Spam Text Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c00f5bd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa4c7b90",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ['which book is this', 'this is book and this is math']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a5405d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 1, 0, 1, 1],\n",
       "       [1, 1, 2, 1, 2, 0]], dtype=int64)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv=CountVectorizer()\n",
    "count=cv.fit_transform(x)\n",
    "count.toarray() #BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e1d6f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['and', 'book', 'is', 'math', 'this', 'which'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.get_feature_names_out() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ff5c6f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>and</th>\n",
       "      <th>book</th>\n",
       "      <th>is</th>\n",
       "      <th>math</th>\n",
       "      <th>this</th>\n",
       "      <th>which</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   and  book  is  math  this  which\n",
       "0    0     1   1     0     1      1\n",
       "1    1     1   2     1     2      0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow=pd.DataFrame(count.toarray(),columns=cv.get_feature_names_out())\n",
    "bow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf47b3d",
   "metadata": {},
   "source": [
    "#### Term frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0dc99a3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 6)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0aeac0cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf=bow.copy()\n",
    "for index, row in enumerate(tf.iterrows()):\n",
    "    for col in row[1].index:\n",
    "        tf.loc[index,col]=tf.loc[index,col]/sum(row[1].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6fa98a04",
   "metadata": {},
   "outputs": [
    {
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       "    .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>and</th>\n",
       "      <th>book</th>\n",
       "      <th>is</th>\n",
       "      <th>math</th>\n",
       "      <th>this</th>\n",
       "      <th>which</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        and      book        is      math      this  which\n",
       "0  0.000000  0.250000  0.250000  0.000000  0.250000   0.25\n",
       "1  0.142857  0.142857  0.285714  0.142857  0.285714   0.00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2f00644",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>and</th>\n",
       "      <th>book</th>\n",
       "      <th>is</th>\n",
       "      <th>math</th>\n",
       "      <th>this</th>\n",
       "      <th>which</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   and  book  is  math  this  which\n",
       "0    0     1   1     0     1      1\n",
       "1    1     1   2     1     2      0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "52086daa",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>and</th>\n",
       "      <th>book</th>\n",
       "      <th>is</th>\n",
       "      <th>math</th>\n",
       "      <th>this</th>\n",
       "      <th>which</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     and  book    is   math  this  which\n",
       "0  False  True  True  False  True   True\n",
       "1   True  True  True   True  True  False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb=bow.astype(\"bool\")\n",
    "bb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c1198806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb[\"and\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "140f6d0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['and', 'book', 'is', 'math', 'this', 'which'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=bb.columns\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1ecc275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 2, 1, 2, 1]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nz=[]\n",
    "for col in cols:\n",
    "    nz.append(bb[col].sum())\n",
    "nz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "00d4af22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 6)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "564cad63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.4054651081081644, 1.0, 1.0, 1.4054651081081644, 1.0, 1.4054651081081644]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf=[]\n",
    "N=bb.shape\n",
    "for index,col in enumerate(cols):\n",
    "    idf.append(np.log((bb.shape[0]+1)/(nz[index]+1))+1)\n",
    "idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bfbfb80a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['which book is this', 'this is book and this is math']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "213370e1",
   "metadata": {},
   "source": [
    "#### TFIDF\n",
    "tf*idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6e556803",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bffcf7e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['which book is this', 'this is book and this is math']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b8671af2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.44832087 0.44832087 0.         0.44832087 0.63009934]\n",
      " [0.39054766 0.27787788 0.55575576 0.39054766 0.55575576 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "tfidf=TfidfVectorizer()\n",
    "X=tfidf.fit_transform(x)\n",
    "print( X.toarray() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "95bd9c2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.40546511 1.         1.         1.40546511 1.         1.40546511]\n"
     ]
    }
   ],
   "source": [
    "print(tfidf.idf_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bce28e4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9b6e0c42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>length</th>\n",
       "      <th>punct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  length  punct\n",
       "0   ham  Go until jurong point, crazy.. Available only ...     111      9\n",
       "1   ham                      Ok lar... Joking wif u oni...      29      6\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155      6\n",
       "3   ham  U dun say so early hor... U c already then say...      49      6\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...      61      2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"spam.tsv\",sep=\"\\t\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c9b91663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 4)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0a869887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label      0\n",
