project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a0242841",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b81f19c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"spam.csv\",encoding='ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f4f4d21b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <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>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     v1                                                 v2 Unnamed: 2  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
       "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
       "3   ham  U dun say so early hor... U c already then say...        NaN   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "\n",
       "  Unnamed: 3 Unnamed: 4  \n",
       "0        NaN        NaN  \n",
       "1        NaN        NaN  \n",
       "2        NaN        NaN  \n",
       "3        NaN        NaN  \n",
       "4        NaN        NaN  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "b00d3ffa",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <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>5567</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        v1                                                 v2 Unnamed: 2  \\\n",
       "5567  spam  This is the 2nd time we have tried 2 contact u...        NaN   \n",
       "5568   ham              Will Ì_ b going to esplanade fr home?        NaN   \n",
       "5569   ham  Pity, * was in mood for that. So...any other s...        NaN   \n",
       "5570   ham  The guy did some bitching but I acted like i'd...        NaN   \n",
       "5571   ham                         Rofl. Its true to its name        NaN   \n",
       "\n",
       "     Unnamed: 3 Unnamed: 4  \n",
       "5567        NaN        NaN  \n",
       "5568        NaN        NaN  \n",
       "5569        NaN        NaN  \n",
       "5570        NaN        NaN  \n",
       "5571        NaN        NaN  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "aecfaaa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 5)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7849a041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27860"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "e20b3c24",
   "metadata": {},
   "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": 128,
   "id": "8319dd4a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5572</td>\n",
       "      <td>5572</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5169</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ham</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "      <td>bt not his girlfrnd... G o o d n i g h t . . .@\"</td>\n",
       "      <td>MK17 92H. 450Ppw 16\"</td>\n",
       "      <td>GNT:-)\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4825</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          v1                      v2  \\\n",
       "count   5572                    5572   \n",
       "unique     2                    5169   \n",
       "top      ham  Sorry, I'll call later   \n",
       "freq    4825                      30   \n",
       "\n",
       "                                               Unnamed: 2  \\\n",
       "count                                                  50   \n",
       "unique                                                 43   \n",
       "top      bt not his girlfrnd... G o o d n i g h t . . .@\"   \n",
       "freq                                                    3   \n",
       "\n",
       "                   Unnamed: 3 Unnamed: 4  \n",
       "count                      12          6  \n",
       "unique                     10          5  \n",
       "top      MK17 92H. 450Ppw 16\"    GNT:-)\"  \n",
       "freq                        2          2  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "4b7db8b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Unnamed: 2'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "7394170c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Unnamed: 3','Unnamed: 4'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "3fb6f666",
   "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>v1</th>\n",
       "      <th>v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        v1                                                 v2\n",
       "0      ham  Go until jurong point, crazy.. Available only ...\n",
       "1      ham                      Ok lar... Joking wif u oni...\n",
       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3      ham  U dun say so early hor... U c already then say...\n",
       "4      ham  Nah I don't think he goes to usf, he lives aro...\n",
       "...    ...                                                ...\n",
       "5567  spam  This is the 2nd time we have tried 2 contact u...\n",
       "5568   ham              Will Ì_ b going to esplanade fr home?\n",
       "5569   ham  Pity, * was in mood for that. So...any other s...\n",
       "5570   ham  The guy did some bitching but I acted like i'd...\n",
       "5571   ham                         Rofl. Its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "1c26310c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.rename(columns={'v1':'TARGET','v2':'MESSAGE'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "962cb951",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TARGET     0\n",
       "MESSAGE    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "a44ed422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "b04660b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(keep='first',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "d632612a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "f9b576d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       1\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "5567    1\n",
       "5568    0\n",
       "5569    0\n",
       "5570    0\n",
       "5571    0\n",
       "Name: TARGET, Length: 5169, dtype: int32"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()\n",
    "df['TARGET']=encoder.fit_transform(df['TARGET'])\n",
    "df['TARGET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "c6ad1301",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>MESSAGE</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
