Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "366aa85d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed367c21",
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('email.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "49ba1f1f",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'DataFrame' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[106], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df(\u001b[38;5;241m2\u001b[39m)\n",
      "\u001b[1;31mTypeError\u001b[0m: 'DataFrame' object is not callable"
     ]
    }
   ],
   "source": [
    "df(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04252fa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5573, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c33339e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category       3\n",
       "Message     5158\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ab2acc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "ham               4825\n",
       "spam               747\n",
       "{\"mode\":\"full\"       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "73fd80e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.unique(df['Category'])\n",
    "df['Category'] == '{\"mode\":\"full\"'\n",
    "df.tail()\n",
    "df = df.drop(5572)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fc726869",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding target variable\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "df['Category'] = encoder.fit_transform(df['Category'])\n",
    "# ham=0, spam=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3480a49e",
   "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>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>0</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>0</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         0  Go until jurong point, crazy.. Available only ...\n",
       "1         0                      Ok lar... Joking wif u oni...\n",
       "2         1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3         0  U dun say so early hor... U c already then say...\n",
       "4         0  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e665be7",
   "metadata": {},
   "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   int32 \n",
      " 1   Message   5572 non-null   object\n",
      "dtypes: int32(1), object(1)\n",
      "memory usage: 65.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9bc102e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category    0\n",
       "Message     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "12558d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category       2\n",
       "Message     5157\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d0a9a367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "415"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "03b5ae53",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(subset='Message', keep='last', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "78c174b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5157, 2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "18c85c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "0    4516\n",
       "1     641\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "536b28de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bMmB3aHdus01nPXmjWe9ux8ZUXpZPRG3z+o398JfecaJjCKGJK7MzT9VgE9duIaKL8M5G7S4gqomiWLEzB9oYNxGRu+zLKUdqYZXoGEKovihsdgdW7+YpsUR08bT6XqL6ovjhcCFvc0pELrF2b54mJ7VVXxSf7tT2aW1E5DpFVRb8mlosOobHqboocstqsUmD31Qich8tHn5SdVF8dSAfGhwlEpEbfX+4EJX1NtExPErVRfHd4ULREYhIZawNDnz3m7beW1RbFEVV9dibXSY6BhGp0H/3nxQdwaNUWxQ/HC7iYScicovNaadQVmO98IYqodqi+PY3cQsQEpG6NThkrD+knfcYVRZFtaUBW9NLRMcgIhVbd0A7h59UWRQ/Hy2C1c57YhOR+2zLKEGpRg4/qbIoeNiJiNzNIQNb0rWx2KjqisLa4MCGY7zIjojcb7NGVqVWXVFsTj+FKkuD6BhEpAGb07QxF6q6otDahTBEJE52aS1yStV/50yD6ACuJMsyfjiivKKQHXaUb/oENYd/gaOmDPqAUAT0Gofg4TdAkhq7Ouu5v5zzuSGjb0HwkGvO+XfVB39AydevNHs86YHPIRl8AAC5S2+FvbKo2TbmflMQPmFBYz5ZRsXmT1C9/1s46qvhE9sFYeMXwCeyXVu+XCJN2Zx2CtMHJ4mO4VaqKopjhVUorlLekuKV21ajet96hE+5Dz4RSbDkp6Jk/avQmfwRNHAqACDhzg+bPKcuYxdK1r8G/64jnO5b8vFH/Nw3mz72e0kAQOzslwHHmTPArKeyULTyUQR0O7Pfyu1rULnzC0Rcfh8MYXGo2LISRZ8tQtxt/4bO5N/mr5tICzaxKLzLnqxy0RHOyXLyKPySh8C/0yAAgCE4GrVHfoW1IO30NnpzaJPn1KZth2+7XjCGxDjfuSQ1e+6f6f2Dm3xet20VDCGxMCX2AtA4mqja9R8ED7sB/l2HAwAiptyPnNdnoubIBgT2ndzir5NIi7aml0CWZUiSJDqK26hqjmKPQtd2MiWkoD5rP2yleQAAa1EG6nMPw6/jwHNub68pQ136Tph7T7jgvmVrHXKX3oLcf81G0eolsBamn39buw01h3+Buff40z/UDRWFsNeUwa9Dv9PbSQYjfBN7wpJ3pDVfJpEmldRYcSRf3bdIVdeIQqFFETTkWjgsNTj59nxApwMcDoSMnIWAlFHn3L760I/Q+fjBv8twp/s1hiU0Hs6KbAeHpRZVu75EwUcPIfaW12AMi2+2fe3xbXDUVyOg52WnH7NXN/6b6fxDmmyrDwhBQ0XzuQ0iam5L+imkxAWJjuE2qimK8lorTpyqER3jnGqP/Iqa335BxBUPwhjZDtbCDJT9+Db05nCYe13WbPvqAz8gIGV0k7mGczHFd4MpvtuZzxNSkL/sf1C1Zx3Cxs07x36/g1/HATAEhjff2dnDZllu/hgRndOmtFO47dKOomO4jWoOPe3NLoes0NViy355H8FDr0VAyij4RLaHuedYBA6aioptq5ptW59zCA2luTD3ufBhp7NJkg6mmM6wlTZfg6ahogj1Wfth7jOxyeN/zG84apqOxuy1FdAHhLQ6A5EW7ThRCpuKlw1STVEo9bATAMg2CyA1/aeWJB0gN//Bqj7wPXxikuET1frfTmRZhrXoxDknt6sPfg+9fzD8fp9Q/4MhOBr6gFDUZe49sx+7DfU5h2CK797qDERaVGu1Y39OuegYbqOaQ097s8tFRzgvv+TBqNiyEvqgSPhEJMFamI7KnV/A3Ht8k+0cllrUHtuE0DF/Ped+Tq17CfrAcISOmgMAKN/0CUxxXWEIi4dsqUXl7i9hLcpA2Pj5TZ4nyw5UH/wBAT0vg6TTN/k7SZIQOHAqKraugjE0DobQOFRsXQWd0YSA7ueeQyGi5g7lVWBg+zDRMdxCFUXhcMjYp+A2Dxs3D+UbP0Lpd2/AUVsBvTkM5r6TETJiepPtao78Csg47yR3Q2Vxk5GJw1KDkm9fh72mDDpTAHyiOiLmxmdhiuva5Hn1mftgryxuVkx/CBpyDeQGC0q/Wwp7fTVMcV0Rdf0TvIaCqBWOFar3zCdJlpV6ZL/ljhZUYtIrG0XHICIN658Ugs/vcH6BrLdSxRyFUi+0IyLtOF5YLTqC26ijKBQ8kU1E2lBtaUBumToXCFRFURzMrRAdgYgIxwrUOU+hiqLIKlXmhXZEpC1qndD2+qIoqqxHvU29F7oQkfc4zhGFMmVp4KYhROQdjrIolCm7hEVBRMqQUVyDBhUu5eH9RcERBREphNXuUOzipBfD64tCC/erJSLvocYJba8vCo4oiEhJMjmiUB4WBREpyalqq+gILufVRVFvs6O42iI6BhHRaadU+J7k1UWRXVqr2JsVEZE2lXBEoSw8NZaIlKakhiMKRclR6QJcROS9OKJQmKIq9TU3EXm3slorHA51HRP36qKoqreJjkBE1IRDBkpr1TWq8OqiqKxrEB2BiKgZtR1+8uqi4IiCiJSoRGWnyHp1UVTWc0RBRMpzqoYjCsWorOOIgoiUhyMKBamxcERBRMrDOQoFqW9Q37rvROT96mx20RFcyruLQmXfDCJSBzuvo1AOC0cURKRALAqFaLA7VPfNICJ1aFDZe5PXFgXnJ4hIqewOdb0/eW1R2FgURKRQahtRGEQHaCuDXhIdgVTEpHPgm06rEVObJjoKqYDVPAFAX9ExXMZri8LPqBcdgVTE4tDhltwrsT7sJfiV/CY6Dnk5v8S+oiO4lNceejLodTByVEEulFnni4mlD6AuvKfoKOTtdOr6RdZriwIAfDmqIBfLrvPF+JIHUBfBsqCLoPPagzXn5NVF4e/DoiDXy603YfypB1Ab0Vt0FPJWLArl4DwFuUtjWdyHmsi+oqOQN9IbRSdwKa8uCh56InfKqzdhXPG9LAtqPc5RKIcfDz2Rm+XX++CyontRHdlPdBTyJgZf0QlcyruLgiMK8oACiw/GFt2L6qgBoqOQtwiIFJ3ApVgURC1QZDFibOE9qIoaKDoKeYPAGNEJXMq7i4KHnsiDiixGjC24G5VRg0RHIaUzsygUgyMK8rRiqxFjCu5CZfRg0VFIyQKjRSdwKa8uikBfdZ2CRt6hxGrEmPw7URE9VHQUUiozi0Ix4kLUdWYBeY8SqxGjT96B8phhoqOQ0viFAgaT6BQu5dVFkRDqLzoCaViZzYBReQtQHjNcdBRSksBY0QlczsuLwk90BNK4CpsBo/LmoyxmhOgopBQqO+wEeHlRJIZxREHiVdgMGJk7D6Wxl4qOQkqgslNjAS8vimA/IwJ91bX4FnmnqgYDLs25HSWxo0RHIdE4olAezlOQUtQ06DEy5zaWhdZxRKE8nKcgJalp0GNk9lwUx40RHYVE4YhCeRI5oiCFqbHrMDrrryiKu0x0FBKBIwrl4YiClKjGrsOorDkojBsnOgp5GotCeVgUpFR1dj1GZ81GQdx40VHIU4z+QEg70SlcTgVFwUNPpFx1dj1GZc5GQfwE0VHIE6J7qu6mRYAKiiIxjCMKUjaLQ4dRJ27GyfhJoqOQu8X1FZ3ALby+KAJ9jYgL5ppPpGwWhw5jTsxEXvxk0VHInWL7ik7gFl5fFADQNylEdASiC7I4dBh9YiZyE6aIjkLuEttHdAK3UEdRJIaIjkDUIjaHhDEZNyIn4S+io5CrGXyByG6iU7iFSooiVHQEohazOSSMTp+OrIQrRUchV4ruCejVuaSQKr6qXvHBMOgkNDhk0VGIWsQu6zA2/Xr81Alol/ulx1//16wGvLDFit0n7civlrH2Bj9M69Z4IzCbXcajP1nwdVoDMsocCDZJGNfRgGfHmRAX2LLfLVccsmHGmjpM7WrAF9PPnJm4dKcVS3dZkVnuAAD0iNLjsZE+mNz5zE3IZFnGkg0WvLXbhrJ6GUPi9fjX5b7oEaXws4lUetgJUMmIws9Hjy7RgaJjELXKH2VxImGax1+7xiqjT7QOr1/e/ESQWhuwp8CORSNN2HN7AD6/wQ/HSxy48tPaFu07q9yBB7+rx6VJzd/YE4IkPDvOhF23B2DX7QEY216PqSvq8FuR/fQ2z2+24n+3WvH65b7YOTcAMWYJ4z+sRZVF4b8IqvSMJ0AlRQFwQpu8k13WYVz6tchIuMqjrzu5sxFPjfXF1d2b30442FfC97MCcH0PI7pG6DE0wYD/m+yL3fkOZFc4nO7X7pBx0+d1WDLahI6hzd9eruhqxOWdjegSrkeXcD2evswXZh9gW25jUciyjFe2W7HwUhOu7m5Ezyg9lk/zQ61NxicHba754t1FpWc8AWoqioQQ0RGI2sQu63BZ+rVIS7xGdJTzqrDIkACE+EpOt3tigwWRARL+2t/ngvu0O2SsOGRDjQ0Yltg4+jhRLqOgWsaETmeOipsMEka1N2BLrv18uxJPbwKiuotO4TaqmKMAOKIg7ybLEsanXY3vkiV0zlktOk4T9Q0yHvmhHjf2MiLIdP6i2JzdgHf32rBvfoDT/R0stGPYuzWobwDMPsDaG/yQEtlYFAXVjSOWaHPT14kOkJB1gdGMUNEpgL756EwtVDOiSI40w2xSTe+RBsmyhAlpV+F44nWio5xms8uYvroODhl4Y8r5L2ytssiYubYOb1/hiwh/528rXSN02DffjG23BWDBQB/M/qIeh4ubjhbOriNZbv6Yoqj4sBOgohGFTiehd0IwtqSXiI5C1GayLGFi2jR8k6xD15yVQrPY7DKuX12HE+UO/HSzv9PRRHqZA5nlMq74tA5AHQDgj5MQDU9U4thdZnQKaywQH72E5LDGfQ2M02PnSTte3WbFm1f4IcbcuE1BtYzYP52fUlQrI9qs4N9rVXzGE6CiogAaL7xjUZC3k2UJE1On4uvOOqTkfCokwx8lkVriwM+z/RF+gVFCtwgdDi5oesjp0Z8sqLLKeHWSLxKDz18yMgDL7wOKDiESYswSvs9oQL/YxsNRVruMDZkNeG6cgpfq6TBSdAK3UlVRDGzPC+9IPS5PvQJfdZbQI+cTl++72iojrfTMMf8TZQ7sK7AjzE9CXKCEa1fVYU++Hetm+MMun5k7CPOT4KNvfNO/eW0d4gMl/HOcL3wNEnqedZ3DHxPff378Hz/WY3KyAYnBOlRZGiezf8m045ubTAAASZJw7xAfPLPRgs5hOnQO1+GZjRb4GyXc2EuhcwARXYDwTqJTuJWqimJYxwj4GHSwNih40ouoFaak/gXrOkvomfOxS/e766QdY5afuS7i/u8sACyY3ceIxaNN+PJYAwCg75s1TZ7382x/jG7f+LaRXeGATmrd4aDCahmz1tYhv1pGsElC72gdvrnJH+P/dJbTQyN8UNcg446v61FWJ2NIgh7fzfJHoJNDX0J1Uf+qwJIsywq/iqV1bn5vB349Xiw6BpFLfdl5PXrnfCg6Bp3LLeuBdsNFp3ArBc8Otc2YrpGiIxC53JWpk7Ev8WbRMehsfmFA4hDRKdxOdUUxtluU6AhEbjEtdRL2JM4RHYP+rPMEVd7R7myqK4p24QHoGOH8gh8ib3V16gTsSrpVdAz6Q1f1z08AKiwKgKMKUrdrj4/DjsS/io5BOiPQ6TLRKTxClUUxsWeM6AhEbnV96mXYnjhXdAxtaz8C8A0SncIjVFkUA5JCERloEh2DyK1uSB2DrYm3i46hXV20c/9zVRaFTidhQkq06BhEbjcjdTQ2J84THUObNDI/Aai0KABgEg8/kUbclDoKGxPni46hLZHdgdD2olN4jGqLYljHcIT4K/SSfyIXm5U6EhsSF4iOoR0aGk0AKi4Kg17Hw0+kKbNTL8XPiXeKjqENXS8XncCjVFsUAHDDoETREYg86pbUEfgx8W7RMdQtrBOQOFh0Co9SdVEMaBeGbjGBF96QSEX+mjoM3yfcIzqGevXX3lIqqi4KALhxSJLoCEQeNzdtKL5N+B/RMdRHZwT63ig6hcepviim9YuHn1H9a7EQnW1e2hCsT7hXdAx16ToJMGtv5QfVF0WQrxFX9IkVHYNIiAVpg/FVwn2iY6hH/9miEwih+qIAgBuHtBMdgUiYO9MG4b8JD0CGQm/84y2CEzWzttPZNFEUfRND0CNOG2uyEJ3L3WkD8GU8y+Ki9JsF6DTxltmMZr5qTmqT1v1Pen+sjX+QZdEWeh9g4C0XtYuioiLMmzcPSUlJMJlMiImJwcSJE7F161YXhXQfzRTF1L7xCPDhpDZp2/3p/bAm7iGWRWv1uOqiJ7GvueYa7N+/H8uXL8fx48fx5ZdfYvTo0SgtLXVRSPfRTFGYTQZc2TdedAwi4R7M6IPP4h6GLGnmv//FG3Jxa2mVl5dj06ZNeO655zBmzBi0a9cOgwcPxt///ndMmTIFACBJEpYuXYrJkyfDz88PHTp0wKpVq5rs5+GHH0aXLl3g7++Pjh07YtGiRbDZbKf/fvHixejbty/ee+89JCUlwWw2Y8GCBbDb7Xj++ecRExODqKgoPP30063Kr6mflJt4+IkIAPBwRm+sjGVZtEjCYCC+/0Xtwmw2w2w244svvoDFYjnvdosWLTo98pg5cyZmzJiBI0eOnP77wMBALFu2DIcPH8arr76Kt99+Gy+//HKTfaSnp2P9+vX45ptv8Omnn+K9997DlClTkJubiw0bNuC5557Do48+im3btrU4vyTLstz6L9t7XbN0C3ZnlYmOQaQIz3Q8iBn5z0GSHaKjKNc17wK9rr3o3axZswZz585FXV0d+vfvj1GjRmH69Ono3bs3gMYRxfz587F06dLTzxk6dCj69++PN95445z7fOGFF7By5Urs2rULQOOI4oUXXkBBQQECAxtXpZg0aRKOHTuG9PR06H6fjO/WrRvmzJmDRx55pEXZNffrxL3jOouOQKQY/8johY9i/g5Z4vzdOQXGASnTXLKra665BidPnsSXX36JiRMn4pdffkH//v2xbNmy09sMGzasyXOGDRvWZESxevVqXHLJJYiJiYHZbMaiRYuQnZ3d5Dnt27c/XRIAEB0djZSUlNMl8cdjRUVFLc6uuaK4tHMkBrUPFR2DSDEWneiBD1kW5zb8LkBvcNnufH19MX78eDz22GPYsmUL5syZg8cff9zpcySp8cSDbdu2Yfr06Zg8eTLWrVuHvXv3YuHChbBarU22NxqNzZ5/rsccjpaPIjVXFABw37guoiMQKcpjJ1KwLOYfLIs/C0oABt3m1pdISUlBTU3N6c/PnjfYtm0bunXrBgDYvHkz2rVrh4ULF2LgwIHo3LkzsrKy3JrvD66rSi8yPDkCQzuGYVuG8k9LI/KUJSe6w9FhIW4tfAaSo0F0HPFGPwIYTC7ZVUlJCa677jrceuut6N27NwIDA7Fr1y48//zzmDp16untVq1ahYEDB+KSSy7Bxx9/jB07duDdd98FACQnJyM7OxsrVqzAoEGD8NVXX2Ht2rUuyXchmhxRABxVEJ3Lkye64Z2ohZB1mvwd8oyIri5dJdZsNmPIkCF4+eWXMXLkSPTs2ROLFi3C3Llz8frrr5/ebsmSJVixYgV69+6N5cuX4+OPP0ZKSgoAYOrUqbjvvvtw1113oW/fvtiyZQsWLVrksozOaO6spz+76Z1t2JxWIjoGkeI80u445hU/Dclhu/DGanT9h0DKlR59SUmSsHbtWkybNs2jr9sSmh1RAMD94zmqIDqXZ7O6YGnko5B1GrzvfPwAj5eE0mm6KAa0C8PILpGiYxAp0vNZnfF6xGOQ9T6io3jWuMWiEyiOpg89AcC+nHJM+9dm0TGIFOuBdum469STkOzWC2/s7TqOAW7+QnQKxdH0iAJoXIJ8TFeOKojO56WsTng1/DHIetecAaRcEjDO+TUNWqX5ogCAByZ0hcTFNInO65XsjvhftZdFylQgrp/oFIrEogDQMz4Y0wdxwUAiZ/4vuwNeDHscssFXdBTX0xmAsZ451dQbsSh+98ikbogwa2zSjqiV/pXTHs+HPqa+suh7ExCRLDqFYrEofhfsb8SjU1JExyBSvKU57fF86OOQDX6io7iGwa/xKmw6LxbFn0zrF48RyeGiYxAp3tKcdvhnyOOQjf6io1y8MX8HguJEp1A0FsVZnpzaEz4G/rMQXchbuUl4KsjLyyJhEDDsLtEpFI/viGfpGGnGHaM7iY5B5BXezUvEksDFkI0BoqO0nsEXmPoGoOOKuRfCojiHBaM7oWOEF/7gEwmw7GQCFntjWYx+BIjkMj4twaI4B5NBjyen9RQdg8hrLD8Zj8cCF0P28ZKyiB8ADL9HdAqvwaI4jxHJEbiqX7zoGERe48OT8Xg0YAlkH7PoKM7pTTzk1EosCicWTumOYD8Nrp5J1EYf58fh7wFLIJsCL7yxKKMfBqK6iU7hVVgUTkSYTVh4eXfRMYi8yor8WDzir9CyiOsHjLhXdAqvw6K4gOsHJeLyXjGiYxB5lZX5Mfib3xOQTUGio5yh9wGmLeUhpzZgUbTAP6/ujfgQlVyFSuQhqwui8aDvEsimYNFRGo18CIjiEYK2YFG0QLCfEa9M7wu9jkvMErXGmsJo3Oe7BA7fELFBYvsAl9wnNoMXY1G00KD2YbhrDBcNI2qtLwqjcJ9pMRy+oWIC/HHISW8Q8/oqwKJohXsu64zBHcJExyDyOv8pjMI9xiVw+An4/3P5C0B0D8+/roqwKFpBr5Pw+ox+iDCr+OYtRG6yrjgCdxkWe7YsBt0GDJjjuddTKRZFK0UF+eK1GZyvIGqLr4sjcKdhMRx+Hlilud0lwKTn3P86GsCiaIPhnSJw/3iuEUPUFuuLI3CHYTEcfhHue5GQJOD6Dzgv4SIsija6Y3QnjOseJToGkVf6pjgc8/SL4fB3Q1kYA4DpnwABvLeMq7Ao2kiSJLx0fV+0C/fitfiJBPr+VBjmSovh8I907Y6nvQHE9HLtPjWORXERgv2MWHbLYIQF8F7bRG3xY0kYbpMWwx7gotH5yL8BPaa5Zl90GoviInWICMC7swfCz8hlAYja4qeSUNyKx2EPiL64HXW9HBiz0DWhqAkWhQv0SwrF6zf245lQRG20oSQUt8iPwx7QxnXVIrsBV78FSPw/6A4sChe5rHs0nuLNjoja7NfSEMyWH4fdHNu6J/qGNE5eK3G1WpVgUbjQjMFJuGcsl/kgaqtNpcG42f4Y7Oa4lj1B0gPXvQ+E8z737sSicLH7J3TF9QMTRMcg8lqby4JxY8NjaAi80B0mJeCKV4FOYz2SS8tYFG7wzFW9MKari0/5I9KQ7eVBuNG2CA2BTn7puvwFoP8sz4XSMBaFGxj0Ovzrpv7onaCQdfiJvNCO8iBMt56nLMYtAQbP9XwojWJRuIm/jwHvzRnEC/KILsKuikBcb12EhqCkMw+Oehi45F5hmbRIkmVZFh1CzXJKa3HjO9uQU1onOgqR1+ofXI2Vpqdh7HklMOEp0XE0h0XhAYWV9bjpne1IK6oWHYXIaz0yKhrzJw8UHUOTeOjJA