SpamEmail.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</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",
       "      <td>NaN</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",
       "      <td>NaN</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",
       "      <td>NaN</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",
       "      <td>NaN</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "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",
       "  Unnamed: 3 Unnamed: 4  \n",
       "0        NaN        NaN  \n",
       "1        NaN        NaN  \n",
       "2        NaN        NaN  \n",
       "3        NaN        NaN  \n",
       "4        NaN        NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_data = pd.read_csv(\"Spam Email Detection - spam.csv\")\n",
    "spam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_data = spam_data.drop(['Unnamed: 2','Unnamed: 3','Unnamed: 4'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_data.columns = ['Category', 'Messsage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Messsage</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
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       "      <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",
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      ],
      "text/plain": [
       "  Category                                           Messsage\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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Messsage</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>5163</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",
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      ],
      "text/plain": [
       "       Category                Messsage\n",
       "count      5572                    5572\n",
       "unique        2                    5163\n",
       "top         ham  Sorry, I'll call later\n",
       "freq       4825                      30"
      ]
     },
     "execution_count": 37,
     "metadata": {},
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   ],
   "source": [
    "spam_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = spam_data.where((pd.notnull(spam_data)), '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Messsage</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",
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       "    <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",
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      "text/plain": [
       "  Category                                           Messsage\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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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   Messsage  5572 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 87.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Spam Email Classification\")\n",
    "plt.pie(data['Category'].value_counts(), labels = ['ham','spam'],autopct='%0.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above distribution data is imbalanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Category'] == 'spam' , 'Category',] = 0\n",
    "data.loc[data['Category'] == 'ham' , 'Category',] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data['Messsage'] \n",
    "\n",
    "Y = data['Category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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: Messsage, Length: 5572, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Splitting the data into training data & test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test , Y_train , Y_test = train_test_split(X, Y, test_size=0.2 , random_state = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_extraction = TfidfVectorizer(min_df = 1, stop_words = 'english' , lowercase = True)\n",
    "\n",
    "X_train_features = features_extraction.fit_transform(X_train)\n",
    "X_test_features = features_extraction.transform(X_test)\n",
    "\n",
    "Y_train = Y_train.astype('int')\n",
    "Y_test = Y_test.astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model : Using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_features , Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the training model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_on_training = model.predict(X_train_features)\n",
    "accuracy_on_training = accuracy_score(Y_train,prediction_on_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data :  0.9661207089970832\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy on training data : \" , accuracy_on_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 55,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Building Predictive Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ham mail\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "#Sample data for illustration\n",
    "inputmail = [\"I've been searching for the right words to thank you for this breather. I promise I won't take your help for granted and will fulfill my promise.\"]\n",
    "\n",
    "# Sample training data \n",
    "training_data = [\"I've been searching for the right words to thank you for this breather. I promise I won't take your help for granted and will fulfill\",\n",
    "                 \"get a free gift now! Click here.\",\n",
    "                 \"Congratulations, you've won a lottery.\",\n",
    "                 \"Meeting postponed\",\n",
    "                 \"Invest in this amazing opportunity today.\"]\n",
    "\n",
    "labels1 = [0,1,1,0,1] # 0 : ham mail , 1 : Spam mail\n",
    "\n",
    "# feature extraction\n",
    "features_extraction = CountVectorizer()\n",
    "input_data_features = features_extraction.fit_transform(training_data)\n",
    "\n",
    "#Train a Naive Bayes Classifier\n",
    "model_1 = MultinomialNB()\n",
    "model_1.fit(input_data_features, labels1)\n",
    "\n",
    "# Convert the input mail to feature vectors\n",
    "input_data_features = features_extraction.transform(inputmail)\n",
    "prediction = model_1.predict(input_data_features)\n",
    "\n",
    "# Display the prediction\n",
    "if prediction[0] == 0:\n",
    "    print(\"ham mail\")\n",
    "else:\n",
    "    print(\"spam mail\")\n"
   ]
  }
 ],
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