Decision_Tree_MNIST_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Decision_Tree_MNIST_Model.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMZ8EtYVVZ8G6rJjaTVEcX+",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aishafarooque/Committee-Based-Classifier/blob/main/Decision_Tree_MNIST_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import output\n",
        "\n",
        "# Standard Libraries\n",
        "import os\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import random as rn\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# PyTorch Libraries\n",
        "import sklearn\n",
        "from sklearn.datasets import fetch_openml\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "output.clear()      # clear output"
      ],
      "metadata": {
        "id": "vQGzNOnWyCAP"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load data\n",
        "batch_size = 32\n",
        "\n",
        "mnist = fetch_openml('mnist_784')\n",
        "\n",
        "mnist.keys()"
      ],
      "metadata": {
        "id": "z1B3FaxqyGz7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c6330dc-b726-4192-8e00-d13d10a02a9f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = mnist['data'] , mnist['target']\n",
        "\n",
        "print(X.shape)\n",
        "print(y.shape)\n",
        "\n",
        "# There are 70,000 images ( 28 × 28 pixels ), and each image has 784 features.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "37EZU6yOAVmZ",
        "outputId": "5abac8be-d12e-4412-ece4-48f93c8a5e91"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(70000, 784)\n",
            "(70000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split (X, y , test_size=0.20 , random_state=42)\n",
        "\n",
        "classifier = DecisionTreeClassifier()\n",
        "classifier.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYKPbXsJBqgT",
        "outputId": "6fad63e0-0a9b-4363-e229-7a6ed76e9850"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "S8sQ8uhegek3"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_dQytfhgks3",
        "outputId": "449e4e04-55a1-4e59-dd05-ee8f8052cd7d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1242    3   19    5    7   14   23    3   18    9]\n",
            " [   1 1519    7   14   13    9    8   10   14    5]\n",
            " [  16   22 1162   38   21   15   25   21   44   16]\n",
            " [   9    8   40 1206    8   60    9   23   32   38]\n",
            " [   8    3   14    8 1143    9   14   13   19   64]\n",
            " [  22   18   11   66   14 1047   26    4   40   25]\n",
            " [  18    4   14    5   25   34 1260    4   28    4]\n",
            " [   3   12   25   19   18    5    1 1364   12   44]\n",
            " [   8   14   51   51   33   38   28    9 1085   40]\n",
            " [   7    4   19   27   75   28    6   43   33 1178]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classification_report = classification_report(y_test, y_pred)\n",
        "print (classification_report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgSVCHMwgpZw",
        "outputId": "f76904f7-c14b-4f64-f41d-4e196f49c2fb"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.92      0.93      1343\n",
            "           1       0.95      0.95      0.95      1600\n",
            "           2       0.85      0.84      0.85      1380\n",
            "           3       0.84      0.84      0.84      1433\n",
            "           4       0.84      0.88      0.86      1295\n",
            "           5       0.83      0.82      0.83      1273\n",
            "           6       0.90      0.90      0.90      1396\n",
            "           7       0.91      0.91      0.91      1503\n",
            "           8       0.82      0.80      0.81      1357\n",
            "           9       0.83      0.83      0.83      1420\n",
            "\n",
            "    accuracy                           0.87     14000\n",
            "   macro avg       0.87      0.87      0.87     14000\n",
            "weighted avg       0.87      0.87      0.87     14000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_predictions(y_test, y_pred)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "tiZwl5qohEQ7",