6KDfLHy9qHoEaegG80TeZG5l3ZgSQjEEYUHVdbbcOv7O7Erq0x0FCKvsWB0Jzw8qZvoGJrGovCwOqsdcz/YhU1pp0RHIVK8e8Ym4/4JXUXH0DwWhQCWBjvu/mQvvjtcKDoKkWI9ML4L7r6ss+gYBBaFMA12Bx5ctR9f7DspOgqRovjodXjm6l64dgBXOFAKFoVAsizj0S8O4ePt2aKjEClCqL8R/545AEM68u50SsKiUIB/rj+CNzdkiI5BJFTHiAC8N2cQ2kcEiI5CZ2FRKMTKndlY9J/fYG1wiI5C5HFDO4bhzZkDEexvFB2FzoFFoSD7csox/8PdKKisFx2FyGOuH5iAp6/qBaOel3UpFYtCYYqrLLjz4z3YkVkqOgqRW0kS8PCkbpg/iveSUDoWhQLZ7A48/dURLNuSKToKkVv4GfV4+YY+mNSzlXezIyFYFAq2Zncu/rH2ICyctyAViQo04d3Zg9CLy/B7DRaFwh3Kq8C8D3cjr5yrz5L3G9M1Ei9c1wcRZpPoKNQKLAovUFpjxV2f7MGW9BLRUYjaxGTQ4R+Xd8fs4e1FR6E2YFF4CbtDxnPfHMXbGzPA7xh5k+6xQXhtel90jg4UHYXaiEXhZTamFuOh1QeQX8FTaEnZJAm4dUQHPDSpK0wGveg4dBFYFF6oos6GJV/+hs/35omOQnRO0UEmvHhdH1zaOVJ0FHIBFoUX++ZQARauPYiSGqvoKESnTUiJxnPX9EZogI/oKOQiLAovd6ragsf+cwhfHywQHYU0zt9Hj0V/ScGMwUmio5CLsShU4tvfCvDYfw6hsNIiOgpp0KWdI/DE1J7owAX9VIlFoSKV9Tb88+ujWLEzm2dGkUckhPrh0SkpmNQzRnQUciMWhQptyyjBPz4/iIxTNaKjkEr5GnVYMCoZ80Z1hK+RZzSpHYtCpWx2Bz7Zno3XfkzlZDe51OSeMVg4pTsSQv1FRyEPYVGoXLWlAW9uSMc7G0+gzmYXHYe8WOcoMxZf2QMjkiNERyEPY1FoRGFlPV7+/jhW7c6F3cFvObVcoK8B943rgpuHtYOB94zQJBaFxqQWVuG5b47ihyNFoqOQwul1Eq7tn4C/TerKRfw0jkWhUdszSvDM+qPYn1MuOgopjF4nYVrfeNw1NpmnuxIAFoXmfXUgHy98exSZJbWio5BgBp2Eq/o1FkS7cBYEncGiINgdMr79rQDvbTqBXVllouOQh/kYdLi6XzzuGJ2MpHCeyUTNsSioif055Xhv8wl8fTAfNjt/NNQsyNeAm4a2wy0j2iMq0Fd0HFIwFgWdU0FFPT7YmolPdmSjvNYmOg65UGywL24d0QEzhiTBbDKIjkNegEVBTtXb7FizJxfvb85EWlG16DjURjoJGJEcgesGJmJyzxgYeZortQKLglpElmVsOF6M9zZn4tfjxaLjUAt1iAjAtQMScHX/eMQG+4mOQ16KRUGtll1Si3UHT2Ld/nwczq8UHYfOEmgyYErvWFw7IAED24eJjkMqwKKgi5JeXI2vDuTjv/tPIpWHpoSRJGB4p3BcOyABk3rEws+HC/WR67AoyGWOFVRh3YGTWHcgHye4cq1HdIk24y+943DNgATEh/DQErkHi4Lc4lBeBdYdyMe6AyeRW1YnOo5qBPoacElyBEZ1icSorpGcdyCPYFGQ2x3Kq8CW9FPYnFaCnZmlqLVyFduWkiSgV3wwRnZuLIZ+iSFcmI88jkVBHmWzO7A3uxyb005ha3oJ9ueWw9LgEB1LUSLMPri0cyRGdYnEpZ0jEM4F+UgwFgUJZW1w4GBeBXZnlWJXZhl2Z5Vp6kZLgSYDuscFISU2CD3igtAzPhjdYgIhSZLoaESnsShIcTJP1eBYYRXSi6uRXlTT+LG4GlX1DaKjXZToINPvhRCMHnFBSIkLQlKYP0uBFI9FQV6jqLIeacXVSC+uQXpR9e9FUo38ynoo5afYz6hHbIgv4kP8EBfsh3YR/qeLgfd0IG/FoiCvV2ttQF5ZHcpqbSivtaK8zobKOhvKa20or7OivNaGirM+r7Y0nC4XvU6CXifB0OSj7szn+saPPnodwgJ8EGE2Idzc+DHi94/RQY3lEBrgI/Yfg8gNWBSkSXaHDIcsw6CTeOiH6AJYFERE5BRPyCYiIqdYFERE5BSLgoiInGJREBGRUywKIiJyikVBREROsSiIiMgpFgURETnFoiAiIqdYFERE5BSLgoiInGJREBGRUywKIiJyikVBREROsSiIiMgpFgURETnFoiAiIqdYFERE5BSLgoiInGJREBGRUywKIiJyikVBREROsSiIiMgpFgURETnFoiAiIqdYFERE5BSLgoiInGJREBGRUywKIiJyikVBREROsSiIiMgpFgURETnFoiAiIqdYFERE5BSLgoiInGJREBGRUywKIiJyikVBREROsSiIiMgpFgURETnFoiAiIqdYFERE5BSLgoiInGJREBGRUywKIiJy6v8BKuTzV4faXVMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df['Category'].value_counts(), labels = ['Ham', 'Spam'], autopct = '%0.3f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8abeea14",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\paria\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "de7d18d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of Sentences\n",
    "df['num_sentences'] = df['Message'].apply(lambda x : len(nltk.sent_tokenize(x)))\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "44c97774",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of words\n",
    "df['num_words'] = df['Message'].apply(lambda x : len(nltk.word_tokenize(x)))\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "27cc7749",
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of characters\n",
    "# Number of Sentences\n",
    "df['num_chars'] = df['Message'].apply(lambda x : len(x))\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "489414bd",
   "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>num_sentences</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_chars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5157.000000</td>\n",
       "      <td>5157.000000</td>\n",
       "      <td>5157.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.969750</td>\n",
       "      <td>18.560403</td>\n",
       "      <td>79.103936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.455526</td>\n",
       "      <td>13.405970</td>\n",
       "      <td>58.382922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>910.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_sentences    num_words    num_chars\n",
       "count    5157.000000  5157.000000  5157.000000\n",
       "mean        1.969750    18.560403    79.103936\n",
       "std         1.455526    13.405970    58.382922\n",
       "min         1.000000     1.000000     2.000000\n",
       "25%         1.000000     9.000000    36.000000\n",
       "50%         1.000000    15.000000    61.000000\n",
       "75%         2.000000    26.000000   118.000000\n",
       "max        38.000000   220.000000   910.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['num_sentences', 'num_words', 'num_chars']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5e2a2061",
   "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>num_sentences</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_chars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4516.000000</td>\n",
       "      <td>4516.000000</td>\n",
       "      <td>4516.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.827724</td>\n",
       "      <td>17.267715</td>\n",
       "      <td>70.869353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.394338</td>\n",
       "      <td>13.588065</td>\n",
       "      <td>56.708301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>91.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>910.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_sentences    num_words    num_chars\n",
       "count    4516.000000  4516.000000  4516.000000\n",
       "mean        1.827724    17.267715    70.869353\n",
       "std         1.394338    13.588065    56.708301\n",
       "min         1.000000     1.000000     2.000000\n",
       "25%         1.000000     8.000000    34.000000\n",
       "50%         1.000000    13.000000    53.000000\n",
       "75%         2.000000    22.000000    91.000000\n",
       "max        38.000000   220.000000   910.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describing on Ham mails\n",
    "df[df['Category'] == 0][['num_sentences', 'num_words', 'num_chars']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "370bba94",
   "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>num_sentences</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_chars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>641.000000</td>\n",
       "      <td>641.000000</td>\n",
