SMS_Spam_Detection.ipynb

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        "<a href=\"https://colab.research.google.com/github/1002Sam/SMS-Spam-Detection/blob/main/SMS_Spam_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "### Intro"
      ],
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      "cell_type": "markdown",
      "source": [
        "In today's digital age, Short Message Service (SMS) has become an essential means of communication, enabling quick and convenient exchange of information. However, with the widespread use of SMS, the prevalence of spam messages has also risen significantly. SMS spam, also known as unwanted or unsolicited text messages, often includes advertisements, phishing attempts, fraudulent schemes, and other malicious content.\n",
        "\n",
        "The SMS Spam Detection Project aims to develop a robust and efficient system that can automatically identify and filter spam messages, protecting users from potential security risks and nuisances. By leveraging machine learning and natural language processing techniques, this project seeks to create a model that can accurately distinguish between legitimate and spam messages in real-time."
      ],
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      "source": [
        "### Importing Libraries & Data"
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      "source": [
        "#importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "sns.set()\n",
        "plt.style.use('ggplot')"
      ]
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    {
      "cell_type": "code",
      "source": [
        "#dataset filepath in my local system\n",
        "smsdata = pd.read_csv('/content/spam.csv', encoding='latin-1')[['v1', 'v2']] #See text"
      ],
      "metadata": {
        "id": "S-4L4Bhst8oJ"
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      "execution_count": 150,
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      "source": [
        "Character Encoding: encoding='latin-1'\n",
        "\n",
        "Character encoding is a method used to represent characters (letters, symbols, and other text elements) in a computer's memory or storage. Different character encodings are used to handle different languages and character sets. If the file contains characters that are outside the standard ASCII range, using the correct encoding is essential to ensure that the data is read and interpreted correctly.\n",
        "By using encoding='latin-1', you are instructing Pandas to read the file using the Latin-1 encoding, which can handle a wider range of characters than the default UTF-8 encoding. This should resolve the UnicodeDecodeError and allow you to read the CSV file successfully.\n",
        "\n",
        "[['v1', 'v2']] taking only specified columns from dataset."
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "### Small EDA"
      ],
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        "id": "tlzA6iYDVrpV"
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    {
      "cell_type": "code",
      "source": [
        "#renaming columns as label and message\n",
        "smsdata.columns = ['label', 'message']\n",
        "smsdata"
      ],
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              "     label                                            message\n",
              "0      ham  Go until jurong point, crazy.. Available only ...\n",
              "1      ham                      Ok lar... Joking wif u oni...\n",
              "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3      ham  U dun say so early hor... U c already then say...\n",
              "4      ham  Nah I don't think he goes to usf, he lives aro...\n",
              "...    ...                                                ...\n",
              "5567  spam  This is the 2nd time we have tried 2 contact u...\n",
              "5568   ham              Will Ì_ b going to esplanade fr home?\n",
              "5569   ham  Pity, * was in mood for that. So...any other s...\n",
              "5570   ham  The guy did some bitching but I acted like i'd...\n",
              "5571   ham                         Rofl. Its true to its name\n",
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              "[5572 rows x 2 columns]"
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              "      <td>Rofl. Its true to its name</td>\n",
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              "ham     4825\n",
              "spam     747\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
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        "#sns.countplot(smsdata['label'])"
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      "execution_count": 154,
      "outputs": []
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              "      message                                                               \n",
              "        count unique                                                top freq\n",
              "label                                                                       \n",
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              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-88c87465-5765-4f4f-9101-84fbdd58296e button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0e3f0619-7fac-4ad0-ad71-96cfc0da143b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0e3f0619-7fac-4ad0-ad71-96cfc0da143b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating 'length' column from length of 'message' column\n",
        "smsdata['length']=smsdata['message'].apply(len)\n",
        "smsdata.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YIeKzsS9OLJF",
        "outputId": "e0b9c559-2515-4f2b-9888-dcdfa5628bcb"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  label                                            message  length\n",
              "0   ham  Go until jurong point, crazy.. Available only ...     111\n",
              "1   ham                      Ok lar... Joking wif u oni...      29\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
              "3   ham  U dun say so early hor... U c already then say...      49\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro...      61"
            ],
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              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>61</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-ba6abf04-bc22-46ac-9343-4c9aee80c87f button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7308a222-3836-4fe8-b391-4a9d6bb342dd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7308a222-3836-4fe8-b391-4a9d6bb342dd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "smsdata.length.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4wQ-TeiSwel",
        "outputId": "fac692f0-32d7-48aa-d6df-84822a87ad90"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    5572.000000\n",
              "mean       80.118808\n",
              "std        59.690841\n",
              "min         2.000000\n",
              "25%        36.000000\n",
              "50%        61.000000\n",
              "75%       121.000000\n",
              "max       910.000000\n",
              "Name: length, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "smsdata['length'].plot(bins=50,kind='hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "5Q9NWUGrOmlJ",
        "outputId": "d6fa181a-9569-475e-e98f-a0594788124c"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 158
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The histogram can provide valuable insights into the distribution of message lengths, helping you understand the typical length of SMS messages in your dataset. It can also be useful for identifying potential outliers or unusual patterns in the message lengths.\n",
        "\n",
        "Describe function also give some useful insights."
