Aula04_Machine_learning_funcionando.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/LucaSSpiller/CP-Claudio-Matheus-Luca/blob/main/Aula04_Machine_learning_funcionando.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Atenção**:\n",
        "Nessa atividade vamos utilizar o seaborn para criar gráficos, para isso precisamos garantir que a última versão esteja atualizada, por isso usamos o comando abaixo para instalá-la."
      ],
      "metadata": {
        "id": "sx0-fq0FhtRF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install seaborn"
      ],
      "metadata": {
        "id": "FXcPrwG-4KBA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e09a621a-9d8e-422f-c1dc-f168b9af4f0b"
      },
      "execution_count": 1,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.8/dist-packages (0.11.2)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.3.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.22.4)\n",
            "Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.8/dist-packages (from seaborn) (3.5.3)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.10.1)\n",
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            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (23.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
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            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.23->seaborn) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.15.0)\n"
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    {
      "cell_type": "markdown",
      "source": [
        "Também vamos precisar do scikit-learn para criar modelos de ML e subsets de treino e teste, para isso, precisamos do comando abaixo para instala-la também."
      ],
      "metadata": {
        "id": "cGTdiD7MkdId"
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    {
      "cell_type": "code",
      "source": [
        "!pip3 install scikit-learn"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1nN40Meeksr3",
        "outputId": "fff1483c-b984-455f-8a96-c7375717bc9d"
      },
      "execution_count": 2,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (1.2.1)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.10.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (3.1.0)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.22.4)\n"
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    {
      "cell_type": "markdown",
      "source": [
        "# **LAB 02 - Aprendizado Supervisionado - Classificação**"
      ],
      "metadata": {
        "id": "h-iQ2NhI4GRw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Objetivos**\n",
        "\n",
        "* Apresentar e utilizar o algoritmo K-Nearest Neighbours (K-NN);\n",
        "* Apresentar e entender como funcionam as etapas de treino e teste do algoritmo;\n",
        "* Apresentar as métricas de performance do modelo;"
      ],
      "metadata": {
        "id": "6toS-GCOBCic"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Como funciona o K-NN?**\n",
        "\n",
        "O KNN (K-Nearest Neighbors) é um algoritmo de aprendizado de máquina supervisionado mais usado para problemas de classificação. A abordagem básica do algoritmo KNN é encontrar os K pontos de dados mais próximos ao ponto de dados de teste e, em seguida, prever a classe do ponto de dados de teste com base nas classes dos pontos de dados mais próximos.\n",
        "\n",
        "O KNN segue os seguintes passos principais:\n",
        "\n",
        "**1.** Receba um dado não classificado e meça distância do novo dado em relação a cada um dos outros dados que já estão classificados;\n",
        "\n",
        "**2.** Selecione as K menores distâncias;\n",
        "\n",
        "**3.** Verifique a(s) classe(s) dos dados que tiveram as K menores distâncias e \n",
        "contabilize a quantidade de vezes que cada classe que apareceu;\n",
        "\n",
        "**4.** Classifique esse novo dado como pertencente à classe que mais apareceu\n",
        "\n",
        "A distância pode ser medida de diferentes maneiras, como a distância Euclidiana, Manhattan, de Minkowski, entre outras. A escolha da medida de distância depende do problema em questão.\n",
        "\n",
        "O algoritmo KNN é considerado um algoritmo simples e rápido de treinar, mas pode não funcionar bem em conjuntos de dados com muitas características, pois a distância entre os pontos pode se tornar muito grande em altas dimensões. Além disso, o valor de K pode afetar a precisão do modelo, portanto, é importante escolher um valor adequado para K."
      ],
      "metadata": {
        "id": "H9XG0HdkBGK9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Definição do Problema**\n",
        "\n",
        "Desenvolver um sistema de machine learning capaz de classificar a espécie da flor íris com base nas suas características de Pétala e Sépala. Para isso vamos usar o dataset da ultima aula (https://archive.ics.uci.edu/ml/datasets/Iris).\n",
        "\n",
        "Esse dataset contém 150 exemplares de flor íris, separadas em três espécies diferentes: **setosa**, **versicolor** e **virginica**, sendo que foram utilizadas 50 amostras de cada espécie. Os atributos de largura e comprimento da sépala e da pétala de cada flor foram medidos manualmente.\n",
        "\n",
        "<img src=\"https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Machine+Learning+R/iris-machinelearning.png\">"
      ],
      "metadata": {
        "id": "oDZ-G1zv8XHT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1** - Do Ponto de vista de Machine Learning, qual a classificação desse problema?"
      ],
      "metadata": {
        "id": "uidB47Ee_i0-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Resposta: Classificação"
      ],
      "metadata": {
        "id": "UmikgtrNA9LF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2** - Carregue o Dataset e mostre as 10 primeiras linhas do dataset. Em seguida mostre as 10 ultimas linhas do dataset. Não esqueça de colocar o cabeçalho nos dados."
      ],
      "metadata": {
        "id": "PIvNaOB8CpKO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Caminho do arquivo\n",
        "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
        "\n",
        "# Define o nome das colunas\n",
        "header = ['sepal_length', 'sepal_widht', 'petal_lenght', 'petal_width', 'species']\n",
        "\n",
        "# Lê e carrega o arquivo para a memória\n",
        "df = pd.read_csv(url, header=None, names=header)\n",
        "\n",
        "# Retorna um trecho das 10 primeiras linhas do dataframe\n",
        "print(df.head(10))\n",
        "\n",
        "print()\n",
        "\n",
        "# Retorna um trecho com as 10 últimas linhas do dataframe\n",
        "print(df.tail(10))"
      ],
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            "\n"
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              "     sepal_length  sepal_widht  petal_lenght  petal_width         species\n",
              "140           6.7          3.1           5.6          2.4  Iris-virginica\n",
              "141           6.9          3.1           5.1          2.3  Iris-virginica\n",
              "142           5.8          2.7           5.1          1.9  Iris-virginica\n",
              "143           6.8          3.2           5.9          2.3  Iris-virginica\n",
              "144           6.7          3.3           5.7          2.5  Iris-virginica\n",
              "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
              "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
              "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
              "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
              "149           5.9          3.0           5.1          1.8  Iris-virginica"
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3** - Verifique se as informações do Dataset estão condizentes com a definição do problema **\"Esse dataset contém 150 exemplares de flor íris, separadas em três espécies diferentes: setosa, versicolor e virginica, sendo que foram utilizadas 50 amostras de cada espécie.\"**"
      ],
      "metadata": {
        "id": "H6uTVsZyDmiu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.info())\n",
        "print()\n",
        "print(df.groupby('species').size())"
      ],
      "metadata": {
        "id": "SVSOHodjD5E_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b61d93e-9c90-4190-9df9-a8df3aa6d2b6"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   sepal_length  150 non-null    float64\n",
            " 1   sepal_widht   150 non-null    float64\n",
            " 2   petal_lenght  150 non-null    float64\n",
            " 3   petal_width   150 non-null    float64\n",
            " 4   species       150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n",
            "None\n",
            "\n",
            "species\n",
            "Iris-setosa        50\n",
            "Iris-versicolor    50\n",
            "Iris-virginica     50\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4** - Agora precisamos analisar os dados e tentar entender como eles se relacionam. Essa etapa faz parte da Analise Exploratória que vimos na última aula. Levante hipóteses sobre como as caracteristcas das Pétalas e das Sépalas influenciam na classificação da Iris e crie gráficos utilizando o **seaborn** para validar a sua hipótese.\n",
        "\n",
        "OBS: Lembre-se que temos 4 atributos numéricos possíveis, com um resultado do tipo objeto (classificação da flor). Verifique a documentação do seaborn em (https://seaborn.pydata.org/examples/index.html) para determinar qual gráfico é mais adequado para validar a sua hipótese."
