pima_decisiontree notebook

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      "name": "pima_decisiontree.ipynb",
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np \n",
        "# for visualisation\n",
        "import matplotlib.pyplot as plt \n",
        "from matplotlib.colors import ListedColormap \n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "import seaborn as sns\n",
        "# for ml\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.tree import DecisionTreeClassifier  \n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import confusion_matrix "
      ],
      "metadata": {
        "id": "Bgx4Cw5SrwAz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Describing the data"
      ],
      "metadata": {
        "id": "FZ9-QUo5r2dK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# using pima dataset from kaggle\n",
        "dataset= pd.read_csv('pima.csv')  "
      ],
      "metadata": {
        "id": "P7SdSeA5sFlH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# attributes of dataset\n",
        "print(\"Describing pima dataset:\")\n",
        "dataset.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "r2HfeEDssV2A",
        "outputId": "054680c2-0458-4917-a1ff-e9b4db941731"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Describing pima dataset:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
              "std      7.884160                  0.331329   11.760232    0.476951  \n",
              "min      0.000000                  0.078000   21.000000    0.000000  \n",
              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
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              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
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              "      <th>Outcome</th>\n",
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              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
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              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.476951</td>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "print(\"Correlations in pima dataset:\")\n",
        "dataset.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Correlations in pima dataset:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
              "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
              "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
              "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
              "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
              "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
              "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
              "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
              "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
              "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
              "\n",
              "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
              "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
              "Glucose                   0.331357  0.221071                  0.137337   \n",
              "BloodPressure             0.088933  0.281805                  0.041265   \n",
              "SkinThickness             0.436783  0.392573                  0.183928   \n",
              "Insulin                   1.000000  0.197859                  0.185071   \n",
              "BMI                       0.197859  1.000000                  0.140647   \n",
              "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
              "Age                      -0.042163  0.036242                  0.033561   \n",
              "Outcome                   0.130548  0.292695                  0.173844   \n",
              "\n",
              "                               Age   Outcome  \n",
              "Pregnancies               0.544341  0.221898  \n",
              "Glucose                   0.263514  0.466581  \n",
              "BloodPressure             0.239528  0.065068  \n",
              "SkinThickness            -0.113970  0.074752  \n",
              "Insulin                  -0.042163  0.130548  \n",
              "BMI                       0.036242  0.292695  \n",
              "DiabetesPedigreeFunction  0.033561  0.173844  \n",
              "Age                       1.000000  0.238356  \n",
              "Outcome                   0.238356  1.000000  "
            ],
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              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
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              "      <th>Outcome</th>\n",
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              "      <th>Pregnancies</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.129459</td>\n",
              "      <td>0.141282</td>\n",
              "      <td>-0.081672</td>\n",
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              "      <td>0.221898</td>\n",
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              "      <th>Glucose</th>\n",
              "      <td>0.129459</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.057328</td>\n",
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              "      <td>0.221071</td>\n",
              "      <td>0.137337</td>\n",
