{
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   "id": "85f84e9d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:08.632192Z",
     "start_time": "2023-03-18T07:58:07.214167Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2818ab6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:08.685976Z",
     "start_time": "2023-03-18T07:58:08.638995Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('spam.csv', encoding='latin 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b1910ff6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:08.751755Z",
     "start_time": "2023-03-18T07:58:08.691018Z"
    }
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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",
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       "      <td>Ok lar... Joking wif u oni...</td>\n",
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       "      <td>spam</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>ham</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  "
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    "df.head()"
   ]
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  {
   "cell_type": "markdown",
   "id": "9fe81b00",
   "metadata": {},
   "source": [
    "#### Stages of the project\n",
    "- Data cleaning\n",
    "- EDA\n",
    "- Text preprocessing(vectorization, stemming, etc)\n",
    "- Model building \n",
    "- Evaluation\n",
    "- Improvement \n",
    "- Website creation \n",
    "- Deployment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9ee3d2f4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:08.808332Z",
     "start_time": "2023-03-18T07:58:08.756782Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5572 entries, 0 to 5571\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   v1          5572 non-null   object\n",
      " 1   v2          5572 non-null   object\n",
      " 2   Unnamed: 2  50 non-null     object\n",
      " 3   Unnamed: 3  12 non-null     object\n",
      " 4   Unnamed: 4  6 non-null      object\n",
      "dtypes: object(5)\n",
      "memory usage: 217.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "606215f1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:08.842442Z",
     "start_time": "2023-03-18T07:58:08.813703Z"
    }
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "42900824",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:08.877454Z",
     "start_time": "2023-03-18T07:58:08.847434Z"
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   "outputs": [
    {
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       "      <td>spam</td>\n",
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       "      <th>3</th>\n",
       "      <td>ham</td>\n",
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       "      <th>4</th>\n",
       "      <td>ham</td>\n",
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      "text/plain": [
       "     v1                                                 v2\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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   "execution_count": 7,
   "id": "21bb37c9",
   "metadata": {
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     "start_time": "2023-03-18T07:58:08.881224Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e78ea6",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Step 1 : Data Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "931e0aef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:08.934342Z",
     "start_time": "2023-03-18T07:58:08.909295Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df.rename(columns = {'v1': 'target', 'v2': 'text'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99e90d53",
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2023-03-18T07:58:08.937473Z"
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    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <td>ham</td>\n",
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       "      <th>2</th>\n",
       "      <td>spam</td>\n",
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       "      <td>ham</td>\n",
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       "      <th>4</th>\n",
       "      <td>ham</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "  target                                               text\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": 9,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f0021df5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:11.473278Z",
     "start_time": "2023-03-18T07:58:08.976146Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Applying label encoder to change ham and spam to 0 and 1\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "289a365f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:11.508797Z",
     "start_time": "2023-03-18T07:58:11.480523Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df['target'] = encoder.fit_transform(df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "866a34f9",
   "metadata": {
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     "end_time": "2023-03-18T07:58:11.538792Z",
     "start_time": "2023-03-18T07:58:11.513794Z"
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    "hidden": true
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   target                                               text\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..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ecd02d85",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:11.568794Z",
     "start_time": "2023-03-18T07:58:11.543801Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target    0\n",
       "text      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking missing values \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca3fb1f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:11.599538Z",
     "start_time": "2023-03-18T07:58:11.573797Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking duplicate values \n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a463b1ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:11.634163Z",
     "start_time": "2023-03-18T07:58:11.604534Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df = df.drop_duplicates(keep = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c66f1fde",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:11.664791Z",
     "start_time": "2023-03-18T07:58:11.639171Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking duplicate values \n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f99bdcd6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:11.694533Z",
     "start_time": "2023-03-18T07:58:11.669791Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "456900dc",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0655f948",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:11.722194Z",
     "start_time": "2023-03-18T07:58:11.698619Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4516\n",
       "1     653\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c40a06b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:14.491498Z",
     "start_time": "2023-03-18T07:58:11.727203Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.pie(df['target'].value_counts(), labels=['ham', 'spam'], autopct=\"%0.2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95c599b3",
   "metadata": {},
   "source": [
    "- 88% messages are Ham, so the data is imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bd8fa405",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:15.740361Z",
     "start_time": "2023-03-18T07:58:14.496506Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading punkt: <urlopen error [Errno 11001]\n",
      "[nltk_data]     getaddrinfo failed>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6db3968d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:15.766665Z",
     "start_time": "2023-03-18T07:58:15.746668Z"
    }
   },
   "outputs": [],
   "source": [
    "df['num_characters'] = df['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4e267284",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:18.818196Z",
     "start_time": "2023-03-18T07:58:15.771623Z"
    }
   },
   "outputs": [],
   "source": [
    "df['num_words'] = df['text'].apply(lambda x: len(nltk.word_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1db91852",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:19.638884Z",
     "start_time": "2023-03-18T07:58:18.825330Z"
    }
   },
   "outputs": [],
   "source": [
    "df['num_sentences'] = df['text'].apply(lambda x: len(nltk.sent_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9d2174c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:19.683252Z",
     "start_time": "2023-03-18T07:58:19.644139Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text  num_characters  \\\n",
       "0       0  Go until jurong point, crazy.. Available only ...             111   \n",
       "1       0                      Ok lar... Joking wif u oni...              29   \n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155   \n",
       "3       0  U dun say so early hor... U c already then say...              49   \n",
       "4       0  Nah I don't think he goes to usf, he lives aro...              61   \n",
       "\n",
       "   num_words  num_sentences  \n",
       "0         24              2  \n",
       "1          8              2  \n",
       "2         37              2  \n",
       "3         13              1  \n",
       "4         15              1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "11965d11",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:19.738651Z",
     "start_time": "2023-03-18T07:58:19.689349Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5169.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78.977945</td>\n",
       "      <td>18.453279</td>\n",
       "      <td>1.947185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>58.236293</td>\n",
       "      <td>13.324793</td>\n",
       "      <td>1.362406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>910.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters    num_words  num_sentences\n",
       "count     5169.000000  5169.000000    5169.000000\n",
       "mean        78.977945    18.453279       1.947185\n",
       "std         58.236293    13.324793       1.362406\n",
       "min          2.000000     1.000000       1.000000\n",
       "25%         36.000000     9.000000       1.000000\n",
       "50%         60.000000    15.000000       1.000000\n",
       "75%        117.000000    26.000000       2.000000\n",
       "max        910.000000   220.000000      28.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking describe \n",
    "df[['num_characters', 'num_words', 'num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c551063e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:19.790871Z",
     "start_time": "2023-03-18T07:58:19.745368Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4516.000000</td>\n",
       "      <td>4516.000000</td>\n",
       "      <td>4516.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>70.459256</td>\n",
       "      <td>17.120903</td>\n",
       "      <td>1.799601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56.358207</td>\n",
       "      <td>13.493725</td>\n",
       "      <td>1.278465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>910.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters    num_words  num_sentences\n",
       "count     4516.000000  4516.000000    4516.000000\n",
       "mean        70.459256    17.120903       1.799601\n",
       "std         56.358207    13.493725       1.278465\n",
       "min          2.000000     1.000000       1.000000\n",
       "25%         34.000000     8.000000       1.000000\n",
       "50%         52.000000    13.000000       1.000000\n",
       "75%         90.000000    22.000000       2.000000\n",
       "max        910.000000   220.000000      28.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analysing Ham messages \n",
    "df[df['target']==0][['num_characters', 'num_words', 'num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "746e7359",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:19.825602Z",
     "start_time": "2023-03-18T07:58:19.795871Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>137.891271</td>\n",
       "      <td>27.667688</td>\n",
       "      <td>2.967841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.137753</td>\n",
       "      <td>7.008418</td>\n",
       "      <td>1.483201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>224.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters   num_words  num_sentences\n",
       "count      653.000000  653.000000     653.000000\n",
       "mean       137.891271   27.667688       2.967841\n",
       "std         30.137753    7.008418       1.483201\n",
       "min         13.000000    2.000000       1.000000\n",
       "25%        132.000000   25.000000       2.000000\n",
       "50%        149.000000   29.000000       3.000000\n",
       "75%        157.000000   32.000000       4.000000\n",
       "max        224.000000   46.000000       8.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analysing Spam messages \n",
    "df[df['target']==1][['num_characters', 'num_words', 'num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd19501",
   "metadata": {},
   "source": [
    "- Mean value for Ham values of words is 70 words where as Spam is 137\n",
    "- Mean words for Ham is 17 where as Spam is 27\n",
    "- Mean sentences for Ham is 1.7 where as Spam is 2.9\n",
    "- Clearly Ham has lesser mean compared to Spam messages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f89ff6b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:20.126465Z",
     "start_time": "2023-03-18T07:58:19.840597Z"
    }
   },
   "outputs": [],
   "source": [
    "# Plotting \n",
    "import seaborn as sns \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4085b895",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:21.201316Z",
     "start_time": "2023-03-18T07:58:20.131475Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for number of characters\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.histplot(df[df['target']==0]['num_characters'])\n",
    "sns.histplot(df[df['target']==1]['num_characters'], color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e5d23e32",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:22.140695Z",
     "start_time": "2023-03-18T07:58:21.206480Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for number of words\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.histplot(df[df['target']==0]['num_words'])\n",
    "sns.histplot(df[df['target']==1]['num_words'], color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "028fcad7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:31.711763Z",
     "start_time": "2023-03-18T07:58:22.145696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x23452da2f70>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 808.875x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# USing pairplot for checking relation between all columns \n",
    "sns.pairplot(df, hue='target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "846ae778",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:32.271681Z",
     "start_time": "2023-03-18T07:58:31.716094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f46f46",
   "metadata": {},
   "source": [
    "- High degree of correlation between columns \n",
    "- We will take only one column, i.e. num_characters "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a303ba9a",
   "metadata": {},
   "source": [
    "### Data Preprocessing \n",
    "- Lower case \n",
    "- Tokenization \n",
    "- Removal of special characters \n",
    "- Removal of stopwords and punctuation \n",
    "- Stemming / Lemmatization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b53095e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:32.303025Z",
     "start_time": "2023-03-18T07:58:32.276677Z"
    }
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stopwords.words('english')\n",
    "\n",
    "import string\n",
    "string.punctuation\n",
    "\n",
    "\n",
    "# Brings the words in root form (loves, loving, loved -- love)\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1958e83a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:32.340201Z",
