Python_Libraries_(Examples).ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MoonlightO2/Python-Libraries-Examples-/blob/main/Python_Libraries_(Examples).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Python Libraries (Examples)**\n",
        "***Article***: https://medium.com/@shashpeiris/key-python-libraries-for-data-analysis-and-code-examples-f15c8a2349c1\n",
        "\n",
        "***Special thanking***: Eugeniu Ghelbur (https://medium.com/@e.ghelbur)"
      ],
      "metadata": {
        "id": "Sdhcy2slGqoE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1. Numpy**"
      ],
      "metadata": {
        "id": "tAKDCyXsG-XI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UjcTrzqkGogW",
        "outputId": "6c4248ec-2a1e-476d-f5d1-27422add99c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: 3.0, Standard Deviation: 1.4142135623730951\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Creating a NumPy array\n",
        "arr = np.array([1, 2, 3, 4, 5])\n",
        "\n",
        "# Calculating mean and standard deviation\n",
        "mean_value = np.mean(arr)\n",
        "std_dev = np.std(arr)\n",
        "\n",
        "print(f\"Mean: {mean_value}, Standard Deviation: {std_dev}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2. Pandas**"
      ],
      "metadata": {
        "id": "dsEH-6bOHV1R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Creating a Pandas DataFrame\n",
        "data = {'Name': ['John', 'Alice', 'Bob'],\n",
        "        'Age': [25, 28, 22]}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Displaying the DataFrame\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdsNOa7lHUpl",
        "outputId": "454392d9-3479-4308-fc83-de578229b951"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Name  Age\n",
            "0   John   25\n",
            "1  Alice   28\n",
            "2    Bob   22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3. Matplotlib**"
      ],
      "metadata": {
        "id": "gm2HqOEeIT5N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Creating a simple line plot\n",
        "x = [1, 2, 3, 4, 5]\n",
        "y = [2, 4, 6, 8, 10]\n",
        "\n",
        "plt.plot(x, y)\n",
        "plt.xlabel('X-axis')\n",
        "plt.ylabel('Y-axis')\n",
        "plt.title('Simple Line Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "rUKyHVVSIWlf",
        "outputId": "17ced5c2-cd96-4392-abdb-0eac9428046e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4. Seaborn**"
      ],
      "metadata": {
        "id": "37JIJs9TJt51"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Using Seaborn to create a scatter plot\n",
        "tips = sns.load_dataset('tips')\n",
        "sns.scatterplot(x='total_bill', y='tip', data=tips)\n",
        "plt.title('Scatter Plot using Seaborn')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "6XqXCg8JJtPW",
        "outputId": "7dc615ef-9027-47c9-eda1-16ecf7f62946"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **5. SciPy**"
      ],
      "metadata": {
        "id": "HdFBFKheJ6gJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats\n",
        "\n",
        "# Performing a t-test\n",
        "data1 = [1, 2, 3, 4, 5]\n",
        "data2 = [2, 4, 6, 8, 10]\n",
        "\n",
        "t_stat, p_value = scipy.stats.ttest_ind(data1, data2)\n",
        "print(f\"T-statistic: {t_stat}, p-value: {p_value}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ba4d3dcZLywU",
        "outputId": "93e302d6-f8d2-48e9-ad4a-a80fcb662dbc"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-statistic: -1.8973665961010275, p-value: 0.09434977284243756\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **6. Scikit-learn**"
      ],
      "metadata": {
        "id": "y9onwoOYJ6cn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Load iris dataset as an example\n",
        "iris = load_iris()\n",
        "X = iris.data\n",
        "y = iris.target\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Standardize the features using StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Create and train a Logistic Regression model\n",
        "model = LogisticRegression(max_iter=1000)\n",
        "model.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "predictions = model.predict(X_test_scaled)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "\n",
        "print(f\"Accuracy: {accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oaa7L3fGMCFo",
        "outputId": "051faa46-0929-46d0-fd65-661edfe6f36c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **7. Statsmodels**"
      ],
      "metadata": {
        "id": "BJhTPxPmJ6Ow"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "\n",
        "# Performing linear regression\n",
        "x = [1, 2, 3, 4, 5]\n",
        "y = [2, 4, 5, 4, 5]\n",
        "\n",
        "x = sm.add_constant(x)\n",
        "model = sm.OLS(y, x).fit()\n",
        "\n",
        "# Displaying regression summary\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnFgASu6MwGt",
        "outputId": "3f778bc3-4b64-4261-9372-0fd7cd21b495"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.600\n",
            "Model:                            OLS   Adj. R-squared:                  0.467\n",
            "Method:                 Least Squares   F-statistic:                     4.500\n",
            "Date:                Sun, 07 Jan 2024   Prob (F-statistic):              0.124\n",
            "Time:                        00:55:22   Log-Likelihood:                -5.2598\n",
            "No. Observations:                   5   AIC:                             14.52\n",
            "Df Residuals:                       3   BIC:                             13.74\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          2.2000      0.938      2.345      0.101      -0.785       5.185\n",
            "x1             0.6000      0.283      2.121      0.124      -0.300       1.500\n",
            "==============================================================================\n",
            "Omnibus:                          nan   Durbin-Watson:                   2.017\n",
            "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.570\n",
            "Skew:                           0.289   Prob(JB):                        0.752\n",
            "Kurtosis:                       1.450   Cond. No.                         8.37\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **8. Dask**"
      ],
      "metadata": {
        "id": "n5xnIvvAJ6LW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import dask.dataframe as dd\n",
        "\n",
        "# Creating a Dask DataFrame\n",
        "data = {'Name': ['John', 'Alice', 'Bob'],\n",
        "        'Age': [25, 28, 22]}\n",
        "df = dd.from_pandas(pd.DataFrame(data), npartitions=2)\n",
        "\n",
        "# Performing a simple computation\n",
        "result = df['Age'].mean().compute()\n",
        "print(f\"Mean Age: {result}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cg-eLLEFNEwG",
        "outputId": "d0d83c51-76db-40f3-bf8d-6d23a16c39c5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Age: 25.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **9. Bokeh**"
      ],
      "metadata": {
        "id": "yZQKfp6eJ6I1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from bokeh.plotting import figure, show\n",
        "from bokeh.io import output_notebook\n",
        "\n",
        "# Creating a simple Bokeh plot\n",
        "output_notebook()\n",
        "\n",
        "x = [1, 2, 3, 4, 5]\n",
        "y = [2, 4, 6, 8, 10]\n",
        "\n",
        "p = figure(title=\"Simple Bokeh Plot\", x_axis_label='X-axis', y_axis_label='Y-axis')\n",
        "p.line(x, y)\n",
        "\n",
        "show(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "UXIGgowdNc_t",
        "outputId": "1facd69a-8c4f-448e-deab-4e9080795602"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function now() {\n",
              "    return new Date();\n",
              "  }\n",
              "\n",
              "  const force = true;\n",
              "\n",
              "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
              "    root._bokeh_onload_callbacks = [];\n",
              "    root._bokeh_is_loading = undefined;\n",
              "  }\n",
              "\n",
              "const JS_MIME_TYPE = 'application/javascript';\n",
              "  const HTML_MIME_TYPE = 'text/html';\n",
              "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
              "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
              "\n",
              "  /**\n",
              "   * Render data to the DOM node\n",
              "   */\n",
              "  function render(props, node) {\n",
              "    const script = document.createElement(\"script\");\n",
              "    node.appendChild(script);\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when an output is cleared or removed\n",
              "   */\n",
              "  function handleClearOutput(event, handle) {\n",
              "    function drop(id) {\n",
              "      const view = Bokeh.index.get_by_id(id)\n",
              "      if (view != null) {\n",
              "        view.model.document.clear()\n",
              "        Bokeh.index.delete(view)\n",
              "      }\n",
              "    }\n",
              "\n",
              "    const cell = handle.cell;\n",
              "\n",
              "    const id = cell.output_area._bokeh_element_id;\n",
              "    const server_id = cell.output_area._bokeh_server_id;\n",
              "\n",
              "    // Clean up Bokeh references\n",
              "    if (id != null) {\n",
              "      drop(id)\n",
              "    }\n",
              "\n",
              "    if (server_id !== undefined) {\n",
              "      // Clean up Bokeh references\n",
              "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
              "      cell.notebook.kernel.execute(cmd_clean, {\n",
              "        iopub: {\n",
              "          output: function(msg) {\n",
              "            const id = msg.content.text.trim()\n",
              "            drop(id)\n",
              "          }\n",
              "        }\n",
              "      });\n",
              "      // Destroy server and session\n",
              "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
              "      cell.notebook.kernel.execute(cmd_destroy);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when a new output is added\n",
              "   */\n",
              "  function handleAddOutput(event, handle) {\n",
              "    const output_area = handle.output_area;\n",
              "    const output = handle.output;\n",
              "\n",
              "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
              "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
              "      return\n",
              "    }\n",
              "\n",
              "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
              "\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
              "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
              "      // store reference to embed id on output_area\n",
              "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
              "    }\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
              "      const bk_div = document.createElement(\"div\");\n",
              "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
              "      const script_attrs = bk_div.children[0].attributes;\n",
