DecisionTree_Iris.ipynb

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      "cell_type": "markdown",
      "source": [
        "Dataset Iris (csv)"
      ],
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        "id": "oV8mzB21JbfV"
      }
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      "execution_count": 55,
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        "id": "6bRSUWZ5_qnH"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import tree\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        },
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/My Drive/dataset/data-iris.csv\")\n",
        "df.head(5)"
      ],
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              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "0   1           7.00          3.02           4.07          1.04   \n",
              "1   2           6.04          3.02           4.05          1.05   \n",
              "2   3           6.09          3.01           4.09          1.05   \n",
              "3   4           5.05          2.03           4.00          1.03   \n",
              "4   5           6.05          2.08           4.06          1.05   \n",
              "\n",
              "           Species  \n",
              "0  Iris-versicolor  \n",
              "1  Iris-versicolor  \n",
              "2  Iris-versicolor  \n",
              "3  Iris-versicolor  \n",
              "4  Iris-versicolor  "
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              "     Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "91   92           6.09          3.01           5.01          2.03   \n",
              "92   93           5.08          2.07           5.01          1.09   \n",
              "93   94           6.08          3.02           5.09          2.03   \n",
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              "99  100           5.09          3.00           5.01          1.08   \n",
              "\n",
              "           Species  \n",
              "91  Iris-virginica  \n",
              "92  Iris-virginica  \n",
              "93  Iris-virginica  \n",
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              "(100, 6)"
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              "Id                 int64\n",
              "SepalLengthCm    float64\n",
              "SepalWidthCm     float64\n",
              "PetalLengthCm    float64\n",
              "PetalWidthCm     float64\n",
              "Species           object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
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        "id": "PI9SXjsvB7l3",
        "outputId": "0f5137e1-6d2f-4263-e552-0c9f61d34331"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count  100.000000     100.000000    100.000000     100.000000    100.000000\n",
              "mean    50.500000       5.828200      2.492200       4.504600      1.328600\n",
              "std     29.011492       0.682243      0.476052       0.828199      0.445877\n",
              "min      1.000000       4.090000      2.000000       3.000000      1.000000\n",
              "25%     25.750000       5.080000      2.070000       4.037500      1.030000\n",
              "50%     50.500000       6.030000      2.090000       4.090000      1.060000\n",
              "75%     75.250000       6.070000      3.002500       5.052500      2.000000\n",
              "max    100.000000       7.090000      3.080000       6.090000      2.050000"
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              "      <th>PetalWidthCm</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>50.500000</td>\n",
              "      <td>5.828200</td>\n",
              "      <td>2.492200</td>\n",
              "      <td>4.504600</td>\n",
              "      <td>1.328600</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>29.011492</td>\n",
              "      <td>0.682243</td>\n",
              "      <td>0.476052</td>\n",
              "      <td>0.828199</td>\n",
              "      <td>0.445877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.090000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>25.750000</td>\n",
              "      <td>5.080000</td>\n",
              "      <td>2.070000</td>\n",
              "      <td>4.037500</td>\n",
              "      <td>1.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>50.500000</td>\n",
              "      <td>6.030000</td>\n",
              "      <td>2.090000</td>\n",
              "      <td>4.090000</td>\n",
              "      <td>1.060000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>75.250000</td>\n",
              "      <td>6.070000</td>\n",
              "      <td>3.002500</td>\n",
              "      <td>5.052500</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>7.090000</td>\n",
              "      <td>3.080000</td>\n",
              "      <td>6.090000</td>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fuyc9ghTCC_-",
        "outputId": "6ed529f6-cad4-4e7f-ef13-ae35c927ab19"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id               0\n",
              "SepalLengthCm    0\n",
              "SepalWidthCm     0\n",
              "PetalLengthCm    0\n",
              "PetalWidthCm     0\n",
              "Species          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mengubah kelas (kolom \"Species\") dari String ke Unique-Integer\n",
        "\n",
        "df[\"Species\"] = pd.factorize(df.Species)[0]"
      ],
      "metadata": {
        "id": "bejPGbTjCFxQ"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 206
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        "id": "faVzDE5YCPks",
        "outputId": "2b84206e-13ac-4878-c585-32eacfadf4b2"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0   1           7.00          3.02           4.07          1.04        0\n",
              "1   2           6.04          3.02           4.05          1.05        0\n",
              "2   3           6.09          3.01           4.09          1.05        0\n",
              "3   4           5.05          2.03           4.00          1.03        0\n",
              "4   5           6.05          2.08           4.06          1.05        0"
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              "      <th>4</th>\n",
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              "      <td>6.05</td>\n",
              "      <td>2.08</td>\n",
              "      <td>4.06</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.tail(5)"
      ],
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          "height": 206
        },
        "id": "Q2KNkLtMCRbu",
        "outputId": "ef37e11e-e299-413e-ad62-b1b2f891006d"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "95   96           6.07          3.00           5.02          2.03        1\n",
              "96   97           6.03          2.05           5.00          1.09        1\n",
              "97   98           6.05          3.00           5.02          2.00        1\n",
              "98   99           6.02          3.04           5.04          2.03        1\n",
              "99  100           5.09          3.00           5.01          1.08        1"
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              "      <th>95</th>\n",
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              "    <tr>\n",
              "      <th>96</th>\n",
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              "      <th>97</th>\n",
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              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>99</td>\n",
              "      <td>6.02</td>\n",
              "      <td>3.04</td>\n",
              "      <td>5.04</td>\n",
              "      <td>2.03</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>100</td>\n",
              "      <td>5.09</td>\n",
              "      <td>3.00</td>\n",
              "      <td>5.01</td>\n",
              "      <td>1.08</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce72b4d9-1d86-41ea-8d43-09b94bc6b5bf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-ce72b4d9-1d86-41ea-8d43-09b94bc6b5bf 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-ce72b4d9-1d86-41ea-8d43-09b94bc6b5bf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Iris-setosa : 0 <br>\n",
        "Iris-virginica: 1"
      ],
      "metadata": {
        "id": "DWLry54pCqjA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# hapus kolom \"Id\"\n",
        "\n",
        "df = df.drop(labels=\"Id\", axis=1)"
      ],
      "metadata": {
        "id": "kO2wuCfUCglv"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zA3btfLpD3cH",
        "outputId": "6d4d5fca-6078-4d77-92a0-7f337e1c2709"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0           7.00          3.02           4.07          1.04        0\n",
              "1           6.04          3.02           4.05          1.05        0\n",
              "2           6.09          3.01           4.09          1.05        0\n",
              "3           5.05          2.03           4.00          1.03        0\n",
              "4           6.05          2.08           4.06          1.05        0"
            ],
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              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
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              "      <th>1</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>4.06</td>\n",
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              "      <td>0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f81dd387-b758-4552-8d71-fb8de1371d33')\"\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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          "metadata": {},
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      ]
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    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "KyVTqxPmETBz",
        "outputId": "c85a0ca7-b563-4b70-fac7-ad112a68bc64"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "SepalLengthCm       1.000000      0.372102       0.709930      0.398166   \n",
              "SepalWidthCm        0.372102      1.000000       0.365902      0.421105   \n",
              "PetalLengthCm       0.709930      0.365902       1.000000      0.636250   \n",
              "PetalWidthCm        0.398166      0.421105       0.636250      1.000000   \n",
