From edaa63497c6e6a6d3fb49f6069278fbf185ab1d6 Mon Sep 17 00:00:00 2001
From: Kenta Edmonda <91776795+KentaEDM@users.noreply.github.com>
Date: Fri, 8 Sep 2023 17:36:16 +0700
Subject: [PATCH] Delete Modeling_Predict_Income.ipynb

---
 Modeling_Predict_Income.ipynb | 7853 ---------------------------------
 1 file changed, 7853 deletions(-)
 delete mode 100644 Modeling_Predict_Income.ipynb

diff --git a/Modeling_Predict_Income.ipynb b/Modeling_Predict_Income.ipynb
deleted file mode 100644
index 28aaa1e..0000000
--- a/Modeling_Predict_Income.ipynb
+++ /dev/null
@@ -1,7853 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "toc_visible": true,
-      "authorship_tag": "ABX9TyPRIHZQ91fElOkUPcITjO6z",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/KentaEDM/Machine-Learning/blob/main/Modeling_Predict_Income.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "rP-joI7LkIXL",
-        "outputId": "b0824157-a512-4e21-8b91-9e5023e853dc"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
-            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
-            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.7.22)\n",
-            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
-            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
-            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.1)\n",
-            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
-            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.4)\n",
-            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.0.0)\n",
-            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
-            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
-            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.2.0)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install kaggle\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!pip install nbconvert\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "sHw4YHOZCDQn",
-        "outputId": "5c7d104c-1280-47ae-8028-b268a18b3431"
-      },
-      "execution_count": 74,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.10/dist-packages (6.5.4)\n",
-            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from nbconvert) (4.9.3)\n",
-            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (4.11.2)\n",
-            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from nbconvert) (6.0.0)\n",
-            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.7.1)\n",
-            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.4)\n",
-            "Requirement already satisfied: jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (3.1.2)\n",
-            "Requirement already satisfied: jupyter-core>=4.7 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (5.3.1)\n",
-            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.2.2)\n",
-            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (2.1.3)\n",
-            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.8.4)\n",
-            "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.8.0)\n",
-            "Requirement already satisfied: nbformat>=5.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (5.9.2)\n",
-            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from nbconvert) (23.1)\n",
-            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (1.5.0)\n",
-            "Requirement already satisfied: pygments>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (2.16.1)\n",
-            "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (1.2.1)\n",
-            "Requirement already satisfied: traitlets>=5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (5.7.1)\n",
-            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core>=4.7->nbconvert) (3.10.0)\n",
-            "Requirement already satisfied: jupyter-client>=6.1.12 in /usr/local/lib/python3.10/dist-packages (from nbclient>=0.5.0->nbconvert) (6.1.12)\n",
-            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.1->nbconvert) (2.18.0)\n",
-            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.1->nbconvert) (4.19.0)\n",
-            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert) (2.5)\n",
-            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert) (1.16.0)\n",
-            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert) (0.5.1)\n",
-            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (23.1.0)\n",
-            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (2023.7.1)\n",
-            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (0.30.2)\n",
-            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (0.10.2)\n",
-            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (23.2.1)\n",
-            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (2.8.2)\n",
-            "Requirement already satisfied: tornado>=4.1 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (6.3.2)\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!pip install nbformat\n",
-        "!pip install nbconvert\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "OXEnRaD7CE8_",
-        "outputId": "5068fc46-70f6-4a1b-951f-8f0cc37363d2"
-      },
-      "execution_count": 75,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: nbformat in /usr/local/lib/python3.10/dist-packages (5.9.2)\n",
-            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.10/dist-packages (from nbformat) (2.18.0)\n",
-            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat) (4.19.0)\n",
-            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.10/dist-packages (from nbformat) (5.3.1)\n",
-            "Requirement already satisfied: traitlets>=5.1 in /usr/local/lib/python3.10/dist-packages (from nbformat) (5.7.1)\n",
-            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (23.1.0)\n",
-            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (2023.7.1)\n",
-            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (0.30.2)\n",
-            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (0.10.2)\n",
-            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core->nbformat) (3.10.0)\n",
-            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.10/dist-packages (6.5.4)\n",
-            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from nbconvert) (4.9.3)\n",
-            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (4.11.2)\n",
-            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from nbconvert) (6.0.0)\n",
-            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.7.1)\n",
-            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.4)\n",
-            "Requirement already satisfied: jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (3.1.2)\n",
-            "Requirement already satisfied: jupyter-core>=4.7 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (5.3.1)\n",
-            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.2.2)\n",
-            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (2.1.3)\n",
-            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.8.4)\n",
-            "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.8.0)\n",
-            "Requirement already satisfied: nbformat>=5.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (5.9.2)\n",
-            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from nbconvert) (23.1)\n",
-            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (1.5.0)\n",
-            "Requirement already satisfied: pygments>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (2.16.1)\n",
-            "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (1.2.1)\n",
-            "Requirement already satisfied: traitlets>=5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (5.7.1)\n",
-            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core>=4.7->nbconvert) (3.10.0)\n",
-            "Requirement already satisfied: jupyter-client>=6.1.12 in /usr/local/lib/python3.10/dist-packages (from nbclient>=0.5.0->nbconvert) (6.1.12)\n",
-            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.1->nbconvert) (2.18.0)\n",
-            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.1->nbconvert) (4.19.0)\n",
-            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert) (2.5)\n",
-            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert) (1.16.0)\n",
-            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert) (0.5.1)\n",
-            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (23.1.0)\n",
-            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (2023.7.1)\n",
-            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (0.30.2)\n",
-            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (0.10.2)\n",
-            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (23.2.1)\n",
-            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (2.8.2)\n",
-            "Requirement already satisfied: tornado>=4.1 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (6.3.2)\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!jupyter nbconvert --to pdf notebook.ipynb\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Vx2KDbFPCHr2",
-        "outputId": "ab4769e4-f225-46f9-9659-1e9729036063"
-      },
-      "execution_count": 76,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "[NbConvertApp] WARNING | pattern 'notebook.ipynb' matched no files\n",
-            "This application is used to convert notebook files (*.ipynb)\n",
-            "        to various other formats.\n",
-            "\n",
-            "        WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.\n",
-            "\n",
-            "Options\n",
-            "=======\n",
-            "The options below are convenience aliases to configurable class-options,\n",
-            "as listed in the \"Equivalent to\" description-line of the aliases.\n",
-            "To see all configurable class-options for some <cmd>, use:\n",
-            "    <cmd> --help-all\n",
-            "\n",
-            "--debug\n",
-            "    set log level to logging.DEBUG (maximize logging output)\n",
-            "    Equivalent to: [--Application.log_level=10]\n",
-            "--show-config\n",
-            "    Show the application's configuration (human-readable format)\n",
-            "    Equivalent to: [--Application.show_config=True]\n",
-            "--show-config-json\n",
-            "    Show the application's configuration (json format)\n",
-            "    Equivalent to: [--Application.show_config_json=True]\n",
-            "--generate-config\n",
-            "    generate default config file\n",
-            "    Equivalent to: [--JupyterApp.generate_config=True]\n",
-            "-y\n",
-            "    Answer yes to any questions instead of prompting.\n",
-            "    Equivalent to: [--JupyterApp.answer_yes=True]\n",
-            "--execute\n",
-            "    Execute the notebook prior to export.\n",
-            "    Equivalent to: [--ExecutePreprocessor.enabled=True]\n",
-            "--allow-errors\n",
-            "    Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.\n",
-            "    Equivalent to: [--ExecutePreprocessor.allow_errors=True]\n",
-            "--stdin\n",
-            "    read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'\n",
-            "    Equivalent to: [--NbConvertApp.from_stdin=True]\n",
-            "--stdout\n",
-            "    Write notebook output to stdout instead of files.\n",
-            "    Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]\n",
-            "--inplace\n",
-            "    Run nbconvert in place, overwriting the existing notebook (only\n",
-            "            relevant when converting to notebook format)\n",
-            "    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]\n",
-            "--clear-output\n",
-            "    Clear output of current file and save in place,\n",
-            "            overwriting the existing notebook.\n",
-            "    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]\n",
-            "--no-prompt\n",
-            "    Exclude input and output prompts from converted document.\n",
-            "    Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]\n",
-            "--no-input\n",
-            "    Exclude input cells and output prompts from converted document.\n",
-            "            This mode is ideal for generating code-free reports.\n",
-            "    Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]\n",
-            "--allow-chromium-download\n",
-            "    Whether to allow downloading chromium if no suitable version is found on the system.\n",
-            "    Equivalent to: [--WebPDFExporter.allow_chromium_download=True]\n",
-            "--disable-chromium-sandbox\n",
-            "    Disable chromium security sandbox when converting to PDF..\n",
-            "    Equivalent to: [--WebPDFExporter.disable_sandbox=True]\n",
-            "--show-input\n",
-            "    Shows code input. This flag is only useful for dejavu users.\n",
-            "    Equivalent to: [--TemplateExporter.exclude_input=False]\n",
-            "--embed-images\n",
-            "    Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.\n",
-            "    Equivalent to: [--HTMLExporter.embed_images=True]\n",
-            "--sanitize-html\n",
-            "    Whether the HTML in Markdown cells and cell outputs should be sanitized..\n",
-            "    Equivalent to: [--HTMLExporter.sanitize_html=True]\n",
-            "--log-level=<Enum>\n",
-            "    Set the log level by value or name.\n",
-            "    Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']\n",
-            "    Default: 30\n",
-            "    Equivalent to: [--Application.log_level]\n",
-            "--config=<Unicode>\n",
-            "    Full path of a config file.\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--JupyterApp.config_file]\n",
-            "--to=<Unicode>\n",
-            "    The export format to be used, either one of the built-in formats\n",
-            "            ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf']\n",
-            "            or a dotted object name that represents the import path for an\n",
-            "            ``Exporter`` class\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--NbConvertApp.export_format]\n",
-            "--template=<Unicode>\n",
-            "    Name of the template to use\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--TemplateExporter.template_name]\n",
-            "--template-file=<Unicode>\n",
-            "    Name of the template file to use\n",
-            "    Default: None\n",
-            "    Equivalent to: [--TemplateExporter.template_file]\n",
-            "--theme=<Unicode>\n",
-            "    Template specific theme(e.g. the name of a JupyterLab CSS theme distributed\n",
-            "    as prebuilt extension for the lab template)\n",
-            "    Default: 'light'\n",
-            "    Equivalent to: [--HTMLExporter.theme]\n",
-            "--sanitize_html=<Bool>\n",
-            "    Whether the HTML in Markdown cells and cell outputs should be sanitized.This\n",
-            "    should be set to True by nbviewer or similar tools.\n",
-            "    Default: False\n",
-            "    Equivalent to: [--HTMLExporter.sanitize_html]\n",
-            "--writer=<DottedObjectName>\n",
-            "    Writer class used to write the\n",
-            "                                        results of the conversion\n",
-            "    Default: 'FilesWriter'\n",
-            "    Equivalent to: [--NbConvertApp.writer_class]\n",
-            "--post=<DottedOrNone>\n",
-            "    PostProcessor class used to write the\n",
-            "                                        results of the conversion\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--NbConvertApp.postprocessor_class]\n",
-            "--output=<Unicode>\n",
-            "    overwrite base name use for output files.\n",
-            "                can only be used when converting one notebook at a time.\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--NbConvertApp.output_base]\n",
-            "--output-dir=<Unicode>\n",
-            "    Directory to write output(s) to. Defaults\n",
-            "                                  to output to the directory of each notebook. To recover\n",
-            "                                  previous default behaviour (outputting to the current\n",
-            "                                  working directory) use . as the flag value.\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--FilesWriter.build_directory]\n",
-            "--reveal-prefix=<Unicode>\n",
-            "    The URL prefix for reveal.js (version 3.x).\n",
-            "            This defaults to the reveal CDN, but can be any url pointing to a copy\n",
-            "            of reveal.js.\n",
-            "            For speaker notes to work, this must be a relative path to a local\n",
-            "            copy of reveal.js: e.g., \"reveal.js\".\n",
-            "            If a relative path is given, it must be a subdirectory of the\n",
-            "            current directory (from which the server is run).\n",
-            "            See the usage documentation\n",
-            "            (https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)\n",
-            "            for more details.\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--SlidesExporter.reveal_url_prefix]\n",
-            "--nbformat=<Enum>\n",
-            "    The nbformat version to write.\n",
-            "            Use this to downgrade notebooks.\n",
-            "    Choices: any of [1, 2, 3, 4]\n",
-            "    Default: 4\n",
-            "    Equivalent to: [--NotebookExporter.nbformat_version]\n",
-            "\n",
-            "Examples\n",
-            "--------\n",
-            "\n",
-            "    The simplest way to use nbconvert is\n",
-            "\n",
-            "            > jupyter nbconvert mynotebook.ipynb --to html\n",
-            "\n",
-            "            Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].\n",
-            "\n",
-            "            > jupyter nbconvert --to latex mynotebook.ipynb\n",
-            "\n",
-            "            Both HTML and LaTeX support multiple output templates. LaTeX includes\n",
-            "            'base', 'article' and 'report'.  HTML includes 'basic', 'lab' and\n",
-            "            'classic'. You can specify the flavor of the format used.\n",
-            "\n",
-            "            > jupyter nbconvert --to html --template lab mynotebook.ipynb\n",
-            "\n",
-            "            You can also pipe the output to stdout, rather than a file\n",
-            "\n",
-            "            > jupyter nbconvert mynotebook.ipynb --stdout\n",
-            "\n",
-            "            PDF is generated via latex\n",
-            "\n",
-            "            > jupyter nbconvert mynotebook.ipynb --to pdf\n",
-            "\n",
-            "            You can get (and serve) a Reveal.js-powered slideshow\n",
-            "\n",
-            "            > jupyter nbconvert myslides.ipynb --to slides --post serve\n",
-            "\n",
-            "            Multiple notebooks can be given at the command line in a couple of\n",
-            "            different ways:\n",
-            "\n",
-            "            > jupyter nbconvert notebook*.ipynb\n",
-            "            > jupyter nbconvert notebook1.ipynb notebook2.ipynb\n",
-            "\n",
-            "            or you can specify the notebooks list in a config file, containing::\n",
-            "\n",
-            "                c.NbConvertApp.notebooks = [\"my_notebook.ipynb\"]\n",
-            "\n",
-            "            > jupyter nbconvert --config mycfg.py\n",
-            "\n",
-            "To see all available configurables, use `--help-all`.\n",
-            "\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!jupyter nbconvert mynotebook.ipynb --to pdf"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "6QUBC_OhCmzc",
-        "outputId": "b5313925-f5f8-450c-adfa-64a119ebaba6"
-      },
-      "execution_count": 78,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "[NbConvertApp] WARNING | pattern 'mynotebook.ipynb' matched no files\n",
-            "This application is used to convert notebook files (*.ipynb)\n",
-            "        to various other formats.\n",
-            "\n",
-            "        WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.\n",
-            "\n",
-            "Options\n",
-            "=======\n",
-            "The options below are convenience aliases to configurable class-options,\n",
-            "as listed in the \"Equivalent to\" description-line of the aliases.\n",
-            "To see all configurable class-options for some <cmd>, use:\n",
-            "    <cmd> --help-all\n",
-            "\n",
-            "--debug\n",
-            "    set log level to logging.DEBUG (maximize logging output)\n",
-            "    Equivalent to: [--Application.log_level=10]\n",
-            "--show-config\n",
-            "    Show the application's configuration (human-readable format)\n",
-            "    Equivalent to: [--Application.show_config=True]\n",
-            "--show-config-json\n",
-            "    Show the application's configuration (json format)\n",
-            "    Equivalent to: [--Application.show_config_json=True]\n",
-            "--generate-config\n",
-            "    generate default config file\n",
-            "    Equivalent to: [--JupyterApp.generate_config=True]\n",
-            "-y\n",
-            "    Answer yes to any questions instead of prompting.\n",
-            "    Equivalent to: [--JupyterApp.answer_yes=True]\n",
-            "--execute\n",
-            "    Execute the notebook prior to export.\n",
-            "    Equivalent to: [--ExecutePreprocessor.enabled=True]\n",
-            "--allow-errors\n",
-            "    Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.\n",
-            "    Equivalent to: [--ExecutePreprocessor.allow_errors=True]\n",
-            "--stdin\n",
-            "    read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'\n",
-            "    Equivalent to: [--NbConvertApp.from_stdin=True]\n",
-            "--stdout\n",
-            "    Write notebook output to stdout instead of files.\n",
-            "    Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]\n",
-            "--inplace\n",
-            "    Run nbconvert in place, overwriting the existing notebook (only\n",
-            "            relevant when converting to notebook format)\n",
-            "    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]\n",
-            "--clear-output\n",
-            "    Clear output of current file and save in place,\n",
-            "            overwriting the existing notebook.\n",
-            "    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]\n",
-            "--no-prompt\n",
-            "    Exclude input and output prompts from converted document.\n",
-            "    Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]\n",
-            "--no-input\n",
-            "    Exclude input cells and output prompts from converted document.\n",
-            "            This mode is ideal for generating code-free reports.\n",
-            "    Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]\n",
-            "--allow-chromium-download\n",
-            "    Whether to allow downloading chromium if no suitable version is found on the system.\n",
-            "    Equivalent to: [--WebPDFExporter.allow_chromium_download=True]\n",
-            "--disable-chromium-sandbox\n",
-            "    Disable chromium security sandbox when converting to PDF..\n",
-            "    Equivalent to: [--WebPDFExporter.disable_sandbox=True]\n",
-            "--show-input\n",
-            "    Shows code input. This flag is only useful for dejavu users.\n",
-            "    Equivalent to: [--TemplateExporter.exclude_input=False]\n",
-            "--embed-images\n",
-            "    Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.\n",
-            "    Equivalent to: [--HTMLExporter.embed_images=True]\n",
-            "--sanitize-html\n",
-            "    Whether the HTML in Markdown cells and cell outputs should be sanitized..\n",
-            "    Equivalent to: [--HTMLExporter.sanitize_html=True]\n",
-            "--log-level=<Enum>\n",
-            "    Set the log level by value or name.\n",
-            "    Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']\n",
-            "    Default: 30\n",
-            "    Equivalent to: [--Application.log_level]\n",
-            "--config=<Unicode>\n",
-            "    Full path of a config file.\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--JupyterApp.config_file]\n",
-            "--to=<Unicode>\n",
-            "    The export format to be used, either one of the built-in formats\n",
-            "            ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf']\n",
-            "            or a dotted object name that represents the import path for an\n",
-            "            ``Exporter`` class\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--NbConvertApp.export_format]\n",
-            "--template=<Unicode>\n",
-            "    Name of the template to use\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--TemplateExporter.template_name]\n",
-            "--template-file=<Unicode>\n",
-            "    Name of the template file to use\n",
-            "    Default: None\n",
-            "    Equivalent to: [--TemplateExporter.template_file]\n",
-            "--theme=<Unicode>\n",
-            "    Template specific theme(e.g. the name of a JupyterLab CSS theme distributed\n",
-            "    as prebuilt extension for the lab template)\n",
-            "    Default: 'light'\n",
-            "    Equivalent to: [--HTMLExporter.theme]\n",
-            "--sanitize_html=<Bool>\n",
-            "    Whether the HTML in Markdown cells and cell outputs should be sanitized.This\n",
-            "    should be set to True by nbviewer or similar tools.\n",
-            "    Default: False\n",
-            "    Equivalent to: [--HTMLExporter.sanitize_html]\n",
-            "--writer=<DottedObjectName>\n",
-            "    Writer class used to write the\n",
-            "                                        results of the conversion\n",
-            "    Default: 'FilesWriter'\n",
-            "    Equivalent to: [--NbConvertApp.writer_class]\n",
-            "--post=<DottedOrNone>\n",
-            "    PostProcessor class used to write the\n",
-            "                                        results of the conversion\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--NbConvertApp.postprocessor_class]\n",
-            "--output=<Unicode>\n",
-            "    overwrite base name use for output files.\n",
-            "                can only be used when converting one notebook at a time.\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--NbConvertApp.output_base]\n",
-            "--output-dir=<Unicode>\n",
-            "    Directory to write output(s) to. Defaults\n",
-            "                                  to output to the directory of each notebook. To recover\n",
-            "                                  previous default behaviour (outputting to the current\n",
-            "                                  working directory) use . as the flag value.\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--FilesWriter.build_directory]\n",
-            "--reveal-prefix=<Unicode>\n",
-            "    The URL prefix for reveal.js (version 3.x).\n",
-            "            This defaults to the reveal CDN, but can be any url pointing to a copy\n",
-            "            of reveal.js.\n",
-            "            For speaker notes to work, this must be a relative path to a local\n",
-            "            copy of reveal.js: e.g., \"reveal.js\".\n",
-            "            If a relative path is given, it must be a subdirectory of the\n",
-            "            current directory (from which the server is run).\n",
-            "            See the usage documentation\n",
-            "            (https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)\n",
-            "            for more details.\n",
-            "    Default: ''\n",
-            "    Equivalent to: [--SlidesExporter.reveal_url_prefix]\n",
-            "--nbformat=<Enum>\n",
-            "    The nbformat version to write.\n",
-            "            Use this to downgrade notebooks.\n",
-            "    Choices: any of [1, 2, 3, 4]\n",
-            "    Default: 4\n",
-            "    Equivalent to: [--NotebookExporter.nbformat_version]\n",
-            "\n",
-            "Examples\n",
-            "--------\n",
-            "\n",
-            "    The simplest way to use nbconvert is\n",
-            "\n",
-            "            > jupyter nbconvert mynotebook.ipynb --to html\n",
-            "\n",
-            "            Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].\n",
-            "\n",
-            "            > jupyter nbconvert --to latex mynotebook.ipynb\n",
-            "\n",
-            "            Both HTML and LaTeX support multiple output templates. LaTeX includes\n",
-            "            'base', 'article' and 'report'.  HTML includes 'basic', 'lab' and\n",
-            "            'classic'. You can specify the flavor of the format used.\n",
-            "\n",
-            "            > jupyter nbconvert --to html --template lab mynotebook.ipynb\n",
-            "\n",
-            "            You can also pipe the output to stdout, rather than a file\n",
-            "\n",
-            "            > jupyter nbconvert mynotebook.ipynb --stdout\n",
-            "\n",
-            "            PDF is generated via latex\n",
-            "\n",
-            "            > jupyter nbconvert mynotebook.ipynb --to pdf\n",
-            "\n",
-            "            You can get (and serve) a Reveal.js-powered slideshow\n",
-            "\n",
-            "            > jupyter nbconvert myslides.ipynb --to slides --post serve\n",
-            "\n",
-            "            Multiple notebooks can be given at the command line in a couple of\n",
-            "            different ways:\n",
-            "\n",
-            "            > jupyter nbconvert notebook*.ipynb\n",
-            "            > jupyter nbconvert notebook1.ipynb notebook2.ipynb\n",
-            "\n",
-            "            or you can specify the notebooks list in a config file, containing::\n",
-            "\n",
-            "                c.NbConvertApp.notebooks = [\"my_notebook.ipynb\"]\n",
-            "\n",
-            "            > jupyter nbconvert --config mycfg.py\n",
-            "\n",
-            "To see all available configurables, use `--help-all`.\n",
-            "\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/gdrive')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "39CZQllllbrA",
-        "outputId": "fdf56408-df4f-44f2-c7f0-a7402beb9c33"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Mounted at /content/gdrive\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from google.colab import files\n",
-        "\n",
-        "files.upload()"
-      ],
-      "metadata": {
-        "id": "a54Q8MpDm3jb",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 92
-        },
-        "outputId": "6dc23fd6-99b4-49a4-a71b-9909fb200f5c"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "     <input type=\"file\" id=\"files-b892594e-a5ee-434b-b999-80efc93bec8b\" name=\"files[]\" multiple disabled\n",
-              "        style=\"border:none\" />\n",
-              "     <output id=\"result-b892594e-a5ee-434b-b999-80efc93bec8b\">\n",
-              "      Upload widget is only available when the cell has been executed in the\n",
-              "      current browser session. Please rerun this cell to enable.\n",
-              "      </output>\n",
-              "      <script>// Copyright 2017 Google LLC\n",
-              "//\n",
-              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-              "// you may not use this file except in compliance with the License.\n",
-              "// You may obtain a copy of the License at\n",
-              "//\n",
-              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
-              "//\n",
-              "// Unless required by applicable law or agreed to in writing, software\n",
-              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-              "// See the License for the specific language governing permissions and\n",
-              "// limitations under the License.\n",
-              "\n",
-              "/**\n",
-              " * @fileoverview Helpers for google.colab Python module.\n",
-              " */\n",
-              "(function(scope) {\n",
-              "function span(text, styleAttributes = {}) {\n",
-              "  const element = document.createElement('span');\n",
-              "  element.textContent = text;\n",
-              "  for (const key of Object.keys(styleAttributes)) {\n",
-              "    element.style[key] = styleAttributes[key];\n",
-              "  }\n",
-              "  return element;\n",
-              "}\n",
-              "\n",
-              "// Max number of bytes which will be uploaded at a time.\n",
-              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
-              "\n",
-              "function _uploadFiles(inputId, outputId) {\n",
-              "  const steps = uploadFilesStep(inputId, outputId);\n",
-              "  const outputElement = document.getElementById(outputId);\n",
-              "  // Cache steps on the outputElement to make it available for the next call\n",
-              "  // to uploadFilesContinue from Python.\n",
-              "  outputElement.steps = steps;\n",
-              "\n",
-              "  return _uploadFilesContinue(outputId);\n",
-              "}\n",
-              "\n",
-              "// This is roughly an async generator (not supported in the browser yet),\n",
-              "// where there are multiple asynchronous steps and the Python side is going\n",
-              "// to poll for completion of each step.\n",
-              "// This uses a Promise to block the python side on completion of each step,\n",
-              "// then passes the result of the previous step as the input to the next step.\n",
-              "function _uploadFilesContinue(outputId) {\n",
-              "  const outputElement = document.getElementById(outputId);\n",
-              "  const steps = outputElement.steps;\n",
-              "\n",
-              "  const next = steps.next(outputElement.lastPromiseValue);\n",
-              "  return Promise.resolve(next.value.promise).then((value) => {\n",
-              "    // Cache the last promise value to make it available to the next\n",
-              "    // step of the generator.\n",
-              "    outputElement.lastPromiseValue = value;\n",
-              "    return next.value.response;\n",
-              "  });\n",
-              "}\n",
-              "\n",
-              "/**\n",
-              " * Generator function which is called between each async step of the upload\n",
-              " * process.\n",
-              " * @param {string} inputId Element ID of the input file picker element.\n",
-              " * @param {string} outputId Element ID of the output display.\n",
-              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
-              " */\n",
-              "function* uploadFilesStep(inputId, outputId) {\n",
-              "  const inputElement = document.getElementById(inputId);\n",
-              "  inputElement.disabled = false;\n",
-              "\n",
-              "  const outputElement = document.getElementById(outputId);\n",
-              "  outputElement.innerHTML = '';\n",
-              "\n",
-              "  const pickedPromise = new Promise((resolve) => {\n",
-              "    inputElement.addEventListener('change', (e) => {\n",
-              "      resolve(e.target.files);\n",
-              "    });\n",
-              "  });\n",
-              "\n",
-              "  const cancel = document.createElement('button');\n",
-              "  inputElement.parentElement.appendChild(cancel);\n",
-              "  cancel.textContent = 'Cancel upload';\n",
-              "  const cancelPromise = new Promise((resolve) => {\n",
-              "    cancel.onclick = () => {\n",
-              "      resolve(null);\n",
-              "    };\n",
-              "  });\n",
-              "\n",
-              "  // Wait for the user to pick the files.\n",
-              "  const files = yield {\n",
-              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
-              "    response: {\n",
-              "      action: 'starting',\n",
-              "    }\n",
-              "  };\n",
-              "\n",
-              "  cancel.remove();\n",
-              "\n",
-              "  // Disable the input element since further picks are not allowed.\n",
-              "  inputElement.disabled = true;\n",
-              "\n",
-              "  if (!files) {\n",
-              "    return {\n",
-              "      response: {\n",
-              "        action: 'complete',\n",
-              "      }\n",
-              "    };\n",
-              "  }\n",
-              "\n",
-              "  for (const file of files) {\n",
-              "    const li = document.createElement('li');\n",
-              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
-              "    li.append(span(\n",
-              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
-              "        `last modified: ${\n",
-              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
-              "                                    'n/a'} - `));\n",
-              "    const percent = span('0% done');\n",
-              "    li.appendChild(percent);\n",
-              "\n",
-              "    outputElement.appendChild(li);\n",
-              "\n",
-              "    const fileDataPromise = new Promise((resolve) => {\n",
-              "      const reader = new FileReader();\n",
-              "      reader.onload = (e) => {\n",
-              "        resolve(e.target.result);\n",
-              "      };\n",
-              "      reader.readAsArrayBuffer(file);\n",
-              "    });\n",
-              "    // Wait for the data to be ready.\n",
-              "    let fileData = yield {\n",
-              "      promise: fileDataPromise,\n",
-              "      response: {\n",
-              "        action: 'continue',\n",
-              "      }\n",
-              "    };\n",
-              "\n",
-              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
-              "    let position = 0;\n",
-              "    do {\n",
-              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
-              "      const chunk = new Uint8Array(fileData, position, length);\n",
-              "      position += length;\n",
-              "\n",
-              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
-              "      yield {\n",
-              "        response: {\n",
-              "          action: 'append',\n",
-              "          file: file.name,\n",
-              "          data: base64,\n",
-              "        },\n",
-              "      };\n",
-              "\n",
-              "      let percentDone = fileData.byteLength === 0 ?\n",
-              "          100 :\n",
-              "          Math.round((position / fileData.byteLength) * 100);\n",
-              "      percent.textContent = `${percentDone}% done`;\n",
-              "\n",
-              "    } while (position < fileData.byteLength);\n",
-              "  }\n",
-              "\n",
-              "  // All done.\n",
-              "  yield {\n",
-              "    response: {\n",
-              "      action: 'complete',\n",
-              "    }\n",
-              "  };\n",
-              "}\n",
-              "\n",
-              "scope.google = scope.google || {};\n",
-              "scope.google.colab = scope.google.colab || {};\n",
-              "scope.google.colab._files = {\n",
-              "  _uploadFiles,\n",
-              "  _uploadFilesContinue,\n",
-              "};\n",
-              "})(self);\n",
-              "</script> "
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Saving kaggle.json to kaggle.json\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "{'kaggle.json': b'{\"username\":\"kentaedmonda\",\"key\":\"3b222347e721389ec8b1fc8f53f7f7c3\"}'}"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 3
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!ls -lha kaggle.json"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bfDmWYUanG59",
-        "outputId": "0685f4f8-7809-454a-9d55-6185d6d693cf"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "-rw-r--r-- 1 root root 68 Sep  8 06:52 kaggle.json\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!mkdir -p ~/.kaggle"
-      ],
-      "metadata": {
-        "id": "DG0inWJInN5i"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!cp kaggle.json ~/.kaggle/"
-      ],
-      "metadata": {
-        "id": "m2ew7JhcnPlZ"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!chmod 600 /root/.kaggle/kaggle.json"
-      ],
-      "metadata": {
-        "id": "mBDmQa8anSH-"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!kaggle datasets download -d wenruliu/adult-income-dataset"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "3AwNKLC1ne9f",
-        "outputId": "c94dc6b7-019a-46ef-e0fc-0192c0855ebb"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Downloading adult-income-dataset.zip to /content\n",
-            "\r  0% 0.00/652k [00:00<?, ?B/s]\n",
-            "\r100% 652k/652k [00:00<00:00, 116MB/s]\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!unzip /content/adult-income-dataset.zip"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "lpnupk9yooLf",
-        "outputId": "759dce23-6d61-4fb4-bddd-09221fac81be"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Archive:  /content/adult-income-dataset.zip\n",
-            "  inflating: adult.csv               \n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Pediksi Pendapatan melalui Algoritma Random Forest Classifer**\n",
-        "\n",
-        "Project ini bertujuan untuk memprediksi pendapatan orang-orang berdasarkan beberapa variabel, seperti umur, pendidikan, gender, total jam kerja, dan lainnya.\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "4GzyLok8ijeZ"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 1. Importing the required libraries for EDA\n",
-        "\n",
-        "Berikut merupakan modul yang digunakan dalam project EDA dan Machine Learning Model."