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              "      <th>BloodPressure</th>\n",
              "      <td>0.141282</td>\n",
              "      <td>0.152590</td>\n",
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              "      <td>0.065068</td>\n",
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              "      <th>SkinThickness</th>\n",
              "      <td>-0.081672</td>\n",
              "      <td>0.057328</td>\n",
              "      <td>0.207371</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.436783</td>\n",
              "      <td>0.392573</td>\n",
              "      <td>0.183928</td>\n",
              "      <td>-0.113970</td>\n",
              "      <td>0.074752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Insulin</th>\n",
              "      <td>-0.073535</td>\n",
              "      <td>0.331357</td>\n",
              "      <td>0.088933</td>\n",
              "      <td>0.436783</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.197859</td>\n",
              "      <td>0.185071</td>\n",
              "      <td>-0.042163</td>\n",
              "      <td>0.130548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>0.017683</td>\n",
              "      <td>0.221071</td>\n",
              "      <td>0.281805</td>\n",
              "      <td>0.392573</td>\n",
              "      <td>0.197859</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.140647</td>\n",
              "      <td>0.036242</td>\n",
              "      <td>0.292695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <td>-0.033523</td>\n",
              "      <td>0.137337</td>\n",
              "      <td>0.041265</td>\n",
              "      <td>0.183928</td>\n",
              "      <td>0.185071</td>\n",
              "      <td>0.140647</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.033561</td>\n",
              "      <td>0.173844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.544341</td>\n",
              "      <td>0.263514</td>\n",
              "      <td>0.239528</td>\n",
              "      <td>-0.113970</td>\n",
              "      <td>-0.042163</td>\n",
              "      <td>0.036242</td>\n",
              "      <td>0.033561</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.238356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outcome</th>\n",
              "      <td>0.221898</td>\n",
              "      <td>0.466581</td>\n",
              "      <td>0.065068</td>\n",
              "      <td>0.074752</td>\n",
              "      <td>0.130548</td>\n",
              "      <td>0.292695</td>\n",
              "      <td>0.173844</td>\n",
              "      <td>0.238356</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7eaa7c0-59ac-47b6-b46d-98b9f1985ca0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e7eaa7c0-59ac-47b6-b46d-98b9f1985ca0 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-e7eaa7c0-59ac-47b6-b46d-98b9f1985ca0');\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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.hist(figsize=(10,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "id": "OwA1ibOosnNP",
        "outputId": "a3e070e0-ee31-4db4-b78a-4162f8c15d18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fcf5171e4d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcf51be56d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcf51685f10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcf51647550>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcf5167db50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcf515c0190>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcf515f8810>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcf515aed50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcf515aed90>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting certain values against outcome\n",
        "sns.pairplot(dataset,x_vars=[\"Glucose\",\"BMI\",\"BloodPressure\"],y_vars=[\"Outcome\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "hUjGCGrrsqcj",
        "outputId": "0f878492-371c-487e-eb1d-d30654f1c037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fcf51414550>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x180 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cleaning the data"
      ],
      "metadata": {
        "id": "jS7nwJZLtOA5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q4TVGnRLs6tF",
        "outputId": "c03e84a4-4ce4-4233-f116-dc5d19d5b610"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LsxsmyX1tTUD",
        "outputId": "5e8c7bf2-0323-4dbb-bbde-8824994a0611"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Predicting the data"
      ],
      "metadata": {
        "id": "B0nNQ18TtXSQ"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJHDRehCtUq2"
      },
      "source": [
        "# independent variables(x): Glucose, BloodPressure  \n",
        "# dependent variables(y): Outcome\n",
        "x= dataset.iloc[:, [1,2]].values\n",
        "y= dataset.iloc[:, 8].values     "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training  \n",
        "x_train, x_test, y_train, y_test= train_test_split(x, y, test_size= 0.25, random_state=0)  \n",
        "  \n",
        "# normalisation \n",
        "st_x= StandardScaler()                            \n",
        "x_train= st_x.fit_transform(x_train)    \n",
        "x_test= st_x.transform(x_test) "
      ],
