naive_spam.ipynb

{
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       "      <th>2</th>\n",
       "      <td>3624</td>\n",
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       "      <td>Subject: neon retreat\\nho ho ho , we ' re arou...</td>\n",
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       "      <th>3</th>\n",
       "      <td>4685</td>\n",
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       "      <td>Subject: photoshop , windows , office . cheap ...</td>\n",
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       "      <th>4</th>\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\\nho ho ho , we ' re arou...   \n",
       "3        4685  spam  Subject: photoshop , windows , office . cheap ...   \n",
       "4        2030   ham  Subject: re : indian springs\\r\\nthis deal is t...   \n",
       "\n",
       "   label_num  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          1  \n",
       "4          0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('Documents\\spam.csv')`\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1fa31203",
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       "    <tr>\n",
       "      <th>5166</th>\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>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>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>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>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 × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     label                                               text  label_num\n",
       "0      ham  Subject: enron methanol ; meter # : 988291\\r\\n...          0\n",
       "1      ham  Subject: hpl nom for january 9 , 2001\\r\\n( see...          0\n",
       "2      ham  Subject: neon retreat\\nho ho ho , we ' re arou...          0\n",
       "3     spam  Subject: photoshop , windows , office . cheap ...          1\n",
       "4      ham  Subject: re : indian springs\\r\\nthis deal is t...          0\n",
       "...    ...                                                ...        ...\n",
       "5166   ham  Subject: put the 10 on the ft\\r\\nthe transport...          0\n",
       "5167   ham  Subject: 3 / 4 / 2000 and following noms\\r\\nhp...          0\n",
       "5168   ham  Subject: calpine daily gas nomination\\r\\n>\\r\\n...          0\n",
       "5169   ham  Subject: industrial worksheets for august 2000...          0\n",
       "5170  spam  Subject: important online banking alert\\r\\ndea...          1\n",
       "\n",
       "[5171 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('Unnamed: 0',axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "571a6d50",
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   "outputs": [
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       "      <th>ham</th>\n",
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       "      <td>1835.5</td>\n",
       "      <td>1060.159422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>917.75</td>\n",
       "      <td>1835.5</td>\n",
       "      <td>2753.25</td>\n",
       "      <td>3671.0</td>\n",
       "      <td>3672.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>spam</th>\n",
       "      <td>1499.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>432.868340</td>\n",
       "      <td>3672.0</td>\n",
       "      <td>4046.50</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4795.50</td>\n",
       "      <td>5170.0</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "      Unnamed: 0                                                         \\\n",
       "           count    mean          std     min      25%     50%      75%   \n",
       "label                                                                     \n",
       "ham       3672.0  1835.5  1060.159422     0.0   917.75  1835.5  2753.25   \n",
       "spam      1499.0  4421.0   432.868340  3672.0  4046.50  4421.0  4795.50   \n",
       "\n",
       "              label_num                                     \n",
       "          max     count mean  std  min  25%  50%  75%  max  \n",
       "label                                                       \n",
       "ham    3671.0    3672.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "spam   5170.0    1499.0  1.0  0.0  1.0  1.0  1.0  1.0  1.0  "
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     "execution_count": 12,
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   "source": [
    "df.groupby('label').describe()"
   ]
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   "cell_type": "code",
   "execution_count": 14,
   "id": "28b7a7a2",
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       "   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\\nho ho ho , we ' re arou...   \n",
       "3        4685  spam  Subject: photoshop , windows , office . cheap ...   \n",
       "4        2030   ham  Subject: re : indian springs\\r\\nthis deal is t...   \n",
       "\n",
       "   label_num  spam  \n",
       "0          0     0  \n",
       "1          0     0  \n",
       "2          0     0  \n",
       "3          1     1  \n",
       "4          0     0  "
      ]
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     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "df['spam']=df['label'].apply(lambda x:1 if x=='spam' else 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e694849f",
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       "   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\\nho ho ho , we ' re arou...   \n",
       "3        4685  spam  Subject: photoshop , windows , office . cheap ...   \n",
       "4        2030   ham  Subject: re : indian springs\\r\\nthis deal is t...   \n",
       "\n",
       "   label_num  spam  \n",
       "0          0     0  \n",
       "1          0     0  \n",
       "2          0     0  \n",
       "3          1     1  \n",
       "4          0     0  "
      ]
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('label_num',axis='columns')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3e2e9711",
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       "      <th colspan=\"8\" halign=\"left\">spam</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>3672.0</td>\n",
       "      <td>1835.5</td>\n",
       "      <td>1060.159422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>917.75</td>\n",
       "      <td>1835.5</td>\n",
       "      <td>2753.25</td>\n",
       "      <td>3671.0</td>\n",
       "      <td>3672.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3672.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spam</th>\n",
       "      <td>1499.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>432.868340</td>\n",
       "      <td>3672.0</td>\n",
       "      <td>4046.50</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4795.50</td>\n",
       "      <td>5170.0</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                                                         \\\n",
       "           count    mean          std     min      25%     50%      75%   \n",
       "label                                                                     \n",
       "ham       3672.0  1835.5  1060.159422     0.0   917.75  1835.5  2753.25   \n",
       "spam      1499.0  4421.0   432.868340  3672.0  4046.50  4421.0  4795.50   \n",
       "\n",
       "              label_num       ...              spam                           \\\n",
       "          max     count mean  ...  75%  max   count mean  std  min  25%  50%   \n",
       "label                         ...                                              \n",
       "ham    3671.0    3672.0  0.0  ...  0.0  0.0  3672.0  0.0  0.0  0.0  0.0  0.0   \n",
       "spam   5170.0    1499.0  1.0  ...  1.0  1.0  1499.0  1.0  0.0  1.0  1.0  1.0   \n",
       "\n",
       "                 \n",
       "       75%  max  \n",
       "label            \n",
       "ham    0.0  0.0  \n",
       "spam   1.0  1.0  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('label').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "48d8f145",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(df.text,df.spam,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "39603466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "v = CountVectorizer()\n",
    "x_train_count = v.fit_transform(x_train.values)\n",
    "x_train_count.toarray()[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b71ce872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "model = MultinomialNB()\n",
    "model.fit(x_train_count,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "facaf709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emails = [\n",
    "    'Hey mohan, can we get together to watch footbal game tomorrow?',\n",
    "    'Upto 20% discount on parking, exclusive offer just for you. Dont miss this reward!'\n",
    "]\n",
    "emails_count = v.transform(emails)\n",
    "model.predict(emails_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4057f1ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.970048309178744"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_count = v.transform(x_test)\n",
    "model.score(x_test_count, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7b7cdfa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "clf = Pipeline([\n",
    "    ('vectorizer', CountVectorizer()),\n",
    "    ('nb', MultinomialNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f763ea01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer', CountVectorizer()), ('nb', MultinomialNB())])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "60c484c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.970048309178744"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_test,y_test)"
   ]
  }
 ],
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