-      ],
-      "metadata": {
-        "id": "_OdXMCs26-Xb"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "from sklearn.ensemble import RandomForestClassifier\n",
-        "from sklearn.model_selection import train_test_split\n"
-      ],
-      "metadata": {
-        "id": "n0n8KvPEouU2"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 2. Loading the data into data frame\n",
-        "\n",
-        "<font size='2'>Konversi data menjadi data frame melalui pandas merupakan aspek penting agar segala proses pengerjaan project lebih efektif dan efisien.\n",
-        "\n",
-        "**About Dataset**\n",
-        "\n",
-        "<font size='2'>Dataset yang bertujuan untuk memprediksi pendapatan tahunan individu dipengaruhi oleh berbagai faktor, seperti tingkat pendidikan, usia, jenis kelamin, pekerjaan, dan lain sebagainya.Secara garis besar, dataset ini berisi *feature* dan *label* sebagai variabel independen dan variabel dependen, di mana kolom income merupakan target label untuk diprediksi. Dataset ini secara mentah berisi sebanyak **15 kolom** dan **48842 baris**. Data diambil dari Kaggle: https://www.kaggle.com/datasets/wenruliu/adult-income-dataset</font>\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "o85XVhRD7nhm"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Membaca dataset\n",
-        "df = pd.read_csv('/content/adult.csv')\n",
-        "#Memunculkan 5 baris data\n",
-        "df.head(5)"
-      ],
-      "metadata": {
-        "id": "ZBoNR-3go30M",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 399
-        },
-        "outputId": "b96486f7-7b3d-44e9-cdec-7a0b0c8e4aea"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   age  workclass  fnlwgt     education  educational-num      marital-status  \\\n",
-              "0   25    Private  226802          11th                7       Never-married   \n",
-              "1   38    Private   89814       HS-grad                9  Married-civ-spouse   \n",
-              "2   28  Local-gov  336951    Assoc-acdm               12  Married-civ-spouse   \n",
-              "3   44    Private  160323  Some-college               10  Married-civ-spouse   \n",
-              "4   18          ?  103497  Some-college               10       Never-married   \n",
-              "\n",
-              "          occupation relationship   race  gender  capital-gain  capital-loss  \\\n",
-              "0  Machine-op-inspct    Own-child  Black    Male             0             0   \n",
-              "1    Farming-fishing      Husband  White    Male             0             0   \n",
-              "2    Protective-serv      Husband  White    Male             0             0   \n",
-              "3  Machine-op-inspct      Husband  Black    Male          7688             0   \n",
-              "4                  ?    Own-child  White  Female             0             0   \n",
-              "\n",
-              "   hours-per-week native-country income  \n",
-              "0              40  United-States  <=50K  \n",
-              "1              50  United-States  <=50K  \n",
-              "2              40  United-States   >50K  \n",
-              "3              40  United-States   >50K  \n",
-              "4              30  United-States  <=50K  "
-            ],
-            "text/html": [
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-              "      <td>160323</td>\n",
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-              "      <td>7688</td>\n",
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-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-f830928f-df42-4716-aed2-58e3305ac696 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 2
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 3. Preprocessing Data\n",
-        "\n",
-        "<font size='2'>Melakukan sebuah pembersihan data dan mengeliminasi beberapa data. Tujuannya adalah untuk memilih beberapa kolom dan baris yang relevan untuk dieksplor dan modelisasi kedepannya.</font>"
-      ],
-      "metadata": {
-        "id": "LOxhQE74_vxq"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.dtypes #Memeriksa tipe data seluruh kolom"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "HxSEZGLWA5_i",
-        "outputId": "df108e1f-b1a7-4188-cc2b-c5279d5fa206"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "age                 int64\n",
-              "workclass          object\n",
-              "fnlwgt              int64\n",
-              "education          object\n",
-              "educational-num     int64\n",
-              "marital-status     object\n",
-              "occupation         object\n",
-              "relationship       object\n",
-              "race               object\n",
-              "gender             object\n",
-              "capital-gain        int64\n",
-              "capital-loss        int64\n",
-              "hours-per-week      int64\n",
-              "native-country     object\n",
-              "income             object\n",
-              "dtype: object"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 3
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.isna().sum() #Melihat apakah ada missing values"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "DWtwf4Hup1sT",
-        "outputId": "1f7cbe2a-07ae-4662-ea9b-97febc1fb22b"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "age                0\n",
-              "workclass          0\n",
-              "fnlwgt             0\n",
-              "education          0\n",
-              "educational-num    0\n",
-              "marital-status     0\n",
-              "occupation         0\n",
-              "relationship       0\n",
-              "race               0\n",
-              "gender             0\n",
-              "capital-gain       0\n",
-              "capital-loss       0\n",
-              "hours-per-week     0\n",
-              "native-country     0\n",
-              "income             0\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.duplicated().sum() #Memeriksa data yang terduplikat\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "xkbWuCQnp8at",
-        "outputId": "0f5df212-625b-478a-9075-10148aa24ddc"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "52"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.drop_duplicates(inplace=True) #Menghapus duplikat data"
-      ],
-      "metadata": {
-        "id": "Dax4xm2rBZcr"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "<font size='2'>Terdapat sebuah nilai \"?\" pada beberapa tabel, sehingga diharuskan untuk membersikan data tersebut demi efektivitas modeling dan eksplorisasi data.</font>"
-      ],
-      "metadata": {
-        "id": "u0_qeU-jBfwa"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Mengubah unformatted value pada dataset\n",
-        "#Terdapat value \"?\", cara mengatasinya dengan mengubah menjadi value NA\n",
-        "df.replace('?', pd.NA, inplace=True)"
-      ],
-      "metadata": {
-        "id": "-tP3a1yuJEMF"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.isna().sum()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Xmbcg3cFKZQ-",
-        "outputId": "71fac107-326a-462b-f0d9-3e870cd5c815"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "age                   0\n",
-              "workclass          2795\n",
-              "fnlwgt                0\n",
-              "education             0\n",
-              "educational-num       0\n",
-              "marital-status        0\n",
-              "occupation         2805\n",
-              "relationship          0\n",
-              "race                  0\n",
-              "gender                0\n",
-              "capital-gain          0\n",
-              "capital-loss          0\n",
-              "hours-per-week        0\n",
-              "native-country      856\n",
-              "income                0\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.dropna(inplace=True) #Menghapus missing value yang dari nilai \"?\""
-      ],
-      "metadata": {
-        "id": "Ne84HFS-Kh5u"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#drop column yang tidak berpengaruh\n",
-        "#fnlwgt column tidak memiliki pengaruh yang signifikan dalam proses modelling data\n",
-        "df = df.drop('fnlwgt', axis=1)"
-      ],
-      "metadata": {
-        "id": "vHuU38HVDx8I"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 755
-        },
-        "id": "pVViaziOERcM",
-        "outputId": "2875224d-a128-4ddd-df69-ee5b7ce5485f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "       age     workclass     education  educational-num      marital-status  \\\n",
-              "0       25       Private          11th                7       Never-married   \n",
-              "1       38       Private       HS-grad                9  Married-civ-spouse   \n",
-              "2       28     Local-gov    Assoc-acdm               12  Married-civ-spouse   \n",
-              "3       44       Private  Some-college               10  Married-civ-spouse   \n",
-              "5       34       Private          10th                6       Never-married   \n",
-              "...    ...           ...           ...              ...                 ...   \n",
-              "48837   27       Private    Assoc-acdm               12  Married-civ-spouse   \n",
-              "48838   40       Private       HS-grad                9  Married-civ-spouse   \n",
-              "48839   58       Private       HS-grad                9             Widowed   \n",
-              "48840   22       Private       HS-grad                9       Never-married   \n",
-              "48841   52  Self-emp-inc       HS-grad                9  Married-civ-spouse   \n",
-              "\n",
-              "              occupation   relationship   race  gender  capital-gain  \\\n",
-              "0      Machine-op-inspct      Own-child  Black    Male             0   \n",
-              "1        Farming-fishing        Husband  White    Male             0   \n",
-              "2        Protective-serv        Husband  White    Male             0   \n",
-              "3      Machine-op-inspct        Husband  Black    Male          7688   \n",
-              "5          Other-service  Not-in-family  White    Male             0   \n",
-              "...                  ...            ...    ...     ...           ...   \n",
-              "48837       Tech-support           Wife  White  Female             0   \n",
-              "48838  Machine-op-inspct        Husband  White    Male             0   \n",
-              "48839       Adm-clerical      Unmarried  White  Female             0   \n",
-              "48840       Adm-clerical      Own-child  White    Male             0   \n",
-              "48841    Exec-managerial           Wife  White  Female         15024   \n",
-              "\n",
-              "       capital-loss  hours-per-week native-country income  \n",
-              "0                 0              40  United-States  <=50K  \n",
-              "1                 0              50  United-States  <=50K  \n",
-              "2                 0              40  United-States   >50K  \n",
-              "3                 0              40  United-States   >50K  \n",
-              "5                 0              30  United-States  <=50K  \n",
-              "...             ...             ...            ...    ...  \n",
-              "48837             0              38  United-States  <=50K  \n",
-              "48838             0              40  United-States   >50K  \n",
-              "48839             0              40  United-States  <=50K  \n",
-              "48840             0              20  United-States  <=50K  \n",
-              "48841             0              40  United-States   >50K  \n",
-              "\n",
-              "[45175 rows x 14 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-330f787e-8183-4f46-8a8d-e58786dd73b1\" class=\"colab-df-container\">\n",
-              "    <div>\n",
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-              "    .dataframe tbody tr th:only-of-type {\n",
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-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>age</th>\n",
-              "      <th>workclass</th>\n",
-              "      <th>education</th>\n",
-              "      <th>educational-num</th>\n",
-              "      <th>marital-status</th>\n",
-              "      <th>occupation</th>\n",
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-              "      <th>capital-gain</th>\n",
-              "      <th>capital-loss</th>\n",
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-              "      <th>native-country</th>\n",
-              "      <th>income</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>25</td>\n",
-              "      <td>Private</td>\n",
-              "      <td>11th</td>\n",
-              "      <td>7</td>\n",
-              "      <td>Never-married</td>\n",
-              "      <td>Machine-op-inspct</td>\n",
-              "      <td>Own-child</td>\n",
-              "      <td>Black</td>\n",
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-              "      <td>0</td>\n",
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-              "      <td>40</td>\n",
-              "      <td>United-States</td>\n",
-              "      <td>&lt;=50K</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>38</td>\n",
-              "      <td>Private</td>\n",
-              "      <td>HS-grad</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Married-civ-spouse</td>\n",
-              "      <td>Farming-fishing</td>\n",
-              "      <td>Husband</td>\n",
-              "      <td>White</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>50</td>\n",
-              "      <td>United-States</td>\n",
-              "      <td>&lt;=50K</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>28</td>\n",
-              "      <td>Local-gov</td>\n",
-              "      <td>Assoc-acdm</td>\n",
-              "      <td>12</td>\n",
-              "      <td>Married-civ-spouse</td>\n",
-              "      <td>Protective-serv</td>\n",
-              "      <td>Husband</td>\n",
-              "      <td>White</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>40</td>\n",
-              "      <td>United-States</td>\n",
-              "      <td>&gt;50K</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>44</td>\n",
-              "      <td>Private</td>\n",
-              "      <td>Some-college</td>\n",
-              "      <td>10</td>\n",
-              "      <td>Married-civ-spouse</td>\n",
-              "      <td>Machine-op-inspct</td>\n",
-              "      <td>Husband</td>\n",
-              "      <td>Black</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>7688</td>\n",
-              "      <td>0</td>\n",
-              "      <td>40</td>\n",
-              "      <td>United-States</td>\n",
-              "      <td>&gt;50K</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>34</td>\n",
-              "      <td>Private</td>\n",
-              "      <td>10th</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Never-married</td>\n",
-              "      <td>Other-service</td>\n",
-              "      <td>Not-in-family</td>\n",
-              "      <td>White</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>30</td>\n",
-              "      <td>United-States</td>\n",
-              "      <td>&lt;=50K</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>48837</th>\n",
-              "      <td>27</td>\n",
-              "      <td>Private</td>\n",
-              "      <td>Assoc-acdm</td>\n",
-              "      <td>12</td>\n",
-              "      <td>Married-civ-spouse</td>\n",
-              "      <td>Tech-support</td>\n",
-              "      <td>Wife</td>\n",
-              "      <td>White</td>\n",
-              "      <td>Female</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>38</td>\n",
-              "      <td>United-States</td>\n",
-              "      <td>&lt;=50K</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>48838</th>\n",
-              "      <td>40</td>\n",
-              "      <td>Private</td>\n",
-              "      <td>HS-grad</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Married-civ-spouse</td>\n",
-              "      <td>Machine-op-inspct</td>\n",
-              "      <td>Husband</td>\n",
-              "      <td>White</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>40</td>\n",
-              "      <td>United-States</td>\n",
-              "      <td>&gt;50K</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>48839</th>\n",
-              "      <td>58</td>\n",
-              "      <td>Private</td>\n",
-              "      <td>HS-grad</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Widowed</td>\n",
-              "      <td>Adm-clerical</td>\n",
-              "      <td>Unmarried</td>\n",
-              "      <td>White</td>\n",
-              "      <td>Female</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>40</td>\n",
-              "      <td>United-States</td>\n",
-              "      <td>&lt;=50K</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>48840</th>\n",
-              "      <td>22</td>\n",
-              "      <td>Private</td>\n",
-              "      <td>HS-grad</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Never-married</td>\n",
-              "      <td>Adm-clerical</td>\n",
-              "      <td>Own-child</td>\n",
-              "      <td>White</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>20</td>\n",
-              "      <td>United-States</td>\n",
-              "      <td>&lt;=50K</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>48841</th>\n",
-              "      <td>52</td>\n",
-              "      <td>Self-emp-inc</td>\n",
-              "      <td>HS-grad</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Married-civ-spouse</td>\n",
-              "      <td>Exec-managerial</td>\n",
-              "      <td>Wife</td>\n",
-              "      <td>White</td>\n",
-              "      <td>Female</td>\n",
-              "      <td>15024</td>\n",
-              "      <td>0</td>\n",
-              "      <td>40</td>\n",
-              "      <td>United-States</td>\n",
-              "      <td>&gt;50K</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>45175 rows × 14 columns</p>\n",
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-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-330f787e-8183-4f46-8a8d-e58786dd73b1 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-330f787e-8183-4f46-8a8d-e58786dd73b1');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-e9515a33-0c33-48d3-be88-560d9a8902d9\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9515a33-0c33-48d3-be88-560d9a8902d9')\"\n",
-              "            title=\"Suggest charts.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-e9515a33-0c33-48d3-be88-560d9a8902d9 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 4. Exploratory Data Analysis\n",
-        " **4.1 Univariate analysis**\n",
-        "<font size='2'>\n",
-        "*   Univariate bertujuan untuk melihat informasi data hanya dari satu variabel saja\n",
-        "*   Juga bertujuan untuk memperoleh gambaran distribusi frekuensi masing-masing variabel yang diteliti\n",
-        "</font>\n",
-        "<br>\n",
-        "<br>\n",
-        "\n",
-        "\n",
-        "Melihat gambaran descriptive statistics - **Numerical columns**"
-      ],
-      "metadata": {
-        "id": "BJTIfX7SEbm7"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.describe()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 300
-        },
-        "id": "d0ZUfKNmIcWQ",
-        "outputId": "2daaef66-a36d-411d-b86a-717850a4d125"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                age  educational-num  capital-gain  capital-loss  \\\n",
-              "count  45175.000000     45175.000000  45175.000000  45175.000000   \n",
-              "mean      38.556170        10.119314   1102.576270     88.687593   \n",
-              "std       13.215349         2.551740   7510.249876    405.156611   \n",
-              "min       17.000000         1.000000      0.000000      0.000000   \n",
-              "25%       28.000000         9.000000      0.000000      0.000000   \n",
-              "50%       37.000000        10.000000      0.000000      0.000000   \n",
-              "75%       47.000000        13.000000      0.000000      0.000000   \n",
-              "max       90.000000        16.000000  99999.000000   4356.000000   \n",
-              "\n",
-              "       hours-per-week  \n",
-              "count    45175.000000  \n",
-              "mean        40.942512  \n",
-              "std         12.007730  \n",
-              "min          1.000000  \n",
-              "25%         40.000000  \n",
-              "50%         40.000000  \n",
-              "75%         45.000000  \n",
-              "max         99.000000  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-87e9d4df-85cc-4f14-b8bf-2a1f144cf529\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>age</th>\n",
-              "      <th>educational-num</th>\n",
-              "      <th>capital-gain</th>\n",
-              "      <th>capital-loss</th>\n",
-              "      <th>hours-per-week</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>45175.000000</td>\n",
-              "      <td>45175.000000</td>\n",
-              "      <td>45175.000000</td>\n",
-              "      <td>45175.000000</td>\n",
-              "      <td>45175.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>38.556170</td>\n",
-              "      <td>10.119314</td>\n",
-              "      <td>1102.576270</td>\n",
-              "      <td>88.687593</td>\n",
-              "      <td>40.942512</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>13.215349</td>\n",
-              "      <td>2.551740</td>\n",
-              "      <td>7510.249876</td>\n",
-              "      <td>405.156611</td>\n",
-              "      <td>12.007730</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>17.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>28.000000</td>\n",
-              "      <td>9.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>40.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>37.000000</td>\n",
-              "      <td>10.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>40.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>47.000000</td>\n",
-              "      <td>13.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>45.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>90.000000</td>\n",
-              "      <td>16.000000</td>\n",
-              "      <td>99999.000000</td>\n",
-              "      <td>4356.000000</td>\n",
-              "      <td>99.000000</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-87e9d4df-85cc-4f14-b8bf-2a1f144cf529')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-87e9d4df-85cc-4f14-b8bf-2a1f144cf529 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-87e9d4df-85cc-4f14-b8bf-2a1f144cf529');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-6449d211-141b-4f0f-ac56-1aabb2728fb9\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6449d211-141b-4f0f-ac56-1aabb2728fb9')\"\n",
-              "            title=\"Suggest charts.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
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-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-6449d211-141b-4f0f-ac56-1aabb2728fb9 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Melihat gambaran descriptive statistics - **Categorical columns**\n"
-      ],
-      "metadata": {
-        "id": "OZ04ClooIuu8"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.describe(exclude='number')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 246
-        },
-        "id": "4IsdCMoOItX-",
-        "outputId": "2b0294d8-52b7-44fe-8c29-7e27991d70e6"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "       workclass education      marital-status    occupation relationship  \\\n",
-              "count      45175     45175               45175         45175        45175   \n",
-              "unique         7        16                   7            14            6   \n",
-              "top      Private   HS-grad  Married-civ-spouse  Craft-repair      Husband   \n",
-              "freq       33262     14770               21042          6010        18653   \n",
-              "\n",
-              "         race gender native-country income  \n",
-              "count   45175  45175          45175  45175  \n",
-              "unique      5      2             41      2  \n",
-              "top     White   Male  United-States  <=50K  \n",
-              "freq    38859  30495          41256  33973  "
-            ],
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-              "    <tr>\n",
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-              "      <td>Private</td>\n",
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-              "      <td>Married-civ-spouse</td>\n",
-              "      <td>Craft-repair</td>\n",
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-              "      <td>&lt;=50K</td>\n",
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-              "    <tr>\n",
-              "      <th>freq</th>\n",
-              "      <td>33262</td>\n",
-              "      <td>14770</td>\n",
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-              "      <td>18653</td>\n",
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-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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-              "        const element = document.querySelector('#df-f5a35547-c91e-4417-957a-b376ca4b7080');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
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-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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-              "          + ' to learn more about interactive tables.';\n",
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-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
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-              "\n",
-              "  @keyframes spin {\n",
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-              "      border-bottom-color: var(--fill-color);\n",
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-              "      border-left-color: var(--fill-color);\n",
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-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
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-              "    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",
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-              "        console.error('Error during call to suggestCharts:', error);\n",
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-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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-              "      let quickchartButtonEl =\n",
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-              "      quickchartButtonEl.style.display =\n",
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-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 13
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df['education'].unique() #melihat kolom unique pada education"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Tfujj6ZLJO4v",
-        "outputId": "a668d95b-b26c-4261-b9ee-b4661f639b0e"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array(['11th', 'HS-grad', 'Assoc-acdm', 'Some-college', '10th',\n",
-              "       'Prof-school', '7th-8th', 'Bachelors', 'Masters', '5th-6th',\n",
-              "       'Assoc-voc', '9th', 'Doctorate', '12th', '1st-4th', 'Preschool'],\n",
-              "      dtype=object)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 14
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df['educational-num'].unique()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "j9lPdsvpJTxi",
-        "outputId": "e26d5a75-91fe-4941-a68c-b38003d5b581"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([ 7,  9, 12, 10,  6, 15,  4, 13, 14,  3, 11,  5, 16,  8,  2,  1])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 15
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "\n",
-        "<font size='2'>\n",
-        "1.Seperti yang dapat dilihat, kolom 'education' dan 'educational-num' memiliki nilai yang mirip.<br>\n",
-        "2.education' memiliki  nilai dalam bentuk string, sedangkan 'educational-num' memiliki nilai numerik.<br>\n",
-        "3.Nantinya, kolom education akan dihapus dan hanya dipakai education-num,\n",
-        "karena data numerical lebih digunakan dalam modeling data.\n",
-        "</font>\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "DUu7g2bqJtMU"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df['age'].describe() #melakukan basic deskripsi statistic untuk melihat distribusi kolom age"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "zE7o4XdSLRKF",
-        "outputId": "0c841bc8-6925-4327-a578-7e93c1700707"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "count    45175.000000\n",
-              "mean        38.556170\n",
-              "std         13.215349\n",
-              "min         17.000000\n",
-              "25%         28.000000\n",
-              "50%         37.000000\n",
-              "75%         47.000000\n",
-              "max         90.000000\n",
-              "Name: age, dtype: float64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 16
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Visualisasi distribusi frekuensi data umur\n",
-        "plt.figure(figsize=(8, 6))\n",
-        "sns.histplot(data=df, x='age', bins=20, kde=True)\n",
-        "plt.title('Distribusi Usia')\n",
-        "plt.xlabel('Usia')\n",
-        "plt.ylabel('Frekuensi')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 564
-        },
-        "id": "tVvQnTWcLaDY",
-        "outputId": "44d03484-7a20-4ac9-c741-be476db98362"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 800x600 with 1 Axes>"
-            ],
