Spam_SMS_Classification.ipynb

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      "source": [
        "**Spam SMS Classification using NLP (Natural Language Processing)**"
      ],
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        "# Importing essential libraries\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 18,
      "outputs": []
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      "source": [
        "# Loading the dataset\n",
        "df = pd.read_csv('/content/Spam SMS Collection.txt', sep='\\t', names=['label', 'message'])"
      ],
      "execution_count": 19,
      "outputs": []
    },
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      "source": [
        "# **Exploratory Data Analysis (EDA)**\n",
        "* Exploring NaN values in dataset\n",
        "* Plotting countplots for Spam vs. Ham\n",
        "\n"
      ]
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              "(5572, 2)"
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              "Index(['label', 'message'], dtype='object')"
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        "df.dtypes"
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              "label      object\n",
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              "  label                                            message\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  U dun say so early hor... U c already then say...\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro..."
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              "     label                                            message\n",
              "5567  spam  This is the 2nd time we have tried 2 contact u...\n",
              "5568   ham               Will ü b going to esplanade fr home?\n",
              "5569   ham  Pity, * was in mood for that. So...any other s...\n",
              "5570   ham  The guy did some bitching but I acted like i'd...\n",
              "5571   ham                         Rofl. Its true to its name"
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-33a48455-37f7-460a-8880-b46935cb3f2e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-33a48455-37f7-460a-8880-b46935cb3f2e')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-33a48455-37f7-460a-8880-b46935cb3f2e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"ham\",\n          \"spam\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"message\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Will \\u00fc b going to esplanade fr home?\",\n          \"Rofl. Its true to its name\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjBi50n3BJk_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6262c27-1854-43ac-dfcf-ca7b9ed7052c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   label    5572 non-null   object\n",
            " 1   message  5572 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 87.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ipncn2wWGnCR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "97c96a99-432b-4eda-fac0-6afa4c5d4969"
      },
      "source": [
        "df.describe(include='object')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       label                 message\n",
              "count   5572                    5572\n",
              "unique     2                    5169\n",
              "top      ham  Sorry, I'll call later\n",
              "freq    4825                      30"
            ],
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              "\n",
              "  <div id=\"df-c0a58879-6218-4f75-b9e0-952dc30376a7\" class=\"colab-df-container\">\n",
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              "      <td>ham</td>\n",
              "      <td>Sorry, I'll call later</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>4825</td>\n",
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              "      display:flex;\n",
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              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "    [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-c0a58879-6218-4f75-b9e0-952dc30376a7 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-c0a58879-6218-4f75-b9e0-952dc30376a7');\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",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    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-0b0277e9-610a-4df1-85da-f14127b0ef4f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"4825\",\n          \"5572\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"message\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5169,\n          \"30\",\n          \"5572\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RI-zu8fXBQoc"
      },
      "source": [
        "_**Note: No NaN values** in the dataset._"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NpIBm-GJWJ6"
      },
      "source": [
        "# Mapping values for label\n",
        "df['label'] = df['label'].map({'ham': 0, 'spam': 1})"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJzlOdnuJsIw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7b590a6d-c7ba-4f4a-99dd-de22555f02d3"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label                                            message\n",
              "0      0  Go until jurong point, crazy.. Available only ...\n",
              "1      0                      Ok lar... Joking wif u oni...\n",
              "2      1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3      0  U dun say so early hor... U c already then say...\n",
              "4      0  Nah I don't think he goes to usf, he lives aro..."