              "Species             0.445772      0.268544       0.777382      0.667205   \n",
              "\n",
              "                Species  \n",
              "SepalLengthCm  0.445772  \n",
              "SepalWidthCm   0.268544  \n",
              "PetalLengthCm  0.777382  \n",
              "PetalWidthCm   0.667205  \n",
              "Species        1.000000  "
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              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
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              "    <tr>\n",
              "      <th>Species</th>\n",
              "      <td>0.445772</td>\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-04246d40-92b0-43f0-a904-cb2161c06986');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# create historgam plot to see the frequency distribution clearly\n",
        "\n",
        "for column in df.columns:\n",
        "    df[column].hist()\n",
        "\n",
        "    plt.title('Histogram of ' + column)\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "    # Show the plot\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "u-5h8uZuKfA-",
        "outputId": "57c72adc-c58b-4f12-c24d-defaa7305135"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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yRZ9++qnT99XkLBodNmyYunTpIm9vb/Xq1UvdunVT+/btNW7cOP3++++65ZZblJCQoM8//1wjRoxwrHdo1qyZoqOjNXPmTJ08edLx9us9e/ZIKti/sIOCgtSmTRtNnTpVdrtdN910kxISEnTw4MEi6Eput9xyi/r37693331XZ86cUdu2bfX9999rwYIF6tGjh9q3b1/oOTMyMtSqVSu1bNlSd999t8LDw3XmzBktX75c3377rXr06KHbbrtNktS3b18tWbJETz/9tNatW6fIyEhlZWXpt99+05IlS7R69WrdcccdjrkbN26sLl26OL39WpLTJzTfd999+uCDDxQcHKyGDRsqOTlZX3/9teMtylfTvHlz+fr6Kjk5We3atXP6gLuAgADdcsstSk5OVkhIyFW/KuPWW29V7969NWvWLKWlpalVq1Zas2ZNnlftcp6P48aNU69eveTj46Nu3boV6kMTa9eurUWLFjneot+vXz81btxYFy5c0KZNm7R06VK+kwzFjiADuGDYsGH64osvlJCQIJvNpurVq2vSpEmODx2TLn12x9ChQ7V48WJ9+OGHMgxDvXr1kpeXl7744gu99NJL+uSTTxQfH68aNWpo2rRpjnfz5Fi4cKHCwsL08ccfa9myZYqKitInn3zi+MC2gli0aJGGDh2quLg4GYahzp07a+XKlapSpYpbe5Kf999/X7Vq1dL8+fO1bNkyhYWFaezYsRo/frxL84WEhOi9997TV199pfj4eB05ckTe3t6qX7++pk2b5vQOHi8vLy1fvlwzZszQwoULtWzZMsf3Mg0fPtyx6DdH27ZtFRERoQkTJiglJUUNGzbU/Pnznd7J9uabb8rb21sfffSRMjMzFRkZqa+//tpxBe1qcj5oLzk5Oc/PY4mMjNTWrVsVEREhL6+rXzSfN2+eKlasqI8++kjLly9Xhw4d9NVXX+Va/3PnnXdq4sSJmjNnjlatWuV42bKwn/58//33a+fOnZo2bZo+//xzzZ49W1arVU2bNtUbb7yhJ554olDzAdfKYrCyCjCV7du367bbbtOHH36oRx55xNPlXDcsFotiYmJyveQHoGRjjQxQguX1BYEzZ86Ul5fXVT9RFwBuBLy0BJRgU6dO1datW9W+fXuVKlVKK1eu1MqVK/Xkk0+69NZhALjeEGSAEqxVq1ZKTEzUxIkTde7cOVWrVk2xsbEaN26cp0sDgBKBNTIAAMC0WCMDAABMiyADAABM67pfI5Odna3Dhw8rMDCwSL/vBQAAuI9hGDp79qyqVKlyxc9Uuu6DzOHDh3l3BwAAJvXnn3+qatWq+W6/7oNMzpfy/fnnn46voXcHu92uhIQEde7cWT4+Pm6b90ZFP92HXroX/XQfeule13s/09PTFR4e7vTlunm57oNMzstJQUFBbg8yAQEBCgoKui6fQMWNfroPvXQv+uk+9NK9bpR+Xm1ZCIt9AQCAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaRFkAACAaZXydAFm1zh2tWxZFk+XUSi/T7nX0yUAAOAWXJEBAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACmRZABAACm5dEgExsbK4vF4vTToEEDx/bMzEzFxMSofPnyKlOmjKKjo3X06FEPVgwAAEoSj1+RadSokVJTUx0/GzZscGwbOXKkvvzySy1dulTr16/X4cOH1bNnTw9WCwAASpJSHi+gVCmFhYXlGk9LS9PcuXO1aNEidejQQZIUHx+vm2++WZs3b1bLli2Lu1QAAFDCePyKzN69e1WlShXVqlVLjzzyiFJSUiRJW7duld1uV1RUlGPfBg0aqFq1akpOTvZUuQAAoATx6BWZFi1aaP78+apfv75SU1M1YcIE3XXXXdq1a5eOHDkiX19fhYSEOB0TGhqqI0eO5DunzWaTzWZz3E5PT5ck2e122e12t9WeM5fVy3DbnMXFnX1wl5yaSmJtZkMv3Yt+ug+9dK/rvZ8F/b0shmGUmDPxmTNnVL16dU2fPl3+/v4aOHCgUyiRpObNm6t9+/Z67bXX8pwjNjZWEyZMyDW+aNEiBQQEFEndAADAvTIyMtSnTx+lpaUpKCgo3/08vkbmn0JCQlSvXj3t27dPnTp10oULF3TmzBmnqzJHjx7Nc01NjrFjx2rUqFGO2+np6QoPD1fnzp2v2IjCstvtSkxM1ItbvGTLtrht3uKwK7aLp0vIJaefnTp1ko+Pj6fLMTV66V70033opXtd7/3MeUXlakpUkDl37pz279+vvn37qlmzZvLx8dGaNWsUHR0tSdq9e7dSUlIUERGR7xxWq1VWqzXXuI+PT5E80LZsi2xZ5goyJfkJX1SP042IXroX/XQfeule12s/C/o7eTTIjB49Wt26dVP16tV1+PBhjR8/Xt7e3urdu7eCg4M1aNAgjRo1SuXKlVNQUJCGDh2qiIgI3rEEAAAkeTjI/PXXX+rdu7dOnjypihUrqnXr1tq8ebMqVqwoSZoxY4a8vLwUHR0tm82mLl26aNasWZ4sGQAAlCAeDTKLFy++4nY/Pz/FxcUpLi6umCoCAABm4vHPkQEAAHAVQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJhWKU8XAAAALqnx/FcF3tfqbWhqc6lx7GrZsixFWNWV/T7lXo/dt8QVGQAAYGIEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFoEGQAAYFolJshMmTJFFotFI0aMcIxlZmYqJiZG5cuXV5kyZRQdHa2jR496rkgAAFCilIgg88MPP+idd95R06ZNncZHjhypL7/8UkuXLtX69et1+PBh9ezZ00NVAgCAksbjQebcuXN65JFH9N5776ls2bKO8bS0NM2dO1fTp09Xhw4d1KxZM8XHx2vTpk3avHmzBysGAAAlhceDTExMjO69915FRUU5jW/dulV2u91pvEGDBqpWrZqSk5OLu0wAAFAClfLknS9evFg//vijfvjhh1zbjhw5Il9fX4WEhDiNh4aG6siRI/nOabPZZLPZHLfT09MlSXa7XXa73T2F/998kmT1Mtw2Z3FxZx/cJaemklib2dBL96Kf7kMvr87qXfBzSs75x9PnoaJ6PAs6r8eCzJ9//qnhw4crMTFRfn5+bpt38uTJmjBhQq7xhIQEBQQEuO1+cky8I9vtcxa1FStWeLqEfCUmJnq6hOsGvXQv+uk+9DJ/U5sX/hhPn4eK6pySkZFRoP0shmF4JMotX75cDzzwgLy9vR1jWVlZslgs8vLy0urVqxUVFaXTp087XZWpXr26RowYoZEjR+Y5b15XZMLDw3XixAkFBQW5rX673a7ExES9uMVLtmyL2+YtDrtiu3i6hFxy+tmpUyf5+Ph4uhxTo5fuRT/dh15eXePY1QXe1+plaOId2R4/DxXVOSU9PV0VKlRQWlraFc/fHrsi07FjR/30009OYwMHDlSDBg3073//W+Hh4fLx8dGaNWsUHR0tSdq9e7dSUlIUERGR77xWq1VWqzXXuI+PT5H8wbFlW2TLMleQKcl/gRTV43QjopfuRT/dh17mz5XziafPQ0X1WBZ0Xo8FmcDAQDVu3NhprHTp0ipfvrxjfNCgQRo1apTKlSunoKAgDR06VBEREWrZsqUnSgYAACWMRxf7Xs2MGTPk5eWl6Oho2Ww2denSRbNmzfJ0WQAAoIQoUUEmKSnJ6bafn5/i4uIUFxfnmYIAAECJ5vHPkQEAAHAVQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJgWQQYAAJiWS0HmwIED7q4DAACg0FwKMnXq1FH79u314YcfKjMz0901AQAAFIhLQebHH39U06ZNNWrUKIWFhempp57S999/7+7aAAAArsilIHPrrbfqzTff1OHDhzVv3jylpqaqdevWaty4saZPn67jx4+7u04AAIBcrmmxb6lSpdSzZ08tXbpUr732mvbt26fRo0crPDxc/fr1U2pqqrvqBAAAyOWagsyWLVs0ePBgVa5cWdOnT9fo0aO1f/9+JSYm6vDhw+revbu76gQAAMillCsHTZ8+XfHx8dq9e7e6du2qhQsXqmvXrvLyupSLatasqfnz56tGjRrurBUAAMCJS0Fm9uzZeuyxxzRgwABVrlw5z30qVaqkuXPnXlNxAAAAV+JSkNm7d+9V9/H19VX//v1dmR4AAKBAXFojEx8fr6VLl+YaX7p0qRYsWHDNRQEAABSES0Fm8uTJqlChQq7xSpUq6dVXX73mogAAAArCpSCTkpKimjVr5hqvXr26UlJSrrkoAACAgnApyFSqVEk7d+7MNb5jxw6VL1/+mosCAAAoCJeCTO/evTVs2DCtW7dOWVlZysrK0tq1azV8+HD16tXL3TUCAADkyaV3LU2cOFG///67OnbsqFKlLk2RnZ2tfv36sUYGAAAUG5eCjK+vrz755BNNnDhRO3bskL+/v5o0aaLq1au7uz4AAIB8uRRkctSrV0/16tVzVy0AAACF4lKQycrK0vz587VmzRodO3ZM2dnZTtvXrl3rluIAAACuxKUgM3z4cM2fP1/33nuvGjduLIvF4u66AAAArsqlILN48WItWbJEXbt2dXc9AAAABebS2699fX1Vp04dd9cCAABQKC4FmWeffVZvvvmmDMO4pjufPXu2mjZtqqCgIAUFBSkiIkIrV650bM/MzFRMTIzKly+vMmXKKDo6WkePHr2m+wQAANcPl15a2rBhg9atW6eVK1eqUaNG8vHxcdr+2WefFWieqlWrasqUKapbt64Mw9CCBQvUvXt3bdu2TY0aNdLIkSP11VdfaenSpQoODtaQIUPUs2dPbdy40ZWyAQDAdcalIBMSEqIHHnjgmu+8W7duTrdfeeUVzZ49W5s3b1bVqlU1d+5cLVq0SB06dJB06Vu3b775Zm3evFktW7a85vsHAADm5lKQiY+Pd3cdysrK0tKlS3X+/HlFRERo69atstvtioqKcuzToEEDVatWTcnJyQQZAADg+gfiXbx4UUlJSdq/f7/69OmjwMBAHT58WEFBQSpTpkyB5/npp58UERGhzMxMlSlTRsuWLVPDhg21fft2+fr6KiQkxGn/0NBQHTlyJN/5bDabbDab43Z6erokyW63y263F+6XvIKcuaxe17ZOyBPc2Qd3yampJNZmNvTSvein+9DLq7N6F/ycknP+8fR5qKgez4LOazFcWLH7xx9/6O6771ZKSopsNpv27NmjWrVqafjw4bLZbJozZ06B57pw4YJSUlKUlpamTz/9VO+//77Wr1+v7du3a+DAgU6hRJKaN2+u9u3b67XXXstzvtjYWE2YMCHX+KJFixQQEFC4XxQAAHhERkaG+vTpo7S0NAUFBeW7n0tBpkePHgoMDNTcuXNVvnx57dixQ7Vq1VJSUpKeeOIJ7d271+XCo6KiVLt2bT388MPq2LGjTp8+7XRVpnr16hoxYoRGjhyZ5/F5XZEJDw/XiRMnrtiIwrLb7UpMTNSLW7xkyzbXBwLuiu3i6RJyyelnp06dci0eR+HQS/ein+5DL6+ucezqAu9r9TI08Y5sj5+Hiuqckp6ergoVKlw1yLj00tK3336rTZs2ydfX12m8Ro0aOnTokCtTOmRnZ8tms6lZs2by8fHRmjVrFB0dLUnavXu3UlJSFBERke/xVqtVVqs117iPj0+R/MGxZVtkyzJXkCnJf4EU1eN0I6KX7kU/3Yde5s+V84mnz0NF9VgWdF6Xgkx2draysrJyjf/1118KDAws8Dxjx47VPffco2rVquns2bNatGiRkpKStHr1agUHB2vQoEEaNWqUypUrp6CgIA0dOlQREREs9AUAAJJcDDKdO3fWzJkz9e6770qSLBaLzp07p/HjxxfqawuOHTumfv36KTU1VcHBwWratKlWr16tTp06SZJmzJghLy8vRUdHy2azqUuXLpo1a5YrJQMAgOuQS0HmjTfeUJcuXdSwYUNlZmaqT58+2rt3rypUqKCPP/64wPPMnTv3itv9/PwUFxenuLg4V8oEAADXOZeCTNWqVbVjxw4tXrxYO3fu1Llz5zRo0CA98sgj8vf3d3eNAAAAeXL5c2RKlSqlRx991J21AAAAFIpLQWbhwoVX3N6vXz+XigEAACgMl4LM8OHDnW7b7XZlZGTI19dXAQEBBBkAAFAsvFw56PTp004/586d0+7du9W6detCLfYFAAC4Fi4FmbzUrVtXU6ZMyXW1BgAAoKi4LchIlxYAHz582J1TAgAA5MulNTJffPGF023DMJSamqr//Oc/ioyMdEthAAAAV+NSkOnRo4fTbYvFoooVK6pDhw5644033FEXAADAVbn8XUsAAACe5tY1MgAAAMXJpSsyo0aNKvC+06dPd+UuAAAArsqlILNt2zZt27ZNdrtd9evXlyTt2bNH3t7euv322x37WSwW91QJAACQB5eCTLdu3RQYGKgFCxaobNmyki59SN7AgQN111136dlnn3VrkQAAAHlxaY3MG2+8ocmTJztCjCSVLVtWkyZN4l1LAACg2LgUZNLT03X8+PFc48ePH9fZs2evuSgAAICCcCnIPPDAAxo4cKA+++wz/fXXX/rrr7/0v//7vxo0aJB69uzp7hoBAADy5NIamTlz5mj06NHq06eP7Hb7pYlKldKgQYM0bdo0txYIAACQH5eCTEBAgGbNmqVp06Zp//79kqTatWurdOnSbi0OAADgSq7pA/FSU1OVmpqqunXrqnTp0jIMw111AQAAXJVLQebkyZPq2LGj6tWrp65duyo1NVWSNGjQIN56DQAAio1LQWbkyJHy8fFRSkqKAgICHOMPP/ywVq1a5bbiAAAArsSlNTIJCQlavXq1qlat6jRet25d/fHHH24pDAAA4GpcuiJz/vx5pysxOU6dOiWr1XrNRQEAABSES0Hmrrvu0sKFCx23LRaLsrOzNXXqVLVv395txQEAAFyJSy8tTZ06VR07dtSWLVt04cIFjRkzRj///LNOnTqljRs3urtGAACAPLl0RaZx48bas2ePWrdure7du+v8+fPq2bOntm3bptq1a7u7RgAAgDwV+oqM3W7X3XffrTlz5mjcuHFFURMAAECBFPqKjI+Pj3bu3FkUtQAAABSKSy8tPfroo5o7d667awEAACgUlxb7Xrx4UfPmzdPXX3+tZs2a5fqOpenTp7ulOAAAgCspVJA5cOCAatSooV27dun222+XJO3Zs8dpH4vF4r7qAAAArqBQQaZu3bpKTU3VunXrJF36SoK33npLoaGhRVIcAADAlRRqjczl3269cuVKnT9/3q0FAQAAFJRLi31zXB5sAAAAilOhgozFYsm1BoY1MQAAwFMKtUbGMAwNGDDA8cWQmZmZevrpp3O9a+mzzz5zX4UAAAD5KFSQ6d+/v9PtRx991K3FAAAAFEahgkx8fHxR1QEAAFBo17TYFwAAwJMIMgAAwLQIMgAAwLQIMgAAwLQIMgAAwLQIMgAAwLQIMgAAwLQIMgAAwLQIMgAAwLQIMgAAwLQIMgAAwLQIMgAAwLQIMgAAwLQIMgAAwLQ8GmQmT56sO++8U4GBgapUqZJ69Oih3bt3O+2TmZmpmJgYlS9fXmXKlFF0dLSOHj3qoYoBAEBJ4tEgs379esXExGjz5s1KTEyU3W5X586ddf78ecc+I0eO1JdffqmlS5dq/fr1Onz4sHr27OnBqgEAQElRypN3vmrVKqfb8+fPV6VKlbR161a1adNGaWlpmjt3rhYtWqQOHTpIkuLj43XzzTdr8+bNatmypSfKBgAAJUSJWiOTlpYmSSpXrpwkaevWrbLb7YqKinLs06BBA1WrVk3JyckeqREAAJQcHr0i80/Z2dkaMWKEIiMj1bhxY0nSkSNH5Ovrq5CQEKd9Q0NDdeTIkTznsdlsstlsjtvp6emSJLvdLrvd7rZ6c+ayehlum7O4uLMP7pJTU0mszWzopXvRT/ehl1dn9S74OSXn/OPp81BRPZ4FnbfEBJmYmBjt2rVLGzZsuKZ5Jk+erAkTJuQaT0hIUEBAwDXNnZeJd2S7fc6itmLFCk+XkK/ExERPl3DdoJfuRT/dh17mb2rzwh/j6fNQUZ1TMjIyCrRfiQgyQ4YM0X//+1998803qlq1qmM8LCxMFy5c0JkzZ5yuyhw9elRhYWF5zjV27FiNGjXKcTs9PV3h4eHq3LmzgoKC3Faz3W5XYmKiXtziJVu2xW3zFoddsV08XUIuOf3s1KmTfHx8PF2OqdFL96Kf7kMvr65x7OoC72v1MjTxjmyPn4eK6pyS84rK1Xg0yBiGoaFDh2rZsmVKSkpSzZo1nbY3a9ZMPj4+WrNmjaKjoyVJu3fvVkpKiiIiIvKc02q1ymq15hr38fEpkj84tmyLbFnmCjIl+S+QonqcbkT00r3op/vQy/y5cj7x9HmoqB7Lgs7r0SATExOjRYsW6fPPP1dgYKBj3UtwcLD8/f0VHBysQYMGadSoUSpXrpyCgoI0dOhQRURE8I4lAADg2SAze/ZsSVK7du2cxuPj4zVgwABJ0owZM+Tl5aXo6GjZbDZ16dJFs2bNKuZKAQBASeTxl5auxs/PT3FxcYqLiyuGigAAgJmUqM+RAQAAKAyCDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2CDAAAMC2PBplvvvlG3bp1U5UqVWSxWLR8+XKn7YZh6KWXXlLlypXl7++vqKgo7d271zPFAgCAEsejQeb8+fO65ZZbFBcXl+f2qVOn6q233tKcOXP03XffqXTp0urSpYsyMzOLuVIAAFASlfLknd9zzz2655578txmGIZmzpyp//mf/1H37t0lSQsXLlRoaKiWL1+uXr16FWepAACgBCqxa2QOHjyoI0eOKCoqyjEWHBysFi1aKDk52YOVAQCAksKjV2Su5MiRI5Kk0NBQp/HQ0FDHtrzYbDbZbDbH7fT0dEmS3W6X3W53W305c1m9DLfNWVzc2Qd3yampJNZmNvTSvein+9DLq7N6F/ycknP+8fR5qKgez4LOW2KDjKsmT56sCRMm5BpPSEhQQECA2+9v4h3Zbp+zqK1YscLTJeQrMTHR