Spam SMS Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a391d3cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all libraries and dependencies\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80244738",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading and Encoding the Dataset\n",
    "\n",
    "df= pd.read_csv(\"spam.csv\", encoding= 'ISO-8859-1', encoding_errors = 'strict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "40a7de76",
   "metadata": {},
   "outputs": [
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       "      <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",
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       "      <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",
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      ],
      "text/plain": [
       "     v1                                                 v2 Unnamed: 2   \n",
       "0   ham  Go until jurong point, crazy.. Available only ...        NaN  \\\n",
       "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
       "3   ham  U dun say so early hor... U c already then say...        NaN   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "\n",
       "  Unnamed: 3 Unnamed: 4  \n",
       "0        NaN        NaN  \n",
       "1        NaN        NaN  \n",
       "2        NaN        NaN  \n",
       "3        NaN        NaN  \n",
       "4        NaN        NaN  "
      ]
     },
     "execution_count": 3,
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   "source": [
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "id": "7d591025",
   "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": 5,
   "id": "6c8783a7",
   "metadata": {},
   "outputs": [
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       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
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       "      <th>count</th>\n",
       "      <td>5572</td>\n",
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       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
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       "      <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",
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       "      <th>freq</th>\n",
       "      <td>4825</td>\n",
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       "          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": 5,
     "metadata": {},
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ef3db73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning the Dataset\n",
    "\n",
    "df=df.drop(df.columns[[2,3,4]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d474656",
   "metadata": {},
   "outputs": [
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       "    <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",
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      "text/plain": [
       "     v1                                                 v2\n",
       "0   ham  Go until jurong point, crazy.. Available only ...\n",
       "1   ham                      Ok lar... Joking wif u oni...\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3   ham  U dun say so early hor... U c already then say...\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro..."
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "01120adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the Columns\n",
    "\n",
    "df.rename(columns={'v1': 'Labels', 'v2': 'Texts'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f7c445b4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>ham</td>\n",
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       "      <th>2</th>\n",
       "      <td>spam</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>ham</td>\n",
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       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>ham</td>\n",
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      "text/plain": [
       "  Labels                                              Texts\n",
       "0    ham  Go until jurong point, crazy.. Available only ...\n",
       "1    ham                      Ok lar... Joking wif u oni...\n",
       "2   spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3    ham  U dun say so early hor... U c already then say...\n",
       "4    ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40bcacc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Texts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f835e2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Texts'] = df['Texts'].apply(lambda x: ' '.join(re.findall(r'[A-Za-z0-9]+', str(x))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dae61cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Go until jurong point crazy Available only in bugis n great world la e buffet Cine there got amore wat'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Texts'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e6176ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Labels=df.Labels.apply(lambda x: 1 if x==\"spam\" else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b491f89",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Labels</th>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "   Labels                                              Texts\n",
       "0       0  Go until jurong point crazy Available only in ...\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 arou..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "37559a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label data are converted into a decimal number using lambda.\n",
    "\n",
    "df.Labels=df.Labels.apply(lambda x: 1 if x==\"spam\" else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "08dbafe0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Go until jurong point crazy Available only in ...</td>\n",
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       "      <td>Ok lar Joking wif u oni</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor U c already then say</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   Labels                                              Texts\n",
       "0       0  Go until jurong point crazy Available only in ...\n",
       "1       0                            Ok lar Joking wif u oni\n",
       "2       0  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 arou..."
