Email_Spam_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f583676d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.svm import SVC\n",
    "import pickle\n",
    "from sklearn.metrics import accuracy_score, classification_report,confusion_matrix\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.naive_bayes import MultinomialNB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f2c7d1a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    <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 ü 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\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 ü 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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the dataset\n",
    "df=pd.read_csv(\"mail_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b65c0d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dimension of the dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ba5ddc28",
   "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": [
    "#Summary\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dca39f7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category    object\n",
       "Message     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Datatypes of each column\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "988d88de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     4825\n",
       "spam     747\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check proportion of each class\n",
    "df['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cf4d8892",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Ok lar... Joking wif u oni...</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
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       "      <td>ham</td>\n",
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      "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..."
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a70bdfef",
   "metadata": {},
   "outputs": [
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       "      <td>ham</td>\n",
       "      <td>Will ü 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",
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      "text/plain": [
       "     Category                                            Message\n",
       "5567     spam  This is the 2nd time we have tried 2 contact u...\n",
       "5568      ham               Will ü 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"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a6de4dc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Category', 'Message'], dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "933d9e8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labelling classes\n",
    "df['Category']=df['Category'].map({'ham':0,'spam':1})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b65536d5",
   "metadata": {},
   "outputs": [
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       "      <td>Ok lar... Joking wif u oni...</td>\n",
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       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
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       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
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       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>0</td>\n",
       "      <td>Will ü b going to esplanade fr home?</td>\n",
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       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>0</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>0</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>0</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\n",
       "0            0  Go until jurong point, crazy.. Available only ...\n",
       "1            0                      Ok lar... Joking wif u oni...\n",
       "2            1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3            0  U dun say so early hor... U c already then say...\n",
       "4            0  Nah I don't think he goes to usf, he lives aro...\n",
       "...        ...                                                ...\n",
       "5567         1  This is the 2nd time we have tried 2 contact u...\n",
       "5568         0               Will ü b going to esplanade fr home?\n",
       "5569         0  Pity, * was in mood for that. So...any other s...\n",
       "5570         0  The guy did some bitching but I acted like i'd...\n",
       "5571         0                         Rofl. Its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3280cb9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[\"Message\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a508ebf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Target\n",
    "y = df[\"Category\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "992544ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Extraction\n",
    "cv=CountVectorizer()\n",
    "X=cv.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bedcffd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data splitting\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": 61,
   "id": "b952495d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Vectorizer.pkl','wb') as file:\n",
    "    pickle.dump(cv,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cda5c9dd",
   "metadata": {},
   "outputs": [
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       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model \n",
    "model=MultinomialNB()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0891f797",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Spam_Detection_model_NB.pkl','wb') as file:\n",
    "    pickle.dump(model,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7847d8d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98206278, 0.98654709, 0.97533632, 0.99103139, 0.97533632,\n",
       "       0.97982063, 0.97085202, 0.97977528, 0.98876404, 0.97752809])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cross Validation\n",
    "scores1=cross_val_score(model,X_train,y_train,cv=10,scoring='accuracy')\n",
    "scores1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e4e4822d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction\n",
    "testing_predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "94041b9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98.56502242152466\n"
     ]
    }
   ],
   "source": [
    "#Accuracy\n",
    "Acc=accuracy_score(testing_predict,y_test)\n",
    "\n",
    "Acc=Acc*100\n",
    "print(Acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a9409dd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       966\n",
      "           1       0.94      0.95      0.95       149\n",
      "\n",
      "    accuracy                           0.99      1115\n",
      "   macro avg       0.97      0.97      0.97      1115\n",
      "weighted avg       0.99      0.99      0.99      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Classification Report\n",
    "report2=classification_report(y_test,testing_predict)\n",
    "print(report2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "819ce48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[957,   9],\n",
       "       [  7, 142]], dtype=int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Confusion matrix\n",
    "confusion_matrix=confusion_matrix(y_test,testing_predict)\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8f0b206a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Testing\n",
    "msg=\"Hello\"\n",
    "data=[msg]\n",
    "result=cv.transform(data).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c98081ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(result)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "80f4a03a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Testing\n",
    "msg=\"Free entry in 2 a wkly comp to win FA Cup final\"\n",
    "data=[msg]\n",
    "result=cv.transform(data).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "97c68540",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(result)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4921860",
   "metadata": {},
   "outputs": [],
   "source": [
    "p"
   ]
  }
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