UTS_Data_Mining.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNxBn+69l3oK/+rRSZHcTs9",
      "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/Aurelio-Naufal/UTS-Datmin-Data/blob/main/UTS_Data_Mining.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": {
        "id": "rKhtbtd_EWza"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from datetime import datetime"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Data dari Github\n",
        "# Data telah dilakukan modifikasi sedikit, pada entri 111 ke bawah data di hapus karena merupakan missing value\n",
        "urldata = 'https://raw.githubusercontent.com/Aurelio-Naufal/UTS-Datmin/main/Copy%20of%20CaseStudy-AlumniData%20-%20CaseStudy-AlumniData.csv'\n",
        "df = pd.read_csv(urldata)"
      ],
      "metadata": {
        "id": "511DJImcGQHV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Understanding\n",
        "Variabel-Variabelnya :\n",
        "* Alumni ID\n",
        "* Timestamp\n",
        "* Tahun Masuk\n",
        "* Tahun Wisuda\n",
        "* Status saat ini\n",
        "* Waktu Mendapatkan Pekerjaan\n",
        "* Tahun masuk saat anda melanjutkan studi setelah lulus dari Prodi Matematika\n",
        "* Posisi anda saat ini di tempat bekerja saat Ini\n",
        "* Nama Universitas dan Kota tempat melanjutkan Studi setelah S1\n",
        "* Nama jurusan/program studi setelah S1\tTahun saudara mulai berwirausaha\n",
        "* Bidang wirausaha\n",
        "* Kesesuaian Kurikulum\n",
        "* Kesesuaian Bidang Ilmu\n",
        "* Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah)\n"
      ],
      "metadata": {
        "id": "-xjtoUCRG8c4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preview Data\n",
        "df.head()"
      ],
      "metadata": {
        "id": "JYMj-7skGqZg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "99b5ece1-5e5e-404f-df2f-b702af2c4bbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Alumni-ID        Timestamp  Tahun Masuk  Tahun Wisuda Status saat ini:  \\\n",
              "0          1   7/25/2023 9:55         2004          2010          ASN/PNS   \n",
              "1          2  7/26/2023 10:10         2015          2020          ASN/PNS   \n",
              "2          3   7/28/2023 2:00         2000          2006          ASN/PNS   \n",
              "3          4  7/27/2023 14:27         2014          2019    karyawan BUMN   \n",
              "4          5   7/25/2023 9:44         2013          2018  Karyawan Swasta   \n",
              "\n",
              "  Waktu Mendapatkan Pekerjaan:  \\\n",
              "0         Kurang dari 3 bulan.   \n",
              "1                   6-12 bulan   \n",
              "2         Kurang dari 3 bulan.   \n",
              "3                   6-12 bulan   \n",
              "4         Kurang dari 3 bulan.   \n",
              "\n",
              "  Tahun masuk saat anda melanjutkan studi setelah lulus dari Prodi Matematika:  \\\n",
              "0                                               2014                             \n",
              "1                                                NaN                             \n",
              "2                                S2 2008 dan S3 2020                             \n",
              "3                                                NaN                             \n",
              "4                                               2021                             \n",
              "\n",
              "  Posisi anda saat ini di tempat bekerja saat Ini:  \\\n",
              "0                                       Guru Kelas   \n",
              "1                                              NaN   \n",
              "2                                            Dosen   \n",
              "3             Officer HC Solution Analysis Support   \n",
              "4                                    Control Tower   \n",
              "\n",
              "  Nama Universitas dan Kota tempat melanjutkan Studi setelah S1  \\\n",
              "0                                Universitas Terbuka              \n",
              "1                                                NaN              \n",
              "2                                   S2 UI dan S3 IPB              \n",
              "3                                                NaN              \n",
              "4                                     ITS - Surabaya              \n",
              "\n",
              "               Nama jurusan/program studi setelah S1  \\\n",
              "0                      Pendidikan Guru Sekolah Dasar   \n",
              "1                                                NaN   \n",
              "2  Magister Teknologi Informasi dan Doktoral Ilmu...   \n",
              "3                                                NaN   \n",
              "4                                       Supply Chain   \n",
              "\n",
              "  Tahun saudara mulai berwirausaha: Bidang wirausaha:  Kesesuaian Kurikulum:  \\\n",
              "0                               NaN               NaN                      3   \n",
              "1                               NaN               NaN                      3   \n",
              "2                               NaN               NaN                      5   \n",
              "3                               NaN               NaN                      3   \n",
              "4                               NaN               NaN                      4   \n",
              "\n",
              "   Kesesuaian Bidang Ilmu:  \\\n",
              "0                        3   \n",
              "1                        3   \n",
              "2                        5   \n",
              "3                        3   \n",
              "4                        3   \n",
              "\n",
              "   Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):  \n",
              "0                                                4.0                                             \n",
              "1                                                6.0                                             \n",
              "2                                                5.0                                             \n",
              "3                                                8.0                                             \n",
              "4                                               15.0                                             "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9c6eb849-9611-4364-a8c0-9da24a08cd0d\" 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>Alumni-ID</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Tahun Masuk</th>\n",
              "      <th>Tahun Wisuda</th>\n",
              "      <th>Status saat ini:</th>\n",
              "      <th>Waktu Mendapatkan Pekerjaan:</th>\n",
              "      <th>Tahun masuk saat anda melanjutkan studi setelah lulus dari Prodi Matematika:</th>\n",
              "      <th>Posisi anda saat ini di tempat bekerja saat Ini:</th>\n",
              "      <th>Nama Universitas dan Kota tempat melanjutkan Studi setelah S1</th>\n",
              "      <th>Nama jurusan/program studi setelah S1</th>\n",
              "      <th>Tahun saudara mulai berwirausaha:</th>\n",
              "      <th>Bidang wirausaha:</th>\n",
              "      <th>Kesesuaian Kurikulum:</th>\n",
              "      <th>Kesesuaian Bidang Ilmu:</th>\n",
              "      <th>Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>7/25/2023 9:55</td>\n",
              "      <td>2004</td>\n",
              "      <td>2010</td>\n",
              "      <td>ASN/PNS</td>\n",
              "      <td>Kurang dari 3 bulan.</td>\n",
              "      <td>2014</td>\n",
              "      <td>Guru Kelas</td>\n",
              "      <td>Universitas Terbuka</td>\n",
              "      <td>Pendidikan Guru Sekolah Dasar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>7/26/2023 10:10</td>\n",
              "      <td>2015</td>\n",
              "      <td>2020</td>\n",
              "      <td>ASN/PNS</td>\n",
              "      <td>6-12 bulan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>7/28/2023 2:00</td>\n",
              "      <td>2000</td>\n",
              "      <td>2006</td>\n",
              "      <td>ASN/PNS</td>\n",
              "      <td>Kurang dari 3 bulan.</td>\n",
              "      <td>S2 2008 dan S3 2020</td>\n",
              "      <td>Dosen</td>\n",
              "      <td>S2 UI dan S3 IPB</td>\n",
              "      <td>Magister Teknologi Informasi dan Doktoral Ilmu...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>7/27/2023 14:27</td>\n",
              "      <td>2014</td>\n",
              "      <td>2019</td>\n",
              "      <td>karyawan BUMN</td>\n",
              "      <td>6-12 bulan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Officer HC Solution Analysis Support</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>7/25/2023 9:44</td>\n",
              "      <td>2013</td>\n",
              "      <td>2018</td>\n",
              "      <td>Karyawan Swasta</td>\n",
              "      <td>Kurang dari 3 bulan.</td>\n",
              "      <td>2021</td>\n",
              "      <td>Control Tower</td>\n",
              "      <td>ITS - Surabaya</td>\n",