            ],
            "text/html": [
              "\n",
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              "    <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>label</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</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>0</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>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7142a84-372d-40cf-9689-c90b37f5cf05')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-d7142a84-372d-40cf-9689-c90b37f5cf05 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-d7142a84-372d-40cf-9689-c90b37f5cf05');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-32362348-b9c8-460f-9b48-ac9429112597\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-32362348-b9c8-460f-9b48-ac9429112597')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-32362348-b9c8-460f-9b48-ac9429112597 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\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\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"message\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5169,\n        \"samples\": [\n          \"K, makes sense, btw carlos is being difficult so you guys are gonna smoke while I go pick up the second batch and get gas\",\n          \"URGENT! Your mobile No *********** WON a \\u00a32,000 Bonus Caller Prize on 02/06/03! This is the 2nd attempt to reach YOU! Call 09066362220 ASAP! BOX97N7QP, 150ppm\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noXpzSrQJ465",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "cfd57c23-d7c2-49a7-8ee4-769317c369d5"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      label                                            message\n",
              "5567      1  This is the 2nd time we have tried 2 contact u...\n",
              "5568      0               Will ü b going to esplanade fr home?\n",
              "5569      0  Pity, * was in mood for that. So...any other s...\n",
              "5570      0  The guy did some bitching but I acted like i'd...\n",
              "5571      0                         Rofl. Its true to its name"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e41c13eb-bddb-4cdc-a0f2-75a1c904e70e\" class=\"colab-df-container\">\n",
              "    <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>label</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5567</th>\n",
              "      <td>1</td>\n",
              "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5568</th>\n",
              "      <td>0</td>\n",
              "      <td>Will ü b going to esplanade fr home?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5569</th>\n",
              "      <td>0</td>\n",
              "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5570</th>\n",
              "      <td>0</td>\n",
              "      <td>The guy did some bitching but I acted like i'd...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5571</th>\n",
              "      <td>0</td>\n",
              "      <td>Rofl. Its true to its name</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e41c13eb-bddb-4cdc-a0f2-75a1c904e70e')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-e41c13eb-bddb-4cdc-a0f2-75a1c904e70e 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-e41c13eb-bddb-4cdc-a0f2-75a1c904e70e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c6e98254-8478-431b-896b-ced6addf58c7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c6e98254-8478-431b-896b-ced6addf58c7')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-c6e98254-8478-431b-896b-ced6addf58c7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"message\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Will \\u00fc b going to esplanade fr home?\",\n          \"Rofl. Its true to its name\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRy7PIlClJiT"
      },
      "source": [
        "# Importing essential libraries for visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xpzp7cqdljhd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "f01643f6-ec6b-4982-cf86-0272909e7bed"
      },
      "source": [
        "# Countplot for Spam vs. Ham as imbalanced dataset\n",
        "plt.figure(figsize=(8,8))\n",
        "g = sns.countplot(x='label', hue='label', data=df)\n",
        "p = plt.title('Countplot for Spam vs. Ham as imbalanced dataset')\n",
        "p = plt.xlabel('Is SMS Spam?')\n",
        "p = plt.ylabel('Count')\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CzNro4H9_Qb3"
      },
      "source": [
        "_**Insight:** From the above countplot, it is evident that the **dataset is imbalanced**._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19UoPYemgITc"
      },
      "source": [
        "# **Feature Engineering**\n",
        "*   Handling imbalanced dataset using Oversampling\n",