      "metadata": {
        "id": "3SlR994Nt4Cs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g11flr1HtUrE",
        "outputId": "811d9be0-8889-4b8f-bfe4-782f3795bdcd"
      },
      "source": [
        "classifier= DecisionTreeClassifier(criterion='entropy', random_state=0)  \n",
        "classifier.fit(x_train, y_train)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tsJxJeF5tUrJ"
      },
      "source": [
        "y_pred= classifier.predict(x_test)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "A351oarMtUrL",
        "outputId": "cb4d3293-e236-4233-a444-6973cb65c0b8"
      },
      "source": [
        "# confusion matrix  \n",
        "cm= confusion_matrix(y_test, y_pred)  \n",
        "cm\n",
        "fig, ax = plot_confusion_matrix(conf_mat=cm, figsize=(3, 3), cmap=plt.cm.Greens)\n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix', fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzeiH_HEtUrN",
        "outputId": "230b7a6d-51e1-4556-eacd-4fd38b114512"
      },
      "source": [
        "print('Accuracy on the training data is {:.2f}%'.format(classifier.score(x_train, y_train)*100))\n",
        "print('Accuracy on the test data is {:.2f}%'.format(classifier.score(x_test, y_test)*100))\n",
        "\n",
        "print(\"\\nr2 score:\",classifier.score(x_train,y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on the training data is 95.49%\n",
            "Accuracy on the test data is 72.40%\n",
            "\n",
            "r2 score: 0.9548611111111112\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean Absolute Error:',metrics.mean_absolute_error(y_test,y_pred))\n",
        "print('Mean Squared Error:',metrics.mean_squared_error(y_test,y_pred))\n",
        "print('Root Mean Squared Error:',np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cyNNfTdUxR_p",
        "outputId": "7d289745-9c60-4a67-ac6c-46d398c9bf95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 0.2760416666666667\n",
            "Mean Squared Error: 0.2760416666666667\n",
            "Root Mean Squared Error: 0.5253966755382705\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "YwWEhPv-tUrP",
        "outputId": "0ce38eca-11f0-4726-b0a9-28f9ad3aba29"
      },
      "source": [
        "# visualising the trianing set result   \n",
        "x_set, y_set = x_train, y_train  \n",
        "x1, x2 = np.meshgrid(np.arange(start = x_set[:, 0].min() - 1, stop = x_set[:, 0].max() + 1, step  =0.01),  \n",
        "np.arange(start = x_set[:, 1].min() - 1, stop = x_set[:, 1].max() + 1, step = 0.01))  \n",
        "plt.contourf(x1, x2, classifier.predict(np.array([x1.ravel(), x2.ravel()]).T).reshape(x1.shape),  \n",
        "alpha = 0.75, cmap = ListedColormap(('purple','green' )))  \n",
        "plt.xlim(x1.min(), x1.max())  \n",
        "plt.ylim(x2.min(), x2.max())  \n",
        "for i, j in enumerate(np.unique(y_set)):  \n",
        "    plt.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1],  \n",
        "    c = ListedColormap(('purple', 'green'))(i), label = j)  \n",
        "plt.title('Decision Tree Algorithm (Training set)')  \n",
        "plt.xlabel('Age')  \n",
        "plt.ylabel('Estimated Salary')  \n",
        "plt.legend()  \n",
        "plt.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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lO0mXYR2m61BXiSQqDMV8ruZ1zcnO38XN9TBZKWVKyLnAfwJNgAJ3q+rXSiWPYUwm+uP9kc9PJFNRLs+VKzXdNb7nJ3Ouh1LOAIaA21X1HOAy4A9E5JwSymMYk4aamH9nln7eNam4HahrUtnfu7/oMo6GqM81Gvob/ZXIZM71UDIFoKpvq+qWxOejQCcw/qIlGcYEpHlmMxWS+uddIRU0z2xOOTfRTEVRn2s0dK3sIl6dOtqf7LkexsUisIjMw8kP/MvSSmIYE5vZm2bTvK6Zmu4atly2hY1XbuR45fFA004xTSrFwJW/GCarA0sOADDn+3OY0TMjxQtoslJyBSAi9cA6YLWqHvG5fitwK0BjTeMYS2cYE4P2ne20drSyZMMSqgerAbjo6Ytofb6VDSs20NHaQeevO+n8dWfmzZJ5KleTSvvO9pTjZfOX5XR/LjTVNxVtjeLAkgP84NQfFKXuktLuf7qkCkBEqnA6/++o6n1+ZVT1buBugPnT5o/fbXiGUSLWPr4WgD2b9xAfTDVhVA9W85Eff4Tb37nd995NszextmUtA7GB5LnRmlRcOVYtXZXzveOJYiqvUtEeoAFK6QUkwDeBTlX951LJYRiThSBvlTAvliUHlgCwrnkd3TWOz3vLrJYx9QLy80IqFRPJI6oQlHIG8F7gt4AOEXkxce7PVPUnJZTJMCYssZqYb2efzYtlyYElSUWwaumqMe/8gzZ2jTWTdfNcGCVTAKr6C3ytj4ZhjIaZzTPZu2Nvcg0AYKBqgA3XOmsAftzaeWuy8y8FQV5I40mWrkNdk1YB2E5gw5gk1DfVs2HFBk40nkBRTjSeYMcndtB4XSPL5i9j4ckLqaiocIZdiZ+1LWvZNHtTyWQeT95GE80jqhCU3AvIMIzC0dHaQeN1/t5yfiPcgdgA65rXlWwWUBOrCe1g072Lik4BPKImEqYADCMLvft7OdR1iHh/PGffcHd03b6rfUwiV4YR1NG6i7+loHlmc4rdHRwvJO+x611UbArpETVRMBOQYYTQu7+X7m3dycVVN0Jk7/7erPe6HYo7qixkmAU3cuWMnhkpcrV2tAbeEzSSbewv3f6apvomWhpbkrLVxGpoaWwpiSxLDixh1bZVTmQyjyyT1f4PpgAMI5RDXYfQ4dTtJ1EjRK5rXpcymoTiR65s29gWfI9PGIXqeDUru1bmLU8+NNU3sXjuYpbNW8biuYtL2uG6prDxIMtYYCYgwwhhNL71LkGmlWyLit5wDv2N/XSt7EqGKXAJilzZ0NMQWG96GAUUVm1bVVIvIKO0mAIwjBBG61sPjmmlu9ZHCWjw4mZ6OIfa7lrOvOdMOg92prhyXtBwQdL846WnoSenhVPr/MsbUwCGEcLM5pl0b+tOMQNFjRC5smtlxqJidbw6dNQdFM5h5X0r+WTnJ5OLz71zeuk+OiLX1tatbGzbSE9DDw1HGrjh1RtY3r88rwVsY/JjCsAwQnA7y9F0oulhFhr7G1nZtTJ01B1mWvKmJ/TK9cJZL7BhxQYGqwcBZxbw3Yu+y8CTA5y97eykkiiHFIdGbpgCMIwseDvcXPGGWYhCkMkJRhafXVlcuf7xwn9Mdv4ug9WD/OSin9DyZEtoHZMZm/1kxxSAYZSQ9E6qrrGOY/uOZXgeufgph57pPb5lexr8z8f746GdY3pAtMa6Rrr7ujOCtXnLjDdc912b/YRjbqCGUSL89hgc23eMqadMDbzHb/G54Yi/50+QR1BFZUXo3ob0FJF7e/emHHce7OS1g6+lnANKGlIinXzcd8sJUwCGUSKCOqm+7j5mLZyFVKTGJQhafL7h1RuoGqhKOVc1UMUHnv+Abx2qGto5RgnGpqTNUMRZ6xgv5OO+W06YCcgwAiiUDdnPzNPX3RfaSdU31XOi5wS9e0d2HE89Zapv+8v7l8PzsP6c9fRM72Ha0Wk0HG7ge1d8j6r3VTFYNUhDTwOLNy3mrdPf4sb7bkR8gt7k2znmE1LC67paiIQs+bjvAmzes7kscgLYDMAwfMgnBES2enr39oZ2trGaGL37ezm271jK+WP7jgW2v7x/OV974WsAXLToIqa9exoVFRXO4rBAz4wefnbtzxi6Yoj+Rv+NaFE7xyDyDSlRyJg/M5tnRp5BeXHNWF7zVqHCd4xHbAZgGD6E2ZBzmQX41ROG20nl235YbPuulV20rG1JCSXh7RzTg7FFQilaSInRpphs7WilbWMbDT0N9DT0sLFtY2BehBTSJkeTOSeAKQCj7Agz7Xiv+ZHNTJJed65mFdfMc7DzYGj7m2Zvythf4NK+qz2w/v54fzKsxJzvz0nuJnaVS2tHK0NXDI2Ei8iB/p5+PnXppxwZdrZTPVzNbzT9RkrHmWvKxVzMQel1dy/p5l/O/xfi6ryzyopKFp60MFSeKDkBJlPayKwKQET+EPi2qhZ8+VxEvgV8EDigqucVun7DSCfMPRDI2PWbTpiZxK/uXDm27xi1DbWByuNww+GREXFipNpd2803z/6mY9fPkmOvaqDKsbefCq1trdy0/qYUeVdsWMGOWTtgEb5hmiukgqHhoYx6Y/EY/3XJfzFcOVJ+IDbAtl+PpFQsZvpHv7r39u5NKTM0PMRrB18LlScI19V1sqWNjDIDaAKeFZEtwLeAh1U1+pw2nLXA14H/LFB9hhE6ws/mHhjW+YfZkHv39waO2tNJCdvQ00DbxjYWdSxKtu/W41eutaOV6Uenc2T6kZQ64xURlI1CbCjG9Q9dz3kvnJc4lfq81YPVNK9r5v659/uakPxMQxVSQeVAJf1TMjvQYRkxn4SZpcLYfnB7Smc+p34OZ806C0gdjUdBUbZ3bw+Uxw9vToDJljYyqwJQ1T8XkS8BVwGfAL4uIj8Avqmqv8qncVV9QkTm5VOHYXjJtgFotKadisoKTlpwkq/93W0z2/3DQ8Nsbd2aGrZhRg8bVmwASCoBILTckWlHGBUCJ6ac4P5r7md4aDilPS813eFZurxUVlSy4KQFdA53BpZJ3y8QdN2P9M4fSB431DZkzFKiENc4+3v3Zx3x+5l4JlvayEheQIkR/77EzxAwE/ihiPx9EWUDQERuFZHnROS5o4NHi92cMcHJNsIPMuHEamKh5h2JSeDia7aF3lhNDIk5tpmNbRt9wzZsbNuYci6sXFjI5yj4teelvzH67t6YxGiqb2La0WlZywbVGdZWeufvPR91BO9H16GuUHmC8hOM5hnGM1HWAD4D/DZwEPh34POqOigiFcDrwBeKKaCq3g3cDTB/2vxCmZ6MSUrYCH9X+y6nIxbwWj68pp1si69h17a2buWhqx+ib0ofAHXH67jmp9dwxdAVyXqDwjOkn89aTkmx91cMVSAI8cpo6w5B9Q9UDdC1sss3VaMf7sj3nPg5/FJ/GbgGEbQw7ZpXOn8dPIPI1vZo6I/3s3DWQt91jrAUkEEpLCdq2sgoawAzgRtV9Q3vSVUdFpEPFkcswxgd2TxvNO70/K5Jpmd6D49e+WjSPfDzf/95fnXmrzJs72fsPiPQHXH1C6vZfcZu1l+/PmURtG9qHz/60I+Y9dos5nbNJd4fd1wSZ2R2vumj+qByKR2sjpR1M4G5cmdbDPabRcRqYmy4dgMchN+84zdZeMZCHr3yUY5MP4IMCxrLHH81HG5gyR8sYcfHdvDC9BcyMqCF4TWvjEYBhHntuDGMgmYQAJ0HO4lJjMqKSoaGhyJ59KQn1ZnUXkAiEgNuUdU1ftdVNfdvzTCKiF/8fj8kJsy7fB63vec2amfWsuyUZQA8cesTbJm6JcP2fsbAGVRUVGSM/FoaW9h78142Tt2Y0vm7aIWyrnkddzbfycHOg7RtbEux7YPjmZOeytGvXOZDOB3wZ7/62eSpRR2LMtYP0vG2JxVCY0tj0rz1yc5PJt/fjI4ZyXUCvzrdeqqPVXP2N8/mwk9fyLMnPRvZLLN47uKsZebUz/HtxOfUz/FdA3C/E7dDzrZOENc4FVSwcNbCyJ14U33ThO3w0wlVAKoaF5FtInKGqu4udOMi8j1gGTBLRN4Evqyq3yx0O0b5kB6/P4igay/OepHBeKbt/VfVvyI9/M2wDvPGvje4ft319Hza36QCToiET3/k08w8PpPlDy9nxYYVyZF63fE6AO678T42tm1MegS5HW+2Eb2fKSf9XreNvil9GV5Hr733NX562U85NOXQyLXhzMXh9DobehpYsG0BG9s2ct+N9yVDTQxf49PRBsi+ec9mmmc207q1ldXfX82unl1cffxqnrjqCUgsxzTUOjOVIC8g8B+Ne72DvKN8PyayF0++RDUBvSIizwDJvemqel2+javqR/OtwzDS8cbv37N5T04xYXK1Kx+PHae2uzbYZAPJDvDQ1EPc96H7kqcXnryQ199+naEqp2Pyevqc+/K5yU73vhvvI4igBWGvEglia+tWNrwvu0eSX51+Xkobr9zIrZ23JvMf9O7v5Q9v+kNf0xE473r7ge20PNPCjJ4ZjtvrlRsZrBxRwNu6t9HS2JLS4XvxG42n++p7R/mdB/2NFt7vPZeUmhOdKArgS0WXwjDyJN33/wfX/oCO1g5Obz6dI9OPcKThyMgI9+VFDA8Ns6t9F5/a8ikevfJR2lvbRyrzG7Gq/3m3A27b2JaxBuCLwJShKVx65qVs3rM52fm7DFYP8tA1D3HSrJM49alTHW+dgBF0xVBFhukonbA9B0GeRg9d/VDGPbtP383zlzyPio68jzQfwsHqQe5eeDd3L7yb1pdaWdm+koueu4jnLn0u8BniFXEeW/YYF7x4ga88YaPzoB25o91v4AaAA1IU2WRgFat8z0fZB/B4oYUxjELi5/u/YsMKpsydwrPvGrFJ98zo4YHrHgBGRrgNRxq4af1NyXy7m2Zv4p6z7kkZhVYNVDFnzxzeaH4jYxF2wbYFKfV5vYAA347veOw4szfNpn+O/2yjr66Pp1Y+ReuC1kBvHRSuffRaWjtaAxe+s+05CKq7b0offdKXvOe+G+5zOnvvswQtMgu8p/M9tP3YUUwffMjxE3n+4ufRCvW9z5UjSB6/WVnYjtzR+uonrwusbVkLMKmUgB9Z9wGIyGUi8qyI9IrIgIjERWSUO1EMo/D4+eFXD1bTGevMGAkOVQ1l+MB79wksObCE6398PQ2HG0CdRdYVG1ZwuPFwZucl8HrL68nDRR2L+OI/fJE1d65hzZ1rQhO1zPn+nIw1BW+9XYe6OLDkADWVAX7nlTUcve0od375TuYunutr0sq25yBwP0H6c8Z8zgVQE6vhvQ+/l+rB6uS5Dz70Qb78V18ObM89H3Tdz8c+bJQf5qsf1V9/IDYwrvIbFIsoJqCvA7cA/w+4GGdPgL9BrozxC8412UcPuVKsHK1BC7pBO2b9RpruPoFYTYzz+s9LhkpwCbLDB41apUK44dUb+O5F303phGNDMY7XHuerq7/qe59L/1A/szfN5qqtV/HTK36a2pErnLPlHM78+Zlc8vQl7OrblZNsPQ093PmlO50RebppK8DUFQXXH37aYf9NYW0b23hgxQMMVY+YvWLDMZa3L09eT/c0CvKxDxvlZ/Pvj7p72JvfYLLmF44UDVRVd4hITFXjwD0i8gLwp8UVLZNd03aNOjTsmOAJzuXaQktNIWOs50Mxc7QGmUCi+tx7CVImUetSlMqaSmY2z6RxdyNH9x5N8cbpq+0jXpt9s1ZDTwNnffMsKuOVvD3z7VQ7usDLC19m/ob5nN53emgdQXsJUhZmEx/rjtelmq9yQeGU+lNoqm/i6IyjTD88PaPIea+cxzOXPMObc98ESewDmNVM9aXVHN55mNaOVo7XHeeJq57geOw4AC2zWnzt/0F7AGpiNZF89b3XhoaHkhFDvbj5DSZzfuEoCuC4iFQDLyZCP7xNiRLJ1NfUc/H8i0vRdCjexaMkMrKlvFSMJ2+GQsXX9yPI9z+qz30UotQ1UDXAhhUbuP2d2wHHA2lR/4jnzF2r76JvavYO1q23Mu78eb7e8nrGqNw15YR5+kTaSwDJ/QRA0vafgo6UC6uju8/pFJ/6zadou68txQw0UDXAjk/s4M1T3wRg2bxlyWsHlhxIhqgGuJRLk7+7Qa6Z2Xbkhvnqp19LX08AqAc6scYAACAASURBVI5XJ0NsF/N3t9REUQC/hWMF/DTwWWAuUJzMDxOUyRYgqhgUKkdrtkif3jj8fr7rXi+YXMhW18sXvsz919zPYNUgnznyGdoebWNRf2o7gQu6kLGr1ytjlPAR6d4+C7Yt4PWW1xmsGozUgYfKFpH+eL8zGFrYz5bPb+H8F87n9ZbXkzJduPdCONUp276r3Xdk7nr2eI/9OvKm+ib2v7afQycdQkURFeYfn0/TuzJdQrd3b0/JCTB7ymy6+7pTZgenTD1lZK+Bwnvffm/ShDuZ8wtH8QJyQ0D0AXcWV5yJSdh01HDIN0crZJ+Ke0djrv9/FH/4qATVtbV1Kxuu3ZD0HOpp8PenD9sr0NCTuqM3/VqY+cnP2yfM9TKIhiMNvorAbSdwn4OLjgx6BqsHU2TomdHDk/VPwjDJTV7psfRTRuKJ+4Ji7fe90MeR6UecdQxARdldvZtZL8yi7kJn49v+3v0Zfv9Dw0Mpm8r64/10HuxMzZEs8NSpT7HgyAKWHFhSkN/d8UqgKUdEOkRka9DPWAo53mme2UyFpL7KiRwgqhiMNkerl2yRPtPbKzgBHerGttTNS+AfcbNtYxsVQ5l/crGhWKhZqm1jG1UDVSnnvOYnP2+fXDr/6ng1t3beyg2v3hDYjp8MKfgtHqcdxyvjyc7fxeufn4v/fmesM2UxGZx33hkb6fCz+f2nip/6e+X1AprZPDPz+60c5OeX/jxy/eOVsBmABXqLyGQLEFUM/Mw02TwpoqZX9Dvv1hs1SUtWxFFYbjA5r8kliPRr7mxgw7UbGKxxOhRR4cLnLwScNYKg0A3e8BFeM9HW1q25m28UJ7hbhdLQ08DNe29mUcciDnUdYsVeTztHGhxTlmcWc9+N943aSyiI/nh/eBrLof4MU1CQh5f3fL4mWNcL6J3X3+GB6x5I5hf+0fU/YusFzhj4cX2cpXuX8vEdH8+rrVIRqADSo3+OB3r7e8fVwmYQ/UP9dP66c1QRDicz6WaaMHJJrzgmU3ElpfOPsrga6G3k2VSlomy5aAsvXPRCMpSzd6HY3by1YsOKDBORK0fOph6PuclVxO679pq53BwG7uh4UcciRzlkMwUVGoHtB7YDI4Mtv6xo7nmXsGihUXC9gAA6WjtovK4xI0HNsAzz2GmPAUxIJRAlH8BlwL8AC4FqnEncMVXN9PMqMvOOzmPN42vGutlITFY/4VKRLcmKS5AZKVuWrrAQCdnwNbmkERuKsWDbguSo3m3DN9xBlvARQR4/UeRIJ91zSeNK9/ZuXjr3pUjvo21jm/8swM2xUKA9BenEK+J0HuxM2vQr6iuQeGqI6oqhChbGFyaPm2c2B8b+SUeQFDOQ1wvIi294aYHH5zw+IRVAFHfOrwMfxUn+Ugf8HvC/iynURMPtbNxRqrs42bu/