              "      for (let i = 0; i < script_attrs.length; i++) {\n",
              "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
              "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
              "      }\n",
              "      // store reference to server id on output_area\n",
              "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function register_renderer(events, OutputArea) {\n",
              "\n",
              "    function append_mime(data, metadata, element) {\n",
              "      // create a DOM node to render to\n",
              "      const toinsert = this.create_output_subarea(\n",
              "        metadata,\n",
              "        CLASS_NAME,\n",
              "        EXEC_MIME_TYPE\n",
              "      );\n",
              "      this.keyboard_manager.register_events(toinsert);\n",
              "      // Render to node\n",
              "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
              "      render(props, toinsert[toinsert.length - 1]);\n",
              "      element.append(toinsert);\n",
              "      return toinsert\n",
              "    }\n",
              "\n",
              "    /* Handle when an output is cleared or removed */\n",
              "    events.on('clear_output.CodeCell', handleClearOutput);\n",
              "    events.on('delete.Cell', handleClearOutput);\n",
              "\n",
              "    /* Handle when a new output is added */\n",
              "    events.on('output_added.OutputArea', handleAddOutput);\n",
              "\n",
              "    /**\n",
              "     * Register the mime type and append_mime function with output_area\n",
              "     */\n",
              "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
              "      /* Is output safe? */\n",
              "      safe: true,\n",
              "      /* Index of renderer in `output_area.display_order` */\n",
              "      index: 0\n",
              "    });\n",
              "  }\n",
              "\n",
              "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
              "  if (root.Jupyter !== undefined) {\n",
              "    const events = require('base/js/events');\n",
              "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
              "\n",
              "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
              "      register_renderer(events, OutputArea);\n",
              "    }\n",
              "  }\n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
              "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
              "     \"<div style='background-color: #fdd'>\\n\"+\n",
              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
              "     \"</p>\\n\"+\n",
              "     \"<ul>\\n\"+\n",
              "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
              "     \"</ul>\\n\"+\n",
              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
              "     \"</code>\\n\"+\n",
              "     \"</div>\"}};\n",
              "\n",
              "  function display_loaded() {\n",
              "    const el = document.getElementById(null);\n",
              "    if (el != null) {\n",
              "      el.textContent = \"BokehJS is loading...\";\n",
              "    }\n",
              "    if (root.Bokeh !== undefined) {\n",
              "      if (el != null) {\n",
              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
              "      });\n",
              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
              "        run_callbacks()\n",
              "      }\n",
              "    }\n",
              "\n",
              "    function on_error(url) {\n",
              "      console.error(\"failed to load \" + url);\n",
              "    }\n",
              "\n",
              "    for (let i = 0; i < css_urls.length; i++) {\n",
              "      const url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    for (let i = 0; i < js_urls.length; i++) {\n",
              "      const url = js_urls[i];\n",
              "      const element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.2.min.js\"];\n",
              "  const css_urls = [];\n",
              "\n",
              "  const inline_js = [    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "function(Bokeh) {\n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "          for (let i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "} else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"e0228213-aee3-461b-bbc4-6766118b814c\" data-root-id=\"p1001\" style=\"display: contents;\"></div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function embed_document(root) {\n",
              "  const docs_json = {\"1cd89f10-8998-4a4c-bba6-af33f025b9fe\":{\"version\":\"3.3.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1001\",\"attributes\":{\"x_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1002\"},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1003\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1011\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1012\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1004\",\"attributes\":{\"text\":\"Simple Bokeh Plot\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1040\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1034\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1035\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1036\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",[1,2,3,4,5]],[\"y\",[2,4,6,8,10]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1041\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1042\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1037\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#1f77b4\"}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1038\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#1f77b4\",\"line_alpha\":0.1}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1039\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#1f77b4\",\"line_alpha\":0.2}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1010\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1023\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1024\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1025\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1026\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1031\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1032\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p1033\"}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1018\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1019\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1020\"},\"axis_label\":\"Y-axis\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1021\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1013\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1014\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1015\"},\"axis_label\":\"X-axis\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1016\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1017\",\"attributes\":{\"axis\":{\"id\":\"p1013\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1022\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1018\"}}}]}}]}};\n",
              "  const render_items = [{\"docid\":\"1cd89f10-8998-4a4c-bba6-af33f025b9fe\",\"roots\":{\"p1001\":\"e0228213-aee3-461b-bbc4-6766118b814c\"},\"root_ids\":[\"p1001\"]}];\n",
              "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
              "  }\n",
              "  if (root.Bokeh !== undefined) {\n",
              "    embed_document(root);\n",
              "  } else {\n",
              "    let attempts = 0;\n",
              "    const timer = setInterval(function(root) {\n",
              "      if (root.Bokeh !== undefined) {\n",
              "        clearInterval(timer);\n",
              "        embed_document(root);\n",
              "      } else {\n",
              "        attempts++;\n",
              "        if (attempts > 100) {\n",
              "          clearInterval(timer);\n",
              "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
              "        }\n",
              "      }\n",
              "    }, 10, root)\n",
              "  }\n",
              "})(window);"
            ],
            "application/vnd.bokehjs_exec.v0+json": ""
          },
          "metadata": {
            "application/vnd.bokehjs_exec.v0+json": {
              "id": "p1001"
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **10. NLTK**"
      ],
      "metadata": {
        "id": "4HrUSLIHJ6GM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "nltk.download('punkt')\n",
        "\n",
        "# Tokenizing a sentence\n",
        "sentence = \"Natural Language Processing is fascinating.\"\n",
        "tokens = word_tokenize(sentence)\n",
        "\n",
        "print(tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jtXcWkrANdg-",
        "outputId": "d11b5d66-adb2-4641-9400-3316e8cc4dcc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Natural', 'Language', 'Processing', 'is', 'fascinating', '.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **11. Beautiful Soup**"
      ],
      "metadata": {
        "id": "EL9f1mq1J569"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from bs4 import BeautifulSoup\n",
        "\n",
        "# Parsing HTML content\n",
        "html_content = \"<html><body><p>This is a paragraph.</p></body></html>\"\n",
        "soup = BeautifulSoup(html_content, 'html.parser')\n",
        "\n",
        "# Extracting text from the paragraph tag\n",
        "paragraph_text = soup.find('p').text\n",
        "print(paragraph_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SrDHzMmiNeAN",
        "outputId": "d0214e4b-e6e8-42be-8e05-3b701f8682c0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is a paragraph.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **12. Plotly**"
      ],
      "metadata": {
        "id": "oE4eGTwyJ53k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "# Creating a Plotly scatter plot\n",
        "df = px.data.iris()\n",
        "fig = px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", color=\"species\", title=\"Iris Dataset\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "hhJu90_pNeXS",
        "outputId": "fc7c1d8d-cb8f-4431-d102-578f3c572600"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"sepal_width\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"sepal_length\"}},\"legend\":{\"title\":{\"text\":\"species\"},\"tracegroupgap\":0},\"title\":{\"text\":\"Iris Dataset\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('460d41ce-8e73-4d6c-a2d1-df41ff417fe8');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **13. Altair**"
      ],
      "metadata": {
        "id": "Rt5Iq8EeJ50f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install altair_viewer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hOpfsw1TP1xJ",
        "outputId": "76506718-6d0f-4d5a-9f1e-0785d34ea1ad"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting altair_viewer\n",
            "  Downloading altair_viewer-0.4.0-py3-none-any.whl (844 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/844.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.7/844.5 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.6/844.5 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m839.7/844.5 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m844.5/844.5 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: altair in /usr/local/lib/python3.10/dist-packages (from altair_viewer) (4.2.2)\n",