-            "image/png": 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iNjaW0aNHs2zZMmJjYwF4+umnsdlsTJ48mdraWiZMmMA//vEP7+vtdjszZ87klltuISMjg5CQEKZNm8ajjz7qbdO9e3dmzZrFnXfeybPPPktycjIvv/wyEyZMaPfPKyIiIiLWszQAv/fee8c8HxgYyPPPP8/zzz9/1DZpaWmHTXH4sbFjx7J69eoW1SgiIiIinYvl6wCLiIiIiLQnBWARERER8SsKwCIiIiLiVxSARURERMSvKACLiIiIiF9RABYRERERv+JTO8GJtKWsrCwKCwvbpa/MzMx26UdERESaTwFY/EJWVhb90tOprqpq134rKiratT8RERE5PgVg8QuFhYVUV1Ux5d6/EJ/as837y1yxmC9ff5aampo270tERESaRwFY/Ep8ak+Sew9o837ysna0eR8iIiLSMroJTkRERET8igKwiIiIiPgVBWARERER8SsKwCIiIiLiVxSARURERMSvKACLiIiIiF9RABYRERERv6IALCIiIiJ+RQFYRERERPyKArCIiIiI+BUFYBERERHxKwrAIiIiIuJXFIBFRERExK8oAIuIiIiIX1EAFhERERG/ogAsIiIiIn5FAVhERERE/IoCsIiIiIj4FYfVBYhI83g8JlV1bjymicc0sRkGHtOaWrKysigsLGy3/mJiYkhNTW23/kREpHNSABbxUaZpUlbTQG5pDfnlNeSX11JaXU9FbQPmjwKvQQBJN73MH/9TzCU1uzmnXxwpUcFtWl9WVhb90tOprqpq035+KCg4mM2ZmQrBIiJyUhSARXxIbYObPUVV7CmqIvtAFeU1DUdsZxhgNwwMAzwecJsQEJnA9zm1fP/ZRh7+bCODukZw81k9OX9gAnab0eq1FhYWUl1VxZR7/0J8as9Wf/8fy8vawdt/vpvCwkIFYBEROSkKwCIWq6xtYGdBJTsKK9hbXI37B8O7NgNiw1zEhwcSF+YiKsRJWGAAIU47htEYak3TZPvmTbz6l99zz5//webyAFbtOcD6faVMf+d7esSEcPv43lw8JMn7mtYUn9qT5N4DWv19RURE2ooCsIgFKmsb2JpXzta8CnLLapqciwwOoHtMCKldgkmKDMLpOPa9qoZhEOSA2r0b+Um/UIYNG0ZRRS1vLN3DjCW72VlYye3vreGT1ft4/KeD6BoZ1JYfTURExOcpAIu0kwa3h52FlWTmlLGnuKrJPN6E8EB6xIbQMzaUqBDnSfcVHeriznP78IsxPXj1m138fcF2Fm0p4LynFvPIxQO4bETKSfchIiLSUSkAi7Sx/LIa1u0rZVteBXVuj/d4fLiL9IRwesaFEupqm9+KoS4Ht43rzQWDErn3X+tYtecAd3+0jg37Snnwwv4E2LUSooiI+B8FYJE2YALBfc9gdU00i7/L9h4PC3TQLyGM9IRwurTCSO+J6hUXyoc3ZfC3Bdt5+qutvL50D5tzy3lh6vBWGXEWERHpSBSARVqRx2OyJa+c7+lB7E/up8zTeCNbr7hQBnWNoGtkUJvciHYibDaD28f3Jj0xjDvfX8PyXcVc9uIS3rxhJEmaFywiIn5EP/8UaQWmabKzoII3l+9h7qY8qnHhri4nzVHO9Wd0Z+LARJK7BFsWfn/ovAEJfDr9DJIiAtlRUMllLy5lV2Gl1WWJiIi0GwVgkZNUUF7Lx6v38fm6HEqq6gkKsJNGPvtevJ5uznJC2mh+78noHR/Gh7ecTo+YEPaVVHPZi0vYmldudVkiIiLtQgFYpIUa3B6W7Cjkve+y2HugGrvNYERaF649vRspFGHWVVtd4jF1jQzig5szGJAUTmFFHT9/aTk7CyqsLktERKTNKQCLtEBOaTXvrMjiu90H8JjQMzaEa0alcUavmOOu2+tLYkJdvHPjKPonhlNYUcuUl5eTXdx+WxuLiIhYoeP8TS3iAzymyYpdxXy4ai8HquoJdtqZNCiRCwcnER4UYHV5LRIRHMCbN5xGr7hQckprmPLycnJLa47/QhERkQ5KAVjkBFXWNvDJ9/tYurMI04Q+8aFcPSqNXnGhVpd20qJDXbx940jSooPJKq5iysvLKKyotbosERGRNqEALHICcstqePe7LPaWVBNgNzi3fzznD0ggMMBudWmtJj48kLdvHOldHWLqy8spqaqzuiwREZFWpwAschybc8v4aNVeKmvdRIU4ueq0VPonhvvEkmatLblLMO/8YhSxYS4255ZzzasrKK+pt7osERGRVqUALHIUpmny7fZC5mzMw+0x6R4TwuUjkukS3Ll3TusWE8LbN44kKsTJur2l3DBjJdV1bqvLEhERaTUKwCJHUNvg5vN1OazccwCA4WlduHBwIi5H55nycCx94sN44/rTCHM5WLG7mJveWkVtg0KwiIh0DgrAIj9SWl3Phyv3squwErvNYMKAeEb3isHWCac8HMvArhHMuP5UggLsfL21gNveXU2D22N1WSIiIidNAVjkB7KLq3hvRRZFlXWEOO38bFgy/RLCrS7LMsPTonjpmhE47TbmbMzjno/W4fGYVpclIiJyUhSARQ5at7eET9fso6bBQ3y4iytPSyUhItDqsiw3uncMz08Zht1m8PHqffz+3xswTYVgERHpuBSAxe+5PSYLNuezcEsBHhP6xofxs2HJhLocVpfmM87tH89Tlw/BMODt5Vn8cVamQrCIiHRY+htevLKysigsLGy3/mJiYkhNTW23/o6kus7NF+tz2FtSDcAZPaMZntalUy5xdrIuGdqV6jo39328nle+2cX+XsGArpOIiHQ8CsACNIbffunpVFdVtVufQcHBbM7MtCwEF1bU8vna/ZTVNOC025gwMJ4eMR1/V7e2dOVpqZjA7z5Zz5fbq4g6/1Y0ECwiIh2NArAAUFhYSHVVFVPu/QvxqT3bvL+8rB28/ee7KSwstCQA7yioYM7GXOrdJhFBAVw0OJHoUFe719ERXXVaKi6Hjd9+uJawIRNYWeSmq8fEZtNosIiIdAwKwNJEfGpPknsPsLqMNmOasGJ3MUt3FAGQ3CWICwYlEtSJtjRuD5cOS2Zf9h7+95tCsqoczN6Yy4QBCdgVgkVEpAPQTXDiNwyHixVFdm/4HZwcwU+GdlX4baEzUoIo+PQJbJhsy69g1vocrRMsIiIdggKw+IXCKjfxP/8Te6vs2Aw4p28cZ/eN04jlSarevpyM2AbsNoNdhZX8e+1+7RgnIiI+TwFYOr0Vu4q5e14hrsTeOG0mPz2lK4OSI6wuq9NICDK5ZEgSAXaDvQeq+fj7fVTWNlhdloiIyFEpAEunZZombyzdzc9fWkZprYe6/F2ck1BPcpdgq0vrdFKigpk8LJmgADv55bV8uGovpdX1VpclIiJyRArA0inV1Lu556N1PPTvjTR4TEanBJL71m8J0W2fbSY+PJDLRiQTHuigtLqeD1ZmU1Bea3VZIiIih1EAlk4np7SaK/5vKR+u2ovNgN9d0I87R0Vi1iuMtbUuwU4uG5FCdIiTqjo3H32/l30Hqq0uS0REpAkFYOlUFm8t4KK/fcPavaVEBgfw+vWn8csxPbWzWzsKdTn42fBkEiMCqWvw8MmafewsqLC6LBERES/9QFg6hboGD/87dwv//HonAOmJ4fzz6uGkRGm+rxUCA+z89JSufLkhl12Flcxcn8P4fvH0Two/6ffOzMxshQpPjC9s1y0iIq1PAVg6vI37S7n3X+vYsK8MgKtHpfHApHQCtb6vpQLsNiYNSmT+5jwyc8qZl5lHdb2b4WldWvR+ZcUFAEydOrU1yzwmq7frFhGRtqEALB1WTb2bZ77axkv/2Ynb07il8Z8nD+b8gQlWlyYH2W0G56bHExRg5/usEr7ZXkhVXQOje8U0e1pKdUXjP3Am3fQAfQcPb4tym7B6u24REWk7CsDS4dQ2uPngu2yeX7iD3LIaACYNSuThi/sTFxZocXXyY4ZhcGbvWIKdDr7ZXsj3WSVU17sZ1y++RRuRRCeldertukVEpO0pAEuHsfdAFf9es5+3l+1hf2lj8O0aGcQjFw/g3P7xFlcnxzM8rQtBAXa+Ojgloq7Bw8SBidqNT0RE2p0CsPikereH/PJaNu4rZd3eUpbtLGLlngPe8/HhLm49uxeXn5qCy6G5vh1F/6RwAgNsfLEhlx0FlczdlMuEAQnYtEqHiIi0IwVgsYRpgiOqK0v3VrO4aCvZB6ooKK/1Poqr6jDNpq8xDBjVPZqfnJLEJUO76ia3DqpHbCgXDEpg1roctuZVYLflcW56vJaqExGRdqMALO2qoraBTfvLWLs/gK6/+D/+sqQEKDliW7vNoFdsKENSIhiSEsk5/eJIjAhqz3KljfSICWXiwES+2JBDZk45AXYbY/vEKgSLiEi7UACWduH2mCzZUcjq7JKDI7sGnvoaeseFMaxHPN1jQ4gLCyQ2zEVcmIvYMBddgp2aH9qJ9YoL5bz+8czZmMe6vaWEuhyc2i3K6rJERMQPKABLmyutrufLDTnklTVuRZwYEUhXewUfPzCFT5cvYdiwIRZXKFbplxBOTb2HxVsLWLKjiBCno1U2yxARETkWBWBpU/sOVPPZuv3UNXhwOWyc2z+enrGh7N22EbOh1uryxAcMTYmksraBlXsO8NXmPIJddrpFh1hdloiIdGI2qwuQzqukqo6ZB8NvYkQgPz8tlZ6xoVaXJT7o9J7RpCeEYZrw5fpcCsr1jyMREWk7CsDSJmrq3Xy2dj81DR7iw11cekpXwoMCrC5LfJRhGIxLjye5SxB1bg+frd1PZW2D1WWJiEgnpQAsrc7tMflifQ4HquoJdTm4aHASDru+anJsdpvBpEGJdAkOoKK2gc/W7qfe7bG6LBER6YSUSqTVrckuIftANQF2g4uHJBHi0lRzOTGBAXYuGdqVoAA7+eW1zN6Qi+fHC0KLiIicJJ8JwH/6058wDIM77rjDe6ympobp06cTHR1NaGgokydPJi8vr8nrsrKymDRpEsHBwcTFxXH33XfT0ND0R6eLFi1i2LBhuFwuevXqxYwZM9rhE/mnipoGlu8qAuCsPrHEhrksrkg6moigAC4c3LhF8s7CSr7ZXmh1SSIi0sn4RAD+7rvv+L//+z8GDx7c5Pidd97J559/zocffsjixYvZv38/l156qfe82+1m0qRJ1NXVsWTJEl5//XVmzJjBQw895G2za9cuJk2axNlnn82aNWu44447uPHGG5kzZ067fT5/8p/tBdS7TRLCA+mfqOWspGWSIoM4Nz0egNVZJazbW2JtQSIi0qlYHoArKiqYMmUKL730El26dPEeLy0t5ZVXXuGpp57inHPOYfjw4bz22mssWbKEZcuWATB37lw2bdrEW2+9xdChQ5k4cSKPPfYYzz//PHV1dQC8+OKLdO/enb/+9a+kp6dz66238rOf/Yynn37aks/bme09UMXWvAoAzu6rXb3k5PRNCCOjRzQAi7YWcAAtjSYiIq3D8gA8ffp0Jk2axPjx45scX7VqFfX19U2O9+vXj9TUVJYuXQrA0qVLGTRoEPHx8d42EyZMoKysjI0bN3rb/Pi9J0yY4H2PI6mtraWsrKzJQ47N4zFZtKUAgEFdI4gLD7S4IukMTu3WhfTExuXRNtOVgJg0q0sSEZFOwNIA/N577/H999/zxBNPHHYuNzcXp9NJZGRkk+Px8fHk5uZ62/ww/B46f+jcsdqUlZVRXV19xLqeeOIJIiIivI+UlJQWfT5/sjW/nKLKOgIdNk7vGW11OdJJGIbBuH7xJEcG4cZO3GUPU2ta/u92ERHp4Cz7myQ7O5vbb7+dt99+m8BA3xotvP/++yktLfU+srOzrS7Jp5mmyao9BwAYmhpJYIDd4oqkM7HbDCYNTiSIWhzhcWyojdLyaCIiclIsC8CrVq0iPz+fYcOG4XA4cDgcLF68mOeeew6Hw0F8fDx1dXWUlJQ0eV1eXh4JCQkAJCQkHLYqxKHnx2sTHh5OUFDQEWtzuVyEh4c3ecjRZRVXUVhRR4DdYEhypNXlSCcUGGCnP9m4q0qp8DiZs1HLo4mISMtZFoDHjRvH+vXrWbNmjfcxYsQIpkyZ4v11QEAA8+fP975my5YtZGVlkZGRAUBGRgbr168nPz/f22bevHmEh4fTv39/b5sfvsehNofeQ07eyoOjvwOSIjT6K20miHoKPv4jBiY7Cir5VsujiYhIC1m2Q0FYWBgDBw5sciwkJITo6Gjv8RtuuIG77rqLqKgowsPD+fWvf01GRgajRo0C4LzzzqN///5cffXVPPnkk+Tm5vLggw8yffp0XK7G9Wdvvvlm/v73v3PPPfdw/fXXs2DBAj744ANmzZrVvh+4k8otq2HvgWpsBgxLjbS6HOnkavdl0s9ZQmZdF77PKiE8MIAhKZFWlyUiIh2MT99N8vTTT3PhhRcyefJkxowZQ0JCAh9//LH3vN1uZ+bMmdjtdjIyMpg6dSrXXHMNjz76qLdN9+7dmTVrFvPmzWPIkCH89a9/5eWXX2bChAlWfKROZ9XuxtHfvvFhhAUGWFyN+IM4R3WT5dE27Cu1uCIREelofGqP2kWLFjV5HhgYyPPPP8/zzz9/1NekpaXxxRdfHPN9x44dy+rVq1ujRPmB8pp6dhQ0rvs7LK3LcVqLtJ5Tu3Whut7NmuwS5m/Ox2YY9E/SXH0RETkxPj0CLL5tU04ZJtA1MoiYUG15LO3HMAzG9I5hSHIEAPMy89i4XyPBIiJyYhSApUVM02TT/sYNQgZo5E0sYBgGZ/WJZVDXxhD8VWY+K3YXY2p1CBEROQ4FYGmR7APVlNU04HTY6BUXanU54qcMw+DsvrGMODgFZ+mOIhZvLdASaSIickwKwNIiGw/eeNQvPowAu75GYh3DMDijVwxjescAsHZvKZ+t3U9NvdviykRExFcpuUizVde72VFQCWj6g/iOU1K7MHFgAg6bwZ6iKt77LpuC8lqryxIRER+kACzNtjmnDLdpEhvmIi7ct7axFv/WJz6My0ekEB7ooLS6ng9WZrM5t8zqskRExMcoAEuzbcrRzW/iu2LDXFx1WippUcE0eEzmbMxj8dYC3B7NCxYRkUYKwNIsxZV1FFbUYTMaR9tEfFFggJ2LhyZxarfGm+PWZJfwyep9VNU1WFyZiIj4AgVgaZYteeUApEYFExRgt7gakaOzGQan94zhwsGJOO029pVU8+6KbPLKaqwuTURELKYALCfMNE22HgzAfRM0+isdQ8/YUK44NYXI4AAqahv4cNVeNudoXrCIiD9TAJYTVlBeS0lVPXabQY8Yrf0rHUdUiJMrT02he0wIbo/JnE15rM46YHVZIiJiEQVgOWFb8yoA6BETgtOhr450LC6HnYsGJ3JKSiQAX28rZOnOIu0cJyLih5Ri5ISYpumd/6ub36SjMgyDM3vHkNEjGoAVu4r5dkeRxVWJiEh7UwCWE5JTWkNFbQNOu41u0cFWlyPSYoZhcFr3KMb2iQVg1Z4DrNtbYm1RIiLSrhSA5YQcuvmtZ2wIDm19LJ3AkJRIRvWIAmDR1gJ2F1VaXJGIiLQXJRk5LtM0vVsf94rXzW/SeZzWLYr0xDBME75cn0thhbZOFhHxBwrAclx5ZbXe6Q+pUZr+IJ2HYRiM6xdPcpcg6tweZm/IpcHjsbosERFpYw6rCxDft72gcfWHbjHBOGz6N5Mvy8zM7JR9tSW7zWDiwATeWpZFUWUdy3YWM7pXjNVliYhIG1IAlmMyTZPt+Y0BuFespj/4qrLiAgCmTp3a7n1XVFS0e5+tLdjpYFx6HDPX5bBqzwF6xIRYXZKIiLQhBWA5psKKOkqrGze/SItWKPBV1RWNO5tNuukB+g4e3i59Zq5YzJevP0tNTefYWrhnbCjpiWFk5pQzd1MeY6OtrkhERNqKArAc046D0x/SooK1+UUHEJ2URnLvAe3SV17Wjnbppz2d1SeW7OJqSqvr2Vxmt7ocERFpI0o0ckyH5v/2itP0B+n8XA47Y/s2rg+8rdyGPUzDwCIinZECsBzVgao6iirqsBnQXXMixU/0iAkhKTIQj2kQeebVVpcjIiJtQAFYjmrnwbV/k7sEExigHweLfzAMgzN7NY4Chww8h50H6i2uSEREWpsCsBzVzoPTH3RHvPibhIhAkoPdGIaN19eWYZqm1SWJiEgrUgCWI6qqayCntPHu/h6xCsDifwZGujEb6lmfX8eSHUVWlyMiIq1IAViOaHdRFSYQG+YiLDDA6nJE2l2IA8rXzgHgxcWdb8ULERF/pgAsR6TpDyJQtuJjbAb8Z1sh6/eWWl2OiIi0EgVgOUyD20NWcRWgACz+zV2Wz5mpQYBGgUVEOhMFYDnM3gPV1LtNQl0OYsNcVpcjYqmf9G38R+AXG3LYVVhpcTUiItIaFIDlMDsP/iXfPSYEwzAsrkbEWmmRAYzrF4dpwj+/1iiwiEhnoAAsTZgm7CzU/F+RH7plbE8A/rVqH4UVtRZXIyIiJ0sBWJooqTeorHUTYDdI7hJkdTkiPmFEtyiGpERS5/bw/nfZVpcjIiInSQFYmsipbpzykBoVjMOur4fIIdeMSgPg7WV7aHB7LK5GREROhhKONJFb3fiV6KbpDyJNTBqcSFSIk/2lNczfnG91OSIichIUgMXLFhzBgbrGEeDu0QrAIj8UGGDn8hEpALy5dI/F1YiIyMlQABavoB7DAYO4MBchLofV5Yj4nCkjUzEM+GZ7ITsObhYjIiIdjwKweAX1PBWAbhr9FTmilKhgxvWLAzQKLCLSkSkACwANHpOgbqcA0C0m2OJqRHzX1IM3w/3r+71U17ktrkZERFpCAVgA2FJYhy0wFKfNJD480OpyRHzWmN6xpEQFUV7TwKz1OVaXIyIiLaAALACsymlc3D8h0INNu7+JHJXNZnDlqakAvLsiy+JqRESkJRSABfhBAA4yLa5ExPddNjwZu81g1Z4DbMktt7ocERFpJgVgYe+BKrLLGjA9buKDtMC/yPHEhQcyPr3xZjiNAouIdDwKwMLXWwsBqN2/Gae+ESIn5KrTGqdBfPz9XmrqdTOciEhHorgjfL21AICaXastrkSk4zizdyxdI4Moq2ngC90MJyLSoSgA+7kGt4dvdzSOAFfv+t7iakQ6DrvN4MpTG3eG0zQIEZGORQHYz63JLqG8poFQp0Fd7naryxHpUC4bkYLdZvDd7gNsz9fNcCIiHYX2u/VzX29rHP0dHO9io9n+N8BlZmZ2qn7EvyREBHJOvzjmbcrj3RXZ/P7C/laXJCIiJ0AB2M8dmv97SryLd9ux37Lixn6nTp3ajr1CRUVFu/Ynnd/PT0tl3qY8/vX9Xu6e0JfAALvVJYmIyHEoAPuxkqo61u0tAWBIgqtd+66uKANg0k0P0Hfw8DbvL3PFYr58/VlqamravC/xL2P6xJIUEcj+0hrmbMzlkqFdrS5JRESOQwHYj32zvRCPCX3iQ4kJtmbUKjopjeTeA9q8n7ysHW3eh/gnu83gilNTefqrrbyzPEsBWESkA9BNcH7s0PSHMb1jLa5EpGO7/NRkbAYs31XMjgJNsxER8XUKwH7KNE3+c/AGuDF9FIBFTkZiRBDn9GvcGe49LYkmIuLzFID91K7CSnJKa3DabZzWPcrqckQ6vEM7w320ai+1DdoZTkTElykA+6klO4oAGJYWqbvWRVrBWX1iSYwI5EBVPXM25lldjoiIHIMCsJ9aejAAn94zxuJKRDoHh93G5SMO7gy3XNMgRER8mQKwH/J4TJbuPBSAoy2uRqTzuPzUFGwGLN1ZxK7CSqvLERGRo1AA9kNb88sprqwj2GlncHKk1eWIdBpdI4MY2/fgzXDfaRRYRMRXKQD7oSXbG0d/T+0WhdOhr4BIa7ry1MZpEB+t3EtdQ/tvLy4iIsen9OOHDt0Al6HpDyKt7px+ccSFuSiqrGP2xlyryxERkSNQAPYzDW4PyzX/V6TNOOw2fj6ycUm0V/6zE9M0La5IRER+TAHYz2zcX0Z5bQNhgQ4GJEVYXY5Ip3T1qDRcDhtr95ayYlex1eWIiMiPKAD7mUPTH0b1iMZuMyyuRqRzig51cemwZABe+s8ui6sREZEfUwD2M1r+TKR93HhmdwDmb85jZ0GFxdWIiMgPKQD7kQa3h1W7G38cO7K7ArBIW+oZG8r49DhME175RqPAIiK+RAHYj2zOLaeyzk2Yy0HfhDCryxHp9G48swcAH63aS2FFrcXViIjIIQrAfmTVngMAnJLWRfN/RdrByO5RDEmOoLbBwz+/3ml1OSIicpACsB9ZeTAAj0jrYnElIv7BMAzuGN8HgDeW7ia/vMbiikREBBSA/crKg/N/R3RTABZpL2P7xnJKaiQ19R7+sXCH1eWIiAgKwH5jX0k1OaU12G0GQ1MirS5HxG8YhsFvzu0LwDsrssgprba4IhERUQD2E4dGfwcmhRPsdFhcjYh/OaNXNKd1j6KuwcPzC7dbXY6IiN9TAPYTK3c3zv8dnhZlcSUi/scwDO46t3Eu8PvfZWtdYBERiykA+wnvDXCa/ytiiVE9ojm7byz1bpPHZm6yuhwREb+mAOwHymrq2ZxbBmgFCBEr/f7C/gTYDRZuKWDB5jyryxER8VsKwH5gdVYJpgmpUcHEhQdaXY6I3+oRG8r1ZzRukfzYzExqG9wWVyQi4p8UgP2Ad/kzjf6KWO7Wc3oRE+piV2Elr3272+pyRET8kpYD8ANrsksAGKYALGK5sMAA7pvYj99+uJZnv9rGpEGJpEQFA5CVlUVhYWG71RITE0Nqamq79Sci4isUgDs50zRZt7cUQOv/iviIS0/pyocrs1m+q5j7Pl7HWzeMJDs7m37p6VRXVbVbHUHBwWzOzFQIFhG/Y2kAfuGFF3jhhRfYvXs3AAMGDOChhx5i4sSJANTU1PCb3/yG9957j9raWiZMmMA//vEP4uPjve+RlZXFLbfcwsKFCwkNDWXatGk88cQTOBz//WiLFi3irrvuYuPGjaSkpPDggw9y7bXXtudHtUxWcRWl1fU47Tb6xIdZXY6IADabwZ8nD+b8Z7/m2+1FvP9dNn0chVRXVTHl3r8Qn9qzzWvIy9rB23++m8LCQgVgEfE7lgbg5ORk/vSnP9G7d29M0+T111/nkksuYfXq1QwYMIA777yTWbNm8eGHHxIREcGtt97KpZdeyrfffguA2+1m0qRJJCQksGTJEnJycrjmmmsICAjgf/7nfwDYtWsXkyZN4uabb+btt99m/vz53HjjjSQmJjJhwgQrP367WHtw9Dc9KRynQ1O+RXxFt5gQfnNuXx7/IpPHZ2Xy1LmNa3THp/YkufcAi6sTEencLA3AF110UZPnjz/+OC+88ALLli0jOTmZV155hXfeeYdzzjkHgNdee4309HSWLVvGqFGjmDt3Lps2beKrr74iPj6eoUOH8thjj3HvvffyyCOP4HQ6efHFF+nevTt//etfAUhPT+ebb77h6aef9osAvH5vCQCDu0ZYW4iIHOb60d2ZuT6HtdklvLCq1OpyRET8hs8MCbrdbt577z0qKyvJyMhg1apV1NfXM378eG+bfv36kZqaytKlSwFYunQpgwYNajIlYsKECZSVlbFx40Zvmx++x6E2h97jSGpraykrK2vy6KgOjQAPTlYAFvE1dpvBX342GKfDxvc5tYQOPs/qkkRE/ILlAXj9+vWEhobicrm4+eab+eSTT+jfvz+5ubk4nU4iIyObtI+Pjyc3NxeA3NzcJuH30PlD547VpqysjOrq6iPW9MQTTxAREeF9pKSktMZHbXduj8mGfYcCcKS1xYjIEfWJD+Pu8/oC0OWcG6losLggERE/YHkA7tu3L2vWrGH58uXccsstTJs2jU2brN0m9P7776e0tNT7yM7OtrSeltpZUEFVnZtgp51ecaFWlyMiR3H96O70j3FicwWzqsiBxzStLklEpFOzfBk0p9NJr169ABg+fDjfffcdzz77LFdccQV1dXWUlJQ0GQXOy8sjISEBgISEBFasWNHk/fLy8rznDv330LEftgkPDycoKOiINblcLlwuV6t8vpNxsmuCLtzduJxSt3A7a9esPmbbzMzMFvcjIifHbjO49bQIbv5kD4UEszqrhOFat1tEpM1YHoB/zOPxUFtby/DhwwkICGD+/PlMnjwZgC1btpCVlUVGRgYAGRkZPP744+Tn5xMXFwfAvHnzCA8Pp3///t42X3zxRZM+5s2b530PX5WVlXXSa4J2GX8T4cMvYtmXHzD87pdP6DUVFRUt7k9EWi4h1MGBBS8TPfE2lu4oIi06mJhQ6/8hLiLSGVkagO+//34mTpxIamoq5eXlvPPOOyxatIg5c+YQERHBDTfcwF133UVUVBTh4eH8+te/JiMjg1GjRgFw3nnn0b9/f66++mqefPJJcnNzefDBB5k+fbp3BPfmm2/m73//O/fccw/XX389CxYs4IMPPmDWrFlWfvTjKiw8+TVBF+Y6KK6D8edfSMrkC47ZNnPFYr58/Vlqampa1JeInLyKdXMZ8NNbya2xMXdjHlecmoLdZlhdlohIp3PCAfi5557jl7/8JYGBgTz33HPHbHvbbbed0Hvm5+dzzTXXkJOTQ0REBIMHD2bOnDmce+65ADz99NPYbDYmT57cZCOMQ+x2OzNnzuSWW24hIyODkJAQpk2bxqOPPupt0717d2bNmsWdd97Js88+S3JyMi+//HKHWQKtpWuCuj0mpXt3ACb9+vSkS7DzmO3zsna0sEIRaU3DoxuYnx9IQUUty3cVcXrPGKtLEhHpdE44AD/99NNMmTKFwMBAnn766aO2MwzjhAPwK6+8cszzgYGBPP/88zz//PNHbZOWlnbYFIcfGzt2LKtXH3sObGdTVFmL22PictiIDAqwuhwROUGBdjinbxxfbMhl5e4D9IgJJSEi0OqyREQ6lRMOwLt27Trir8U35ZfVAhAX7sIw9CNUkY6kd3wYfQsq2ZJXzleb87jq1FRNhRARaUWtsgya2+1mzZo1HDhwoDXeTlpBQfnBAByqkSORjmhMnxgCA2wUVdTxfZb+bBURaU0tCsB33HGHd/qC2+1mzJgxDBs2jJSUFBYtWtSa9UkLFVY0BuCYsGPP/RUR3xTsdDCmdywAy3cVU1JVZ3FFIiKdR4sC8EcffcSQIUMA+Pzzz9m9ezebN2/mzjvv5IEHHmjVAqX5TNOksKLxL0stoyTScfVLCCM1Khi3x2TB5nxMbZAhItIqWhSACwsLvRtNfPHFF1x22WX06dOH66+/nvXr17dqgdJ85TUN1Lk92AyOu/qDiPguwzA4p18cDptB9oFqtuVrnW4RkdbQogAcHx/Ppk2bcLvdzJ4927tsWVVVFXa7vVULlOY7NP0hKsSpG2dEOriIoABGdGvcFe6b7YU0uD0WVyQi0vG1KABfd911XH755QwcOBDDMBg/fjwAy5cvp1+/fq1aoDSfpj+IdC7DU7sQFuigvKaB77NKrC5HRKTDa9FOcI888ggDBw4kOzubyy67zLvrmt1u57777mvVAqX5vDfAKQCLdAoOu43RvWL4ckMu3+0upn9iOKGBPreTvYhIh9HiP0F/9rOfHXZs2rRpJ1WMtI7/BmDN/xXpLHrHhbImIpCc0hq+3VHIhAEJVpckItJhtTgAz58/n/nz55Ofn4/H03RO2quvvnrShUnLNLg9lFTVAxoBFulMDMPgrD6xvPddNptzyxmW2oXYMP0eFxFpiRbNAf7DH/7Aeeedx/z58yksLOTAgQNNHmKdoso6TCAowE6wUzckinQm8eGB9IkPBWDpziKLqxER6bhaNAL84osvMmPGDK6++urWrkdO0g+nP2gLZJHOZ1SPaLblVbCrsJKc0moSI4KsLklEpMNp0QhwXV0dp59+emvXIq1AK0CIdG5dgp2kJ4YDGgUWEWmpFgXgG2+8kXfeeae1a5FWoBUgRDq/kd2jsBmQXVzN3gNVVpcjItLhtGgKRE1NDf/85z/56quvGDx4MAEBAU3OP/XUU61SnDRP4xbIWgFCpLMLDwpgYFIE6/aVsmRHEZcND9KUJxGRZmhRAF63bh1Dhw4FYMOGDU3O6Q9h61TWuamp92DQuAuciHRep3aPYuP+MnJKa9hfUkPXLpoLLCJyoloUgBcuXNjadUgrKDo4+hsZHIDD3qLZLSLSQYS6HPRPCmf9vlK+211M1y5drS5JRKTDOKmUtH37dubMmUN1dTXQ+CN4sU5RZeMNcNEhmv8r4g+Gp3XBMGBPcRV5ZTVWlyMi0mG0KAAXFRUxbtw4+vTpwwUXXEBOTg4AN9xwA7/5zW9atUA5cQcOBuAuIQHHaSkinUFEUAB948MAWLlba7CLiJyoFgXgO++8k4CAALKysggODvYev+KKK5g9e3arFSfNU1zVGIA1/1fEf4xI6wLA9oIKig/+I1hERI6tRQF47ty5/PnPfyY5ObnJ8d69e7Nnz55WKUya70Bl4xbIXYIVgEX8RXSoix4xIQCs3FNscTUiIh1DiwJwZWVlk5HfQ4qLi3G5NP/UCtX1bqrr3YACsIi/GdGtcRR4a24FlbUNFlcjIuL7WrQKxJlnnskbb7zBY489BjQufebxeHjyySc5++yzW7VAOTGH5v+Guhw4HVoBQqS1ZGZm+nw/iRFBJEYEklNaw7q9pWT0jG7FykREOp8WBeAnn3yScePGsXLlSurq6rjnnnvYuHEjxcXFfPvtt61do5wAzf8VaV1lxQUATJ06tV37raioaNHrTkmJJKc0l/X7Sjm1WxcthSgicgwtCsADBw5k69at/P3vfycsLIyKigouvfRSpk+fTmJiYmvXKCfg0AhwlKY/iLSK6ooyACbd9AB9Bw9v8/4yVyzmy9efpaamZcuZ9YwNJSzQQXlNA5tzyxnYNaKVKxQR6TxaFIABIiIieOCBB1qzFjkJxVoCTaRNRCelkdx7QJv3k5e146Reb7MZDE2O5D/bC1mTXcKApHDtzCkichQtCsBff/31Mc+PGTOmRcVIyx2oalwBQlMgRPzXgK7hLNtVRFFlHVnFVaRFh1hdkoiIT2pRAB47duxhx3440uB2u1tckDRfg9tDabWWQBPxdy6HnQGJEazZW8Lq7BIFYBGRo2jRXRIHDhxo8sjPz2f27NmceuqpzJ07t7VrlOM4NPrrctgIdtotrkZErDQ0NRKAPUVVFFXUWluMiIiPatEIcETE4TdXnHvuuTidTu666y5WrVp10oXJiTtwcAWILsFOzfkT8XMRQQH0jA1hR0Ela7JLGJceb3VJIiI+p1XXyYmPj2fLli2t+ZZyAg7dAKf5vyICcEpK48YYmbnlVNdpSpqIyI+1aAR43bp1TZ6bpklOTg5/+tOfGDp0aGvUJc3gHQHWChAiAiRFBhIX5iK/vJb1+0o5rXuU1SWJiPiUFgXgoUOHYhgGpmk2OT5q1CheffXVVilMTlyx1gAWkR8wDINTUiOZszGPtXtLGJYWicOmjTFERA5pUQDetWtXk+c2m43Y2FgCAwNbpSg5caZpem+C66IpECJyUO+4ML7ZXkhlrZtteRWkJ4ZbXZKIiM9o0ZDAkiVLSEtL8z5SUlK84ffuu+9u1QLl2MprGnB7TGwGRARqCoSINLLbDIYkRwKwOrvksJ/YiYj4sxYF4FtuuYUvv/zysON33nknb7311kkXJSeu5OD6vxFBAdhsWgFCRP5rYNcIHDaDgvJa9pVUW12OiIjPaFEAfvvtt7nqqqv45ptvvMd+/etf88EHH7Bw4cJWK06Or+TgDXCRmv8rIj8SFGCnX2IYAGuyS6wtRkTEh7QoAE+aNIl//OMfXHzxxaxatYpf/epXfPzxxyxcuJB+/fq1do1yDKU/GAEWEfmxQ0ui7Sio9P55ISLi71p0ExzAz3/+c0pKSjjjjDOIjY1l8eLF9OrVqzVrkxNQcvAGuEgFYBE5gqgQJ2nRwewpqmJNdgln9Ym1uiQREcudcAC+6667jng8NjaWYcOG8Y9//MN77Kmnnjr5yuSEHBrRiQxWABaRIzslJZI9RVVs2l/GqB5RuBzaMl1E/NsJB+DVq1cf8XivXr0oKyvzntdWvO3HNE3vTXCaAywiR5MaFUxUsJPiqjo27i9jWGoXq0sSEbHUCQdg3dzmeypq/7sEWpirxbNZRKSTMwyDoamRLNicz9rsEoamRFpdkoiIpU5qa6Dt27czZ84cqqsbl9fROpPt69D83/BALYEmIsfWLyGMQIeNspoGdhZUWl2OiIilWhSAi4qKGDduHH369OGCCy4gJycHgBtuuIHf/OY3rVqgHJ13BQjN/xWR4wiw2xjYNQKA1dkHLK5GRMRaLQrAd955JwEBAWRlZREcHOw9fsUVVzB79uxWK06OzTv/VytAiMgJGJIcic2A/SU1HKjTT41ExH+1aOLo3LlzmTNnDsnJyU2O9+7dmz179rRKYXJ82gRDRJojNNBB77gwtuSVs73spGbAiYh0aC36E7CysrLJyO8hxcXFuFyuky5KTow2wRCR5hqaGglAdpUNe1i0tcWIiFikRQH4zDPP5I033vA+NwwDj8fDk08+ydlnn91qxcnRmab5300wNAdYRE5QQnggSZGBmBiEDb/Y6nJERCzRoikQTz75JOPGjWPlypXU1dVxzz33sHHjRoqLi/n2229bu0Y5gqo6Nw0eE4PGVSBERE7UiLQoPivZT9jQiVTWeawuR0Sk3bVoBHjgwIFs3bqV0aNHc8kll1BZWcmll17K6tWr6dmzZ2vXKEdwaPQ3LNCBXUugiUgzdIsOJjzAg80VzJwdVVaXIyLS7po9AlxfX8/555/Piy++yAMPPNAWNckJKKnWDXAi0jKGYdAnzMPKYhszt1XyYL2bwABtjywi/qPZI8ABAQGsW7euLWqRZijVEmgichJSQjw0lOVTUuPhk9X7rC5HRKRdtWgKxNSpU3nllVdauxZphkNTILQJhoi0hM2Asu8+BeD/Fu+gwa25wCLiP1p0E1xDQwOvvvoqX331FcOHDyckJKTJ+aeeeqpVipOj0wiwiJysirVzSZt4E7uLqvh83X5+ekry8V8kItIJNCsA79y5k27durFhwwaGDRsGwNatW5u0MQzdkNXWfrgEmtYAFpGWMutruLhvKG+vL+dvC7Zz8ZCuuqlWRPxCswJw7969ycnJYeHChUDj1sfPPfcc8fHxbVKcHFlNg4e6gz+uVAAWkZNxQa9gZu2oYWdBJTPX7eeSoV2tLklEpM01aw6waZpNnn/55ZdUVla2akFyfGUHpz8EO+047NrOVERaLijAxg1ndAfgbwu24/GYx3mFiEjHd1Lp6ceBWNpHmbZAFpFWNO2MboQHOtieX8EXG3KsLkdEpM01KwAbhnHYHF/N+W1/ZTUNgHaAE5HWER4YwPWjG0eBn/lqm1aEEJFOr1lzgE3T5Nprr8XlcgFQU1PDzTfffNgqEB9//HHrVSiHObQCRHhQixbxEBE5zPWju/P6kt1sz6/g4+/3cfmpKVaXJCLSZpqVoKZNm9bk+dSpU1u1GDkxZTWHArBGgEWkdYQHBjD97F78cVYmT3+1lYuHJml3OBHptJoVgF977bW2qkOawTsHWFMgRKQVTR2Vxqvf7GJ/aQ1vLN3NL8f0tLokEZE2oZ+hdzCmaf53DrBGgEXkJGVmZjZ5/tM+Lp7/robnvtpCuvMAIc7WXWkmJiaG1NTUVn1PEZHmUgDuYCrr3Lg9JoYBYS797xORlikrLgCOMJXNsJF4/d+oiEnj4vuep2TxjFbtNyg4mM2ZmQrBImIpJagO5tD0hzCXA5t2bBKRFqquKANg0k0P0Hfw8CbncqoNlhRA5KjJXPbTiwlrpR825WXt4O0/301hYaECsIhYSgG4gymr1g1wItJ6opPSSO49oMmxrqbJvrX72VNUxda6SC7pr93hRKRz0TZiHUzpoRUgdAOciLQRwzA4q08sNgN2F1Wxq1A7fopI56IA3MGUVTfeAKdd4ESkLXUJdnJKahcAFm8toMGjzTFEpPNQAO5gyrQJhoi0k9O6RRHstFNaXc/K3QesLkdEpNUoAHcwmgIhIu3F6bBxVp9YAL7bXUxhRa3FFYmItA4F4A7E7TGpqNEUCBFpP73jQukRE4LHhK8y8/CYptUliYicNAXgDqSitgETsNsMgp3aolRE2p5hGJzdLw6nw0ZeWS1rskqsLklE5KQpAHcgpYfm/wY6MAytASwi7SPU5eDMXjEALNlZRHFlncUViYicHAXgDkRrAIuIVQYkhZMaFYzbYzJ7Q65WhRCRDk0BuAMp0w1wImIRwzA4t388gQE2Cipq+XZ7kdUliYi0mAJwB3JoCoRugBMRK4S6HJzbPx6ANdkl7NYGGSLSQSkAdyDlB1eACA/UGsAiYo0eMaEMSY4AYO6mPO9PpkREOhJLA/ATTzzBqaeeSlhYGHFxcfzkJz9hy5YtTdrU1NQwffp0oqOjCQ0NZfLkyeTl5TVpk5WVxaRJkwgODiYuLo67776bhoaGJm0WLVrEsGHDcLlc9OrVixkzZrT1x2t13ikQGgEWEQuN7hVDbJiL6no3s9bl0ODWfGAR6VgsDcCLFy9m+vTpLFu2jHnz5lFfX895551HZeV/f6x255138vnnn/Phhx+yePFi9u/fz6WXXuo973a7mTRpEnV1dSxZsoTXX3+dGTNm8NBDD3nb7Nq1i0mTJnH22WezZs0a7rjjDm688UbmzJnTrp/3ZLg9JpW1bgDCNAIsIhZy2G1cOCiRoAA7+eW1zN+cj6n1gUWkA7E0Sc2ePbvJ8xkzZhAXF8eqVasYM2YMpaWlvPLKK7zzzjucc845ALz22mukp6ezbNkyRo0axdy5c9m0aRNfffUV8fHxDB06lMcee4x7772XRx55BKfTyYsvvkj37t3561//CkB6ejrffPMNTz/9NBMmTGj3z90SFbWNI9p2m0FQgNYAFhFrhQcFMHFgAp+s2cfm3HJiQ10MS+tidVkiIifEp+YAl5aWAhAVFQXAqlWrqK+vZ/z48d42/fr1IzU1laVLlwKwdOlSBg0aRHx8vLfNhAkTKCsrY+PGjd42P3yPQ20OvceP1dbWUlZW1uRhtUNLoIVpDWAR8REpUcGM6d24VfJ/theyPb/C4opERE6MzwRgj8fDHXfcwRlnnMHAgQMByM3Nxel0EhkZ2aRtfHw8ubm53jY/DL+Hzh86d6w2ZWVlVFdXH1bLE088QUREhPeRkpLSKp/xZJTXHroBTvN/RcR3DEmOYFDXxpviZm/MZX/J4X+mioj4Gp8JwNOnT2fDhg289957VpfC/fffT2lpqfeRnZ1tdUmU/2AEWETEVxiGwdg+sXSPCcHtMfl87X4OaKc4EfFxPhGAb731VmbOnMnChQtJTk72Hk9ISKCuro6SkpIm7fPy8khISPC2+fGqEIeeH69NeHg4QUFBh9XjcrkIDw9v8rDaoRFgBWAR8TU2m8HEgQkkhAdS0+Dh49X7vNO2RER8kaUB2DRNbr31Vj755BMWLFhA9+7dm5wfPnw4AQEBzJ8/33tsy5YtZGVlkZGRAUBGRgbr168nPz/f22bevHmEh4fTv39/b5sfvsehNofeoyPQLnAi4ssC7DYuGpJIl+AAKmob+Hj1PiprG47/QhERC1gagKdPn85bb73FO++8Q1hYGLm5ueTm5nrn5UZERHDDDTdw1113sXDhQlatWsV1111HRkYGo0aNAuC8886jf//+XH311axdu5Y5c+bw4IMPMn36dFwuFwA333wzO3fu5J577mHz5s384x//4IMPPuDOO++07LM3V3m1RoBFxLcFOx389JSuhAc6KK2u55PV+6iud1tdlojIYSwNwC+88AKlpaWMHTuWxMRE7+P999/3tnn66ae58MILmTx5MmPGjCEhIYGPP/7Ye95utzNz5kzsdjsZGRlMnTqVa665hkcffdTbpnv37syaNYt58+YxZMgQ/vrXv/Lyyy93mCXQTNP8wRQIjQCLiO8KCwzg0mHJhLjsFFXW8enqfdQ2KASLiG+xdDjxRBZODwwM5Pnnn+f5558/apu0tDS++OKLY77P2LFjWb16dbNr9AVVdW7cnsZrFerSCLCI+LaIoAAuPSWZj1btJb+8ls/W7ucnQ7taXZaIiJdP3AQnx3Zo9DfU5cBu0xrAIuL7okKc/OSUJJwOG/tLapi5Lge3NosTER+hANwBaAk0EemI4sIC+cnQJALsBlnFVawodIBNO1mKiPUUgDsALYEmIh1VYkQQFw1Owm4z2F9tI/qCO/CcwPQ3EZG2pADcAfx3BQjdACciHU9KVDAXDErAwCR0wNn8c1XZCd0DIiLSVhSAO4DyWk2BEJGOrUdMKKdGuzE9buburOJ/vshUCBYRyygAdwBlNZoCISIdX0qIh6LZfwfgpf/s4tn52yyuSET8lQJwB3DoJjjtAiciHV3l+nncMLRxe/lnvtrGy//ZaXFFIuKPFIB9XIMHaho8gEaARaRzmNQnhN+e1weAP87K5Iv1ORZXJCL+RgHYx1Ud3EDJ6bDhcmj5IBHpHKaf3YvrzugGwF0frGFtdoml9YiIf1EA9nFVDY0bX4Rr9FdEOhHDMHhwUn/O7htLTb2HG99Yyf6SaqvLEhE/oQDs4w4FYC2BJiKdjd1m8NxVp9A3PoyC8lp++eZKaurdVpclIn5AAdjHVbkPBWCNAItI5xMWGMDL00bQJTiADfvKeHTmJqtLEhE/oADs46oaV0BTABaRTislKphnrjwFw4B3lmfx8fd7rS5JRDo5BWAfV+0+NAdYUyBEpPM6q08st53TG4AHPtnAltxyiysSkc5MAdjH/XcOsEaARaRzu21cb0b3iqG63s1t767WfGARaTMKwL7MsFF98M9/3QQnIp2d3WbwzJVDiQl1siWvnL/O3WJ1SSLSSSkA+zB7aDQmBjYDQpxaA1hEOr+YUBd/njwYgJe/2cWSHYUWVyQinZECsA9zRMQBjaO/hmFYXI2ISPsYlx7PVaelYprw2w/WUnpwO3gRkdaiAOzDHOGxAIS5NP9XRPzLg5PS6RYdzP7SGv6opdFEpJUpAPsw+6EAHKQALCL+JcTl4H8vG4JhwIer9vLtdk2FEJHWowDswxzhB6dAuHQDnIj4nxHdorh6VBoA93+8nuo6rQohIq1DAdiHOSI0Aiwi/u2e8/uRGBFIVnEVz3y11epyRKSTUAD2YYemQGgTDBHxV6EuB4//dCAAL/1nJxv2lVpckYh0BgrAPso0zf9OgdAmGCLix87pF89FQ5LwmPDwZxsxTdPqkkSkg1MA9lEVdSY2ZxCgVSBERB64IJ1gp51Vew7wyep9VpcjIh2cArCPKqhqvNnDZTNx2PW/SUT8W0JEILee0wuAJ77cTHmN1gYWkZZTsvJRhwJwsEM/6hMRAbhhdHe6x4RQUF7L3xZst7ocEenAFIB9VGHlwQCsHZBFRABwOew8dGF/AF79Zhfb8yssrkhEOioFYB+VrxFgEZHDnN0vjnH94mjwmPzhc90QJyItowDsowoPBuAgu/5wFxH5od9f2B+n3cZ/thUyb1Oe1eWISAekAOyjNAdYROTIusWE8Isx3QF4bNYmauq1Q5yINI8CsI8q9AZgiwsREfFB08/uRUJ4INnF1fzz651WlyMiHYwCsA+qqXdTUuMBIFhTIEREDhPsdPC7SekA/GPRdnJKqy2uSEQ6EgVgH5RTWgOAp64Gp/4PiYgc0UWDEzmtWxQ19R7+MnuL1eWISAeieOWD9h1oHMloKMvHMCwuRkTERxmGwYMXNo4Cf7x6H+v2llhbkIh0GArAPmhfSRUA7rJ8iysREfFtg5MjufSUrgA8NnOTlkUTkROiAOyD9pU0ToFoKCuwuBIREd939/l9CQyw8d3uA8zekGt1OSLSASgA+6BpGWk8fnY05as+t7oUERGflxgRxC/H9ATgiS83U9ugZdFE5NgUgH1QdKiL9Fgn9YVZVpciItIh3DSmB3FhLrKKq3h9yW6ryxERH6cALCIiHV6Iy8HdE/oC8Lf52ymqqLW4IhHxZQrAIiLSKUwelsyApHDKaxt45qttVpcjIj5MAVhERDoFm83gwUn9AXhnRRbb8sotrkhEfJUCsIiIdBoZPaM5r388bo/J419kWl2OiPgoBWAREelU7r8gnQC7waItBSzequUkReRwCsAiItKpdI8J4ZqMbgA8PmsTDW6PtQWJiM9xWF2AiIj4l8zMtp+acFaMh/edBlvzKnh/ZTZTRqa1eZ8i0nEoAIuISLsoK26cjjB16tR26S9s+EVEjb+Jv8zO5OIhSYQFBrRLvyLi+xSARUSkXVRXlAEw6aYH6Dt4eJv3l5O1g8VFeymJTub5hTu4b2K/Nu9TRDoGBWAREWlX0UlpJPce0C59HfjoT8T97CFe/WYXU0amkhIV3C79iohv001wIiLSaVXvWMHgOCd1bg9/mr3Z6nJExEcoAIuISKc2bWg4hgGz1uWwak+x1eWIiA9QABYRkU6te2QAlw9PAeDRmZl4PKbFFYmI1RSARUSk0/vNhD4EO+2szS7h83X7rS5HRCymACwiIp1eXFggvxrbE4A/f7mZmnq3xRWJiJUUgEVExC/ceGYPkiIC2V9aw8v/2Wl1OSJiIS2DJiIindoPd567vF8gzyyv4e8LtpHuKqFLkL1V+4qJiSE1NbVV31NEWp8CsIiIdEpH3nnOIOHq/4Wkvlz+xzcpnv23Vu0zKDiYzZmZCsEiPk4BWEREOqWj7TxXVGuwKA/ChpzHJRPOoYuzdVaFyMvawdt/vpvCwkIFYBEfpwAsIiKd2o93nksG9pPD1rwKNlWFcXn/ZAzDsK5AEWl3uglORET8zpm9YgmwG+SW1bApp8zqckSknSkAi4iI3wkNdDCqRzQA324v0rJoIn5GAVhERPzSkORIokOcVNe7WbKjyOpyRKQdKQCLiIhfstsMxvaNBWD9vlLyymosrkhE2osCsIiI+K3kLsH0TQgDYOGWfEyzdVaEEBHfpgAsIiJ+7cxeMTjtNvLKatm4XzfEifgDBWAREfFrIS4Ho3pEAfDt9kKq63RDnEhnpwAsIiJ+b0hyJNGhTmoaPHy7o9DqckSkjSkAi4iI37PZDM7uGwfAxv1l7DtQbXFFItKWFIBFRESArpFBDEwKB+CrzXk0uD0WVyQibUUBWERE5KDRvWIIcdopqapnxe5iq8sRkTaiACwiInKQK8DO2INTIVbtOUBBea3FFYlIW1AAFhER+YFecaH0jA3BY8L8zXl4tDawSKejACwiIvIjY/vGedcGXptdYnU5ItLKFIBFRER+JNTlYHTvGACW7iyirLre4opEpDUpAIuIiBzBwKRwukYGUe82WaBtkkU6FQVgERGRIzAMg3H94rDbDPYUVbElt9zqkkSklSgAi4iIHEWXECendWvcJnnR1gIqaxssrkhEWoOlAfjrr7/moosuIikpCcMw+PTTT5ucN02Thx56iMTERIKCghg/fjzbtm1r0qa4uJgpU6YQHh5OZGQkN9xwAxUVFU3arFu3jjPPPJPAwEBSUlJ48skn2/qjiYhIJzE8rQtxYS5qGzzM36ypECKdgaUBuLKykiFDhvD8888f8fyTTz7Jc889x4svvsjy5csJCQlhwoQJ1NTUeNtMmTKFjRs3Mm/ePGbOnMnXX3/NL3/5S+/5srIyzjvvPNLS0li1ahV/+ctfeOSRR/jnP//Z5p9PREQ6PrvN4Nz+8dgM2FVYyWZNhRDp8BxWdj5x4kQmTpx4xHOmafLMM8/w4IMPcskllwDwxhtvEB8fz6effsqVV15JZmYms2fP5rvvvmPEiBEA/O1vf+OCCy7gf//3f0lKSuLtt9+mrq6OV199FafTyYABA1izZg1PPfVUk6AsIiJyNDGhLkb2iGbpjiIWby0gJSqYUJelf4WKyEnw2TnAu3btIjc3l/Hjx3uPRUREMHLkSJYuXQrA0qVLiYyM9IZfgPHjx2Oz2Vi+fLm3zZgxY3A6nd42EyZMYMuWLRw4cOCIfdfW1lJWVtbkISIi/m1E6n+nQizQVAiRDs1nA3Bubi4A8fHxTY7Hx8d7z+Xm5hIXF9fkvMPhICoqqkmbI73HD/v4sSeeeIKIiAjvIyUl5eQ/kIiIdGg2m8F5/eOxG4amQoh0cD4bgK10//33U1pa6n1kZ2dbXZKIiPiA6FAXI3v8d1WIihqtCiHSEflsAE5ISAAgLy+vyfG8vDzvuYSEBPLz85ucb2hooLi4uEmbI73HD/v4MZfLRXh4eJOHiIgIwPDULsSHu6hr8DB/c56mQoh0QD4bgLt3705CQgLz58/3HisrK2P58uVkZGQAkJGRQUlJCatWrfK2WbBgAR6Ph5EjR3rbfP3119TX/3cby3nz5tG3b1+6dOnSTp9GREQ6C5vN4Nz0xqkQu4uq2Lhf94mIdDSWBuCKigrWrFnDmjVrgMYb39asWUNWVhaGYXDHHXfwxz/+kc8++4z169dzzTXXkJSUxE9+8hMA0tPTOf/88/nFL37BihUr+Pbbb7n11lu58sorSUpKAuDnP/85TqeTG264gY0bN/L+++/z7LPPctddd1n0qUVEpKOLDnWR0TMagK+3FVBaXX+cV4iIL7F0DZeVK1dy9tlne58fCqXTpk1jxowZ3HPPPVRWVvLLX/6SkpISRo8ezezZswkMDPS+5u233+bWW29l3Lhx2Gw2Jk+ezHPPPec9HxERwdy5c5k+fTrDhw8nJiaGhx56SEugiYjISTklNZJdhZXsK6lmzsZcMjRbTqTDsDQAjx079phzpwzD4NFHH+XRRx89apuoqCjeeeedY/YzePBg/vOf/7S4ThERkR+zGY2rQry9PIuc0hq2Gj47q1BEfkS/W0VERFooPCiAMX1iANhYYicgtrvFFYnIiVAAFhEROQn9E8PpEROCiUHMhXdR79aqECK+TgFYRETkJBiGwbj0OFw2E2dcd97doA0yRHydArCIiMhJCnY6GBbVuCnGv7dUsnxnkcUVicixKACLiIi0gqRgk4p1czGBuz5Yq6XRRHyYArCIiEgrKZ7/EvEhdvaVVPO7j9drlzgRH6UALCIi0krMumruGhWJw2Ywa30OH6zMtrokETkCBWAREZFW1DvayV3n9QHgkc82sT2/wuKKROTHFIBFRERa2c1jenJGr2iq693c9u5qahvcVpckIj+gACwiItLKbDaDpy4fSpfgADbllPHnL7dYXZKI/IACsIiISBuIDw/kfy8bAsCr3+5i4ZZ8iysSkUMUgEVERNrIuPR4rj29GwC//WAt+eU11hYkIoACsIiISJu6b2I/+iWEUVRZx6/fWU2D22N1SSJ+TwFYRESkDQUG2Pn7z4cR6nKwfFcxf5692eqSRPyeArCIiEgb6xUXyv9eNhiAl/6zi5nr9ltckYh/UwAWERFpB+cPTOSms3oAcM9H69iaV25xRSL+SwFYRESkndx9Xl9O7xlNVZ2bm99cRXlNvdUlifglBWAREZF24rDbeO6qU0iMCGRnYSW//XAtpmlaXZaI33FYXYCIiEhnkpmZedw2t48I4cGFNczZmMfv3/kPl6aHtqivmJgYUlNTW/RaEX+mACwiItIKyooLAJg6deoJtQ8dcj7R59/Km2tLeOqh31C9c2Wz+wwKDmZzZqZCsEgzKQCLiIi0guqKMgAm3fQAfQcPP25704Tvi93srrSTePnDjI1vINJ54tMh8rJ28Paf76awsFABWKSZFIBFRERaUXRSGsm9B5xQ2ySPyb/X7CP7QDXLDgRyxYgUwgID2rhCEdFNcCIiIhax2wwmDUokKthJZa2bz9fmUNegneJE2poCsIiIiIVcAXYuHppEUICdgopavtyQg8ejlSFE2pICsIiIiMUiggK4eEgSdpvB7qIqvt5WYHVJIp2aArCIiIgPSIgIZMKAeADW7i1lddYBiysS6bwUgEVERHxE77gwRveKAeDrbYVk5pRZXJFI56QALCIi4kOGpUYyNDkSgHmZeewoqLC2IJFOSAFYRETEhxiGwZg+MaQnhmGa8OWGXLKLq6wuS6RTUQAWERHxMYZhML5fPD1jQ3B7TD5ft5/c0hqryxLpNBSARUREfJDNZnD+gARSugRR727cMKOwotbqskQ6BQVgERERH+Ww27hwcBIJ4YHUNHj4dPU+SqrqrC5LpMNTABYREfFhToeNS4YmER3ipLLOzb++38cBhWCRk6IALCIi4uMCA+z89JSuRIU4qaht4F+r9lJWb3VVIh2XArCIiEgHEOJyMHlYV6JDG0eCv84LICAm1eqyRDokBWAREZEOItjpYPIpycSGuqj1GMRf9QS7SzQULNJcCsAiIiIdSJDTzqXDuhLp9GAPjuChRUVs2FdqdVkiHYoCsIiISAcTGGDnzLgGavdvpqLO5OcvLeO73cVWlyXSYSgAi4iIdEBOG+S9/3v6xQRQVtPA1JeXM2djrtVliXQICsAiIiIdlFlXzcNjohmfHk9tg4db3lrFm8v2WF2WiM9TABYREenAXA6DF6cO46rTUvCY8PtPN/CHzzfS4PZYXZqIz1IAFhER6eAcdhv/89NB/ObcPgC89u1urn99JWU1WiFC5EgUgEVERDoBwzD49bjevDBlGEEBdr7eWsDFf/uGTfvLrC5NxOcoAIuIiHQiEwcl8uHNGSRFBLK7qIqf/uNb3luRhWmaVpcm4jMUgEVERDqZgV0jmHXbmZzdN5baBg/3fbyeX7+7mgOVdVaXJuITFIBFREQ6oS4hTl6Zdir3nt8Pu81g5rocznvmaxZszrO6NBHLKQCLiIh0UjabwS1je/LJr06nV1woBeW1XD9jJb9+dzV5ZTVWlydiGQVgERGRTm5wciQzfz2aX5zZHZsBn6/dzzn/u4iXvt5JbYPb6vJE2p0CsIiIiB8IDLDzwKT+fHbraE5JjaSyzs3jX2Qy7q+L+WT1Xjwe3SQn/kMBWERExI8M7BrBv24+nScnDyYuzMXeA9Xc+f5aJj77H/69Zp820BC/oAAsIiLiZ2w2g8tPTWHx3Wdzz/l9CQ90sCWvnNvfW8M5f13MG0t3U65NNKQTUwAWERHxU0FOO78a24v/3HsOvz2vD1EhTrKKq3jo3xsZ9T/zefDT9WzO1UYa0vk4rC5ARERErBURFMCt5/Tm+tHd+eC7bN5YtoedBZW8tSyLt5ZlcVq3KKaMSuX8gQm4HHaryxU5aQrAIiIiAkCw08G1Z3Rn2undWLqjiDeX7WHupjxW7C5mxe5iugQH8NNTkrni1BT6JoRZXa5IiykAi4iIdGCZmZlt8r6BwC/6G1zaLZavdlUxb2cVxVX1vPrtLl79dhdDUiK58tQULhycSFhgQJvUINJWFIBFREQ6oLLiAgCmTp3aPh0aNiLSM7jotidYuqectdklrM0u4dHPNzFpcCJXnprC8LQuGIbRPvWInAQFYBERkQ6ouqLx5rRJNz1A38HD27y/vKwdvP3nuzk/NIvrLuzDot3VzN9Vxb5yNx+t2stHq/bSNczOuO7BjO0WRGTgyc8VjomJITU1tRWqF2lKAVhERKQDi05KI7n3gDbv52gjzq6u6YQOPo/gfmeyrzyQN9aV8/rqA1RvX0H5mi+p2b0GaNkmG0HBwWzOzFQIllanACwiIiLHdbwR53oP7K1qYHeFjeI6B8F9Tye47+mEOEy6h7pJC/HQnEHhQyPOhYWFCsDS6hSARURE5IQda8S5O3AmUFhRy4Z9pWTmllPZ4GFDiYNNpdArNpSBXSNI7hKkucJiKQVgERERaVUxoS7G9o3jjF4xbM0rZ/2+UvLKatmaX8HW/Aq6BAcwsGsE6YnhBAVoXWFpfwrAIiIi0iYC7DYGJEUwICmC/PIa1u8rZUtuOQeq6vnPtkKW7Ciid1wog7pGkBgRqFFhaTcKwCIiItLm4sICGdcvkDN7xbIlt5z1+0spKK9lc245m3PLiQ5xMqhrBP0Sw7TbnLQ5BWARERFpN06HjUHJEQzsGk5eWS3r95WyNa+coso6Fm0t4JvthfSJDyPeo9FgaTsKwCIiItLuDMMgISKQhIhAxvSOYXNu41zhoso6NuWUsYkAEqY9w5wdlfQZ0ECoS5FFWo++TSIiImIpV4CdISmRDE6OIKe0ca7w1twyXAm9+L9VZby1/isuHJzET4d15bRuUdhsGh2Wk6MALCIiIj7BMAySIoNIigyil72IN958g4GX3MT+cjfvr8zm/ZXZdI0M4ienJPHTU7rSKy7M6pKlg1IAFhEREZ/jskP5d5/ytxcepL5LNz75fh9frM9hX0k1zy/cwfMLdzCoawQ/PaUrFw1JIjbMZXXJ0oEoAIuIiIjPMgyDUT2iGdUjmj9cMoCvMvP4dPU+Fm0pYP2+UtbvK+XxLzIZ3SuG8wcmMD49XmFYjksBWERERDqEwAA7Fw5O4sLBSRRV1DJzXQ6frN7HmuwSFm8tYPHWAn5nrGd4ahfOGxDPef0T6BYTYnXZ4oMUgEVERMRnZWZmHvXcoEAYlBHE/oEBLMmuYcX+GrYX17NyzwFW7jnA/3yxma5hdgbHuxgc76J/jJMwl+2Y/cXExJCamtraH0N8jAKwiIiI+Jyy4gIApk6d2qzX2cNiCOo1kuA+owhMGcS+cthXXsWX26sAqC/eR+3+LdQX7KK+MJv6omwaSvMBE4Cg4GA2Z2YqBHdyCsAiIiLic6orygCYdNMD9B08vEXvUefxUFBTT36NjYIaG+UNBgFRXQmI6tqknd0wCXWYBNSXs2PZXN5ctoeMKhdJkUF0jQwiLDDgpD+P+BYFYBEREfFZ0UlpJPce0OLX9/jBr2vq3eSW1ZBbWkNxZR3FVXWUVNbjNqG03gAiiBh1GS99X8ZL36/0vi4s0EHXg2E4KTKIrl2CvOG4a2QQsWEu7FqbuENRABYRERG/EBhgp1t0CN2i/3tjnMdjUlpTz4HKOnZnZfPtV18w/uLLqMTFvpJqSqrqKa9pYHNuOZtzy4/4vk67jR6xIfRLCKNPQljjf+PD6BoZhGEoGPsiBWARERHxWzabQZdgJ12CnThLPMyc/08uuW4M6enpQAjV9R6Kqt3kV7oprPJQUNXQ+N9KNwVVboqq3dS5PUcMyMEBBmkRDrpFBtAtIoBukQ5SIwJwOf4binXTnTUUgEVERERo4Y13hg1HeCwBMWkExKbhjE0jILYbAVHJVOEgs7CezMJ6b3PT46bhwH7q8ndRl78Lo2w/X334OsPTe7TLaHFWVhaFhYWt+p6maVJW66Gs1kN5nUlVvQePCR7TJDQsnAtO7UNMqG+tzawALCIiIkLr3Hh3iMf0UF5fT2m9QUmdQWm9QWmdQS12AqJTCIhOISR9DAA/e2MzEUE76B4TQveYxika3WKC6R4TQmpUMBFBAa0SjrOysuiXnk51VVWLXm84gwjokoTj4I2EAV264ohKIiCqKzbX0dZbLiHMZeenp/dveeFtwK8C8PPPP89f/vIXcnNzGTJkCH/729847bTTrC5LREREfMjJ3nh3LJW1DRRW1FJQUUvW/nx27MkmMK4bpdX1rMkuYU12yWGvCQqwkxgRSMLBR2JEIIkRQSRGBBIfHkh0aOMUjsAA+zH7LiwspLqqiin3/oX41J6HnfeYUOuGardBtRsqGwwqGgzK6xv/W+M+Vgg3cdrAaYMAm4kBNNTVkJ+1g/qq6OZdpHbgNwH4/fff56677uLFF19k5MiRPPPMM0yYMIEtW7YQFxdndXkiIiLiB0JcDkJcDtKiQ0ioy+Gbh29lxhtvEdq1FznlDeRUuA/+t/HXJTUequvd7CysZGdh5THf22U3CHMZhDlthATYcNoNHHZw2gwC7AYVZSVETbiVPcF9yKkOp8Fj4nab1Hs8VNa6qaxtOLga8tEFBdjpEhJAl2AnkcEB3vnT4UEOHLamm4zs3baRp/58P71+u+okr1rr85sA/NRTT/GLX/yC6667DoAXX3yRWbNm8eqrr3LfffdZXJ2IiIj4m0Nzjq+95uhzjg2HE3toFPawWBxh0djDYrCHxXh/7QiLwRYUhmF3UOs2qa0yKazyHOXdXIQNPZ/sKqDqyGHaZjSG9FCXgzCXg8hgJ12CA4gMcdIlKADXcUaZOwq/CMB1dXWsWrWK+++/33vMZrMxfvx4li5delj72tpaamtrvc9LS0sBKCsra/tiD6qoqAAa//VUW92yuTrNkZe1A4Dc3VvZERKs/tSfz/Wp/jp2f1b0qf7Un6/3uXvTagBOnXgFyd17t+AdisAswqwCNzYacNBgOHBjp8GwYWLDg4GJgQcbJUX57N+eSe9hGcQndsVucPBh4rJDkN3EZQPvdGMTqGx8VBY0/rI5CvbuAhozTXtkqEN9mObxxrHBME+kVQe3f/9+unbtypIlS8jIyPAev+eee1i8eDHLly9v0v6RRx7hD3/4Q3uXKSIiIiInKTs7m+Tk5GO28YsR4Oa6//77ueuuu7zPPR4PxcXFREdHd4oFrcvKykhJSSE7O5vw8HCry+kQdM2aT9es+XTNWkbXrfl0zZpP16z52vuamaZJeXk5SUlJx23rFwE4JiYGu91OXl5ek+N5eXkkJCQc1t7lcuFyNV2vLjIysi1LtER4eLh+EzeTrlnz6Zo1n65Zy+i6NZ+uWfPpmjVfe16ziIiIE2pnO36Tjs/pdDJ8+HDmz5/vPebxeJg/f36TKREiIiIi0vn5xQgwwF133cW0adMYMWIEp512Gs888wyVlZXeVSFERERExD/4TQC+4oorKCgo4KGHHiI3N5ehQ4cye/Zs4uPjrS6t3blcLh5++OHDpnnI0emaNZ+uWfPpmrWMrlvz6Zo1n65Z8/nyNfOLVSBERERERA7xiznAIiIiIiKHKACLiIiIiF9RABYRERERv6IALCIiIiJ+RQG4k3riiSc49dRTCQsLIy4ujp/85Cds2bKlSZuamhqmT59OdHQ0oaGhTJ48+bDNQvzJCy+8wODBg70LdmdkZPDll196z+t6Hd+f/vQnDMPgjjvu8B7TdTvcI488gmEYTR79+vXzntc1O7J9+/YxdepUoqOjCQoKYtCgQaxcudJ73jRNHnroIRITEwkKCmL8+PFs27bNwoqt1a1bt8O+Z4ZhMH36dEDfsyNxu938/ve/p3v37gQFBdGzZ08ee+wxfrhegL5nhysvL+eOO+4gLS2NoKAgTj/9dL777jvveZ+8ZqZ0ShMmTDBfe+01c8OGDeaaNWvMCy64wExNTTUrKiq8bW6++WYzJSXFnD9/vrly5Upz1KhR5umnn25h1db67LPPzFmzZplbt241t2zZYv7ud78zAwICzA0bNpimqet1PCtWrDC7detmDh482Lz99tu9x3XdDvfwww+bAwYMMHNycryPgoIC73lds8MVFxebaWlp5rXXXmsuX77c3Llzpzlnzhxz+/bt3jZ/+tOfzIiICPPTTz81165da1588cVm9+7dzerqagsrt05+fn6T79i8efNMwFy4cKFpmvqeHcnjjz9uRkdHmzNnzjR37dplfvjhh2ZoaKj57LPPetvoe3a4yy+/3Ozfv7+5ePFic9u2bebDDz9shoeHm3v37jVN0zevmQKwn8jPzzcBc/HixaZpmmZJSYkZEBBgfvjhh942mZmZJmAuXbrUqjJ9TpcuXcyXX35Z1+s4ysvLzd69e5vz5s0zzzrrLG8A1nU7socfftgcMmTIEc/pmh3Zvffea44ePfqo5z0ej5mQkGD+5S9/8R4rKSkxXS6X+e6777ZHiT7v9ttvN3v27Gl6PB59z45i0qRJ5vXXX9/k2KWXXmpOmTLFNE19z46kqqrKtNvt5syZM5scHzZsmPnAAw/47DXTFAg/UVpaCkBUVBQAq1ator6+nvHjx3vb9OvXj9TUVJYuXWpJjb7E7Xbz3nvvUVlZSUZGhq7XcUyfPp1JkyY1uT6g79mxbNu2jaSkJHr06MGUKVPIysoCdM2O5rPPPmPEiBFcdtllxMXFccopp/DSSy95z+/atYvc3Nwm1y0iIoKRI0f69XU7pK6ujrfeeovrr78ewzD0PTuK008/nfnz57N161YA1q5dyzfffMPEiRMBfc+OpKGhAbfbTWBgYJPjQUFBfPPNNz57zfxmJzh/5vF4uOOOOzjjjDMYOHAgALm5uTidTiIjI5u0jY+PJzc314IqfcP69evJyMigpqaG0NBQPvnkE/r378+aNWt0vY7ivffe4/vvv28y3+sQfc+ObOTIkcyYMYO+ffuSk5PDH/7wB84880w2bNiga3YUO3fu5IUXXuCuu+7id7/7Hd999x233XYbTqeTadOmea/Nj3f39Pfrdsinn35KSUkJ1157LaDfm0dz3333UVZWRr9+/bDb7bjdbh5//HGmTJkCoO/ZEYSFhZGRkcFjjz1Geno68fHxvPvuuyxdupRevXr57DVTAPYD06dPZ8OGDXzzzTdWl+Lz+vbty5o1aygtLeWjjz5i2rRpLF682OqyfFZ2dja333478+bNO+xf/3J0h0aTAAYPHszIkSNJS0vjgw8+ICgoyMLKfJfH42HEiBH8z//8DwCnnHIKGzZs4MUXX2TatGkWV+f7XnnlFSZOnEhSUpLVpfi0Dz74gLfffpt33nmHAQMGsGbNGu644w6SkpL0PTuGN998k+uvv56uXbtit9sZNmwYV111FatWrbK6tKPSFIhO7tZbb2XmzJksXLiQ5ORk7/GEhATq6uooKSlp0j4vL4+EhIR2rtJ3OJ1OevXqxfDhw3niiScYMmQIzz77rK7XUaxatYr8/HyGDRuGw+HA4XCwePFinnvuORwOB/Hx8bpuJyAyMpI+ffqwfft2fdeOIjExkf79+zc5lp6e7p06cuja/HgVA3+/bgB79uzhq6++4sYbb/Qe0/fsyO6++27uu+8+rrzySgYNGsTVV1/NnXfeyRNPPAHoe3Y0PXv2ZPHixVRUVJCdnc2KFSuor6+nR48ePnvNFIA7KdM0ufXWW/nkk09YsGAB3bt3b3J++PDhBAQEMH/+fO+xLVu2kJWVRUZGRnuX67M8Hg+1tbW6Xkcxbtw41q9fz5o1a7yPESNGMGXKFO+vdd2Or6Kigh07dpCYmKjv2lGcccYZhy3luHXrVtLS0gDo3r07CQkJTa5bWVkZy5cv9+vrBvDaa68RFxfHpEmTvMf0PTuyqqoqbLam0chut+PxeAB9z44nJCSExMREDhw4wJw5c7jkkkt895pZdvudtKlbbrnFjIiIMBctWtRkGZyqqipvm5tvvtlMTU01FyxYYK5cudLMyMgwMzIyLKzaWvfdd5+5ePFic9euXea6devM++67zzQMw5w7d65pmrpeJ+qHq0CYpq7bkfzmN78xFy1aZO7atcv89ttvzfHjx5sxMTFmfn6+aZq6ZkeyYsUK0+FwmI8//ri5bds28+233zaDg4PNt956y9vmT3/6kxkZGWn++9//NtetW2decsklli+1ZDW3222mpqaa995772Hn9D073LRp08yuXbt6l0H7+OOPzZiYGPOee+7xttH37HCzZ882v/zyS3Pnzp3m3LlzzSFDhpgjR4406+rqTNP0zWumANxJAUd8vPbaa9421dXV5q9+9SuzS5cuZnBwsPnTn/7UzMnJsa5oi11//fVmWlqa6XQ6zdjYWHPcuHHe8Guaul4n6scBWNftcFdccYWZmJhoOp1Os2vXruYVV1zRZD1bXbMj+/zzz82BAweaLpfL7Nevn/nPf/6zyXmPx2P+/ve/N+Pj402Xy2WOGzfO3LJli0XV+oY5c+aYwBGvg75nhysrKzNvv/12MzU11QwMDDR79OhhPvDAA2Ztba23jb5nh3v//ffNHj16mE6n00xISDCnT59ulpSUeM/74jUzTPMH25uIiIiIiHRymgMsIiIiIn5FAVhERERE/IoCsIiIiIj4FQVgEREREfErCsAiIiIi4lcUgEVERETErygAi4iIiIhfUQAWEREREb+iACwiIjzyyCMMHTrU6jJERNqFArCISCcwduxY7rjjjsOOz5gxg8jIyOO+/re//S3z589v/cJERHyQw+oCRETEeqGhoYSGhlpdhohIu9AIsIiIn1i0aBGnnXYaISEhREZGcsYZZ7Bnzx7g8CkQ3333Heeeey4xMTFERERw1lln8f3331tUuYhI61IAFhHxAw0NDfzkJz/hrLPOYt26dSxdupRf/vKXGIZxxPbl5eVMmzaNb775hmXLltG7d28uuOACysvL27lyEZHWpykQIiJ+oKysjNLSUi688EJ69uwJQHp6+lHbn3POOU2e//Of/yQyMpLFixdz4YUXtmmtIiJtTSPAIiJ+ICoqimuvvZYJEyZw0UUX8eyzz5KTk3PU9nl5efziF7+gd+/eREREEB4eTkVFBVlZWe1YtYhI21AAFhHpBMLDwyktLT3seElJCREREQC89tprLF26lNNPP53333+fPn36sGzZsiO+37Rp01izZg3PPvssS5YsYc2aNURHR1NXV9emn0NEpD0oAIuIdAJ9+/Y94k1q33//PX369PE+P+WUU7j//vtZsmQJAwcO5J133jni+3377bfcdtttXHDBBQwYMACXy0VhYWGb1S8i0p4UgEVEOoFbbrmFrVu3ctttt7Fu3Tq2bNnCU089xbvvvstvfvMbdu3axf3338/SpUvZs2cPc+fOZdu2bUedB9y7d2/efPNNMjMzWb58OVOmTCEoKKidP5WISNtQABYR6QR69OjB119/zebNmxk/fjwjR47kgw8+4MMPP+T8888nODiYzZs3M3nyZPr06cMvf/lLpk+fzk033XTE93vllVc4cOAAw4YN4+qrr+a2224jLi6unT+ViEjbMEzTNK0uQkRERESkvWgEWERERET8igKwiIiIiPgVBWARERER8SsKwCIiIiLiVxSARURERMSvKACLiIiIiF9RABYRERERv6IALCIiIiJ+RQFYRERERPyKArCIiIiI+BUFYBERERHxK/8PhpObnkPpnLQAAAAASUVORK5CYII=\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "<font size='2'>Berdasarkan visualisasi histogram dan metode describe diatas,\n",