      ],
      "metadata": {
        "id": "lPP8iHoBQkU-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Playing, code for the message (string) with a length of 120 characters\n",
        "smsdata[smsdata['length']==120]['message'].iloc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "vgbppZLaNd7K",
        "outputId": "ef37ebfb-0f8d-4584-bd5b-5b295b859b7a"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'SMS. ac Sptv: The New Jersey Devils and the Detroit Red Wings play Ice Hockey. Correct or Incorrect? End? Reply END SPTV'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Text Preprocessing"
      ],
      "metadata": {
        "id": "_-VSEgKxVKS8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import nltk\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rrExzETsNd3v",
        "outputId": "65385c4b-ab0e-4165-c801-d7555648d1f4"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating ps instance for PorterStemmer class\n",
        "ps = PorterStemmer()"
      ],
      "metadata": {
        "id": "61hiNDTcNd1w"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Stemming\n",
        "corpus = []\n",
        "for i in range(0, len(smsdata)):\n",
        "    review = re.sub('[^a-zA-Z]', ' ', smsdata['message'][i])\n",
        "    review = review.lower()\n",
        "    review = review.split()\n",
        "\n",
        "    review = [ps.stem(word) for word in review if not word in stopwords.words('english')]\n",
        "    review = ' '.join(review)\n",
        "    corpus.append(review)"
      ],
      "metadata": {
        "id": "Zek0d4mBNdzp"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Preprocessed list of sentences\n",
        "#corpus"
      ],
      "metadata": {
        "id": "GPCy8uXGXjLk"
      },
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the Bag of Words model with Vectorization\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer(max_features=2500)\n",
        "X = cv.fit_transform(corpus).toarray()"
      ],
      "metadata": {
        "id": "XJ299ORmNdpQ"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Array of vector matrix of corpus limited to 2500 token features\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MQ7rHq6JYGbc",
        "outputId": "03dbba5f-5940-461b-eaf5-ec6d889d7977"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#X.shape"
      ],
      "metadata": {
        "id": "jPxeLk-cqvq3"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Spam/Class Prediction Model by BOG"
      ],
      "metadata": {
        "id": "2YvABHkkccK1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y=pd.get_dummies(smsdata['label']) #OHE for label categorical column\n",
        "y=y.iloc[:,1].values #This step to reduce variables if single column giving us info of two,\n",
        "y                    #resulted 'y' will be a NumPy array containing binary values (0 and 1), where 1 represents 'spam' and 0 represents 'ham'."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nD3U-Wf_ZQad",
        "outputId": "df7495e7-220c-40b2-a9c6-8709b7623d79"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 0, 0, 0], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train Test Split\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0) #here X is message's numerical vector matrix and y is label's array"
      ],
      "metadata": {
        "id": "avFWKOKQZmWR"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training model using Naive bayes classifier\n",
        "\n",
        "from sklearn.naive_bayes import MultinomialNB  #GaussianNB also can be used but for continuous or floating based features\n",
        "spam_detect_model = MultinomialNB().fit(X_train, y_train)\n",
        "spam_detect_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "X_vsEAqNYGSW",
        "outputId": "fb4e9198-0e4a-41da-dd35-07514a2a8ba3"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_pred=spam_detect_model.predict(X_train)\n",
        "y_pred=spam_detect_model.predict(X_test)"
      ],
      "metadata": {
        "id": "E5Px5fZadM5D"
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report"
      ],
      "metadata": {
        "id": "JR-x4YD0YGGC"
      },
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating accuracy on train and test\n",
        "train_accuracy = accuracy_score(y_train,X_pred)\n",
        "test_accuracy = accuracy_score(y_test,y_pred)\n",
        "\n",
        "print(\"The accuracy on train dataset is\", train_accuracy)\n",
        "print(\"The accuracy on test dataset is\", test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8NB2DJRrjXnl",
        "outputId": "a0bd843b-a8ca-48e0-d607-a003c40d58fe"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy on train dataset is 0.9885573255553063\n",
            "The accuracy on test dataset is 0.9865470852017937\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_m = confusion_matrix(y_test, y_pred)\n",
        "confusion_m   #Refer below text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJ0gJQm9YEYg",
        "outputId": "9e8aff12-fd5c-4a9c-c44b-480276633a15"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[943,   6],\n",
              "       [  9, 157]])"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "REMARK - HERE we try to minimize FALSE POSITIVE by optimizing PRECISION because the mathematical model wrongly predicting important mail as SPAM."