       "      <td>641.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.970359</td>\n",
       "      <td>27.667707</td>\n",
       "      <td>137.118565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.485575</td>\n",
       "      <td>7.103501</td>\n",
       "      <td>30.399707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>130.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>148.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>157.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>223.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_sentences   num_words   num_chars\n",
       "count     641.000000  641.000000  641.000000\n",
       "mean        2.970359   27.667707  137.118565\n",
       "std         1.485575    7.103501   30.399707\n",
       "min         1.000000    2.000000    7.000000\n",
       "25%         2.000000   25.000000  130.000000\n",
       "50%         3.000000   29.000000  148.000000\n",
       "75%         4.000000   32.000000  157.000000\n",
       "max         9.000000   46.000000  223.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describing on Spam mails\n",
    "df[df['Category'] == 1][['num_sentences', 'num_words', 'num_chars']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "422e2d56",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\paria\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x258bcfe6a10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 824.861x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, hue = 'Category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d1d90903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='num_words', ylabel='Count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 6))\n",
    "sns.histplot(df[df['Category'] == 0]['num_words'], kde = True)\n",
    "sns.histplot(df[df['Category'] == 1]['num_words'], color = 'red', kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fa1be275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='num_chars', ylabel='Count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 6))\n",
    "sns.histplot(df[df['Category'] == 0]['num_chars'], kde = True)\n",
    "sns.histplot(df[df['Category'] == 1]['num_chars'], color = 'red', kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fa97a076",
   "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>num_sentences</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_chars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.259023</td>\n",
       "      <td>0.255968</td>\n",
       "      <td>0.374409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_sentences</th>\n",
       "      <td>0.259023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.679396</td>\n",
       "      <td>0.622428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_words</th>\n",
       "      <td>0.255968</td>\n",
       "      <td>0.679396</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.966155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_chars</th>\n",
       "      <td>0.374409</td>\n",
       "      <td>0.622428</td>\n",
       "      <td>0.966155</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Category  num_sentences  num_words  num_chars\n",
       "Category       1.000000       0.259023   0.255968   0.374409\n",
       "num_sentences  0.259023       1.000000   0.679396   0.622428\n",
       "num_words      0.255968       0.679396   1.000000   0.966155\n",
       "num_chars      0.374409       0.622428   0.966155   1.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.drop(labels = ['Message'], axis = 1)\n",
    "df1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3eabd8c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df1.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eb95d879",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\paria\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\paria\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re, nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "762215ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_message(text):\n",
    "    text = text.lower()\n",
    "    def clean_text(text):\n",
    "        rgx = r\"[^A-Za-z0-9\\s\\.]\"\n",
    "        return re.sub(rgx, '', text)\n",
    "    \n",
    "    def remove_stop_words(text):\n",
    "        tokens = text.split()\n",
    "        tokens = [word for word in tokens if word not in stopwords.words('english')]\n",
    "        return ' '.join(tokens)\n",
    "    \n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    def lemmatize_text(text): \n",
    "        return ' '.join([lemmatizer.lemmatize(word) for word in text.split()])\n",
    "    \n",
    "    return lemmatize_text(remove_stop_words(clean_text(text)))\n",
    "\n",
    "#transform_message('Hey how are you on this fine morning on 4th May?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "837c89d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Transformed_Message'] = df['Message'].apply(transform_message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4eae584b",
   "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",
       "      <th>num_sentences</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_chars</th>\n",
       "      <th>Transformed_Message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>111</td>\n",
       "      <td>go jurong point crazy.. available bugis n grea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>ok lar... joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>49</td>\n",
       "      <td>u dun say early hor... u c already say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>61</td>\n",
       "      <td>nah dont think go usf life around though</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>FreeMsg Hey there darling it's been 3 week's n...</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>147</td>\n",
       "      <td>freemsg hey darling 3 week word back id like f...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Category                                            Message  num_sentences  \\\n",
       "0         0  Go until jurong point, crazy.. Available only ...              2   \n",
       "1         0                      Ok lar... Joking wif u oni...              2   \n",
       "3         0  U dun say so early hor... U c already then say...              1   \n",