-        "\n",
-        "*   <font size='2'>Kolom 'age' memiliki 25% umur dibawah 28\n",
-        "\n",
-        "*   Kolom 'age' memiliki 50% umur dibawah 37 </font>\n",
-        "*   Kolom 'age' memiliki 75% umur dibawah 47 </font>\n",
-        "<br> <font size='2'>Nilai umur yang terbanyak berada pada antara umur 17 hingga 50</font>\n",
-        "\n",
-        "\n",
-        " </font>\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "4EqkBSg5Ljzo"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(f'Total jumlah orang yang berumur 17-50 : {len(df[df[\"age\"].between(17,50)])} orang')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "dbZmDGwjLeJ9",
-        "outputId": "b6231ec0-f70a-4087-9d7d-bc951307eb84"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Total jumlah orang yang berumur 17-50 : 36495 orang\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "<font size='2'>Bagaimana distribusi dari kolom workclass?</font>"
-      ],
-      "metadata": {
-        "id": "xwF7Tv8naekM"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df['workclass'].describe()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "5iaoLsnaaqZd",
-        "outputId": "dd49729f-754d-4df7-fe6f-7101894f3e65"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "count       45175\n",
-              "unique          7\n",
-              "top       Private\n",
-              "freq        33262\n",
-              "Name: workclass, dtype: object"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 19
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Melihat perbandingan jumlah total pekerja pada seluruh data melalui visualisasi\n",
-        "plt.figure(figsize=(12,8))\n",
-        "\n",
-        "total = float(len(df[\"income\"]) )\n",
-        "\n",
-        "ax = sns.countplot(x=\"workclass\", data=df)\n",
-        "for p in ax.patches:\n",
-        "    height = p.get_height()\n",
-        "    ax.text(p.get_x()+p.get_width()/2.,\n",
-        "            height + 3,\n",
-        "            '{:1.2f}'.format((height/total)*100),\n",
-        "            ha=\"center\")\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 508
-        },
-        "id": "Taf25VxNavMj",
-        "outputId": "5c6c6614-8a41-4bde-aea4-f650e7ffb685"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1200x800 with 1 Axes>"
-            ],
-            "image/png": 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AbI7wDwAAAACAzRVr+J85c6ZatmypMmXKyMfHR7169VJCQoJDzb333isnJyeH27BhwxxqEhMT1b17d5UqVUo+Pj4aN26c/v77b4eaqKgoNW/eXG5ubqpdu7bmz5+fq593331X1atXl7u7u1q1aqXNmzcX+ToDAAAAAHCjFWv4j46O1ogRI7Rp0yZFRETo/Pnz6ty5s9LT0x3qhg4dqqSkJOs2a9Ysa1xWVpa6d++uzMxMbdy4UQsWLND8+fM1ZcoUq+bQoUPq3r27OnTooLi4OI0ZM0ZPPPGE1qxZY9UsWbJE4eHhmjp1qn7++Wc1bdpUoaGhOn78+PV/IAAAAAAAuI6cjDGmuJvIceLECfn4+Cg6Olrt2rWTdOHIf7NmzfTWW2/lOc2qVav0j3/8Q0ePHpWvr68kae7cuZowYYJOnDghV1dXTZgwQStWrNDOnTut6fr27auUlBStXr1aktSqVSu1bNlSs2fPliRlZ2crMDBQo0aN0sSJE6/ae1pamry9vZWamiovL698r3PQuIX5rkXxin1tQHG3AAAAAACWguTQm+qc/9TUVElS+fLlHYYvWrRIFStWVKNGjTRp0iT9+eef1riYmBg1btzYCv6SFBoaqrS0NO3atcuqCQkJcZhnaGioYmJiJEmZmZmKjY11qHF2dlZISIhVc6mMjAylpaU53AAAAAAAuBmVKO4GcmRnZ2vMmDFq06aNGjVqZA1/9NFHVa1aNQUEBGj79u2aMGGCEhIStHTpUklScnKyQ/CXZN1PTk6+Yk1aWpr++usvnT59WllZWXnW7NmzJ89+Z86cqenTp1/bSgMAAAAAcAPcNOF/xIgR2rlzp3788UeH4U8++aT1/8aNG8vf31/33XefDhw4oFq1at3oNi2TJk1SeHi4dT8tLU2BgYHF1g8AAAAAAJdzU4T/kSNHavny5Vq3bp2qVKlyxdpWrVpJkvbv369atWrJz88v11X5jx07Jkny8/Oz/s0ZdnGNl5eXPDw85OLiIhcXlzxrcuZxKTc3N7m5ueV/JQEAAAAAKCbFes6/MUYjR47UsmXL9P3336tGjRpXnSYuLk6S5O/vL0kKDg7Wjh07HK7KHxERIS8vLzVs2NCqiYyMdJhPRESEgoODJUmurq4KCgpyqMnOzlZkZKRVAwAAAADArapYj/yPGDFCixcv1v/+9z+VKVPGOkff29tbHh4eOnDggBYvXqxu3bqpQoUK2r59u8aOHat27dqpSZMmkqTOnTurYcOGevzxxzVr1iwlJydr8uTJGjFihHVkftiwYZo9e7bGjx+vwYMH6/vvv9fnn3+uFStWWL2Eh4crLCxMLVq00F133aW33npL6enpGjRo0I1/YAAAAAAAKELFGv7nzJkj6cLP+V1s3rx5GjhwoFxdXfXdd99ZQTwwMFC9e/fW5MmTrVoXFxctX75cw4cPV3BwsEqXLq2wsDDNmDHDqqlRo4ZWrFihsWPH6u2331aVKlX00UcfKTQ01Krp06ePTpw4oSlTpig5OVnNmjXT6tWrc10EEAAAAACAW42TMcYUdxN2UJDfV7xY0LiF17ErFKXY1wYUdwsAAAAAYClIDi3Wc/4BAAAAAMD1R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHPFGv5nzpypli1bqkyZMvLx8VGvXr2UkJDgUHPu3DmNGDFCFSpUkKenp3r37q1jx4451CQmJqp79+4qVaqUfHx8NG7cOP39998ONVFRUWrevLnc3NxUu3ZtzZ8/P1c/7777rqpXry53d3e1atVKmzdvLvJ1BgAAAADgRivW8B8dHa0RI0Zo06ZNioiI0Pnz59W5c2elp6dbNWPHjtW3336rL774QtHR0Tp69KgefPBBa3xWVpa6d++uzMxMbdy4UQsWLND8+fM1ZcoUq+bQoUPq3r27OnTooLi4OI0ZM0ZPPPGE1qxZY9UsWbJE4eHhmjp1qn7++Wc1bdpUoaGhOn78+I15MAAAAAAAuE6cjDGmuJvIceLECfn4+Cg6Olrt2rVTamqqKlWqpMWLF+uhhx6SJO3Zs0cNGjRQTEyMWrdurVWrVukf//iHjh49Kl9fX0nS3LlzNWHCBJ04cUKurq6aMGGCVqxYoZ07d1rL6tu3r1JSUrR69WpJUqtWrdSyZUvNnj1bkpSdna3AwECNGjVKEydOvGrvaWlp8vb2Vmpqqry8vPK9zkHjFua7FsUr9rUBxd0CAAAAAFgKkkNvqnP+U1NTJUnly5eXJMXGxur8+fMKCQmxaurXr6+qVasqJiZGkhQTE6PGjRtbwV+SQkNDlZaWpl27dlk1F88jpyZnHpmZmYqNjXWocXZ2VkhIiFVzqYyMDKWlpTncAAAAAAC4Gd004T87O1tjxoxRmzZt1KhRI0lScnKyXF1dVbZsWYdaX19fJScnWzUXB/+c8TnjrlSTlpamv/76S3/88YeysrLyrMmZx6Vmzpwpb29v6xYYGFi4FQcAAAAA4Dq7acL/iBEjtHPnTn322WfF3Uq+TJo0SampqdbtyJEjxd0SAAAAAAB5KlHcDUjSyJEjtXz5cq1bt05VqlSxhvv5+SkzM1MpKSkOR/+PHTsmPz8/q+bSq/Ln/BrAxTWX/kLAsWPH5OXlJQ8PD7m4uMjFxSXPmpx5XMrNzU1ubm6FW2EAAAAAAG6gYj3yb4zRyJEjtWzZMn3//feqUaOGw/igoCCVLFlSkZGR1rCEhAQlJiYqODhYkhQcHKwdO3Y4XJU/IiJCXl5eatiwoVVz8TxyanLm4erqqqCgIIea7OxsRUZGWjUAAAAAANyqivXI/4gRI7R48WL973//U5kyZazz6729veXh4SFvb28NGTJE4eHhKl++vLy8vDRq1CgFBwerdevWkqTOnTurYcOGevzxxzVr1iwlJydr8uTJGjFihHVkftiwYZo9e7bGjx+vwYMH6/vvv9fnn3+uFStWWL2Eh4crLCxMLVq00F133aW33npL6enpGjRo0I1/YAAAAAAAKELFGv7nzJkjSbr33nsdhs+bN08DBw6UJL355ptydnZW7969lZGRodDQUL333ntWrYuLi5YvX67hw4crODhYpUuXVlhYmGbMmGHV1KhRQytWrNDYsWP19ttvq0qVKvroo48UGhpq1fTp00cnTpzQlClTlJycrGbNmmn16tW5LgIIAAAAAMCtxskYY4q7CTsoyO8rXixo3MLr2BWKUuxrA4q7BQAAAACwFCSH3jRX+wcAAAAAANcH4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRUq/Hfs2FEpKSm5hqelpaljx47X2hMAAAAAAChChQr/UVFRyszMzDX83LlzWr9+/TU3BQAAAAAAik6JghRv377d+v/u3buVnJxs3c/KytLq1atVuXLlousOAAAAAABcswKF/2bNmsnJyUlOTk55fr3fw8ND77zzTpE1BwAAAAAArl2Bwv+hQ4dkjFHNmjW1efNmVapUyRrn6uoqHx8fubi4FHmTAAAAAACg8AoU/qtVqyZJys7Ovi7NAAAAAACAoleg8H+xffv26YcfftDx48dzfRgwZcqUa24MAAAAAAAUjUKF/w8//FDDhw9XxYoV5efnJycnJ2uck5MT4R8AAAAAgJtIocL/iy++qJdeekkTJkwo6n4AAAAAAEARcy7MRKdPn9bDDz9c1L0AAAAAAIDroFDh/+GHH9batWuLuhcAAAAAAHAdFOpr/7Vr19bzzz+vTZs2qXHjxipZsqTD+KeffrpImgMAAAAAANeuUOH/gw8+kKenp6KjoxUdHe0wzsnJifAPAAAAAMBNpFDh/9ChQ0XdBwAAAAAAuE4Kdc4/AAAAAAC4dRTqyP/gwYOvOP7jjz8uVDMAAAAAAKDoFSr8nz592uH++fPntXPnTqWkpKhjx45F0hgAAAAAACgahQr/y5YtyzUsOztbw4cPV61ata65KQAAAAAAUHSK7Jx/Z2dnhYeH68033yyqWQIAAAAAgCJQpBf8O3DggP7++++inCUAAAAAALhGhfraf3h4uMN9Y4ySkpK0YsUKhYWFFUljAAAAAACgaBQq/G/bts3hvrOzsypVqqTXX3/9qr8EAAAAAAAAbqxChf8ffvihqPsAAAAAAADXSaHCf44TJ04oISFBklSvXj1VqlSpSJoCAAAAAABFp1AX/EtPT9fgwYPl7++vdu3aqV27dgoICNCQIUP0559/FnWPAAAAAADgGhQq/IeHhys6OlrffvutUlJSlJKSov/973+Kjo7WM888U9Q9AgAAAACAa1Cor/1/9dVX+vLLL3Xvvfdaw7p16yYPDw898sgjmjNnTlH1BwAAAAAArlGhjvz/+eef8vX1zTXcx8eHr/0DAAAAAHCTKVT4Dw4O1tSpU3Xu3Dlr2F9//aXp06crODi4yJoDAAAAAADXrlBf+3/rrbfUpUsXValSRU2bNpUk/fLLL3Jzc9PatWuLtEEAAAAAAHBtChX+GzdurH379mnRokXas2ePJKlfv37q37+/PDw8irRBAAAAAABwbQoV/mfOnClfX18NHTrUYfjHH3+sEydOaMKECUXSHAAAAAAAuHaFOuf//fffV/369XMNv+OOOzR37txrbgoAAAAAABSdQoX/5ORk+fv75xpeqVIlJSUlXXNTAAAAAACg6BQq/AcGBmrDhg25hm/YsEEBAQHX3BQAAAAAACg6hTrnf+jQoRozZozOnz+vjh07SpIiIyM1fvx4PfPMM0XaIAAAAAAAuDaFCv/jxo3TyZMn9dRTTykzM1OS5O7urgkTJmjSpElF2iAAAAAAALg2hQr/Tk5OevXVV/X8888rPj5eHh4eqlOnjtzc3Iq6PwAAAAAAcI0KFf5zeHp6qmXLlkXVCwAAAAAAuA4KdcE/AAAAAABw6yD8AwAAAABgc4R/AAAAAABsjvAPAAAAAIDNEf4BAAAAALA5wj8AAAAAADZH+AcAAAAAwOYI/wAAAAAA2BzhHwAAAAAAmyP8AwAAAABgc4R/AAAAAABsjvAPAAAAAIDNEf4BAAAAALA5wj8AAAAAADZH+AcAAAAAwOYI/wAAAAAA2BzhHwAAAAAAmyvW8L9u3Tr16NFDAQEBcnJy0tdff+0wfuDAgXJycnK4denSxaHm1KlT6t+/v7y8vFS2bFkNGTJEZ8+edajZvn277rnnHrm7uyswMFCzZs3K1csXX3yh+vXry93dXY0bN9bKlSuLfH0BAAAAACgOxRr+09PT1bRpU7377ruXrenSpYuSkpKs26effuowvn///tq1a5ciIiK0fPlyrVu3Tk8++aQ1Pi0tTZ07d1a1atUUGxur1157TdOmTdMHH3xg1WzcuFH9+vXTkCFDtG3bNvXq1Uu9evXSzp07i36lAQAAAAC4wZyMMaa4m5AkJycnLVu2TL169bKGDRw4UCkpKbm+EZAjPj5eDRs21JYtW9SiRQtJ0urVq9WtWzf99ttvCggI0Jw5c/Tcc88pOTlZrq6ukqSJEyfq66+/1p49eyRJffr0UXp6upYvX27Nu3Xr1mrWrJnmzp2br/7T0tLk7e2t1NRUeXl55Xu9g8YtzHctilfsawOKuwUAAAAAsBQkh9705/xHRUXJx8dH9erV0/Dhw3Xy5ElrXExMjMqWLWsFf0kKCQmRs7OzfvrpJ6umXbt2VvCXpNDQUCUkJOj06dNWTUhIiMNyQ0NDFRMTc9m+MjIylJaW5nADAAAAAOBmdFOH/y5dumjhwoWKjIzUq6++qujoaHXt2lVZWVmSpOTkZPn4+DhMU6JECZUvX17JyclWja+vr0NNzv2r1eSMz8vMmTPl7e1t3QIDA69tZQEAAAAAuE5KFHcDV9K3b1/r/40bN1aTJk1Uq1YtRUVF6b777ivGzqRJkyYpPDzcup+WlsYHAAAAAACAm9JNfeT/UjVr1lTFihW1f/9+SZKfn5+OHz/uUPP333/r1KlT8vPzs2qOHTvmUJNz/2o1OePz4ubmJi8vL4cbAAAAAAA3o1sq/P/22286efKk/P39JUnBwcFKSUlRbGysVfP9998rOztbrVq1smrWrVun8+fPWzURERGqV6+eypUrZ9VERkY6LCsiIkLBwcHXe5UAAAAAALjuijX8nz17VnFxcYqLi5MkHTp0SHFxcUpMTNTZs2c1btw4bdq0SYcPH1ZkZKR69uyp2rVrKzQ0VJLUoEEDdenSRUOHDtXmzZu1YcMGjRw5Un379lVAQIAk6dFHH5Wrq6uGDBmiXbt2acmSJXr77bcdvrI/evRorV69Wq+//rr27NmjadOmaevWrRo5cuQNf0wAAAAAAChqxRr+t27dqjvvvFN33nmnJCk8PFx33nmnpkyZIhcXF23fvl3333+/6tatqyFDhigoKEjr16+Xm5ubNY9Fixapfv36uu+++9StWze1bdtWH3zwgTXe29tba9eu1aFDhxQUFKRnnnlGU6ZM0ZNPPmnV3H333Vq8eLE++OADNW3aVF9++aW+/vprNWrU6MY9GAAAAAAAXCdOxhhT3E3YQUF+X/FiQeMWXseuUJRiXxtQ3C0AAAAAgKUgOfSWOucfAAAAAAAUHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0Va/hft26devTooYCAADk5Oenrr792GG+M0ZQpU+Tv7y8PDw+FhIRo3759DjWnTp1S//795eXlpbJly2rIkCE6e/asQ8327dt1zz33yN3dXYGBgZo1a1auXr744gvVr19f7u7uaty4sVauXFnk6wsAAAAAQHEo1vCfnp6upk2b6t13381z/KxZs/Tvf/9bc+fO1U8//aTSpUsrNDRU586ds2r69++vXbt2KSIiQsuXL9e6dev05JNPWuPT0tLUuXNnVatWTbGxsXrttdc0bdo0ffDBB1bNxo0b1a9fPw0ZMkTbtm1Tr1691KtXL+3cufP6rTwAAAAAADeIkzHGFHcTkuTk5KRly5apV69eki4c9Q8ICNAzzzyjZ599VpKUmpoqX19fzZ8/X3379lV8fLwaNmyoLVu2qEWLFpKk1atXq1u3bvrtt98UEBCgOXPm6LnnnlNycrJcXV0lSRMnTtTXX3+tPXv2SJL69Omj9PR0LV++3OqndevWatasmebOnZuv/tPS0uTt7a3U1FR5eXnle72Dxi3Mdy2KV+xrA4q7BQAAAACwFCSH3rTn/B86dEjJyckKCQmxhnl7e6tVq1aKiYmRJMXExKhs2bJW8JekkJAQOTs766effrJq2rVrZwV/SQoNDVVCQoJOnz5t1Vy8nJyanOXkJSMjQ2lpaQ43AAAAAABuRjdt+E9OTpYk+fr6Ogz39fW1xiUnJ8vHx8dhfIkSJVS+fHmHmrzmcfEyLleTMz4vM2fOlLe3t3ULDAws6CoCAAAAAHBD3LTh/2Y3adIkpaamWrcjR44Ud0sAAAAAAOTppg3/fn5+kqRjx445DD927Jg1zs/PT8ePH3cY//fff+vUqVMONXnN4+JlXK4mZ3xe3Nzc5OXl5XADAAAAAOBmdNOG/xo1asjPz0+RkZHWsLS0NP30008KDg6WJAUHByslJUWxsbFWzffff6/s7Gy1atXKqlm3bp3Onz9v1URERKhevXoqV66cVXPxcnJqcpYDAAAAAMCtrFjD/9mzZxUXF6e4uDhJFy7yFxcXp8TERDk5OWnMmDF68cUX9c0332jHjh0aMGCAAgICrF8EaNCggbp06aKhQ4dq8+bN2rBhg0aOHKm+ffsqICBAkvToo4/K1dVVQ4YM0a5du7RkyRK9/fbbCg8Pt/oYPXq0Vq9erddff1179uzRtGnTtHXrVo0cOfJGPyQAAAAAABS5EsW58K1bt6pDhw7W/ZxAHhYWpvnz52v8+PFKT0/Xk08+qZSUFLVt21arV6+Wu7u7Nc2iRYs0cuRI3XfffXJ2dlbv3r3173//2xrv7e2ttWvXasSIEQoKClLFihU1ZcoUPfnkk1bN3XffrcWLF2vy5Mn6f//v/6lOnTr6+uuv1ahRoxvwKAAAAAAAcH05GWNMcTdhBwX5fcWLBY1beB27QlGKfW1AcbcAAAAAAJaC5NCb9px/AAAAAABQNAj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGzupg7/06ZNk5OTk8Otfv361vhz585pxIgRqlChgjw9PdW7d28dO3bMYR6JiYnq3r27SpUqJR8fH40bN05///23Q01UVJSaN28uNzc31a5dW/Pnz78RqwcAAAAAwA1xU4d/SbrjjjuUlJRk3X788Udr3NixY/Xtt9/qiy++UHR0tI4ePaoHH3zQGp+VlaXu3bsrMzNTGzdu1IIFCzR//nxNmTLFqjl06JC6d++uDh06KC4uTmPGjNETTzyhNWvW3ND1BAAAAADgeilR3A1cTYkSJeTn55dreGpqqv7zn/9o8eLF6tixoyRp3rx5atCggTZt2qTWrVtr7dq12r17t7777jv5+vqqWbNmeuGFFzRhwgRNmzZNrq6umjt3rmrUqKHXX39dktSgQQP9+OOPevPNNxUaGnpD1xUAAAAAgOvhpj/yv2/fPgUEBKhmzZrq37+/EhMTJUmxsbE6f/68QkJCrNr69euratWqiomJkSTFxMSocePG8vX1tWpCQ0OVlpamXbt2WTUXzyOnJmceAAAAAADc6m7qI/+tWrXS/PnzVa9ePSUlJWn69Om65557tHPnTiUnJ8vV1VVly5Z1mMbX11fJycmSpOTkZIfgnzM+Z9yVatLS0vTXX3/Jw8Mjz94yMjKUkZFh3U9LS7umdQUAAAAA4Hq5qcN/165drf83adJErVq1UrVq1fT5559fNpTfKDNnztT06dOLtQcAAAAAAPLjpv/a/8XKli2runXrav/+/fLz81NmZqZSUlIcao4dO2ZdI8DPzy/X1f9z7l+txsvL64ofMEyaNEmpqanW7ciRI9e6egAAAAAAXBe3VPg/e/asDhw4IH9/fwUFBalkyZKKjIy0xickJCgxMVHBwcGSpODgYO3YsUPHjx+3aiIiIuTl5aWGDRtaNRfPI6cmZx6X4+bmJi8vL4cbAAAAAAA3o5s6/D/77LOKjo7W4cOHtXHjRj3wwANycXFRv3795O3trSFDhig8PFw//PCDYmNjNWjQIAUHB6t169aSpM6dO6thw4Z6/PHH9csvv2jNmjWaPHmyRowYITc3N0nSsGHDdPDgQY0fP1579uzRe++9p88//1xjx44tzlUHAAAAAKDI3NTn/P/222/q16+fTp48qUqVKqlt27batGmTKlWqJEl688035ezsrN69eysjI0OhoaF67733rOldXFy0fPlyDR8+XMHBwSpdurTCwsI0Y8YMq6ZGjRpasWKFxo4dq7fffltVqlTRRx99xM/8AQAAAABsw8kYY4q7CTtIS0uTt7e3UlNTC3QKQNC4hdexKxSl2NcGFHcLAAAAAGApSA69qb/2DwAAAAAArh3hHwAAAAAAmyP8AwAAAABgc4R/AAAAAABsjvAPAAAAAIDNEf4BAIWWlZWl559/XjVq1JCHh4dq1aqlF154Qfn9IZkNGzaoRIkSatasWa5x7777rqpXry53d3e1atVKmzdvLuLuAQAAbh+EfwBAob366quaM2eOZs+erfj4eL366quaNWuW3nnnnatOm5KSogEDBui+++7LNW7JkiUKDw/X1KlT9fPPP6tp06YKDQ3V8ePHr8dqAAAA2B7hHwBQaBs3blTPnj3VvXt3Va9eXQ899JA6d+6cr6P0w4YN06OPPqrg4OBc49544w0NHTpUgwYNUsOGDTV37lyVKlVKH3/88fVYDQAAANsj/AMACu3uu+9WZGSk9u7dK0n65Zdf9OOPP6pr165XnG7evHk6ePCgpk6dmmtcZmamYmNjFRISYg1zdnZWSEiIYmJiinYFAAAAbhMlirsBAMCta+LEiUpLS1P9+vXl4uKirKwsvfTSS+rfv/9lp9m3b58mTpyo9evXq0SJ3C9Df/zxh7KysuTr6+sw3NfXV3v27CnydQAAALgdEP4BAIX2+eefa9GiRVq8eLHuuOMOxcXFacyYMQoICFBYWFiu+qysLD366KOaPn266tatWwwdAwAA3J4I/wCAQhs3bpwmTpyovn37SpIaN26sX3/9VTNnzswz/J85c0Zbt27Vtm3bNHLkSElSdna2jDEqUaKE1q5dq7Zt28rFxUXHjh1zmPbYsWPy8/O7/isFAABgQ5zzDwAotD///FPOzo4vJS4uLsrOzs6z3svLSzt27FBcXJx1GzZsmOrVq6e4uDi1atVKrq6uCgoKUmRkpDVddna2IiMj87w4IAAAAK6OI/8AgELr0aOHXnrpJVWtWlV33HGHtm3bpjfeeEODBw+2aiZNmqTff/9dCxculLOzsxo1auQwDx8fH7m7uzsMDw8PV1hYmFq0aKG77rpLb731ltLT0zVo0KAbtm4AAAB2QvgHABTaO++8o+eff15PPfWUjh8/roCAAP3zn//UlClTrJqkpCQlJiYWaL59+vTRiRMnNGXKFCUnJ6tZs2ZavXp1rosAAgAAIH+cjDGmuJuwg7S0NHl7eys1NVVeXl75ni5o3MLr2BWKUuxrA4q7Bdym2rzTprhbQD5sGLWhuFsAAAC3mYLkUM75BwAAAADA5gj/AArk999/12OPPaYKFSrIw8NDjRs31tatW684zaJFi9S0aVOVKlVK/v7+Gjx4sE6ePOlQk5KSohEjRsjf319ubm6qW7euVq5ceT1XBQAAALhtcM4/gHw7ffq02rRpow4dOmjVqlWqVKmS9u3bp3Llyl12mg0bNmjAgAF688031aNHD/3+++8aNmyYhg4dqqVLl0qSMjMz1alTJ/n4+OjLL79U5cqV9euvv6ps2bI3aM0AAAAAeyP8A8i3V199VYGBgZo3b541rEaNGlecJiYmRtWrV9fTTz9t1f/zn//Uq6++atV8/PHHOnXqlDZu3KiSJUtKkqpXr170KwAAAADcpvjaP4B8++abb9SiRQs9/PDD8vHx0Z133qkPP/zwitMEBwfryJEjWrlypYwxOnbsmL788kt169bNYb7BwcEaMWKEfH191ahRI7388svKysq63qsEAAAA3BYI/wDy7eDBg5ozZ47q1KmjNWvWaPjw4Xr66ae1YMGCy07Tpk0bLVq0SH369JGrq6v8/Pzk7e2td99912G+X375pbKysrRy5Uo9//zzev311/Xiiy/eiNUCAAAAbI/wDyDfsrOz1bx5c7388su688479eSTT2ro0KGaO3fuZafZvXu3Ro8erSlTpig2NlarV6/W4cOHNWzYMIf5+vj46IMPPlBQUJD69Omj55577orzBQAAAJB/nPMPIN/8/f3VsGFDh2ENGjTQV199ddlpZs6cqTZt2mjcuHGSpCZNmqh06dK655579OKLL8rf31/+/v4qWbKkXFxcHOabnJyszMxMubq6Xp8VAgAAAG4THPkHkG9t2rRRQkKCw7C9e/eqWrVql53mzz//lLOz464mJ+QbY6z57t+/X9nZ2Q7z9ff3J/gDAAAARYDwDyDfxo4dq02bNunll1/W/v37tXjxYn3wwQcaMWKEVTNp0iQNGDDAut+jRw8tXbpUc+bM0cGDB7VhwwY9/fTTuuuuuxQQECBJGj58uE6dOqXRo0dr7969WrFihV5++WWH+QIAAAAoPL72DyDfWrZsqWXLlmnSpEmaMWOGatSoobfeekv9+/e3apKSkpSYmGjdHzhwoM6cOaPZs2frmWeeUdmyZdWxY0eHn/oLDAzUmjVrNHbsWDVp0kSVK1fW6NGjNWHChBu6fgAAAIBdOZmc793imqSlpcnb21upqany8vLK93RB4xZex65QlGJfG3D1oiKQOKPxDVkOrl3VKTtuyHLavNPmhiwH12bDqA3F3QIAALjNFCSH8rV/AAAAAABsjvAPAAAAAIDNEf4BAAAAALA5wj8AAAAAADZH+AcAAAAAwOYI/wAAAAAA2BzhHwAAAAAAmyP8AwAAAABgc4R/AAAAAABsjvAPAAAAAIDNEf4BAABwQ7zyyitycnLSmDFjLluzdOlStWjRQmXLllXp0qXVrFkzffLJJ7lqOnfurAoVKsjJyUlxcXHXt3EAsAHCPwAAAK67LVu26P3331eTJk2uWFe+fHk999xziomJ0fbt2zVo0CANGjRIa9assWrS09PVtm1bvfrqq9e7bQCwjRLF3QAAAADs7ezZs+rfv78+/PBDvfjii1esvffeex3ujx49WgsWLNCPP/6o0NBQSdLjjz8uSTp8+PD1aBcAbIkj/wAAALiuRowYoe7duyskJKRA0xljFBkZqYSEBLVr1+46dQcAtweO/AMAAOC6+eyzz/Tzzz9ry5Yt+Z4mNTVVlStXVkZGhlxcXPTee++pU6dO17FLALA/jvwDAADgujhy5IhGjx6tRYsWyd3dPd/TlSlTRnFxcdqyZYteeuklhYeHKyoq6vo1ihtqzpw5atKkiby8vOTl5aXg4GCtWrXqitOkpKRoxIgR8vf3l5ubm+rWrauVK1fmWZufC0sCtyOO/AMAAOC6iI2N1fHjx9W8eXNrWFZWltatW6fZs2dbR/Yv5ezsrNq1a0uSmjVrpvj4eM2cOTPX9QBwa6pSpYpeeeUV1alTR8YYLViwQD179tS2bdt0xx135KrPzMxUp06d5OPjoy+//FKVK1fWr7/+qrJly+aqze+FJYHbEeEfAAAA18V9992nHTt2OAwbNGiQ6tevrwkTJuQZ/POSnZ2tjIyM69EiikGPHj0c7r/00kuaM2eONm3alGf4//jjj3Xq1Clt3LhRJUuWlCRVr149V11BLiwJ3I742j8AAACuizJlyqhRo0YOt9KlS6tChQpq1KiRJGnAgAGaNGmSNc3MmTMVERGhgwcPKj4+Xq+//ro++eQTPfbYY1bNqVOnFBcXp927d0uSEhISFBcXp+Tk5Bu7grhmWVlZ+uyzz5Senq7g4OA8a7755hsFBwdrxIgR8vX1VaNGjfTyyy8rKyvLoa6wF5YEbhcc+QcAAEVmzpw5mjNnjvUTbHfccYemTJmirl275lm/a9cuTZkyRbGxsfr111/15ptv5jpPd926dXrttdcUGxurpKQkLVu2TL169bq+K3KLmf3Mt8XdQr79fuCknNIOWj3H/LBN5b1+1+zMC/ejN8TpzVnvKOXMSZUs4Srf8lX0WOhYnYv3tabZtCtSi9a8bc2zb9++kqSurfuq292P3uA1yr+Rr/e4etFtYseOHQoODta5c+fk6empZcuWqWHDhnnWHjx4UN9//7369++vlStXav/+/Xrqqad0/vx5TZ06VVLhLiwJ3G4I/wAAoMgU9FzeP//8UzVr1tTDDz+ssWPH5jnP9PR0NW3aVIMHD9aDDz54vVcB19noR16+4v1/tHlM/2jzmK6k9R33qfUd9xV5b7hx6tWrp7i4OKWmpurLL79UWFiYoqOj8/wAIDs7Wz4+Pvrggw/k4uKioKAg/f7773rttdc0depU68KSERERBbqwJHC7IfwDAIAiU9BzeVu2bKmWLVtKkiZOnJjnPLt27XrZbw4AuDW5urpaF3UMCgrSli1b9Pbbb+v999/PVevv76+SJUs6XCOiQYMGSk5OVmZmZqEvLAncbgj/AADgusjKytIXX3xxxXN5AUC68kUd27Rpo8WLFys7O1vOzhcuWbZ37175+/vL1dW1yC4sCdgd4R8AABSpgpzLC+D2M2nSJHXt2lVVq1bVmTNntHjxYkVFRWnNmjWSLlwEsnLlypo5c6Ykafjw4Zo9e7ZGjx6tUaNGad++fXr55Zf19NNPS/q/C0te7NILSwIg/AMAgCJWkHN5Adx+jh8/rgEDBigpKUne3t5q0qSJ1qxZo06dOkmSEhMTrSP8khQYGKg1a9Zo7NixatKkiSpXrqzRo0drwoQJxbUKwC2J8A8AAIpUQc7lBXB9vPTYQ8XdwmXVlFSzbQuHYZsXvK/NCy7sIzpVqSj9fTbXOvSoXUU9aleRJJndP+uVsD6XXUanKhWlP367qR8HSXruv18Wdwu4jThfvQQAAKDwrnQuLwAAuDE48g8AAIpMQc/lzczM1O7du63///7774qLi5Onp6f17YGzZ89q//791jIOHTqkuLg4lS9fXlWrVr3BawgAwK2J8A8AAIpMQc/lPXr0qO68807r/r/+9S/961//Uvv27RUVFSVJ2rp1qzp06GDVhIeHS5LCwsI0f/78679SAADYAOEfAIBbTHS79sXdwmUNkDSgajWparULAzLPSy+8qOgXXpQkTZekg4cc1iHqnna5Z5RtrBqny9VcMp+bTft10cXdAgAAFs75BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAsDnCPwAAAAAANkf4BwAAAADA5gj/AAAAAADYHOEfAAAAAACbI/wDAAAAAGBzhH8AAAAAAGyO8A8AAAAAgM0R/gEAAAAAxerdd99V9erV5e7urlatWmnz5s1XrP/iiy9Uv359ubu7q3Hjxlq5cqXD+IEDB8rJycnh1qVLl+u5Cjc9wj8AAAAAoNgsWbJE4eHhmjp1qn7++Wc1bdpUoaGhOn78eJ71GzduVL9+/TRkyBBt27ZNvXr1Uq9evbRz506Hui5duigpKcm6ffrppzdidW5ahH8AAAAAQLF54403NHToUA0aNEgNGzbU3LlzVapUKX388cd51r/99tvq0qWLxo0bpwYNGuiFF15Q8+bNNXv2bIc6Nzc3+fn5Wbdy5crdiNW5aRH+AQAAAADFIjMzU7GxsQoJCbGGOTs7KyQkRDExMXlOExMT41AvSaGhobnqo6Ki5OPjo3r16mn48OE6efJk0a/ALYTwDwAAAAAoFn/88YeysrLk6+vrMNzX11fJycl5TpOcnHzV+i5dumjhwoWKjIzUq6++qujoaHXt2lVZWVlFvxK3CML/JQp6oQkAAAAAwM2lb9++uv/++9W4cWP16tVLy5cv15YtWxQVFVXcrRUbwv9FCnqhCQAAAABA4VWsWFEuLi46duyYw/Bjx47Jz88vz2n8/PwKVC9JNWvWVMWKFbV///5rb/oWRfi/SEEvNAEAAAAAKDxXV1cFBQUpMjLSGpadna3IyEgFBwfnOU1wcLBDvSRFRERctl6SfvvtN508eVL+/v5F0/gtiPD//yvMhSYAAAAAANcmPDxcH374oRYsWKD4+HgNHz5c6enpGjRokCRpwIABmjRpklU/evRorV69Wq+//rr27NmjadOmaevWrRo5cqQk6ezZsxo3bpw2bdqkw4cPKzIyUj179lTt2rUVGhpaLOt4MyhR3A3cLK50oYk9e/bkqs/IyFBGRoZ1PzU1VZKUlpZWoOVmZfxViG5RHAr6ty2sM+du34uQ3Gpu1Dbx919/35Dl4NrcqO1BktL/Zpu4FdzIbeKvjD9v2LJQeDdymzh3/vwNWxYK70ZuEwmvRd+wZRVUTZXSqHvDNPHpcTqZflp1fWvqjQeeU+LszUqUtCt6m06V/V1bSnwrSSopacb94fr3y29q0oSJCiwfoFm9J+mvJYe0ZckhnTufofVLI/Wf9z7UmXPpqlSmvFrVbKZ/dh+t7S+uLdZ1vZp649oXqD5nGzLGXLXWyeSn6jZw9OhRVa5cWRs3bnT4usj48eMVHR2tn376yaF+2rRpmj59+o1uEwAAAAAAB0eOHFGVKlWuWMOR//9fQS80MWnSJIWHh1v3s7OzderUKVWoUEFOTk7Xvd+bWVpamgIDA3XkyBF5eXkVdzsoZmwPuBTbBC7FNoFLsU3gUmwTuBTbxAXGGJ05c0YBAQFXrSX8//8uvtBEr169JP3fhSZyzh25mJubm9zc3ByGlS1b9gZ0euvw8vK6rZ+IcMT2gEuxTeBSbBO4FNsELsU2gUuxTUje3t75qiP8XyQ8PFxhYWFq0aKF7rrrLr311lsOF5oAAAAAAOBWRPi/SJ8+fXTixAlNmTJFycnJatasmVavXp3rIoAAAAAAANxKCP+XGDlyZJ5f80f+ubm5aerUqblOi8Dtie0Bl2KbwKXYJnAptglcim0Cl2KbKDiu9g8AAAAAgM05F3cDAAAAAADg+iL8AwAAAABgc4R/AAAAAABsjvCPAqlevbreeuut4m4Dt5hp06apWbNmxd3GbSevx33atGny9fWVk5OTvv7662Lp62Z1+PBhOTk5KS4urrhbuWXcqO0oKipKTk5OSklJue7LAlA4dnjNuffeezVmzJjibuOWld99NXmi+BD+b2MDBw6Uk5OTnJyc5Orqqtq1a2vGjBn6+++/LzvNli1b9OSTTxZZD7zZvnEGDhyoXr16FXcbKIATJ05o+PDhqlq1qtzc3OTn56fQ0FBt2LChUPOLj4/X9OnT9f777yspKUldu3Yt4o5vTvn98CkwMFBJSUlq1KjR9W+qmFy837/4tn///uJuDTehq+2DChvoeON/c+I1R1q6dKleeOGF4m7jpjB37lyVKVPGIRecPXtWJUuW1L333utQmxP6/f39lZSUJG9vb0nS/PnzVbZs2RvYtSP2NbnxU3+3uS5dumjevHnKyMjQypUrNWLECJUsWVKTJk1yqMvMzJSrq6sqVapUTJ0Ct5/evXsrMzNTCxYsUM2aNXXs2DFFRkbq5MmThZrfgQMHJEk9e/aUk5NTUbZqCy4uLvLz8yvuNq67nP3+xYpr357z2oKbU1Hvg3Bz4zVHKl++fHG3cNPo0KGDzp49q61bt6p169aSpPXr18vPz08//fSTzp07J3d3d0nSDz/8oKpVq6pevXrF2TLyw+C2FRYWZnr27OkwrFOnTqZ169bWuBdffNH4+/ub6tWrG2OMqVatmnnzzTeNMcb069fPPPLIIw7TZ2ZmmgoVKpgFCxYYY4xZtWqVadOmjfH29jbly5c33bt3N/v377fqJTnc2rdvb4378MMPTf369Y2bm5upV6+eeffdd4v+QbiN5PX3zhEVFWVatmxpXF1djZ+fn5kwYYI5f/68NT4rK8u8+uqrplatWsbV1dUEBgaaF1980Ro/fvx4U6dOHePh4WFq1KhhJk+ebDIzM63xU6dONU2bNr1if2lpaebRRx81pUqVMn5+fuaNN94w7du3N6NHj7ZqTp06ZR5//HFTtmxZ4+HhYbp06WL27t1rjDEmNTXVuLu7m5UrVzrMd+nSpcbT09Okp6fn85G6OZw+fdpIMlFRUVesGTJkiKlYsaIpU6aM6dChg4mLi7PGX/y4T506Ndfz7XK+/vprc+eddxo3NzdTo0YNM23aNIftQZKZO3eu6d69u/Hw8DD169c3GzduNPv27TPt27c3pUqVMsHBwQ7P9Zxe5s6da6pUqWI8PDzMww8/bFJSUq74OLRv396MGjXKjBs3zpQrV874+vqaqVOnOtT8+uuv5v777zelS5c2ZcqUMQ8//LBJTk42xhgzb968XOs9b968PJd16NAhI8ls27bNGGPMDz/8YCSZ7777zgQFBRkPDw8THBxs9uzZ4zDdN998Y1q0aGHc3NxMhQoVTK9eva64TsXpSvuBq/3d9+7da+655x7j5uZmGjRoYNauXWskmWXLllk1iYmJ5uGHHzbe3t6mXLly5v777zeHDh3KtfxLX1sWLlxogoKCjKenp/H19TX9+vUzx44ds6bL+VucPn36iuv3wgsvmEqVKhlPT08zZMgQM2HCBId9T1ZWlpk+fbqpXLmycXV1NU2bNjWrVq2yxgcHB5vx48c7zPP48eOmRIkSJjo6+orLtpur7YOqVavm8LyqVq2aMcaY/fv3m/vvv9/4+PiY0qVLmxYtWpiIiAhruvbt2192X7R+/XrTtm1b4+7ubqpUqWJGjRplzp49e8U+4+PjTZs2baztMiIiItd2uX37dtOhQwfj7u5uypcvb4YOHWrOnDljjDFmzZo1xs3NLde29fTTT5sOHToU4BG7tfGac8Gl7zuqVatmXnrpJTNo0CDj6elpAgMDzfvvv+8wzZEjR0zfvn1NuXLlTKlSpUxQUJDZtGnTFZdzq/D39zczZ8607o8fP96MGDHCNGjQwPzwww/W8Hbt2pmwsDCHfXXO/y++5bx+5+dxvdLz1pjcfytjjOnZs6cJCwuzxud3G8zP9rJ582YTEhJiKlSoYLy8vEy7du1MbGysNX7QoEGme/fuDvPNzMw0lSpVMh999NEVH+cbifB/G8vrTeD9999vmjdvbsLCwoynp6d5/PHHzc6dO83OnTuNMY7hf/ny5cbDw8Phifjtt98aDw8Pk5aWZowx5ssvvzRfffWV2bdvn9m2bZvp0aOHady4scnKyjLGXHgi5by5TkpKMidPnjTGGPPf//7X+Pv7m6+++socPHjQfPXVV6Z8+fJm/vz51/lRsa/Lven/7bffTKlSpcxTTz1l4uPjzbJly0zFihUdAtb48eNNuXLlzPz5883+/fvN+vXrzYcffmiNf+GFF8yGDRvMoUOHzDfffGN8fX3Nq6++ao3PT/h/4oknTLVq1cx3331nduzYYR544AFTpkwZhx37/fffbxo0aGDWrVtn4uLiTGhoqKldu7b1QcNDDz1kHnvsMYf59u7dO9ewW8H58+eNp6enGTNmjDl37lyeNSEhIaZHjx5my5YtZu/eveaZZ54xFSpUsJ5HFz/uZ86csYJwUlKSSUpKynOe69atM15eXmb+/PnmwIEDZu3ataZ69epm2rRpVo0kU7lyZbNkyRKTkJBgevXqZapXr246duxoVq9ebXbv3m1at25tunTpYk0zdepUU7p0adOxY0ezbds2Ex0dbWrXrm0effTRKz4O7du3N15eXmbatGlm7969ZsGCBcbJycmsXbvWGHMhzDVr1sy0bdvWbN261WzatMkEBQVZHyT++eef5plnnjF33HGHtd5//vlnnsu6XPhv1aqViYqKMrt27TL33HOPufvuu61pli9fblxcXMyUKVPM7t27TVxcnHn55ZevuE7F6XL7gav93bOyskyjRo3MfffdZ+Li4kx0dLS58847HUJWZmamadCggRk8eLDZvn272b17t3n00UdNvXr1TEZGhrX8vF5b/vOf/5iVK1eaAwcOmJiYGBMcHGy6du1q9Zef8P/f//7XuLu7m48//tgkJCSY6dOnGy8vL4d9zxtvvGG8vLzMp59+avbs2WPGjx9vSpYsaX2IOHv2bFO1alWTnZ1tTfPOO+/kGnY7uNo+6Pjx49aHaUlJSeb48ePGGGPi4uLM3LlzzY4dO8zevXvN5MmTjbu7u/n111+NMcacPHnSVKlSxcyYMcNhX7R//35TunRp8+abb5q9e/eaDRs2mDvvvNMMHDjwsj3+/fffpl69eqZTp04mLi7OrF+/3tx1110O2+XZs2eNv7+/efDBB82OHTtMZGSkqVGjhhUQ/v77b+Pr6+vw5jyvYXbHa84FeYX/8uXLm3fffdfs27fPzJw50zg7O1sfAp85c8bUrFnT3HPPPWb9+vVm3759ZsmSJWbjxo1XfcxvBY8++qjp3Lmzdb9ly5bmiy++MMOGDTNTpkwxxlx4nXVzczPz58932FdnZGSYt956y3h5eVnbQE5muNrjerXnrTFXD/+X29fkJT/bS2RkpPnkk09MfHy82b17txkyZIjx9fW1Ms+GDRuMi4uLOXr0qDXN0qVLTenSpR2yUnEj/N/GLn4TmJ2dbSIiIoybm5t59tlnTVhYmPH19bXesOW4OPyfP3/eVKxY0SxcuNAa369fP9OnT5/LLvPEiRNGktmxY4cxJveb7Ry1atUyixcvdhj2wgsvmODg4EKuLS73pv///b//Z+rVq+fwxvbdd981np6eJisry6SlpRk3NzeHsH81r732mgkKCrLuXy38p6WlmZIlS5ovvvjCGpaSkmJKlSpl7dj37t1rJJkNGzZYNX/88Yfx8PAwn3/+uTHGmGXLljkc5c/5NsDFR/ZuJV9++aUpV66ccXd3N3fffbeZNGmS+eWXX4wxF46QeXl55XqTVqtWLevT80sf92XLll3xk29jjLnvvvtyhddPPvnE+Pv7W/clmcmTJ1v3Y2JijCTzn//8xxr26aefGnd3d+v+1KlTjYuLi/ntt9+sYatWrTLOzs5XfEFu3769adu2rcOwli1bmgkTJhhjjFm7dq1xcXExiYmJ1vhdu3YZSWbz5s15Pg6Xc6Uj/zlWrFhhJJm//vrLGHPhSHH//v2vOu+bRVhYmHFxcTGlS5e2bg899NBV/+5r1qwxJUqUML///rs1ftWqVQ4h65NPPsm1L8nIyDAeHh5mzZo11vLzem251JYtW4wk6w1TfsJ/q1atzIgRIxyGtWnTxuFvHxAQYF566SWHmpYtW5qnnnrKGPN/R/nXrVtnjQ8ODra2t9vNlfZBxphcR9gv54477jDvvPOOdf/i9xI5hgwZYp588kmHYevXrzfOzs7W8+1Sq1atMiVKlHDYh1x65P+DDz4w5cqVc/gGwYoVK4yzs7P1DaHRo0ebjh07WuMv920Au+M1J+/wf/EBhOzsbOPj42PmzJljjDHm/fffN2XKlLE+ALGbDz/80JQuXdqcP3/epKWlmRIlSpjjx4+bxYsXm3bt2hljLoRiSebXX3/Nta+eN2+e8fb2zjXfqz2u+XneXi385yzn0n1NXgqzvWRlZZkyZcqYb7/91hrWsGFDh4NfPXr0uOIHmMWBC/7d5pYvXy5PT0+5u7ura9eu6tOnj6ZNmyZJaty48RXPxSxRooQeeeQRLVq0SJKUnp6u//3vf+rfv79Vs2/fPvXr1081a9aUl5eXqlevLklKTEy87HzT09N14MABDRkyRJ6entbtxRdftM4fQ9GJj49XcHCww/l4bdq00dmzZ/Xbb78pPj5eGRkZuu+++y47jyVLlqhNmzby8/OTp6enJk+efNm/8fr16x3+rosWLdLBgwd1/vx53XXXXVadt7e3w7lj8fHxKlGihFq1amUNq1ChgurVq6f4+HhJUrdu3VSyZEl98803kqSvvvpKXl5eCgkJKdyDU8x69+6to0eP6ptvvlGXLl0UFRWl5s2ba/78+frll1909uxZVahQweHxPHToUL6fJxdPN2zYMEnSL7/8ohkzZjiMGzp0qJKSkvTnn39a0zZp0sT6v6+vr6QL+4yLh507d05paWnWsKpVq6py5crW/eDgYGVnZyshISHP7SKvZUmSv7+/jh8/LunCdhEYGKjAwEBrfMOGDVW2bFlru8jLsGHDHJZ3JRcv39/fX5Ks5cfFxV3xuXEz6tChg+Li4qzbv//976v+3XMe54CAAGs+wcHBDvP95ZdftH//fpUpU8aaR/ny5XXu3DmHbTKv15bY2Fj16NFDVatWVZkyZdS+fXtJl3+tyGvbTUhIcNiHSHK4n5aWpqNHj6pNmzYONW3atLG2lUqVKqlz587W9nfo0CHFxMQ4vK7dTq60D7qcs2fP6tlnn1WDBg1UtmxZeXp6Kj4+/oqv+9KF7Wf+/PkOf9vQ0FBlZ2fr0KFDevnllx3GJSYmKiEhQYGBgQ7X6rh0G4iPj1fTpk1VunRpa1ibNm2sfY8k9e/fX1FRUTp69KgkadGiRerevXuxXqisOPCak7eLl+3k5CQ/Pz+H14A777zTttcKuPfee5Wenq4tW7Zo/fr1qlu3ripVqqT27dtb5/1HRUWpZs2aqlq1aoHmfaXHNT/P28LKazuUrry9SNKxY8c0dOhQ1alTR97e3vLy8tLZs2cd9m1PPPGEdU2dY8eOadWqVRo8ePA19VvUuODfba5Dhw6aM2eOXF1dFRAQoBIl/m+TuPgJdzn9+/dX+/btdfz4cUVERMjDw0NdunSxxvfo0UPVqlXThx9+qICAAGVnZ6tRo0bKzMy87DzPnj0rSfrwww8dgp504YJcuLE8PDyuOD7njfH06dMVGhoqb29vffbZZ3r99dfzrG/RooXDrzv4+vrq4MGDRdKrq6urHnroIS1evFh9+/bV4sWL1adPH4ft+lbj7u6uTp06qVOnTnr++ef1xBNPaOrUqXrqqafk7++vqKioXNPk9w3rxX8HLy8vSReef9OnT9eDDz6YZy85SpYsaf0/54OjvIZlZ2fnq5e8tou8lpUz7/zO93JmzJihZ599Nl+1V1qvqz0/bkalS5dW7dq1HYbl9+9+JWfPnlVQUFCeb6IvvqDgpa8t6enpCg0NVWhoqBYtWqRKlSopMTFRoaGhl32tyGvbLSr9+/fX008/rXfeeUeLFy9W48aNHULG7eZy+6CBAwfmWf/ss88qIiJC//rXv1S7dm15eHjooYceuuLrvnRh+/nnP/+pp59+Ote4qlWratiwYXrkkUesYRd/EHWtWrZsqVq1aumzzz7T8OHDtWzZsit+wGFnvObkdqXXoFvxNaAgateurSpVquiHH37Q6dOnrQ9mAwICFBgYqI0bN+qHH35Qx44dCzzva31td3Z2ljHGYdj58+evOl1hXz/CwsJ08uRJvf3226pWrZrc3NwUHBzssG8bMGCAJk6cqJiYGG3cuFE1atTQPffck+9l3Ai37jtiFIm83gQWxN13363AwEAtWbJEq1at0sMPP2w9mU+ePKmEhAR9+OGH1ob/448/Okyfc/QnKyvLGubr66uAgAAdPHjwtj3aciM1aNBAX331lYwx1ovnhg0bVKZMGVWpUkU+Pj7y8PBQZGSknnjiiVzTb9y4UdWqVdNzzz1nDfv1118vuzwPD49c21zNmjVVsmRJbdmyxfrkODU1VXv37lW7du2sPv/++2/99NNPuvvuuyX93zbWsGFDa179+/dXp06dtGvXLn3//fd68cUXC/nI3JwaNmyor7/+Ws2bN1dycrJKlChhfaOmoPJ67jdv3lwJCQnXtF+4nMTERB09etR6075p0yY5OzurXr16eW4X+dGgQQMdOXJER44csY7+7969WykpKdZ24erq6rCPkSQfHx/5+Phc4xpdOHIRGRmpQYMGXfO8itPV/u45j3NSUpL17YdNmzblmseSJUvk4+NToDdUe/bs0cmTJ/XKK69Yf8OtW7decZq8+qxXr562bNmiAQMGWMO2bNli/d/Ly0sBAQHasGGD9QZWurC/u/hocc+ePfXkk09q9erVWrx4scP88H/7IOnCm/dLn1sbNmzQwIED9cADD0i6EO4OHz7sUJPXc7J58+bavXv3ZbfB8uXL5zq6Wq9ePR05ckTHjh2zwtvFf3PpwrY7f/58paenWx88bdiwwdr35Ojfv78WLVqkKlWqyNnZWd27d8/Ho2F/vOZcWZMmTfTRRx/p1KlTtj3636FDB0VFRen06dMaN26cNbxdu3ZatWqVNm/erOHDh+c5bV7P9fzIz/O2UqVKSkpKsqbJysrSzp071aFDhysu/3J/9yttLznLf++999StWzdJ0pEjR/THH384zKNChQrq1auX5s2bp5iYmJvyvQFf+8c1e/TRRzV37lxFREQ4hPVy5cqpQoUK+uCDD7R//359//33Cg8Pd5g2J1iuXr1ax44dU2pqqiRp+vTpmjlzpv79739r79692rFjh+bNm6c33njjhq6b3aSmpjp83TcuLk5PPvmkjhw5olGjRmnPnj363//+p6lTpyo8PFzOzs5yd3fXhAkTNH78eC1cuFAHDhzQpk2b9J///EeSVKdOHSUmJuqzzz7TgQMH9O9//1vLli0rUF9lypRRWFiYxo0bpx9++EG7du3SkCFD5OzsbH0gUadOHfXs2VNDhw7Vjz/+qF9++UWPPfaYKleurJ49e1rzateunfz8/NS/f3/VqFEj17dHbhUnT55Ux44d9d///lfbt2/XoUOH9MUXX2jWrFnq2bOnQkJCFBwcrF69emnt2rU6fPiwNm7cqOeee+6qwelKpkyZooULF2r69OnatWuX4uPj9dlnn2ny5MnXvE7u7u4KCwvTL7/8ovXr1+vpp5/WI488ck0/rxcSEqLGjRurf//++vnnn7V582YNGDBA7du3V4sWLSRd+J3fQ4cOKS4uTn/88YcyMjKueV1yTJ06VZ9++qmmTp2q+Ph47dixQ6+++mqRzf9GudrfPSQkRHXr1nX4+138gZ90ITxVrFhRPXv21Pr163Xo0CFFRUXp6aef1m+//XbZZVetWlWurq565513dPDgQX3zzTeF+p3tUaNG6T//+Y8WLFigffv26cUXX9T27dsdTmkaN26cXn31VS1ZskQJCQmaOHGi4uLiNHr0aKumdOnS6tWrl55//nnFx8erX79+Be7FDq62D5IuPLciIyOVnJys06dPS7qwr166dKni4uL0yy+/6NFHH811NK969epat26dfv/9d+vN84QJE7Rx40aNHDlScXFx2rdvn/73v/9p5MiRl+2xU6dOqlWrlsLCwrR9+3Zt2LDB2mZz/u79+/e39j07d+7UDz/8oFGjRunxxx93ONqbsw956aWX9NBDD8nNza3oHsxbAK85hdOvXz/5+fmpV69e2rBhgw4ePKivvvpKMTExRbaM4tahQwf9+OOPiouLc/jgtH379nr//feVmZnpELgvVr16dZ09e1aRkZH6448/HE7luJL8PG87duyoFStWaMWKFdqzZ4+GDx+ulJSUXMu/dF9zOVfbXurUqaNPPvlE8fHx+umnn9S/f/88v/nxxBNPaMGCBYqPj1dYWFi+1veGKu6LDqD4XOknny43Lq8LZ+zevdv6mZ9Lr4YcERFhGjRoYNzc3EyTJk1MVFRUrgsEffjhhyYwMNA4Ozs7/NTfokWLTLNmzYyrq6spV66cadeunVm6dGkh1xZhYWG5fvJEkhkyZEi+furvxRdfNNWqVTMlS5Y0VatWdbhAz7hx40yFChWMp6en6dOnj3nzzTcdLvBS2J/6u+uuu8zEiROtmpyf+vP29jYeHh4mNDTUukr3xcaPH28kWVeivRWdO3fOTJw40TRv3tx4e3ubUqVKmXr16pnJkydbV6tPS0szo0aNMgEBAaZkyZImMDDQ9O/f37r4XWEuvmSMMatXrzZ333238fDwMF5eXuauu+4yH3zwgTX+0udwXhfuvPSiPzm9vPfeeyYgIMC4u7ubhx56yJw6deqKveTngj5X+qm/nMeyd+/epmzZsoX6qb+LL/q1bds2I8nh5+u++uora19VsWJF8+CDD15xnYrTlfb7V/u7JyQkmLZt2xpXV1dTt25ds3r16lzbQlJSkhkwYICpWLGicXNzMzVr1jRDhw41qampV1z+4sWLTfXq1Y2bm5sJDg4233zzzVX/FnmZMWOGqVixovH09DSDBw82Tz/9tGndurU1Pisry0ybNs1UrlzZlCxZMtdP/eVYuXKlkWRd0Op2lJ990DfffGNq165tSpQoYf3U36FDh0yHDh2Mh4eHCQwMNLNnz871PI6JiTFNmjQxbm5uDvukzZs3m06dOhlPT09TunRp06RJk1wXaLxUzk/9ubq6mvr165tvv/3WSDKrV6+2aq72k2E5cn4p4Pvvv7+GR+7WxGvOBXld8O/S971NmzZ1+EWkw4cPm969exsvLy9TqlQp06JFC/PTTz9ddb1vFTmPd/369R2GHz582Egy9erVs4blta8eNmyYqVChQq6f+rva43q1521mZqYZPny4KV++vPHx8TEzZ87M9f7gcvuaS+Vne/n5559NixYtjLu7u6lTp4754osv8lyP7OxsU61aNdOtW7fLLq84ORlzyckSAHATSE9PV+XKlfX6669ryJAhxd0OrtG0adP09ddfO5xrB1xvnTp1kp+fnz755JPibgU3yIYNG9S2bVvt379ftWrVKu52UEx4zUFBFOX2cvbsWVWuXFnz5s3L81oWxY1z/gHcFLZt26Y9e/borrvuUmpqqmbMmCFJDl/pB4DL+fPPPzV37lyFhobKxcVFn376qb777jtFREQUd2u4jpYtWyZPT0/VqVNH+/fv1+jRo9WmTRuCP4AbKjs7W3/88Ydef/11lS1bVvfff39xt5Qnwj+Am8a//vUvJSQkyNXVVUFBQVq/fr0qVqxY3G0BuAU4OTlp5cqVeumll3Tu3DnVq1dPX3311S37U5/InzNnzmjChAlKTExUxYoVFRISctlfmwGA6yUxMVE1atRQlSpVNH/+/Jv2l6b42j8AAAAAADbH1f4BAAAAALA5wj8AAAAAADZH+AcAAAAAwOYI/wAAAAAA2BzhHwAAXHeHDx+Wk5NToX9Hef78+SpbtmyR9gQAwO2E8A8AAAAAgM0R/gEAwHWVmZlZ3C0AAHDbI/wDAHCbW758ucqWLausrCxJUlxcnJycnDRx4kSr5oknntBjjz0mSfrqq690xx13yM3NTdWrV9frr7/uML/q1avrhRde0IABA+Tl5aUnn3wy1zKzsrI0ePBg1a9fX4mJiZKklJQU/fOf/5Svr6/c3d3VqFEjLV++PM+eDxw4oJ49e8rX11eenp5q2bKlvvvuO4ea9957T3Xq1JG7u7t8fX310EMPWeO+/PJLNW7cWB4eHqpQoYJCQkKUnp5eiEcPAIBbQ4nibgAAABSve+65R2fOnNG2bdvUokULRUdHq2LFioqKirJqoqOjNWHCBMXGxuqRRx7RtGnT1KdPH23cuFFPPfWUKlSooIEDB1r1//rXvzRlyhRNnTo11/IyMjLUr18/HT58WOvXr1elSpWUnZ2trl276syZM/rvf/+rWrVqaffu3XJxccmz57Nnz6pbt2566aWX5ObmpoULF6pHjx5KSEhQ1apVtXXrVj399NP65JNPdPfdd+vUqVNav369JCkpKUn9+vXTrFmz9MADD+jMmTNav369jDFF+rgCAHAzcTK80gEAcNsLCgpSv3799Oyzz+qBBx5Qy5YtNX36dJ08eVKpqamqUqWK9u7dq2nTpunEiRNau3atNe348eO1YsUK7dq1S9KFI/933nmnli1bZtUcPnxYNWrU0Pr16zVt2jRlZGRo+fLl8vb2liStXbtWXbt2VXx8vOrWrZurv/nz52vMmDFKSUm57Do0atRIw4YN08iRI7V06VINGjRIv/32m8qUKeNQ9/PPPysoKEiHDx9WtWrVruVhAwDglsHX/gEAgNq3b6+oqCgZY7R+/Xo9+OCDatCggX788UdFR0crICBAderUUXx8vNq0aeMwbZs2bbRv3z7rtAFJatGiRZ7L6devn9LT07V27Vor+EsXTjWoUqVKnsE/L2fPntWzzz6rBg0aqGzZsvL09FR8fLx1CkGnTp1UrVo11axZU48//rgWLVqkP//8U5LUtGlT3XfffWrcuLEefvhhffjhhzp9+nSBHi8AAG41hH8AAKB7771XP/74o3755ReVLFlS9evX17333quoqChFR0erffv2BZpf6dKl8xzerVs3bd++XTExMQ7DPTw8CjT/Z599VsuWLdPLL7+s9evXKy4uTo0bN7YuLlimTBn9/PPP+vTTT+Xv768pU6aoadOmSklJkYuLiyIiIrRq1So1bNhQ77zzjurVq6dDhw4VqAcAAG4lhH8AAGCd9//mm29aQT8n/EdFRenee++VJDVo0EAbNmxwmHbDhg2qW7fuZc/Pv9jw4cP1yiuv6P7771d0dLQ1vEmTJvrtt9+0d+/efPW7YcMGDRw4UA888IAaN24sPz8/HT582KGmRIkSCgkJ0axZs7R9+3YdPnxY33//vSTJyclJbdq00fTp07Vt2za5uro6nKYAAIDdcME/AACgcuXKqUmTJlq0aJFmz54tSWrXrp0eeeQRnT9/3vpA4JlnnlHLli31wgsvqE+fPoqJidHs2bP13nvv5XtZo0aNUlZWlv7xj39o1apVatu2rdq3b6927dqpd+/eeuONN1S7dm3t2bNHTk5O6tKlS6551KlTR0uXLlWPHj3k5OSk559/XtnZ2db45cuX6+DBg2rXrp3KlSunlStXKjs7W/Xq1dNPP/2kyMhIde7cWT4+Pvrpp5904sQJNWjQ4BofRQAAbl4c+QcAAJIunPeflZVlHeUvX768GjZsKD8/P9WrV0+S1Lx5c33++ef67LPP1KhRI02ZMkUzZsxwuNJ/fowZM0bTp09Xt27dtHHjRkkXfkKwZcuW6tevnxo2bKjx48c7XEfgYm+88YbKlSunu+++Wz169FBoaKiaN29ujS9btqyWLl2qjh07qkGDBpo7d64+/fRT3XHHHfLy8tK6devUrVs31a1bV5MnT9brr7+url27FvxBAwDgFsHV/gEAAAAAsDmO/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwOcI/AAAAAAA2R/gHAAAAAMDmCP8AAAAAANgc4R8AAAAAAJsj/AMAAAAAYHOEfwAAAAAAbI7wDwAAAACAzRH+AQAAAACwuf8PNrh0zw9/I+UAAAAASUVORK5CYII=\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "\n",
-        "<font size='2'>\n",
-        "\n",
-        "\n",
-        "*   Sektor Private merupakan pekerjaan yang paling dominan dalam data ini\n",
