Spam_Mail_Prediction_using_Machine_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='color:black' align='center'>Spam Mail Prediction Project</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kqB21QOgMg-G"
   },
   "source": [
    "<h2 style='color:blue'>Importing the Libraries</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "rALI06-oHusw"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YyKe9o2ONeFv"
   },
   "source": [
    "<h2 style='color:blue'>Load the Dataset</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "CpStHH8KNcYB"
   },
   "outputs": [],
   "source": [
    "# loading the data from csv file to a pandas Dataframe\n",
    "mail_data = pd.read_csv('e:/mail_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pdn-7VE2NxsZ",
    "outputId": "28c19d96-23a2-43c0-86ad-5c1aee7f1b58"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</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": [
       "  Category                                            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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mail_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>ham</td>\n",
       "      <td>Tired. I haven't slept well the past few nights.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3532</th>\n",
       "      <td>ham</td>\n",
       "      <td>Prepare to be pounded every night...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>ham</td>\n",
       "      <td>It,,s a taxt massage....tie-pos argh ok! Lool!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>ham</td>\n",
       "      <td>Watching cartoon, listening music &amp;amp; at eve...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4119</th>\n",
       "      <td>ham</td>\n",
       "      <td>Watch lor. I saw a few swatch one i thk quite ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category                                            Message\n",
       "217       ham   Tired. I haven't slept well the past few nights.\n",
       "3532      ham               Prepare to be pounded every night...\n",
       "2648      ham     It,,s a taxt massage....tie-pos argh ok! Lool!\n",
       "646       ham  Watching cartoon, listening music &amp; at eve...\n",
       "4119      ham  Watch lor. I saw a few swatch one i thk quite ..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mail_data.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Remove Null Values</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mail_data['Message'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "yhakjIE1N011"
   },
   "outputs": [],
   "source": [
    "mail_data.dropna(axis = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "id": "SJey6H-SOWeK",
    "outputId": "af1b0dfd-2ff9-4af9-cfcd-d0c177dd6ab9"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</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",
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      ],
      "text/plain": [
       "  Category                                            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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mail_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IbK82N2gOdar",
    "outputId": "4d1840a1-22b5-468f-d4d0-a4528ef4313c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of rows and columns in the dataframe\n",
    "mail_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vhR4U3ATPBdk"
   },
   "source": [
    "<h2 style = 'color:blue'>Label Encoding</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "9EW7QSgeOt4p"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>Category</th>\n",
       "      <th>Message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>0</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>0</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": [
       "   Category                                            Message\n",
       "0         0  Go until jurong point, crazy.. Available only ...\n",
       "1         0                      Ok lar... Joking wif u oni...\n",
       "2         1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3         0  U dun say so early hor... U c already then say...\n",
       "4         0  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label spam mail as 0;  ham mail as 1;\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "mail_data['Category'] = le.fit_transform(mail_data['Category'])\n",
    "mail_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uxZK1fWwPwII"
   },
   "source": [
    "<h2 style='color:blue'>spam  -  1</h2>\n",
