sms_spam_using_RNN.ipynb

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      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle\n",
        "import tensorflow\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.layers import Dense, Embedding, SimpleRNN,Dropout\n",
        "from keras.models import Sequential"
      ]
    },
    {
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        "df=pd.read_pickle('sms_cleanedData.pkl')\n",
        "df"
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              "3           u dun say so early hor u c already then say      0\n",
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              "5568                will ì b going to esplanade fr home      0\n",
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              "5570  the guy did some bitching but i acted like id ...      0\n",
              "5571                            rofl it true to it name      0\n",
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              "      <td>wow so healthy old airport rd lor cant thk of ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5569</th>\n",
              "      <td>i know you are can you pls open the back</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5570</th>\n",
              "      <td>might ax well im there</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5571</th>\n",
              "      <td>sitting in mu waiting for everyone to get out ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5572 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a9e1462-615a-4962-ac6f-8363381bcee5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-1a9e1462-615a-4962-ac6f-8363381bcee5 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-1a9e1462-615a-4962-ac6f-8363381bcee5');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-45202dac-5547-4756-8813-5fe5f091a0ef\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-45202dac-5547-4756-8813-5fe5f091a0ef')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-45202dac-5547-4756-8813-5fe5f091a0ef button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_bc61d781-1fbf-4fdf-b7af-74db51226ec5\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bc61d781-1fbf-4fdf-b7af-74db51226ec5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5572,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5130,\n        \"samples\": [\n          \"no drama plsi have had enough from you and family while i am struggling in the hot sun in a strange placeno reason why there should be an ego of not going if not invited when actually it necessity to gowait for very serious reppurcussions\",\n          \"when did i use soc i use it only at home \\u00ec\\u00ef dunno how 2 type it in word ar\",\n          \"this message is from a great doctor in india 1 do not drink appy fizz it contains cancer causing age\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['label'].value_counts()"
      ],
      "metadata": {
        "id": "VraaXyZ4VF17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "outputId": "8e8b8b91-a33b-4987-a538-4bd056555647"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "0    4825\n",
              "1     747\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>747</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer=Tokenizer()\n",
        "tokenizer.fit_on_texts(df['text'])\n",
        "len(tokenizer.word_index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4WqDEZCWaa3",
        "outputId": "0c11fbfc-0d98-4800-e2f3-8dd295339708"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9011"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tokenizer.word_index"
      ],
      "metadata": {
        "id": "jEAov8a1WnLW"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sequences=tokenizer.texts_to_sequences(df['text'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvVSOKfNWqqZ",
        "outputId": "58039cee-7905-481c-e1d7-1de266e0c884"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5572"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(sequences[130])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sb5q3ZvtXVYS",
        "outputId": "29d9e582-16e1-4b22-f358-56997ffedc0c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(sequences,df['label'],test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "id": "TXAbaamuXAFC"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train=pad_sequences(X_train,padding=\"post\",maxlen=50)\n",
        "X_test=pad_sequences(X_test,padding=\"post\",maxlen=50)"
      ],
      "metadata": {
        "id": "J-kBq9fyXggM"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2cl-4x0Xj4u",
        "outputId": "17c7c654-37cd-4e51-aa47-923cc5c3be60"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4457, 50)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_padded=pad_sequences(sequences,padding=\"post\",maxlen=50)"
      ],
      "metadata": {
        "id": "0xK9DcBYfDGb"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()\n",
        "model.add(Embedding(len(tokenizer.word_index)+1,32,input_length=50))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(SimpleRNN(32))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1,activation='sigmoid'))"
      ],
      "metadata": {
        "id": "LlBfY2HhYQEn"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "vqksEHsgYj35",