              "      <td>Supply Chain</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c6eb849-9611-4364-a8c0-9da24a08cd0d')\"\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-9c6eb849-9611-4364-a8c0-9da24a08cd0d 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-9c6eb849-9611-4364-a8c0-9da24a08cd0d');\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-61d0e705-49b3-4abf-b222-e6ae810a7341\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-61d0e705-49b3-4abf-b222-e6ae810a7341')\"\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-61d0e705-49b3-4abf-b222-e6ae810a7341 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": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PreProcessing"
      ],
      "metadata": {
        "id": "_S42zouWHzcA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaWoqj7wlSkJ",
        "outputId": "e83cbe79-48c2-429d-b100-7bea9fdf26d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 110 entries, 0 to 109\n",
            "Data columns (total 15 columns):\n",
            " #   Column                                                                                        Non-Null Count  Dtype  \n",
            "---  ------                                                                                        --------------  -----  \n",
            " 0   Alumni-ID                                                                                     110 non-null    int64  \n",
            " 1   Timestamp                                                                                     110 non-null    object \n",
            " 2   Tahun Masuk                                                                                   110 non-null    int64  \n",
            " 3   Tahun Wisuda                                                                                  110 non-null    int64  \n",
            " 4   Status saat ini:                                                                              110 non-null    object \n",
            " 5   Waktu Mendapatkan Pekerjaan:                                                                  110 non-null    object \n",
            " 6   Tahun masuk saat anda melanjutkan studi setelah lulus dari Prodi Matematika:                  24 non-null     object \n",
            " 7   Posisi anda saat ini di tempat bekerja saat Ini:                                              84 non-null     object \n",
            " 8   Nama Universitas dan Kota tempat melanjutkan Studi setelah S1                                 24 non-null     object \n",
            " 9   Nama jurusan/program studi setelah S1                                                         24 non-null     object \n",
            " 10  Tahun saudara mulai berwirausaha:                                                             13 non-null     object \n",
            " 11  Bidang wirausaha:                                                                             12 non-null     object \n",
            " 12  Kesesuaian Kurikulum:                                                                         110 non-null    int64  \n",
            " 13  Kesesuaian Bidang Ilmu:                                                                       110 non-null    int64  \n",
            " 14  Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):  66 non-null     float64\n",
            "dtypes: float64(1), int64(5), object(9)\n",
            "memory usage: 13.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek Missing Value\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8jl533GBnwiN",
        "outputId": "8bfcff48-f8ea-41db-9c9b-c6fabfeb532c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Alumni-ID                                                                                        0\n",
              "Timestamp                                                                                        0\n",
              "Tahun Masuk                                                                                      0\n",
              "Tahun Wisuda                                                                                     0\n",
              "Status saat ini:                                                                                 0\n",
              "Waktu Mendapatkan Pekerjaan:                                                                     0\n",
              "Tahun masuk saat anda melanjutkan studi setelah lulus dari Prodi Matematika:                    86\n",
              "Posisi anda saat ini di tempat bekerja saat Ini:                                                26\n",
              "Nama Universitas dan Kota tempat melanjutkan Studi setelah S1                                   86\n",
              "Nama jurusan/program studi setelah S1                                                           86\n",
              "Tahun saudara mulai berwirausaha:                                                               97\n",
              "Bidang wirausaha:                                                                               98\n",
              "Kesesuaian Kurikulum:                                                                            0\n",
              "Kesesuaian Bidang Ilmu:                                                                          0\n",
              "Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):    44\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Telihat bahwa terdapat banyak missing value pada beberapa variabel tersebut, sehingga untuk mendapatkan insight yang maksimal, variabel-variabel yang memiliki missing value tersebut akan dihindari dalam proses pencarian insight"
      ],
      "metadata": {
        "id": "CnG_jzJ3lZS0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kemudian, karena pada beberapa variabel yang mempunyai data type objek seharusnya kategorik, maka akan diubah"
      ],
      "metadata": {
        "id": "DJASKqyXl40z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Status saat ini:\"] = df[\"Status saat ini:\"].astype(\"category\")\n",
        "df[\"Waktu Mendapatkan Pekerjaan:\"] = df[\"Waktu Mendapatkan Pekerjaan:\"].astype(\"category\")"
      ],
      "metadata": {
        "id": "Ff5M1mfXmEER"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Cek Statistika Deskriptif\n",
        "df.describe(include =\"all\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "rDwTUfKhpW6R",
        "outputId": "2f8d3e97-8dda-4bee-86e2-7d6e111fd0ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Alumni-ID        Timestamp  Tahun Masuk  Tahun Wisuda  \\\n",
              "count   110.000000              110   110.000000    110.000000   \n",
              "unique         NaN              102          NaN           NaN   \n",
              "top            NaN  7/27/2023 20:48          NaN           NaN   \n",
              "freq           NaN                3          NaN           NaN   \n",
              "mean     55.500000              NaN  2014.081818   2018.927273   \n",
              "std      31.898276              NaN     4.854076      4.948745   \n",
              "min       1.000000              NaN  2000.000000   2006.000000   \n",
              "25%      28.250000              NaN  2011.250000   2016.250000   \n",
              "50%      55.500000              NaN  2016.000000   2021.000000   \n",
              "75%      82.750000              NaN  2017.000000   2022.750000   \n",
              "max     110.000000              NaN  2019.000000   2023.000000   \n",
              "\n",
              "       Status saat ini: Waktu Mendapatkan Pekerjaan:  \\\n",
              "count               110                          110   \n",
              "unique               11                            7   \n",
              "top     Karyawan Swasta         Kurang dari 3 bulan.   \n",
              "freq                 70                           60   \n",
              "mean                NaN                          NaN   \n",
              "std                 NaN                          NaN   \n",
              "min                 NaN                          NaN   \n",
              "25%                 NaN                          NaN   \n",
              "50%                 NaN                          NaN   \n",
              "75%                 NaN                          NaN   \n",
              "max                 NaN                          NaN   \n",
              "\n",
              "       Tahun masuk saat anda melanjutkan studi setelah lulus dari Prodi Matematika:  \\\n",
              "count                                                  24                             \n",
              "unique                                                 14                             \n",
              "top                                                     -                             \n",
              "freq                                                    6                             \n",
              "mean                                                  NaN                             \n",
              "std                                                   NaN                             \n",
              "min                                                   NaN                             \n",
              "25%                                                   NaN                             \n",
              "50%                                                   NaN                             \n",
              "75%                                                   NaN                             \n",
              "max                                                   NaN                             \n",
              "\n",
              "       Posisi anda saat ini di tempat bekerja saat Ini:  \\\n",
              "count                                                84   \n",
              "unique                                               73   \n",
