Email-spam(TechnoHacks) part-3.ipynb

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   "cell_type": "markdown",
   "id": "12fbc276",
   "metadata": {},
   "source": [
    "# Email Spam Filtering-Task-3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a1df729",
   "metadata": {},
   "source": [
    " ''' Use a dataset of emails and build a model that\n",
    "can identify spam emails.\n",
    "The SMS Spam Collection is a set of SMS tagged messages that have been␣\n",
    "↪collected for SMS Spam research.\n",
    "It contains one set of SMS messages in English of 5,572 messages,\n",
    "tagged acording being ham (legitimate) or spam."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363bc22a",
   "metadata": {},
   "source": [
    "'''Email spam filtering is a technique used to automatically identify and\n",
    "categorize unwanted or unsolicited emails, commonly known as spam, from␣\n",
    "↪legitimate and\n",
    "relevant emails in a user's inbox. The goal of email spam filtering is to␣\n",
    "↪reduce the clutter in users' email accounts,\n",
    "enhance security by minimizing the risk of phishing and malware attacks, and␣\n",
    "↪improve the overall email experience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "679a537f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix, roc_curve,roc_auc_score\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from collections import Counter\n",
    "# Libraries for visualisation\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e1f524c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(f'C:\\\\Users\\\\abhin\\\\Desktop\\\\Data_set\\\\spam1.csv',encoding='IBM437')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6cd987c2",
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   "outputs": [
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       "      <td>spam</td>\n",
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       "    </tr>\n",
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       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5572 rows × 5 columns</p>\n",
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      ],
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       "        v1                                                 v2 Unnamed: 2  \\\n",
       "0      ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
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       "...    ...                                                ...        ...   \n",
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     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\abhin\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
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       "True"
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       "0      ham  Go until jurong point, crazy.. Available only ...        NaN   \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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       "4      ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "...    ...                                                ...        ...   \n",
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       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        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",
       "3      ham  U dun say so early hor... U c already then say...\n",
       "4      ham  Nah I don't think he goes to usf, he lives aro...\n",
       "...    ...                                                ...\n",
       "5567  spam  This is the 2nd time we have tried 2 contact u...\n",
       "5568   ham              Will ╠_ b going to esplanade fr home?\n",
       "5569   ham  Pity, * was in mood for that. So...any other s...\n",
       "5570   ham  The guy did some bitching but I acted like i'd...\n",
       "5571   ham                         Rofl. Its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f558cee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['v1', 'v2'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ea00273f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_column_names = {\"v1\":\"Category\",\"v2\":\"Message-in-email\"}\n",
    "df.rename(columns = new_column_names,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7b3f8cd8",
   "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-in-email</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will ╠_ b going to esplanade fr home?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category                                   Message-in-email\n",
       "0         ham  Go until jurong point, crazy.. Available only ...\n",
       "1         ham                      Ok lar... Joking wif u oni...\n",
       "2        spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3         ham  U dun say so early hor... U c already then say...\n",
       "4         ham  Nah I don't think he goes to usf, he lives aro...\n",
       "...       ...                                                ...\n",
       "5567     spam  This is the 2nd time we have tried 2 contact u...\n",
       "5568      ham              Will ╠_ b going to esplanade fr home?\n",
