 Thailand\n",
            "168 : This is:    Timor-Leste\n",
            "-8.7443169 126.063482 Timor-Leste\n",
            "169 : This is:    Togo\n",
            "8.7800265 1.0199765 Togo\n",
            "170 : This is:    Trinidad and Tobago\n",
            "10.7466905 -61.0840075 Trinidad and Tobago\n",
            "171 : This is:    Tunisia\n",
            "36.8002068 10.1857757 Tunisia\n",
            "172 : This is:    Turkey\n",
            "39.294076 35.2316631 Turkey\n",
            "173 : This is:    US\n",
            "39.7837304 -100.445882 US\n",
            "174 : This is:    Uganda\n",
            "1.5333554 32.2166578 Uganda\n",
            "175 : This is:    Ukraine\n",
            "49.4871968 31.2718321 Ukraine\n",
            "176 : This is:    United Arab Emirates\n",
            "24.0002488 53.9994829 United Arab Emirates\n",
            "177 : This is:    United Kingdom\n",
            "54.7023545 -3.2765753 United Kingdom\n",
            "178 : This is:    Uruguay\n",
            "-32.8755548 -56.0201525 Uruguay\n",
            "179 : This is:    Uzbekistan\n",
            "41.32373 63.9528098 Uzbekistan\n",
            "180 : This is:    Venezuela\n",
            "8.0018709 -66.1109318 Venezuela\n",
            "181 : This is:    Vietnam\n",
            "15.9266657 107.9650855 Vietnam\n",
            "182 : This is:    West Bank and Gaza\n",
            "31.9049661 35.2023413 West Bank and Gaza\n",
            "183 : This is:    Western Sahara\n",
            "24.1797324 -13.7667848 Western Sahara\n",
            "184 : This is:    Yemen\n",
            "16.3471243 47.8915271 Yemen\n",
            "185 : This is:    Zambia\n",
            "-14.5189121 27.5589884 Zambia\n",
            "186 : This is:    Zimbabwe\n",
            "-18.4554963 29.7468414 Zimbabwe\n"
          ]
        }
      ],
      "source": [
        "from geopy.geocoders import Nominatim\n",
        "import time\n",
        "#Geolocation is a simple and clever application which uses google maps api.\n",
        "#This application allows you to easily and quickly get information about given localisation.\n",
        "geolocator = Nominatim(user_agent=\"Vahid Rafe\")\n",
        "lati=[]\n",
        "longi=[]\n",
        "counter=0\n",
        "for index, row in df.iterrows():\n",
        "    time.sleep(2)\n",
        "    i=row['Country/Region']\n",
        "\n",
        "    print(counter, \": This is:   \", i)\n",
        "    if i != None:\n",
        "\n",
        "        location = geolocator.geocode(i)\n",
        "        if location!=None:\n",
        "            lati.append(location.latitude)\n",
        "            longi.append(location.longitude)\n",
        "        else:\n",
        "            lati.append(float(\"Nan\"))\n",
        "            longi.append(float(\"Nan\"))\n",
        "        print(lati[counter], longi[counter], i)\n",
        "        counter=counter+1\n",
        "\n",
        "    else:\n",
        "        print(\"Nothing\")\n",
        "        lati.append(float(\"Nan\"))\n",
        "        longi.append(float(\"Nan\"))\n",
        "        print(lati[counter], longi[counter], i)\n",
        "        counter=counter+1\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e94a7b24-8804-45a7-9503-1037f5e42fa8",
      "metadata": {
        "id": "e94a7b24-8804-45a7-9503-1037f5e42fa8"
      },
      "outputs": [],
      "source": [
        "df['Country/Region']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cc64956b-98e2-42a4-8dfd-1c5684788a6e",
      "metadata": {
        "id": "cc64956b-98e2-42a4-8dfd-1c5684788a6e"
      },
      "outputs": [],
      "source": [
        "df['lat']=lati\n",
        "df['long']=longi\n",
        "for index, row in df.iterrows():\n",
        "    print(row['Country/Region'], row['lat'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fe75df49-2ebd-4549-b327-4dc9a40541a8",
      "metadata": {
        "id": "fe75df49-2ebd-4549-b327-4dc9a40541a8"
      },
      "outputs": [],
      "source": [
        "# ---- Visualizing Clusters on a World Map ----\n",
        "world_map = folium.Map(location=[20, 0], zoom_start=2)\n",
        "\n",
        "marker_cluster = MarkerCluster().add_to(world_map)\n",
        "\n",
        "for _, row in df.iterrows():\n",
        "    folium.Marker(\n",
        "        location=[row['lat'], row['long']],\n",
        "        popup=f\"Country: {row['Country/Region']}<br>New cases: {row['New cases']}<br>New Deaths: {row['New deaths']}<br>New Recovered: {row['New recovered']}\",\n",
        "        icon=folium.Icon(color=[\"red\", \"blue\", \"green\", \"purple\", \"orange\", \"black\"][row[\"Cluster\"]])\n",
        "    ).add_to(marker_cluster)\n",
        "\n",
        "# Save map to an HTML file\n",
        "world_map.save(\"covid18_clusters_map.html\")\n",
        "print(\"✅ World Map with Clusters Saved as 'covid18_clusters_map.html'\")\n",
        "#print(\"✅ Map saved successfully!\")\n",
        "\n",
        "# Open the saved file in Jupyter Notebook\n",
        "import webbrowser\n",
        "webbrowser.open(\"covid18_clusters_map.html\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8c5cbf8e-6904-468f-b349-3cea521ff63b",
      "metadata": {
        "id": "8c5cbf8e-6904-468f-b349-3cea521ff63b"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python [conda env:base] *",
      "language": "python",
      "name": "conda-base-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.7"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}