            "Collecting altair-data-server>=0.4.0 (from altair_viewer)\n",
            "  Downloading altair_data_server-0.4.1-py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: portpicker in /usr/local/lib/python3.10/dist-packages (from altair-data-server>=0.4.0->altair_viewer) (1.5.2)\n",
            "Requirement already satisfied: tornado in /usr/local/lib/python3.10/dist-packages (from altair-data-server>=0.4.0->altair_viewer) (6.3.2)\n",
            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.10/dist-packages (from altair->altair_viewer) (0.4)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from altair->altair_viewer) (3.1.2)\n",
            "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.10/dist-packages (from altair->altair_viewer) (4.19.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from altair->altair_viewer) (1.23.5)\n",
            "Requirement already satisfied: pandas>=0.18 in /usr/local/lib/python3.10/dist-packages (from altair->altair_viewer) (1.5.3)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from altair->altair_viewer) (0.12.0)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair->altair_viewer) (23.1.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair->altair_viewer) (2023.11.2)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair->altair_viewer) (0.32.0)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair->altair_viewer) (0.15.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.18->altair->altair_viewer) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.18->altair->altair_viewer) (2023.3.post1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->altair->altair_viewer) (2.1.3)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from portpicker->altair-data-server>=0.4.0->altair_viewer) (5.9.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.18->altair->altair_viewer) (1.16.0)\n",
            "Installing collected packages: altair-data-server, altair_viewer\n",
            "Successfully installed altair-data-server-0.4.1 altair_viewer-0.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import altair as alt\n",
        "import pandas as pd\n",
        "\n",
        "# Creating a simple Altair chart\n",
        "data = pd.DataFrame({'x': [1, 2, 3, 4, 5], 'y': [2, 4, 6, 8, 10]})\n",
        "chart = alt.Chart(data).mark_point().encode(x='x', y='y').properties(title='Altair Chart')\n",
        "\n",
        "chart"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "GVUvxR1ENevG",
        "outputId": "170c90bf-d9ff-47a6-fabf-2167ebb389ae"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<div id=\"altair-viz-e6cd48ae6a27428db4c8d223478dd154\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
              "  (function(spec, embedOpt){\n",
              "    let outputDiv = document.currentScript.previousElementSibling;\n",
              "    if (outputDiv.id !== \"altair-viz-e6cd48ae6a27428db4c8d223478dd154\") {\n",
              "      outputDiv = document.getElementById(\"altair-viz-e6cd48ae6a27428db4c8d223478dd154\");\n",
              "    }\n",
              "    const paths = {\n",
              "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
              "    };\n",
              "\n",
              "    function maybeLoadScript(lib, version) {\n",
              "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
              "      return (VEGA_DEBUG[key] == version) ?\n",
              "        Promise.resolve(paths[lib]) :\n",
              "        new Promise(function(resolve, reject) {\n",
              "          var s = document.createElement('script');\n",
              "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "          s.async = true;\n",
              "          s.onload = () => {\n",
              "            VEGA_DEBUG[key] = version;\n",
              "            return resolve(paths[lib]);\n",
              "          };\n",
              "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
              "          s.src = paths[lib];\n",
              "        });\n",
              "    }\n",
              "\n",
              "    function showError(err) {\n",
              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
              "      throw err;\n",
              "    }\n",
              "\n",
              "    function displayChart(vegaEmbed) {\n",
              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
              "    }\n",
              "\n",
              "    if(typeof define === \"function\" && define.amd) {\n",
              "      requirejs.config({paths});\n",
              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
              "    } else {\n",
              "      maybeLoadScript(\"vega\", \"5\")\n",
              "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
              "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
              "        .catch(showError)\n",
              "        .then(() => displayChart(vegaEmbed));\n",
              "    }\n",
              "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-4159737ac45a13c822387d6595911db8\"}, \"mark\": \"point\", \"encoding\": {\"x\": {\"field\": \"x\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"y\", \"type\": \"quantitative\"}}, \"title\": \"Altair Chart\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-4159737ac45a13c822387d6595911db8\": [{\"x\": 1, \"y\": 2}, {\"x\": 2, \"y\": 4}, {\"x\": 3, \"y\": 6}, {\"x\": 4, \"y\": 8}, {\"x\": 5, \"y\": 10}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ],
            "text/plain": [
              "alt.Chart(...)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **14. Vaex**"
      ],
      "metadata": {
        "id": "GoNgwdQzJ5yD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install vaex"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zxOlPibcQ1ux",
        "outputId": "ec71dca2-1fd2-4b5c-dad9-ace25d20d941"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting vaex\n",
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            "Building wheels for collected packages: aplus\n",
            "  Building wheel for aplus (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for aplus: filename=aplus-0.11.0-py3-none-any.whl size=4366 sha256=03da1ef21b61e85333df5510aff7b8a45c628e27c105b96883417be7823bcbdc\n",
            "  Stored in directory: /root/.cache/pip/wheels/74/ed/14/4d7bffd9475a07582e0c256b9b1ddbbc1f49c58e3150d356f4\n",
            "Successfully built aplus\n",
            "Installing collected packages: ipywebrtc, blake3, aplus, websockets, uvloop, typing-extensions, python-dotenv, httptools, h11, watchfiles, uvicorn, starlette, fastapi, vaex-core, vaex-viz, vaex-server, vaex-ml, vaex-hdf5, vaex-astro, ipyvue, ipympl, ipydatawidgets, pythreejs, ipyvuetify, ipyvolume, vaex-jupyter, vaex\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.5.0\n",
            "    Uninstalling typing_extensions-4.5.0:\n",
            "      Successfully uninstalled typing_extensions-4.5.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aplus-0.11.0 blake3-0.3.4 fastapi-0.108.0 h11-0.14.0 httptools-0.6.1 ipydatawidgets-4.3.5 ipympl-0.9.3 ipyvolume-0.6.3 ipyvue-1.10.1 ipyvuetify-1.8.10 ipywebrtc-0.6.0 python-dotenv-1.0.0 pythreejs-2.4.2 starlette-0.32.0.post1 typing-extensions-4.9.0 uvicorn-0.25.0 uvloop-0.19.0 vaex-4.17.0 vaex-astro-0.9.3 vaex-core-4.17.1 vaex-hdf5-0.14.1 vaex-jupyter-0.8.2 vaex-ml-0.18.3 vaex-server-0.9.0 vaex-viz-0.5.4 watchfiles-0.21.0 websockets-12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import vaex\n",
        "\n",
        "# Creating a Vaex DataFrame\n",
        "data = {'Name': ['John', 'Alice', 'Bob'],\n",
        "        'Age': [25, 28, 22]}\n",
        "df = vaex.from_dict(data)\n",
        "\n",
        "# Displaying the Vaex DataFrame\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20r71oiNNfOC",
        "outputId": "3dea979b-c637-46d7-8516-f956322414ff"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  #  Name      Age\n",
            "  0  John       25\n",
            "  1  Alice      28\n",
            "  2  Bob        22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **15. Geopandas**"
      ],
      "metadata": {
        "id": "nV-mHJ-tJ5ux"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import geopandas as gpd\n",
        "from shapely.geometry import Point\n",
        "\n",
        "# Creating a GeoDataFrame with points\n",
        "geometry = [Point(-74.0059, 40.7128), Point(-73.9862, 40.7306)]\n",
        "gdf = gpd.GeoDataFrame(geometry=geometry, crs=\"EPSG:4326\")\n",
        "\n",
        "# Plotting the GeoDataFrame\n",
        "gdf.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "2Wu55n-cNfl_",
        "outputId": "46689b29-63bf-48b2-9cfa-86b3963b6c60"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **16. Folium**"
      ],
      "metadata": {
        "id": "sfKyozlRJ5rm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import folium\n",
        "\n",
        "# Creating a Folium map\n",
        "map = folium.Map(location=[37.7749, -122.4194], zoom_start=8)\n",
        "\n",
        "# Adding a marker\n",
        "folium.Marker(location=[37.7749, -122.4194], popup='San Francisco').add_to(map)\n",
        "\n",
        "# Displaying the map\n",
        "map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "3YZAEF29NhRL",
        "outputId": "74168ced-54c9-4c20-989b-9476100ba935"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x7c88daf9bcd0>"
            ],
            "text/html": [
              "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;html&gt;\n",
              "&lt;head&gt;\n",
              "    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    \n",
              "        &lt;script&gt;\n",
              "            L_NO_TOUCH = false;\n",
              "            L_DISABLE_3D = false;\n",
              "        &lt;/script&gt;\n",
              "    \n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
              "    \n",
              "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "            &lt;style&gt;\n",
              "                #map_e6c48da06260d1311f74519b6f08b374 {\n",
              "                    position: relative;\n",
              "                    width: 100.0%;\n",
              "                    height: 100.0%;\n",
              "                    left: 0.0%;\n",
              "                    top: 0.0%;\n",
              "                }\n",
              "                .leaflet-container { font-size: 1rem; }\n",
              "            &lt;/style&gt;\n",
              "        \n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;\n",
              "    \n",
              "    \n",
              "            &lt;div class=&quot;folium-map&quot; id=&quot;map_e6c48da06260d1311f74519b6f08b374&quot; &gt;&lt;/div&gt;\n",
              "        \n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;\n",
              "    \n",
              "    \n",
              "            var map_e6c48da06260d1311f74519b6f08b374 = L.map(\n",
              "                &quot;map_e6c48da06260d1311f74519b6f08b374&quot;,\n",
              "                {\n",
              "                    center: [37.7749, -122.4194],\n",
              "                    crs: L.CRS.EPSG3857,\n",
              "                    zoom: 8,\n",
              "                    zoomControl: true,\n",
