Cópia_de_algoritmosMineracao.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Cópia de algoritmosMineracao.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/raulc27/python_testes/blob/master/C%C3%B3pia_de_algoritmosMineracao.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwneKkBDeh0t"
      },
      "source": [
        "**Exemplo do algoritmo  não supervisionado k-means**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_BCwTgUekvn"
      },
      "source": [
        "#importa as bibliotecas necessárias\n",
        "from pandas import DataFrame\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQa4FmLdezc5"
      },
      "source": [
        "#cria dados aleatórios\n",
        "dados = {'x': [25,34,22,27,33,33,31,22,35,34,67,54,57,43,50,57,59,52,65,47,49,48,35,33,44,45,38,43,51,46],\n",
        "        'y': [79,51,53,78,59,74,73,57,69,75,51,32,40,47,53,36,35,58,59,50,25,20,14,12,20,5,29,27,8,7]\n",
        "       }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9P9Vu3Kt8lbN"
      },
      "source": [
        "dados"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ez1rXh6cfAEt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "617febee-40d2-46a1-c3fd-9e7ade913a98"
      },
      "source": [
        "#cria o dataframe\n",
        "df = DataFrame(dados,columns=['x','y'])\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>34</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    x   y\n",
              "0  25  79\n",
              "1  34  51\n",
              "2  22  53\n",
              "3  27  78\n",
              "4  33  59"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "0hSW0fIF9JYB",
        "outputId": "faefae42-74c3-4839-e55f-9e2ed1dc7fea"
      },
      "source": [
        "df.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e21ae30f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0u5EUBVfJIm"
      },
      "source": [
        "#adiciona as bibliotecas para construir o algoritmo\n",
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4xbuiWefUgH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f86fa0d5-3fd1-4ab6-a6a4-ee2b8675ac50"
      },
      "source": [
        "kmeans = KMeans(n_clusters=2)    # cria o objeto de para o algoritmo k-means para encontrar 2 clusters\n",
        "kmeans.fit(df)  #aplica o algoritmo\n",
        "centroides = kmeans.cluster_centers_   #encontra as coordenadas dos centroids\n",
        "print(centroides)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[38.75       61.625     ]\n",
            " [47.07142857 22.14285714]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sgnmM89gMOO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "31aa85b5-1821-49d9-db88-fd8373f56d8d"
      },
      "source": [
        "#realiza o plot do gráfico da saída\n",
        "plt.scatter(df['x'], df['y'], c= kmeans.labels_.astype(float), s=50, alpha=0.5)\n",