        "*   Creating new features e.g. word_count, contains_currency_symbol, contains_numbers, etc.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUjh1AFEgOab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66662a59-5af8-49d3-f495-2eceb77c443c"
      },
      "source": [
        "# Handling imbalanced dataset using Oversampling\n",
        "only_spam = df[df['label']==1]\n",
        "print('Number of Spam records: {}'.format(only_spam.shape[0]))\n",
        "print('Number of Ham records: {}'.format(df.shape[0]-only_spam.shape[0]))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Spam records: 747\n",
            "Number of Ham records: 4825\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPMQ8YDJgOWH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "daa8c2b1-de3d-43a4-c3de-0b93f1d3fe46"
      },
      "source": [
        "count = int((df.shape[0]-only_spam.shape[0])/only_spam.shape[0])\n",
        "for i in range(0, count-1):\n",
        "  df = pd.concat([df, only_spam])\n",
        "\n",
        "df.shape"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9307, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKgIDjAbMsdT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "5ebdc81f-7dbb-49a0-b061-a2146dbeb47d"
      },
      "source": [
        "# Countplot for Spam vs. Ham as balanced dataset\n",
        "plt.figure(figsize=(8,8))\n",
        "g = sns.countplot(x='label', hue='label', data=df)\n",
        "p = plt.title('Countplot for Spam vs. Ham as balanced dataset')\n",
        "p = plt.xlabel('Is SMS Spam?')\n",
        "p = plt.ylabel('Count')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGyIBoY4NICC"
      },
      "source": [
        "# Creating new feature word_count\n",
        "df['word_count'] = df['message'].apply(lambda x: len(x.split()))"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dx2yYqZAU3xj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2fbd945a-2df2-4d54-f5f6-76f5d64fdd62"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label                                            message  word_count\n",
              "0      0  Go until jurong point, crazy.. Available only ...          20\n",
              "1      0                      Ok lar... Joking wif u oni...           6\n",
              "2      1  Free entry in 2 a wkly comp to win FA Cup fina...          28\n",
              "3      0  U dun say so early hor... U c already then say...          11\n",
              "4      0  Nah I don't think he goes to usf, he lives aro...          13"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>message</th>\n",
              "      <th>word_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7a8ad451-c4ba-436f-bf69-43f41b58decd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\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",
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              "    }\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-7a8ad451-c4ba-436f-bf69-43f41b58decd 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-7a8ad451-c4ba-436f-bf69-43f41b58decd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5f2f534d-e194-4bcd-9ee6-b62f93677190\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5f2f534d-e194-4bcd-9ee6-b62f93677190')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-5f2f534d-e194-4bcd-9ee6-b62f93677190 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\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\": 9307,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"message\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5169,\n        \"samples\": [\n          \"K, makes sense, btw carlos is being difficult so you guys are gonna smoke while I go pick up the second batch and get gas\",\n          \"URGENT! Your mobile No *********** WON a \\u00a32,000 Bonus Caller Prize on 02/06/03! This is the 2nd attempt to reach YOU! Call 09066362220 ASAP! BOX97N7QP, 150ppm\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"word_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 1,\n        \"max\": 171,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          7,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dRdACe8cONu9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "ce3fb38f-63ba-412f-d9b7-7308b6ea5565"
      },
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# 1-row, 2-column, go to the first subplot\n",
        "plt.subplot(1, 2, 1)\n",
        "g1 = sns.histplot(data=df[df['label']==0], x='word_count', kde=True, stat=\"density\", color='blue')\n",
        "g1.set(title='Distribution of word_count for Ham messages')\n",
        "\n",
        "# 1-row, 2-column, go to the second subplot\n",
        "plt.subplot(1, 2, 2)\n",
        "g2 = sns.histplot(data=df[df['label']==1], x='word_count', kde=True, stat=\"density\", color='red')\n",
        "g2.set(title='Distribution of word_count for Spam messages')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCAT3fDHQKmr"
      },
      "source": [