              "top                                               Dosen   \n",
              "freq                                                  4   \n",
              "mean                                                NaN   \n",
              "std                                                 NaN   \n",
              "min                                                 NaN   \n",
              "25%                                                 NaN   \n",
              "50%                                                 NaN   \n",
              "75%                                                 NaN   \n",
              "max                                                 NaN   \n",
              "\n",
              "       Nama Universitas dan Kota tempat melanjutkan Studi setelah S1  \\\n",
              "count                                                  24              \n",
              "unique                                                 17              \n",
              "top                                                     -              \n",
              "freq                                                    6              \n",
              "mean                                                  NaN              \n",
              "std                                                   NaN              \n",
              "min                                                   NaN              \n",
              "25%                                                   NaN              \n",
              "50%                                                   NaN              \n",
              "75%                                                   NaN              \n",
              "max                                                   NaN              \n",
              "\n",
              "       Nama jurusan/program studi setelah S1  \\\n",
              "count                                     24   \n",
              "unique                                    15   \n",
              "top                                        -   \n",
              "freq                                       6   \n",
              "mean                                     NaN   \n",
              "std                                      NaN   \n",
              "min                                      NaN   \n",
              "25%                                      NaN   \n",
              "50%                                      NaN   \n",
              "75%                                      NaN   \n",
              "max                                      NaN   \n",
              "\n",
              "       Tahun saudara mulai berwirausaha: Bidang wirausaha:  \\\n",
              "count                                 13                12   \n",
              "unique                                 6                 7   \n",
              "top                                    -                 -   \n",
              "freq                                   6                 6   \n",
              "mean                                 NaN               NaN   \n",
              "std                                  NaN               NaN   \n",
              "min                                  NaN               NaN   \n",
              "25%                                  NaN               NaN   \n",
              "50%                                  NaN               NaN   \n",
              "75%                                  NaN               NaN   \n",
              "max                                  NaN               NaN   \n",
              "\n",
              "        Kesesuaian Kurikulum:  Kesesuaian Bidang Ilmu:  \\\n",
              "count              110.000000               110.000000   \n",
              "unique                    NaN                      NaN   \n",
              "top                       NaN                      NaN   \n",
              "freq                      NaN                      NaN   \n",
              "mean                 3.872727                 3.563636   \n",
              "std                  0.879002                 1.045105   \n",
              "min                  1.000000                 1.000000   \n",
              "25%                  3.000000                 3.000000   \n",
              "50%                  4.000000                 4.000000   \n",
              "75%                  4.000000                 4.000000   \n",
              "max                  5.000000                 5.000000   \n",
              "\n",
              "        Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):  \n",
              "count                                           66.000000                                             \n",
              "unique                                                NaN                                             \n",
              "top                                                   NaN                                             \n",
              "freq                                                  NaN                                             \n",
              "mean                                             7.646212                                             \n",
              "std                                              8.999497                                             \n",
              "min                                              1.000000                                             \n",
              "25%                                              4.125000                                             \n",
              "50%                                              6.000000                                             \n",
              "75%                                              7.000000                                             \n",
              "max                                             65.000000                                             "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9f34d2a0-d6f3-4a02-a20f-d35f47ef9b91\" 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>Alumni-ID</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Tahun Masuk</th>\n",
              "      <th>Tahun Wisuda</th>\n",
              "      <th>Status saat ini:</th>\n",
              "      <th>Waktu Mendapatkan Pekerjaan:</th>\n",
              "      <th>Tahun masuk saat anda melanjutkan studi setelah lulus dari Prodi Matematika:</th>\n",
              "      <th>Posisi anda saat ini di tempat bekerja saat Ini:</th>\n",
              "      <th>Nama Universitas dan Kota tempat melanjutkan Studi setelah S1</th>\n",
              "      <th>Nama jurusan/program studi setelah S1</th>\n",
              "      <th>Tahun saudara mulai berwirausaha:</th>\n",
              "      <th>Bidang wirausaha:</th>\n",
              "      <th>Kesesuaian Kurikulum:</th>\n",
              "      <th>Kesesuaian Bidang Ilmu:</th>\n",
              "      <th>Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>110.000000</td>\n",
              "      <td>110</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>24</td>\n",
              "      <td>84</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>66.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>102</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>73</td>\n",
              "      <td>17</td>\n",
              "      <td>15</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>7/27/2023 20:48</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Karyawan Swasta</td>\n",
              "      <td>Kurang dari 3 bulan.</td>\n",
              "      <td>-</td>\n",
              "      <td>Dosen</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70</td>\n",
              "      <td>60</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>55.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014.081818</td>\n",
              "      <td>2018.927273</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.872727</td>\n",
              "      <td>3.563636</td>\n",
              "      <td>7.646212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>31.898276</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.854076</td>\n",
              "      <td>4.948745</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.879002</td>\n",
              "      <td>1.045105</td>\n",
              "      <td>8.999497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2006.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>28.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011.250000</td>\n",
              "      <td>2016.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>82.750000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>2022.750000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>110.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>2023.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>65.000000</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-9f34d2a0-d6f3-4a02-a20f-d35f47ef9b91')\"\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-9f34d2a0-d6f3-4a02-a20f-d35f47ef9b91 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-9f34d2a0-d6f3-4a02-a20f-d35f47ef9b91');\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-a58d37f9-e590-4ecc-940b-5a753b34a7ce\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a58d37f9-e590-4ecc-940b-5a753b34a7ce')\"\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-a58d37f9-e590-4ecc-940b-5a753b34a7ce 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": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA"
      ],
      "metadata": {