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6fca4444",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DATA VISUALIZATION\n",
    "\n",
    "le = LabelEncoder()\n",
    "df[\"Labels\"] = le.fit_transform(df[\"Labels\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1a762d3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I6OK988MxOH1w6aPPFMSyLb69GoGILt2xKjPWHRK7PF8EnyiIo+WN2JxbLToGESnYEh/c2NMnCmL51uOiIxCRwu04UYvDZQ2iY3iV6gvC5nBizb5S0TGISAVW+djJatUXxMYjlahr5m0EiajzVu8p8alNPlVfEF/sLREdgYhUorKxFd/nVIqO4TWqLogWqwPf+uDKAyLyHF+aZlJ1QXxzsAzNVt+8ApKIPGPdwXLUt/jGtLWqC+I/nF4iIjdrtTt95n4yqi2IumYrNh71nblCIvKeL31kZaRqC2Lt/jLYHL6z2oCIvGdzbhXqmq0XfqDCqbYgPt/DmwIRkWfYnRK+2q/+aSZVFkR5gwXbj9WIjkFEKuYLF+CqsiD+s7eE95wmIo/aml+N6qZW0TE8SpUF8d+f1N/sRCSWwylhU06V6BgepbqCaLTYsLeoXnQMIvIBm3PVXRB60QHcbfuxGsXslSI5Haj/4UM0HdwAp7kWuqAIBPa7BqEjpkGj0bQ9zlZViNqNS2Ep2A9IDhgikxF945PQh8S4PG7TT9+i+r+vtX+nzoCUxz5re9NhrkXthmWwHN8Np8UMv6S+iBhzDwwRCZ54qkSqtCVP3bcRUF1BKOkb1pC9Co171iJy4iMwRiWjtTQH1WsXQesXiJBBkwAAttpSlH3wOwRljkXYFTOgMZpgqyqARmc877E1RhMS7vrXGe/4+a+SJKHi02eh0eoRPXUBtEYTGn5cjfKPFqDLnYuhNfp74ukSqU5xXQtOVJuREhkoOopHqG6KaauCCqK1+BACegyFqftg6ENjEdj7CgR0HQBr6dG2x9R9vxwB3QchfPQdMMZ2hyE8Hqa0odAFhp3/4BoNdEHhP/8XGN72IXttCawlRxAx7j74xfeEITIREePvg2S3wnxoo4eeLZE6qflmZKoqiLpmKw4p6IYefgl9YDmxF7aak9dsWCvyYSk6CP/UgQAASXKiJX8H9OFdUP7RQhT+fQZKlz+K5qNbL3hsydqCosVzUPTGbFSs+hOslSd+/pjj5D4yGv3PoxCNRguNzoDWooPufIpEqrc5T73nIVQ1xbQtvxqSMk4/AABCht0MZ2szSpbcC2i1gNOJsJGzENR3NADAaa6HZG1BQ/YnCLtyFsJHzUHLsZ2o/Ox5xN72PPyTM1we1xCRgMjrHoIxuhucrWY0bP8UZe8/ji53vgF9SBQMEYnQhUSjbuO7iJjwG2gNfmj48XM4GqvgaOL1I0QXY1teNSRJanfeUC1UVRBKOv8AAM2HNsF8cAOibngMhugUWMvzUbt+CXRBkQjKuAaS5AQABPQYhpDBUwAAxthUtBYfQuOetecsCL+EPvBL6NPu7ZK35qJpz1qEjZwFjU6P6Bvno3rtIhQtmgZotPDv2v/kyEVBBUskB9VmKw6XNaJPfIjoKG7HghCodsNShA67GYHpVwEAjNFdYW+oQP22jxGUcQ10phBAq4MhKqnd5xkiky5qKkij08MYmwpb3c/Xh/jF9UCXOX+Hs9UMyWGHzhSK0uWPwhiX5p4nR+RDNudWqbIgVHMOoqLRgtyKJtExLopkawU07b8FGo0WODVy0OgM8ItLg72m/b5Stppi6M6xxNXl13E6YK080e5E9Wlav0DoTKGw1RTDWpYLU9rQS3gmRL5NaS9OO0o1IwglrV46LaDHENRv+Qi6kGgYo5JhLc9Dw4+rEZQ5tu0xIUOnovLzF+GX2Bf+KZloyd+JltztiJ3+57bHVK15BbrgSIRfNRsAULd5Bfy69II+vAucliY0bP8UjoYKBF02vu1zzId/gM4UAl1IDGyVx1Hz7ZswpQ1DQLcsrz1/IrXYfqwGdocTep1qXnMDYEEIFTHmHtRteh8137wBZ3M9dEERCOp/LcIun9b2GFPPEYgcfx/qt32M2vVvQh+RgOgbfw//xL5tj7E3VLYbiTgtTaj+6u9wmGuh9Q+CX2wPxM18Ccao5LbHOJpqUPvdW3CY66ALCkdQ36sResbXJaKOa2q1Y29RPQamnD1KVzKNJClp3c+5jX55A45VmUXHICIftWBiH/zflamiY7iVKsZDLVYHjlezHIhInKPljaIjuJ0qCiK3oklR1z8QkfocKVfWIpmOUEVBqLG5iUhZcssboZIZ+zbqKIgKFgQRiWW2OlBU2yI6hlupoiByVDi0IyLlOVKmrherqigITjERkRwcUdnvIsUXRLPVjuI6dQ3riEiZ1PZiVfEFkVPOFUxEJA+cYpIZtTU2ESlXfpUZdodTdAy3UXxB5Chsgz4iUi+r3amqi3YVXxAcQRCRnBwpU8+LVsUXBJe4EpGc5Feq53eSogvC7nCitJ4rmIhIPiqbWkVHcBtFF0S12QonVzARkYxUm62iI7iNoguiokE9TU1E6lDNEYQ8VDZZREcgImqnuokjCFngCIKI5IZTTDJR0ciCICJ5qWu2wqGSk6OKLogaFTU1EamDU1LP7yZFF0Rdszq+CUSkLiwIGahvsYmOQER0FrWsZGJBEBG5WRVHEOLVsSCISIY4gpCBBhYEEckQz0HIQKPFLjoCEdFZmq0O0RHcQtEFwTvJEZEc8ToIOdCIDkBEdDa7Ux13lVN0QbAfiEiO1HLXUWUXBBuCiGTIwRGEeBqOIYhIhuwqOQehFx2gMziCIHcJ0DmwrtsKRLYUiI5CKmANGgugv+gYnabsghAdgFSjxaHDo1WTsFK7ENrmStFxSOECEjNFR3ALZU8xcQhBbrS9LgSPGOZDMgaKjkJKp1H0r9Y2in4WrAdyt8/LY/Bi6HxIWoPoKKRkKnnxquiCYEOQJywu7IoP4x4XHYOUTO8nOoFbKLog2A/kKfPz+2Fj0lzRMUip/IJFJ3ALZReESoZxJE+351yJw0m3io5BSmQMEp3ALRRdECajTnQEUrmJuTegLGGs6BikNH4hohO4haILIipIHfN8JF8OSYuxJ2ahMWaQ6CikJH4cQQgXFWQUHYF8QKNdj2sr7kdreE/RUUgpeA5CvEiOIMhLiix+uNX8GBxB8aKjkBLwHIR4nGIib9rTEIT7Nb+HpJL5ZfIglfyMKLwgOMVE3vVVZST+FDQfko4/e3QegVGiE7iFogsiOpgjCPK+d4qTsDTmCUi8Eodc0gDB6piKVHRBcIqJRHnmWB+sS3xAdAySI1MkoFfHCJMFQXSJ7s4dhn1JM0XHILkJUcfoAVB8QaijpUm5Judei6LE60THIDkJ7iI6gdsouiDCTUbotJwHJnEkSYPxx6ajLm646CgkFxxByINWq0G4iaMIEsvs0GJC6T2wRPQRHYXkgCMI+YgN4XkIEq+s1YibGh+FPThRdBQSLTRBdAK3UXxB9IhRxxWLpHwHGgPxf44n4fQPFx2FRIrsITqB2yi+IHrFqWPPE1KHDTXhWBgwH5LeX3QUEiVKPXt2Kb4gerMgSGY+KO2Cf0Y+CUkl9yWmi2CKAkwRolO4jeJ/gnvFqWPPE1KXF06kYU3CI6JjkLdF9xKdwK0UXxAJYQEI9tOLjkF0lgdyB2JH8h2iY5A3qWh6CVBBQQBAT04zkUzdfHQMjidOFh2DvIUjCPnhiWqSs/H5t6A6fqToGOQNUWmiE7iVKgqCJ6pJzlqdWkwovhPNUZmio5CnxaSLTuBWqiiIXrEsCJK3SqsBk2sfgi0kRXQU8pTgLkCIeq6iBlRSEL25kokUIMccgNm2eXAGqONmMvQLiQNFJ3A7VRREqMmAuBBemETyt7k2FL/zmw/JECg6CrlbAgtCtniimpTik7JYvBb+JCStfJZnf3/CjhtWNKPLK43QPN2A1Ydt7T7+1AYLer/ehMDnGxD+QgPGLDcju8je4eP/5YdWaJ5uwMNfWdq9/82dVoxaZkbInxugeboBdRbJLc9HiIRBohO4nWoKon9SmOgIRB22qCAVq+J+KzpGG7NVwmWxWvzjOtcj8Z6ROrx+nT9+mhuEH+YEomuYFuPeb0al2XnBY/9Y7MC/dlqRGXv2r5tmm4QJPfT4/ZUK33RTowW6DBCdwu1UUxDDUiNFRyC6KI/lX4YtSXeLjgEAuDbNgGev9seNfQwuPz49w4AxqXqkhmvRN0aHv473R0MrsK/8/AXRZJUw49MWLLkhAOH+Z9+75eFhfph3hR+GJerc8jyEie4D+Klv41DVFMSA5DAY9ap5OuQjpueMQk7SzaJjXBSrQ8KbO60I9QMuizv/v7n7/2vBxDQ9xqTKZzrNIxKyRCfwCNV81/wNOgxICkP2sRrRUYguynV5N2Jz11rElKwXHeW81hy1YdonLWi2AfHBGqybFYgo07kLYuV+G3aVOvDjXT5wQr7rlaITeISqXnIP5TQTKZDNqcHYwtvRFC3vOezRXfXYc28QttxpwoTuevzqk2ZUnOMcRGG9Ew99ZcEHUwPgr/eB2wKnjhKdwCNUVRDDUtWzzS75lnqbHtdXPQBrWKroKOcUaNSgR4QWwxL1eHtyAPRaDd7eZXP52J2lDlSYJWT9ywz9Mw3QP9OAjScc+Fu2FfpnGuBwKni10i/FpAPBsaJTeIRqppgAYGBKOAIMOrTYHKKjEF204y3+mG58Ah8FLoTOXCE6zgU5JQmtDte/6K/ppsdPc9tPLc35vAW9o3R44nIjdFoVjSpSR4tO4DGqGkH46XUYylEEKdiO+mA8rJsPyejdFTFNVgl7yhzYU3byxdWxWif2lDlQUO+E2Srh9+st2FZkx4k6J3aWOHDH5y0obpBwS/rPq56uWW7G69utAIBgPw36xeja/Rdo0CAy4OT7TytrOvl1cmtOTlX9VH4yQ02LgkYY3dVbEKoaQQDAVT2jseFIpegYRJfsPxXR6JK4APNqFkLjdD2F4247ShwY/W5z29uPftMKoBW3X2bAP6/3x+EqJ97d24KqZgmRARoMTtBh05xA9D3jl31ejRNVzRe+LuJM/9xhxdMbrW1vj1x2MsPSyf6Y3d/YuSflDTojkHK56BQeo5EkSUFVfWH5lU24+pWNomMQddqzqQcwo+R5aKCqf6Lq0vVKYPYa0Sk8RlVTTACQGh2EpIgA0TGIOm1Bfl9sSJorOgadT/erRSfwKNUVBACMTIsWHYHILebkXIGDSbeJjkHnkq7uuwWqsiDG9Y0THYHIbW7InYjShPGiY9AvxfYDIruLTuFRqiyIK3pEISpI4Zt/EZ3ikLQYc2ImGmKHiI5CZ+ozSXQCj1NlQei0GtxwWbzoGERuY7brcG35XLSG9xIdhU5T+fQSoNKCAIAbBySIjkDkVsUWP9xi/i0cQeq6raUiRfUCYnqLTuFxqi2IzMQwpEb7wCZh5FP2NQRhLn4PyS9UdBTflq7+6SVAxQUBAFP6cxRB6vNNVQSeDpwPScfzbMKkTxGdwCtUXRCcZiK1WlaSiLej50GCivY0UorYDCCun+gUXqHqgkiKMGFgSrjoGEQe8ezxXvg68UHRMXzPgJmiE3iNqgsCAKZwFEEqdm/uUOxNmiU6hu/Q+QGZvxKdwmtUXxDXZ8TDoOMwnNRrSu4EFCZeLzqGb+g9ETD5zo7Rqi+I8EAjrurJrTdIvSRJg3HHbkVtnHp3FZWNLN8aram+IADgxgGJoiMQeVSLQ4fxpXejJbKv6CjqFZoMdBslOoVX+URBjE2PRWwIlwSSulW0GjC1/hHYQ5JER1Gn/tMBrU/8ymzjE8/WqNdi9ohuomMQedyhJhPudMyDM8B35sm9QqsHsn4tOoXX+URBAMCMYckI8lPdDfSIzrKxOhwL/OdD0vO+KG7TZxIQ6nsrIn2mIEL8DZg2mENv8g0flsbjH5FPQtLoLvxgurDh94tOIITPFAQA3HFFN+i1XPJKvuHlEz3wecIjomMoX+JgIHGQ6BRC+FRBdAkLwA2XcSdM8h0P52Zhe9KdomMo24jOXa1eWVmJuXPnIjk5GX5+foiLi8P48eOxefNmNwX0HJ+blL97ZCo+210sOgaR1/wq5xr8L60a3QpXi46iPBHdgd6duwjxpptugtVqxbvvvovU1FSUl5dj/fr1qK6udlNIz/GpEQQA9IkPwUheOEc+ZkLezaiOv0p0DOUZfn+nlrbW1dVh06ZNeOGFFzB69GikpKRgyJAhePLJJzFp0sktwzUaDRYvXoxrr70WAQEBSE1NxSeffNLuOE888QR69uwJk8mE1NRULFy4EDabre3jTz31FPr374933nkHycnJCAoKwn333QeHw4EXX3wRcXFxiImJwXPPPXdR+X2uIADgnpGpoiMQeVWrU4txxXegOeoy0VGUIzge6D+jU4cICgpCUFAQVq9ejdbW1nM+buHChbjpppuwd+9ezJgxA9OmTcOhQ4d+jhIcjGXLluHgwYNYtGgRlixZgldffbXdMfLy8rB27Vp89dVXWLFiBd5++21MnDgRRUVF2LhxI1544QUsWLAA2dnZHc6vkSRJuvinrXwT/7YJB0oaRMcg8qruphZ8FfwnGOqPi44if9e9DAy5q9OHWbVqFe666y60tLQgKysLV111FaZNm4bMzEwAJ0cQ9957LxYvXtz2OcOGDUNWVhbeeOMNl8d8+eWXsXLlSuzYsQPAyRHESy+9hLKyMgQHBwMAJkyYgCNHjiAvLw/aU6Og3r17Y/bs2Zg3b16HsvvkCAI4eS6CyNfkNQfg19Yn4DRFiY4ib6FJQNbtbjnUTTfdhJKSEnzxxReYMGECNmzYgKysLCxbtqztMcOHD2/3OcOHD283gvjoo49w+eWXIy4uDkFBQViwYAEKCgrafU7Xrl3bygEAYmNjkZ6e3lYOp99XUVHR4ew+WxATM+LRLYq3JCXfs7U2FI8b5kMy8Of/nEY+DuiNbjucv78/xo4di4ULF2LLli2YPXs2/vjHP3boc7du3YoZM2bguuuuw5o1a7B7927Mnz8fVqu13eMMBkO7tzUajcv3OZ3ODuf22YLQ67SYd636bzpO5Mqq8lj8Nfz3kLQ+t5DxwsK7dfrcw4Wkp6fDbDa3vb1t27Z2H9+2bRv69OkDANiyZQtSUlIwf/58DBo0CGlpaThx4oRH853m0z8d4/vGYVhqBLbl14iOQuR1fy/ohoTUxzGt5M+io8jLVU8AOvf8aqyursYtt9yCO+64A5mZmQgODsaOHTvw4osvYvLkyW2P+/jjjzFo0CBcccUV+OCDD7B9+3a8/fbbAIC0tDQUFBRg5cqVGDx4ML788kt89tlnbsl3IT47gjhtwcR08OJq8lXz8jPwQ9I9omPIR1RPIPNWtx0uKCgIQ4cOxauvvoqRI0eiX79+WLhwIe666y68/vrrbY97+umnsXLlSmRmZmL58uVYsWIF0tPTAQCTJk3CI488gt/85jfo378/tmzZgoULF7ot4/n47CqmMz328V58srNIdAwiYb5J+ww9Cz8WHUO8Wz8A+nj37nwajQafffYZpkyZ4tWv2xE+P4IAgN+N7wWTkZuake+amDcFFV2uER1DrNRRXi8HuWNBAIgJ8cc9I7uLjkEkjM2pwdjC29EUM1B0FDG0emDCX0SnkB1OMZ3SYnXg6lc2oLTeIjoKkTDJARZ8G/o8jHW5oqN415B7gOteFJ1CdjiCOCXAqMPj43uJjkEkVEGLP6a1PA5HYKzoKN5jigRGPyk6hSyxIM5w44AEXJYYKjoGkVC76oPxgHY+JL/gCz9YDUbPBwLCRaeQJRbEGTQaDRZcny46BpFw/62Mwp+D50PSue9qYlmKzQAGzhGdQrZYEL8wuGsEJvfnTYWI3ixKxnsxv4MElV4opNEBNyzq1Hbeasf/My48dUNfRAWp/JUTUQf84Vg6vktS6f2Yh98HJProqq0OYkG4EB5oxJ8m9xMdg0gW7swZgQNJ00XHcK+I7ifPPdB5sSDO4dqMeEzMiBcdg0gWrs+diJKECaJjuIkGmPw6YAgQHUT2WBDn8czkvogI5FQTkSRpMPbEDNTHDhMdpfMG3wmkjBCdQhFYEOcRGeTHqSaiU8x2HSaU34vWCAVfLxSaDIx5WnQKxWBBXMDEzHjcOCBBdAwiWSi1GHFz42OwByvx34QGmPQ3wC9IdBDFYEF0wDOT+yIhjPOVRADwU2Mg5kpPwukfJjrKxbn8IaD7aNEpFIUF0QHB/ga8emt/3jeC6JR1VRF4yrQAks5PdJSOSRwMXO2deyioCQuig4Z0i8A9V3HHV6LTlpd0wVvR8yBpZP5rxC8UuOltt90lzpfI/DsrL4+O7YlM7tVE1Oa5472wNuEh0THO74bXgPAU0SkUiQVxEQw6LRbPHMirrInOcF/uYOxOul10DNeyfg30myo6hWKxIC5SQlgAFs8cCIOOJySITpuaOw4FiTeIjtFedB9gwguiUygaC+ISDO4agacn8foIotMkSYPxx36F2rjLRUc5KSAcuO1DwGgSnUTRWBCXaPrQZMwaxnlNotNaHDqML70bLZGCXzxpdMDNS4GIVLE5VIAF0Ql/vCEdQ7tFiI5BJBsVrQbcWP8w7CHJ4kKMf47XO7gJC6IT9KdOWieG8yI6otMON5lwh30enAECXjz1nwkMm+v9r6tSLIhOigg0YsmvB8Fk1ImOQiQb39eE4Un/BZAMXjwHkDgEuP5V7309H8CCcIM+8SF45ZbLoOHCJqI2H5XG4fWIJyFpvPDiKSQRuPV9QM8l6O7EgnCTazPi8cDoHqJjEMnKKye647Muj3r2iwSEAzNXAcGxnv06PogF4UaPjO2JiZm8yRDRmR7NG4DspLs8c3CDCZj+byCmt2eO7+NYEG6k0Wjw2q39MaYPX8kQnenWnNHIT3LzFc1aPXDLMiBpiHuPS21YEG5m0GnxxowsXNUzWnQUIlm5Nm8qqrqMct8BJ/0d6Dnefcejs7AgPMCo1+JfswZieGqk6ChEstHq1GJs4R0wR/fv/MHGPAX0n97549B5sSA8xN+gw9uzB2FQSrjoKESyUWvTY1L1A7CFduIq5xEPAlc84r5QdE4sCA8yGfVYOmcwLksKEx2FSDbymgMws/V3cJouYRr28oeAcX9yfyhyiQXhYcH+Biy/Ywj6dgkRHYVINrLrQvCoYT4kY2DHP+mKR4Cxz3guFJ2FBeEFoQEGvHfnUPSM5c3SiU5bXR6Dl0PnQ9J24E5vV/725HkH8ioWhJdEBBrxwf8NQ2r0RbxiIlK5fxR2xcq4x8//oJG/A675g3cCUTsaSZIk0SF8SVm9BTPfzkZuRZPoKESysTxtE0YWLj77A1fNA0Y/6f1ABIAFIUR9iw33vLcD2/JrREchko2v0z5Hr8KPTr2lAcY9C4z4jdBMvo4FIYjV7sTvPtmL1XtKREchkgWdxonN3d5FXNkGYMobQOavREfyeSwIgSRJwivfHMXr/8sVHYVIFuJNwHfTQxHQ4wrRUQgsCFlYub0AC1bvh93JbwX5rtgQP7wzezD6dgkVHYVOYUHIxMajlbj/g11oarWLjkLkdb3jgrF0zmDEh/LujHLCgpCRgyUNuGPZjyhrsIiOQuQ1V6ZFYfHMgQjy68D1EORVLAiZKa1vwZylP+JwWaPoKEQeN31oMp6Z1Bd6HS/JkiMWhAw1Wmy4/8Pd+P5opegoRB5hMurw7JR+mJqVKDoKnQcLQqacTgl//y4Xi9YfBc9dk5r0ig3GP2ZkoUcMt56ROxaEzG3JrcKDK/egqqlVdBSiTrt1UBKentwX/gad6CjUASwIBahotOChFXuwNb9adBSiS2Iy6vDcjf1w4wBOKSkJC0IhHE4Jf1ufg9f/lwsH55xIQXrHBeP16ZxSUiIWhMLsOF6Dhz/ag6LaFtFRiC6IU0rKxoJQoEaLDQtX7+c+TiRbnFJSBxaEgn2+pxgLVu9Ho4VXX5N8DE+NxPNTM9Ativc+UToWhMKV1LXgqS8O4JuD5aKjkI+LCDTi99f1wc0DOWpQCxaESnx3uBx//OIACmt4boK87+aBiZh/XR+EBxpFRyE3YkGoiMXmwBv/y8U/v8+H1e4UHYd8QPfoQDx3YwaGpUaKjkIewIJQoWNVZvzh8/3YlFMlOgqplFGvxf2jemDuqO4w6rmPklqxIFRszb4SPLvmEHeHJbca0T0Sz07ph9RoXtegdiwIlTO32vHquqNYtuU4b0hEnRJ56iT0TTwJ7TNYED7icFkDnvriALbl14iOQgoTGmDA3SNTMXtEVwTyng0+hQXhY7bmVWPR+qMsCrqgYD897riiG+68shtC/A2i45AALAgflZ1fjde+zeEGgHQWk1GH2SO64u6RqQgzcdmqL2NB+Ljtx2rw2rdHsSWPReHr/A1azByagrmjuiMyyE90HJIBFgQBAH48XoNF3+bgh1wujfU1Rp0Wtw1Jwv2jeyAmxF90HJIRFgS1s/NEDV77NofXUPgAk1GHqVkJuG9UD3QJCxAdh2SIBUEu7S6oxXvbTuC/P5XCYuNV2WqSHGHCr4en4JZBSQgN4MlnOjcWBJ1XfYsNq3cXY8X2AhwuaxQdhy6RRgNcmRaN24enYHSvGGi1GtGRSAFYENRhewrrsHJ7Af6ztwRmq0N0HOqA6GA/3JSViGmDk9CV22/TRWJB0EVrarXjiz0lWLG9AD8V14uOQ7+g1QBX9YzGrYOTMaZPDPQ67pVEl4YFQZ2yv7geK38swOd7SnjjIoE0GmBAUhjG9Y3D5P5dEB/Kk87UeSwIcgur3YkteVX4+kA5vj1UjsrGVtGRVM+o12JE90iMS4/DmPQYxARziSq5FwuC3M7plLC7sBZfHyjHuoPlOFZlFh1JNYL99BjVOwbj+8ZiVK8YBHFvJPIgFgR53IlqMzYcqcSGIxXYml/NZbMXKTbED2P6xGJc3zgMT43k/RfIa1gQ5FUWmwPZx2rwQ04l9hTWYX9xA1psXBF1mkYDpEYFIis5HAOSw5GVEoZescHQaLgslbyPBUFCOZwSjpY3Ym9hHfYW1WNvYR2Oljf6zL0rgv306J8chgFJYRiQEo6spHCEmnjxGskDC4Jkx2Jz4EBJPfYUniyMfUV1OF7dLDpWp5mMOiRHmJCREIqslHBkJYcjLSaIF62RbLEgSBHqW2woqG5GcV0zimpbUFTbguK6U3/WNqNBBktsNRogJtgPyREmJEWYkBIRiOTIACRHmJAcEYjoYO6QSsrCgiBVaLTYTpXFyeIormtBVWMrLHYHWqwOWGxOtNgcsNgcaLU7T77v1Mda7WefNNdrNfDTa+Fv0MHfoIPJqENogAEhAQaE+OtP/WlAZJARSeEmpESeLAV/g07AsyfyDBYE+TxJktBqd8Jic0Cv08Jfr+XVx0RgQRAR0TnwZRIREbnEgiAiIpdYEERE5BILgoiIXGJBEBGRSywIIiJyiQVBREQusSCIiMglFgQREbnEgiAiIpdYEERE5BILgoiIXGJBEBGRSywIIiJyiQVBREQusSCIiMglFgQREbnEgiAiIpdYEERE5BILgoiIXGJBEBGRSywIIiJyiQVBREQusSCIiMglFgQREbnEgiAiIpdYEERE5BILgoiIXGJBEBGRSywIIiJyiQVBREQusSCIiMglFgQREbnEgiAiIpdYEERE5BILgoiIXGJBEBGRSywIIiJyiQVBREQusSCIiMglFgQREbnEgiAiIpdYEERE5BILgoiIXGJBEBGRS/8PpVlXA0sItmoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df[\"Labels\"].value_counts(),autopct = \"%.2f\", labels=['Ham','Spam'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da22f112",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAINING AND SPLITTING THE DATA\n",
    "# Data is divied into training and testing dataset.\n",
    "\n",
    "X_train, X_test, y_train, y_test=train_test_split(df.Texts, df.Labels, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2138722f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4457,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8fcb868a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CountVectorization for text transfrom \n",
    "\n",
    "cv=CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7ee6510",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_cv= cv.fit_transform(X_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d040b18c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2119                 I hope you know I m still mad at you\n",