        "_**Insight:** **Spam messages** word_count fall in the range of **15-30 words**, whereas majority of the **Ham messages** fall in the range of **below 25 words**._"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bxntzd8jSEDE"
      },
      "source": [
        "# Creating feature contains_currency_symbol\n",
        "def currency(x):\n",
        "  currency_symbols = ['€', '$', '¥', '£', '₹']\n",
        "  for i in currency_symbols:\n",
        "    if i in x:\n",
        "      return 1\n",
        "  return 0\n",
        "\n",
        "df['contains_currency_symbol'] = df['message'].apply(currency)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lBLNtDiUlDM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9796429d-bfff-4e65-f5f0-c9e13c2a163d"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      label                                            message  word_count  \\\n",
              "5537      1  Want explicit SEX in 30 secs? Ring 02073162414...          16   \n",
              "5540      1  ASKED 3MOBILE IF 0870 CHATLINES INCLU IN FREE ...          33   \n",
              "5547      1  Had your contract mobile 11 Mnths? Latest Moto...          28   \n",
              "5566      1  REMINDER FROM O2: To get 2.50 pounds free call...          28   \n",
              "5567      1  This is the 2nd time we have tried 2 contact u...          30   \n",
              "\n",
              "      contains_currency_symbol  \n",
              "5537                         0  \n",
              "5540                         1  \n",
              "5547                         0  \n",
              "5566                         0  \n",
              "5567                         1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cdee1b2d-427b-45ed-9623-2030169459d6\" class=\"colab-df-container\">\n",
              "    <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>label</th>\n",
              "      <th>message</th>\n",
              "      <th>word_count</th>\n",
              "      <th>contains_currency_symbol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5537</th>\n",
              "      <td>1</td>\n",
              "      <td>Want explicit SEX in 30 secs? Ring 02073162414...</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5540</th>\n",
              "      <td>1</td>\n",
              "      <td>ASKED 3MOBILE IF 0870 CHATLINES INCLU IN FREE ...</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5547</th>\n",
              "      <td>1</td>\n",
              "      <td>Had your contract mobile 11 Mnths? Latest Moto...</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5566</th>\n",
              "      <td>1</td>\n",
              "      <td>REMINDER FROM O2: To get 2.50 pounds free call...</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5567</th>\n",
              "      <td>1</td>\n",
              "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdee1b2d-427b-45ed-9623-2030169459d6')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-cdee1b2d-427b-45ed-9623-2030169459d6 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-cdee1b2d-427b-45ed-9623-2030169459d6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-225634bb-04b1-41c8-bd8d-90fde5edf0cc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-225634bb-04b1-41c8-bd8d-90fde5edf0cc')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-225634bb-04b1-41c8-bd8d-90fde5edf0cc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"message\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"ASKED 3MOBILE IF 0870 CHATLINES INCLU IN FREE MINS. INDIA CUST SERVs SED YES. L8ER GOT MEGA BILL. 3 DONT GIV A SHIT. BAILIFF DUE IN DAYS. I O \\u00a3250 3 WANT \\u00a3800\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"word_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 16,\n        \"max\": 33,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contains_currency_symbol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMZcauWZYHKV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "9f90f49e-ea83-4356-bb4a-79759d2f006a"
      },
      "source": [
        "# Countplot for contains_currency_symbol\n",
        "plt.figure(figsize=(8,8))\n",
        "g = sns.countplot(x='contains_currency_symbol', data=df, hue='label')\n",
        "p = plt.title('Countplot for contain_currency')\n",
        "p = plt.xlabel('Does SMS contain currency symbol?')\n",
        "p = plt.ylabel('Count')\n",
        "p = plt.legend(labels=['Ham', 'Spam'], loc=9)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-fLzXMsYY2N"
      },
      "source": [
        "_**Insight: Almost 1/3 of Spam messages contain currency symbols**, and currency symbols are **rarely used in Ham messages.**_"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OqgKvMkTVCJL"
      },
      "source": [
        "# Creating feature contains_number\n",
        "def numbers(x):\n",
        "  for i in x:\n",
        "    if ord(i)>=48 and ord(i)<=57:\n",
        "      return 1\n",
        "  return 0\n",