t8SSTVyijPhjNbGUUMZewhSIO3LumeFE2XRH2Vtboy1tRTG5DMswWy7aktHGaL1t/O7LWpc6PxKXlF3N3o59eGiYl855KfL7yKYkvTuos8nW0NMQvBs6C8MVw8kF4CQxOLJgZFbQVN/EwlkLicnIDLGyopI59XOSDho1sRoWzlrI2bPOHnHaUFi1bVVOGzmHZXSZyUrNhNoINl6ZzH7CpSLM82Lu4rlZ7w9TIGEhEqLMAkIjfybQmGYsLA5WD2aMWqPiZ04KStLivSfIs8hL1PdxuOFwst4gryRve3f+xZ0jweN8+Oxdn81aJpS02YU3Ab1LLrH73XLtO9tz3sVfoSXZGpU3E2oj2HhlMvsJlwq/zV25eA2FLRpHTcsYROQNVj5ohVI1UJXTvUGb1zJGwB5iQzEGqgZY8+U1GYvG6Wkrg3b/pr8Pb+TTjOf3BMYDiFfHQzt2t66Lnr2I597zXNqDJf4fhfmo2HtvfBPUKCzdu7So7RaLKB35byXKfRonH4BtBEsjLNG4MTrqm+ppbGmMZO7xI0hRhJl5oiZbX9SxiBUbViTNHRKP3lPVHa+LdK8MB5ttsj2HDDv27L6pfSkmnQeveZD7r78/eR4hdGdy+vuQxL9FHYs4/4XzU803Ai+++0W2tm7lcMNh1n9wffBLUJIK7aonr6IuVpf2AMG3ZqPYe2/OmnUWc+rnOAcKFcMVLH9r+YS0/0MOG8FEJA48ALylqgfC7yov8h2tGv7k4jXkd++JnhP07k1dhwmMr+/plLxIhZNE3vUAcglLjuLWF9SRZbu3aqDKt9OP+hy1fbUZHftg9aATktnPZOSzgJstZIZfeIqhqiEev/Zx/unpf+KCdy5gU+cm1rasTc0TrHDxMxePPJtCX9xHCfktKmdhrPbenDXrLM6adRbtO9v51hPfKnp7xSRQAYjIN4B/UdVXRKQB2AzEgZNE5I9V9Xv5Ni4iVwNfw/Es+ndV/dt86ywFo/FxN6Lj9bCSmCAiDA8NB75nb/l0wsw8vh2uT+cfdJ/XiyYs1n46lYOVTsgGnBnCuS+fm5Jq0c8jJ+w5gkw6YSYjcEazwzKcTF0ZpIAevObBwPa9ETRdO7obJdcbomLNpSOmqTAaDnveZRZlcMrUU0a19yY9sUwQKS6gCrGBGKuWrqLhSAM3vHoDy/uX59x2qQmbAbxPVW9LfP4EsF1VbxCRU4CHgLwUQCLh/P8G3g+8CTwrIg+o6qv51Fsq8hmtGsGk7wfQ+Mjiql9UxvTy6QQuYB5pYN6yeb7tRSE9TMRdq+/KGj3Ub/Q/UDPAlou2JF1Dg9I0hkX6DCTLtWEZpjpezYff+DBLTlriZOsmNa3mg9c8GBpmwus7D44SWHJgCXs27+GFs17wT1ATNNJXR9Etm78seco38GKCfcf20VDbkJMS8EssA0549/SF4BTbv0C8xnknPQ09fPei78LzTDglELYG4Jm38X5gPYCq7itQ25cCO1S1S1UHgHuB6wtUtzFJyLYfID0URLbyQWEVbnj1hkj3RyVb+Abw976JV8Yz9gUEhZUYrQtlGH6JULxhPJ6/+PnAzr9qsMrXd96tI8jbKIia4czRuF/YFZcoaR/T8Qs/gZBzMpjB6kHWnxOy7jFOCZsBHBaRDwJvAe8FfhdARCpx9gPky2nAHs/xm8B70guJyK3ArQCNNY3pl8cNj9U8xvpz1tMzvWdCTwnHG1E8qbzJXLKVD4rqOb9jPrvYBeS3SSxbO1GifPrhF1YiLFk8MOqNWF4zDqSaOAPNSApVQ8Gxguqb6nPeA9Ef62f1V1ez9+a9yXDR7ug+SnJ3l6DcwUHlwXkH3plPFHqmj/EO6QIQpgA+Cfwv4BRgtWfk3wb8uNiCuajq3cDdAPOnzS/CmCd/Hqt5LCXz00SeEo43onTqLlHLhUUIzZZHNxeyRSKNsp/AWzaduuN14fkFRulN09DTQO/+3hSTpmvirNAK/01PAsfrjofm0g2KNioqvi6jDT0NzOiZwbS1ToYxrxJwO/R00m34YbmDm+qbiEnMNxlM3fG65O9T1A2CQSlAxzNhsYC2A1f7nH8YeLgAbb+F41Lqcnri3IRj/Tnrfae2689Zz/IXTAHkQ1Cyl1yQCmHqKVMzPIL8GO0msaizBm+5uuN1VAxVpJp83EBtHm+ddNORW8eosndl8bF32wraxLh071In9k3A/QOxAb5/2vdZ1LEo436/FJkonDxwMgfrDqaYYrzPHBuI0byuOSVpTLaEMC5hEUab6psQkaymtECPKy8Ki/YXJuz4WBJpJ3CReBZYICLzcTr+W4D/XkJ5Rk3Q1G8iTgnHG+keVl4voDDcmYPXUyiKAhjNJrGos4b0cn1T+6iIV6SaasQZEdceq02JBhrqcppOmOnHda8MuM91P43jP5v6+I6POwogRJH0TO/xTZm4vH85Xbu6eHLBkynPe7DuIKdMPcVJ0jLU76tAa7pTR/ZRI/BmS9Y0NDzke92rXAO/+7Tv7ckFT7J53uZkJrdxRbv/6ZJJqqpDIvJpnNlEDPiWqr5SKnnyITCRxgScEo5Hgjysgmy0UcNF+DGaPMJRZw2+geBimYpsuHKY6t5qvnjnFyO1lU62EBFByqGhpyG5W3jjlRvpmd5DY38jK7tWpph0/vZrf8uJnhPc9ZlgT6egUCivnvFqZggHHaa7r5vFcxdz2e2XUdtdm1Fnf2NmRx4lzEO2ZE1B173fd2SPK8HJUTCB4iSUVFWp6k+An5RShkLgN7X1epYYxSHIPKRxzbBhR8UvzEHFUEUyrII3jr47Sg2bNXijgea66HvX6rtyXjiuGqji/BfOT3El9SVg41f6DKO7tpt7zryH7m3dnP/K+Sn3Z8uT7Kec0xeYXdxOuGtlFy1rW4gNjOyij1fH6VrZFbqYG0RQRFDXVORnSnLdT933n2voj8vPuDxSubGkPWAKELYR7HNhFarqP+cn0uRhef9yeB7zAhpj3A6+e3t3ir/+8NCw7/6AKKR779Qdr2OgZiC52OqOrL1mnrDFXPd8z4ye3Nw2xd+UFNhWYgfwBx76AGftPYvYWTG2TN/CYMWg74i/ariKiuoK+uP9TBmawn979L+xqGMRd62+y3c2s+5D67jvQyNeR5+56zNZPZ3SQ6H07u8NlH/K0BRgZKG3eV0zNd019Df207Wyi45FHaGLueGvMjXUszf9Y7opKVEAGHn/V//8at597N28WPdiUvnENe5rPppoaWBF1f+3UkS+nPjYAlyCEwYCYAXwjKr+j+KLl8r8afN1zcVrxrpZY5wTxRSUq0ufS9CGLpeGww2BwdGCNjd5z8eGYsRj8dBFxobDI1E2t7ZuDc7KlfanvPqrq9l9xm7/Ufqjbfz0mp+mlF/7+FpWLV0Vqe7WjlZWbFhB9WC1r8xSIRmxm/w2g3nlOfE/T/jWBcEbwGpiNSyeu7gg9+VSNt27CJxF6JbGlnGZCbB9Vfvzqnpx+vkwL6A7AUTkCeDdqno0cbyGMXQDNQxvaIeKygpUFY1rcoE3SjTW0XT+UdIt9jT05BQKAkjpTKv7q7N686Sbg8L8/9c+vjb5+YEzHnDWDKoSYajTTFe3TLkl4/4ZvTM4PO1wxvnG/kb+6el/SjnXe2bq9zIcH05ZHD7Rc4L22nYeWfqI814u8E9vuWDbAjYv2UzPrp5RL+YGmYey3ZdLG14mSxrYKGsATaTuCh5InDOMopMemsHr/eOGgqiorPD1CvKaIHLZTwDR0y26i4VRQ0Gk19c3tS+7aUiim5xWLV2V/Fw5VMlQlWOm0Jgm7fOLOhZxuOFwsmxrR2sy3+0V3VdkjtDTdvh623Bp7Whl5X0jZTSubGrcxIZrMr2jvOkt09ccgkw7YYu5Yb7+2RaB089FLevKN9E6/HSirFf/J/CMiKxJjP5/CfxHUaUyJiW9+3vZs3kPu9p3sWfznkh2+SihIPw6f6kQ6hrrku0NDw3ntDEqirdNWMRMv1AQgYS5ZnpwPYuWPrbUv7wkwkjj2Lndzj/9/uHYMHtv3suy+cv4yNsf4YYHb2D3Gbv56uqvct+N91E5WEndsbqRcNQPrGDu/XNTvq9l85ex8OSF1FTWgMCuebsyNkyFeUeFlfEL6eAXAsL1+w/y9e882EljXWPgfekERRIdiwijpSJKOOiviMhDwPsSpz6hqi8UVyxjsuGX5N3PVzyd0ZhuYjUx6hrrOLbvWEZQt6DZQjqhvt+QNUSEez6ZfCWC8okS+bKnoYedv7Ez8Lq7SJ2ejcx7/7MXPstDpz4EO2H191fzSssrGfsTqgaquPG+G5PPESeekmM5fdR9dPrRjAXrMO8oV76gMukj8TCTS1je333H9o3sMYhgqglbMJ6MRHUDnQIcUdV7RORkEZmvqjuLKZgxuRht2sxcTTfuwu+ezXt8Zw4SE+ZdPi95vKt9l289UdMehrGoY5GzY1ey79h168266NzTQEdrR6C5Iko7SzqWcHP9zQDs6tnFPW33RNrH4A285zfqTr8n6B3WVNbw+NrHnc97optdgkwuYe/Cu8cgG12HujIUp6IZaSYnE1lNQAlvoC8ykgO4Cvh2MYUyJh+jTZvpjUSZSztD/f47POP98RQzVBBRonlGIYrvv7fesPJuudaO1tComNnu976DsDb9zsf746z+6mr6h/w7XO89fu8wNhxLMamEmXaisL93v28sHy9RFWUui8CThSgzgA8BFwJbAFR1r4hMK6pUxqQjLMl7GOmhILxeQEHtgNMRzeiZ4VvGlSNM+USJ5hmFML9997rbqcclTm1fLSemZLpDSlySYRrOfu1sdszaAYsSJpGh/sDdv37eP+nPnuvu5xk9M5jeM50jM46E3pP+DqsGqxisGqTz1510/trHbCPk5E3j54rpR1Tf/FwXgScDURTAgKqqiBOuT0SmFlkmYxKST9pMv1AQfolfvPVtbNsY6qcehQu3X8iS7iWRYggFEbRbNj3lo8SE+e+bz2/v+u2MNIrp5asHq5PB0Zrqm+h+oJsFry8YVWrJMBnDZjtXbrwy6z1SIVwxdAXXvXhdaPvgeBYtm7csazkvvrH808hlNhE1wNxkIooC+IGI/CswQ0R+H/gd4N+LK5YxmXD9+L2ddb5pM4PScIKz4ejG/hs5Xnc8qQByXUsAZ5Q866xZDB4fpP/w6MwAUWcSGlf2bN7DouZFrGJVShpFv/Lb52x3Ni4N9TNt3jQWvL4gw7/+A89/gLNfPTtp1w5aAHfrfvzax0Pb9H0uT8ygD77wQc7cfiZxxiYtajbTTK6++ZPFtz8XAncCpxQSeT9wFY7vwcOq+kixBfPDdgJPPIJG6um7RIvVFkT3/EnH7cTyDUedC+nvxm8H89bWrTxw3QMpbp6+s4qA3biF3jVdiO9z1dJVKakfozDa3cHlSNBO4CiLwH+nqo+o6udV9Y9V9RER+bviiGlMNsK8f8aiLWBUnb9rTipUisiopL8bv0XwR9seDfTxD6srqL50U1yuC+/F+j6zke8CshFtI9j7fc5dU2hBjMnJaL1/CtlWrsRqYkw9ZWrSvDRa6ueMbkTsbbO+qZ7GlsaRxXKBIw2Zi68Q7LWTIlNafbGaWMbo3a9M/Zz60AX70bynTbM3cftltyd3Fu/v3Z/T/U31TbQ0tqSEdh6vsXjGK2HRQD8F/E+gWUS8W/ymAU8VWzBjcjBa759CtpUL85bNCzQl5YJUCLUNtRzbf8zXY0liQkVlRaR3410E3/fivozyLn5eO96QD4Vg9VdX+3pXjbodz0QjanRPL5MhHEMpCVsE/i7wEPA3wJ94zh9V1XeKKpUxacjH+ycK6YHi8sHteAth9tFh5WDnQSTmb0oRkYK+Gz+vnXh1PBnyoVDsvXkv09ZOy4jXH9ROUJA2P/u9G74BclMCxugJiwbaA/QAHwUQkdlALVAvIvWquntsRDQmMkHeOoVYAPYNFCdOJzocH+Z43XGm9qV6Lbv5gb1hItzzbsdbSPPUcHzYN5xAfCjOpz/y6ZRAbD0NPWxs20hHa0dwhUt9zimc/8L5nN11NgP1A1T1ViXj6Hvz6BaCoHj9fu2EBWkL8+AZzUzAGB1Z3UBFZAXwz8Ac4ADwLqATOHe0jYrIh4E1wELgUlV9brR1GeOfoJSO+eI7UlfH6+fLf+6ks1jzl2uIVWfmB65tqA1USoUwJbkExZLpb+x3Rszz4cXrXkyeb6SRZSwLrM/X80WgY0kH9TcXz+XSy4ElByIplrCE7NnCN0zm8AvjiSj7AP4auAx4VFUvFJHlQL7JYF4GbgT+Nc96jDImqJN+4ayRWIV3feYubt57c0pOW6/ZyG9GMrN5Jgc7D2YXIGIUT0VTFIGb4jAXvKYUP/rj/UnlMF7818NkXThrYegu3kKEX4iaQnI0qSYnC1GMpoOq2g1UiEiFqj4GZPiT5oKqdqrqtnzqMAy/heSUOP6JOPprW9ayafYmYMRs5A0H0b2tOyXUcX1Tfdb1hIrKCmadPYtZC2dlXdAenDrIicYTKMqJxhNsW7UtJ9OMa0rJ1im6111TS65eNYUmKIRCTayGpvomLnnnEiTuP0PKN/xC+jsLeidRy01WoswADotIPfAE8B0ROQAcK65YI4jIrcCtAI01jWPVrDEB8FtE9YsvPxAbYF3zOpYcWBI5KulJC04K9QSSmCTL1zfVB26eUpQdH9uRly0+SsiDdMaDGSVbaIXl317OaXNOywgpUTlYSfOs/Hz5w8xP3ncStdxkJYoCuB44AXwW+BjQAPxltptE5FHgFJ9Ld6jq/VEFVNW7gbvB2Qkc9T5j8uO3wBwU2bK7xollH3Vfglt3kCkovXzYmkG+C7GBI3+F6Uenc2TaEd/8AP3xftp3tZfMrJEttEJNdw2Luv1DZbxze36OhlEje5ZjBFAvURLCHAMQkenAhqgVq+qVechlGJFIX2CeeXwmh6Zm7kqdedzx8MllX0J9U33gZrD08kH19jT00L6zPetzZMWng5/eM53PffVzWXMIBKVZHAvC/PT7G/up7a7NSKd5ovEET/N0Xu1GjexZjhFAvUTxAvokcCfOLGCYkaUv229tjDvaHm3j/mvuz4xS+WgbNOS+LyFq+aByZ845MyVR+2jYNHuTb4TQKzc6Yyy/aJ7pRDVrzN40O5KLZyHoWtnFmfecmRKxdTQL5H5EjexZjhFAvUQxAf0xcJ6qRnCLiIaIfAj4F+Bk4Mci8qKq/mah6jfKl/NeOI/hoeEMk8J5HefBstz3JUQtX8z9Dq4HkxshtLG/kaU/XpocNadHHAUCTUK/2P2LwHbOfelcljywJNkh13bXcuY9Z7KtexuvnP9K3s+Rwelw7nXncsUjVzD9yPSCKpyokT3LMQKol6zRQEXkp8CNqnp8bEQKxqKBGtmIEu1yMhAWsTPQJDQMtf21gXV+6v9+ioYjmeEkeqb38H8/9X9HLWs2vvHLb4wqGqgRnaBooFFmAH8KbBKRXwJJY5mq/lEB5TOMglDs0BPZyLbHIOh6tvvSCQtT7WcSqo5Xs2rbqpT9EOnsOrLL93zDkQa+8ctvRHh6Y6IRRQH8K/BzoANnDcAwxi3FNMVkIz00hbvHwJUr6PqJnhMpoSnS7/PD7zk1rgwPDWcmoTnSwM1v3Rza+cPYBu4zxgdRFECVqn6u6JIYRoEoVuiJbGTbYxB03S/lpN/ehHTSn9OrYFzPmlyStZR69mSMPVEUwEOJzVgbSDUBWURQw/CQbY/BaFJS5kK+s59Szp6M0hBFAXw08f+fes6ZG6hhpJHNhJJrkLnRmF6CZj+3vec2TtSeyLk+Y3ITZSPY/LEQxDAmOtlMKEHXs4WnLhSVsUouP+PygtZpTGzCMoJdoao/F5Eb/a6r6n3FE8swJh7ZTChh18PCUxebco6GWe6EzQCW4nj/rPC5poApAMNII9sCdND1Ui1chyVtMSUw+QnLCPblxMe/VNWd3msiYmYhwxglufr8F5Nyj4ZZ7kRZBF4HvDvt3A+BiwovjmEUhtvecxsD1QPZC+ZJ9UB1Tpuksu0VGGvKPRpmuRO2BnA2TtrHhrR1gOk4uYENY9xyovYEM+pmFL2dwxWHcyofNR/BWFHu0TDLnbAZQAvwQWAGqesAR4HfL6ZQhlEILjjlgqK3kWuo56j5CMaKco+GWe6ErQHcD9wvIotVdfMYymQYk5bxFm6h3KNhljtR1gA+JCKvAH3AT4FFwGdV9dtFlcwwJiHjMdxCWNIWY3ITJSn8Vap6BMcctAs4E/h8MYUyjMlKfVM9jS2NKbuDo8bqMYxCEykYXOL/a4H/p6o9Ij7ZJgzDiESpfP4NI50oCmCDiLyGYwL6lIicjJMe0jAMw5jAZDUBqeqfAEuAi1V1EDgOXJ9PoyLyDyLymohsFZEfiUjx/fUMwzCMFAIVgIh8wXPYpqpxAFU9BuSbDewRnDzDi4DtpEYaNQzDMMaAsBnALZ7P6R301fk0qqo/U9WhxOHTwOn51GcY+bK/dz+b92ymfVc7m/dsZn/v/lKLZBhFJ0wBSMBnv+N8+B3goUAhRG4VkedE5Lmjg0cL2KxhOLgB0dwdsW5ANFMCxmQnTAFowGe/4wxE5FERednn53pPmTuAIeA7gUKo3q2qF6vqxdOqpmVr1jByJiwgmmFMZsK8gM4XkSM4o/26xGcSx1ljAanqlWHXRWQVzt6CNlXNqlAMo1hYQDSjXAkLBVG0vekicjXwBWCpqh4vVjuGEQULiGaUK1F2AheDrwPTgEdE5EURiR5P1zAKTPPMZiok9U/BAqIZ5UCUjWAFR1XPLEW7huGHBUQzypWSKADDGG8UIiDaqqWrAFj7+Nr8BTKMMaBUJiDDMAyjxJgCMAzDKFNMARiGYZQppgAMwzDKFFMAhmEYZYopAMMwjDLFFIBhGEaZYgrAMAyjTDEFYBiGUaaYAjAMwyhTTAEYhmGUKaYADMMwyhRTAIZhGGWKKQDDMIwyxRSAYRhGmWL5AIxJS/vO9lKLYBjjmpIoABH5K+B6YBg4AKxS1b2lkMWYnFhSFsPITqlMQP+gqotU9QLgQeAvSiSHYRhG2VISBaCqRzyHUwEthRyGYRjlTMnWAETkK8BvAz3A8pBytwK3AjTWNI6NcIZhGGVA0WYAIvKoiLzs83M9gKreoapzge8Anw6qR1XvVtWLVfXiaVXTiiWuYRhG2VG0GYCqXhmx6HeAnwBfLpYshmEYRiYlWQMQkQWew+uB10ohh2EYRjlTqjWAvxWRFhw30DeA20okh2EYRtlSEgWgqitL0a5hGIYxgoWCMAzDKFNMARiGYZQppgAMwzDKFFMAhmEYZYopAMMwjDLFFIBhGEaZYgrAMAyjTDEFYBiGUaZYRjBjwtK7v5d3Xn+H4aFhACQmNJ7VSH1TfUqZQ12HiPfHidXEmNk8EyDjXPo96fVObZpKX3cf8f44FZUVqCoaVxBAYWvrVhp6Guhp6OH2y25nZddKlhxYMqpn6t7e7dTtRUAqBI2rr8x+PFbzGOvPWU/P9B6mHZ3GlANT2Cyb6Y/3UxOrrj2+yAAACLVJREFUoXlmM031Tb737u/dT9ehrpSyQPKcS7Z68sFPhlzbSa+jsa6R7r7uvOqcTJgCMCYkvft7OfjawZRMEhpXDnYeBKC+qd7pTLd1o8NOoXh/3Lme6LTdc93bulPu8au3d29v8thVDM5Fp/PfsGIDg9WDAHTXdrO2ZS1ATkqgd39vUv4MlKRSSJfZj8dqHuO7F303KdPR6UedZ4o71/vj/Wzr3gaQ0QHu793Ptu5tDOtwsuxrB19DfdJ2hNWTD34y5NqOXx17e0cSDxZL9omEmYCMCcmhrkOBaYQOdR1K/u92/imkndJhTbkn1/REG9s2Jjtal4HYAOua1+VUjytDFLwy+7H+nPUZMiGph8M6TNehrox7uw51JTvNZHshLyWonnzwkyHXdvzqSKcYsk8kTAEYE5J4fzzrtbAyhbjHpaehx/d8d013TvXk2nZY+Z7p/jKl4zXnhJ0bTT35EFRfLu1ELVto2ScSpgCMCUmsJpb1WliZQtzj0tDT4Hu+sT+3DHa5th1WvuGIv0zp1MRqIp0bTT35EFRfLu1ELVto2ScSpgCMCcnM5pkZJo2Ua4n/pcKnUNopqZCUe4LqDaJtYxtVA1Up56rj1azsyi3orStDFLwy+3HDqzdkyJRuxamQiuTirpfmmc1USGrXICEvJaiefPCTIdd2/OpIpxiyTyRsEdiYkLiLn2FeQO7/uXgBBdUb5gW0qGMR4KwF9DT00NjfOCovILftQngBLe9fDs+T4gVUNVDFsaZjWT1g3HOl9AIKkiGXdvzqMC+gVEQ1xxWvEjJ/2nxdc/GaUothGL6sWroKgLWPry2pHH7c9p7bGJo6xOVnXF5qUYwS0L6q/XlVvTj9fElNQCJyu4ioiMwqpRyGYRjlSMkUgIjMBa4CdpdKBsMwjHKmlDOAu4AvkLPXtWEYhlEISqIAROR64C1VfakU7RuGYRhF9AISkUeBU3wu3QH8GY75J0o9twK3AjTW5OZXbRiGYQRTNAWgqlf6nReRVmA+8JKIAJwObBGRS1V1n089dwN3g+MFVCx5DcMwyo0x3wegqh3AbPdYRHYBF6tqQBQswzAMoxjYTmDDMIwypeQ7gVV1XqllMAzDKEdsBmAYhlGmmAIwDMMoU0wBGIZhlCmmAAzDMMoUUwCGYRhliikAwzCMMsUUgGEYRpliCsAwDKNMMQVgGIZRppgCMAzDKFNMARiGYZQppgAMwzDKFFGdOCH2ReTXwBslFmMWMN5DV5uM+TPe5YPxL+N4lw/KR8Z3qerJ6ScnlAIYD4jIc6p6canlCMNkzJ/xLh+MfxnHu3xgMpoJyDAMo0wxBWAYhlGmmALInbtLLUAETMb8Ge/