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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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET                                            MESSAGE\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": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "ab9e67d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df[\"TARGET\"].value_counts(),labels=['ham','spam'],autopct=\"%0.2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "6652e292",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df['MESSAGE']\n",
    "y=df['TARGET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "b81767aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Go until jurong point, crazy.. Available only ...\n",
       "1                           Ok lar... Joking wif u oni...\n",
       "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       U dun say so early hor... U c already then say...\n",
       "4       Nah I don't think he goes to usf, he lives aro...\n",
       "                              ...                        \n",
       "5567    This is the 2nd time we have tried 2 contact u...\n",
       "5568                Will Ì_ b going to esplanade fr home?\n",
       "5569    Pity, * was in mood for that. So...any other s...\n",
       "5570    The guy did some bitching but I acted like i'd...\n",
       "5571                           Rofl. Its true to its name\n",
       "Name: MESSAGE, Length: 5169, dtype: object"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "7e64e1c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       1\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "5567    1\n",
       "5568    0\n",
       "5569    0\n",
       "5570    0\n",
       "5571    0\n",
       "Name: TARGET, Length: 5169, dtype: int32"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "86d3fda5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c97959b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 8)\t2\n",
      "  (0, 52)\t1\n",
      "  (0, 34)\t1\n",
      "  (0, 51)\t1\n",
      "  (0, 46)\t1\n",
      "  (0, 50)\t1\n",
      "  (0, 1)\t1\n",
      "  (0, 9)\t1\n",
      "  (0, 42)\t1\n",
      "  (1, 52)\t1\n",
      "  (1, 45)\t1\n",
      "  (1, 40)\t1\n",
      "  (1, 44)\t2\n",
      "  (1, 38)\t1\n",
      "  (1, 30)\t1\n",
      "  (1, 19)\t1\n",
      "  (2, 9)\t1\n",
      "  (2, 42)\t1\n",
      "  (2, 49)\t1\n",
      "  (2, 16)\t1\n",
      "  (2, 22)\t1\n",
      "  (2, 41)\t2\n",
      "  (2, 24)\t1\n",
      "  (2, 12)\t1\n",
      "  (2, 17)\t1\n",
      "  :\t:\n",
      "  (4, 18)\t1\n",
      "  (4, 39)\t1\n",
      "  (4, 23)\t1\n",
      "  (5, 52)\t1\n",
      "  (5, 9)\t1\n",
      "  (5, 42)\t1\n",
      "  (5, 2)\t1\n",
      "  (5, 18)\t1\n",
      "  (5, 27)\t1\n",
      "  (5, 47)\t1\n",
      "  (5, 48)\t1\n",
      "  (5, 28)\t1\n",
      "  (6, 20)\t1\n",
      "  (6, 26)\t1\n",
      "  (6, 43)\t1\n",
      "  (6, 4)\t1\n",
      "  (6, 0)\t1\n",
      "  (6, 3)\t1\n",
      "  (7, 26)\t2\n",
      "  (7, 4)\t1\n",
      "  (7, 37)\t1\n",
      "  (7, 11)\t1\n",
      "  (7, 15)\t1\n",
      "  (7, 29)\t1\n",
      "  (7, 33)\t1\n"
     ]
    }
   ],
   "source": [
    "print(x_train_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "d0069222",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn import svm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "99f954d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv=CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "7477e8d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_cv=cv.fit_transform(x_train)\n",
    "x_test_cv=cv.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3d3eadce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 1879)\t1\n",
      "  (0, 1170)\t1\n",
      "  (0, 6840)\t1\n",
      "  (0, 6610)\t1\n",
      "  (0, 2779)\t1\n",
      "  (1, 1939)\t1\n",
      "  (1, 4467)\t1\n",
      "  (1, 453)\t1\n",
      "  (1, 7176)\t1\n",
      "  (1, 7594)\t1\n",
      "  (1, 1577)\t1\n",
      "  (1, 203)\t1\n",
      "  (1, 4768)\t1\n",
      "  (1, 7175)\t1\n",
      "  (1, 7390)\t1\n",
      "  (1, 7590)\t1\n",
      "  (1, 4309)\t1\n",
      "  (1, 5157)\t1\n",
      "  (1, 3732)\t1\n",
      "  (1, 3015)\t1\n",
      "  (1, 2333)\t1\n",
      "  (1, 5210)\t1\n",
      "  (1, 4577)\t1\n",
      "  (1, 4731)\t1\n",
      "  (1, 5615)\t1\n",
      "  :\t:\n",
      "  (4134, 3290)\t2\n",
      "  (4134, 4817)\t1\n",
      "  (4134, 1546)\t1\n",
      "  (4134, 4195)\t1\n",
      "  (4134, 891)\t1\n",
      "  (4134, 1092)\t1\n",
      "  (4134, 1261)\t1\n",
      "  (4134, 7302)\t1\n",
      "  (4134, 6595)\t1\n",
      "  (4134, 1624)\t1\n",
      "  (4134, 1977)\t1\n",
      "  (4134, 7438)\t1\n",
      "  (4134, 6189)\t1\n",
      "  (4134, 6815)\t1\n",
      "  (4134, 2357)\t1\n",
      "  (4134, 4093)\t1\n",
      "  (4134, 6583)\t1\n",
      "  (4134, 5934)\t1\n",
      "  (4134, 1661)\t1\n",
      "  (4134, 5153)\t1\n",
      "  (4134, 6292)\t1\n",
      "  (4134, 3707)\t1\n",
      "  (4134, 6172)\t1\n",
      "  (4134, 3624)\t1\n",
      "  (4134, 4785)\t1\n"
     ]
    }
   ],
   "source": [
    "print(x_train_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "0da256aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "794f79bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr.fit(x_train_cv,y_train)\n",
    "prediction_train=lr.predict(x_train_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "ef635f95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.75816203143893\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_train,prediction_train)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "d32fc743",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr.fit(x_test_cv,y_test)\n",
    "prediction_test=lr.predict(x_test_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "0220b6ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.80657640232108\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test,prediction_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8240629",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a7b315e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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