     "start_time": "2023-03-18T07:58:32.307486Z"
    }
   },
   "outputs": [],
   "source": [
    "def transform_text(text):\n",
    "    '''This function will take the text as return it after preprocessing \n",
    "    - Lower case \n",
    "    - Tokenization \n",
    "    - Removal of special characters \n",
    "    - Removal od stopwords and punctuations\n",
    "    - Stemming '''\n",
    "\n",
    "    text = text.lower()\n",
    "    text = nltk.word_tokenize(text)\n",
    "\n",
    "    y = []\n",
    "    for i in text:\n",
    "        if i.isalnum():\n",
    "            y.append(i)\n",
    "\n",
    "    text = y.copy()\n",
    "    y.clear()\n",
    "\n",
    "    for i in text:\n",
    "        if i not in stopwords.words('english') and i not in string.punctuation:\n",
    "            y.append(i)\n",
    "\n",
    "    text = y[:]  # another way to copy\n",
    "    y.clear()\n",
    "\n",
    "    for i in text:\n",
    "        y.append(ps.stem(i))\n",
    "\n",
    "    return \" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "88177c92",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:32.370204Z",
     "start_time": "2023-03-18T07:58:32.345201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'love hero'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_text(\"I loves being @hero but?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bb3cd765",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:58:32.404024Z",
     "start_time": "2023-03-18T07:58:32.375211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'call messag miss call'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_text(df['text'][45])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4677d524",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:30.223207Z",
     "start_time": "2023-03-18T07:58:32.409016Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the new column using the above function \n",
    "df['transformed_text'] = df['text'].apply(transform_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1b8006e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:30.265814Z",
     "start_time": "2023-03-18T07:59:30.228207Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "      <th>transformed_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>go jurong point crazi avail bugi n great world...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>ok lar joke wif u oni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>free entri 2 wkli comp win fa cup final tkt 21...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>u dun say earli hor u c alreadi say</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>nah think goe usf live around though</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text  num_characters  \\\n",
       "0       0  Go until jurong point, crazy.. Available only ...             111   \n",
       "1       0                      Ok lar... Joking wif u oni...              29   \n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155   \n",
       "3       0  U dun say so early hor... U c already then say...              49   \n",
       "4       0  Nah I don't think he goes to usf, he lives aro...              61   \n",
       "\n",
       "   num_words  num_sentences                                   transformed_text  \n",
       "0         24              2  go jurong point crazi avail bugi n great world...  \n",
       "1          8              2                              ok lar joke wif u oni  \n",
       "2         37              2  free entri 2 wkli comp win fa cup final tkt 21...  \n",
       "3         13              1                u dun say earli hor u c alreadi say  \n",
       "4         15              1               nah think goe usf live around though  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a6d508e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:30.326329Z",
     "start_time": "2023-03-18T07:59:30.271381Z"
    }
   },
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "wc = WordCloud(width = 500, height=500, min_font_size=10, background_color='white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "63a34e8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:32.570519Z",
     "start_time": "2023-03-18T07:59:30.331336Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23454b98ca0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spam_wc = wc.generate(df[df['target']==1]['transformed_text'].str.cat(sep=' '))\n",
    "plt.imshow(spam_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6b8f84a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:35.899948Z",
     "start_time": "2023-03-18T07:59:32.576834Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23453361ca0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ham_wc = wc.generate(df[df['target']==0]['transformed_text'].str.cat(sep=' '))\n",
    "plt.imshow(ham_wc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed2e072",
   "metadata": {},
   "source": [
    "##### checking the top 30 words in Spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "10749d75",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:35.929168Z",
     "start_time": "2023-03-18T07:59:35.906732Z"
    }
   },
   "outputs": [],
   "source": [
    " \n",
    "spam_corpus = []\n",
    "for msg in df[df['target']==1]['transformed_text'].tolist():\n",
    "    for word in msg.split():\n",
    "        spam_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "18bd2d5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:36.010737Z",
     "start_time": "2023-03-18T07:59:35.935208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['free',\n",
       " 'entri',\n",
       " '2',\n",
       " 'wkli',\n",
       " 'comp',\n",
       " 'win',\n",
       " 'fa',\n",
       " 'cup',\n",
       " 'final',\n",
       " 'tkt',\n",
       " '21st',\n",
       " 'may',\n",
       " 'text',\n",
       " 'fa',\n",
       " '87121',\n",
       " 'receiv',\n",
       " 'entri',\n",
       " 'question',\n",
       " 'std',\n",
       " 'txt',\n",
       " 'rate',\n",
       " 'c',\n",
       " 'appli',\n",
       " '08452810075over18',\n",
       " 'freemsg',\n",
       " 'hey',\n",
       " 'darl',\n",
       " '3',\n",
       " 'week',\n",
       " 'word',\n",
       " 'back',\n",
       " 'like',\n",
       " 'fun',\n",
       " 'still',\n",
       " 'tb',\n",
       " 'ok',\n",
       " 'xxx',\n",
       " 'std',\n",
       " 'chg',\n",
       " 'send',\n",
       " 'rcv',\n",
       " 'winner',\n",
       " 'valu',\n",
       " 'network',\n",
       " 'custom',\n",
       " 'select',\n",
       " 'receivea',\n",
       " 'prize',\n",
       " 'reward',\n",
       " 'claim',\n",
       " 'call',\n",
       " 'claim',\n",
       " 'code',\n",
       " 'kl341',\n",
       " 'valid',\n",
       " '12',\n",
       " 'hour',\n",
       " 'mobil',\n",
       " '11',\n",
       " 'month',\n",
       " 'u',\n",
       " 'r',\n",
       " 'entitl',\n",
       " 'updat',\n",
       " 'latest',\n",
       " 'colour',\n",
       " 'mobil',\n",
       " 'camera',\n",
       " 'free',\n",
       " 'call',\n",
       " 'mobil',\n",
       " 'updat',\n",
       " 'co',\n",
       " 'free',\n",
       " '08002986030',\n",
       " 'six',\n",
       " 'chanc',\n",
       " 'win',\n",
       " 'cash',\n",
       " '100',\n",
       " 'pound',\n",
       " 'txt',\n",
       " 'csh11',\n",
       " 'send',\n",
       " 'cost',\n",
       " '6day',\n",
       " 'tsandc',\n",
       " 'appli',\n",
       " 'repli',\n",
       " 'hl',\n",
       " '4',\n",
       " 'info',\n",
       " 'urgent',\n",
       " '1',\n",
       " 'week',\n",
       " 'free',\n",
       " 'membership',\n",
       " 'prize',\n",
       " 'jackpot',\n",
       " 'txt',\n",
       " 'word',\n",
       " 'claim',\n",
       " '81010',\n",
       " 'c',\n",
       " 'lccltd',\n",
       " 'pobox',\n",
       " '4403ldnw1a7rw18',\n",
       " 'xxxmobilemovieclub',\n",
       " 'use',\n",
       " 'credit',\n",
       " 'click',\n",
       " 'wap',\n",
       " 'link',\n",
       " 'next',\n",
       " 'txt',\n",
       " 'messag',\n",
       " 'click',\n",
       " 'http',\n",
       " 'england',\n",
       " 'v',\n",
       " 'macedonia',\n",
       " 'dont',\n",
       " 'miss',\n",
       " 'news',\n",
       " 'txt',\n",
       " 'ur',\n",
       " 'nation',\n",
       " 'team',\n",
       " '87077',\n",
       " 'eg',\n",
       " 'england',\n",
       " '87077',\n",
       " 'tri',\n",
       " 'wale',\n",
       " 'scotland',\n",
       " 'poboxox36504w45wq',\n",
       " 'thank',\n",
       " 'subscript',\n",
       " 'rington',\n",
       " 'uk',\n",
       " 'mobil',\n",
       " 'charg',\n",
       " 'pleas',\n",
       " 'confirm',\n",
       " 'repli',\n",
       " 'ye',\n",
       " 'repli',\n",
       " 'charg',\n",
       " '07732584351',\n",
       " 'rodger',\n",
       " 'burn',\n",
       " 'msg',\n",
       " 'tri',\n",
       " 'call',\n",
       " 'repli',\n",
       " 'sm',\n",
       " 'free',\n",
       " 'nokia',\n",
       " 'mobil',\n",
       " 'free',\n",
       " 'camcord',\n",
       " 'pleas',\n",
       " 'call',\n",
       " '08000930705',\n",
       " 'deliveri',\n",
       " 'tomorrow',\n",
       " 'sm',\n",
       " 'ac',\n",
       " 'sptv',\n",
       " 'new',\n",
       " 'jersey',\n",
       " 'devil',\n",
       " 'detroit',\n",
       " 'red',\n",
       " 'wing',\n",
       " 'play',\n",
       " 'ice',\n",
       " 'hockey',\n",
       " 'correct',\n",
       " 'incorrect',\n",
       " 'end',\n",
       " 'repli',\n",
       " 'end',\n",
       " 'sptv',\n",
       " 'congrat',\n",
       " '1',\n",
       " 'year',\n",
       " 'special',\n",
       " 'cinema',\n",
       " 'pass',\n",
       " '2',\n",
       " 'call',\n",
       " '09061209465',\n",
       " 'c',\n",
       " 'suprman',\n",
       " 'v',\n",
       " 'matrix3',\n",
       " 'starwars3',\n",
       " 'etc',\n",
       " '4',\n",
       " 'free',\n",
       " '150pm',\n",
       " 'dont',\n",
       " 'miss',\n",
       " 'valu',\n",
       " 'custom',\n",
       " 'pleas',\n",
       " 'advis',\n",
       " 'follow',\n",
       " 'recent',\n",
       " 'review',\n",
       " 'mob',\n",
       " 'award',\n",
       " 'bonu',\n",
       " 'prize',\n",
       " 'call',\n",
       " '09066364589',\n",
       " 'urgent',\n",
       " 'ur',\n",
       " 'award',\n",
       " 'complimentari',\n",
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       " 'pobox12n146tf150p',\n",
       " 'urgent',\n",
       " 'mobil',\n",
       " 'number',\n",
       " 'award',\n",
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       " 'guarante',\n",
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       " '09058094455',\n",
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       " 'video',\n",
       " 'camera',\n",
       " ...]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_corpus"
   ]
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  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "57ff2811",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:36.037747Z",
     "start_time": "2023-03-18T07:59:36.014286Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9939"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spam_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7ed8d29f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:36.132921Z",
     "start_time": "2023-03-18T07:59:36.039984Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'free': 191,\n",
       "         'entri': 21,\n",
       "         '2': 155,\n",
       "         'wkli': 9,\n",
       "         'comp': 8,\n",
       "         'win': 48,\n",
       "         'fa': 2,\n",
       "         'cup': 3,\n",
       "         'final': 14,\n",
       "         'tkt': 2,\n",
       "         '21st': 1,\n",
       "         'may': 6,\n",
       "         'text': 122,\n",
       "         '87121': 2,\n",
       "         'receiv': 31,\n",
       "         'question': 9,\n",
       "         'std': 6,\n",
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       "         'rstm': 2,\n",
       "         'sw7': 2,\n",
       "         '3ss': 2,\n",
       "         '88800': 1,\n",
       "         '89034': 1,\n",
       "         'premium': 2,\n",
       "         '08718711108': 1,\n",
       "         'sun0819': 1,\n",
       "         'hello': 4,\n",
       "         'seem': 1,\n",
       "         'cool': 1,\n",
       "         'gr8': 7,\n",
       "         '20': 3,\n",
       "         'everi': 26,\n",
       "         'wk': 17,\n",
       "         'opt': 10,\n",
       "         '08452810071': 1,\n",
       "         'hi': 15,\n",
       "         'sue': 2,\n",
       "         'old': 2,\n",
       "         'work': 3,\n",
       "         'lapdanc': 1,\n",
       "         'sex': 10,\n",
       "         'bedroom': 1,\n",
       "         'textoper': 3,\n",
       "         'g2': 1,\n",
       "         '1da': 1,\n",
       "         '150ppmsg': 1,\n",
       "         'forward': 4,\n",
       "         '448712404000': 1,\n",
       "         '08712404000': 1,\n",
       "         'immedi': 5,\n",
       "         'fantast': 7,\n",
       "         'deck': 1,\n",
       "         'alert': 5,\n",
       "         '08714712388': 1,\n",
       "         '09071512433': 2,\n",
       "         'b4': 6,\n",
       "         '050703': 2,\n",
       "         'csbcm4235wc1n3xx': 2,\n",
       "         'callcost': 2,\n",
       "         'mobilesvari': 2,\n",
       "         '50': 3,\n",
       "         '08714712394': 1,\n",
       "         'email': 2,\n",
       "         'alertfrom': 1,\n",
       "         'jeri': 1,\n",
       "         'stewarts': 1,\n",
       "         '2kbsubject': 1,\n",
       "         'prescripiton': 1,\n",
       "         'drvgsto': 1,\n",
       "         'listen': 3,\n",
       "         '123': 1,\n",
       "         'nokia6650': 2,\n",
       "         'txtauction': 5,\n",
       "         '81151': 2,\n",
       "         '4t': 3,\n",
       "         'ctxt': 2,\n",
       "         'subscrib': 9,\n",
       "         'best': 7,\n",
       "         'helplin': 2,\n",
       "         '08706091795': 2,\n",
       "         'realiz': 1,\n",
       "         '40': 2,\n",
       "         'thousand': 1,\n",
       "         'run': 1,\n",
       "         'around': 2,\n",
       "         'tattoo': 1,\n",
       "         'premier': 2,\n",
       "         'romant': 1,\n",
       "         'pari': 3,\n",
       "         'night': 7,\n",
       "         'book': 6,\n",
       "         '08704439680t': 1,\n",
       "         'unclaim': 1,\n",
       "         '09066368327': 1,\n",
       "         'claimcod': 1,\n",
       "         'm39m51': 1,\n",
       "         'citi': 3,\n",
       "         'break': 5,\n",
       "         'could': 7,\n",
       "         'summer': 7,\n",
       "         'store': 7,\n",
       "         '88039': 2,\n",
       "         'skilgm': 3,\n",
       "         'tscs087147403231winawk': 2,\n",
       "         '0578': 2,\n",
       "         'ever': 3,\n",
       "         'thought': 1,\n",
       "         'good': 12,\n",
       "         'life': 3,\n",
       "         'perfect': 1,\n",
       "         'commun': 2,\n",
       "         '5': 8,\n",
       "         'polyphon': 4,\n",
       "         '087018728737': 1,\n",
       "         'toppoli': 1,\n",
       "         'tune': 1,\n",
       "         'subpoli': 2,\n",
       "         '81618': 1,\n",
       "         'pole': 1,\n",
       "         '08718727870': 2,\n",
       "         '14thmarch': 1,\n",
       "         'availa': 1,\n",
       "         'pobox84': 4,\n",
       "         'm263uz': 2,\n",
       "         'no1': 5,\n",
       "         '8077': 1,\n",
       "         'tell': 13,\n",
       "         'mate': 8,\n",
       "         '36504': 4,\n",
       "         'w45wq': 3,\n",
       "         'cashto': 2,\n",
       "         '08000407165': 2,\n",
       "         'getstop': 2,\n",
       "         '88222': 2,\n",
       "         'php': 2,\n",
       "         'rg21': 1,\n",
       "         '4jx': 1,\n",
       "         'either': 8,\n",
       "         'gift': 16,\n",
       "         'outbid': 1,\n",
       "         'simonwatson5120': 1,\n",
       "         'shinco': 1,\n",
