Untitled2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dca4f7f3",
   "metadata": {},
   "source": [
    "$$\n",
    "P(A|B) = \\frac{P(B|A)P(A)}{P(B)}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d90e109",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8aacecc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5952b7d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "727acf80",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "33cdadf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "332b05f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c4ca1f67",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "36070738",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d04aa85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e06de77",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95602f78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "285033b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b042beef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "634c24b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "164f5a8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f652744",
   "metadata": {},
   "source": [
    "*Go to the \"Models\" tab and select \"punkt\" from the \"Identifier\" column.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "118e0237",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fbd4bf86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0b1146fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_table(\n",
    "    \"SMSSpamCollection\", sep=\"\\t\", header=None,\n",
    "    names=[\"label\", \"message\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1f5445a9",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            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..."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e98a64f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"label\"] = df.label.map({\"ham\": 0, \"spam\": 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "632f475a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Ok lar... Joking wif u oni...</td>\n",
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       "      <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",
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      "text/plain": [
       "   label                                            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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a8fbe7ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"message\"] = df.message.map(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6304771",
   "metadata": {},
   "source": [
    "```python\n",
    "\n",
    "sum = lambda x, y: x +y\n",
    "\n",
    "def sum(x, y):\n",
    "    return x + y\n",
    "\n",
    "---\n",
    "\n",
    "def t_lower(x):\n",
    "    return x.lower()\n",
    "\n",
    "df[\"message\"] = df.message.map(to_lower)\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1cbc06d5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>free entry in 2 a wkly comp to win fa cup fina...</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>u dun say so early hor... u c already then say...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>nah i don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>freemsg hey there darling it's been 3 week's n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>even my brother is not like to speak with me. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>as per your request 'melle melle (oru minnamin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>winner!! as a valued network customer you have...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>had your mobile 11 months or more? u r entitle...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                            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...\n",
       "5      1  freemsg hey there darling it's been 3 week's n...\n",
       "6      0  even my brother is not like to speak with me. ...\n",
       "7      0  as per your request 'melle melle (oru minnamin...\n",
       "8      1  winner!! as a valued network customer you have...\n",
       "9      1  had your mobile 11 months or more? u r entitle..."
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "23eacd5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gk/fddl43fd163gdtc83k14z_h00000gn/T/ipykernel_30752/2904496813.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"message\"] = df.message.str.replace(\"[^\\w\\s]\", \"\")\n"
     ]
    }
   ],
   "source": [
    "df[\"message\"] = df.message.str.replace(\"[^\\w\\s]\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eadffc5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>go until jurong point crazy available only in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>ok lar joking wif u oni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>free entry in 2 a wkly comp to win fa cup fina...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>u dun say so early hor u c already then say</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>nah i dont think he goes to usf he lives aroun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>freemsg hey there darling its been 3 weeks now...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>even my brother is not like to speak with me t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>as per your request melle melle oru minnaminun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>winner as a valued network customer you have b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>had your mobile 11 months or more u r entitled...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                            message\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 dont think he goes to usf he lives aroun...\n",
       "5      1  freemsg hey there darling its been 3 weeks now...\n",
       "6      0  even my brother is not like to speak with me t...\n",
       "7      0  as per your request melle melle oru minnaminun...\n",
       "8      1  winner as a valued network customer you have b...\n",
       "9      1  had your mobile 11 months or more u r entitled..."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2e909b25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function word_tokenize in module nltk.tokenize:\n",
      "\n",
      "word_tokenize(text, language='english', preserve_line=False)\n",
      "    Return a tokenized copy of *text*,\n",
      "    using NLTK's recommended word tokenizer\n",
      "    (currently an improved :class:`.TreebankWordTokenizer`\n",
      "    along with :class:`.PunktSentenceTokenizer`\n",
      "    for the specified language).\n",
      "    \n",
      "    :param text: text to split into words\n",
      "    :type text: str\n",
      "    :param language: the model name in the Punkt corpus\n",
      "    :type language: str\n",
      "    :param preserve_line: A flag to decide whether to sentence tokenize the text or not.\n",
