EO_2_emails_naive_bayes.ipynb

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        "# Estudo Orientado | Unidade 2: Calssificação de emails com Naive-Bayes\n",
        "\n",
        "Após criar a base de dados, realizar a conversão dos atributos, treinar o classificador e medir o seu desempenho, realize predições para dois casos de teste.\n",
        "\n",
        "Por fim, no mesmo notebook, escreva um breve parágrafo com suas considerações/hipóteses sobre o resultado obtido."
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      "source": [
        "# Importando bibliotecas\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "remetente = ['promo@icasas.com','promo@icasas.com','promo@icasas.com','promotions@gmail.com','promotions@gmail.com','atendente@loja.com','atendente@loja.com']\n",
        "assunto = ['Aproveite agora','Promocao imperdivel','Acesse agora','Videos prontos','Pague agora','Aproveite as promocoes','Promocao']\n",
        "resultado = ['Sim','Sim','Sim','Sim','Sim','Nao','Nao']\n",
        "\n",
        "df = pd.DataFrame({'remetente':remetente, 'assunto': assunto,'resultado':resultado })\n",
        "df"
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              "              remetente                 assunto resultado\n",
              "0      promo@icasas.com         Aproveite agora       Sim\n",
              "1      promo@icasas.com     Promocao imperdivel       Sim\n",
              "2      promo@icasas.com            Acesse agora       Sim\n",
              "3  promotions@gmail.com          Videos prontos       Sim\n",
              "4  promotions@gmail.com             Pague agora       Sim\n",
              "5    atendente@loja.com  Aproveite as promocoes       Nao\n",
              "6    atendente@loja.com                Promocao       Nao"
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        "target = df['resultado']\n",
        "\n",
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        "inputs"
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            "text/plain": [
              "              remetente                 assunto\n",
              "0      promo@icasas.com         Aproveite agora\n",
              "1      promo@icasas.com     Promocao imperdivel\n",
              "2      promo@icasas.com            Acesse agora\n",
              "3  promotions@gmail.com          Videos prontos\n",
              "4  promotions@gmail.com             Pague agora\n",
              "5    atendente@loja.com  Aproveite as promocoes\n",
              "6    atendente@loja.com                Promocao"
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              "      <td>promo@icasas.com</td>\n",
              "      <td>Acesse agora</td>\n",
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              "      <th>3</th>\n",
              "      <td>promotions@gmail.com</td>\n",
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        "# Definindo variável de Encoding (strings para numérico)\n",
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            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#  conversão dos atributos. Vou criar isso diretamente, ao invés de criar uma variavel numerica\n",
        "\n",
        "inputs['remetente'] = label_encoder.fit_transform(inputs['remetente'])\n",
        "\n",
        "inputs['assunto'] = label_encoder.fit_transform(inputs['assunto'])\n",
        "\n",
        "inputs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "894816e2",
      "metadata": {
        "id": "894816e2"
      },
      "outputs": [],
      "source": [
        "# Definindo o classificador Gaussian Naive Bayes\n",
        "\n",
        "classifier = GaussianNB()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "a7cd2300",
      "metadata": {
        "id": "a7cd2300",
        "outputId": "90da9191-d05b-4d92-d71a-bbb7557686e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Treinando o classificador Gaussian Naive Bayes\n",
        "\n",
        "classifier.fit(inputs, target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "b1b483d6",
      "metadata": {
        "id": "b1b483d6",
        "outputId": "16c1b345-0e4c-4bd7-a478-24e5b5ba888b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Calculando o score do classificador\n",
        "\n",
        "classifier.score(inputs, target)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Realizando predição em um dado novo"
      ],
      "metadata": {
        "id": "dqyUgCRV8Vmj"
      },
      "id": "dqyUgCRV8Vmj"
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "6505abc3",
      "metadata": {
        "id": "6505abc3",
        "outputId": "54b0b23b-5551-4b84-f357-115c9040bf5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Sim'], dtype='<U3')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# se o email for promo@icasas.com e o assunto for Pague agora\n",
        "\n",
        "classifier.predict([[1,3]]) \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "eda5c5dd",
      "metadata": {
        "id": "eda5c5dd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "171e930d-7007-46cd-efd5-29a7d7eef996"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Nao'], dtype='<U3')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# se o email for atendente@loja.com e o assunto for Pague agora\n",
        "classifier.predict([[0,3]]) "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusão\n",
        "\n",
        "Os filtros de SPAM, assim como as classificações de textos é a aplicação mais comum do uso de modelo de Naive-Bayes. Isto porque, Naive-Bayes gera uma tabela de probabilidade a partir de uma base de dados histórica, no intuito de encontrar uma correlação entre os atributos previsores e o target. Embora uma das grandes desvantagens do modelo probabilistico de Naive-Bayes seja a forma como este trata os atributos previsores de forma independente (cada par de caracteristica é independente), nos exemplos testados acima os resultados estavam corretos. Isto pode ter sido por temos obtido um bom score de classificação. No entanto, talvez com uma base de dados maior e com mais atributos previsores o score do classificador talvez não fosse tão bom e as predições de dados novos falhassem.."
      ],
      "metadata": {
        "id": "L2EgDSen9E-b"
      },
      "id": "L2EgDSen9E-b"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5z_X_fJ98eRN"
      },
      "id": "5z_X_fJ98eRN",
      "execution_count": null,
      "outputs": []
    }
  ],
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    "colab": {
      "name": "EO_2_emails_naive-bayes.ipynb",
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