       "4         0  Nah I don't think he goes to usf, he lives aro...              1   \n",
       "5         1  FreeMsg Hey there darling it's been 3 week's n...              4   \n",
       "\n",
       "   num_words  num_chars                                Transformed_Message  \n",
       "0         24        111  go jurong point crazy.. available bugis n grea...  \n",
       "1          8         29                      ok lar... joking wif u oni...  \n",
       "3         13         49          u dun say early hor... u c already say...  \n",
       "4         15         61           nah dont think go usf life around though  \n",
       "5         39        147  freemsg hey darling 3 week word back id like f...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2c42a5b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "wc = WordCloud(width=500,height=500,min_font_size=10,background_color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c5136d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x258c22c5e90>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spam_wc = wc.generate(df[df['Category'] == 1]['Transformed_Message'].str.cat(sep=\" \"))\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.imshow(spam_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "86dd463a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x258c6a4a5d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ham_wc = wc.generate(df[df['Category'] == 0]['Transformed_Message'].str.cat(sep=\" \"))\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.imshow(ham_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cb56aeb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_corpus = []\n",
    "for msg in df[df['Category'] == 1]['Transformed_Message'].tolist():\n",
    "    for word in msg.split():\n",
    "        spam_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5774024a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10712"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spam_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8086fdae",
   "metadata": {},
   "outputs": [],
   "source": [
    "ham_corpus = []\n",
    "for msg in df[df['Category'] == 0]['Transformed_Message'].tolist():\n",
    "    for word in msg.split():\n",
    "        ham_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "db5da768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38199"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ham_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0d2dd37f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "#cv = CountVectorizer()\n",
    "tfidf = TfidfVectorizer(max_features= 3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7f22093c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_cv = cv.fit_transform(df['Transformed_Message']).toarray()\n",
    "X_tfidf = tfidf.fit_transform(df['Transformed_Message']).toarray()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "78817a6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5157, 3000)\n"
     ]
    }
   ],
   "source": [
    "#print(X_cv.shape)\n",
    "print(X_tfidf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f9b35b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Category'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b32fb7c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'from sklearn.preprocessing import MinMaxScaler\\nscaler = MinMaxScaler()\\nX_tfidf_scaled = scaler.fit_transform(X_tfidf)'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_tfidf_scaled = scaler.fit_transform(X_tfidf)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "181917d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"# append the num_character col to X\\nX_tfidf_num_chars = np.hstack((X_tfidf,df['num_chars'].values.reshape(-1,1)))\""
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"# append the num_character col to X\n",
    "X_tfidf_num_chars = np.hstack((X_tfidf,df['num_chars'].values.reshape(-1,1)))\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "4fdb4fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#X_cv_train, X_cv_test, y_cv_train, y_cv_test = train_test_split(X_cv, y, test_size=0.15, random_state=2)\n",
    "X_tfidf_train, X_tfidf_test, y_tfidf_train, y_tfidf_test = train_test_split(X_tfidf, y, test_size=0.15, random_state=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4763b833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_tfidf_scaled_train, X_tfidf_scaled_test, y_tfidf_scaled_train, y_tfidf_scaled_test = train_test_split(X_tfidf_scaled, y, test_size=0.15, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2661fd6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_tfidf_num_chars_train, X_tfidf_num_chars_test, y_tfidf_num_chars_train, y_tfidf_num_chars_test = train_test_split(X_tfidf_num_chars, y, test_size=0.15, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "17b138ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score\n",
    "\n",
    "nb_clfs = {\n",
    "    #'gb' : GaussianNB(),\n",
    "    'mnb' : MultinomialNB(),\n",
    "    #'bnb ': BernoulliNB()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0997d1eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"print('USING COUNTVECTORIZER\\n')\\nfor name, clf in nb_clfs.items():\\n    clf.fit(X_cv_train,y_cv_train)\\n    y_pred1 = clf.predict(X_cv_test)\\n    print(name)\\n    print('-'*30)\\n    print(f'Accuracy: {accuracy_score(y_cv_test,y_pred1)}')\\n    print(f'Confusion Matrix:\\n {confusion_matrix(y_cv_test,y_pred1)}')\\n    print(f'Precision: {precision_score(y_cv_test,y_pred1)}')\\n    print('\\n')\""
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"print('USING COUNTVECTORIZER\\n')\n",
    "for name, clf in nb_clfs.items():\n",
    "    clf.fit(X_cv_train,y_cv_train)\n",
    "    y_pred1 = clf.predict(X_cv_test)\n",
    "    print(name)\n",
    "    print('-'*30)\n",
    "    print(f'Accuracy: {accuracy_score(y_cv_test,y_pred1)}')\n",
    "    print(f'Confusion Matrix:\\n {confusion_matrix(y_cv_test,y_pred1)}')\n",
    "    print(f'Precision: {precision_score(y_cv_test,y_pred1)}')\n",
    "    print('\\n')\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d46a0ea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USING TFIDFVECTORIZER\n",
      "\n",
      "mnb\n",
      "------------------------------\n",
      "Accuracy: 0.9728682170542635\n",
      "Confusion Matrix:\n",
      " [[680   0]\n",
      " [ 21  73]]\n",