        "id": "qrXy2wnAmtAW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek Korelasi\n",
        "corr = df.corr()\n",
        "plt.figure(figsize=(12, 10))\n",
        "sns.heatmap(corr,\n",
        " cmap='inferno', vmax=1.0, vmin=-1.0, linewidths=0.1,\n",
        " annot=True, annot_kws={\"size\": 8}, square=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Pgbd5-nCmwOB",
        "outputId": "b6d54f65-941a-480f-c4e3-b9aa5df23712"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-36-9c31b36031a2>:2: 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",
            "  corr = df.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Diperoleh terdapat korelasi yang cukup signifikan antara Kesesuaian bidang ilmu dan Kesesuaiain kurikulum, Selain itu korelasi yang sangat tinggi juga didapatkan pada tahun masuk dan tahun wisuda dimana hal ini dapat diexpect (masuk akal, karena rata-rata kuliah 4-6 tahun)"
      ],
      "metadata": {
        "id": "4GL2bwaSoyYf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Insight 1\n",
        "Analisis Hubungan antara Lama Studi(Kuliah) dengan Perkiraan Penghasilan"
      ],
      "metadata": {
        "id": "0EZUFkpgIAft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Akan dibuat variabel baru yakni Lama Studi yang terbuat dari Tahun Wisuda dikurang Tahun Masuk\n",
        "df[\"Lama Studi\"]= df[\"Tahun Wisuda\"] - df[\"Tahun Masuk\"]\n",
        "df[\"Lama Studi\"].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5-OFKsslyHu",
        "outputId": "96232d50-cb61-4d55-a154-c2075f9fb49f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    6\n",
              "1    5\n",
              "2    6\n",
              "3    5\n",
              "4    5\n",
              "Name: Lama Studi, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Lama Studi'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HMojOXhtvvnP",
        "outputId": "6999f338-0245-43b0-c1f9-b126d39cdee0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    110.000000\n",
              "mean       4.845455\n",
              "std        0.814844\n",
              "min        4.000000\n",
              "25%        4.000000\n",
              "50%        5.000000\n",
              "75%        5.000000\n",
              "max        7.000000\n",
              "Name: Lama Studi, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rata-rata alumni lulus dalam waktu 4.8 tahun, atau berada di antara 4 dan 5 tahun"
      ],
      "metadata": {
        "id": "ziJ8j4yGvzm-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Selanjutnya akan dicek korelasinya dengan perkiraan penghasilan"
      ],
      "metadata": {
        "id": "L58PpOQAquXK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p1 = sns.catplot(data=df, x='Lama Studi',y='Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):', kind = 'strip', palette = 'muted')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "aBdTLH2Hq5xZ",
        "outputId": "43073e80-4236-43fc-b941-b85b148af209"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-44-41ede2721282>:1: FutureWarning: Passing `palette` without assigning `hue` is deprecated.\n",
            "  p1 = sns.catplot(data=df, x='Lama Studi',y='Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):', kind = 'strip', palette = 'muted')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek Pairplot\n",
        "p = sns.pairplot(data =df,\n",
        "                 x_vars=['Lama Studi','Tahun Masuk', 'Tahun Wisuda'],\n",
        "                 y_vars=['Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):'],\n",
        "                 hue = \"Waktu Mendapatkan Pekerjaan:\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "p3Z7BBeyrK6f",
        "outputId": "a3db8d09-f42c-43d5-c16f-0e397eba5fdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1050.38x250 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dapat dilihat bahwa Lama Studi dibawah 5 tahun cenderung mendapatkan perkiraan penghasilan yang lebih tinggi dibanding yang lulusnya 6 tahun ke atas. Kemudian dapat dilihat juga bahwa tidak terdapat pengaruh antara tahun masuk dan wisuda terhadap rata-rata perkiraan penghasilan karena dalam plot tetap konstan (pengecualian adanya outlier)"
      ],
      "metadata": {
        "id": "lF57BnjIswVY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek korelasi\n",
        "import scipy.stats\n",
        "df[\"Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):\"].corr(df[\"Lama Studi\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F00Q77Qdswu8",
        "outputId": "e16cbcfb-c52a-4bc6-83b4-8e38ef840670"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.11432441581428764"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):\"].corr(df[\"Lama Studi\"], method = \"spearman\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNkJTUJzut1w",
        "outputId": "9adb8f1a-d184-44dc-a4ba-1d8d1b313899"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.1481487541803329"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):\"].corr(df[\"Lama Studi\"], method = \"kendall\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q1kECNm0uyWb",
        "outputId": "cc9a8821-0acb-48f9-bcf2-9a01b521e956"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.12188930214768505"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Diperoleh korelasi yang kurang signifikan, akan tetapi hal ini juga dipengaruhi adanya missing value dan outlier, jika melihat dari plot, lama Studi dibawah 5 tahun cenderung mendapatkan perkiraan penghasilan yang lebih tinggi dibanding yang lulusnya 6 tahun ke atas"
      ],
      "metadata": {
        "id": "mSgB-HCiu4IS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Insight 2\n",
        "Analisis Hubungan antara status saat ini dan waktu mendapatkan pekerjaan dengan rata-rata perkiraan penghasilan"
      ],
      "metadata": {
        "id": "HNrVadPpwEsY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p2 = sns.catplot(data=df, x='Status saat ini:',y='Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):', kind = 'strip', palette = 'muted')\n",
        "plt.xticks(rotation = 90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        },
        "id": "sDEfHjdTu3Ms",
        "outputId": "3e3e2170-7fe4-473c-9b9f-999489197218"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-46-c12b20ae62d4>:1: FutureWarning: Passing `palette` without assigning `hue` is deprecated.\n",
            "  p2 = sns.catplot(data=df, x='Status saat ini:',y='Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):', kind = 'strip', palette = 'muted')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
              " [Text(0, 0, 'ASN/PNS'),\n",
              "  Text(1, 0, 'Dosen'),\n",
              "  Text(2, 0, 'Guru'),\n",
              "  Text(3, 0, 'Karyawan Swasta'),\n",
              "  Text(4, 0, 'Karyawan, Melanjutkan Studi'),\n",
              "  Text(5, 0, 'Melamar Pekerjaan'),\n",
              "  Text(6, 0, 'Melanjutkan Studi'),\n",
              "  Text(7, 0, 'Melanjutkan Studi, Wirausaha'),\n",
              "  Text(8, 0, 'Mengurus Rumah Tangga'),\n",
              "  Text(9, 0, 'Wirausaha'),\n",
              "  Text(10, 0, 'karyawan BUMN')])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p3 =  sns.catplot(data=df, x='Waktu Mendapatkan Pekerjaan:',y='Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):', kind = 'strip', palette = 'muted')\n",
        "plt.xticks(rotation = 90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 937
        },
        "id": "T3mcuQNFzO44",
        "outputId": "66c6f061-6048-49ec-cd29-57289fdf5f2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-48-60a75912d6a6>:1: FutureWarning: Passing `palette` without assigning `hue` is deprecated.\n",
            "  p3 =  sns.catplot(data=df, x='Waktu Mendapatkan Pekerjaan:',y='Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):', kind = 'strip', palette = 'muted')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([0, 1, 2, 3, 4, 5, 6],\n",
              " [Text(0, 0, '3-6 Bulan'),\n",
              "  Text(1, 0, '6-12 bulan'),\n",
              "  Text(2, 0, 'Belum mendapatkan pekerjaan'),\n",
              "  Text(3, 0, 'Kurang dari 3 bulan.'),\n",
              "  Text(4, 0, 'Tidak berencana mencari pekerjaan'),\n",
              "  Text(5, 0, 'Wirausaha'),\n",
              "  Text(6, 0, 'lebih dari 12 bulan.')])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Terlihat bahwa terindikasi adanya asosiasi antara Status saat ini dengan waktu mendapatkan pekerjaan (dikarenakan saat di plot terhadap perkiraan rata-rata penghasilannya, memiliki bentuk plot yang mirip). Karena bukan merupakan variable numerik, akan diuji asosiasinya dengan crosstab dan dicek dengan menggunakan chi square\n",
        "\n",
        "H0: Kedua variabel tidak saling berkaitan\n",
        "H1: Kedua variabel saling berkaitan"
      ],
      "metadata": {
        "id": "yLBFaxP80vqB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cross tabulation antara Status saat ini dan waktu mendapatkan pekerjaan\n",
        "CrosstabResult=pd.crosstab(index=df['Status saat ini:'],columns=df['Waktu Mendapatkan Pekerjaan:'])\n",
        "print(CrosstabResult)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PikBb29fzdK8",
        "outputId": "c92a6848-67db-4ffd-dd8c-54232ac305f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Waktu Mendapatkan Pekerjaan:  3-6 Bulan  6-12 bulan  \\\n",
            "Status saat ini:                                      \n",
            "ASN/PNS                               0           1   \n",
            "Dosen                                 0           0   \n",