      ],
      "metadata": {
        "id": "g2UEYzQSEvSG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "g = sns.violinplot(y='species', x='sepal_length', data=df, inner='quartile')\n",
        "plt.show()\n",
        "\n",
        "g = sns.violinplot(y='species', x='petal_lenght', data=df, inner='quartile')\n",
        "plt.show()\n",
        "\n",
        "g = sns.violinplot(y='species', x='sepal_widht', data=df, inner='quartile')\n",
        "plt.show()\n",
        "\n",
        "g = sns.violinplot(y='species', x='petal_width', data=df, inner='quartile')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3ENXkuiVK7h1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "dd8bd7ab-35bf-47a8-9371-636852e8efc6"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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HwzuffCPs7g/rL6FQiKamJhoaGqirq6OmpoZDhw6xd+8+9u3bRaTWbZKRQCaRrFJieUOJ5Q9H05LvxNozLs9ikp86SLgLiXTXljqRSGf8ugtftAuJdrnrxZ3mC2FGZia5uXkUlOaTf8KAkZMvOTk5xy9ZWVkDasXwAR1qwCLgT0QkArQD3zzFNjfgBlEEqAV+qqpNIvIT4A0R8QAR4F7gAG4tapOIrIsPFHkQWBV/rgdUdb2IXAX8XESc+GPvUdVmEbkf2BLfz+o+OuZBpXtQSKKkpaUdP1foZNFolIMHD7J161Y2bdrE2nXradj/PgCaVUSkoNIdzp/e+99Gz0ekYnq/7q8/JzROif5CVYgG8QRb8QRbkVArEmrHE2rDF+lAQ+2fCSuP10t+fgHFZUUUF484Pqy/ID7aseCkUY+D4Rw3m6U/gWyW/tSiqhw4cIAPPviAd1esYOuWLe7t2aWESy4iWjR6UC3wOeBn6e9LkS48nU3upesY3mAL3mDL8aVuwP1CV1BYxNChFQwpL6esrIzS0lJKSkooLi6mpKSEvLw8PB5PAg8kMWyWfmP6gYhQWVlJZWUlN998M0ePHuWdd97h5Zdf4eC+FaRXf0S4cAyRIVNsJv7BJNKJt70eT0cDno4G/F1NaOiTYQI5uXmMHl3JyJHzGDFiBMOHD2fo0KGUl5enxHlj/c1CzZg+UlZWxs0338xNN93Eli1bePHFF3nr7bfx1+0gWjCcyJBpODnl/VaeAVebOQdJ01+ojlv7ajuKt70Of0cdGmwD3P7g4SNGMGHepYwbN44xY8YwZsyYXh/9N9hZqBnTx0SEqVOnMnXqVO6++26ef/55nnn2Odq3LcHJHUKoYiZO7hAbPXkB+ru/8LjuhURbD+Npq8XffvT4+VyFRUVMmz+byZMnM2nSJMaNG0d6up2c39cs1IzpR0VFRdx5553ccsstvPTSSzz62GMc2/EKTk45oWGzcHIrEl1EcyaqSFezuxJ2y2H87bXHQ2zosGHMuuwqpk+fzrRp0ygrKxuUcy8mmg0USSAbKGJCoRCvvPIKD/72IY41NRLLrSA8rAonp7TX9+Vtrgb6r6kuJUYkEp9ZPx5igbbDx/vDysqHMHdOFbNmzWLGjBkUFfXfXJeD3ZkGilioJZCFmukWCoV44YUXeOihh2ltbSFaMILwsCo0szDRRRsQerW/0Iniba3F21KDv60GOpoAyM7JZe6cKmbPns3s2bOpqLBadaLY6EdjklxaWho33HAD11xzDU899RSPPfY4XZufJVo0lvCwWUlzrltKUsXT2YS3pQZvaw2+tlrUieH1+Zg2dRpz515PVVUV48aNG5TD5wcaCzVjkkhmZibf+ta3+OpXv8pjjz3G008/g2/zXiLFFxEZOqPnk9aeQvfcj/25JEyyknCnG2Ith/C3HTm+fMvIykrmXfV15syZw/Tp021gxwBkzY8JZM2P5mwaGhp48MEHWfLyy6hCuHQCkSHT0cC5z0qfykP6z9pf6MTwtB/F23wIf2sNEg/43Nw85s6dw5w57iUZpoYzZ2d9aknKQs301OHDh3nooYd47bXXUDyES8YTGTLtnOaYTOVQOxUJd+JtrsbbfNCtjUXDeLxepk6Zwrx585gzZ441KQ5QFmpJykLNnKtDhw7x6KOP8tprrxFzlEjRGKLlU3rUpJjyodbdN9Z8EH/zQaS9HoCi4hIWXDyf+fPnM3v2bDIzB/HaaynCQi1JWaiZ83X06FF+97vfsWTJy4RCQZzcIYRLJxErGAGeU08ALfF+o/NpujwfvvpdAERLxvfdTtTB03YU37H9+JsPQrANEWHixIlceumlXHzxxYwePdrOF0sxFmpJykLNXKi2tjaWLFnCU08/Q0N9HRLIIFQ0lljRWJzMwoTOUtJnNUPHwdt6GG/TPgItB9FwFz6/nzlVVVx22WUsWLCAwkI7FSKV2ZB+Y1JUTk4ON998MzfccAOrV6/mpZde4v2VK3GObIbMfMIFo4gVjMTJLBrY03A5UXe0YtN+N8giIdLS07nk0gVcfvnlzJ8/35oVDWChZkxK8Hq9zJ/v9hs1NzezfPly3nzrLTZv2oDWrEfSsogEctD0fCLDZvVbE+QFiYbwNh9ymxZbD6HRCJlZWVx2xSIWLlzInDlzSEtLS3QpTZKxUDMmxeTn5/OVr3yFr3zlKzQ1NfHRRx+xcuVKVq1aTVd9Lf76HZCRRySrBCerFCe7GCejELwJ/jhQRbrck6B9zdV422pBlbz8fC6/+otcfvnlzJo1a1AsdGnOn4WaMSmssLCQq6++mquvvppYLMbu3btZv349mzdvZvOWLbQc2ONuKAIZeUTTC3Ay8nEy8tH0PJz0XPD20ZpeTsxdpqX9KN62Ondy4PhglspRo7j02lu5+OKLmTRpUkJXPzcDi4WaMYOE1+tlwoQJTJgwgZtvvhlV5ejRo+zatYs9e/awe/duPt67j6OHN3DiADIJZBILZOEEstFANk4gA/Vnov4M8KWhvjTU43dHXUp3+DigenxpFqJBPKF2JNyOJ9iKL3gMuprBcQB32P2shZdQVeXOrVha2vsTOpvBwUY/JpCNfjTJKBQKUV1dzaFDhzh06BA1NTXU1tZy+EgtDfX1RCLh835uEaGouJhxY8cyevRoxo0bx+TJkykrK+vFIzCpzkY/GmN6LC0tjbFjxzJ27NjP3KeqtLe309jYSHNzM21tbbS2thIMBgmHw4TDYUQEr9eLz+cjKyuLnJwccnNzKSsro6SkxPrETJ+yUDPG9JiIkJOTQ05OTqKLYswp2aRnxhhjUoaFmjHGmJRhoWaMMSZlWKgZY4xJGRZqxhhjUoaFmjHGmJRhoWaMMSZlWKgZY4xJGRZqxhhjUoaFmjHGmJRhoWaMMSZlWKgZY4xJGRZqxhhjUoaFmjHGmJRhoWaMMSZlWKgZY4xJGRZqxhhjUoatfG1MgqkqjY2N1NfX09bWRigUwuPxkJ6eTlFREaWlpWRmZia6mMYMCBZqxvSz1tZW1q1bx6ZNm9i+bRt7935MVzB0xsdUDCln4qTJVFVVMW/ePIqLi/uptMYMLBZqxvSDw4cPs3z5ct5dvpxt27ejqgS8UJkT5bKSKOWZDkXpDjl+h4AHFOiKCs1h4Winl4PtB1n7fi1vv/02IsKMGdP54hev5oorriAtLS3Rh2dM0hBVTXQZBq2qqipds2ZNooth+khLSwtvvfUWb7zxOtu37wAgzat8aUSQqUURRufG8J1Dr7YqHOrwsLouwMqj6dR2CPl5uXz1uq9x/fXXk5OT00dHYkxyEZG1qlp1yvss1BLHQi31OI7DmjVrWLJkCe+tWEE0FmNEjsOCsiCr6wL4PcpPqtoveD+q8OOPcmgKemiPesjKzODGm27mxhtvJCMjoxeOxJjkdaZQs+ZHY3pBQ0MDr7zyCi+9+AJH6+rJCQi/V9HF5RVhRubEANjQ4O+1/YlApk/JzI7xjYvaeW5vmN/85je88PxzfO/ue7jqqqsQkV7bnzEDhYWaMecpGo2yevVqXnrxRVZ+8AGO4zC5MMr1U4LMLo3g76cTZkbmxPjh9A52NQd5dHeMn/70p7z00ov86Ef/jTFjxvRPIYxJEhZqxpyjgwcP8tprr/HqKy/T2HSM3DT40vAgi4aGKM90+q0cv18Z/NTv4/Nj/HVVKysOB/jdzi3cdded3HLLrXzzm9+0wSRm0LBQM6YHGhsbWbp0KW++8Trbd+zEIzCtKMJt00LMLI6c04CP3jK9OPqZ2zwCC4eGmVUS4dHdGTz88MMsX7aUv/jLHzN58uT+L6Qx/cxCzZjTOHr0KO+99x7Lly1j46ZNqCojcx1uGRdkQXmY/LRzG2TV3bfWH3ICyt2TO1lQHuY3Ow5x7/e/zw033sidd95ptTaT0mz0YwLZ6MfkEg6H2bp1K6tWreLDD1by8d59AAzNVuaWBJlfFmZodv81L57NgTYvcPaw7IzC73Zn8k5NGiOGD+Mvf/wTJk2a1B9FNKZP2JD+JGWhllihUIgdO3awceNGNmxYz+ZNmwiFI3gFxudHmV4UZnZJhCFZyRNkJ/q7NdkAPT5FYHOjj/t35NAcFG659VZuv/12AoFAXxbRmD5hQ/rNoKeq1NbWsm3bNrZt28aWLZuPnxANMDxHWVgWYnJhlIkFETJT8D9jalGUf5h3jEd3ZfLII4/w3op3+Yu//DETJ05MdNGM6TUp+K9rDHR0dLB9+/bjIbZt6xaaW1oB8HthVE6UwjQPmT7lx7PbyAkMjhaLTB98Z1Inc0vD/OeOg9xz993ceNNN3HHHHaSnpye6eMZcsB41P4rID4D/AtqAB4CZwJ+r6ht9W7zUZs2Pvae2tpZNmzaxadMmNm/ayP4DB+l+b1dkK2NywozOjTE2L8rwbHd6qnNtvks2zSH35OpzHbDSrTMKj+3KZNnhNCqGlPMnf/pnzJ49uzeLaEyfuOA+NRHZqKrTReQq4HvAfwceVtVZvVvUwcVC7fw1Njaydu1a1q5dy7q1azhaVw9Ahl8YmxtmXF6UsXlRxuTGyPKf+j2+scFtqDjV0PjBZFuTj//cmc3RDuHKK6/k3nvvpbCwMNHFMua0eqNPrXu+nS/hhtlWsTl4TD+KxWJs27aNlStX8uEHH/Dx3r0AZAeEiXkhrhof5aICtxbm6eE7s7/D7N3D7qCMyyvC/brfs5lUGOV/zW3mxf3pvPT2m6x8/z2+feddXHfddfh81kNhBpaevmPXisgbwCjgL0QkB0jOIWEmZYTDYdasWcO7777L+++toKW1DU98ZOINYyJMLYowMqfnIZZovR1qvfl8AS8sHuOef/fwrii//OUvefGF5/n+vfcxf/58m0fSDBg9DbU7gRnAXlXtFJEi4I4+K5UZtMLhMKtWrWLp0qW8/94KOruCZPqFGUVBZo2IMLUoetrmxHPV0/O8klVf1Pwqshz+dEYb6+r9PP7xQf7sz/6MmTNn8N3vfs9mJDEDQk9DTYFJwDXA3wBZgA2VMr0iGo2ybt063n77bd5dvoyOzi6yA8Kc4iBzLwozuTDaJ9NQPbzTXaJloA4U6SsiMLs0wvTiZt6pSeP5bRu45557uPji+XzrW7fbidsmqfU01H6F29x4BW6otQHPAHP6qFwmxTmOw5YtW3jrrbdYtvQdmltayfALVcVB5o/vuyBLJec76rGnfB74wvAQlw8J8Xp1Oq+u/Yi7P/iQ2bNncfPNtzBnzhxrljRJp6ehNk9VZ4nIegBVPSYiNhWBOSeqyvbt21m6dClL33mLuvpG/F6YWRTm4mlhphdFCHgTXcqB476pHf2yn3QffGVUkC8MD/L2oTRe27qOP/7jdYweVcnXvr6YK6+80hYmNUmjp6EWEREvbjMkIlKCDRQxPRCLxdiyZQsrVqxg+bKlHK2rx+uBaYURvjY5zOySMBkJGmDXX6GQKjJ8cE1liKtGhFhZG+D16r380z/9E7/693/nyi98gS996UtMmDDBam8moXp6ntqtwI3ALOC3wGLgJ6r6VN8WL7Wl6nlqra2trFmzhg8++IAPP1hJS2sbPg9MKYwwt9SdT7G3BnsMJAN9YMrJVGFXi5elh9L4qD6dSEwZMXwYV37hKhYtWsTIkSMTXUSTonplQmMRmQD8Hu45a2+r6vbeK+LglCqh1tHRwdatW1m/fj3r1q5h585dOKpkBYTphUFmFUeYVpya8ykmUvdAl29c1JXgkkBHRFhV5+e9I2nsbHZf6MqRI7j0sstZsGABEydOxOu1tmXTO8471EQkV1VbReSU0wuoalMvlXFQGoihFolE2L9/Pzt37mTHjh1s3bKFffv24ajiFRiTF2NyQZhpRRFG58bwJvFgj2Q9GbqnknWar6agsLouwNr6ADuafTgKOdlZzJpdxaxZs5gxYwYjR47E40niN4dJahcyo8hjuMP41xLvT+t+zvjvo8+w03ZVzT7NfStVdcFZ9t1nRKQC+IWqLj6Pxy4D/lhVB1YanaPOzk5qamqorq7m4MGDHDhwgH17P+bAwYPEYm53aqZfGJ0T5trKKBflRxmXFyV9ANXGBnqoJavCdOWqEW7fW3tE2NLoY2NjiC2rlrN8+XLADbmJkyYzceJELrroIsaNG0dpaan1x5kLdsaPIFW9Jn49qjd2JiI+VY32V6B17+/k21X1MG6/YH+UwauqSdWJ0tnZybFjx2hqaqKhoYH6+nrq6+upq6uj9shhjhw5cnxG+26lmVCRGeZLw2OMyI4xKjdGaYYzYGbzSEXTiiOJLsJZZfuV+eUR5pdHUO2krsvDzmYfu5pDfLz9I1avXkV3Y1FWZgYjKysZObKSYcOGMXToUMrLyyktLaWgoMCaL02P9Oh7tYhcB7yjqi3x3/OBRar6fA8euwj4W+AYMAEY312LE5EhwBNAbrws96jqihMemwdsAkapqiMiWcAO3BriCODfgRKgE/