spam_mail_detection.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
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   "cell_type": "code",
   "source": "df = pd.read_csv('spam.csv', encoding='latin-1')",
   "id": "ccec24a14ff9c7b5",
   "outputs": [],
   "execution_count": 3
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   "source": "df.head()\n",
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       "     v1                                                 v2 Unnamed: 2  \\\n",
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       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
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      " 3   Unnamed: 3  12 non-null     object\n",
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       "        v1                                                 v2\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",
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       "4      ham  Nah I don't think he goes to usf, he lives aro...\n",
       "...    ...                                                ...\n",
       "5567  spam  This is the 2nd time we have tried 2 contact u...\n",
       "5568   ham              Will Ì_ b going to esplanade fr home?\n",
       "5569   ham  Pity, * was in mood for that. So...any other s...\n",
       "5570   ham  The guy did some bitching but I acted like i'd...\n",
       "5571   ham                         Rofl. Its true to its name\n",
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       "        v1                                                 v2\n",
       "102    ham  As per your request 'Melle Melle (Oru Minnamin...\n",
       "153    ham  As per your request 'Melle Melle (Oru Minnamin...\n",
       "206    ham  As I entered my cabin my PA said, '' Happy B'd...\n",
       "222    ham                             Sorry, I'll call later\n",
       "325    ham                   No calls..messages..missed calls\n",
       "...    ...                                                ...\n",
       "5524  spam  You are awarded a SiPix Digital Camera! call 0...\n",
       "5535   ham  I know you are thinkin malaria. But relax, chi...\n",
       "5539   ham                         Just sleeping..and surfing\n",
       "5553   ham                        Hahaha..use your brain dear\n",
       "5558   ham                             Sorry, I'll call later\n",
       "\n",
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       "      <td>ham</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
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      "Index: 5169 entries, 0 to 5571\n",
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      " 0   v1      5169 non-null   object\n",
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       "          v1                                                 v2\n",
       "count   5169                                               5169\n",
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       "top      ham  Go until jurong point, crazy.. Available only ...\n",
       "freq    4516                                                  1"
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   "source": "df['v1'].value_counts()",
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     "data": {
      "text/plain": [
       "v1\n",
       "ham     4516\n",
       "spam     653\n",
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    "sns.countplot(data=df, x='v1')\n",
    "plt.xlabel('v1')\n",
    "plt.ylabel('count')\n",
    "plt.title('Distribution of mails')\n",
    "plt.show()"
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:02:36.878904Z",
     "start_time": "2024-08-24T06:02:36.851157Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df.loc[df[\"v1\"] == \"spam\", \"Category\"] = 0\n",
    "df.loc[df[\"v1\"] == \"ham\", \"Category\"] = 1\n",
    "\n",
    "df.head()"
   ],
   "id": "46f71b65aa03f017",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     v1                                                 v2  Category\n",
       "0   ham  Go until jurong point, crazy.. Available only ...       1.0\n",
       "1   ham                      Ok lar... Joking wif u oni...       1.0\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...       0.0\n",
       "3   ham  U dun say so early hor... U c already then say...       1.0\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...       1.0"
      ],
      "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>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>Category</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",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</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",
       "      <td>1.0</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",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:02:44.485093Z",