       "message    0\n",
       "length     0\n",
       "punct      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cb8ba6f6",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>punct</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5572.000000</td>\n",
       "      <td>5572.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>80.489950</td>\n",
       "      <td>4.177495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>59.942907</td>\n",
       "      <td>4.623919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>122.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>910.000000</td>\n",
       "      <td>133.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            length        punct\n",
       "count  5572.000000  5572.000000\n",
       "mean     80.489950     4.177495\n",
       "std      59.942907     4.623919\n",
       "min       2.000000     0.000000\n",
       "25%      36.000000     2.000000\n",
       "50%      62.000000     3.000000\n",
       "75%     122.000000     6.000000\n",
       "max     910.000000   133.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f9b368fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     4825\n",
       "spam     747\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"label\"].value_counts()\n",
    "# Unbalanced dataset we have here\n",
    "# we have to make it balanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "78fc1e21",
   "metadata": {},
   "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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>length</th>\n",
       "      <th>punct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ham</td>\n",
       "      <td>Even my brother is not like to speak with me. ...</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  length  punct\n",
       "0   ham  Go until jurong point, crazy.. Available only ...     111      9\n",
       "1   ham                      Ok lar... Joking wif u oni...      29      6\n",
       "3   ham  U dun say so early hor... U c already then say...      49      6\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...      61      2\n",
       "6   ham  Even my brother is not like to speak with me. ...      77      2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ham=df[df[\"label\"]==\"ham\"]\n",
    "ham.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "66b5c1e1",
   "metadata": {},
   "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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>length</th>\n",
       "      <th>punct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>spam</td>\n",
       "      <td>FreeMsg Hey there darling it's been 3 week's n...</td>\n",
       "      <td>147</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>spam</td>\n",
       "      <td>WINNER!! As a valued network customer you have...</td>\n",
       "      <td>157</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>spam</td>\n",
       "      <td>Had your mobile 11 months or more? U R entitle...</td>\n",
       "      <td>154</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>spam</td>\n",
       "      <td>SIX chances to win CASH! From 100 to 20,000 po...</td>\n",
       "      <td>136</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                            message  length  punct\n",
       "2   spam  Free entry in 2 a wkly comp to win FA Cup fina...     155      6\n",
       "5   spam  FreeMsg Hey there darling it's been 3 week's n...     147      8\n",
       "8   spam  WINNER!! As a valued network customer you have...     157      6\n",
       "9   spam  Had your mobile 11 months or more? U R entitle...     154      2\n",
       "11  spam  SIX chances to win CASH! From 100 to 20,000 po...     136      8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam=df[df[\"label\"]==\"spam\"]\n",
    "spam.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "319a6d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4825, 4), (747, 4))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ham.shape , spam.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49e168b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ham=ham.sample(spam.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0bd53e54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((747, 4), (747, 4))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ham.shape , spam.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3f55f49c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data=ham.append(spam,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5e0cc492",
   "metadata": {},
   "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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>length</th>\n",
       "      <th>punct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>ham</td>\n",
       "      <td>3 pa but not selected.</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>ham</td>\n",
       "      <td>Dont worry. I guess he's busy.</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404</th>\n",
       "      <td>spam</td>\n",
       "      <td>Want the latest Video handset? 750 anytime any...</td>\n",
       "      <td>132</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>spam</td>\n",
       "      <td>Natalja (25/F) is inviting you to be her frien...</td>\n",
       "      <td>136</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>ham</td>\n",
       "      <td>U coming 2 pick me?</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     label                                            message  length  punct\n",
       "680    ham                             3 pa but not selected.      22      1\n",
       "325    ham                     Dont worry. I guess he's busy.      30      3\n",
       "1404  spam  Want the latest Video handset? 750 anytime any...     132      3\n",
       "1453  spam  Natalja (25/F) is inviting you to be her frien...     136     12\n",
       "290    ham                                U coming 2 pick me?      19      1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.concat([ham,spam],ignore_index=True)\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "07b42c90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     747\n",
       "spam    747\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"label\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "798da602",
   "metadata": {},
   "source": [
