first commit

.ipynb_checkpoints/Spam_Filter-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "b9bb9dcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
+      "[nltk_data]     C:\\Users\\shiva\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
+      "[nltk_data]       date!\n",
+      "[nltk_data] Downloading package punkt to\n",
+      "[nltk_data]     C:\\Users\\shiva\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nltk\n",
+    "nltk.download(\"averaged_perceptron_tagger\")\n",
+    "nltk.download(\"punkt\")\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "import pandas as pd\n",
+    "import csv\n",
+    "import numpy as np\n",
+    "from sklearn import preprocessing , svm , model_selection, metrics\n",
+    "from sklearn.preprocessing import MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c1e93bc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                 msg label  label_no  \\\n",
+      "0  Go until jurong point, crazy.. Available only ...   ham         0   \n",
+      "1                      Ok lar... Joking wif u oni...   ham         0   \n",
+      "2  Free entry in 2 a wkly comp to win FA Cup fina...  spam         1   \n",
+      "3  U dun say so early hor... U c already then say...   ham         0   \n",
+      "4  Nah I don't think he goes to usf, he lives aro...   ham         0   \n",
+      "\n",
+      "        NNP      IN        JJ        NN         ,        RB         :  ...  \\\n",
+      "0  0.071429  0.1250  0.142857  0.152174  0.076923  0.176471  0.054054  ...   \n",
+      "1  0.047619  0.0000  0.047619  0.043478  0.000000  0.000000  0.054054  ...   \n",
+      "2  0.142857  0.0625  0.190476  0.152174  0.000000  0.000000  0.000000  ...   \n",
+      "3  0.023810  0.0000  0.095238  0.021739  0.000000  0.176471  0.054054  ...   \n",
+      "4  0.023810  0.0625  0.000000  0.000000  0.076923  0.176471  0.000000  ...   \n",
+      "\n",
+      "    MD  PRP$  JJR  JJS   UH   RP   WP  WDT    #   ''  \n",
+      "0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+      "1  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+      "2  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+      "3  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+      "4  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+      "\n",
+      "[5 rows x 38 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tok_dict={}\n",
+    "\n",
+    "lst=['NNP', 'IN', 'JJ', 'NN', ',', 'RB', ':', 'EX', 'VBD', 'WRB', 'CD', 'DT', 'TO', 'VB', '.',\n",
+    "     '(', ')', 'CC', 'POS', 'VBP', 'NNS', 'PRP', 'VBZ', 'VBG', 'VBN', 'MD', 'PRP$', 'JJR', 'JJS', 'UH', 'RP', 'WP', 'WDT', '#', \"''\"]\n",
+    "\n",
+    "pd_dict={'msg':[],'label':[],'label_no':[],'NNP':[], 'IN':[], 'JJ':[], 'NN':[], ',':[], 'RB':[], ':':[], 'EX':[], 'VBD':[], \n",
+    "'WRB':[], 'CD':[], 'DT':[], 'TO':[], 'VB':[], '.':[], '(':[], ')':[], 'CC':[],\n",
+    "'POS':[], 'VBP':[], 'NNS':[], 'PRP':[], 'VBZ':[], 'VBG':[], 'VBN':[], 'MD':[], \n",
+    "'PRP$':[], 'JJR':[], 'JJS':[], 'UH':[], 'RP':[], 'WP':[], 'WDT':[], '#':[], \"''\":[]}\n",
+    "\n",