      ],
      "metadata": {
        "id": "kEe5zTHGmWp_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0n3k14ZYEJW",
        "outputId": "02a4f406-12e8-4bdf-908f-087cb884687f"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       949\n",
            "           1       0.96      0.95      0.95       166\n",
            "\n",
            "    accuracy                           0.99      1115\n",
            "   macro avg       0.98      0.97      0.97      1115\n",
            "weighted avg       0.99      0.99      0.99      1115\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### By using TF-IDF"
      ],
      "metadata": {
        "id": "N0vlRugvm81t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('wordnet')\n",
        "from nltk.stem import WordNetLemmatizer   #lemmatizing algorithm used for word normalization"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6zu_rMPFohiN",
        "outputId": "2a3eaf6c-a9f4-420f-c538-d9423010942d"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating instance named stemmer for class PorterStemmer()\n",
        "lemmatizer = WordNetLemmatizer()"
      ],
      "metadata": {
        "id": "Q1z1tGZUop7e"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Lemmatization\n",
        "corpus1 = []\n",
        "for i in range(0, len(smsdata)):\n",
        "    review = re.sub('[^a-zA-Z]', ' ', smsdata['message'][i])\n",
        "    review = review.lower()\n",
        "    review = review.split()\n",
        "\n",
        "    review = [lemmatizer.lemmatize(word) for word in review if not word in stopwords.words('english')]\n",
        "    review = ' '.join(review)\n",
        "    corpus1.append(review)"
      ],
      "metadata": {
        "id": "-n2ZxLq3pJ5v"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#processed list sentences by lemmatization\n",
        "#corpus1"
      ],
      "metadata": {
        "id": "DZKQvBiHqC4m"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the TF-IDF model\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "cv1 = TfidfVectorizer()\n",
        "X = cv.fit_transform(corpus).toarray()"
      ],
      "metadata": {
        "id": "xFU2VRBAnExs"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#X #see TF-IDF has given more importance to words but not others"
      ],
      "metadata": {
        "id": "mwRbRTNtnEuB"
      },
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#X.shape"
      ],
      "metadata": {
        "id": "5zUBixvYnEr5"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Spam-Ham Model by TF-IDF"
      ],
      "metadata": {
        "id": "nejvAV02rPhe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y=pd.get_dummies(smsdata['label']) #OHE for label categorical column\n",
        "y=y.iloc[:,1].values #This step to reduce variables if single column giving us info of two,\n",
        "y                    #resulted 'y' will be a NumPy array containing binary values (0 and 1), where 1 represents 'spam' and 0 represents 'ham'."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nTL67vvdnEp9",
        "outputId": "048346e0-f50a-4936-ab43-f85503dc914d"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 0, 0, 0], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shape of X1:\", X.shape)\n",
        "print(\"Shape of y1:\", y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EtYjEMXEtquz",
        "outputId": "d8b147e1-53d0-4510-8bb2-d8f00c8f9718"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X1: (5572, 2500)\n",
            "Shape of y1: (5572,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train Test Split\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0) #here X is message's numerical vector matrix and y is label's array"
      ],
      "metadata": {
        "id": "1Q53b27QnEnr"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training model using Naive bayes classifier\n",
        "\n",
        "from sklearn.naive_bayes import MultinomialNB  #GaussianNB also can be used but for continuous or floating based features\n",
        "spam_detect_model1 = MultinomialNB().fit(X_train, y_train)\n",
        "spam_detect_model1"
      ],
      "metadata": {
        "id": "bwK4wxD9YD5-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "eba12065-3961-402b-93ff-00454d2cb214"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_pred=spam_detect_model.predict(X_train)\n",
        "y_pred=spam_detect_model.predict(X_test)"
      ],
      "metadata": {
        "id": "4Doe45Z3YDdZ"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report"
      ],
      "metadata": {
        "id": "zW9VSVeQu3Ce"
      },
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating accuracy on train and test\n",
        "train_accuracy = accuracy_score(y_train,X_pred)\n",
        "test_accuracy = accuracy_score(y_test,y_pred)\n",
        "\n",
        "print(\"The accuracy on train dataset is\", train_accuracy)\n",
        "print(\"The accuracy on test dataset is\", test_accuracy)    #not much difference in accuracies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xUJqzOcFu-vE",
        "outputId": "e6503705-a119-4b7e-cac2-2113a07f038b"
      },
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy on train dataset is 0.9885573255553063\n",
            "The accuracy on test dataset is 0.9865470852017937\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_m = confusion_matrix(y_test, y_pred)\n",
        "confusion_m   #Refer text in BOG confusion matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_o5aJmrTvIwC",
        "outputId": "397016e9-e89f-4f43-f6e8-cd04a8019870"
      },
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[943,   6],\n",
              "       [  9, 157]])"
            ]
          },
          "metadata": {},
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRX2a-dNvX5l",
        "outputId": "0b702660-1fb9-4095-b5c2-7df50f1bf92e"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       949\n",
            "           1       0.96      0.95      0.95       166\n",
            "\n",
            "    accuracy                           0.99      1115\n",
            "   macro avg       0.98      0.97      0.97      1115\n",
            "weighted avg       0.99      0.99      0.99      1115\n",
            "\n"
          ]
        }
      ]
    }
  ]
}