Classificador_de_SPAM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Classificador de SPAM",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nnly6nIRfr4T"
      },
      "source": [
        "# Verificador de SPAM\n",
        "---\n",
        "O objetivo desse projeto é desenvolver um classificador para verificar se determinada mensagem é SPAM ou não"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zNCfHEyQ8dZv",
        "outputId": "23e9e655-3a84-4047-ed47-5adc6980efad"
      },
      "source": [
        "!pip install nltk"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZnIgh379rWB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb58d2d8-508e-439c-daab-37b292da8224"
      },
      "source": [
        "import nltk\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import string\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AStk3WdB9GIX"
      },
      "source": [
        ""
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjmz932mgwEd"
      },
      "source": [
        "## Lendo o arquivo e transformando-o num DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0sBGxxf9p1D"
      },
      "source": [
        "messages = [line.rstrip() for line in open('SMSSpamCollection')]"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSnwuH3r-zNC"
      },
      "source": [
        "label_list = []\n",
        "text_list = []\n",
        "\n",
        "for i in range(len(messages)):\n",
        "  label_list.append(messages[i].split('\\t')[0])\n",
        "  text_list.append(messages[i].split('\\t')[1])\n",
        "\n",
        "df = pd.DataFrame(dict(zip(text_list, label_list)), index=['label']).T.reset_index().rename(columns={'index':'text'})"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "li_zWEGJ_DKy",
        "outputId": "51645f81-0a26-4f27-d2f8-a9f34f6d52cc"
      },
      "source": [
        "ax = df.label.value_counts().plot(kind='bar')\n",
        "plt.box(False)\n",
        "plt.title('Spam x non-spam messages\\n',fontsize=16)\n",
        "plt.xticks(rotation=360)\n",
        "for b in ax.patches:\n",
        "  plt.annotate(f\"{b.get_height()}\",(b.get_x()+ 0.2 , b.get_height()+80))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lkZoLyWbBtMr",
        "outputId": "228a26ad-5b62-4f69-b75f-6165bf035b55"
      },
      "source": [
        "df.label.value_counts()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ham     4518\n",
              "spam     642\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcnreyONinyL"
      },
      "source": [
        "Agora vamos criar uma coluna com o tamanho dos textos das mensagens"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYW4XUC3CDuj"
      },
      "source": [
        "df['length'] = df['text'].apply(lambda x: len(x))"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "i8pbbcu_CImc",
        "outputId": "12bed27c-233f-4bd4-b8e4-93dbd8a4f373"
      },
      "source": [
        "sns.kdeplot(x='length', hue='label', data=df)\n",
        "plt.box(False)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kR9Patl-iu0j"
      },
      "source": [
        "Podemos notar uma clara diferença entre mensagens spam e não spam. Essa diferença está na concentração do tamanho das mensagens."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4gLhoW30Eto3"
      },
      "source": [
        "# Pré-processamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aD5Pt7wEEtF3"
      },
      "source": [
        ""
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2SDwH5PjBwg"
      },
      "source": [
        "Primeiro criamos a função que tira as pontuações e depois tira as stopwords"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgIS5L4BIjqk"
      },
      "source": [
        "def pre_processing(mess):\n",
        "  sem_pont = [letra for letra in mess if letra not in string.punctuation]\n",
        "  texto = ''.join(sem_pont)\n",
        "  sem_stop = [palavra for palavra in texto.split() if palavra.lower() not in stopwords.words('english')]\n",
        "  return sem_stop"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NCw78M0IkEH-"
      },
      "source": [
        "Aplicando"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rM5aFG6SKhaS"
      },
      "source": [
        "df['text_clean'] = df['text'].apply(pre_processing)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlD8rrWsPTqS"
      },
      "source": [
        "Não utilizaremos essa coluna, mas aplicaremos a função para criar nosso transformador"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJsZ3HNuPT9P"
      },
      "source": [
        ""
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r28ZNQQYPdOV"
      },
      "source": [
        "transformer = CountVectorizer(analyzer=pre_processing).fit(df['text'])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2i79DiIyP8_m"
      },
      "source": [
        "text3 = transformer.transform([df['text'][3]])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "etbC1661TQkZ"
      },
      "source": [
        "textos = transformer.transform([df['text']])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-b2vztxh2fZ"
      },
      "source": [
        "## Método TF-idf\n",
        "---\n",
