Email_spam_naive_bayes.ipynb

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      "name": "Email_spam_naive_bayes.ipynb",
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      "mount_file_id": "1gD1rlxIk3FMLixwZv9EaS0qWfNX3gP1M",
      "authorship_tag": "ABX9TyMYlmMTTUrJ2EiAvOiQt7lQ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Vivek2509/Email_spam_detection/blob/main/Email_spam_naive_bayes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhPHJdEdJ_I3"
      },
      "source": [
        "# Email Spam Detector"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqp-12jGVJdv"
      },
      "source": [
        "## Import library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNKl_1G4J0fL"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xydkCbPfLXY2"
      },
      "source": [
        "## Import dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AM4q5jxKK7DC"
      },
      "source": [
        "email = pd.read_csv('/content/drive/My Drive/email/emails.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABM0zXiALIbB",
        "outputId": "8b33acbf-b86a-4067-8993-2858286a71f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "email.head(10)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>spam</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Subject: naturally irresistible your corporate...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Subject: the stock trading gunslinger  fanny i...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Subject: unbelievable new homes made easy  im ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Subject: 4 color printing special  request add...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Subject: do not have money , get software cds ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Subject: great nnews  hello , welcome to medzo...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Subject: here ' s a hot play in motion  homela...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Subject: save your money buy getting this thin...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Subject: undeliverable : home based business f...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Subject: save your money buy getting this thin...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text  spam\n",
              "0  Subject: naturally irresistible your corporate...     1\n",
              "1  Subject: the stock trading gunslinger  fanny i...     1\n",
              "2  Subject: unbelievable new homes made easy  im ...     1\n",
              "3  Subject: 4 color printing special  request add...     1\n",
              "4  Subject: do not have money , get software cds ...     1\n",
              "5  Subject: great nnews  hello , welcome to medzo...     1\n",
              "6  Subject: here ' s a hot play in motion  homela...     1\n",
              "7  Subject: save your money buy getting this thin...     1\n",
              "8  Subject: undeliverable : home based business f...     1\n",
              "9  Subject: save your money buy getting this thin...     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDdaG4WXLJ3t",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(email)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5728"
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          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVjQGNacVg8r",
        "outputId": "75410dbe-2b40-4f59-afc8-cff406dd1186",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "email.isna().sum()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "text    0\n",
              "spam    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "email.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5728, 2)"
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          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yOCzfFx8awWh",
        "outputId": "29efed64-558b-4707-8e3e-6a43de593751",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "email.tail()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>spam</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5723</th>\n",
              "      <td>Subject: re : research and development charges...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5724</th>\n",
              "      <td>Subject: re : receipts from visit  jim ,  than...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5725</th>\n",
              "      <td>Subject: re : enron case study update  wow ! a...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5726</th>\n",
              "      <td>Subject: re : interest  david ,  please , call...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5727</th>\n",
              "      <td>Subject: news : aurora 5 . 2 update  aurora ve...</td>\n",
              "      <td>0</td>\n",
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            ],
            "text/plain": [
              "                                                   text  spam\n",
              "5723  Subject: re : research and development charges...     0\n",
              "5724  Subject: re : receipts from visit  jim ,  than...     0\n",
              "5725  Subject: re : enron case study update  wow ! a...     0\n",
              "5726  Subject: re : interest  david ,  please , call...     0\n",
              "5727  Subject: news : aurora 5 . 2 update  aurora ve...     0"
            ]
          },
          "metadata": {
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          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1xyVuvSLNf7"
      },
      "source": [
        "## Cleaning the text"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hOsI2hHbLVhA",
        "outputId": "124ff149-5825-4ec0-fe60-71cff3596d7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "import re\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "corpus = []\n",
        "for i in range(0, len(email)):\n",
        "  e_mail = re.sub('[^a-zA-Z]', ' ', email['text'][i])\n",
        "  e_mail = e_mail.split()\n",
        "  ps = PorterStemmer()\n",