        "\n",
        "df['contains_number'] = df['message'].apply(numbers)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dLrxUfDsVmgj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "acaeb96d-4ac2-4ca1-ef87-5ec136c3ecfc"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label                                            message  word_count  \\\n",
              "0      0  Go until jurong point, crazy.. Available only ...          20   \n",
              "1      0                      Ok lar... Joking wif u oni...           6   \n",
              "2      1  Free entry in 2 a wkly comp to win FA Cup fina...          28   \n",
              "3      0  U dun say so early hor... U c already then say...          11   \n",
              "4      0  Nah I don't think he goes to usf, he lives aro...          13   \n",
              "\n",
              "   contains_currency_symbol  contains_number  \n",
              "0                         0                0  \n",
              "1                         0                0  \n",
              "2                         0                1  \n",
              "3                         0                0  \n",
              "4                         0                0  "
            ],
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              "      <th></th>\n",
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              "    <tr>\n",
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              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>11</td>\n",
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              "      <th>4</th>\n",
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              "      <td>13</td>\n",
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              "    }\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-44a06090-120a-45e3-a143-d2268e260e00 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-44a06090-120a-45e3-a143-d2268e260e00');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-44bcf9ff-b3a5-41ec-942c-700087f99696\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-44bcf9ff-b3a5-41ec-942c-700087f99696')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-44bcf9ff-b3a5-41ec-942c-700087f99696 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\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\": 9307,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"message\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5169,\n        \"samples\": [\n          \"K, makes sense, btw carlos is being difficult so you guys are gonna smoke while I go pick up the second batch and get gas\",\n          \"URGENT! Your mobile No *********** WON a \\u00a32,000 Bonus Caller Prize on 02/06/03! This is the 2nd attempt to reach YOU! Call 09066362220 ASAP! BOX97N7QP, 150ppm\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"word_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 1,\n        \"max\": 171,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          7,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contains_currency_symbol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contains_number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SylEzeOlV0Kj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "1887404a-85ad-4436-c888-fbca025099a8"
      },
      "source": [
        "# Countplot for contains_number\n",
        "plt.figure(figsize=(8,8))\n",
        "g = sns.countplot(x='contains_number', data=df, hue='label')\n",
        "p = plt.title('Countplot for contain_numbers')\n",
        "p = plt.xlabel('Does SMS contain number?')\n",
        "p = plt.ylabel('Count')\n",
        "p = plt.legend(labels=['Ham', 'Spam'], loc=9)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJTf9Z9GXKfN"
      },
      "source": [
        "_**Insight:** It is evident that **most of the Spam messages contain numbers,** and **majority of the Ham messages donot contain numbers.**_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLCtNBBQnRDR"
      },
      "source": [
        "# **Data Cleaning**\n",
        "* Removing special character and numbers using regular expression\n",
        "* Converting the entire sms into lower case\n",
        "* Tokenizing the sms by words\n",
        "* Removing the stop words\n",
        "* Lemmatizing the words\n",
        "* Joining the lemmatized words\n",
        "* Building a corpus of messages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2IlhceAnQb7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a24e7060-fbea-469a-d2c7-d66a0ea92353"
      },
      "source": [
        "# Importing essential libraries for performing NLP\n",
        "import nltk\n",
        "import re\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBzPDWYglyeS"
      },
      "source": [
        "# Cleaning the messages\n",
        "corpus = []\n",
        "wnl = WordNetLemmatizer()\n",
        "\n",
        "for sms_string in list(df.message):\n",