      "Precision: 1.0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('USING TFIDFVECTORIZER\\n')\n",
    "for name, clf in nb_clfs.items():\n",
    "    clf.fit(X_tfidf_train,y_tfidf_train)\n",
    "    y_pred1 = clf.predict(X_tfidf_test)\n",
    "    print(name)\n",
    "    print('-'*30)\n",
    "    print(f'Accuracy: {accuracy_score(y_tfidf_test,y_pred1)}')\n",
    "    print(f'Confusion Matrix:\\n {confusion_matrix(y_tfidf_test,y_pred1)}')\n",
    "    print(f'Precision: {precision_score(y_tfidf_test,y_pred1)}')\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bf1dd07d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"print('USING TFIDFVECTORIZER and MINMAX SCALING\\n')\\nfor name, clf in nb_clfs.items():\\n    clf.fit(X_tfidf_scaled_train,y_tfidf_scaled_train)\\n    y_pred1 = clf.predict(X_tfidf_scaled_test)\\n    print(name)\\n    print('-'*30)\\n    print(f'Accuracy: {accuracy_score(y_tfidf_scaled_test,y_pred1)}')\\n    print(f'Confusion Matrix:\\n {confusion_matrix(y_tfidf_scaled_test,y_pred1)}')\\n    print(f'Precision: {precision_score(y_tfidf_scaled_test,y_pred1)}')\\n    print('\\n')\""
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"print('USING TFIDFVECTORIZER and MINMAX SCALING\\n')\n",
    "for name, clf in nb_clfs.items():\n",
    "    clf.fit(X_tfidf_scaled_train,y_tfidf_scaled_train)\n",
    "    y_pred1 = clf.predict(X_tfidf_scaled_test)\n",
    "    print(name)\n",
    "    print('-'*30)\n",
    "    print(f'Accuracy: {accuracy_score(y_tfidf_scaled_test,y_pred1)}')\n",
    "    print(f'Confusion Matrix:\\n {confusion_matrix(y_tfidf_scaled_test,y_pred1)}')\n",
    "    print(f'Precision: {precision_score(y_tfidf_scaled_test,y_pred1)}')\n",
    "    print('\\n')\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f0717172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"print('USING TFIDFVECTORIZER and NUM_CHARS\\n')\\nfor name, clf in nb_clfs.items():\\n    clf.fit(X_tfidf_num_chars_train,y_tfidf_num_chars_train)\\n    y_pred1 = clf.predict(X_tfidf_num_chars_test)\\n    print(name)\\n    print('-'*30)\\n    print(f'Accuracy: {accuracy_score(y_tfidf_num_chars_test,y_pred1)}')\\n    print(f'Confusion Matrix:\\n {confusion_matrix(y_tfidf_num_chars_test,y_pred1)}')\\n    print(f'Precision: {precision_score(y_tfidf_num_chars_test,y_pred1)}')\\n    print('\\n')\""
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"print('USING TFIDFVECTORIZER and NUM_CHARS\\n')\n",
    "for name, clf in nb_clfs.items():\n",
    "    clf.fit(X_tfidf_num_chars_train,y_tfidf_num_chars_train)\n",
    "    y_pred1 = clf.predict(X_tfidf_num_chars_test)\n",
    "    print(name)\n",
    "    print('-'*30)\n",
    "    print(f'Accuracy: {accuracy_score(y_tfidf_num_chars_test,y_pred1)}')\n",
    "    print(f'Confusion Matrix:\\n {confusion_matrix(y_tfidf_num_chars_test,y_pred1)}')\n",
    "    print(f'Precision: {precision_score(y_tfidf_num_chars_test,y_pred1)}')\n",
    "    print('\\n')\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6006c2a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8efc77d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs = {\n",
    "    'SVC' : SVC(kernel='sigmoid', gamma=1.0),\n",
    "    'KNN' : KNeighborsClassifier(), \n",
    "    'Multinomial NB': MultinomialNB(), \n",
    "    'Decision Tree': DecisionTreeClassifier(max_depth=5), \n",
    "    'Logistic Regression': LogisticRegression(solver='liblinear', penalty='l1'), \n",
    "    'Random Forest': RandomForestClassifier(n_estimators=50, random_state=2), \n",
    "    'AdaBoost': AdaBoostClassifier(n_estimators=50, random_state=2), \n",
    "    'BgC': BaggingClassifier(n_estimators=50, random_state=2),\n",
    "    'GBDT': GradientBoostingClassifier(n_estimators=50,random_state=2),\n",
    "    'XGBoost':XGBClassifier(n_estimators=50,random_state=2)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5ad36912",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USING TFIDFVECTORIZER\n",
      "\n",
      "SVC\n",
      "------------------------------\n",
      "Accuracy: 0.979328165374677\n",
      "Confusion Matrix:\n",
      " [[679   1]\n",
      " [ 15  79]]\n",
      "Precision: 0.9875\n",
      "\n",
      "\n",
      "KNN\n",
      "------------------------------\n",
      "Accuracy: 0.917312661498708\n",
      "Confusion Matrix:\n",
      " [[680   0]\n",
      " [ 64  30]]\n",
      "Precision: 1.0\n",
      "\n",
      "\n",
      "Multinomial NB\n",
      "------------------------------\n",
      "Accuracy: 0.9728682170542635\n",
      "Confusion Matrix:\n",
      " [[680   0]\n",
      " [ 21  73]]\n",
      "Precision: 1.0\n",
      "\n",
      "\n",
      "Decision Tree\n",
      "------------------------------\n",
      "Accuracy: 0.9418604651162791\n",
      "Confusion Matrix:\n",
      " [[672   8]\n",
      " [ 37  57]]\n",
      "Precision: 0.8769230769230769\n",
      "\n",
      "\n",
      "Logistic Regression\n",
      "------------------------------\n",
      "Accuracy: 0.9573643410852714\n",
      "Confusion Matrix:\n",
      " [[676   4]\n",
      " [ 29  65]]\n",
      "Precision: 0.9420289855072463\n",
      "\n",
      "\n",
      "Random Forest\n",
      "------------------------------\n",
      "Accuracy: 0.9715762273901809\n",
      "Confusion Matrix:\n",
      " [[678   2]\n",
      " [ 20  74]]\n",
      "Precision: 0.9736842105263158\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\paria\\anaconda3\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost\n",
      "------------------------------\n",
      "Accuracy: 0.9573643410852714\n",
      "Confusion Matrix:\n",
      " [[672   8]\n",
      " [ 25  69]]\n",
      "Precision: 0.8961038961038961\n",
      "\n",
      "\n",
      "BgC\n",
      "------------------------------\n",
      "Accuracy: 0.9638242894056848\n",
      "Confusion Matrix:\n",
      " [[670  10]\n",
      " [ 18  76]]\n",
      "Precision: 0.8837209302325582\n",
      "\n",
      "\n",
      "GBDT\n",
      "------------------------------\n",
      "Accuracy: 0.9573643410852714\n",
      "Confusion Matrix:\n",
      " [[677   3]\n",
      " [ 30  64]]\n",
      "Precision: 0.9552238805970149\n",
      "\n",
      "\n",
      "XGBoost\n",
      "------------------------------\n",
      "Accuracy: 0.9689922480620154\n",
      "Confusion Matrix:\n",
      " [[675   5]\n",
      " [ 19  75]]\n",