            "Guru                                  0           0   \n",
            "Karyawan Swasta                      12           7   \n",
            "Karyawan, Melanjutkan Studi           0           0   \n",
            "Melamar Pekerjaan                     0           0   \n",
            "Melanjutkan Studi                     1           0   \n",
            "Melanjutkan Studi, Wirausaha          0           0   \n",
            "Mengurus Rumah Tangga                 0           0   \n",
            "Wirausaha                             0           0   \n",
            "karyawan BUMN                         0           1   \n",
            "\n",
            "Waktu Mendapatkan Pekerjaan:  Belum mendapatkan pekerjaan  \\\n",
            "Status saat ini:                                            \n",
            "ASN/PNS                                                 0   \n",
            "Dosen                                                   0   \n",
            "Guru                                                    0   \n",
            "Karyawan Swasta                                         0   \n",
            "Karyawan, Melanjutkan Studi                             0   \n",
            "Melamar Pekerjaan                                      19   \n",
            "Melanjutkan Studi                                       1   \n",
            "Melanjutkan Studi, Wirausaha                            0   \n",
            "Mengurus Rumah Tangga                                   0   \n",
            "Wirausaha                                               0   \n",
            "karyawan BUMN                                           0   \n",
            "\n",
            "Waktu Mendapatkan Pekerjaan:  Kurang dari 3 bulan.  \\\n",
            "Status saat ini:                                     \n",
            "ASN/PNS                                          2   \n",
            "Dosen                                            1   \n",
            "Guru                                             1   \n",
            "Karyawan Swasta                                 46   \n",
            "Karyawan, Melanjutkan Studi                      2   \n",
            "Melamar Pekerjaan                                0   \n",
            "Melanjutkan Studi                                1   \n",
            "Melanjutkan Studi, Wirausaha                     0   \n",
            "Mengurus Rumah Tangga                            2   \n",
            "Wirausaha                                        3   \n",
            "karyawan BUMN                                    2   \n",
            "\n",
            "Waktu Mendapatkan Pekerjaan:  Tidak berencana mencari pekerjaan  Wirausaha  \\\n",
            "Status saat ini:                                                             \n",
            "ASN/PNS                                                       0          0   \n",
            "Dosen                                                         0          0   \n",
            "Guru                                                          0          0   \n",
            "Karyawan Swasta                                               1          0   \n",
            "Karyawan, Melanjutkan Studi                                   0          0   \n",
            "Melamar Pekerjaan                                             0          0   \n",
            "Melanjutkan Studi                                             0          0   \n",
            "Melanjutkan Studi, Wirausaha                                  1          1   \n",
            "Mengurus Rumah Tangga                                         0          0   \n",
            "Wirausaha                                                     0          1   \n",
            "karyawan BUMN                                                 0          0   \n",
            "\n",
            "Waktu Mendapatkan Pekerjaan:  lebih dari 12 bulan.  \n",
            "Status saat ini:                                    \n",
            "ASN/PNS                                          0  \n",
            "Dosen                                            0  \n",
            "Guru                                             0  \n",
            "Karyawan Swasta                                  4  \n",
            "Karyawan, Melanjutkan Studi                      0  \n",
            "Melamar Pekerjaan                                0  \n",
            "Melanjutkan Studi                                0  \n",
            "Melanjutkan Studi, Wirausaha                     0  \n",
            "Mengurus Rumah Tangga                            0  \n",
            "Wirausaha                                        0  \n",
            "karyawan BUMN                                    0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import chi2_contingency\n",
        "\n",
        "# Uji Chi-sq test\n",
        "ChiSqResult = chi2_contingency(CrosstabResult)\n",
        "\n",
        "# P-Value adalah H0 probabilitas H0 benar\n",
        "# Jika P-value < 0.05 (taraf 95%) maka H0 ditolak\n",
        "\n",
        "print('P-Valuenya:', ChiSqResult[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPuWIycQ0XkO",
        "outputId": "de414f24-f003-4eca-b261-23cf7d74a3ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P-Valuenya: 1.0971374597180914e-14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Karena P-valuenya sangat kecil, maka H0 ditolak, artinya kedua variabel tersebut saling berkaitan"
      ],
      "metadata": {
        "id": "EQVLXOZw1Z5r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Insight 3\n",
        "Hubungan antara Kesesuaian kurikulum dan Kesesuaian bidang ilmu terhadap rata-rata penghasilan"
      ],
      "metadata": {
        "id": "Ny46J1Fj1jTm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Membuat boxplot Kesesuaian Kurikulum terhadap rata-rata Penghasilan\n",
        "plt.figure(figsize=(12,6))\n",
        "boxplot = df.boxplot(column='Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):', by='Kesesuaian Kurikulum:', vert=False)\n",
        "plt.title('Box Plot Kesesuaian Kurikulum dan Rata-Rata Penghasilan')\n",
        "plt.suptitle('')\n",
        "plt.ylabel('Kesesuaian Kurikulum')\n",
        "plt.xlabel('Penghasilan')\n",
        "plt.xlim(0,40)\n",
        "plt.grid(True)\n",
        "# outlier > 40 dihilangkan\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "UhAtLpZnnOyU",
        "outputId": "80a9972e-9fc7-40d0-b1eb-9cab3929ccc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAG9CAYAAAD5ixlRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABZ/UlEQVR4nO3deVwU9f8H8NdyLbDIoYKCByKoqIAmKeKFqKh4kqGZlmBW39IyD6zs8PqWlgdemR2WV6UmoaVpioZ4pKaoiSamhjeKIoKcLsvn9wc/9uuyoLuwywLzej4ePJadnfnM+z0zO/vemc/MyoQQAkREREQSZGbqAIiIiIhMhYUQERERSRYLISIiIpIsFkJEREQkWSyEiIiISLJYCBEREZFksRAiIiIiyWIhRERERJLFQoiIiIgki4VQDXT58mXIZDKsWbPG1KFIkkwmw6xZs0wdhlGVtY1FRkbCzs7OYPPo2bMnevbsabD2jGXfvn2QyWTYt2+fqUMhiZHJZHjjjTeqdJ6zZs2CTCbTGNasWTNERkZWaRxVSdKF0Jo1ayCTyTT+XFxcEBwcjJ07d1Z5PCU73JI/S0tLNG/eHGPGjMG///5rkHn88ccfmDVrFu7fv6/T+OV9+J0+fRr169dHs2bNcPnyZYPERo9XsoO6e/euxvBr167B09MTdevWxYkTJ0wUHRlDz549NfYJNjY28PPzw5IlS1BUVFShNvXdB+ijWbNmGvEqFAp06tQJ69atq3CbO3bsMMoXj8jISI1Y7e3t0a5dOyxatAgFBQUGnx9VXxamDqA6mDNnDjw8PCCEwO3bt7FmzRoMGDAA27Ztw6BBg6o8nokTJ6Jjx45QKpU4ceIEvvrqK/z6669ISkqCm5tbpdr+448/MHv2bERGRsLR0bFCbZw5cwa9e/eGQqFAfHw8mjVrVqmYapq8vDxYWFSPt86NGzcQHByMe/fuYc+ePejQoYNB2nV3d0deXh4sLS0N0h5VXOPGjTFv3jwAwN27d/HDDz9g8uTJuHPnDj7++GO92zPEPuBx2rdvj6lTpwIAUlNTsWrVKkRERKCgoACvvPKK3u3t2LEDK1asMEoxJJfLsWrVKgDA/fv38dNPPyEqKgrHjh3Dxo0bDT6/muCDDz7Au+++a+owqlT12JubWGhoKJ5++mn183HjxqFBgwbYsGGDSQqh7t27Izw8HAAwduxYtGzZEhMnTsTatWsxffr0Ko/nUWfPnkWvXr1gY2OD+Ph4eHh4mDQeU7C2tjZ1CACAmzdvIjg4GOnp6YiLi4O/v3+l2ywsLERRURGsrKyqTZ5S5+DggBdeeEH9/LXXXoO3tzeWL1+OOXPmwNzc3ITRaWvUqJFGvJGRkWjevDkWL15coULImCwsLDRiHT9+PAICArBp0yZER0dX+otnTWRhYVFtvuhVFUmfGiuPo6MjbGxstDaGnJwcTJ06FU2aNIFcLkerVq2wcOFCCCEAFB8p8Pb2hre3N/Ly8tTT3bt3D66urujSpQtUKpXe8fTq1QsAkJKS8tjxfv/9d3Tv3h0KhQKOjo4YOnQozp07p3591qxZmDZtGgDAw8NDfUhY11Nb586dQ+/evSGXyxEfH4/mzZtrvH706FH0798fDg4OsLW1RVBQEA4dOqQxzoMHDzBp0iQ0a9YMcrkcLi4uCAkJ0TqlY6i2yju3Xbp/ysOHDzFjxgz4+/vDwcEBCoUC3bt3R3x8vNa0pfsIXblyBePHj0erVq1gY2ODevXqYfjw4VrLteRU7KFDhzBlyhQ4OztDoVDgmWeewZ07d8pa5OVKTU1FcHAw0tLSsHv3bo1Cvry+N5GRkRpH70r6AS1cuBBLliyBp6cn5HI5/v77b537oZ06dQrOzs7o2bMnsrOzy+xf8Gjuj9vWyhunrD46PXv2hI+PD06fPo2goCDY2trCy8sLMTExAICEhAQEBATAxsYGrVq1wp49ex6bR4nr168jLCwMCoUCLi4umDx5cpmnSQ4cOIDhw4ejadOmkMvlaNKkCSZPnqzxvgf+d2r5xo0bCAsLg52dHZydnREVFVWhfQFQXIh37NgRDx48QFpamnr46dOn1UWHtbU1GjZsiJdeegnp6enqcZ60D1i9ejV69eoFFxcXyOVytGnTBitXrqxQnCWcnZ3h7e2NS5cuaQzXZRlGRkZixYoVAKBxGqvEwoUL0aVLF9SrVw82Njbw9/dXbwMVYWZmpn7vlCyTgoICzJw5E15eXuo43377ba3toqQ/z9atW+Hj4wO5XI62bdvit99+05rPvn378PTTT8Pa2hqenp748ssvy33vAHhim7rug5RKJWbPno0WLVrA2toa9erVQ7du3RAXF6ce53FxlLh37x6ioqLg6+sLOzs72NvbIzQ0FH/99ZdWnjKZDD/++CM+/vhjNG7cGNbW1ujduzcuXrz42HlUJWmVfeXIzMzE3bt3IYRAWloali9fjuzsbI1vCkIIDBkyBPHx8Rg3bhzat2+PXbt2Ydq0abhx4wYWL14MGxsbrF27Fl27dsX777+P6OhoAMCECROQmZmJNWvWVOjbW8kOpF69euWOs2fPHoSGhqJ58+aYNWsW8vLysHz5cnTt2hUnTpxAs2bNMGzYMPzzzz/YsGEDFi9ejPr16wMo3lE9yfnz59GrVy9YWFggPj4enp6eGq///vvvCA0Nhb+/P2bOnAkzMzP1TvXAgQPo1KkTgOJvszExMXjjjTfQpk0bpKen4+DBgzh37pz6tI4h29JVVlYWVq1aheeffx6vvPIKHjx4gG+++Qb9+vXDn3/+ifbt25c77bFjx/DHH39g5MiRaNy4MS5fvoyVK1eiZ8+e+Pvvv2Fra6sx/ptvvgknJyfMnDkTly9fxpIlS/DGG29g06ZNOsV6+/ZthIeH49atW9i9ezc6duyoV66lrV69Gvn5+Xj11Vchl8tRt25dnfqfHDt2DP369cPTTz+Nn3/+GTY2NpWKQ18ZGRkYNGgQRo4cieHDh2PlypUYOXIkvv/+e0yaNAmvvfYaRo0ahQULFiA8PBzXrl1DnTp1ym0vLy8PvXv3xtWrVzFx4kS4ublh/fr1+P3337XG3bx5M3Jzc/H666+jXr16+PPPP7F8+XJcv34dmzdv1hhXpVKhX79+CAgIwMKFC7Fnzx4sWrQInp6eeP311yuUe0mh+uiprbi4OPz7778YO3YsGjZsiLNnz+Krr77C2bNnceTIEchksifuA1auXIm2bdtiyJAhsLCwwLZt2zB+/HgUFRVhwoQJFYq1sLAQ169fh5OTk8ZwXZbhf/7zH9y8eRNxcXFYv369VttLly7FkCFDMHr0aDx8+BAbN27E8OHDsX37dgwcOLBC8T66vy0qKsKQIUNw8OBBvPrqq2jdujWSkpKwePFi/PPPP9i6davGtAcPHkRsbCzGjx+POnXqYNmyZXj22Wdx9epV9f775MmT6N+/P1xdXTF79myoVCrMmTOn3P2wLm3qug+aNWsW5s2bh5dffhmdOnVCVlYWjh8/jhMnTiAkJETnZfTvv/9i69atGD58ODw8PHD79m18+eWXCAoKwt9//611JO2TTz6BmZkZoqKikJmZifnz52P06NE4evSozvM0KiFhq1evFgC0/uRyuVizZo3GuFu3bhUAxEcffaQxPDw8XMhkMnHx4kX1sOnTpwszMzOxf/9+sXnzZgFALFmy5InxxMfHCwDi22+/FXfu3BE3b94Uv/76q2jWrJmQyWTi2LFjQgghUlJSBACxevVq9bTt27cXLi4uIj09XT3sr7/+EmZmZmLMmDHqYQsWLBAAREpKik7LKCIiQlhaWgpXV1fh5uYm/vnnH61xioqKRIsWLUS/fv1EUVGRenhubq7w8PAQISEh6mEODg5iwoQJ5c7PkG0JIYS7u7uIiIjQGh4UFCSCgoLUzwsLC0VBQYHGOBkZGaJBgwbipZde0hgOQMycOVMjttIOHz4sAIh169aph5Vsb3369NHIbfLkycLc3Fzcv3//sbnMnDlTABDu7u7C3t5eHD58uMzxSudWIiIiQri7u6ufl2xH9vb2Ii0tTWPcsraxiIgIoVAohBBCHDx4UNjb24uBAweK/Px8rRhLK8n90e2udJxljSPE/94X8fHxGtMCED/88IN6WHJysgAgzMzMxJEjR9TDd+3apZVLWZYsWSIAiB9//FE9LCcnR3h5eWnNv6x1Pm/ePCGTycSVK1fUwyIiIgQAMWfOHI1xn3rqKeHv7//YeEry9Pb2Fnfu3BF37twRycnJYtq0aQKAGDhwoMa4ZcW0YcMGAUDs379fPexx+4Cy2ujXr59o3rz5E2MVovj91rdvX3W8SUlJ4sUXXxQAtN6rui7DCRMmlLlNldXGw4cPhY+Pj+jVq9cTYy3ZnktivXjxopg7d66QyWTCz89PCCHE+vXrhZmZmThw4IDGtF988YUAIA4dOqQeBkBYWVlpfBb89ddfAoBYvny5etjgwYOFra2tuHHjhnrYhQsXhIWFhVaeurap6z6oXbt2WttNaWW9h0vvR/Pz84VKpdIYJyUlRcjlco1tveS927p1a43969KlSwUAkZSU9NhYqgpPjQFYsWIF4uLiEBcXh++++w7BwcF4+eWXERsbqx5nx44dMDc3x8SJEzWmnTp1KoQQGleZzZo1C23btkVERATGjx+PoKAgreke56WXXoKzszPc3NwwcOBA5OTkYO3atRqnPx6VmpqKU6dOITIyEnXr1lUP9/PzQ0hICHbs2KHzvMuiUqlw9+5d1K1bV/0N8lGnTp3ChQsXMGrUKKSnp+Pu3bu4e/cucnJy0Lt3b+zfv199hMHR0RFHjx7FzZs3y5yXIdvSh7m5OaysrAAARUVFuHfvHgoLC/H0008/8UqsR4+EKJVKpKenw8vLC46OjmVO++qrr2oceu7evTtUKhWuXLmiU6y3b9+GnZ0dXF1ddRr/SZ599lmdjgqWiI+PR79+/dC7d2/ExsZCLpcbJA592dnZYeTIkernrVq1gqOjI1q3bo2AgAD18JL/n3Tl5Y4dO+Dq6qrunwcAtra2ePXVV7XGfXSd5+Tk4O7du+jSpQuEEDh58qTW+K+99prG8+7du+t8JWhycjKcnZ3Vp5gWLFiAIUOGaJ22fDSm/Px83L17F507dwYAna8mfLSNkiPlQUFB+Pfff5GZmalTG7t371bH6+vri/Xr12Ps2LFYsGBBufPSZRk+Kd6MjAxkZmaie/fuOuebk5OjjtXLywvvvfceAgMDsWXLFgDFR61at24Nb29v9b7o7t276u4KpU+d9+nTR+NouZ+fH+zt7dXrWqVSYc+ePQgLC9M4auLl5YXQ0NAyY3xSm6WXw+P2QY6Ojjh79iwuXLig0/Ipj1wuh5mZmTqn9PR02NnZoVWrVmUu+7Fjx6r3r0Dx9g88+T1ZVXhqDECnTp00ioznn38eTz31FN544w0MGjQIVlZWuHLlCtzc3LQOrbdu3RoAND7ErKys8O2336Jjx46wtrbG6tWrn3jO9VEzZsxA9+7dYW5ujvr166N169aP7bxWMu9WrVppvda6dWvs2rULOTk5UCgUOsfwKBsbG6xatQqjR4/GwIEDERcXp9FWyZsqIiKi3DYyMzPh5OSE+fPnIyIiAk2aNIG/vz8GDBiAMWPGqPsbGbItfa1duxaLFi1CcnIylEqleviTOoTn5eVh3rx5WL16NW7cuKHuM1YSa2lNmzbVeF5yyiAjI0OnOL/77ju88MILCAkJwcGDB+Hi4qLTdOXRp8N7fn4+Bg4cCH9/f/z4448m7VTZuHFjrfeVg4MDmjRpojUMePLyvXLlCry8vLTaLOt9dfXqVcyYMQO//PKLVrul17m1tbVWoenk5KTz+m7WrBm+/vprFBUV4dKlS/j4449x584drc7s9+7dw+zZs7Fx40aNvkNlxVSeQ4cOYebMmTh8+DByc3O12nBwcEBmZqZGPx4rKyuNL2ABAQH46KOPoFKpcObMGXz00UfIyMjQ+CAE9FuG5dm+fTs++ugjnDp1SqPPjq77W2tra2zbtg1A8Ye7h4cHGjdurH79woULOHfuXLlfFEov59LvbUBzXaelpSEvLw9eXl5a45U1TJc2Ad33QXPmzMHQoUPRsmVL+Pj4oH///njxxRfh5+dX5rzLU1RUhKVLl+Lzzz9HSkqKRn+3srpwVHafZ2wshMpgZmaG4OBgLF26FBcuXEDbtm31bmPXrl0Aij84Lly4oNeHja+vL/r06aP3PI1p5MiRyMjIwPjx4zFs2DBs27ZN4wgKACxYsKDcvjQl9yIaMWIEunfvji1btmD37t1YsGABPv30U8TGxiI0NNSgbQHl7xBVKpVGf63vvvsOkZGRCAsLw7Rp0+Di4gJzc3PMmzdPq5NnaW+++SZWr16NSZMmITAwEA4ODpDJZBg5cmSZfW3K6yf26M7rcYKCgvDjjz9i2LBh6NevH/bt26f+sAeKcy6rrfI65+rTt0cul2PAgAH4+eef8dtvv2ldVfm45f0k+k5b3nKs7PJ9EpVKhZCQENy7dw/vvPMOvL29oVAocOPGDURGRmqt88pe1aVQKDT2B127dkWHDh3w3nvvYdmyZerhI0aMwB9//IFp06ahffv2sLOzQ1FREfr3769Tn69Lly6hd+/e8Pb2RnR0NJo0aQIrKyvs2LEDixcvVrfx1ltvYe3aterpgoKCNDqy169fXx1vv3794O3tjUGDBmHp0qWYMmUKAP2XYVkOHDiAIUOGoEePHvj888/h6uoKS0tLrF69Gj/88MMTpweK183j9rVFRUXw9fVV9/csrXTRbYxtT5c2dd0H9ejRA5cuXcLPP/+M3bt3Y9WqVVi8eDG++OILvPzyyzrHNHfuXHz44Yd46aWX8N///hd169aFmZkZJk2aZJR9nrGxECpHYWEhACA7OxtA8X1V9uzZgwcPHmgcFUpOTla/XuL06dOYM2cOxo4di1OnTuHll19GUlKSxoeVIZXM+/z581qvJScno379+uojOPocmSrt9ddfx7179/DBBx/ghRdewMaNG2FmZqY+bGtvb69TAefq6orx48dj/PjxSEtLQ4cOHfDxxx8jNDTUoG0Bxd88yrpx3JUrVzSOHMXExKB58+aIjY3VWEYzZ858YgwxMTGIiIjAokWL1MPy8/ONcsO6EoMHD8a3336LiIgIDBo0CLt371YXNE5OTmUectb11NvjyGQyfP/99xg6dCiGDx+OnTt3alyhVvJN7/79+xodeXWZ96PTGjpuXbi7u+PMmTMQQmhsA6XfV0lJSfjnn3+wdu1ajBkzRj380StvjMnPzw8vvPACvvzyS0RFRaFp06bIyMjA3r17MXv2bMyYMUM9blmnQMrbB2zbtg0FBQX45ZdfNL7Blz798/bbb2tcSFK6E3RpAwcORFBQEObOnYv//Oc/UCgUei3D8uL96aefYG1tjV27dmmcnl29evVj49GHp6cn/vrrL/Tu3btS+84SLi4usLa2LvOKqcpcRaXPPqhu3boYO3Ysxo4di+zsbPTo0QOzZs3SqxCKiYlBcHAwvvnmG43h9+/fL7P7RHXHPkJlUCqV2L17N6ysrNSnvgYMGACVSoXPPvtMY9zFixdDJpOpP3iVSiUiIyPh5uaGpUuXYs2aNbh9+zYmT55stHhdXV3Rvn17rF27VmPDP3PmDHbv3o0BAwaoh5UURBX9kH7//fcxefJkbN68Gf/5z38AAP7+/vD09MTChQvVheOjSi4NV6lUWoe8XVxc4Obmpj6sbci2gOId2ZEjR/Dw4UP1sO3bt+PatWsa05Z8Y3n0G8rRo0dx+PDhJyyR4mlLf7NZvnx5hS+P1tWLL76IJUuW4ODBg3j22WfVp/M8PT2RnJyscUn+X3/9pXX7gYqysrJCbGwsOnbsiMGDB+PPP/9Uv1ZSyO7fv189rKSP25OUNa1KpcJXX31lkLifZMCAAbh586bG5de5ubla8y9rWxFCYOnSpVUSJ1BcjCiVSvWRirJiAoAlS5ZoTVvePqCsNjIzM7UKizZt2qBPnz7qP13uX/XOO+8gPT0dX3/9dbnzKm8ZPi5emUym8T67fPmy1pVclTFixAjcuHFDHfej8vLykJOTo1d7JUegtm7dqtG38eLFi5X6NQNd90GP3koBKD667uXlpfedtMua3+bNm3Hjxg292qkueEQIwM6dO9VHdtLS0vDDDz/gwoULePfdd2Fvbw+g+Bt4cHAw3n//fVy+fBnt2rXD7t278fPPP2PSpEnqnXjJ+eq9e/eiTp068PPzw4wZM/DBBx8gPDxcoygxpAULFiA0NBSBgYEYN26c+vJ5BwcHjXvelOy03n//fYwcORKWlpYYPHiwXv2HFi1ahIyMDKxatQp169bFp59+ilWrViE0NBRt27bF2LFj0ahRI9y4cQPx8fGwt7fHtm3b8ODBAzRu3Bjh4eFo164d7OzssGfPHhw7dkz9TcbMzMxgbQHAyy+/jJiYGPTv3x8jRozApUuX8N1332ld/j9o0CDExsbimWeewcCBA5GSkoIvvvgCbdq0KbMgKz3t+vXr4eDggDZt2uDw4cPYs2fPY293YCgTJ05U9w0ZM2YMvv/+e7z00kuIjo5Gv379MG7cOKSlpeGLL75A27ZtkZWVZZD52tjYYPv27ejVqxdCQ0ORkJAAHx8f9O3bF02bNsW4ceMwbdo0mJub49tvv4WzszOuXr362Dbbtm2Lzp07Y/r06bh37x7q1q2LjRs3qo/OGtsrr7yCzz77DGPGjEFiYiJcXV2xfv16rdsfeHt7w9PTE1FRUbhx4wbs7e3x008/VWl/hzZt2mDAgAFYtWoVPvzwQ9SrVw89evTA/PnzoVQq0ahRI+zevbvMe4+Vtw/o27cvrKysMHjwYPznP/9BdnY2vv76a7i4uCA1NbVS8YaGhsLHxwfR0dGYMGGCXsuwJN6JEyeiX79+MDc3x8iRIzFw4EBER0ejf//+GDVqFNLS0rBixQp4eXnh9OnTlYq3xIsvvogff/wRr732GuLj49G1a1eoVCokJyfjxx9/xK5du8q9iKU8s2bNwu7du9G1a1e8/vrr6i/YPj4+OHXqVIXi1HUf1KZNG/Ts2RP+/v6oW7cujh8/rr4Fib7zKznr0aVLFyQlJeH777+vcP9Mk6vKS9Sqm7Iun7e2thbt27cXK1eu1LjEWQghHjx4ICZPnizc3NyEpaWlaNGihViwYIF6vMTERGFhYSHefPNNjekKCwtFx44dhZubm8jIyCg3npJLDTdv3vzYuMu6tFkIIfbs2SO6du0qbGxshL29vRg8eLD4+++/tab/73//Kxo1aiTMzMyeeCn9o5dMl84pLCxMABDz5s0TQghx8uRJMWzYMFGvXj0hl8uFu7u7GDFihNi7d68QQoiCggIxbdo00a5dO1GnTh2hUChEu3btxOeff67VviHbWrRokWjUqJGQy+Wia9eu4vjx41qXbhcVFYm5c+cKd3d3IZfLxVNPPSW2b9+udcm5ENqXz2dkZIixY8eK+vXrCzs7O9GvXz+RnJysdclpyfZWchuEEmVdHl6Wksta79y5o/Xam2++KQCI1157TQghxHfffSeaN28urKysRPv27cWuXbvKvXx+wYIFWu096fL5Enfv3hVt2rQRDRs2FBcuXBBCFL8PAgIChJWVlWjatKmIjo7W6fJ5IYS4dOmS6NOnj5DL5aJBgwbivffeE3FxcWVePt+2bVutuN3d3cu8PBhlXL5dlitXroghQ4YIW1tbUb9+ffHWW2+J3377TWv+f//9t+jTp4+ws7MT9evXF6+88or6suYnLTMhyr/NQGnl5SmEEPv27dPYFq9fvy6eeeYZ4ejoKBwcHMTw4cPFzZs3tbZXIcrfB/zyyy/Cz89PWFtbi2bNmolPP/1UfPvttzrfcqO85S+EEGvWrNFYProuw8LCQvHmm28KZ2dnIZPJNJbbN998I1q0aCHkcrnw9vYWq1ev1nnZlrduSnv48KH49NNPRdu2bYVcLhdOTk7C399fzJ49W2RmZqrHK28bK+sWHnv37hVPPfWUsLKyEp6enmLVqlVi6tSpwtraWmM8XdvUdR/00UcfiU6dOglHR0dhY2MjvL29xccffywePnyoHkfXy+enTp0qXF1dhY2Njejatas4fPiw1nu6vM+08j7DTEUmRDXprURERCRRYWFhBrm0nfTHPkJERERVqPRPsVy4cAE7duwo86dxyPh4RIiIiKgKubq6qn8X7sqVK1i5ciUKCgpw8uRJtGjRwtThSQ47SxMREVWh/v37Y8OGDbh16xbkcjkCAwMxd+5cFkEmwiNCREREJFnsI0RERESSxUKIiIiIJKtG9xEqKirCzZs3UadOHYPc/pyIiIiqPyEEHjx4ADc3N5iZVe6YTo0uhG7evKn1o3dEREQkDdeuXUPjxo0r1UaNLoRKfvw0JSUFdevWNXE0Va/kN9H69u0LS0tLU4dT5Zg/82f+zJ/5SzP/e/fuwcPDQ+NH0CuqRhdCJafD6tSpo/5NMClRKpWwtbWFvb29JN8IzJ/5M3/mz/ylmz8Ag3SLYWdpIiIikiwWQkRERCRZLISIiIhIslgIERERkWSxECIiIiLJYiFEREREksVCiIiIiCSLhRARERFJFgshIiIikiwWQkRERCRZLISIiIhIslgIERERkWSxECIiIiLJYiFEREREkmVh6gBINyl3c5BTUKgxLKcgGyce3ETDa2lQyO0qPQ+F3AIe9RWVboeIiKimYCFUA6TczUHwwn1aw82sb0Dh8TnWrzNHUX4jg8wrPqoniyEiIpIMFkI1QMmRoCXPtYeXy/+O/FzM+BsfJgLR4b7wcmpTqXlcTMvGpE2ntI46ERER1WYshGoQLxc7+DRyUD8vsig+ctPcWQGfBg7lTUZERETlYGdpIiIikiwWQkRERCRZLISIiIhIslgIERERkWSxECIiIiLJYiGkg9zcXJw4cQK5ubmmDqXG47IkIqLqhIWQDpKTk+Hv74/k5GRTh1LjcVkSEVF1wkKIiIiIJIuFEBEREUkWCyEiIiKSLBZCRDWQSqVCQkIC9u/fj4SEBKhUKlOHRERUJVQqFQ4ePGiw9kxaCM2aNQsymUzjz9vb25QhEVV7sbGx8PLyQkhICKKjoxESEgIvLy/ExsaaOjQiIqMq2f+FhYUZrE2THxFq27YtUlNT1X+GrPKIapvY2FiEh4fD19cXBw4cwIYNG3DgwAH4+voiPDycxRAR1VqP7v9+++03g7Vr8l+ft7CwQMOGDU0dBlG1p1KpMHXqVAwaNAhbt26FSqVCeno6AgICsHXrVoSFhSEqKgpDhw6Fubm5qcMlIjKY0vu/jIwMg7Vt8kLowoULcHNzg7W1NQIDAzFv3jw0bdq0zHELCgpQUFCgfp6VlQUAUCqVUCqVRosxO694nudT78PsSrrR5lOeS3dyAAA5eQUaeRYWFqofK5t/jjrHTHW7xnA+9T6A4mVa2ZhLpjfmuq9OEhIScPnyZaxfvx4qlUor/2nTpqFHjx6Ij49HUFCQKUOtElJb/6Uxf+b/6GNtV97+zxBMWggFBARgzZo1aNWqFVJTUzF79mx0794dZ86cQZ06dbTGnzdvHmbPnq01PD4+Hra2tkaL84+kSwCAKZuTID+QZ7T5PMn2fYdxy/l/z28W3gQAHD1yFNcsrlWq7eN3AMACU2OSKtXOkxTcuggA+DX+MDLuphmkzbi4OIO0U93t378fAHD9+nWkp/+vIC/JPy+veNvcuXMncnJyqj5AE5HK+i8P82f+UlB6/2fIXyeQCSGEwVqrpPv378Pd3R3R0dEYN26c1utlHRFq0qQJUlNTUa9ePaPFdfjP4wjq1gU/bN+L1j7tjDaf8ly6k4OpMUnY9HJHdHB3Ug9PSktCxJ4IrO2zFr4uvpWax4krGXhu1TEsCveFp7OisiGX69yZvzBqUG8kHPwDgZ2erlRbSqUScXFxCAkJgaWlpYEirL4SEhIQEhKCAwcOICAgQCv/I0eOoEePHoiLi5PMESEprf/SmD/zl1L+pfd/6enpcHV1RWZmJuzt7SvVtslPjT3K0dERLVu2xMWLF8t8XS6XQy6Xaw23tLQ06oZgZ1M8z1aujmjvbryCqzwWFsWrSWEj18izZLiFhUWl81eoc3SATyOHSrX1OEXpjgCKl6mh1pmx1391ERwcjGbNmmH+/PnYunWrerilpSXMzc2xYMECeHh4IDg4WFJ9hKSy/svD/Jm/FPIvvf8zZM4mv2rsUdnZ2bh06RJcXV1NHQpRtWNubo5FixZh+/btCAsLw5EjR5CXl4cjR44gLCwM27dvx8KFCyVVBBGRNJTe/x07dsxgbZu0EIqKilJ3gPrjjz/wzDPPwNzcHM8//7wpwyKqtoYNG4aYmBgkJSWhR48eeP7559GjRw+cOXMGMTExGDZsmKlDJCIyikf3f6GhoQZr16SF0PXr1/H888+jVatWGDFiBOrVq4cjR47A2dn5yRMTSdSwYcNw8eJFxMXFYcqUKYiLi8OFCxdYBBFRrVey/3u0e0BlmbSP0MaNG005e6Iay9zcHEFBQcjJyUFQUBBPhxGRZJibm6Nbt24Ga69a9REiIiIiqkoshIiIiEiyWAgRERGRZLEQ0oG3tzcSExPh7e1t6lBqPC5LIiKqTqrVDRWrK1tbW3To0MHUYdQKXJZERFSd8IgQERERSRYLISIiIpIsFkJEREQkWSyEiIiISLJYCBEREZFk8aqxGiBPqQIAnLmRqTH834yc4sc7OTArzNSaTh8X07IrNT0REVFNxEKoBrj0/0XKu7FJGsPNrG9A4QFMiUlCUf49g8xLIecmQURE0sFPvRqgb9uGAABPFzvYWP7vxzVzCrKx4w8VBowZCIXcrtLzUcgt4FFfUel2iIiIagoWQjVAXYUVRnZqqjVcqbTFrTpu6NDEBZaWliaIjIiIqGZjZ2kiIiKSLBZCREREJFkshIiIiEiyWAgRERGRZLEQIiIiIsliIURERESSxUKIiIiIJIuFEBEREUkWCyEiIiKSLBZCREREJFkshIiIiEiyWAgRERGRZLEQIiIiIsliIURERESSxUKIiIiIJIuFEBEREUkWCyEiIiKSLBZCREREJFkshIiIiEiyWAgRERGRZLEQIiIiIsliIURERESSxUKIiIiIJIuFEBEREUkWCyEiIiKSLBZCREREJFkshIiIiEiyWAgRERGRZLEQIiIiIsliIURERESSxUKIiIiIJIuFEBEREUmWhakDoP9JuZuDnIJCnccvLCzEtWzg7M0sWFjotioLVPnIUF5HV/c2sLGwqWioREREtQILoWoi5W4Oghfuq8CUFliYdETnsc2sb0DhsRyLsQZ9PP0rMD8iIqLag4VQNVFyJGjJc+3h5WKn0zSFhYU4ePAgunXrpvMRof2XT2LlRSDvoarCsRIREdUWLISqGS8XO/g0ctBpXKVSiSt2QFs3e1haWuo0TUqWbWXCIyIiqlXYWZqIiIgki4UQERERSRYLISIiIpIsFkJEREQkWSyEiIiISLJYCOkgNzcXJ06cQG5urqlDqTG4zIiIqCZgIaSD5ORk+Pv7Izk52dSh1BhcZkREVBOwECIiIiLJYiFEREREksVCiIiIiCSLP7FBNZJKpUJCQgL2798PhUKB4OBgmJubmzqsClGpVDhw4ABSU1Ph6uqK7t27PzGX2pQ/EZEpVZsjQp988glkMhkmTZpk6lComouNjYWXlxdCQkIQHR2NkJAQeHl5ITY21tSh6a0kl+DgYIwaNQrBwcFPzKU25U9EZGrVohA6duwYvvzyS/j5+Zk6FKrmYmNjER4eDl9fXxw4cAAbNmzAgQMH4Ovri/Dw8BpVDDyay+HDh/HgwQMcPnz4sbnUpvyJiKqDCp0aO3bsGOLj45GWloaioiKN16Kjo/VqKzs7G6NHj8bXX3+Njz76qCLhkESoVCpMnToVgwYNwtatW6FSqZCeno6AgABs3boVYWFhiIqKwtChQ6v9aaLSuZiZFX8n6dy5c7m51Kb8iYiqC70Loblz5+KDDz5Aq1at0KBBA8hkMvVrj/6vqwkTJmDgwIHo06fPEwuhgoICFBQUqJ9nZWUBAJRKJZRKpd7z1lV2XvE8z6feh9mVdKPM49KdHABATl6BzrmUjKdP7nkFDwEAl+9m45SRcgGKlxVQvOwMtW4SEhJw+fJlrF+/HiqVSiv/adOmoUePHoiPj0dQUJBB5mkspXNRqVQar5eVS23K3xAqsv3XJsyf+T/6KDWGzFvvQmjp0qX49ttvERkZWemZb9y4ESdOnMCxY8d0Gn/evHmYPXu21vD4+HjY2tpWOp7y/JF0CQAwZXMS5AfyjDYfANi+7zBuOes3TVxcnM7jHrhzE7AEFu+9iEX5xsul4NZFAMCv8YeRcTfNIG3u378fAHD9+nWkp/+viCvJPy+vOJ+dO3ciJyfHIPM0lvJyKVFWLrUpf0PSZ/uvjZg/85ciQ/5qgd6FkJmZGbp27VrpGV+7dg1vvfUW4uLiYG1trdM006dPx5QpU9TPs7Ky0KRJEwQHB6NevXqVjqk8TvWPYz6A6OG+aO3TzijzuHQnB1NjkjCoZyA6uDvpNI1SqURcXBxCQkJgaWmp0zQFZ49i31/A5N5e6N7UOLkAwLkzNhi1FhgYHIjATk8bpE2FQoHo6Gg0btwYAQEBWvkfOXIEABAaGlrtj4iUzqW0snKpTfkbQkW2/9qE+TN/Kedf1hfIitK7EJo8eTJWrFiBJUuWVGrGiYmJSEtLQ4cOHdTDVCoV9u/fj88++wwFBQVa/RzkcjnkcrlWW5aWlkbdEOxsiufZytUR7d2NU3BZWBSvCoWNXO9c9MnfRm4FAGhW385ouQBAUbojgOJlZ6h1ExwcjGbNmmH+/PnYunWrerilpSXMzc2xYMECeHh41IhLyUvnUtJHCACKiorKzKU25W9Ixn7/V3fMn/lLMX9D5qx3IRQVFYWBAwfC09MTbdq00QpG16tWevfujaSkJI1hY8eOhbe3N9555x1J7chJN+bm5li0aBHCw8MRFhaGadOmIS8vD0eOHMGCBQuwfft2xMTE1Ihtp3Qu06dPh4+PD86cOYN58+aVmUttyp+IqLrQuxCaOHEi4uPj1aejKtJBGgDq1KkDHx8fjWEKhQL16tXTGk5UYtiwYYiJicHUqVPRo0cP9XAPDw/ExMRg2LBhJoxOP4/m0qVLF/Xwx+VSm/InIqoO9C6E1q5di59++gkDBw40RjxETzRs2DAMHToU8fHx2LlzJ0JDQ2vs6aCSXPS5s3Rtyp+IyNT0LoTq1q0LT09PY8SCffv2GaVdqn3Mzc0RFBSEnJwcBAUF1egiwNzcHD179tR7mtqSPxGRKel9Z+lZs2Zh5syZBr10jYiIiMgU9D4itGzZMly6dAkNGjRAs2bNtDpLnzhxwmDBERERERmT3oVQWFiYEcIgIiIiqnp6F0IzZ840RhzVmre3NxITE+Ht7W3qUGoMLjMiIqoJKvSjq1Jja2urceNHejIuMyIiqgkq9BMbj7t3UOkfjyQiIiKqrvQuhLZs2aLxXKlU4uTJk1i7dm2ZP4hKREREVF3pXQgNHTpUa1h4eDjatm2LTZs2Ydy4cQYJjIiIiMjY9L6PUHk6d+6MvXv3Gqo5IiIiIqMzSCGUl5eHZcuWoVGjRoZojoiIiKhK6H1qzMnJSaOztBACDx48gK2tLb777juDBiclecriTuZnbmTqPE1hYSGuZQNnb2bBwkK3VXntHu8ITkREVELvQmjx4sUahZCZmRmcnZ0REBAAJycngwYnJZfSsgEA78Ym6TmlBRYmHdF5bDPrG1B4ADZW/G0qIiIivQuhyMhII4RBfds2BAB4utjBxlK3IqWwsBAHDx5Et27ddD4iVKDKR4bSH13d21Q4ViIiotpCp0/P06dP69ygn59fhYORsroKK4zs1FSvaZRKJa7YAW3d7LV+8618DgAa6B0fERFRbaRTIdS+fXvIZDIIIR47nkwm4w0ViYiIqMbQqRBKSUkxdhxEREREVU6nQsjd3d3YcRARERFVOb07S69bt+6xr48ZM6bCwRARERFVJb0LobfeekvjuVKpRG5uLqysrGBra8tCiIiIiGoMve8snZGRofGXnZ2N8+fPo1u3btiwYYMxYiQiIiIyCoP8xEaLFi3wySefaB0tIiIiIqrODPajqxYWFrh586ahmiMiIiIyOr37CP3yyy8az4UQSE1NxWeffYauXbsaLDAiIiIiY9O7EAoLC9N4LpPJ4OzsjF69emHRokWGiouIiIjI6PQuhIqKisp97Ul3niYiIiKqTvTuI7RgwYIyh6tUKowaNarSARERERFVlQoVQt98843GsMLCQowcORKnTp0yVFxERERERqf3qbFff/0Vffv2hYODA8LDw1FYWIgRI0YgOTkZ8fHxxoiRiIiIyCj0LoQ6duyIn376CWFhYbCyssI333yDixcvIj4+Hg0aNDBGjERERERGUaH7CPXq1Qvr1q3Ds88+i5SUFCQkJLAIIiIiohpHpyNCw4YNK3O4s7MzHB0d8eqrr6qHxcbGGiYyIiIiIiPTqRBycHAoc3i/fv0MGgwRERFRVdKpEFq9erWx4yAiIiKqcgb7rTEiIiKimkanI0IdOnTA3r174eTkhKeeegoymazccU+cOGGw4IiIiIiMSadCaOjQoZDL5QC0f2uMiIiIqKbSqRCaOXMmgOKf0QgODoafnx8cHR2NGRcRERGR0enVR8jc3Bx9+/ZFRkaGseIhIiIiqjJ6d5b28fHBv//+a4xYiIiIiKqU3oXQRx99hKioKGzfvh2pqanIysrS+CMiIiKqKfT+rbEBAwYAAIYMGaJx9ZgQAjKZDCqVynDRERERERmR3oUQf2GeiIiIagu9C6GgoCBjxEFERERU5fQuhPbv3//Y13v06FHhYIiIiIiqkt6FUM+ePbWGPdpXiH2EiIiIqKbQ+6qxjIwMjb+0tDT89ttv6NixI3bv3m2MGImIiIiMQu8jQg4ODlrDQkJCYGVlhSlTpiAxMdEggVGxlLs5yCkoLPO1wsJCXMsGzt7MgoWFbquyQJWPmzlX4KZwh9zcukIxKeQW8KivqNC0RERE1YnehVB5GjRogPPnzxuqOUJxERS8cN8TxrLAwqQjOrdpZn0DCo/lyEl5E0X5jSocW3xUTxZDRERU4+ldCJ0+fVrjuRACqamp+OSTT9C+fXtDxUWA+kjQkufaw8vFTuv1wsJCHDx4EN26ddP5iFBK1nm89yew9Ln28LBvpXdMF9OyMWnTqXKPUhEREdUkehdC7du3h0wmgxBCY3jnzp2xevVqgwVG/+PlYgefRtqnJJVKJa7YAW3d7GFpaalTW2bWxQWVp4sd2tTTbpOIiEhKdC6Ebt68CTc3N6SkpGgMNzMzg7OzM6ytrbFx40a0aqX/UQYiIiIiU9D5qrG+ffvi/v37cHd31/hr0qQJrK2tsWnTJowZM8aYsRIREREZlM6FkLOzM0JDQ5Gbm6v12o8//ogXXngBH3/8sUGDIyIiIjImnQuhbdu2QalUIiwsDEqlUj188+bNePHFF/HRRx9h2rRpRgmSiIiIyBh0LoTs7Oywc+dOXLt2DaNGjYIQAjExMRg9ejRmzpyJd955x5hxEhERERmcXneWdnZ2xu7du/Hnn38iJCQEo0ePxowZM/Dee+8ZKz6Dy83NxYkTJ8o8xUfGx+VPRETVic6F0OnTp3H69GlkZGRgwYIFOHjwIMLCwjBkyBD1a6XvMVQdJScnw9/fH8nJyaYORZK4/ImIqDrR+fL5R+8fVPK4efNmxMTEqO8pJJPJ+KOrREREVGPoXAiVvn8QERERUU2ncyHk7u5uzDiIiIiIqpzBfnSVqDZSqVQ4cOAAUlNT4erqiu7du8Pc3NzUYVVrVbHMVCoVEhISsH//figUCgQHB3O9EFGF6HXVmKGtXLkSfn5+sLe3h729PQIDA7Fz505ThkSkFhsbCy8vLwQHB2PUqFEIDg6Gl5cXYmNjTR1atVUVy6xkHiEhIYiOjkZISAjXCxFVmEkLocaNG+OTTz5BYmIijh8/jl69emHo0KE4e/asKcMiQmxsLMLDw+Hr64vDhw/jwYMHOHz4MHx9fREeHs4P3TJUxTJ7dB4HDhzAhg0bcODAAa4XIqowkxZCgwcPxoABA9CiRQu0bNkSH3/8Mezs7HDkyBFThkUSp1KpMHXqVAwaNAhbt25F586dYWdnh86dO2Pr1q0YNGgQoqKieIXkI6pimZWeR0BAAGxsbBAQEMD1QkQVVm36CKlUKmzevBk5OTkIDAwsc5yCggIUFBSon2dlZQEAlEqlxs9+PE52XvH051Pvw+xKeiWjNq5Ld3IAADl5BWXmVzJM19wBoLCwUP2oz3QlctTLL1Pdlj7Op94HULweKjL/R1Ukf10kJCTg8uXLWL9+PVQqldYH67Rp09CjRw/Ex8cjKCjIoPPWh7Hyr4iqWGal51E6/+qyXqpKdVr/psD8mb+h6F0I3b59G1FRUdi7dy/S0tLU9xAqoe+3saSkJAQGBiI/Px92dnbYsmUL2rRpU+a48+bNw+zZs7WGx8fHw9bWVqf5/ZF0CQAwZXMS5Afy9IrVVLbvO4xbzuW/HhcXp3NbNwtvAgAOHTyEFAv9b4lw/A4AWGBqTJLe0wJAwa2LAIBf4w8j425ahdooTZ/8dbF//34AwPXr15Gerl0s5+UVbzc7d+5ETk6OQeddEYbOvyKqYpmVN4+S/Kvbeqkq1WH9mxLzl2b+hvx1Ar0LocjISFy9ehUffvghXF1dIZPJKhVAq1atcOrUKWRmZiImJgYRERFISEgosxiaPn06pkyZon6elZWFJk2aIDg4GPXq1dNpfk71j2M+gOjhvmjt065SsRvbpTs5mBqThEE9A9HB3UnrdaVSibi4OISEhMDS0lKnNs/dO4fPf/scXbt1Reu6rfWOqeGVDKy/eAyLwn3h6azQe/pzZ2wwai0wMDgQgZ2e1nv6R1Ukf10oFApER0ejcePGCAgI0Hq95NRtaGioyY8IGSP/iqiKZVZ6HqXzry7rpapUp/VvCsxf2vmX9YWrovQuhA4ePIgDBw6gffv2BgnAysoKXl5eAAB/f38cO3YMS5cuxZdffqk1rlwuh1wu1xpuaWmp84ZgZ1M8fStXR7R31614MhULi+LVo7CRPzY/ffIvadPCwqJCbx6Fevk5wKeRg97TF6U7AiheD4Z68+qTvy6Cg4PRrFkzzJ8/H1u3boWZ2f+60hUVFWHBggXw8PCoNpdsGzr/iqiKZVZ6HiUsLS1hbm5e7dZLVakO69+UmL808zdkznp3lm7SpInW6TBDKioq0ugHRFTVzM3NsWjRImzfvh1hYWEaV0CFhYVh+/btWLhwoaQ+bJ+kKpZZ6XkcOXIEeXl5OHLkCNcLEVWY3keElixZgnfffRdffvklmjVrVqmZT58+HaGhoWjatCkePHiAH374Afv27cOuXbsq1S5RZQ0bNgwxMTGYOnUqunTpoh7u4eGBmJgYDBs2zITRVU9VscwenUePHj2MMg8ikha9C6HnnnsOubm58PT0hK2trdbhqXv37uncVlpaGsaMGYPU1FQ4ODjAz88Pu3btQkhIiL5hERncsGHDMHToUN5ZWg9VscxK5hEfH4+dO3ciNDRUcqfDiMhwKnREyFC++eYbg7VFZAzm5ubo2bOnqcOoUapimZmbmyMoKAg5OTkICgpiEUREFaZ3IRQREWGMOIiIiIiqXKVuqJifn4+HDx9qDLO3t69UQERERERVRe+rxnJycvDGG2/AxcUFCoUCTk5OGn9ERERENYXehdDbb7+N33//HStXroRcLseqVaswe/ZsuLm5Yd26dcaI0aC8vb2RmJgIb29vU4ciSVz+RERUneh9amzbtm1Yt24devbsibFjx6J79+7w8vKCu7s7vv/+e4wePdoYcRqMra0tOnToYOowJIvLn4iIqhO9jwjdu3cPzZs3B1DcH6jkcvlu3bqpfwuIiIiIqCbQuxBq3rw5UlKKf6zT29sbP/74I4DiI0WOjo4GDY6IiIjImPQuhMaOHYu//voLAPDuu+9ixYoVsLa2xuTJkzFt2jSDB0hERERkLHr3EZo8ebL6/z59+iA5ORmJiYnw8vKCn5+fQYMjIiIiMqZK3UcIANzd3eHu7m6IWIiIiIiqlE6F0LJly/Dqq6/C2toay5Yte+y4EydONEhgBOQpVQCAMzcyy3y9sLAQ17KBszezYGGhW02bkpUNALiUlo2i/LLbfZyLadl6T0NERFRd6fTpuXjxYowePRrW1tZYvHhxuePJZDIWQgZ06f+Ljndjkx4zlgUWJh3RuU0z6xtQeABvbTqFovw7FY5NIa/0wUQiIiKT0+nTrOQqsdL/k3H1bdsQAODpYgcbS+0flSwsLMTBgwfRrVs3nY8IFajycTOnPdyC3SE3t65QXAq5BTzqKyo0LRERUXXCr/XVWF2FFUZ2alru60qlElfsgLZu9rC0tNSxVQf4o4FhAiQiIqrhKlQIXb9+Hb/88guuXr2q9aOr0dHRBgmMiIiIyNj0LoT27t2LIUOGoHnz5khOToaPjw8uX74MIQR/OoGIiIhqFL1vqDh9+nRERUUhKSkJ1tbW+Omnn3Dt2jUEBQVh+PDhxoiRiIiIyCj0LoTOnTuHMWPGAAAsLCyQl5cHOzs7zJkzB59++qnBAyQiIiIyFr0LIYVCoe4X5OrqikuXLqlfu3v3ruEiIyIiIjIyvfsIde7cGQcPHkTr1q0xYMAATJ06FUlJSYiNjUXnzp2NESMRERGRUehdCEVHRyM7u/hGf7Nnz0Z2djY2bdqEFi1a8IoxIiIiqlH0LoSaN2+u/l+hUOCLL74waEBEREREVUXvPkJEREREtYXeR4TMzMwgk8nKfV2lUlUqICIiIqKqonchtGXLFo3nSqUSJ0+exNq1azF79myDBUZERERkbHoXQkOHDtUaFh4ejrZt22LTpk0YN26cQQIjIiIiMjaD9RHq3Lkz9u7da6jmiIiIiIzOIIVQXl4eli1bhkaNGhmiOSIiIqIqofepMScnJ43O0kIIPHjwALa2tvjuu+8MGhwRERGRMeldCC1evFijEDIzM4OzszMCAgLg5ORk0OCIiIiIjEnvQigyMtIIYRARERFVPb37CP322284ePCg+vmKFSvQvn17jBo1ChkZGQYNjoiIiMiY9C6Epk2bhqysLABAUlISpkyZggEDBiAlJQVTpkwxeIBERERExqL3qbGUlBS0adMGAPDTTz9h8ODBmDt3Lk6cOIEBAwYYPEAiIiIiY9H7iJCVlRVyc3MBAHv27EHfvn0BAHXr1lUfKSIiIiKqCfQ+ItStWzdMmTIFXbt2xZ9//olNmzYBAP755x80btzY4AESERERGYveR4Q+++wzWFhYICYmBitXrlTfRHHnzp3o37+/wQMkIiIiMha9jwg1bdoU27dv1xq+ePFigwREREREVFUq9BMbly5dwgcffIDnn38eaWlpAIqPCJ09e9agwREREREZk96FUEJCAnx9fXH06FHExsYiOzsbAPDXX39h5syZBg+QiIiIyFj0LoTeffddfPTRR4iLi4OVlZV6eK9evXDkyBGDBkdERERkTHoXQklJSXjmmWe0hru4uODu3bsGCYqIiIioKuhdCDk6OiI1NVVr+MmTJ9VXkBERERHVBHoXQiNHjsQ777yDW7duQSaToaioCIcOHUJUVBTGjBljjBiJiIiIjELvQmju3Lnw9vZGkyZNkJ2djTZt2qBHjx7o0qULPvjgA2PESERERGQUet9HyMrKCl9//TVmzJiBpKQkZGdn46mnnkKLFi2MER8RERGR0ehdCJVo0qQJmjRpApVKhaSkJGRkZMDJycmQsdET5BXm4UzOTTS8lgaF3K7S7SnkFvCorzBAZERERDWD3oXQpEmT4Ovri3HjxkGlUiEoKAh//PEHbG1tsX37dvTs2dMIYVJZ/rh2DhuVn+ObdeYoyjdMR/X4qJ4shoiISDL0LoRiYmLwwgsvAAC2bduGf//9F8nJyVi/fj3ef/99HDp0yOBBUtnyHxYBACb39kJwc/9KtXUxLRuTNp1CTkGhIUIjIiKqEfQuhO7evYuGDRsCAHbs2IERI0agZcuWeOmll7B06VKDB0hP1sTJBj6NHEwdBhERUY2j91VjDRo0wN9//w2VSoXffvsNISEhAIDc3FyYm5sbPEAiIiIiY9H7iNDYsWMxYsQIuLq6QiaToU+fPgCAo0ePwtvb2+ABEhERERmL3oXQrFmz4OPjg2vXrmH48OGQy+UAAHNzc7z77rsGD5CIiIjIWCp0+Xx4eDgAID8/Xz0sIiLCMBERERERVRG9+wipVCr897//RaNGjWBnZ4d///0XAPDhhx/im2++MXiARERERMaidyH08ccfY82aNZg/fz6srKzUw318fLBq1SqDBkePl1+Qr/FYneTm5uLEiRPIzc01dShERETl0rsQWrduHb766iuMHj1a4yqxdu3aITk52aDB0eOlXr+q8VidJCcnw9/fn9sEERFVa3oXQjdu3ICXl5fW8KKiIiiVSoMERURERFQV9C6E2rRpgwMHDmgNj4mJQfv27Q0RExEREVGV0PuqsRkzZiAiIgI3btxAUVERYmNjcf78eaxbtw7bt283RoxERERERqH3EaGhQ4di27Zt2LNnDxQKBWbMmIFz585h27ZtCAwMNEaMREREREahcyG0ePFi9f/du3dHXFwc0tLSkJubi4MHDyIwMBD9+vXTa+bz5s1Dx44dUadOHbi4uCAsLAznz5/Xqw0iIiKiitK5EHrvvfewbt26Ml/LyclB//79kZ6ertfMExISMGHCBBw5cgRxcXFQKpXo27cvcnJy9GqHiIiIqCJ07iO0fv16vPjii3B0dMSQIUPUw7Ozs9G/f3/cuXMHCQkJes38t99+03i+Zs0auLi4IDExET169NCrLSIiIiJ96VwIhYeH4/79+3j++efx66+/omfPnsjJyUFoaChu376NhIQEuLq6ViqYzMxMAEDdunXLfL2goAAFBQXq51lZWQAApVIpyUv3VYVF6sfK5p+TV7xcz6dmorCwsNKxnU+9DwDIzisw2ropaVeK6x5g/syf+T/6KDXM33B5y4QQQp8J5s+fj48//hg///wzZsyYgRs3biAhIQGNGzeuVCBFRUUYMmQI7t+/j4MHD5Y5zqxZszB79myt4T/88ANsbW0rNf+aaGfyURxquA1dbw1GqHdApdo6fgdYf7FCPz1XpoJbF3Fr7SS8/d9F6OLrabB2iYiIcnNzMWrUKGRmZsLe3r5Sben9yff222/j3r176N27N5o1a4Z9+/ZVuggCgAkTJuDMmTPlFkEAMH36dEyZMkX9PCsrC02aNEFwcDDq1atX6RhqmguF93AoG/DyaoEBAwZUqq2GVzKw/uIxLAr3haezotKxnTtjg1FrgYHBgQjs9HSl2yuLUqlEXFwcQkJCYGlpaZR5VGfMn/kzf+Yv1fz17ZP8ODoXQsOGDdN4bmlpifr16+Ott97SGB4bG6t3EG+88Qa2b9+O/fv3P7aoksvlkMvlWsMtLS0luSGYW5ipHyubv8KmeLm2cnWATyOHSsdWlO4IALCzkRt93Uh1/Zdg/syf+TN/qTFkzjoXQg4Omh+Ozz//fKVnLoTAm2++iS1btmDfvn3w8PCodJtEREREutK5EFq9erXBZz5hwgT88MMP+Pnnn1GnTh3cunULQHHRZWNjY/D5ERERET1K7ztLG9LKlSuRmZmJnj17wtXVVf23adMmU4ZFREREEmG4y4QqQM8L1oiIiIgMyqRHhIiIiIhMiYUQERERSRYLISIiIpIsFkI1mGvjphqP1Ym3tzcSExPh7e1t6lCIiIjKZdLO0lQ51nJrjcfqxNbWFh06dDB1GERERI/FI0JEREQkWSyEiIiISLJYCBEREZFksRAiIiIiyWIhRERERJLFq8ZqsPxCFQDg0p0cnLmRWam2LqZlGyIkIiKiGoWFUA12IyMfALAi4V8s31VgkDYVcm4SREQkHfzUq8HCfZ/CxYvj8dzz/VHfzqHS7SnkFvCorzBAZERERDUDC6EazM3BHv1d3NC1uSssLS1NHQ4REVGNw87SREREJFkshIiIiEiyWAgRERGRZLEQIiIiIsliIURERESSxUKIiIiIJIuFEBEREUkWCyEiIiKSLBZCREREJFkshIiIiEiyWAgRERGRZLEQIiIiIsliIURERESSxUKIiIiIJIuFEBEREUkWCyEiIiKSLBZCREREJFkshIiIiEiyWAgRERGRZLEQIiIiIsliIURERESSxUKIiIiIJIuFEBEREUkWCyEiIiKSLBZCREREJFkshIiIiEiyWAgRERGRZLEQIiIiIsliIURERESSxUKIiIiIJIuFEBEREUkWCyEiIiKSLBZCEpNXmIddFxKRV5hn6lCIiIhMjoWQxGxJOomoPyKx9cxJU4dCRERkciyEJOZ2Vj4A4FZmvokjISIiMj0WQkRERCRZLISIiIhIslgIERERkWSxECIiIiLJYiFEREREksVCiIiIiCSLhZDE5GRnaTwSERFJGQshibmQlKjxSEREJGUshIiIiEiyWAgRERGRZLEQIiIiIsliIURERESSZdJCaP/+/Rg8eDDc3Nwgk8mwdetWU4ZDREREEmPSQignJwft2rXDihUrTBkGERERSZSFKWceGhqK0NBQU4ZAREREEmbSQkhfBQUFKCgoUD/Pyiq+KaBSqYRSqTRVWCZTkrM+uYsioX6s6cusIvnXJsyf+T/6KDXMn/kbikwIIQzWWiXIZDJs2bIFYWFh5Y4za9YszJ49W2v4Dz/8AFtbWyNGV3us+H07UjscgeuJzpjQa5CpwyEiItJbbm4uRo0ahczMTNjb21eqrRp1RGj69OmYMmWK+nlWVhaaNGmC4OBg1KtXz4SRmYZSqURcXBxCQkJgaWmp0zQ/nT2GVAANGzTAgAEDjBugkVUk/9qE+TN/5s/8pZp/enq6wdqqUYWQXC6HXC7XGm5paSnJDaGEPvnLzGTqx9qyzLj+mT/zZ/5SJdX8DZkz7yNEREREkmXSI0LZ2dm4ePGi+nlKSgpOnTqFunXromnTpiaMjIiIiKTApIXQ8ePHERwcrH5e0v8nIiICa9asMVFUREREJBUmLYR69uyJanLRGhEREUkQ+wgRERGRZLEQIiIiIsliIURERESSxUJIYlr4+ms8EhERSRkLIYlR2NlrPBIREUkZCyEiIiKSLBZCREREJFkshIiIiEiyWAgRERGRZLEQIiIiIsliIURERESSxUKIiIiIJMukP7pKVe+59v4Alv7/IxERkbSxEJIYNwcHTA7qZeowiIiIqgWeGiMiIiLJYiFEREREksVCiIiIiCSLhRARERFJFgshIiIikiwWQkRERCRZLISIiIhIslgIERERkWSxECIiIiLJYiFEREREksVCiIiIiCSLhRARERFJFgshIiIikiwWQkRERCRZFqYOoDKEEACABw8ewNLS0sTRVD2lUonc3FxkZWUxf+Zv6nCqHPNn/sxfuvk/ePAAwP/qgMqo0YVQeno6AMDDw8PEkRAREVFVS09Ph4ODQ6XaqNGFUN26dQEAV69erfSCqImysrLQpEkTXLt2Dfb29qYOp8oxf+bP/Jk/85dm/pmZmWjatKm6DqiMGl0ImZkVd3FycHCQ5IZQwt7envkzf1OHYTLMn/kzf+nmX1IHVKoNA8RBREREVCOxECIiIiLJqtGFkFwux8yZMyGXy00dikkwf+bP/Jk/82f+UmTI/GXCENeeEREREdVANfqIEBEREVFlsBAiIiIiyWIhRERERJLFQoiIiIgkq0YXQitWrECzZs1gbW2NgIAA/Pnnn6YOqUrMmjULMplM48/b29vUYRnN/v37MXjwYLi5uUEmk2Hr1q0arwshMGPGDLi6usLGxgZ9+vTBhQsXTBOsETwp/8jISK3toX///qYJ1sDmzZuHjh07ok6dOnBxcUFYWBjOnz+vMU5+fj4mTJiAevXqwc7ODs8++yxu375toogNS5f8e/bsqbX+X3vtNRNFbFgrV66En5+f+qaBgYGB2Llzp/r12rzugSfnX5vXfVk++eQTyGQyTJo0ST3MENtAjS2ENm3ahClTpmDmzJk4ceIE2rVrh379+iEtLc3UoVWJtm3bIjU1Vf138OBBU4dkNDk5OWjXrh1WrFhR5uvz58/HsmXL8MUXX+Do0aNQKBTo168f8vPzqzhS43hS/gDQv39/je1hw4YNVRih8SQkJGDChAk4cuQI4uLioFQq0bdvX+Tk5KjHmTx5MrZt24bNmzcjISEBN2/exLBhw0wYteHokj8AvPLKKxrrf/78+SaK2LAaN26MTz75BImJiTh+/Dh69eqFoUOH4uzZswBq97oHnpw/UHvXfWnHjh3Dl19+CT8/P43hBtkGRA3VqVMnMWHCBPVzlUol3NzcxLx580wYVdWYOXOmaNeunanDMAkAYsuWLernRUVFomHDhmLBggXqYffv3xdyuVxs2LDBBBEaV+n8hRAiIiJCDB061CTxVLW0tDQBQCQkJAghite1paWl2Lx5s3qcc+fOCQDi8OHDpgrTaErnL4QQQUFB4q233jJdUFXMyclJrFq1SnLrvkRJ/kJIZ90/ePBAtGjRQsTFxWnkbKhtoEYeEXr48CESExPRp08f9TAzMzP06dMHhw8fNmFkVefChQtwc3ND8+bNMXr0aFy9etXUIZlESkoKbt26pbEtODg4ICAgQDLbAgDs27cPLi4uaNWqFV5//XWkp6ebOiSjyMzMBPC/H1xOTEyEUqnUWP/e3t5o2rRprVz/pfMv8f3336N+/frw8fHB9OnTkZuba4rwjEqlUmHjxo3IyclBYGCg5NZ96fxLSGHdT5gwAQMHDtRY14Dh3v818kdX7969C5VKhQYNGmgMb9CgAZKTk00UVdUJCAjAmjVr0KpVK6SmpmL27Nno3r07zpw5gzp16pg6vCp169YtAChzWyh5rbbr378/hg0bBg8PD1y6dAnvvfceQkNDcfjwYZibm5s6PIMpKirCpEmT0LVrV/j4+AAoXv9WVlZwdHTUGLc2rv+y8geAUaNGwd3dHW5ubjh9+jTeeecdnD9/HrGxsSaM1nCSkpIQGBiI/Px82NnZYcuWLWjTpg1OnToliXVfXv5A7V/3ALBx40acOHECx44d03rNUO//GlkISV1oaKj6fz8/PwQEBMDd3R0//vgjxo0bZ8LIyBRGjhyp/t/X1xd+fn7w9PTEvn370Lt3bxNGZlgTJkzAmTNnanV/uMcpL/9XX31V/b+vry9cXV3Ru3dvXLp0CZ6enlUdpsG1atUKp06dQmZmJmJiYhAREYGEhARTh1Vlysu/TZs2tX7dX7t2DW+99Rbi4uJgbW1ttPnUyFNj9evXh7m5uVbP8Nu3b6Nhw4Ymisp0HB0d0bJlS1y8eNHUoVS5kvXNbeF/mjdvjvr169eq7eGNN97A9u3bER8fj8aNG6uHN2zYEA8fPsT9+/c1xq9t67+8/MsSEBAAALVm/VtZWcHLywv+/v6YN28e2rVrh6VLl0pm3ZeXf1lq27pPTExEWloaOnToAAsLC1hYWCAhIQHLli2DhYUFGjRoYJBtoEYWQlZWVvD398fevXvVw4qKirB3716Nc6dSkZ2djUuXLsHV1dXUoVQ5Dw8PNGzYUGNbyMrKwtGjRyW5LQDA9evXkZ6eXiu2ByEE3njjDWzZsgW///47PDw8NF739/eHpaWlxvo/f/48rl69WivW/5PyL8upU6cAoFas/7IUFRWhoKCg1q/78pTkX5batu579+6NpKQknDp1Sv339NNPY/To0er/DbINGLZvd9XZuHGjkMvlYs2aNeLvv/8Wr776qnB0dBS3bt0ydWhGN3XqVLFv3z6RkpIiDh06JPr06SPq168v0tLSTB2aUTx48ECcPHlSnDx5UgAQ0dHR4uTJk+LKlStCCCE++eQT4ejoKH7++Wdx+vRpMXToUOHh4SHy8vJMHLlhPC7/Bw8eiKioKHH48GGRkpIi9uzZIzp06CBatGgh8vPzTR16pb3++uvCwcFB7Nu3T6Smpqr/cnNz1eO89tpromnTpuL3338Xx48fF4GBgSIwMNCEURvOk/K/ePGimDNnjjh+/LhISUkRP//8s2jevLno0aOHiSM3jHfffVckJCSIlJQUcfr0afHuu+8KmUwmdu/eLYSo3eteiMfnX9vXfXlKXylniG2gxhZCQgixfPly0bRpU2FlZSU6deokjhw5YuqQqsRzzz0nXF1dhZWVlWjUqJF47rnnxMWLF00dltHEx8cLAFp/ERERQojiS+g//PBD0aBBAyGXy0Xv3r3F+fPnTRu0AT0u/9zcXNG3b1/h7OwsLC0thbu7u3jllVdqzReCsvIGIFavXq0eJy8vT4wfP144OTkJW1tb8cwzz4jU1FTTBW1AT8r/6tWrokePHqJu3bpCLpcLLy8vMW3aNJGZmWnawA3kpZdeEu7u7sLKyko4OzuL3r17q4sgIWr3uhfi8fnX9nVfntKFkCG2AZkQQlTiyBURERFRjVUj+wgRERERGQILISIiIpIsFkJEREQkWSyEiIiISLJYCBEREZFksRAiIiIiyWIhRERERJLFQoiIiIgki4UQEVVrPXv2xKRJk4zW/r59+yCTydQ/3LhmzRo4OjoabX5EVL2wECKickVGRkImk0Emk6l/BXvOnDkoLCw0dWgG06VLF6SmpsLBwcHUoRCRCViYOgAiqt769++P1atXo6CgADt27MCECRNgaWmJ6dOnmzo0g7CyskLDhg1NHQYRmQiPCBHRY8nlcjRs2BDu7u54/fXX0adPH/zyyy8oKChAVFQUGjVqBIVCgYCAAOzbt089Xckppl27dqF169aws7ND//79kZqaqh6nsLAQEydOhKOjI+rVq4d33nkHERERCAsL04ihqKgIb7/9NurWrYuGDRti1qxZGq9HR0fD19cXCoUCTZo0wfjx45Gdna1+/cqVKxg8eDCcnJygUCjQtm1b7NixA4D2qbHSLl26hKFDh6JBgwaws7NDx44dsWfPHo1xmjVrhrlz5+Kll15CnTp10LRpU3z11Vf6L2wiqnIshIhILzY2Nnj48CHeeOMNHD58GBs3bsTp06cxfPhw9O/fHxcuXFCPm5ubi4ULF2L9+vXYv38/rl69iqioKPXrn376Kb7//nusXr0ahw4dQlZWFrZu3ao1z7Vr10KhUODo0aOYP38+5syZg7i4OPXrZmZmWLZsGc6ePYu1a9fi999/x9tvv61+fcKECSgoKMD+/fuRlJSETz/9FHZ2djrlm52djQEDBmDv3r04efIk+vfvj8GDB+Pq1asa4y1atAhPP/00Tp48ifHjx+P111/H+fPndV2sRGQqev1WPRFJSkREhBg6dKgQQoiioiIRFxcn5HK5iIyMFObm5uLGjRsa4/fu3VtMnz5dCCHE6tWrBQBx8eJF9esrVqwQDRo0UD9v0KCBWLBggfp5YWGhaNq0qXqeQggRFBQkunXrpjGfjh07infeeafcuDdv3izq1aunfu7r6ytmzZpV5rjx8fECgMjIyFDH7eDgUG7bQgjRtm1bsXz5cvVzd3d38cILL6ifFxUVCRcXF7Fy5crHtkNEpsc+QkT0WNu3b4ednR2USiWKioowatQohIeHY82aNWjZsqXGuAUFBahXr576ua2tLTw9PdXPXV1dkZaWBgDIzMzE7du30alTJ/Xr5ubm8Pf3R1FRkUa7fn5+Gs8fbQcA9uzZg3nz5iE5ORlZWVkoLCxEfn4+cnNzYWtri4kTJ+L111/H7t270adPHzz77LNabZYnOzsbs2bNwq+//orU1FQUFhYiLy9P64jQo+3JZDI0bNhQI0Yiqp54aoyIHis4OBinTp3ChQsXkJeXh7Vr1yI7Oxvm5uZITEzEqVOn1H/nzp3D0qVL1dNaWlpqtCWTySCE0DuGstopKZYuX76MQYMGwc/PDz/99BMSExOxYsUKAMDDhw8BAC+//DL+/fdfvPjii0hKSsLTTz+N5cuX6zTvqKgobNmyBXPnzsWBAwdw6tQp+Pr6qtvWJUYiqr5YCBHRYykUCnh5eaFp06awsCg+iPzUU09BpVIhLS0NXl5eGn+6XoHl4OCABg0a4NixY+phKpUKJ06c0Cu+xMREFBUVYdGiRejcuTNatmyJmzdvao3XpEkTvPbaa4iNjcXUqVPx9ddf69T+oUOHEBkZiWeeeQa+vr5o2LAhLl++rFeMRFR98dQYEemtZcuWGD16NMaMGYNFixbhqaeewp07d7B37174+flh4MCBOrXz5ptvYt68efDy8oK3tzeWL1+OjIwMyGQynWPx8vKCUqnE8uXLMXjwYBw6dAhffPGFxjiTJk1CaGgoWrZsiYyMDMTHx6N169Y6td+iRQvExsZi8ODBkMlk+PDDD3mkh6gW4REhIqqQ1atXY8yYMZg6dSpatWqFsLAwHDt2DE2bNtW5jXfeeQfPP/88xowZg8DAQNjZ2aFfv36wtrbWuY127dohOjoan376KXx8fPD9999j3rx5GuOoVCpMmDABrVu3Rv/+/dGyZUt8/vnnOrUfHR0NJycndOnSBYMHD0a/fv3QoUMHneMjoupNJipywp6IyAiKiorQunVrjBgxAv/9739NHQ4RSQBPjRGRyVy5cgW7d+9GUFAQCgoK8NlnnyElJQWjRo0ydWhEJBE8NUZEJmNmZoY1a9agY8eO6Nq1K5KSkrBnzx6d++8QEVUWT40RERGRZPGIEBEREUkWCyEiIiKSLBZCREREJFkshIiIiEiyWAgRERGRZLEQIiIiIsliIURERESSxUKIiIiIJOv/AKynk+0Os1NLAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Membuat boxplot Kesesuaian Bidang Ilmu terhadap rata-rata Penghasilan\n",
        "plt.figure(figsize=(12,6))\n",
        "boxplot = df.boxplot(column='Perkiraan rata-rata penghasilan (take-home-pay) anda per-bulan saat ini (dalam juta rupiah):', by='Kesesuaian Bidang Ilmu:', vert=False)\n",
        "plt.title('Box Plot Kesesuaian Bidang Ilmu dan Rata-Rata Penghasilan')\n",
        "plt.suptitle('')\n",
        "plt.ylabel('Kesesuaian Bidang Ilmu')\n",
        "plt.xlabel('Penghasilan')\n",
        "plt.xlim(0,40)\n",
        "plt.grid(True)\n",
        "# outlier > 40 dihilangkan\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "LYSFcmfo5kQI",
        "outputId": "e3cc1ef1-930a-4abc-fa77-fb50298395a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan plot correlation yang telah dibuat telah terindikasi juga adanya korelasi/asosiasi antara Kesesuaian Kurikulum dan Kesesuaian bidang Ilmu, dimana"
      ],
      "metadata": {
        "id": "dpedJ--H56zm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Kesesuaian Kurikulum:\"].corr(df[\"Kesesuaian Bidang Ilmu:\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GlSVHYXM6UPl",
        "outputId": "d617b75b-20b5-4a29-fd8d-573e17e7bf24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6180888947825135"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yang mana korelasi tersebut cukup signifikan"
      ],
      "metadata": {
        "id": "PDCBKOkZ6b2p"
      }
    }
  ]
}