        "\n",
        "  # Cleaning special character from the sms\n",
        "  message = re.sub(pattern='[^a-zA-Z]', repl=' ', string=sms_string)\n",
        "\n",
        "  # Converting the entire sms into lower case\n",
        "  message = message.lower()\n",
        "\n",
        "  # Tokenizing the sms by words\n",
        "  words = message.split()\n",
        "\n",
        "  # Removing the stop words\n",
        "  filtered_words = [word for word in words if word not in set(stopwords.words('english'))]\n",
        "\n",
        "  # Lemmatizing the words\n",
        "  lemmatized_words = [wnl.lemmatize(word) for word in filtered_words]\n",
        "\n",
        "  # Joining the lemmatized words\n",
        "  message = ' '.join(lemmatized_words)\n",
        "\n",
        "  # Building a corpus of messages\n",
        "  corpus.append(message)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-qBfMYzpyoS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f806bf44-a734-47aa-8e60-fefbf82b0737"
      },
      "source": [
        "corpus[0:3]"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['go jurong point crazy available bugis n great world la e buffet cine got amore wat',\n",
              " 'ok lar joking wif u oni',\n",
              " 'free entry wkly comp win fa cup final tkts st may text fa receive entry question std txt rate c apply']"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDL2Jfcbp6hi"
      },
      "source": [
        "# Creating the Bag of Words model\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf = TfidfVectorizer(max_features=500)\n",
        "vectors = tfidf.fit_transform(corpus).toarray()\n",
        "feature_names = tfidf.get_feature_names_out()\n",
        "\n",
        "# Extracting independent and dependent variables from the dataset\n",
        "X = pd.DataFrame(vectors, columns=feature_names)\n",
        "y = df['label']"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yvo6cT8rrKas"
      },
      "source": [
        "# **Model Building & Evaluation**\n",
        "_**Metric: F1-Score**_\n",
        "\n",
        "* Multiomail Naive Bayes: 0.943\n",
        "* Decision Tree: 0.98\n",
        "* **Random Forest (Ensemble): 0.994**\n",
        "* Voting (Multinomial Naive Bayes + Decision Tree): 0.98\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-GI83Ie5SEak"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMzc9nvtUgMk"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Using Naive Bayes Model**"
      ],
      "metadata": {
        "id": "AySqjz-BxK46"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsqsZ6OorYHq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f24899de-bc61-435f-f2ca-f02d6a961099"
      },
      "source": [
        "# Fitting Naive Bayes to the Training set\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "mnb = MultinomialNB()\n",
        "cv = cross_val_score(mnb, X, y, scoring='f1', cv=10)\n",
        "print('--- Average F1-Score for MNB model: {} ---'.format(round(cv.mean(), 3)))\n",
        "print('Standard Deviation: {}'.format(round(cv.std(), 3)))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Average F1-Score for MNB model: 0.943 ---\n",
            "Standard Deviation: 0.004\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INb9wkThW45r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f86ff343-4e41-4f9d-babf-8832adb159b2"
      },
      "source": [
        "# Classification report for MNB model\n",
        "mnb = MultinomialNB()\n",
        "mnb.fit(X_train, y_train)\n",
        "y_pred = mnb.predict(X_test)\n",
        "\n",
        "print('--- Classification report for MNB model ---')\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Classification report for MNB model ---\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.94      0.94       959\n",
            "           1       0.94      0.94      0.94       903\n",
            "\n",
            "    accuracy                           0.94      1862\n",
            "   macro avg       0.94      0.94      0.94      1862\n",
            "weighted avg       0.94      0.94      0.94      1862\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bjAauN_XMNN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "f9bf5d6f-f50c-4397-b27b-327b31dd6e0d"
      },
      "source": [
        "# Confusion matrix of MNB model\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "axis_labels = ['ham', 'spam']\n",
        "g = sns.heatmap(data=cm, annot=True, cmap=\"Blues\", xticklabels=axis_labels, yticklabels=axis_labels, fmt='g', cbar_kws={\"shrink\": 0.5})\n",
        "p = plt.xlabel('Actual values')\n",
        "p = plt.ylabel('Predicted values')\n",
        "p = plt.title('--- Confusion Matrix for Multinomial Naive Bayes model ---')"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Using Decision Tree Model**"
      ],
      "metadata": {