iOqu4QkQeBIO5KAu+LyAvAv8afUoHLgSJgiapOiY/q/BnwRdwRnfer6i9F5PeAf4qXa3W8bKGTju1m4C9xa64vq+qfxW9vB/4f8HngXuC9s/2dzlVnZydbt24lFAoRDocJhUIEg0GCwSBdXV10dnbS3t5OR0cHbW1ttLY009rSQktrK6HwZz8M/V4oSofitAgzshxKS2KUZTiUZzqUZcZIT8HPk74+z6uvXVsZOvtGSUQEyjIdyjLD8dpxJ8EYVLd5OdDu5VB7kENH2vhw7w6OBT/92ng8HgoL8igsLCK/oJDc3Fxyc3PJysoiOzubjIwMMjIySEtLY9y4cQwdOjQxB2kSrqeNRX+tqs91/6KqzSLy18DzPXz8LGCKqu476fZbgNdV9e/j4ZJ54p2q2iIiG4CFwFLcptDXVTUiIr8G7lbV3SIyD/cE8SviDx0GLFDVmIi8BNyrqu+LSDZu4J3ou0AlMENVoyJSKCLpwIPA76nqLhF5CLgH+JfuB8WbMH8GzMYN7DdE5KvxoM8CPlLV/3byH0JEvhvfJyNGjOjJ3+6UHnroIR577LFzekxewGFMVoyhpTGGZ8coznAoSHPIDyjZfmWwtfzYkP7ES/fCuPwY4/I/3ZgRjEJ90EN9l5eDbV4+bvWyvy3KrsZjZ33OsWPG8Jv/+q++KrJJcj0NtVP16J5L78mqUwQauDWg34iIH3heVTecYpsncE8nWArcBPwqHk4LgKdOaINPO+ExT53Q5Pc+8M8i8ijwrKoeOqnd/vPAf3Q3U6pqk4hMB/ap6q74Nr/FrXH9ywmPmwMsU9V6gPjzX44b9DHcGVc+Q1V/Dfwa3IEip9qmJxYtWsSmTZtobW3FcZwT+iIUJxYl2BWko7OTYOiT/qKWsIeWsIdtx/wAZPmF/DSHgkCUwjSHwnSHknSH4gyH0gyHonRrXuxNyTqwIxl0RITqdi81HR6OdHo52umhPuSnvstDKPrZfxOPx0N2ViZZWVl4vT4kPujE4/Fw3de+1t/FN0mkp8G0RkT+Gbe5D9wP+LXnsJ9TfiVW1XdF5HLgy8CD8X20AX8d3+Qu4EXgp/ERmLOBd3BrQs2qOuNs+1PVfxCRl3GXzXk/vibcybW13hbs6360CRMm8Ktf/eqs20WjUdrb22ltbaWlpYWWlhaam5tpamqisbGRxsZGGurr2FZ3lMba5uMLawJ4PVCWCUMywlRkxRia5TAiO0ZFVsymsEoCAzUkg1HY0+rj4xYfe1u9HGgP0HDCWQlpAT9Dhw5l1MThLBgyhNLSUoqLiykqKqKwsJD8/HyysrJs9KQ5pZ6G2h/gLgz6BG6/1Ju4wXZBRGQkcEhV7xeRNGCWqv4QeO6k7Vbj9ostiYdFq4jsE5HrVfWp+Npu01R14yn2MUZVNwObRWQObr/ehhM2eRP4nogs7W5+BHYClSIyVlX3AN8Alp/01KuAX4hIMW7z483ALy/wT9LrfD4f+fn55Ofnn3XbaDRKXV0dR44c4ciRIxw6dMgd/XhgPxuqa46PevR6YHi2w6icCGNzo4zNj1KR6Qy45stkOs8rlYVjsLPZx5YmP9uPBdjX5jk+OGTY0CFMXzCJsWPHMmbMGCorKyktLbXAMuetR6Gmqh3An4tIVvzn3rII+BMRiQDtwDdPs90TwFPx7bvdCvxfEfkJ4Ad+B3wm1IAfisjncAeBbAVeBYaccP8DwHhgU7wc96vqv4nIHbjNm90DRf7jxCdV1SMi8ue4zaLdA0Ve6OmBJyOfz0dFRQUVFRWfuS8ajVJdXc3evXvZtWsXu3btYtX2bSytcQMhJyBclBdiUmGUyYWRARFy3TN8mN7XHBLW1ftZ1xBg6zE/kRj4vF4mTZrEN2bOZOrUqUyaNImcnJxEF9WkmJ5Ok7UA98M/W1VHxPucvqeq3+/rAqaygXjy9Ykcx6G6upotW7awadMm1q9bS+3ROgCKM2BqYYhZxREmFybnRMX93XzX2/vb2OB+J+3vFbxPpy0sfFTn58Ojaew85kOBIeVlLLjkUubNm8eMGTNIT7cVq8yFu5CTr7v9H+Aq3P4tVHVjvC/MDGIej4eRI0cycuRIvvzlLwNw+PBh1qxZw0cffcSHq1extCZEmleYWhiiqjTCrJJw0kyXNRDO8zqTZAgzR2FTo49lNWmsbwwQc2DkiOF86yu/x8KFCxk9erSdUG36VY8/XlS1+qQ3Z1KdUGySQ0VFBddeey3XXnst4XCYDRs28N5777Hi3eWs2XoMnyeLqYVh5pe5AZeoGfqh/8/zSpWJjMGtlS07HODtmkwauiA/L5evL/4iV111FWPHjrUgMwnT0+bHp4F/Bv4NmAf8AKhS1Zv6tnipbaA3P54Lx3HYtm0by5YtY+k7b1PfcMJaauW2ltr5SMSs/3WdHl49mMbyIxmEY8rMmTO47rqvcckll+D3+/utHGZwu+BZ+uMj/P4V95wuD/A68ANVbezNgg42gynUTtS96vXbb7/N0nfe/tSq1xeXh5lUYKte90R/9gke6fDwwv50Vtam4fF4ufILX+DGG29k9OjTTv9qTJ+54D41VW3AHW1ozAXzeDxMmzaNadOm8Qd/8AesW7eOt99+m+XLl7HiSBc5AagqCTGvLMzE/GifzfQ/UM/z6k+NQeHZvRmsOJKG3+9n8fXXcdNNN1FcXJzoohlzSj2d+3E0bk1tPu55ah8Af6Sqe/uwbGYQ8Pl8zJ07l7lz5/KjH/2IVatW8c477/D+e++xtCZETkCYVRxkdkmEKUk6ijIVdUbhpf3pvFadCeLha1+/jttuu43CwlOuQmVM0uhpN/1juLOJXBf//Sbgcdz+NWN6RVpaGpdddhmXXXYZwWCQVatWsXz5cla+/x7LD3cR8AqTC0LMKI4wtShKaYaT6CInVF/MXekovHckwBMfZ9ESgiuv/Dzf+c53KC8v7/V9GdMXetqntklVp51020ZVnd5nJRsEBmuf2rmKRCKsX7+elStXsvL9946fC1eepUzODzO5MMJFBVHyAuc2lWayneeVaAfavPzXjiz2tHiZNHEiP/yjP2LChAmJLpYxn9EbA0V+hjsV1O9wmx9vBAqAn4OtgH2+LNTOnapy8OBBVq1axZo1q9mwfj1dQXdofkW2Mj43zPj8KGPzogxJsllNknVR0lAMntmbwWsH08nNzeWe79/LVVddZVNVmaTVG6F24gz73Q84Pi28qtoQqPNgoXbhotEoO3fuZMOGDWzatJHNmzbR3tEJQHZAGJMTZkxelDG5Ucbkxcj2J24Ntd4emNIbIbmlycd/7sihvhO+/OUvc88995Cbm9sr5TOmr/TGjCJ/Brymqq0i8t9x10f7W1Vd11uFNOZ8+Hw+Jk+ezOTJk7n11ltxHIcDBw6wdetWtm7dyratW3hu38Hjqw+UZcHYnBBj82Jk+xzKsxxG5Q7Mk6IvJNQ6o/D47kyW1qQxbGgFv/iHP2fGjBm9XEJj+l9PQ+0nqvqkiFyKuxDnPwH/FxsoYpKMx+Nh1KhRjBo1imuuuQZwVwnfsWMH27ZtY9u2bWzdspn3a1sAyPAJY3PDTCiIMqkgwujcWJ+dQpAsNjf6eGBHDseCws0338S3v/1t0tLSzv5AYwaAnoZa91fZL+POYv+yiPxdH5XJmF6VmZnJrFmzmDVrFuD2y9XW1rJlyxY2b97Mxg3reerjA0AGGT5hYn6I6cURZhRHKEpPXHPl2eSnnVvZuuK1s3dq0hgxfBh//+OfMGnSpD4qnTGJ0dNQqxGR/wdcCfwsvvZZin+fNalKRBgyZAhDhgzhyiuvBKC5uZkNGzawevVqVn30Iet21ANQmeswu9g9EbwiK7lOITiXIf3bmnzcvyOHhi648cYbueuuu6x2ZlJST0PtBuCLwD+parOIDAH+pO+KZUz/ys/PZ9GiRSxatAhV5cCBA6xcuZIV777LM9u28czeDEbkOFxc5k7lVZzENbgTdUXhd3syePtQOsOGVvBvP/8xU6dOTXSxjOkzPRr9aPqGjX4cGOrr61m+fDlvv/UWW7dtQ4BJhVEuGxJibmm4x7Oc9PcExBsbfPzXzhwag3D99Tdw11132XpmJiVc8JB+0zcs1Aaew4cP88Ybb/DaKy9zuPYomX5hQVkXnxsa7velZR7emQHANy7q+tTtx0LCY7sy+eBogJEjhvOnf/bnVjszKaU3hvQbY3DXi7v99tv55je/ycaNG1myZAnLli3lrUNRRuc5fK6ii4vLwqT3w39Wd82vW9SBN6vTeHZfFlHxcvvtt3HbbbcRCAT6vjDGJAkLNWPOg8fjYebMmcycOZMf/OAHvPHGG7z4wvP85/aDPLo7m4vLgiysCDEmN9bns5o4Cqvq/Dy1N4ujHcLcuVX84Ac/ZPjw4X27Y2OSkDU/JpA1P6YWVWXLli0sWbKEpe+8TTAUpiJLuXRIFwvKIhT38gTMUccNsxf3Z3Ko3UPlyBHce98fMG+enT5qUpv1qSUpC7XU1dHRwdKlS3nllZfZsmUrAOPyY8wuDjGrJHJB81Ie7vCwsjbA8iMZHAvCiOHDuP2Ob/O5z30Or9fW5jGpz0ItSVmoDQ6HDx92F0FdtpRdu/cAUJQBE/NCjMmLUZkTpTzTIduvnwm6mAP1QQ8H27zsavGxpSmNQ+2CR4Sqqiq+vngx8+bNs8mHzaBioZakLNQGn9raWj766CPWrFnDxg3raW5pPX5fwOtOwpzmURQIxoSWkNL9Lxrw+5k2fRrz51/MFVdcYatPm0HLQi1JWagNbqrK0aNH2bt3L4cOHaKuro729naCwSAej4f09HSKioooKytj3LhxVFZW2iwgxmBD+o1JSiJCeXm5rSptTC+yhnhjjDEpw0LNGGNMyrBQM8YYkzIs1IwxxqQMCzVjjDEpw0LNGGNMyrBQM8YYkzIs1IwxxqQMCzVjjDEpw0LNGGNMyrBQM8YYkzIs1IwxxqQMm9DYmCTW1dXFwYMHqa6uprGxkaamJkKhEI7j4PV6ycvLo6CggOHDhzNy5EiKiooSXWRjEspCzZgk0tDQwOrVq9m4cSNbtm6h+mA1n1oeygMevwcEnJADJ60cVVZexswZM5k3bx4XX3wxmZmZ/XsAxiSYraeWQLaemlFVdu/ezYoVK1j+7nL279sPgKQJToGDFiqap5ADZAB+IL46tmeZBxSceQ60gbQI0iB4G7w4IQef38eCBQu49vevpaqqylbHNinD1lMzJsnU1NTw+uuv88abb3C45rAbVMXgTHPQMoU8jofXGQmQ6V60TNHxiqMONIBzyGHFhyt4d/m7lA8p5+abbubqq68mPT29T4/NmESymloCWU1tcAmFQixdupSXXnqJzZs3uzeWgjPcQYcqnOOi1p5lbs3LWeScfqMYSI3g2eOBRsjJzeG2W2/jq1/9KhkZGed5JMYk1plqahZqCWShNjhUV1fz3HPP8cqrr9DZ0YnkCLHKGDpC3VrWeepRqHVToBG8271QC3n5edxx+x1ce+21+HzWYGMGFgu1JGWhlrpUldWrV/Pkk0+yatUq8ID6FdLBudLpWdPiWZxTqJ2oAbxbvFAPQ4cN5fv3fJ9LL70UkV4olDH9wPrUjOkn4XCYN998k8d/9zgHDxxEMgRnkoOOVjwfxQdq9FJ2aP55fiEthtjCGByBmg9r+PGPf8ys2bP4wR/+gFGjRvVO4YxJEAs1Y3pBR0cHL7zwAk88+QTHmo4h+YIzx0GHK3j7Zp864wJaWQSoAC1Q6IL1m9dzxx13sHjxYm6//Xays7N7rZzG9CcLNWMuQHNzM0899RRPP/M0XZ1dUAqxy2JQxmdqZM6Ec2wm7A/x0ZPRi6PIFuHJJ5/k9Tde57577+MLX/iCNUmaAcf61BLI+tQGrrq6On73u9/xwosvEAlH0GHqhlZBokt2jmrj1+Xx6ybwrvdCE0ydOpUf/ehHjBkzJlGlM+aUbKBIkrJQG3gOHz7MI488wquvvkpMYzgjHHRC/OToVKEg+wTvFi8SERYvXswdd9xBVlZWoktmDGChlrQs1AaO/fv388gjj/DWW2+hKLFRMfQihXP5nG+OX+f3fvn6RAhki+DZ66GgsID77r2Pz3/+89YkaRLOQi1JWaglvz179vDQQw+xfPly8PJJmJ3HecvnPQS/LzXHr/PPsM0JTZLTpk3jhz/8IWPHju37shlzGjak35hztHXrVn770G/58IMPEb8QuyiGjj/3WT/6kux3a0xaef5fTD0behC0hRC7IobsEzZv2cydd97JV77yFe68807y8vLOe9/G9AULNWPiVJW1a9fy0MMPsWH9BndS4ckOOlYhkOjSfVZvhFrPdwY6WokOiyJbhedfeJ433nyDO799J9ddd53NSmKShr0TzaAXi8VYsWIFjzz6CLt27kIyBWe6e8J0b/6HOPOTqNnxfAVAZyqx0TE6N3byy1/+kmeefYZ7v3+vzUpikoKFmhm0QqEQb775Jo8+9ig1h2qQHMGZ7aAj++iE6SScHP+8gzYvfj5eLRzZdIQf//jHTJo0ibvvvpsZM2b0ahmNORcWambQaWtr44UXXuDJp56k+VgzFLgf7jpMe20KqwHjQoJWgCEQLYsi+4Xt27fzh3/4h8yZM4c777yTSZMm9VYpjekxCzUzaBw+fJinnnqKJS8vIRQMQRnELo9BKf0SZv3aB9afPPH+tpFRZI+wZvMaVt+9mrlz53Lbbbcxffp0a5Y0/cZCzaQ0VWXdunU8/fTTrFy5EgRiw+MjGfP7tyzJGGq9WiYv6EVKdIwbbqs3rWbVH65i0qRJ3HTTTVx66aU2oMT0OXuHmZTU1tbGG2+8wbPPPUv1wWokXYhNiKFjzu8cs2TkzLjwgSd9ErQ+0AlKdGwU2Sds37Odv/qrv6K4tJivXvtVvvSlL1FcXNx7+zPmBHbydQLZyde9y3EcNm3axCuvvMLb77xNJByBQnDG9O1s+T0lH8YDZH7y/M/1ywnhChwG7x4v1IHH42HO3Dl84covcMkll5CZeQErpZpByU6+NilLVdm7dy/vvPMOb7z5Bkdrj7onSw+PoaMUChNdwk8kU5h10/T+OceNoRAbGoM2cPY5rNq8io8+/Ai/309VVRWXXHIJVVVVVFRU9H15TEqzmloCWU3t/ESjUbZu3crKlSt5d8W71ByqcT84S8EZ6aBDe/f8MtMHFGgAqRG8h71oh/s5VFZexrSp05g0aRLjx49n9OjRNpGy+Qyb+zFJWaj1TDgcZvfu3WzevJmNGzeydt1agl1Bd9RdiaJD3UsyngdmekCBNpCjgtQL3iYvTtcnzaEFhQUMHz6cIeVDKC4uprCwkLy8PHJzc8nMzCQzM5O0tDQCgQCBQAC/34/P58Pn8+H1em3kZQpKquZHEWlX1VMuqysiK1V1wQU+/98A76rqW+fwmGuBSar6D2fYpgL4haouvpDymdOLRqMcPXqUQ4cOsX//fvbt28fOXTvZt28fTsz9kJMcITYkhpapuxCnP7FlPheyId6ndiErVqciAXJBcxUdpzjqQBfQDNIiNLY3cqzmGFv2bHHD7hy7/7xeLz6fD3/ATyAQIC09jcyMTLKzssnKyiInJ4fc3Fzy8vIoKCigoKCA4uJiiouLKSgowOtNcGesOSf9XlM7VaiJiE9Vo328X6+qxvpyH+dqsNTUYrEYra2tNDc309zcTFNTE01NTTQ0NNDQ0EBdXR2Haw/TWN+I43zyieXJ8BDLiaGFihYoFDOga2PJOEv/gAtaBcInXCJAFCQmEMMNvJMvyif3xdyLRAUi4Il6kKigIUUjn/0beDzusjvlZeWUl5dTWlpKWVnZpy7Z2dlWG+xnSVVT6yYii4C/BY4BE4Dx3YEnIkOAJ4DceBnvUdUVJzw2D9gEjFJVR0SygB3AaOB+YImqPi0i++PPcyXwjyLSCvwz0AG8D4xW1WtE5HagSlXvE5EHgVagCnc94D+NP1dl/HmniIgX+BnwRdx/lftV9Zci8lfA7+MOGl8JfE9ToH03FosRCoXo7Oykq6uLzs5O2tvb6ejooKOjg7a2Ntrb22lra6OtrY3W1lZaWltobm6mtbWVzo5OTvVnEI+48yymOziZDowHskGzFXIhlpZU30GSTm+EpDTHQ40B8jYV3JUSTlot4VzL3729c2K1LwaEgKB7kS7B6XKo76ynoa6B7Qe3ox2KOp/eV1p6GsXFxZSWlFJcXExRUdHxGl9eXh55eXnk5OSQk5NDZmYmgUASzo6dQhLdnT4LmKKq+066/RbgdVX9+3iAfGrMr6q2iMgGYCGwFLgmvn3kFN+YGlV1loikA7uBy1V1n4g8foZyDQEuxQ3bF4GnT7r/u0AlMENVoyLSPcbu31T1bwBE5OF4uV46418gCRw5coT77ruP+vr6C3oeT8DjTngbUByfg6bFmwjTcGe5T8e9LQ039v0MqmmptHyABMdg1f1JE/+0OTEoFXUDUHFDrxPoAukUujq7ONR1iJqDNXh2e9AuRWNnfq1vuOEG7rvvvj46kMEt0aG26hSBBrAa+I2I+IHnVXXDKbZ5ArgRN9RuAn51mn08Eb+eAOw9YX+P44bTqTyvqg6wTUTKTnH/54H/6G4yVdWm+O2fE5E/xf23KAS2clKoich3u/c7YsSI0+y+fzU2Np5/oPlAMxUyIZoWdcPrhIsG9JNv1mkk/FyxRNIJyRdqFrTnSHC/kGUAMdAuPR5w2qU4Qbc/UNoFaZXT9v9VV1f3X5kHmUSHWsepblTVd0XkcuDLwIMi8s9AG/DX8U3uwq1B/TReS5oNvHMu+ziL0Ak/96guEa8J/gq3GbNaRP4Hp+gBUtVfA78Gt0/tPMrW66ZMmcIrr7xCMBgkHA4TDocJhUKEw2GCwSDBYJCurq7jTY+dnZ3Hmx67mx9b21ppbWulvb6drs6u0+5L/IKkC06aW5PTDD3+7VgzFbJxA3EQ1eASKRmDNik4uJ8CXUCnWyPrvpYuwdPl+dQIzW7pGekUFhZSNKKIgvwC8vPzjzc/Zme7A1OysrIYP358fx/RoJHoUDslERkJHFLV+0UkDZilqj8Enjtpu9XAv+L2dZ2tA2YnMFpEKlV1P24t73y9CXxPRJae0PzY/Q5vEJFsYDGfbbZMWtnZ2WRnn3JQ6jmLRqOf9K21tBy/NDc3c+zYMY4dO0ZTUxN19XU01DS4w/NPIAFBcxQn14E83EEi+STpu9Uk3AkDQE45SMThUwNFJCYQxR1k0n0Jg4QET9iDhNy+tJO76QJpAUpKSqgYU3F8kEhpaSmlpaWUlJRQXFxss6MkgWT9mFgE/ImIRIB24Jun2e4J4Kn49mekql0i8n3gNRHpwG3iPF8P4A5r2BQv4/2q+m8icj+wBai9wOcf0Hw+3/GO8p7o6OigtraWI0eOcPjwYaqrq9m/fz8f7/2Y9n3t7kaCu0RMsYOWKpSQvO/e00jG0Y9JzcFtn2l3m/PodAdvSFDwRDwQBo2cvf/qTESE9Ix0cnNzyc/PPz7Io3vAR0lJyfHgys3NtVGOA8CgOvlaRLJVtV3cd+a/A7tV9f8kqjyDZUj/+VJVGhoa2LFjB9u3b2fTpk1s276NaCSKeASnxEErFK3Qk4YSJafeDrXeGI6fVEHbAVIv0AjeY160RT/VJ5WRmXF8lGFubi45OTlkZWWd8eRrn893/Pfu+9PT00lPTycrK4uMjAw8Hk/ijtmcl6Qc0p8g3xGRb+H22qwH/l+Cy2POQEQoKSmhpKSEyy67DHBXq968eTOrVq3ivfff49D6Q+4rWQzO8PjExWlnft5UMWDOLTsdh0+myjrqRdvc48nIzGDy5MmMH+dOkzVy5EgqKirIyclJbHnNgDCoamrJxmpqF+7gwYMsW7aMt95+i/379rs1uHIHZ7TjnmWYTK1FtfHr8oSW4lP6vaamQBPIAcF7yIuGFL/fz+yq2cypmsOsWbMYNWqU1Z7MGdncj0nKQq13ffzxx7z22mu89vprtDS3IFlCrDKGjrZ5IU+rv4I24q7d5t3nNiv6A34uu/QyrrjiCubMmUNGRooscmf6hYVakrJQ6xvRaJT333+f5194nrVr1iIeITYsho5LrqVoBoV2kN2C94AXjSgTJk7g2t+/liuuuMJGCprzZn1qZlDx+XwsXLiQhQsXUl1dzXPPPceSl5cQPBiEYoiNi8FQ+r9psjl+nd/P+02EFpAdgqfag9fj5fOf/zyLFy/moosuSnTJTIqzmloCWU2t/3R0dPDqq6/y5FNPUnukFskWYuNiaGX/rb2WVCMNuzXHr/N76flaQbYKnkMe0tLTuO6r13HDDTdQXFzcSzswxmpqxpCVlcXixYu57rrrWLFiBY8//jjb129HtgmxMTF07MAbNSn740P6Ky9gSP+GXgraDpBtgueAh7S0NG74xg3ccMMN5OXlXdjzGnOOLNTMoOL1elm0aBELFy5k8+bNPPbYY6xcuRLZJcRGxdDxA+OcN+idULtgYZDtgnePF6/Xy9dv+Dq33nor+fn5iSuTGdQs1MygJCJMmzaNadOmsW/fPh577DHefPNNnI8dnBGOOydiL58W5cxPombHC+WAfCx4t3shDFdddRV33nknZWWnmv/bmP5jfWoJZH1qyaW2tpYnnniCF198kUgkgjPMQSdq0g7s6JU+uu5pN8/llIej4NvgQ1uVmTNnct999zFu3LjzL4Mx58iG9CcpC7XkdOzYMZ588kmeefYZgl1BdIjiTHSgKNEl+7R+H3jS4fbByWGhfEg5f3DfH3DppZfafIim31moJSkLteTW1tbGs88+yxNPPkF7WzuUQWxCzJ1M+Tw+x3u7D6zfQi3mDs/37vQS8Af41je/xQ033GArOJuEsdGPxpyHnJwcvvWtb3H99dfzwgsv8Njjj9GyvMU9121izF3V+xzCLSkGdpzkrGU6Em9qbFcWXbGIe79/L6Wlpf1YQmPOjYWaMWeRmZnJzTffzNe+9jWWLFnCI48+QuOKRqRQiE6IQgUJmWPSmXHhNbTThtoJTY3DRgzjR3/7I2bPnn3B+zOmr1moGdNDaWlpfP3rX+faa6/l9ddf56GHH6J2ZS2SJ8QuirkrBJxhHl5N7+UaWn7vPh3gNjXuFLw73KbGO+6+g+uvvx6/398HOzOm91mfWgJZn9rAFo1GWbZsGb996Lcc2H/AnUB5XAwd1X+zlFwo+TBeU5uvblPjRh/apixatIh7773XhuibpGQDRZKUhVpqcByHDz/8kIcfeZitW7YiaUJsdHyWkoGwOkAbeDZ6kCPCsOHD+KMf/hFz5sxJdKmMOS0bKGJMH/J4PCxYsIAFCxawefNmHn/8cd5//33YBbHh8dUB8hNdylM4YTaQtLQ07rjnDhYvXmxNjWZAs1AzphdNnTqVqVOnUl1dzdNPP83Lr7xMeH8YikF9imYpOivBrSOx+GwgO9zZQK6++mq++93vUlho6/KYgc+aHxPImh9TX1tbG6+++ipPP/O0uzpAuhAbEV8doL/n+o2B7HPPN9NOZc6cOdxzzz2MHTu2nwtizIWxPrUkZaE2eDiOw5o1a3jxpRd57733cGIOFIIz3HFHTZ7Hws89Pvk6HK+ZfexFu5TJUybznbu+w6xZs87jSIxJPOtTMybBPB4Pc+fOZe7cuRw7dow333yTV159hb0b98JGoBicCgetUMjmws97c4B69zw0b40XjSlVc6q45ZZbmDVrlk1tZVKW1dQSyGpqZv/+/bzzzju8u+Jd9n68F8A9NaA4BkWghQq5gPezj/1UTU1x1zRrEKgH7xEvGlIyMjP44lVf5Nprr2XMmDH9d2DG9CFrfkxSFmrmRIcPH2b16tWsWbOGdevX0dbadvw+T7aHWHoM0kG96tbkHJCw4Al5oA006v4vZ+VkcfG8i1m4cCHz5s0jPX0gnFdgTM9ZqCUpCzVzOqrKkSNH2L59OwcPHqS6upr6+noamxoJBoM46uDz+sjPz6cgv4ARI0YwcuRIpkyZQmVlJR7PGaY2MWaAsz41YwYYEaGiooKKiopEF8WYAcW+zhljjEkZFmrGGGNShoWaMcaYlGGhZowxJmVYqBljjEkZNqQ/gUSkHjiQ6HKcg2KgIdGF6CWpcix2HMnFjqN/jFTVklPdYaFmekxE1pzu3JCBJlWOxY4judhxJJ41PxpjjEkZFmrGGGNShoWaORe/TnQBelGqHIsdR3Kx40gw61MzxhiTMqymZowxJmVYqBljjEkZFmrmU0RkuIgsFZFtIrJVRH5wim1ERH4hIntEZJOIzEpEWc+kh8exSERaRGRD/PJXiSjr2YhIuoisEpGN8WP5n6fYJk1Enoi/Jh+JSGUCinpGPTyO20Wk/oTX5K5ElLUnRMQrIutFZMkp7kv616PbWY5jwLwe3WzpGXOyKPDfVHWdiOQAa0XkTVXddsI2VwPj4pd5wP+NXyeTnhwHwApVvSYB5TsXIeAKVW0XET/wnoi8qqofnrDNncAxVR0rIjcBPwNuTERhz6AnxwHwhKrel4DynasfANtx1yY/2UB4Pbqd6Thg4LwegNXUzElU9Yiqrov/3Ib7Zh960mZfAR5S14dAvogM6eeinlEPj2NAiP+d2+O/+uOXk0d4fQX4bfznp4HfExHppyL2SA+PY0AQkWHAl4EHTrNJ0r8e0KPjGHAs1MxpxZtMZgIfnXTXUKD6hN8PkcSBcYbjALg43hz2qohM7t+S9Vy8iWgDUAe8qaqnfU1UNQq0AEX9Wsge6MFxAHw93qz9tIgM798S9ti/AH8KOKe5f0C8Hpz9OGBgvB7HWaiZUxKRbOAZ4Ieq2pro8pyvsxzHOtw55KYDvwSe7+fi9ZiqxlR1BjAMmCsiUxJcpPPSg+N4CahU1WnAm3xS20kaInINUKeqaxNdlgvRw+NI+tfjZBZq5jPi/R3PAI+q6rOn2KQGOPEb27D4bUnlbMehqq3dzWGq+grgF5Hifi7mOVHVZmAp8MWT7jr+moiID8gDGvu1cOfgdMehqo2qGor/+gAwu5+L1hOXANeKyH7gd8AVIvLISdsMhNfjrMcxQF6PT7FQM58Sb/f/T2C7qv7zaTZ7EfhmfBTkfKBFVY/0WyF7oCfHISLl3f0cIjIX9/8h2T54EJESEcmP/5wBXAnsOGmzF4FvxX9eDLyjSTazQk+O46S+2Wtx+0KTiqr+haoOU9VK4Cbcv/VtJ22W9K9HT45jILweJ7PRj+ZklwDfADbH+z4A/hIYAaCq/wG8AnwJ2AN0Anf0fzHPqifHsRi4R0SiQBdwU7J98MQNAX4rIl7c4H1SVZeIyN8Aa1T1RdwAf1hE9gBNuB9SyaYnx/GHInIt7ujVJuD2hJX2HA3A1+OUBvrrYdNkGWOMSRnW/GiMMSZlWKgZY4xJGRZqxhhjUoaFmjHGmJRhoWaMMSZlWKgZY4xJGRZqxgxy8SV4PrPsyDk8vkpEfnGa+/aLSLGIVIrIltNsc7uIVJzv/o05kZ18bYy5IKq6BlhzAU9xO7AFONwrBTKDmtXUjBkARCRLRF6OryiwRURuFJHZIrJcRNaKyOvdUxqJyDIR+df4oo5b4lOAISJzReSD+IKQK0Xkoh7ue7OI5MenRWsUkW/Gb39IRK48saYnIkUi8oa4i4A+AJy43IpXRO6P3/eGiGSIyGKgCng0Xt6M3vy7mcHHQs2YgeGLwGFVna6qU4DXcFcWWKyqs4HfAH9/wvaZ8dnwvx+/D9x5Fi9T1ZnAXwE/7eG+38eddmwysBe4LH77xcDKk7b9a+A9VZ0MPEd8WrK4ccC/x+9rBr6uqk/j1vJuVdUZqtrVwzIZc0rW/GjMwLAZ+N8i8jNgCXAMmAK8GZ+T2QucOKn04wCq+q6I5MYnEs7BnXtxHO7inP4e7nsFcDlwAHeV8++KyFDclZ075NNrX14OfC2+75dF5NgJ9+1T1Q3xn9cClT3cvzE9ZjU1YwYAVd0FzMINt78Dvg5sjdduZqjqVFX9wokPOfkpgL8FlsZrer8PpPdw9+/i1s4uA5YB9biTQa84x8MInfBzDPtSbfqAhZoxA0B8dGCnqj4C/ByYB5SIyMXx+/3y6ZW7b4zffinu0kAtuGt6da97d3tP962q1UAxME5V9wLvAX+MG3Ynexe4Jb7vq4GCHuyiDbcWacwFs29KxgwMU4Gfi4gDRIB7cJcD+YWI5OH+L/8LsDW+fVBE1uM2MX47fts/4jY//gR4+Rz3/xFuEye4NbT/hRtuJ/ufwOMishW3v+1gD577QeA/RKQLuNj61cyFsKVnjEkxIrIM+OP4UHtjBhVrfjTGGJMyrKZmjAFARO4AfnDSze+r6r2JKI8x58NCzRhjTMqw5kdjjDEpw0LNGGNMyrBQM8YYkzIs1IwxxqSM/w9gy+jnBDErnQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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": "markdown",
      "source": [
        "Resposta: A melhor correlação que pode determinar a espécie da flor é..."