0yVcN+ile9FP96GX+ZvavPDHePo8VFTnlIyMjALtV2KDTFhYmCTp6NGjqly5smP86NGjuvXWW/M9buzYsRo1apTjdnp6usLDw9W5c2cFBQW5rT673a7ExES9uMVLtmyL2+YtDrtiu3i6hFxy+tmpUyf5+Ph4uhxTo5fuRT/dh15eXePY1QXe1+plaOId2R4/DxXVOSXnFZWrKbFBpmbNmgoLC9OaNWscwSU9PV3fffednnnmmXyPs1qtslqtucZ9fHyK5A+OLdsiW5a5gkxJ/gukqB6nGxG9dC/66T70Mn+unE88fR4qqseyoPN6NMicO3dO+/btc9w+ePCgtm/frnLlyqlatWoaMWKEJk2apLp166pmzZp68cUXVaVKFfXo0cNzRQMAgBLDo0Fmy5Ytat++veN2zktC/fv31/z58zVmzBidP39eTz75pM6cOaPWrVtr1apV8vPz81TJAACgBPFokGnXrp0MI//V1haLRS+//LJefvnlYqwKAACYRYn9HBkAAICrIcgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTIsgAAADTMkWQiYuLU40aNeTn56cWLVro+++/93RJAACgBCjxQeaTTz7RqFGjNH78eP3444+65ZZb1KVLFx07dszTpQEAAA8r8UFm+vTpeuKJJzRw4EA1bNhQc+bMUUBAgObNm+fp0gAAgIeV6CBz4cIFbd26VVFRUY4xLy8vRUVFKTk52YOVAQCAkqCUpwu4khMnTigrK0uhoaFO46Ghofrtt9/yPMZms8lmszlup6WlSZJOnTolu93uttrsdrsyMjJUyu6lrGyL2+YtDidPnvR0Cbnk9PPkyZPy8fHxdDmmRi/di366D728ulIXzxd832xDGRnZHj8PFdU55ezZs5IkwzCuuF+JDjKumDx5siZMmJBrvGbNmh6opmSq8IanKwAAuEMfTxegoj+nnD17VsHBwfluL9FBpkKFCvL29tbRo0edxo8ePaqwsLA8jxk7dqxGjRrluJ2dna1Tp06pfPnysljcl1jT09MVHh6uP//8U0FBQW6b90ZFP92HXroX/XQfeule13s/DcPQ2bNnVaVKlSvuV6KDjK+vr5o1a6Y1a9aoR48eki4FkzVr1mjIkCF5HmO1WmW1Wp3GQkJCiqzGoKCg6/IJ5Cn0033opXvRT/ehl+51PffzSldicpToICNJo0aNUv/+/XXHHXeoefPmmjlzps6fP6+BAwd6ujQAAOBhJT7IPPzwwzp+/LheeuklHTlyRLfeeqtWrVqVawEwAAC48ZT4ICNJQ4YMyfelJE+xWq0aP358rpex4Br66T700r3op/vQS/ein5dYjKu9rwkAAKCEKtEfiAcAAHAlBBkAAGBaBBkAAGBaBBkAAGBaBJl8TJ48WXfeeacCAwNVqVIl9ejRQ7t3777qcUuXLlWDBg3k5+enJk2aaMWKFcVQbcnmSi/fe+893XXXXSpbtqzKli2rqKgoff/998VUccnm6nMzx+LFi2WxWBwfMnkjc7WXZ86cUUxMjCpXriyr1ap69erd8H/WXe3lzJkzVb9+ffn7+ys8PFwjR45UZmZmMVRcss2ePVtNmzZ1fNhdRESEVq5cecVjbtTzD0EmH+vXr1dMTIw2b96sxMRE2e12de7cWefP5/+FXps2bVLv3r01aNAgbdu2TT169FCPHj20a9euYqy85HGll0lJSerdu7fWrVun5ORkhYeHq3Pnzjp06FAxVl4yudLPHL///rtGjx6tu+66qxgqLflc6eWFCxfUqVMn/f777/r000+1e/duvffee7rpppuKsfKSx5VeLlq0SM8//7zGjx+vX3/9VXPnztUnn3yiF154oRgrL5mqVq2qKVOmaOvWrdqyZYs6dOig7t276+eff85z/xv6/GOgQI4dO2ZIMtavX5/vPg899JBx7733Oo21aNHCeOqpp4q6PFMpSC8vd/HiRSMwMNBYsGBBEVZmTgXt58WLF41WrVoZ77//vtG/f3+je/fuxVOgiRSkl7NnzzZq1aplXLhwoRgrM5+C9DImJsbo0KGD09ioUaOMyMjIoi7PlMqWLWu8//77eW67kc8/XJEpoLS0NElSuXLl8t0nOTlZUVFRTmNdunRRcnJykdZmNgXp5eUyMjJkt9sLdcyNoqD9fPnll1WpUiUNGjSoOMoypYL08osvvlBERIRiYmIUGhqqxo0b69VXX1VWVlZxlWkKBellq1attHXrVsfLxgcOHNCKFSvUtWvXYqnRLLKysrR48WKdP39eERERee5zI59/TPHJvp6WnZ2tESNGKDIyUo0bN853vyNHjuT66oTQ0FAdOXKkqEs0jYL28nL//ve/VaVKlVx/UG90Be3nhg0bNHfuXG3fvr34ijOZgvbywIEDWrt2rR555BGtWLFC+/bt0+DBg2W32zV+/PhirLjkKmgv+/TpoxMnTqh169YyDEMXL17U008/zUtL/+enn35SRESEMjMzVaZMGS1btkwNGzbMc98b+fxDkCmAmJgY7dq1Sxs2bPB0KabnSi+nTJmixYsXKykpSX5+fkVYnfkUpJ9nz55V37599d5776lChQrFWJ25FPS5mZ2drUqVKundd9+Vt7e3mjVrpkOHDmnatGkEmf9T0F4mJSXp1Vdf1axZs9SiRQvt27dPw4cP18SJE/Xiiy8WU7UlV/369bV9+3alpaXp008/Vf/+/bV+/fp8w8wNy9OvbZV0MTExRtWqVY0DBw5cdd/w8HBjxowZTmMvvfSS0bRp0yKqzlwK08sc06ZNM4KDg40ffvihCCszp4L2c9u2bYYkw9vb2/FjsVgMi8VieHt7G/v27Sumikuuwjw327RpY3Ts2NFpbMWKFYYkw2azFVWJplGYXrZu3doYPXq009gHH3xg+Pv7G1lZWUVVoml17NjRePLJJ/PcdiOff1gjkw/DMDRkyBAtW7ZMa9euVc2aNa96TEREhNasWeM0lpiYmO9rmjcKV3opSVOnTtXEiRO1atUq3XHHHUVcpXkUtp8NGjTQTz/9pO3btzt+7r//frVv317bt29XeHh4MVVe8rjy3IyMjNS+ffuUnZ3tGNuzZ48qV64sX1/foiy3RHOllxkZGfLycj4NeXt7O+aDs+zsbNlstjy33dDnH0+mqJLsmWeeMYKDg42kpCQjNTXV8ZORkeHYp2/fvsbzzz/vuL1x40ajVKlSxuuvv278+uuvxvjx4w0fHx/jp59+8sSvUGK40sspU6YYvr6+xqeffup0zNmzZz3xK5QorvTzcrxr6RJXepmSkmIEBgYaQ4YMMXbv3m3897//NSpVqmRMmjTJE79CieFKL8ePH28EBgYaH3/8sXHgwAEjISHBqF27tvHQQw954lcoUZ5//nlj/fr1xsGDB42dO3cazz//vGGxWIyEhATDMDj//BNBJh+S8vyJj4937NO2bVujf//+TsctWbLEqFevnuHr62s0atTI+Oqrr4q38BLIlV5Wr149z2PGjx9f7PWXNK4+N/+JIHOJq73ctGmT0aJFC8NqtRq1atUyXnnlFePixYvFW3wJ40ov7Xa7ERsba9SuXdvw8/MzwsPDjcGDBxunT58u9vpLmscee8yoXr264evra1SsWNHo2LGjI8QYBueff7IYBtfvAACAObFGBgAAmBZBBgAAmBZBBgAAmBZBBgAAmBZBBgAAmBZBBgAAmBZBBgAAmBZBBoDHWSwWLV++/JrmGDBggHr06HHFfdq1a6cRI0ZccZ/58+crJCTkmmoBUHwIMsAN7Pjx43rmmWdUrVo1Wa1WhYWFqUuXLtq4caPHamrZsqWefvppp7E5c+bIYrFo/vz5TuMDBgzQXXfdJUl68803c22/mho1amjmzJku1Zmenq5x48apQYMG8vPzU1hYmKKiovTZZ5/xPUFAMSrl6QIAeE50dLQuXLigBQsWqFatWjp69KjWrFmjkydPeqym9u3ba9myZU5j69atU3h4uJKSkjRgwADHeFJSkvr37y9JCg4OLrYaz5w5o9atWystLU2TJk3SnXfeqVKlSmn9+vUaM2aMOnTowFUdoLh4+CsSAHjI6dOnDUlGUlLSFfcZNGiQUaFCBSMwMNBo3769sX37dsf28ePHG7fccosxZ84co2rVqoa/v7/x4IMPGmfOnHHs8/333xtRUVFG+fLljaCgIKNNmzbG1q1bne5HkrFs2TLDMAxj9erVhiQjNTXVsT00NNSIi4szqlev7hg7cOCAIclYt26dYRi5vz/q3LlzRt++fY3SpUsbYWFhxuuvv260bdvWGD58uGEYl76rRpd9L5BhGEZ8fLwRHBxsrFq1ymjQoIFRunRpo0uXLsbhw4cdcz/zzDNG6dKljUOHDuXq2dmzZw273W4YxqXvDJs4caKjjmrVqhmff/65cezYMeP+++83SpcubTRp0sT44Ycf8n0MAFwZLy0BN6gyZcqoTJkyWr58uWw2W577PPjggzp27JhWrlyprVu36vbbb1fHjh116tQpxz779u3TkiVL9OWXX2rVqlXatm2bBg8e7Nh+9uxZ9e/fXxs2bNDmzZtVt25dde3aVWfPns3zPiMjI+Xj46N169ZJkn755Rf9/fffGjRokE6ePKmDBw9KunSVxs/PTxEREXnO89xzz2n9+vX6/PPPlZCQoKSkJP3444+O7Z999pmqVq2ql19+WampqUpNTXVsy8jI0Ouvv64PPvhA33zzjVJSUjR69GhJUnZ2thYvXqxHHnlEVapUybOvpUr9/4vdM2bMUGRkpLZt26Z7771Xffv2Vb9+/fToo4/qxx9/VO3atdWvXz9ejgJc5ekkBcBzPv30U6Ns2bKGn5+f0apVK2Ps2LHGjh07DMMwjG+//dYICgoyMjMznY6pXbu28c477xiGcemKjLe3t/HXX385tq9cudLw8vJyuqLyT1lZWUZgYKDx5ZdfOsb0jysyhmEYkZGRxpNPPmkYhmHExcUZXbt2NQzDMDp37mzMmzfPMAzD6Nu3r9G+fXvHMf+8InP27FnD19fXWLJkiWP7yZMnDX9/f8cVGcO4dMVkxowZTvXFx8cbkox9+/Y5xuLi4ozQ0FDDMAzj6NGjhiRj+vTpef5+/1S9enXj0UcfddxOTU01JBkvvviiYyw5OTnXFSgABccVGeAGFh0drcOHD+uLL77Q3XffraSkJN1+++2aP3++duzYoXPnzql8+fKOqzdlypTRwYMHtX//fscc1apV00033eS4HRERoezsbO3evVuSdPToUT3xxBOqW7eugoODFRQUpHPnziklJSXfutq1a6ekpCRJl9bBtGvXTpLUtm1bp/H27dvnefz+/ft14cIFtWjRwjFWrlw51a9fv0B9CQgIUO3atR23K1eurGPHjklSoa+cNG3a1PH/oaGhkqQmTZrkGsuZH0DhsNgXuMH5+fmpU6dO6tSpk1588UU9/vjjGj9+vAYPHqzKlSs7gsM/FWYha//+/XXy5Em9+eabql69uqxWqyIiInThwoV8j2nfvr1eeeUVHTp0SElJSY6Xddq2bat33nlH+/fv159//qkOHToU9tctEB8fH6fbFovFEWAqVqyokJAQ/fbbb4Wey2Kx5DuWnZ19TTUDNyquyABw0rBhQ50/f1633367jhw5olKlSqlOnTpOPxUqVHDsn5KSosOHDztub968WV5eXo6rHxs3btSwYcPUtWtXNWrUSFarVSdOnLhiDa1atZKvr69mzZqlzMxMNWvWTJJ055136vjx45o3b55Kly6t5s2b53l87dq15ePjo++++84xdvr0ae3Zs8dpP19fX2VlZRWqP15eXurVq5c++ugjp987x7lz53Tx4sVCzQnAdQQZ4AZ18uRJdejQQR9++KF27typgwcPaunSpZo6daq6d++uqKgoRUREqEePHkpISNDvv/+uTZs2ady4cdqyZYtjHj8/P/Xv3187duzQt99+q2HDhumhhx5SWFiYJKlu3br64IMP9Ouvv+q7777TI488In9//yvW5u/vr5YtW+rtt99WZGSkvL29JV0KHv8cv/zKSY4yZcpo0KBBeu6557R27Vrt2rVLAwYMkJeX8195NWrU0DfffKNDhw5dNVz90yuvvKLw8HC1aNFCCxcu1C+//KK9e/dq3rx5uu2223Tu3LkCzwXg2vDSEnCDKlOmjFq0aKEZM2Zo//79stvtCg8P1xNPPKEXXnhBFotFK1as0Lhx4zRw4EAdP35cYWFhatOmjWNdhyTVqVNHPXv2VNeuXXXq1Cndd999mjVrlmP73Llz9eSTT+r2229XeHi4Xn31VcdLRVfSvn17ffPNN471MTnatm2rdevW5bs+Jse0adN07tw5devWTYGBgXr22WeVlpbmtM/LL7+sp556SrVr15bNZivw+pdy5cpp8+bNmjJliiZNmqQ//vhDZcuWVZMmTTRt2rRi/Uwb4EZnMQq7cg0A/k9sbKyWL1+u7du3e7oUADcoXloCAACmRZABAACmxUtLAADAtLgiAwAATIsgAwAATIsgAwAATIsgAwAATIsgAwAATIsgAwAATIsgAwAATIsgAwAATIsgAwAATOv/Abk0oB6SxB0xAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "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": [
        "**Visualisasi Seaborn**"
      ],
      "metadata": {
        "id": "_MF6YVuIEqZA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.heatmap(df.corr(), annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "hTIf5Jp9ElBW",
        "outputId": "b99f5f05-ffc6-4408-c5e9-9d8dce58543c"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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dqmP+hCUq9lK+ydLSIJfmQcNM+dppmJlB+qEnlXV1oevcBRm7dpRihOVLWnI6pHlSmFmaKpWbWZgh5V8ttwBQpXplVK5mg293LlWUiTTyv19PPD6GMZ3H4uWTT3MM2auUV5DmSWFiYapUbmJpgrR/tSwCgFV1G1jaWWP6Nh9F2ZtrtSPyAOZ2mYL4mIJjyBKexiE9KQ1W9jafTjLGlrHiq1+/Pg4fPoxmzZohNjYWYrEY9vb2hdaPiYnBixcvUKVKFQDA5cuXFV1sQP74rM2bN6Nnz/zBtU+fPkViYuJHxWZtbY0qVarg0aNHGDly5EftAwDq1KmDa9euKZX9+/ObrqDiGjFiBDZs2IDNmzerfGoyNTW1WMns+/j4+MDLy0upTOPV8xLZN5WevNw8hN0OR8v2zXE24DyA/KS7Rftm+H3XoSLvR6ShAS3tggl/3+E98SA0DBH3o1RsVc7l5SEvPBzazZpDciH/2kEkgnazZnh96P3XTrdTZ4i0tZB96lQZBFo+5OXmIfxOBJq1a4qLJ/IfchKJRGja3hF/7jpSoH5M1FOMcxmvVOY+awz0DPSwefEWJLxIKJO4hSDNzUP03SjUb9sIN09eBZB/req3bYzTPx8vUP9l1HPM+3y6UtlA7xHQNdDF3iU7kPRS9awFZjbmMDQzQlp8wWSYyodiJWNJSUkYPHgwvvrqKzRu3BhGRka4fv06Vq5ciX79+sHFxQVOTk7o378/Vq5cidq1a+PFixf4+++/MWDAALRo0QJAfsuam5sbVq1ahfT0dEydOhVDhgxRjLWqVasW9uzZgxYtWiA9PR2zZs2Cnp7eB+N7/PgxQkJClMpq1aqFJUuWYOrUqTAxMUH37t0hkUhw/fp1pKSkFEhMCjNlyhR07NgRa9asQZ8+ffDPP//g+PHjihY0IL8L8U0MVatWhZGRUZG6/1q3bo3Zs2dj5syZeP78OQYMGIAqVaogMjIS/v7+aN++fYlNbaGjo1Mgptycj0t0S9Pr11mIefZC8fn5iziEhUfBxNgIlW2sBIxMOL/+eAC+63zwIDQM926FYZjHIOjp6+Gvfflf6ovXz0N8bAI2+/0EAHDzHIkHtx/iWfRzaGtro23X1ug58HOs8FmjtF8DQ3107dMZ65dsLnDMT0Xm7wdgMtcHueFhyH0QBv1BgyDS1UN2QP61M/aZB1lCAjK2/aS0nV7PXpCcPw95esHuNZGRETStrKFhkT8sQ7Na/ngpWXL+E5ifsj9+OojZa7zx8HY4HoY8xBdjB0BXTxcBB04CAOasnYXE2ERsX7ETuZJcRD98orR9Rnp+j8W/yz9FAduOwmP1FDy+E4VHIRFwHdsbOvo6CP79HwDA+NVTkBKXjN9X7kWuJBfPw58qbf/mKck35Tr6uug/bQiuB1xCWkIqrKrZYKjPKMRHx+LOuZAyPbdSxZaxwhkaGqJ169ZYu3YtoqKikJubCzs7O3h4eGDevHkQiUQ4duwY5s+fD3d3dyQkJMDGxgYdO3aEtbW1Yj8ODg744osv0LNnTyQnJ6N3795KU01s374d48ePR7NmzWBnZ4fly5crBs6/j6rEKjg4GOPGjYO+vj6+//57zJo1CwYGBmjUqBGmT59e5HNv164d/P39sWTJEixYsACurq6YMWMGfvjhB0WdgQMHKqbHSE1Nxc6dOzFmzJgi7X/FihVo3rw5Nm3aBH9/f8hkMtSsWRODBg1SesigorgbFoGvpryd6HDlxh8BAP16uGDZgsIHtn7KTh85A7NKphg/6ytUsjRH+L1ITBs5C8mJ+b+GrW2tlLqn9fR1MXv5DFhVtoQkW4InUTFYNOVbnD5yRmm/3fp1hUgkwonDhT+5Vd5JzpzBKxNTGI75Chrm5siLikTKnFmKQf2aVlYFvvw17eyg3bgxUrxV3286bdvBZO7b7iTTRYsBABm7diJz965SOQ91EXT0LEzMTTBm5miYWZoh6v4j+Iyaj9T/H6huZWsJWQUb81OYq39dhLG5Cb6YMQwmlqaIefAYq9y+RXpi/nRO5rYWkBVj3LBMKoNdvepoP7Az9I31kRKfgnvnQvHHmt+Ql5NXWqdR5uTyijXPmEhenNHjJWDx4sU4fPhwgRas8sjDwwNhYWEIDg4WOpT/JDfxkdAhlGvtGrsLHUK5dbRok7ZTIUZG6godQrllq8mpID7W7ug/Sv0YWed2ldi+9DqOKbF9lRa+KLwYVq1ahW7dusHAwADHjx/H7t27lVr0iIiIqASwm5IKc/XqVaxcuRKvXr1CjRo1sGHDBowbN07osIiIiD4tFaybu8yTscWLF2Px4sVlfdgSceDAAaFDICIiok8MW8aIiIhIvbCbkoiIiEhAFaybslgvCiciIiL6lG3atAn29vbQ1dVF69atcfXq1ULr5ubmYunSpahZsyZ0dXXRpEkTBAQEFPuYTMaIiIhIvchkJbcUw/79++Hl5QVfX1/cvHkTTZo0gaura6Hve16wYAG2bt2KjRs34v79+5gwYQIGDBiAW7duFeu4TMaIiIhIvchlJbZIJBKkp6crLRKJROVh16xZAw8PD7i7u6N+/frw9/eHvr4+duxQ/X7aPXv2YN68eejZsydq1KiBiRMnomfPnli9enWxTpfJGBEREamXEmwZ8/Pzg4mJidLi5+dX4JA5OTm4ceMGXFxcFGUaGhpwcXHBpUuXVIYpkUigq6s8+bKenh7Onz9frNPlAH4iIiL6ZPn4+BR4XaKq90YnJiZCKpUqvb4RAKytrREWFqZy366urlizZg06duyImjVrIjAwEAcPHoRUWrzXObFljIiIiNRLCbaM6ejowNjYWGlRlYx9jPXr16NWrVqoW7cutLW14enpCXd3d2hoFC+9YjJGRERE6qUEx4wVlYWFBTQ1NREXF6dUHhcXBxsbG5XbWFpa4vDhw8jMzMSTJ08QFhYGQ0ND1KhRo1iny2SMiIiIKjxtbW00b94cgYGBijKZTIbAwEA4OTm9d1tdXV3Y2toiLy8Pf/zxB/r161esY3PMGBEREakXgWbg9/LygpubG1q0aIFWrVph3bp1yMzMhLu7OwBg9OjRsLW1VTwAcOXKFTx//hyOjo54/vw5Fi9eDJlMhtmzZxfruEzGiIiISL0INAP/0KFDkZCQgEWLFiE2NhaOjo4ICAhQDOqPiYlRGg+WnZ2NBQsW4NGjRzA0NETPnj2xZ88emJqaFuu4TMaIiIiI/p+npyc8PT1VrgsKClL63KlTJ9y/f/8/H5PJGBEREakXviiciIiISEB8UTgRERERlRW2jBEREZF6YTclERERkYCYjBEREREJSC4XOoIyxTFjRERERAJiyxgRERGpF3ZTEhEREQmogiVj7KYkIiIiEhBbxoiIiEi9VLBJX5mMERERkXphNyURERERlRW2jBEREZF6qWDzjDEZIyIiIvXCbkoiIiIiKitsGSO0a+wudAjl2oXbO4UOodxyauQmdAjlmp2WttAhlFt7X1wWOoRya3dZHKSCtYwxGSMiIiL1wqktiIiIiIQjl1WsAfwcM0ZEREQkILaMERERkXrhmDEiIiIiAVWwMWPspiQiIiISEFvGiIiISL1UsAH8TMaIiIhIvVSwMWPspiQiIiISEFvGiIiISL1UsJYxJmNERESkXuQVa8wYuymJiIiIBMSWMSIiIlIv7KYkIiIiEhCntiAiIiISEGfgJyIiIqKywpYxIiIiUi/spiQiIiISjryCDeBnNyURERGRgNgyRkREROqF3ZREREREAuLTlERERERUVpiMERERkXqRyUtuKaZNmzbB3t4eurq6aN26Na5evfre+uvWrUOdOnWgp6cHOzs7zJgxA9nZ2cU6JrspiYiISL0I9DTl/v374eXlBX9/f7Ru3Rrr1q2Dq6srHj58CCsrqwL1f/31V8ydOxc7duxA27ZtER4ejjFjxkAkEmHNmjVFPi5bxoiIiIgArFmzBh4eHnB3d0f9+vXh7+8PfX197NixQ2X9ixcvol27dhgxYgTs7e3x+eefY/jw4R9sTfs3JmNERESkXkqwm1IikSA9PV1pkUgkBQ6Zk5ODGzduwMXFRVGmoaEBFxcXXLp0SWWYbdu2xY0bNxTJ16NHj3Ds2DH07NmzWKfLZIyIiIjUi1xWYoufnx9MTEyUFj8/vwKHTExMhFQqhbW1tVK5tbU1YmNjVYY5YsQILF26FO3bt4eWlhZq1qyJzp07Y968ecU6XSZjREREpF5KsGXMx8cHaWlpSouPj0+JhBkUFITly5dj8+bNuHnzJg4ePIi///4b33zzTbH2wwH8RERE9MnS0dGBjo7OB+tZWFhAU1MTcXFxSuVxcXGwsbFRuc3ChQsxatQojBs3DgDQqFEjZGZmYvz48Zg/fz40NIrW5vVJtoyJRCIcPnz4P+1jzJgx6N+//3vrdO7cGdOnT39vnV27dsHU1PQ/xUJERFSRyGWyEluKSltbG82bN0dgYKCiTCaTITAwEE5OTiq3ef36dYGES1NTM/8c5EWfVqNEk7GEhARMnDgR1apVg46ODmxsbODq6ooLFy6U5GGKpU2bNpgwYYJSmb+/P0QiEXbt2qVUPmbMGHTo0AEAsH79+gLrP8Te3h7r1q37qDjT09Mxf/581K1bF7q6urCxsYGLiwsOHjxYrP9Dy4NBY/rj8JV9CH50Ejv+2oL6jnULrdu5RwfsPr4VgQ/+wtnIAPxyaht6DPxcqc7VF2dVLl9OHFbap6K2rofcweTZvnDuOxIN2/VA4LmLQockuMFjBuDI1QO48Pg0dv29FQ0c6xVa17lnR/wc8BPOhB1DcNRJ7D21Az0HuSrVuf4yWOUyauLw0j4VQfQY3RNbL2zD/vA/sOLPVajVpFaRtmvfpwMOxRzF3J/mK8o0xZoY5eOGdSc34rew37H92i5MXTsDZtbmpRW+oCZOcENk+GVkpEfh4vmjaNnCsdC6Y78agaB/DiIh7h4S4u7hxPF9Bern5TxXucz0mqB6p+WRQPOMeXl54aeffsLu3bvx4MEDTJw4EZmZmXB3dwcAjB49WqmLs0+fPtiyZQv27duHx48f49SpU1i4cCH69OmjSMqKokS7KQcOHIicnBzs3r0bNWrUQFxcHAIDA5GUlFSShykWZ2dnHDp0SKnszJkzsLOzQ1BQEMaMGaMoDwoKgpubGwDAxMSkzGJMTU1F+/btkZaWhm+//RYtW7aEWCzG2bNnMXv2bHTp0uWTaV1z6euM6b6T8d3cNbh38z6GeQzGhl9XYXCHL5GSlFqgfnrqK+xc/wuiI2OQm5uL9i5OWLh2DlISU3D57DUAQI8mA5S2cerSGgtWz8Y/f58ti1NSS1lZ2ajjUAMDen2O6fO+FTocwXXr2wUzFnvCb85q3L11H8M9BmPjb6sxsP0I1fddSjp2rP85/77LyUWHbm2xaO1cJCem4HJQ/lNTro37KW3TtksbLFwzB//8HVQGZ1S22vVpD/eF4+A/bxPCQ8LRZ2xfLPplKTw7T0BaUlqh21lWtYLbgq9w78pdpXIdPR3UaFgTBzbsR/T9xzA0McTYxR6Yt30BZvX2Ku3TKVODB/fFqu99MWnyXFy9dgtTp4zDsb/3on7DjkhIKPi3sVMnJ+zb/ycuXb6O7OxszPKejOPHfkVjxy548SJ/ELmtnaPSNt1dnfHTj6tx8NCxsjilT9rQoUORkJCARYsWITY2Fo6OjggICFAM6o+JiVFqCVuwYAFEIhEWLFiA58+fw9LSEn369MGyZcuKddwSaxlLTU1FcHAwVqxYAWdnZ1SvXh2tWrWCj48P+vbtq6gzbtw4WFpawtjYGF26dEFoaKhiH4sXL4ajoyO2bt0KOzs76OvrY8iQIUhLe/uP/dq1a+jWrRssLCxgYmKCTp064ebNm4XG5ezsjIcPHyo9CXH27FnMnTsXQUFBirLHjx/jyZMncHZ2BlCwmzIzMxOjR4+GoaEhKleujNWrVysdp3Pnznjy5AlmzJgBkUgEkUiktP7EiROoV68eDA0N0b17d7x8+VKxbt68eYiOjsaVK1fg5uaG+vXro3bt2vDw8EBISAgMDQ0B5Le8ffvtt4o4qlevjiNHjiAhIQH9+vWDoaEhGjdujOvXr3/o/y7BjBg/BId//Qt/7T+OxxFP8N2c1cjOykaf4aofA755KQRBAcGIjnyC509eYP/2PxD54BGatGqkqJOUkKy0dHJthxsXbuFFzEuV+6wIOji1xNTxbnDp1E7oUNTCyK+H4vDeozi6/xgeh0fDb/YqZGdlo+/wXirr37gUgqDjwYiOyL/v9m37HyIfPILj++677u1x/cItPP8E77u+4/rj1G8n8M/vgXgW8RT+PpshyZKg69BuhW6joaGBGRtmYt+aXxEXozwG5/Wr11gychEu/nUeLx49R/ith/hp4VY4NK4FiyqWpX06ZWrGNA9s2/4rdv98AA8eRGDS5Ll4/ToL7mNUt9yPdpsC/627ERp6Dw8fRmH8197Q0NBAly7tFXXi4hKUlr59XREUdBGPH8eU1WmVPgFn4Pf09MSTJ08gkUhw5coVtG7dWrEuKChIqddMLBbD19cXkZGRyMrKQkxMDDZt2lTsBpQSS8YMDQ1haGiIw4cPq5y/AwAGDx6M+Ph4HD9+HDdu3ECzZs3QtWtXJCcnK+pERkbiwIEDOHr0KAICAnDr1i1MmjRJsf7Vq1dwc3PD+fPncfnyZdSqVQs9e/bEq1evVB6zXbt20NLSwpkzZwAA9+/fR1ZWFsaOHYukpCQ8fvwYQH5rma6ubqH9wrNmzcLZs2fx559/4uTJkwgKClJKAg8ePIiqVati6dKlePnypVKy9fr1a6xatQp79uzBuXPnEBMTA29vbwD5/dH79u3DyJEjUaVKFZXXVSx+24C5du1atGvXDrdu3UKvXr0watQojB49Gl9++SVu3ryJmjVrYvTo0WrZtSnWEqNu49q4FnxDUSaXy3Et+AYaNW9QpH20bN8M1Wva4daV2yrXm1uYoV1XJxzZx1+IlO/NfXflX/fd1eDraFzk+655/n13OVTlenMLM7Tv6oQ/f/urRGJWJ2ItMWo2ckDo+bfnLpfLcft8COo0q1PodkOmD0NaYhoC958q0nH0jfUhk8mQmZ7xn2NWF1paWmjWrDEC/wlWlMnlcgT+cx5t2jQv0j709fWgpSVGSnKqyvVWVhbo2aMrduz6rSRCVh8lOLVFeVBi3ZRisRi7du2Ch4cH/P390axZM3Tq1AnDhg1D48aNcf78eVy9ehXx8fGKpxpWrVqFw4cP43//+x/Gjx8PAMjOzsbPP/8MW1tbAMDGjRvRq1cvrF69GjY2NujSpYvScX/88UeYmpri7Nmz6N27d4G4DAwM0KpVKwQFBWH48OEICgpC+/btoaOjg7Zt2yIoKAifffYZgoKC4OTkpPKJi4yMDGzfvh2//PILunbtCgDYvXs3qlatqqhjbm4OTU1NGBkZFXjqIjc3F/7+/qhZsyaA/Kx76dKlAPLnNUlJSUHduoWPm3pXz5498fXXXwMAFi1ahC1btqBly5YYPHgwAGDOnDlwcnJ679MfQjE1N4FYLEZyQopSeXJiCqo7VCt0OwMjA/x983/Q1taGVCrFynnrcPWc6ta/XkO6IzPjNc4cO1eisVP59fa+S1YqT05Igb1D9UK3MzAywPFbBxX33QqfNbhSyH3Xe0iPT/a+MzI3hqZYE2mJyv9uUxNTYVuzqspt6rWsj65Du8Gr+7QiHUNLRwujfcYg+M9zyMrI+s8xqwsLC3OIxWLExyUqlcfHJ6BunZpF2off8vl48SIOpwODVa4fPWowXr3KwKFDx/9zvCScEh8z1qtXLwQHB+Py5cs4fvw4Vq5ciW3btiEzMxMZGRmoVKmS0jZZWVmIiopSfK5WrZoiEQMAJycnyGQyPHz4EDY2NoiLi8OCBQsQFBSE+Ph4SKVSvH79GjExhTfPdu7cGb///juA/CbGzp07AwA6deqEoKAguLu7IygoCB4eHiq3j4qKQk5OjlJTpbm5OerUKfxX4bv09fUViRgAVK5cGfHx8QCK97QFADRu3Fjx32/6sBs1alSgLD4+XmUyJpFICrRcyuQyaIjU98Ha1xmv8WW3cdAz0EPL9s0w3XcSnj95gZuXQgrU7TOsB04cOo0cSU7ZB0qflNcZrzHC5SvoG+ihZfvmmLHYE8+fvMANFfdd3+E9EXDwFO87ALoGepi21gtb5vyAVynpH6yvKdaE9+Y5AETYOn9z6QdYjsyeNRlDh/RF126DC+1xGjNmGH797VCh68utj+heLM9KfJ4xXV1ddOvWDd26dcPChQsxbtw4+Pr6YtKkSahcubLSOK03itO36ubmhqSkJKxfvx7Vq1eHjo4OnJyckJNT+Jegs7Mzli1bhufPnyMoKEjRRdipUyds3boVUVFRePr0aYFWt5KipaWl9FkkEimSMEtLS5iamiIsLKzY+3ozLk1VmayQx3n9/PywZMkSpbIqhtVga2RfpOP/F6nJacjLy4O5pZlSubmFGZL+1WrxLrlcjmfRzwEAEfci8Vmt6hgzZWSBZMyxVWPYO1TH/AlLVOyFKqq3953yk3rmlmZIii/84aJ377vwe5H4rJY9xkwdVSAZc2ydf9/5fO1b4rGrg1fJ6ZDmSWFiofzv1tTCFKn/auUGAJvqNrCuZo15OxYqykQa+d9L/3t0GJ7OExD7JH8M75tEzNLWCr7D5n9SrWIAkJiYjLy8PFhZWyiVW1lZIjYu4b3bes34GrNnTYZr92G4c+eByjrt27VC3ToOGDFyYonFrC7kFSwZK/XmkPr16yMzMxPNmjVDbGwsxGIxHBwclBYLi7c3akxMDF68eKH4fPnyZWhoaChaoS5cuICpU6eiZ8+eaNCgAXR0dJCYmFjguO9q27YttLW1sXnzZmRnZ6N58/y++pYtWyIhIQE7duxQdGeqUrNmTWhpaeHKlSuKspSUFISHhyvVe9OdURwaGhoYNmwY9u7dq3Teb2RkZCAvL69Y+3wfVTMRVzYsvIuwJOXl5iHsdjhatn87VkIkEqFF+2a4c+Nekfcj0tCAlrZWgfK+w3viQWgYIu5HqdiKKqo3912rf913Lds3x+1i3HcaGiJoq7jv+g3vjfuf8H2Xl5uHqDuRaNzubau8SCRCo3ZN8PDmwwL1n0c9wzSXyfDqPlWxXDt1FXcv3YFX96lIfJH/ff0mEavyWRUsHrEAr1JVj/stz3Jzc3Hz5m10cX47+F4kEqGLc3tcvnyj0O28Z07E/HnT0av3l7hxU/X4WABwdx+O6zdCcfv2/RKNm8peiSVjSUlJ6NKlC3755Rfcvn0bjx8/xu+//46VK1eiX79+cHFxgZOTE/r374+TJ08iOjoaFy9exPz585We/tPV1YWbmxtCQ0MRHByMqVOnYsiQIYout1q1amHPnj148OABrly5gpEjR0JPT++9senp6aFNmzbYuHEj2rVrp5j7Q1tbW6n83y1YbxgaGmLs2LGYNWsW/vnnH9y9exdjxowpMNGbvb09zp07h+fPn38wQXzXsmXLYGdnh9atW+Pnn3/G/fv3ERERgR07dqBp06bIyCi5Aa06OjowNjZWWsqyi/LXHw+g34he6DXYFfYO1THnOy/o6evhr3354x0Wr5+HST5vu4vdPEeiVccWqFKtMuwdqmPE10PQc+DnCDioPCjYwFAfXft0xp+//l1m56LOXr/OQlh4FMLC8xOE5y/iEBYehZex8QJHJoy9W/ej/8je6DW4O+xrVYfPipnQ09fD0f9/0GPJhvmYPO9rRf0xU75E644tYFutMuxrVcfIr4ei5yBXHPvjpNJ+DQz14dKnM/789dMbuP+uI9sOo9twVzgP6oKqDlXx9fJJ0NXXReCB0wCAqWtn4Ms5owEAuZJcxITHKC2Z6ZnIyshCTHgM8nLzoCnWxGz/uXBo7IC1U1dBQ1MDppamMLU0hVjr03oxzNr1P2Hc2BEYNWow6tZ1wKYfvoOBgR527d4PANi5Yz2WfTtXUX+W9yQsWTwL48bPRPSTp7C2toS1tSUMDPSV9mtkZIhBA3tjx45PbOD+GwI+TSmEErvrDQ0N0bp1a6xduxZRUVHIzc2FnZ0dPDw8MG/ePIhEIhw7dgzz58+Hu7s7EhISYGNjg44dOyq9lNPBwQFffPEFevbsieTkZPTu3RubN78dR7B9+3aMHz8ezZo1g52dHZYvX67odnwfZ2dnnDt3TjFe7I1OnTrhzJkziiktCvP9998jIyMDffr0gZGREWbOnKk05QYALF26FF9//TVq1qwJiURS5PFg5ubmuHz5Mr777jt8++23ePLkCczMzNCoUSN8//33ZTrnWWk7feQMzCqZYvysr1DJ0hzh9yIxbeQsJP//4GBrWyulLlY9fV3MXj4DVpUtIcmW4ElUDBZN+Ranj5xR2m+3fl0hEolw4nAgCLgbFoGvpsxRfF658UcAQL8eLli2YKZQYQnm1JF/YFbJFBNmj1Xcd1NGeCvuOxtba8je+dLW09fFHD8vWFW2giRbgujIJ1jo+Q1OHflHab+f98+/7wIOnS7T8ylrF46eh7G5CYZ5jYSZpRke33+EpaN8kZaYCgCwrGJZrG4lc5tKaPV5GwDA2hMbldYtGOKDe5fvqtqsXPr99yOwtDDH4kXesLGxRGjoPfTq/SXi4/N/sFezq6L0nff1+NHQ0dHB7/t/UtrP0m9WY+k3axSfhw7pB5FIhH37D5fJeZS5Ysyc/ykQydVoDoTFixfj8OHDCAkJETqUCqVVlU5Ch1CuXbi9U+gQyi2nRm5Ch1Cu2WmZCh1CuXU0tvD5Ken98nKel/oxXk3qUWL7Mtqs/k+aqu8jdEREREQVwKfVOU9ERETlXzkZ61VS1KplbPHixeyiJCIiquDkcnmJLeWBWiVjRERERBUNuymJiIhIvVSwbkomY0RERKReKlgyxm5KIiIiIgGxZYyIiIjUSkV7NyWTMSIiIlIvFSwZYzclERERkYDYMkZERETqpWK9mpLJGBEREakXjhkjIiIiElIFS8Y4ZoyIiIhIQGwZIyIiIvXCMWNEREREwqloY8bYTUlEREQkILaMERERkXphNyURERGRcNhNSURERERlhi1jREREpF7YTUlEREQkHHkFS8bYTUlEREQkILaMERERkXqpYC1jTMaIiIhIrVS0bkomY0RERKReKlgyxjFjRERERAJiyxgRERGpFXZTEhEREQmooiVj7KYkIiIiEhBbxoiIiEitVLSWMSZjREREpF7kIqEjKFNMxghH6wgdQfnm1MhN6BDKrUt3dgsdQrkW6ugldAjl1s+TmgkdAqmpTZs24fvvv0dsbCyaNGmCjRs3olWrVirrdu7cGWfPni1Q3rNnT/z9999FPibHjBEREZFakctKbimO/fv3w8vLC76+vrh58yaaNGkCV1dXxMfHq6x/8OBBvHz5UrHcvXsXmpqaGDx4cLGOy2SMiIiI1IpcJiqxpTjWrFkDDw8PuLu7o379+vD394e+vj527Nihsr65uTlsbGwUy6lTp6Cvr89kjIiIiOgNiUSC9PR0pUUikRSol5OTgxs3bsDFxUVRpqGhARcXF1y6dKlIx9q+fTuGDRsGAwODYsXIZIyIiIjUSkl2U/r5+cHExERp8fPzK3DMxMRESKVSWFtbK5VbW1sjNjb2gzFfvXoVd+/exbhx44p9vhzAT0RERGpFXoJPU/r4+MDLS/lhFx0dnRLb/xvbt29Ho0aNCh3s/z5MxoiIiEitlOQ8Yzo6OkVKviwsLKCpqYm4uDil8ri4ONjY2Lx328zMTOzbtw9Lly79qBjZTUlEREQVnra2Npo3b47AwEBFmUwmQ2BgIJycnN677e+//w6JRIIvv/zyo47NljEiIiJSK8V9CrKkeHl5wc3NDS1atECrVq2wbt06ZGZmwt3dHQAwevRo2NraFhhztn37dvTv3x+VKlX6qOMyGSMiIiK1IpcLc9yhQ4ciISEBixYtQmxsLBwdHREQEKAY1B8TEwMNDeVOxYcPH+L8+fM4efLkRx+XyRgRERHR//P09ISnp6fKdUFBQQXK6tSpA/l/zB6ZjBEREZFaEaqbUihMxoiIiEitVLRkjE9TEhEREQmILWNERESkVoQawC8UJmNERESkVthNSURERERlhi1jREREpFZK8t2U5QGTMSIiIlIrJfluyvKAyRgRERGpFVkFaxnjmDEiIiIiAbFljIiIiNQKx4wRERERCYhTWxARERFRmWHLGBEREakVzsBPREREJCB2UxIRERFRmWHLGBEREamVijbPGJMxIiIiUisVbWoLdlMSERERCahcJmNBQUEQiURITU0VOpQPKk+xEhERqQO5vOSW8uA/d1OOGTMGu3fvBgBoaWmhWrVqGD16NObNmwex+P2737VrF6ZPn/6fE5WgoCA4OzsjJSUFpqam/2lf/0Xnzp3h6OiIdevWFXvbnJwcrFu3Dnv37kVERAT09fVRp04djBs3Dl9++SW0tLRKPmCB6PXvD4Ohw6Bhbo68qCikb1iPvLAwlXXN1q6DtmPTAuWSy5eQ6jMXAKDToQP0+vSDVu3a0DAxQdK4sciLiizVcxDK4DEDMGrScFSyNEfE/Sh8P38d7oU8UFnXuWdHuE8dBTt7W4i1xIh59Ax7t+7Hsf+dUNS5/jJY5bbrl27Gni2/lco5qLvrIXew89f/4X5YJBKSkrHebyG6dmwrdFiCs3TrAZsJA6BlaYrXD6LxdOFPyAyJKLS+prEBbGePhGmPNhCbGiHneTyeLt6BtH9uFKhrM/kLVPUZjbhtR/F08fbSPA1BaLXvCe0uX0BkZAbZi8fI/mMrZDGqr52e53KIHRoVKM+7dw1ZPy3N/6CtC50+bhA3agORvhFkyXHIPXcUuRcDSvM0yhTHjH2E7t27Y+fOnZBIJDh27BgmT54MLS0t+Pj4lMTuP3k5OTlwdXVFaGgovvnmG7Rr1w7Gxsa4fPkyVq1ahaZNm8LR0VHoMEuEjrMzjCZORvraNch9cB/6gwbDbOUqJI7+EnIVSXnqooUQid8moiITY1Tath3ZQUFvy3T1kHv3DiRBZ2A8a3YZnIUwuvXtghmLPeE3ZzXu3rqP4R6DsfG31RjYfgRSklIL1E9PSceO9T8jOjIGuTm56NCtLRatnYvkxBRcDroKAHBt3E9pm7Zd2mDhmjn45++gMjgj9ZSVlY06DjUwoNfnmD7vW6HDUQtmfdrBbtFXeOKzBZm3wmE9ri9q/eKLu50mIy8prUB9kZYYtX9djNykNDz6eiVyYpOhXdUS0rTMAnX1mzjAcqQrXt9/XBanUubETdtDp/84ZB/YBNmTcGh16gv9CUuRuXwC5BkFr13WjuUQab790ywyMIb+rA3IDb2gKNPpPxbiWo2R/ctqyJLjIa7TFDqDJkKWlgzpvatlcl6ljWPGPoKOjg5sbGxQvXp1TJw4ES4uLjhy5AgkEgm8vb1ha2sLAwMDtG7dGkH//0c0KCgI7u7uSEtLg0gkgkgkwuLFiwEAe/bsQYsWLWBkZAQbGxuMGDEC8fHxHx3f++IA8lvoTE1NceLECdSrVw+Ghobo3r07Xr58qaiTl5eHqVOnwtTUFJUqVcKcOXPg5uaG/v37A8hvITx79izWr1+vOJ/o6GjF9jdu3ECLFi2gr6+Ptm3b4uHDh4p169atw7lz5xAYGIjJkyfD0dERNWrUwIgRI3DlyhXUqlULQH7L25QpUzB9+nSYmZnB2toaP/30EzIzM+Hu7g4jIyM4ODjg+PHjH32tSpvB4CHI+vsvZAcch/TJE7xasxry7Gzo9eipsr781SvIUpIVi07zFpBnS5B9NkhRJ/vUSWT+vBuSGwV/cX9KRn49FIf3HsXR/cfwODwafrNXITsrG32H91JZ/8alEAQdD0Z0xBM8f/IC+7b9D5EPHsGx1dtf3UkJyUpLp+7tcf3CLTyPealynxVBB6eWmDreDS6d2gkditqwHt8Pib+dRNKBf5Ad8QxP5m6BLFsCi