        "  e_mail = [ps.stem(word) for word in e_mail if not word in set(stopwords.words('english'))]\n",
        "  e_mail = ' '.join(e_mail)\n",
        "  corpus.append(e_mail)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_DDypCjL_p9",
        "outputId": "011aa853-7dfe-4610-8bc3-aebe67456462",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "corpus[2509]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'subject enron mid year perform manag process enron mid year perform manag process begun process requir select suggest review provid perform relat feedback may also request provid feedback fellow employe need access perform manag system pep http pep enron com question direct pep help desk follow number u option europ option canada canada employe e mail question perfmgmt enron com log pep enter user id password provid log immedi prompt chang secur password user id password user id wkamin password welcom'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVMCS0fZN35x"
      },
      "source": [
        "## Creating the Bag of Words model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ESG0YkwcNC4e"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer()\n",
        "X = cv.fit_transform(corpus).toarray()\n",
        "y = email['spam']"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdmCLHQkOJf7",
        "outputId": "3afe4a6b-2b70-4ed1-c6e6-e1ce76159b82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(X)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5728"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwqjGQ4jQs_p",
        "outputId": "d77db408-6efe-4a3a-a49d-b3b60f52ddfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5728, 25607)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RSCl5sw9OMaC",
        "outputId": "b80be939-6f14-44b7-c20e-f9ad6269289d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(y)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5728"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YuHcG1UtORXS"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O95pTScKOOjF"
      },
      "source": [
        "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)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_l3ghzQOXyo"
      },
      "source": [
        "## Training the Naive Bayes model on the Training set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aL_Nil98b3IC"
      },
      "source": [
        "### 1.GaussianNB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WuvuwU2sOU8C",
        "outputId": "2fc7d7e7-3277-4119-cc0f-99d98072e6b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "GN_classifier = GaussianNB()\n",
        "GN_classifier.fit(X_train, y_train)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fit1kl2XObLw",
        "outputId": "76bee4d2-0f4f-45b5-b62d-a63cf9b1bfc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "GN_score = GN_classifier.score(X_test,y_test)\n",
        "GN_score"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9607329842931938"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9kO7900OkFe"
      },
      "source": [
        "# Predicting the Test set results\n",
        "y_GN_pred = GN_classifier.predict(X_test)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qwE6VPoOmOt",
        "outputId": "a4ffc04b-ef11-4ef9-9ca3-53635eddf4ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_GN_pred"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 0, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6k5FFOIKPRst"
      },
      "source": [
        "### 2.MultinomialNB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRC8D4FLRz0y",
        "outputId": "feed879c-d0e6-4639-c2fb-a1a073220140",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "MN_classifier = MultinomialNB()\n",
        "MN_classifier.fit(X_train, y_train)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rs27t2kSZKP",
        "outputId": "c1c3f5d7-f168-49dc-8c9e-f3becbf43888",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "MN_score = MN_classifier.score(X_test,y_test)\n",
        "MN_score"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9825479930191972"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hEWHgFbqSgGF"
      },
      "source": [
        "y_MN_pred = MN_classifier.predict(X_test)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLfXX4c6Onh_"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6z67gQ0OrgZ",
        "outputId": "c2912ca5-f413-45dd-e9c7-9dc99e3d051c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "GN_cm = confusion_matrix(y_test, y_GN_pred)\n",
        "print(GN_cm)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[875  10]\n",
            " [ 35 226]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XgHY9TTSqiI",
        "outputId": "9409773e-35c0-40d6-e35e-01e5b43ecbbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "MN_cm = confusion_matrix(y_test, y_MN_pred)\n",
        "print(MN_cm)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[870  15]\n",
            " [  5 256]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEwvGRp1dEN0"
      },
      "source": [
        "## Compare Both models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmxM8H3tS4jw",
        "outputId": "276dd9c6-c9cc-472e-a3cb-efa12a6c4ec7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "models = pd.DataFrame({\"GaussianNB\": GN_score,\n",
        "                           \"MultinomialNB\": MN_score\n",
        "                           },\n",
        "                          index=[0])\n",
        "\n",
        "models.T.plot.bar(title=\"Compare different models\",\n",
        "                      legend=False)\n",
        "plt.xticks(rotation=0);"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdZnBdTFPHXJ"
      },
      "source": [
        "## SAVE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8noTVjCUh4R"
      },
      "source": [
        "import pickle\n",
        "pickle.dump(MN_classifier,open(\"/content/drive/My Drive/email/Email_spam_naive_bayes_MN.pkl\",\"wb\"))"
      ],
      "execution_count": 25,
      "outputs": []
    }
  ]
}