       "         'plyr': 1,\n",
       "         'bid': 8,\n",
       "         'notif': 1,\n",
       "         'smsservic': 1,\n",
       "         'yourinclus': 1,\n",
       "         'pl': 8,\n",
       "         '3qxj9': 3,\n",
       "         'extra': 6,\n",
       "         '9ae': 3,\n",
       "         'alfi': 3,\n",
       "         'moon': 3,\n",
       "         'children': 3,\n",
       "         'need': 11,\n",
       "         'song': 3,\n",
       "         'm8': 4,\n",
       "         'chariti': 9,\n",
       "         '8007': 19,\n",
       "         'zed': 6,\n",
       "         '08701417012': 3,\n",
       "         'profit': 3,\n",
       "         'cust': 3,\n",
       "         'care': 6,\n",
       "         '07821230901': 1,\n",
       "         'five': 3,\n",
       "         '08002888812': 2,\n",
       "         'wed': 2,\n",
       "         '09066350750': 2,\n",
       "         'ibiza': 4,\n",
       "         'await': 24,\n",
       "         '434': 3,\n",
       "         'sk3': 3,\n",
       "         '8wp': 3,\n",
       "         'ppm': 4,\n",
       "         'talk': 5,\n",
       "         'fall': 1,\n",
       "         'world': 2,\n",
       "         'discreet': 2,\n",
       "         'vip': 4,\n",
       "         '83110': 1,\n",
       "         'suppli': 2,\n",
       "         'virgin': 2,\n",
       "         'record': 6,\n",
       "         'mysteri': 1,\n",
       "         '09061104283': 1,\n",
       "         'approx': 1,\n",
       "         '07808': 1,\n",
       "         'xxxxxx': 1,\n",
       "         '08719899217': 1,\n",
       "         '41685': 2,\n",
       "         'posh': 1,\n",
       "         'bird': 1,\n",
       "         'chap': 1,\n",
       "         'user': 8,\n",
       "         'trial': 1,\n",
       "         'prod': 1,\n",
       "         'champney': 1,\n",
       "         'put': 1,\n",
       "         'address': 3,\n",
       "         'dob': 1,\n",
       "         'asap': 6,\n",
       "         'ta': 1,\n",
       "         '0721072': 1,\n",
       "         'till': 2,\n",
       "         'drop': 1,\n",
       "         '10k': 2,\n",
       "         '5k': 1,\n",
       "         'travel': 1,\n",
       "         'ntt': 7,\n",
       "         'cr01327bt': 1,\n",
       "         'fixedlin': 1,\n",
       "         'liverpool': 2,\n",
       "         'mid': 1,\n",
       "         '09058094565': 2,\n",
       "         'remind': 2,\n",
       "         'alreadi': 1,\n",
       "         'paid': 1,\n",
       "         'mymobi': 1,\n",
       "         'lastest': 1,\n",
       "         'stereophon': 1,\n",
       "         'marley': 1,\n",
       "         'dizze': 1,\n",
       "         'racal': 1,\n",
       "         'libertin': 1,\n",
       "         'stroke': 1,\n",
       "         'nookii': 1,\n",
       "         'bookmark': 1,\n",
       "         'januari': 1,\n",
       "         'male': 3,\n",
       "         'sale': 4,\n",
       "         'gay': 6,\n",
       "         'cheaper': 2,\n",
       "         'cheap': 3,\n",
       "         'peak': 2,\n",
       "         '08712460324': 8,\n",
       "         'money': 4,\n",
       "         'lucki': 8,\n",
       "         '88600': 2,\n",
       "         'give': 8,\n",
       "         'away': 4,\n",
       "         'box403': 1,\n",
       "         'w1t1ji': 1,\n",
       "         'matthew': 1,\n",
       "         '09063440451': 1,\n",
       "         'lux': 2,\n",
       "         'ppm150': 1,\n",
       "         'box334': 1,\n",
       "         'sk38xh': 4,\n",
       "         '09061749602': 1,\n",
       "         '528': 1,\n",
       "         'hp20': 1,\n",
       "         '1yf': 1,\n",
       "         'touch': 1,\n",
       "         'folk': 1,\n",
       "         'compani': 3,\n",
       "         'enjoy': 12,\n",
       "         '08718720201': 5,\n",
       "         'filthi': 2,\n",
       "         'stori': 1,\n",
       "         'girl': 9,\n",
       "         '09050001808': 1,\n",
       "         'm95': 1,\n",
       "         'valid12hr': 2,\n",
       "         '3g': 3,\n",
       "         'videophon': 3,\n",
       "         '09063458130': 2,\n",
       "         'videochat': 3,\n",
       "         'wid': 3,\n",
       "         'java': 3,\n",
       "         'dload': 3,\n",
       "         'polyph': 2,\n",
       "         'nolin': 3,\n",
       "         'rentl': 3,\n",
       "         'panason': 1,\n",
       "         'bluetoothhdset': 1,\n",
       "         'doublemin': 1,\n",
       "         'doubletxt': 1,\n",
       "         'contract': 4,\n",
       "         'guess': 6,\n",
       "         'somebodi': 2,\n",
       "         'secretli': 2,\n",
       "         'wan': 11,\n",
       "         'na': 10,\n",
       "         '09065394514': 1,\n",
       "         'datebox1282essexcm61xn': 2,\n",
       "         '09058097218': 1,\n",
       "         '6': 5,\n",
       "         'ls15hb': 2,\n",
       "         'bloke': 1,\n",
       "         'zoe': 1,\n",
       "         'kickoff': 1,\n",
       "         'inform': 9,\n",
       "         'euro': 2,\n",
       "         'eastend': 1,\n",
       "         'flower': 2,\n",
       "         'dot': 1,\n",
       "         'compar': 1,\n",
       "         'violet': 1,\n",
       "         'tulip': 1,\n",
       "         'lili': 1,\n",
       "         'e': 1,\n",
       "         'f': 3,\n",
       "         '84025': 2,\n",
       "         'lot': 2,\n",
       "         'peopl': 3,\n",
       "         'regist': 5,\n",
       "         'replys150': 1,\n",
       "         'ask': 5,\n",
       "         'cant': 3,\n",
       "         '09058091854': 1,\n",
       "         'box385': 1,\n",
       "         'm6': 1,\n",
       "         '6wu': 1,\n",
       "         '09050003091': 2,\n",
       "         'c52': 2,\n",
       "         'xchat': 5,\n",
       "         'sipix': 3,\n",
       "         'digit': 6,\n",
       "         '09061221061': 1,\n",
       "         '28day': 1,\n",
       "         'box177': 1,\n",
       "         'm221bp': 1,\n",
       "         '2yr': 1,\n",
       "         'warranti': 1,\n",
       "         'p': 2,\n",
       "         '09061790121': 2,\n",
       "         '3030': 1,\n",
       "         'b': 10,\n",
       "         'receipt': 3,\n",
       "         'an': 6,\n",
       "         'elvi': 1,\n",
       "         'presley': 1,\n",
       "         'birthday': 1,\n",
       "         'o2': 5,\n",
       "         'log': 7,\n",
       "         'onto': 7,\n",
       "         'surpris': 5,\n",
       "         '449050000301': 1,\n",
       "         '09050000301': 1,\n",
       "         'bore': 3,\n",
       "         'speed': 1,\n",
       "         'speedchat': 2,\n",
       "         '80155': 1,\n",
       "         'em': 1,\n",
       "         'swap': 1,\n",
       "         'chatter': 1,\n",
       "         'chat80155': 1,\n",
       "         'rcd': 1,\n",
       "         '08000776320': 2,\n",
       "         'part': 8,\n",
       "         'survey': 1,\n",
       "         'yesterday': 3,\n",
       "         'howev': 1,\n",
       "         'wish': 2,\n",
       "         '80160': 1,\n",
       "         'hmv1': 1,\n",
       "         'forget': 2,\n",
       "         'place': 4,\n",
       "         'mani': 3,\n",
       "         'request': 2,\n",
       "         '08707808226': 1,\n",
       "         'let': 3,\n",
       "         'notifi': 1,\n",
       "         'luck': 6,\n",
       "         'futur': 3,\n",
       "         'market': 1,\n",
       "         '84122': 1,\n",
       "         '08450542832': 1,\n",
       "         ...})"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dictionary of how many times a word is repeated in the list of spam_corpus\n",
    "from collections import Counter\n",
    "Counter(spam_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5ee783dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:36.162925Z",
     "start_time": "2023-03-18T07:59:36.137920Z"
    }
   },
   "outputs": [],
   "source": [
    "# Collecting the top 30 words repeated the most \n",
    "spam_corpus_top_30 = pd.DataFrame(Counter(spam_corpus).most_common(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5b7290e9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:36.203956Z",
     "start_time": "2023-03-18T07:59:36.167874Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>call</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>free</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>txt</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>text</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>u</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ur</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mobil</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>stop</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>repli</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>claim</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>prize</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>get</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>new</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>servic</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>tone</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>send</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>urgent</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>nokia</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>contact</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>award</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>phone</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>cash</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>pleas</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>week</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>win</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>c</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>collect</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>min</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0    1\n",
       "0      call  320\n",
       "1      free  191\n",
       "2         2  155\n",
       "3       txt  141\n",
       "4      text  122\n",
       "5         u  119\n",
       "6        ur  119\n",
       "7     mobil  114\n",
       "8      stop  104\n",
       "9     repli  103\n",
       "10    claim   98\n",
       "11        4   97\n",
       "12    prize   82\n",
       "13      get   74\n",
       "14      new   64\n",
       "15   servic   64\n",
       "16     tone   63\n",
       "17     send   60\n",
       "18   urgent   57\n",
       "19    nokia   57\n",
       "20  contact   56\n",
       "21    award   55\n",
       "22    phone   52\n",
       "23     cash   51\n",
       "24    pleas   51\n",
       "25     week   49\n",
       "26      win   48\n",
       "27        c   45\n",
       "28  collect   45\n",
       "29      min   45"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_corpus_top_30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c046b339",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:36.996131Z",
     "start_time": "2023-03-18T07:59:36.208911Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.barplot(spam_corpus_top_30[0],spam_corpus_top_30[1])\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12456953",
   "metadata": {},
   "source": [
    "##### checking the top 30 words in Spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "75feba8e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:37.037367Z",
     "start_time": "2023-03-18T07:59:36.999831Z"
    }
   },
   "outputs": [],
   "source": [
    "ham_corpus = []\n",
    "for msg in df[df['target']==0]['transformed_text'].tolist():\n",
    "    for word in msg.split():\n",
    "        ham_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7eaa0e70",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:37.101498Z",
     "start_time": "2023-03-18T07:59:37.042322Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['go',\n",
       " 'jurong',\n",
       " 'point',\n",
       " 'crazi',\n",
       " 'avail',\n",
       " 'bugi',\n",
       " 'n',\n",
       " 'great',\n",
       " 'world',\n",
       " 'la',\n",
       " 'e',\n",
       " 'buffet',\n",
       " 'cine',\n",
       " 'got',\n",
       " 'amor',\n",
       " 'wat',\n",
       " 'ok',\n",
       " 'lar',\n",
       " 'joke',\n",
       " 'wif',\n",
       " 'u',\n",
       " 'oni',\n",
       " 'u',\n",
       " 'dun',\n",
       " 'say',\n",
       " 'earli',\n",
       " 'hor',\n",
       " 'u',\n",
       " 'c',\n",
       " 'alreadi',\n",
       " 'say',\n",
       " 'nah',\n",
       " 'think',\n",
       " 'goe',\n",
       " 'usf',\n",
       " 'live',\n",
       " 'around',\n",
       " 'though',\n",
       " 'even',\n",
       " 'brother',\n",
       " 'like',\n",
       " 'speak',\n",
       " 'treat',\n",
       " 'like',\n",
       " 'aid',\n",
       " 'patent',\n",
       " 'per',\n",
       " 'request',\n",
       " 'mell',\n",
       " 'oru',\n",
       " 'minnaminungint',\n",
       " 'nurungu',\n",
       " 'vettam',\n",
       " 'set',\n",
       " 'callertun',\n",
       " 'caller',\n",
       " 'press',\n",
       " '9',\n",
       " 'copi',\n",
       " 'friend',\n",
       " 'callertun',\n",
       " 'gon',\n",
       " 'na',\n",
       " 'home',\n",
       " 'soon',\n",
       " 'want',\n",
       " 'talk',\n",
       " 'stuff',\n",
       " 'anymor',\n",
       " 'tonight',\n",
       " 'k',\n",
       " 'cri',\n",
       " 'enough',\n",
       " 'today',\n",
       " 'search',\n",
       " 'right',\n",
       " 'word',\n",
       " 'thank',\n",
       " 'breather',\n",
       " 'promis',\n",
       " 'wont',\n",
       " 'take',\n",
       " 'help',\n",
       " 'grant',\n",
       " 'fulfil',\n",
       " 'promis',\n",
       " 'wonder',\n",
       " 'bless',\n",
       " 'time',\n",
       " 'date',\n",
       " 'sunday',\n",
       " 'oh',\n",
       " 'k',\n",
       " 'watch',\n",
       " 'eh',\n",
       " 'u',\n",
       " 'rememb',\n",
       " '2',\n",
       " 'spell',\n",
       " 'name',\n",
       " 'ye',\n",
       " 'v',\n",
       " 'naughti',\n",
       " 'make',\n",
       " 'v',\n",
       " 'wet',\n",
       " 'fine',\n",
       " 'thatåõ',\n",
       " 'way',\n",
       " 'u',\n",
       " 'feel',\n",
       " 'thatåõ',\n",
       " 'way',\n",
       " 'gota',\n",
       " 'b',\n",
       " 'serious',\n",
       " 'spell',\n",
       " 'name',\n",
       " 'go',\n",
       " 'tri',\n",
       " '2',\n",
       " 'month',\n",
       " 'ha',\n",
       " 'ha',\n",
       " 'joke',\n",
       " 'pay',\n",
       " 'first',\n",
       " 'lar',\n",
       " 'da',\n",
       " 'stock',\n",
       " 'comin',\n",
       " 'aft',\n",
       " 'finish',\n",
       " 'lunch',\n",
       " 'go',\n",
       " 'str',\n",
       " 'lor',\n",
       " 'ard',\n",
       " '3',\n",
       " 'smth',\n",
       " 'lor',\n",
       " 'u',\n",
       " 'finish',\n",
       " 'ur',\n",
       " 'lunch',\n",
       " 'alreadi',\n",
       " 'ffffffffff',\n",
       " 'alright',\n",
       " 'way',\n",
       " 'meet',\n",
       " 'sooner',\n",
       " 'forc',\n",
       " 'eat',\n",
       " 'slice',\n",
       " 'realli',\n",
       " 'hungri',\n",
       " 'tho',\n",
       " 'suck',\n",
       " 'mark',\n",
       " 'get',\n",
       " 'worri',\n",
       " 'know',\n",
       " 'sick',\n",
       " 'turn',\n",
       " 'pizza',\n",
       " 'lol',\n",
       " 'lol',\n",
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       " 'thought',\n",
       " 'get',\n",
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       " 'littl',\n",
       " 'thing',\n",
       " '4',\n",
       " 'lovabl',\n",
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       " 'somtim',\n",
       " 'littl',\n",
       " 'thing',\n",
       " 'occupi',\n",
       " 'biggest',\n",
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       " 'heart',\n",
       " 'gud',\n",
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       " 'open',\n",
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       " 'name',\n",
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       " 'us',\n",
       " 'call',\n",
       " 'check',\n",
       " 'life',\n",
       " 'begin',\n",
       " 'qatar',\n",