ywfiXcbzLB2Uuo60BGIZhlCk2AzAMwyhTTAEYhmGUKaYA8kBEbhcRFZFZpZYlHRH5KxHZKiIvisjPRGROqWXyIiL/ICKvJWT8kYjMKLVM6YjIh0XkFREZFpFx4yooIleLyDYR2SEif1JqedIRkW+JyAERebnUsvghInNF5DEReTXx/X6m1DKlIyK1IvKMiLyUkPHOYrRjCmCUiMhc4Cpgd6llCeAfVHWRql4APAj8RakFSuMR4DxVXQRsB/60xPL48TJwI/BEqQVxEZEY8L+Ba4BzgI+KyDmllSqDtcDVpRYihCHgdlU9B7gM+INx+A77gStU9XzgAuBqEbms0I2YAhg9dwFfAMblKrqqHvEcTmWcyamqP1PVocTh08DppZTHD1XtVNVtpZYjjUuBHarapaoDwL3A9SWWKQVVfQJ4p9RyBKGqb6vqlsTno0AncFpppUpFHXoTh1WJn4L/DZsCGAUicj3wlqq+VGpZwhCRr4jIHuBjjL8ZgJffAR4qtRAThNOAPZ7jNxlnnddEQkTmARcCvyytJJmISExEXgQOAI+oasFlrCx0hZMFEXkUOMXn0h3An+GYf0pKmIyqer+q3gHcISJ/Cnwa+PJ4ki9R5g6cKfl3xlI2lygyGpMTEakH1gGr02bM4wJVjQMXJNbHfiQi56lqQddVTAEEoKpX+p0XkVZgPvCSiIBjutgiIpeq6r4xFDFQRh++A/yEMVYA2eQTkVXAB4E2LdGGlBze4XjhLWCu5/j0xDkjB0SkCqfz/46q3ldqecJQ1cMi8hjOukpBFYCZgHJEVTtUdbaqzlPVeThT8HePdeefDRFZ4Dm8HnitVLL4ISJX46yhXKeqx0stzwTiWWCBiMwXkWrgFuCBEss0oRBn5PZNoFNV/7nU8vghIie7nnEiUge8nyL8DZsCmLz8rYi8LCJbccxV483V7evANOCRhKvqN0otUDoi8iEReRNYDPxYRB4utUyJhfNPAw/jLF7+QFVfKa1UqYjI94DNQIuIvCkiv1tqmdJ4L/BbwBWJ370XReQDpRYqjVOBxxJ/v8/irAE8WOhGLBSEYRhGmWIzAMMwjDLFFIBhGEaZYgrAMAyjTDEFYBiGUaaYAjAMwyhTTAEYRkRE5IZE9NezSy2LYRQCUwCGEZ2PAr9I/G8YEx5TAIYRgUTcmMuB38XZfYuIVIjI/0nkNXhERH4iIjclrl0kIo+LyPMi8rCInFpC8Q3DF1MAhhGN64Gfqup2oFtELsLJFTAPJy7/b+HsGHbjzPwLcJOqXgR8C/hKKYQ2jDAsGJxhROOjwNcSn+9NHFcC/09Vh4F9iYBdAC3AeThhLgBiwNtjK65hZMcUgGFkQUROAq4AWkVEcTp0BX4UdAvwiqouHiMRDWNUmAnIMLJzE/BfqvquRBTYucBOnKxXKxNrAU3AskT5bcDJIpI0CYnIuaUQ3DDCMAVgGNn5KJmj/XU4iWTeBF4Fvg1sAXoSqRpvAv5ORF4CXgSWjJ24hhENiwZqGHkgIvWq2isijcAzwHvHW24IwwjC1gAMIz8eTCTuqAb+yjp/YyJhMwDDMIwyxdYADMMwyhRTAIZhGGWKKQDDMIwyxRSAYRhGmWIKwDAMo0z5/wF2oyLC0uTHawAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "SU7lEsSXtUrR",
        "outputId": "55080ba9-027c-46dc-e05f-066aa5a10651"
      },
      "source": [
        "# Visualising the test set result   \n",
        "x_set, y_set = x_test, y_test  \n",
        "x1, x2 = np.meshgrid(np.arange(start = x_set[:, 0].min() - 1, stop = x_set[:, 0].max() + 1, step  =0.01),  \n",
        "np.arange(start = x_set[:, 1].min() - 1, stop = x_set[:, 1].max() + 1, step = 0.01))  \n",
        "plt.contourf(x1, x2, classifier.predict(np.array([x1.ravel(), x2.ravel()]).T).reshape(x1.shape),  \n",
        "alpha = 0.75, cmap = ListedColormap(('purple','green' )))  \n",
        "plt.xlim(x1.min(), x1.max())  \n",
        "plt.ylim(x2.min(), x2.max())  \n",
        "for i, j in enumerate(np.unique(y_set)):  \n",
        "    plt.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1],  \n",
        "    c = ListedColormap(('purple', 'green'))(i), label = j)  \n",
        "plt.title('Decision Tree Algorithm(Test set)')  \n",
        "plt.xlabel('Age')  \n",
        "plt.ylabel('Estimated Salary')  \n",
        "plt.legend()  \n",
        "plt.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame({'Actual':y_test.flatten(),'Predicted':y_pred.flatten()})\n",
        "df1=df.head(36)\n",
        "df1.plot(kind='bar',figsize=(12,5))\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "gZx7IkGuxAXG",
        "outputId": "e5209bb7-6171-4c40-a992-32fde526fb5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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JKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSqioIR0RbRCyPiBURccFWlvkfEbE0Ih6KiOuKbVOSJEkq1rDeFoiIocDVwDuBlcCiiJiXUlraZZn9gf8LHJtSWhMRf9VfDUuSJElFqOaM8FHAipTSIymlPwNzgTMqlvkn4OqU0hqAlNJzxbYpSZIkFStSStteIGIK0JZSmp6PnwUcnVI6p8syNwEPA8cCQ4GLU0o/7aHWDGAGwLhx446YO3dut/lLnlrbbbx59FCamppqX6sedHR0DHqt7WH9ilo3gOaR62la/3T3iXsfWkytKur0pCH3g/7cTlXW6knH6ud2mG1eVJ1+3+aDvH5F1urX/XwHOnb2Z61G7AkKPLZU1ulLLbd5fXX6UmuAjy2TJ0++N6XUUjm910sjqjQM2B9oBcYDCyPi4JTSn7oulFKaBcwCaGlpSa2trd2KTLvg5m7js9t2pXKZerW3tw96re1h/YpaN4DZhzxK6/KLuk+cuuWTWl21qqjTk4bcD/pzO1VZqyftcy7fYbZ5UXX6fZsP8voVWatf9/Md6NjZn7UasSco8NhSWacvtdzm9dXpS61BPrZ0qubSiKeAfbuMj8+ndbUSmJdS2pBSepTs7PD+VXchSZIkDbBqgvAiYP+IaI6I4cD7gXkVy9xEdjaYiBgLHAA8UmCfkiRJUqF6DcIppY3AOcCtwDLghpTSQxFxSUScni92K7AqIpYCC4BPpZRW9VfTkiRJUl9VdY1wSmk+ML9i2oVdbifgE/kgSZIkNTx/WU6SJEmlZBCWJElSKRmEJUmSVEoGYUmSJJWSQViSJEmlZBCWJElSKRmEJUmSVEoGYUmSJJWSQViSJEmlZBCWJElSKRmEJUmSVEoGYUmSJJWSQViSJEmlZBCWJElSKRmEJUmSVEoGYUmSJJWSQViSJEmlZBCWJElSKRmEJUmSVEoGYUmSJJWSQViSJEmlZBCWJElSKRmEJUmSVEoGYUmSJJWSQViSJEmlZBCWJElSKRmEJUmSVEoGYUmSJJWSQViSJEmlZBCWJElSKRmEJUmSVEoGYUmSJJWSQViSJEmlZBCWJElSKRmEJUmSVEoGYUmSJJWSQViSJEmlZBCWJElSKRmEJUmSVEoGYUmSJJWSQViSJEmlZBCWJElSKVUVhCOiLSKWR8SKiLhgG8udGREpIlqKa1GSJEkqXq9BOCKGAlcDpwATgakRMbGH5UYBHwPuLrpJSZIkqWjVnBE+CliRUnokpfRnYC5wRg/L/QvwBeDVAvuTJEmS+kU1Qfj1wJNdxlfm0zaLiMOBfVNKNxfYmyRJktRvIqW07QUipgBtKaXp+fhZwNEppXPy8SHA7cC0lNJjEdEOnJ9SuqeHWjOAGQDjxo07Yu7cud3mL3lqbbfx5tFDaWpqqm/NKnR0dAx6re1h/YpaN4DmketpWv9094l7H1pMrSrq9KQh94P+3E5V1upJx+rndphtXlSdft/mg7x+Rdbq1/18Bzp29metRuwJCjy2VNbpSy23eX11+lJrgI8tkydPvjeltMVn2IZV8ZhPAft2GR+fT+s0CjgIaI8IgL2AeRFxemUYTinNAmYBtLS0pNbW1m4PNO2C7ieUZ7ftSuUy9Wpvbx/0WtvD+hW1bgCzD3mU1uUXdZ84dcsntbpqVVGnJw25H/TndqqyVk/a51y+w2zzour0+zYf5PUrsla/7uc70LGzP2s1Yk9Q4LGlsk5farnN66vTl1qDfGzpVM2lEYuA/SOiOSKGA+8H5nXOTCmtTSmNTSlNSClNAO4CtgjBkiRJUiPpNQinlDYC5wC3AsuAG1JKD0XEJRFxen83KEmSJPWHai6NIKU0H5hfMe3CrSzb2ve2JEmSpP7lL8tJkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkrJICxJkqRSMghLkiSplAzCkiRJKiWDsCRJkkqpqiAcEW0RsTwiVkTEBT3M/0RELI2IxRHxi4h4Q/GtSpIkScXpNQhHxFDgauAUYCIwNSImVix2P9CSUpoE3Aj8e9GNSpIkSUWq5ozwUcCKlNIjKaU/A3OBM7oukFJakFJ6OR+9CxhfbJuSJElSsSKltO0FIqYAbSml6fn4WcDRKaVztrL8VcAfU0qf72HeDGAGwLhx446YO3dut/lLnlrbbbx59FCampqqXplt6ejoGPRa28P6FbVuAM0j19O0/