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Fake News Classifier.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### <b>Fake News Classifier model using TfidfVectorizer and PassiveAggressiveClassifier</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sb\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.linear_model import PassiveAggressiveClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reading data into pandas dataframe\n",
+    "df=pd.read_csv('news.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "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>Unnamed: 0</th>\n",
+       "      <th>title</th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>8476</td>\n",
+       "      <td>You Can Smell Hillary’s Fear</td>\n",
+       "      <td>Daniel Greenfield, a Shillman Journalism Fello...</td>\n",
+       "      <td>FAKE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>10294</td>\n",
+       "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
+       "      <td>Google Pinterest Digg Linkedin Reddit Stumbleu...</td>\n",
+       "      <td>FAKE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>3608</td>\n",
+       "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
+       "      <td>U.S. Secretary of State John F. Kerry said Mon...</td>\n",
+       "      <td>REAL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>10142</td>\n",
+       "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
+       "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
+       "      <td>FAKE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>875</td>\n",
+       "      <td>The Battle of New York: Why This Primary Matters</td>\n",
+       "      <td>It's primary day in New York and front-runners...</td>\n",
+       "      <td>REAL</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0                                              title  \\\n",
+       "0        8476                       You Can Smell Hillary’s Fear   \n",
+       "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
+       "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
+       "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
+       "4         875   The Battle of New York: Why This Primary Matters   \n",
+       "\n",
+       "                                                text label  \n",
+       "0  Daniel Greenfield, a Shillman Journalism Fello...  FAKE  \n",
+       "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE  \n",
+       "2  U.S. Secretary of State John F. Kerry said Mon...  REAL  \n",
+       "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE  \n",
+       "4  It's primary day in New York and front-runners...  REAL  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#printing first 5 entries in the table/dataframe\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#deciding predicate and predicator variables\n",
+    "x=df['text']\n",
+    "y=df['label']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#splitting data into train dataset and test dataset\n",
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#vectorization and fitting data \n",
+    "tf_vect=TfidfVectorizer(stop_words='english',max_df=0.6)\n",
+    "tf_train=tf_vect.fit_transform(x_train) \n",
+    "tf_test=tf_vect.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#predicting lables of test data using PassiveAgrressiveClassifier\n",
+    "passiveac=PassiveAggressiveClassifier(max_iter=60) #more the value of max_iter, more accuracy can be obtained\n",
+    "passiveac.fit(tfidf_train,y_train)\n",
+    "pred = passiveac.predict(tfidf_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#calculating accuracy\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        FAKE       0.94      0.93      0.93       933\n",
+      "        REAL       0.93      0.94      0.93       968\n",
+      "\n",
+      "    accuracy                           0.93      1901\n",
+      "   macro avg       0.93      0.93      0.93      1901\n",
+      "weighted avg       0.93      0.93      0.93      1901\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test, pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[864  69]\n",
+      " [ 60 908]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(confusion_matrix(y_test,pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.932140978432404\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(accuracy_score(y_test,pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Fake News classifier model "
+   ]
+  }
+ ],
+ "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}