sms-spam-detection.ipynb

{
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   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d660cd48",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('spam.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8e3e9cf",
   "metadata": {
    "pycharm": {
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   "outputs": [
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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",
       "    <tr>\n",
       "      <th>2464</th>\n",
       "      <td>ham</td>\n",
       "      <td>They will pick up and drop in car.so no problem..</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>ham</td>\n",
       "      <td>HI HUN! IM NOT COMIN 2NITE-TELL EVERY1 IM SORR...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413</th>\n",
       "      <td>spam</td>\n",
       "      <td>Dear U've been invited to XCHAT. This is our f...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>ham</td>\n",
       "      <td>They released vday shirts and when u put it on...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4458</th>\n",
       "      <td>spam</td>\n",
       "      <td>Welcome to UK-mobile-date this msg is FREE giv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "        v1                                                 v2 Unnamed: 2  \\\n",
       "2464   ham  They will pick up and drop in car.so no problem..        NaN   \n",
       "1248   ham  HI HUN! IM NOT COMIN 2NITE-TELL EVERY1 IM SORR...        NaN   \n",
       "1413  spam  Dear U've been invited to XCHAT. This is our f...        NaN   \n",
       "2995   ham  They released vday shirts and when u put it on...        NaN   \n",
       "4458  spam  Welcome to UK-mobile-date this msg is FREE giv...        NaN   \n",
       "\n",
       "     Unnamed: 3 Unnamed: 4  \n",
       "2464        NaN        NaN  \n",
       "1248        NaN        NaN  \n",
       "1413        NaN        NaN  \n",
       "2995        NaN        NaN  \n",
       "4458        NaN        NaN  "
      ]
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "df.sample(5)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4c93050",
   "metadata": {
    "pycharm": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 5)"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c27b2bd6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 1. Data cleaning\n",
    "# 2. EDA\n",
    "# 3. Text Preprocessing\n",
    "# 4. Model building\n",
    "# 5. Evaluation\n",
    "# 6. Improvement\n",
    "# 7. Website\n",
    "# 8. Deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cad57ac",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 1. Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c81e0f5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 7,
   "id": "fdb73374",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# drop last 3 cols\n",
    "df.drop(columns=['Unnamed: 2','Unnamed: 3','Unnamed: 4'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b9b463ff",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>ham</td>\n",
       "      <td>The battery is for mr adewale my uncle. Aka Egbon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2712</th>\n",
       "      <td>ham</td>\n",
       "      <td>Hey you still want to go for yogasana? Coz if ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4428</th>\n",
       "      <td>ham</td>\n",
       "      <td>Hey they r not watching movie tonight so i'll ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3944</th>\n",
       "      <td>ham</td>\n",
       "      <td>I will be gentle princess! We will make sweet ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>ham</td>\n",
       "      <td>U don't know how stubborn I am. I didn't even ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "       v1                                                 v2\n",
       "1947  ham  The battery is for mr adewale my uncle. Aka Egbon\n",
       "2712  ham  Hey you still want to go for yogasana? Coz if ...\n",
       "4428  ham  Hey they r not watching movie tonight so i'll ...\n",
       "3944  ham  I will be gentle princess! We will make sweet ...\n",
       "49    ham  U don't know how stubborn I am. I didn't even ..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e902916",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>ham</td>\n",
       "      <td>Lmao. Take a pic and send it to me.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2338</th>\n",
       "      <td>ham</td>\n",
       "      <td>Alright, see you in a bit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>ham</td>\n",
       "      <td>I'm really not up to it still tonight babe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3735</th>\n",
       "      <td>ham</td>\n",
       "      <td>Hows the street where the end of library walk is?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3859</th>\n",
       "      <td>ham</td>\n",
       "      <td>Yep. I do like the pink furniture tho.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     target                                               text\n",
       "1418    ham                Lmao. Take a pic and send it to me.\n",
       "2338    ham                          Alright, see you in a bit\n",
       "88      ham         I'm really not up to it still tonight babe\n",
       "3735    ham  Hows the street where the end of library walk is?\n",
       "3859    ham             Yep. I do like the pink furniture tho."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# renaming the cols\n",
    "df.rename(columns={'v1':'target','v2':'text'},inplace=True)\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5018a1d7",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "81d6e7b8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['target'] = encoder.fit_transform(df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "06bb183f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   "outputs": [
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       "      <th>0</th>\n",
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       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking 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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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      "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..."