       " 'pl',\n",
       " 'pray',\n",
       " 'hard',\n",
       " 'k',\n",
       " 'delet',\n",
       " 'contact',\n",
       " 'sindu',\n",
       " 'got',\n",
       " 'job',\n",
       " 'birla',\n",
       " 'soft',\n",
       " 'wine',\n",
       " 'flow',\n",
       " 'never',\n",
       " 'yup',\n",
       " 'thk',\n",
       " 'cine',\n",
       " 'better',\n",
       " 'co',\n",
       " 'need',\n",
       " '2',\n",
       " 'go',\n",
       " '2',\n",
       " 'plaza',\n",
       " 'mah',\n",
       " 'ok',\n",
       " 'ur',\n",
       " 'typic',\n",
       " 'repli',\n",
       " 'everywher',\n",
       " 'dirt',\n",
       " 'floor',\n",
       " 'window',\n",
       " 'even',\n",
       " 'shirt',\n",
       " 'sometim',\n",
       " 'open',\n",
       " 'mouth',\n",
       " 'come',\n",
       " 'flow',\n",
       " 'dream',\n",
       " ...]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ham_corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "31c37182",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:37.132820Z",
     "start_time": "2023-03-18T07:59:37.106471Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35394"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ham_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "808dc087",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:37.231101Z",
     "start_time": "2023-03-18T07:59:37.137763Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'go': 404,\n",
       "         'jurong': 1,\n",
       "         'point': 17,\n",
       "         'crazi': 10,\n",
       "         'avail': 13,\n",
       "         'bugi': 7,\n",
       "         'n': 120,\n",
       "         'great': 97,\n",
       "         'world': 28,\n",
       "         'la': 7,\n",
       "         'e': 77,\n",
       "         'buffet': 2,\n",
       "         'cine': 7,\n",
       "         'got': 236,\n",
       "         'amor': 1,\n",
       "         'wat': 108,\n",
       "         'ok': 217,\n",
       "         'lar': 38,\n",
       "         'joke': 14,\n",
       "         'wif': 27,\n",
       "         'u': 883,\n",
       "         'oni': 4,\n",
       "         'dun': 55,\n",
       "         'say': 127,\n",
       "         'earli': 33,\n",
       "         'hor': 2,\n",
       "         'c': 57,\n",
       "         'alreadi': 90,\n",
       "         'nah': 10,\n",
       "         'think': 149,\n",
       "         'goe': 26,\n",
       "         'usf': 10,\n",
       "         'live': 25,\n",
       "         'around': 59,\n",
       "         'though': 26,\n",
       "         'even': 77,\n",
       "         'brother': 17,\n",
       "         'like': 234,\n",
       "         'speak': 24,\n",
       "         'treat': 19,\n",
       "         'aid': 2,\n",
       "         'patent': 1,\n",
       "         'per': 9,\n",
       "         'request': 6,\n",
       "         'mell': 1,\n",
       "         'oru': 2,\n",
       "         'minnaminungint': 1,\n",
       "         'nurungu': 1,\n",
       "         'vettam': 1,\n",
       "         'set': 19,\n",
       "         'callertun': 4,\n",
       "         'caller': 3,\n",
       "         'press': 5,\n",
       "         '9': 18,\n",
       "         'copi': 8,\n",
       "         'friend': 76,\n",
       "         'gon': 59,\n",
       "         'na': 96,\n",
       "         'home': 152,\n",
       "         'soon': 54,\n",
       "         'want': 208,\n",
       "         'talk': 55,\n",
       "         'stuff': 44,\n",
       "         'anymor': 8,\n",
       "         'tonight': 57,\n",
       "         'k': 107,\n",
       "         'cri': 6,\n",
       "         'enough': 25,\n",
       "         'today': 123,\n",
       "         'search': 16,\n",
       "         'right': 80,\n",
       "         'word': 30,\n",
       "         'thank': 87,\n",
       "         'breather': 1,\n",
       "         'promis': 12,\n",
       "         'wont': 32,\n",
       "         'take': 143,\n",
       "         'help': 37,\n",
       "         'grant': 1,\n",
       "         'fulfil': 1,\n",
       "         'wonder': 36,\n",
       "         'bless': 7,\n",
       "         'time': 219,\n",
       "         'date': 12,\n",
       "         'sunday': 9,\n",
       "         'oh': 106,\n",
       "         'watch': 65,\n",
       "         'eh': 12,\n",
       "         'rememb': 35,\n",
       "         '2': 284,\n",
       "         'spell': 5,\n",
       "         'name': 31,\n",
       "         'ye': 72,\n",
       "         'v': 43,\n",
       "         'naughti': 6,\n",
       "         'make': 129,\n",
       "         'wet': 3,\n",
       "         'fine': 44,\n",
       "         'thatåõ': 5,\n",
       "         'way': 95,\n",
       "         'feel': 83,\n",
       "         'gota': 1,\n",
       "         'b': 55,\n",
       "         'serious': 6,\n",
       "         'tri': 72,\n",
       "         'month': 36,\n",
       "         'ha': 16,\n",
       "         'pay': 38,\n",
       "         'first': 52,\n",
       "         'da': 138,\n",
       "         'stock': 6,\n",
       "         'comin': 11,\n",
       "         'aft': 19,\n",
       "         'finish': 67,\n",
       "         'lunch': 39,\n",
       "         'str': 3,\n",
       "         'lor': 159,\n",
       "         'ard': 22,\n",
       "         '3': 40,\n",
       "         'smth': 16,\n",
       "         'ur': 197,\n",
       "         'ffffffffff': 1,\n",
       "         'alright': 22,\n",
       "         'meet': 112,\n",
       "         'sooner': 4,\n",
       "         'forc': 3,\n",
       "         'eat': 45,\n",
       "         'slice': 3,\n",
       "         'realli': 83,\n",
       "         'hungri': 12,\n",
       "         'tho': 16,\n",
       "         'suck': 7,\n",
       "         'mark': 9,\n",
       "         'get': 349,\n",
       "         'worri': 41,\n",
       "         'know': 236,\n",
       "         'sick': 10,\n",
       "         'turn': 12,\n",
       "         'pizza': 8,\n",
       "         'lol': 74,\n",
       "         'alway': 52,\n",
       "         'convinc': 5,\n",
       "         'catch': 13,\n",
       "         'bu': 27,\n",
       "         'fri': 16,\n",
       "         'egg': 5,\n",
       "         'tea': 6,\n",
       "         'mom': 21,\n",
       "         'left': 29,\n",
       "         'dinner': 35,\n",
       "         'love': 216,\n",
       "         'back': 127,\n",
       "         'amp': 58,\n",
       "         'pack': 4,\n",
       "         'car': 43,\n",
       "         'let': 86,\n",
       "         'room': 35,\n",
       "         'ahhh': 1,\n",
       "         'work': 118,\n",
       "         'vagu': 2,\n",
       "         'wait': 96,\n",
       "         'still': 143,\n",
       "         'clear': 8,\n",
       "         'sure': 76,\n",
       "         'sarcast': 2,\n",
       "         'x': 35,\n",
       "         'us': 52,\n",
       "         'yeah': 86,\n",
       "         'apologet': 1,\n",
       "         'fallen': 1,\n",
       "         'actin': 1,\n",
       "         'spoilt': 1,\n",
       "         'child': 3,\n",
       "         'caught': 3,\n",
       "         'till': 19,\n",
       "         'wo': 21,\n",
       "         'badli': 1,\n",
       "         'cheer': 8,\n",
       "         'tell': 133,\n",
       "         'anyth': 71,\n",
       "         'fear': 2,\n",
       "         'faint': 1,\n",
       "         'housework': 1,\n",
       "         'quick': 8,\n",
       "         'cuppa': 1,\n",
       "         'yup': 41,\n",
       "         'look': 60,\n",
       "         'msg': 53,\n",
       "         'xuhui': 3,\n",
       "         'learn': 6,\n",
       "         '2nd': 7,\n",
       "         'may': 38,\n",
       "         'lesson': 26,\n",
       "         '8am': 2,\n",
       "         'oop': 10,\n",
       "         'roommat': 8,\n",
       "         'done': 45,\n",
       "         'see': 147,\n",
       "         'letter': 8,\n",
       "         'decid': 25,\n",
       "         'hello': 36,\n",
       "         'saturday': 9,\n",
       "         'text': 85,\n",
       "         'tomo': 19,\n",
       "         'invit': 9,\n",
       "         'pl': 83,\n",
       "         'ahead': 6,\n",
       "         'watt': 1,\n",
       "         'weekend': 27,\n",
       "         'abiola': 11,\n",
       "         'forget': 16,\n",
       "         'need': 170,\n",
       "         'crave': 12,\n",
       "         'sweet': 29,\n",
       "         'arabian': 1,\n",
       "         'steed': 1,\n",
       "         'mmmmmm': 2,\n",
       "         'yummi': 3,\n",
       "         'hope': 122,\n",
       "         'man': 34,\n",
       "         'well': 109,\n",
       "         'endow': 1,\n",
       "         'lt': 287,\n",
       "         'gt': 288,\n",
       "         'inch': 6,\n",
       "         'call': 233,\n",
       "         'messag': 55,\n",
       "         'miss': 106,\n",
       "         'hep': 1,\n",
       "         'immunis': 1,\n",
       "         'nigeria': 8,\n",
       "         'fair': 3,\n",
       "         'tyler': 6,\n",
       "         'ca': 57,\n",
       "         'could': 57,\n",
       "         'mayb': 42,\n",
       "         'ask': 121,\n",
       "         'bit': 43,\n",
       "         'stubborn': 1,\n",
       "         'hospit': 11,\n",
       "         'kept': 4,\n",
       "         'weak': 7,\n",
       "         'sucker': 2,\n",
       "         'saw': 24,\n",
       "         'class': 45,\n",
       "         'gram': 4,\n",
       "         'usual': 14,\n",
       "         'run': 29,\n",
       "         'half': 23,\n",
       "         'eighth': 2,\n",
       "         'smarter': 1,\n",
       "         'almost': 13,\n",
       "         'whole': 14,\n",
       "         'second': 20,\n",
       "         'fyi': 6,\n",
       "         'ride': 5,\n",
       "         'tomorrow': 69,\n",
       "         'morn': 68,\n",
       "         'crash': 4,\n",
       "         'place': 53,\n",
       "         'wow': 9,\n",
       "         'never': 40,\n",
       "         'realiz': 6,\n",
       "         'embarass': 5,\n",
       "         'accomod': 2,\n",
       "         'thought': 45,\n",
       "         'sinc': 25,\n",
       "         'best': 35,\n",
       "         'seem': 15,\n",
       "         'happi': 104,\n",
       "         'sorri': 121,\n",
       "         'give': 102,\n",
       "         'offer': 9,\n",
       "         'mallika': 1,\n",
       "         'sherawat': 1,\n",
       "         'yesterday': 20,\n",
       "         'find': 49,\n",
       "         'url': 3,\n",
       "         'later': 100,\n",
       "         'reach': 44,\n",
       "         'gauti': 1,\n",
       "         'sehwag': 1,\n",
       "         'odi': 2,\n",
       "         'seri': 4,\n",
       "         'pick': 71,\n",
       "         '1': 40,\n",
       "         'burger': 1,\n",
       "         'move': 17,\n",
       "         'pain': 26,\n",
       "         'kill': 6,\n",
       "         'good': 213,\n",
       "         'girl': 36,\n",
       "         'situat': 8,\n",
       "         'seeker': 1,\n",
       "         'part': 18,\n",
       "         'check': 46,\n",
       "         'iq': 2,\n",
       "         'took': 17,\n",
       "         'forev': 9,\n",
       "         'come': 275,\n",
       "         'doubl': 5,\n",
       "         'hair': 24,\n",
       "         'dresser': 3,\n",
       "         'said': 76,\n",
       "         'wun': 7,\n",
       "         'cut': 15,\n",
       "         'short': 9,\n",
       "         'nice': 47,\n",
       "         'dedic': 2,\n",
       "         'day': 190,\n",
       "         'song': 10,\n",
       "         'send': 120,\n",
       "         'valuabl': 2,\n",
       "         'frnd': 18,\n",
       "         'rpli': 3,\n",
       "         'plane': 3,\n",
       "         'end': 36,\n",
       "         'wah': 4,\n",
       "         'lucki': 9,\n",
       "         'save': 12,\n",
       "         'money': 55,\n",
       "         'hee': 13,\n",
       "         'hi': 111,\n",
       "         'babe': 74,\n",
       "         'im': 73,\n",
       "         'wan': 81,\n",
       "         'someth': 69,\n",
       "         'xx': 10,\n",
       "         'perform': 3,\n",
       "         'machan': 4,\n",
       "         'free': 55,\n",
       "         'that': 37,\n",
       "         'cool': 40,\n",
       "         'gentleman': 3,\n",
       "         'digniti': 3,\n",
       "         'respect': 4,\n",
       "         'peopl': 39,\n",
       "         'much': 107,\n",
       "         'shi': 1,\n",
       "         'pa': 21,\n",
       "         'oper': 1,\n",
       "         'job': 40,\n",
       "         'ta': 17,\n",
       "         'earn': 3,\n",
       "         'ah': 32,\n",
       "         'next': 46,\n",
       "         'stop': 41,\n",
       "         'network': 5,\n",
       "         'urgnt': 3,\n",
       "         'sm': 14,\n",
       "         'real': 22,\n",
       "         'yo': 36,\n",
       "         'ticket': 13,\n",
       "         'one': 165,\n",
       "         'jacket': 2,\n",
       "         'use': 59,\n",
       "         'multi': 1,\n",
       "         'start': 51,\n",
       "         'came': 23,\n",
       "         'bed': 32,\n",
       "         'coin': 6,\n",
       "         'factori': 1,\n",
       "         'cash': 13,\n",
       "         'nitro': 3,\n",
       "         'ela': 2,\n",
       "         'il': 10,\n",
       "         'download': 11,\n",
       "         'wen': 17,\n",
       "         'stand': 12,\n",
       "         'close': 18,\n",
       "         'anoth': 33,\n",
       "         'night': 107,\n",
       "         'spent': 8,\n",
       "         'late': 57,\n",
       "         'afternoon': 26,\n",
       "         'casualti': 1,\n",
       "         'mean': 45,\n",
       "         'stuff42moro': 1,\n",
       "         'includ': 6,\n",
       "         'sheet': 4,\n",
       "         'smile': 55,\n",
       "         'pleasur': 7,\n",
       "         'troubl': 7,\n",
       "         'pour': 2,\n",
       "         'rain': 14,\n",
       "         'sum1': 2,\n",
       "         'hurt': 27,\n",
       "         'becoz': 3,\n",
       "         'someon': 42,\n",
       "         'havent': 24,\n",
       "         'plan': 53,\n",
       "         'buy': 67,\n",
       "         'lido': 3,\n",
       "         '530': 5,\n",
       "         'show': 31,\n",
       "         'telugu': 2,\n",
       "         'movi': 26,\n",
       "         'abt': 26,\n",
       "         'load': 11,\n",
       "         'loan': 6,\n",
       "         'wk': 8,\n",
       "         'hol': 3,\n",
       "         'forgot': 28,\n",
       "         'hairdress': 1,\n",
       "         'appoint': 5,\n",
       "         'four': 2,\n",
       "         'shower': 13,\n",
       "         'beforehand': 1,\n",
       "         'caus': 21,\n",
       "         'prob': 16,\n",
       "         'ham': 3,\n",
       "         'pleas': 74,\n",
       "         'noth': 34,\n",
       "         'els': 23,\n",
       "         'okay': 26,\n",
       "         'price': 9,\n",
       "         'long': 42,\n",
       "         'legal': 4,\n",
       "         'ave': 5,\n",
       "         'am': 1,\n",
       "         'gone': 16,\n",
       "         '4the': 1,\n",
       "         'drive': 39,\n",
       "         'test': 26,\n",
       "         'yet': 48,\n",
       "         'guess': 35,\n",
       "         'gave': 10,\n",
       "         'boston': 5,\n",
       "         'men': 7,\n",
       "         'chang': 31,\n",
       "         'locat': 2,\n",
       "         'nyc': 4,\n",
       "         'cuz': 7,\n",
       "         'signin': 1,\n",
       "         'page': 10,\n",
       "         'umma': 7,\n",
       "         'life': 62,\n",
       "         'vava': 3,\n",
       "         'lot': 48,\n",
       "         'dear': 81,\n",
       "         'wish': 55,\n",
       "         'birthday': 25,\n",
       "         'truli': 2,\n",
       "         'memor': 1,\n",
       "         'aight': 33,\n",
       "         'hit': 11,\n",
       "         'would': 75,\n",
       "         'ip': 1,\n",
       "         'address': 17,\n",
       "         'consid': 6,\n",
       "         'comput': 9,\n",
       "         'minecraft': 1,\n",
