Email spam detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "012b9d57",
   "metadata": {},
   "source": [
    "# Email Spam Detection ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6071a0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8560dc4",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf9be977",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>Unnamed: 2</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</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>NaN</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>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "      <td>NaN</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",
       "      <td>NaN</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        v1                                                 v2 Unnamed: 2\n",
       "0      ham  Go until jurong point, crazy.. Available only ...        NaN\n",
       "1      ham                      Ok lar... Joking wif u oni...        NaN\n",
       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN\n",
       "3      ham  U dun say so early hor... U c already then say...        NaN\n",
       "4      ham  Nah I don't think he goes to usf, he lives aro...        NaN\n",
       "...    ...                                                ...        ...\n",
       "5567  spam  This is the 2nd time we have tried 2 contact u...        NaN\n",
       "5568   ham              Will Ì_ b going to esplanade fr home?        NaN\n",
       "5569   ham  Pity, * was in mood for that. So...any other s...        NaN\n",
       "5570   ham  The guy did some bitching but I acted like i'd...        NaN\n",
       "5571   ham                         Rofl. Its true to its name        NaN\n",
       "\n",
       "[5572 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Email_spam.csv',encoding='ISO-8859-1')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f461af",
   "metadata": {},
   "source": [
    "# Droping Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fdf270b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </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": [
       "     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..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop(columns=['Unnamed: 2'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a6f63c",
   "metadata": {},
   "source": [
    "# Renameing Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e8a888a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Category                                            Message\n",
       "0      ham  Go until jurong point, crazy.. Available only ...\n",
       "1      ham                      Ok lar... Joking wif u oni...\n",
       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3      ham  U dun say so early hor... U c already then say...\n",
       "4      ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns={'v1':'Category','v2':'Message'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597dfad1",
   "metadata": {},
   "source": [
    "# Basic EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4220318f",
   "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   5572 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 87.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "68f77a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ham', 'spam'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "602258ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5169"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Message'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ab3a5905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>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\n",
       "count      5572                    5572\n",
       "unique        2                    5169\n",
       "top         ham  Sorry, I'll call later\n",
       "freq       4825                      30"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d13d23",
   "metadata": {},
   "source": [
    "# Feature Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d764c04",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Category']=df['Category'].map({'ham':1,'spam':0})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd4458d3",
   "metadata": {},
   "source": [
    "# Selecting Independent(X) and Dependent Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a025e4db",
   "metadata": {},
   "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: Message, Length: 5572, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df['Message']\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c2b9e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df['Category']\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43f9978",
   "metadata": {},
   "source": [
    "# Splitting data into Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04b36cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14f971f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1978    No I'm in the same boat. Still here at my moms...\n",
       "3989    (Bank of Granite issues Strong-Buy) EXPLOSIVE ...\n",
       "3935       They r giving a second chance to rahul dengra.\n",
       "4078       O i played smash bros  &lt;#&gt;  religiously.\n",
       "4086    PRIVATE! Your 2003 Account Statement for 07973...\n",
       "                              ...                        \n",
       "3772    I came hostel. I m going to sleep. Plz call me...\n",
       "5191                               Sorry, I'll call later\n",
