EMAIL_SPAM_DETECTION_WITH_MACHINE_LEARNING.ipynb

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      "source": [
        "Import the necessary libraries"
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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n"
      ],
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        "id": "nm7o12nhbUot"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "Load and preprocess the email data"
      ],
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        "id": "UfCIepAYbhz9"
      }
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    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/spam.csv', encoding='latin1')\n",
        "data.head()\n",
        "\n"
      ],
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              "     v1                                                 v2 Unnamed: 2  \\\n",
              "0   ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
              "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
              "3   ham  U dun say so early hor... U c already then say...        NaN   \n",
              "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
              "\n",
              "  Unnamed: 3 Unnamed: 4  \n",
              "0        NaN        NaN  \n",
              "1        NaN        NaN  \n",
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              "     v1                                                 v2\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
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      "source": [
        "data.columns = ['label', 'email_text']  # Rename the columns"
      ],
      "metadata": {
        "id": "9tQpimRudMZn"
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      "execution_count": null,
      "outputs": []
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        "data.dropna(inplace=True)  # Drop any rows with missing values"
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      "execution_count": null,
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      "source": [
        "data['email_text'] = data['email_text'].str.strip()  # Remove leading/trailing white spaces"
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        "id": "BAzjG0iVdTA9"
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      "execution_count": null,
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    {
      "cell_type": "markdown",
      "source": [
        " Perform data visualization"
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      "cell_type": "markdown",
      "source": [
        "1)count plot"
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      "cell_type": "code",
      "source": [
        "sns.countplot(data=data, x='label')\n",
        "plt.xlabel('Label')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of Spam vs Non-spam Emails')\n",
        "plt.show()\n"
      ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2)pie chart"
      ],
      "metadata": {
        "id": "JYFGAG1reR3w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "label_counts = data['label'].value_counts()\n",
        "plt.pie(label_counts, labels=label_counts.index, autopct='%1.1f%%')\n",
        "plt.title('Distribution of Spam vs Non-spam Emails')\n",
        "plt.axis('equal')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "e9-DIlXXeUuW",
        "outputId": "ccf71123-a619-4b96-b380-2569e14ffeb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "3)Histogram"
      ],
      "metadata": {
        "id": "XJt-IJCOergB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.hist(data['email_text'].str.len(), bins=20)\n",
        "plt.xlabel('Email Text Length')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of Email Text Lengths')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "VkkREVZNevao",
        "outputId": "360331d0-40ea-43e5-ce93-d3bb0089eaf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        " Preprocess the data"
      ],
      "metadata": {
        "id": "F3HccnXwgt53"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['label'] = data['label'].map({'ham': 0, 'spam': 1})"
      ],
      "metadata": {
        "id": "DLHk-ajcg3cd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split the dataset into features (email text) and target (spam or non-spam)"
      ],
      "metadata": {
        "id": "XBHBUob9hBS5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data['email_text']  # Email text column\n",
        "y = data['label']  # Spam or non-spam label column"
      ],
      "metadata": {
        "id": "IkW7-g4Og7lp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " Perform feature extraction using TF-IDF vectorizer"
      ],
      "metadata": {
        "id": "Kd6Qrv0ahGuv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer = TfidfVectorizer()\n",
        "X = vectorizer.fit_transform(X)"
      ],
      "metadata": {
        "id": "3mYLvwhghJ11"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split the data into training and testing sets"
      ],
      "metadata": {
        "id": "aGB0vwszhMXF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "Ofq96DwJhO-F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create and train the Support Vector Machine (SVM) classifier"
      ],
      "metadata": {
        "id": "5AAOlv9UhUU-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = SVC(kernel='linear')\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "pDigwQgUhZfA",
        "outputId": "fc972c9f-680d-4297-edda-2bbfeb2ed36d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make predictions on the test set"
      ],
      "metadata": {
        "id": "RfQpkKJlhdIl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "uPZEpf7HhgYk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluate the model"
      ],
      "metadata": {
        "id": "7ia1UPSQhi2g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "confusion_mat = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_mat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GOCxknOxhmXP",
        "outputId": "fb4327a9-c4d7-4238-9bf4-a3c7489f3bb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.979372197309417\n",
            "Confusion Matrix:\n",
            "[[963   2]\n",
            " [ 21 129]]\n"
          ]
        }
      ]
    }
  ]
}