Email Spam Filtering/Email_Spam_filtering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# importing required modules\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>spam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Subject: naturally irresistible your corporate...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Subject: the stock trading gunslinger  fanny i...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Subject: unbelievable new homes made easy  im ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Subject: 4 color printing special  request add...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Subject: do not have money , get software cds ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  spam\n",
       "0  Subject: naturally irresistible your corporate...     1\n",
       "1  Subject: the stock trading gunslinger  fanny i...     1\n",
       "2  Subject: unbelievable new homes made easy  im ...     1\n",
       "3  Subject: 4 color printing special  request add...     1\n",
       "4  Subject: do not have money , get software cds ...     1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing the dataset\n",
    "dataset = pd.read_csv('emails.csv', encoding='latin-1')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4360\n",
       "1    1368\n",
       "Name: spam, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count observations in each label\n",
    "dataset.spam.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for splitting dataset into train set and test set\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(dataset[\"text\"],dataset[\"spam\"], test_size = 0.2, random_state = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4582,)\n",
      "(1146,)\n",
      "(4582,)\n",
      "(1146,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for vectorizing words\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words='english',\n",
       "        strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "        tokenizer=None, vocabulary=None)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect = CountVectorizer(stop_words='english')\n",
    "vect.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['00', '000', '0000', '00000000', '000000000003619', '000000000003991', '000000000003997', '000000000005168', '000000000005411', '000000000005413', '000000000005820', '000000000006238', '000000000007494', '000000000007498', '000000000007876', '000000000010552', '000000000011185', '000000000012677', '000000000012735', '000000000012736']\n",
      "['zunaechst', 'zunf', 'zur', 'zurich', 'zusaetzlich', 'zuzana', 'zwabic', 'zwischen', 'zwlaszcza', 'zwrocic', 'zwwyw', 'zwzm', 'zxghlajf', 'zyban', 'zyc', 'zygoma', 'zymg', 'zzn', 'zzncacst', 'zzzz']\n"
     ]
    }
   ],
   "source": [
    "print(vect.get_feature_names()[0:20])\n",
    "print(vect.get_feature_names()[-20:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse.csr.csr_matrix"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_df = vect.transform(X_train)\n",
    "X_test_df = vect.transform(X_test)\n",
    "type(X_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = dict()\n",
    "# Naive Bayes Machine Learning Model\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "model = MultinomialNB()\n",
    "model.fit(X_train_df,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction[\"naive_bayes\"] = model.predict(X_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99127399650959858"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get accuracy\n",
    "accuracy_score(y_test,prediction[\"naive_bayes\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        Ham       0.99      1.00      0.99       861\n",
      "       Spam       0.99      0.98      0.98       285\n",
      "\n",
      "avg / total       0.99      0.99      0.99      1146\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, prediction['naive_bayes'], target_names = [\"Ham\", \"Spam\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type here something and see if it belongs to spam: IMPORTANT - You could be entitled up to £3,160 in compensation from mis-sold PPI on a credit card or loan. Please reply PPI for info or STOP to opt out.\n",
      "spam\n"
     ]
    }
   ],
   "source": [
    "def classify(user_input):\n",
    "    custom_train = [('text', [user_input])]\n",
    "    custom_train = pd.DataFrame.from_items(custom_train)\n",
    "    text = custom_train.iloc[:, 0].values\n",
    "    return model.predict(vect.transform(text))\n",
    "\n",
    "text = input('Type here something and see if it belongs to spam: ')\n",
    "if classify(text):\n",
    "    print('spam')\n",
    "else:\n",
    "    print('ham')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type here something and see if it belongs to spam: Hey there! I hope you're free tonight, let's catch up at dinner!\n",
      "ham\n"
     ]
    }
   ],
   "source": [
    "text = input('Type here something and see if it belongs to spam: ')\n",
    "if classify(text):\n",
    "    print('spam')\n",
    "else:\n",
    "    print('ham')"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}