              "                    preferCanvas: false,\n",
              "                }\n",
              "            );\n",
              "\n",
              "            \n",
              "\n",
              "        \n",
              "    \n",
              "            var tile_layer_3300fdb820fd2af6326b6e5eac0cac3a = L.tileLayer(\n",
              "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
              "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
              "            ).addTo(map_e6c48da06260d1311f74519b6f08b374);\n",
              "        \n",
              "    \n",
              "            var marker_7264f7c1bceb2b1b80862d3be05c61d9 = L.marker(\n",
              "                [37.7749, -122.4194],\n",
              "                {}\n",
              "            ).addTo(map_e6c48da06260d1311f74519b6f08b374);\n",
              "        \n",
              "    \n",
              "        var popup_54b0049c80bbb525ed2943ed59b53a86 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_c805ee909075275177f47884d2e59d4c = $(`&lt;div id=&quot;html_c805ee909075275177f47884d2e59d4c&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;San Francisco&lt;/div&gt;`)[0];\n",
              "                popup_54b0049c80bbb525ed2943ed59b53a86.setContent(html_c805ee909075275177f47884d2e59d4c);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_7264f7c1bceb2b1b80862d3be05c61d9.bindPopup(popup_54b0049c80bbb525ed2943ed59b53a86)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "&lt;/script&gt;\n",
              "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **17. Xarray**"
      ],
      "metadata": {
        "id": "6RikY2DSJ5n6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import xarray as xr\n",
        "\n",
        "# Creating a simple xarray dataset\n",
        "data = xr.DataArray([[1, 2], [3, 4]], dims=('x', 'y'), coords={'x': [0, 1], 'y': [0, 1]})\n",
        "\n",
        "# Displaying the xarray dataset\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2rsreArjNhm9",
        "outputId": "3dd042cc-2425-45ea-de2f-a469e895bdbe"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<xarray.DataArray (x: 2, y: 2)>\n",
            "array([[1, 2],\n",
            "       [3, 4]])\n",
            "Coordinates:\n",
            "  * x        (x) int64 0 1\n",
            "  * y        (y) int64 0 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **18. LightGBM**"
      ],
      "metadata": {
        "id": "tJAAVJX6J5lF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Loading Iris dataset and splitting it\n",
        "iris = load_iris()\n",
        "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)\n",
        "\n",
        "# Training a LightGBM classifier\n",
        "model = lgb.LGBMClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Making predictions and calculating accuracy\n",
        "predictions = model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "\n",
        "print(f\"Accuracy: {accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBBGxcwPNiG5",
        "outputId": "43bd1636-482f-4feb-8164-60af2f522044"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 93\n",
            "[LightGBM] [Info] Number of data points in the train set: 120, number of used features: 4\n",
            "[LightGBM] [Info] Start training from score -1.026292\n",
            "[LightGBM] [Info] Start training from score -1.149906\n",
            "[LightGBM] [Info] Start training from score -1.123930\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "Accuracy: 0.9666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **19. Keras**"
      ],
      "metadata": {
        "id": "5kshMJhOJ5ey"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "# Creating a simple Keras model\n",
        "model = Sequential()\n",
        "model.add(Dense(units=64, activation='relu', input_dim=10))\n",
        "model.add(Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "# Displaying the model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vY3-rW68Nic_",
        "outputId": "c3eca12b-6787-4361-81fa-bb8f1570a4d9"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 64)                704       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 769 (3.00 KB)\n",
            "Trainable params: 769 (3.00 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **20. Arrow**"
      ],
      "metadata": {
        "id": "UpeFvdAhJ5ZG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install arrow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVoiRrM9S657",
        "outputId": "4619fcab-a3e7-4525-aef2-66a833e64d1c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting arrow\n",
            "  Downloading arrow-1.3.0-py3-none-any.whl (66 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/66.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m61.4/66.4 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.0 in /usr/local/lib/python3.10/dist-packages (from arrow) (2.8.2)\n",
            "Collecting types-python-dateutil>=2.8.10 (from arrow)\n",
            "  Downloading types_python_dateutil-2.8.19.20240106-py3-none-any.whl (9.7 kB)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7.0->arrow) (1.16.0)\n",
            "Installing collected packages: types-python-dateutil, arrow\n",
            "Successfully installed arrow-1.3.0 types-python-dateutil-2.8.19.20240106\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import arrow\n",
        "\n",
        "# Getting the current time in a specific timezone\n",
        "local_time = arrow.now()\n",
        "utc_time = arrow.utcnow()\n",
        "\n",
        "print(f\"Local Time: {local_time}\")\n",
        "print(f\"UTC Time: {utc_time}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGcPDRBNNjvv",
        "outputId": "e15a910e-28bc-495a-9bf1-279fd74e62b8"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Local Time: 2024-01-07T00:56:07.391772+00:00\n",
            "UTC Time: 2024-01-07T00:56:07.391891+00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **21. NetworkX**"
      ],
      "metadata": {
        "id": "F7RlYfqqJ4wN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Creating a simple graph\n",
        "G = nx.Graph()\n",
        "G.add_nodes_from([1, 2, 3])\n",
        "G.add_edges_from([(1, 2), (2, 3)])\n",
        "\n",
        "# Visualizing the graph\n",
        "nx.draw(G, with_labels=True, font_weight='bold')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "q6dlDCOmNkKP",
        "outputId": "171869b4-060b-4cda-b1c5-0c254919f40d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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mxwIaFDUdAADAwmpqavT4448rOjpaZWVlWr16tbKzsykyCAgsMwAAABa1c+dOuVwuffjhh3I4HJo2bZoiIyPNjgU0GsoMAACAxRiGoUWLFmnGjBlq2bKlli1bps6dO5sdC2h0nGYGAABgIXv37tXQoUOVnp6u2NhYFRQUUGQQsFhmAAAALMAwDC1evFiZmZmKiIjQa6+9pp49e5odCzAVywwAAICPO3TokCZMmKD4+HhFR0fL7XZTZACxzAAAAPi0VatWKSUlRVVVVXr22Wc1aNAgsyMBPoNlBgAAwAeVlJQoKSlJI0aMUMeOHeXxeCgywG+wzAAAAPgYt9stl8ulI0eO6JFHHtGYMWNks9nMjgX4HJYZAAAAH1FWVqapU6cqJiZGl112mQoKCjR27FiKDHAKLDMAAAA+YPPmzXI4HPrpp590zz33yG63KyiIz52B0+FPCAAAgIkqKys1Z84c9e7dW02bNtWGDRvkdDopMkAtsMwAAACYpLCwUE6nU4WFhcrIyFB6erpCQ0PNjgVYBpUfAACgkdXU1Oixxx5TdHS0KioqtHr1amVnZ1NkgDpimQEAAGhEO3fulNPp1KZNm+R0OjVt2jRFRESYHQuwJMoMAABAIzAMQ4sWLdKMGTPUqlUrLVu2TJ07dzY7FmBpnGYGAADQwPbu3auhQ4cqPT1dgwcPVn5+PkUGqAcsMwAAAA3EMAy99dZbyszMVGRkpF577TX17NnT7FiA32CZAQAAaACHDh3S+PHjNXnyZPXo0UMej4ciA9QzlhkAAIB69t577yk1NVVVVVV69tlnNWjQILMjAX6JZQYAAKCelJSUKDExUSNHjtTVV18tj8dDkQEaEMsMAABAPSgoKFBiYqKOHDmiRx99VKNHj5bNZjM7FuDXWGYAAADOQVlZmXJzczVw4EBddtllcrvdGjNmDEUGaAQsMwAAAGfp008/ldPp1K5duzR79mwlJCQoKIjPioHGwp82AACAOqqsrNTs2bPVp08fNW3aVOvXr5fD4aDIAI2MZQYAAKAOCgsL5XQ6VVhYqKysLKWmpio0NNTsWEBA4uMDAACAWqipqdFjjz2m6OhoVVRUaM2aNcrMzKTIACZimQEAADiDH374QS6XS5s2bZLL5dLUqVMVERFhdiwg4FFmAAAATsEwDD3//POaOXOmWrVqpeXLl6tTp05mxwLwL5xmBgAAcBJ79+7VkCFDNGXKFA0ZMkQFBQUUGcDHsMwAAAD8imEYevPNN5WVlaUmTZro9ddfV48ePcyOBeAkWGYAAAD+paioSOPGjVNCQoJ69Oght9tNkQF8GMsMAACApJUrVyo1NVU1NTV67rnndNttt5kdCcAZsMwAAICAVlJSosTERI0aNUrXXHONPB4PRQawCJYZAAAQsAoKCuRyuVRcXKzHHntMo0aNks1mMzsWgFpimQEAAAHnxIkTysnJ0cCBA3X55ZfL7XZr9OjRFBnAYlhmAABAQPnkk0/kdDq1e/duzZkzR5MnT1ZQEJ/vAlbEn1wAABAQKisrNXv2bPXp00fNmjXThg0bZLfbKTKAhdkMwzDMDgEAANCQCgsL5XA4tG3bNmVmZiotLU0hIZygAlgdH0UAAAC/VVNTo8cee0zR0dGqqqrSmjVrlJmZSZEB/AR/kgEAgF/6/vvv5XK59NFHHykxMVG5ubmKiIgwOxaAekSZAQAAfsUwDC1cuFAzZ87UhRdeqOXLl6tTp05mxwLQADjNDAAA+I09e/Zo8ODBysjI0LBhw5Sfn0+RAfwYywwAALA8wzD05ptvKisrS02aNNHrr7+uHj16mB0LQANjmQEAAJZWVFSkcePGKSEhQT179pTH46HIAAGCZQYAAFjWypUrlZqaqpqaGi1cuFADBw40OxKARsQyAwAALKekpEQul0ujRo3SX/7yF3k8HooMEIBYZgAAgKXk5+crMTFRxcXFevzxxzVy5EjZbDazYwEwAcsMAACwhBMnTignJ0e33XabLr/8cnk8Ho0aNYoiAwQwlhkAAODzPvnkEzmdTu3evVtz5szR5MmTFRTEZ7JAoOOnAAAA8FmVlZWaPXu2+vTpo2bNmmnDhg2y2+0UGQCSJJthGIbZIQAAAH6rsLBQDodD27ZtU1ZWllJTUxUSwkklAP6NjzUAAIBPqamp0aOPPqro6GhVV1dr7dq1ysjIoMgA+B1+KgAAAJ/x/fffy+l06uOPP1ZSUpJyc3MVHh5udiwAPooyAwAATGcYhp577jnNmjVLF154oVasWKEbb7zR7FgAfBynmQEAAFPt2bNHgwcPVmZmpoYPH678/HyKDIBaYZkBAACmMAxDb7zxhrKyshQVFaU33nhD3bt3NzsWAAthmQEAAI2uqKhIcXFxstvt6t27tzweD0UGQJ2xzAAAgEb17rvvKi0tTTU1NVq4cKEGDhxodiQAFsUyAwAAGkVJSYlcLpdGjx6ta6+9Vh988AFFBsA5YZkBAAANLi8vT4mJiTp69KjmzZunESNGyGazmR0LgMWxzAAAgAZz4sQJZWdna9CgQbriiivk8Xg0cuRIigyAesEyAwAAGsTHH38sl8ul3bt3695771V8fLyCgvgcFUD9ocwAAIB6VVlZqQceeECPPPKIrr76auXl5enKK680OxYAP2QzDMMwOwQAAPAPX331lRwOh77++mtlZ2crJSVFISF8dgqgYbD1AgCAc1ZTU6NHHnlE0dHRqqmp0dq1azVlyhSKDIAGxU8YAABwTnbs2CGn06lPP/1USUlJysnJUXh4uNmxAAQAygwAADgrhmHoueee06xZs3TRRRdp+fLluvHGG82OBSCAcJoZAACos927dys2NlaZmZkaPny48vLyKDIAGh3LDAAAqDXDMPT6668rOztbUVFRevPNNxUdHW12LAABimUGAADUSlFRkeLi4uRwONSnTx95PB6KDABTscwAAIAzWrFihdLS0mQYhp5//nnFxMSYHQkAWGYAAMCplZSUyOVyacyYMbr++uvl8XgoMgB8BssMAAA4qby8PCUmJqqkpETz5s3TiBEjZLPZzI4FAL9gmQEAAP/hxIkTys7O1qBBg9SuXTt5PB6NHDmSIgPA57DMAACAX3z88cdyOp3au3ev7rvvPk2aNElBQXz2CcA38dMJAACosrJSd999t/r27avmzZsrLy9PkydPpsgA8Gk2wzAMs0MAAADzfPXVV7Lb7dq+fbuys7OVnJyskBBO3gDg+/i4BQCAAFVdXa25c+cqOjpaXq9Xa9euVXp6OkUGgGXw0woAgAC0Y8cOOZ1Offrpp0pKSlJOTo7Cw8PNjgUAdUKZAQAggHi9Xj333HO64447dNFFF2nFihW64YYbzI4FAGeF08wAAAgQu3fvVmxsrLKysjRixAjl5+dTZABYGssMAAB+zjAMvfbaa8rJyVFUVJTefPNNRUdHmx0LAM4ZywwAAH7s4MGDGjt2rJxOp/r27SuPx0ORAeA3WGYAAPBTy5cvV3p6ugzD0KJFi3TrrbeaHQkA6hXLDAAAfubo0aNyOp0aO3asrr/+enk8HooMAL/EMgMAgB9Zv369kpKSdOzYMf3973/X8OHDZbPZzI4FAA2CZQYAAD9QWlqqrKwsxcbG6sorr5TH49GIESMoMgD8GssMAAAW99FHH8npdGrfvn26//77NXHiRAUF8XklAP/HTzoAACyqoqJCd911l/r166cWLVooLy9P8fHxFBkAAcNmGIZhdggAAFA3X375pRwOh7Zv367s7GwlJycrJIQTLgAEFj66AQDAQqqrqzV37lx1795dhmHo/fffV3p6OkUGQEDiJx8AABaxY8cOORwObd68WcnJycrOzlZ4eLjZsQDANJQZAAB8nNfr1bPPPqs77rhDrVu31ooVK3TDDTeYHQsATEeZAQDAh+3evVtJSUnKy8vTpEmTNGvWLEVFRZkdCwB8AmUGAAAfZBiGXn31VeXk5Oi8887TW2+9pW7dupkdCwB8CjcAAADAxxw8eFBjx46Vy+VS//795fF4KDIAcBIsMwAA+JDly5crPT1dkvTCCy9owIABJicCAN/FMgMAgA84evSoHA6Hxo4dqxtuuEEej4ciAwBnwDIDAIDJ1q9fr6SkJB07dkxPPPGEhg0bJpvNZnYsAPB5LDMAAJiktLRUmZmZio2N1ZVXXimPx6Phw4dTZACgllhmAAAwwaZNm+RyubRv3z498MADmjBhgoKC+IwRAOqCn5oAADSiiooK3XXXXerfv79atGjxy/NjKDIAUHc2wzAMs0MAABAIvvzyS9ntdn377bfKyclRUlKSQkI4SQIAzhYfAwEA0MCqq6v18MMPq3v37rLZbHr//feVlpZGkQGAc8RPUQAAGtB3330np9OpzZs3KyUlRVlZWQoPDzc7FgD4BcoMAAANwOv16plnntGdd96p1q1b691339X1119vdiwA8CuUGQAA6tnu3buVmJio/Px8TZo0SbNmzVJUVJTZsQDA71BmAACoJ4Zh6NVXX1VOTo7OO+88LV68WF27djU7FgD4LW4AAABAPTh48KDGjBkjl8ul/v37y+PxUGQAoIGxzAAAcI6WLVum9PR02Ww2vfjii+rfv7/ZkQAgILDMAABwlo4ePSq73a64uDh16tRJHo+HIgMAjYhlBgCAs7Bu3TolJSWptLRU8+fP19ChQ2Wz2cyOBQABhWUGAIA6KC0tVWZmpgYPHqw//vGPcrvdGjZsGEUGAEzAMgMAQC1t2rRJLpdL+/bt0wMPPKAJEyYoKIjPBQHALPwEBgDgDCoqKnTnnXeqf//+atmy5S/Pj6HIAIC5bIZhGGaHAADAV33xxRdyOBz69ttvlZubq6SkJAUHB5sdCwAglhkAAE6qurpaDz30kHr06CGbzaZ169YpNTWVIgMAPoRrZgAA+I1vv/1WTqdTn332mVJTU5WVlaWwsDCzYwEAfoMyAwDAv3i9Xi1YsEB33XWX2rRpo3fffVfXX3+92bEAAKdAmQEAQNLu3buVmJio/Px8xcfHa+bMmYqKijI7FgDgNCgzAICAZhiGXnnlFeXm5qpZs2Z6++23dcstt5gdCwBQC9wAAAAQsH7++WeNHj1aiYmJGjBggDweD0UGACyEZQYAEJCWLl2qKVOmyGaz6cUXX1T//v3NjgQAqCOWGQBAQCkuLlZCQoLGjRunTp06yePxUGQAwKJYZgAAAWPdunVKSkpSaWmpnnzySQ0ZMkQ2m83sWACAs8QyAwDwe8ePH1dGRoYGDx6sq666Sm63W0OHDqXIAIDFscwAAPzahx9+KJfLpQMHDujBBx/UhAkTKDEA4CdYZgAAfqmiokJ33HGH+vfvrz/84Q/Ky8vTxIkTKTIA4EdshmEYZocAAKA+ffHFF7Lb7dqxY4dyc3OVmJio4OBgs2MBAOoZywwAwG9UV1froYceUvfu3RUcHKx169YpJSWFIgMAfoprZgAAfuHbb7+Vw+HQli1blJqaqqysLIWFhZkdCwDQgCgzAABL83q9WrBgge68805deumlWrlypa677jqzYwEAGgFlBgBgWbt27VJiYqIKCgo0efJkzZw5U02aNDE7FgCgkVBmAACWYxiG/vGPfyg3N1fnn3++3n77bd1yyy1mxwIANDJuAAAAsJQDBw5o9OjRSkpK0q233iqPx0ORAYAAxTIDALCMJUuWaMqUKQoODtZLL72kfv36mR0JAGAilhkAgM8rLi5WQkKCxo8fr86dO8vj8VBkAAAsMwAA3/b+++8rOTlZpaWlevLJJzVkyBDZbDazYwEAfABlBgBQrw6XVmrJlj0q3FeiQ8crVO01FBJkU8um4erQupkGXn2JWkSd+fkvx48f16xZs7Rw4UJ17dpVjz/+uC655JJG+DcAAFiFzTAMw+wQAADr27qrWPPzdmjLrmLtLyk/5fsubhahq9s2l+OWdurYtvlJ3/Phhx/K6XTq559/1l133aXx48ezxgAAfocyAwA4J5XVXt27cpuWfb5XRccra/19rZqGKaZjG+X2ba/Q4H9ewlleXq57771X8+bN03XXXacnnnhCV1xxRUNFBwBYHGUGAHDWSiuqNenFT7Rxx6GzPkandi31zJhrteObQtntdn3//feaOnWqXC6XgoOD6zEtAMDfUGYAAGelqsarsQs/Oqci878uDSnV9qdT9N9XXaknn3xSHTp0qIeEAAB/R5kBAJyVO5d/pYWenfVyLMPw6k9hRVo8fZTCws58cwAAACTuZgYAOAtbdxVr2da9p3zdqKlW5RfvqbJwnYzSI7I1aa6QNv+t8GtjFXReq9+932YL0v7wS7XtwAl1bEuZAQDUDg/NBADU2fy8Hae92L/c84IqNr0mVVcq9I9dZAuLVNV2t0rfuVNGRelJv6foeKXm5+1oqMgAAD9EmQEA1Mnh0kpt2VV8yte9ZSWq2rZBkhTZM0mRt0xU1MAZUlCIjBPFqvrh01N+75ZdxTpcWvs7ogEAAhunmQEA6mTJlj2nfY6MLShETW7NlSQFX3zlP78WFimFhEmV1TLKik/5vftLyrV06x6N6/x/6jUzAMA/UWYAAHVSuK/ktK/bwpsopE37//ha1Y5NUuUJSVJQs4vO6fgAAPwvTjMDANTJoeMVdXp/TdFOleU/J0kKat5GIZf/5bTvLzpWt+MDAAIXywwAoE6qvbW/o3/1ge904t0H/7nKhDVRZLRDtuDT/9VTl+MDAAIbZQYAUCchQbZava/6wHc6seJ+qapctsjz1aTvFAX/4fJ6Oz4AAJQZAECdtGwafsb3eI8VqWzVXKmqXEHNW6tJv6yTPl/mZFqdd+bjAwAgUWYAAHXUoXWzM76nLP85GWUlCjr/YjWJmaGgyPPq9fgAAEiUGQBAHQ28+hI9lf/9KW/PXL13m2p2fyFJskVdoMrNS/7j9eALr1DolZ1P+r0XN4tQTMdL6jcwAMBvUWYAAHXSIipMHS89X/sLT15mvMcO/vK/a/ZuU83ebf/xeugfu5yyzFzdtrlaRIXVX1gAgF+jzAAA6mT37t36YcVT8l7YVUFRzX/3ethVNyvsqpvrfNxWTcPkuKVdPSQEAAQKnjMDAKgVwzD0+uuvq0uXLtrzxQfq3b52F/TXVkzHNurYtnm9HhMA4N8oMwCAMzp8+LDGjx8vu92uXr16yePx6InJPdWpXct6OX7ndi2V27d9vRwLABA4bIZh8HQyAMAprVmzRsnJyaqoqNBDDz2kQYMG/fJaaUW14l/8RB/sOHTWx+/crqUWjLlWUeGc+QwAqBvKDADgpI4fP66ZM2fq+eefV3R0tB5//HG1bt36d++rqvFqzsptWrZ1r4qOV9b6+K2ahimmYxvl9m2v0GBOFAAA1B1lBgDwO5s2bZLT6dSBAwd01113afz48bLZbKf9nq27ijU/b4e27Co+5W2bpX/efvnqts3luKUd18gAAM4JZQYA8IvKykrdf//9evTRR/XnP/9ZTz75pNq1q9sdxg6XVmrp1j0q3FeiomMVqvYaCgmyqdV54erQupliOl7C7ZcBAPWCMgMAkCQVFhbK4XBo27Ztys7OVkpKikJCuI4FAOC7OEkZAAKc1+vVvHnzFB0drcrKSq1Zs0ZTpkyhyAAAfB5/UwFAAPvpp5/kcrnk8XjkcDg0ffp0RUZGmh0LAIBaocwAQAAyDEOvvPKKcnJydP7552vJkiW66aabzI4FAECdcJoZAASYgwcPauzYsUpMTNSAAQPk8XgoMgAAS2KZAYAAsnLlSqWmpqqmpkaLFi3SrbfeanYkAADOGssMAASAkpISJSUladSoUbrmmmvk8XgoMgAAy2OZAQA/t3HjRjkcDh06dEiPPPKIxowZc8YHYAIAYAUsMwDgpyoqKjRr1iwNGDBArVu3VkFBgcaOHUuRAQD4DR6aCQB+6Msvv5Tdbte3336rqVOnKjExUcHBwWbHAgCgXrHMAIAfqamp0SOPPKLu3btLkt5//32lpKRQZAAAfolrZgDAT+zcuVMOh0MfffSREhMTNXXqVIWHh5sdCwCABkOZAQCLMwxDL7zwgqZPn66WLVtq+fLl6tSpk9mxAABocJxmBgAWduDAAY0cOVJpaWkaNGiQCgoKKDIAgIDBMgMAFrVs2TKlpaUpODhYL7/8svr27Wt2JAAAGhXLDABYTElJiZxOp+Li4tSpUyd5PB6KDAAgILHMAICF5Ofny+Vy6ejRo5o3b55GjBjBc2MAAAGLZQYALKCsrExTp07Vbbfdpssvv1xut1sjR46kyAAAAhrLDAD4uK1bt8put2vnzp26++675XA4FBTEZ1EAAPC3IQD4qOrqav3tb39Tz549FRYWpvfff18ul4siAwDAv7DMAIAP2rFjhxwOhzZv3qzU1FRlZWUpLCzM7FgAAPgUygwA+BDDMLRw4ULNnDlTF110kVasWKEbbrjB7FgAAPgkzlUAAB+xb98+DRkyRBkZGRo6dKjy8vIoMgAAnAbLDAD4gMWLFysjI0Ph4eF67bXX1LNnT7MjAQDg81hmAMBExcXFio+P16RJk3TzzTfL7XZTZAAAqCWWGQAwyfr165WYmKjS0lI99dRTGjx4MM+NAQCgDlhmAKCRnThxQtnZ2YqNjdUf//hHud1uDRkyhCIDAEAdscwAQCPavHmzHA6Hdu3apXvvvVfx8fE8NwYAgLPE36AA0Aiqqqp03333qXfv3oqKitL69euVkJBAkQEA4BywzABAA9u+fbscDoc+//xzpaenKyMjQ6GhoWbHAgDA8vhIEAAaiNfr1VNPPaWuXbvq2LFjeu+995Sbm0uRAQCgnrDMAEAD2L17t5KSkpSXl6f4+HjNmjVLTZo0MTsWAAB+hTIDAPXIMAy9+eabyszMVFRUlN58801FR0ebHQsAAL/EaWYAUE8OHz6sCRMmKCEhQT179pTH46HIAADQgFhmAKAerFmzRikpKSovL9czzzyj22+/3exIAAD4PZYZADgHx48f15QpUzRs2DB16NBBbrebIgMAQCNhmQGAs/TRRx/J4XDowIED+tvf/qbx48fLZrOZHQsAgIDBMgMAdVRZWal77rlH/fr1U4sWLZSXl6cJEyZQZAAAaGQ2wzAMs0MAgFVs27ZNDodDhYWFysrKUmpqqkJCGLkBADADywwA1ILX69Xf//53RUdHq7y8XKtXr1ZGRgZFBgAAE/G3MACcwa5du+RyueR2u2W32zVjxgxFRkaaHQsAgIBHmQGAUzAMQ6+++qpycnLUrFkzvfPOO7r55pvNjgUAAP6F08wA4CSKiooUFxcnl8ul/v37y+12U2QAAPAxLDMA8BvvvfeeUlJSVFNTo+eff14xMTFmRwIAACfBMgMA/3Ls2DElJydr5MiR+vOf/yyPx0ORAQDAh7HMAICkjRs3yul0qqioSHPnztXYsWN5bgwAAD6OZQZAQKuoqNAdd9yhAQMG6OKLL1Z+fr7i4uIoMgAAWAAPzQQQsL766ivZ7XZt375dubm5SkpKUnBwsNmxAABALbHMAAg4NTU1evTRRxUdHS2v16u1a9cqNTWVIgMAgMVwzQyAgLJz5045nU5t2rRJiYmJmjp1qsLDw82OBQAAzgJlBkBAMAxDL774oqZPn64WLVpo2bJl6ty5s9mxAADAOeA0MwB+7+eff9aoUaOUmpqqgQMHKj8/nyIDAIAfYJkB4NeWL1+utLQ02Ww2vfTSS+rXr5/ZkQAAQD1hmQHgl0pKSuRyuTR27FjdcMMN8ng8FBkAAPwMywwAv+N2u+V0OlVcXKx58+ZpxIgRPDcGAAA/xDIDwG+Ul5dr2rRpiomJ0WWXXSa3262RI0dSZAAA8FM8NBOAX/j888+VkJCgH374QdOnT5fT6VRQEJ/XAADgz/ibHoClVVdX66GHHlKPHj0UGhqqdevWKTExkSIDAEAA4JoZAJb1/fffy263a/PmzUpJSVF2drbCwsLMjgUAABoJZQaA5RiGoeeff14zZszQhRdeqBUrVuiGG24wOxYAAGhknIcBwFL27dunoUOHasqUKRo6dKjy8/MpMgAABCiWGQCW8fbbbysjI0NhYWF67bXX1LNnT7MjAQAAE7HMAPB5xcXFmjx5siZOnKibbrpJbrebIgMAAFhmAPi2DRs2yOVyqbS0VE8++aSGDBnCc2MAAIAklhkAPurEiRPKycnR7bffriuvvFJut1tDhw6lyAAAgF+wzADwOZs3b5bD4dCuXbs0Z84cTZ48mefGAACA3+G3AwA+o6qqSvfdd5969+6tqKgorV+/Xna7nSIDAABOimUGgE/Yvn27nE6ntm7dqvT0dGVkZCg0NNTsWAAAwIfxcScAU3m9Xj399NPq2rWrSkpKtHLlSuXm5lJkAADAGbHMADDNnj17lJiYqLy8PE2aNEl33HGHmjRpYnYsAABgEZQZAI3OMAy99dZbyszMVJMmTfTmm28qOjra7FgAAMBiOM0MQKM6cuSIJk6cqMmTJ6t79+5yu90UGQAAcFZYZgA0mrVr1yo5OVnl5eVasGCBYmNjzY4EAAAsjGUGQIMrLS1VRkaGhg4dqvbt28vtdlNkAADAOWOZAdCgPv74YzkcDu3bt08PPvigJkyYIJvNZnYsAADgB1hmADSIyspKzZ49W3379lXz5s2Vl5eniRMnUmQAAEC9sRmGYZgdAoB/+frrr+VwOPTVV18pMzNTaWlpCglhCAYAAPWLZQZAvfF6vXriiSfUrVs3nThxQqtXr1ZmZiZFBgAANAh+wwBQL3bv3i2Xy6WCggLZ7XbNmDFDkZGRZscCAAB+jDID4JwYhqHXXntN2dnZatasmd555x3dfPPNZscCAAABgNPMAJy1oqIixcXFyel0ql+/fnK73RQZAADQaFhmAJyVVatWKSUlRdXV1Xr++ecVExNjdiQAABBgWGYA1MmxY8eUkpKiESNGqGPHjnK73RQZAABgCpYZALX24YcfyuFwqKioSA8//LDi4uJ4bgwAADANywyAM6qoqNCdd96p/v3766KLLlJ+fr7GjRtHkQEAAKbioZkATquwsFAJCQnavn27cnNzlZSUpODgYLNjAQAAsMwAOLmamho99thjio6Oltfr1dq1a5WamkqRAQAAPoNrZgD8zo8//iin06kPP/xQLpdLU6dOVUREhNmxAAAA/gNlBsAvDMPQSy+9pGnTpumCCy7Q0qVL9de//tXsWAAAACfFaWYAJEk///yzRo0apZSUFMXExKigoIAiAwAAfBrLDACtWLFCaWlpkqSXXnpJ/fr1MzkRAADAmbHMAAGspKRELpdLY8aM0fXXXy+Px0ORAQAAlsEyAwQot9stl8ulI0eO6PHHH9fIkSN5bgwAALAUlhkgwJSXl2v69OkaOHCg2rZtK7fbrVGjRlFkAACA5fDQTCCAfP7557Lb7fr+++81bdo0OZ1OnhsDAAAsi2UGCADV1dV6+OGH1bNnT4WEhGjdunVKSkqiyAAAAEvjmhnAz33//fdyOBz69NNPlZKSoqysLIWHh5sdCwAA4JxRZgA/ZRiGFi1apOnTp+vCCy/U8uXLdeONN5odCwAAoN5wmhngh/bv369hw4YpPT1dQ4YMUX5+PkUGAAD4HZYZwM+88847mjJlikJDQ/Xqq6+qV69eZkcCAABoECwzgJ84evSoEhISNGHCBHXp0kUej4ciAwAA/BrLDOAH8vLy5HK5dOzYMc2fP19Dhw7luTEAAMDvscwAFlZWVqacnBwNGjRI//Vf/yWPx6Nhw4ZRZAAAQEBgmQEs6rPPPpPdbteuXbs0Z84cTZ48WUFBfD4BAAACB7/5ABZTVVWlBx54QL1791ZUVJTWrVsnu91OkQEAAAGHZQawkG+//VYOh0Nbt25VWlqaMjMzFRoaanYsAAAAU1BmAAvwer169tlndccdd6hNmzZauXKlrr32WrNjAQAAmIrzUgAft3fvXg0ePFjZ2dkaOXKkNmzYQJEBAAAQywzg09566y1lZGSoSZMmeuONN9S9e3ezIwEAAPgMlhnABx05ckQTJ05UfHy8unfvLrfbTZEBAAD4DZYZwMesXbtWycnJKisr04IFCxQbG2t2JAAAAJ/EMgP4iNLSUmVkZGjo0KFq3769PB4PRQYAAOA0WGYAH/Dxxx/L6XRq7969euCBBzRx4kTZbDazYwEAAPg0lhnARFVVVZo9e7b69u2r888/Xxs2bNCkSZMoMgAAALVgMwzDMDsEEIi+/vprORwOffXVV8rMzFRaWppCQhhLAQAAaotlBmhkXq9X8+fPV7du3XTixAmtWrVKmZmZFBkAAIA64rcnoBHt3r1bLpdLBQUFSkhI0MyZMxUZGWl2LAAAAEuizACNwDAMvfbaa8rOztZ5552nt99+W7fccovZsQAAACyN08yABnbo0CHFxcXJ6XSqb9++8ng8FBkAAIB6wDIDNKDVq1crOTlZVVVVWrhwoQYOHGh2JAAAAL/BMgM0gOPHjys1NVXDhw9Xx44d5fF4KDIAAAD1jGUGqGcffvihnE6nDh48qIcfflhxcXE8NwYAAKABsMwA9aSyslJ33XWXBgwYoAsvvFB5eXkaN24cRQYAAKCB8NBMoB4UFhbKbrfrm2++UU5OjpKTkxUcHGx2LAAAAL/GMgOcg5qaGj3++OOKjo5WdXW11q5dq7S0NIoMAABAI+CaGeAs/fjjj3K5XNq4caOcTqemTZumiIgIs2MBAAAEDMoMUEeGYejll1/W1KlTdcEFF2jp0qX661//anYsAACAgMNpZkAdHDx4UGPGjFFycrJiYmJUUFBAkQEAADAJywxQS++++65SU1MlSS+++KL69+9vbiAAAIAAxzIDnEFJSYkSExM1evRoXXfddfJ4PBQZAAAAH8AyA5yGx+OR0+nUkSNH9Nhjj2nUqFE8NwYAAMBHsMwAJ1FeXq4ZM2YoJiZGl156qQoKCjR69GiKDAAAgA/hoZnAb3zxxRey2+3asWOHpk2bJqfTyXNjAAAAfBDLDPAvNTU1mjt3rnr06KHg4GCtW7dOSUlJFBkAAAAfxTUzgKQffvhBDodDn3zyiZKTk5Wdna3w8HCzYwEAAOA0KDMIaIZhaNGiRZoxY4b+8Ic/aPny5brxxhvNjgUAAIBa4DQzBKz9+/dr+PDhSk9PV2xsrPLy8igyAAAAFsIyg4C0ZMkSTZkyRSEhIXrllVfUu3dvsyMBAACgjlhmEFCOHj0qu92u8ePHq3PnznK73RQZAAAAi2KZQcDIy8tTYmKiSkpKNH/+fA0dOpTnxgAAAFgYywz8XllZmXJzczVo0CBdccUV8ng8GjZsGEUGAADA4lhm4Ne2bNkiu92uH3/8UbNnz1ZCQoKCgujwAAAA/oDf6uCXqqur9eCDD6pXr16KjIzU+vXr5XA4KDIAAAB+hGUGfue7776T3W7Xli1blJaWpszMTIWFhZkdCwAAAPWMMgO/YRiGnn32Wc2aNUtt2rTRypUrdd1115kdCwAAAA2Ec27gF/bu3avBgwcrKytLI0eO1IYNGygyAAAAfo5lBpb31ltvKTMzUxEREXrjjTfUvXt3syMBAACgEbDMwLKOHDmiSZMmKT4+Xt26dZPH46HIAAAABBCWGVjS+++/r+TkZJ04cUILFixQbGys2ZEAAADQyFhmYCmlpaXKysrSkCFDdNVVV8ntdlNkAAAAAhTLDCzjk08+kcPh0N69e/XAAw9owoQJPDcGAAAggPGbIHxeVVWV5syZo759++r888/Xhg0bNGnSJIoMAABAgGOZgU/75ptv5HA49MUXXygzM1Pp6ekKCeE/WwAAALDMwEd5vV49+eST6tatm0pLS7V69WplZWVRZAAAAPALfjOEz9m9e7cSExOVn5+vyZMna9asWYqMjDQ7FgAAAHwMZQY+wzAMvf7668rOzlbTpk21ePFide3a1exYAAAA8FGcZgafcOjQIY0bN04Oh0N9+vSRx+OhyAAAAOC0WGZgutWrVyslJUWVlZVauHChBg4caHYkAAAAWADLDExz/PhxpaWlafjw4frTn/4kj8dDkQEAAECtsczAFJs2bZLT6dSBAwf08MMPKy4uTjabzexYAAAAsBCWGTSqyspK3X333erfv79atWql/Px8jRs3jiIDAACAOrMZhmGYHQKBobCwUHa7XV9//bVycnKUnJzMc2MAAABw1lhm0OBqamr0+OOPKzo6WtXV1Vq7dq3S09MpMgAAADgn/DaJBvXTTz/J6XRq48aNcjqdmjZtmiIiIsyOBQAAAD9AmUGDMAxD//jHP5Sbm6sLLrhAS5YsUZcuXcyOBQAAAD/CaWaodwcPHtSYMWOUlJSkW2+9VQUFBRQZAAAA1DuWGdSrd999V6mpqTIMQy+88IIGDBhgdiQAAAD4KZYZ1IuSkhIlJSVp9OjRuvbaa+XxeCgyAAAAaFAsMzhnH3zwgZxOpw4fPqzHHntMo0aN4rkxAAAAaHAsMzhrFRUVmjlzpm699VZdcsklKigo0OjRoykyAAAAaBQ8NBNn5csvv1RCQoJ27NihqVOnyuVyKTg42OxYAAAACCAsM6iTmpoaPfLII+revbuCgoL0/vvvKzk5mSIDAACARsc1M6i1H374QU6nUx999JGSk5OVk5Oj8PBws2MBAAAgQFFmcEb/e5vl6dOnq1WrVlqxYoVuvPFGs2MBAAAgwHGaGU7rwIEDGjFihNLS0hQbG6v8/HyKDAAAAHwCywxOaenSpUpPT1dISIheeeUV9e7d2+xIAAAAwC9YZvA7JSUlcjgcGjdunDp37iy3202RAQAAgM9hmcF/yM/Pl8vlUklJiZ544gkNGzaM58YAAADAJ7HMQJJUVlamqVOn6rbbbtMVV1wht9ut4cOHU2QAAADgs1hmoK1btyohIUE//vij7rnnHtntdgUF0XMBAADg2/iNNYBVV1frb3/7m3r27KmIiAitW7dOTqeTIgMAAABLYJkJUN99950cDoc+++wzpaWlKTMzU2FhYWbHAgAAAGqNMhNgDMPQc889p1mzZuniiy/WypUrdd1115kdCwAAAKgzzicKIPv27dOQIUOUmZmp4cOHKy8vjyIDAAAAy2KZCRCLFy9WRkaGIiIi9Prrr6tHjx5mRwIAAADOCcuMnysuLlZ8fLwmTZqkrl27yu12U2QAAADgF1hm/Nj69euVmJioEydO6Omnn1ZsbCzPjQEAAIDfYJnxQydOnFBWVpZiY2N11VVXye12a/DgwRQZAAAA+BWWGT/z6aefyuFwaM+ePbr//vs1ceJEnhsDAAAAv8RvuX6iqqpK9957r/r06aNmzZppw4YNio+Pp8gAAADAb7HM+IHt27fL4XDo888/V0ZGhtLT0xUaGmp2LAAAAKBB8bG9hXm9Xj311FPq2rWrjh8/rlWrVik7O5siAwAAgIDAMmNRu3fvVlJSkvLy8jR58mTNnDlTTZo0MTsWAAAA0GgoMxZjGIbeeOMNZWVlqWnTplq8eLG6du1qdiwAAACg0XGamYUcPnxY48ePl91uV+/eveV2uykyAAAACFgsMxaxZs0aJScnq7KyUs8995xuu+02syMBAAAApmKZ8XHHjx9Xenq6hg0bpj/96U9yu90UGQAAAEAsMz7to48+ksPh0IEDB/TQQw9p3LhxstlsZscCAAAAfALLjA+qrKzUPffco379+qlly5bKy8vT+PHjKTIAAADAr9gMwzDMDoF/KywslMPh0LZt25STk6Pk5GSFhDCgAQAAAL/FMuMjvF6v5s2bp+7du6uyslJr165Veno6RQYAAAA4BX5T9gG7du2S0+nUBx98IIfDoenTpysiIsLsWAAAAIBPo8yYyDAMvfLKK8rJyVHz5s21ZMkSdenSxexYAAAAgCVQZurgcGmllmzZo8J9JTp0vELVXkMhQTa1bBquDq2baeDVl6hFVFitjlVUVKS0tDStWLFCI0aM0L333qtmzZo18L8BAAAA4D+4AUAtbN1VrPl5O7RlV7H2l5Sf8n0XN4vQ1W2by3FLO3Vs2/yU71u5cqVSU1Pl9Xo1d+5cDRgwoAFSAwAAAP6NMnMaldVe3btym5Z9vldFxytr/X2tmoYppmMb5fZtr9Dgf99j4dixY5o6dapefvll9enTR4888oguvPDChogOAAAA+D3KzCmUVlRr0oufaOOOQ2d9jE7tWuqZMdcqKjxEGzdulMPh0OHDhzV79myNHj2a58YAAAAA54AycxJVNV6NXfjRORWZ/9Xp/1yg/9q9Wk/8/XHdcMMNeuKJJ3T55Zefe0gAAAAgwFFmTuLO5V9poWdn/RzMMFT11Rpl92wnl8ul4ODg+jkuAAAAEOC4m9lvbN1VrGVb9572Pd7yYyovWKTq7zdJkpqOfFhB5/3h5G+22fSHa/volkGdKDIAAABAPQo681sCy/y8Hae92L9q52aVvp77S5GpjeJyr+bn7aiPeAAAAAD+hTLzK4dLK7VlV/Fp31Oe/5wUFKyILnF1OvaWXcU6XFr7O6IBAAAAOD3KzK8s2bLntM+RkaTQ9t3UdOh9CrmsY52Ovb+kXEu37jmXeAAAAAB+hTLzK4X7Ss74nojrYmULi2yw4wMAAACoHcrMrxw6XtGgxy861rDHBwAAAAIJZeZXqr0Ne5fqhj4+AAAAEEgoM78SEmSz9PEBAACAQEKZ+ZWWTcMb9PitzmvY4wMAAACBhDLzKx1aN7P08QEAAIBAEmJ2AF8y8OpL9FT+96e9PfOJlQ9Jkozqfz8zpmzDM7KFhCmoxaWKuGHYSb/v4mYRiul4Sf0GBgAAAAIYZeZXWkSF6eq2zfXeV/tP+Z7qn7b87ms1ewslScFVpy5BV7dtrhZRYeecEQAAAMA/2QzD4BZbv7J1V7EmvvCxio5XnvnNtdSqaZieHXudOrZtXm/HBAAAAAId18z8Rse2zRXTsU29HjOmYxuKDAAAAFDPKDMnkdu3vTq1a1kvx+rcrqVy+7avl2MBAAAA+DfKzEmEBgfpmTHXqvM5FprO7VpqwZhrFRrM/80AAABAfeOamdOoqvFqzsptWrZ1b52uoWnVNEwxHdsot297igwAAADQQCgztbB1V7Hm5+3Qll3Fp71t88XNInR12+Zy3NKOa2QAAACABkaZqYPDpZVaunWPCveVqOhYhaq9hkKCbGp1Xrg6tG6mmI6XcPtlAAAAoJFQZgAAAABYEhd0AAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS6LMAAAAALAkygwAAAAAS/r/AatYSHXF0VgsAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **22. Pygame**"