-        "\n",
-        "\n",
-        "\n",
-        "</font>\n",
-        "\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "or0asjOAbOFo"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Visualisasi persebaran peminatan pekerjaan dan perbandingan pendapatan pada masing-masing pekerjaan\n",
-        "plt.figure(figsize=(12,10))\n",
-        "total = float(len(df[\"income\"]) )\n",
-        "\n",
-        "ax = sns.countplot(x=\"workclass\", hue=\"income\", data=df)\n",
-        "for p in ax.patches:\n",
-        "    height = p.get_height()\n",
-        "    ax.text(p.get_x()+p.get_width()/2.,\n",
-        "            height + 3,\n",
-        "            '{:1.2f}'.format((height/total)*100),\n",
-        "            ha=\"center\")\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 619
-        },
-        "id": "SIc5jSi0mxI0",
-        "outputId": "a2c1abde-6295-497f-c462-2c3da8581e22"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1200x1000 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Perbandingan Total Income Berdasarkan Pekerjaan**\n",
-        "\n",
-        "*   <font size='2'>Private Sector merupakan pekerjaan paling banyak yang memiliki pendapatan kurang dari 50K\n",
-        "*   Private sector juga merupakan pekerjaan yang total pendapatan lebih dari 50K\n",
-        " </font>\n",
-        "\n",
-        "\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "c0-xQiViCp8Z"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Melihat sebaran level pendidikan\n",
-        "sns.displot(x=df['education'], aspect=3)\n",
-        "plt.title('Sebaran Level Pendidikan')\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 271
-        },
-        "id": "h-1v69DQbncP",
-        "outputId": "8c122755-8fdf-4ef3-9203-9210161c5096"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1500x500 with 1 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Melihat distribusi waktu yang dihabiskan pekerja dalam satu minggu\n",
-        "df['hours-per-week'].hist(figsize=(8,8))\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 676
-        },
-        "id": "GNsCD8SoiPub",
-        "outputId": "c0686a3e-86ba-4804-8534-b9fdb0c305ca"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 800x800 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Total Jam Kerja Per Minggu**\n",
-        "<font size='2'>\n",
-        "\n",
-        "\n",
-        "*   Kebanyakan orang-orang bekerja selama 30-40 jam per minggu, dengan perkiraan 27,000 orang.\n",
-        "*   Terdapat juga orang-orang yang bekerja selama 80-100 jam per minggu dengan total perkiraan di bawah 100 orang.\n",
-        "*  Sebanyak 75% orang menghabiskan 45 jam atau kurang selama seminggu</font>\n"
-      ],
-      "metadata": {
-        "id": "OEJYzZgmDuo1"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**4.1 Bivariate Analysis**\n",
-        "<font size='2'>\n",
-        "*   Bivariate bertujuan untuk melihat hubungan antara dua variabel\n",
-        "*   Mengetahui pengaruh satu variabel dengan yang lain positif atau negatif\n",
-        "</font>\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "Q7PoWkKRoBXS"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Visualisai dua variabel x dan y antara income dan hours-per-week\n",
-        "#Menggunakan visualisasi boxplot untuk deteksi outlier\n",
-        "plt.figure(figsize=(10,10))\n",
-        "sns.boxplot(x='income', y='hours-per-week', data=df)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 742
-        },
-        "id": "qSs2A1Uwj51D",
-        "outputId": "3a9cc274-179b-4c79-cd10-c75f9ad6553a"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x1000 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Pendapatan Berdasarkan Jam Kerja Per-Minggu**\n",
-        "<font size='2'>\n",
-        "\n",
-        "\n",
-        "*   Median \"jam kerja per minggu\" untuk kelompok pendapatan yang berpenghasilan >50 ribu lebih besar daripada kelompok pendapatan yang berpenghasilan <=50 ribu.\n",
-        "*   Kelompok pendapatan dengan penghasilan >50 ribu menghabiskan ~44 \"jam per minggu\" (jam kerja panjang)\n",
-        "*   Kelompok pendapatan yang berpenghasilan <=50 ribu menghabiskan ~37 \"jam per minggu\".\n",
-        "*   Boxplot untuk kelompok pendapatan <=50rb memiliki rentang yang kecil untuk nilai batas minimum (q1-1.5 IQR) dan batas maksimum (q3+ 1.5 IQR) yaitu (28,48), namun boxplot untuk kelompok pendapatan >50rb memiliki rentang yang besar untuk minimum (q1-1.5 IQR) dan maksimum (q3+ 1.5 IQR) yaitu (23,68).  \n",
-        "*   Kelompok pendapatan dengan penghasilan >50k memiliki jam kerja yang fleksibel\n",
-        "*   Lebih banyak Outlier yang ada pada kelompok pendapatan yang berpenghasilan <=50k.</font>\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "N9MFAFxUkOj5"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Visualisasi hubungan income dan age menggunakan catplot\n",
-        "sns.catplot(data=df, x='income', y='age', kind='box', aspect=2)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 401
-        },
-        "id": "-PdDjVScbq4p",
-        "outputId": "6dde822d-8970-4dc9-db64-11a1ccb04bc2"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<seaborn.axisgrid.FacetGrid at 0x78e0bc7bbd30>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 25
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x500 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Pendapatan Berdasarkan Umur**\n",
-        "\n",
-        "<font size='2'>\n",
-        "\n",
-        "*   Rata-rata orang-orang memiliki pendapatan kurang dari atau sama dengan 50K\n",
-        "dalam rentang umur antara 25-45\n",
-        "*   Sedangkan pendapatan diatas 50K antara umur 37-50  </font>\n",
-        "\n",
-        "\n",
-        "\n",
-        "<br>\n",
-        "<br>\n",
-        "\n",
-        "\n",
-        "\n",
-        " <font size='3'>**Menambah kolom income baru dengan kategori nilai 0 (<=50) dan 1  (>50)**</font>\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "oi6i8S6gcSjW"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Menambah kolom baru sebagai representasi income dengan nilai 0 dibawah 50k dan 1 diatas 50k\n",
-        "df['income_num'] = df['income'].map({'<=50K': 0, '>50K': 1})"
-      ],
-      "metadata": {
-        "id": "-CCoexyx_09q"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [],
-      "metadata": {
-        "id": "CsN0k4MZ-4eZ"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Visualisasi perbandingan income dan hours-per-week\n",
-        "plt.figure(figsize=(12,6))\n",
-        "sns.boxplot(x='income_num', y='hours-per-week', hue='gender', data=df)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 403
-        },
-        "id": "D-XI9tc1og1u",
-        "outputId": "49990aa1-f468-4a83-a486-22d200bd30b4"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1200x600 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Pendapatan Berdasarkan Jam Kerja dan Korelasinya pada Gender**\n",
-        "\n",
-        "<font size='2'>\n",
-        "\n",
-        "*   Data menunjukkan bahwa pendapatan dibawah 50k gender pria lebih banyak menghabiskan waktu dibandingkan perempuan\n",
-        "*   Perbandingan nilai median pendapatan di bawah 50K antara pria dan perempuan cukup tinggi, yaitu pria dengan rentang 40-45 jam setiap minggu, perempuan 30-40 jam.\n",
-        "*   Begitupun dengan pendapatan diatas 50k lebih menghabiskan banyak waktu dalam satu minggu.\n",
-        "*   Distribusi data pada pendapatan 50k keatas adalah rata-rata diatas 45-50 jam untuk pria, sedangkan perempuan 40-47 jam. </font>\n"
-      ],
-      "metadata": {
-        "id": "okEVkcKHpu4Y"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "plt.figure(figsize=(12,6))\n",
-        "sns.boxplot(x='income_num', y='age', hue='gender', data=df)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 407
-        },
-        "id": "N-nTcpMZo5N_",
-        "outputId": "94f102e7-2e5e-4961-afb6-2d6239d87fa2"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1200x600 with 1 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Pendapatan Berdasarkan Umur dan Korelasinya pada Gender**\n",
-        "\n",
-        "<font size='2'>\n",
-        "\n",
-        "*   Data menunjukkan bahwa total pendapatan dibawah 50k bbaik itu perempuan dan pria banyak tersebar pada rentang umur 25-45\n",
-        "*   Sedangan dengan pendapatan diatas 50k banyak terdistribusi pada rentang umur 37-55.\n",
-        "*   Ini menunjukkan semakin tinggi umur maka akan semakin meningkat pendapatan\n",
-        "</font>"
-      ],
-      "metadata": {
-        "id": "8C2YtDI8riD_"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.corr() #Melihat korelasi antara masing-masing kolom"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 345
-        },
-        "id": "vMjdFEULNR_E",
-        "outputId": "1e9f5a18-5232-423b-b269-cf666afb67b6"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "<ipython-input-29-646fd27f4ccb>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
-            "  df.corr() #Melihat korelasi antara masing-masing kolom\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                      age  educational-num  capital-gain  capital-loss  \\\n",
-              "age              1.000000         0.037269      0.079649      0.059258   \n",
-              "educational-num  0.037269         1.000000      0.126982      0.081719   \n",
-              "capital-gain     0.079649         0.126982      1.000000     -0.032137   \n",
-              "capital-loss     0.059258         0.081719     -0.032137      1.000000   \n",
-              "hours-per-week   0.101604         0.146463      0.083868      0.054141   \n",
-              "income_num       0.236839         0.332981      0.221071      0.148679   \n",
-              "\n",
-              "                 hours-per-week  income_num  \n",
-              "age                    0.101604    0.236839  \n",
-              "educational-num        0.146463    0.332981  \n",
-              "capital-gain           0.083868    0.221071  \n",
-              "capital-loss           0.054141    0.148679  \n",
-              "hours-per-week         1.000000    0.227146  \n",
-              "income_num             0.227146    1.000000  "
-            ],
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-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-9a1cc1d4-d94a-4ae5-834a-3f65aec33585 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-9a1cc1d4-d94a-4ae5-834a-3f65aec33585');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
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-              "        element.appendChild(docLink);\n",
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-              "\n",
-              "\n",
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-              "            title=\"Suggest charts.\"\n",
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-              "\n",
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-              "    </g>\n",
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-              "\n",
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-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
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-              "      --disabled-bg-color: #DDD;\n",
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-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
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-              "\n",
-              "  .colab-df-quickchart {\n",
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-              "    border-radius: 50%;\n",
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-              "    display: none;\n",
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-              "    padding: 0;\n",
-              "    width: 32px;\n",
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-              "  .colab-df-quickchart:hover {\n",
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-              "    fill: var(--button-hover-fill-color);\n",
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-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
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-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
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-              "    20% {\n",
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-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
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-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-655a357e-75d4-4b29-a67f-eaf533539676 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 29
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# **Preparing data for Modeling**\n",
-        "\n",
-        "\n",
-        "*   Konversi data categorical menjadi number values agar bisa diproses oleh model\n",
-        "*   Menggunakan teknik one-hot encoding"
-      ],
-      "metadata": {
-        "id": "9TRXZf9SBQcf"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Proses one-hot encoding data, dengan tujuan untuk merubah value menjadi kategorikal numeric\n",
-        "df = pd.concat([df.drop('occupation', axis=1), pd.get_dummies(df.occupation).add_prefix('occupation_')], axis=1)\n",
-        "df = pd.concat([df.drop('workclass', axis=1), pd.get_dummies(df.workclass).add_prefix('workclass_')], axis=1)\n",
-        "df = df.drop('education', axis =1)\n",
-        "df = pd.concat([df.drop('marital-status', axis=1), pd.get_dummies(df['marital-status']).add_prefix('marital-status_')], axis=1)\n",
-        "df = pd.concat([df.drop('relationship', axis=1), pd.get_dummies(df.relationship).add_prefix('relationship_')], axis=1)\n",
-        "df = pd.concat([df.drop('race', axis=1), pd.get_dummies(df.race).add_prefix('race_')], axis=1)\n",
-        "df = df.drop('capital-loss', axis=1)\n",
-        "df = pd.concat([df.drop('native-country', axis=1), pd.get_dummies(df['native-country']\t).add_prefix('native-country_')], axis=1)\n"
-      ],
-      "metadata": {
-        "id": "CKs1hZ8MsdYV"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 478
-        },
-        "id": "PjeSie-1sFen",
-        "outputId": "0298bcd6-70a4-4a03-d4a7-1fa8f09aaaeb"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "       age  educational-num  gender  capital-gain  hours-per-week income  \\\n",
-              "0       25                7    Male             0              40  <=50K   \n",
-              "1       38                9    Male             0              50  <=50K   \n",
-              "2       28               12    Male             0              40   >50K   \n",
-              "3       44               10    Male          7688              40   >50K   \n",
-              "5       34                6    Male             0              30  <=50K   \n",
-              "...    ...              ...     ...           ...             ...    ...   \n",
-              "48837   27               12  Female             0              38  <=50K   \n",
-              "48838   40                9    Male             0              40   >50K   \n",
-              "48839   58                9  Female             0              40  <=50K   \n",
-              "48840   22                9    Male             0              20  <=50K   \n",
-              "48841   52                9  Female         15024              40   >50K   \n",
-              "\n",
-              "       income_num  occupation_Adm-clerical  occupation_Armed-Forces  \\\n",
-              "0               0                        0                        0   \n",
-              "1               0                        0                        0   \n",
-              "2               1                        0                        0   \n",
-              "3               1                        0                        0   \n",
-              "5               0                        0                        0   \n",
-              "...           ...                      ...                      ...   \n",
-              "48837           0                        0                        0   \n",
-              "48838           1                        0                        0   \n",
-              "48839           0                        1                        0   \n",
-              "48840           0                        1                        0   \n",
-              "48841           1                        0                        0   \n",
-              "\n",
-              "       occupation_Craft-repair  ...  native-country_Portugal  \\\n",
-              "0                            0  ...                        0   \n",
-              "1                            0  ...                        0   \n",
-              "2                            0  ...                        0   \n",
-              "3                            0  ...                        0   \n",
-              "5                            0  ...                        0   \n",
-              "...                        ...  ...                      ...   \n",
-              "48837                        0  ...                        0   \n",
-              "48838                        0  ...                        0   \n",
-              "48839                        0  ...                        0   \n",
-              "48840                        0  ...                        0   \n",
-              "48841                        0  ...                        0   \n",
-              "\n",
-              "       native-country_Puerto-Rico  native-country_Scotland  \\\n",
-              "0                               0                        0   \n",
-              "1                               0                        0   \n",
-              "2                               0                        0   \n",
-              "3                               0                        0   \n",
-              "5                               0                        0   \n",
-              "...                           ...                      ...   \n",
-              "48837                           0                        0   \n",
-              "48838                           0                        0   \n",
-              "48839                           0                        0   \n",
-              "48840                           0                        0   \n",
-              "48841                           0                        0   \n",
-              "\n",
-              "       native-country_South  native-country_Taiwan  native-country_Thailand  \\\n",
-              "0                         0                      0                        0   \n",
-              "1                         0                      0                        0   \n",
-              "2                         0                      0                        0   \n",
-              "3                         0                      0                        0   \n",
-              "5                         0                      0                        0   \n",
-              "...                     ...                    ...                      ...   \n",
-              "48837                     0                      0                        0   \n",
-              "48838                     0                      0                        0   \n",
-              "48839                     0                      0                        0   \n",
-              "48840                     0                      0                        0   \n",
-              "48841                     0                      0                        0   \n",
-              "\n",
-              "       native-country_Trinadad&Tobago  native-country_United-States  \\\n",
-              "0                                   0                             1   \n",
-              "1                                   0                             1   \n",
-              "2                                   0                             1   \n",
-              "3                                   0                             1   \n",
-              "5                                   0                             1   \n",
-              "...                               ...                           ...   \n",
-              "48837                               0                             1   \n",
-              "48838                               0                             1   \n",
-              "48839                               0                             1   \n",
-              "48840                               0                             1   \n",
-              "48841                               0                             1   \n",
-              "\n",
-              "       native-country_Vietnam  native-country_Yugoslavia  \n",
-              "0                           0                          0  \n",
-              "1                           0                          0  \n",
-              "2                           0                          0  \n",
-              "3                           0                          0  \n",
-              "5                           0                          0  \n",
-              "...                       ...                        ...  \n",
-              "48837                       0                          0  \n",
-              "48838                       0                          0  \n",
-              "48839                       0                          0  \n",
-              "48840                       0                          0  \n",
-              "48841                       0                          0  \n",
-              "\n",
-              "[45175 rows x 87 columns]"
-            ],
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-              "      <th>income_num</th>\n",
-              "      <th>occupation_Adm-clerical</th>\n",
-              "      <th>occupation_Armed-Forces</th>\n",
-              "      <th>occupation_Craft-repair</th>\n",
-              "      <th>...</th>\n",
-              "      <th>native-country_Portugal</th>\n",
-              "      <th>native-country_Puerto-Rico</th>\n",
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-              "      <th>2</th>\n",
-              "      <td>28</td>\n",
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-              "    <tr>\n",
-              "      <th>48837</th>\n",
-              "      <td>27</td>\n",
-              "      <td>12</td>\n",
-              "      <td>Female</td>\n",
-              "      <td>0</td>\n",
-              "      <td>38</td>\n",
-              "      <td>&lt;=50K</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>48838</th>\n",
-              "      <td>40</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>0</td>\n",
-              "      <td>40</td>\n",
-              "      <td>&gt;50K</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>48839</th>\n",
-              "      <td>58</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Female</td>\n",
-              "      <td>0</td>\n",
-              "      <td>40</td>\n",
-              "      <td>&lt;=50K</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>48840</th>\n",
-              "      <td>22</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>0</td>\n",
-              "      <td>20</td>\n",
-              "      <td>&lt;=50K</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>48841</th>\n",
-              "      <td>52</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Female</td>\n",
-              "      <td>15024</td>\n",
-              "      <td>40</td>\n",
-              "      <td>&gt;50K</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>45175 rows × 87 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bec10ac6-f9ba-40fb-bb49-ea0595e93cbc')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-bec10ac6-f9ba-40fb-bb49-ea0595e93cbc button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-bec10ac6-f9ba-40fb-bb49-ea0595e93cbc');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-9e2bae3f-df57-43d3-a171-7b3533f6d430\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9e2bae3f-df57-43d3-a171-7b3533f6d430')\"\n",
-              "            title=\"Suggest charts.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-9e2bae3f-df57-43d3-a171-7b3533f6d430 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 31
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Mengubah kategori kolom gender dan income\n",
-        "df=df.drop('income_num',axis=1)\n",
-        "df['gender'] = df['gender'].apply(lambda x: 1 if x=='Male' else 0)\n",
-        "df['income'] = df['income'].apply(lambda x: 1 if x=='>50K' else 0)"
-      ],
-      "metadata": {
-        "id": "cgtRK-7gtuYU"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.columns.values\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "b5b-p77korLK",
-        "outputId": "e2e2d0b7-17a8-4d48-eeb9-cbfc5d20079c"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array(['age', 'educational-num', 'gender', 'capital-gain',\n",
-              "       'hours-per-week', 'income', 'occupation_Adm-clerical',\n",
-              "       'occupation_Armed-Forces', 'occupation_Craft-repair',\n",
-              "       'occupation_Exec-managerial', 'occupation_Farming-fishing',\n",
-              "       'occupation_Handlers-cleaners', 'occupation_Machine-op-inspct',\n",
-              "       'occupation_Other-service', 'occupation_Priv-house-serv',\n",
-              "       'occupation_Prof-specialty', 'occupation_Protective-serv',\n",
-              "       'occupation_Sales', 'occupation_Tech-support',\n",
-              "       'occupation_Transport-moving', 'workclass_Federal-gov',\n",
-              "       'workclass_Local-gov', 'workclass_Private',\n",
-              "       'workclass_Self-emp-inc', 'workclass_Self-emp-not-inc',\n",
-              "       'workclass_State-gov', 'workclass_Without-pay',\n",
-              "       'marital-status_Divorced', 'marital-status_Married-AF-spouse',\n",
-              "       'marital-status_Married-civ-spouse',\n",
-              "       'marital-status_Married-spouse-absent',\n",
-              "       'marital-status_Never-married', 'marital-status_Separated',\n",
-              "       'marital-status_Widowed', 'relationship_Husband',\n",
-              "       'relationship_Not-in-family', 'relationship_Other-relative',\n",
-              "       'relationship_Own-child', 'relationship_Unmarried',\n",
-              "       'relationship_Wife', 'race_Amer-Indian-Eskimo',\n",
-              "       'race_Asian-Pac-Islander', 'race_Black', 'race_Other',\n",
-              "       'race_White', 'native-country_Cambodia', 'native-country_Canada',\n",
-              "       'native-country_China', 'native-country_Columbia',\n",
-              "       'native-country_Cuba', 'native-country_Dominican-Republic',\n",
-              "       'native-country_Ecuador', 'native-country_El-Salvador',\n",
-              "       'native-country_England', 'native-country_France',\n",
-              "       'native-country_Germany', 'native-country_Greece',\n",
-              "       'native-country_Guatemala', 'native-country_Haiti',\n",
-              "       'native-country_Holand-Netherlands', 'native-country_Honduras',\n",
-              "       'native-country_Hong', 'native-country_Hungary',\n",
-              "       'native-country_India', 'native-country_Iran',\n",
-              "       'native-country_Ireland', 'native-country_Italy',\n",
-              "       'native-country_Jamaica', 'native-country_Japan',\n",
-              "       'native-country_Laos', 'native-country_Mexico',\n",
-              "       'native-country_Nicaragua',\n",
-              "       'native-country_Outlying-US(Guam-USVI-etc)', 'native-country_Peru',\n",
-              "       'native-country_Philippines', 'native-country_Poland',\n",
-              "       'native-country_Portugal', 'native-country_Puerto-Rico',\n",
-              "       'native-country_Scotland', 'native-country_South',\n",
-              "       'native-country_Taiwan', 'native-country_Thailand',\n",
-              "       'native-country_Trinadad&Tobago', 'native-country_United-States',\n",
-              "       'native-country_Vietnam', 'native-country_Yugoslavia'],\n",
-              "      dtype=object)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 33
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import seaborn as sns\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "plt.figure(figsize=(18,12))\n",
-        "sns.heatmap(df.corr(), annot=False, cmap='coolwarm')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 616
-        },
-        "id": "bth_eSbzR1lI",
-        "outputId": "2dc0a3ce-e55e-4ee1-d23d-434ab4ab5388"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<Axes: >"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 34
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1800x1200 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df.corr()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 530
-        },
-        "id": "hxy4D3YsSiIu",
-        "outputId": "26a7e196-caee-47ca-ae17-6d974d62becf"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                                     age  educational-num    gender  \\\n",
-              "age                             1.000000         0.037269  0.081920   \n",
-              "educational-num                 0.037269         1.000000  0.003455   \n",
-              "gender                          0.081920         0.003455  1.000000   \n",
-              "capital-gain                    0.079649         0.126982  0.047471   \n",
-              "hours-per-week                  0.101604         0.146463  0.231392   \n",
-              "...                                  ...              ...       ...   \n",
-              "native-country_Thailand        -0.000670         0.007377 -0.006673   \n",
-              "native-country_Trinadad&Tobago  0.002831        -0.010165 -0.008969   \n",
-              "native-country_United-States    0.011394         0.131344 -0.008482   \n",
-              "native-country_Vietnam         -0.014440        -0.010312 -0.002239   \n",
-              "native-country_Yugoslavia       0.003283        -0.006439  0.005184   \n",
-              "\n",
-              "                                capital-gain  hours-per-week    income  \\\n",
-              "age                                 0.079649        0.101604  0.236839   \n",
-              "educational-num                     0.126982        0.146463  0.332981   \n",
-              "gender                              0.047471        0.231392  0.215741   \n",
-              "capital-gain                        1.000000        0.083868  0.221071   \n",
-              "hours-per-week                      0.083868        1.000000  0.227146   \n",
-              "...                                      ...             ...       ...   \n",
-              "native-country_Thailand            -0.002872        0.010311 -0.004434   \n",
-              "native-country_Trinadad&Tobago     -0.003138       -0.004190 -0.009505   \n",
-              "native-country_United-States        0.010872        0.010497  0.038571   \n",
-              "native-country_Vietnam             -0.002719       -0.008060 -0.016257   \n",
-              "native-country_Yugoslavia          -0.000560       -0.001363  0.005219   \n",
-              "\n",
-              "                                occupation_Adm-clerical  \\\n",
-              "age                                           -0.037821   \n",
-              "educational-num                               -0.002603   \n",
-              "gender                                        -0.277672   \n",
