Spam ham classifier CV Naive bayes.ipynb

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   "id": "da317773-3bb0-41f3-a2ee-8d1a2b284fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "messages=pd.read_csv('/Users/iambu/Desktop/spam.csv',encoding = \"latin-1\")\n",
    "messages.rename(columns={\"v1\":\"label\",\"v2\":\"text\"},inplace=True)\n",
    "messages.shape\n",
    "import re \n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9548d2b4-22fb-44b8-8311-2fde100f78cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps=PorterStemmer()\n",
    "w=WordNetLemmatizer()\n",
    "\n",
    "corpus=[]\n",
    "\n",
    "for i in range(0,len(messages)):\n",
    "    rev=re.sub('[^a-zA-Z]',' ',messages['text'][i])\n",
    "    rev=rev.lower()\n",
    "    rev=rev.split()\n",
    "    rev=[ps.stem(word) for word in rev if not word in set(stopwords.words('english'))]\n",
    "    rev=' '.join(rev)\n",
    "    corpus.append(rev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7a04d657-9a88-41c0-b540-c35f5a2de49b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv=CountVectorizer(max_features=2500)\n",
    "X=cv.fit_transform(corpus).toarray()\n",
    "\n",
    "y=pd.get_dummies(messages['label'])\n",
    "y=y.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a8097a6f-1ff8-4ed3-b103-48b103b66b7a",
   "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, test_size = 0.20, random_state = 0)\n",
    "\n",
    "# Training model using Naive bayes classifier\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "spam_detect_model = MultinomialNB().fit(X_train, y_train)\n",
    "\n",
    "y_pred=spam_detect_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a250ea69-3581-4eda-a1f7-c819ae83cef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d8acc3fd-0d74-4600-b5fd-142441489b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[943,   6],\n",
       "       [  9, 157]], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_m = confusion_matrix(y_test,y_pred)\n",
    "\n",
    "confusion_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2a9bb5b0-c27b-4a88-a3f1-2a2d8810690a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9865470852017937"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3d87fcd-21fe-4c52-bd2e-d490c96a0666",
   "metadata": {},
   "outputs": [],
   "source": []
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