      "    :type preserve_line: bool\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(nltk.word_tokenize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d251147a",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.message[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "854c2dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['go',\n",
       " 'until',\n",
       " 'jurong',\n",
       " 'point',\n",
       " 'crazy',\n",
       " 'available',\n",
       " 'only',\n",
       " 'in',\n",
       " 'bugis',\n",
       " 'n',\n",
       " 'great',\n",
       " 'world',\n",
       " 'la',\n",
       " 'e',\n",
       " 'buffet',\n",
       " 'cine',\n",
       " 'there',\n",
       " 'got',\n",
       " 'amore',\n",
       " 'wat']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.word_tokenize(df.message[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1735a4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"message\"] = df[\"message\"].apply(nltk.word_tokenize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "933be563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>[go, until, jurong, point, crazy, available, o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>[ok, lar, joking, wif, u, oni]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>[free, entry, in, 2, a, wkly, comp, to, win, f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>[u, dun, say, so, early, hor, u, c, already, t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>[nah, i, dont, think, he, goes, to, usf, he, l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>[freemsg, hey, there, darling, its, been, 3, w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>[even, my, brother, is, not, like, to, speak, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>[as, per, your, request, melle, melle, oru, mi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>[winner, as, a, valued, network, customer, you...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>[had, your, mobile, 11, months, or, more, u, r...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                            message\n",
       "0      0  [go, until, jurong, point, crazy, available, o...\n",
       "1      0                     [ok, lar, joking, wif, u, oni]\n",
       "2      1  [free, entry, in, 2, a, wkly, comp, to, win, f...\n",
       "3      0  [u, dun, say, so, early, hor, u, c, already, t...\n",
       "4      0  [nah, i, dont, think, he, goes, to, usf, he, l...\n",
       "5      1  [freemsg, hey, there, darling, its, been, 3, w...\n",
       "6      0  [even, my, brother, is, not, like, to, speak, ...\n",
       "7      0  [as, per, your, request, melle, melle, oru, mi...\n",
       "8      1  [winner, as, a, valued, network, customer, you...\n",
       "9      1  [had, your, mobile, 11, months, or, more, u, r..."
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3492ce75",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3981b1c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9403d715",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"message\"] = df[\"message\"].apply(\n",
    "    lambda x: [stemmer.stem(y) for y in x]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "319ff4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>[go, until, jurong, point, crazi, avail, onli,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>[ok, lar, joke, wif, u, oni]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>[free, entri, in, 2, a, wkli, comp, to, win, f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>[u, dun, say, so, earli, hor, u, c, alreadi, t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>[nah, i, dont, think, he, goe, to, usf, he, li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>[freemsg, hey, there, darl, it, been, 3, week,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>[even, my, brother, is, not, like, to, speak, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>[as, per, your, request, mell, mell, oru, minn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>[winner, as, a, valu, network, custom, you, ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>[had, your, mobil, 11, month, or, more, u, r, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                            message\n",
       "0      0  [go, until, jurong, point, crazi, avail, onli,...\n",
       "1      0                       [ok, lar, joke, wif, u, oni]\n",
       "2      1  [free, entri, in, 2, a, wkli, comp, to, win, f...\n",
       "3      0  [u, dun, say, so, earli, hor, u, c, alreadi, t...\n",
       "4      0  [nah, i, dont, think, he, goe, to, usf, he, li...\n",
       "5      1  [freemsg, hey, there, darl, it, been, 3, week,...\n",
       "6      0  [even, my, brother, is, not, like, to, speak, ...\n",
       "7      0  [as, per, your, request, mell, mell, oru, minn...\n",
       "8      1  [winner, as, a, valu, network, custom, you, ha...\n",
       "9      1  [had, your, mobil, 11, month, or, more, u, r, ..."
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9b47e61c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e2012783",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"message\"] = df[\"message\"].apply(lambda x: ' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2a070de2",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>go until jurong point crazi avail onli in bugi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                            message\n",
       "0      0  go until jurong point crazi avail onli in bugi..."
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "58e63cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vect = CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e47c724d",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = count_vect.fit_transform(df[\"message\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ac85d362",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bf9c3561",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer = TfidfTransformer().fit(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "522e4639",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = transformer.transform(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fb79b602",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    counts, df[\"label\"], test_size=0.1, random_state=71\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5e11a0c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "480a25a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MultinomialNB().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "37b4d8f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "69cf6e30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a5d663a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9372759856630825"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y_pred == y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "64342023",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cee486f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[471   0]\n",
      " [ 35  52]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c058752e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4825\n",
       "1     747\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e8249f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}