        "id": "sbh5WWcbxhyE"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rhkwwq3Hb-M-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5edc1d6-42de-4ab5-a724-23c9adb523df"
      },
      "source": [
        "# Fitting Decision Tree to the Training set\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dt = DecisionTreeClassifier()\n",
        "cv = cross_val_score(dt, X, y, scoring='f1', cv=10)\n",
        "print('--- Average F1-Score for Decision Tree model: {} ---'.format(round(cv.mean(), 3)))\n",
        "print('Standard Deviation: {}'.format(round(cv.std(), 3)))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Average F1-Score for Decision Tree model: 0.981 ---\n",
            "Standard Deviation: 0.003\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFB2qr6YchC-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25f0a4aa-fe59-489a-e19a-49fd87c7c5ae"
      },
      "source": [
        "# Classification report for Decision Tree model\n",
        "dt = DecisionTreeClassifier()\n",
        "dt.fit(X_train, y_train)\n",
        "y_pred = dt.predict(X_test)\n",
        "\n",
        "print('--- Classification report for Decision Tree model ---')\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Classification report for Decision Tree model ---\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.97      0.98       959\n",
            "           1       0.97      1.00      0.98       903\n",
            "\n",
            "    accuracy                           0.98      1862\n",
            "   macro avg       0.98      0.98      0.98      1862\n",
            "weighted avg       0.98      0.98      0.98      1862\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcNIfLAMc_cN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "76cb3983-3b20-4bd9-a3ec-2f1be1e7f695"
      },
      "source": [
        "# Confusion matrix of Decision Tree model\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "axis_labels = ['ham', 'spam']\n",
        "g = sns.heatmap(data=cm, annot=True, cmap=\"Blues\", xticklabels=axis_labels, yticklabels=axis_labels, fmt='g', cbar_kws={\"shrink\": 0.5})\n",
        "p = plt.xlabel('Actual values')\n",
        "p = plt.ylabel('Predicted values')\n",
        "p = plt.title('--- Confusion Matrix for Decision Tree model ---')"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Using Random Forest Model**"
      ],
      "metadata": {
        "id": "Sygbzf4ryTNO"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOuy-BaLSqd5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8bb1593-f89b-4d61-f4c6-1220fe599969"
      },
      "source": [
        "# Fitting Random Forest to the Training set\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier(n_estimators=10)\n",
        "cv = cross_val_score(rf, X, y, scoring='f1', cv=10,)\n",
        "print('--- Average F1-Score for Random Forest model: {} ---'.format(round(cv.mean(), 3)))\n",
        "print('Standard Deviation: {}'.format(round(cv.std(), 3)))"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Average F1-Score for Random Forest model: 0.995 ---\n",
            "Standard Deviation: 0.003\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F02lOVivsrna",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fa78267-9185-4dec-9231-9c496f0d5886"
      },
      "source": [
        "# Classification report for Random Forest model\n",
        "rf = RandomForestClassifier(n_estimators=20)\n",
        "rf.fit(X_train, y_train)\n",
        "y_pred = rf.predict(X_test)\n",
        "\n",
        "print('--- Classification report for Random Forest model ---')\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Classification report for Random Forest model ---\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99       959\n",
            "           1       0.99      1.00      0.99       903\n",
            "\n",
            "    accuracy                           0.99      1862\n",
            "   macro avg       0.99      0.99      0.99      1862\n",
            "weighted avg       0.99      0.99      0.99      1862\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-UL5fUSWL_N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "a7bf1f4f-0f43-4eb4-8df0-914686555c85"
      },
      "source": [
        "# Confusion matrix of Random Forest model\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "axis_labels = ['ham', 'spam']\n",
        "g = sns.heatmap(data=cm, annot=True, cmap=\"Blues\", xticklabels=axis_labels, yticklabels=axis_labels, fmt='g', cbar_kws={\"shrink\": 0.5})\n",
        "p = plt.xlabel('Actual values')\n",
        "p = plt.ylabel('Predicted values')\n",
        "p = plt.title('--- Confusion Matrix for Random Forest model ---')"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ySKNOiwaop3f"
      },
      "source": [
        "_**Note:** Decision Tree & MNB algorithms are selected and fed to Voting algorithm to increase the F1-Score!_"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Using Voting Algorithm** (Decison Tree + Naive Bayes Model)"
      ],
