Spam-Ham Classification Pipeline/Spam-Ham Classification Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "messages = pd.read_csv('SMSSpamCollection', sep='\\t',names=[\"label\", \"message\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bd12ecd160>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6DoUkxzEIhI9W1ada80Ozw0LteV9rnwZWD718FfBAn/VJkp6uz6OPAlwD3FlVHxhatAPY1KY3ATcOtV/SjkLaADwyO8w06TwMVdJ86fPaR68D3gF8Pcntre09wPuA65NsBu4DLmrLbgLOB3YDjwGX9ljbMWX2MFTwGkqS+tVbKFTV3zH3fgKAc+foX8BlfdUjSXp2ntEsSeoYCpKkjqEgSeoYCpKkjqEgSeoYCgvI8PkI3tNZ0jh4j+YF5GnnI7z/LZ6sJmneuaWwUA1daluS5ouhMA5DN9xZevyJXsJC0oLh8NE4tK0AGFy2wktYSFoo3FKQJHUMBUlSx1CQJHUMBUlSx1CQJHUMBUlSx1CQJHUMBUlSx1CQJHV6C4Ukf55kX5JvDLWdkuTmJHe355Nbe5JclWR3kjuSnNNXXZKkZ9bnlsL/BN50QNvlwK6qWgfsavMA5wHr2mMLcHWPdUmSnkFvoVBVfwv88IDmjcD2Nr0duHCo/doa+DKwLMmKvmqTJM1tvvcpnF5VDwK059Na+0pgz1C/6dZ2kCRbkkwlmZqZmTkqRQ3f3EaSJtlC2dE817/GNVfHqtpWVeurav3y5cuPyofP3tzGexdImnTzHQoPzQ4Lted9rX0aWD3UbxXwwDzXJkkTb75DYQewqU1vAm4car+kHYW0AXhkdphJkjR/ervJTpKPAb8GnJpkGngv8D7g+iSbgfuAi1r3m4Dzgd3AY8ClfdUlSXpmvYVCVb3tGRadO0ffAi7rqxZJ0mgWyo5mSdICYChIkjqGgiSpM7Gh4AlrknSwiQ0FT1iTpINNbChIkg5mKEiSOoaCJKljKEiSOoaCJKljKCxGS47rDrddseqMcVcj6RjS27WPNEZPPdEdanvvFReMuRhJxxK3FCRJHUNhsXMoSdJhcPhosXMoSdJhcEtBktQxFCRJHUNBktQxFCbJ0E7npcefOOe0O6OlybagQiHJm5J8O8nuJJePu55Fp+10XrN1J089/tM5p/fev2fOlw7ff+JIguO5vl7S/FgwoZBkCfDfgfOAVwJvS/LK8VY12Yb/IR++/8TevXsP+x/4p71+KHhGCYuFHCgLuTbpSCyYUABeA+yuqnuq6nHg48DGMdc0eYaGmJ7xRkRDWxzDAXGoYalRPuOZwuK5BNLw+/QxTDZK/Qs9MAw2DUtVjbsGAJK8FXhTVf12m38H8E+r6vcO6LcF2NJmzwS+fYQfeSrw/SN87WLjutjPdbGf62K/xbYu1lTV8rkWLKST1+a6WfJBiVVV24Btz/nDkqmqWv9c32cxcF3s57rYz3Wx3ySti4U0fDQNrB6aXwU8MKZaJGkiLaRQ+CqwLslLkjwfuBjYMeaaJGmiLJjho6p6MsnvAZ8DlgB/XlXf7PEjn/MQ1CLiutjPdbGf62K/iVkXC2ZHsyRp/BbS8JEkacwMBUlSZ+JCYdIupZFkdZIvJrkzyTeTvLO1n5Lk5iR3t+eTW3uSXNXWzx1JzhnvNzj6kixJcluSnW3+JUluaevir9uBDiQ5vs3vbsvXjrPuoy3JsiQ3JLmr/T5eO6m/iyT/of338Y0kH0tywqT+LiYqFCb0UhpPAn9QVa8ANgCXte98ObCrqtYBu9o8DNbNuvbYAlw9/yX37p3AnUPzVwBXtnXxMLC5tW8GHq6qlwFXtn6LyYeAz1bVy4FXMVgnE/e7SLIS+H1gfVX9MoMDXS5mUn8XVTUxD+C1wOeG5t8NvHvcdc3zOrgReCODM8FXtLYVwLfb9IeBtw317/othgeD8192Aa8HdjI4afL7wNIDfyMMjoR7bZte2vpl3N/hKK2HFwLfPfD7TOLvAlgJ7AFOaX/nncC/nsTfRVVN1pYC+//4s6Zb20Rom7lnA7cAp1fVgwDt+bTWbbGvow8Cfwj8vM2/GPhRVT3Z5oe/b7cu2vJHWv/F4KXADPAXbSjtI0lewAT+LqrqfuD9wH3Agwz+zrcymb+LiQuFkS6lsRgl+QXgk8C7qurRQ3Wdo21RrKMkFwD7qurW4eY5utYIy451S4FzgKur6mzgx+wfKprLol0Xbb/JRuAlwD8CXsBguOxAk/C7mLhQmMhLaSQ5jkEgfLSqPtWaH0qyoi1fAexr7Yt5Hb0OeHOS7zG4Cu/rGWw5LEsyeyLn8Pft1kVb/iLgh/NZcI+mgemquqXN38AgJCbxd/EG4LtVNVNVTwCfAv4Zk/m7mLhQmLhLaWRw3eprgDur6gNDi3YAm9r0Jgb7GmbbL2lHm2wAHpkdTjjWVdW7q2pVVa1l8Lf/QlW9Hfgi8NbW7cB1MbuO3tr6L4r/I6yqvcCeJGe2pnOBbzGBvwsGw0YbkpzU/nuZXRcT97sAJmtHc/u7nQ/8P+A7wH8adz3z8H1/lcGm7R3A7e1xPoMx0F3A3e35lNY/DI7Q+g7wdQZHZIz9e/SwXn4N2NmmXwp8BdgNfAI4vrWf0OZ3t+UvHXfdR3kdnAVMtd/GZ4CTJ/V3Afxn4C7gG8B1wPGT+rvwMheSpM6kDR9Jkg7BUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLn/wOqWR9l1e1wjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "messages['length'] = messages['message'].apply(len)\n",