        "outputId": "36d8eb6e-f60f-4f5b-f01e-a75d3e4e9d96"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vbc3dl3QgoVWFV0+h6a2IpqcaWm3Mm96S0f1O4YeFsVx0vX9qaEYojIVHOUgals91A7pyTe/uBIc6GfjfBh/l9BFB6bCyadR3ZqSSX90ILg+3xsy/7vxqqrfZ8F/QxRNi4h1YrZpOw/BRuezY0PCdeMDQAl5evJMXv95Jq/blJLYra/CcSvRWRNNLDa0hflgYy5k6DFH1Vhg79awiMvYFkp8bgNMh/Lwkmq59/VdCq+Rgto3Yplpdxja1k5fj/wDJqO/MSCW/ulBoPT9PtsZM4y6dQeRkVjfmX5ZE0aZTw44sL1s7pzDPytfz4jjvGs97GnoroumjhlY7LdrWUEMb5p8aWiV6K4xlpdvo0ruEoGAXoOh1ZiH7duqnsvbr8kgGX659v4Mvz2XNsii/bRrxnRmt5Fcfx8OCxzEV6jL19b30SCrSFLH+2Mr8WQks+7jhOY5JF3YgPzeAUX26cu3dGWxaE86uLSGIQEJiBROnV2fAHtOvK8VFFhwVwpplUTz98S5adyznjYdasnurpp85alIGie09G6LroYhmhBpaXXb7DSwgsV0ZLgVZaYG87IcaWiV6K4ztWB/G6sVRvLZsB06HkLwlxOe5rqmvpdRoU1uYP7MZn76WwLQ3Uzjv6hyy0rRQF6/tGvSd1cRIJb/6UPicqLRRYZh6W808XUAm9efpAiBSYlV/GaR7WY6plFYGqaEZhkEprY4pjPrOXPrn9VqrVvit3taqe6Sa/NkAj46d3PW7etXb/k2MXO2tN0+XiYnJsUrjFyT3hGNq2GtiYvLvozg+nvAwnZ+JiYnXmD0/ExOTEw6lxOz5mZiYnHgoaPSPrnnCse++TUxMjjKiW5BzbQlQRGSGiGwXkU0islBEomt8dr+IJIvIDhEZVmP/ee59ySIy1ZP/ovH1/AxQLzvvJGNW2tus1eeJjZrsO9uY0CPlcBhj12XM/VOsBvUsDAjNkUD9MrTUxFVmwOOeOkTPaAseus35zQNeBd6vse874H6llENEngPuB+4Tka7AVUA3oAXwvYhUprJ5DRiCpvP7u4h8pZTaWt+FzZ6fiYmJ1+j1hIdSahWQe9i+5Uqpyrv1r0Ci+/VI4BOlVLlSag+afm8/95aslNqtlKoAPnEfWy+Nr+dnYmLSqPHyCY84Efmjxvs5Sqk5XlzuBuBT9+uWaM6wkjT3PoDUw/b3b8iw6fxMTEy8xgsBo2xfn/AQkWmAA/jQl/MbwnR+JiYmXqEU2A2a661ERMYCI4BBqvoZ3HSg5kPyie591LO/Tsw5PxMTE6/Qhr0WjzZfEJHzgCnARUqpmjm6vgKuEpEgEWkLdAB+A34HOohIWxEJRFsU+aqh65g9PxMTE6/R6wmPmglQRCQNeARtdTcI+E5EAH5VSt2qlNoiIguArWjD4QlKKafbzu3AMsAKzFVKbWno2o3e+dWlgnXR9VlcdN0BXE5h7Q+RvPNUYj1WjiSueQX3vrCH6HgHKPj2ozgWzU3gpgfS6D84D4fdwj97g3j+ntZHpGBSTicoRfpVTlp+ouVPK/7eSd5bduwpiubvBhHUtfquV7HTRc4zFbiKAQs0nxcECg7cX4E9TSEWCDnLSuzttYfO2AJdzPh0K7ZAhdWq+GlpLB+8mEiv0/O5ceo+xAJlJRZm3due/Xs9z+dWVx2cdcFBRk/6h1Ynl3HnRZ3ZuSnMq7qt7TsbPfkfhl+TQ747Mei7z7Xg9x88z5MX17xcK2uc3V3WeBa924zRd6Vz3tUHquzOm5HI7z96ni/QqDqoxGJRvPzlJrIzAnl0XJeq/bc+tIehl2Xx354NzssfglG/B2/QM9SljgQodWZ/Uko9BTxVy/5vgW+9ubZhzk9EWqHF7iSg1dccpdRL3tpZ/lksX82L594XU6r29Ty9kNOH5jN+aBfsFZaqZJne4HIKbz3ZiuTNoYSEOXll8TbWr45k3epI5j7XEpdTuOH+NK6ckMHcZw5rSJZKx1YdM2ZrLzSdHkj2M4eWRTkUBx6pIP7RQAI7WnDmKSQAlB0iRwUQ0teKsisybqug5Bcntc1E2CuEqaO6UFZixRrgYuaCrfyxMooJT6Tw+LiOpO4K4YLRmVw9IZ3np7T3uw5SdgTzxLj2THxmr8e2alLbdwaw8K2mfD47wSeb1WUN08r6zRbW/6Q5z4XvJPB/c5r7aVffOqhk5Nj97EsOITS8OsCuQ/ciwqN8i7s06vfgHcfH421G/gcO4G6lVFdgADDBHaToFbWpYI249gCfvpaAvUIrfn6O98HGuVk2kjdr6etLi62kJgfTpJmddasjcTm1u9r2dWHENTuyIbm74ocQ2NaCrfWR1Vm61kXgyRYCO2qfWaMFsQqWYCGkr/Z/iU0I7Cw4s+oKcBbKSrRjAwIUAQFK05VQVP2owiIc5GR5F2xbVx2kJof4lWq+LuUyf8jNCiR5s9b70soaQpOECh3sGlMHAHHNyul3zkGWLah2+BaL4sape3nnudY+2TTq9+Atx4OGh5H5/PYD+92vC0VkG1pMTr1R157Qsl053fsXMfa+f6got/DWEy35e6NvwxKAhMRy2ncrYcf6Q20MvTKHVV/H+FVW+z4FAhl3lOPKU4QNsRI15tDG6SxUlK52EXlV3V+HxaJ4+avNtGhdxjcfJLBjYzgv3t+Wx+fuoKJMKCmyMunSbj6Xs6460JMLxx5g0GU57NwYxpwnWnotBF5JVVk3hNOtbxEXjcli8H9z+PuvMN56ohVFPmaK1rsObnkwhXeea01IjV7fhddm8OuKGA4e0O+pEL1/Dw2hrfaaz/Z6hFvC8lRgbS2fea3eZrUqIqKd3HlhJ95+siXT3tiDNrL2nuBQJw/O3s3sx1odoll71e37cTqEHxbG+mS3CqeifIOL+CcCafZWECUrnZT+Vv1jUA5F9oMVRFwZgK1l3V+HyyXcPuIUrj39VDr2KKJ1xxIuuSGDh2/oxLVn9Gb55/HcPG2fT0Wsqw705Jv347n+jG7cNrQLuVkBjHuowUiEWgkOdfLgm8nMflwr6zcfNOX6s3tw2/Bu5GbZuPmh1IaN1GVXxzrod+5B8nJsJG+pFrePbVrBWcNz+Op934bodaHn78ETKoOcPdkaM4Y7PxEJB/4PuEspVXD4576ot2VnBPLzkmhA2LEhDJcLomK9n0OxBigemr2bHxfG8vPS6h7ekMuy6T8on+kT24KfXXdrUyHoVAvWaPdQ9wwrFTuq5wpznrET0EqIutqz3kpxYQCbfo2k73/yaNe5hB0btR/XqsVN6Nq70Pvy1VEHepOXbcPl0mQgl3wUR6de3qutWQNcPPRmMj9+2YSfl8YeYXfpx/F06umLXf3roGufAgYMOsi8leuY+uJOeiYV8OaSDTRvXcbcFeuZt3IdQSEu3lmxzu9r6fV78IbjYdhrqPMTERua4/tQKfWFXnZ/WRpFz9O1H3rLtmXYAhX5ud4OdRSTZqSwLzmYL96unpPp8598LhufyaM3tqe8zP/qCRlgxb7LhatMoRyKsnUubG01uwffsOMqUsROrn+OJirWTliE1pgDg1ycemYBqbtCCI1w0rJtKQCnnpnPvl0hXpau9jowgkopSIDTz8sjZYcPZZ2ewr7kEL54u1kNu9XzfqcPO+ibXQPqYN7M1lx7Zh/GntObZ+/qwMY1kVzRpx+jkvoy9pzejD2nN+WlFm4c1Nvva+nze/CcytXeY73nZ+Rqr6AtWW9TSj3vq51aVbA+bcLkWXuZ/f1W7HZhxl1t8LaH1u20YgZfmsuebSG8tkSbhpw3vSXjH0vFFuji6Q93ArB9fRivPHDo5HRlqIt9L6SOKCX6ZhuWSCF3VgXOg5A5uZzADhaavRKENVKIvCaA/deVg0DI6VZCz7TiyFTkv+vA1kb451ptuB95eQBamNKhxDS1c8+MXVisChFY/W0sv/0Qw8sPtGXa6ztRLqEo38oL97XTpQ5sgS7GP55KVKyDx99NZvfWUKZd28Fju7V9Zz2SimjfrQSlIDM1iJeneqcK161vEYMvzdHK+q2W/WjejETOuSiXdl1LQEFmWhAvP+DdQoJRdWAURv0evOV4WO01Ur3tTGA18BfVMSEPuONxaiVSYlV/61D9y2IxpiG0/sVMaaVcBrUfM6WVISmt1jqX+63eFtO5qRo49zKPjv3ijDdOSPW2nzD69mNiYvKv0NiHtJ7Q6J/wMDExaVzonMz0X8N0fiYmJl5jOj8TE5MTDi+TmTZaTOdnYmLiNY09hs8TGp/zM2A1Tukg2lIb+87Wv/pOXVPS8EE+8GdvY1ZPLeHhDR/kA66iIkPsGrGKrJzGNDBLoP7RBFLmv9NSChwGJzM9GjQ+52diYtLoMYe9JiYmJxzmnJ+JickJizKdn4mJyYmIueBhYmJywqGUOednYmJyQiI4zdXeo8/FNx5g+DU5iMCSj2JZ+HZTXexecnMWw6/ORSnYsz2YWZNPwl7u2xdssSheXrSZ7MxAHr2pEwmJZUx9OZnIaAc7N4cx8+72OOxH2nbZ7WwcKATEQrfPtYQBaS8IeavAYoOgRGj9mCIgQjt+/zuQs0jAAq2mKKJO1/Y7CmHvY0LpLhCB1o94lnxAzzoIi3Bw15M7ad1Ry+TywgMdSNsTwv0v7CChZRmZ6cE8c1dnn7Mu69UO6hIwGnN3OklD83G5IC8ngFl3tyE30/MEBrZAFzMXbMMW6MJqhdVLYvjgxUSmvLCLjj2KcdiFHRvDeHlaG5wOz+vYKDErb9Frzk9E5qLp82Yppbq798UCnwJtgBTgCqXUQXemqJeA84ESYKxSap37nOuAB91mn1RKvdfQtQ1z3yISLCK/ichGEdkiIo/5a7N1p1KGX5PDxAs6cuuQTvQfXECLNv5nvmjSrIKLb8jm9vM7csugzlitcM7Igz7bG3l9xiG59W64L5Uv5zbnxoG9KCoIYNgVB2o9T6xWOrx2qKOKHKDo9pmi6wJFUGvImKs1utJdcHCZ0PVzRYfXFPuekap4xtTpQtTpiu4LFV0+VQR7kOlK7zq4ddpu/lgdw7jhfZgw8lRSd4Vyxbg0NqyJ4qZhfdmwJoorxvmWdVnPdlApYHTLoG7cNbIzF445wEkdSvl8djPGD+vKhOFd+W1FNKPu3O+VXXuFcN81nbnt/FO47YJu9P1PPp17FfHjoibcNOgUbj2vO0HBLs67sva2UJ/dqaO6MOGCU5gwojt9zs6jc69CJjyRwvRJJ3P7iFP48as4rp7gW6ZsT9A5n9884LzD9k0FViilOgAr3O8BhqNp9XYAxgFvQJWzfAToD/QDHhGRBrPSGtl3LQcGKqV6Ar2A80RkgD8GT+pQzvb1oZSXWXA5hU2/hnPG8DxdCmsNUAQFu7BYFUEhLnIyfAswjWtWTr9z81j2abx7j6JnUgGrl2iZh7//vziShtTuVMRiwXqYmmNkEoi7cxR2isKeqb3OWwkxwxSWQAhqCcGtoHgzOAuhaB00uUQ7zmKjqqfYEHrVQWi4g+6n5bPscy05qMNuobgwgKRBuXz/pbbv+y8TSBqc65N9PdtBXQJGNVPZB4c68T7zWy2iU8DvK7WMyyDs2BhOXHNvRZiMEbPyCqXN+3myNWhKqVXA4Q1hJFDZc3sPuLjG/veVxq9AtIg0B4YB3ymlcpVSB4HvONKhHoGRKa0UUBmmb3NvfiV/S9kezNj79hMR46Ci1MJpAwvYuTHU36KSkxHI5282Zf5vWykvE9b9L5J1qyJ9snXLQ3t559mTCAnTGmJkjIPiAmuVIlx2RqDPqmM5i4SYoVoV2g8IYadUV6etKdizwBIMATGw9xGh5G8I7aINiRu0rWMdNEssIz/XxuRndtKuczE7t4Tz5lPtiG5SUSXcc/CAjegmvtWDUe3gcAGj6+5NZ/ClORQXWrnvyo5e27NYFK98vYUWrcv4en4COzZUPw1jDXAx6JJs3njcexU3o8WsPMGL1d44Efmjxvs5Sqk5DZyT4BZAA8hAk78FTQCt5nAhzb2vrv31YnQae6uIbACy0DyzXwJGqcnBLHitKc98tIunPtzF7i0huHR4Gi48ykHSsHyuG9CVa3p3JzjUycD/et8r6TfQLVqzWX/lrP1vg1gh9vz6j1MOKNkO8Zcrun6isIZUD5XrQ0peIiQAACAASURBVK86AK0HeXLXIhZ/3JzbLzmVslILV4xLO+wo8aE3pWFEO6hNwOi9GS25dkAPfvwylgvHejc8BU10asIF3Rmd1ItOPTXRqUpuf2Ivf/0WwZbfPeyWH2bXKDErT1DuBQ9PNiC7UqPHvTXk+A69ltaJMiRjrqHOTynlVEr1AhKBfiLSvZZjvBIwWvZJE24f3ol7Lu1AUb7Vb21VgFPPKiJjXyD5uQE4HcLPS6Lp2td7IZyufQo10ZpV65n6cjI9kwq49eG9hEU6sVi17y+uWQU5XkycA2R/BfmrhLZPaWnsAWzxioqM6mPsWVrvLzABAptC2Cna/ujBipLtDV9DrzoAyM4IIjsjiB2btB/2T0vjOLlrEXk5gcTEa729mPgK8nN9H5rp2Q4aEjD6YWETzhzu+/xncWEAG9dE0vc/+QCMmphOVKydOU96l8q/Nrt6ill5g17D3jrIdA9ncf/Ncu9PB1rVOC7Rva+u/fVyVNarlVJ5wI94MA5viEo1+vgWFZwxPJ8fF0b7a5KsdBtdepcQFOwCFL3OLGTfTu9/TPNmnMS1Z/Rm7Nmn8uzEk9m4JpLpk05m06+RnDVc60UNvjSbNd97rhCW/zNkzhPav6iw1NDmiT5HW/BwVUB5OpTtg7DuYIuDwGZQlqIdV/ibEOLBgodedQBwMDuQAxlBtGyr9XR6JeWxb1cov/4Qy+CLtUnLwRdnsmaF77Kg+rWD2gWMWrQpq3qdNDSP1F3e1cXholO9z8ondVcw512ZRZ+z83l24sk+rZgaJ2blHUqJR5uPfAVc5359HbCoxv4xojEAyHcPj5cBQ0Ukxr3QMdS9r16MFDCKB+xKqTwRCQGGAM/5a/fht1KIiHHgdAivTkuk2MdQiZrsWB/G6sVRvLZsB06HkLwlhCUfNvHbbiVzn2vF1JeTGTM5lV1bw1i+IL7W41wVFWy/TnDkwaZhQotbFRnvag5u53itIYWdAq0fVIS0h5ihii2XCmKFk6YqxD1H3+o+xZ4HBOWAwJbQ5jFF5vyjWwdvPNGOKTP/xmZzsT81mBfu74hYFA+8uJ1hl2WS9U8QT9/V2Wf7erWDugSMhl2ZTWL7MpRLyEwP5JX7veulxTa1c/fM3VjdolOrFmuiU4t3/kZmehAvfKFd6+elMXz0SoPTU1UYJWblDVqvTrdQl4+Bc9DmBtPQVm2fBRaIyI3AXuAK9+HfooW5JKOFulyvlUflisgTwO/u4x5XSjU4Z2OkgFEPtJUaK1oPc4FS6vH6zomUWNXfMtiQ8hiBJcgznWFvMFNaaRxLKa0wSGxJRP+nKH4t+5Z8V45fhkNObqHazRrn0bFbL37shBQw2gScapR9ExOTfw+D+kxHlWPuCQ8TE5N/F4XgMh9vMzExORE5Djp+pvMzMTHxEh0XPP5NTOdnYmLiPcdB1890fiYmJl5zXPf8ROQV6vHvSqmJhpTICAxamnJV2HW3aVRISos1xoSk/JNkTEgKYsyEunLp3xYEY9TbJNT/55WPoEIH9Ta0R+yOderr+f1Rz2cmJiYnKgo4nnt+hycDFJFQpZQxEbgmJibHFMdDnF+DYwsRSRKRrcB29/ueIvK64SUzMTFpvCgPt0aMJxMrL6IlC8wBUEptBM42slAmJiaNGc+SGjT2RRGPVnuVUqmHPWdozAyviYnJsUEj79V5gifOL1VETgeUiNiAO4FtxhbLxMSk0aJAHeervZXciqaY1BL4By1P1gQjC1UfeiqMVWILcjHri2RNEStAsXpxNPNnNvPJ1uSZe+k/OJ+87ABuGdz1kM8uHZfJuIfTufyUHhQc9D3E0us6UIqM4UVYYoSmH2lZpktX2Cl8uwJHiou4uaEEdjk0xMaR4eLA1cVE3BRE+KhAVLkie3wJqgJwQvDAACJv9iyrjV5Ka0bUbW02x9zzD0nD8lAuIS87gJmTW3ul3AYwaUYK/Qflk5cTwK1DtJTybbuUMPHpfQSHOclMC2L6xLaHaIV4SliEgzuf/JvWHTRlvBendWT7Bk1y4JLr07j5vj1cNWAABXm+abB4xrHv/Br0GkqpbKXUKKVUglIqXik1WimV4+kF3Kns14vIN/4VVX+FsUrs5cKUy9szfkgnxg/pRN9zCunc27csxss/i2Xa6JOP2B/fvILeZxeQmeafsIyvdRD7wqHJLQPaWYh9NoTAXrX/+ApeKicoqYYTCYQmr4bS9IMw4ueHUr7GQcXmhmc/9FRaM6Jua7P5+ZsJjB/SlduGdWHtiihG35VRx9l1891nTXhwTIdD9k2avpe5z7Zk/NBu/LI0mstu8d4uwC3TdvHn6lhuOb8vt1/cm9RdWjxgXLNyep9xkKx0/VOtHcGJsOAhIu1E5GsROSAiWSKySES8yZSo6zBZL4WxQ6mhiGVTWG3K56X8zWsjKMw70qHc8mga7zzVUpcQAa/rQARL5KF3altbKwGta//6S/9nx9pCCGhb/bmIYAnVbCgH4PCsrHoqrRlRt7XZPES5LcTlm93fjrTbsm0Zf63Vgs3XrY7kjPO9r4fQcAfd+x6pjAcw7v5dzJ3R9uj4nBPB+QEfAQuA5kAL4DPgY0+Mi0gicAHwtq8FrElNhbGP12+muMDqs8LY4Vgsite/28Gnm7awflV4lYKXHiQNzSM7w8bubfoqzeldBwCuEkXR/Aoibjyy96Cciqxri8kcXkRQvwACuzc8ZEvZHkz3/sVExDgICnZx2sAC4lvo92SMnnVbk7FT0vngt78YeEku789srovNvX+HkDRU0/E4+4KDxHstW1mtjDfpmb955Yt13PnE3wSFOBkwMIeczCD27DDmSZ5DqAxy9mRrxHji/EKVUvOVUg739gHgqaDBi8AUoE5tLW/U2/RUGDscl0u4bUgnRvXpSqdeJbTuVKqL3aBgF1fdkcH7M1voYs/IOgAofLuc8KsCq3p5NRGr0HR+GAlfhVOx1Yl9V8PDXqMU90D/uq3JvOktGd3vFH5YGMtF13uv3FYbz9/bhhFjsnhl8TZCwp047N47h0plvG8/bs4d/+1NWamVUbfv5cpbUpn/svcymL5isIDRUaFO5ycisW4l9CUiMlVE2ohIaxGZgpZLv15EZASQpZT6s77jvFFv01NhrC6KC6xs/CWc087VR/2qeZtymrWq4I3l23hvzWbim1fw2tJtxMT71vsxug7sW1wUvFpO5sVFFH9aQeF75RR/dmgPxRIhBPWxUv6rZxFPRijugf51Wxs/LIzlTB+H6YeTtiuYaaM7cscFXVi5KJb9e72fm8vOCCI7M4gdm7Te/k/LNGW8hMQyXlu0jndX/EZcQjkvf7GemDjfdJE9wiWebY2Y+pbF/kTr4Fb+B7fU+EwB9zdg+wzgIhE5H62nGCkiHyilRvta2JoKY+VlQq8zC/lbB7HqqFgHDodQXGAlMNhF77OLWPCabyuSh5OyPYQre/Woev/ems3ccX5nn1d7jaqDSuJmV9sqeKscS6gQdnkgzoMuJECwRAiqTFH+m5Pwaz1bYIhqYic/x1altHbnhR0aPskD9K7bSlq0LeOfPZqDThrmvXJbXVTWg4ji6on7WfxB7UJW9XEwO5AD+zVlvPQ9ofRKyiN5azgPXF9dD++u+I07Lz3V0NVe0alXJyKTgJvQfMpfaKJEzYFPgCZofuhapVSFiAQB7wN90B66uFIpleLrtet7tretr0bd59+P20GKyDnAPf44PjBOZS02wc49L+3DYgGLBVZ9HcXa732bR5v66h56JBUSFevgg9//Yv6s5iz7JM7vMlbiUx0oRfbNJbjyFBkXFhFxcyCWSCF/VjmuPEXu5FJsHS00ealuJ+rKVhx8olQLb1cQMiiA4DM9czJ6Ka0ZUbe12ew3sIDEdmW4FGSlBfKyl8ptAFNf2U2PpEIiYxzMX7uJD55vQXCYkwvHaEPon5dGs3yBb233zSfbM2XGDgJsLjJSQ3jhAX1uJh6j02KGiLQEJgJdlVKlIrIAuApNoe0FpdQnIvImcCPwhvvvQaXUySJyFZoa5JU+X98T9Ta32HhXasz1KaXe9/gi1c5vRH3HGabeZtTkg8WA9FNKpwmxwzBTWhmHWIwZ3lkMSGm1pmgR+Y5svwoc1LqVav7AnR4du/fWe+tUb3M7v1+BnkAB8CXwCvAh0Ewp5RCRJOBRpdQwEVnmfr1GRAKADCBe+ShB2eAtWEQeQdPV7Io21zcc+Amt++kRSqmVwEpfCmhiYtII8dzdxIlIzfR4c5RScwCUUukiMhPYB5QCy9GGuXlKqcpgqjS0Byxw/011n+sQkXy0oXG2L/+CJ+OPy9A883ql1PUikgB84MvFTExMjhM8H6Bk19PziwFGAm2BPLQwuvP0KJ4neDK2KFVKuQCHiEQCWUArY4tlYmLSaNEvzm8wsEcpdUApZQe+QFsojXYPawESgXT363Tcvsf9eRTubFO+4Inz+0NEooG30Lqk64A1vl7QxMTk2EeUZ1sD7AMGiEioaGmjBgFbgR/RRpwA1wGL3K+/cr/H/fkPvs73gQfDXqXUbe6Xb4rIUiBSKbXJ1wuamJgcB+iwhqiUWisin6N1qBzAemAOsBj4RESedO97x33KO8B8EUkGctFWhn2mPgGj3vV9ppRa58+FTUxMTJRSjwCPHLZ7N9CvlmPLgMv1unZ9Pb9Z9XymgIF6FaIKAbHqHz5ihGKX27DuJq0REbrbBPjndH2fhKmkYrn3MXCeEHT+fkPsKrv+Tz1IoD5B0IfjKi3T3aZevwW9gpz/TeoLcj73aBbExMTkGEHR6B9d8wRTtNzExMR7jueen4mJiUldHNfDXhMTE5M6OQ6cnyeZnEVERovIw+73J4nIESsxJiYmJxAnSCbn14Ek4Gr3+0LgNcNKZGJi0qjxNMC5sQ+NPRn29ldK9RaR9QBKqYMi4p8KjxfENa/g3hf2EB3vAAXffhTHorkJ3PRAGv0H5+GwW/hnbxDP39Paq1RJtal2tetawsRn9xEYpNzpl1qxY4N36ewnz9pH/8EFmt1BnQE4a0Qe107OoFWHMiZe0JGdm7zP1lGXYteFo9MZcc1+XE7h9//FMnem55nI/FZDc7mwXZ6KirbieMudTbnAScBT2UiGA9UsAMeDcRBRHb4kO8oJmJiBY1oc6uwwZEMZ1jeqM1FLqh3HtCPz3MU1L9faQZzd3Q7iWfSuprB30dhMLrw2C5cLfvshmnee8e/pS4tF8crSv8nZb+Ph67yRq6nd1suLNpOdGcijN3UiIbGMqS8nExntYOfmMGbe3R6H3fMMNnXVw+i70jnv6gPk52jf1bwZifz+Y7RfZa+XE2S11y4iVtydWBGJx8PHmkUkBa2n6AQcdT3gXB8up/DWk61I3hxKSJiTVxZvY/3qSNatjmTucy1xOYUb7k/jygkZzH0m0WO7yz+L5at58dz7YkrVvpumpfPBC83548coThuYz43T0plyeUevyrt8QSxfvRvHvS/tq9qXsj2Yx29uw8RnU72yVZNKxa6n7+xKgM1FULCLHv3zGDAwlwkje+OwW4iK9S6GrbY6AC/U0ERwPN0U6/TqxyutnxbgOjUY11VRWD7Jx/pJAc6bY7QPnQrr2wdRfarj4lSvYByzqx2nbew/7s/zD7lUdTsI09rBN1tY/1MU0XF2kobkcdvwbtgrLEQ18T+L88U3ZZO6M5jQcM8yVdfHyOsz2LcrpMrWDfel8uXc5vzvmybc/uQehl1xgMUfJnhsr656AFj4TgL/N0cfvZGGaOy9Ok/w5JbzMrAQaCoiT6Gls3rai2ucq5Tq5YvjA8jNspG8WesplRZbSU0OpkkzO+tWR+Jyanef7evCiGvmXaOvTbVLKQhzN9KwCCe5md5nwt28NvwIu6nJwaT5kQ24LsWuC67az2dvJVb1HPJzveuQ+62GJoKKOPR8yy8luIZovWXXkDAsv5RUf7aoENeZYajo2gPZLatLcJ0WDMFHNsvcrECSN7s1h4utpCaH0CShghGjs1jwejPsFe46yPEve3Fc8wr6DSpgyUexftkBTUqy37l5LPu0sier6JlUwOolmu3v/y+OpCHeSa/WVQ9HnRNhzk8p9SGaCNEzwH7gYqXUZ0YXrDYSEstp363kCGW1oVfm8MdK/xXM3nw0kZse1FS7bn4onbnP6C+M4wt1KXa1aFNKt74FvPDpBp6bv5EO3f3XHfFbDe2gE5q4BxSxVu09QLYDy08luC6sO6mqZWUxrnMbnmaoagcbwmnZtoxu/Yp48cutTP90Ox17+Jdc9dbH/uHtJ5ujdBjW3fLQXt559iRcbluRMQ6KC6xVN+3sjEC/HFfNegC4aEwWbyzdzKQZewiP9FBb1BeOkzk/T1Z7TwJKgK/RsioUu/d5ggKWi8ifIjKuDvvV6m2qbvW24FAnD87ezezHWh2iq3rV7ftxOoQfFvp/px4xJpvZjyUyut8pzH40kckz9zV80lGgNsWuK25OxWpVRETZmXRlT96Z3o77X9yGP7db3dXQRLQNCHj9IM6bYqCurMc5DmSPHdU3pPbP3QSHOnnwzWRmP661A2sAREQ7uOviLrz9dCIPvL4LX+ugcq42+S//Myj3G3iQvBxbVS9Nbw6vh28+aMr1Z/fgtuHdyM2ycfNDvk+xeMRx0PPzZM5vMdVCRsFoiQd3AN08OPdMd7bWpsB3IrJdKbWq5gHurK5zACItsbVWlzVA8dDs3fy4MJafl8ZU7R9yWTb9B+Uz9eqOVOss+c6Qy3J442Ft3nDVN9HcNWOv3zb1oDbFrstvTiU7M4hfvosDhL//ikC5hMgYOwUHfVuPqqmGBlSpoU0c0ZmDBzwcTsZYIceh9f5yHBCt3V9lZzkBT7slIPNdWH4vxWEV1Bmao7H8rwTXGaEQUPf3aA1w8dCbyfz4ZRN+Xqrd7LL329xtQvh7YzgulxAV6yA/1/vhb9fTihkwtIDTBm0lMEgRGuFkyit7mX6H95KQXfsUMmDQQU47Jw9bkCI03MmtD+8lLNKJxapwOYW4ZhXkZHr/XdVWD3nZ1f/v0o/jeWzuTq/teoMYo7ZwVPEkpdUpNd+7s73cVsfhh5+b7v6bJSIL0TI1rKr/rCOsMGlGCvuSg/ni7eqJ4T7/yeey8ZlMubwj5WX66D3kZNrokVTEpjUR9DqjkH/2eC8taAS1KXbt2xVKxr4QevTLY9PaaFq2KSHA5qLgoO9zXnqoobmSQrF8V6wteHxXjOt0zbnZ51cvRlmnZ+MaEFLl+AAsPxbjvDHmCHvVKCZNT2FfcghfvN2sau8vy2PomVTIpjWRtGxbhs3mIj/Xt9j9d59pzrvPaAsGPZKKuOzWLJ8cH8C8GScxb4Y2QDqlfwGX3ryf6ZNO5oFXd3LW8Fz+900TBl+azZrv6/ufa6P2eohtWkFuluZITx92kJQd9fegTXx4wkMptU5E+jd0nIiEARalVKH79VDgcW+v1+20YgZfmsuebSG8tmQroAlKj38sFVugi6c/1O5w29eH8coDnjfU2lS7XpzSmvGPpWINgIpy4cX7vG/4U19LoUdSkWb3jy3Mn9mMwjwrtz2ZTlSsgyfe382uLSFMG9XeK7u1KXaVlVq566m/ef2rP3HYheendsKbHrDfamguF7Y7MyDfie3qNJxjonBeFUnAE9lYlxShEtyhLg2R4UAOOFE96r7ZdOtbxOBLc7R28O1mQAvnWL4gjskz9vDm8s047MLMu9uhxyjAKOY+14qpLyczZnIqu7aGsXyBd/KVddXDORfl0q5rCSjITAviZS9+Cz7RyIe0ntCgepuITK7x1gL0BpoopYY1cF47tFVi0JzsR0qpp+o7J9ISqwYE1GvWJ8yUVuAsMiil1TLPw4u84VhKaWUJNiallXLq375+tS+lwJXj190huEUr1eaWyQ0fCOx4dHKd6m3/Np70/Gr+Gh1oc4D/19BJSqndaMJHJiYmxxvHQc+vXufnDm6OUErdc5TKY2JicixwPDs/EQlwa2OecTQLZGJi0rgRjv/V3t/Q5vc2iMhXaJqaVRNHSqkvDC6biYlJY+QYCGD2BE9iRILRtDEHAiOAC91/TUxMTlR0CnIWkWgR+VxEtovINhFJEpFYEflORHa6/8a4jxUReVlEkkVkU30ia55Qn/Nr6l7p3Qz85f67xf13sz8XNTExOcbR7wmPl4ClSqnOaAuk24CpwAqlVAdghfs9wHCgg3sbB7zhz79Q37DXCoRTe9CUMZ1eZVBYisv/7By1YtFfac5ZUKC7TQCxGZOFLGhEhiF2z9uQZYjdJd30T/NkREgKGBOW03C2Cs/QY9grIlHA2cBYAKVUBVAhIiOBc9yHvQesBO4DRgLvu4XKf3X3GpsrpXyKi6rP+e1XSnkdlGxiYnIC4LnzixORP2q8n+N+pBW0R2UPAO+KSE/gT+BOIKGGQ8sAKh/tagnUfGg5zb1Pd+fXeMPkTUxM/j2UV6u92fUEOQegLareoZRaKyIvUT3E1S6llBIxZnmlvjm/QUZc0MTE5DhAnzm/NCBNKbXW/f5zNGeYKSLNAdx/K+dA0oGaaboT3ft8ok7np5TKreszExOTExs98vkppTKAVBHp5N41CNiKljrvOve+64BF7tdfAWPcq74DgHxf5/vAlK40MTHxBf0GoncAH7p1gXYD16N1yhaIyI3AXuAK97HfAucDyWg5Rq/358Km8zMxMfEOHROVKqU2ALXNCR4x7eZe5Z2gz5WPAednlMpabeih2uW3IpoHvLd2K6VFVlwucDqEO4Z7J7JUiVFKYLZAFzMXbMMW6MJqhdVLYvjgxersL+Mf2cvQyw9wSfcj27yy28HlYvXICM5apKXl3z4zmKyVNiw2CG3l5JQnS7FFar++gh0WtjwWiqNIwAKnf1qINQjyt1jZNC0UVxnEn+2gy/2llUmlG0SPdlBX3bbrWsIdT6UQGOTC6RRefbA1f2+sO7X/0SirtwjHxxMehjo/EYkG3ga6o90rblBKrfHGhlEqa7Whh2qX34poHjLl8vYU+Ji0sxKjlMDsFcJ913SmrMSKNcDFrM+28cfKaLZvCKfDKUWER9WjL2G1ahvV30GTJAcd7yrDEgDbZwWz660gOt9dhssBm6aG0eOZYiI7u6jIEyzuKtnyeAjdHyshuoeTP24NI/unAOLP8kzXQo92UFfd3nh/Kh++1II/VkZz2rl53HR/GlOu6uzzdfRUmvOG48H56ZMCuW5qi972CqNU1g5HL9UuvxXRjiLGKYEJZSVaHQQEKAICFAqtl3LT/an16uqKxcLhXbT4MxxVTi26p5OyTK3ZZv8SQERHJ5GdtbiLwGiFWKHsgOAoFmJ6OhGBlhdVkLnCszaiVzuos24VVY4qLMJJTpbvbVdPpTmvOUE0PHyiruhtPWy/+WgiT3+YzM0PpSMWmDTS/15fpWpXaLj+0fp+K6IdjhKe/ng3KFg8vwlLPmzit8maSmDd+hZx0ZgsBv83h7//CuOtJ1pR5IUgPLiHY19voUXrMr6en8CODeGMHJvBr9/HkHvA995v2heBNB+uNaPiFCsI/H5zGBUHhebD7bS7sZzyTAvBCdXfY3AzF2VZnt3njWgHNev2zcdP4qn3/+bmaamIBSb/t4vPdo1ssw3SyB2bJxjZ86sZvb1eRN52p7M/hEPU26hbva0mequs6anadTi6K6IBky8+mduHdWTaqLZcNDab7v39k2s0QgnM5RImXNCd0Um96NSziO79Cjj7/FwWvee5QPfhJM8OwhKgaDFC02hWTji4zkrP6SUMmF9E5gob2b/6fj83oh0cXrcjRmcx+4lWXJvUi9mPn8Sk6SmNpqwec6JIV/pBZfT2G0qpU9HSYU09/CCl1BylVF+lVF8bngkGDbksh5++1SbgV30TTcde/qVor1Ttem/tVu5/Yy89zyxiyiv6KLfVVER7b83mKkW0mHjvRNZrkpOhDZXyc2z8vDSKzqeWNHBG3dSlBOZyCUoJSz+Op1NP3+u3uDCAjWsi6TmgkOZtynl35UbeW72BoBAXc3/c6LGdtIWBHPifjZ7PlVSNioMTXMT2cRIYo7CGQPxZdgq2WglKcFUNjQHKMiwEN224d6R3O6itbgdfmsPPSzTRotWLY+jY07cbl5Ft1iPMYW+91Ba9fYTz8wW9Vdb0VO06HD0U0WoSFOLEYtHmkYJCnPT5TyEfPu9rb8oYJbCoWDsOu1BcGEBgkIveZ+Wz4M3mXNPv1KpjFm7+gxvO9Uzl4MDqAHbPDaL/e0VYaxQl/gwHe+YG4SwFsUHuHwG0GVNOcLwiIExxcKOV6B5O0r8KpPWohkcV+raD2us2J8tGjwGFbPo1Umu7Kb7pfxjZZj3heE9m6hdKqQwRSRWRTkqpHVRHb3uFUSprRuG3IloDxMQ7eOSdFEDTM/5xYQx/rIz0yZZRSmCxTe