spam_mail_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5ZycEQcRVkYn",
   "metadata": {
    "id": "5ZycEQcRVkYn"
   },
   "source": [
    "**Spam Email Prediction Model**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jl5bR_KVVtsc",
   "metadata": {
    "id": "jl5bR_KVVtsc"
   },
   "source": [
    "Importing required packages or dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "05a65aef-b608-42c6-934c-e0ffa4a244d2",
   "metadata": {
    "id": "05a65aef-b608-42c6-934c-e0ffa4a244d2"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "RC5gIASNV48N",
   "metadata": {
    "id": "RC5gIASNV48N"
   },
   "source": [
    "Reading the csv dataset files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "33903893-13b7-4311-8407-207af46d8380",
   "metadata": {
    "id": "33903893-13b7-4311-8407-207af46d8380"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('mail_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JmbJbdhvV7jO",
   "metadata": {
    "id": "JmbJbdhvV7jO"
   },
   "source": [
    "printing the top 5 lines of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84bfc604-2149-48ce-9b41-b4294a1a0ff1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "84bfc604-2149-48ce-9b41-b4294a1a0ff1",
    "outputId": "3adf8dc6-0bce-499c-cae9-586c49633483"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "PsuuJWrnV-Nq",
   "metadata": {
    "id": "PsuuJWrnV-Nq"
   },
   "source": [
    "Replacing NaN values with empy string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6804bd38-86da-46ad-b47e-9523bf5c9455",
   "metadata": {
    "id": "6804bd38-86da-46ad-b47e-9523bf5c9455"
   },
   "outputs": [],
   "source": [
    "data = df.where((pd.notnull(df)),'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4024fa6f-49ae-4ee2-b044-ea184276e2dd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4024fa6f-49ae-4ee2-b044-ea184276e2dd",
    "outputId": "ebc9efcf-0890-4472-b8a6-130bed2b9f7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e694f639-1ebc-4c41-be66-d88a81092c13",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e694f639-1ebc-4c41-be66-d88a81092c13",
    "outputId": "1112cb09-3af0-4a0b-c34a-aed41a8bc7e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "q-uA9I2ZWBpO",
   "metadata": {
    "id": "q-uA9I2ZWBpO"
   },
   "source": [
    "Allocating identification to spam and ham by 0 and 1 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "872f49a8-6f1f-4dbf-a429-c63309003224",
   "metadata": {
    "id": "872f49a8-6f1f-4dbf-a429-c63309003224"
   },
   "outputs": [],
   "source": [
    "data.loc[data['Category'] == 'spam','Category',] = 0\n",
    "data.loc[data['Category'] == 'ham','Category',] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a889d32-e493-4aec-ae8d-d26ae9183edf",
   "metadata": {
    "id": "8a889d32-e493-4aec-ae8d-d26ae9183edf"
   },
   "outputs": [],
   "source": [
    "x = data['Message']\n",
    "y = data['Category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "01ec5c63-17a6-4798-9e10-42dc4f76e99e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "01ec5c63-17a6-4798-9e10-42dc4f76e99e",
    "outputId": "70013414-99ed-4ae8-ff73-64ffa4deb519"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
      "Name: Message, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(x.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c68a6d90-bc6b-4f43-9d1d-4fe0f7b7394e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c68a6d90-bc6b-4f43-9d1d-4fe0f7b7394e",
    "outputId": "b67aa4a7-6238-4d06-e315-dd4fc7e43509"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    1\n",
      "2    0\n",
      "3    1\n",
      "4    1\n",
      "Name: Category, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ob2iI9gIWFa5",
   "metadata": {
    "id": "ob2iI9gIWFa5"
   },
   "source": [
    "Splitting the data for training and testing into 80:20 ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50898e57-2a39-4bd2-9fd0-0fa8000e403f",
   "metadata": {
    "id": "50898e57-2a39-4bd2-9fd0-0fa8000e403f"
   },
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(x , y , test_size=0.2 , random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ji1Qs6GbWKDC",
   "metadata": {
    "id": "ji1Qs6GbWKDC"
   },
   "source": [
    "In logistic Regression the input data must be converted into feature data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3788f649-21f0-4f94-a200-3cb637b133e1",
   "metadata": {
    "id": "3788f649-21f0-4f94-a200-3cb637b133e1"
   },
   "outputs": [],
   "source": [
    "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",
    "Y_train = Y_train.astype('int')\n",
    "Y_test = Y_test.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96cb6c7b-5224-4ebf-b518-c3a91ae903b5",
   "metadata": {
    "id": "96cb6c7b-5224-4ebf-b518-c3a91ae903b5"
   },
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7-zHHaFCWTgn",