      "Precision: 0.9375\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "print('USING TFIDFVECTORIZER\\n')\n",
    "for name, clf in clfs.items():\n",
    "    clf.fit(X_tfidf_train,y_tfidf_train)\n",
    "    y_pred1 = clf.predict(X_tfidf_test)\n",
    "    print(name)\n",
    "    print('-'*30)\n",
    "    print(f'Accuracy: {accuracy_score(y_tfidf_test,y_pred1)}')\n",
    "    print(f'Confusion Matrix:\\n {confusion_matrix(y_tfidf_test,y_pred1)}')\n",
    "    print(f'Precision: {precision_score(y_tfidf_test,y_pred1)}')\n",
    "    print('\\n')\n",
    "    accuracy_scores.append(accuracy_score(y_tfidf_test,y_pred1))\n",
    "    precision_scores.append(precision_score(y_tfidf_test,y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d924950a",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy_scores,'Precision':precision_scores}).sort_values('Precision',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8433606d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.979328165374677,\n",
       " 0.917312661498708,\n",
       " 0.9728682170542635,\n",
       " 0.9418604651162791,\n",
       " 0.9573643410852714,\n",
       " 0.9715762273901809,\n",
       " 0.9573643410852714,\n",
       " 0.9638242894056848,\n",
       " 0.9573643410852714,\n",
       " 0.9689922480620154]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "32d78f1b",
   "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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.917313</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Multinomial NB</td>\n",
       "      <td>0.972868</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.979328</td>\n",
       "      <td>0.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.971576</td>\n",
       "      <td>0.973684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.957364</td>\n",
       "      <td>0.955224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.957364</td>\n",
       "      <td>0.942029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.968992</td>\n",
       "      <td>0.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.957364</td>\n",
       "      <td>0.896104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.963824</td>\n",
       "      <td>0.883721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.941860</td>\n",
       "      <td>0.876923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Algorithm  Accuracy  Precision\n",
       "1                  KNN  0.917313   1.000000\n",
       "2       Multinomial NB  0.972868   1.000000\n",
       "0                  SVC  0.979328   0.987500\n",
       "5        Random Forest  0.971576   0.973684\n",
       "8                 GBDT  0.957364   0.955224\n",
       "4  Logistic Regression  0.957364   0.942029\n",
       "9              XGBoost  0.968992   0.937500\n",
       "6             AdaBoost  0.957364   0.896104\n",
       "7                  BgC  0.963824   0.883721\n",
       "3        Decision Tree  0.941860   0.876923"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8dd834c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Voting Classifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "svc = SVC(kernel='sigmoid', gamma=1.0,probability=True)\n",
    "mnb = MultinomialNB()\n",
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "46a137ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting = VotingClassifier(estimators=[('svm', svc), ('nb', mnb), ('knn', knn)],voting='hard')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ca1ba91e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --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",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  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",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\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",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 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",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 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",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 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-5 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-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;svm&#x27;,\n",
       "                              SVC(gamma=1.0, kernel=&#x27;sigmoid&#x27;,\n",
       "                                  probability=True)),\n",
       "                             (&#x27;nb&#x27;, MultinomialNB()),\n",
       "                             (&#x27;knn&#x27;, KNeighborsClassifier())])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;VotingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.VotingClassifier.html\">?<span>Documentation for VotingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>VotingClassifier(estimators=[(&#x27;svm&#x27;,\n",
       "                              SVC(gamma=1.0, kernel=&#x27;sigmoid&#x27;,\n",
       "                                  probability=True)),\n",
       "                             (&#x27;nb&#x27;, MultinomialNB()),\n",
       "                             (&#x27;knn&#x27;, KNeighborsClassifier())])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>svm</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(gamma=1.0, kernel=&#x27;sigmoid&#x27;, probability=True)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>nb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;MultinomialNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>knn</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('svm',\n",
       "                              SVC(gamma=1.0, kernel='sigmoid',\n",
       "                                  probability=True)),\n",
       "                             ('nb', MultinomialNB()),\n",
       "                             ('knn', KNeighborsClassifier())])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting.fit(X_tfidf_train,y_tfidf_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c000561d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USING VOTING CLASSIFIER\n",
      "------------------------------\n",
      "Accuracy: 0.9728682170542635\n",
      "Confusion Matrix:\n",
      " [[680   0]\n",
      " [ 21  73]]\n",
      "Precision: 1.0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = voting.predict(X_tfidf_test)\n",
    "print('USING VOTING CLASSIFIER')\n",
    "print('-'*30)\n",