3cPXM3VqtCBFYtjuW3HzyTaKwMdSlOsfDDwEg6TChj91tBuOzC7zdpISHRPR10f6QUW5SizXXl/HJlBIjW82v6H21Ft9tDpWyaFoqzHOLPdHi80qsXddXtS/e14dZH92G1KirKLbw0tc1RLZdeNPYhrSc0qN7ml3GRXmihLlXR20qpg3UdHymxqr91qP4FOYZSWhlVVqNSWmExJv/FeesyDbFrREoro+rWiJRWa9UKClSuX19aaHwr1flSz9Tb1s8+ttXbfKae6G0TE5NjmeOg59fon/AwMTFpXJhPeJiYmJywiBEZ148ypvMzMTHxjmMgjMUTTOdnYmLiNeaw18TE5MTEdH4GYESoh6e5jLw1azUg1MWg0BHlNCjcx26MXSNCUgCm7PpLd5vTT+7R8EE+YAnV/9E1KdXnoS6z52diYnJiYjo/ExOTEw7v1NsaLabzMzEx8YrjJc7P6GSmJiYmxyNKebZ5gIhY3WnvvnG/bysia0UkWUQ+dYsbISJB7vfJ7s/b+PMvmM7PxMTEa3TO53cnh2Z5fw54kx6EFQAAIABJREFUQSl1MnAQuNG9/0bgoHv/C+7jfOaYGvbaglzM+iIZW6DCGqBYvTia+TObNXxiLUyeta8qIeQtgzQNhZseTGfAkALsFcL+vUHMmtyKYi8yGBslCDRpRgr9B+WTlxPArUO6AdC2SwkTn95HcJiTzLQgpk9sS0mRd6vPR0scSi/BpUPK/vw++g8u1Mo+sFPDJ7iZe14Hbli6E4DVzyeQ/H0EYoHQJg6GT08jIkHL/rLv1zB+eLI5TocQEuPgmo/3VNlwOeH9i08mPMHOZW/XnkPPiPZVicWiePnLTWRnBPLouOpM0Lc+tIehl2Xx3579vbbpFToGOYtIInAB8BQwWUQEGAhc4z7kPeBR4A1gpPs1aCnyXhURUT5mZzGs5ycinURkQ42tQETu8semvVyYcnl7xg/pxPghneh7TiGde/uWaHP5glimjTpU7WrdqgjGDezM+CGdSd8dxFW3Z9Vxdu1UitbcMvgU7rq4KxeOyeKkDqWAJgg04fzuTDi/u1eOD+C7z5rw4JgOh+ybNH0vc59tyfih3fhlaTSX3ZLhlU3QxJamjT75kH2V4lC3DevC+7Oac+O0dK/t1saUy9tz25BOujg+gOWfxjJtVFu/bPS7+QDXf5vM2G+SaX9uIb+80hSAsgIL3z3Sgv/O2cuNS3cy8pVDM4X/OS+OJu3rzw9oRPuqZOTY/exLPjTHYofuDQhD6Yy4PNs84EVgClB5dBMgTylV+c+kAS3dr1sCqQDuz/Pdx/uEYc5PKbVDKdVLKdUL6IMmMrzQP6s1hHFsCqtN+SwItHlt+BFCQ+tWReJyanF229aFEtfcu2zLRgkCbf7tSFGklm3L+Gutlt9u3epIzjg/z3u7R0kcygg2rw2n0A/5T4CgiOpfp73UUhUOuu2raDoOLSCyhfb9h8VVxzIW7g9g148R9Lgit+Hy6dy+AOKaldPvnIMsW1CdwNZiUdw4dS/vPHd0k5l66PziKmUq3Nu4KhsiI4AspdSfR63gNThaw95BwC6llN95ti0WxavL/qZFmwq+nteEHev91+utjWFX5fK/r3wPtNVbEOhw9v4dQtLQfNYsj+bsCw4S31yf3G9GiEMZIbikF6tmJrBlYTRBES6u+nA3ALl7gnA5hI+vaUtFkZU+Y7Pp/l/t5rLiyRacc99+Kor9C3D3tX3d8mAK7zzXmpAaUpUXXpvBrytiOOiHMJRXKDxezACy68nndwZwkYicDwQDkWiKj9EiEuDu3SUClcOPdKAVkCYiAUAUkOPbP3H0FjyuAj6u7QNvBYxcLuG2IZ0Y1acrnXqV0LpTqd5l5eqJGTgdwg9feJZ9+HCMEAQ6nOfvbcOIMVm8sngbIeFOHHZ9ngzRWxwK9Bdc0pOz78lk/M876Doyj3XzNafscgoZm0O49O0ULp+3h19ebUrunkCSf4ggtImDZqeU+XVNX9tXv3MPkpdjI3lLtch5bNMKzhqew1fv+6ax7Ct6LHgope5XSiUqpdqg+YgflFKjgB+By9yHXQcscr/+yv0e9+c/+DrfB0eh5+depr4IuL+2z5VSc4A5oGVy9tRucYGVjb+Ec9q5hez1UmOiPoZckUO/wQVMveJktIgm76hLEKiSpR/H89jcnX6XM21XMNNGa72ylm3L6Dcw32+boIlDvfFwIqCJQ901w39RnNoElzavDW/grKNL15F5fH5DG868K4uIZnZCoh0EhioCQ5206ldM1rZgMreEkLwikt0r/7+98w6Pqkz78P3MpDcg1NACSJNFQESkiSioILq7Luta1rKKol4oihV7L6tgwV5QbKxi21WQoq4s6KcogigQlQAh9JCeEEIyM8/3xzmBAClTzlFC3vu65srkzJnfeWfmnOe89fkl498j7Cn1Mue69iEdJ5Lzq9cxxQwaWcCxJywnOjZAQpKf5+f9QGWFh1c+XwFAbHyAGZ8vZ/zI/iFph4y78/xuBt4WkfuBFcAMe/sM4A0RyQTysQJm2PwWzd4xwHJVjTgveZNUHz6fsKvYS0xcgP7DS5n9TCsHimgxYEQxZ12Zw43jurGnPJxKsTuGQDXRpHklRXnRiCjnTtrG3DdbRqwJzptDOWu45Cz5G2JI7Wx1F6z9NIVUexCj26hiPr27LQEf+CuFbT8kMODiXHqeVswJN1qncfY3iXz7cgtOf2wzaz4KzjQ80vNr5tR0Zk61+vWOOq6IceO37jfaC/DByqWuBz43Jjmr6iJgkf18PTCwhn3KgbOcOuZvEfzOpZYmb6iktq7khiez8XjA44HFHzdh6WfhmfdMeSaLPoNLLaOhZat5Y2obzrlqB9GxykNvZwLw8/JEpk/pELSmW4ZAU55aT5/BJaQ08/HG0h9587G2xCX6OePCnQB8Nb8pC2eH3o/2W5hDOWm4tF/Zn91Y7fdbwxvTWrPgX/V/B/kbYnl2aE+GXbOD9YuSyV8fi3iUlHaVnHKf1bXUvOseOg8v4dWx3RCBPmfn07JHcJ7Se8vnwvl1yKB6WCQzddvAKBHIBrqoar3tshRJ1eNkpBsFcV4TkCgXRkLVnUWT6tbJ6pY5lEs0qKwu8c5151Txze65FPlzI7ogkpu216OHXxPUvks+vqnRGhjtIoJ5OAaD4dDkcFjb26BWeBgMhkMABQ6DZq8JfgaDIXQafuwzwc9gMISOafYaDIZGyeEw2muCn8FgCA1jXekSbkxLEXdW8UnMobnov0Z87mT8EHHnO1C/O1N+3JiWEv1FeGnV6sM/usB5UQemtlmTnBt+9Dv0gp/BYDj0MR4eBoOhMWJqfgaDofFh+vwMBkPj5PBY22uCn8FgCB3T7DUYDI0OY1r++3DmZTmMOTcfVdjwcxzTrutI5Z7Qp7LU5FxWxbgJO5hw5xbOOqoPxSH6RMz84nvKdu1zKrvmL30ZNjqX8ydtosMRu7l2XB/Wrgo9kacbutExAabOziA6JoDXC0vmNePNJ9oz+eH1dOuzCxHYvCGOaTd02eudEqzuo++ssVz2vMqX81N584n29BtSxPgp2YgHyss8TLvxCLZtjAtKszZnvC69yrj6gSxiYgP4/cLTt6fz68rwE6X+efxOxpyXhwjMm5XKhy/Xky9Slco/74CmHqJnWjkVtTiA/54CdLsfaePFe3czJNmDlgbwP1CI5vjBD96zE/GMSQCg8qRt0Nk616S1l6gHa88R6PEo0/+zitwdMdx9aQ9aty9nyvRMUpr6WLsqkanXH4Gv0uUk7abmVzciMhm4FKt79CfgYjshYVg0b1PBny/J5bITe1JR7uG257MY8acCPg0jl93Cd1P5aGZLbnwia7/tLdMq6D+8mB2bw/dDmHLBHygu2Df/bePaBO6b2JNJ960LW9MN3coK4ebzelJe5sUbFWDauxksW9SUF+5P32uDOeG2jfzxwh3Mfr5tSLpT/n7kXt2ps9ewbFETJt6Xxb0TurNpXTxjz9/BuRO38NhNRwSlWeWMl7kqkfhEP0/NWc2KL5sw/pZNvPVkW5YtasqxJxZy6S2buemcnmF9H+k9djPmvDwmje1OZaXw4FvrWPpZE7Zm1Z3QNeqRVHwP7jOQCswqRfrHEvX3JPxvlRKYVYr38hQC/y5DOkUR9VAqWujHd8FOZFQ8Ei0