       "         'server': 1,\n",
       "         'grumpi': 1,\n",
       "         'old': 18,\n",
       "         'better': 41,\n",
       "         'lie': 6,\n",
       "         'play': 25,\n",
       "         'dont': 118,\n",
       "         'busi': 20,\n",
       "         'plural': 1,\n",
       "         'noun': 2,\n",
       "         'research': 4,\n",
       "         'co': 76,\n",
       "         'new': 61,\n",
       "         'thing': 109,\n",
       "         'scare': 5,\n",
       "         'mah': 13,\n",
       "         'loud': 3,\n",
       "         'wa': 3,\n",
       "         'openin': 1,\n",
       "         'sentenc': 2,\n",
       "         'formal': 1,\n",
       "         'anyway': 32,\n",
       "         'juz': 25,\n",
       "         'tt': 5,\n",
       "         'eatin': 7,\n",
       "         'puttin': 2,\n",
       "         'weight': 8,\n",
       "         'haha': 51,\n",
       "         'anythin': 3,\n",
       "         'special': 22,\n",
       "         'happen': 50,\n",
       "         'enter': 5,\n",
       "         'cabin': 2,\n",
       "         'boss': 6,\n",
       "         'felt': 10,\n",
       "         'askd': 6,\n",
       "         '4': 156,\n",
       "         'apart': 6,\n",
       "         'went': 48,\n",
       "         'goodo': 2,\n",
       "         'must': 21,\n",
       "         'friday': 14,\n",
       "         'ratio': 1,\n",
       "         'tortilla': 2,\n",
       "         'hmm': 14,\n",
       "         'uncl': 14,\n",
       "         'inform': 10,\n",
       "         'school': 27,\n",
       "         'directli': 6,\n",
       "         'food': 22,\n",
       "         'pair': 1,\n",
       "         'malarki': 1,\n",
       "         'sao': 1,\n",
       "         'mu': 15,\n",
       "         '12': 4,\n",
       "         'ìï': 53,\n",
       "         'predict': 5,\n",
       "         'yetund': 4,\n",
       "         'sent': 51,\n",
       "         'bother': 8,\n",
       "         'involv': 2,\n",
       "         'impos': 1,\n",
       "         'apologis': 3,\n",
       "         'hey': 100,\n",
       "         'r': 120,\n",
       "         'del': 1,\n",
       "         'bak': 8,\n",
       "         'sum': 2,\n",
       "         'lucyxx': 1,\n",
       "         'cost': 12,\n",
       "         'answer': 19,\n",
       "         'question': 18,\n",
       "         'haf': 23,\n",
       "         'msn': 2,\n",
       "         'yiju': 7,\n",
       "         'befor': 2,\n",
       "         'activ': 3,\n",
       "         'lazi': 9,\n",
       "         'type': 13,\n",
       "         'lect': 9,\n",
       "         'pouch': 2,\n",
       "         'sir': 33,\n",
       "         'mail': 27,\n",
       "         'swt': 4,\n",
       "         'tire': 12,\n",
       "         'littl': 27,\n",
       "         'lovabl': 3,\n",
       "         'person': 39,\n",
       "         'coz': 16,\n",
       "         'somtim': 2,\n",
       "         'occupi': 3,\n",
       "         'biggest': 1,\n",
       "         'heart': 32,\n",
       "         'gud': 44,\n",
       "         'ni8': 10,\n",
       "         'open': 21,\n",
       "         'ya': 56,\n",
       "         'dot': 1,\n",
       "         'what': 12,\n",
       "         'staff': 2,\n",
       "         'begin': 5,\n",
       "         'qatar': 4,\n",
       "         'pray': 9,\n",
       "         'hard': 12,\n",
       "         'delet': 6,\n",
       "         'contact': 12,\n",
       "         'sindu': 1,\n",
       "         'birla': 3,\n",
       "         'soft': 3,\n",
       "         'wine': 11,\n",
       "         'flow': 3,\n",
       "         'thk': 50,\n",
       "         'plaza': 2,\n",
       "         'typic': 1,\n",
       "         'repli': 40,\n",
       "         'everywher': 2,\n",
       "         'dirt': 1,\n",
       "         'floor': 3,\n",
       "         'window': 4,\n",
       "         'shirt': 7,\n",
       "         'sometim': 10,\n",
       "         'mouth': 2,\n",
       "         'dream': 21,\n",
       "         'without': 23,\n",
       "         'chore': 1,\n",
       "         'joy': 7,\n",
       "         'tv': 21,\n",
       "         'exist': 1,\n",
       "         'hail': 1,\n",
       "         'mist': 1,\n",
       "         'becom': 7,\n",
       "         'aaooooright': 1,\n",
       "         'leav': 75,\n",
       "         'hous': 36,\n",
       "         'interview': 5,\n",
       "         'boy': 32,\n",
       "         'keep': 68,\n",
       "         'safe': 12,\n",
       "         'envi': 1,\n",
       "         'everyon': 15,\n",
       "         'parent': 14,\n",
       "         'hand': 17,\n",
       "         'excit': 3,\n",
       "         'spend': 14,\n",
       "         'cultur': 1,\n",
       "         'modul': 4,\n",
       "         'avoid': 5,\n",
       "         'missunderstd': 1,\n",
       "         'wit': 15,\n",
       "         'belov': 2,\n",
       "         'escap': 3,\n",
       "         'fanci': 6,\n",
       "         'bridg': 1,\n",
       "         'lager': 1,\n",
       "         'complet': 16,\n",
       "         'form': 2,\n",
       "         'clark': 1,\n",
       "         'also': 65,\n",
       "         'utter': 2,\n",
       "         'wast': 9,\n",
       "         'axi': 1,\n",
       "         'bank': 15,\n",
       "         'account': 20,\n",
       "         'hmmm': 11,\n",
       "         'hop': 7,\n",
       "         'muz': 11,\n",
       "         'discuss': 7,\n",
       "         'liao': 37,\n",
       "         'bloodi': 4,\n",
       "         'hell': 6,\n",
       "         'cant': 49,\n",
       "         'believ': 18,\n",
       "         'surnam': 1,\n",
       "         'mr': 10,\n",
       "         'ill': 42,\n",
       "         'clue': 1,\n",
       "         'spanish': 1,\n",
       "         'bath': 25,\n",
       "         'carlo': 16,\n",
       "         'mall': 3,\n",
       "         'stay': 33,\n",
       "         'til': 23,\n",
       "         'smoke': 24,\n",
       "         'worth': 4,\n",
       "         'doesnt': 9,\n",
       "         'log': 8,\n",
       "         'spoke': 4,\n",
       "         'maneesha': 1,\n",
       "         'satisfi': 1,\n",
       "         'experi': 5,\n",
       "         'toll': 1,\n",
       "         'lift': 7,\n",
       "         'especi': 9,\n",
       "         'approach': 2,\n",
       "         'studi': 20,\n",
       "         'gr8': 10,\n",
       "         'trust': 10,\n",
       "         'guy': 54,\n",
       "         'handsom': 2,\n",
       "         'toward': 6,\n",
       "         'net': 8,\n",
       "         'mummi': 4,\n",
       "         'boytoy': 16,\n",
       "         'awesom': 19,\n",
       "         'minut': 36,\n",
       "         'xma': 8,\n",
       "         'radio': 3,\n",
       "         'ju': 35,\n",
       "         'si': 20,\n",
       "         'join': 17,\n",
       "         'leagu': 1,\n",
       "         'touch': 17,\n",
       "         'deal': 11,\n",
       "         'final': 15,\n",
       "         'cours': 13,\n",
       "         'howev': 7,\n",
       "         'suggest': 5,\n",
       "         'abl': 26,\n",
       "         'or': 1,\n",
       "         'everi': 39,\n",
       "         'stool': 1,\n",
       "         'settl': 7,\n",
       "         'year': 59,\n",
       "         'wishin': 1,\n",
       "         'mrng': 8,\n",
       "         'hav': 23,\n",
       "         'stori': 11,\n",
       "         'hamster': 2,\n",
       "         'dead': 9,\n",
       "         'tmr': 28,\n",
       "         '1pm': 1,\n",
       "         'orchard': 11,\n",
       "         'mrt': 9,\n",
       "         'kate': 11,\n",
       "         'babyjontet': 1,\n",
       "         'txt': 13,\n",
       "         'xxx': 15,\n",
       "         'found': 14,\n",
       "         'enc': 1,\n",
       "         'buck': 7,\n",
       "         'darlin': 15,\n",
       "         'ive': 9,\n",
       "         'colleg': 17,\n",
       "         'refil': 1,\n",
       "         'success': 6,\n",
       "         'inr': 1,\n",
       "         'decim': 20,\n",
       "         'keralacircl': 1,\n",
       "         'prepaid': 1,\n",
       "         'balanc': 3,\n",
       "         'rs': 8,\n",
       "         'transact': 3,\n",
       "         'id': 16,\n",
       "         'kr': 1,\n",
       "         'goodmorn': 13,\n",
       "         'sleep': 72,\n",
       "         'ga': 14,\n",
       "         'alter': 1,\n",
       "         '11': 4,\n",
       "         'dat': 37,\n",
       "         'ericsson': 2,\n",
       "         'oso': 23,\n",
       "         'oredi': 15,\n",
       "         'straight': 4,\n",
       "         'dogg': 1,\n",
       "         'connect': 8,\n",
       "         'refund': 1,\n",
       "         'bill': 9,\n",
       "         'shoot': 4,\n",
       "         'big': 33,\n",
       "         'readi': 35,\n",
       "         'bruv': 2,\n",
       "         'break': 14,\n",
       "         'reward': 2,\n",
       "         'semest': 10,\n",
       "         'chat': 14,\n",
       "         'noe': 20,\n",
       "         'leh': 30,\n",
       "         'sound': 26,\n",
       "         'match': 5,\n",
       "         'head': 22,\n",
       "         'draw': 5,\n",
       "         'slept': 8,\n",
       "         'past': 7,\n",
       "         'easi': 15,\n",
       "         'sen': 4,\n",
       "         'select': 4,\n",
       "         'exam': 16,\n",
       "         'march': 12,\n",
       "         'atm': 3,\n",
       "         'regist': 4,\n",
       "         'os': 2,\n",
       "         'ubandu': 1,\n",
       "         'instal': 3,\n",
       "         'disk': 1,\n",
       "         'import': 14,\n",
       "         'file': 7,\n",
       "         'system': 7,\n",
       "         'repair': 3,\n",
       "         'shop': 37,\n",
       "         'romant': 2,\n",
       "         'nite': 21,\n",
       "         'sceneri': 1,\n",
       "         'appreci': 9,\n",
       "         'compani': 14,\n",
       "         'elama': 1,\n",
       "         'po': 3,\n",
       "         'mudyadhu': 1,\n",
       "         'strict': 1,\n",
       "         'teacher': 4,\n",
       "         'bcoz': 8,\n",
       "         'teach': 11,\n",
       "         'conduct': 2,\n",
       "         'gandhipuram': 1,\n",
       "         'walk': 27,\n",
       "         'cross': 2,\n",
       "         'road': 11,\n",
       "         'side': 13,\n",
       "         'street': 7,\n",
       "         'rubber': 1,\n",
       "         'batteri': 7,\n",
       "         'die': 17,\n",
       "         'print': 5,\n",
       "         'upstair': 2,\n",
       "         'closer': 4,\n",
       "         'wil': 17,\n",
       "         'theori': 2,\n",
       "         'argument': 4,\n",
       "         'win': 12,\n",
       "         'lose': 15,\n",
       "         'argu': 3,\n",
       "         'kick': 5,\n",
       "         'correct': 8,\n",
       "         'tomarrow': 3,\n",
       "         'hear': 29,\n",
       "         'laptop': 11,\n",
       "         'case': 13,\n",
       "         'pleassssssseeeee': 1,\n",
       "         'tel': 13,\n",
       "         'avent': 3,\n",
       "         'sportsx': 1,\n",
       "         'shine': 2,\n",
       "         'meant': 14,\n",
       "         'sign': 7,\n",
       "         'although': 2,\n",
       "         'told': 51,\n",
       "         'baig': 1,\n",
       "         'face': 30,\n",
       "         'fr': 13,\n",
       "         'thanx': 30,\n",
       "         'everyth': 27,\n",
       "         'commerci': 2,\n",
       "         'websit': 5,\n",
       "         'slipper': 3,\n",
       "         'kalli': 6,\n",
       "         'bat': 3,\n",
       "         'inning': 3,\n",
       "         'didnt': 28,\n",
       "         'goodnight': 7,\n",
       "         'fix': 12,\n",
       "         'wake': 30,\n",
       "         'dearli': 3,\n",
       "         'ranjith': 2,\n",
       "         'cal': 5,\n",
       "         'drpd': 1,\n",
       "         'deeraj': 1,\n",
       "         'deepak': 1,\n",
       "         '5min': 2,\n",
       "         'hold': 17,\n",
       "         'bcum': 2,\n",
       "         'angri': 11,\n",
       "         'wid': 9,\n",
       "         'dnt': 7,\n",
       "         'childish': 2,\n",
       "         'true': 19,\n",
       "         'deep': 7,\n",
       "         'affect': 2,\n",
       "         'care': 68,\n",
       "         'luv': 26,\n",
       "         'kettoda': 1,\n",
       "         'manda': 1,\n",
       "         'up': 2,\n",
       "         '3day': 1,\n",
       "         'ship': 7,\n",
       "         '2wk': 1,\n",
       "         'usp': 1,\n",
       "         'week': 66,\n",
       "         'lag': 2,\n",
       "         'bribe': 1,\n",
       "         'nipost': 1,\n",
       "         'lem': 8,\n",
       "         'necessarili': 2,\n",
       "         'expect': 11,\n",
       "         'headin': 2,\n",
       "         'mmm': 4,\n",
       "         'jolt': 2,\n",
       "         'suzi': 1,\n",
       "         'lover': 7,\n",
       "         'park': 16,\n",
       "         'mini': 2,\n",
       "         'disturb': 11,\n",
       "         'luton': 1,\n",
       "         '0125698789': 1,\n",
       "         'ring': 17,\n",
       "         'h': 2,\n",
       "         'dint': 6,\n",
       "         'wana': 11,\n",
       "         'trip': 19,\n",
       "         'sometm': 1,\n",
       "         'evo': 1,\n",
       "         'flash': 3,\n",
       "         'jealou': 3,\n",
       "         'sort': 17,\n",
       "         'narcot': 1,\n",
       "         'sunni': 4,\n",
       "         'ray': 3,\n",
       "         'blue': 9,\n",
       "         'bay': 2,\n",
       "         'might': 34,\n",
       "         'object': 1,\n",
       "         'bf': 5,\n",
       "         'rob': 2,\n",
       "         'mack': 1,\n",
       "         'gf': 2,\n",
       "         'theater': 1,\n",
       "         'celebr': 8,\n",
       "         'full': 20,\n",
       "         'swing': 12,\n",
       "         'tool': 2,\n",
       "         'far': 16,\n",
       "         'oki': 15,\n",
       "         'pass': 10,\n",
       "         'last': 62,\n",
       "         'ahold': 1,\n",
       "         'anybodi': 5,\n",
       "         'throw': 6,\n",
       "         'babi': 31,\n",
       "         'cruisin': 1,\n",
       "         'hour': 42,\n",
       "         'fone': 10,\n",
       "         'jenni': 2,\n",
       "         'ge': 6,\n",
       "         'shall': 29,\n",
       "         'updat': 6,\n",
       "         'edukkukaye': 1,\n",
       "         'raksha': 1,\n",
       "         'sens': 7,\n",
       "         'gautham': 3,\n",
       "         'stupid': 9,\n",
       "         'cam': 2,\n",
       "         'buzi': 1,\n",
       "         'accident': 4,\n",
       "         'resend': 1,\n",
       "         'unless': 9,\n",
       "         'gurl': 1,\n",
       "         'appropri': 1,\n",
       "         'teas': 8,\n",
       "         'plz': 18,\n",
       "         'rose': 6,\n",
       "         'grave': 1,\n",
       "         'bslvyl': 8,\n",
       "         'phone': 73,\n",
       "         'coffe': 7,\n",
       "         'somebodi': 9,\n",
       "         'high': 4,\n",
       "         'diesel': 1,\n",
       "         'shit': 34,\n",
       "         'shock': 3,\n",
       "         'scari': 3,\n",
       "         'imagin': 8,\n",
       "         'def': 4,\n",
       "         'somewher': 9,\n",
       "         'taxi': 2,\n",
       "         'fridg': 1,\n",
       "         'meal': 4,\n",
       "         'womdarful': 1,\n",
       "         'actor': 2,\n",
       "         'remb': 1,\n",
       "         'book': 32,\n",
       "         'jo': 3,\n",
       "         'friendship': 14,\n",
       "         'hang': 6,\n",
       "         'thread': 2,\n",
       "         'garag': 3,\n",
       "         'key': 6,\n",
       "         'bookshelf': 1,\n",
       "         'accept': 5,\n",
       "         'sister': 16,\n",
       "         'dear1': 1,\n",
       "         'best1': 1,\n",
       "         'clos1': 1,\n",
       "         'lvblefrnd': 1,\n",
       "         'jstfrnd': 1,\n",
       "         'cutefrnd': 1,\n",