     "start_time": "2024-08-24T06:02:44.475790Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = df['v2']\n",
    "Y = df['Category']"
   ],
   "id": "ec6f56a480f0d06b",
   "outputs": [],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:02:51.736614Z",
     "start_time": "2024-08-24T06:02:51.706932Z"
    }
   },
   "cell_type": "code",
   "source": "X",
   "id": "b4f0a629194fb1a3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Go until jurong point, crazy.. Available only ...\n",
       "1                           Ok lar... Joking wif u oni...\n",
       "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       U dun say so early hor... U c already then say...\n",
       "4       Nah I don't think he goes to usf, he lives aro...\n",
       "                              ...                        \n",
       "5567    This is the 2nd time we have tried 2 contact u...\n",
       "5568                Will Ì_ b going to esplanade fr home?\n",
       "5569    Pity, * was in mood for that. So...any other s...\n",
       "5570    The guy did some bitching but I acted like i'd...\n",
       "5571                           Rofl. Its true to its name\n",
       "Name: v2, Length: 5169, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:03:07.934672Z",
     "start_time": "2024-08-24T06:03:07.893410Z"
    }
   },
   "cell_type": "code",
   "source": "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.3, random_state=2)",
   "id": "cccc82a217749129",
   "outputs": [],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:03:14.123977Z",
     "start_time": "2024-08-24T06:03:14.100236Z"
    }
   },
   "cell_type": "code",
   "source": "X_train",
   "id": "9175978cea975dfb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2118    U can WIN å£100 of Music Gift Vouchers every w...\n",
       "1019                      Don know..wait i will check it.\n",
       "1881                              Ok.. ÌÏ finishing soon?\n",
       "2575                 Now thats going to ruin your thesis!\n",
       "4858    Hey, a guy I know is breathing down my neck to...\n",
       "                              ...                        \n",
       "3534                             I'm at home. Please call\n",
       "1124                   Aiyar sorry lor forgot 2 tell u...\n",
       "2628    Haha... They cant what... At the most tmr forf...\n",
       "3833           Watching tv lor. Nice one then i like lor.\n",
       "2694    Hey sexy buns! What of that day? No word from ...\n",
       "Name: v2, Length: 3618, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:03:23.902132Z",
     "start_time": "2024-08-24T06:03:23.894255Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(X.shape)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ],
   "id": "a42241238e014162",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5169,)\n",
      "(3618,)\n",
      "(1551,)\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:03:32.819109Z",
     "start_time": "2024-08-24T06:03:32.677293Z"
    }
   },
   "cell_type": "code",
   "source": [
    "feature_extraction = TfidfVectorizer(min_df=1, stop_words='english',lowercase=True)\n",
    "\n",
    "# Convert the training and testing text messages into numerical features using TF-IDF\n",
    "\n",
    "X_train_features = feature_extraction.fit_transform(X_train)\n",
    "X_test_features = feature_extraction.transform(X_test)\n",
    "\n",
    "# Convert the target values into 0 and 1\n",
    "\n",
    "Y_train = Y_train.astype(int)\n",
    "Y_test = Y_test.astype(int)"
   ],
   "id": "5d94b034a5a435c4",
   "outputs": [],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:03:39.445250Z",
     "start_time": "2024-08-24T06:03:39.434457Z"
    }
   },
   "cell_type": "code",
   "source": "print(X_train_features)",
   "id": "9343cb17f6d8788e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 274)\t0.28469841570534354\n",
      "  (0, 757)\t0.2382703692823207\n",
      "  (0, 309)\t0.28469841570534354\n",
      "  (0, 5479)\t0.28469841570534354\n",
      "  (0, 1666)\t0.18521698912601275\n",
      "  (0, 3176)\t0.28469841570534354\n",
      "  (0, 6726)\t0.16969158756163835\n",
      "  (0, 6243)\t0.28469841570534354\n",
      "  (0, 603)\t0.24274243659325892\n",
      "  (0, 2157)\t0.1929056107731498\n",
      "  (0, 6682)\t0.2059490803121428\n",
      "  (0, 6265)\t0.15269186047504035\n",
      "  (0, 5701)\t0.24274243659325892\n",
      "  (0, 6566)\t0.16515206653974776\n",
      "  (0, 6468)\t0.23079712301516783\n",
      "  (0, 2768)\t0.21505634607080928\n",
      "  (0, 4120)\t0.22760511302317224\n",
      "  (0, 234)\t0.20290236667023745\n",
      "  (0, 6626)\t0.18521698912601275\n",
      "  (1, 1536)\t0.5888338190512723\n",
      "  (1, 6486)\t0.5280141801118141\n",
