Spam_Mail_Filtering.ipynb

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        "<a href=\"https://colab.research.google.com/github/tejaswiniii-11/Spam-Mails-Detection/blob/main/Spam_Mail_Filtering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
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      "source": [
        "Data Preprocessing"
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      "source": [
        "#loading data from csv to panda dataframe\n",
        "raw_mail_data = pd.read_csv('/content/drive/MyDrive/mail_data.csv')"
      ],
      "metadata": {
        "id": "lp_HwJwwkNXz"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "print(raw_mail_data)"
      ],
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            "        v1                                                 v2 Unnamed: 2  \\\n",
            "0      ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
            "1      ham                      Ok lar... Joking wif u oni...        NaN   \n",
            "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
            "3      ham  U dun say so early hor... U c already then say...        NaN   \n",
            "4      ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
            "...    ...                                                ...        ...   \n",
            "5567  spam  This is the 2nd time we have tried 2 contact u...        NaN   \n",
            "5568   ham              Will Ì_ b going to esplanade fr home?        NaN   \n",
            "5569   ham  Pity, * was in mood for that. So...any other s...        NaN   \n",
            "5570   ham  The guy did some bitching but I acted like i'd...        NaN   \n",
            "5571   ham                         Rofl. Its true to its name        NaN   \n",
            "\n",
            "     Unnamed: 3 Unnamed: 4  \n",
            "0           NaN        NaN  \n",
            "1           NaN        NaN  \n",
            "2           NaN        NaN  \n",
            "3           NaN        NaN  \n",
            "4           NaN        NaN  \n",
            "...         ...        ...  \n",
            "5567        NaN        NaN  \n",
            "5568        NaN        NaN  \n",
            "5569        NaN        NaN  \n",
            "5570        NaN        NaN  \n",
            "5571        NaN        NaN  \n",
            "\n",
            "[5572 rows x 5 columns]\n"
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      "source": [
        "# replace the null values with a null string\n",
        "mail_data = raw_mail_data.where((pd.notnull(raw_mail_data)),'')"
      ],
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      "execution_count": null,
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      "source": [
        "# printing the first 5 rows of the dataframe\n",
        "mail_data.head()"
      ],
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              "  Category                                            Message\n",
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              "      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-ca31eb4b-8d56-4a5f-a1bb-10fba9db9979 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-ca31eb4b-8d56-4a5f-a1bb-10fba9db9979');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-54d167ad-3c3b-453e-b82b-fb0e94ada0d3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
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              "      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-54d167ad-3c3b-453e-b82b-fb0e94ada0d3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the number of rows and columns in the dataframe\n",
        "mail_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6o-a1-uzpHqo",
        "outputId": "5df13af2-e036-4e5f-e7f0-ab165183b9f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# label spam mail as 0;  ham mail as 1;\n",
        "\n",
        "mail_data.loc[mail_data['Category'] == 'spam', 'Category',] = 0\n",
        "mail_data.loc[mail_data['Category'] == 'ham', 'Category',] = 1"
      ],
      "metadata": {
        "id": "WBkynLlepJsP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "spam --> 0\n",
        "\n",
        "ham (non-sapm) --> 1"
      ],
      "metadata": {
        "id": "Bieqd1I4pOrs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# separating the data as texts and label\n",
        "\n",
        "X = mail_data['Message']\n",
        "\n",
        "Y = mail_data['Category']"
      ],
      "metadata": {
        "id": "zDn8IR_TpMnm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X contains texts\n",
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M38bjUWapVu4",