-              "capital-gain                                  -0.031159   \n",
-              "hours-per-week                                -0.100499   \n",
-              "...                                                 ...   \n",
-              "native-country_Thailand                       -0.001474   \n",
-              "native-country_Trinadad&Tobago                 0.007922   \n",
-              "native-country_United-States                   0.019229   \n",
-              "native-country_Vietnam                         0.010768   \n",
-              "native-country_Yugoslavia                     -0.005441   \n",
-              "\n",
-              "                                occupation_Armed-Forces  \\\n",
-              "age                                           -0.009020   \n",
-              "educational-num                                0.003612   \n",
-              "gender                                         0.012216   \n",
-              "capital-gain                                  -0.001363   \n",
-              "hours-per-week                                 0.001132   \n",
-              "...                                                 ...   \n",
-              "native-country_Thailand                       -0.000446   \n",
-              "native-country_Trinadad&Tobago                -0.000423   \n",
-              "native-country_United-States                   0.005427   \n",
-              "native-country_Vietnam                        -0.000755   \n",
-              "native-country_Yugoslavia                     -0.000397   \n",
-              "\n",
-              "                                occupation_Craft-repair  \\\n",
-              "age                                            0.012293   \n",
-              "educational-num                               -0.155179   \n",
-              "gender                                         0.227955   \n",
-              "capital-gain                                  -0.020525   \n",
-              "hours-per-week                                 0.043266   \n",
-              "...                                                 ...   \n",
-              "native-country_Thailand                       -0.004782   \n",
-              "native-country_Trinadad&Tobago                -0.003965   \n",
-              "native-country_United-States                   0.001708   \n",
-              "native-country_Vietnam                         0.002980   \n",
-              "native-country_Yugoslavia                     -0.000173   \n",
-              "\n",
-              "                                occupation_Exec-managerial  ...  \\\n",
-              "age                                               0.108037  ...   \n",
-              "educational-num                                   0.201621  ...   \n",
-              "gender                                            0.030577  ...   \n",
-              "capital-gain                                      0.058018  ...   \n",
-              "hours-per-week                                    0.131222  ...   \n",
-              "...                                                    ...  ...   \n",
-              "native-country_Thailand                           0.005573  ...   \n",
-              "native-country_Trinadad&Tobago                   -0.006650  ...   \n",
-              "native-country_United-States                      0.032668  ...   \n",
-              "native-country_Vietnam                           -0.010657  ...   \n",
-              "native-country_Yugoslavia                         0.014349  ...   \n",
-              "\n",
-              "                                native-country_Portugal  \\\n",
-              "age                                            0.005589   \n",
-              "educational-num                               -0.047193   \n",
-              "gender                                         0.007848   \n",
-              "capital-gain                                  -0.004358   \n",
-              "hours-per-week                                 0.004311   \n",
-              "...                                                 ...   \n",
-              "native-country_Thailand                       -0.000940   \n",
-              "native-country_Trinadad&Tobago                -0.000890   \n",
-              "native-country_United-States                  -0.120282   \n",
-              "native-country_Vietnam                        -0.001591   \n",
-              "native-country_Yugoslavia                     -0.000837   \n",
-              "\n",
-              "                                native-country_Puerto-Rico  \\\n",
-              "age                                               0.004710   \n",
-              "educational-num                                  -0.043694   \n",
-              "gender                                           -0.009992   \n",
-              "capital-gain                                     -0.006283   \n",
-              "hours-per-week                                   -0.010741   \n",
-              "...                                                    ...   \n",
-              "native-country_Thailand                          -0.001581   \n",
-              "native-country_Trinadad&Tobago                   -0.001496   \n",
-              "native-country_United-States                     -0.202334   \n",
-              "native-country_Vietnam                           -0.002675   \n",
-              "native-country_Yugoslavia                        -0.001407   \n",
-              "\n",
-              "                                native-country_Scotland  native-country_South  \\\n",
-              "age                                            0.011855              0.000207   \n",
-              "educational-num                                0.001078              0.016338   \n",
-              "gender                                        -0.001125             -0.006183   \n",
-              "capital-gain                                  -0.003090             -0.001277   \n",
-              "hours-per-week                                 0.001415              0.015410   \n",
-              "...                                                 ...                   ...   \n",
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-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-c0a84dfe-8e40-4a3b-b278-2d3a2e43a7e1');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-fefe63d7-48a0-4dc9-8cb7-68cf35cecd4c\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fefe63d7-48a0-4dc9-8cb7-68cf35cecd4c')\"\n",
-              "            title=\"Suggest charts.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-fefe63d7-48a0-4dc9-8cb7-68cf35cecd4c button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 35
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Menghapus kolom yang tidak memiliki korelasi tinggi**\n",
-        "\n",
-        "*   Tahap ini ditujukan untuk menghapus beberapa kolom yang korelasi tidak terlalu signifikan\n",
-        "*   Korelasinya berdasarkan pada tabel Income\n",
-        "*   Tahapannya melalui sortir dari data yang berkorelasi tinggi hingga korelasi terendah\n",
-        "*   Apabila kolom yang memiliki korelasi rendah, maka akan dihapus melalui serangkaian fungsi yang diterapkan.\n",
-        "\n",
-        "\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "RXBZwMxzM8TA"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "korelasi = df.corr()['income'].abs()\n",
-        "korelasi_tersortir = korelasi.sort_values()\n",
-        "num_cols_to_drop = int (0.8 * len(df.columns))\n",
-        "cols_to_drop = korelasi_tersortir.iloc[:num_cols_to_drop].index\n",
-        "df_dropped = df.drop(cols_to_drop, axis = 1)"
-      ],
-      "metadata": {
-        "id": "jwLAKi8sVlc5"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df_dropped"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 478
-        },
-        "id": "qCH6brmxWPL-",
-        "outputId": "ca589219-079d-4397-ce97-959528584813"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "       age  educational-num  gender  capital-gain  hours-per-week  income  \\\n",
-              "0       25                7       1             0              40       0   \n",
-              "1       38                9       1             0              50       0   \n",
-              "2       28               12       1             0              40       1   \n",
-              "3       44               10       1          7688              40       1   \n",
-              "5       34                6       1             0              30       0   \n",
-              "...    ...              ...     ...           ...             ...     ...   \n",
-              "48837   27               12       0             0              38       0   \n",
-              "48838   40                9       1             0              40       1   \n",
-              "48839   58                9       0             0              40       0   \n",
-              "48840   22                9       1             0              20       0   \n",
-              "48841   52                9       0         15024              40       1   \n",
-              "\n",
-              "       occupation_Exec-managerial  occupation_Other-service  \\\n",
-              "0                               0                         0   \n",
-              "1                               0                         0   \n",
-              "2                               0                         0   \n",
-              "3                               0                         0   \n",
-              "5                               0                         1   \n",
-              "...                           ...                       ...   \n",
-              "48837                           0                         0   \n",
-              "48838                           0                         0   \n",
-              "48839                           0                         0   \n",
-              "48840                           0                         0   \n",
-              "48841                           1                         0   \n",
-              "\n",
-              "       occupation_Prof-specialty  workclass_Self-emp-inc  \\\n",
-              "0                              0                       0   \n",
-              "1                              0                       0   \n",
-              "2                              0                       0   \n",
-              "3                              0                       0   \n",
-              "5                              0                       0   \n",
-              "...                          ...                     ...   \n",
-              "48837                          0                       0   \n",
-              "48838                          0                       0   \n",
-              "48839                          0                       0   \n",
-              "48840                          0                       0   \n",
-              "48841                          0                       1   \n",
-              "\n",
-              "       marital-status_Divorced  marital-status_Married-civ-spouse  \\\n",
-              "0                            0                                  0   \n",
-              "1                            0                                  1   \n",
-              "2                            0                                  1   \n",
-              "3                            0                                  1   \n",
-              "5                            0                                  0   \n",
-              "...                        ...                                ...   \n",
-              "48837                        0                                  1   \n",
-              "48838                        0                                  1   \n",
-              "48839                        0                                  0   \n",
-              "48840                        0                                  0   \n",
-              "48841                        0                                  1   \n",
-              "\n",
-              "       marital-status_Never-married  relationship_Husband  \\\n",
-              "0                                 1                     0   \n",
-              "1                                 0                     1   \n",
-              "2                                 0                     1   \n",
-              "3                                 0                     1   \n",
-              "5                                 1                     0   \n",
-              "...                             ...                   ...   \n",
-              "48837                             0                     0   \n",
-              "48838                             0                     1   \n",
-              "48839                             0                     0   \n",
-              "48840                             1                     0   \n",
-              "48841                             0                     0   \n",
-              "\n",
-              "       relationship_Not-in-family  relationship_Own-child  \\\n",
-              "0                               0                       1   \n",
-              "1                               0                       0   \n",
-              "2                               0                       0   \n",
-              "3                               0                       0   \n",
-              "5                               1                       0   \n",
-              "...                           ...                     ...   \n",
-              "48837                           0                       0   \n",
-              "48838                           0                       0   \n",
-              "48839                           0                       0   \n",
-              "48840                           0                       1   \n",
-              "48841                           0                       0   \n",
-              "\n",
-              "       relationship_Unmarried  relationship_Wife  \n",
-              "0                           0                  0  \n",
-              "1                           0                  0  \n",
-              "2                           0                  0  \n",
-              "3                           0                  0  \n",
-              "5                           0                  0  \n",
-              "...                       ...                ...  \n",
-              "48837                       0                  1  \n",
-              "48838                       0                  0  \n",
-              "48839                       1                  0  \n",
-              "48840                       0                  0  \n",
-              "48841                       0                  1  \n",
-              "\n",
-              "[45175 rows x 18 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-241ef329-6101-489c-bff8-53c38797892e\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>age</th>\n",
-              "      <th>educational-num</th>\n",
-              "      <th>gender</th>\n",
-              "      <th>capital-gain</th>\n",
-              "      <th>hours-per-week</th>\n",
-              "      <th>income</th>\n",
-              "      <th>occupation_Exec-managerial</th>\n",
-              "      <th>occupation_Other-service</th>\n",
-              "      <th>occupation_Prof-specialty</th>\n",
-              "      <th>workclass_Self-emp-inc</th>\n",
-              "      <th>marital-status_Divorced</th>\n",
-              "      <th>marital-status_Married-civ-spouse</th>\n",
-              "      <th>marital-status_Never-married</th>\n",
-              "      <th>relationship_Husband</th>\n",
-              "      <th>relationship_Not-in-family</th>\n",
-              "      <th>relationship_Own-child</th>\n",
-              "      <th>relationship_Unmarried</th>\n",
-              "      <th>relationship_Wife</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
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-              "      <th>0</th>\n",
-              "      <td>25</td>\n",
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-              "      <th>1</th>\n",
-              "      <td>38</td>\n",
-              "      <td>9</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>50</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
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-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
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-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>28</td>\n",
-              "      <td>12</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>40</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
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-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>44</td>\n",
-              "      <td>10</td>\n",
-              "      <td>1</td>\n",
-              "      <td>7688</td>\n",
-              "      <td>40</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
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-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>34</td>\n",
-              "      <td>6</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>30</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
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-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
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-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
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-              "      <th>48837</th>\n",
-              "      <td>27</td>\n",
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-              "      <th>48838</th>\n",
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-              "      <th>48839</th>\n",
-              "      <td>58</td>\n",
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-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>48840</th>\n",
-              "      <td>22</td>\n",
-              "      <td>9</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>20</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>48841</th>\n",
-              "      <td>52</td>\n",
-              "      <td>9</td>\n",
-              "      <td>0</td>\n",
-              "      <td>15024</td>\n",
-              "      <td>40</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>45175 rows × 18 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-241ef329-6101-489c-bff8-53c38797892e')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-241ef329-6101-489c-bff8-53c38797892e button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-241ef329-6101-489c-bff8-53c38797892e');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-777bffaf-e1f4-40aa-9abd-9b855eba93d6\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-777bffaf-e1f4-40aa-9abd-9b855eba93d6')\"\n",
-              "            title=\"Suggest charts.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-777bffaf-e1f4-40aa-9abd-9b855eba93d6 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 56
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**4.3 Multivariate Analysis**\n",
-        "\n",
-        "\n",
-        "*   Analisis multivariate ini digunakan untuk melihat korelasi banyak variabel\n",
-        "*   Salah satu tekniknya adalah dengan memanggil fungsi corr untuk melihat korelasi antar satu variabel dengan variabel lainnya\n",
-        "* Tanda dari tingginya korelasi di bawah adlaah angka yang mendekati 1 atau warna gelap merah.\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "FxXG6X8y_-vn"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "plt.figure(figsize=(18,12))\n",
-        "sns.heatmap(df_dropped.corr(), annot=True, cmap='coolwarm')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 609
-        },
-        "id": "uWmmdegTxCZj",
-        "outputId": "fff8d46d-3e50-41ee-d1f1-5c1bdba59a34"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<Axes: >"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 57
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1800x1200 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Interpretasi Heatmap Correlation**\n",
-        "\n",
-        "\n",
-        "\n",
-        "*   Tanda yang cenderung mendekati warna merah memiliki korelasi tinggi satu sama lain\n",
-        "*   Tabel Income memiliki korelasi positif terbanyak dalam seluruh kolom, sehingga dapat dikatakan income sebagai variabel dependen dan lainnya sebagai independen\n",
-        "\n",
-        "\n",
-        "<br>\n",
-        "<br>\n",
-        "<br>\n",
-        "\n",
-        "**5.Modeling Data**\n",
-        "\n",
-        " **Tahapan Modeling Data Menggunakan Algoritma Random Forest Classifier**<br>\n",
-        "* Tahap pertama melakukan training data dan testing data menggunakan modul\n",
-        "train_test_split\n",
-        "* Import algoritma RFC (RandomForestClassifier)\n",
-        "\n",
-        "\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "ae481Nd-Nv_q"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from sklearn.ensemble import RandomForestClassifier\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "\n",
-        "train_df, test_df = train_test_split(df, test_size= 0.2)\n"
-      ],
-      "metadata": {
-        "id": "7zFphYunxO36"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "test_df"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 478
-        },
-        "id": "DYttVqIn2iTE",
-        "outputId": "1fa0b818-a005-4b8c-d277-c005ee26f906"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "       age  educational-num  gender  capital-gain  hours-per-week  income  \\\n",
-              "25951   19                9       0             0              20       0   \n",
-              "13388   26               13       0             0              75       1   \n",
-              "19761   59               11       1          7298              40       1   \n",
-              "22330   44                9       1             0              40       0   \n",
-              "41850   59                3       1          2580              15       0   \n",
-              "...    ...              ...     ...           ...             ...     ...   \n",
-              "22844   49                9       1             0              52       1   \n",
-              "17541   30                9       1             0              40       0   \n",
-              "22260   37                6       1             0              50       1   \n",
-              "1961    26               13       1             0              40       0   \n",
-              "43446   45               14       1             0              40       1   \n",
-              "\n",
-              "       occupation_Adm-clerical  occupation_Armed-Forces  \\\n",
-              "25951                        1                        0   \n",
-              "13388                        0                        0   \n",
-              "19761                        0                        0   \n",
-              "22330                        0                        0   \n",
-              "41850                        0                        0   \n",
-              "...                        ...                      ...   \n",
-              "22844                        0                        0   \n",
-              "17541                        0                        0   \n",
-              "22260                        0                        0   \n",
-              "1961                         0                        0   \n",
-              "43446                        0                        0   \n",
-              "\n",
-              "       occupation_Craft-repair  occupation_Exec-managerial  ...  \\\n",
-              "25951                        0                           0  ...   \n",
-              "13388                        0                           0  ...   \n",
-              "19761                        0                           0  ...   \n",
-              "22330                        0                           0  ...   \n",
-              "41850                        0                           0  ...   \n",
-              "...                        ...                         ...  ...   \n",
-              "22844                        0                           0  ...   \n",
-              "17541                        1                           0  ...   \n",
-              "22260                        0                           0  ...   \n",
-              "1961                         0                           0  ...   \n",
-              "43446                        0                           1  ...   \n",
-              "\n",
-              "       native-country_Portugal  native-country_Puerto-Rico  \\\n",
-              "25951                        0                           0   \n",
-              "13388                        0                           0   \n",
-              "19761                        0                           0   \n",
-              "22330                        0                           0   \n",
-              "41850                        0                           0   \n",
-              "...                        ...                         ...   \n",
-              "22844                        0                           0   \n",
-              "17541                        0                           0   \n",
-              "22260                        0                           0   \n",
-              "1961                         0                           0   \n",
-              "43446                        0                           0   \n",
-              "\n",
-              "       native-country_Scotland  native-country_South  native-country_Taiwan  \\\n",
-              "25951                        0                     0                      0   \n",
-              "13388                        0                     0                      0   \n",
-              "19761                        0                     0                      0   \n",
-              "22330                        0                     0                      0   \n",
-              "41850                        0                     0                      0   \n",
-              "...                        ...                   ...                    ...   \n",
-              "22844                        0                     0                      0   \n",
-              "17541                        0                     0                      0   \n",
-              "22260                        0                     0                      0   \n",
-              "1961                         0                     0                      0   \n",
-              "43446                        0                     0                      0   \n",
-              "\n",
-              "       native-country_Thailand  native-country_Trinadad&Tobago  \\\n",
-              "25951                        0                               0   \n",
-              "13388                        0                               0   \n",
-              "19761                        0                               0   \n",
-              "22330                        0                               0   \n",
-              "41850                        0                               0   \n",
-              "...                        ...                             ...   \n",
-              "22844                        0                               0   \n",
-              "17541                        0                               0   \n",
-              "22260                        0                               0   \n",
-              "1961                         0                               0   \n",
-              "43446                        0                               0   \n",
-              "\n",
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-              "<p>9035 rows × 86 columns</p>\n",
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-              "            title=\"Convert this dataframe to an interactive table.\"\n",
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-              "                                                    [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",
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-              "  .colab-df-quickchart {\n",
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-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
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-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
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-              "    background-color: var(--bg-color);\n",
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-              "    display: none;\n",
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-              "      border-color: transparent;\n",
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-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-c5435793-af5f-42a4-8447-de9bdfd54bc3 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 59
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Membagi data menjadi train data dan test data\n",
-        "x_train = train_df.drop('income', axis=1)\n",
-        "y_train = train_df.income\n",
-        "\n",
-        "x_test = test_df.drop('income', axis=1)\n",
-        "y_test = test_df.income"
-      ],
-      "metadata": {
-        "id": "ypwmI0vK2sjY"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Fitting data yang telah dilatih dengan model RFC\n",
-        "forest = RandomForestClassifier()\n",
-        "forest.fit(x_train, y_train)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "7VxvrHzZ3VRA",
-        "outputId": "10caff35-6647-4c23-978f-3345421e61f4"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "RandomForestClassifier()"
-            ],
-            "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>RandomForestClassifier()</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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 61
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Uji presentase score dari model data\n",
-        "forest.score(x_test, y_test)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "hoWRz2Lq3h_1",
-        "outputId": "f7b10eaa-0a5a-4ab2-c972-ccb83b53b19b"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.8296624239070283"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 62
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Hasil Akurasi Model**\n",
-        "\n",
-        "\n",
-        "*   Akurasi Model hanya mencapai 82,9%, maka dari itu diperlukan pengoptimalisasi lagi untuk meningkatkan nilai akurasi\n",
-        "\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "zom5GgV3PwS1"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "forest.feature_importances_ #Feature Importances untuk melihat peran hubungan tinggi pada target variabel"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "EKEoIogA4LYy",
-        "outputId": "276a6160-9c23-49f4-d71a-9387807b9640"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([2.47141187e-01, 1.32126621e-01, 1.40690945e-02, 1.11415874e-01,\n",
-              "       1.21212684e-01, 5.20805338e-03, 3.45469385e-05, 6.09580066e-03,\n",
-              "       2.06547941e-02, 5.13450970e-03, 3.52202008e-03, 4.15931233e-03,\n",
-              "       9.64777618e-03, 2.02032140e-04, 1.87207615e-02, 3.00487746e-03,\n",
