spam_ham2.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>4</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>5</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>5568</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>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</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>5572</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": [
       "     label                                            message\n",
       "1      ham  Go until jurong point, crazy.. Available only ...\n",
       "2      ham                      Ok lar... Joking wif u oni...\n",
       "3     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "4      ham  U dun say so early hor... U c already then say...\n",
       "5      ham  Nah I don't think he goes to usf, he lives aro...\n",
       "...    ...                                                ...\n",
       "5568  spam  This is the 2nd time we have tried 2 contact u...\n",
       "5569   ham              Will Ì_ b going to esplanade fr home?\n",
       "5570   ham  Pity, * was in mood for that. So...any other s...\n",
       "5571   ham  The guy did some bitching but I acted like i'd...\n",
       "5572   ham                         Rofl. Its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"spam.csv\" , sep=\",\", encoding='latin-1' , names=[\"label\",\"message\",\"a\",\"aa\",\"sa\"])\n",
    "data=data.drop(0).dropna(axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>message</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  length\n",
       "1   ham  Go until jurong point, crazy.. Available only ...     111\n",
       "2   ham                      Ok lar... Joking wif u oni...      29\n",
       "3  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
       "4   ham  U dun say so early hor... U c already then say...      49\n",
       "5   ham  Nah I don't think he goes to usf, he lives aro...      61"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['length'] = data['message'].apply(len)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hdata=data.loc[data['label'] == 'ham']\n",
    "sdata=data.loc[data['label'] == 'spam']\n",
    "hdata['length'].plot(bins=50,kind='hist',color='blue')\n",
    "# sdata['length']  = hdata['length'].twinx()\n",
    "sdata['length'].plot(bins=50,kind='hist',color='red')\n",
    "# hdata['length'].plot(bins=100,kind='hist',color='b')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "text preproseccing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "stopwords , punctuation, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "# from nltk.corpus import stopwords\n",
    "stopwords = ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n",
    "def process(msg):\n",
    "    nopunc = [c for c in msg if c not in string.punctuation]\n",
    "    nopunc=''.join(nopunc)\n",
    "    return [w for w in nopunc.split() if w.lower() not in stopwords]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       [Go, jurong, point, crazy, Available, bugis, n...\n",
       "2                          [Ok, lar, Joking, wif, u, oni]\n",
       "3       [Free, entry, 2, wkly, comp, win, FA, Cup, fin...\n",
       "4           [U, dun, say, early, hor, U, c, already, say]\n",
       "5       [Nah, dont, think, goes, usf, lives, around, t...\n",
       "                              ...                        \n",
       "5568    [2nd, time, tried, 2, contact, u, U, å£750, Po...\n",
       "5569                   [Ì, b, going, esplanade, fr, home]\n",
       "5570                     [Pity, mood, Soany, suggestions]\n",
       "5571    [guy, bitching, acted, like, id, interested, b...\n",
       "5572                                   [Rofl, true, name]\n",
       "Name: message, Length: 5572, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['message'].apply(process)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "#bow -> bag of words\n",
    "bow_transformer = CountVectorizer(analyzer=process).fit(data['message'])\n",
    "data_bow=bow_transformer.transform(data['message'])\n",
    "bow4=bow_transformer.transform([data['message'][3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow4.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "tfidf_transformer=TfidfTransformer().fit(data_bow)\n",
    "data_tfidf=tfidf_transformer.transform(data_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 11003)\t0.1909725859033143\n",
      "  (0, 10964)\t0.15964606600812142\n",
      "  (0, 10570)\t0.1399031851067661\n",
      "  (0, 10398)\t0.2118051872602214\n",
      "  (0, 9143)\t0.1611563042398638\n",
      "  (0, 9091)\t0.2253022095695746\n",
      "  (0, 9050)\t0.2253022095695746\n",
      "  (0, 6470)\t0.18460060785524732\n",
      "  (0, 6244)\t0.3477748370983395\n",
      "  (0, 5535)\t0.1886735733993242\n",
      "  (0, 4652)\t0.16446336178389345\n",
      "  (0, 3882)\t0.15434394973306392\n",
      "  (0, 2754)\t0.17653182127701653\n",
      "  (0, 1909)\t0.15260120989906273\n",
      "  (0, 1808)\t0.4506044191391492\n",
      "  (0, 1529)\t0.21770108079052697\n",
      "  (0, 860)\t0.2118051872602214\n",
      "  (0, 442)\t0.21770108079052697\n",
      "  (0, 429)\t0.21770108079052697\n",
      "  (0, 422)\t0.09655718066459201\n",
      "  (0, 73)\t0.2253022095695746\n"
     ]
    }
   ],
   "source": [
    "tfidf4 = tfidf_transformer.transform(bow4)\n",
    "print(tfidf4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "spam_detect_model = MultinomialNB().fit(data_tfidf,data['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: spam\n",
      "expected: spam\n"
     ]
    }
   ],
   "source": [
    "print('predicted:',spam_detect_model.predict(tfidf4)[0])\n",
    "print('expected:',data.label[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions = spam_detect_model.predict(data_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4825    0]\n",
      " [ 114  633]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "# print(classification_report(data['label'],all_predictions))\n",
    "print(confusion_matrix(data['label'],all_predictions))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "training model using pipelining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "msg_train,msg_test,label_train,label_test = train_test_split(data['message'],data['label'],test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "pipeline = Pipeline([\n",
    "    ( 'bow',CountVectorizer(analyzer=process)),\n",
    "    # ('tfidf',TfidfTransformer()),\n",
    "    ('classifier',MultinomialNB()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;bow&#x27;,\n",
       "                 CountVectorizer(analyzer=&lt;function process at 0x0000025ED8BB01F0&gt;)),\n",
       "                (&#x27;classifier&#x27;, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;bow&#x27;,\n",
       "                 CountVectorizer(analyzer=&lt;function process at 0x0000025ED8BB01F0&gt;)),\n",
       "                (&#x27;classifier&#x27;, MultinomialNB())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer(analyzer=&lt;function process at 0x0000025ED8BB01F0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('bow',\n",
       "                 CountVectorizer(analyzer=<function process at 0x0000025ED8BB01F0>)),\n",
       "                ('classifier', MultinomialNB())])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(msg_train,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pipeline.predict(msg_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       1.00      0.98      0.99      1472\n",
      "        spam       0.89      0.98      0.94       200\n",
      "\n",
      "    accuracy                           0.98      1672\n",
      "   macro avg       0.95      0.98      0.96      1672\n",
      "weighted avg       0.99      0.98      0.98      1672\n",
      "\n",
      "[[1449    4]\n",
      " [  23  196]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predictions,label_test))\n",
    "print(confusion_matrix(label_test,predictions))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "prediction after pipelining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THE MESSAGE:  click here to win 10K dollars  \n",
      "is  spam\n"
     ]
    }
   ],
   "source": [
    "to_pred = input()\n",
    "pred=pipeline.predict([to_pred])\n",
    "print(\"THE MESSAGE: \",to_pred,\"\\nis \",pred[0])"
   ]
  }
 ],
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