        "plt.scatter(centroides[:, 0], centroides[:, 1], c='red', s=50)\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"Y\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Y')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a7EG1TTVc+elLog5DCszMuPzWj/DkXz1NsiR5qOY/PDhC3/5+PvVvPh5xhFNDqiTJWZcs5qyIa/ijTbnEjzuQq/+55SwVKVbnXL6UvgP9vPzoKjKZAAMSqQQ3feU65p0zO+rwJI+mXOI/69IlrH3pPepHteXv79zP/AvmqbYvkmVmXPLJ5Zx/9dm0be7AzDhjYROp0tSHHyyT2pSbnfPim5aTLEuxd9c+MpmAIAjY19ZNEDhXffriqMMTmXDKKkqZf+4c5p0zW0m/SEy5Gn9tYzVf/E+f5ZUnXmfdyvW4O4uXz+fy2y4+qluViEixyttcPeOppaXFW1tbT/q4IAhwdw0/F5GiFMVcPZGLusuUiMhENGUT/8jwCO1bOgkCZ8a8BrVdypTn7uzZuY/+A/1Mq59GbePYbs0iMEUT/7uvvs+//N8XGR4MFz2IJ+J89K7LuPDa86KfalgkD7o79/PkXz/D7o3txOJGkHEWLZ/PjV++hrLKsg9/AykqUy7xb123g59972nqmmupaQhrPCNDIzz99y9QXlXGmRdPrIEUIqdrZHiEn/75E/TtH6BxTv2htRU2vLGZJwaHuev3b1OFR44y5RrBX32ilfJp5UdNPJYsSVLdMI2X/+k1zdYpU86mt7bR3XGAuuzcVBD20W+cXc/WtTto39oZcYQy0Uy5xL9z/W6q6sZOeVpeVca+tm5GhibGmpci46V9c/sx11aIxYx9uzXhmhxtyiX+iuqyQ237R0qPZEgkE8ST6topU0t5dTmZdCbnPnco0doKMsqUa+Nffv35PP/jl5kxr/Gods19u7toufEC9ek/QQN9g6x++k3WPPcOw4PDzD1nNpfd0sIZmrVxwlm8fAEvrHiF4aGRo6bTPri2wuwzZ0YYnZysTDrD279ax2u/eIMDe3tomlvPJTdfxOLlC8btXs2Uq/Evu/Y8Fpw/l7YtnXR17Ke78wBtWzqYMb+RSz+Va2kAGW1oYIif/tkTvPL465RWlFA3o5YdH+zmh3/yCJvf2RZ1eDJKdf00PvGbH6OrvZvOHXs5sLeHju17GOgZ4NavfkJrK0wi7s4vf/Acv/zB83jg1M+czoG9fTz6lz/n9X9eM27nmXI1/lRJks/cfzOb39nGe69tIEhnWNKykIXLNEHbiXrvtfXhl+URC7HXNlbTt7+fZ/7h//Gv/scXNDhuHGTSGdq3dpJJZ2iYXX9ay12ed9XZNC9o4p2X36OrfT9Ncxs45/KlVNdPG8eIJd92bWxj7SvvM2N+I7FYWLuvqq2grLKEXz28knMuX0rFtPLTPs+US/wQ9ttftGw+i5bNjzqUSWnty+/nXBO0orqc9m172NfWTf0ZdRFENnVsensrv/zBc/Tv7wczYvEYl9/awsU3LT/lL9X6mdP52F1XjHOkUkib3tpKIhE/lPQPSiQTeBCwc/1ully08LTPk9fEb2ZbgB4gA6TdvcXM6oAVwDxgC3CXu6vbwQQSBH7MtkQzsmseyKnq2NbJI995kqraShrnNAAwMpzmhRWvkCpNsfz68yOOUKISZAKO9elyBw/G57NXiL/Xr3H3ZUdMFPQA8Ky7LwaezW7LBHLWJYvp6eodUz7QO0hldTm1M2oiiGrqaH36TZLJxFFLHiZTCaafUccrT7Qes4eOTH3zz5tLeiQ9ZrxRJp0J10tYND6dK6JoqL0NeCj7/CHg9ghikOM4+/Kl1M6ooWP7HjLpDO5OT1cv3Z37ueY3rlTPqNO0bd1OKmvHNqWVlKUY7Bukt7svgqhkIpi1pJnFyxfQtqXjULf0gd5B2rfu4ZJPLaeqtnJczpPvxO/A02a22szuy5Y1ufvu7PM2oCnXgWZ2n5m1mllrZ6dGHhZSWUUpn3/g05x/9dl0tXfTsX0P06ZXcufv38rSlkVRhzfpVVSX5xxrEmQCAFJlmlCwWMViMW756se56rOX0t87QPu2TjC46SvXccXt47eQVF7n4zezme6+08wagWeArwFPuHvNEa/pcvfa473Pqc7HL6cvk86QyQQkUwnN9zJO3nn5PZ7866dpnt901DXt3LGXJRct4JavfiLC6GSiCIKA9HCaZEnylD97kczH7+47s48dZvYYcDHQbmbN7r7bzJqBjnzGIKcnnojnnA5ATt1Zlyxm05tbWLdqPaXlpcQTMQZ6B6lpquajd18edXgyQcRisbxNJ5+3xG9mFUDM3Xuyzz8O/DfgCeAe4FvZx8fzFYPIycqkM3Rs20MmE9A4e3pePnjxRJyb//UNnHfVWax99QOGB4ZZuGweSz+y6LT68oucqHzW+JuAx7J/oiSAf3T3X5rZ68BPzOxeYCtwVx5jEDlh69/YxNMPvcDAgQEwI5GMc/Udl3HhdeO/jkM8Hmf+eXOZf97ccX1fkRORt8Tv7puAC3KU7wWuy9d5RU7Fro1tPPa/fkH19KpDfeuHh0Z4+qEXKClPcc7lZ0Ycocj40bh7EWDVz39NSWmKssrSQ2WpkiQ1jdW8/E+vEwRBhNGJjC8lfhFg23s7qaob20e6vKqMA3t6GOwbiiAqkfxQ4hcByqtKcy7Sk0lnsJiRLJmS01pJkVLiFyFcx6G7Y/+YofJ7d3dx7pVnamZXmVJUjREBzv/o2Wx6ayub3txKWVUZsZjR3ztA/cw6rvz0+I2YFJkIlPhFgGQqXMdh01tbeffVD0iPZFjSsoAlFy2gpEx962VqUeIXyYon4ixevoDFyxdEHYpIXqmNX0SkyCjxi4gUGSV+EZEio8QvIlJklPhFRIqMEr+ISJE5ZuI3s6fMbF7hQhERkUI4Xo3/7wjXy/1DM9N4dRGRKeKYA7jc/adm9gvgj4BWM/sHIDhi/18UID4RERlnHzZydxjoA0qAKo5I/CIi48V9BNLr8fQWsHIseQ4Wb4g6rCnrmInfzG4E/oJwjdzl7t5/KicwszjQCux090