QI0TPCC4h7Z8u3k72ungSbB+PS27exL9fSeN/c5pz1f0bOPVvO5n7lssJYxt+7HPVurIdMAkYoKq9AS8Rpp0GKyFmbFwAj1eJjQ/sTZEeKjU5lwFcfvdmZjzQztEb26Z1CWzZ4Hxutsh1q7nhRSlRUYpCNf9fJSYuHIe8GnRVQNl7wSYm+8jLCf4GU6sz3n6afvJywp903bHbHn5ekcCecg8Bv/DjN0kMHVOPK54IJO8/MT/wVTme0dbv4hkdT+BL+34vQJmiqrBbIdljXRUh0KLNHgaeWMiCd6oCpdJ3cDFL5lk1xc/eb8Hgk12YHH0AEggE9TiUcbvZGwXEi0glkABsjUQsb3sM7z3fije+XcOecmH5/1JYvjjyrMBVDD6lkNzt0azPSAhbQxUeeHUNqjDv7dbMe8eZ2f9u6Xo8ylMfr6Ztejkfv9GaX36wmozXPbKeY08sJHttPC89EHq2YY9Hmf7RKtqmlzPnzdb8sjKJJ27pzL2v/EJFuVBW6mXyuD+EVebqznjP39uRB17/lctu24R44Lq/HFm/QC1k/RzHP27eRnIzHxW7PRx7UjFrV4ZxLuQHkOZ2VEv1QL4VBDxnJuC/tQDfuBwoU7x3NUU8duCsUHwTcsELnvOS8Bxfc3fA5XdsZMbDHYlPtAJ+SjMfu4q9ey0xc7fHOGKXWieKmeRcF6q6RUSmYmVy3g0sVNWFB+5n+3hOAIij7hMtqYmPwacWcdGgXpQWe7n9hQ2c9Jd8/vtBcB4KdREbF+Ccq7dzy3nd6t+5Dm44tzd5O2JpklrBgzPXsGl9PKu+axJx+dzSDQSEiWN7k5js484X1pLevYyNvybw2E1d8HiUK+/eyPDT8/n0vdA8QgIB4arTjyIx2ccdz/9KevcyzrxkO3de0oNfViYx7rKtXHZbNk/e0qV+sWoc6Ix30fk5vHBfB76al8rxY/OZ/EgWt/y9R0iaVWzKjGP2M614aNY6yss8rF8dT6SVFxHZ61Ok3+5BukbjfTwVtvjx3ZCP9IlBEj1EvdMKaelFt/rwTc5Huhx8aQ48yXZvW5XIUccVR1awCBD0sJjk7GaztxnwJ6Az0BZIFJHzD9xPVV9U1QGqOiCauvtWjj6+lO3ZMRTlR+H3CV/Na0qvAbscKW9apz206VDBcwszeO3rVbRMq+CZ+Rk0axmasXTeDuszFOXH8H+fptKjjzM2jW7pVrGrJIqVX6cw4IR9bgOBgPC/OakMG123QXd9uj9+k8KAEwrp0rOMX+zBiMVzm9Orf0lIWjU5440al8dX8ywLyCVzm9G9b2Tfy4K3m3PVmB7cMK4bpUVeNq8PbkBmP1I9aJ5VM9M8PzSzLrPA/N3I8DhEBGkfhaR50WxrzbW0tGqK0jYK6ReDrj34vOt1TAmDRhYwc/EKpkzPpO/gYq64cyOJKX48XisYtWhTQd6O38C/VzW4Rx2ISAcR+UJE1ojIahG5xt6eKiKfisha+28ze7uIyHQRyRSRH0UkIqcmN4eERgEbVHWnqlYCHwBDIhHM2RLNkf3LiI0LAEq/YSVkrw3j5KyBrJ/jObtfHy4a3JuLBvdm57YYJo4+koKdwfchxcb79zZHYuP99B9WRNav4Teh3dZtklpJYrJ18cXEBuh/fBGb18eRll41JqUMGlXIpvWh9SseqHv0sGI2rYsnIdlPu86Wz/LRw4rIXheKbs3OeHk50fQZZAXRfkNL2JoV2fnQpLkVdFq2rWDomCK++DB0Y3HPkDgC863PGZi/G89Qq0zSyot+bxkhab4f3eRD0qLQkgBaYQUKLQygqyqQTgfX/GY+2pELhvbnH8OP5uFJXVn5dQqPTO7Kj9+kcPwY6wY1alwuX38Wnt90SDgQ/AAfcL2q9gIGARNFpBcwBfhcVbsBn9v/g+UE2c1+TACei+QjuNnnlw0MEpEErGbvSGBZJIK/rEhkydwmPLPgF/w+IXN1PPPeCs8ipCbnsgVvt4ikeDRrUckdz/wMWAMziz5uyfdLmjHk5DyuvHMDTVIrueelDNZnJHL7Jb3qUXNfN7VVJddPXY/Xq4jA4rmpfPvfpkydnUFCkh8RWJ+RwNN3dArte2hVyQ2PrsNj6y75JJVv/9uM6bd25rZn16IBobTIy+M3B9/krc0Z78mbO3HF3dl4vUrFHg9PTgmtrAdy50tZJDfz4fcJT9/Wnl3F9Vwiqvgm5kFRgMq/7sB7cTKe85Lw31NA5SdlSGtrqguA58Ik/A8XUnnxTlDwTkhGmnoIrKrAP63IqooEwHteEtIp+JvuK//swJTpmVx43SbWrUlk4WxnbExrxaE+P1XdBmyzn5eISAbQDqvFOMLe7TUsS8ub7e2v20bl34hIUxFJs3VCxm33tnuAs7Ei/ArgUlWt1QMwRVL1OM8oFwriTgXXE+9MrfO3QF1LaeWOM55bKa3UF1o3RjA0pJRW35R/QlEgL6IfrUlCWx3cbXxQ+y748f6NQG61TS+q6osH7icinYDFQG8gW1Wb2tsFKFDVpiIyB3hYVb+0X/scuFlVw6pUue3edhdwl5vHMBgMvzVBNWmryK3PulJEkoD3gWtVtbj6DVVVVcSdpPkuTwM3GAyHHYpTfX6IlQ33feAtVf3A3rxDRNLs19OAHHv7FqD6vKv29rawMMHPYDCETiDIRx3YTdoZQIaqPlbtpY+Ai+znFwH/qbb9QnvUdxBQFG5/HzTAtb0Gg+H3x6F5fkOBC4CfROQHe9utwMPAbBEZD2wE/ma/9glwGpAJlAEXR3JwE/wMBkPoOBD87IGL2gZfRtawvwITIz6wjQl+BoMhNFTBpdH435LGEfwCfldkdU+ts3bCx61pOUmJrugGSp1ZYXMgWunO+lRPQuSTww/Ed2r4K2DqovtXzgeYFQetsQqTw2B5W+MIfgaDwVlM8DMYDI0OBYyHh8FgaHwoqOnzMxgMjQ3FDHgYDIZGiunzMxgMjRIT/H5b2h9Rzq3PZe39v03HCt6Y2qZ+h60g8XiUp+b/St62aO68KLQMw1VMfjSL40YWUZgXxRUnW2naOx9ZxqQHs4lL9LNjcyyPTOpczSejfmpzLrvl6Uzad7Fy7yWl+Ckt9jLxtN5B67brVMaUaav3/p/Wvpw3nu5EUrKPU/+6jSLbLOm1J7qwbEnwqcN+C6e1xBQ/k6duolPPclThses6kPF9+NN5PB5l+r9/JHd7DHdPOJJrH8qkW2/LwW5LVhzTbuoasoNdJM54WllJ5skBvM2g82zrdyj5LEDui34qNkD6a17ielnToornBch/Y990rj1rIf3NKOJ6CMXzA+S96geBqJaC1xe+x0m10h0Wwc/tlFbXAJdhzeJ+SVWfqGv/UFJaeTzKW9+v5prTu5OzpZ7MtUF+xr9M2En3vmUkJPmDCn4SdfC9o/fAEsrLvNzw+Ia9wW/6xxm8dH97flqazCl/y6VNhz28Pq1dLaIHz/NLbVVBaqvK/ZzL7p3Qjey1+5KBXnZ7NruKvcyaXrNuffP8PB7l9S/+j+vOOYaTz9zG7jIvH8zsWOd7oOZ5frWV9/I7s/lwRuu9TmtnXb69Vqe1+ub53fBENqu+TWT+rOZERQeIjVd2FdcfnGqb53fmJVvp1ruUhCQ/d084koQkH2Wl1u972a1ZFOZF8+4LNX+36q9pHqkSlxDYzxnv+XvSyc6M388ZrzAvukZnPA0ESH9V2Xanb2/w27PByo+4/UE/ra717A1+1dmTqWy53keX/0SjPmXdGB+d3o0iqqmQ86SfjE/W8dPOZZGltIpupUNanBXUvvO3P/t9fVldfi/cTGPfGyvwDQT6AqeLSFen9PsNK2Hbxtj6A1+QtEirYODIYubNiswPZNW3B7vCtetczk9LrRrO8iUpDD2tHkewA6jVuWwvyvCx+Sz6KLzErgB9BxWwfVM8Odsiz1HottNaQrKfowbtYr79W/kqPUEFvtpo0WYPA0cUsGD2PrvHqsAHSmxsIAyrxsic8cTjwXuAN1dsZyGmU91xq3hBgORTql3WCrobVJXALqiMdmhivkNZXX5P3Gz2HgksVdUyABH5H/AX4BEnxEf8qZBF/w49xXhtXHHPVl6+P42EJOdHsTb+Gs/gU4r4emFTho8toGVa+KsXqjuXVdF7YCkFudERpXA/YUwOiz7Z131wxnlbGPnHHaxdnczLjx5BaXF4