2FdVda3GNoVmqZGiBrrh4zrYch5Fo+My/eQ9SBaqZ6Gvi5qbJyB6NmbVCZqnwLtzv2Re+kE8q4GQhb3FJLfN0OeI4FW626qN3idAfmrVMWiWccRyJUgL+S8oormZ/WQe+0fSCPvQp4cj9xLJyB78Ria1WuXzUlRiSuVMWN6enrIycmBp6cnLl26hH379uH27dsYPHgwunfvjoiICLRt2xbr1q2DsbExXr58iZcvX8Lb2xsAkJubi2+++QahoaE4fPgwoqOjMWbMmI+O531xvPH69WusWrUKe/bswblz5xATE6OIBwBWrFiBvXv3YufOnbhw4QLS09Nx+PBhxfr169fDyckJHh4eivOxs7NTrJ8/fz5Wr16N69evQywW46uvvlKs27t3L1xcXNC0acHuOC0tLRgYGCg+7969GxYWFrh69SqmTJmCiRMnYvDgwWjbti1u3ryJzz//HKNGjcLr168/+nqVGrEY4tq1kfNu0iSXI+fmDWg1aFCkXej27IXsM/8A2dmlFKR6EmuJUbdxbVwJfnvt5HI5rgZfR+PmRbt2Lds3R/Wadrh1OVTlenMLM7Tv6oQ/f/urRGKmT4NISwyDRjWRHnz7baFcjvTgUBg0q6NyG9PPWyHzZhiqLfsaTW7tQoPT62HjOQjQUP6TU23ZeKQF3sCr87dV7qfc0xRDo6oDpOHv/JuTyyEND4GGvepr929arbsh9+Y5IEeiKJM+fgBxw9YQmZjnH8ahETQsq0AadqtEwxcSx4z9B3K5HIGBgThx4gSGDx+OnTt3IiYmBlWqVAEAeHt7IyAgADt37sTy5cthYmICkUgEGxsbpf28m6jUqFEDGzZsQMuWLZGRkQFDQ8NixRQTE/PBOID8BNDf3x81a9YEkJ/ALV26VLGfjRs3wsfHBwMGDAAA/PDDDzh27JhivYmJCbS1taGvr1/gfABg2bJl6NSpEwBg7ty56NWrF7Kzs6Grq4uIiAh07ty5SOfTpEkTLFiwAADg4+OD7777DhYWFvDw8AAALFq0CFu2bMHt27fRpk2b4lyqUqdhYgKRphiylBSlcllKCrSrVfvg9uK6daFVowbSv19RWiGqLVNzE4jFYiQnJCuVJyekwN6heqHbGRgZ4Pitg9DW1oZUKsUKnzW4cu66yrq9h/RAZsZrnDl2rkRjp/JNbG4EkVgTuQmpSuV5iWnQdaiqchvtatYwatsISYfPIWL0N9Cxt0H15V9DpKWJl2v3AwDM+raHfqOaeNDLW+U+PgUiA2OINDUhe6X8nSd/lQpNa9XX7l0a1WpBs4o9svdtUCqX/LEVukM9YbhkN+TSPEAuR/b+jZA+ulei8QuJY8Y+wl9//QVDQ0Pk5uZCJpNhxIgRGDRoEHbt2oXatZWbTSUSCSpVqvTe/d24cQOLFy9GaGgoUlJSIJPJAOQnVvXr1y9WbHfu3IFUKv1gHPr6+opEDAAqV66s6BpNS0tDXFwcWrVqpVivqamJ5s2bK2L7kMaNGyvtGwDi4+NRrVo1yIuRur+7H01NTVSqVAmNGr3tdrK2tlbsWxWJRAKJRKJcJpNBR0P9H6zV69kLuVFRhQ72p4JeZ7zGCJevoG+gh5btm2PGYk88f/ICNy6FFKjbd3hPBBw8hRxJTtkHSp8UkYYIuUlpeDJ7MyCT4fWdKGjbVIL1hP54uXY/tCpboNqScQgf4Qu5JFfocNWWVpvPIX3xuMBgf62OfaBpXwevf1oKeXICNGs2gO7ACchKS1ZuhaNyo0SSMWdnZ2zZsgXa2tqoUqUKxGIx9u/fD01NTdy4cQOamppK9d/XupWZmQlXV1e4urpi7969sLS0RExMDFxdXZGTU/w/EhkZGUWK499PK4pEomIlSR/y7v5FovyM/00iV7t2bYQVMcFQFef79v1vfn5+WLJkiVLZzOrVMOsz+yId/7+QpaVBLs2DhpmZUrmGmRmkycmFbPX/dHWh69wFGbt2lGKE6is1OQ15eXkwtzRXKje3NENSfFKh28nlcjyLfg4ACL8Xic9q2WPM1FEFkjHH1o1h71AdPl/7lnjsVL7lJb+CPE8KLUtTpXKxhQly41NUbpMbnwJ5rhR453soK/IZtK3N87s9G9eElqUp6h9fo1gvEmvCsHV9WI3piRs1BittW17JM9Mhl0qhYWSGd89GZGQKWbrqa6egrQOtph0gOb5XuVxLGzq9RiFrx3JI7+e3csteRkPDtga0nQcg6xNJxjiA/yMYGBjAwcEB1apVU0xn0bRpU0ilUsTHx8PBwUFpedON96br5F1hYWFISkrCd999hw4dOqBu3br/afB+UeL4EBMTE1hbW+PatWuKMqlUips3byrVU3U+RTFixAicPn0at24V7O/Pzc1FZmbJDWz18fFBWlqa0jK1+oe7CEtEXh7ywsOh3az52zKRCNrNmiH33vub13U7dYZIWwvZp06VcpDqKS83D2G3w9Gq/dtrJxKJ0LJ9c9y+UfSuCQ0NEbS1C06T0m94b9wPDUPE/agSiZc+HfLcPGTeiYJR+7et8hCJYNy+MTJvPlS5Tca1MOjYVwZEb/+g6taogpzYZMhz85B+PhR3u07FPdcZiiUzJALJh87hnuuMTyIRAwBI8yB7FgnNWsrXTrN2E8iiVV+7N8SO7QGxFnKvBymv0NDMf8L8340FchkgUv8ejqKSyUUltpQHpfY6pNq1a2PkyJEYPXo0Vq9ejaZNmyIhIQGBgYFo3LgxevXqBXt7e2RkZCAwMBBNmjSBvr4+qlWrBm1tbWzcuBETJkzA3bt38c033xTpmHfu3IGRkZHis0gkQpMmTT4YR1FMmTIFfn5+cHBwQN26dbFx40akpKQoWqIAwN7eHleuXEF0dDQMDQ1hbm7+nj2+NX36dPz999/o2rUrvvnmG7Rv3x5GRka4fv06VqxYge3bt5fY1BY6OjrQ0dFRKssqwy7KzN8PwGSuD3LDw5D7IAz6gwZBpKuH7ID8J0CNfeZBlpCAjG0/KW2n17MXJOfPQ56eXmCfIiMjaFpZQ8Miv9tZs1r+gxOy5PwnMD8Ve7fux+L183A/NAz3Qh5ghMdg6Onr4ei+/LGLSzbMR3xsIjYt3woAGDPlSzwIDcOz6OfQ0tFGuy5t0HOQK/zmrlbar4GhPlz6dMa6JZvK/JzU0evXWYh59kLx+fmLOISFR8HE2AiVbawEjEw4cT/+ic/WTsPr0EhkhkTAelwfaOjpInF/IADAft005MYm4fl3vwAA4n8OgNWYnrBbOg7xO/6G7meVUdlzEOJ35D8cIsvMRvbDGKVjyLIkyEt5VaC8vMsJOgzdETMgfRoJWUw4tDr1g0hbF7lXTgMAdEfOgCwtCTl//ay0nVbrbsi7cxl4/Up5h5Is5EXegU5fd0hyJZAlJ0DToSG0WjhD8uenN0dbRVGq76bcuXMnvv32W8ycORPPnz+HhYUF2rRpg969ewMA2rZtiwkTJmDo0KFISkqCr68vFi9ejF27dmHevHnYsGEDmjVrhlWrVqFv374fPF7Hjh2VPmtqaiIvL++DcRTFnDlzEBsbi9GjR0NTUxPjx4+Hq6urUtent7c33NzcUL9+fWRlZeHx46LNm6Ojo4NTp05h7dq12Lp1K7y9vaGvr4969eph6tSpaNiwYZHjVHeSM2fwysQUhmO++v9JXyORMmeWYlC/ppVVgV/FmnZ20G7cGCneM1XuU6dtO5jMfTunnemixQCAjF07kbl7V6mchxBOHfkHZpVMMWH2WFSyNEf4vUhMGeGN5MT8a2djaw2Z7O2vZT19Xczx84JVZStIsiWIjnyChZ7f4NSRf5T2+3n/rhCJRAg4dLpMz0dd3Q2LwFdT5ig+r9z4IwCgXw8XLFug+h781KUcvQBxJRNU8R4OLUszvL7/GBGjliAvMX+eLB1bS+Cdey/3ZSLCRy6B3eKv0ODUOuTEJiNu+1+I3XxQqFMQTN6t85AYmECnx0iIjM0ge/4Ir7f6Qp6RCgAQmVlC41+tXCIrW4hrNsDrzQtV7jN790ro9HaD7pfeEOkbQpaSAMmxPci9oL7TGhVXOXkIssSI5CU5MKoCkclkqFevHoYMGVLkljt1FefcSegQyrVeYZ9Il4oALt3ZLXQI5Vqoo5fQIZRbdQYVf0gJ5TNad7TUj3Gx8sAS21fbl3+U2L5KS6m2jH1Knjx5gpMnT6JTp06QSCT44Ycf8PjxY4wYMULo0IiIiKgcYzJWRBoaGti1axe8vb0hl8vRsGFDnD59GvXq1RM6NCIiok9KRXuakslYEdnZ2eHChQsfrkhERET/SUUb/MFkjIiIiNSKHBWrZezTmZSEiIiIqBxiyxgRERGpFVkFm+eByRgRERGpFRm7KYmIiIiorDAZIyIiIrUih6jEluLatGkT7O3toauri9atW+Pq1avvrZ+amorJkyejcuXK0NHRQe3atXHs2LFiHZPdlERERKRWhJraYv/+/fDy8oK/vz9at26NdevWwdXVFQ8fPoSVVcF30+bk5KBbt26wsrLC//73P9ja2uLJkycwNTUt1nGZjBEREREBWLNmDTw8PODu7g4A8Pf3x99//40dO3Zg7ty5Berv2LEDycnJuHjxIrS0tAAA9vb2xT4uuymJiIhIrZRkN6VEIkF6errSIpFIChwzJycHN27cgIuLi6JMQ0MDLi4uuHTpkso4jxw5AicnJ0yePBnW1tZo2LAhli9fDqm0eO8+ZTJGREREakVWgoufnx9MTEyUFj8/vwLHTExMhFQqhbW1tVK5tbU1YmNjVcb56NEj/O9//4NUKsWxY8ewcOFCrF69Gt9++22xzpfdlERERKRWSnLMmI+PD7y8vJTKdHR0SmTfMpkMVlZW+PHHH6GpqYnmzZvj+fPn+P777+Hr61vk/TAZIyIiok+Wjo5OkZIvCwsLaGpqIi4uTqk8Li4ONjY2KrepXLkytLS0oKmpqSirV68eYmNjkZOTA21t7SLFyG5KIiIiUitCTG2hra2N5s2bIzAwUFEmk8kQGBgIJycnldu0a9cOkZGRkMnetuWFh4ejcuXKRU7EACZjREREpGZkopJbisPLyws//fQTdu/ejQcPHmDixInIzMxUPF05evRo+Pj4KOpPnDgRycnJmDZtGsLDw/H3339j+fLlmDx5crGOy25KIiIiIgBDhw5FQkICFi1ahNjYWDg6OiIgIEAxqD8mJgYaGm/bsezs7HDixAnMmDEDjRs3hq2tLaZNm4Y5c+YU67hMxoiIiEitCPluSk9PT3h6eqpcFxQUVKDMyckJly9f/k/HZDJGREREakUudABljGPGiIiIiATEljEiIiJSK0K9m1IoTMaIiIhIrchEwo0ZEwK7KYmIiIgExJYxIiIiUisVbQA/kzEiIiJSKxwzRkRERCSg4s6cX95xzBgRERGRgNgyRkRERGpFyBn4hcBkjIiIiNRKRRvAz25KIiIiIgGxZYyIiIjUSkUbwM9kjDAyUlfoEMo1Oy1toUMot0IdvYQOoVxrErJG6BDKrccdJgkdQrllVAbHqGhTW7CbkoiIiEhAbBkjIiIitVLRBvAzGSMiIiK1UtHGjLGbkoiIiEhAbBkjIiIitVLRBvAzGSMiIiK1wmSMiIiISEByjhkjIiIiorLCljEiIiJSK+ymJCIiIhJQRUvG2E1JREREJCC2jBEREZFa4Qz8RERERALiDPxEREREVGbYMkZERERqpaIN4GcyRkRERGqloiVj7KYkIiIiEhBbxoiIiEit8GlKIiIiIgFVtKcpmYwRERGRWuGYMSIiIiIqM2wZIyIiIrXCMWNEREREApJVsHSM3ZREREREAmLLGBEREamVijaAn8kYERERqZWK1UnJbkoiIiIihU2bNsHe3h66urpo3bo1rl69WmjdXbt2QSQSKS26urrFPiaTMSIiIlIrshJcimP//v3w8vKCr68vbt68iSZNmsDV1RXx8fGFbmNsbIyXL18qlidPnhTzqEzGiIiISM3IRCW3FMeaNWvg4eEBd3d31K9fH/7+/tDX18eOHTsK3UYkEsHGxkaxWFtbF/t8P7lkLCgoCCKRCKmpqf9pP/b29li3bt1764hEIhw+fPi9dcaMGYP+/fv/p1iIiIjo40gkEqSnpystEomkQL2cnBzcuHEDLi4uijINDQ24uLjg0qVLhe4/IyMD1atXh52dHfr164d79+4VO8ZSTcbGjBmj6EPV1taGg4MDli5diry8vA9uu2vXLpiamv6n42dkZEBLSwv79u1TKh82bBhEIhGio6OVyu3t7bFw4UIAwLVr1zB+/PgiHys6OhoikQghISEfFWtkZCTc3d1RtWpV6Ojo4LPPPsPw4cNx/fr1j9pfedLXrQ9+ubgbxyKOYuOR9ajjWKdI23Xu2wmnn57Akm2+pRyh+ugxuie2XtiG/eF/YMWfq1CrSa0ibde+TwccijmKuT/NV5RpijUxyscN605uxG9hv2P7tV2YunYGzKzNSyt8QVm69UCjSz+iWeQB1D26EgaO7792msYGqPbteDS+sQPNon5Hw3ObYNKlucq6NpO/QItnh2G3eGxphF5uXA+5g8mzfeHcdyQatuuBwHMXhQ5JLZiM6IPPTu+GQ8gR2O1bB91Gtd9bX8PIAFYLJ6PGuV/hEHoE9se3waBjS8X6z07vRu0HAQUWq4WTS/tUyowM8hJb/Pz8YGJiorT4+fkVOGZiYiKkUmmBli1ra2vExsaqjLNOnTrYsWMH/vzzT/zyyy+QyWRo27Ytnj17VqzzLfWWse7du+Ply5eIiIjAzJkzsXjxYnz//felfVgAgKGhIVq0aIGgoCCl8qCgINjZ2SmVP378GE+ePEGXLl0AAJaWltDX1y+TOK9fv47mzZsjPDwcW7duxf3793Ho0CHUrVsXM2fOLJMYhNK5TydMWDgee9btxYSek/Ho/iN8t2cZTCuZvHc766rW+HqBB25fuVNGkQqvXZ/2cF84DvvX/YaZvaYj+sFjLPplKUw+cK0sq1rBbcFXuHflrlK5jp4OajSsiQMb9mNmz+lYMd4PtjVsMW/7gtI8DUGY9WkHu0Vf4cXafbjfwwtZ96NR6xdfiAu5diItMWr/uhjadlZ49PVK3O00GdGzNyPnZVKBuvpNHGA50hWv7z8u7dNQe1lZ2ajjUAPzZ04SOhS1YdijIyzneCBp0y+IGegJycNHsP1pGTTNC/l3qyVG1e1+ENta48W0bxHdwwNxi9YjNy5RUSVm8FREdRiuWJ595QMAeBUQXBanVCbkJbj4+PggLS1NafHx8SmROJ2cnDB69Gg4OjqiU6dOOHjwICwtLbF169Zi7afUkzEdHR3Y2NigevXqmDhxIlxcXHDkyBFIJBJ4e3vD1tYWBgYGaN26tSI5CgoKgru7O9LS0hQta4sXLwYA7NmzBy1atICRkRFsbGwwYsSI9w6sc3Z2Vkq6Hjx4gOzsbEycOFGpPCgoCDo6OnBycgJQsJsyIiICHTt2hK6uLurXr49Tp04pHeezzz4DADRt2hQikQidO3dWWr9q1SpUrlwZlSpVwuTJk5GbmwsAkMvlGDNmDGrVqoXg4GD06tULNWvWhKOjI3x9ffHnn38CeNvyduDAAXTo0AF6enpo2bIlwsPDce3aNbRo0QKGhobo0aMHEhISivp/j+AGenyBY78F4MSBk4iJiME6nw2QZEvQfahrodtoaGjAZ8Mc7F69By9jXpZhtMLqO64/Tv12Av/8HohnEU/h77MZkiwJug7tVug2GhoamLFhJvat+RVxMXFK616/eo0lIxfh4l/n8eLRc4TfeoifFm6FQ+NasKhiWdqnU6asx/dD4m8nkXTgH2RHPMOTuVsgy5bAYlhXlfUthnaFpqkRosb6IeN6GHKexSPj8j1kPYhWqqehr4saG2cgevYmSNMyy+BM1FsHp5aYOt4NLp3aCR2K2jBz+wLpvwcg/dAp5ETFIH7xRsizJTD+QvV3nMkXn0PDxBAvPJcg+9Z95L2IQ9a1O8h5+DbZl6akQZqYolgMOrdCzpMXyLp2u6xOq9SV5AB+HR0dGBsbKy06OjoFjmlhYQFNTU3ExSl/V8bFxcHGxqZIcWtpaaFp06aIjIws1vmW+ZgxPT095OTkwNPTE5cuXcK+fftw+/ZtDB48GN27d0dERATatm2LdevWKT2h4O3tDQDIzc3FN998g9DQUBw+fBjR0dEYM2ZMocdzdnbGw4cP8fJl/h/tM2fOoH379ujSpYtSMnbmzBk4OTmpfCRVJpPhiy++gLa2Nq5cuQJ/f3/MmTNHqc6bR19Pnz6Nly9f4uDBg0r7joqKwpkzZ7B7927s2rULu3btAgCEhITg3r17mDlzJjQ0Cv7f8e+uWl9fXyxYsAA3b96EWCzGiBEjMHv2bKxfvx7BwcGIjIzEokWLCr0e6kSsJUbtRrVw8/xNRZlcLsfN4Fuo37x+odt9OX0kUpNSEbD/RFmEqRbEWmLUbOSA0POhijK5XI7b50NQp1nh3bpDpg9DWmIaAvefKrTOu/SN9SGTyZCZnvGfY1YXIi0xDBrVRHrwO3+o5HKkB4fCoJBrZ/p5K2TeDEO1ZV+jya1daHB6PWw8BwH/+jdabdl4pAXewKvzn84fQSpBWmLoNqiFzEu33pbJ5ci8dAt6jvVUbmLYpQ2yQ8LyuymDf0P1I/4wHz+0wL337jGM+3RB+sGK831YWrS1tdG8eXMEBgYqymQyGQIDAxUNNR8ilUpx584dVK5cuVjHLrNJX+VyOQIDA3HixAkMHz4cO3fuRExMDKpUqQIA8Pb2RkBAAHbu3Inly5fDxMRE8YTCu7766ivFf9eoUQMbNmxAy5YtkZGRAUNDwwLHbdeuHbS1tREUFIThw4cjKCgInTp1QvPmzZGYmIjHjx/js88+w9mzZzF2rOrxHqdPn0ZYWBhOnDihiHf58uXo0aOHoo6lZX5LQqVKlQrEbGZmhh9++AGampqoW7cuevXqhcDAQHh4eCAiIgIAULdu3SJdR29vb7i65v+imjZtGoYPH47AwEC0a5f/S3Ts2LGKRE/dmZgbQ1OsiZSEVKXylMQU2DnYqdymYcsG6DHMFV+7VqxuEKP/v1ZpiSlK5amJqbCtWVXlNvVa1kfXod3g1X1akY6hpaOF0T5jEPznOWRlZP3nmNWF2NwIIrEmcv91n+UlpkHXQfW1065mDaO2jZB0+BwiRn8DHXsbVF/+NURamni5dj8AwKxve+g3qokHvbxL+xSonNI0NYZIrAlpUqpSuTQpFdqfqf6O06paGXqtrfHqrzN4/vVCaFWvAutFnoBYjOTNewvUN+zqBA0jQ6QdKtoPrvJCqHdTenl5wc3NDS1atECrVq2wbt06ZGZmwt3dHQAwevRo2NraKsacLV26FG3atIGDgwNSU1Px/fff48mTJxg3blyxjlvqydhff/0FQ0ND5ObmQiaTYcSIERg0aBB27dqF2rWVBzFKJBJUqlTpvfu7ceMGFi9ejNDQUKSkpEAmy59FJCYmBvXrF2xN0dfXR8uWLRXJ2NmzZzFr1iyIxWK0bdsWQUFBkMvliImJgbOzs8pjPnjwAHZ2dopEDECRs2QAaNCgATQ1NRWfK1eujDt38sc6yeXFu+EaN26s+O83gwwbNWqkVPa+bluJRFLgKRKZXAYNkfo/WKtnoIc562Zjzex1SE9JFzoctaZroIdpa72wZc4PeFWEa6Up1oT35jkARNg6f3PpB6jmRBoi5Cal4cnszYBMhtd3oqBtUwnWE/rj5dr90KpsgWpLxiF8hC/kklyhw6VPiYYI0qRUxC1aD8hkkNyPhNjKAuZjB6lMxkwGdkdm8DVIE5IFCLb0CDUD/9ChQ5GQkIBFixYhNjYWjo6OCAgIUPy9jYmJUerFSklJgYeHB2JjY2FmZobmzZvj4sWLKvOR9yn1ZMzZ2RlbtmyBtrY2qlSpArFYjP3790NTUxM3btxQSlIAqGzdeiMzMxOurq5wdXXF3r17YWlpiZiYGLi6uiInJ+e9Mezfvx/37t1DVlYWmjVrBgDo1KkTzpw5A5lMBn19fbRu3bpkTvpftLS0lD6LRCJFEvkmIQ0LC0PTpk2LtS+RSKSy7M2+VfHz88OSJUuUyj4zqoEaJg4fPHZJS0tOhzRPCjNLU6VyMwszpCSkFKhfpXplVK5mg293LlWUiTTyr8GJx8cwpvNYvHzyaY4he/X/18rEwkyp3NTCFKkqrpVNdRtYV7PGvB0LFWVvrtX/Hh2Gp/MExD7JfzroTSJmaWsF32HzP6lWMQDIS34FeZ4UWv+6z8QWJsiNL3jtACA3PgXyXCnwzr+lrMhn0LY2z+/2bFwTWpamqH98jWK9SKwJw9b1YTWmJ27UGKy0LVVM0tR0yPOk0KxkqlSuWckU0kTV915eQjLkecr3Xs6jGIgtzQEtMZD7djYCcRUr6Ds54sXUb0ol/orK09MTnp6eKtf9+4HAtWvXYu3atf/5mKWejBkYGMDBQfkPfdOmTSGVShEfH48OHTqo3E5bWxtSqVSpLCwsDElJSfjuu+9gZ5ffxFuUqR+cnZ3x7bff4tdff0X79u0VCWDHjh3x448/Qi6