       "5226        Prabha..i'm soryda..realy..frm heart i'm sory\n",
       "5390                           Nt joking seriously i told\n",
       "860                   In work now. Going have in few min.\n",
       "Name: Message, Length: 4457, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "39a2cce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3245    Funny fact Nobody teaches volcanoes 2 erupt, t...\n",
       "944     I sent my scores to sophas and i had to do sec...\n",
       "1044    We know someone who you know that fancies you....\n",
       "2484    Only if you promise your getting out as SOON a...\n",
       "812     Congratulations ur awarded either å£500 of CD ...\n",
       "                              ...                        \n",
       "4264     &lt;DECIMAL&gt; m but its not a common car he...\n",
       "2439    Rightio. 11.48 it is then. Well arent we all u...\n",
       "5556    Yes i have. So that's why u texted. Pshew...mi...\n",
       "4205                               Get the door, I'm here\n",
       "4293    Kit Strip - you have been billed 150p. Netcoll...\n",
       "Name: Message, Length: 1115, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d5a7bb93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1978    1\n",
       "3989    0\n",
       "3935    1\n",
       "4078    1\n",
       "4086    0\n",
       "       ..\n",
       "3772    1\n",
       "5191    1\n",
       "5226    1\n",
       "5390    1\n",
       "860     1\n",
       "Name: Category, Length: 4457, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1c662e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3245    1\n",
       "944     1\n",
       "1044    0\n",
       "2484    1\n",
       "812     0\n",
       "       ..\n",
       "4264    1\n",
       "2439    1\n",
       "5556    1\n",
       "4205    1\n",
       "4293    0\n",
       "Name: Category, Length: 1115, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7bbb4c9",
   "metadata": {},
   "source": [
    "# Feature Extraction and Logistic Regression(Modelling)&Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79a7dfae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.75      0.86       150\n",
      "           1       0.96      1.00      0.98       965\n",
      "\n",
      "    accuracy                           0.97      1115\n",
      "   macro avg       0.98      0.88      0.92      1115\n",
      "weighted avg       0.97      0.97      0.96      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "#creat a pipeline for project\n",
    "clf=Pipeline([\n",
    "    ('vectorizer_tfidf',TfidfVectorizer()),\n",
    "    ('logistic_reg',LogisticRegression())\n",
    "])\n",
    "\n",
    "#fit with X_train and y_train\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "#get the prediction for X_test and store it in y_pred\n",
    "y_pred=clf.predict(X_test)\n",
    "\n",
    "#print the classification report\n",
    "print(classification_report(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e05d4d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3245    Funny fact Nobody teaches volcanoes 2 erupt, t...\n",
       "944     I sent my scores to sophas and i had to do sec...\n",
       "1044    We know someone who you know that fancies you....\n",
       "2484    Only if you promise your getting out as SOON a...\n",
       "812     Congratulations ur awarded either å£500 of CD ...\n",
       "Name: Message, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0a9bed25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3245    1\n",
       "944     1\n",
       "1044    0\n",
       "2484    1\n",
       "812     0\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fec9a93a",
   "metadata": {},
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2c4edf0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a7a983",
   "metadata": {},
   "source": [
    "# Random Forest & Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4678552f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.83      0.90       150\n",
      "           1       0.97      1.00      0.99       965\n",
      "\n",
      "    accuracy                           0.98      1115\n",
      "   macro avg       0.98      0.91      0.94      1115\n",
      "weighted avg       0.98      0.98      0.97      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#creat a pipeline for project\n",
    "clf1=Pipeline([\n",
    "    ('vectorizer_tfidf',TfidfVectorizer()),\n",
    "    ('random_forest',RandomForestClassifier())\n",
    "])\n",
    "\n",
    "#fit with X_train and y_train\n",
    "clf1.fit(X_train,y_train)\n",
    "\n",
    "#get the prediction for X_test and store it in y_pred\n",
    "y_pred=clf1.predict(X_test)\n",
    "\n",
    "#print the classification report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eba665a",
   "metadata": {},
   "source": [
    "# Email Detection based on Random forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5f8ab405",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "Spam Email\n"
     ]
    }
   ],
   "source": [
    "emails=[\"Congratulations!You've won a $1000 Walmart gift card.Go to http://bit.ly/123456 tp claim now\"\n",
    "]\n",
    "\n",
    "prediction=clf1.predict(emails)\n",
    "print(prediction)\n",
    "if (prediction[0]==1):\n",
    "    print(\"Ham Email\")\n",
    "else:\n",
    "    print(\"Spam Email\")\n",
    "\n",
    "#My final model is Random forest classifier because it gives 98% Accuracy    "
   ]
  }
 ],
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