Project_Spam_Mail_Prediction_using_Machine_Learning.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqB21QOgMg-G"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rALI06-oHusw"
      },
      "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"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YyKe9o2ONeFv"
      },
      "source": [
        "Data Collection & Pre-Processing"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from google.colab import files\n",
        "\n",
        "\n",
        "uploaded = files.upload()\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
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      "outputs": [
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              "        style=\"border:none\" />\n",
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              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
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              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
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              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
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              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
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              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
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              "\n",
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              "\n",
              "  // Wait for the user to pick the files.\n",
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              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
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              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
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              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
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              "\n",
              "  for (const file of files) {\n",
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              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
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              "\n",
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              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
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              "    } while (position < fileData.byteLength);\n",
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              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving mail_data.csv to mail_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpStHH8KNcYB"
      },
      "source": [
        "# loading the data from csv file to a pandas Dataframe\n",
        "raw_mail_data = pd.read_csv('mail_data.csv')"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdn-7VE2NxsZ",
        "outputId": "0ecc60ea-754f-4db0-e3a7-5335bf9ff1a8"
      },
      "source": [
        "print(raw_mail_data)"
      ],
      "execution_count": 11,
      "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",
      "metadata": {
        "id": "yhakjIE1N011"
      },
      "source": [
        "# replace the null values with a null string\n",
        "mail_data = raw_mail_data.where((pd.notnull(raw_mail_data)),'')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "SJey6H-SOWeK",
        "outputId": "03d29b62-1ae4-43a6-ff20-d7958f18da6c"
      },
      "source": [
        "# printing the first 5 rows of the dataframe\n",
        "mail_data.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Category                                            Message\n",
              "0      ham  Go until jurong point, crazy.. Available only ...\n",
              "1      ham                      Ok lar... Joking wif u oni...\n",
              "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3      ham  U dun say so early hor... U c already then say...\n",
              "4      ham  Nah I don't think he goes to usf, he lives aro..."
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbK82N2gOdar",
        "outputId": "e2c2b632-dc95-46e5-e281-e97dfab5be4a"
      },
      "source": [
        "# check the number of rows and columns in the dataframe\n",
        "mail_data.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhR4U3ATPBdk"
      },
      "source": [
        "Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9EW7QSgeOt4p"
      },
      "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"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uxZK1fWwPwII"
      },
      "source": [
        "spam  -  0\n",
        "\n",
        "ham  -  1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8Rt-FaNPtPE"
      },
      "source": [
        "# separating the data as texts and label\n",
        "\n",
        "X = mail_data['Message']\n",
        "\n",
        "Y = mail_data['Category']"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "QnQeUBGtQPP7",
        "outputId": "ee5754b7-3ac2-4f92-caf4-b299ee861e77"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 17,
      "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",
      "metadata": {
        "colab": {
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        },
        "id": "cuWDNy5KQQjY",
        "outputId": "5a18566f-653d-4eda-d379-a8fa0e6cae85"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": 18,
      "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",
      "metadata": {
        "id": "jvHyqdH8QZPH"
      },
      "source": [
        "Splitting the data into training data & test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RO2GmbSNQSQH"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tS2c7A4NRa46",
        "outputId": "588a8e66-8fc7-45fe-9e0e-0ecfdfb98e35"
      },
      "source": [
        "print(X.shape)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5572,)\n",
            "(4457,)\n",
            "(1115,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYQpiACGSBYM"
      },
      "source": [
        "Feature Extraction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLs847nSRibm"
      },
      "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', lowercase='True')\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')"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBMAcw9RUkUY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8098bf23-c728-4010-e068-42c6b645429e"
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": 22,
      "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",
      "metadata": {
        "id": "1NFuGogZUpt0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "606ca52c-2bf8-46f5-b6c2-9b44b4d1f350"
      },
      "source": [
        "print(X_train_features)"
      ],
      "execution_count": 23,
      "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",
      "metadata": {
        "id": "q86FvELbU_SV"
      },
      "source": [
        "Training the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hV6BAIZQVBbo"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JeAOwzpUv0V"
      },
      "source": [
        "model = LogisticRegression()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWGRHWAPVI_z",
        "outputId": "ca2dd82c-9836-4ecf-bf9f-2f4a6233ccc4"
      },
      "source": [
        "# training the Logistic Regression model with the training data\n",
        "model.fit(X_train_features, Y_train)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZ01fa8dVeL5"
      },
      "source": [
        "Evaluating the trained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExiF2kKxVYtC"
      },
      "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)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7t4DI5UWCkB",
        "outputId": "ecc70627-5cbc-4a12-83e4-acc9095bfa6a"
      },
      "source": [
        "print('Accuracy on training data : ', accuracy_on_training_data)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data :  0.9670181736594121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTin5rXTWKg3"
      },
      "source": [
        "# prediction on test data\n",
        "\n",
        "prediction_on_test_data = model.predict(X_test_features)\n",
        "accuracy_on_test_data = accuracy_score(Y_test, prediction_on_test_data)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gvoMK4OWnJY",
        "outputId": "3e1cda3a-198c-4c55-e02f-08e893fffc15"
      },
      "source": [
        "print('Accuracy on test data : ', accuracy_on_test_data)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test data :  0.9659192825112107\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bXdOKxYAXaHC"
      },
      "source": [
        "Building a Predictive System"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h60z1__mWql6",
        "outputId": "d9da1821-dff1-4a78-aa91-b577542370b1"
      },
      "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')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n",
            "Ham mail\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_mail1 = [\"This is the 2nd time we have tried 2 contact u. U have won the Â£750 Pound prize. 2 claim is easy, call 087187272008 NOW1! Only 10p per minute. BT-national-rate.\"]\n",
        "\n",
        "# convert text to feature vectors\n",
        "input_data_features = feature_extraction.transform(input_mail1)\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": "bsSNbSZZdg17",
        "outputId": "161ed53c-c184-46a9-a148-ece02dc4079b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "Spam mail\n"
          ]
        }
      ]
    }
  ]
}