       "3050    Awesome question with a cute answer Someone as...\n",
       "2093    Final Chance Claim ur 150 worth of discount vo...\n",
       "4143       That s a shame Maybe cld meet for few hrs tomo\n",
       "1141    I don t know u and u don t know me Send CHAT t...\n",
       "                              ...                        \n",
       "4397    Can you tell Shola to please go to college of ...\n",
       "1084    For me the love should start with attraction i...\n",
       "3967    Did u turn on the heater The heater was on and...\n",
       "1884             I love your ass Do you enjoy doggy style\n",
       "4095           Hey is rite u put 10 evey mnth is that all\n",
       "Name: Texts, Length: 4457, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0460e8d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sparse Matrix\n",
    "\n",
    "X_train_cv.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9bbee1dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4457, 7737)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_cv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a4ab33c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['consider', 'considering', 'consistently', 'console', 'constant',\n",
       "       'constantly', 'contact', 'contacted', 'contacts', 'contains',\n",
       "       'content', 'contented', 'contention', 'contents', 'continent',\n",
       "       'continue', 'continued', 'contract', 'contribute', 'control',\n",
       "       'convenience', 'conversations', 'convey', 'conveying', 'convinced',\n",
       "       'convincing', 'cook', 'cooked', 'cookies', 'cooking', 'cool',\n",
       "       'cooperative', 'copies', 'coping', 'cops', 'copy', 'corect',\n",
       "       'cornwall', 'corrct', 'correct', 'correction', 'correctly',\n",
       "       'corrupt', 'cos', 'cosign', 'cost', 'costa', 'costing', 'costs',\n",
       "       'costume', 'costumes', 'couch', 'cougar', 'cough', 'coughing',\n",
       "       'could', 'coulda', 'couldn', 'count', 'countinlots', 'country',\n",
       "       'counts', 'coupla', 'couple', 'courage', 'courageous', 'course',\n",
       "       'court', 'cousin', 'cover', 'coveragd', 'covers', 'coz', 'cozy',\n",
       "       'cps', 'cr', 'cr01327bt', 'cr9', 'crab', 'crack', 'cramps', 'crap',\n",
       "       'crash', 'crashed', 'crashing', 'crave', 'craving', 'craziest',\n",
       "       'crazy', 'crazyin', 'crckt', 'cream', 'created', 'credit',\n",