unuE/c+tJhaVdTpSUPuB/25naqs1ZOO1c/tMNu8qDr9vs0Hef2KrNWv+/kOdOzsz1qN2BMUeGyprNOXWm7z+ur0pdYAH1smT558b0qppXL6sJo72IaI+J9AC/D2nuanlGYBswBaWlpSa2trt/nTLri52/jstl2pXKZe7e3tg15re1i/otYNYPYhj9K6/KLuE6du+aRWV60q6vSkIfeD/txOVdbqSfucy3eYbV5UnX7f5oO8fkXW6tf9fAc6dvZnrUbsCQo8tlTW6Ustt3l9dfpSa5CPLZ2qCcJPAft2GR+fT+smIk4CPgO8PaW0vuoOJEmSpEFQzTXCi4D9I6I5IoYD7wfmdV0gIg4DrgFOTyk9V3ybkiRJUrF6DcIppY3AOcCtwDLghpTSQxFxSUScni/2RaAJ+H5EPBAR87ZSTpIkSWoIVV0jnFKaD8yvmHZhl9snFdyXJEmS1K/8ZTlJkiSVkkFYkiRJpWQQliRJUikZhCVJklRKBmFJkiSVkkFYkiRJpWQQliRJUikZhCVJklRKBmFJkiSVkkFYkiRJpWQQliRJUikZhCVJklRKBmFJkiSVkkFYkiRJpWQQliRJUikZhCVJklRKBmFJkiSVkkFYkiRJpWQQliRJUikZhCVJklRKBmFJkiSVkkFYkiRJpWQQliRJUikZhCVJklRKBmFJkiSVkkFYkiRJpWQQliRJUikZhCVJklRKBmFJkiSVkkFYkiRJpWQQliRJUikZhCVJklRKBmFJkiSVkkFYkiRJpWQQliRJUikZhCVJklRKBmFJkiSVkkFYkiRJpWQQliRJUikZhCVJklRKBmFJkiSVkkFYkiRJpWQQliRJUilVFYQjoi0ilkfEioi4oIf5O0fE9fn8uyNiQtGNSpIkSUXqNQhHxFDgauAUYCIwNSImViz2EWBNSulvgK8AXyi6UUmSJKlI1ZwRPgpYkVJ6JKX0Z2AucEbFMmcA38pv3wicGBFRXJuSJElSsSKltO0FIqYAbSml6fn4WcDRKaVzuizzYL7Mynz8v/NlXqioNQOYkY++CVjeS39jgRd6WaZajVjLnga+lj0NfC17Gvha9jTwtexp4GvZ08DX2p57ekNKac/KicMKaKBqKaVZwKxql4+Ie1JKLUU8diPWsqeBr2VPA1/Lnga+lj0NfC17Gvha9jTwtXbEnqq5NOIpYN8u4+PzaT0uExHDgNHAqnqbkiRJkvpbNUF4EbB/RDRHxHDg/cC8imXmAR/Kb08Bbk+9XXMhSZIkDaJeL41IKW2MiHOAW4GhwDdSSg9FxCXAPSmlecC1wHciYgWwmiwsF6Hqyyi201r2NPC17Gnga9nTwNeyp4GvZU8DX8ueBr7WDtdTrx+WkyRJknZE/rKcJEmSSskgLEmSpFIyCEuSJKmUBvR7hHsTEQeS/Urd6/NJTwHzUkrLBrmn1wN3p5Q6ukxvSyn9tMZaRwEppbQo/5nqNuD3KaX5fezx2ymlf+hLjbzOcWS/JPhgSum2Gu97NLAspfRiRIwELgAOB5YCl6aU1lZZ5zzgRymlJ2vrvsdand9y8nRK6ecR8QHgGGAZMCultKGGWn8N/B3Z1wRuAh4GrkspvdjXPiVJ0uBomA/LRcSngalkP+G8Mp88nizIzE0pXVbQ43w4pfTNKpc9DzibLDgdCnwspfTjfN59KaXDa3jci4BTyF58/Aw4GlgAvBO4NaX0r1XWqfzqugAmA7cDpJROr6Gn36aUjspv/xPZuv4IOBn4SS3bPCIeAg7Jv2VkFvAy+c9t59P/rso6a4GXgP8G5gDfTyk9X20fFbW+R7a9dwH+BDQBP8x7ipTSh7Zx9651zgNOAxYC7wbuz+u9F/jfKaX2evqTpJ5ExF+llJ4b7D66iogxKSV/H0A7npRSQwxkZ9h26mH6cOAPBT7OEzUsuwRoym9PAO4hC8MA99f4uEvIvn5uF+BF4HX59JHA4hrq3Ad8F2gF3p7/+0x+++019nR/l9uLgD3z27sCS2qstaxrjxXzHqilJ7JLdk4m+1q+54Gfkn1P9agae1qc/zsMeBYYmo9Hjdt8SZf77gK057f3q3U/cOjbAPzVYPfQQ09jBruHRhvIflTpMuD3ZF+puYrshMJlwG4FPs4tNSz7OuDfgO8AH6iY99UaH3cv4GvA1cAY4OL8OHEDsHeNtfaoGMYAjwG7A3vUUKetYvtfCywGrgPG1djTZcDY/HYL8AiwAni8lueZ/Pnqs8AbC/hbt5CdPPou2TtzPwPW5s9dh9VYqwm4BHgor/E8cBcwrcY6w4D/lT9HLc6HW4CP0kOe6cO6z6ph2aF5T/8CHFsx77M1Pu4uwP8BPgWMAKaR/W7Ev5Nnoz6u18N13GdSl9s75fvXPOBSYJd6+mika4RfA/bpYfre+byqRcTirQxLgHE1lBqS8sshUkqPkYXOUyLiy2RhqhYbU0qbUkovA/+d8rfUU0qvUNv6tQD3Ap8B1qbsbOQrKaU7Ukp31NjTkIjYPSLGkJ0hfT7v6SVgY421HoyID+e3fxcRLQARcQBQ9SUI2cOn11JKt6WUPkK2T3yV7DKSR2rsaUh+ecQosv/Qo/PpO5P9B6pF52VEO5MdREkpPVFHHSJidERcFhG/j4jVEbEqIpbl03artd5WHuOWGpd/XUT8W0R8J7+EpOu8r9ZQZ6+I+FpEXB0RYyLi4ohYEhE3RMTeNfa0R8UwBvhtvs/uUUOdti63R0fEtfnx4LqIqOV4QP43GpvfbomIR4C7I+LxiHh7jbXui4jPRsQba7lfD3VaImJBRHw3IvaNiJ9FxNqIWBQRh9VYqykiLomIh/Iaz0fEXRExrca2bgDWAK0ppT1SSmPI3rlak8+rpafDtzIcQfZOXbW+SXbc/gHw/oj4QUTsnM97ay09AbPJLvt6kiycvUL2btEvgZk11nqB7JjeOdxDdjneffntal3a5faXyE6QvIcsKF5TY0+nppReyG9/Efj7lNLfkL2D+aUa6uwO7AYsiIjfRsTHI6Kn5/lqfJUsgN0M3Alck1IaTXYZXtXHqNz3yJ5P3gV8DrgCOAuYHBGXbuuOFb5Dtg9eTPb3f3de7xCywF61Ho53XY97766h1DVkJ8ZWAVfkeaVTVe/MdjGbLDM1k233FrL9IcheCFYtItZFxIv5sC4i1gFv7JxeY0+dLgP+hmyfHEnt//cyRb1iKeCVQRvZK85byL4ceRbZq6wVdHmlW2WtZ8l2zjdUDBPIrhetts7twKEV04YB3wY21djT3eSvVsgCduf00VScQa2y3njg+8BV1HCWu6LGYzaGRbcAAAYCSURBVGQHg0fzf/fOpzdRw1ncLusxm+yShrvJwu8jwB1kl0ZUW2erZ1ip8dUe8PG8h8eB84BfAP9JdubmohrqfIzslf5/kp3h+nA+fU9gYR3b/Vbg08BeXabtlU+7rYY6h29lOAJ4psaefkB2UPlbslfXPwB2zudVvX/m/2fPJXtyWpyv0775tB/X2NNr+b7ZddjQub/WUOe+Lre/Dnw+Px58HLipxp6WdLm9ADgyv30A2Q8M1VLrUeD/AU8Av8372aeO/em3ZJddTSULZlPy6ScCv6mx1o/JzvqMBz4B/DOwP/Atsmv9q62zvJ55W1l+E9mxeEEPwys11HmgYvwzwK/JzsDWdAym+7tpT2zrcaqo9cn8/83BXfeNOvaD+7bWQx09LQOG5bfvqphX9buFFT0dTxZY/5j/7WYUuM1rfYf2dxXji/J/h5B9bqfaOls9o7mteVtZfhN/eT7uHDrH/1xDncVdbg8jy1I/JDuJU+t2eiD/N/K/W3QZr/pd1fw+V5Blp3Fdpj1aS40e9oMHyM+819PT5jr13Km/hnwnfCtwZj68lfwt6RrrXAsct5V519VQZzxdwkrFvGNr7GnnrUwf2/UAWMe6nkoNT1BV1twFaK7zvq8jezV8BDW+HZff/4CC12Uf8nBBdmZiCnBUHXXekt/3wAJ6KiQkUFBAyGsVEhJ6ebIyIGy9r7pDQi/bfLACwm1kb6l2fdIbR/bC6Oc19vQgsP9W5j1Z499uSMW0aWRvjz9e73YCPt+X/SC/T+eJjS+TvYNV9Qu9LjVWkr14+SRZgIou82oNLefmf8N3kJ3t/A+ys4yfA75TQ50tjh1kb923Ad+ssaffkF0y9z6ykxt/m09/O7W/CL2TPCMAp5N9TqdzXi3H4Lvyfrqe3BoC/D3ZB+xr6ekPwH5bmVfLfr7F/1PgIrLjeU2XmXY9PpL9qnCP/wdqqHcE2XPWefl2qmc/f4TszPaZdLkks96eUmqwIOzgUIahqJBQVEDIly8kJBgQaqpVSEho0ICwO/AFsndQ1pBdJ7wsn1b1da95rSnAm7Yy729rqPPvwEk9TG+rIyBcQg/XSJK9TXtjrftol/ufThau/ljHfS+qGDo/87EX8O066rUC15N9bmMJMB+YQQ3XvpJ90L2ubdFDrUPI3k27BTgw/7/3p/wYdUwdtX6b75u/6ty/yN7lO6+GOhPybfQc2eecHs5vX0+NJ5PIPqze47unwLk11PkuPbyLDkwHNtTY09e3sp+/EfhVnX/HIWRB+JfU8A59l/t/s2IYl0/fC/hFXT0VtZM6ODhUN1SEhNUVIWH3GuoUEhDy5QsJCSUPCMNqrFNISCg4IEyqCAgH5NNrCgj5fQ4ETqrcH3p6kq6y1ol9rbWNOqcMVk+VtciudTyo4PVrxG1eT09vLrhWn/dPsm+AOors3bNjgfOBd9faT17rKP5yudVEshfwNdcqqs42ap1KlxMKddY6HriwzvU7uqj1S8kg7ODQUAP59ceNUqeReqoICA3RU6PXGqyeyM74LAduIvsswhld5tV6PW4htcjO5hfVU5G1GnH9GrWn3zdSLbIX1neRfajx38g+h/LPZF+1+Zkae6qsdXs9tYqqMwC16tpWRW7zzTXruZODg0P/DNT5wcf+qmNP23etweqJ4r96ss+1GrGnHX39GrGnfli/Pn8tapG1GrGnRl2/zqGhfllOKoOIWLy1WdTw9X5F1bGnge+pyFqN2BMVXz0ZEa3AjRHxBmr/6smiajViT0XWsqeBr7UxpbQJeDkiun0takTU9LWvBdZqxJ6KrFVkT0CD/cSyVBLjyL6/ck3F9CD7sNJA17Gn7btWI/b0bEQcmlJ6ACCl1BERpwHfAA6usaeiajViT0XWsqeBr/XniNglZb8PcETnxIgYTY2/f1BgrUbsqchaRfaUqec0soODQ/0DxX29XyF17Gnge9rR149iv3qykFqN2NOOvn6N2FPB61fY16IWVasRe2rU9escOr8cWZIkSSqVRvqJZUmSJGnAGIQlSZJUSgZhSZIklZJBWJIkSaX0/wH8GZvt67l6AgAAAABJRU5ErkJggg==\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}