+    "with open(\"spam_db.csv\", 'r') as file:\n",
+    "  csvreader = csv.reader(file)\n",
+    "  j=0\n",
+    "  k=0\n",
+    "  for row in csvreader:\n",
+    "    if j==0:\n",
+    "        j=1\n",
+    "        continue\n",
+    "    pd_dict['msg'].append(row[1])\n",
+    "    pd_dict['label'].append(row[0])\n",
+    "    if row[0]=='spam':\n",
+    "        pd_dict['label_no'].append(1)\n",
+    "    else:\n",
+    "        pd_dict['label_no'].append(0)\n",
+    "    for label in lst:\n",
+    "        pd_dict[label].append(0)\n",
+    "    text=row[1]\n",
+    "    tokens=word_tokenize(text)\n",
+    "    tokens_tagged=nltk.pos_tag(tokens)\n",
+    "    for i in tokens_tagged:\n",
+    "        if i[1] in tok_dict:\n",
+    "            tok_dict[i[1]].append(i[0])\n",
+    "        else:\n",
+    "            tok_dict[i[1]]=[i[0]]\n",
+    "        if i[1] in pd_dict:\n",
+    "          pd_dict[i[1]][k]+=1\n",
+    "    k+=1\n",
+    "        \n",
+    "tok_dict1={}\n",
+    "for i in tok_dict:\n",
+    "    tok_dict1[i]=len(tok_dict[i])\n",
+    "\n",
+    "del_lst=[]\n",
+    "for i in tok_dict1:\n",
+    "    if tok_dict1[i]<100:\n",
+    "        del_lst.append(i)\n",
+    "\n",
+    "for i in del_lst:\n",
+    "    tok_dict1.pop(i)\n",
+    "\n",
+    "lst=[]\n",
+    "for i in tok_dict1:\n",
+    "    lst.append(i)\n",
+    "\n",
+    "df=pd.DataFrame(pd_dict)\n",
+    "numeric_columns = df.drop(['msg', 'label', 'label_no'], axis=1).columns\n",
+    "\n",
+    "# Create the MinMaxScaler object\n",
+    "scaler = MinMaxScaler()\n",
+    "\n",
+    "# Normalize the numeric columns using min-max normalization\n",
+    "df[numeric_columns] = scaler.fit_transform(df[numeric_columns])\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "35824c58",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\shiva\\AppData\\Local\\Temp\\ipykernel_9568\\3238635716.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
+      "  X=np.array(df.drop(['msg','label','label_no'],1))\n"
+     ]
+    }
+   ],
+   "source": [
+    "X=np.array(df.drop(['msg','label','label_no'],1))\n",
+    "y=np.array(df['label_no'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "aec84e0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9676956209619526\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25)\n",
+    "clf=svm.SVC(kernel='poly')\n",
+    "clf.fit(X_train, y_train)\n",
+    "accuracy = clf.score(X_test, y_test)\n",
+    "print(accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "62e97e65",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision: 0.9669448190530422\n",
+      "Recall: 0.9676956209619526\n",
+      "F1 score: 0.9667034979766862\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[1208,   11],\n",
+       "       [  34,  140]], dtype=int64)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = clf.predict(X_test)\n",
+    "\n",
+    "precision = metrics.precision_score(y_test, y_pred, average='weighted')\n",
+    "recall = metrics.recall_score(y_test, y_pred, average='weighted')\n",
+    "f1 = metrics.f1_score(y_test, y_pred, average='weighted')\n",
+    "\n",