-              "       6.57207034e-03, 4.60240197e-03, 4.48591614e-03, 5.12389057e-03,\n",
-              "       5.58438300e-03, 9.72856425e-03, 6.95813245e-03, 8.04420304e-03,\n",
-              "       4.38705113e-03, 2.26303619e-04, 7.30108136e-03, 3.98730687e-04,\n",
-              "       7.49182595e-02, 9.78729618e-04, 3.12489389e-02, 2.10295715e-03,\n",
-              "       2.10702158e-03, 4.13240722e-02, 1.30370735e-02, 1.87182750e-03,\n",
-              "       8.22379829e-03, 6.67344851e-03, 9.48052344e-03, 1.37918483e-03,\n",
-              "       3.09446796e-03, 5.03342064e-03, 9.26842351e-04, 6.26205720e-03,\n",
-              "       3.37262913e-04, 1.44973497e-03, 5.72532617e-04, 3.20255699e-04,\n",
-              "       8.02006850e-04, 2.28392039e-04, 1.75103538e-04, 2.80021368e-04,\n",
-              "       1.07987672e-03, 4.24175730e-04, 1.10418631e-03, 4.65545157e-04,\n",
-              "       1.94117396e-04, 2.23386645e-04, 0.00000000e+00, 4.07720891e-05,\n",
-              "       1.22606915e-04, 2.44712090e-04, 1.12573322e-03, 6.17509369e-04,\n",
-              "       4.90965950e-04, 8.11998703e-04, 4.94655629e-04, 6.15570328e-04,\n",
-              "       1.43111797e-04, 2.61288688e-03, 1.04232131e-04, 7.78334547e-05,\n",
-              "       2.22986561e-04, 1.16078260e-03, 4.62264461e-04, 4.68298539e-04,\n",
-              "       4.30077012e-04, 1.21481042e-04, 5.26531787e-04, 2.43872272e-04,\n",
-              "       9.68835810e-05, 1.56851804e-04, 5.86072059e-03, 3.51926352e-04,\n",
-              "       3.12840004e-04])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 49
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "forest.feature_names_in_ #Feature dengan nama kolom"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "DoMtLTl8-I0W",
-        "outputId": "a56d09fa-e424-40c1-ddfc-a1adaca243b2"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array(['age', 'educational-num', 'gender', 'capital-gain',\n",
-              "       'hours-per-week', 'occupation_Adm-clerical',\n",
-              "       'occupation_Armed-Forces', 'occupation_Craft-repair',\n",
-              "       'occupation_Exec-managerial', 'occupation_Farming-fishing',\n",
-              "       'occupation_Handlers-cleaners', 'occupation_Machine-op-inspct',\n",
-              "       'occupation_Other-service', 'occupation_Priv-house-serv',\n",
-              "       'occupation_Prof-specialty', 'occupation_Protective-serv',\n",
-              "       'occupation_Sales', 'occupation_Tech-support',\n",
-              "       'occupation_Transport-moving', 'workclass_Federal-gov',\n",
-              "       'workclass_Local-gov', 'workclass_Private',\n",
-              "       'workclass_Self-emp-inc', 'workclass_Self-emp-not-inc',\n",
-              "       'workclass_State-gov', 'workclass_Without-pay',\n",
-              "       'marital-status_Divorced', 'marital-status_Married-AF-spouse',\n",
-              "       'marital-status_Married-civ-spouse',\n",
-              "       'marital-status_Married-spouse-absent',\n",
-              "       'marital-status_Never-married', 'marital-status_Separated',\n",
-              "       'marital-status_Widowed', 'relationship_Husband',\n",
-              "       'relationship_Not-in-family', 'relationship_Other-relative',\n",
-              "       'relationship_Own-child', 'relationship_Unmarried',\n",
-              "       'relationship_Wife', 'race_Amer-Indian-Eskimo',\n",
-              "       'race_Asian-Pac-Islander', 'race_Black', 'race_Other',\n",
-              "       'race_White', 'native-country_Cambodia', 'native-country_Canada',\n",
-              "       'native-country_China', 'native-country_Columbia',\n",
-              "       'native-country_Cuba', 'native-country_Dominican-Republic',\n",
-              "       'native-country_Ecuador', 'native-country_El-Salvador',\n",
-              "       'native-country_England', 'native-country_France',\n",
-              "       'native-country_Germany', 'native-country_Greece',\n",
-              "       'native-country_Guatemala', 'native-country_Haiti',\n",
-              "       'native-country_Holand-Netherlands', 'native-country_Honduras',\n",
-              "       'native-country_Hong', 'native-country_Hungary',\n",
-              "       'native-country_India', 'native-country_Iran',\n",
-              "       'native-country_Ireland', 'native-country_Italy',\n",
-              "       'native-country_Jamaica', 'native-country_Japan',\n",
-              "       'native-country_Laos', 'native-country_Mexico',\n",
-              "       'native-country_Nicaragua',\n",
-              "       'native-country_Outlying-US(Guam-USVI-etc)', 'native-country_Peru',\n",
-              "       'native-country_Philippines', 'native-country_Poland',\n",
-              "       'native-country_Portugal', 'native-country_Puerto-Rico',\n",
-              "       'native-country_Scotland', 'native-country_South',\n",
-              "       'native-country_Taiwan', 'native-country_Thailand',\n",
-              "       'native-country_Trinadad&Tobago', 'native-country_United-States',\n",
-              "       'native-country_Vietnam', 'native-country_Yugoslavia'],\n",
-              "      dtype=object)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 50
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Prediksi peran feature pada gaji\n",
-        "importances = dict(zip(forest.feature_names_in_,forest.feature_importances_))\n",
-        "imporances = {key: value for key, value in sorted(importances.items(), key=lambda x: x[1], reverse=True)}"
-      ],
-      "metadata": {
-        "id": "CDdK8unv-Nvi"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "importances"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "NGot_Pfq-9nz",
-        "outputId": "742de973-8fa1-42f8-b56e-b1c17a455984"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "{'age': 0.2471411869548978,\n",
-              " 'educational-num': 0.1321266209394863,\n",
-              " 'gender': 0.014069094502772637,\n",
-              " 'capital-gain': 0.1114158743025889,\n",
-              " 'hours-per-week': 0.12121268398693752,\n",
-              " 'occupation_Adm-clerical': 0.005208053377454361,\n",
-              " 'occupation_Armed-Forces': 3.454693852215542e-05,\n",
-              " 'occupation_Craft-repair': 0.006095800658456923,\n",
-              " 'occupation_Exec-managerial': 0.020654794061114243,\n",
-              " 'occupation_Farming-fishing': 0.005134509700466332,\n",
-              " 'occupation_Handlers-cleaners': 0.003522020081879046,\n",
-              " 'occupation_Machine-op-inspct': 0.004159312328629127,\n",
-              " 'occupation_Other-service': 0.009647776182429284,\n",
-              " 'occupation_Priv-house-serv': 0.00020203213977277936,\n",
-              " 'occupation_Prof-specialty': 0.01872076148765566,\n",
-              " 'occupation_Protective-serv': 0.0030048774637478092,\n",
-              " 'occupation_Sales': 0.006572070337000821,\n",
-              " 'occupation_Tech-support': 0.00460240196957732,\n",
-              " 'occupation_Transport-moving': 0.00448591614183137,\n",
-              " 'workclass_Federal-gov': 0.005123890567262365,\n",
-              " 'workclass_Local-gov': 0.0055843830003597895,\n",
-              " 'workclass_Private': 0.009728564248627532,\n",
-              " 'workclass_Self-emp-inc': 0.006958132451311564,\n",
-              " 'workclass_Self-emp-not-inc': 0.008044203042415985,\n",
-              " 'workclass_State-gov': 0.004387051133618794,\n",
-              " 'workclass_Without-pay': 0.0002263036192760843,\n",
-              " 'marital-status_Divorced': 0.007301081358426683,\n",
-              " 'marital-status_Married-AF-spouse': 0.0003987306874112746,\n",
-              " 'marital-status_Married-civ-spouse': 0.07491825948616024,\n",
-              " 'marital-status_Married-spouse-absent': 0.0009787296179846851,\n",
-              " 'marital-status_Never-married': 0.031248938900411652,\n",
-              " 'marital-status_Separated': 0.002102957147391818,\n",
-              " 'marital-status_Widowed': 0.002107021575366537,\n",
-              " 'relationship_Husband': 0.041324072232444664,\n",
-              " 'relationship_Not-in-family': 0.013037073543924227,\n",
-              " 'relationship_Other-relative': 0.001871827497910145,\n",
-              " 'relationship_Own-child': 0.008223798290044798,\n",
-              " 'relationship_Unmarried': 0.006673448508775857,\n",
-              " 'relationship_Wife': 0.009480523439201353,\n",
-              " 'race_Amer-Indian-Eskimo': 0.0013791848288267992,\n",
-              " 'race_Asian-Pac-Islander': 0.003094467956030067,\n",
-              " 'race_Black': 0.005033420635688592,\n",
-              " 'race_Other': 0.0009268423505952479,\n",
-              " 'race_White': 0.006262057198443522,\n",
-              " 'native-country_Cambodia': 0.0003372629132852367,\n",
-              " 'native-country_Canada': 0.0014497349696312603,\n",
-              " 'native-country_China': 0.0005725326173555739,\n",
-              " 'native-country_Columbia': 0.0003202556989830795,\n",
-              " 'native-country_Cuba': 0.0008020068499670093,\n",
-              " 'native-country_Dominican-Republic': 0.00022839203912517693,\n",
-              " 'native-country_Ecuador': 0.00017510353792390395,\n",
-              " 'native-country_El-Salvador': 0.00028002136832100725,\n",
-              " 'native-country_England': 0.0010798767225512478,\n",
-              " 'native-country_France': 0.0004241757304603595,\n",
-              " 'native-country_Germany': 0.0011041863113242368,\n",
-              " 'native-country_Greece': 0.00046554515699228223,\n",
-              " 'native-country_Guatemala': 0.00019411739647491285,\n",
-              " 'native-country_Haiti': 0.0002233866446887041,\n",
-              " 'native-country_Holand-Netherlands': 0.0,\n",
-              " 'native-country_Honduras': 4.0772089065578265e-05,\n",
-              " 'native-country_Hong': 0.00012260691538506613,\n",
-              " 'native-country_Hungary': 0.0002447120897466533,\n",
-              " 'native-country_India': 0.0011257332235042352,\n",
-              " 'native-country_Iran': 0.0006175093691524696,\n",
-              " 'native-country_Ireland': 0.0004909659495798987,\n",
-              " 'native-country_Italy': 0.0008119987032688317,\n",
-              " 'native-country_Jamaica': 0.000494655629490808,\n",
-              " 'native-country_Japan': 0.000615570328222533,\n",
-              " 'native-country_Laos': 0.0001431117974824957,\n",
-              " 'native-country_Mexico': 0.0026128868841877374,\n",
-              " 'native-country_Nicaragua': 0.00010423213126079944,\n",
-              " 'native-country_Outlying-US(Guam-USVI-etc)': 7.783345472125392e-05,\n",
-              " 'native-country_Peru': 0.0002229865605318169,\n",
-              " 'native-country_Philippines': 0.0011607826034669296,\n",
-              " 'native-country_Poland': 0.0004622644606587957,\n",
-              " 'native-country_Portugal': 0.0004682985386997742,\n",
-              " 'native-country_Puerto-Rico': 0.00043007701229378476,\n",
-              " 'native-country_Scotland': 0.00012148104213521055,\n",
-              " 'native-country_South': 0.0005265317874096462,\n",
-              " 'native-country_Taiwan': 0.00024387227186922262,\n",
-              " 'native-country_Thailand': 9.688358104610872e-05,\n",
-              " 'native-country_Trinadad&Tobago': 0.00015685180412999608,\n",
-              " 'native-country_United-States': 0.0058607205856922116,\n",
-              " 'native-country_Vietnam': 0.0003519263524320661,\n",
-              " 'native-country_Yugoslavia': 0.00031284000435139055}"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 53
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Peningkatan Akurasi melalui Hyperparameters Tunning**"
-      ],
-      "metadata": {
-        "id": "ZEQsi5UN-i2t"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from sklearn.model_selection import GridSearchCV\n",
-        "#Hyperparameter tunning melalui GridSearchSV\n",
-        "param_grid = {\n",
-        "    'n_estimators': [50, 100, 250],\n",
-        "    'max_depth': [5,10, 30, 50],\n",
-        "    'min_samples_split':[2, 6],\n",
-        "    'max_features': ['sqrt', 'log2']\n",
-        "}\n",
-        "grid_search = GridSearchCV(estimator=RandomForestClassifier(),\n",
-        "                           param_grid=param_grid, verbose=10)\n"
-      ],
-      "metadata": {
-        "id": "FlpN4W6t_4P2"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "grid_search.fit(x_train, y_train) #Fitting hyperparamter pada data"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "xDzbicOoAwmO",
-        "outputId": "5991d7ca-2f00-4379-f218-d43c3d88d281"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n",
-            "[CV 1/5; 1/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 1/5; 1/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.832 total time=   0.6s\n",
-            "[CV 2/5; 1/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 2/5; 1/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.830 total time=   0.6s\n",
-            "[CV 3/5; 1/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 3/5; 1/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.837 total time=   0.6s\n",
-            "[CV 4/5; 1/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 4/5; 1/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.833 total time=   0.6s\n",
-            "[CV 5/5; 1/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 5/5; 1/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.832 total time=   0.6s\n",
-            "[CV 1/5; 2/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 1/5; 2/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.835 total time=   1.2s\n",
-            "[CV 2/5; 2/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 2/5; 2/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.839 total time=   1.2s\n",
-            "[CV 3/5; 2/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 3/5; 2/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.836 total time=   1.2s\n",
-            "[CV 4/5; 2/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 4/5; 2/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.834 total time=   1.2s\n",
-            "[CV 5/5; 2/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 5/5; 2/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.836 total time=   1.5s\n",
-            "[CV 1/5; 3/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 1/5; 3/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.836 total time=   4.1s\n",
-            "[CV 2/5; 3/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 2/5; 3/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.837 total time=   2.9s\n",
-            "[CV 3/5; 3/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 3/5; 3/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.833 total time=   2.9s\n",
-            "[CV 4/5; 3/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 4/5; 3/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.829 total time=   2.9s\n",
-            "[CV 5/5; 3/48] START max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 5/5; 3/48] END max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.836 total time=   4.3s\n",
-            "[CV 1/5; 4/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 1/5; 4/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.829 total time=   0.8s\n",
-            "[CV 2/5; 4/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 2/5; 4/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.833 total time=   0.6s\n",
-            "[CV 3/5; 4/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 3/5; 4/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.832 total time=   0.6s\n",
-            "[CV 4/5; 4/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 4/5; 4/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.828 total time=   0.6s\n",
-            "[CV 5/5; 4/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 5/5; 4/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.833 total time=   0.6s\n",
-            "[CV 1/5; 5/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 1/5; 5/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.834 total time=   1.2s\n",
-            "[CV 2/5; 5/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 2/5; 5/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.836 total time=   1.2s\n",
-            "[CV 3/5; 5/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 3/5; 5/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.833 total time=   1.2s\n",
-            "[CV 4/5; 5/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 4/5; 5/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.834 total time=   1.2s\n",
-            "[CV 5/5; 5/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 5/5; 5/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.835 total time=   1.2s\n",
-            "[CV 1/5; 6/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 1/5; 6/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.833 total time=   3.9s\n",
-            "[CV 2/5; 6/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 2/5; 6/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.835 total time=   3.4s\n",
-            "[CV 3/5; 6/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 3/5; 6/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.834 total time=   2.9s\n",
-            "[CV 4/5; 6/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 4/5; 6/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.830 total time=   2.9s\n",
-            "[CV 5/5; 6/48] START max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 5/5; 6/48] END max_depth=5, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.834 total time=   3.3s\n",
-            "[CV 1/5; 7/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 1/5; 7/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.813 total time=   0.8s\n",
-            "[CV 2/5; 7/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 2/5; 7/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.817 total time=   0.8s\n",
-            "[CV 3/5; 7/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 3/5; 7/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.810 total time=   0.8s\n",
-            "[CV 4/5; 7/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 4/5; 7/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.819 total time=   0.7s\n",
-            "[CV 5/5; 7/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 5/5; 7/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.816 total time=   0.5s\n",
-            "[CV 1/5; 8/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 1/5; 8/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.819 total time=   1.0s\n",
-            "[CV 2/5; 8/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 2/5; 8/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.818 total time=   1.0s\n",
-            "[CV 3/5; 8/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 3/5; 8/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.810 total time=   1.0s\n",
-            "[CV 4/5; 8/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 4/5; 8/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.814 total time=   1.0s\n",
-            "[CV 5/5; 8/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 5/5; 8/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.822 total time=   1.0s\n",
-            "[CV 1/5; 9/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 1/5; 9/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.814 total time=   2.4s\n",
-            "[CV 2/5; 9/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 2/5; 9/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.817 total time=   2.6s\n",
-            "[CV 3/5; 9/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 3/5; 9/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.811 total time=   3.7s\n",
-            "[CV 4/5; 9/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 4/5; 9/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.810 total time=   2.4s\n",
-            "[CV 5/5; 9/48] START max_depth=5, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 5/5; 9/48] END max_depth=5, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.815 total time=   2.4s\n",
-            "[CV 1/5; 10/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 1/5; 10/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.805 total time=   0.5s\n",
-            "[CV 2/5; 10/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 2/5; 10/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.811 total time=   0.5s\n",
-            "[CV 3/5; 10/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 3/5; 10/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.812 total time=   0.5s\n",
-            "[CV 4/5; 10/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 4/5; 10/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.820 total time=   0.5s\n",
-            "[CV 5/5; 10/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 5/5; 10/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.814 total time=   0.5s\n",
-            "[CV 1/5; 11/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 1/5; 11/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.813 total time=   1.0s\n",
-            "[CV 2/5; 11/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 2/5; 11/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.817 total time=   1.0s\n",
-            "[CV 3/5; 11/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 3/5; 11/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.812 total time=   1.2s\n",
-            "[CV 4/5; 11/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 4/5; 11/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.810 total time=   1.5s\n",
-            "[CV 5/5; 11/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 5/5; 11/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.823 total time=   1.6s\n",
-            "[CV 1/5; 12/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 1/5; 12/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.814 total time=   2.5s\n",
-            "[CV 2/5; 12/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 2/5; 12/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.819 total time=   2.4s\n",
-            "[CV 3/5; 12/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 3/5; 12/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.813 total time=   2.4s\n",
-            "[CV 4/5; 12/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 4/5; 12/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.811 total time=   2.4s\n",
-            "[CV 5/5; 12/48] START max_depth=5, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 5/5; 12/48] END max_depth=5, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.814 total time=   3.5s\n",
-            "[CV 1/5; 13/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 1/5; 13/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.853 total time=   1.4s\n",
-            "[CV 2/5; 13/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 2/5; 13/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.854 total time=   1.0s\n",
-            "[CV 3/5; 13/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 3/5; 13/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.850 total time=   1.0s\n",
-            "[CV 4/5; 13/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 4/5; 13/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.847 total time=   0.9s\n",
-            "[CV 5/5; 13/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 5/5; 13/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.852 total time=   1.0s\n",
-            "[CV 1/5; 14/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 1/5; 14/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.855 total time=   1.9s\n",
-            "[CV 2/5; 14/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 2/5; 14/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.855 total time=   1.9s\n",
-            "[CV 3/5; 14/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 3/5; 14/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.848 total time=   1.9s\n",
-            "[CV 4/5; 14/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 4/5; 14/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.847 total time=   2.9s\n",
-            "[CV 5/5; 14/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 5/5; 14/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.854 total time=   2.3s\n",
-            "[CV 1/5; 15/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 1/5; 15/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.854 total time=   4.6s\n",
-            "[CV 2/5; 15/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 2/5; 15/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.856 total time=   5.1s\n",
-            "[CV 3/5; 15/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 3/5; 15/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.849 total time=   5.9s\n",
-            "[CV 4/5; 15/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 4/5; 15/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.848 total time=   4.6s\n",
-            "[CV 5/5; 15/48] START max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 5/5; 15/48] END max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.853 total time=   5.8s\n",
-            "[CV 1/5; 16/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 1/5; 16/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.854 total time=   1.4s\n",
-            "[CV 2/5; 16/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 2/5; 16/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.856 total time=   1.0s\n",
-            "[CV 3/5; 16/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 3/5; 16/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.850 total time=   1.0s\n",
-            "[CV 4/5; 16/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 4/5; 16/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.848 total time=   1.0s\n",
-            "[CV 5/5; 16/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 5/5; 16/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.855 total time=   1.0s\n",
-            "[CV 1/5; 17/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 1/5; 17/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.854 total time=   1.9s\n",
-            "[CV 2/5; 17/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 2/5; 17/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.855 total time=   1.9s\n",
-            "[CV 3/5; 17/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 3/5; 17/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.850 total time=   1.9s\n",
-            "[CV 4/5; 17/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 4/5; 17/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.848 total time=   2.9s\n",
-            "[CV 5/5; 17/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 5/5; 17/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.853 total time=   2.4s\n",
-            "[CV 1/5; 18/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 1/5; 18/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.855 total time=   4.6s\n",
-            "[CV 2/5; 18/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 2/5; 18/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.856 total time=   4.8s\n",
-            "[CV 3/5; 18/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 3/5; 18/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.849 total time=   6.0s\n",
-            "[CV 4/5; 18/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 4/5; 18/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.848 total time=   4.6s\n",
-            "[CV 5/5; 18/48] START max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 5/5; 18/48] END max_depth=10, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.852 total time=   5.5s\n",
-            "[CV 1/5; 19/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 1/5; 19/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.852 total time=   1.3s\n",
-            "[CV 2/5; 19/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 2/5; 19/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.852 total time=   0.9s\n",
-            "[CV 3/5; 19/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 3/5; 19/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.847 total time=   0.8s\n",
-            "[CV 4/5; 19/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 4/5; 19/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.845 total time=   0.8s\n",
-            "[CV 5/5; 19/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 5/5; 19/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.850 total time=   0.8s\n",
-            "[CV 1/5; 20/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 1/5; 20/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.852 total time=   1.5s\n",
-            "[CV 2/5; 20/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 2/5; 20/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.850 total time=   1.5s\n",
-            "[CV 3/5; 20/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 3/5; 20/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.846 total time=   2.2s\n",
-            "[CV 4/5; 20/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 4/5; 20/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.846 total time=   2.5s\n",
-            "[CV 5/5; 20/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 5/5; 20/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.850 total time=   2.4s\n",
-            "[CV 1/5; 21/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 1/5; 21/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.851 total time=   4.2s\n",
-            "[CV 2/5; 21/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 2/5; 21/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.851 total time=   3.7s\n",
-            "[CV 3/5; 21/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 3/5; 21/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.846 total time=   3.9s\n",
-            "[CV 4/5; 21/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 4/5; 21/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.846 total time=   5.1s\n",
-            "[CV 5/5; 21/48] START max_depth=10, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 5/5; 21/48] END max_depth=10, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.850 total time=   3.7s\n",
-            "[CV 1/5; 22/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 1/5; 22/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.851 total time=   0.8s\n",
-            "[CV 2/5; 22/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 2/5; 22/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.850 total time=   0.8s\n",
-            "[CV 3/5; 22/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 3/5; 22/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.845 total time=   0.8s\n",
-            "[CV 4/5; 22/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 4/5; 22/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.844 total time=   0.8s\n",
-            "[CV 5/5; 22/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 5/5; 22/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.850 total time=   0.8s\n",
-            "[CV 1/5; 23/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 1/5; 23/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.852 total time=   1.9s\n",
-            "[CV 2/5; 23/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 2/5; 23/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.851 total time=   2.4s\n",
-            "[CV 3/5; 23/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 3/5; 23/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.848 total time=   1.8s\n",