       "5569      ham  Pity, * was in mood for that. So...any other s...\n",
       "5570      ham  The guy did some bitching but I acted like i'd...\n",
       "5571      ham                         Rofl. Its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "618b3618",
   "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-in-email</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>spam</td>\n",
       "      <td>FreeMsg Hey there darling it's been 3 week's n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ham</td>\n",
       "      <td>Even my brother is not like to speak with me. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ham</td>\n",
       "      <td>As per your request 'Melle Melle (Oru Minnamin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>spam</td>\n",
       "      <td>WINNER!! As a valued network customer you have...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>spam</td>\n",
       "      <td>Had your mobile 11 months or more? U R entitle...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ham</td>\n",
       "      <td>I'm gonna be home soon and i don't want to tal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>spam</td>\n",
       "      <td>SIX chances to win CASH! From 100 to 20,000 po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>spam</td>\n",
       "      <td>URGENT! You have won a 1 week FREE membership ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ham</td>\n",
       "      <td>I've been searching for the right words to tha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ham</td>\n",
       "      <td>I HAVE A DATE ON SUNDAY WITH WILL!!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Category                                   Message-in-email\n",
       "0       ham  Go until jurong point, crazy.. Available only ...\n",
       "1       ham                      Ok lar... Joking wif u oni...\n",
       "2      spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       ham  U dun say so early hor... U c already then say...\n",
       "4       ham  Nah I don't think he goes to usf, he lives aro...\n",
       "5      spam  FreeMsg Hey there darling it's been 3 week's n...\n",
       "6       ham  Even my brother is not like to speak with me. ...\n",
       "7       ham  As per your request 'Melle Melle (Oru Minnamin...\n",
       "8      spam  WINNER!! As a valued network customer you have...\n",
       "9      spam  Had your mobile 11 months or more? U R entitle...\n",
       "10      ham  I'm gonna be home soon and i don't want to tal...\n",
       "11     spam  SIX chances to win CASH! From 100 to 20,000 po...\n",
       "12     spam  URGENT! You have won a 1 week FREE membership ...\n",
       "13      ham  I've been searching for the right words to tha...\n",
       "14      ham                I HAVE A DATE ON SUNDAY WITH WILL!!"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d56dea80",
   "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-in-email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5572</td>\n",
       "      <td>5572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ham</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4825</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Category        Message-in-email\n",
       "count      5572                    5572\n",
       "unique        2                    5169\n",
       "top         ham  Sorry, I'll call later\n",
       "freq       4825                      30"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a962ad52",
   "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   object\n",
      " 1   Message-in-email  5572 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 87.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "979b09a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c167cc78",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df[\"Category\"] == \"spam\", \"Category\"] = 0\n",
    "df.loc[df[\"Category\"] == \"ham\", \"Category\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5c883ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[\"Message-in-email\"]\n",
    "Y = df[\"Category\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "966d4db3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       Go until jurong point, crazy.. Available only ...\n",
      "1                           Ok lar... Joking wif u oni...\n",
      "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
      "3       U dun say so early hor... U c already then say...\n",
      "4       Nah I don't think he goes to usf, he lives aro...\n",
      "                              ...                        \n",
      "5567    This is the 2nd time we have tried 2 contact u...\n",
      "5568                Will ╠_ b going to esplanade fr home?\n",
      "5569    Pity, * was in mood for that. So...any other s...\n",
      "5570    The guy did some bitching but I acted like i'd...\n",
      "5571                           Rofl. Its true to its name\n",
      "Name: Message-in-email, Length: 5572, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8b786475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1\n",
      "1       1\n",
      "2       0\n",
      "3       1\n",