+    "print(\"Precision:\", precision)\n",
+    "print(\"Recall:\", recall)\n",
+    "print(\"F1 score:\", f1)\n",
+    "\n",
+    "confusion_mat = metrics.confusion_matrix(y_test, y_pred)\n",
+    "confusion_mat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ccce58e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text='''WINNER!! As a valued network customer you have been selected to receivea \n",
+    "å£900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f53b1187",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokens=word_tokenize(text)\n",
+    "tokens_tagged=nltk.pos_tag(tokens)\n",
+    "x=[]\n",
+    "for i in range(35):\n",
+    "    x.append(0)\n",
+    "pos_dict={'NNP':[0], 'IN':[1], 'JJ':[2], 'NN':[3], ',':[4], 'RB':[5], ':':[6], 'EX':[7], 'VBD':[8], \n",
+    "'WRB':[9], 'CD':[10], 'DT':[11], 'TO':[12], 'VB':[13], '.':[14], '(':[15], ')':[16], 'CC':[17],\n",
+    "'POS':[18], 'VBP':[19], 'NNS':[20], 'PRP':[21], 'VBZ':[22], 'VBG':[23], 'VBN':[24], 'MD':[25], \n",
+    "'PRP$':[26], 'JJR':[27], 'JJS':[28], 'UH':[29], 'RP':[30], 'WP':[31], 'WDT':[32], '#':[33], \"''\":[34]}\n",
+    "for i in tokens_tagged:\n",
+    "    x[pos_dict[i[1]][0]]+=1\n",
+    "x=np.array(x)\n",
+    "x=x.reshape(1,-1)\n",
+    "# x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "1d0066d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SPAM\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred=clf.predict(x)\n",
+    "if pred==0:\n",
+    "    print(\"NOT SPAM\")\n",
+    "else:\n",
+    "    print(\"SPAM\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7440777a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Spam_Filter.ipynb

@@ -0,0 +1,307 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "b9bb9dcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
+      "[nltk_data]     C:\\Users\\shiva\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
+      "[nltk_data]       date!\n",
+      "[nltk_data] Downloading package punkt to\n",
+      "[nltk_data]     C:\\Users\\shiva\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nltk\n",
+    "nltk.download(\"averaged_perceptron_tagger\")\n",
+    "nltk.download(\"punkt\")\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "import pandas as pd\n",
+    "import csv\n",
+    "import numpy as np\n",
+    "from sklearn import preprocessing , svm , model_selection, metrics\n",
+    "from sklearn.preprocessing import MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c1e93bc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                 msg label  label_no  \\\n",
+      "0  Go until jurong point, crazy.. Available only ...   ham         0   \n",
+      "1                      Ok lar... Joking wif u oni...   ham         0   \n",
+      "2  Free entry in 2 a wkly comp to win FA Cup fina...  spam         1   \n",
+      "3  U dun say so early hor... U c already then say...   ham         0   \n",
+      "4  Nah I don't think he goes to usf, he lives aro...   ham         0   \n",
+      "\n",
+      "        NNP      IN        JJ        NN         ,        RB         :  ...  \\\n",