    "\n",
    "<h2 style='color:blue'>ham  -  0</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "t8Rt-FaNPtPE"
   },
   "outputs": [],
   "source": [
    "# separating the data as texts and label\n",
    "\n",
    "X = mail_data['Message']\n",
    "Y = mail_data['Category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QnQeUBGtQPP7",
    "outputId": "a2640f4b-2a1d-4742-9742-3ecbb6017668"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       Go until jurong point, crazy.. Available only ...\n",
      "1                           Ok lar... Joking wif u oni...\n",
      "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
      "3       U dun say so early hor... U c already then say...\n",
      "4       Nah I don't think he goes to usf, he lives aro...\n",
      "                              ...                        \n",
      "5567    This is the 2nd time we have tried 2 contact u...\n",
      "5568                 Will ü b going to esplanade fr home?\n",
      "5569    Pity, * was in mood for that. So...any other s...\n",
      "5570    The guy did some bitching but I acted like i'd...\n",
      "5571                           Rofl. Its true to its name\n",
      "Name: Message, Length: 5572, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cuWDNy5KQQjY",
    "outputId": "1a0a109b-d63a-4cf0-fe4e-b486f1d3d623"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       0\n",
      "1       0\n",
      "2       1\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "5567    1\n",
      "5568    0\n",
      "5569    0\n",
      "5570    0\n",
      "5571    0\n",
      "Name: Category, Length: 5572, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jvHyqdH8QZPH"
   },
   "source": [
    "<h2 style='color:blue'>Splitting the data into training data & test data</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "RO2GmbSNQSQH"
   },
   "outputs": [],
   "source": [
    "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 = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wYQpiACGSBYM"
   },
   "source": [
    "<h2 style='color:blue'>Feature Extraction</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "nLs847nSRibm"
   },
   "outputs": [],
   "source": [
    "# transform the text data to feature vectors that can be used as input\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "feature_extraction = TfidfVectorizer(min_df = 1, stop_words = 'english', lowercase = 'True')\n",
    "\n",
    "X_train_features = feature_extraction.fit_transform(X_train)\n",
    "X_test_features = feature_extraction.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "dBMAcw9RUkUY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 3422)\t0.6418008618863358\n",
      "  (0, 3960)\t0.40459749284424307\n",
      "  (0, 4776)\t0.2937599690543961\n",
      "  (0, 4486)\t0.4933198981059812\n",
      "  (0, 3101)\t0.30778739607068667\n",
      "  (1, 3855)\t0.4410710256765374\n",
      "  (1, 4574)\t0.4410710256765374\n",
      "  (1, 2534)\t0.4410710256765374\n",
      "  (1, 814)\t0.4410710256765374\n",
      "  (1, 4555)\t0.4205367990464199\n",
      "  (1, 2902)\t0.2120712188920981\n",
      "  (2, 3398)\t0.5133141633463273\n",
      "  (2, 1317)\t0.34462014146959175\n",
      "  (2, 432)\t0.4077104256374456\n",
      "  (2, 4294)\t0.36445133334144264\n",
      "  (2, 2503)\t0.5133141633463273\n",
      "  (2, 4776)\t0.2349500626979615\n",
      "  (3, 1138)\t0.6489221209014988\n",
      "  (3, 1160)\t0.44843330753299465\n",
      "  (3, 3378)\t0.38536596088088965\n",
      "  (3, 3118)\t0.3618113574629584\n",
      "  (3, 3778)\t0.31367701143832527\n",
      "  (4, 3805)\t1.0\n",
      "  (5, 3731)\t0.6020708068994186\n",
      "  (5, 7381)\t0.7984426989330436\n",
      "  :\t:\n",
      "  (4454, 348)\t0.2816333253882664\n",
      "  (4454, 110)\t0.3000941484572203\n",
      "  (4454, 2067)\t0.25658354936739225\n",
      "  (4454, 4488)\t0.3000941484572203\n",
      "  (4454, 651)\t0.3000941484572203\n",
      "  (4454, 373)\t0.23959800001827322\n",
      "  (4454, 796)\t0.2816333253882664\n",
      "  (4454, 7430)\t0.1801037942222884\n",
      "  (4454, 7343)\t0.4498896407891956\n",
      "  (4454, 6911)\t0.1653057679612594\n",
      "  (4454, 6902)\t0.21937394005137992\n",
      "  (4454, 1621)\t0.18784292855815676\n",
      "  (4454, 7311)\t0.1949753381818719\n",
      "  (4454, 1665)\t0.21130971337962476\n",
      "  (4454, 7011)\t0.14040083857773877\n",
      "  (4455, 2434)\t0.617585164773377\n",
      "  (4455, 5750)\t0.5526101356478642\n",
      "  (4455, 4311)\t0.3634744995680554\n",
