Spam_Detection_using_RNN_.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/gokulakrishnanbalaji/Spam-classification-using-RNN/blob/main/Spam_Detection_using_RNN_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##SPAM DETECTION\n"
      ],
      "metadata": {
        "id": "607Y11ibFWOb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "bxJjg2Z5tOZm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**RNN**"
      ],
      "metadata": {
        "id": "sNb5d4WSWghH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "Z43wb6VfWpTQ",
        "outputId": "72de8fb4-9819-417a-8089-2921de9802b9",
        "collapsed": true
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      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkvWKj9QX8_l",
        "outputId": "1977784f-b540-4e25-98f5-92819aa6297e"
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      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (2.12.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "#from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import pad_sequences\n",
        "from keras.utils.data_utils import pad_sequences\n",
        "import tensorflow as tf\n",
        "from keras.layers import Dense\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM, Embedding"
      ],
      "metadata": {
        "id": "kFrSwfN1YFGz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('spam.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wBNDQ3moYOWc",
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      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Category                                            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..."
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Message</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JRh_uHYkecJ4",
        "outputId": "21a58bc0-7c67-4093-ef27-2ac4d26ae4c9"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['target'] = df['Category'].map( {'spam':1, 'ham':0 })"
      ],
      "metadata": {
        "id": "QKZ2ehCIYQRO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_train = df.sample(frac=.8, random_state=11)\n",
        "df_test = df.drop(df_train.index)\n",
        "print(df_train.shape, df_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6YlU6uTdYWQL",
        "outputId": "069d8aab-b2c1-4718-e52b-7a6deabd0439"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4458, 3) (1114, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = df_train['target'].values\n",
        "y_test = df_test['target'].values\n",
        "y_train.shape,y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kH7Bhv3YYZkE",
        "outputId": "dbe86d0c-8843-466e-da70-fc646e0d6a36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4458,), (1114,))"
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          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = df_train['Message'].values\n",
        "X_test = df_test['Message'].values"
      ],
      "metadata": {
        "id": "lya9BF-7YnfO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "word_dict = tokenizer.index_word\n"
      ],
      "metadata": {
        "id": "-G1vH3i6Yo17"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_seq = tokenizer.texts_to_sequences(X_train)\n",
        "X_test_seq = tokenizer.texts_to_sequences(X_test)\n",
        "print(X_train_seq[:5])\n",
        "print(df_train.iloc[0,:])\n",
        "for el in X_train_seq[0]:\n",
        "    print(word_dict[el], end=' ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0zuYM0zYrsZ",
        "outputId": "94818b28-19f3-4e90-c233-3c87ff0c99d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[667, 2, 233, 93, 640, 40, 132, 9, 51, 1252, 3, 51, 571, 2, 3823, 1020, 3824, 2596, 26, 188, 1427, 2, 1253, 69, 50, 86, 2, 3825], [1, 2016, 121, 404, 20, 157, 6, 3826, 19, 484, 1, 169, 322, 376, 6, 668, 170, 2017], [405, 74, 164, 4, 264, 15, 11, 2018], [51, 146, 2597, 30, 5, 938, 3827, 1121, 22, 12, 641, 71, 3828, 12, 356, 71, 3829, 2, 2598, 274, 938, 606, 12, 36, 154, 2019, 118, 260, 65, 189, 642, 377], [31, 3, 104, 1, 27, 807, 45, 43, 8, 4, 118]]\n",
            "Category                                                 spam\n",
            "Message     Welcome to UK-mobile-date this msg is FREE giv...\n",
            "target                                                      1\n",
            "Name: 4460, dtype: object\n",
            "welcome to uk mobile date this msg is free giving you free calling to 08719839835 future mgs billed at 150p daily to cancel send go stop to 89123 "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train_seq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_JHuBPB0ujra",
        "outputId": "f5338951-dc18-489e-cf70-10ebf7115f1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4458"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_pad = pad_sequences(X_train_seq, maxlen=20, padding='post')\n",
        "X_test_pad = pad_sequences(X_test_seq, maxlen=20, padding='post')\n",
        "\n",
        "X_train_pad.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7T2iXgtrYvlE",