+      "0  0.071429  0.1250  0.142857  0.152174  0.076923  0.176471  0.054054  ...   \n",
+      "1  0.047619  0.0000  0.047619  0.043478  0.000000  0.000000  0.054054  ...   \n",
+      "2  0.142857  0.0625  0.190476  0.152174  0.000000  0.000000  0.000000  ...   \n",
+      "3  0.023810  0.0000  0.095238  0.021739  0.000000  0.176471  0.054054  ...   \n",
+      "4  0.023810  0.0625  0.000000  0.000000  0.076923  0.176471  0.000000  ...   \n",
+      "\n",
+      "    MD  PRP$  JJR  JJS   UH   RP   WP  WDT    #   ''  \n",
+      "0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+      "1  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+      "2  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+      "3  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+      "4  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+      "\n",
+      "[5 rows x 38 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tok_dict={}\n",
+    "\n",
+    "lst=['NNP', 'IN', 'JJ', 'NN', ',', 'RB', ':', 'EX', 'VBD', 'WRB', 'CD', 'DT', 'TO', 'VB', '.',\n",
+    "     '(', ')', 'CC', 'POS', 'VBP', 'NNS', 'PRP', 'VBZ', 'VBG', 'VBN', 'MD', 'PRP$', 'JJR', 'JJS', 'UH', 'RP', 'WP', 'WDT', '#', \"''\"]\n",
+    "\n",
+    "pd_dict={'msg':[],'label':[],'label_no':[],'NNP':[], 'IN':[], 'JJ':[], 'NN':[], ',':[], 'RB':[], ':':[], 'EX':[], 'VBD':[], \n",
+    "'WRB':[], 'CD':[], 'DT':[], 'TO':[], 'VB':[], '.':[], '(':[], ')':[], 'CC':[],\n",
+    "'POS':[], 'VBP':[], 'NNS':[], 'PRP':[], 'VBZ':[], 'VBG':[], 'VBN':[], 'MD':[], \n",
+    "'PRP$':[], 'JJR':[], 'JJS':[], 'UH':[], 'RP':[], 'WP':[], 'WDT':[], '#':[], \"''\":[]}\n",
+    "\n",
+    "with open(\"spam_db.csv\", 'r') as file:\n",
+    "  csvreader = csv.reader(file)\n",
+    "  j=0\n",
+    "  k=0\n",
+    "  for row in csvreader:\n",
+    "    if j==0:\n",
+    "        j=1\n",
+    "        continue\n",
+    "    pd_dict['msg'].append(row[1])\n",
+    "    pd_dict['label'].append(row[0])\n",
+    "    if row[0]=='spam':\n",
+    "        pd_dict['label_no'].append(1)\n",
+    "    else:\n",
+    "        pd_dict['label_no'].append(0)\n",
+    "    for label in lst:\n",
+    "        pd_dict[label].append(0)\n",
+    "    text=row[1]\n",
+    "    tokens=word_tokenize(text)\n",
+    "    tokens_tagged=nltk.pos_tag(tokens)\n",
+    "    for i in tokens_tagged:\n",
+    "        if i[1] in tok_dict:\n",
+    "            tok_dict[i[1]].append(i[0])\n",
+    "        else:\n",
+    "            tok_dict[i[1]]=[i[0]]\n",
+    "        if i[1] in pd_dict:\n",
+    "          pd_dict[i[1]][k]+=1\n",
+    "    k+=1\n",
+    "        \n",
+    "tok_dict1={}\n",
+    "for i in tok_dict:\n",
+    "    tok_dict1[i]=len(tok_dict[i])\n",
+    "\n",
+    "del_lst=[]\n",
+    "for i in tok_dict1:\n",
+    "    if tok_dict1[i]<100:\n",
+    "        del_lst.append(i)\n",
+    "\n",
+    "for i in del_lst:\n",
+    "    tok_dict1.pop(i)\n",
+    "\n",
+    "lst=[]\n",
+    "for i in tok_dict1:\n",
+    "    lst.append(i)\n",
+    "\n",
+    "df=pd.DataFrame(pd_dict)\n",
+    "numeric_columns = df.drop(['msg', 'label', 'label_no'], axis=1).columns\n",
+    "\n",