+Z2Xzgx8B0YDXwJXcfPpX3FpHJz4MDeN8PINgNngACfPAXeOnNxEqvjjq8Kel4bfx/CNzp7g+catLPuh9Yd8T2t4HvuPsioAu49zTeW0QmOR94FIJOiM2EeBPEmyHWCINPhn8ByLg7ZuJ396vcfe3pvLmZzQJuBv42u23AtcDD2Zc8BNx+OucQkcnLgy4YeQ9sVLOOJYESfHhVJHFNdfnux/+XwDc4fG9gOtDt7uns9g5gZq4Dzew+M2s1s9bOzs48hykikQh6gRhYjlRkZRDsLXhIxSBvid/MPgV0uPvqUzne3R909xZ3b2lo0E0ekSkpVgvm4JkcO/sgPqvgIRWDfM7HfwVwq5ndBJQC04DvAjVmlsjW+mcBO/MYg4hMYBarxJMfgeGVYRu/WbjDB8ADLKXVz/IhbzV+d/+P7j7L3ecBnwOec/cvAM8Dd2Rfdg/weL5iEJGJz8puhuR5EOyCzE7I7ALvg/IvYPEZUYc3JUWxAtc3gR+b2Z8AbwDfjyAGEZkgzEqg/IsQtEOmDSwFiQWYlUYd2pRVkMTv7i8AL2SfbwL095uIHGJmEJ8R/kjeaXZOEZEio8XWRSRyntmND70I6Q+Acii5DEu1YJaKOrQpSYlfRCLl6Y143/eBOFg1+DAMPIaPrIWKe5T880BNPSISGfcA738EqIBYQ3hjN1YBsVnhpG0j6z7keMcze/HMTtwHChP0FKAav4hEJ+iAoCucn+dIZmCVMNIKqQtyHuqZTnzgEUhvzo78jeGpq7DS6zFTajseXR0RiVAG7Fj7YuAjOfd40If3/Q34IMTOCL8ofASGnsXJhGMD5JjU1CMi0Yk1AqVhAh/NeyF5Ts7DfOQtCA5ArP7waF9Lhl8Cwy/jQW/+Yp4ClPhFJDJmSSi9Mdvk0xcWeiYczBWrxpIX5j4wvT6cxG3MG8YBD4+XY1JTj4hEylIX45aCwX8Op2vAIHkeVvZJLFZ5jIPK4dAkv6M5oJ5Ax6PELyKRMjMsdSGevCBs3rEklqs2f+QxqQvx4dfAg6OndA4OgNVCPOds75KlxC8iE4JZDGzaib04vgBSl8Pwy2HtnxKgF4hj5V8M30uOSYlfRE6JBz3gPWDTjt0kkydmBmW3QvIsfHhl+JdC/EIsdTEWn17QWCYjJX4ROSke9OGDP4ORNwn7YjqevAgru/lDm2jGk1kMkkux5NKCnXOqUOIXkRMWjrT9B0hvgVhT2IvGMzD8Oh4cgIovh7VxmdDUECYiJy6zJRwpG2vOdp0kfIydAen3IbM90vDkxCjxi8gJ8/QOwA4Pmjro4HZmd8FjkpOnxC8iJ85KCPvJH2u/+s9PBkr8InLCLLk0Oy/O8NE7fDhs8kksjiYwOSl5S/xmVmpmr5nZm2a21sz+a7Z8vpmtMrMNZrbCNNm2yKRhsRoovSWcYiHTGU6zEHSGP6WfKXi3Tjk1+azxDwHXuvsFwDLgRjO7FPg28B13XwR0AffmMQaZRDzowjNt+OjapEwosZIrsMrfhtS5ECsPp1eo/HfESlqiDk1OUN66c7q7Ew6lA0hmfxy4FviNbPlDwH8BvpevOGTiC+dVfwzSG4EYWBIvuRYruVojMCcoS8zFEnOjDkNOUV4/VWYWN7M1QAfwDLAR6HY/NLvSDiDnpBpmdp+ZtZpZa2dnZz7DlAh50Iv3/TVkdoRdAuPN4bD9wSfxoeejDk9kSspr4nf3jLsvA2YBFwNnnsSxD7p7i7u3NDQ05C1GiZYPvwFB76h51VNhP/Gh57WcnkgeFOTvaHfvBp4HLgNq7PC6aLOAnYWIQSao9HvhEnujWTIcEZrpKHxMIlNcPnv1NJhZTfZ5GXADsI7wC+CO7MvuAR7PVwwyCVgZkHt5vXC/On2JjLd8ztXTDDxkZnHCL5ifuPuTZvYu8GMz+xPgDeD7eYxBJjhLteAjb4LXHT0aNNgP8enhfDAF4j6AD7WGC3x7GlLnY6lLsVh1wWIQKYR89up5Cxizbpq7byJs7xeBxBJIXQTDq7NNPimgB4hjZfcUrFePB/1434PhlANWEy7uMfhCuNhHxb/VVL8ypWh2TomUWQzK7sQT58DIaxD0QOK8sKYdry9YHD78arjsX3zW4cJ4GQQd+OAvsYovFCwWkXxT4pfImcWx1HmQOi+6IIZXQSxHrd7qYeQd3IcwKyl8XCJ5oNExIpCdeyZXPciA4DgLe4tMPkr8IgDJs8C7xpZ7T3ZQWXnhY8rBPY2PfEAw+Ct8+A086Is6JJmE1NQjAljJR/GRtyHoyt7ctXBgmR+A0jsnxKpSHnThfX8PmfZwGwdL4WWfJ5Y6O9rgZFJRjV8EsPgMrOJfh+38we7wx5JQ/mViE2BNV3fH+38EwT6In5H9mRn2hBr4IR7sizpEmURU4xfJssRsqPxt8P1AAFYzcSaJC3ZDems4n9GRrAyCbnz4Taz0mmhik0lHiV/kCGYWNvVMNMF+ID52yUMIV8XKtBU8JJm8Jkh1RkSOK1YDZMBzLHvoQxCfUfCQZPJS4heZDGIzIDEPfNQU5d4PFsNSF0QSlkxOSvwik4CZYeWfD6evzuwM1y/I7ATvg7IvYbG6qEOUSURt/CJR6umBFStg/XpYvBjuvhuqqnK+1GI1UPk1yGzGMx2YVUByCeHktyInTolfJCovvQQ33QRBAH19UFEBX/86PPUUXHllzkPM4pBYhCUWFThYmUrU1CMShZ6eMOn39IRJH8LHg+W9vcc/XuQ0KPGLRGHFirCmn0sQhPtF8kSJXyQK69cfrumP1tcHGzYUNh4pKkr8IifIPYMH+/DgwOm/2eLFYZt+LhUV+MIF2XN147n67oucBt3cFfkQ7o6PrIHBX4SzdbrjiflY2W3YqQ6cuvvu8EZurvPFwG/aDT1/Fg7Yis+GstuwxKycrxc5WflcbH22mT1vZu+a2Vozuz9bXmdmz5jZ+uxjbb5iEBkPPrIG+v8RiEGsOfzJ7MJ7/+rUJ0erqgp771RVHa75V1TgVRX4ilugInH4XMEevO+v8UzHuP2bpLjls8