    "print(f'Accuracy: {accuracy_score(y_tfidf_test,y_pred)}')\n",
    "print(f'Confusion Matrix:\\n {confusion_matrix(y_tfidf_test,y_pred)}')\n",
    "print(f'Precision: {precision_score(y_tfidf_test,y_pred)}')\n",
    "print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "65eeb4b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying stacking\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "\n",
    "estimators=[('svm', svc), ('nb', mnb), ('knn', knn)]\n",
    "final_estimator=RandomForestClassifier()\n",
    "\n",
    "clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "58bceb8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --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",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  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",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\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",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 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",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 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",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 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-6 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-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 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-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;svm&#x27;,\n",
       "                                SVC(gamma=1.0, kernel=&#x27;sigmoid&#x27;,\n",
       "                                    probability=True)),\n",
       "                               (&#x27;nb&#x27;, MultinomialNB()),\n",
       "                               (&#x27;knn&#x27;, KNeighborsClassifier())],\n",
       "                   final_estimator=RandomForestClassifier())</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;StackingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>StackingClassifier(estimators=[(&#x27;svm&#x27;,\n",
       "                                SVC(gamma=1.0, kernel=&#x27;sigmoid&#x27;,\n",
       "                                    probability=True)),\n",
       "                               (&#x27;nb&#x27;, MultinomialNB()),\n",
       "                               (&#x27;knn&#x27;, KNeighborsClassifier())],\n",
       "                   final_estimator=RandomForestClassifier())</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>svm</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(gamma=1.0, kernel=&#x27;sigmoid&#x27;, probability=True)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>nb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;MultinomialNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>knn</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "StackingClassifier(estimators=[('svm',\n",
       "                                SVC(gamma=1.0, kernel='sigmoid',\n",
       "                                    probability=True)),\n",
       "                               ('nb', MultinomialNB()),\n",
       "                               ('knn', KNeighborsClassifier())],\n",
       "                   final_estimator=RandomForestClassifier())"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_tfidf_train,y_tfidf_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6cd76edd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USING STACKING CLASSIFIER\n",
      "------------------------------\n",
      "Accuracy: 0.9780361757105943\n",
      "Confusion Matrix:\n",
      " [[676   4]\n",
      " [ 13  81]]\n",
      "Precision: 0.9529411764705882\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_tfidf_test)\n",
    "print('USING STACKING CLASSIFIER')\n",
    "print('-'*30)\n",
    "print(f'Accuracy: {accuracy_score(y_tfidf_test,y_pred)}')\n",
    "print(f'Confusion Matrix:\\n {confusion_matrix(y_tfidf_test,y_pred)}')\n",
    "print(f'Precision: {precision_score(y_tfidf_test,y_pred)}')\n",
    "print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "626b2e22",
   "metadata": {},
   "source": [
    "After all these we can observe that multinomial naive bayes has the highest accuracy while keeping the precision as 1 as precision is the determining criteria here(we don't want false positives at any cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e37c6099",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "23892184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --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",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  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",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\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",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 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",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 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",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 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-7 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-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 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-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</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-24\" type=\"checkbox\" checked><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MultinomialNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = mnb\n",
    "model.fit(X_tfidf_train, y_tfidf_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "29e4140a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_tfidf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "90ead2f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_tfidf_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "be2edb6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9728682170542635"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_tfidf_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cbec3c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(tfidf,open('vectorizer.pkl','wb'))\n",
    "pickle.dump(mnb,open('model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4a580d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0415d309",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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