      "  (4455, 3424)\t0.4255548030452034\n",
      "  (4456, 6077)\t0.5490155787253892\n",
      "  (4456, 4416)\t0.4542018586423069\n",
      "  (4456, 2724)\t0.40079007123433724\n",
      "  (4456, 5910)\t0.3950554110813791\n",
      "  (4456, 6769)\t0.2873431830527075\n",
      "  (4456, 2135)\t0.30498360947948605\n"
     ]
    }
   ],
   "source": [
    "print(X_train_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "1NFuGogZUpt0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 7478)\t0.11238527640544621\n",
      "  (0, 7420)\t0.19398477272351503\n",
      "  (0, 7260)\t0.14384114349719565\n",
      "  (0, 6602)\t0.19398477272351503\n",
      "  (0, 6350)\t0.19398477272351503\n",
      "  (0, 5782)\t0.11386086683184304\n",
      "  (0, 5452)\t0.13293730388210073\n",
      "  (0, 5038)\t0.19398477272351503\n",
      "  (0, 5014)\t0.13293730388210073\n",
      "  (0, 4496)\t0.1102105484474495\n",
      "  (0, 4466)\t0.16951508500886583\n",
      "  (0, 4302)\t0.17357598220109133\n",
      "  (0, 4250)\t0.19398477272351503\n",
      "  (0, 4004)\t0.17357598220109133\n",
      "  (0, 3867)\t0.3120289176577842\n",
      "  (0, 3630)\t0.19398477272351503\n",
      "  (0, 3310)\t0.3946343133427287\n",
      "  (0, 3136)\t0.19398477272351503\n",
      "  (0, 3060)\t0.38796954544703005\n",
      "  (0, 3059)\t0.2772755538586351\n",
      "  (0, 2114)\t0.1302342007925869\n",
      "  (0, 1493)\t0.1368549641200954\n",
      "  (0, 524)\t0.19398477272351503\n",
      "  (1, 7163)\t0.4130504232751816\n",
      "  (1, 7014)\t0.39097385770203663\n",
      "  :\t:\n",
      "  (1109, 2086)\t0.3511723226114505\n",
      "  (1110, 6677)\t0.278010885413778\n",
      "  (1110, 6020)\t0.35653930858248284\n",
      "  (1110, 5001)\t0.39666689104890435\n",
      "  (1110, 3863)\t0.2030867005199339\n",
      "  (1110, 3207)\t0.3623676133884122\n",
      "  (1110, 2346)\t0.23047008239041109\n",
      "  (1110, 2243)\t0.24205279104825103\n",
      "  (1110, 2124)\t0.2117142985315063\n",
      "  (1110, 1781)\t0.4309661687093966\n",
      "  (1110, 1754)\t0.35132571227515735\n",
      "  (1111, 6588)\t0.40238437393479387\n",
      "  (1111, 3964)\t0.38365121885138903\n",
      "  (1111, 3301)\t0.3196021385759522\n",
      "  (1111, 1544)\t0.7673024377027781\n",
      "  (1112, 4720)\t0.2822562123792488\n",
      "  (1112, 4551)\t0.4782808737746853\n",
      "  (1112, 3911)\t0.3333542507601604\n",
      "  (1112, 3118)\t0.23303080850834387\n",
      "  (1112, 1850)\t0.23729509164672005\n",
      "  (1112, 1067)\t0.3546488048040951\n",
      "  (1112, 952)\t0.4782808737746853\n",
      "  (1112, 852)\t0.3395778153140287\n",
      "  (1113, 2557)\t1.0\n",
      "  (1114, 2338)\t1.0\n"
     ]
    }
   ],
   "source": [
    "print(X_test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert Y_train and Y_test values as integers\n",
    "\n",
    "Y_train = Y_train.astype('int')\n",
    "Y_test = Y_test.astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q86FvELbU_SV"
   },
   "source": [
    "<h2 style='color:blue'>Training the Model</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hV6BAIZQVBbo"
   },
   "source": [
    "<h3 style='color:purple'>Logistic Regression</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "1JeAOwzpUv0V"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy using Logistic Regression: 96.68161434977578\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logistic_model = LogisticRegression(max_iter = 100, random_state = 0)\n",
    "logistic_model.fit(X_train_features, Y_train)\n",
    "\n",
    "logistic_test_score = logistic_model.score(X_test_features, Y_test)\n",
    "print('Test set accuracy using Logistic Regression:', logistic_test_score * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gWGRHWAPVI_z",
    "outputId": "1c5e15dd-0e07-4871-c4fa-b908ee400b55"
   },
   "source": [
    "<h3 style='color:purple'>Linear Regression</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy using Logistic Regression: 80.27366054420114\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(X_train_features, Y_train)\n",
    "\n",
    "linear_test_score = linear_model.score(X_test_features, Y_test)\n",
    "print('Test set accuracy using Logistic Regression:', linear_test_score * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>Support Vector Machine</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy using SVC: 98.83408071748879\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "svc = LinearSVC(loss = 'squared_hinge', random_state = 0)\n",
    "svc.fit(X_train_features, Y_train)\n",
    "\n",