afBn7P3X9tZlXAajN7BvhN4Fl3/5aZPQA8AHwzj3GInDL3TFjTj00/PCe/Gdh0CNrwoZVY2U2n9uZXXgm7doU3cjdswBcuxG/ugHKHWOUR56qFoB0fegkr/8z4/MOkqOVzsfXdwO7s8x4zWwfMBG4DPpZ92d/+qo8AAAr9SURBVEPACyjxy0QVdIfNO7HmsfusGkbWwqkmfoDKSrj33uy5euDAn4LlOlctpNcCSvxy+gpyc9fM5gEXAquApuyXAkAb0HSMY+4zs1Yza+3s7Mz1EpH8s0TYzp5zcrR0ODPmuDk4+2aum7lpQGv+yvjIe+I3s0rgEeB33f2o7hAedlfI2WXB3R909xZ3b2loaMh3mCK52bTs5Gij2vLdwbshdcn4nSpWDokzwfeO3RnsG9dzSXHLa+I3syRh0v+huz+aLW43C/+WzT7qjpVMWGaGld0ebgRt4ANhk0ywExILsdSF43u+0puAJGR2Z8/VG07IFp+NlVw8rueS4pXPXj0GfB9Y5+5/ccSuJ4B7ss/vAR7PVwwi48HiM7Cq+yF1BWHPnioo+yxW8VuYpcb5XA1Y1e9A6cey56qAstuxyq9oMjYZN5avwSFmdiXwK+Bt4ODY9D8gbOf/CTAH2Arc5T767+ijtbS0eGtra17iFBGZqsxstbu3jC7PZ6+el4Ac68QBcF2+zisiIsenKRtERIqMEr+ISJFR4hcRKTJK/CIiRUaJX0SkyGhaZjkl7g5+ADCwKsJhGyIyGSjxy0kLRjbA4M/D0aWQnS/+FiwxJ9rAROSEqKlHToqnt0Df30JwIMd88buiDk9EToASv5wUH3warAxi1dm54g1iteAxfPDZqMMTkROgxC8nzH0E0hvBasbujNVB+l2tDysyCSjxy0mIgcU5PPXSkTJASjd5RSYB3dwtcp7ejA+/BplOiJ+BlVyKxc/I+VqzOJ68EEbeABu1yHiwB0quLEDEInK6lPiLWDD0Kxj4GVASttsHu/HhVXj554mlluU8xkqvw9Prwx49sWyTT9AFsTqs5OrCBS8ip0yJv0h5Zi8M/BxiTWDJbGkl+CAMPIwnloQrQo1isTqo/G18eCUMrwFiUHoDlroEO7hAuIhMaEr8RcpH3g2fHEr6WVYKwV7IbITYeTmPtVg1VvoJKP1EnqMUkXzQzd2iNcixl0sAfLhgkYhIYSnxFymLzyFn75yD3THjzQWNR0QKJ59r7v7AzDrM7J0jyurM7BkzW599rM3X+eVDJBZCfFZ2AfHsF4BnINgFybPDEbkiMiXls8b/98CNo8oeAJ5198XAs9ltiYBZAqv4MiTODpN/0AbeAamLsfLPqT++yBSWzzV3XzSzeaOKbwM+ln3+EPAC8M18xSDHZ7FKrOKLeLAfvBesWj1zRIpAoXv1NLl7dkpH2oCmAp9fcrBYNVAddRgiUiCR3dz1cFKXY07sYmb3mVmrmbV2dnYWMDKRqcl9AA96NJ+SFLzG325mze6+28yagY5jvdDdHwQeBGhpadFvqsgp8mAfPvAUjKwFHGL1eOmNxFLnRh2aRKTQNf4ngHuyz+8BHi/w+UWKigc9eO9fQXpdOEo7fkY4RqP/IYLhNVGHJxHJZ3fOHwGvAkvNbIeZ3Qt8C7jBzNYD12e3RaYs9xF85H18+Nd4elvBm1l8uDW7aE4TWPbjHquE2HQYfAr3TEHjkYkhn716Pn+MXdfl65wiE4mnt+H9/we85/DdrMQcKP8SFptWmCBG3gLLcS4rDyfaC/ZCvLEwsciEoZG7InngQS/e931wg9hMiM+E2BmQ2YX3/6hwNX9LcswR2uZAvDBxyISixC+SBz7yFvgQxKoOF5qBNUJ6EwS7j33weEpeBH4gR4AHIDYjXDlNio4Sv0g+ZHaBpcaWmwEWrmFQAJZaBol5kNkB3h/e2A06gEGs7NMaoV2kNC2zyDhwz4APgJVglgxvnvrIsQ+wwoyQNiuBit/Ch16H4ZVAPyQvwEquwuIzPvR4mZqU+EVOg3uAD78CQ89D0AeWwFOXQuoiGHomXNjGSg8fEOyDeAPEZxcsRrNSrPQqKL2qYOeUiU2JX+Q0+OAvwqQfa4B4dVjLH/oVZHZC6d0w+JOw5wwJIA2xaqz8i5iplVWio8Qvcoo86A6TfGwmWLZ3jCXD3jvpjVjJNVD1DXxkLQT7w8Xsk2eGzS8iEVLiFzlVme3ho43qEmkGJPH0B8SSS7CSywsemsjx6O9NkVN2nD7w5mCqV8nEpMQvcqri88La/uj1iT0AT2OJsyMJS+TDKPGLnCKLlUPpLRC0h/3yPQ1BDwQ7IPWRgvbcETkZ+ltU5DTESi7FY9PxoRfCnjyxGkjdiKWWaXCUTFhK/CKnyZKLseTiqMMQOWFq6hERKTJK/CIiRUaJX0SkyCjxi4gUGSV+EZEiY4VeA/RUmFknsBWoB/ZEHM5EoWtxNF2Pw3QtjlbM12OuuzeMLpwUif8gM2t195ao45gIdC2OputxmK7F0XQ9xlJTj4hIkVHiFxEpMpMt8T8YdQATiK7F0XQ9DtO1OJquxyiTqo1fRERO32Sr8YuIyGlS4hcRKTITMvGb2Wwze97M3jWztWZ2f7a8zsyeMbP12cfaqGMtBDMrNbPXzOzN7PX4r9ny+Wa2ysw2mNkKM0tFHWuhmFnczN4wsyez28V8LbaY2dtmtsbMWrNlxfpZqTGzh83sPTNbZ2aXFeu1OJ4JmfiBNPB77n42cCnw22Z2NvAA8Ky7LwaezW4XgyHgWne/AFgG3GhmlwLfBr7j7ouALuDeCGMstPuBdUdsF/O1ALjG3Zcd0V+9WD8r3wV+6e5nAhcQ/o4U67U4pgmZ+N19t7v/Ovu8h/A/byZwG/BQ9mUPAbdHE2Fheag3u5nM/jhwLfBwtrxoroeZzQJuBv42u20U6bU4jqL7rJhZNXA18H0Adx92926K8Fp8mAmZ+I9kZvOAC4FVQJO7787uagOaIgqr4LJNG2uADuAZYCPQ7e7p7Et2EH45FoO/BL4BBNnt6RTvtYCwEvC0ma02s/uyZcX4WZkPdAJ/l20G/Fszq6A4r8VxTejEb2aVwCPA77r7gSP3edgPtWj6orp7xt2XAbOAi4EzIw4pEmb2KaDD3VdHHcsEcqW7Lwc+SdgsevWRO4vos5IAlgPfc/cLgT5GNesU0bU4rgmb+M0sSZj0f+juj2aL282sObu/mbD2W1Syf7o+D1wG1JjZweUzZwE7IwuscK4AbjWzLcCPCZt4vktxXgsA3H1n9rEDeIywYlCMn5UdwA53X5Xdfpjwi6AYr8VxTcjEn22z/T6wzt3/4ohdTwD3ZJ/fAzxe6NiiYGYNZlaTfV4G3EB43+N54I7sy4rierj7f3T3We4+D/gc8Jy7f4EivBYAZlZhZlUHnwMfB96hCD8r7t4GbDezpdmi64B3KcJr8WEm5MhdM7sS+BXwNofbcf+AsJ3/J8Acwmma73L3fZEEWUBmdj7hTak44Zf1T9z9v5nZAsJabx3wBvBFdx+KLtLCMrOPAb/v7p8q1muR/Xc/lt1MAP/o7n9qZtMpzs/KMsKb/ilgE/Blsp8ZiuxaHM+ETPwiIpI/E7KpR0RE8keJX0SkyCjxi4gUGSV+EZEio8QvIlJklPhFTlJ29tjNZlaX3a7Nbs+LNjKRE6PEL3KS3H078D3gW9mibwEPuvuWyIISOQnqxy9yCrJTiqwGfgB8BVjm7iPRRiVyYhIf/hIRGc3dR8zsPwC/BD6upC+TiZp6RE7dJ4HdwLlRByJyMpT4RU5Bdk6YGwhXiPv3B2d/FJkMlPhFTlJ29tjvEa4TsQ34M+DPo41K5MQp8YucvK8A29z9mez2/wbOMrOPRhiTyAlTrx4RkSKjGr+ISJFR4hcRKTJK/CIiRUaJX0SkyCjxi4gUGSV+EZEio8QvIlJk/j+TvzX34fatXQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hl1BoTgChPJt"
      },
      "source": [
        "**Exemplo do algoritmo Supervisionado  - KNN**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_X9rCmrBhBKg"
      },
      "source": [
        "#importando as bibliotecas \n",
        "import pylab as pl\n",
        "from sklearn import neighbors, datasets\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eqju5IkYh7tX"
      },
      "source": [
        "#cria a rotina para utilizar o dataset Iris\n",
        "iris = datasets.load_iris()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wo24X2Y2iq52"
      },
      "source": [
        "#Converte o banco de dados iris para o dataframe\n",
        "df_iris = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
        "                     columns= iris['feature_names'] + ['target'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_LLyy_3ieQ3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "0812931e-52c7-405e-9574-a5e87096b28d"
      },
      "source": [
        "print(df_iris.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   sepal length (cm)  sepal width (cm)  ...  petal width (cm)  target\n",
            "0                5.1               3.5  ...               0.2     0.0\n",
            "1                4.9               3.0  ...               0.2     0.0\n",
            "2                4.7               3.2  ...               0.2     0.0\n",
            "3                4.6               3.1  ...               0.2     0.0\n",
            "4                5.0               3.6  ...               0.2     0.0\n",
            "\n",
            "[5 rows x 5 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sSZef5jjBo_"
      },
      "source": [
        "#transforma os dados em array\n",
        "X = df_iris.iloc[:, :-1].values  #dados de entrada\n",
        "y = df_iris.iloc[:, 4].values  # saídas ou target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuvjtzJkjNvP"
      },
      "source": [
        "#realiza a divisão dos dados entre treinamento e teste\n",