        "A ideia do TF-idf é redimensionar a frequência das palavras pela frequência com que aparecem em todos os documentos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C32eybnTSUc5"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfTransformer"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F7Tyy6j_SfqV"
      },
      "source": [
        "transformer_tf = TfidfTransformer()\n",
        "transformer_tf = transformer_tf.fit(textos)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6jagJDSPkpnt"
      },
      "source": [
        "Vamos ver um exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTTCtN9iTBB3",
        "outputId": "a0b31645-3e52-45ef-8962-5fb8844ff90b"
      },
      "source": [
        "tfidf4 = transformer_tf.transform(text3)\n",
        "print(tfidf4)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 9554)\t0.5187068876586773\n",
            "  (0, 7186)\t0.43912355218815696\n",
            "  (0, 6222)\t0.25935344382933867\n",
            "  (0, 6204)\t0.25935344382933867\n",
            "  (0, 5261)\t0.25935344382933867\n",
            "  (0, 4629)\t0.25935344382933867\n",
            "  (0, 4068)\t0.5187068876586773\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2tRiUNpRUz6O"
      },
      "source": [
        "## Modelagem preditiva"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YB5MP4Q-U3TT"
      },
      "source": [
        "**Modelo Naive Bayes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSFalpwAWggx"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3tJXfdQYoWD"
      },
      "source": [
        "text_tfidf = transformer_tf.transform(textos)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ccjypzcpekXy"
      },
      "source": [
        "Separando em treino e teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ow2a15T8fOaT"
      },
      "source": [
        "txt_treino, txt_teste, label_treino, label_teste = train_test_split(df['text'], df['label'])"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzXwDHxFfe7F"
      },
      "source": [
        "O scikit learn nos fornece a função Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hI_tG04kg_bn"
      },
      "source": [
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJX5Ff3rhGrb"
      },
      "source": [
        "pipeline = Pipeline([\n",
        "                    ('bow', CountVectorizer()),\n",
        "                    ('tfidf', TfidfTransformer()),\n",
        "                    ('classifiet', MultinomialNB())\n",
        "])"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYH6T70EidBs",
        "outputId": "f938aee3-f6bb-484f-a317-0a3af11a8540"
      },
      "source": [
        "pipeline.fit(txt_treino, label_treino)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('bow',\n",
              "                 CountVectorizer(analyzer='word', binary=False,\n",
              "                                 decode_error='strict',\n",
              "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
              "                                 input='content', lowercase=True, max_df=1.0,\n",
              "                                 max_features=None, min_df=1,\n",
              "                                 ngram_range=(1, 1), preprocessor=None,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
              "                                 tokenizer=None, vocabulary=None)),\n",
              "                ('tfidf',\n",
              "                 TfidfTransformer(norm='l2', smooth_idf=True,\n",
              "                                  sublinear_tf=False, use_idf=True)),\n",
              "                ('classifiet',\n",
              "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5ud9wDqigH4"
      },
      "source": [
        "pred = pipeline.predict(df['text'])"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ua_VoVphfpvQ"
      },
      "source": [
        "## Avaliando o modelo\n",
        "---\n",
        "Para avaliar nosso classificador, vamos utilizar o relatório de classificação e a matriz de confusão."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8suv7PaTi44S"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZEmYLz_i9Zc",
        "outputId": "0261fe81-7678-4f49-e8e1-b3fb481f03eb"
      },
      "source": [
        "print(classification_report(df['label'], pred))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.96      1.00      0.98      4518\n",
            "        spam       1.00      0.71      0.83       642\n",
            "\n",
            "    accuracy                           0.96      5160\n",
            "   macro avg       0.98      0.86      0.91      5160\n",
            "weighted avg       0.97      0.96      0.96      5160\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "9kWeW6Lnk9ck",
        "outputId": "53b0525d-2523-45be-ee8b-d3e10cde5a9b"
      },
      "source": [
        "sns.heatmap(confusion_matrix(df['label'], pred), annot=True, cmap='Blues', fmt='g')\n",
        "plt.suptitle('Matriz de confusão do classificador\\n', fontsize=16)\n",
        "plt.title('0 = ham // 1 = spam');"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uyZ0acigl8OB"
      },
      "source": [
        "Nosso modelo demonstrou um bom desempenho, conseguimos classificar as mensagems que são e as que não são spam com uma precisão alta."
      ]
    }
  ]
}