spam.ipynb

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            "Requirement already satisfied: scikit_learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (1.23.5)\n",
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      "source": [
        "Importing the Dependencies"
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        "id": "wdxl40MIyJdH"
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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.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mail_data=pd.read_csv(\"/content/drive/MyDrive/mail_data.csv\")"
      ],
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        "id": "MRxkfHYY6NTp"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "print(mail_data)"
      ],
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        "id": "QZqSSWVl7M-d",
        "outputId": "020d3a8a-6343-45f4-9070-e0ddb9750908"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Category                                            Message\n",
            "0         ham  Go until jurong point, crazy.. Available only ...\n",
            "1         ham                      Ok lar... Joking wif u oni...\n",
            "2        spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
            "3         ham  U dun say so early hor... U c already then say...\n",
            "4         ham  Nah I don't think he goes to usf, he lives aro...\n",
            "...       ...                                                ...\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\n",
            "\n",
            "[5572 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#replace the null values with null string\n",
        "data=mail_data.where((pd.notnull(mail_data)),'')"
      ],
      "metadata": {
        "id": "jFjK6lrB7Q4_"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "data.head()"
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              "  Category                                            Message\n",
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              "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",
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              "(5572, 2)"
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              "       Category                 Message\n",
              "count      5572                    5572\n",
              "unique        2                    5157\n",
              "top         ham  Sorry, I'll call later\n",
              "freq       4825                      30"
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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-d113b5ca-8a70-4cae-a72d-e2f125acbef2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d113b5ca-8a70-4cae-a72d-e2f125acbef2')\"\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-d113b5ca-8a70-4cae-a72d-e2f125acbef2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7oIvkcrPb-kE",
        "outputId": "fe4d58c2-b9d0-4c96-9ec1-4147e6860fa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Category    0\n",
              "Message     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcKtXhxXcH5w",
        "outputId": "8d2fbf08-1366-4c7c-a2ee-d1c0524af0b8"
      },
      "execution_count": null,
      "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   Category  5572 non-null   object\n",
            " 1   Message   5572 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 87.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Label Encoding"
      ],
      "metadata": {
        "id": "nO_Z9dWj9n71"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# label spam mail as 0; ham mail as 1:\n",
        "data.loc[data['Category']=='spam','Category',]=0\n",
        "data.loc[data['Category']=='ham','Category',]=1"
      ],
      "metadata": {
        "id": "K3WuDf459c7l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Spam-0\n",
        "\n",
        "Ham-1"
      ],
      "metadata": {
        "id": "WzaXlbTO9yCE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#separating the data  as texts and labels\n",
        "X=data['Message']\n",
        "Y=data['Category']"
      ],
      "metadata": {
        "id": "ARc_Pf5_98zq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FAWl_Gs-_DTT",
        "outputId": "58a55615-07a0-4aa8-861b-2d0804717a9e"
      },
      "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": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ztTu8zed_HRg",
        "outputId": "8c2601c3-904b-418e-af21-db30c6dd6cc1"
      },
      "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 the data into training data & test data"
      ],
      "metadata": {
        "id": "COvKSTR9_hEI"
      }
    },
    {
      "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": "dZbPEOpB_gTM"
      },
      "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": "07Oa2hbtAN3U",
        "outputId": "38abd748-77e7-4c20-b7d4-2c1e9d17b672"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5572,)\n",
            "(4457,)\n",
            "(1115,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature Extraction"
      ],
      "metadata": {
        "id": "Jp45tY1RKUbZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#transform the test data to feature vectors that can be used as input to the logistic regression\n",
        "feature_extraction=TfidfVectorizer(min_df=1,stop_words='english',lowercase=True)\n",
        "X_train_features=feature_extraction.fit_transform(X_train)\n",
        "X_test_features=feature_extraction.transform(X_test)\n",
        "#convert Y_train and Y_test values as integers\n",
        "Y_train=Y_train.astype('int')\n",
        "Y_test=Y_test.astype('int')"
      ],
      "metadata": {
        "id": "vqa68oT8JDzs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "id": "RCegSwRSKYXP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1fdcb751-0479-4743-be0f-8a3f8f3c82fd"
      },
      "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": "RCtKQm2gmnwX",
        "outputId": "aef4dd1f-bf89-477d-c71c-e7e30a399da8"
      },
      "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": [
        "Training the Model"
      ],
      "metadata": {
        "id": "pFcNewGZPXz-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistic Regression"
      ],
      "metadata": {
        "id": "VJ9RT2UvPctY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=LogisticRegression()"
      ],
      "metadata": {
        "id": "Oadz5G8_O_O1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training the logistic regression model with training data\n",
        "model.fit(X_train_features, Y_train)"
      ],
      "metadata": {
        "id": "cZdcGnfeP9oi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "88673a7c-b29f-4ad3-d301-f0dfd91f76ac"
      },
      "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": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluating the trained model"
      ],
      "metadata": {
        "id": "4CUwEmzEQx3F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#prediction on training data\n",
        "y_pred=model.predict(X_train_features)\n",
        "accuracy=accuracy_score(Y_train,y_pred)"
      ],
      "metadata": {
        "id": "KluE6kiUQ2uK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on training data:',accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JS8bDApsRtAP",
        "outputId": "b171e2a7-22d9-49a3-cd76-b4fdd9e86178"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data: 0.9670181736594121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#prediction on testing data\n",
        "y_pred=model.predict(X_test_features)\n",
        "accuracy=accuracy_score(Y_test,y_pred)"
      ],
      "metadata": {
        "id": "LV_1pYq0SBEf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on testing data:',accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCcmTsaFSP_B",
        "outputId": "3063ba38-1ee5-4905-f050-f2cab59f8643"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on testing data: 0.9659192825112107\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating the Confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(Y_test, y_pred)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xmLRTC93ejmF",
        "outputId": "bd0c1b21-e165-4b6a-a726-c09717e820ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[117,  38],\n",
              "       [  0, 960]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix graph using seaborn\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.heatmap(cm, annot=True, fmt='.2f', cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "E7SoCGwJfMwu",
        "outputId": "7758e9c5-8139-437b-cbe2-cc0cf75e63e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score\n",
        "precision = precision_score(Y_test, y_pred)\n",
        "recall = recall_score(Y_test, y_pred)\n",
        "f1 = f1_score(Y_test, y_pred)"
      ],
      "metadata": {
        "id": "YS10MkGVfthR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print the results\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w6WLvDreieP7",
        "outputId": "3895d57e-6401-47c5-a5ab-70c9e6e59915"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.9619238476953907\n",
            "Recall: 1.0\n",
            "F1 Score: 0.9805924412665985\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building a Predictive System"
      ],
      "metadata": {
        "id": "OtvEx2frTVaW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_mail=[\"WINNER!! As a valued network customer you have been selected to receivea Â£900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.\"]\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",
        "if prediction[0]==1:\n",
        "  print(\"Ham mail\")\n",
        "else:\n",
        "  print(\"spam mail\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZR841yuTjL5",
        "outputId": "a6e6da4b-d59a-4df5-876f-70fbc8826aa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "spam mail\n"
          ]
        }
      ]
    }
  ]
}