       "         'lifpartnr': 1,\n",
       "         'belovd': 2,\n",
       "         'swtheart': 1,\n",
       "         'bstfrnd': 1,\n",
       "         'enemi': 3,\n",
       "         'definit': 7,\n",
       "         '2day': 4,\n",
       "         'normal': 11,\n",
       "         'uniqu': 2,\n",
       "         'rest': 11,\n",
       "         'mylif': 1,\n",
       "         'wot': 24,\n",
       "         'lost': 11,\n",
       "         'made': 24,\n",
       "         'advanc': 5,\n",
       "         'pongal': 4,\n",
       "         'kb': 7,\n",
       "         'power': 7,\n",
       "         'yoga': 7,\n",
       "         'dunno': 31,\n",
       "         'tahan': 2,\n",
       "         'anot': 2,\n",
       "         'lo': 2,\n",
       "         'dude': 24,\n",
       "         'afraid': 3,\n",
       "         'cake': 6,\n",
       "         'merri': 7,\n",
       "         'christma': 12,\n",
       "         'kiss': 36,\n",
       "         'cud': 4,\n",
       "         'ppl': 4,\n",
       "         'gona': 3,\n",
       "         'l8': 1,\n",
       "         'buse': 2,\n",
       "         'waitin': 4,\n",
       "         'pete': 8,\n",
       "         'guild': 1,\n",
       "         'bristol': 2,\n",
       "         'flight': 2,\n",
       "         'problem': 40,\n",
       "         'track': 6,\n",
       "         'record': 4,\n",
       "         'read': 21,\n",
       "         'women': 2,\n",
       "         'light': 12,\n",
       "         'apo': 2,\n",
       "         'return': 12,\n",
       "         'immedi': 3,\n",
       "         'chanc': 11,\n",
       "         'evapor': 1,\n",
       "         'violat': 2,\n",
       "         'privaci': 1,\n",
       "         'steal': 2,\n",
       "         'number': 64,\n",
       "         ...})"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dictionary of how many times a word is repeated in the list of ham_corpus\n",
    "from collections import Counter\n",
    "Counter(ham_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fa36225b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:37.263543Z",
     "start_time": "2023-03-18T07:59:37.233194Z"
    }
   },
   "outputs": [],
   "source": [
    "# Collecting the top 30 words repeated the most \n",
    "ham_corpus_top_30 = pd.DataFrame(Counter(ham_corpus).most_common(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0676043a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:37.297094Z",
     "start_time": "2023-03-18T07:59:37.270866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
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       "      <td>883</td>\n",
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       "      <td>got</td>\n",
       "      <td>236</td>\n",
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       "      <th>8</th>\n",
       "      <td>know</td>\n",
       "      <td>236</td>\n",
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       "      <th>9</th>\n",
       "      <td>like</td>\n",
       "      <td>234</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>call</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>time</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ok</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>love</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>good</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>want</td>\n",
       "      <td>208</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ur</td>\n",
       "      <td>197</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>day</td>\n",
       "      <td>190</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>need</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>one</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>lor</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>home</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>think</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>see</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>take</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>still</td>\n",
       "      <td>143</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>da</td>\n",
       "      <td>138</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>tell</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>make</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0    1\n",
       "0       u  883\n",
       "1      go  404\n",
       "2     get  349\n",
       "3      gt  288\n",
       "4      lt  287\n",
       "5       2  284\n",
       "6    come  275\n",
       "7     got  236\n",
       "8    know  236\n",
       "9    like  234\n",
       "10   call  233\n",
       "11   time  219\n",
       "12     ok  217\n",
       "13   love  216\n",
       "14   good  213\n",
       "15   want  208\n",
       "16     ur  197\n",
       "17    day  190\n",
       "18   need  170\n",
       "19    one  165\n",
       "20    lor  159\n",
       "21      4  156\n",
       "22   home  152\n",
       "23  think  149\n",
       "24    see  147\n",
       "25   take  143\n",
       "26  still  143\n",
       "27     da  138\n",
       "28   tell  133\n",
       "29   make  129"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ham_corpus_top_30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a5e64c70",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:38.061542Z",
     "start_time": "2023-03-18T07:59:37.302042Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.barplot(ham_corpus_top_30[0],ham_corpus_top_30[1])\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06bb8855",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "50cae416",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:38.074641Z",
     "start_time": "2023-03-18T07:59:38.066444Z"
    }
   },
   "outputs": [],
   "source": [
    "# We are going to start with Naive Base modelling as it is known for good results in textual for of data\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "07de7310",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:38.402120Z",
     "start_time": "2023-03-18T07:59:38.082259Z"
    }
   },
   "outputs": [],
   "source": [
    "X = cv.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6e7f6866",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:38.430933Z",
     "start_time": "2023-03-18T07:59:38.407054Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a3214a43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:38.461198Z",
     "start_time": "2023-03-18T07:59:38.433004Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 6708)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d368cf1e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:38.481289Z",
     "start_time": "2023-03-18T07:59:38.466204Z"
    }
   },
   "outputs": [],
   "source": [
    "y = df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c332fc7a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:38.811557Z",
     "start_time": "2023-03-18T07:59:38.486394Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d89ee425",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:38.843715Z",
     "start_time": "2023-03-18T07:59:38.816489Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bcb898fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:38.871135Z",
     "start_time": "2023-03-18T07:59:38.848884Z"
    }
   },
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a8553bf9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:40.360748Z",
     "start_time": "2023-03-18T07:59:38.876133Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score 0.8800773694390716\n",
      "confusion matrix [[792 104]\n",
      " [ 20 118]]\n",
      "precision score 0.5315315315315315\n"
     ]
    }
   ],
   "source": [
    "gnb.fit(X_train, y_train)\n",
    "y_pred_gnb = gnb.predict(X_test)\n",
    "print('accuracy score', accuracy_score(y_test, y_pred_gnb))\n",
    "print('confusion matrix', confusion_matrix(y_test, y_pred_gnb))\n",
    "print('precision score', precision_score(y_test, y_pred_gnb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "354271a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:41.694142Z",
     "start_time": "2023-03-18T07:59:40.368644Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score 0.9642166344294004\n",
      "confusion matrix [[871  25]\n",
      " [ 12 126]]\n",
      "precision score 0.8344370860927153\n"
     ]
    }
   ],
   "source": [
    "mnb.fit(X_train, y_train)\n",
    "y_pred_mnb = mnb.predict(X_test)\n",
    "print('accuracy score', accuracy_score(y_test, y_pred_mnb))\n",
    "print('confusion matrix', confusion_matrix(y_test, y_pred_mnb))\n",
    "print('precision score', precision_score(y_test, y_pred_mnb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c17cd439",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:43.426843Z",
     "start_time": "2023-03-18T07:59:41.699143Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score 0.9700193423597679\n",
      "confusion matrix [[893   3]\n",
      " [ 28 110]]\n",
      "precision score 0.9734513274336283\n"
     ]
    }
   ],
   "source": [
    "bnb.fit(X_train, y_train)\n",
    "y_pred_bnb = bnb.predict(X_test)\n",
    "print('accuracy score', accuracy_score(y_test, y_pred_bnb))\n",
    "print('confusion matrix', confusion_matrix(y_test, y_pred_bnb))\n",
    "print('precision score', precision_score(y_test, y_pred_bnb))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5231b660",
   "metadata": {},
   "source": [
    "- So far Bernoulli NB is performing better \n",
    "- Let's try on tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ba1e81b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:43.439028Z",
     "start_time": "2023-03-18T07:59:43.430551Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0fc17d3c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:43.739927Z",
     "start_time": "2023-03-18T07:59:43.444141Z"
    }
   },
   "outputs": [],
   "source": [
    "X = tfidf.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b99f3813",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:43.767778Z",
     "start_time": "2023-03-18T07:59:43.747118Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "00a91e01",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:44.223701Z",
     "start_time": "2023-03-18T07:59:43.771193Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0ed96292",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:44.240719Z",
     "start_time": "2023-03-18T07:59:44.228621Z"
    }
   },
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "411801cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:45.573821Z",
     "start_time": "2023-03-18T07:59:44.248509Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score 0.8762088974854932\n",
      "confusion matrix [[793 103]\n",
      " [ 25 113]]\n",
      "precision score 0.5231481481481481\n"
     ]
    }
   ],
   "source": [
    "gnb.fit(X_train, y_train)\n",
    "y_pred_gnb = gnb.predict(X_test)\n",
    "print('accuracy score', accuracy_score(y_test, y_pred_gnb))\n",
    "print('confusion matrix', confusion_matrix(y_test, y_pred_gnb))\n",
    "print('precision score', precision_score(y_test, y_pred_gnb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "93d56205",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:19:14.023643Z",
     "start_time": "2023-03-18T08:19:13.977644Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score 0.9709864603481625\n",
      "confusion matrix [[896   0]\n",
      " [ 30 108]]\n",
      "precision score 1.0\n"
     ]
    }
   ],
   "source": [
    "mnb.fit(X_train, y_train)\n",
    "y_pred_mnb = mnb.predict(X_test)\n",
    "print('accuracy score', accuracy_score(y_test, y_pred_mnb))\n",
    "print('confusion matrix', confusion_matrix(y_test, y_pred_mnb))\n",
    "print('precision score', precision_score(y_test, y_pred_mnb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a966a9f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:46.412033Z",
     "start_time": "2023-03-18T07:59:45.759190Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score 0.9700193423597679\n",
      "confusion matrix [[893   3]\n",
      " [ 28 110]]\n",
      "precision score 0.9734513274336283\n"
     ]
    }
   ],
   "source": [
    "bnb.fit(X_train, y_train)\n",
    "y_pred_bnb = bnb.predict(X_test)\n",
    "print('accuracy score', accuracy_score(y_test, y_pred_bnb))\n",
    "print('confusion matrix', confusion_matrix(y_test, y_pred_bnb))\n",
    "print('precision score', precision_score(y_test, y_pred_bnb))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2de1cba",
   "metadata": {},
   "source": [
    "- We can see that the TFIDF vectorizer is performing better on Multinomial NB as precision is 1 and accuaracy is 0.97\n",
    "- Because data is imbalance so accuracy is not a good measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5610b544",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:50.818508Z",
     "start_time": "2023-03-18T07:59:46.415725Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\bisha\\anaconda3\\lib\\site-packages (1.7.4)\n",
      "Requirement already satisfied: numpy in c:\\users\\bisha\\anaconda3\\lib\\site-packages (from xgboost) (1.21.5)\n",
      "Requirement already satisfied: scipy in c:\\users\\bisha\\anaconda3\\lib\\site-packages (from xgboost) (1.9.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f1383928",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:51.150593Z",
     "start_time": "2023-03-18T07:59:50.823459Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3e5195f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:51.176757Z",
     "start_time": "2023-03-18T07:59:51.155533Z"
    }
   },
   "outputs": [],
   "source": [
    "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
    "knc = KNeighborsClassifier()\n",
    "mnb = MultinomialNB()\n",
    "dtc = DecisionTreeClassifier(max_depth=5)\n",
    "lrc = LogisticRegression(solver='liblinear', penalty='l1')\n",
    "rfc = RandomForestClassifier(n_estimators=50, random_state=2)\n",
    "abc = AdaBoostClassifier(n_estimators=50, random_state=2)\n",
    "bc = BaggingClassifier(n_estimators=50, random_state=2)\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "gbdt = GradientBoostingClassifier(n_estimators=50,random_state=2)\n",
    "xgb = XGBClassifier(n_estimators=50,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b1505863",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:51.205792Z",
     "start_time": "2023-03-18T07:59:51.180570Z"
    }
   },
   "outputs": [],
   "source": [
    "clfs = {\n",
    "    'SVC' : svc,\n",
    "    'KN' : knc, \n",
    "    'NB': mnb, \n",
    "    'DT': dtc, \n",
    "    'LR': lrc, \n",
    "    'RF': rfc, \n",
    "    'AdaBoost': abc, \n",
    "    'BgC': bc, \n",
    "    'ETC': etc,\n",
    "    'GBDT':gbdt,\n",
    "    'xgb':xgb\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1efb713d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T07:59:51.226418Z",
     "start_time": "2023-03-18T07:59:51.210489Z"
    }
   },
   "outputs": [],
   "source": [
    "def train_classifier(clf, X_train, y_train, X_test, y_test):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    \n",
    "    return accuracy, precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c01b5f13",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:08:25.789623Z",
     "start_time": "2023-03-18T07:59:51.229466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For SVC\n",
      "accuracy 0.9729206963249516\n",
      "precision 0.9741379310344828\n",
      "For KN\n",
      "accuracy 0.9003868471953579\n",
      "precision 1.0\n",
      "For NB\n",
      "accuracy 0.9593810444874274\n",
      "precision 1.0\n",
      "For DT\n",
      "accuracy 0.9352030947775629\n",
      "precision 0.8380952380952381\n",
      "For LR\n",
      "accuracy 0.9516441005802708\n",
      "precision 0.94\n",
      "For RF\n",
      "accuracy 0.9738878143133463\n",
      "precision 1.0\n",
      "For AdaBoost\n",
      "accuracy 0.9613152804642167\n",
      "precision 0.9454545454545454\n",
      "For BgC\n",
      "accuracy 0.9584139264990329\n",
      "precision 0.8625954198473282\n",
      "For ETC\n",
      "accuracy 0.9758220502901354\n",
      "precision 0.9829059829059829\n",
      "For GBDT\n",
      "accuracy 0.9526112185686654\n",
      "precision 0.9238095238095239\n",
      "For xgb\n",