        "from sklearn.model_selection import train_test_split   # função que realiza a divisão do dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)# divide 20% para teste"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2R8tINMjcAn"
      },
      "source": [
        "# realiza o processo de normalização dos dados\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()  #objeto que normaliza os dados\n",
        "scaler.fit(X_train)  #realiza a normalização dos dados\n",
        "\n",
        "X_train = scaler.transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVjY_ztIjxTW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "6a8614f3-daf0-442a-a7dc-970afee490c2"
      },
      "source": [
        "#treina o modelo \n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier = KNeighborsClassifier(n_neighbors=5) #utiliza a construção por meio de 5 vizinhos\n",
        "classifier.fit(X_train, y_train) # aplica a classificação"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNF2OPdLj6qn"
      },
      "source": [
        "#realiza a previsão\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGukx0Xfj-gX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "820cc518-ebe9-438b-fae2-3ff6812e63f4"
      },
      "source": [
        "#constroi a matriz de confusão para comparar o modelo criado\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 8  0  0]\n",
            " [ 0  9  2]\n",
            " [ 0  0 11]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00         8\n",
            "         1.0       1.00      0.82      0.90        11\n",
            "         2.0       0.85      1.00      0.92        11\n",
            "\n",
            "    accuracy                           0.93        30\n",
            "   macro avg       0.95      0.94      0.94        30\n",
            "weighted avg       0.94      0.93      0.93        30\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kfPi_AwDk5vq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "8d329ccc-66ee-4381-93b4-0eba42d17240"
      },
      "source": [
        "#realiza o plot da matriz de confusão\n",
        "matriz_confusao = confusion_matrix(y_test, y_pred)\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "fig, ax = plot_confusion_matrix(conf_mat=matriz_confusao)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jDCIS0BaDiARUDPUws+FmtsbMKs3sG7HzZI2Z3WNmm8xseewsWWRm3c1snpmtNLMVZvbl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\nFQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AAmVUrH9lqLF"
      },
      "source": [
        "**Exemplo do algoritmo Supervisionado  - Árvore de Decisão**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZoX_UV1qv8y"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier # importa o classificador árvore de decisão\n",
        "from sklearn import metrics #importa as métricas para avaliação\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "768_ZtTFrVZF"
      },
      "source": [
        "# Cria o objeto de classificação através do \n",
        "clf = DecisionTreeClassifier()\n",