      ]
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     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f19b769",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target    0\n",
       "text      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ac2b1849",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicate values\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "32622c97",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# remove duplicates\n",
    "df = df.drop_duplicates(keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a9e7b344",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ccdd4b0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb8bd6d6",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 2.EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7dc565ad",
   "metadata": {
    "pycharm": {
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   "outputs": [
    {
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       "      <td>Ok lar... Joking wif u oni...</td>\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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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
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       "    <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",
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      "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f49b1fcb",
   "metadata": {
    "pycharm": {
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   "outputs": [
    {
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     "execution_count": 31,
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   "source": [
    "df['target'].value_counts()"
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  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b8cc21ab",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 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": "code",
   "execution_count": 34,
   "id": "f6f0f92f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Data is imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a3af6e80",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c31cde0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "!pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e6502f8d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\91842\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers\\punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "35152bc6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['num_characters'] = df['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3a2a2606",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8318ea23",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# num of words\n",
    "df['num_words'] = df['text'].apply(lambda x:len(nltk.word_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "920e699e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0         24  \n",
       "1          8  \n",
       "2         37  \n",
       "3         13  \n",
       "4         15  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3a2adba4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['num_sentences'] = df['text'].apply(lambda x:len(nltk.sent_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4e3c5e54",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
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       "  </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",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\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",
       "      <td>155</td>\n",
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       "    <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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3d8c30f6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>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>78.923776</td>\n",
       "      <td>18.456375</td>\n",
       "      <td>1.962275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>58.174846</td>\n",
       "      <td>13.323322</td>\n",
       "      <td>1.433892</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>38.000000</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        78.923776    18.456375       1.962275\n",
       "std         58.174846    13.323322       1.433892\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      38.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2ab64881",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>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>70.456820</td>\n",
       "      <td>17.123339</td>\n",
       "      <td>1.815545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56.356802</td>\n",
       "      <td>13.491315</td>\n",
       "      <td>1.364098</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>38.000000</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        70.456820    17.123339       1.815545\n",
       "std         56.356802    13.491315       1.364098\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      38.000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ham\n",
    "df[df['target'] == 0][['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d186cb91",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>137.479326</td>\n",
       "      <td>27.675345</td>\n",
       "      <td>2.977029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.014336</td>\n",
       "      <td>7.011513</td>\n",
       "      <td>1.493676</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>131.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>148.000000</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>223.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>9.000000</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       137.479326   27.675345       2.977029\n",
       "std         30.014336    7.011513       1.493676\n",
       "min         13.000000    2.000000       1.000000\n",
       "25%        131.000000   25.000000       2.000000\n",
       "50%        148.000000   29.000000       3.000000\n",
       "75%        157.000000   32.000000       4.000000\n",
       "max        223.000000   46.000000       9.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#spam\n",
    "df[df['target'] == 1][['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e848663f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "97352818",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_characters', ylabel='Count'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.histplot(df[df['target'] == 0]['num_characters'])\n",
    "sns.histplot(df[df['target'] == 1]['num_characters'],color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5a1532ec",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_words', ylabel='Count'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.histplot(df[df['target'] == 0]['num_words'])\n",
    "sns.histplot(df[df['target'] == 1]['num_words'],color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8c44f867",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x16f88c4a4f0>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 582.375x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,hue='target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e8f5a80b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab74288",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 3. Data Preprocessing\n",
