spam_classification_with_naive_bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reset -f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Prepare the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  label                                               text\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...\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_table('SMSSpamCollection', header=None)\n",
    "data.columns = ['label', 'text']\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  label                                               text\n",
      "0   ham  go until jurong point crazy available only in ...\n",
      "1   ham                            ok lar joking wif u oni\n",
      "2  spam  free entry in  a wkly comp to win fa cup final...\n",
      "3   ham        u dun say so early hor u c already then say\n",
      "4   ham  nah i dont think he goes to usf he lives aroun...\n"
     ]
    }
   ],
   "source": [
    "# remove words with numbers, punctuation and capital letters\n",
    "alphanumeric = lambda x: re.sub(r\"\\w*\\d\\w*\", '', x)\n",
    "punc_lower = lambda x: re.sub('[%s]' % re.escape(string.punctuation), '', x.lower())\n",
    "\n",
    "data['text'] = data.text.map(alphanumeric).map(punc_lower)\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2 :Split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.text\n",
    "Y = data.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    go until jurong point crazy available only in ...\n",
       "1                              ok lar joking wif u oni\n",
       "2    free entry in  a wkly comp to win fa cup final...\n",
       "3          u dun say so early hor u c already then say\n",
       "4    nah i dont think he goes to usf he lives aroun...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     ham\n",
       "1     ham\n",
       "2    spam\n",
       "3     ham\n",
       "4     ham\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3900,)\n",
      "708             quite late lar ard  anyway i wun b drivin\n",
      "4338                         on a tuesday night r u  real\n",
      "5029    go chase after her and run her over while shes...\n",
      "4921       g says you never answer your texts confirmdeny\n",
      "2592            still work going onit is very small house\n",
      "Name: text, dtype: object\n",
      "\n",
      "\n",
      "(1672,)\n",
      "3245    squeeeeeze this is christmas hug if u lik my f...\n",
      "944     and also ive sorta blown him off a couple time...\n",
      "1044    mmm thats better now i got a roast down me id...\n",
      "2484        mm have some kanji dont eat anything heavy ok\n",
      "812     so theres a ring that comes with the guys cost...\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_train.head())\n",
    "print(\"\\n\");\n",
    "print(X_test.shape)\n",
    "print(X_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3900,)\n",
      "708     ham\n",
      "4338    ham\n",
      "5029    ham\n",
      "4921    ham\n",
      "2592    ham\n",
      "Name: label, dtype: object\n",
      "\n",
      "\n",
      "(1672,)\n",
      "3245    ham\n",
      "944     ham\n",
      "1044    ham\n",
      "2484    ham\n",
      "812     ham\n",
      "Name: label, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(Y_train.shape)\n",
    "print(Y_train.head())\n",
    "print(\"\\n\")\n",
    "print(Y_test.shape)\n",
    "print(Y_test.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Numerically encode the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3900, 6645)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "cv = CountVectorizer(stop_words='english')\n",
    "\n",
    "X_train_cv = cv.fit_transform(X_train)    # remember mean and standard deviation while fitting\n",
    "X_test_cv = cv.transform(X_test)    # apply mean and standard deviation while transforming\n",
    "\n",
    "print(X_train_cv.toarray().shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: Fit model and predict outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ham', 'ham', 'ham', ..., 'ham', 'spam', 'ham'], dtype='<U4')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "nb = MultinomialNB()\n",
    "\n",
    "# train the model\n",
    "nb.fit(X_train_cv, Y_train)\n",
    "\n",
    "# take the trained model and apply it to the X_test_cv\n",
    "Y_pred_cv = nb.predict(X_test_cv)\n",
    "Y_pred_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5: Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.985\n",
      "Precisision: 0.95\n",
      "Recall: 0.938\n",
      "F1 Score: 0.944\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "cm = confusion_matrix(Y_test, Y_pred_cv)\n",
    "sns.heatmap(cm, xticklabels=['actual_ham', 'actual_spam'], yticklabels=['predicted_ham', 'predicted_spam'], annot=True, fmt='d', annot_kws={'fontsize':20}, cmap=\"YlGnBu\")\n",
    "\n",
    "tn, fp = cm[0]\n",
    "fn, tp = cm[1]\n",
    "\n",
    "accuracy = round((tp+tn)/(tp+tn+fp+fn), 3)\n",
    "precision = round((tp)/(tp+fp), 3)\n",
    "recall = round((tp)/(tp+fn), 3)\n",
    "f1 = round((2*precision*recall)/(precision+recall), 3)\n",
    "\n",
    "print('Accuracy: {}'.format(accuracy))\n",
    "print('Precisision: {}'.format(precision))\n",
    "print('Recall: {}'.format(recall))\n",
    "print(\"F1 Score: {}\".format(f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}