MultinomialNB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TEXT CLASSIFICATION INTO SPAM or HAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages = pd.read_csv('smsspamcollection/SMSSpamCollection', sep='\\t',\n",
    "                           names=[\"label\", \"message\"])\n",
    "messages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>count</th>\n",
       "      <td>5572</td>\n",
       "      <td>5572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ham</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4825</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       label                 message\n",
       "count   5572                    5572\n",
       "unique     2                    5169\n",
       "top      ham  Sorry, I'll call later\n",
       "freq    4825                      30"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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>label</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>653</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 freq\n",
       "label                                                                       \n",
       "ham      4825   4516                             Sorry, I'll call later   30\n",
       "spam      747    653  Please call our customer service representativ...    4"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.groupby('label').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>0</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>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</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>155</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>49</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>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  length\n",
       "0   ham  Go until jurong point, crazy.. Available only ...     111\n",
       "1   ham                      Ok lar... Joking wif u oni...      29\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
       "3   ham  U dun say so early hor... U c already then say...      49\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...      61"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages['length'] = messages['message'].apply(len)\n",
    "messages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1738b8f8088>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "messages['length'].plot(bins=50, kind='hist') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5572.000000\n",
       "mean       80.489950\n",
       "std        59.942907\n",
       "min         2.000000\n",
       "25%        36.000000\n",
       "50%        62.000000\n",
       "75%       122.000000\n",
       "max       910.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.length.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001738C8674C8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001738C8A0E48>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "messages.hist(column='length', by='label', bins=50,figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to remove punctuation, stopwords , returns tokenized messages\n",
    "def text_process(mess):\n",
    "   \n",
    "\n",
    "    nopunc = [char for char in mess if char not in string.punctuation]\n",
    "\n",
    "\n",
    "    nopunc = ''.join(nopunc)\n",
    "    \n",
    "    \n",
    "    return [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>0</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>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</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>155</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>49</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>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  length\n",
       "0   ham  Go until jurong point, crazy.. Available only ...     111\n",
       "1   ham                      Ok lar... Joking wif u oni...      29\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
       "3   ham  U dun say so early hor... U c already then say...      49\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...      61"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "msg_train, msg_test, label_train, label_test = train_test_split(messages['message'], messages['label'], test_size=0.2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('bow', CountVectorizer(analyzer=text_process)),  # strings to token integer counts\n",
    "#     ('tfidf', TfidfTransformer()),  # integer counts to weighted TF-IDF scores\n",
    "    ('classifier', MultinomialNB()),  # train on TF-IDF vectors w/ Naive Bayes classifier\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('bow',\n",
       "                 CountVectorizer(analyzer=<function text_process at 0x000001738CC5DF78>,\n",
       "                                 binary=False, decode_error='strict',\n",
       "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
       "                                 input='content', lowercase=True, max_df=1.0,\n",
       "                                 max_features=None, min_df=1,\n",
       "                                 ngram_range=(1, 1), preprocessor=None,\n",
       "                                 stop_words=None, strip_accents=None,\n",
       "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                 tokenizer=None, vocabulary=None)),\n",
       "                ('classifier',\n",
       "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(msg_train,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pipeline.predict(msg_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[950,  15],\n",
       "       [  6, 144]], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "confusion_matrix(predictions,label_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.99      0.98      0.99       965\n",
      "        spam       0.91      0.96      0.93       150\n",
      "\n",
      "    accuracy                           0.98      1115\n",
      "   macro avg       0.95      0.97      0.96      1115\n",
      "weighted avg       0.98      0.98      0.98      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predictions,label_test))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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