        "outputId": "055306b7-e353-4330-8afd-aacaf9c20dc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       Go until jurong point, crazy.. Available only ...\n",
            "1                           Ok lar... Joking wif u oni...\n",
            "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
            "3       U dun say so early hor... U c already then say...\n",
            "4       Nah I don't think he goes to usf, he lives aro...\n",
            "                              ...                        \n",
            "5567    This is the 2nd time we have tried 2 contact u...\n",
            "5568                 Will ü b going to esplanade fr home?\n",
            "5569    Pity, * was in mood for that. So...any other s...\n",
            "5570    The guy did some bitching but I acted like i'd...\n",
            "5571                           Rofl. Its true to its name\n",
            "Name: Message, Length: 5572, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Y contains labels\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V2xRdBjdpkR5",
        "outputId": "6baddda3-58cd-49d2-dd2c-67ff6a3a205f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       1\n",
            "1       1\n",
            "2       0\n",
            "3       1\n",
            "4       1\n",
            "       ..\n",
            "5567    0\n",
            "5568    1\n",
            "5569    1\n",
            "5570    1\n",
            "5571    1\n",
            "Name: Category, Length: 5572, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting data into Train & Test datasets"
      ],
      "metadata": {
        "id": "GkTE6yQapqbO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)"
      ],
      "metadata": {
        "id": "LxvPTNtlpoew"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LufD0yBGpwxe",
        "outputId": "564cee76-0072-4c59-9f92-2cd9a91473c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5572,)\n",
            "(4457,)\n",
            "(1115,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature Extraction"
      ],
      "metadata": {
        "id": "cgYBN75qp1ry"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# transform the text data to feature vectors that can be used as input to the Logistic regression\n",
        "\n",
        "feature_extraction = TfidfVectorizer(min_df = 1, stop_words='english')\n",
        "\n",
        "X_train_features = feature_extraction.fit_transform(X_train)\n",
        "X_test_features = feature_extraction.transform(X_test)\n",
        "\n",
        "# convert Y_train and Y_test values as integers\n",
        "\n",
        "Y_train = Y_train.astype('int')\n",
        "Y_test = Y_test.astype('int')"
      ],
      "metadata": {
        "id": "6_tYiDvGpyav"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F-M3aYfop3xa",
        "outputId": "cd6d3ce7-1bfd-465a-9fd9-4c4eabb230f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3075                  Don know. I did't msg him recently.\n",
            "1787    Do you know why god created gap between your f...\n",
            "1614                         Thnx dude. u guys out 2nite?\n",
            "4304                                      Yup i'm free...\n",
            "3266    44 7732584351, Do you want a New Nokia 3510i c...\n",
            "                              ...                        \n",
            "789     5 Free Top Polyphonic Tones call 087018728737,...\n",
            "968     What do u want when i come back?.a beautiful n...\n",
            "1667    Guess who spent all last night phasing in and ...\n",
            "3321    Eh sorry leh... I din c ur msg. Not sad alread...\n",
            "1688    Free Top ringtone -sub to weekly ringtone-get ...\n",
            "Name: Message, Length: 4457, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80znKVrJsvv3",
        "outputId": "d54710c0-9b8a-41c2-f502-4984f9cbea79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 5413)\t0.6198254967574347\n",
            "  (0, 4456)\t0.4168658090846482\n",
            "  (0, 2224)\t0.413103377943378\n",
            "  (0, 3811)\t0.34780165336891333\n",
            "  (0, 2329)\t0.38783870336935383\n",
            "  (1, 4080)\t0.18880584110891163\n",
            "  (1, 3185)\t0.29694482957694585\n",
            "  (1, 3325)\t0.31610586766078863\n",
            "  (1, 2957)\t0.3398297002864083\n",
            "  (1, 2746)\t0.3398297002864083\n",
            "  (1, 918)\t0.22871581159877646\n",
            "  (1, 1839)\t0.2784903590561455\n",
            "  (1, 2758)\t0.3226407885943799\n",
            "  (1, 2956)\t0.33036995955537024\n",
            "  (1, 1991)\t0.33036995955537024\n",