    "- Lower case\n",
    "- Tokenization\n",
    "- Removing special characters\n",
    "- Removing stop words and punctuation\n",
    "- Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "b91ca026",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def transform_text(text):\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[:]\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[:]\n",
    "    y.clear()\n",
    "    \n",
    "    for i in text:\n",
    "        y.append(ps.stem(i))\n",
    "    \n",
    "            \n",
    "    return \" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "e2bfd624",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gon na home soon want talk stuff anymor tonight k cri enough today'"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_text(\"I'm gonna be home soon and i don't want to talk about this stuff anymore tonight, k? I've cried enough today.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "a8e50b45",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"I'm gonna be home soon and i don't want to talk about this stuff anymore tonight, k? I've cried enough today.\""
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['text'][10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "5f22b78b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'love'"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()\n",
    "ps.stem('loving')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "467d03cd",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['transformed_text'] = df['text'].apply(transform_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "c88541f5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "0a470afc",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 233,
   "id": "e53afa54",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "spam_wc = wc.generate(df[df['target'] == 1]['transformed_text'].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "f6d31b53",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16f87ea8cd0>"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.imshow(spam_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "23aa35d6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ham_wc = wc.generate(df[df['target'] == 0]['transformed_text'].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "de135c88",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16f87f6c280>"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.imshow(ham_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "097c12f8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "e7b704c1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "spam_corpus = []\n",
    "for msg in df[df['target'] == 1]['transformed_text'].tolist():\n",
    "    for word in msg.split():\n",
    "        spam_corpus.append(word)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "012f6b89",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9941"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spam_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "67a8d65d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91842\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "sns.barplot(pd.DataFrame(Counter(spam_corpus).most_common(30))[0],pd.DataFrame(Counter(spam_corpus).most_common(30))[1])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "5aa7ab51",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 282,
   "id": "bb3a8c3e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35303"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ham_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "e4c0824a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91842\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "sns.barplot(pd.DataFrame(Counter(ham_corpus).most_common(30))[0],pd.DataFrame(Counter(ham_corpus).most_common(30))[1])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "a27316ab",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Text Vectorization\n",
    "# using Bag of Words\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1fea545",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 4. Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "id": "b56b6a0d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "cv = CountVectorizer()\n",
    "tfidf = TfidfVectorizer(max_features=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "id": "50c5946a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X = tfidf.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "c4809ab6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import MinMaxScaler\n",
    "#scaler = MinMaxScaler()\n",
    "#X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "016c2bdb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# appending the num_character col to X\n",
    "#X = np.hstack((X,df['num_characters'].values.reshape(-1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "id": "d19d30fd",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 3000)"
      ]
     },
     "execution_count": 524,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "id": "c2d74f16",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y = df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "07d00277",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "d2b2861d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 528,
   "id": "c680564b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 489,
   "id": "c9920d0e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "3ca60ba9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8916827852998066\n",
      "[[808  88]\n",
      " [ 24 114]]\n",
      "0.5643564356435643\n"
     ]
    }
   ],
   "source": [
    "gnb.fit(X_train,y_train)\n",
    "y_pred1 = gnb.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred1))\n",
    "print(confusion_matrix(y_test,y_pred1))\n",
    "print(precision_score(y_test,y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "d4652432",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.971953578336557\n",
      "[[896   0]\n",
      " [ 29 109]]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "mnb.fit(X_train,y_train)\n",
    "y_pred2 = mnb.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred2))\n",
    "print(confusion_matrix(y_test,y_pred2))\n",
    "print(precision_score(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "d776bff4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9835589941972921\n",
      "[[895   1]\n",
      " [ 16 122]]\n",
      "0.991869918699187\n"
     ]
    }
   ],
   "source": [
    "bnb.fit(X_train,y_train)\n",
    "y_pred3 = bnb.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred3))\n",
    "print(confusion_matrix(y_test,y_pred3))\n",
    "print(precision_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "f5f42bdf",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# tfidf --> MNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "635d3da1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 495,
   "id": "69aecbbb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 496,
   "id": "e5b3a99f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 497,
   "id": "f6e6bb0d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 348,
