Naive Bayes - Spam Detection - 4th Sem.ipynb

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   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40021346",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"/Users/bitanpaul/Documents/Academics/ML/CSVs/spam_ham_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53161403",
   "metadata": {},
   "outputs": [
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     "data": {
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       "      <th>text</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>605</td>\n",
       "      <td>ham</td>\n",
       "      <td>Subject: enron methanol ; meter # : 988291\\r\\n...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2349</td>\n",
       "      <td>ham</td>\n",
       "      <td>Subject: hpl nom for january 9 , 2001\\r\\n( see...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3624</td>\n",
       "      <td>ham</td>\n",
       "      <td>Subject: neon retreat\\r\\nho ho ho , we ' re ar...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4685</td>\n",
       "      <td>spam</td>\n",
       "      <td>Subject: photoshop , windows , office . cheap ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2030</td>\n",
       "      <td>ham</td>\n",
       "      <td>Subject: re : indian springs\\r\\nthis deal is t...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5166</th>\n",
       "      <td>1518</td>\n",
       "      <td>ham</td>\n",
       "      <td>Subject: put the 10 on the ft\\r\\nthe transport...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5167</th>\n",
       "      <td>404</td>\n",
       "      <td>ham</td>\n",
       "      <td>Subject: 3 / 4 / 2000 and following noms\\r\\nhp...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5168</th>\n",
       "      <td>2933</td>\n",
       "      <td>ham</td>\n",
       "      <td>Subject: calpine daily gas nomination\\r\\n&gt;\\r\\n...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5169</th>\n",
       "      <td>1409</td>\n",
       "      <td>ham</td>\n",
       "      <td>Subject: industrial worksheets for august 2000...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5170</th>\n",
       "      <td>4807</td>\n",
       "      <td>spam</td>\n",
       "      <td>Subject: important online banking alert\\r\\ndea...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5171 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "      Unnamed: 0 label                                               text  \\\n",
       "0            605   ham  Subject: enron methanol ; meter # : 988291\\r\\n...   \n",
       "1           2349   ham  Subject: hpl nom for january 9 , 2001\\r\\n( see...   \n",
       "2           3624   ham  Subject: neon retreat\\r\\nho ho ho , we ' re ar...   \n",
       "3           4685  spam  Subject: photoshop , windows , office . cheap ...   \n",
       "4           2030   ham  Subject: re : indian springs\\r\\nthis deal is t...   \n",
       "...          ...   ...                                                ...   \n",
       "5166        1518   ham  Subject: put the 10 on the ft\\r\\nthe transport...   \n",
       "5167         404   ham  Subject: 3 / 4 / 2000 and following noms\\r\\nhp...   \n",
       "5168        2933   ham  Subject: calpine daily gas nomination\\r\\n>\\r\\n...   \n",
       "5169        1409   ham  Subject: industrial worksheets for august 2000...   \n",
       "5170        4807  spam  Subject: important online banking alert\\r\\ndea...   \n",
       "\n",
       "      label_num  \n",
       "0             0  \n",
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "data"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "08c05434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "label         0\n",
       "text          0\n",
       "label_num     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum() #we need to detect & delete every null values from the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6221ae42",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Y = data.text, data.label #defining x=subject of mail & y= label i.e. ham/spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bc9bcb52",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, x_test, Y_train, y_test = train_test_split(X,Y, test_size = .3) #spliting subject & label into traing & testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1380bd61",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer =CountVectorizer() #CountVectorizer is a function used for converting texts of subject into numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72508ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "count = vectorizer.fit_transform(X_train.values) #CountVectorizer converting texts of train subjects into numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b9d263d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3619x38627 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 315859 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count #count of numbers that are converted from subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee14e3e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = MultinomialNB() #conditional probablity formula is defined inside Multinomial function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7aa2ef5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2c5f2993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(count, targets) #count=input & target=output is to be fitted in the naive bayes algo i.e. clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9f262fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict (vectorizer.transform(x_test)) #predicting the accuracy using predict part of data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd7f9a15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1090,   14],\n",
       "       [  22,  426]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred) #tn & tp should be higher than fn & fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "93c09735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9768041237113402"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred) #accuracy of this ml model is 97% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6070f153",
   "metadata": {},
   "outputs": [],
   "source": []
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