-            "[CV 4/5; 23/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 4/5; 23/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.846 total time=   1.5s\n",
-            "[CV 5/5; 23/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 5/5; 23/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.848 total time=   1.5s\n",
-            "[CV 1/5; 24/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 1/5; 24/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.852 total time=   3.7s\n",
-            "[CV 2/5; 24/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 2/5; 24/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.849 total time=   4.6s\n",
-            "[CV 3/5; 24/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 3/5; 24/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.846 total time=   4.4s\n",
-            "[CV 4/5; 24/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 4/5; 24/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.848 total time=   3.7s\n",
-            "[CV 5/5; 24/48] START max_depth=10, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 5/5; 24/48] END max_depth=10, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.849 total time=   3.8s\n",
-            "[CV 1/5; 25/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 1/5; 25/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.851 total time=   3.0s\n",
-            "[CV 2/5; 25/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 2/5; 25/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.852 total time=   2.2s\n",
-            "[CV 3/5; 25/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 3/5; 25/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.844 total time=   1.9s\n",
-            "[CV 4/5; 25/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 4/5; 25/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.844 total time=   1.9s\n",
-            "[CV 5/5; 25/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 5/5; 25/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.848 total time=   1.9s\n",
-            "[CV 1/5; 26/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 1/5; 26/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.850 total time=   4.1s\n",
-            "[CV 2/5; 26/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 2/5; 26/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.849 total time=   4.8s\n",
-            "[CV 3/5; 26/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 3/5; 26/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.846 total time=   3.7s\n",
-            "[CV 4/5; 26/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 4/5; 26/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.844 total time=   3.7s\n",
-            "[CV 5/5; 26/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 5/5; 26/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.850 total time=   5.3s\n",
-            "[CV 1/5; 27/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 1/5; 27/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.850 total time=   9.6s\n",
-            "[CV 2/5; 27/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 2/5; 27/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.851 total time=  11.2s\n",
-            "[CV 3/5; 27/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 3/5; 27/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.847 total time=  11.1s\n",
-            "[CV 4/5; 27/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 4/5; 27/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.846 total time=  11.2s\n",
-            "[CV 5/5; 27/48] START max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 5/5; 27/48] END max_depth=30, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.851 total time=  10.2s\n",
-            "[CV 1/5; 28/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 1/5; 28/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.856 total time=   2.6s\n",
-            "[CV 2/5; 28/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 2/5; 28/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.856 total time=   1.8s\n",
-            "[CV 3/5; 28/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 3/5; 28/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.855 total time=   1.8s\n",
-            "[CV 4/5; 28/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 4/5; 28/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.850 total time=   1.8s\n",
-            "[CV 5/5; 28/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 5/5; 28/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.856 total time=   1.8s\n",
-            "[CV 1/5; 29/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 1/5; 29/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.858 total time=   3.9s\n",
-            "[CV 2/5; 29/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 2/5; 29/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.854 total time=   4.5s\n",
-            "[CV 3/5; 29/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 3/5; 29/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.854 total time=   3.5s\n",
-            "[CV 4/5; 29/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 4/5; 29/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.851 total time=   3.5s\n",
-            "[CV 5/5; 29/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 5/5; 29/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.857 total time=   4.8s\n",
-            "[CV 1/5; 30/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 1/5; 30/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.857 total time=   8.8s\n",
-            "[CV 2/5; 30/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 2/5; 30/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.856 total time=  10.1s\n",
-            "[CV 3/5; 30/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 3/5; 30/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.854 total time=  10.2s\n",
-            "[CV 4/5; 30/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 4/5; 30/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.853 total time=   8.6s\n",
-            "[CV 5/5; 30/48] START max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 5/5; 30/48] END max_depth=30, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.857 total time=  10.7s\n",
-            "[CV 1/5; 31/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 1/5; 31/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.849 total time=   2.5s\n",
-            "[CV 2/5; 31/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 2/5; 31/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.853 total time=   1.7s\n",
-            "[CV 3/5; 31/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 3/5; 31/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.843 total time=   2.6s\n",
-            "[CV 4/5; 31/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 4/5; 31/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.844 total time=   2.2s\n",
-            "[CV 5/5; 31/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 5/5; 31/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.851 total time=   1.7s\n",
-            "[CV 1/5; 32/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 1/5; 32/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.851 total time=   3.6s\n",
-            "[CV 2/5; 32/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 2/5; 32/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.853 total time=   3.3s\n",
-            "[CV 3/5; 32/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 3/5; 32/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.845 total time=   4.8s\n",
-            "[CV 4/5; 32/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 4/5; 32/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.847 total time=   3.3s\n",
-            "[CV 5/5; 32/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 5/5; 32/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.853 total time=   3.4s\n",
-            "[CV 1/5; 33/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 1/5; 33/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.853 total time=  10.1s\n",
-            "[CV 2/5; 33/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 2/5; 33/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.853 total time=   9.1s\n",
-            "[CV 3/5; 33/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 3/5; 33/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.845 total time=   9.2s\n",
-            "[CV 4/5; 33/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 4/5; 33/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.846 total time=   9.9s\n",
-            "[CV 5/5; 33/48] START max_depth=30, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 5/5; 33/48] END max_depth=30, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.853 total time=   9.0s\n",
-            "[CV 1/5; 34/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 1/5; 34/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.854 total time=   2.3s\n",
-            "[CV 2/5; 34/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 2/5; 34/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.854 total time=   1.5s\n",
-            "[CV 3/5; 34/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 3/5; 34/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.853 total time=   1.6s\n",
-            "[CV 4/5; 34/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 4/5; 34/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.850 total time=   1.5s\n",
-            "[CV 5/5; 34/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 5/5; 34/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.854 total time=   1.5s\n",
-            "[CV 1/5; 35/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 1/5; 35/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.857 total time=   3.0s\n",
-            "[CV 2/5; 35/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 2/5; 35/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.856 total time=   4.5s\n",
-            "[CV 3/5; 35/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 3/5; 35/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.853 total time=   3.1s\n",
-            "[CV 4/5; 35/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 4/5; 35/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.854 total time=   3.2s\n",
-            "[CV 5/5; 35/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 5/5; 35/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.860 total time=   3.0s\n",
-            "[CV 1/5; 36/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 1/5; 36/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.856 total time=   9.1s\n",
-            "[CV 2/5; 36/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 2/5; 36/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.854 total time=   8.5s\n",
-            "[CV 3/5; 36/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 3/5; 36/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.854 total time=   8.1s\n",
-            "[CV 4/5; 36/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 4/5; 36/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.852 total time=   9.3s\n",
-            "[CV 5/5; 36/48] START max_depth=30, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 5/5; 36/48] END max_depth=30, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.859 total time=   7.7s\n",
-            "[CV 1/5; 37/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 1/5; 37/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.839 total time=   3.3s\n",
-            "[CV 2/5; 37/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 2/5; 37/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.842 total time=   2.5s\n",
-            "[CV 3/5; 37/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 3/5; 37/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.839 total time=   2.1s\n",
-            "[CV 4/5; 37/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 4/5; 37/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.834 total time=   2.1s\n",
-            "[CV 5/5; 37/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=50\n",
-            "[CV 5/5; 37/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=50;, score=0.840 total time=   2.2s\n",
-            "[CV 1/5; 38/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 1/5; 38/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.840 total time=   5.6s\n",
-            "[CV 2/5; 38/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 2/5; 38/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.844 total time=   4.4s\n",
-            "[CV 3/5; 38/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 3/5; 38/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.836 total time=   4.2s\n",
-            "[CV 4/5; 38/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 4/5; 38/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.836 total time=   5.4s\n",
-            "[CV 5/5; 38/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100\n",
-            "[CV 5/5; 38/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100;, score=0.844 total time=   4.4s\n",
-            "[CV 1/5; 39/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 1/5; 39/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.842 total time=  13.3s\n",
-            "[CV 2/5; 39/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 2/5; 39/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.842 total time=  15.9s\n",
-            "[CV 3/5; 39/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 3/5; 39/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.840 total time=  11.8s\n",
-            "[CV 4/5; 39/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 4/5; 39/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.837 total time=  11.7s\n",
-            "[CV 5/5; 39/48] START max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=250\n",
-            "[CV 5/5; 39/48] END max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=250;, score=0.844 total time=  10.9s\n",
-            "[CV 1/5; 40/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 1/5; 40/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.851 total time=   3.2s\n",
-            "[CV 2/5; 40/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 2/5; 40/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.850 total time=   3.1s\n",
-            "[CV 3/5; 40/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 3/5; 40/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.847 total time=   2.0s\n",
-            "[CV 4/5; 40/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 4/5; 40/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.844 total time=   1.9s\n",
-            "[CV 5/5; 40/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=50\n",
-            "[CV 5/5; 40/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=50;, score=0.851 total time=   1.8s\n",
-            "[CV 1/5; 41/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 1/5; 41/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.851 total time=   4.3s\n",
-            "[CV 2/5; 41/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 2/5; 41/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.850 total time=   7.8s\n",
-            "[CV 3/5; 41/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 3/5; 41/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.847 total time=   3.7s\n",
-            "[CV 4/5; 41/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 4/5; 41/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.845 total time=   4.2s\n",
-            "[CV 5/5; 41/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=100\n",
-            "[CV 5/5; 41/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=100;, score=0.852 total time=   4.7s\n",
-            "[CV 1/5; 42/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 1/5; 42/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.853 total time=   9.5s\n",
-            "[CV 2/5; 42/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 2/5; 42/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.850 total time=  10.5s\n",
-            "[CV 3/5; 42/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 3/5; 42/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.850 total time=  10.5s\n",
-            "[CV 4/5; 42/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 4/5; 42/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.848 total time=  10.6s\n",
-            "[CV 5/5; 42/48] START max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=250\n",
-            "[CV 5/5; 42/48] END max_depth=50, max_features=sqrt, min_samples_split=6, n_estimators=250;, score=0.852 total time=   9.1s\n",
-            "[CV 1/5; 43/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 1/5; 43/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.837 total time=   4.7s\n",
-            "[CV 2/5; 43/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 2/5; 43/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.841 total time=   2.0s\n",
-            "[CV 3/5; 43/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 3/5; 43/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.838 total time=   1.9s\n",
-            "[CV 4/5; 43/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 4/5; 43/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.833 total time=   1.9s\n",
-            "[CV 5/5; 43/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=50\n",
-            "[CV 5/5; 43/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=50;, score=0.841 total time=   1.9s\n",
-            "[CV 1/5; 44/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 1/5; 44/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.839 total time=   4.7s\n",
-            "[CV 2/5; 44/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 2/5; 44/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.840 total time=   4.2s\n",
-            "[CV 3/5; 44/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 3/5; 44/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.836 total time=   3.8s\n",
-            "[CV 4/5; 44/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 4/5; 44/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.836 total time=   4.0s\n",
-            "[CV 5/5; 44/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100\n",
-            "[CV 5/5; 44/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100;, score=0.842 total time=   4.9s\n",
-            "[CV 1/5; 45/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 1/5; 45/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.841 total time=  11.0s\n",
-            "[CV 2/5; 45/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 2/5; 45/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.842 total time=   9.9s\n",
-            "[CV 3/5; 45/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 3/5; 45/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.838 total time=  10.9s\n",
-            "[CV 4/5; 45/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 4/5; 45/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.836 total time=  10.8s\n",
-            "[CV 5/5; 45/48] START max_depth=50, max_features=log2, min_samples_split=2, n_estimators=250\n",
-            "[CV 5/5; 45/48] END max_depth=50, max_features=log2, min_samples_split=2, n_estimators=250;, score=0.841 total time=  10.1s\n",
-            "[CV 1/5; 46/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 1/5; 46/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.851 total time=   2.3s\n",
-            "[CV 2/5; 46/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 2/5; 46/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.852 total time=   1.6s\n",
-            "[CV 3/5; 46/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 3/5; 46/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.847 total time=   1.7s\n",
-            "[CV 4/5; 46/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 4/5; 46/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.843 total time=   1.7s\n",
-            "[CV 5/5; 46/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=50\n",
-            "[CV 5/5; 46/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=50;, score=0.851 total time=   1.7s\n",
-            "[CV 1/5; 47/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 1/5; 47/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.850 total time=   3.7s\n",
-            "[CV 2/5; 47/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 2/5; 47/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.848 total time=   4.4s\n",
-            "[CV 3/5; 47/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 3/5; 47/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.848 total time=   3.3s\n",
-            "[CV 4/5; 47/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 4/5; 47/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.843 total time=   3.3s\n",
-            "[CV 5/5; 47/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=100\n",
-            "[CV 5/5; 47/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=100;, score=0.850 total time=   3.9s\n",
-            "[CV 1/5; 48/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 1/5; 48/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.852 total time=   9.0s\n",
-            "[CV 2/5; 48/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 2/5; 48/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.851 total time=   9.8s\n",
-            "[CV 3/5; 48/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 3/5; 48/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.849 total time=  11.1s\n",
-            "[CV 4/5; 48/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 4/5; 48/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.845 total time=   8.9s\n",
-            "[CV 5/5; 48/48] START max_depth=50, max_features=log2, min_samples_split=6, n_estimators=250\n",
-            "[CV 5/5; 48/48] END max_depth=50, max_features=log2, min_samples_split=6, n_estimators=250;, score=0.853 total time=  10.6s\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "GridSearchCV(estimator=RandomForestClassifier(),\n",
-              "             param_grid={'max_depth': [5, 10, 30, 50],\n",
-              "                         'max_features': ['sqrt', 'log2'],\n",
-              "                         'min_samples_split': [2, 6],\n",
-              "                         'n_estimators': [50, 100, 250]},\n",
-              "             verbose=10)"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=RandomForestClassifier(),\n",
-              "             param_grid={&#x27;max_depth&#x27;: [5, 10, 30, 50],\n",
-              "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
-              "                         &#x27;min_samples_split&#x27;: [2, 6],\n",
-              "                         &#x27;n_estimators&#x27;: [50, 100, 250]},\n",
-              "             verbose=10)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=RandomForestClassifier(),\n",
-              "             param_grid={&#x27;max_depth&#x27;: [5, 10, 30, 50],\n",
-              "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
-              "                         &#x27;min_samples_split&#x27;: [2, 6],\n",
-              "                         &#x27;n_estimators&#x27;: [50, 100, 250]},\n",
-              "             verbose=10)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 64
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "grid_search.best_estimator_"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "hOQI65BYFzf7",
-        "outputId": "dc61f0e1-4e9c-484d-ff1c-83423ddaadff"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "RandomForestClassifier(max_depth=30, max_features='log2', min_samples_split=6)"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=30, max_features=&#x27;log2&#x27;, min_samples_split=6)</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=30, max_features=&#x27;log2&#x27;, min_samples_split=6)</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 70
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "forest = grid_search.best_estimator_\n",
-        "forest.score(x_test, y_test) #Uji skor akurasi model"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "soyng3VsF8gA",
-        "outputId": "e8550384-b25e-4d2d-f2d0-f24941f59428"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.8450470392916436"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 71
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "importances = dict(zip(forest.feature_names_in_,forest.feature_importances_))\n",
-        "imporances = {key: value for key, value in sorted(importances.items(), key=lambda x: x[1], reverse=True)}"
-      ],
-      "metadata": {
-        "id": "CvG-ZVMuGcOM"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "importances #Hasil prediksi"
-      ],
-      "metadata": {
-        "id": "Lp6A9IcpGh9R",
-        "outputId": "7a4a2221-d8f0-4fc4-bee3-d302317dca57",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "{'age': 0.1551518676541976,\n",
-              " 'educational-num': 0.14082081305774524,\n",
-              " 'gender': 0.0143860560543573,\n",
-              " 'capital-gain': 0.13555713111340167,\n",
-              " 'hours-per-week': 0.09208654424590207,\n",
-              " 'occupation_Adm-clerical': 0.005335711687251273,\n",
-              " 'occupation_Armed-Forces': 8.844184055235584e-05,\n",
-              " 'occupation_Craft-repair': 0.0063616924604688934,\n",
-              " 'occupation_Exec-managerial': 0.03032085855609601,\n",
-              " 'occupation_Farming-fishing': 0.006554437564765853,\n",
-              " 'occupation_Handlers-cleaners': 0.004739797593016007,\n",
-              " 'occupation_Machine-op-inspct': 0.005167386061046641,\n",
-              " 'occupation_Other-service': 0.011932086821915067,\n",
-              " 'occupation_Priv-house-serv': 0.00029362338114340906,\n",
-              " 'occupation_Prof-specialty': 0.02433221312299907,\n",
-              " 'occupation_Protective-serv': 0.003007527913110482,\n",
-              " 'occupation_Sales': 0.005980789252326362,\n",
-              " 'occupation_Tech-support': 0.004466916436188848,\n",
-              " 'occupation_Transport-moving': 0.004350873995547464,\n",
-              " 'workclass_Federal-gov': 0.005138257868831246,\n",
-              " 'workclass_Local-gov': 0.005006988243756945,\n",
-              " 'workclass_Private': 0.009254701879301504,\n",
-              " 'workclass_Self-emp-inc': 0.007904179631000654,\n",
-              " 'workclass_Self-emp-not-inc': 0.008570089952130024,\n",
-              " 'workclass_State-gov': 0.003999462043154903,\n",
-              " 'workclass_Without-pay': 0.00026525133683732424,\n",
-              " 'marital-status_Divorced': 0.010856013194149142,\n",
-              " 'marital-status_Married-AF-spouse': 0.0004409794216627202,\n",
-              " 'marital-status_Married-civ-spouse': 0.08595394041439582,\n",
-              " 'marital-status_Married-spouse-absent': 0.0010390780530758078,\n",
-              " 'marital-status_Never-married': 0.044391169892995845,\n",
-              " 'marital-status_Separated': 0.002931510407089085,\n",
-              " 'marital-status_Widowed': 0.0022960618452821033,\n",
-              " 'relationship_Husband': 0.05770091956655229,\n",
-              " 'relationship_Not-in-family': 0.019860396635223685,\n",
-              " 'relationship_Other-relative': 0.0027416228209812604,\n",
-              " 'relationship_Own-child': 0.014504052693021116,\n",
-              " 'relationship_Unmarried': 0.01161335476723861,\n",
-              " 'relationship_Wife': 0.011721104720960841,\n",
-              " 'race_Amer-Indian-Eskimo': 0.001531772876503895,\n",
-              " 'race_Asian-Pac-Islander': 0.0027620068513976486,\n",
-              " 'race_Black': 0.004779390349561768,\n",
-              " 'race_Other': 0.0010447490364312935,\n",
-              " 'race_White': 0.005822380335828926,\n",
-              " 'native-country_Cambodia': 0.0002731231829443815,\n",
-              " 'native-country_Canada': 0.0016207090484281238,\n",
-              " 'native-country_China': 0.0005493179651202208,\n",
-              " 'native-country_Columbia': 0.00035959337899627995,\n",
-              " 'native-country_Cuba': 0.0007997384566023021,\n",
-              " 'native-country_Dominican-Republic': 0.0003028964328009091,\n",
-              " 'native-country_Ecuador': 0.00019156529704232556,\n",
-              " 'native-country_El-Salvador': 0.00038960206567691894,\n",
-              " 'native-country_England': 0.0012180412903735447,\n",
-              " 'native-country_France': 0.0004081736437243918,\n",
-              " 'native-country_Germany': 0.0009466084268267089,\n",
-              " 'native-country_Greece': 0.0005632206266543231,\n",
-              " 'native-country_Guatemala': 0.0002267930931375788,\n",
-              " 'native-country_Haiti': 0.00020823890036450539,\n",
-              " 'native-country_Holand-Netherlands': 1.0690878777850074e-08,\n",
-              " 'native-country_Honduras': 5.010944830084493e-05,\n",
-              " 'native-country_Hong': 0.0001658418988696363,\n",
-              " 'native-country_Hungary': 0.0002874948694308454,\n",
-              " 'native-country_India': 0.0010229057298837426,\n",
-              " 'native-country_Iran': 0.000506334322790496,\n",
-              " 'native-country_Ireland': 0.0005330600247442642,\n",
-              " 'native-country_Italy': 0.0008153030723668685,\n",
-              " 'native-country_Jamaica': 0.0004301382950291879,\n",
-              " 'native-country_Japan': 0.0007571109663310874,\n",
-              " 'native-country_Laos': 0.00011013186400365366,\n",
-              " 'native-country_Mexico': 0.0033861546419876667,\n",
-              " 'native-country_Nicaragua': 0.00015968108179372355,\n",
-              " 'native-country_Outlying-US(Guam-USVI-etc)': 6.274587904164244e-05,\n",
-              " 'native-country_Peru': 0.0001669308605827094,\n",
-              " 'native-country_Philippines': 0.0009092291689811275,\n",
-              " 'native-country_Poland': 0.0006336507169091614,\n",
-              " 'native-country_Portugal': 0.0004699605304933213,\n",
-              " 'native-country_Puerto-Rico': 0.0007274003701525728,\n",
-              " 'native-country_Scotland': 0.00013048615930935086,\n",
-              " 'native-country_South': 0.0005885792986937148,\n",
-              " 'native-country_Taiwan': 0.000348623872906966,\n",
-              " 'native-country_Thailand': 0.00012722772103054985,\n",
-              " 'native-country_Trinadad&Tobago': 0.0001656843296211075,\n",
-              " 'native-country_United-States': 0.005693769971148482,\n",
-              " 'native-country_Vietnam': 0.00026139273653266016,\n",
-              " 'native-country_Yugoslavia': 0.00036821602009732044}"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 73
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "**Hasil Model Prediksi Pendapatan**\n",
-        "\n",
-        "*   Dictionary diatas menunjukkan feature mana yang paling berperan dalam menentukan tingkatan gaji pekerja.\n",
-        "*   5 Teratas dari feature ini adalah umur, pendidikan, gender, capital-gain, dan jam kerja.\n",
-        "* Semakin tinggi pendidikan, semakin besar kemungkinan pendapatan yang didapatkan\n",
-        "* Begitu pula dengan umur, rata-rata umur semakin tinggi, akan berdampak pada peningkatan pendapatan, karena pengalaman biasanya tidak terlepas dari umur.\n",
-        "* Gender pada data ini menunjukkan, pria lebih cenderung memiliki pendapatan lebih besar dari perempuan, salah satu alasannya adalah pria lebih banyak menghabiskan waktu untuk bekerja selama satu minggu\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "-fH-fR2W-1if"
-      }
-    }
-  ]
-}
\ No newline at end of file