      ],
      "metadata": {
        "id": "zUJZVu8BK71q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pygame\n",
        "import sys\n",
        "\n",
        "# Initializing Pygame\n",
        "pygame.init()\n",
        "\n",
        "# Creating a Pygame window\n",
        "window = pygame.display.set_mode((400, 300))\n",
        "pygame.display.set_caption('Pygame Window')\n",
        "\n",
        "# Main game loop\n",
        "while True:\n",
        "    for event in pygame.event.get():\n",
        "        if event.type == pygame.QUIT:\n",
        "            pygame.quit()\n",
        "            sys.exit()\n",
        "\n",
        "    pygame.display.flip()"
      ],
      "metadata": {
        "id": "ZL8z8ISHNkfO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **23. Dash**"
      ],
      "metadata": {
        "id": "JVmipyzZK9HM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install dash"
      ],
      "metadata": {
        "id": "jCvMT4aQU_ef"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dash\n",
        "from dash import dcc, html\n",
        "\n",
        "# Creating a simple Dash app\n",
        "app = dash.Dash(__name__)\n",
        "\n",
        "app.layout = html.Div(children=[\n",
        "    html.H1(children='Dash Example'),\n",
        "    dcc.Graph(\n",
        "        id='example-graph',\n",
        "        figure={\n",
        "            'data': [{'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'Bar Chart'}],\n",
        "            'layout': {'title': 'Dash Bar Chart'}\n",
        "        }\n",
        "    )\n",
        "])\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    app.run_server(debug=True)"
      ],
      "metadata": {
        "id": "5u0E69qNNkyP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **24. PyCaret**"
      ],
      "metadata": {
        "id": "cTtzrYV9K8iw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pycaret"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHPw7PLcVsPB",
        "outputId": "341aa82e-6f2d-4232-d1f1-ff1b66f9000b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pycaret in /usr/local/lib/python3.10/dist-packages (3.2.0)\n",
            "Requirement already satisfied: category-encoders>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (2.6.3)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from pycaret) (2.2.1)\n",
            "Requirement already satisfied: deprecation>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (2.1.0)\n",
            "Requirement already satisfied: imbalanced-learn>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (0.10.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.12.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (7.0.0)\n",
            "Requirement already satisfied: ipython>=5.5.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (7.34.0)\n",
            "Requirement already satisfied: ipywidgets>=7.6.5 in /usr/local/lib/python3.10/dist-packages (from pycaret) (7.7.1)\n",
            "Requirement already satisfied: jinja2>=1.2 in /usr/local/lib/python3.10/dist-packages (from pycaret) (3.1.2)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.3.2)\n",
            "Requirement already satisfied: kaleido>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (0.2.1)\n",
            "Requirement already satisfied: lightgbm>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (4.1.0)\n",
            "Requirement already satisfied: markupsafe>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (2.1.3)\n",
            "Requirement already satisfied: matplotlib<=3.6,>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (3.6.0)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (5.9.2)\n",
            "Requirement already satisfied: numba>=0.55.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (0.58.1)\n",
            "Requirement already satisfied: numpy<1.27,>=1.21 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.23.5)\n",
            "Requirement already satisfied: pandas<2.0.0,>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.5.3)\n",
            "Requirement already satisfied: plotly-resampler>=0.8.3.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (0.9.1)\n",
            "Requirement already satisfied: plotly>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (5.15.0)\n",
            "Requirement already satisfied: pmdarima!=1.8.1,<3.0.0,>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (2.0.4)\n",
            "Requirement already satisfied: psutil>=5.9.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (5.9.5)\n",
            "Requirement already satisfied: pyod>=1.0.8 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.1.2)\n",
            "Requirement already satisfied: requests>=2.27.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (2.31.0)\n",
            "Requirement already satisfied: schemdraw==0.15 in /usr/local/lib/python3.10/dist-packages (from pycaret) (0.15)\n",
            "Requirement already satisfied: scikit-learn<1.3.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.2.2)\n",
            "Requirement already satisfied: scikit-plot>=0.3.7 in /usr/local/lib/python3.10/dist-packages (from pycaret) (0.3.7)\n",
            "Requirement already satisfied: scipy~=1.10.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.10.1)\n",
            "Requirement already satisfied: sktime!=0.17.1,!=0.17.2,!=0.18.0,<0.22.0,>=0.16.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (0.21.1)\n",
            "Requirement already satisfied: statsmodels>=0.12.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (0.14.1)\n",
            "Requirement already satisfied: tbats>=1.1.3 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.1.3)\n",
            "Requirement already satisfied: tqdm>=4.62.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (4.66.1)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from pycaret) (3.4.1)\n",
            "Requirement already satisfied: yellowbrick>=1.4 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.5)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.datasets import get_data\n",
        "from pycaret.classification import *\n",
        "\n",
        "# Loading a classification dataset\n",
        "data = get_data('diabetes')\n",
        "\n",
        "# Setting up PyCaret environment\n",
        "exp1 = setup(data, target='Class variable')\n",
        "\n",
        "# Comparing different models\n",
        "compare_models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "68316115fa4a42f198997fd0b5ce2748",
            "f938f519529e416881902d88c6fd2ca1",
            "864e2afe8f584f5088b0b5c4120fbbf4",
            "946cd65280d147c499f9f8e0b01f5452",
            "c69dfa3f46d947939a46f1af1a0eaf24",
            "20b12e80b31d4c9f8d058a4fd0d20205",
            "cce53142382d415583f2a6ca06acd41d",
            "4cb742a850f2452aa04bc04e16e9e0f4",
            "0fdced2329e24b1eac410ceae45ba54a",
            "dde7f24e72074d0c9b3ddbf2c3aed0bf",
            "36475a12d336401aa69b212fefc13b45"
          ]
        },
        "id": "BngJSBwgNl4k",
        "outputId": "d8a63247-0e41-49e7-90d0-0ab9d0110f57"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Number of times pregnant  \\\n",
              "0                         6   \n",
              "1                         1   \n",
              "2                         8   \n",
              "3                         1   \n",
              "4                         0   \n",
              "\n",
              "   Plasma glucose concentration a 2 hours in an oral glucose tolerance test  \\\n",
              "0                                                148                          \n",
              "1                                                 85                          \n",
              "2                                                183                          \n",
              "3                                                 89                          \n",
              "4                                                137                          \n",
              "\n",
              "   Diastolic blood pressure (mm Hg)  Triceps skin fold thickness (mm)  \\\n",
              "0                                72                                35   \n",
              "1                                66                                29   \n",
              "2                                64                                 0   \n",
              "3                                66                                23   \n",
              "4                                40                                35   \n",
              "\n",
              "   2-Hour serum insulin (mu U/ml)  \\\n",
              "0                               0   \n",
              "1                               0   \n",
              "2                               0   \n",
              "3                              94   \n",
              "4                             168   \n",
              "\n",
              "   Body mass index (weight in kg/(height in m)^2)  Diabetes pedigree function  \\\n",
              "0                                            33.6                       0.627   \n",
              "1                                            26.6                       0.351   \n",
              "2                                            23.3                       0.672   \n",
              "3                                            28.1                       0.167   \n",
              "4                                            43.1                       2.288   \n",
              "\n",
              "   Age (years)  Class variable  \n",
              "0           50               1  \n",
              "1           31               0  \n",
              "2           32               1  \n",
              "3           21               0  \n",
              "4           33               1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7865b0f5-88e7-4448-bdc3-c191a55f050f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Number of times pregnant</th>\n",
              "      <th>Plasma glucose concentration a 2 hours in an oral glucose tolerance test</th>\n",
              "      <th>Diastolic blood pressure (mm Hg)</th>\n",
              "      <th>Triceps skin fold thickness (mm)</th>\n",
              "      <th>2-Hour serum insulin (mu U/ml)</th>\n",
              "      <th>Body mass index (weight in kg/(height in m)^2)</th>\n",
              "      <th>Diabetes pedigree function</th>\n",
              "      <th>Age (years)</th>\n",
              "      <th>Class variable</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7865b0f5-88e7-4448-bdc3-c191a55f050f')\"\n",
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              "\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-7865b0f5-88e7-4448-bdc3-c191a55f050f button.colab-df-convert');\n",
