15[email classifcation].ipynb

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       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"csvs\\\\spam.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a86c99f-a662-4caa-8fa7-f61aa1ac97a1",
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    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</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>4825</td>\n",
       "      <td>4516</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spam</th>\n",
       "      <td>747</td>\n",
       "      <td>641</td>\n",
       "      <td>Please call our customer service representativ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         Message                                                            \\\n",
       "           count unique                                                top   \n",
       "Category                                                                     \n",
       "ham         4825   4516                             Sorry, I'll call later   \n",
       "spam         747    641  Please call our customer service representativ...   \n",
       "\n",
       "               \n",
       "         freq  \n",
       "Category       \n",
       "ham        30  \n",
       "spam        4  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Category']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f87fbcbb-b8b7-4c34-a4b4-0a8aaba69571",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Message</th>\n",
       "      <th>spam</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>0</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>1</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>0</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>0</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>1</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",
       "      <td>0</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>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category                                            Message  spam\n",
       "0         ham  Go until jurong point, crazy.. Available only ...     0\n",
       "1         ham                      Ok lar... Joking wif u oni...     0\n",
       "2        spam  Free entry in 2 a wkly comp to win FA Cup fina...     1\n",
       "3         ham  U dun say so early hor... U c already then say...     0\n",
       "4         ham  Nah I don't think he goes to usf, he lives aro...     0\n",
       "...       ...                                                ...   ...\n",
       "5567     spam  This is the 2nd time we have tried 2 contact u...     1\n",
       "5568      ham               Will ü b going to esplanade fr home?     0\n",
       "5569      ham  Pity, * was in mood for that. So...any other s...     0\n",
       "5570      ham  The guy did some bitching but I acted like i'd...     0\n",
       "5571      ham                         Rofl. Its true to its name     0\n",
       "\n",
       "[5572 rows x 3 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['spam'] = df['Category'].apply(lambda x: 1 if x =='spam'  else 0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "879f94c8-521c-4480-a674-1fd7d66abc15",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "v = CountVectorizer()\n",
    "df_count = v.fit_transform(df['Message'].values)\n",
    "#df_count.toarray()[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5cdc10be-eb71-46b1-b7f5-4a8ed08cd603",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6c2db4e9-24ad-4768-a1f9-d68d81312cbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#using K folds instead\n",
    "kf = KFold(n_splits = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca4d85b6-8ed5-4068-bc6e-c311bfbb7601",
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index, test_index in kf.split(df_count, df['spam']):\n",
    "    X_train, X_test, y_train, y_test = df_count[train_index], df_count[test_index],df.spam[train_index], df.spam[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1106bbda-612a-4cde-953e-b93cf0d3cfae",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "model = MultinomialNB()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "21bce300-91f8-46cf-b589-1e1d70fd1bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "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": 39,
   "id": "d7627b80-d3f3-4619-9c19-d8be9fbd0169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9811523963381799"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X_test_count = v.transform(X_test)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ada4ffa1-1a28-4b83-99b5-6cd32857a882",
   "metadata": {},
   "outputs": [],
   "source": [
    "##using pipelines\n",
    "from sklearn.pipeline import Pipeline\n",
    "clf = Pipeline([\n",
    "    ('vectorizer', CountVectorizer()),\n",
    "    ('classfier', MultinomialNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a90a609a-f1fc-40bd-b05a-a0206a6fdf9b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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