   "id": "f294fb4b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9729206963249516, 0.9741379310344828)"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_classifier(svc,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "01a350d2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91842\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For  SVC\n",
      "Accuracy -  0.8665377176015474\n",
      "Precision -  0.0\n",
      "For  KN\n",
      "Accuracy -  0.9284332688588007\n",
      "Precision -  0.7711864406779662\n",
      "For  NB\n",
      "Accuracy -  0.9400386847195358\n",
      "Precision -  1.0\n",
      "For  DT\n",
      "Accuracy -  0.9439071566731141\n",
      "Precision -  0.8773584905660378\n",
      "For  LR\n",
      "Accuracy -  0.9613152804642167\n",
      "Precision -  0.9711538461538461\n",
      "For  RF\n",
      "Accuracy -  0.9748549323017408\n",
      "Precision -  0.9827586206896551\n",
      "For  AdaBoost\n",
      "Accuracy -  0.971953578336557\n",
      "Precision -  0.9504132231404959\n",
      "For  BgC\n",
      "Accuracy -  0.9680851063829787\n",
      "Precision -  0.9133858267716536\n",
      "For  ETC\n",
      "Accuracy -  0.97678916827853\n",
      "Precision -  0.975\n",
      "For  GBDT\n",
      "Accuracy -  0.9487427466150871\n",
      "Precision -  0.9292929292929293\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91842\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14:16:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "For  xgb\n",
      "Accuracy -  0.9700193423597679\n",
      "Precision -  0.9421487603305785\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name,clf in clfs.items():\n",
    "    \n",
    "    current_accuracy,current_precision = train_classifier(clf, X_train,y_train,X_test,y_test)\n",
    "    \n",
    "    print(\"For \",name)\n",
    "    print(\"Accuracy - \",current_accuracy)\n",
    "    print(\"Precision - \",current_precision)\n",
    "    \n",
    "    accuracy_scores.append(current_accuracy)\n",
    "    precision_scores.append(current_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "ffcb9b12",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 387,
   "id": "7c614d5d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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",
       "    </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>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.977756</td>\n",
       "      <td>0.991453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.970019</td>\n",
       "      <td>0.990826</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>0.962282</td>\n",
       "      <td>0.954128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.950413</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>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>0.957447</td>\n",
       "      <td>0.861538</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",
       "8        ETC  0.977756   0.991453\n",
       "5         RF  0.970019   0.990826\n",
       "0        SVC  0.972921   0.974138\n",
       "6   AdaBoost  0.962282   0.954128\n",
       "10       xgb  0.971954   0.950413\n",
       "4         LR  0.951644   0.940000\n",
       "9       GBDT  0.951644   0.931373\n",
       "7        BgC  0.957447   0.861538\n",
       "3         DT  0.935203   0.838095"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "6515228a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "performance_df1 = pd.melt(performance_df, id_vars = \"Algorithm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "e2968237",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<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>ETC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.977756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.972921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>xgb</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.971954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RF</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.970019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.962282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NB</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.959381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BgC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.957447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LR</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.951644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.951644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DT</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.935203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KN</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.900387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ETC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.991453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SVC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.974138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>xgb</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.950413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RF</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.990826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.954128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NB</td>\n",
       "      <td>Precision</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BgC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.861538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LR</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.931373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DT</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.838095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KN</td>\n",
       "      <td>Precision</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm   variable     value\n",
       "0        ETC   Accuracy  0.977756\n",
       "1        SVC   Accuracy  0.972921\n",
       "2        xgb   Accuracy  0.971954\n",
       "3         RF   Accuracy  0.970019\n",
       "4   AdaBoost   Accuracy  0.962282\n",
       "5         NB   Accuracy  0.959381\n",
       "6        BgC   Accuracy  0.957447\n",
       "7         LR   Accuracy  0.951644\n",
       "8       GBDT   Accuracy  0.951644\n",
       "9         DT   Accuracy  0.935203\n",
       "10        KN   Accuracy  0.900387\n",
       "11       ETC  Precision  0.991453\n",
       "12       SVC  Precision  0.974138\n",
       "13       xgb  Precision  0.950413\n",
       "14        RF  Precision  0.990826\n",
       "15  AdaBoost  Precision  0.954128\n",
       "16        NB  Precision  1.000000\n",
       "17       BgC  Precision  0.861538\n",
       "18        LR  Precision  0.940000\n",
       "19      GBDT  Precision  0.931373\n",
       "20        DT  Precision  0.838095\n",
       "21        KN  Precision  1.000000"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "a6c626fb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 444.975x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Algorithm', y='value', \n",
    "               hue = 'variable',data=performance_df1, kind='bar',height=5)\n",
    "plt.ylim(0.5,1.0)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b36faeac",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# model improve\n",