+    "# Create the MinMaxScaler object\n",
+    "scaler = MinMaxScaler()\n",
+    "\n",
+    "# Normalize the numeric columns using min-max normalization\n",
+    "df[numeric_columns] = scaler.fit_transform(df[numeric_columns])\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "35824c58",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\shiva\\AppData\\Local\\Temp\\ipykernel_9568\\3238635716.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
+      "  X=np.array(df.drop(['msg','label','label_no'],1))\n"
+     ]
+    }
+   ],
+   "source": [
+    "X=np.array(df.drop(['msg','label','label_no'],1))\n",
+    "y=np.array(df['label_no'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "aec84e0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9676956209619526\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25)\n",
+    "clf=svm.SVC(kernel='poly')\n",
+    "clf.fit(X_train, y_train)\n",
+    "accuracy = clf.score(X_test, y_test)\n",
+    "print(accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "62e97e65",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision: 0.9669448190530422\n",
+      "Recall: 0.9676956209619526\n",
+      "F1 score: 0.9667034979766862\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[1208,   11],\n",
+       "       [  34,  140]], dtype=int64)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = clf.predict(X_test)\n",
+    "\n",
+    "precision = metrics.precision_score(y_test, y_pred, average='weighted')\n",
+    "recall = metrics.recall_score(y_test, y_pred, average='weighted')\n",
+    "f1 = metrics.f1_score(y_test, y_pred, average='weighted')\n",
+    "\n",
+    "print(\"Precision:\", precision)\n",
+    "print(\"Recall:\", recall)\n",
+    "print(\"F1 score:\", f1)\n",
+    "\n",
+    "confusion_mat = metrics.confusion_matrix(y_test, y_pred)\n",
+    "confusion_mat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ccce58e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text='''WINNER!! As a valued network customer you have been selected to receivea \n",
+    "å£900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f53b1187",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokens=word_tokenize(text)\n",
+    "tokens_tagged=nltk.pos_tag(tokens)\n",
+    "x=[]\n",
+    "for i in range(35):\n",
+    "    x.append(0)\n",
+    "pos_dict={'NNP':[0], 'IN':[1], 'JJ':[2], 'NN':[3], ',':[4], 'RB':[5], ':':[6], 'EX':[7], 'VBD':[8], \n",
+    "'WRB':[9], 'CD':[10], 'DT':[11], 'TO':[12], 'VB':[13], '.':[14], '(':[15], ')':[16], 'CC':[17],\n",
+    "'POS':[18], 'VBP':[19], 'NNS':[20], 'PRP':[21], 'VBZ':[22], 'VBG':[23], 'VBN':[24], 'MD':[25], \n",
+    "'PRP$':[26], 'JJR':[27], 'JJS':[28], 'UH':[29], 'RP':[30], 'WP':[31], 'WDT':[32], '#':[33], \"''\":[34]}\n",
+    "for i in tokens_tagged:\n",
+    "    x[pos_dict[i[1]][0]]+=1\n",
+    "x=np.array(x)\n",
+    "x=x.reshape(1,-1)\n",
+    "# x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "1d0066d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SPAM\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred=clf.predict(x)\n",
+    "if pred==0:\n",
+    "    print(\"NOT SPAM\")\n",