gcoNp7U2HSsoyvNy/eOb6PKH3az9MYHn7mjLnt3hBcDLb89ixj/TiU/avwY3+eFMjh1RSHZmPC89lB6yrlvOeHVRsjBAu2m2+XmU0HqKl6xzfEgcxHQU8ptEZKBoc3gsb3MzpdUq4HgRaW6nsj+N/XNxAZZ7m4gsE5FllQR3V4qKDjDolCIWz3Em+B03qpjC3Cgyf3J+6RPAYzd24vQLc3hqbgbxSX58leG1Og50LqtixB/zIqr1RUUHOO7EXL5cYAW/ue+0Y/zoQVw1bgD5O2O49MbwTNEPLO/pttPaBYP78cK9HZn8SFZYul6v0vWo3cx5vTkTT+lBeZmHs6/Kqf+NNTDwRNsRbfXBfY+PT+nK+UOOYdO6eIaPzQtZu8oZ7/zB/ejRt5T07mUAPHZTF/5+3NFkZ8Yz/HTnlsbtXhVA4iC2q3V+qU8pfD9A+ltRHDE/itiuQqu8TpEfSEE1ENTjUMa14KeqGcA/gYXAfOAH4KDOkVDc26o49sQSMn9KoDDXic5b6HXsLgadUsxrS9dwy3Mb6TuslJue2uiINsDmdXHcdn53rh57JIv+k8q2jcF9zurU5FwG4PEqQ0cXsPjj8JvrA4bls25NMoV5VhdCYV4MgYCgKsx/L43uR4Vuk+im01rutmh2bovmlxVW0/rLOU3oetTusLR6HVNsOaItWs6UJ9bSd3AxN05bu/d1y8GuBUNPDT34VeGWM96BlCxQUk7dd0mX/2I1O2PaCyJC8slC4m6HfK4DGtzjEMbVZKaqOkNVj1HV4UAB8KsTuiP+XOBok/fVh9I4f0AvLjquFw9dmc7KL5N45OrQmzm1UeUIJqKcO2kbc98M1WCmZucyYK8zWu728Ps+TzhtB/+r1uRt1mJfDXzIqFw2rg11FNVdp7WCndHkbo2h/RHWSHe/40vDdvGbOTWdC4Ydwz9G9Ofha7ux8usUHr2+K2npVcFUGTQyn80ROtg55YxXGxpQSj7bv78vupWwZ73iK7CCUNlSpTymzJHjmT6/ehCRVqqaIyIdsfr7BkWqGRvvp//wEp682dm+EqeY8tR6+gwuIaWZjzeW/sibj7UlLtHPGRfuBOCr+U1ZODu0JmptzmXffdGUEWfkseij8Gt9sfF+jh5SwFP37DP6Hn/9err0LEUVdmyN46m7uztSXied1p65vR03P51NVLSyPTuGaZOdOx9E4PpH1pGQ5EMENmQk8vRdnUPSiNQZTysr2XhxAH8hrDutkuYTvHibQM6jfvwFsPlaP7HdA3R42rqEdy9XoloLMe33dalEtRRaXOZh02U+iILoNCGnRVa4X0u1wikRu7kfArg91WUJ0ByoBK5T1c/r2t819zaXPmNNU10iFzUpraBhpbSqeapL5LiR0mr2+QvIWROhe5u3hQ5OPCOofReUzDxkp7q47d52vJv6BoPh90BdC/i/JQ1qhYfBYDgEMCmtDAZDo+UQn8YSDCb4GQyGkFCs0eWGjgl+BoMhNNQkMzUYDI2Uw2HAw9WpLqEiIjuxTIrrowWQ60IRjG7DKmtD0z0UypquqqHOst8PEZlvHzMYclV1dCTHc4tDKvgFi4gsc2PukNFtWGVtaLoNqayNAVeXtxkMBsOhigl+BoOhUdJQg9+LRtc13YZU1oam25DKetjTIPv8DAaDIVIaas3PYDAYIsIEP4PB0ChpcMFPREaLyC8ikikiUxzSfEVEckRklRN6tmYHEflCRNaIyGrbyc4J3TgR+VZEVtq69zihW03fKyIrRGSOg5pZIvKTiPwgIssc0mwqIu+JyM8ikiEigx3Q7GGXsepRLCLXOlTeyfbvtUpE/iUikTtFWbrX2JqrnSpro0FVG8wD8ALrgC5ADLAS6OWA7nCgP7DKwbKmAf3t58lYWaydKKsASfbzaGApMMjBcl8HzALmOKiZBbRw+Fx4DbjUfh4DNHXhXNuONSk4Uq12wAYg3v5/NvAPB3R7Y3nlJGCt1voM6Ork93A4PxpazW8gkKmq61W1Angb+FOkoqq6GHDOTMHS3Kaqy+3nJUAG1kUQqa6qapX5RbT9cGTUSkTaA2OBl53QcwsRaYJ1w5oBoKoVqhqaH2j9jATWqapTZi5RQLyIRGEFKyds1PY6JKqqD6hySDQEQUMLfu2ATdX+34wDAcVtRKQTcDRWLc0JPa+I/ADkAJ+qqiO6wBPATYDTq9YVWCgi34vIBAf0OgM7gVftJvrLIuJ0qupzgH85IaSqW4CpQDawDShS1YUOSAflkGiomYYW/BocIpIEvA9cq6qh26DVgKr6VbUf0B4YaBvER4SInA7kqOr3ERfwYIapan9gDDBRRIZHqBeF1U3xnKoeDewCHOn/BRCRGOCPwLsO6TXDaqF0BtoCiSJyfqS6GqRDoqFmGlrw28L+d7b29rZDEhGJxgp8b6nqB07r2029LwAnFo4PBf4oIllY3QknicibDuhW1XxQ1RzgQ6zui0jYDGyuVuN9DysYOsUYYLmq7nBIbxSwQVV3qmol8AEwxAlhdckhsTHQ0ILfd0A3Eels353PAT76nctUIyIiWH1SGar6mIO6LUWkqf08HjgZ+DlSXVW9RVXbq2onrO/1v6oace1ERBJFJLnqOXAKVnMtkrJuBzaJSJXl3EhgTUQF3Z9zcajJa5MNDBKRBPu8GInVBxwxItLK/lvlkDjLCd3GQIPK56eqPhG5CliANRr3iqqujlRXRP4FjABaiMhm4C5VnRGh7FDgAuAnu38O4FZV/SRC3TTgNRHxYt28ZquqY9NSXKA18KF1zRMFzFLV+Q7oXg28Zd8E1wMXO6BZFaBPBi53Qg9AVZeKyHvAcsAHrMC5JWnvi0iVQ+JEFwZ+DlvM8jaDwdAoaWjNXoPBYHAEE/wMBkOjxAQ/g8HQKDHBz2AwNEpM8DMYDI0SE/waECLit7ONrBKRd+0lTeFqzRSRv9rPXxaRXnXsO0JEQp6Ua2dzOcjlq7btB+xTWtfrNex/t4jcEGoZDY0XE/waFrtVtZ+q9gYqgCuqv2gvmg8ZVb1UVeuaJDwCh1YkGAyHCib4NVyWAF3tWtkSEfkIWGMnPXhURL4TkR9F5HKwVpyIyNN2LsTPgFZVQiKySEQG2M9Hi8hyO1/g53ZShiuAyXat83h7lcn79jEZLnsMAAACc0lEQVS+E5Gh9nubi8hCO7fcy1jpt+pERP5tJzxYfWDSAxF53N7+uYi0tLcdISLz7fcsEZGeTnyZhsZHg1rhYbCwa3hjsBazg7WutbeqbrADSJGqHisiscBXIrIQK6tMD6AX1qqLNcArB+i2BF4ChttaqaqaLyLPA6WqOtXebxbwuKp+aS+rWoCVXuku4EtVvVdExgLjg/g4l9jHiAe+E5H3VTUPSASWqepkEbnT1r4Ka2XEFaq6VkSOA54FTgrjazQ0ckzwa1jEV1sqtwRr7fAQ4FtV3WBvPwXoU9WfBzQBumHlv/uXqvqBrSLy3xr0BwGLq7RUtbYch6OAXvaSNYAUO3vNcOx8cqo6V0QKgvhMk0TkTPt5B7useVhptd6xt78JfGAfYwjwbrVjxwZxDIPhIEzwa1jstlNZ7cUOAruqbwKuVtUFB+x3moPl8GBljy6voSxBIyIjsALpYFUtE5FFQG3p3dU+buGB34HBEA6mz+/wYwFwpZ1OCxHpbi/WXwycbfcJpgEn1vDeb4DhItLZfm+qvb0EKxV/FQuxEgtg71cVjBYD59nbxgDN6ilrE6DADnw9sWqeVXiAqtrreVjN6WJgg4icZR9DRKRvPccwGGrEBL/Dj5ex+vOWi2XI9AJWDf9DYK392uvA1we+UVV3AhOwmpgr2dfs/Bg4s2rAA5gEDLAHVNawb9T5HqzguRqr+ZtdT1nnA1EikgE8jBV8q9iFlah1FVaf3r329r8D4+3yrcYBGwND48RkdTEYDI0SU/MzGAyNEhP8DAZDo8QEP4PB0Cgxwc9gMDRKTPAzGAyNEhP8DAZDo8QEP4PB0Cj5f+5l6OI0iN48AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model has an average accuracy of around 87%. "
      ],
      "metadata": {
        "id": "uPuHlIqFhu1Z"
      }
    }
  ]
}