    "# 1. Change the max_features parameter of TfIdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "231b2c67",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "d6c44dc0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "temp_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_scaling':accuracy_scores,'Precision_scaling':precision_scores}).sort_values('Precision_scaling',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "401c5f50",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new_df = performance_df.merge(temp_df,on='Algorithm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "200919bd",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new_df_scaled = new_df.merge(temp_df,on='Algorithm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "id": "23bc6b1f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "temp_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_num_chars':accuracy_scores,'Precision_num_chars':precision_scores}).sort_values('Precision_num_chars',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "id": "129f1fdc",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>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>0.976190</td>\n",
       "      <td>0.928433</td>\n",
       "      <td>0.771186</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>0.971954</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978723</td>\n",
       "      <td>0.946154</td>\n",
       "      <td>0.940039</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.977756</td>\n",
       "      <td>0.991453</td>\n",
       "      <td>0.979691</td>\n",
       "      <td>0.975610</td>\n",
       "      <td>0.979691</td>\n",
       "      <td>0.975610</td>\n",
       "      <td>0.976789</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.970019</td>\n",
       "      <td>0.990826</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.982759</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>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.943089</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>0.962282</td>\n",
       "      <td>0.954128</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.950413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>0.950413</td>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.933884</td>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.933884</td>\n",
       "      <td>0.970019</td>\n",
       "      <td>0.942149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.956480</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.967118</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.971154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.931373</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.927835</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.927835</td>\n",
       "      <td>0.948743</td>\n",
       "      <td>0.929293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.861538</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>0.869231</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>0.869231</td>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.913386</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>0.931335</td>\n",
       "      <td>0.831683</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.943907</td>\n",
       "      <td>0.877358</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              0.971954   \n",
       "2        ETC  0.977756   0.991453              0.979691   \n",
       "3         RF  0.970019   0.990826              0.975822   \n",
       "4        SVC  0.972921   0.974138              0.974855   \n",
       "5   AdaBoost  0.962282   0.954128              0.961315   \n",
       "6        xgb  0.971954   0.950413              0.968085   \n",
       "7         LR  0.951644   0.940000              0.956480   \n",
       "8       GBDT  0.951644   0.931373              0.946809   \n",
       "9        BgC  0.957447   0.861538              0.959381   \n",
       "10        DT  0.935203   0.838095              0.931335   \n",
       "\n",
       "    Precision_max_ft_3000  Accuracy_scaling  Precision_scaling  \\\n",
       "0                1.000000          0.905222           0.976190   \n",
       "1                1.000000          0.978723           0.946154   \n",
       "2                0.975610          0.979691           0.975610   \n",
       "3                0.982906          0.975822           0.982906   \n",
       "4                0.974576          0.971954           0.943089   \n",
       "5                0.945455          0.961315           0.945455   \n",
       "6                0.933884          0.968085           0.933884   \n",
       "7                0.969697          0.967118           0.964286   \n",
       "8                0.927835          0.946809           0.927835   \n",
       "9                0.869231          0.959381           0.869231   \n",
       "10               0.831683          0.932302           0.840000   \n",
       "\n",
       "    Accuracy_num_chars  Precision_num_chars  \n",
       "0             0.928433             0.771186  \n",
       "1             0.940039             1.000000  \n",
       "2             0.976789             0.975000  \n",
       "3             0.974855             0.982759  \n",
       "4             0.866538             0.000000  \n",
       "5             0.971954             0.950413  \n",
       "6             0.970019             0.942149  \n",
       "7             0.961315             0.971154  \n",
       "8             0.948743             0.929293  \n",
       "9             0.968085             0.913386  \n",
       "10            0.943907             0.877358  "
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df_scaled.merge(temp_df,on='Algorithm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "dd092029",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Voting Classifier\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": 515,
   "id": "a08d7fe4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "voting = VotingClassifier(estimators=[('svm', svc), ('nb', mnb), ('et', etc)],voting='soft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "f2a74210",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 516,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "465b16f8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9816247582205029\n",
      "Precision 0.9917355371900827\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": 518,
   "id": "9df1eefd",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Applying stacking\n",
    "estimators=[('svm', svc), ('nb', mnb), ('et', etc)]\n",
    "final_estimator=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "id": "4cd689a2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import StackingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "08c14dd5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "2c841ef0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9787234042553191\n",
      "Precision 0.9328358208955224\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": 530,
   "id": "06e4f088",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": "a36a3173",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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