      "  (1, 3501)\t0.4067379480769643\n",
      "  (1, 2132)\t0.45720892460283746\n",
      "  (2, 5579)\t0.4697175168946937\n",
      "  (2, 2524)\t0.6743730717061566\n",
      "  :\t:\n",
      "  (3612, 4940)\t0.4429207609150278\n",
      "  (3612, 3694)\t0.38321599910309895\n",
      "  (3612, 6141)\t0.4110511297018481\n",
      "  (3613, 3066)\t1.0\n",
      "  (3614, 782)\t0.6262002443728721\n",
      "  (3614, 2608)\t0.468640976347078\n",
      "  (3614, 5588)\t0.36937006848543374\n",
      "  (3614, 5988)\t0.3605704584204915\n",
      "  (3614, 3714)\t0.3490037052200379\n",
      "  (3615, 2602)\t0.5526654848490291\n",
      "  (3615, 6121)\t0.402966817413515\n",
      "  (3615, 2911)\t0.7295057271332491\n",
      "  (3616, 4209)\t0.39641512487909236\n",
      "  (3616, 6530)\t0.42462645266346505\n",
      "  (3616, 6259)\t0.4312579153217267\n",
      "  (3616, 3628)\t0.2870113903720111\n",
      "  (3616, 3714)\t0.6278443252388438\n",
      "  (3617, 6777)\t0.44477543282893783\n",
      "  (3617, 1360)\t0.45786114340694495\n",
      "  (3617, 5339)\t0.37301010052886446\n",
      "  (3617, 4059)\t0.31592454413164556\n",
      "  (3617, 1937)\t0.2537122894817113\n",
      "  (3617, 6054)\t0.2739877149031144\n",
      "  (3617, 3016)\t0.2900143375128283\n",
      "  (3617, 6682)\t0.36061839292205816\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:03:46.796827Z",
     "start_time": "2024-08-24T06:03:46.781498Z"
    }
   },
   "cell_type": "code",
   "source": "print(Y_train)",
   "id": "a47d9c3cc9b54a66",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2118    0\n",
      "1019    1\n",
      "1881    1\n",
      "2575    1\n",
      "4858    1\n",
      "       ..\n",
      "3534    1\n",
      "1124    1\n",
      "2628    1\n",
      "3833    1\n",
      "2694    1\n",
      "Name: Category, Length: 3618, dtype: int64\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:03:57.256068Z",
     "start_time": "2024-08-24T06:03:57.118781Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train_features,Y_train)"
   ],
   "id": "aacbb067b81910",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:04:05.430326Z",
     "start_time": "2024-08-24T06:04:05.409428Z"
    }
   },
   "cell_type": "code",
   "source": [
    "prediction_on_training_data = model.predict(X_train_features)\n",
    "accuracy_on_training_data = accuracy_score(Y_train, prediction_on_training_data)\n",
    "\n",
    "prediction_on_test_data = model.predict(X_test_features)\n",
    "accuracy_on_test_data = accuracy_score(Y_test, prediction_on_test_data)\n",
    "\n",
    "# Print accuracy\n",
    "print('Accuracy on training data: {} %'.format(accuracy_on_training_data * 100))\n",
    "print('Accuracy on test data: {} %'.format(accuracy_on_test_data * 100))"
   ],
   "id": "44431d03e57e19be",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data: 96.01990049751244 %\n",
      "Accuracy on test data: 95.80915538362346 %\n"
     ]
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:06:09.153219Z",
     "start_time": "2024-08-24T06:06:09.132699Z"
    }
   },
   "cell_type": "code",
   "source": [
    "input_mail = [\"Congratulations on your recent achievement! Well done.\"]\n",
    "input_data_features = feature_extraction.transform(input_mail)\n",
    "prediction = model.predict(input_data_features)\n",
    "\n",
    "if (prediction)[0] == 1:\n",
    "    print(\"Ham Mail\")\n",
    "else:\n",
    "    print(\"Spam Mail\")"
   ],
   "id": "f11c07b33f1ebf34",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ham Mail\n"
     ]
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:06:24.980438Z",
     "start_time": "2024-08-24T06:06:24.964899Z"
    }
   },
   "cell_type": "code",
   "source": [
    "input_mail = [\"Congratulations! You have won $10000 from an online game. Click the link to claim your prize now!\"]\n",
    "input_data_features = feature_extraction.transform(input_mail)\n",
    "prediction = model.predict(input_data_features)\n",
    "\n",
    "if (prediction)[0] == 1:\n",
    "    print(\"Ham Mail\")\n",
    "else:\n",
    "    print(\"Spam Mail\")"
   ],
   "id": "5a9333847a59b3e3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spam Mail\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T06:06:34.612035Z",
     "start_time": "2024-08-24T06:06:34.392333Z"
    }
   },
   "cell_type": "code",
   "source": [
    "cm = confusion_matrix(Y_test,prediction_on_test_data)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(cm, annot=True, fmt='d',cmap=\"plasma\", cbar=False)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel('True')\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ],
   "id": "2aa2e7867f98013b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "13cdbb23c517dbc8"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}