    "messages['length'].plot.hist(bins=100,histtype='bar',ec='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001BD11313B70>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001BD1609A7F0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "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', \n",
    "              by='label', \n",
    "              bins=50,\n",
    "              figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_process(mess):\n",
    "    nopunc = [char for char in mess if char not in string.punctuation]\n",
    "    \n",
    "    nopunc = ''.join(nopunc)\n",
    "    \n",
    "    return [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "bow_transformer = CountVectorizer(analyzer=text_process).fit(messages['message'])\n",
    "messages_bow = bow_transformer.transform(messages['message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "tfidf_transformer = TfidfTransformer().fit(messages_bow)\n",
    "\n",
    "messages_tfidf = tfidf_transformer.transform(messages_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: ham\n",
      "expected: ham\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "spam_detect_model = MultinomialNB().fit(messages_tfidf, messages['label'])\n",
    "\n",
    "# Let's check out the output\n",
    "message4 = messages['message'][3]\n",
    "bow4 = bow_transformer.transform([message4])\n",
    "tfidf_transformer = TfidfTransformer().fit(messages_bow)\n",
    "tfidf4 = tfidf_transformer.transform(bow4)\n",
    "print('predicted:', spam_detect_model.predict(tfidf4)[0])\n",
    "print('expected:', messages.label[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.98      1.00      0.99      4825\n",
      "        spam       1.00      0.85      0.92       747\n",
      "\n",
      "   micro avg       0.98      0.98      0.98      5572\n",
      "   macro avg       0.99      0.92      0.95      5572\n",
      "weighted avg       0.98      0.98      0.98      5572\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "all_predictions = spam_detect_model.predict(messages_tfidf)\n",
    "print (classification_report(messages['label'], all_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4457 1115 5572\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "msg_train, msg_test, label_train, label_test = \\\n",
    "train_test_split(messages['message'], messages['label'], test_size=0.2)\n",
    "\n",
    "print(len(msg_train), len(msg_test), len(msg_train) + len(msg_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       1.00      0.95      0.98      1008\n",
      "        spam       0.69      1.00      0.81       107\n",
      "\n",
      "   micro avg       0.96      0.96      0.96      1115\n",
      "   macro avg       0.84      0.98      0.89      1115\n",
      "weighted avg       0.97      0.96      0.96      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('bow', CountVectorizer(analyzer=text_process)),  \n",
    "    ('tfidf', TfidfTransformer()),  \n",
    "    ('classifier', MultinomialNB()), \n",
    "])\n",
    "\n",
    "pipeline.fit(msg_train,label_train)\n",
    "\n",
    "predictions = pipeline.predict(msg_test)\n",
    "\n",
    "print(classification_report(predictions,label_test))"
   ]
  }
 ],
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