+    "else:\n",
+    "    print(\"SPAM\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7440777a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -0,0 +1,189 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[25]:
+
+
+import nltk
+nltk.download("averaged_perceptron_tagger")
+nltk.download("punkt")
+from nltk.tokenize import word_tokenize
+import pandas as pd
+import csv
+import numpy as np
+from sklearn import preprocessing , svm , model_selection, metrics
+from sklearn.preprocessing import MinMaxScaler
+import gradio as gr
+
+
+# In[26]:
+
+
+tok_dict={}
+
+lst=['NNP', 'IN', 'JJ', 'NN', ',', 'RB', ':', 'EX', 'VBD', 'WRB', 'CD', 'DT', 'TO', 'VB', '.',
+     '(', ')', 'CC', 'POS', 'VBP', 'NNS', 'PRP', 'VBZ', 'VBG', 'VBN', 'MD', 'PRP$', 'JJR', 'JJS', 'UH', 'RP', 'WP', 'WDT', '#', "''"]
+
+pd_dict={'msg':[],'label':[],'label_no':[],'NNP':[], 'IN':[], 'JJ':[], 'NN':[], ',':[], 'RB':[], ':':[], 'EX':[], 'VBD':[], 
+'WRB':[], 'CD':[], 'DT':[], 'TO':[], 'VB':[], '.':[], '(':[], ')':[], 'CC':[],
+'POS':[], 'VBP':[], 'NNS':[], 'PRP':[], 'VBZ':[], 'VBG':[], 'VBN':[], 'MD':[], 
+'PRP$':[], 'JJR':[], 'JJS':[], 'UH':[], 'RP':[], 'WP':[], 'WDT':[], '#':[], "''":[]}
+
+with open("spam_db.csv", 'r') as file:
+  csvreader = csv.reader(file)
+  j=0
+  k=0
+  for row in csvreader:
+    if j==0:
+        j=1
+        continue
+    pd_dict['msg'].append(row[1])
+    pd_dict['label'].append(row[0])
+    if row[0]=='spam':
+        pd_dict['label_no'].append(1)
+    else:
+        pd_dict['label_no'].append(0)
+    for label in lst:
+        pd_dict[label].append(0)
+    text=row[1]
+    tokens=word_tokenize(text)
+    tokens_tagged=nltk.pos_tag(tokens)
+    for i in tokens_tagged:
+        if i[1] in tok_dict:
+            tok_dict[i[1]].append(i[0])
+        else:
+            tok_dict[i[1]]=[i[0]]
+        if i[1] in pd_dict:
+          pd_dict[i[1]][k]+=1
+    k+=1
+        
+tok_dict1={}
+for i in tok_dict:
+    tok_dict1[i]=len(tok_dict[i])
+
+del_lst=[]
+for i in tok_dict1:
+    if tok_dict1[i]<100:
+        del_lst.append(i)
+
+for i in del_lst:
+    tok_dict1.pop(i)
+
+lst=[]
+for i in tok_dict1:
+    lst.append(i)
+
+df=pd.DataFrame(pd_dict)
+numeric_columns = df.drop(['msg', 'label', 'label_no'], axis=1).columns
+
+# Create the MinMaxScaler object
+scaler = MinMaxScaler()
+
+# Normalize the numeric columns using min-max normalization
+df[numeric_columns] = scaler.fit_transform(df[numeric_columns])
+
+print(df.head())
+
+
+# In[27]:
+
+
+X=np.array(df.drop(['msg','label','label_no'],axis = 1))
+y=np.array(df['label_no'])
+
+
+# In[32]:
+
+
+X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25)
+clf=svm.SVC(kernel='poly')
+clf.fit(X_train, y_train)
+accuracy = clf.score(X_test, y_test)
+print(accuracy)
+
+
+# In[36]:
+
+
+y_pred = clf.predict(X_test)
+
+precision = metrics.precision_score(y_test, y_pred, average='weighted')
+recall = metrics.recall_score(y_test, y_pred, average='weighted')
+f1 = metrics.f1_score(y_test, y_pred, average='weighted')
+
+print("Precision:", precision)
+print("Recall:", recall)
+print("F1 score:", f1)
+
+confusion_mat = metrics.confusion_matrix(y_test, y_pred)
