spam_ham_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spam or Ham classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1)Reading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data_source=https://archive.ics.uci.edu/ml/datasets/sms+spam+collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>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": [
       "  Label                                            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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(r'SMSSpamCollection_data',sep='\\t',names=['Label','message'])  # reading the data here seperator is tab  column labels as names\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) Text preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Label', 'message'], dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5572\n",
      "(5572, 5000)\n"
     ]
    },
    {
     "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.]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing libraries\n",
    "import nltk   #NLP library\n",
    "import re    #For text processing\n",
    "from nltk.stem.porter import PorterStemmer   # for stemming of words (to get the stem of words/root word)\n",
    "from nltk.corpus import stopwords    # to get the list of stop words in required longuage\n",
    "from nltk.stem import WordNetLemmatizer  # for lemmatization\n",
    "\n",
    "\n",
    "\n",
    "#===========================cleaning the text================================\n",
    "\n",
    "\n",
    "\n",
    "ps=PorterStemmer()\n",
    "l_s=WordNetLemmatizer()\n",
    "# sentences=nltk.sent_tokenize(paragraph)\n",
    "corpus=[]\n",
    "\n",
    "for i in range(len(df)):\n",
    "    review=re.sub('[^a-zA-Z]',' ',df['message'][i])  #replace all non alphabatic terms by space\n",
    "    review=review.lower()                       # lower the each sentence\n",
    "    review=review.split()                       # split the senetence into list of words\n",
    "    review=[l_s.lemmatize(word) for word in review if word not in set(stopwords.words('english'))]   # Remove stopwords and lemmatize it\n",
    "    review=' '.join(review)      # join the all words with space\n",
    "    corpus.append(review)        #append the final cleaned sentence into corpus list\n",
    "    \n",
    "\n",
    "# print(corpus)          # Corpus list output\n",
    "\n",
    "# ============================Creating TF-IDF model==========================\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "cv=TfidfVectorizer(max_features=5000) #build a vocabulary that only consider the top max_features ordered by term frequency across the corpus.\n",
    "\n",
    "x=cv.fit_transform(corpus).toarray()\n",
    "print(len(corpus))\n",
    "print(x.shape)      \n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# * Now independent feature x is ready\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ham', 'spam'], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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: Label, Length: 5572, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df['Label'].replace({'ham':0,'spam':1})\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Now the data is ready in erms of x and y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3) Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4) Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "model=MultinomialNB()\n",
    "model.fit(x_train,y_train)\n",
    "y_pred=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5) Peformance evaluation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuaracy is 0.9662598707824839\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import plot_confusion_matrix,confusion_matrix\n",
    "print('Model accuaracy is',accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1206,    1],\n",
       "       [  46,  140]], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_confusion_matrix(model,x_test,y_test,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}