fake_news.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\panka\\Machine Learning\\Fake News\\data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "                                                   URLs  \\\n",
       "0     http://www.bbc.com/news/world-us-canada-414191...   \n",
       "1     https://www.reuters.com/article/us-filmfestiva...   \n",
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       "3     https://www.reuters.com/article/us-mexico-oil-...   \n",
       "4     http://www.cnn.com/videos/cnnmoney/2017/10/08/...   \n",
       "...                                                 ...   \n",
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       "\n",
       "                                               Headline  \\\n",
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       "2     Trump’s Fight With Corker Jeopardizes His Legi...   \n",
       "3     Egypt's Cheiron wins tie-up with Pemex for Mex...   \n",
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       "...                                                 ...   \n",
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       "4006        Ron Paul on Trump, Anarchism & the AltRight   \n",
       "4007  China to accept overseas trial data in bid to ...   \n",
       "4008  Vice President Mike Pence Leaves NFL Game Beca...   \n",
       "\n",
       "                                                   Body  Label  \n",
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       "2     The feud broke into public view last week when...      1  \n",
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       "4006                                                NaN      0  \n",
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_main=df[['Headline','Label','Body']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                                               Headline  Label  \\\n",
       "0            Four ways Bob Corker skewered Donald Trump      1   \n",
       "1     Linklater's war veteran comedy speaks to moder...      1   \n",
       "2     Trump’s Fight With Corker Jeopardizes His Legi...      1   \n",
       "3     Egypt's Cheiron wins tie-up with Pemex for Mex...      1   \n",
       "4           Jason Aldean opens 'SNL' with Vegas tribute      1   \n",
       "...                                                 ...    ...   \n",
       "4004                                    Trends to Watch      0   \n",
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       "4006        Ron Paul on Trump, Anarchism & the AltRight      0   \n",
       "4007  China to accept overseas trial data in bid to ...      1   \n",
       "4008  Vice President Mike Pence Leaves NFL Game Beca...      0   \n",
       "\n",
       "                                                   Body  \n",
       "0     Image copyright Getty Images\\nOn Sunday mornin...  \n",
       "1     LONDON (Reuters) - “Last Flag Flying”, a comed...  \n",
       "2     The feud broke into public view last week when...  \n",
       "3     MEXICO CITY (Reuters) - Egypt’s Cheiron Holdin...  \n",
       "4     Country singer Jason Aldean, who was performin...  \n",
       "...                                                 ...  \n",
       "4004  Trends to Watch\\n% of readers think this story...  \n",
       "4005  Trump Jr. Is Soon To Give A 30-Minute Speech F...  \n",
       "4006                                                NaN  \n",
       "4007  SHANGHAI (Reuters) - China said it plans to ac...  \n",
       "4008  Vice President Mike Pence Leaves NFL Game Beca...  \n",
       "\n",
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     "execution_count": 5,
     "metadata": {},
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    "df_main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new=df_main.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new['Body'].fillna('no info', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Headline    0\n",
       "Label       0\n",
       "Body        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x=df_new['Body']\n",
    "df_y=df_new['Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test=train_test_split(df_x,df_y, test_size=0.2, random_state=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv1=TfidfVectorizer(min_df=1,stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_xtrain=tv1.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=new_xtrain.toarray()"
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       " ...]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tv1.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.0247837 , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
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       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.03118581, 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['000', '04', '15', '16', '2003', '2017', '25', '73', 'addition',\n",
       "        'address', 'affordable', 'aim', 'appoint', 'area', 'attempt',\n",
       "        'bay', 'belt', 'billion', 'blueprint', 'bought', 'bring', 'build',\n",
       "        'buyers', 'capital', 'carrie', 'china', 'class', 'come', 'coming',\n",
       "        'committees', 'continue', 'country', 'criteria', 'current',\n",
       "        'decades', 'deficit', 'developers', 'development', 'developments',\n",
       "        'domestic', 'double', 'dropping', 'economic', 'economy',\n",
       "        'education', 'efforts', 'eligibility', 'enormous', 'enterprises',\n",
       "        'excess', 'expanding', 'expected', 'expenditure', 'families',\n",
       "        'fiscal', 'flats', 'following', 'goal', 'government', 'grew',\n",
       "        'gross', 'growth', 'guangdong', 'half', 'heard', 'higher',\n",
       "        'highlights', 'hk', 'home', 'homes', 'hong', 'housing', 'increase',\n",