+confusion_mat
+
+
+# In[33]:
+
+
+text='''WINNER!! As a valued network customer you have been selected to receivea 
+å£900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.'''
+
+
+# In[34]:
+
+
+tokens=word_tokenize(text)
+tokens_tagged=nltk.pos_tag(tokens)
+x=[]
+for i in range(35):
+    x.append(0)
+pos_dict={'NNP':[0], 'IN':[1], 'JJ':[2], 'NN':[3], ',':[4], 'RB':[5], ':':[6], 'EX':[7], 'VBD':[8], 
+'WRB':[9], 'CD':[10], 'DT':[11], 'TO':[12], 'VB':[13], '.':[14], '(':[15], ')':[16], 'CC':[17],
+'POS':[18], 'VBP':[19], 'NNS':[20], 'PRP':[21], 'VBZ':[22], 'VBG':[23], 'VBN':[24], 'MD':[25], 
+'PRP$':[26], 'JJR':[27], 'JJS':[28], 'UH':[29], 'RP':[30], 'WP':[31], 'WDT':[32], '#':[33], "''":[34]}
+for i in tokens_tagged:
+    x[pos_dict[i[1]][0]]+=1
+x=np.array(x)
+x=x.reshape(1,-1)
+# x
+
+
+# In[35]:
+
+
+pred=clf.predict(x)
+if pred==0:
+    print("NOT SPAM")
+else:
+    print("SPAM")
+
+
+# In[ ]:
+def spam_detection(txt):
+    tokens=word_tokenize(text)
+    tokens_tagged=nltk.pos_tag(tokens)
+    x=[]
+    for i in range(35):
+        x.append(0)
+    pos_dict={'NNP':[0], 'IN':[1], 'JJ':[2], 'NN':[3], ',':[4], 'RB':[5], ':':[6], 'EX':[7], 'VBD':[8], 
+    'WRB':[9], 'CD':[10], 'DT':[11], 'TO':[12], 'VB':[13], '.':[14], '(':[15], ')':[16], 'CC':[17],
+    'POS':[18], 'VBP':[19], 'NNS':[20], 'PRP':[21], 'VBZ':[22], 'VBG':[23], 'VBN':[24], 'MD':[25], 
+    'PRP$':[26], 'JJR':[27], 'JJS':[28], 'UH':[29], 'RP':[30], 'WP':[31], 'WDT':[32], '#':[33], "''":[34]}
+    for i in tokens_tagged:
+        x[pos_dict[i[1]][0]]+=1
+    x=np.array(x)
+    x=x.reshape(1,-1)
+    # x
+
+
+    # In[35]:
+
+
+    pred=clf.predict(x)
+    if pred==0:
+        return "NOT SPAM"
+    else:
+        return "SPAM"
+
+iface = gr.Interface(fn=spam_detection, inputs="text", outputs="text")
+iface.launch()
+
+
+

requirements.txt

@@ -0,0 +1,2 @@
+nltk
+scikit-learn

test.py

@@ -0,0 +1,86 @@
+import nltk
+from nltk.tokenize import word_tokenize
+import pandas as pd
+import csv
+
+
+tok_dict={}
+
+lst=['NNP', 'IN', 'JJ', 'NN', ',', 'RB', ':', 'EX', 'VBD', 'WRB', 'CD', 'DT', 'TO', 'VB', '.',
+     '(', ')', 'CC', 'POS', 'VBP', 'NNS', 'PRP', 'VBZ', 'VBG', 'VBN', 'MD', 'PRP$', 'JJR', 'JJS', 'UH', 'RP', 'WP', 'WDT', '#', "''"]
+
+pd_dict={'msg':[],'label':[],'label_no':[],'NNP':[], 'IN':[], 'JJ':[], 'NN':[], ',':[], 'RB':[], ':':[], 'EX':[], 'VBD':[], 
+'WRB':[], 'CD':[], 'DT':[], 'TO':[], 'VB':[], '.':[], '(':[], ')':[], 'CC':[],
+'POS':[], 'VBP':[], 'NNS':[], 'PRP':[], 'VBZ':[], 'VBG':[], 'VBN':[], 'MD':[], 
+'PRP$':[], 'JJR':[], 'JJS':[], 'UH':[], 'RP':[], 'WP':[], 'WDT':[], '#':[], "''":[]}
+
+with open("spam.csv", 'r') as file:
+  csvreader = csv.reader(file)
+  j=0
+  k=0
+  for row in csvreader:
+    if j==0:
+        j=1
+        continue
+    pd_dict['msg'].append(row[1])
+    pd_dict['label'].append(row[0])
+    if row[0]=='spam':
+        pd_dict['label_no'].append(1)
+    else:
+        pd_dict['label_no'].append(0)
+    for label in lst:
+        pd_dict[label].append(0)
+    text=row[1]
+    tokens=word_tokenize(text)
+    tokens_tagged=nltk.pos_tag(tokens)