      ],
      "metadata": {
        "id": "zwh86NKqhBB0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5** - Agora que você identificou uma correlação entre os tamanhos de pétalas e sépalas, e como elas podem determinar a classificação da flor, vamos nos preparar para implementar o modelo. Primeiro temos que criar um novo dataset, somente com os dados que vamos utilizar, aqueles que identificamos a correlação.\n",
        "\n",
        "Obs: Lembre-se que estamos começando o projeto do nosso modelo, então precisamos criar duas \"tabelas\", uma com os atributos (entradas) e outra com a classificação (classe)."
      ],
      "metadata": {
        "id": "dkOcel9VQrGp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecionando um sub-dataframe com os campos petal_lenght e sepal_widht,\n",
        "# e outro com a variável de classes\n",
        "entradas = df[['sepal_length', 'petal_width']]\n",
        "classes = df['species']\n",
        "print(f\"Formato das tabelas de dados {entradas.shape} e classes {classes.shape}\")"
      ],
      "metadata": {
        "id": "idzl_o-tRxkW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7e71106-7b96-476b-bde8-e66da3403591"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Formato das tabelas de dados (150, 2) e classes (150,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **6 -** Com os dois suconjuntos criados precisamos separa-los em Treino e Teste! \n",
        "Como assim você deve estar se perguntando. É simples: Imagine que o modelo é uma criança a qual você está tentando ensinar o que é um cachorro. Um jeito de fazer isso é mostrar vários cachorros para ela, dizendo \"Olá só, isso é um cachorro. Ele tem quatro patas, tem rabo e faz \"au-au\"\". Depois de mostrar muitos cachorros para a criança, você mostra outros animais e pergunta: \"Isso é um cachorro?\". A criança vai lembrar das caracteristicas que aprendeu e vai responder sim ou não. \n",
        "\n",
        "Para algoritmos de ML é a mesma coisa, nós vamos pegar um dataset de Treino e vamos passar no algoritmo, ja dando as respostas. Ele vai entender a correlação dos dados com a classificação. No nosso caso, ele vai relacionar as caracteristicas dos atributos com o tipo de flor. Depois vamos passar no algoritmo os dados de Teste, sem as respostas, e ele vai precisar lembrar do treino e classficar esses novos dados.\n",
        "\n",
        "Normalmente usamos 80% do Dataset para Treino e 20% para Teste, e para isso, vamos usar o **Scikit-Learn** com a função **train_test_split()**. Ele vai pegar o nosso dataset e fazer essa separação de forma aleatória."
      ],
      "metadata": {
        "id": "5APpMx0mhS2K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Separamos 20% para o teste\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "entradas_treino, entradas_teste, classes_treino, classes_teste = train_test_split(entradas, classes, test_size=0.2)\n",
        "\n",
        "print(f\"Formato das tablas de dados de treino {entradas_treino.shape} e teste {entradas_teste.shape}\")\n",
        "\n",
        "# Primeiras linhas do dataframe\n",
        "entradas_treino.head()\n",
        "\n",
        "print()\n",
        "\n",
        "classes_treino.head()"
      ],
      "metadata": {
        "id": "Vvz9cB4Zl1aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4278cca2-7c78-43ae-ad6e-4d2555bc4ca0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Formato das tablas de dados de treino (120, 2) e teste (30, 2)\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5         Iris-setosa\n",
              "12        Iris-setosa\n",
              "134    Iris-virginica\n",
              "141    Iris-virginica\n",
              "104    Iris-virginica\n",
              "Name: species, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **7 -** Agora vamos treinar o nosso modelo baseado no Algoritmo K-NN!\n",
        "\n",
        "Treinar um modelo no python é simples se usar o Scikit-Learn e treinar um modelo no Scikit-Learn é mais simples ainda: basta criar o classificador, e chamar o método fit().\n",
        "\n",
        "Uma observação sobre a sintaxe dos classificadores do scikit-learn:\n",
        "\n",
        "* O método fit(X,Y) recebe uma matriz ou dataframe X onde cada linha é uma amostra de aprendizado, e um array Y contendo as saídas esperadas do classificador, seja na forma de texto ou de inteiros;\n",
        "* O método predict(X) recebe uma matriz ou dataframe X onde cada linha é uma amostra de teste, retornando um array de classes;\n",
        "* Não esqueça de importar as bibliotécas com \"from sklearn.neighbors import KNeighborsClassifier\";\n",
        "\n",
        "Faça o treinamento do modelo usando um K = 3"
      ],
      "metadata": {
        "id": "p3CuLPv6r_9v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importa a biblioteca\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "#Cria o classificar KNN\n",
        "k = 3\n",
        "modelo = KNeighborsClassifier(n_neighbors=k)\n",
        "\n",
        "#Cria o modelo de machine learning\n",
        "modelo.fit(entradas_treino, classes_treino)"
      ],
      "metadata": {
        "id": "mlSA3bdZtF4q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "6a3ec5aa-9fcc-47a0-c764-96ff4081cd52"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **8 -** Agora que temos o modelo treinado, vamos testar!\n",
        "\n",
        "Para testar o modelo, vamos usar o método predict() com os dados de teste que separamos. Verifique se as resposta fazem sentido e determine a acurácia do modelo, ou seja, a quatidade de acertos que ele teve. Você vai precisar importar essa bibliotéca também com \"from sklearn.metrics import accuracy_score\""
      ],
      "metadata": {
        "id": "9Uxo_gA1tVn2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Para obter as previsões, basta chamar o método predict()\n",
        "classes_encontradas = modelo.predict(entradas_teste)\n",
        "print(\"Predição: {}\".format(classes_encontradas))\n",
        "print()\n",
        "\n",
        "# Para determinar a quantidade de acertos (acurácia)\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "acertos = accuracy_score(classes_teste, classes_encontradas)\n",
        "print(\"Acerto médio de classificação: \", acertos)"
      ],
      "metadata": {
        "id": "L_H4SSbEtuBg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60f00e62-1627-4444-adab-0cb774298882"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predição: ['Iris-virginica' 'Iris-setosa' 'Iris-versicolor' 'Iris-setosa'\n",
            " 'Iris-virginica' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-virginica']\n",
            "\n",
            "Acerto médio de classificação:  0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **9 -** Fizemos o treinamento do algortimo com um K igual a 3 e obtivemos uma determinada acurácia, mas se mudarmos esse valor de K, será que teremos resultados melhors?\n",
        "\n",
        "Faça um programa que treina o algoritmo com valores de K iniciando em 1 e indo até 26. Verifique a acurácia para cada K, armazenando esses valores em uma lista. Por fim, plote um gráfico com os valores de acurácia obtidos e determine se se houve mudanças de perfomance com K maiores."
      ],
      "metadata": {
        "id": "bsVedqNOugV4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#### Reposta loop for para diferentes k\n",
        "k_range = list(range(1, 26))\n",
        "acertos = []\n",
        "for k in k_range:\n",
        "  modelo = KNeighborsClassifier(n_neighbors)"
      ],
      "metadata": {
        "id": "qA1C4PVWvl80"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **10 -** Por fim, vamos exportar o nosso modelo para ultizar em nossas aplicações. Para fazer isso, usaremos o Pickle, com o método dump()"
      ],
      "metadata": {
        "id": "H-EBadAuxQXF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('knn_model.pkl', 'wb') as file:\n",
        "  pickle.dump(modelo,file)"
      ],
      "metadata": {
        "id": "7pX_JBTZwwst",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "97546bb9-223a-4772-edba-e58eebb73b60"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-9d27a685850f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'knn_model.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m   \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodelo\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'modelo' is not defined"
          ]
        }
      ]
    }
  ]
}