    "#### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fc2a75e7",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.hist(ham[\"length\"],bins=100,alpha=0.7,label=\"Ham\")\n",
    "plt.hist(spam[\"length\"],bins=100,alpha=0.7,label=\"Spam\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f1b85bbb",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.hist(ham[\"punct\"],bins=60,alpha=0.7,label=\"Ham punct\")\n",
    "plt.hist(spam[\"punct\"],bins=60,alpha=0.7,label=\"Spam punct\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c48b374",
   "metadata": {},
   "source": [
    "#### Data preparation for Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ff506c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "af673514",
   "metadata": {},
   "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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>length</th>\n",
       "      <th>punct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>So now my dad is gonna call after he gets out ...</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>You still coming tonight?</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ham</td>\n",
       "      <td>Come to my home for one last time i wont do an...</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>To the wonderful Okors, have a great month. We...</td>\n",
       "      <td>101</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>MY NO. IN LUTON 0125698789 RING ME IF UR AROUN...</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  length  punct\n",
       "0   ham  So now my dad is gonna call after he gets out ...      88      1\n",
       "1   ham                          You still coming tonight?      25      1\n",
       "2   ham  Come to my home for one last time i wont do an...      63      2\n",
       "3   ham  To the wonderful Okors, have a great month. We...     101      3\n",
       "4   ham  MY NO. IN LUTON 0125698789 RING ME IF UR AROUN...      51      3"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fcd803ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf=TfidfVectorizer()\n",
    "X=tfidf.fit_transform(data[\"message\"])\n",
    "X.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c6faf988",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=X.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "25e4695f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1494, 4509)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape\n",
    "#4595 are the unique words in data[\"message\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "99d4e3a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,data[\"label\"],test_size=0.2, random_state=42, stratify=data[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "eab4a9f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1195, 4509), (299, 4509))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f4f26708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1195,), (299,))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b53ca94",
   "metadata": {},
   "source": [
    "### Train text classifier\n",
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e7311314",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=RandomForestClassifier(n_estimators=100,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ad51fa1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2a21945e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c221d096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[146   4]\n",
      " [ 14 135]]\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8f095e82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.91      0.97      0.94       150\n",
      "        spam       0.97      0.91      0.94       149\n",
      "\n",
      "    accuracy                           0.94       299\n",
      "   macro avg       0.94      0.94      0.94       299\n",
      "weighted avg       0.94      0.94      0.94       299\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print( classification_report(y_test,y_pred) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f076bae3",
   "metadata": {},
   "source": [
    "#### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1b4d4981",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=SVC(C=1500,gamma=\"auto\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7312ff42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1500, gamma=&#x27;auto&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1500, gamma=&#x27;auto&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=1500, gamma='auto')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6c811d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "580892a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[146   4]\n",
      " [ 12 137]]\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6c73f1f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.92      0.97      0.95       150\n",
      "        spam       0.97      0.92      0.94       149\n",
      "\n",
      "    accuracy                           0.95       299\n",
      "   macro avg       0.95      0.95      0.95       299\n",
      "weighted avg       0.95      0.95      0.95       299\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print( classification_report(y_test,y_pred) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "577ba4f7",
   "metadata": {},
   "source": [
    "### Test model with Unseen Data - Real Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dc1691ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1500, gamma=&#x27;auto&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1500, gamma=&#x27;auto&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=1500, gamma='auto')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "dc7d43dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(x):\n",
    "    x=tfidf.transform([x])\n",
    "    x=x.toarray()\n",
    "    prediction = clf.predict(x)\n",
    "    return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a5ee844d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ham'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict('what are you doing?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "97b50697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ham'], dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict('Do you want to win a free ticket ?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3f1fe05b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ham'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict('Can ı make an offer to you?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70b3f572",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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