      "accuracy 0.9690522243713733\n",
      "precision 0.9344262295081968\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name, clf in clfs.items():\n",
    "    currenct_acc, currenct_pre = train_classifier(clf, X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    print('For', name)\n",
    "    print(\"accuracy\", currenct_acc)\n",
    "    print('precision', currenct_pre)\n",
    "    \n",
    "    accuracy_scores.append(currenct_acc)\n",
    "    precision_scores.append(currenct_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d762da3a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:08:25.805004Z",
     "start_time": "2023-03-18T08:08:25.791106Z"
    }
   },
   "outputs": [],
   "source": [
    "  performance_df = pd.DataFrame({'Algorithm': clfs.keys(), 'Accuracy': accuracy_scores, 'Precision': precision_scores}).sort_values('Precision', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ec146461",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:08:25.835528Z",
     "start_time": "2023-03-18T08:08:25.824398Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.973888</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.969052</td>\n",
       "      <td>0.934426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision\n",
       "1         KN  0.900387   1.000000\n",
       "2         NB  0.959381   1.000000\n",
       "5         RF  0.973888   1.000000\n",
       "8        ETC  0.975822   0.982906\n",
       "0        SVC  0.972921   0.974138\n",
       "6   AdaBoost  0.961315   0.945455\n",
       "4         LR  0.951644   0.940000\n",
       "10       xgb  0.969052   0.934426\n",
       "9       GBDT  0.952611   0.923810\n",
       "7        BgC  0.958414   0.862595\n",
       "3         DT  0.935203   0.838095"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e8a0740",
   "metadata": {},
   "source": [
    "- Here we can see that RF is performing better than NB on accuracy\n",
    "- KN is also giving precision 1, but accuracy is very poor\n",
    "- ETC is also considerable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a4d2c370",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:08:25.850778Z",
     "start_time": "2023-03-18T08:08:25.836528Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KN</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.900387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.959381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.973888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.975822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.972921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.961315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.951644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgb</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.969052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.952611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.958414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.935203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KN</td>\n",
       "      <td>Precision</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NB</td>\n",
       "      <td>Precision</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RF</td>\n",
       "      <td>Precision</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ETC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.982906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SVC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.974138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.945455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LR</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>xgb</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.934426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.923810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BgC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.862595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>DT</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.838095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm   variable     value\n",
       "0         KN   Accuracy  0.900387\n",
       "1         NB   Accuracy  0.959381\n",
       "2         RF   Accuracy  0.973888\n",
       "3        ETC   Accuracy  0.975822\n",
       "4        SVC   Accuracy  0.972921\n",
       "5   AdaBoost   Accuracy  0.961315\n",
       "6         LR   Accuracy  0.951644\n",
       "7        xgb   Accuracy  0.969052\n",
       "8       GBDT   Accuracy  0.952611\n",
       "9        BgC   Accuracy  0.958414\n",
       "10        DT   Accuracy  0.935203\n",
       "11        KN  Precision  1.000000\n",
       "12        NB  Precision  1.000000\n",
       "13        RF  Precision  1.000000\n",
       "14       ETC  Precision  0.982906\n",
       "15       SVC  Precision  0.974138\n",
       "16  AdaBoost  Precision  0.945455\n",
       "17        LR  Precision  0.940000\n",
       "18       xgb  Precision  0.934426\n",
       "19      GBDT  Precision  0.923810\n",
       "20       BgC  Precision  0.862595\n",
       "21        DT  Precision  0.838095"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df1 = pd.melt(performance_df, id_vars='Algorithm')\n",
    "performance_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e9ae7259",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:08:26.053256Z",
     "start_time": "2023-03-18T08:08:25.852776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "sns.barplot(data = performance_df1, x='Algorithm', y='value', hue='variable')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8011e6bc",
   "metadata": {},
   "source": [
    "### Model improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "1e16a010",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:18:22.279445Z",
     "start_time": "2023-03-18T08:18:22.264440Z"
    }
   },
   "outputs": [],
   "source": [
    "# Setting max_features of tfidf to 3000\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(max_features=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "84930006",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:18:25.320627Z",
     "start_time": "2023-03-18T08:18:25.247032Z"
    }
   },
   "outputs": [],
   "source": [
    "X = tfidf.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5a386001",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:18:29.598063Z",
     "start_time": "2023-03-18T08:18:29.532659Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b1ec097d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:10:01.371203Z",
     "start_time": "2023-03-18T08:08:26.205985Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For SVC\n",
      "accuracy 0.9758220502901354\n",
      "precision 0.9747899159663865\n",
      "For KN\n",
      "accuracy 0.9052224371373307\n",
      "precision 1.0\n",
      "For NB\n",
      "accuracy 0.9709864603481625\n",
      "precision 1.0\n",
      "For DT\n",
      "accuracy 0.9303675048355899\n",
      "precision 0.8173076923076923\n",
      "For LR\n",
      "accuracy 0.9584139264990329\n",
      "precision 0.9702970297029703\n",
      "For RF\n",
      "accuracy 0.9748549323017408\n",
      "precision 0.9827586206896551\n",
      "For AdaBoost\n",
      "accuracy 0.960348162475822\n",
      "precision 0.9292035398230089\n",
      "For BgC\n",
      "accuracy 0.9574468085106383\n",
      "precision 0.8671875\n",
      "For ETC\n",
      "accuracy 0.9748549323017408\n",
      "precision 0.9745762711864406\n",
      "For GBDT\n",
      "accuracy 0.9477756286266924\n",
      "precision 0.92\n",
      "For xgb\n",
      "accuracy 0.971953578336557\n",
      "precision 0.943089430894309\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name, clf in clfs.items():\n",
    "    currenct_acc, currenct_pre = train_classifier(clf, X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    print('For', name)\n",
    "    print(\"accuracy\", currenct_acc)\n",
    "    print('precision', currenct_pre)\n",
    "    \n",
    "    accuracy_scores.append(currenct_acc)\n",
    "    precision_scores.append(currenct_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ed8f3e0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:10:01.386392Z",
     "start_time": "2023-03-18T08:10:01.372203Z"
    }
   },
   "outputs": [],
   "source": [
    "temp_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_max_ft_3000':accuracy_scores,'Precision_max_ft_3000':precision_scores}).sort_values('Precision_max_ft_3000',ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7c6fcd41",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:10:01.401634Z",
     "start_time": "2023-03-18T08:10:01.387877Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy_max_ft_3000</th>\n",
       "      <th>Precision_max_ft_3000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.930368</td>\n",
       "      <td>0.817308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy_max_ft_3000  Precision_max_ft_3000\n",
       "1         KN              0.905222               1.000000\n",
       "2         NB              0.970986               1.000000\n",
       "5         RF              0.974855               0.982759\n",
       "0        SVC              0.975822               0.974790\n",
       "8        ETC              0.974855               0.974576\n",
       "4         LR              0.958414               0.970297\n",
       "10       xgb              0.971954               0.943089\n",
       "6   AdaBoost              0.960348               0.929204\n",
       "9       GBDT              0.947776               0.920000\n",
       "7        BgC              0.957447               0.867188\n",
       "3         DT              0.930368               0.817308"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2ad020f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:10:01.416676Z",
     "start_time": "2023-03-18T08:10:01.402634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.973888</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.969052</td>\n",
       "      <td>0.934426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision\n",
       "1         KN  0.900387   1.000000\n",
       "2         NB  0.959381   1.000000\n",
       "5         RF  0.973888   1.000000\n",
       "8        ETC  0.975822   0.982906\n",
       "0        SVC  0.972921   0.974138\n",
       "6   AdaBoost  0.961315   0.945455\n",
       "4         LR  0.951644   0.940000\n",
       "10       xgb  0.969052   0.934426\n",
       "9       GBDT  0.952611   0.923810\n",
       "7        BgC  0.958414   0.862595\n",
       "3         DT  0.935203   0.838095"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "dc99d953",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:10:01.432015Z",
     "start_time": "2023-03-18T08:10:01.417677Z"
    }
   },
   "outputs": [],
   "source": [
    "new_df = performance_df.merge(temp_df, on='Algorithm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7ab1dda6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:10:01.447509Z",
     "start_time": "2023-03-18T08:10:01.433021Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Accuracy_max_ft_3000</th>\n",
       "      <th>Precision_max_ft_3000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.973888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.969052</td>\n",
       "      <td>0.934426</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "      <td>0.930368</td>\n",
       "      <td>0.817308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision  Accuracy_max_ft_3000  Precision_max_ft_3000\n",
       "0         KN  0.900387   1.000000              0.905222               1.000000\n",
       "1         NB  0.959381   1.000000              0.970986               1.000000\n",
       "2         RF  0.973888   1.000000              0.974855               0.982759\n",
       "3        ETC  0.975822   0.982906              0.974855               0.974576\n",
       "4        SVC  0.972921   0.974138              0.975822               0.974790\n",
       "5   AdaBoost  0.961315   0.945455              0.960348               0.929204\n",
       "6         LR  0.951644   0.940000              0.958414               0.970297\n",
       "7        xgb  0.969052   0.934426              0.971954               0.943089\n",
       "8       GBDT  0.952611   0.923810              0.947776               0.920000\n",
       "9        BgC  0.958414   0.862595              0.957447               0.867188\n",
       "10        DT  0.935203   0.838095              0.930368               0.817308"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "211755a5",
   "metadata": {},
   "source": [
    "- Can't find much difference in side by side comparision\n",
    "- NB performance has increased \n",
    "- RF performance has decreased"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3528064d",
   "metadata": {},
   "source": [
    "- Lets try to scale the data and see the difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "630ca4bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:10:01.633634Z",
     "start_time": "2023-03-18T08:10:01.448509Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler  \n",
    "# Didn't use std. scaler as it gives negative values as well which is not accepted by NB\n",
    "scaler = MinMaxScaler()\n",
    "X1 = tfidf.fit_transform(df['transformed_text']).toarray()\n",
    "scaler.fit_transform(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "cc57361b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:10:01.680482Z",
     "start_time": "2023-03-18T08:10:01.634640Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X1, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b4975f42",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:11:37.832064Z",
     "start_time": "2023-03-18T08:10:01.681482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For SVC\n",
      "accuracy 0.9758220502901354\n",
      "precision 0.9747899159663865\n",
      "For KN\n",
      "accuracy 0.9052224371373307\n",
      "precision 1.0\n",
      "For NB\n",
      "accuracy 0.9709864603481625\n",
      "precision 1.0\n",
      "For DT\n",
      "accuracy 0.9323017408123792\n",
      "precision 0.8333333333333334\n",
      "For LR\n",
      "accuracy 0.9584139264990329\n",
      "precision 0.9702970297029703\n",
      "For RF\n",
      "accuracy 0.9748549323017408\n",
      "precision 0.9827586206896551\n",
      "For AdaBoost\n",
      "accuracy 0.960348162475822\n",
      "precision 0.9292035398230089\n",
      "For BgC\n",
      "accuracy 0.9574468085106383\n",
      "precision 0.8671875\n",
      "For ETC\n",
      "accuracy 0.9748549323017408\n",
      "precision 0.9745762711864406\n",
      "For GBDT\n",
      "accuracy 0.9477756286266924\n",
      "precision 0.92\n",
      "For xgb\n",
      "accuracy 0.971953578336557\n",
      "precision 0.943089430894309\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name, clf in clfs.items():\n",
    "    currenct_acc, currenct_pre = train_classifier(clf, X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    print('For', name)\n",
    "    print(\"accuracy\", currenct_acc)\n",
    "    print('precision', currenct_pre)\n",
    "    \n",
    "    accuracy_scores.append(currenct_acc)\n",
    "    precision_scores.append(currenct_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bd1949e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:11:37.847504Z",
     "start_time": "2023-03-18T08:11:37.834381Z"
    }
   },
   "outputs": [],
   "source": [
    "temp_df1 = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_scaling':accuracy_scores,'Precision_scaling':precision_scores}).sort_values('Precision_scaling',ascending=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9c43820b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:11:37.862806Z",
     "start_time": "2023-03-18T08:11:37.849412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy_scaling</th>\n",
       "      <th>Precision_scaling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy_scaling  Precision_scaling\n",
       "1         KN          0.905222           1.000000\n",
       "2         NB          0.970986           1.000000\n",
       "5         RF          0.974855           0.982759\n",
       "0        SVC          0.975822           0.974790\n",
       "8        ETC          0.974855           0.974576\n",
