Spam Filtering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "46791352",
   "metadata": {},
   "source": [
    "## Install the requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73f98411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not install packages due to an OSError: [WinError 5] Access is denied: 'C:\\\\Users\\\\haykd\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\~andas\\\\_libs\\\\algos.cp311-win_amd64.pyd'\n",
      "Check the permissions.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Requirement already satisfied: numpy==1.25.0 in c:\\users\\haykd\\appdata\\roaming\\python\\python311\\site-packages (from -r requirements.txt (line 1)) (1.25.0)\n",
      "Requirement already satisfied: scikit-learn==1.3.0 in c:\\users\\haykd\\appdata\\roaming\\python\\python311\\site-packages (from -r requirements.txt (line 2)) (1.3.0)\n",
      "Collecting pandas==2.0.3 (from -r requirements.txt (line 3))\n",
      "  Using cached pandas-2.0.3-cp311-cp311-win_amd64.whl (10.6 MB)\n",
      "Requirement already satisfied: scipy>=1.5.0 in c:\\users\\haykd\\appdata\\roaming\\python\\python311\\site-packages (from scikit-learn==1.3.0->-r requirements.txt (line 2)) (1.10.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\haykd\\appdata\\roaming\\python\\python311\\site-packages (from scikit-learn==1.3.0->-r requirements.txt (line 2)) (1.3.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\haykd\\appdata\\roaming\\python\\python311\\site-packages (from scikit-learn==1.3.0->-r requirements.txt (line 2)) (3.1.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\haykd\\appdata\\roaming\\python\\python311\\site-packages (from pandas==2.0.3->-r requirements.txt (line 3)) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\haykd\\appdata\\roaming\\python\\python311\\site-packages (from pandas==2.0.3->-r requirements.txt (line 3)) (2023.3)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\haykd\\appdata\\roaming\\python\\python311\\site-packages (from pandas==2.0.3->-r requirements.txt (line 3)) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\haykd\\appdata\\roaming\\python\\python311\\site-packages (from python-dateutil>=2.8.2->pandas==2.0.3->-r requirements.txt (line 3)) (1.16.0)\n",
      "Installing collected packages: pandas\n",
      "  Attempting uninstall: pandas\n",
      "    Found existing installation: pandas 2.0.1\n",
      "    Uninstalling pandas-2.0.1:\n",
      "      Successfully uninstalled pandas-2.0.1\n"
     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "217d9de5",
   "metadata": {},
   "source": [
    "## Download the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b653a63e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.request import urlretrieve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ccc86709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('spam.csv', <http.client.HTTPMessage at 0x25c86daba10>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/HaykDanghyan/email-spam-filtering/master/spam.csv'\n",
    "urlretrieve(url, 'spam.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a385eb7e",
   "metadata": {},
   "source": [
    "## Import all the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f3fed53b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98e8597",
   "metadata": {},
   "source": [
    "## Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3940f5d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_email = pd.read_csv('spam.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "232333c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will ü b going to esplanade fr home?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category                                            Message\n",
       "0         ham  Go until jurong point, crazy.. Available only ...\n",
       "1         ham                      Ok lar... Joking wif u oni...\n",
       "2        spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3         ham  U dun say so early hor... U c already then say...\n",
       "4         ham  Nah I don't think he goes to usf, he lives aro...\n",
       "...       ...                                                ...\n",
       "5567     spam  This is the 2nd time we have tried 2 contact u...\n",
       "5568      ham               Will ü b going to esplanade fr home?\n",
       "5569      ham  Pity, * was in mood for that. So...any other s...\n",
       "5570      ham  The guy did some bitching but I acted like i'd...\n",
       "5571      ham                         Rofl. Its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_email"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0254f280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_email.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "18a99aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category    0\n",
       "Message     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_email.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28ef4c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>4825</td>\n",
       "      <td>4516</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spam</th>\n",
       "      <td>747</td>\n",
       "      <td>641</td>\n",
       "      <td>Please call our customer service representativ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Message                                                            \\\n",
       "           count unique                                                top   \n",
       "Category                                                                     \n",
       "ham         4825   4516                             Sorry, I'll call later   \n",
       "spam         747    641  Please call our customer service representativ...   \n",
       "\n",
       "               \n",
       "         freq  \n",
       "Category       \n",
       "ham        30  \n",
       "spam        4  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_email.groupby('Category').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c5dac483",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turning ham/spam into numerical data by creating a new column\n",
    "df_email['Spam'] = df_email['Category'].apply(lambda x: 1 if x == 'spam' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc6656bc",
   "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>Category</th>\n",
       "      <th>Message</th>\n",
       "      <th>Spam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will ü b going to esplanade fr home?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category                                            Message  Spam\n",
       "0         ham  Go until jurong point, crazy.. Available only ...     0\n",
       "1         ham                      Ok lar... Joking wif u oni...     0\n",
       "2        spam  Free entry in 2 a wkly comp to win FA Cup fina...     1\n",
       "3         ham  U dun say so early hor... U c already then say...     0\n",
       "4         ham  Nah I don't think he goes to usf, he lives aro...     0\n",
       "...       ...                                                ...   ...\n",
       "5567     spam  This is the 2nd time we have tried 2 contact u...     1\n",
       "5568      ham               Will ü b going to esplanade fr home?     0\n",
       "5569      ham  Pity, * was in mood for that. So...any other s...     0\n",
       "5570      ham  The guy did some bitching but I acted like i'd...     0\n",
       "5571      ham                         Rofl. Its true to its name     0\n",
       "\n",
       "[5572 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_email"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb30a56",
   "metadata": {},
   "source": [
    "## Create the training/testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a076a546",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_email.Message, df_email.Spam, test_size=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a1b6fbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()\n",
    "X_train_count = cv.fit_transform(X_train.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa38fb80",
   "metadata": {},
   "source": [
    "## Create the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e07a193",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fb9168ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-1 {color: black;}#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>MultinomialNB()</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\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_count, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd6f8ee",
   "metadata": {},
   "source": [
    "## Predict wheter the email is ham or spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c9983df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pre-test ham prediction\n",
    "email_ham = ['Hey, wanna meet up for the game?']\n",
    "email_ham_count = cv.transform(email_ham)\n",
    "model.predict(email_ham_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7acdd3d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pre-test spam prediction\n",
    "email_spam = ['Win 10000$ right now, click and register by the link']\n",
    "email_spam_count = cv.transform(email_spam)\n",
    "model.predict(email_spam_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1900ee6",
   "metadata": {},
   "source": [
    "## Testing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "448a7ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_count = cv.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6ff0612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9883355764917003"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test_count, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "23cf962c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b3c79458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9883355764917003"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred == y_test) / len(y_test)"
   ]
  }
 ],
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