        "outputId": "55d1a71f-4b81-463e-f758-0e9f1c7e57ee"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_10\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_10\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding_9 (\u001b[38;5;33mEmbedding\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m32\u001b[0m)              │         \u001b[38;5;34m288,384\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m32\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ simple_rnn_8 (\u001b[38;5;33mSimpleRNN\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m2,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m33\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">288,384</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ simple_rnn_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m871,493\u001b[0m (3.32 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">871,493</span> (3.32 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m290,497\u001b[0m (1.11 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">290,497</span> (1.11 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m580,996\u001b[0m (2.22 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">580,996</span> (2.22 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "GxvKKqriYsjF"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history=model.fit(X_train,y_train,epochs=10,validation_data=(X_test,y_test),batch_size=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pE7CLIjgZEmW",
        "outputId": "1dd26135-ad9d-4e78-a615-a26206bef5f0"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - accuracy: 0.8103 - loss: 0.4816 - val_accuracy: 0.9435 - val_loss: 0.2285\n",
            "Epoch 2/10\n",
            "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - accuracy: 0.9291 - loss: 0.2313 - val_accuracy: 0.9462 - val_loss: 0.1796\n",
            "Epoch 3/10\n",
            "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 27ms/step - accuracy: 0.9727 - loss: 0.1201 - val_accuracy: 0.9516 - val_loss: 0.1683\n",
            "Epoch 4/10\n",
            "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 19ms/step - accuracy: 0.9875 - loss: 0.0701 - val_accuracy: 0.9507 - val_loss: 0.1679\n",
            "Epoch 5/10\n",
            "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 18ms/step - accuracy: 0.9885 - loss: 0.0523 - val_accuracy: 0.9561 - val_loss: 0.1845\n",
            "Epoch 6/10\n",
            "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 19ms/step - accuracy: 0.9913 - loss: 0.0378 - val_accuracy: 0.9507 - val_loss: 0.1844\n",
            "Epoch 7/10\n",
            "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 18ms/step - accuracy: 0.9929 - loss: 0.0370 - val_accuracy: 0.9552 - val_loss: 0.1922\n",
            "Epoch 8/10\n",
            "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 19ms/step - accuracy: 0.9967 - loss: 0.0177 - val_accuracy: 0.9444 - val_loss: 0.2210\n",
            "Epoch 9/10\n",
            "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 0.9858 - loss: 0.0500 - val_accuracy: 0.9507 - val_loss: 0.2294\n",
            "Epoch 10/10\n",
            "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 18ms/step - accuracy: 0.9930 - loss: 0.0291 - val_accuracy: 0.9552 - val_loss: 0.1998\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "acc=history.history['accuracy']\n",
        "val_acc=history.history['val_accuracy']\n",
        "loss=history.history['loss']\n",
        "val_loss=history.history['val_loss']\n",
        "epochs=range(1,len(acc)+1)\n",
        "plt.plot(epochs,acc,'-',label='Training acc',color='orange')\n",
        "plt.plot(epochs,val_acc,'-',label='Validation acc',color='green')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "eQPIWO-JZIGP",
        "outputId": "fb318356-2a46-4cac-fb7d-7f0e5d48cc94"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7da626bb6ef0>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(epochs,loss,'-',label='Training Loss',color='orange')\n",
        "plt.plot(epochs,val_loss,'-',label='Validation Loss',color='green')\n",
        "plt.title('Training and validation Loss')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "Uz6ss2AJZwHG",
        "outputId": "4b85a032-aaba-483f-953b-dc98bfe33b6d"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7da6249824a0>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "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": [
        "# Confusion Matrix"
      ],
      "metadata": {
        "id": "uQcAQ7Avc9Nk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Make predictions on the test set\n",
        "predict_x = model.predict(X_padded)\n",
        "pred = np.argmax(predict_x, axis=1)\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "acc = model.evaluate(X_padded, df['label'])\n",
        "\n",
        "# Note: You don't need to pass y_test to the predict method\n",
        "predict_prob = model.predict(X_padded)\n",
        "proba_rnn = np.argmax(predict_prob, axis=1)\n",
        "\n",
        "# Print the test loss and accuracy\n",
        "print(\"Test loss is {0:.2f} accuracy is {1:.2f}\".format(acc[0], acc[1]))\n",
        "\n",
        "# Print the confusion matrix\n",
        "print(confusion_matrix(df['label'], pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "enxxfZYUa4eg",
        "outputId": "8f277000-e4e6-43db-cb15-1ff27c74cf69"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n",
            "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.0428\n",
            "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n",
            "Test loss is 0.04 accuracy is 0.99\n",
            "[[4825    0]\n",
            " [ 747    0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('sms_classifier.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "852rL5kwdKT1",
        "outputId": "fcc58d02-4a0c-4bfd-edef-7ad46761eb2c"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('tokenizer.pickle','wb') as handle:\n",
        "    pickle.dump(tokenizer,handle,protocol=pickle.HIGHEST_PROTOCOL)"
      ],
      "metadata": {
        "id": "PRog_i9OgDz6"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jgOMOtUTgaaS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}