Mail Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Relavant Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import nltk\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting environment to ignore future warnings\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Dataset"
   ]
  },
  {
   "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>mail</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                                               mail\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"data\", sep=\"\\t\", names=[\"label\", \"mail\"])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5572 entries, 0 to 5571\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   label   5572 non-null   object\n",
      " 1   mail    5572 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 87.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 7))\n",
    "sns.countplot(data.label)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = nltk.stem.PorterStemmer()\n",
    "clean_data = []\n",
    "for i in range(len(data)):\n",
    "    temp = re.sub('[^a-zA-Z]', ' ', data[\"mail\"][i]) # Removing Special words\n",
    "    temp = temp.lower()\n",
    "    temp = temp.split()\n",
    "    temp = [stemmer.stem(word) for word in temp if word not in nltk.corpus.stopwords.words(\"english\")] # Stemming th words\n",
    "    temp = ' '.join(temp)\n",
    "    clean_data.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['go jurong point crazi avail bugi n great world la e buffet cine got amor wat',\n",
       " 'ok lar joke wif u oni',\n",
       " 'free entri wkli comp win fa cup final tkt st may text fa receiv entri question std txt rate c appli',\n",
       " 'u dun say earli hor u c alreadi say',\n",
       " 'nah think goe usf live around though']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_features=5000)   # Usinf max_features argument we'll get just important features like important words that repeating in most of mails.\n",
    "x = cv.fit_transform(clean_data).toarray()\n",
    "x[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding the label column data\n",
    "def encoder(x):\n",
    "    if x=='ham':\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "y = list(map(encoder, data[\"label\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting into train test\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.30, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Naive Bayes Classifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "model = MultinomialNB()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1428   29]\n",
      " [  18  197]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "result_table = confusion_matrix(y_test, y_pred)\n",
    "print(result_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.98      1457\n",
      "           1       0.87      0.92      0.89       215\n",
      "\n",
      "    accuracy                           0.97      1672\n",
      "   macro avg       0.93      0.95      0.94      1672\n",
      "weighted avg       0.97      0.97      0.97      1672\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model is  97.19 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "score = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy of the Model is \",round(score*100, 2), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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