    "svc_test_score = svc.score(X_test_features, Y_test)\n",
    "print(\"Test Set Accuracy using SVC:\", svc_test_score * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>Multinomial Naive Bayes</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy using Multinomial NB: 97.57847533632287\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "multi_nb = MultinomialNB()\n",
    "multi_nb.fit(X_train_features, Y_train)\n",
    "\n",
    "multi_nb_test_score = multi_nb.score(X_test_features, Y_test)\n",
    "print('Test Set Accuracy using Multinomial NB:', multi_nb_test_score * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>Decision Tree</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy using Decision Tree: 96.8609865470852\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dtree_clf = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
    "dtree_clf.fit(X_train_features, Y_train)\n",
    "\n",
    "dtree_clf_test_score = dtree_clf.score(X_test_features, Y_test)\n",
    "print('Test Set Accuracy using Decision Tree:', dtree_clf_test_score * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wZ01fa8dVeL5"
   },
   "source": [
    "<h2 style='color:blue'>Accuracy Score</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "ExiF2kKxVYtC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data : 99.95512676688355\n"
     ]
    }
   ],
   "source": [
    "# prediction on training data\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "prediction_on_training_data = svc.predict(X_train_features)\n",
    "accuracy_on_training_data = accuracy_score(Y_train, prediction_on_training_data)\n",
    "print('Accuracy on training data :', accuracy_on_training_data * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "cTin5rXTWKg3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test data :  98.83408071748879\n"
     ]
    }
   ],
   "source": [
    "# prediction on test data\n",
    "\n",
    "prediction_on_test_data = svc.predict(X_test_features)\n",
    "accuracy_on_test_data = accuracy_score(Y_test, prediction_on_test_data)\n",
    "print('Accuracy on test data : ', accuracy_on_test_data * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Classification Report</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       955\n",
      "           1       0.99      0.93      0.96       160\n",
      "\n",
      "    accuracy                           0.99      1115\n",
      "   macro avg       0.99      0.96      0.98      1115\n",
      "weighted avg       0.99      0.99      0.99      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "matrix = classification_report(Y_test, prediction_on_test_data)\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Confusion Matrix</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[954,   1],\n",
       "       [ 12, 148]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(Y_test, prediction_on_test_data)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(51.0, 0.5, 'Actual Value')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 8))\n",
    "sns.heatmap(cm, annot = True)\n",
    "plt.xlabel('Predicted Value')\n",
    "plt.ylabel('Actual Value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bXdOKxYAXaHC"
   },
   "source": [
    "<h2 style='color:blue'>Building a Predictive System</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h60z1__mWql6",
    "outputId": "3aac53f3-13f2-4afb-e9f2-75d337cbcd44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I've been searching for the right words to thank you for this breather. I promise i wont take your help for granted and will fulfil my promise. You have been wonderful and a blessing at all times --> Ham mail\n"
     ]
    }
   ],
   "source": [
    "input_mail = [\"I've been searching for the right words to thank you for this breather. I promise i wont take your help for granted and will fulfil my promise. You have been wonderful and a blessing at all times\"]\n",
    "\n",
    "# convert text to feature vectors\n",
    "input_data_features = feature_extraction.transform(input_mail)\n",
    "\n",
    "# making prediction\n",
    "\n",
    "prediction = svc.predict(input_data_features)\n",
    "\n",
    "if (prediction[0] == 0):\n",
    "    print(input_mail[0], '--> Ham mail')\n",
    "else:\n",
    "    print(input_mail[0], '--> Spam mail')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "v_LqbM_ZYwS1"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Project 17. Spam Mail Prediction using Machine Learning.ipynb",
   "provenance": []
  },
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}