       "4         LR          0.958414           0.970297\n",
       "10       xgb          0.971954           0.943089\n",
       "6   AdaBoost          0.960348           0.929204\n",
       "9       GBDT          0.947776           0.920000\n",
       "7        BgC          0.957447           0.867188\n",
       "3         DT          0.932302           0.833333"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "560a715f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:11:37.893189Z",
     "start_time": "2023-03-18T08:11:37.864806Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Accuracy_max_ft_3000</th>\n",
       "      <th>Precision_max_ft_3000</th>\n",
       "      <th>Accuracy_scaling</th>\n",
       "      <th>Precision_scaling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.973888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.969052</td>\n",
       "      <td>0.934426</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "      <td>0.930368</td>\n",
       "      <td>0.817308</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision  Accuracy_max_ft_3000  \\\n",
       "0         KN  0.900387   1.000000              0.905222   \n",
       "1         NB  0.959381   1.000000              0.970986   \n",
       "2         RF  0.973888   1.000000              0.974855   \n",
       "3        ETC  0.975822   0.982906              0.974855   \n",
       "4        SVC  0.972921   0.974138              0.975822   \n",
       "5   AdaBoost  0.961315   0.945455              0.960348   \n",
       "6         LR  0.951644   0.940000              0.958414   \n",
       "7        xgb  0.969052   0.934426              0.971954   \n",
       "8       GBDT  0.952611   0.923810              0.947776   \n",
       "9        BgC  0.958414   0.862595              0.957447   \n",
       "10        DT  0.935203   0.838095              0.930368   \n",
       "\n",
       "    Precision_max_ft_3000  Accuracy_scaling  Precision_scaling  \n",
       "0                1.000000          0.905222           1.000000  \n",
       "1                1.000000          0.970986           1.000000  \n",
       "2                0.982759          0.974855           0.982759  \n",
       "3                0.974576          0.974855           0.974576  \n",
       "4                0.974790          0.975822           0.974790  \n",
       "5                0.929204          0.960348           0.929204  \n",
       "6                0.970297          0.958414           0.970297  \n",
       "7                0.943089          0.971954           0.943089  \n",
       "8                0.920000          0.947776           0.920000  \n",
       "9                0.867188          0.957447           0.867188  \n",
       "10               0.817308          0.932302           0.833333  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df1 = new_df.merge(temp_df1, on='Algorithm')\n",
    "new_df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76cd7d58",
   "metadata": {},
   "source": [
    "- There is no great difference after applying scaling \n",
    "- So we will not consider scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ea3473e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:11:37.955454Z",
     "start_time": "2023-03-18T08:11:37.895689Z"
    }
   },
   "outputs": [],
   "source": [
    "# Adding number of characters\n",
    "X1 = np.hstack((X1, df['num_characters'].values.reshape(-1, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "4508e06c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:11:37.970600Z",
     "start_time": "2023-03-18T08:11:37.957465Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   0., ...,   0.,   0., 111.],\n",
       "       [  0.,   0.,   0., ...,   0.,   0.,  29.],\n",
       "       [  0.,   0.,   0., ...,   0.,   0., 155.],\n",
       "       ...,\n",
       "       [  0.,   0.,   0., ...,   0.,   0.,  57.],\n",
       "       [  0.,   0.,   0., ...,   0.,   0., 125.],\n",
       "       [  0.,   0.,   0., ...,   0.,   0.,  26.]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ca97e20a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:11:37.986301Z",
     "start_time": "2023-03-18T08:11:37.972229Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 3001)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "cbde361c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:11:38.049684Z",
     "start_time": "2023-03-18T08:11:37.988299Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X1, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1cb67aaa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:13:18.266541Z",
     "start_time": "2023-03-18T08:11:38.051680Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For SVC\n",
      "accuracy 0.8665377176015474\n",
      "precision 0.0\n",
      "For KN\n",
      "accuracy 0.9274661508704062\n",
      "precision 0.7739130434782608\n",
      "For NB\n",
      "accuracy 0.9410058027079303\n",
      "precision 1.0\n",
      "For DT\n",
      "accuracy 0.9429400386847195\n",
      "precision 0.8691588785046729\n",
      "For LR\n",
      "accuracy 0.9613152804642167\n",
      "precision 0.9622641509433962\n",
      "For RF\n",
      "accuracy 0.9680851063829787\n",
      "precision 0.981651376146789\n",
      "For AdaBoost\n",
      "accuracy 0.9642166344294004\n",
      "precision 0.9316239316239316\n",
      "For BgC\n",
      "accuracy 0.9661508704061895\n",
      "precision 0.8992248062015504\n",
      "For ETC\n",
      "accuracy 0.9806576402321083\n",
      "precision 0.9758064516129032\n",
      "For GBDT\n",
      "accuracy 0.9516441005802708\n",
      "precision 0.9313725490196079\n",
      "For xgb\n",
      "accuracy 0.9709864603481625\n",
      "precision 0.9426229508196722\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name, clf in clfs.items():\n",
    "    currenct_acc, currenct_pre = train_classifier(clf, X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    print('For', name)\n",
    "    print(\"accuracy\", currenct_acc)\n",
    "    print('precision', currenct_pre)\n",
    "    \n",
    "    accuracy_scores.append(currenct_acc)\n",
    "    precision_scores.append(currenct_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "00b9ac8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:13:18.281798Z",
     "start_time": "2023-03-18T08:13:18.268089Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy_num_chars</th>\n",
       "      <th>Precision_num_chars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.941006</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.981651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.980658</td>\n",
       "      <td>0.975806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.962264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>0.942623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.964217</td>\n",
       "      <td>0.931624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.931373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.966151</td>\n",
       "      <td>0.899225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.942940</td>\n",
       "      <td>0.869159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.927466</td>\n",
       "      <td>0.773913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.866538</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy_num_chars  Precision_num_chars\n",
       "2         NB            0.941006             1.000000\n",
       "5         RF            0.968085             0.981651\n",
       "8        ETC            0.980658             0.975806\n",
       "4         LR            0.961315             0.962264\n",
       "10       xgb            0.970986             0.942623\n",
       "6   AdaBoost            0.964217             0.931624\n",
       "9       GBDT            0.951644             0.931373\n",
       "7        BgC            0.966151             0.899225\n",
       "3         DT            0.942940             0.869159\n",
       "1         KN            0.927466             0.773913\n",
       "0        SVC            0.866538             0.000000"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df2 = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_num_chars':accuracy_scores,'Precision_num_chars':precision_scores}).sort_values('Precision_num_chars',ascending=False)\n",
    "temp_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "524dd927",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:13:18.296846Z",
     "start_time": "2023-03-18T08:13:18.283645Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Accuracy_max_ft_3000</th>\n",
       "      <th>Precision_max_ft_3000</th>\n",
       "      <th>Accuracy_scaling</th>\n",
       "      <th>Precision_scaling</th>\n",
       "      <th>Accuracy_num_chars</th>\n",
       "      <th>Precision_num_chars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.927466</td>\n",
       "      <td>0.773913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.941006</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.973888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</td>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.981651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "      <td>0.980658</td>\n",
       "      <td>0.975806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "      <td>0.866538</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "      <td>0.964217</td>\n",
       "      <td>0.931624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.962264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.969052</td>\n",
       "      <td>0.934426</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>0.942623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.947776</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.931373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.867188</td>\n",
       "      <td>0.966151</td>\n",
       "      <td>0.899225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "      <td>0.930368</td>\n",
       "      <td>0.817308</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.942940</td>\n",
       "      <td>0.869159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision  Accuracy_max_ft_3000  \\\n",
       "0         KN  0.900387   1.000000              0.905222   \n",
       "1         NB  0.959381   1.000000              0.970986   \n",
       "2         RF  0.973888   1.000000              0.974855   \n",
       "3        ETC  0.975822   0.982906              0.974855   \n",
       "4        SVC  0.972921   0.974138              0.975822   \n",
       "5   AdaBoost  0.961315   0.945455              0.960348   \n",
       "6         LR  0.951644   0.940000              0.958414   \n",
       "7        xgb  0.969052   0.934426              0.971954   \n",
       "8       GBDT  0.952611   0.923810              0.947776   \n",
       "9        BgC  0.958414   0.862595              0.957447   \n",
       "10        DT  0.935203   0.838095              0.930368   \n",
       "\n",
       "    Precision_max_ft_3000  Accuracy_scaling  Precision_scaling  \\\n",
       "0                1.000000          0.905222           1.000000   \n",
       "1                1.000000          0.970986           1.000000   \n",
       "2                0.982759          0.974855           0.982759   \n",
       "3                0.974576          0.974855           0.974576   \n",
       "4                0.974790          0.975822           0.974790   \n",
       "5                0.929204          0.960348           0.929204   \n",
       "6                0.970297          0.958414           0.970297   \n",
       "7                0.943089          0.971954           0.943089   \n",
       "8                0.920000          0.947776           0.920000   \n",
       "9                0.867188          0.957447           0.867188   \n",
       "10               0.817308          0.932302           0.833333   \n",
       "\n",
       "    Accuracy_num_chars  Precision_num_chars  \n",
       "0             0.927466             0.773913  \n",
       "1             0.941006             1.000000  \n",
       "2             0.968085             0.981651  \n",
       "3             0.980658             0.975806  \n",
       "4             0.866538             0.000000  \n",
       "5             0.964217             0.931624  \n",
       "6             0.961315             0.962264  \n",
       "7             0.970986             0.942623  \n",
       "8             0.951644             0.931373  \n",
       "9             0.966151             0.899225  \n",
       "10            0.942940             0.869159  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df2 = new_df1.merge(temp_df2, on='Algorithm')\n",
    "new_df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32192187",
   "metadata": {},
   "source": [
    "- We can see that NB performance has decreased after using the num_character column \n",
    "- In all these experiments NB is performing better, and in that tfidf max_feature 3000 without scaling is what we want to keep for further modelling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "5e39733e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:13:18.312087Z",
     "start_time": "2023-03-18T08:13:18.297871Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create a voting classifier of the top 3 performing models\n",
    "# Its an algorithm which is combination of best performing models\n",
    "svc = SVC(kernel='sigmoid', gamma=1.0,probability=True)\n",
    "mnb = MultinomialNB()\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "\n",
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "59fb5047",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:13:18.327646Z",
     "start_time": "2023-03-18T08:13:18.313087Z"
    }
   },
   "outputs": [],
   "source": [
    "voting = VotingClassifier(estimators=[('svm', svc), ('nb', mnb), ('et', etc)],voting='soft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "2bdace4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:13:57.538331Z",
     "start_time": "2023-03-18T08:13:18.329154Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('svm',\n",
       "                              SVC(gamma=1.0, kernel='sigmoid',\n",
       "                                  probability=True)),\n",
       "                             ('nb', MultinomialNB()),\n",
       "                             ('et',\n",
       "                              ExtraTreesClassifier(n_estimators=50,\n",
       "                                                   random_state=2))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d548f872",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:13:59.581958Z",
     "start_time": "2023-03-18T08:13:57.540015Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.941972920696325\n",
      "Precision 1.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = voting.predict(X_test)\n",
    "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
    "print(\"Precision\",precision_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "77f5d32a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:13:59.598008Z",
     "start_time": "2023-03-18T08:13:59.582958Z"
    }
   },
   "outputs": [],
   "source": [
    "# Applying stacking\n",
    "estimators=[('svm', svc), ('nb', mnb), ('et', etc)]\n",
    "final_estimator=RandomForestClassifier()\n",
    "\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "\n",
    "clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "dd2d9a9d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:16:56.047937Z",
     "start_time": "2023-03-18T08:13:59.600155Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9709864603481625\n",
      "Precision 0.8802816901408451\n"
     ]
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
    "print(\"Precision\",precision_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "89ace26b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-18T08:19:39.154239Z",
     "start_time": "2023-03-18T08:19:39.140234Z"
    }
   },
   "outputs": [],
   "source": [
    "import pickle \n",
    "pickle.dump(tfidf, open('vectorizer.pkl', 'wb'))\n",
    "pickle.dump(mnb, open('model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb156535",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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