      "4       1\n",
      "       ..\n",
      "5567    0\n",
      "5568    1\n",
      "5569    1\n",
      "5570    1\n",
      "5571    1\n",
      "Name: Category, Length: 5572, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4c29a5b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2,\n",
    "random_state = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7e39fa46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5572,)\n",
      "(4457,)\n",
      "(1115,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "90b85d25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5572,)\n",
      "(4457,)\n",
      "(1115,)\n"
     ]
    }
   ],
   "source": [
    "print(Y.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4cb57575",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_extraction = TfidfVectorizer(min_df=1, stop_words=\"english\",\n",
    "lowercase=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "db33ec14",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_features = feature_extraction.fit_transform(X_train)\n",
    "X_test_features = feature_extraction.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cb904999",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = Y_train.astype(\"int\")\n",
    "Y_test = Y_test.astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7c9c18ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3075    Mum, hope you are having a great day. Hoping t...\n",
      "1787                           Yes:)sura in sun tv.:)lol.\n",
      "1614    Me sef dey laugh you. Meanwhile how's my darli...\n",
      "4304                Yo come over carlos will be here soon\n",
      "3266                    Ok then i come n pick u at engin?\n",
      "                              ...                        \n",
      "789                          Gud mrng dear hav a nice day\n",
      "968             Are you willing to go for aptitude class.\n",
      "1667    So now my dad is gonna call after he gets out ...\n",
      "3321    Ok darlin i supose it was ok i just worry too ...\n",
      "1688                     Nan sonathaya soladha. Why boss?\n",
      "Name: Message-in-email, Length: 4457, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fa2eaf1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 731)\t0.32207229533730536\n",
      "  (0, 3977)\t0.2411608243124387\n",
      "  (0, 4295)\t0.3893042361045832\n",
      "  (0, 6602)\t0.20305518394534605\n",
      "  (0, 3383)\t0.32207229533730536\n",
      "  (0, 2116)\t0.38519642807943744\n",
      "  (0, 3132)\t0.4403035234544808\n",
      "  (0, 3258)\t0.258880502955985\n",
      "  (0, 3377)\t0.21816477736422235\n",
      "  (0, 4513)\t0.2910887633154199\n",
      "  (1, 4060)\t0.380431198316959\n",
      "  (1, 6875)\t0.4306015894277422\n",
      "  (1, 6419)\t0.4769136859540388\n",
      "  (1, 6444)\t0.5652509076654626\n",
      "  (1, 7448)\t0.35056971070320353\n",
      "  (2, 923)\t0.4917598465723273\n",
      "  (2, 2103)\t0.42972812260098503\n",
      "  (2, 3914)\t0.40088501350982736\n",
      "  (2, 2220)\t0.413484525934624\n",
      "  (2, 5826)\t0.4917598465723273\n",
      "  (3, 6142)\t0.4903863168693604\n",
      "  (3, 1592)\t0.5927091854194291\n",
      "  (3, 1835)\t0.3708680641487708\n",
      "  (3, 7458)\t0.5202633571003087\n",
      "  (4, 2527)\t0.7419319091456392\n",
      "  :\t:\n",
      "  (4452, 2116)\t0.3092200696489299\n",
      "  (4453, 989)\t0.6760129013031282\n",
      "  (4453, 7278)\t0.5787739591782677\n",
      "  (4453, 1755)\t0.45610005640082985\n",
      "  (4454, 3025)\t0.42618909997886\n",
      "  (4454, 2080)\t0.3809693742808703\n",
      "  (4454, 3084)\t0.34475593009514444\n",
      "  (4454, 1995)\t0.4166919007849217\n",
      "  (4454, 1039)\t0.31932060116006045\n",
      "  (4454, 7351)\t0.31166263834107377\n",
      "  (4454, 5369)\t0.42618909997886\n",
      "  (4455, 1138)\t0.38998123077430413\n",
      "  (4455, 6435)\t0.38998123077430413\n",
      "  (4455, 6363)\t0.25697343671652706\n",
      "  (4455, 2760)\t0.3226323745940581\n",
      "  (4455, 7363)\t0.2915949626395065\n",
      "  (4455, 7412)\t0.3028481995557642\n",
      "  (4455, 2102)\t0.3136468384526087\n",
      "  (4455, 4250)\t0.30616657078392584\n",
      "  (4455, 3760)\t0.16807158405536876\n",
      "  (4455, 4772)\t0.35860460546223444\n",
      "  (4456, 6119)\t0.5304350313291551\n",
      "  (4456, 6135)\t0.5304350313291551\n",
      "  (4456, 1376)\t0.4460036316446079\n",
      "  (4456, 4557)\t0.48821933148688146\n"
     ]
    }
   ],
   "source": [
    "print(X_train_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64df4065",
   "metadata": {},
   "source": [
    "**Logical_Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "22f2efd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a logistic regression model and train it on the training data\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train_features, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "529dd573",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_on_training_data = model.predict(X_train_features)\n",