        "\n",
        "# Realiza o treinamento do classificador\n",
        "clf = clf.fit(X_train,y_train)\n",
        "\n",
        "#Realiza a previsão de classificaçao \n",
        "y_pred = clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPNeLhNsxksK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "aa941430-b3b9-4670-a33f-1605caf39383"
      },
      "source": [
        "#Avaliando o modelo \n",
        "\n",
        "#realiza o plot da matriz de confusão\n",
        "matriz_confusao = confusion_matrix(y_test, y_pred)\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "fig, ax = plot_confusion_matrix(conf_mat=matriz_confusao)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zr2soyTsxwQu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "00ab8eed-fa79-40dc-a5f6-c75584617d7c"
      },
      "source": [
        "#visualização da árvore de decisão\n",
        "#impor das bibliotecas necessárias para realizar a visualização da árvore de decisão\n",
        "from sklearn.tree import export_graphviz\n",
        "from sklearn.externals.six import StringIO  \n",
        "from IPython.display import Image  \n",
        "import pydotplus"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bn-PglIUyADm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753
        },
        "outputId": "833ee92c-2be3-4746-e13c-f5629cf4d0f0"
      },
      "source": [
        "#Constrói a árvore de decisão para o DB Iris\n",
        "dot_data = StringIO()\n",
        "export_graphviz(clf, out_file=dot_data,  \n",
        "                filled=True, rounded=True,\n",
        "                special_characters=True,feature_names = iris.feature_names,class_names=['0','1','2'])\n",
        "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
        "graph.write_png('iris.png')\n",
        "Image(graph.create_png())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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JpDRBlW7UaN2GjUgPfS38C0GjDKyvCwEAgJmFZWLSuwZide9NIfaB9XUhloDn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\nLqDpTHUDAOANVQkmahWBibVJJXwWt/JtavL82xojHQAA1TEF4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NTzewQgQLPCuZpTXQhX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\n70MQBHUu/wd70sEFJ0b87wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djoy3TUNzG7m"
      },
      "source": [
        "**Exemplo do algoritmo Supervisionado  - Suppor Vector Machine (SVM)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxtVhyRlzM_G"
      },
      "source": [
        "#biblioteca necessária para a construção do SVM\n",
        "from sklearn.svm import SVC"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpv8dHRuzW6O"
      },
      "source": [
        "#cria o objeto SVM\n",
        "clf = SVC(gamma='auto')  #escolhe o kernel linear"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q45M0Sjjze4o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "24d55aa8-7442-47f9-d85c-cdeef066fb39"
      },
      "source": [
        "#realiza a classificação via SVM\n",