XK7ozValXrx6ePn2Kly9fKvqBL1++XCBeAAVi/hBHR0fUr18fq1evxtChQwuMG0tNTf3PU3y8y8fHB15eXkpl/esPLLH9F0debh7C70SgWbumuHgifw4XkUiEpu0d8eeuIwXqx0Q9xTgX5elG3GeNgZ6BHjYv3oKEF+XnwYXiysvNQ9SdSDRu1xhXT+bfeyKRCI3aNcHx3X8XqP886hmmuSg/5j5i1ijoGephu++PSHyR/2TWm0SsymdVsHDoPLxKfVX6J1PG5Ll5yLwTBaP2jZF64kp+oUgE4/aNEb/rmMptMq6Fwbx/R0AkAv6/9Vq3RhXkxCZDnpuH9POhuNt1qtI2n62eguyo53i5+SATMcqXm4fsexHQb+OIzMD/n6dKJIJ+G0ek7j2qcpOsm/dh3NtZ6d7TtrdFXnySUiIGACYDPoc0OQ2ZZwt/XU95VdH+BQnSN1W7dm2MHDkSo0ePxsGDB/H48WNcvXoVfn5++Pvv/D8s9vb2yMjIQGBgIBITE/H69WtUq1YN2tra2LhxIx49eoQjR47gm28+/Iugbdu20NHRwcaNG9GpUydFeatWrRAfH48///yz0C5KAHBxcUHt2rXh5uaG0NBQBAcHY/78+Up1rKysoKenh4CAAMTFxSEtLa1I10IkEmHnzp0IDw9Hhw4dcOzYMTx69Ai3b9/GsmXL0K9fvyLtp6hUPVUiZBflHz8dRM/hPdBtkAuqOdhh2vIp0NXTRcCBkwCAOWtnYeyc/L76XEkuoh8+UVoy0jOQlZmF6IdPkJf74fnryrMj2w6j23BXOA/qgqoOVfH18knQ1ddF4IHTAICpa2fgyzmjAeRfq5jwGKUlMz0TWRlZiAmPQV5uHjTFmpjtPxcOjR2wduoqaGhqwNTSFKaWphBrldlw0jIR9+OfsBzeDZUGOUPXoSqq+02Ahp4uEvfnD9S1XzcNtnO/VNSP/zkAYlND2C0dB53PqsCkS3NU9hyEhN35yZssMxvZD2OUFlmWBHkpr5D9MEaQc1QHr19nISw8CmHhUQCA5y/iEBYehZexhQ+d+NSl7D4Ik8E9YNzPBdo17GDlOwUaerpIP5T/HWfznTcsZrgr6qft+wsaJoawnDcBWva2MOjUCubjhyH1138lbyIRjL/ohvTDpwDpp5e6lOQ8Y+WBYN+4O3fuxLfffouZM2fi+fPnsLCwQJs2bdC7d28A+QnUhAkTMHToUCQlJcHX1xeLFy/Grl27MG/ePGzYsAHNmjXDqlWr0Ldv3/ceS1dXF23atMHZs2eVppzQ0dFBmzZtEBQU9N5kTENDA4cOHcLYsWPRqlUr2NvbY8OGDejevbuijlgsxoYNG7B06VIsWrQIHTp0KNCcWZhWrVrh+vXrWLZsGTw8PJCYmIjKlSsrnir9lAUdPQsTcxOMmTkaZpZmiLr/CD6j5iM1MRUAYGVrCZn80/ui+RgXjp6HsbkJhnmNhJmlGR7ff4Slo3yR9v/XyrKKJeSyon/xmNtUQqvP2wAA1p7YqLRuwRAf3Lt8V9Vm5VLK0QsQVzJBFe/h0LI0w+v7jxExagnyEvN/NOnYWgLvXLvcl4kIH7kEdou/QoNT65ATm4y47X8hdvPBwg5BAO6GReCrKW+fNF+58UcAQL8eLli24NOeM7EwGcfPIdHMBJWmjoKmhRkkDx7h+fgFikH94spWSv9u82IT8dxjASznjkf1w1uQF5eI1D2Hkbztd6X96js1hVYVa6QdPFmWp0OlRCQv7ghy+uS42BU+pxd9mJFIdfc2fdh8meaHK1GhmoSs+XAlUulxh4r1RHZJqv0goNSPMcN+WInta230vg9XEtin1RdBRERE5V5F6w9R//kMiIiIiD5hbBkjIiIitSIvJwPvSwqTMSIiIlIrFa2bkskYERERqZXyMiVFSeGYMSIiIiIBsWWMiIiI1ErFahdjMkZERERqht2URERERFRm2DJGREREaoVPUxIREREJqKLNM8ZuSiIiIiIBsWWMiIiI1Aq7KYmIiIgExG5KIiIiIiozbBkjIiIitcJuSiIiIiIByeQVq5uSyRgRERGplYqVinHMGBEREZGg2DJGREREaqWivZuSyRgRERGpFU5tQURERERlhi1jREREpFY4tQURERGRgCramDF2UxIREREJiC1jREREpFYq2gB+JmNERESkViramDF2UxIREREJiC1jREREpFbkfDclERERkXAq2tOUTMaIiIhIrXDMGBERERGVGbaMEWw1DYUOoVzb++Ky0CGUWz9PaiZ0COXa4w6ThA6h3PoseLPQIdB7cGoLIiIiIgFVtDFj7KYkIiIiEhCTMSIiIlIrcrm8xJbi2rRpE+zt7aGrq4vWrVvj6tWrRdpu3759EIlE6N+/f7GPyWSMiIiI1IqsBJfi2L9/P7y8vODr64ubN2+iSZMmcHV1RXx8/Hu3i46Ohre3Nzp06FDMI+ZjMkZEREQEYM2aNfDw8IC7uzvq168Pf39/6OvrY8eOHYVuI5VKMXLkSCxZsgQ1atT4qOMyGSMiIiK1Ii/B/0kkEqSnpystEomkwDFzcnJw48YNuLi4KMo0NDTg4uKCS5cuFRrr0qVLYWVlhbFjx370+TIZIyIiIrUig7zEFj8/P5iYmCgtfn5+BY6ZmJgIqVQKa2trpXJra2vExsaqjPP8+fPYvn07fvrpp/90vpzagoiIiD5ZPj4+8PLyUirT0dH5z/t99eoVRo0ahZ9++gkWFhb/aV9MxoiIiEitlOSLwnV0dIqUfFlYWEBTUxNxcXFK5XFxcbCxsSlQPyoqCtHR0ejTp4+iTCbLf2RALBbj4cOHqFmzZpFiZDclERERqZWS7KYsKm1tbTRv3hyBgYFv45DJEBgYCCcnpwL169atizt37iAkJESx9O3bF87OzggJCYGdnV2Rj82WMSIiIlIrQr0OycvLC25ubmjRogVatWqFdevWITMzE+7u7gCA0aNHw9bWFn5+ftDV1UXDhg2Vtjc1NQWAAuUfwmSMiIiICMDQoUORkJCARYsWITY2Fo6OjggICFAM6o+JiYGGRsl3KjIZIyIiIrUiK8ExY8Xl6ekJT09PleuCgoLeu+2uXbs+6phMxoiIiEitVKzXhHMAPxEREZGg2DJGREREaqU4T0F+CpiMERERkVqpaMkYuymJiIiIBMSWMSIiIlIrJTkDf3nAZIyIiIjUCrspiYiIiKjMsGWMiIiI1IpQr0MSCpMxIiIiUiscM0ZEREQkII4ZIyIiIqIyw5YxIiIiUivspiQiIiISELspiYiIiKjMsGWMiIiI1EpFm9qCLWNqSCQS4fDhw0KHQUREJAiZXF5iS3nAlrF/SUhIwKJFi/D3338jLi4OZmZmaNKkCRYtWoR27dqVSQwvX76EmZlZmRxLCF1HdUePr/vBxNIUTx9E4xff7XgUGvnB7Vr3aYdJG71w4+RVbBi/QlE+bpUnOgxyVqp7++wtrHb7tsRjF9rECW6Y6TURNjaWuH37PqZNX4hr10NU1h371QiM+nIQGjSoAwC4efMOFiz8Tql+Xs5zldvOmfsNVq/xL+nwBaXVvie0u3wBkZEZZC8eI/uPrZDFRKisq+e5HGKHRgXK8+5dQ9ZPS/M/aOtCp48bxI3aQKRvBFlyHHLPHUXuxYDSPA3BmIzoA/OvBkHTwgySsEdIWLYZ2XfCC62vYWQAi+ljYNitHTRMDJH3Ih4JfluRee4aAOCz07uhZWtdYLvUX48i/ptNpXYe6ux6yB3s/PV/uB8WiYSkZKz3W4iuHdsKHRaVASZj/zJw4EDk5ORg9+7dqFGjBuLi4hAYGIikpKQyi8HGxqbMjlXWWvVui+ELxmD3gq2IuhUB1696w/vnhZjTZQpeJaUXup1FVUsMm+eGh1fuq1x/O+gmts16+wWeK8kt8diFNnhwX6z63heTJs/F1Wu3MHXKOBz7ey/qN+yIhISC92enTk7Yt/9PXLp8HdnZ2ZjlPRnHj/2Kxo5d8OJFLADA1s5RaZvurs746cfVOHjoWFmcUpkRN20Pnf7jkH1gE2RPwqHVqS/0JyxF5vIJkGekFaiftWM5RJpvvx5FBsbQn7UBuaEXFGU6/cdCXKsxsn9ZDVlyPMR1mkJn0ETI0pIhvXe1TM6rrBj26AjLOR6IX7wR2bcfwnR0f9j+tAzRPcdBmlzw+kFLjKrb/ZCXnIoX075FXlwStGytIE3PUFSJGTwV0HzbOaNTyx5Vd/jhVUBwWZySWsrKykYdhxoY0OtzTJ/36f2YLA52U1ZgqampCA4OxooVK+Ds7Izq1aujVatW8PHxQd++fQHkdyFu2bIFPXr0gJ6eHmrUqIH//e9/Svt5+vQphgwZAlNTU5ibm6Nfv36Ijo5WqrNjxw40aNAAOjo6qFy5Mjw9PRXr/t1N+aH9BQUFoVWrVjAwMICpqSnatWuHJ0+elPj1KQndx/XB2X2nEfz7GbyIfIZd87ciJ0uCjkO6FrqNSEMDE9ZNx6G1+xH/NE5lndycPKQlpCqW1+mZpXUKgpkxzQPbtv+K3T8fwIMHEZg0eS5ev86C+5hhKuuPdpsC/627ERp6Dw8fRmH8197Q0NBAly7tFXXi4hKUlr59XREUdBGPH8eU1WmVCe3O/ZF76QTyrgZCFvcUkt83Q54jgVbrbqo3eJ0B+atUxaJZxxHIlSAv5LyiiuZn9ZB77R9II+9CnhyP3EsnIHvxGJrVa5fNSZUhM7cvkP57ANIPnUJOVAziF2+EPFsC4y9cVdY3+eJzaJgY4oXnEmTfuo+8F3HIunYHOQ8fK+pIU9IgTUxRLAadWyHnyQtkXbtdVqeldjo4tcTU8W5w6VQ2vTDqrKJ1UzIZe4ehoSEMDQ1x+PBhSCSSQustXLgQAwcORGhoKEaOHIlhw4bhwYMHAIDc3Fy4urrCyMgIwcHBuHDhAgwNDdG9e3fk5OQAALZs2YLJkydj/PjxuHPnDo4cOQIHBweVx/rQ/vLy8tC/f3906tQJt2/fxqVLlzB+/HiIRKKSv0D/kaaWGPYNa+LehbdftnK5HPcu3IZDs8L/gPWfNhjpSWk4dyCw0Dp12zTAxus78F3gBrh9Ox4GpoYlGrvQtLS00KxZYwT+87bVQC6XI/Cf82jTpnmR9qGvrwctLTFSklNVrreyskDPHl2xY9dvJRGy+tAUQ6OqA6ThoW/L5HJIw0OgYV+nSLvQat0NuTfPATlvvxekjx9A3LA1RCbm+YdxaAQNyyqQht0q0fAFpyWGboNayLz0znnJ5ci8dAt6jvVUbmLYpQ2yQ8JgtXAyagT/hupH/GE+fiigUcifHC0xjPt0QfrBE6VwAkTqj92U7xCLxdi1axc8PDzg7++PZs2aoVOnThg2bBgaN26sqDd48GCMGzcOAPDNN9/g1KlT2LhxIzZv3oz9+/dDJpNh27ZtioRo586dMDU1RVBQED7//HN8++23mDlzJqZNm6bYZ8uWLVXG9KH9tWjRAmlpaejduzdq1qwJAKhXT/UXpNCMzIygKdZEWmKqUnlaQhoq17RVuU2tFnXRcUhXLOw5s9D93jl7CzcCLiPhaTysqttg0KwR8N61AEu/mAe5TFaSpyAYCwtziMVixMclKpXHxyegbp2aRdqH3/L5ePEiDqcDVXcDjR41GK9eZeDQoeP/OV51IjIwhkhTE7JXKUrl8lep0LSu+sHtNarVgmYVe2Tv26BULvljK3SHesJwyW7IpXmAXI7s/RshfXSvROMXmqapMURiTUiTUpXKpUmp0P7MTuU2WlUrQ6+1NV79dQbPv14IrepVYL3IExCLkbx5b4H6hl2doGFkiLRDp0rjFKgcqmjdlEzG/mXgwIHo1asXgoODcfnyZRw/fhwrV67Etm3bMGbMGACAk5OT0jZOTk4ICQkBAISGhiIyMhJGRkZKdbKzsxEVFYX4+Hi8ePECXbsW3i33rg/t7/PPP8eYMWPg6uqKbt26wcXFBUOGDEHlypVV7k8ikRRo9ZPKpdAUaRYpnrKka6CLr9dOxU6fLchIeVVovStH347jefYwBk8fPMGq4M2o16YB7l+8Uxahqr3ZsyZj6JC+6NptcKGtvmPGDMOvvx16b6twRaTV5nNIXzwuMNhfq2MfaNrXweuflkKenADNmg2gO3ACstKSlVvhKiINEaRJqYhbtB6QySC5HwmxlQXMxw5SmYyZDOyOzOBrkCYkCxAsqaPy0r1YUpiMqaCrq4tu3bqhW7duWLhwIcaNGwdfX19FMvY+GRkZaN68OfbuLfiFY2lpCY3Cmuk/cn9AfkvZ1KlTERAQgP3792PBggU4deoU2rRpU2AbPz8/LFmyRKmssUldOJrWL1ZcH+NVyitI86QwsTBVKjexNEFaQmqB+lbVbWBpZ43p23wUZSKN/NbBHZEHMLfLFMTHFBxDlvA0DulJabCyt/lkkrHExGTk5eXBytpCqdzKyhKxcQnv3dZrxteYPWsyXLsPw507D1TWad+uFerWccCIkRNLLGZ1Ic9Mh1wqhYaRGd5tJxUZmUKWnlLodgAAbR1oNe0AyfF//fvT0oZOr1HI2rEc0vvXAQCyl9HQsK0BbecByPqEkjFpajrkeVJoVjJVKtesZAppourrl5eQDHmeFHinZTrnUQzEluaAlhjIzVOUi6tYQd/JES+mflMq8VP5VNFaxjhmrAjq16+PzMy3A8IvX76stP7y5cuKrsFmzZohIiICVlZWcHBwUFpMTExgZGQEe3t7BAYWPv7pXR/a3xtNmzaFj48PLl68iIYNG+LXX39VuT8fHx+kpaUpLY1MijZu5r+S5uYh+m4U6rd9O2WASCRC/baNEXmz4CPyL6OeY97n07Gw50zFcuv0dTy4dBcLe85E0kvVT7ia2ZjD0MwIafEf+ENbjuTm5uLmzdvo4vx28L1IJEIX5/a4fPlGodt5z5yI+fOmo1fvL3HjZuEDo93dh+P6jVDcvq36adVyTZoH2bNIaNZ6O9QAIhE0azeBLPrhezcVO7YHxFrIvR6kvEJDEyKxFvDvX+9yGSD6xL5Wc/OQfS8C+m0c35aJRNBv44isENXJfdbN+9CuVgV4Z+yqtr0t8uKTlBIxADAZ8DmkyWnIPPtpPYFKVByf2LfGf5OUlIQuXbrgl19+we3bt/H48WP8/vvvWLlyJfr166eo9/vvv2PHjh0IDw+Hr68vrl69qngacuTIkbCwsEC/fv0QHByMx48fIygoCFOnTsWzZ88AAIsXL8bq1auxYcMGRERE4ObNm9i4caPKmD60v8ePH8PHxweXLl3CkydPcPLkSURERBQ6bkxHRwfGxsZKS1l2UQZsO4pOw13QbmBnVK5pC7dl46Gjr4Pg3/8BAIxfPQWDZ48EkD89xfPwp0rL6/RMZGdm43n4U0hz86Cjr4uhPqNRs2ktWFS1RP22jTD9p7mIj47FnXMhZXZeZWHt+p8wbuwIjBo1GHXrOmDTD9/BwEAPu3bvBwDs3LEey76dq6g/y3sSliyehXHjZyL6yVNYW1vC2toSBgb6Svs1MjLEoIG9sWPHJzZw/x05QYeh5eQKccsu0LCuCp3BkyDS1kXuldMAAN2RM6Dde3SB7bRad0PencvA6391k0uykBd5Bzp93aHp0BAic2uIW3WFVgtn5N25VBanVKZSdh+EyeAeMO7nAu0adrDynQINPV2kHzoJALD5zhsWM9wV9dP2/QUNE0NYzpsALXtbGHRqBfPxw5D661HlHYtEMP6iG9IPnwKkn8b4zv/i9esshIVHISw8CgDw/EUcwsKj8DI2XuDIyl5Fe5qS3ZTvMDQ0ROvWrbF27VpERUUhNzcXdnZ28PDwwLx58xT1lixZgn379mHSpEmoXLkyfvvtN9Svn9/Np6+vj3PnzmHOnDn44osv8OrVK9ja2qJr164wNjYGALi5uSE7Oxtr166Ft7c3LCwsMGjQIJUxfWh/WVlZCAsLw+7du5GUlITKlStj8uTJ+Prrr0v/gn2Eq39dhLG5Cb6YMQwmlqaIefAYq9y+RXpi/lxF5rYWxfrHI5PKYFevOtoP7Ax9Y32kxKfg3rlQ/LHmN+Tl5H14B+XI778fgaWFORYv8oaNjSVCQ++hV+8vER+fP6i/ml0VyN7pFvp6/Gjo6Ojg9/0/Ke1n6TersfSbNYrPQ4f0g0gkwr79h8vkPISQd+s8JAYm0OkxEiJjM8ieP8Lrrb6QZ6QCAERmltD4130nsrKFuGYDvN68UOU+s3evhE5vN+h+6Q2RviFkKQmQHNuD3Auf1gMQAJBx/BwSzUxQaeqo/ElfHzzC8/ELFIP6xZWtIJe9vX55sYl47rEAlnPHo/rhLciLS0TqnsNI3va70n71nZpCq4o10g6eLMvTUVt3wyLw1ZQ5is8rN/4IAOjXwwXLFhT+ENOnqKJ1U4rk8nKSNqoJkUiEQ4cOoX///kKHUmLc7AcKHUK5tvfF5Q9XIpVSJjUTOoRy7eWJT29y47LyWfBmoUMot7QsapT6MWpYNC2xfT1KVP/pZtgyRkRERGpFLq9Y3dZMxoiIiEityCpYNyWTsWJiry4RERGVJCZjREREpFYqWsMHkzEiIiJSKxWtm5LzjBEREREJiC1jREREpFbYTUlEREQkoPIyc35JYTJGREREaqWizcDPMWNEREREAmLLGBEREakVjhkjIiIiEhCntiAiIiKqoDZt2gR7e3vo6uqidevWuHr1aqF1Dx48iBYtWsDU1BQGBgZwdHTEnj17in1MJmNERESkVuRyeYktxbF//354eXnB19cXN2/eRJMmTeDq6or4+HiV9c3NzTF//nxcunQJt2/fhru7O9zd3XHixIliHZfJGBEREakVmVxeYktxrFmzBh4eHnB3d0f9+vXh7+8PfX197NixQ2X9zp07Y8CAAahXrx5q1qyJadOmoXHjxjh//nyxjstkjIiIiD5ZEokE6enpSotEIilQLycnBzdu3ICLi4uiTENDAy4uLrh06dIHjyOXyxEYGIiHDx+iY8eOxYqRyRgRERGplZLspvTz84OJiYnS4ufnV+CYiYmJkEqlsLa2Viq3trZGbGxsobGmpaXB0NAQ2tra6NWrFzZu3Ihu3boV63z5NCURERGplZJ8mtLHxwdeXl5KZTo6OiW2fyMjI4SEhCAjIwOBgYHw8vJCjRo10Llz5yLvg8kYERERfbJ0dHSKlHxZWFhAU1MTcXFxSuVxcXGwsbEpdDsNDQ04ODgAABwdHfHgwQP4+fkVKxljNyURERGpFSGeptTW1kbz5s0RGBioKJPJZAgMDISTk1OR9yOTyVSOSXsftowRERGRWhHqReFeXl5wc3NDixYt0KpVK6xbtw6ZmZlwd3cHAIwePRq2traKMWd+fn5o0aIFatasCYlEgmPHjmHPnj3YsmVLsY7LZIyIiIjUilAvCh86dCgSEhKwaNEixMbGwtHREQEBAYpB/TExMdDQeNupmJmZiUmTJuHZs2fQ09ND3bp18csvv2Do0KHFOq5IXtFeAEUFuNkPFDqEcm3vi8tCh1BupUxqJnQI5drLE7lCh1BufRa8WegQyi0tixqlfgwDffsS21fm6+gS21dpYcsYERERqRWhuimFwmSMiIiI1EpF67Tj05REREREAmLLGBEREakVoQbwC4XJGBEREakVdlMSERERUZlhyxgRERGplYrWMsZkjIiIiNRKxUrF2E1JREREJCjOwE9qTSKRwM/PDz4+PtDR0RE6nHKH1+/j8dp9PF67/4bXr+JhMkZqLT09HSYmJkhLS4OxsbHQ4ZQ7vH4fj9fu4/Ha/Te8fhUPuymJiIiIBMRkjIiIiEhATMaIiIiIBMRkjNSajo4OfH19OYj1I/H6fTxeu4/Ha/ff8PpVPBzAT0RERCQgtowRERERCYjJGBEREZGAmIwRERERCYjJGBEREZGAmIwRERERCUgsdABEVPLi4+MRHx8PmUymVN64cWOBIiKiwjx9+hQikQhVq1YFAFy9ehW//vor6tevj/HjxwscHZUFTm1BaunatWs4c+aMyoRizZo1AkWl/m7cuAE3Nzc8ePAAb/5pi0QiyOVyiEQiSKVSgSNUf9nZ2bh9+7bKe69v374CRUWfsg4dOmD8+PEYNWoUYmNjUadOHTRo0AARERGYMmUKFi1aJHSIVMrYMkZqZ/ny5ViwYAHq1KkDa2triEQixbp3/5sK+uqrr1C7dm1s3769wLWjDwsICMDo0aORmJhYYB2T2Q9LSkrCokWLCv0hlZycLFBk6u3u3bto1aoVAODAgQNo2LAhLly4gJMnT2LChAlMxioAJmOkdtavX48dO3ZgzJgxQodS7jx69Ah//PEHHBwchA6lXJoyZQoGDx6MRYsWwdraWuhwyp1Ro0YhMjISY8eO5Y+BYsjNzVXMtn/69GlFC2zdunXx8uVLIUOjMsJkjNSOhoYG2rVrJ3QY5VLXrl0RGhrKZOwjxcXFwcvLi4nYRwoODsb58+fRpEkToUMpVxo0aAB/f3/06tULp06dwjfffAMAePHiBSpVqiRwdFQWmIyR2pkxYwY2bdqEdevWCR1KubNt2za4ubnh7t27aNiwIbS0tJTWc8zT+w0aNAhBQUGoWbOm0KGUS3Xr1kVWVpbQYZQ7K1aswIABA/D999/Dzc1NkcweOXJE0X1JnzYO4Ce1I5PJ0KtXL4SHh6N+/foFEoqDBw8KFJn6O3r0KEaNGoX09PQC6zjm6cNev36NwYMHw9LSEo0aNSpw702dOlWgyMqHa9euYe7cuVi0aJHKHwPGxsYCRab+pFIp0tPTYWZmpiiLjo6Gvr4+rKysBIyMygJbxkjtTJ06FWfOnIGzszMqVarEcSfFMGXKFHz55ZdYuHAhu9o+wm+//YaTJ09CV1cXQUFBBR4eYTL2fqampkhPT0eXLl2Uyvk074fJ5XLcuHEDUVFRGDFiBIyMjKCtrQ19fX2hQ6MywJYxUjtGRkbYt28fevXqJXQo5Y6RkRFCQkLYzfaRbGxsMHXqVMydOxcaGpwTu7hatWoFsViMadOmqRzA36lTJ4EiU29PnjxB9+7dERMTA4lEgvDwcNSoUQPTpk2DRCKBv7+/0CFSKWPLGKkdc3NzJhMf6YsvvsCZM2d4/T5STk4Ohg4dykTsI929exe3bt1CnTp1hA6lXJk2bRpatGiB0NBQpQH7AwYMgIeHh4CRUVlhMkZqZ/HixfD19cXOnTvZRF9MtWvXho+PD86fP88xTx/Bzc0N+/fvx7x584QOpVxq0aIFnj59ymSsmIKDg3Hx4kVoa2srldvb2+P58+cCRUVlickYqZ0NGzYgKioK1tbWsLe3L5BQ3Lx5U6DI1N+2bdtgaGiIs2fP4uzZs0rrOObpw6RSKVauXIkTJ06gcePGBe49vv3h/aZMmYJp06Zh1qxZKn8M8HVcqslkMpXj6Z49ewYjIyMBIqKyxjFjpHaWLFny3vW+vr5lFAlVNM7OzoWuE4lE+Oeff8owmvJHVfcuX8f1YUOHDoWJiQl+/PFHGBkZ4fbt27C0tES/fv1QrVo17Ny5U+gQqZQxGSP6RKSnp8PQ0LDAH0SZTIaMjAxOK0Cl7smTJ+9dX7169TKKpHx59uwZXF1dIZfLERERgRYtWiAiIgIWFhY4d+4cp7aoAJiMkdpISUnBL7/8Ajc3twKJQ1paGn7++WeV6wg4dOgQ5syZg5CQkALj7DIzM9GsWTOsWrUKffr0EShC9SaVSnHv3j3UqlULenp6SuuysrIQERGBhg0bcmA/lZq8vDzs27cPt2/fRkZGBpo1a4aRI0cWuB/p08RvFlIbP/zwA86dO6cy2TIxMUFwcDA2btwoQGTqb8uWLZg9e7bKBx4MDAwwZ84c/PDDDwJEVj7s2bMHX331VYEB1ACgpaWFr776Cr/++qsAkZUPN27cgLOzs8rJhtPS0uDs7IzQ0FABIis/xGIxvvzyS6xcuRKbN2/GuHHjmIhVIGwZI7Xh6OiI1atXo2vXrirXBwYGwtvbG7du3SrjyNRflSpVcO7cuULfSRkZGYmOHTvixYsXZRxZ+dChQwdMnjwZw4YNU7n+wIEDih8LVNCIESNQr149LFy4UOX65cuX4/79+/jll1/KODL1deTIEfTo0QNaWlo4cuTIe+vyNWafPj5NSWojKioKtWrVKnR9rVq1EBUVVYYRlR8pKSnIy8srdH1ubi5SUlLKMKLy5eHDh2jTpk2h61u2bIkHDx6UYUTly5UrVzB37txC1/fp0wfbtm0rw4jUX//+/REbGwsrKyv079+/0Hp88KFiYDclqQ1NTc33tty8ePGCY3YKYW9vj+vXrxe6/vr16xw8/R6ZmZkqu9jeePXqFV6/fl2GEZUvz58/f+8UDIaGhnj58mUZRqT+ZDKZYmC+TCYrdGEiVjHwLxupjaZNm+Lw4cOFrj906BCaNm1adgGVI1988QXmz5+PuLi4AutiY2OxYMECDBw4UIDIyodatWrh4sWLha4/f/78e1ttKzpLS0s8fPiw0PVhYWGwsLAow4iIyhcmY6Q2PD09sXr1avzwww9KvwalUik2btyItWvXYvLkyQJGqL7mzp0LIyMj1KpVC5MmTcL69euxfv16TJw4EbVr14ahoeF7u5EquhEjRmDBggW4fft2gXWhoaFYtGgRRowYIUBk5YOLiwuWLVumcp1cLseyZcvg4uJSxlGVH1OnTsWGDRsKlP/www+YPn162QdEZY4D+EmtzJ8/H35+fjAyMkKNGjUAAI8ePUJGRgZmzZqF7777TuAI1VdaWhp8fHywf/9+xfgwU1NTDBs2DMuWLYOZmZnAEaqv3NxcfP755zh//jxcXFxQt25dAPktOqdPn0a7du1w6tSpAjPKU76oqCg0b94cderUwcyZMxWvQwoLC8Pq1asRHh6O69evF/qASUVna2uLI0eOoHnz5krlN2/eRN++ffHs2TOBIqOywmSM1M7Vq1exd+9eREZGQi6Xo3bt2hgxYgRatWoldGjlglwuR2JiIuRyOSwtLSESiYQOqVzIzc3F2rVr8euvvyIiIkLp3ps+fbrKaS/orevXr2PMmDG4f/++4p6Ty+WoX78+du7ciZYtWwocofrS1dXF3bt3CySrkZGRaNiwIbKzswWKjMoKkzEiIioxISEhSsmso6Oj0CGpvYYNG2LChAnw9PRUKt+4cSO2bNmC+/fvCxQZlRVObUFqKTU1FVevXkV8fDxkMpnSutGjRwsUlfqLi4uDt7c3AgMDER8fj3//1uKTWUWTk5Oj8t6rVq2aQBGVH46OjkzAisnLywuenp5ISEhAly5dAOTPq7h69WqsW7dO2OCoTLBljNTO0aNHMXLkSMX7FN/tZhOJREhOThYwOvXWo0cPxMTEwNPTE5UrVy7QRdmvXz+BIisfwsPDMXbs2AJPVvJF10UjlUqxa9cuxY+BfyezfNF64bZs2YJly5Yppvext7fH4sWL+eOzgmAyRmqndu3a6NmzJ5YvX67y9T5UOCMjIwQHB7Nl4iO1a9cOYrEYc+fOVZnMNmnSRKDIygdPT0/s2rULvXr1Unn91q5dK1Bk5UdCQgL09PRgaGgodChUhthNSWrn+fPnmDp1KhOxj2BnZ1ega5KKLiQkBDdu3FA8TUnFs2/fPhw4cAA9e/YUOpRyJy8vD0FBQYiKilJMo/LixQsYGxszMasAOM8YqR1XV9f3ziZPhVu3bh3mzp2L6OhooUMpl+rXr4/ExEShwyi3tLW1OX3FR3jy5AkaNWqEfv36YfLkyUhISAAArFixAt7e3gJHR2WB3ZSkFt59UW5CQgKWLl0Kd3d3NGrUqMDcTnxprjIzMzOl7qDMzEzk5eVBX1+/wLXjeLuC3n0N0vXr17FgwQIsX75c5b1nbGxc1uGVK6tXr8ajR4/www8/cEqVYujfvz+MjIywfft2VKpUCaGhoahRowaCgoLg4eGBiIgIoUOkUsZkjNRCUd85yUHUBe3evbvIdd3c3EoxkvJJQ0NDKXF4M1j/XRzAX7gvvvhC6fM///wDc3NzNGjQoEAye/DgwbIMrdyoVKkSLl68iDp16sDIyEiRjEVHR6N+/fp8L2oFwDFjpBb+/dQVFR0TrP/mzJkzQodQrpmYmCh9HjBggECRlF+FvRD82bNn730BO3062DJGaufnn3/G0KFDoaOjo1Sek5ODffv28VHv99DU1MTLly9hZWWlVJ6UlAQrKyu27HxATEwM7OzsVLaMPX36lPOMUakYOnQoTExM8OOPP8LIyAi3b9+GpaUl+vXrh2rVqmHnzp1Ch0iljMkYqR0mFB9PQ0MDsbGxBa7dixcvULNmTWRlZQkUWfnAe++/6dKlCw4ePAhTU1Ol8vT0dPTv35/zjBXi2bNncHV1hVwuR0REBFq0aIGIiAhYWFjg3LlzBe5H+vSwm5LUjqoxO0D+F9a/u0Qo34YNGwDkj6nbtm2b0qPwUqkU586d43QNRVDYvZeRkQFdXV0BIipfgoKCkJOTU6A8OzsbwcHBAkRUPlStWhWhoaHYt28fbt++jYyMDIwdOxYjR46Enp6e0OFRGWAyRmqjadOmEIlEEIlE6Nq1K8Tit7enVCrF48eP0b17dwEjVF9vJtOUy+Xw9/eHpqamYp22tjbs7e3h7+8vVHhqz8vLC0B+Mrtw4UKlOe6kUimuXLnCiXTf4/bt24r/vn//PmJjYxWfpVIpAgICYGtrK0Ro5YZYLMaXX34pdBgkECZjpDb69+8PIH/iTVdXV6XWnTcJxcCBAwWKTr09fvwYAODs7IyDBw/CzMxM4IjKl1u3bgHIT2bv3LkDbW1txTptbW00adKE8z29h6Ojo+KH1Jt3K75LT08PGzduFCCy8uPhw4fYuHEjHjx4AACoV68ePD092aJdQXDMGKmd3bt3Y+jQoewWojLn7u6O9evXcz6xYnry5Ankcjlq1KiBq1evwtLSUrFOW1sbVlZWSq21pOyPP/7AsGHD0KJFCzg5OQEALl++jGvXrmHfvn38EVoBMBkjKufedLEVxZo1a0oxEiL6GDVr1sTIkSOxdOlSpXJfX1/88ssviIqKEigyKitMxkjt/HtG+TdEIhF0dXXh4OCAMWPGwN3dXYDo1I+zs7PS55s3byIvLw916tQBAISHh0NTUxPNmzfn02wf8O8JTN94994bMWKE4tqS8tszPoRvz1BNX18ft2/fLvAqqYiICDRp0oSTvlYAHDNGamfRokVYtmwZevTogVatWgEArl69ioCAAEyePBmPHz/GxIkTkZeXBw8PD4GjFd67k5auWbMGRkZG2L17t2LcWEpKCtzd3dGhQwehQiw3jI2NcfjwYZiamqJ58+YA8pPb1NRUfP7559i/fz9WrFiBwMBAtGvXTuBo1cObsZ5viEQipZfVv/vDilODqNa5c2cEBwcXSMbOnz/Pf7cVBFvGSO0MHDgQ3bp1w4QJE5TKt27dipMnT+KPP/7Axo0b8eOPP+LOnTsCRamebG1tcfLkSTRo0ECp/O7du/j888/x4sULgSIrH+bOnYv09HT88MMPild0yWQyTJs2DUZGRli2bBkmTJiAe/fu4fz58wJHq35Onz6NOXPmYPny5YqxT5cuXVK877Nbt24CR6ie/P39sWjRIgwZMgRt2rQBkD9m7Pfff8eSJUtQpUoVRV22Ln6amIyR2jE0NERISEiBX4mRkZFwdHRERkYGoqKi0LhxY2RmZgoUpXoyMjLC0aNH0blzZ6XyM2fOoG/fvnj16pUwgZUTlpaWuHDhAmrXrq1UHh4ejrZt2yIxMRF37txBhw4dkJqaKkyQaqxhw4bw9/dH+/btlcqDg4Mxfvx4xZOCpIzv5qWi3QFEZcjc3BxHjx4tUH706FGYm5sDADIzM/nONhUGDBgAd3d3HDx4EM+ePcOzZ8/wxx9/YOzYsYWOh6K38vLyEBYWVqA8LCxM8UdQV1dX5ZhGAqKiogrMvg/kv78yOjq6zOMpL2QyWZEWJmKfLo4ZI7WzcOFCTJw4EWfOnFGMGbt27RqOHTummLj01KlT6NSpk5BhqiV/f394e3tjxIgRyM3NBZA/meTYsWPx/fffCxyd+hs1ahTGjh2LefPmoWXLlgDy773ly5cr3ol69uzZAt3AlK9ly5bw8vLCnj17YG1tDQCIi4vDrFmzFP+W6a1Lly4hKSkJvXv3VpT9/PPP8PX1RWZmJvr374+NGzcWeE8vfXrYTUlq6cKFC/jhhx/w8OFDAECdOnUwZcoUtG3bVuDIyofMzEzF4/A1a9aEgYGBwBGVD1KpFN999x1++OEHxMXFAQCsra0xZcoUzJkzB5qamoiJiYGGhgaqVq0qcLTqJzIyEgMGDEB4eDjs7OwAAE+fPkWtWrVw+PDhAkMPKroe/9fenQdVVf5/AH8Dsl/ZtGtoINcGBMsFBWRRL7ilmIaoaWQuae5jxeDg9mVR0dxm0CbT0UAyzcTBXGbUFuQqpiipQBkYiICJICrKohLC74+G+4tQFESe5+r7NeM499z7x3uYy/A553nO+wwfDl9fX4SGhgIAMjIy0Lt3b0yZMgUuLi5Yu3YtZs6ciYiICLFB6bnjMEZE9Ah3794FABbANlFtbS1+/PFH7XKvi4sLBg8ezKXdR7C1tcXBgwfh5uYGAFiyZAk0Go325pD4+HiEh4fj4sWLImNSK+AyJUmppqYG2dnZKC4uRk1NTb33BgwYICiVnAIDA7F9+3ZYWFg8cV9YQkJCK6XSfRzCmkdPTw9Dhw7F0KFDRUeR3u3bt7XLucA/S+DDhw/XvnZ3d0dBQYGIaNTKOIyRdE6fPo2goCDtI1b+jXcTNWRpaam96mBpaSk4jW4rKipCSEgIfv75ZxQXFzf4/vG719DGjRsxY8YMmJiYYOPGjY1+dv78+a2USjd06NABubm5sLOzQ1VVFc6dO4fIyEjt+2VlZTA0NBSYkFoLlylJOr169YKTkxMiIyNha2vbYHmDAwc9L8OHD0d+fj7mzZv3yO/eO++8IyiZvFQqFVJTU9GuXTuoVKrHfk5PTw+XL19uxWTymz17NtLS0rB69Wp8//33iIuLw7Vr17QPqt+5cyeio6Nx9uxZwUnpeeMwRtIxNzdHWloaN/s2gVqtxqBBg+Dn5wdPT0+eTTdT27ZtceLECfTq1Ut0FHoJlJSUIDAwEMnJyVAoFIiLi8Po0aO17w8aNAienp6IiooSmJJaA5cpSTp9+/ZFdnY2h7EmUKlUiI2NRUREBExNTeHl5QU/Pz8MHDgQHh4eMDAwEB1RJ9jZ2TVYmqQnCw8P1w4OdVd16Mnat2+P48eP486dO1AoFA1+T+Pj46FQKASlo9bEK2MknX379mHp0qVYsGABunfv3uAqT48ePQQlk9+VK1eQmJgIjUaDpKQkFBQUQKFQwMfHBwMHDsSCBQtER5TaDz/8gPXr12PLli1wcHAQHUdnqFQq5OXlwcTERHsi4Ofnh759+6JNG57zEz0JhzGSzqMeDVL38GFu4G+ay5cvIyYmBp9//jnKy8v5s3sCa2trVFZWorq6GmZmZg1OBG7duiUomfyuXLmCY8eOISkpCRqNBvn5+TA3N4ePj492OGPxK9GjcRgj6eTl5TX6fufOnVspiW7Ky8tDUlKS9l9xcTE8PT2hVqsRFhYmOp7U4uLiGn1/8uTJrZRE9+Xm5mqHs/3796OiogLV1dWiYxFJicMY0Qvg66+/1g5fJSUl8Pb2hlqthlqthru7Ozf0U6uqOyGoWzKvOyFITEwUHY1IShzGSEo7duzA5s2bkZubi1OnTqFz586Ijo6GSqVivcAj6Ovrw97eHgsXLsS0adM4fD2DnJwcxMbGIicnBxs2bIBSqcThw4dhb2/PZ1I+Rn5+PpKSkrRXwv59QjBgwAB4eHhwYz9RIxpuziES7Msvv0RwcDD8/f1RWlqq3edkZWWF6OhoseEktWnTJnh6eiIyMhJKpRIjR47E+vXrkZqayrsDm0Cj0aB79+5ISUlBQkICysvLAQBpaWkIDw8XnE5eDg4OCAsLg7OzM7777juUlpbi6NGjWLx4Mfr168dBjOgJeGWMpNOtWzesXLkSAQEBaNu2LdLS0tClSxf89ttv8PX1RUlJieiIUrt48aL2bsqkpCQ8ePBAu4k6JCREdDypeXl5Ydy4cQgODq733Ttz5gwCAwNx9epV0RGlNGHCBGg0Gjx48AD9+vWDWq2Gn58fXF1d+UxKoqfAYYykY2pqiszMTHTu3LneH8Q///wTPXr0wL1790RH1BnXrl3Dpk2beDflU1IoFMjIyIBKpar33bty5QqcnZ1x//590RGllpmZWe+Oyvv372uHM19fX7i7u4uOSCQlFsCQdFQqFS5cuNDgrskjR47AxcVFUCrdUFxcrP1jmJSUhEuXLsHQ0BCenp7w8/MTHU96VlZWKCwsbPBYn/Pnz6NTp06CUukOZ2dnODs7Y/bs2QD+uUq7a9curFixAosWLeLdlESPwWGMpBMcHIy5c+fi/v37qK2txZkzZ/Dtt99i1apV2LZtm+h4UpozZw6SkpKQlZWFNm3awMPDA2PHjoWfnx+8vb1hYmIiOqJOmDBhAkJDQxEfHw89PT3U1NTg5MmTCAkJwaRJk0TH0wlFRUXak4Fjx47h0qVLMDY2Rv/+/UVHI5IWlylJSjt37kRERARycnIAAB07dkRkZCSmTZsmOJmc/t167uPjAzMzM9GRdFJVVRXmzp2L7du34+HDh2jTpg0ePnyIoKAgxMbGsk3+Mfbs2aMdwLKysmBoaAh3d3ftd9Lb2xvGxsaiYxJJi8MYSa2yshLl5eVQKpWorKzEhQsX4O3tLToWveAKCgqQkZGB8vJyuLq6wtHRUXQkqRkZGcHNza3eCYGpqanoWEQ6g8MY6Yy0tDT07t2bm9D/48CBA0/92VGjRj3HJC+u9PR0uLm5oaqqSnQUKVVUVMDc3Fx0DCKdxWvuRDouICDgqT7H53o2X21tLX92j3H37t16/zfGwsLiecch0kkcxoh0XE1NjegI9BKzsrJ6YpdYbW0tTwaIGsFhjIiImu3YsWOiIxDpPA5jJI0n7X3Kzc1tpSS6raKiAhqNBvn5+Q32OM2fP19QKrk9aYmtrKyslZLoHrVaLToCkc7jBn6Shr7+kx+VyqWOxp0/fx7+/v6orKxERUUFbGxsUFJSAjMzMyiVSly+fFl0RCnp6+s3utTGZbamqaysfOTJQI8ePQQlIpIbr4yRNLj36dl9+umnGDlyJDZv3gxLS0ucPn0ahoaGmDhxIj7++GPR8aTFpbaWcePGDUydOhWHDx9+5PscZokejVfGiF4gVlZWSElJQdeuXWFlZYVTp07BxcUFKSkpmDx5MjIzM0VHpBfY+++/j7y8PERHR8PX1xf79u1DUVERVqxYgfXr12PEiBGiIxJJiVfGSArsymoZhoaG2uVepVKJ/Px8uLi4wNLSEgUFBYLTyelpKhnqsJqhcYmJidi/fz/c3Nygr6+Pzp07Y8iQIbCwsMCqVas4jBE9BocxkgK7slqGq6srzp49C0dHR6jVaoSFhaGkpAQ7duzAm2++KTqelFjN0HIqKiqgVCoBANbW1rhx4wacnJzQvXt3nDt3TnA6InlxGCMpcL9Yy1i5cqX2zr+oqChMmjQJs2fPhqOjI2JiYgSnkxP3i7Wcrl27IisrCw4ODujZsye2bNkCBwcHbN68Gba2tqLjEUmLe8aIiKhFfPPNN6iursaUKVPw66+/YtiwYbh16xaMjIywfft2jB8/XnREIilxGCMpsSvr2RQXFyMrKwsA4OzsjFdeeUVwIt3CaoaWUVlZiczMTNjb26N9+/ai4xBJi8MYSYddWc1XVlaGOXPmYPfu3dr9TQYGBhg/fjy++OILWFpaCk4oN1YzPJtly5YhJCQEZmZm9Y7fu3cPa9euRVhYmKBkRHJ7cssmUSur68q6ffs2TE1Ncfr0aeTl5aFPnz5Yt26d6HhSmz59OlJSUnDo0CGUlpaitLQUhw4dQmpqKmbOnCk6nvQ++eQTlJaWIiUlBaampjhy5Aji4uLg6OjYpDt+X1aRkZEoLy9vcLyyshKRkZECEhHpBl4ZI+mwK6v5zM3NcfToUfTr16/e8RMnTmDYsGGoqKgQlEw32NraYv/+/fDw8ICFhQVSU1Ph5OSEAwcOYM2aNUhOThYdUWr6+vooKipqsCyemJiI8ePH48aNG4KSEcmNd1OSdNiV1Xzt2rV75FKkpaUlrK2tBSTSLaxmaB5ra2vo6elBT08PTk5O9apCHj58iPLycsyaNUtgQiK5cRgj6bArq/mWLl2K4OBg7NixA6+++ioA4Pr161iwYAH+97//CU4nP1YzNE90dDRqa2vx4YcfIjIyst4JgZGRERwcHODl5SUwIZHcuExJ0klNTUVZWRn8/PxQXFyMSZMm4ZdfftF2ZfXs2VN0RGm5uroiOzsbDx48gL29PQAgPz8fxsbGcHR0rPdZXulpiNUMz0aj0cDHxwdt2vA8n6gpOIwRvUCaskk6PDz8OSZ5MbCaoelycnIQGxuLnJwcbNiwAUqlEocPH4a9vT3eeOMN0fGIpMRhjKTFrixqbaxmeDYajQbDhw+Hj48Pjh8/jj/++ANdunTBZ599htTUVOzdu1d0RCIpcRgj6bAr69mUlpZi7969yMnJwYIFC2BjY4Nz586hQ4cO6NSpk+h4UjMwMEBhYaF2E3+dmzdvQqlUsmfsCby8vDBu3DgEBwejbdu2SEtLQ5cuXXDmzBkEBgbi6tWroiMSSYk9YyQddmU1X3p6OpycnLB69WqsW7cOpaWlAICEhAQsWrRIbDgdUPdA8P9KS0uDjY2NgES6JSMjA6NHj25wXKlUoqSkREAiIt3AXZYknUOHDjXoynrrrbewdetWDBs2TGAy+QUHB2PKlClYs2YN2rZtqz3u7++PoKAggcnkxmqGlmFlZYXCwkKoVKp6x8+fP8+rskSN4DBG0mFXVvOdPXsWW7ZsaXC8U6dOuH79uoBEuoHVDC1jwoQJCA0NRXx8PPT09FBTU4OTJ08iJCQEkyZNEh2PSFocxkg67MpqPmNjY9y9e7fB8UuXLvEGiEZMnjwZAKBSqVjN8AxWrlyJefPmwd7eHtXV1ejWrRsePnyIoKAgLF26VHQ8ImlxAz9Jh11ZzTd9+nTcvHkTe/bsgY2NDdLT02FgYICAgAAMGDAA0dHRoiNKj9UMTVdTU4O1a9fiwIEDqKqqQo8ePTBmzBiUl5fD1dW1we8tEdXH0z+STkBAgOgIOmv9+vUYO3YslEol7t27B7VajcLCQnh5eSEqKkp0POn9t5ohKioKSqUSaWlp+Oqrr1jN8BhRUVGIiIjA4MGDYWpqil27dqG2thYxMTGioxHpBF4ZI3oBJScnIz09HeXl5ejTpw8GDRokOpJOYDVD8zg6OiIkJER7t/NPP/2EESNG4N69e9rnzBLR4/G3hKRUWlqKbdu2YdGiRbh16xaAf5Yk//rrL8HJ5HTq1CkcOnRI+7pfv34wNzfHpk2b8N5772HGjBl48OCBwIS6gdUMzZOfnw9/f3/t68GDB0NPTw/Xrl0TmIpId3AYI+mwK6vpli1bht9//137OiMjAx999BGGDBmChQsX4uDBg1i1apXAhLqhrprhv1jN0Ljq6mqYmJjUO2ZoaIi///5bUCIi3cI9YyQddmU13YULF7B8+XLt6927d8PDwwNbt24FANjZ2SE8PBwRERGCEuoGVjM0T21tLaZMmQJjY2Ptsfv372PWrFkwNzfXHktISBARj0h6HMZIOuzKarrbt2+jQ4cO2td1G9HruLu7o6CgQEQ0ncJqhuapqwb5t4kTJwpIQqSbOIyRdNiV1XQdOnRAbm4u7OzsUFVVhXPnziEyMlL7fllZGQwNDQUmlNt/qxk++OADVjM0QWxsrOgIRDqNe8ZIOqNGjcKyZcu0+0309PSQn5+P0NBQjBkzRnA6Ofn7+2PhwoU4ceIEFi1aBDMzM/Tv31/7fnp6Ol5//XWBCeUWFRWFxYsXQ6FQoFOnTti1axf27t2Ld999l4MYET13rLYg6dy5cwdjx45FamoqysrK0LFjR21X1uHDh+vtQaF/lJSUIDAwEMnJyVAoFIiLi6t3V+CgQYPg6enJrrHHYDUDEYnEYYykxa6sprtz5w4UCgUMDAzqHb916xYUCgWMjIwEJZObsbExsrOzYWdnpz1mYmKC7OxsvPbaawKTEdHLgKd8JA12ZT07S0vLBoMYANjY2HAQawSrGYhIJG7gJ2ksW7YMvr6+ePvttwH8f1fW5MmT4eLigrVr16Jjx46sZ6AWx2oGIhKJy5QkDVtbWxw8eBBubm4AgCVLlkCj0SA5ORkAEB8fj/DwcFy8eFFkTHoBTZ069ak+x7sGieh54JUxkga7skgUDllEJBL3jJE06rqyAGi7sjw9PbXvsyuLiIheRBzGSBrsyiIiopcRlylJGsuXL0dgYCDUarW2K+vfdwDGxMRg6NChAhMSERG1PG7gJ+mwK4uIiF4mHMaIiIiIBOKeMSIiIiKBOIwRERERCcRhjIiIiEggDmNEREREAnEYIyIiIhKIwxgRERGRQBzGiIiIiAT6Pyr2vjDaBoBdAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# change dataframe (df) ke array Numpy\n",