    "accuracy_on_training_data = accuracy_score(Y_train, prediction_on_training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c5f690aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data: 0.965896342831501\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy on training data:\",accuracy_on_training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "61fa5b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_on_test_data = model.predict(X_test_features)\n",
    "accuracy_on_test_data = accuracy_score(Y_test,prediction_on_test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5c64e96e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test data: 0.9623318385650225\n"
     ]
    }
   ],
   "source": [
    " print(\"Accuracy on test data:\",accuracy_on_test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2ba16d39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "Spam Mail\n"
     ]
    }
   ],
   "source": [
    "# Test the model with some custom email messages\n",
    "input_your_mail = [\"Congratulations! You have won a free vacation to an exoticdestination.Click the link to claim your prize now!\"]\n",
    "input_data_features = feature_extraction.transform(input_your_mail)\n",
    "prediction = model.predict(input_data_features)\n",
    "print(prediction)\n",
    "# Print the prediction result\n",
    "if (prediction)[0] == 1:\n",
    "    print(\"Ham Mail\")\n",
    "else:\n",
    "    print(\"Spam Mail\")       \n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a433152b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "Ham Mail\n"
     ]
    }
   ],
   "source": [
    "input_your_mail = [\"Meeting reminder: Tomorrow, 10 AM, conference room. See you␣there!\"]\n",
    "input_data_features = feature_extraction.transform(input_your_mail)\n",
    "prediction = model.predict(input_data_features)\n",
    "print(prediction)\n",
    "# Print the prediction result\n",
    "if (prediction)[0] == 1:\n",
    "    print(\"Ham Mail\")\n",
    "else:\n",
    "    print(\"Spam Mail\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "36b45c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spam_count = df[df['Category'] == 0].shape[0]\n",
    "ham_count = df[df['Category'] == 1].shape[0]\n",
    "plt.bar(['Spam', 'Ham'], [spam_count, ham_count])\n",
    "plt.xlabel('Email Type')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Spam and Ham Emails')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b4a61b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data visualization - Confusion Matrix\n",
    "cm = confusion_matrix(Y_test, prediction_on_test_data)\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap='Blues', cbar=False)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d36d4063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data visualization - ROC Curve\n",
    "probabilities = model.predict_proba(X_test_features)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(Y_test, probabilities)\n",
    "roc_auc = roc_auc_score(Y_test, probabilities)\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.plot(fpr, tpr, label=f'ROC curve (AUC = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], 'k--', label='Random Guess')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c394734f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data visualization - Top 10 Most Common Words in Spam Emails\n",
    "stop_words = set(stopwords.words('english'))\n",
    "spam_words = \" \".join(df[df['Category'] == 0]['Message-in-email']).split()\n",
    "ham_words = \" \".join(df[df['Category'] == 1]['Message-in-email']).split()\n",
    "spam_word_freq = Counter([word.lower() for word in spam_words if word.lower()\n",
    "not in stop_words and word.isalpha()])\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(*zip(*spam_word_freq.most_common(10)), color='r')\n",
    "plt.xlabel('Words')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Top 10 Most Common Words in Spam Emails')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6d12e651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data visualization - Top 10 Most Common Words in Ham Emails\n",
    "ham_word_freq = Counter([word.lower() for word in ham_words if word.lower() not\n",
    "in stop_words and word.isalpha()])\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(*zip(*ham_word_freq.most_common(10)), color='g')\n",
    "plt.xlabel('Words')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Top 10 Most Common Words in Ham Emails')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f2c97855",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0a464e32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "1    4825\n",
       "0     747\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "df[\"Category\"] = le.fit_transform(df[\"Category\"])\n",
    "df['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7a2da4e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Category', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6d18f383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pie chart representation of the mails-spam and ham\n",
    "plt.pie(df[\"Category\"].value_counts(),autopct = \"%.2f\", labels=['ham','spam'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "110f57d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}