        "clf.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoUUauiHznxY"
      },
      "source": [
        "#Realiza a previsão de classificaçao \n",
        "y_pred = clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vR4iiboNzsUX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "61a91674-fd36-42e3-ec59-9c8795a6cfed"
      },
      "source": [
        "#Avaliando o modelo \n",
        "\n",
        "#realiza o plot da matriz de confusão\n",
        "matriz_confusao = confusion_matrix(y_test, y_pred)\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "fig, ax = plot_confusion_matrix(conf_mat=matriz_confusao)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jDCIS0BaDiARUDPUws+FmtsbMKs3sG7HzZI2Z3WNmm8xseewsWWRm3c1snpmtNLMVZvbl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\nFQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Obj6HiS4FCbU"
      },
      "source": [
        "**Exemplo de Redes Neurais**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32YaKQIFFHw5"
      },
      "source": [
        "#definição da biblioteca\n",
        "from sklearn.neural_network import MLPClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgx35Hq5FjUS"
      },
      "source": [
        "#define a configuração da rede\n",
        "clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 5), random_state=1) #rede com 2 camadas escondidas com 5 neurônios cada "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t1x75i_5FydR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "72a5a436-bd94-4738-e154-c25dfd424232"
      },
      "source": [
        "#realiza o fit do modelo\n",
        "clf.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
              "              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
              "              hidden_layer_sizes=(5, 5), learning_rate='constant',\n",
              "              learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
              "              n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
              "              random_state=1, shuffle=True, solver='lbfgs', tol=0.0001,\n",
              "              validation_fraction=0.1, verbose=False, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWYlLVUfF55p"
      },
      "source": [
        "#realiza a previsão\n",
        "y_pred=clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pw7ofX0-H0Z5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "908cd6ed-34aa-463b-98da-8d3fe73fec9a"
      },
      "source": [
        "#Avaliando o modelo \n",
        "\n",
        "#realiza o plot da matriz de confusão\n",
        "matriz_confusao = confusion_matrix(y_test, y_pred)\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "fig, ax = plot_confusion_matrix(conf_mat=matriz_confusao)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jDCIS0BaDiARUDPUws+FmtsbMKs3sG7HzZI2Z3WNmm8xseewsWWRm3c1snpmtNLMVZvbl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\nFQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}