Spam Mail Prediction .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1d9adbd8",
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ca27ce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading Data  \n",
    "raw_mail_data=pd.read_csv(\"C:\\\\Users\\\\busra\\\\OneDrive\\\\Masaüstü\\\\mail_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91a3293c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_mail_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5aacd56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_mail_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c161e84",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace the null values with a null string \n",
    "mail_data=raw_mail_data.where((pd.notnull(raw_mail_data)),'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2495f53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mail_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68162b7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mail_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "18008036",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ef9fed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Label spam mail as 0, other 1\n",
    "mail_data.loc[mail_data['Category']=='spam', 'Category']=0\n",
    "mail_data.loc[mail_data['Category']=='ham', 'Category']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fe3f198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</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>1</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>1</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Category                                            Message\n",
       "0        1  Go until jurong point, crazy.. Available only ...\n",
       "1        1                      Ok lar... Joking wif u oni...\n",
       "2        0  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3        1  U dun say so early hor... U c already then say...\n",
       "4        1  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mail_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2989362c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seperate data text and labels\n",
    "X=mail_data[\"Message\"]\n",
    "Y=mail_data[\"Category\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd77971c",
   "metadata": {},
   "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",
      "                              ...                        \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"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a45ef215",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "49a1c3d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting data into train and test\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5aa09446",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5572,) (4457,) (1115,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape , X_train.shape , X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1593af9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Extraction\n",
    "#We need to all the text data convert to meaningful numerics\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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1888c9e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "print(X_train_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a46b0942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 7271)\t0.1940327008179069\n",
      "  (0, 6920)\t0.20571591693537986\n",
      "  (0, 5373)\t0.2365698724638063\n",
      "  (0, 5213)\t0.1988547357502182\n",
      "  (0, 4386)\t0.18353336340308998\n",
      "  (0, 1549)\t0.2646498848307188\n",
      "  (0, 1405)\t0.3176863938914351\n",
      "  (0, 1361)\t0.25132445289897426\n",
      "  (0, 1082)\t0.2451068436245027\n",
      "  (0, 1041)\t0.28016206931555726\n",
      "  (0, 405)\t0.2381316303003606\n",
      "  (0, 306)\t0.23975986557206702\n",
      "  (0, 20)\t0.30668032384591537\n",
      "  (0, 14)\t0.26797874471323896\n",
      "  (0, 9)\t0.2852706805264544\n",
      "  (0, 1)\t0.2381316303003606\n",
      "  (1, 7368)\t0.29957800964520975\n",
      "  (1, 6732)\t0.42473488678029325\n",
      "  (1, 6588)\t0.3298937975962767\n",
      "  (1, 6507)\t0.26731535902873493\n",
      "  (1, 6214)\t0.3621564482127515\n",
      "  (1, 4729)\t0.22965776503163893\n",
      "  (1, 4418)\t0.3457696891316818\n",
      "  (1, 3491)\t0.496093956101028\n",
      "  (2, 7205)\t0.22341717215670331\n",
      "  :\t:\n",
      "  (1110, 3167)\t0.5718357066163949\n",
      "  (1111, 7353)\t0.4991205841293424\n",
      "  (1111, 6787)\t0.40050175714278885\n",
      "  (1111, 6033)\t0.4714849709283488\n",
      "  (1111, 3227)\t0.44384935772735523\n",
      "  (1111, 2440)\t0.4137350055985486\n",
      "  (1112, 7071)\t0.33558524648843113\n",
      "  (1112, 6777)\t0.32853717524096393\n",
      "  (1112, 6297)\t0.3056896872268727\n",
      "  (1112, 5778)\t0.22807428098549426\n",
      "  (1112, 5695)\t0.3381604952481646\n",
      "  (1112, 5056)\t0.2559183043595413\n",
      "  (1112, 4170)\t0.3307835623173863\n",
      "  (1112, 2329)\t0.241856898377491\n",
      "  (1112, 1683)\t0.4017087436272034\n",
      "  (1112, 1109)\t0.35334496762883244\n",
      "  (1113, 4080)\t0.3045947361955407\n",
      "  (1113, 4038)\t0.37023520529413706\n",
      "  (1113, 3811)\t0.28103080586555096\n",
      "  (1113, 3281)\t0.33232508601719535\n",
      "  (1113, 3113)\t0.33840833425155675\n",
      "  (1113, 2852)\t0.5956422931588335\n",
      "  (1113, 2224)\t0.3337959267435311\n",
      "  (1114, 4557)\t0.5196253874825217\n",
      "  (1114, 4033)\t0.8543942045002639\n"
     ]
    }
   ],
   "source": [
    "print(X_test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b537f8d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c2ff37a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "model=LogisticRegression()\n",
    "model.fit(X_train_features, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8dc87282",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data 0.9670181736594121\n"
     ]
    }
   ],
   "source": [
    "#Prediction on Training Data\n",
    "X_train_predict=model.predict(X_train_features)\n",
    "train_score=accuracy_score(X_train_predict,Y_train)\n",
    "print(\"Accuracy on training data {}\".format(train_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "87b49f47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data 0.9659192825112107\n"
     ]
    }
   ],
   "source": [
    "#Prediction on Test Data\n",
    "X_test_predict=model.predict(X_test_features)\n",
    "test_score=accuracy_score(X_test_predict,Y_test)\n",
    "print(\"Accuracy on training data {}\".format(test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1f4a853b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spam!\n"
     ]
    }
   ],
   "source": [
    "#Build A Predictive System \n",
    "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",
    "prediction=model.predict(input_data_features)\n",
    "\n",
    "if prediction==0:\n",
    "    print(\"Ham mail\")\n",
    "else:\n",
    "    print(\"Spam!\")"
   ]
  }
 ],
 "metadata": {
  "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": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}