        "# untuk memudahkan proses operasi matriks dalam python\n",
        "\n",
        "df = df.to_numpy()"
      ],
      "metadata": {
        "id": "aXvzemNZF4p7"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xScIBBlGYZu",
        "outputId": "61a2f5a0-7ecf-4f9f-d304-b5d399acc73d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7.  , 3.02, 4.07, 1.04, 0.  ],\n",
              "       [6.04, 3.02, 4.05, 1.05, 0.  ],\n",
              "       [6.09, 3.01, 4.09, 1.05, 0.  ],\n",
              "       [5.05, 2.03, 4.  , 1.03, 0.  ],\n",
              "       [6.05, 2.08, 4.06, 1.05, 0.  ],\n",
              "       [5.07, 2.08, 4.05, 1.03, 0.  ],\n",
              "       [6.03, 3.03, 4.07, 1.06, 0.  ],\n",
              "       [4.09, 2.04, 3.03, 1.  , 0.  ],\n",
              "       [6.06, 2.09, 4.06, 1.03, 0.  ],\n",
              "       [5.02, 2.07, 3.09, 1.04, 0.  ],\n",
              "       [5.  , 2.  , 3.05, 1.  , 0.  ],\n",
              "       [5.09, 3.  , 4.02, 1.05, 0.  ],\n",
              "       [6.  , 2.02, 4.  , 1.  , 0.  ],\n",
              "       [6.01, 2.09, 4.07, 1.04, 0.  ],\n",
              "       [5.06, 2.09, 3.06, 1.03, 0.  ],\n",
              "       [6.07, 3.01, 4.04, 1.04, 0.  ],\n",
              "       [5.06, 3.  , 4.05, 1.05, 0.  ],\n",
              "       [5.08, 2.07, 4.01, 1.  , 0.  ],\n",
              "       [6.02, 2.02, 4.05, 1.05, 0.  ],\n",
              "       [5.06, 2.05, 3.09, 1.01, 0.  ],\n",
              "       [5.09, 3.02, 4.08, 1.08, 0.  ],\n",
              "       [6.01, 2.08, 4.  , 1.03, 0.  ],\n",
              "       [6.03, 2.05, 4.09, 1.05, 0.  ],\n",
              "       [6.01, 2.08, 4.07, 1.02, 0.  ],\n",
              "       [6.04, 2.09, 4.03, 1.03, 0.  ],\n",
              "       [6.06, 3.  , 4.04, 1.04, 0.  ],\n",
              "       [6.08, 2.08, 4.08, 1.04, 0.  ],\n",
              "       [6.07, 3.  , 5.  , 1.07, 0.  ],\n",
              "       [6.  , 2.09, 4.05, 1.05, 0.  ],\n",
              "       [5.07, 2.06, 3.05, 1.  , 0.  ],\n",
              "       [5.05, 2.04, 3.08, 1.01, 0.  ],\n",
              "       [5.05, 2.04, 3.07, 1.  , 0.  ],\n",
              "       [5.08, 2.07, 3.09, 1.02, 0.  ],\n",
              "       [6.  , 2.07, 5.01, 1.06, 0.  ],\n",
              "       [5.04, 3.  , 4.05, 1.05, 0.  ],\n",
              "       [6.  , 3.04, 4.05, 1.06, 0.  ],\n",
              "       [6.07, 3.01, 4.07, 1.05, 0.  ],\n",
              "       [6.03, 2.03, 4.04, 1.03, 0.  ],\n",
              "       [5.06, 3.  , 4.01, 1.03, 0.  ],\n",
              "       [5.05, 2.05, 4.  , 1.03, 0.  ],\n",
              "       [5.05, 2.06, 4.04, 1.02, 0.  ],\n",
              "       [6.01, 3.  , 4.06, 1.04, 0.  ],\n",
              "       [5.08, 2.06, 4.  , 1.02, 0.  ],\n",
              "       [5.  , 2.03, 3.03, 1.  , 0.  ],\n",
              "       [5.06, 2.07, 4.02, 1.03, 0.  ],\n",
              "       [5.07, 3.  , 4.02, 1.02, 0.  ],\n",
              "       [5.07, 2.09, 4.02, 1.03, 0.  ],\n",
              "       [6.02, 2.09, 4.03, 1.03, 0.  ],\n",
              "       [5.01, 2.05, 3.  , 1.01, 0.  ],\n",
              "       [5.07, 2.08, 4.01, 1.03, 0.  ],\n",
              "       [6.03, 3.03, 6.  , 2.05, 1.  ],\n",
              "       [5.08, 2.07, 5.01, 1.09, 1.  ],\n",
              "       [7.01, 3.  , 5.09, 2.01, 1.  ],\n",
              "       [6.03, 2.09, 5.06, 1.08, 1.  ],\n",
              "       [6.05, 3.  , 5.08, 2.02, 1.  ],\n",
              "       [7.06, 3.  , 6.06, 2.01, 1.  ],\n",
              "       [4.09, 2.05, 4.05, 1.07, 1.  ],\n",
              "       [7.03, 2.09, 6.03, 1.08, 1.  ],\n",
              "       [6.07, 2.05, 5.08, 1.08, 1.  ],\n",
              "       [7.02, 3.06, 6.01, 2.05, 1.  ],\n",
              "       [6.05, 3.02, 5.01, 2.  , 1.  ],\n",
              "       [6.04, 2.07, 5.03, 1.09, 1.  ],\n",
              "       [6.08, 3.  , 5.05, 2.01, 1.  ],\n",
              "       [5.07, 2.05, 5.  , 2.  , 1.  ],\n",
              "       [5.08, 2.08, 5.01, 2.04, 1.  ],\n",
              "       [6.04, 3.02, 5.03, 2.03, 1.  ],\n",
              "       [6.05, 3.  , 5.05, 1.08, 1.  ],\n",
              "       [7.07, 3.08, 6.07, 2.02, 1.  ],\n",
              "       [7.07, 2.06, 6.09, 2.03, 1.  ],\n",
              "       [6.  , 2.02, 5.  , 1.05, 1.  ],\n",
              "       [6.09, 3.02, 5.07, 2.03, 1.  ],\n",
              "       [5.06, 2.08, 4.09, 2.  , 1.  ],\n",
              "       [7.07, 2.08, 6.07, 2.  , 1.  ],\n",
              "       [6.03, 2.07, 4.09, 1.08, 1.  ],\n",
              "       [6.07, 3.03, 5.07, 2.01, 1.  ],\n",
              "       [7.02, 3.02, 6.  , 1.08, 1.  ],\n",
              "       [6.02, 2.08, 4.08, 1.08, 1.  ],\n",
              "       [6.01, 3.  , 4.09, 1.08, 1.  ],\n",
              "       [6.04, 2.08, 5.06, 2.01, 1.  ],\n",
              "       [7.02, 3.  , 5.08, 1.06, 1.  ],\n",
              "       [7.04, 2.08, 6.01, 1.09, 1.  ],\n",
              "       [7.09, 3.08, 6.04, 2.  , 1.  ],\n",
              "       [6.04, 2.08, 5.06, 2.02, 1.  ],\n",
              "       [6.03, 2.08, 5.01, 1.05, 1.  ],\n",
              "       [6.01, 2.06, 5.06, 1.04, 1.  ],\n",
              "       [7.07, 3.  , 6.01, 2.03, 1.  ],\n",
              "       [6.03, 3.04, 5.06, 2.04, 1.  ],\n",
              "       [6.04, 3.01, 5.05, 1.08, 1.  ],\n",
              "       [6.  , 3.  , 4.08, 1.08, 1.  ],\n",
              "       [6.09, 3.01, 5.04, 2.01, 1.  ],\n",
              "       [6.07, 3.01, 5.06, 2.04, 1.  ],\n",
              "       [6.09, 3.01, 5.01, 2.03, 1.  ],\n",
              "       [5.08, 2.07, 5.01, 1.09, 1.  ],\n",
              "       [6.08, 3.02, 5.09, 2.03, 1.  ],\n",
              "       [6.07, 3.03, 5.07, 2.05, 1.  ],\n",
              "       [6.07, 3.  , 5.02, 2.03, 1.  ],\n",
              "       [6.03, 2.05, 5.  , 1.09, 1.  ],\n",
              "       [6.05, 3.  , 5.02, 2.  , 1.  ],\n",
              "       [6.02, 3.04, 5.04, 2.03, 1.  ],\n",
              "       [5.09, 3.  , 5.01, 1.08, 1.  ]])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split Dataset\n",
        "# 80 baris untuk training dan 20 baris data untuk testing\n",
        "\n",
        "dataTraining = np.concatenate((df[0:40, :], df[50:90, :]), axis = 0)\n",
        "dataTesting = np.concatenate((df[40:50, :], df[90:100, :]), axis = 0)"
      ],
      "metadata": {
        "id": "FX9srkIzGZ_I"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataTraining.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mD2fxAjbG8g9",
        "outputId": "f079ec82-c017-4e7e-e3d6-888486a1aa18"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataTesting.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_tUX6BkWHiBh",
        "outputId": "324e0655-e022-4e99-d2c5-03483fbc9524"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Memecah dataset ke input dan label\n",
        "\n",
        "inputTraining = dataTraining[:, 0:4]\n",
        "inputTesting = dataTesting[:, 0:4]\n",
        "labelTraining = dataTraining[:, 4]\n",
        "labelTesting = dataTesting[:, 4]"
      ],
      "metadata": {
        "id": "QGQl5cpVHk1G"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Modelling**"
      ],
      "metadata": {
        "id": "_K37Eg94H8Pq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tree.DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "zBRYyDTcH6pq"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = model.fit(inputTraining, labelTraining)"
      ],
      "metadata": {
        "id": "qowR12YsIAS7"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# memprediksi input Data Testing\n",
        "\n",
        "hasilPrediksi = model.predict(inputTesting)\n",
        "print(\"Label Sebenarnya: \", labelTesting)\n",
        "print(\"Hasil Prediksi: \", hasilPrediksi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RpwjcbgjIDkb",
        "outputId": "7f82fe03-e841-48d9-817e-90c23b537674"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label Sebenarnya:  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
            "Hasil Prediksi:  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Menghitung Akurasi\n",
        "\n",
        "prediksiBenar = (hasilPrediksi == labelTesting).sum()\n",
        "prediksiSalah = (hasilPrediksi != labelTesting).sum()\n",
        "print(\"Prediksi Benar :\", prediksiBenar, \"data\")\n",
        "print(\"Prediksi Salah :\", prediksiSalah, \"data\")\n",
        "print(\"Akurasi :\", prediksiBenar/(prediksiBenar+prediksiSalah) * 100, \"%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZU9YI0vSIOo-",
        "outputId": "da8b829f-b8ac-4849-8f88-f786f3d0186e"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediksi Benar : 20 data\n",
            "Prediksi Salah : 0 data\n",
            "Akurasi : 100.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9-0hUCGBIyt2"
      },
      "execution_count": 53,
      "outputs": []
    }
  ]
}