       "        'increasing', 'infrastructure', 'initiative', 'innovation',\n",
       "        'interests', 'introduce', 'kong', 'laid', 'lam', 'land', 'leader',\n",
       "        'leases', 'legislation', 'level', 'levels', 'lowered', 'lowest',\n",
       "        'macao', 'maiden', 'meet', 'members', 'middle', 'million', 'mixed',\n",
       "        'months', 'national', 'number', 'offer', 'opportunities',\n",
       "        'overall', 'owned', 'ownership', 'people', 'percent', 'percentage',\n",
       "        'policy', 'pool', 'priorities', 'private', 'product', 'profits',\n",
       "        'proposes', 'provisions', 'public', 'pursue', 'quarters', 'rate',\n",
       "        'ratio', 'real', 'recent', 'recruit', 'rental', 'require',\n",
       "        'research', 'reserves', 'reuters', 'reviews', 'road', 'sale',\n",
       "        'say', 'scheme', 'security', 'seek', 'senior', 'set', 'shorten',\n",
       "        'sites', 'sovereignty', 'specified', 'standard', 'starter', 'step',\n",
       "        'subject', 'supply', 'talent', 'talents', 'target', 'tax',\n",
       "        'taxation', 'technology', 'tenure', 'term', 'terms', 'threaten',\n",
       "        'tier', 'time', 'unemployment', 'units', 'venture', 'voices',\n",
       "        'waiting', 'wednesday', 'went', 'year', 'years', 'young', 'youth'],\n",
       "       dtype='<U148')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tv1.inverse_transform(a[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3207x42271 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 557780 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_xtrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HONG KONG (Reuters) - The following are highlights of the maiden policy address of Hong Kong leader Carrie Lam on Wednesday as she laid out a blueprint of her government’s priorities in the coming five years.\\nNational security\\n- Say “no” to any attempt to threaten country’s (China‘s) sovereignty, security and development interests\\nInfrastructure\\n- Belt and Road Initiative and the Guangdong-Hong Kong-Macao Bay Area development will bring enormous opportunities for Hong Kong’s economy\\nEconomy\\n- The government last went into deficit in 2003-04 and now fiscal reserves are in excess of HK$1,000 billion.\\n- Hong Kong grew by 4.0 percent in real terms year-on-year in the first two quarters, with unemployment dropping to 3.1 percent in recent months, the lowest level in two decades\\n- Overall economic growth in 2017 is expected to be higher than 3.5 percent\\nInnovation and Technology\\n- The government will step up efforts in research and development, expanding talent pool, venture capital, legislation reviews, and I&T development\\n- The government has set a goal to double the Gross Domestic Expenditure on R&D as a percentage of the Gross Domestic Product from the current 0.73 percent to 1.5 percent within the current-term government’s five-year tenure\\nTaxation\\n- On the two-tier profits tax system, the profits tax rate for the first HK$2 million of profits of enterprises will be lowered to 8.25 percent, or half of the standard profits tax rate\\n- Profits above that amount will continue to be subject to the standard tax rate of 16.5 percent\\nHousing\\n- To step up efforts to increase the supply of housing units\\n- Seek to shorten the waiting time for public rental housing and increase supply of flats under the home ownership scheme.\\n- Proposes to introduce affordable “Starter Homes” for middle-class families in Hong Kong\\n- Land for “Starter Homes” to come from sites already owned by private developers or to be bought from the government\\n- Provisions in land leases to require developers to pursue mixed developments, to build and offer for sale a specified number of “Starter Homes” units in addition to private housing units to target buyers who meet the eligibility criteria\\nEducation and talents\\n- Increase education expenditure by HK$5 billion a year\\n- Appoint more young people to government committees, with the aim of increasing the overall ratio of youth members to 15 percent within the current-term government\\n- Recruit young people in policy research units so that the voices of young people can be heard at senior levels of the government'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# USING SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model_svm=SVC(kernel=\"linear\")\n",
    "#y_train=y_train.astype('int')\n",
    "model_svm.fit(new_xtrain,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_xtest=tv1.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred2=model_svm.predict(new_xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test1=y_test.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9850374064837906"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score as ac\n",
    "ac(pred2,y_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# USING KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=4, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "knn = KNeighborsClassifier(n_neighbors=4) \n",
    "knn.fit(new_xtrain,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1=knn.predict(new_xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9014962593516209"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score as ac\n",
    "ac(pred1,y_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# USING RANDOM FOREST CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf=RandomForestClassifier()\n",
    "rf.fit(new_xtrain,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=rf.predict(new_xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9663341645885287"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score as ac\n",
    "ac(pred,y_test1)"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}