       "       'credited', 'credits', 'creep', 'creepy', 'cres', 'cribbs'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.get_feature_names_out()[2000:2100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bc16148d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_np=X_train_cv.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37230597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_np[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1ccb3cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2119                 I hope you know I m still mad at you\n",
       "3050    Awesome question with a cute answer Someone as...\n",
       "2093    Final Chance Claim ur 150 worth of discount vo...\n",
       "4143       That s a shame Maybe cld meet for few hrs tomo\n",
       "1141    I don t know u and u don t know me Send CHAT t...\n",
       "4146    Pls help me tell sura that i m expecting a bat...\n",
       "5043    Dunno my dad said he coming home 2 bring us ou...\n",
       "Name: Texts, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "656b0f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1790, 6065, 6856, 7454], dtype=int64),)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(X_train_np[2925]!=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f95679eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_np[2925][1661]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "89bbe3c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# USING NAIVE BAYES CLASSIFIER FOR PREDICTION\n",
    "\n",
    "NB=MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e526234b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NB.fit(X_train_cv, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "99dcca5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_cv= cv.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4cc69121",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=NB.predict(X_test_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "04a434f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       984\n",
      "           1       0.93      0.93      0.93       131\n",
      "\n",
      "    accuracy                           0.98      1115\n",
      "   macro avg       0.96      0.96      0.96      1115\n",
      "weighted avg       0.98      0.98      0.98      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0f9e2a83",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9838565022421525\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, y_pred))"
   ]
  }
 ],
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