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              "        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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              "  .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
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              "\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6b6dc3b6-7302-4513-b798-a62a79300037 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      <th id=\"T_66d1f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
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              "      <td id=\"T_66d1f_row0_col1\" class=\"data row0 col1\" >8758</td>\n",
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              "      <td id=\"T_66d1f_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_66d1f_row1_col1\" class=\"data row1 col1\" >Class variable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_66d1f_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
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              "      <td id=\"T_66d1f_row5_col1\" class=\"data row5 col1\" >(537, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td id=\"T_66d1f_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_66d1f_row6_col1\" class=\"data row6 col1\" >(231, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_66d1f_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_66d1f_row7_col1\" class=\"data row7 col1\" >8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_66d1f_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_66d1f_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_66d1f_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_66d1f_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_66d1f_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_66d1f_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_66d1f_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_66d1f_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_66d1f_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_66d1f_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_66d1f_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_66d1f_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_66d1f_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_66d1f_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_66d1f_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_66d1f_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_66d1f_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_66d1f_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_66d1f_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_66d1f_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_66d1f_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td id=\"T_66d1f_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_66d1f_row18_col1\" class=\"data row18 col1\" >f95a</td>\n",
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              "  text-align: left;\n",
              "}\n",
              "#T_41942_row0_col0, #T_41942_row0_col2, #T_41942_row0_col3, #T_41942_row0_col4, #T_41942_row1_col0, #T_41942_row1_col1, #T_41942_row1_col2, #T_41942_row1_col3, #T_41942_row1_col4, #T_41942_row1_col5, #T_41942_row1_col6, #T_41942_row1_col7, #T_41942_row2_col0, #T_41942_row2_col1, #T_41942_row2_col2, #T_41942_row2_col3, #T_41942_row2_col4, #T_41942_row2_col5, #T_41942_row2_col6, #T_41942_row2_col7, #T_41942_row3_col0, #T_41942_row3_col1, #T_41942_row3_col2, #T_41942_row3_col3, #T_41942_row3_col5, #T_41942_row3_col6, #T_41942_row3_col7, #T_41942_row4_col0, #T_41942_row4_col1, #T_41942_row4_col3, #T_41942_row4_col4, #T_41942_row4_col5, #T_41942_row4_col6, #T_41942_row4_col7, #T_41942_row5_col0, #T_41942_row5_col1, #T_41942_row5_col2, #T_41942_row5_col3, #T_41942_row5_col4, #T_41942_row5_col5, #T_41942_row5_col6, #T_41942_row5_col7, #T_41942_row6_col0, #T_41942_row6_col1, #T_41942_row6_col2, #T_41942_row6_col3, #T_41942_row6_col4, #T_41942_row6_col5, #T_41942_row6_col6, #T_41942_row6_col7, #T_41942_row7_col0, #T_41942_row7_col1, #T_41942_row7_col2, #T_41942_row7_col4, #T_41942_row7_col5, #T_41942_row7_col6, #T_41942_row7_col7, #T_41942_row8_col0, #T_41942_row8_col1, #T_41942_row8_col2, #T_41942_row8_col3, #T_41942_row8_col4, #T_41942_row8_col5, #T_41942_row8_col6, #T_41942_row8_col7, #T_41942_row9_col0, #T_41942_row9_col1, #T_41942_row9_col2, #T_41942_row9_col3, #T_41942_row9_col4, #T_41942_row9_col5, #T_41942_row9_col6, #T_41942_row9_col7, #T_41942_row10_col0, #T_41942_row10_col1, #T_41942_row10_col2, #T_41942_row10_col3, #T_41942_row10_col4, #T_41942_row10_col5, #T_41942_row10_col6, #T_41942_row10_col7, #T_41942_row11_col0, #T_41942_row11_col1, #T_41942_row11_col2, #T_41942_row11_col3, #T_41942_row11_col4, #T_41942_row11_col5, #T_41942_row11_col6, #T_41942_row11_col7, #T_41942_row12_col0, #T_41942_row12_col1, #T_41942_row12_col2, #T_41942_row12_col3, #T_41942_row12_col4, #T_41942_row12_col5, #T_41942_row12_col6, #T_41942_row12_col7, #T_41942_row13_col0, #T_41942_row13_col1, #T_41942_row13_col2, #T_41942_row13_col3, #T_41942_row13_col4, #T_41942_row13_col5, #T_41942_row13_col6, #T_41942_row13_col7, #T_41942_row14_col0, #T_41942_row14_col1, #T_41942_row14_col2, #T_41942_row14_col3, #T_41942_row14_col4, #T_41942_row14_col5, #T_41942_row14_col6, #T_41942_row14_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_41942_row0_col1, #T_41942_row0_col5, #T_41942_row0_col6, #T_41942_row0_col7, #T_41942_row3_col4, #T_41942_row4_col2, #T_41942_row7_col3 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_41942_row0_col8, #T_41942_row1_col8, #T_41942_row2_col8, #T_41942_row3_col8, #T_41942_row4_col8, #T_41942_row5_col8, #T_41942_row6_col8, #T_41942_row7_col8, #T_41942_row9_col8, #T_41942_row10_col8, #T_41942_row11_col8, #T_41942_row12_col8, #T_41942_row13_col8, #T_41942_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_41942_row8_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_41942\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_41942_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_41942_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_41942_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_41942_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_41942_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_41942_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_41942_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_41942_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_41942_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row0\" class=\"row_heading level0 row0\" >gbc</th>\n",
              "      <td id=\"T_41942_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_41942_row0_col1\" class=\"data row0 col1\" >0.7656</td>\n",
              "      <td id=\"T_41942_row0_col2\" class=\"data row0 col2\" >0.8125</td>\n",
              "      <td id=\"T_41942_row0_col3\" class=\"data row0 col3\" >0.5789</td>\n",
              "      <td id=\"T_41942_row0_col4\" class=\"data row0 col4\" >0.7035</td>\n",
              "      <td id=\"T_41942_row0_col5\" class=\"data row0 col5\" >0.6288</td>\n",
              "      <td id=\"T_41942_row0_col6\" class=\"data row0 col6\" >0.4615</td>\n",
              "      <td id=\"T_41942_row0_col7\" class=\"data row0 col7\" >0.4705</td>\n",
              "      <td id=\"T_41942_row0_col8\" class=\"data row0 col8\" >0.1390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row1\" class=\"row_heading level0 row1\" >rf</th>\n",
              "      <td id=\"T_41942_row1_col0\" class=\"data row1 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_41942_row1_col1\" class=\"data row1 col1\" >0.7616</td>\n",
              "      <td id=\"T_41942_row1_col2\" class=\"data row1 col2\" >0.8123</td>\n",
              "      <td id=\"T_41942_row1_col3\" class=\"data row1 col3\" >0.5611</td>\n",
              "      <td id=\"T_41942_row1_col4\" class=\"data row1 col4\" >0.6997</td>\n",
              "      <td id=\"T_41942_row1_col5\" class=\"data row1 col5\" >0.6140</td>\n",
              "      <td id=\"T_41942_row1_col6\" class=\"data row1 col6\" >0.4473</td>\n",
              "      <td id=\"T_41942_row1_col7\" class=\"data row1 col7\" >0.4579</td>\n",
              "      <td id=\"T_41942_row1_col8\" class=\"data row1 col8\" >0.2220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
              "      <td id=\"T_41942_row2_col0\" class=\"data row2 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_41942_row2_col1\" class=\"data row2 col1\" >0.7560</td>\n",
              "      <td id=\"T_41942_row2_col2\" class=\"data row2 col2\" >0.8038</td>\n",
              "      <td id=\"T_41942_row2_col3\" class=\"data row2 col3\" >0.5673</td>\n",
              "      <td id=\"T_41942_row2_col4\" class=\"data row2 col4\" >0.6832</td>\n",
              "      <td id=\"T_41942_row2_col5\" class=\"data row2 col5\" >0.6142</td>\n",
              "      <td id=\"T_41942_row2_col6\" class=\"data row2 col6\" >0.4399</td>\n",
              "      <td id=\"T_41942_row2_col7\" class=\"data row2 col7\" >0.4473</td>\n",
              "      <td id=\"T_41942_row2_col8\" class=\"data row2 col8\" >0.1910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row3\" class=\"row_heading level0 row3\" >ridge</th>\n",
              "      <td id=\"T_41942_row3_col0\" class=\"data row3 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_41942_row3_col1\" class=\"data row3 col1\" >0.7543</td>\n",
              "      <td id=\"T_41942_row3_col2\" class=\"data row3 col2\" >0.0000</td>\n",
              "      <td id=\"T_41942_row3_col3\" class=\"data row3 col3\" >0.5143</td>\n",
              "      <td id=\"T_41942_row3_col4\" class=\"data row3 col4\" >0.7060</td>\n",
              "      <td id=\"T_41942_row3_col5\" class=\"data row3 col5\" >0.5900</td>\n",
              "      <td id=\"T_41942_row3_col6\" class=\"data row3 col6\" >0.4222</td>\n",
              "      <td id=\"T_41942_row3_col7\" class=\"data row3 col7\" >0.4359</td>\n",
              "      <td id=\"T_41942_row3_col8\" class=\"data row3 col8\" >0.0290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row4\" class=\"row_heading level0 row4\" >lda</th>\n",
              "      <td id=\"T_41942_row4_col0\" class=\"data row4 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_41942_row4_col1\" class=\"data row4 col1\" >0.7524</td>\n",
              "      <td id=\"T_41942_row4_col2\" class=\"data row4 col2\" >0.8173</td>\n",
              "      <td id=\"T_41942_row4_col3\" class=\"data row4 col3\" >0.5143</td>\n",
              "      <td id=\"T_41942_row4_col4\" class=\"data row4 col4\" >0.7007</td>\n",
              "      <td id=\"T_41942_row4_col5\" class=\"data row4 col5\" >0.5880</td>\n",
              "      <td id=\"T_41942_row4_col6\" class=\"data row4 col6\" >0.4185</td>\n",
              "      <td id=\"T_41942_row4_col7\" class=\"data row4 col7\" >0.4317</td>\n",
              "      <td id=\"T_41942_row4_col8\" class=\"data row4 col8\" >0.0330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row5\" class=\"row_heading level0 row5\" >lr</th>\n",
              "      <td id=\"T_41942_row5_col0\" class=\"data row5 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_41942_row5_col1\" class=\"data row5 col1\" >0.7487</td>\n",
              "      <td id=\"T_41942_row5_col2\" class=\"data row5 col2\" >0.8161</td>\n",
              "      <td id=\"T_41942_row5_col3\" class=\"data row5 col3\" >0.5088</td>\n",
              "      <td id=\"T_41942_row5_col4\" class=\"data row5 col4\" >0.6922</td>\n",