        "outputId": "110d91aa-6719-4f46-e7bb-ffcbfb123f19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4458, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "laenge_pads = 20\n",
        "anz_woerter = 7982\n",
        "\n",
        "rnn_model = Sequential()\n",
        "rnn_model.add(Embedding(input_dim=anz_woerter+1, output_dim=20, input_length=laenge_pads))\n",
        "rnn_model.add(RNN(400))\n",
        "rnn_model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "rnn_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "rnn_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ay7ldQMdYykw",
        "outputId": "b87722e1-4f96-4f81-f2de-4bd15e082776"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 20, 20)            159660    \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 400)               673600    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 401       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 833,661\n",
            "Trainable params: 833,661\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = rnn_model.fit(X_train_pad, y_train, epochs=10, batch_size=64,validation_data=(X_test_pad, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_vwxA9yZY4HC",
        "outputId": "15b1f851-9efd-4f88-ad35-f0187fd06cd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "70/70 [==============================] - 24s 198ms/step - loss: 0.1877 - accuracy: 0.9345 - val_loss: 0.0877 - val_accuracy: 0.9695\n",
            "Epoch 2/10\n",
            "70/70 [==============================] - 5s 76ms/step - loss: 0.0357 - accuracy: 0.9913 - val_loss: 0.1281 - val_accuracy: 0.9713\n",
            "Epoch 3/10\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.0162 - accuracy: 0.9969 - val_loss: 0.3373 - val_accuracy: 0.9614\n",
            "Epoch 4/10\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.0104 - accuracy: 0.9980 - val_loss: 0.0751 - val_accuracy: 0.9740\n",
            "Epoch 5/10\n",
            "70/70 [==============================] - 2s 25ms/step - loss: 0.0074 - accuracy: 0.9987 - val_loss: 0.0942 - val_accuracy: 0.9731\n",
            "Epoch 6/10\n",
            "70/70 [==============================] - 2s 24ms/step - loss: 0.0033 - accuracy: 0.9993 - val_loss: 0.0811 - val_accuracy: 0.9785\n",
            "Epoch 7/10\n",
            "70/70 [==============================] - 2s 28ms/step - loss: 0.0058 - accuracy: 0.9982 - val_loss: 0.2123 - val_accuracy: 0.9731\n",
            "Epoch 8/10\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 1.5273e-04 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9740\n",
            "Epoch 9/10\n",
            "70/70 [==============================] - 1s 21ms/step - loss: 1.9231e-05 - accuracy: 1.0000 - val_loss: 0.2232 - val_accuracy: 0.9713\n",
            "Epoch 10/10\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1294e-05 - accuracy: 1.0000 - val_loss: 0.2354 - val_accuracy: 0.9713\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sms_test = ['hi, completed ml assignment?']\n",
        "sms_seq = tokenizer.texts_to_sequences(sms_test)\n",
        "sms_pad = pad_sequences(sms_seq, maxlen=20, padding='post')\n",
        "# Use the predict() method to obtain the model's output probabilities\n",
        "predictions=(lstm_model.predict(sms_pad) > 0.5).astype(\"int32\")\n",
        "\n",
        "# Print the predicted class label\n",
        "print(\"Predicted class label:\", predictions)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P0iQSW_-ZgY3",
        "outputId": "780a9ede-95ae-4aa4-978c-2e3a693c8253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 359ms/step\n",
            "Predicted class label: [[0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sms_test = ['Congratulations, you’ve won 10000 rupees. click the link']\n",
        "sms_seq = tokenizer.texts_to_sequences(sms_test)\n",
        "sms_pad = pad_sequences(sms_seq, maxlen=20, padding='post')\n",
        "\n",
        "# Use the predict() method to obtain the model's output probabilities\n",
        "predictions=(lstm_model.predict(sms_pad) > 0.5).astype(\"int32\")\n",
        "\n",
        "# Print the predicted class label\n",
        "print(\"Predicted class label:\", predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mNp2ilkdZpPN",
        "outputId": "f40a7fca-b6c1-4b95-c140-077bb8511d3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 18ms/step\n",
            "Predicted class label: [[1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores2 = rnn_model.evaluate(X_test_pad,y_test,verbose=1,batch_size=5)\n",
        "print('Accurracy: {}'.format(scores2[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EVdsPRPWy_7e",
        "outputId": "108b9a0c-e48d-4686-9d7c-e2ccdb1c360c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "223/223 [==============================] - 1s 3ms/step - loss: 0.2354 - accuracy: 0.9713\n",
            "Accurracy: 0.9712746739387512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "THAKFgyjzNG-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}