            "  (1, 3046)\t0.2503712792613518\n",
            "  (1, 3811)\t0.17419952275504033\n",
            "  (2, 407)\t0.509272536051008\n",
            "  (2, 3156)\t0.4107239318312698\n",
            "  (2, 2404)\t0.45287711070606745\n",
            "  (2, 6601)\t0.6056811524587518\n",
            "  (3, 2870)\t0.5864269879324768\n",
            "  (3, 7414)\t0.8100020912469564\n",
            "  (4, 50)\t0.23633754072626942\n",
            "  (4, 5497)\t0.15743785051118356\n",
            "  :\t:\n",
            "  (4454, 4602)\t0.2669765732445391\n",
            "  (4454, 3142)\t0.32014451677763156\n",
            "  (4455, 2247)\t0.37052851863170466\n",
            "  (4455, 2469)\t0.35441545511837946\n",
            "  (4455, 5646)\t0.33545678464631296\n",
            "  (4455, 6810)\t0.29731757715898277\n",
            "  (4455, 6091)\t0.23103841516927642\n",
            "  (4455, 7113)\t0.30536590342067704\n",
            "  (4455, 3872)\t0.3108911491788658\n",
            "  (4455, 4715)\t0.30714144758811196\n",
            "  (4455, 6916)\t0.19636985317119715\n",
            "  (4455, 3922)\t0.31287563163368587\n",
            "  (4455, 4456)\t0.24920025316220423\n",
            "  (4456, 141)\t0.292943737785358\n",
            "  (4456, 647)\t0.30133182431707617\n",
            "  (4456, 6311)\t0.30133182431707617\n",
            "  (4456, 5569)\t0.4619395404299172\n",
            "  (4456, 6028)\t0.21034888000987115\n",
            "  (4456, 7154)\t0.24083218452280053\n",
            "  (4456, 7150)\t0.3677554681447669\n",
            "  (4456, 6249)\t0.17573831794959716\n",
            "  (4456, 6307)\t0.2752760476857975\n",
            "  (4456, 334)\t0.2220077711654938\n",
            "  (4456, 5778)\t0.16243064490100795\n",
            "  (4456, 2870)\t0.31523196273113385\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Taining the model\n",
        "\n",
        "using Logistic Regression"
      ],
      "metadata": {
        "id": "91ltV67is4CO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "pcEU21nVsyHr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the Logistic Regression model with the training data\n",
        "model.fit(X_train_features, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "-awaDXtvs-jv",
        "outputId": "7cd5ca62-aa2f-442e-db44-9016d175cf51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prediction on training data\n",
        "\n",
        "prediction_on_training_data = model.predict(X_train_features)\n",
        "accuracy_on_training_data = accuracy_score(Y_train, prediction_on_training_data)"
      ],
      "metadata": {
        "id": "T2ihiFQ9tAqa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on training data : ', accuracy_on_training_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vD3wd_PNtC9j",
        "outputId": "e9c34d9f-92b3-4de9-e9b8-6cb738db9adf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data :  0.9670181736594121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building a Predictive System"
      ],
      "metadata": {
        "id": "aIXefgDotlZr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_mail = [\"I've been searching for the right words to thank you for this breather. I promise i wont take your help for granted and will fulfil my promise. You have been wonderful and a blessing at all times\"]\n",
        "\n",
        "# convert text to feature vectors\n",
        "input_data_features = feature_extraction.transform(input_mail)\n",
        "\n",
        "# making prediction\n",
        "\n",
        "prediction = model.predict(input_data_features)\n",
        "print(prediction)\n",
        "\n",
        "\n",
        "if (prediction[0]==1):\n",
        "  print('Ham mail')\n",
        "\n",
        "else:\n",
        "  print('Spam mail')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qhMAthJqtFkW",
        "outputId": "075e0420-cd6d-48c8-c1b5-390c05202a66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n",
            "Ham mail\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "An example for the above conditional loop"
      ],
      "metadata": {
        "id": "u77L8g-luA1u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_list = [1, 2, 3]"
      ],
      "metadata": {
        "id": "1tvOmZ3GtwgL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(my_list[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g6PO48sst2Vu",
        "outputId": "56dc04a0-2d36-46c5-97b6-c3052821ba26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model built is, doesn't overfit"
      ],
      "metadata": {
        "id": "4FHaTIeFta-j"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z5RTJmJftI9k"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}