              "      <td id=\"T_41942_row5_col5\" class=\"data row5 col5\" >0.5825</td>\n",
              "      <td id=\"T_41942_row5_col6\" class=\"data row5 col6\" >0.4100</td>\n",
              "      <td id=\"T_41942_row5_col7\" class=\"data row5 col7\" >0.4221</td>\n",
              "      <td id=\"T_41942_row5_col8\" class=\"data row5 col8\" >0.7310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row6\" class=\"row_heading level0 row6\" >ada</th>\n",
              "      <td id=\"T_41942_row6_col0\" class=\"data row6 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_41942_row6_col1\" class=\"data row6 col1\" >0.7470</td>\n",
              "      <td id=\"T_41942_row6_col2\" class=\"data row6 col2\" >0.8117</td>\n",
              "      <td id=\"T_41942_row6_col3\" class=\"data row6 col3\" >0.5789</td>\n",
              "      <td id=\"T_41942_row6_col4\" class=\"data row6 col4\" >0.6567</td>\n",
              "      <td id=\"T_41942_row6_col5\" class=\"data row6 col5\" >0.6121</td>\n",
              "      <td id=\"T_41942_row6_col6\" class=\"data row6 col6\" >0.4263</td>\n",
              "      <td id=\"T_41942_row6_col7\" class=\"data row6 col7\" >0.4305</td>\n",
              "      <td id=\"T_41942_row6_col8\" class=\"data row6 col8\" >0.2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row7\" class=\"row_heading level0 row7\" >nb</th>\n",
              "      <td id=\"T_41942_row7_col0\" class=\"data row7 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_41942_row7_col1\" class=\"data row7 col1\" >0.7469</td>\n",
              "      <td id=\"T_41942_row7_col2\" class=\"data row7 col2\" >0.8041</td>\n",
              "      <td id=\"T_41942_row7_col3\" class=\"data row7 col3\" >0.6053</td>\n",
              "      <td id=\"T_41942_row7_col4\" class=\"data row7 col4\" >0.6469</td>\n",
              "      <td id=\"T_41942_row7_col5\" class=\"data row7 col5\" >0.6212</td>\n",
              "      <td id=\"T_41942_row7_col6\" class=\"data row7 col6\" >0.4328</td>\n",
              "      <td id=\"T_41942_row7_col7\" class=\"data row7 col7\" >0.4364</td>\n",
              "      <td id=\"T_41942_row7_col8\" class=\"data row7 col8\" >0.0300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row8\" class=\"row_heading level0 row8\" >qda</th>\n",
              "      <td id=\"T_41942_row8_col0\" class=\"data row8 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_41942_row8_col1\" class=\"data row8 col1\" >0.7338</td>\n",
              "      <td id=\"T_41942_row8_col2\" class=\"data row8 col2\" >0.7981</td>\n",
              "      <td id=\"T_41942_row8_col3\" class=\"data row8 col3\" >0.5617</td>\n",
              "      <td id=\"T_41942_row8_col4\" class=\"data row8 col4\" >0.6363</td>\n",
              "      <td id=\"T_41942_row8_col5\" class=\"data row8 col5\" >0.5882</td>\n",
              "      <td id=\"T_41942_row8_col6\" class=\"data row8 col6\" >0.3953</td>\n",
              "      <td id=\"T_41942_row8_col7\" class=\"data row8 col7\" >0.4025</td>\n",
              "      <td id=\"T_41942_row8_col8\" class=\"data row8 col8\" >0.0280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row9\" class=\"row_heading level0 row9\" >xgboost</th>\n",
              "      <td id=\"T_41942_row9_col0\" class=\"data row9 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_41942_row9_col1\" class=\"data row9 col1\" >0.7281</td>\n",
              "      <td id=\"T_41942_row9_col2\" class=\"data row9 col2\" >0.7775</td>\n",
              "      <td id=\"T_41942_row9_col3\" class=\"data row9 col3\" >0.5775</td>\n",
              "      <td id=\"T_41942_row9_col4\" class=\"data row9 col4\" >0.6263</td>\n",
              "      <td id=\"T_41942_row9_col5\" class=\"data row9 col5\" >0.5944</td>\n",
              "      <td id=\"T_41942_row9_col6\" class=\"data row9 col6\" >0.3919</td>\n",
              "      <td id=\"T_41942_row9_col7\" class=\"data row9 col7\" >0.3974</td>\n",
              "      <td id=\"T_41942_row9_col8\" class=\"data row9 col8\" >0.0800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row10\" class=\"row_heading level0 row10\" >lightgbm</th>\n",
              "      <td id=\"T_41942_row10_col0\" class=\"data row10 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_41942_row10_col1\" class=\"data row10 col1\" >0.7244</td>\n",
              "      <td id=\"T_41942_row10_col2\" class=\"data row10 col2\" >0.7756</td>\n",
              "      <td id=\"T_41942_row10_col3\" class=\"data row10 col3\" >0.5292</td>\n",
              "      <td id=\"T_41942_row10_col4\" class=\"data row10 col4\" >0.6290</td>\n",
              "      <td id=\"T_41942_row10_col5\" class=\"data row10 col5\" >0.5659</td>\n",
              "      <td id=\"T_41942_row10_col6\" class=\"data row10 col6\" >0.3688</td>\n",
              "      <td id=\"T_41942_row10_col7\" class=\"data row10 col7\" >0.3772</td>\n",
              "      <td id=\"T_41942_row10_col8\" class=\"data row10 col8\" >0.3870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row11\" class=\"row_heading level0 row11\" >knn</th>\n",
              "      <td id=\"T_41942_row11_col0\" class=\"data row11 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_41942_row11_col1\" class=\"data row11 col1\" >0.7151</td>\n",
              "      <td id=\"T_41942_row11_col2\" class=\"data row11 col2\" >0.7315</td>\n",
              "      <td id=\"T_41942_row11_col3\" class=\"data row11 col3\" >0.5474</td>\n",
              "      <td id=\"T_41942_row11_col4\" class=\"data row11 col4\" >0.6119</td>\n",
              "      <td id=\"T_41942_row11_col5\" class=\"data row11 col5\" >0.5688</td>\n",
              "      <td id=\"T_41942_row11_col6\" class=\"data row11 col6\" >0.3594</td>\n",
              "      <td id=\"T_41942_row11_col7\" class=\"data row11 col7\" >0.3668</td>\n",
              "      <td id=\"T_41942_row11_col8\" class=\"data row11 col8\" >0.0470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row12\" class=\"row_heading level0 row12\" >dt</th>\n",
              "      <td id=\"T_41942_row12_col0\" class=\"data row12 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_41942_row12_col1\" class=\"data row12 col1\" >0.6852</td>\n",
              "      <td id=\"T_41942_row12_col2\" class=\"data row12 col2\" >0.6558</td>\n",
              "      <td id=\"T_41942_row12_col3\" class=\"data row12 col3\" >0.5573</td>\n",
              "      <td id=\"T_41942_row12_col4\" class=\"data row12 col4\" >0.5468</td>\n",
              "      <td id=\"T_41942_row12_col5\" class=\"data row12 col5\" >0.5438</td>\n",
              "      <td id=\"T_41942_row12_col6\" class=\"data row12 col6\" >0.3068</td>\n",
              "      <td id=\"T_41942_row12_col7\" class=\"data row12 col7\" >0.3124</td>\n",
              "      <td id=\"T_41942_row12_col8\" class=\"data row12 col8\" >0.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row13\" class=\"row_heading level0 row13\" >dummy</th>\n",
              "      <td id=\"T_41942_row13_col0\" class=\"data row13 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_41942_row13_col1\" class=\"data row13 col1\" >0.6518</td>\n",
              "      <td id=\"T_41942_row13_col2\" class=\"data row13 col2\" >0.5000</td>\n",
              "      <td id=\"T_41942_row13_col3\" class=\"data row13 col3\" >0.0000</td>\n",
              "      <td id=\"T_41942_row13_col4\" class=\"data row13 col4\" >0.0000</td>\n",
              "      <td id=\"T_41942_row13_col5\" class=\"data row13 col5\" >0.0000</td>\n",
              "      <td id=\"T_41942_row13_col6\" class=\"data row13 col6\" >0.0000</td>\n",
              "      <td id=\"T_41942_row13_col7\" class=\"data row13 col7\" >0.0000</td>\n",
              "      <td id=\"T_41942_row13_col8\" class=\"data row13 col8\" >0.0390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_41942_level0_row14\" class=\"row_heading level0 row14\" >svm</th>\n",
              "      <td id=\"T_41942_row14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_41942_row14_col1\" class=\"data row14 col1\" >0.5699</td>\n",
              "      <td id=\"T_41942_row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
              "      <td id=\"T_41942_row14_col3\" class=\"data row14 col3\" >0.4520</td>\n",
              "      <td id=\"T_41942_row14_col4\" class=\"data row14 col4\" >0.3212</td>\n",
              "      <td id=\"T_41942_row14_col5\" class=\"data row14 col5\" >0.3313</td>\n",
              "      <td id=\"T_41942_row14_col6\" class=\"data row14 col6\" >0.0771</td>\n",
              "      <td id=\"T_41942_row14_col7\" class=\"data row14 col7\" >0.0904</td>\n",
              "      <td id=\"T_41942_row14_col8\" class=\"data row14 col8\" >0.0290</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/65 [00:00<?, ?it/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": []
          },
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        {
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          "data": {
            "text/plain": [
              "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
              "                           learning_rate=0.1, loss='log_loss', max_depth=3,\n",
              "                           max_features=None, max_leaf_nodes=None,\n",
              "                           min_impurity_decrease=0.0, min_samples_leaf=1,\n",
              "                           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                           n_estimators=100, n_iter_no_change=None,\n",
              "                           random_state=8758, subsample=1.0, tol=0.0001,\n",
              "                           validation_fraction=0.1, verbose=0,\n",
              "                           warm_start=False)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(ccp_alpha=0.0, criterion=&#x27;friedman_mse&#x27;, init=None,\n",
              "                           learning_rate=0.1, loss=&#x27;log_loss&#x27;, max_depth=3,\n",
              "                           max_features=None, max_leaf_nodes=None,\n",
              "                           min_impurity_decrease=0.0, min_samples_leaf=1,\n",
              "                           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                           n_estimators=100, n_iter_no_change=None,\n",
              "                           random_state=8758, subsample=1.0, tol=0.0001,\n",
              "                           validation_fraction=0.1, verbose=0,\n",
              "                           warm_start=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(ccp_alpha=0.0, criterion=&#x27;friedman_mse&#x27;, init=None,\n",
              "                           learning_rate=0.1, loss=&#x27;log_loss&#x27;, max_depth=3,\n",
              "                           max_features=None, max_leaf_nodes=None,\n",
              "                           min_impurity_decrease=0.0, min_samples_leaf=1,\n",
              "                           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                           n_estimators=100, n_iter_no_change=None,\n",
              "                           random_state=8758, subsample=1.0, tol=0.0001,\n",
              "                           validation_fraction=0.1, verbose=0,\n",
              "                           warm_start=False)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING: Runtime no longer has a reference to this dataframe, please re-run this cell and try again.\n"
          ]
        }
      ]
    }
  ]
}