   "metadata": {
    "id": "7-zHHaFCWTgn"
   },
   "source": [
    "Train the model on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "baa2f705-eaa4-4bac-9419-bb7e169db246",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "baa2f705-eaa4-4bac-9419-bb7e169db246",
    "outputId": "7113df8e-d691-4940-ab53-717eaeaef709"
   },
   "outputs": [
    {
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       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
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       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
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       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
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       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
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       "\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
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       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
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       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
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       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
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       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
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       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
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       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
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       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_features,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "j2JdgUmXWVrF",
   "metadata": {
    "id": "j2JdgUmXWVrF"
   },
   "source": [
    "Checking accuracy on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd06bf6b-09b0-47c3-a8fb-ffe6c363c2b8",
   "metadata": {
    "id": "bd06bf6b-09b0-47c3-a8fb-ffe6c363c2b8"
   },
   "outputs": [],
   "source": [
    "prediction_on_training_data = model.predict(X_train_features)\n",
    "accuracy_on_training_data = accuracy_score(Y_train,prediction_on_training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "42f87192-e409-463f-bb31-9ee66f0bedb9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "42f87192-e409-463f-bb31-9ee66f0bedb9",
    "outputId": "57834f9b-f735-47cd-a3cd-ca78119f96e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data :  0.9670181736594121\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on training data : ',accuracy_on_training_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eMy5nA-NWYlD",
   "metadata": {
    "id": "eMy5nA-NWYlD"
   },
   "source": [
    "Checking accuracy on testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "920e414e-08e3-44d1-a3bf-83ccb023de55",
   "metadata": {
    "id": "920e414e-08e3-44d1-a3bf-83ccb023de55"
   },
   "outputs": [],
   "source": [
    "prediction_on_testing_data = model.predict(X_test_features)\n",
    "accuracy_on_testing_data = accuracy_score(Y_test,prediction_on_testing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dc2ca60c-59bb-4261-914a-7c7702f646fc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dc2ca60c-59bb-4261-914a-7c7702f646fc",
    "outputId": "4ae4a07a-4ba0-4bb8-e148-f4f94424f0dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on testing data :  0.967713004484305\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on testing data : ',accuracy_on_testing_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "w1ke3nF3Way6",
   "metadata": {
    "id": "w1ke3nF3Way6"
   },
   "source": [
    "Testing on user input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44ee927d-efee-448a-8131-82f57d073f5f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "44ee927d-efee-448a-8131-82f57d073f5f",
    "outputId": "7d2f708b-1e1a-45e5-c848-7872373eaa6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "# Testing on user input data\n",
    "usrInput = [\"U've been selected to stay in 1 of 250 top British hotels-FOR NOTHING! Holiday valued at Â£350! Dial 08712300220 to claim-National Rate Call.Bx526,SW73SS\"]\n",
    "\n",
    "usrInput_data_features = feature_extraction.transform(usrInput)\n",
    "\n",
    "predict = model.predict(usrInput_data_features)\n",
    "\n",
    "print(predict)\n",
    "# 0 means spam\n",
    "# 1 means ham"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "660713f4-f723-4370-99b5-419dda9cc963",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "afa9b5ea-8b79-4ff6-b947-e56c37fae3c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model,open('model.pkl','wb'))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
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}