      "metadata": {
        "id": "557HN6q5ycmD"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scx5eN7TaE3V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c404a15d-1ea3-42a3-a1fe-9763e04ebe05"
      },
      "source": [
        "# Fitting Decision Tree and MNB to VotingClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "vc = VotingClassifier([('decision_tree', dt), ('m_naive_bayes', mnb)], weights=[2,1])\n",
        "cv = cross_val_score(vc, X, y, cv=10, scoring='f1')\n",
        "\n",
        "print('--- Average F1-Score for VotingClassifier model: {} ---'.format(round(cv.mean(), 3)))\n",
        "print('Standard Deviation: {}'.format(round(cv.std(), 3)))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Average F1-Score for VotingClassifier model: 0.98 ---\n",
            "Standard Deviation: 0.005\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cy6K6Ps0oKnq"
      },
      "source": [
        "_**Note:** Voting algorithm did not out-perform Random Forest algorithm, hence **Random Forest algorithm is selected for predicting the results of this problem statement.**_\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LAtjiTWSuaYD"
      },
      "source": [
        "# **Making Predictions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "culehxsLuZ0a"
      },
      "source": [
        "def predict_spam(sample_message):\n",
        "  sample_message = re.sub(pattern='[^a-zA-Z]',repl=' ', string = sample_message)\n",
        "  sample_message = sample_message.lower()\n",
        "  sample_message_words = sample_message.split()\n",
        "  sample_message_words = [word for word in sample_message_words if not word in set(stopwords.words('english'))]\n",
        "  final_message = [wnl.lemmatize(word) for word in sample_message_words]\n",
        "  final_message = ' '.join(final_message)\n",
        "\n",
        "  temp = tfidf.transform([final_message]).toarray()\n",
        "  return rf.predict(temp)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QzJJjG8fu7OI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0adc1e0d-6f6c-43c6-c057-32f05dfbd39a"
      },
      "source": [
        "# Prediction 1 - Lottery text message\n",
        "sample_message = 'IMPORTANT - You could be entitled up to £3,160 in compensation from mis-sold PPI on a credit card or loan. Please reply PPI for info or STOP to opt out.'\n",
        "\n",
        "if predict_spam(sample_message):\n",
        "  print('Gotcha! This is a SPAM message.')\n",
        "else:\n",
        "  print('This is a HAM (normal) message.')"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gotcha! This is a SPAM message.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wl4QRloavhNa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca964c02-7cd6-467e-e756-5ccdcc928aa3"
      },
      "source": [
        "# Prediction 2 - Casual text chat\n",
        "sample_message = 'Came to think of it. I have never got a spam message before.'\n",
        "\n",
        "if predict_spam(sample_message):\n",
        "  print('Gotcha! This is a SPAM message.')\n",
        "else:\n",
        "  print('This is a HAM (normal) message.')"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is a HAM (normal) message.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3y7O714gAu3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1db96354-701a-4cc8-ef8a-e3fd81cb4fc8"
      },
      "source": [
        "# Prediction 3 - Transaction confirmation text message\n",
        "sample_message = 'Sam, your rent payment for Jan 19 has been received. $1,300 will be drafted from your Wells Fargo Account ******0000 within 24-48 business hours. Thank you!'\n",
        "\n",
        "if predict_spam(sample_message):\n",
        "  print('Gotcha! This is a SPAM message.')\n",
        "else:\n",
        "  print('This is a HAM (normal) message.')"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is a HAM (normal) message.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Qa7U43sh4__",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08fd20ae-8d92-48a2-a9eb-4624915e8f13"
      },
      "source": [
        "# Predicting values 4 - Feedback message\n",
        "sample_message = 'Tammy, thanks for choosing Carl’s Car Wash for your express polish. We would love to hear your thoughts on the service. Feel free to text back with any feedback. Safe driving!'\n",
        "\n",
        "if predict_spam(sample_message):\n",
        "  print('Gotcha! This is a SPAM message.')\n",
        "else:\n",
        "  print('This is a HAM (normal) message.')"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gotcha! This is a SPAM message.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "pickle.dump(tfidf,open('vectorizer.pkl','wb'))\n",
        "pickle.dump(rf,open('model.pkl','wb'))"
      ],
      "metadata": {
        "id": "-XLssiB50GQb"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J1xsvyKWX8HS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}