+#        print(tokens_tagged,end='\n\n')
+    for i in tokens_tagged:
+        if i[1] in tok_dict:
+            tok_dict[i[1]].append(i[0])
+        else:
+            tok_dict[i[1]]=[i[0]]
+        if i[1] in pd_dict:
+          pd_dict[i[1]][k]+=1
+    k+=1
+        
+
+
+
+#text=""
+#tokens=word_tokenize(text)
+#tokens_tagged=nltk.pos_tag(tokens)
+#print(tokens_tagged,end='\n\n')
+#for i in tokens_tagged:
+#    if i[1] in tok_dict:
+#        tok_dict[i[1]].append(i[0])
+#    else:
+#        tok_dict[i[1]]=[i[0]]
+
+#print(tok_dict, end="\n\n")
+
+tok_dict1={}
+for i in tok_dict:
+    tok_dict1[i]=len(tok_dict[i])
+
+del_lst=[]
+for i in tok_dict1:
+    print(i," ",tok_dict1[i])
+    if tok_dict1[i]<100:
+        del_lst.append(i)
+
+print(del_lst)
+for i in del_lst:
+    tok_dict1.pop(i)
+
+
+print(tok_dict1)
+
+lst=[]
+for i in tok_dict1:
+    lst.append(i)
+
+
+print(lst,len(lst))
+
+df=pd.DataFrame(pd_dict)
+print(df.head())

testing.py

@@ -0,0 +1,86 @@
+import nltk
+from nltk.tokenize import word_tokenize
+import pandas as pd
+import csv
+
+
+tok_dict={}
+
+lst=['NNP', 'IN', 'JJ', 'NN', ',', 'RB', ':', 'EX', 'VBD', 'WRB', 'CD', 'DT', 'TO', 'VB', '.',
+     '(', ')', 'CC', 'POS', 'VBP', 'NNS', 'PRP', 'VBZ', 'VBG', 'VBN', 'MD', 'PRP$', 'JJR', 'JJS', 'UH', 'RP', 'WP', 'WDT', '#', "''"]
+
+pd_dict={'msg':[],'label':[],'label_no':[],'NNP':[], 'IN':[], 'JJ':[], 'NN':[], ',':[], 'RB':[], ':':[], 'EX':[], 'VBD':[], 
+'WRB':[], 'CD':[], 'DT':[], 'TO':[], 'VB':[], '.':[], '(':[], ')':[], 'CC':[],
+'POS':[], 'VBP':[], 'NNS':[], 'PRP':[], 'VBZ':[], 'VBG':[], 'VBN':[], 'MD':[], 
+'PRP$':[], 'JJR':[], 'JJS':[], 'UH':[], 'RP':[], 'WP':[], 'WDT':[], '#':[], "''":[]}
+
+with open("spam_db.csv", 'r') as file:
+  csvreader = csv.reader(file)
+  j=0
+  k=0
+  for row in csvreader:
+    if j==0:
+        j=1
+        continue
+    pd_dict['msg'].append(row[1])
+    pd_dict['label'].append(row[0])
+    if row[0]=='spam':
+        pd_dict['label_no'].append(1)
+    else:
+        pd_dict['label_no'].append(0)
+    for label in lst:
+        pd_dict[label].append(0)
+    text=row[1]
+    tokens=word_tokenize(text)
+    tokens_tagged=nltk.pos_tag(tokens)
+#        print(tokens_tagged,end='\n\n')
+    for i in tokens_tagged:
+        if i[1] in tok_dict:
+            tok_dict[i[1]].append(i[0])
+        else:
+            tok_dict[i[1]]=[i[0]]
+        if i[1] in pd_dict:
+          pd_dict[i[1]][k]+=1
+    k+=1
+        
+
+
+
+#text=""
+#tokens=word_tokenize(text)
+#tokens_tagged=nltk.pos_tag(tokens)
+#print(tokens_tagged,end='\n\n')
+#for i in tokens_tagged:
+#    if i[1] in tok_dict:
+#        tok_dict[i[1]].append(i[0])
+#    else:
+#        tok_dict[i[1]]=[i[0]]
+
+#print(tok_dict, end="\n\n")
+
+tok_dict1={}
+for i in tok_dict:
+    tok_dict1[i]=len(tok_dict[i])
+
+del_lst=[]
+for i in tok_dict1:
+    print(i," ",tok_dict1[i])
+    if tok_dict1[i]<100:
+        del_lst.append(i)
+
+print(del_lst)
+for i in del_lst:
+    tok_dict1.pop(i)
+
+
+print(tok_dict1)
+
+lst=[]
+for i in tok_dict1:
+    lst.append(i)
+
+
+print(lst,len(lst))
+
+df=pd.DataFrame(pd_dict)
+print(df.head())