Spam Detection Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "baaccabf",
   "metadata": {},
   "source": [
    "# Spam Detection Project\n",
    "\n",
    "In this project, I have prepared a spam detector system that classifies a text as a spam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "409d5bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a007aa93",
   "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>messages</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                                           messages\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": [
    "data = pd.read_table('smscollection/SMSCollection',\n",
    "                    sep='\\t',\n",
    "                    header=None,\n",
    "                    names=['label','messages'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0e3a141",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data: (5572, 2)\n",
      "No. of Null values: 0\n",
      "<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   messages  5572 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 87.2+ KB\n",
      "Info. of data: None\n",
      "Description of data:        label                messages\n",
      "count   5572                    5572\n",
      "unique     2                    5169\n",
      "top      ham  Sorry, I'll call later\n",
      "freq    4825                      30\n"
     ]
    }
   ],
   "source": [
    "def explore_data(df):\n",
    "    print('Shape of data:',df.shape)\n",
    "    print('No. of Null values:',df.isnull().sum().sum())\n",
    "    print('Info. of data:',df.info())\n",
    "    print('Description of data:',df.describe())\n",
    "    \n",
    "explore_data(data)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe25eee4",
   "metadata": {},
   "source": [
    "# Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d2d1448",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no. of hams:  4825\n",
      "Total no. of spams:  747\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len_ham=len(data['label'][data.label=='ham'])\n",
    "len_spam=len(data['label'][data.label=='spam'])\n",
    "\n",
    "arr=np.array([len_ham, len_spam])\n",
    "labels=['ham','spam']\n",
    "\n",
    "print('Total no. of hams: ',len_ham)\n",
    "print('Total no. of spams: ',len_spam)\n",
    "\n",
    "plt.pie(arr, labels=labels, explode=[0.2,0.0],shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d4fc03",
   "metadata": {},
   "source": [
    "# Text Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9d4575c9",
   "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>messages</th>\n",
       "      <th>Preprocessed Text</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>nah i do not think he goes to usf, he lives ar...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                           messages  \\\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",
       "\n",
       "                                   Preprocessed Text  \n",
       "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 do not think he goes to usf, he lives ar...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def text_preprocess(x):\n",
    "    x=str(x).lower()\n",
    "    x = x.replace(\",000,000\", \"m\").replace(\",000\", \"k\").replace(\"′\", \"'\").replace(\"’\", \"'\")\\\n",
    "                           .replace(\"won't\", \"will not\").replace(\"cannot\", \"can not\").replace(\"can't\", \"can not\")\\\n",
    "                           .replace(\"n't\", \" not\").replace(\"what's\", \"what is\").replace(\"it's\", \"it is\")\\\n",
    "                           .replace(\"'ve\", \" have\").replace(\"i'm\", \"i am\").replace(\"'re\", \" are\")\\\n",
    "                           .replace(\"he's\", \"he is\").replace(\"she's\", \"she is\").replace(\"'s\", \" own\")\\\n",
    "                           .replace(\"%\", \" percent \").replace(\"₹\", \" rupee \").replace(\"$\", \" dollar \")\\\n",
    "                           .replace(\"€\", \" euro \").replace(\"'ll\", \" will\") \n",
    "    return x  \n",
    "\n",
    "data[\"Preprocessed Text\"] = data[\"messages\"].apply(lambda x: text_preprocess(x))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f5018e55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
      "=================================================\n",
      "go until jurong point, crazy.. available only in bugis n great world la e buffet... cine there got amore wat...\n"
     ]
    }
   ],
   "source": [
    "print(data['messages'][0])\n",
    "print(\"=================================================\")\n",
    "print(data['Preprocessed Text'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8149d65",
   "metadata": {},
   "source": [
    "# Feature and Data Deveploment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ffbec6bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['label']=data.label.map({'ham':0,'spam':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0194a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(data['messages'],data['label'],random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f367433",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for converting text to numbers\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "count_vector=CountVectorizer()\n",
    "training_data=count_vector.fit_transform(x_train)\n",
    "testing_data=count_vector.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64efe66",
   "metadata": {},
   "source": [
    "# Model Development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f8b450a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "naive_bayes=MultinomialNB()\n",
    "naive_bayes.fit(training_data, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a748a82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions=naive_bayes.predict(testing_data)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "91d01faf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.9885139985642498\n",
      "Precision Score:  0.9720670391061452\n",
      "Recall Score:  0.9405405405405406\n",
      "f1 Score:  0.9560439560439562\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "print(\"Accuracy Score: \",format(accuracy_score(y_test, predictions)))\n",
    "print(\"Precision Score: \",format(precision_score(y_test, predictions)))\n",
    "print(\"Recall Score: \",format(recall_score(y_test, predictions)))\n",
    "print(\"f1 Score: \",format(f1_score(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cf4575a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc=pd.Series('This is the 2nd time we have tried 2 cuntact u...')\n",
    "test=count_vector.transform(doc)\n",
    "naive_bayes.predict(test)"
   ]
  }
 ],
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