DAY 18 - Fake News Detection - Naive Bayes Classifier.ipynb

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    "# What is Naive Bayes Algorithm?\n",
    "\n",
    "### Principle of Naive Bayes Classifier\n",
    "A Naive Bayes classifier is a probabilistic machine learning model that's used for classification task. The crux of the classifier is based on the Bayes theorem.\n",
    "P(A|B) = (P(B|A).P(A))/P(B)\n",
    "\n",
    "\n",
    "### Terms\n",
    "- P(A|B) is the posterior probability of class (A, target) given predictor (B, attributes)\n",
    "- P(A) is the prior probability of class.\n",
    "- P(B|A) is the likelihood which is the probability of predictor given class.\n",
    "- P(B) is the prior probability of predictor.\n",
    "\n",
    "\n",
    "### Naive Bayes Algorithm - Working\n",
    "- Take a training dataset of weather and corresponding target variable play.\n",
    "- We need to classify if the players will play or not based on some condition.\n",
    "- Steps:\n",
    "    1. Convert the dataset into frequency table.\n",
    "    2. Create likelihood table by finding probabilities like Overcast probability = 0.29 and the probability of playing is 0.64\n",
    "    3. Naive Bayesian equation is used to find the posterior probability of each class. The class with the highest posterior probability is the outcome of the prediction.\n",
    "\n",
    "\n",
    "### Problem\n",
    "- Players will plat if the weather is sunny.\n",
    "- We can solve this by the method of posterier probability.\n",
    "- P(Yes|Sunny) = P(Sunny|Yes) * P(Yes) / P(Sunny)\n",
    "- Here we have P(Sunny|Yes) = 3/9 = 0.33, P(Sunny) =5/14 = 0.36, P(Yes) = 9/14 = 0.64\n",
    "- Now, P(Yes|Sunny) = 0.33 * 0.64 / 0.36 = 0.60, which has higher probability.\n",
    "- Naive Bayes uses a similar method to predict the probability of different classes based on various attributes.\n",
    "- It is mostly used in text classification and with problems having multiple classes.\n",
    "\n",
    "\n",
    "### Applications of Naive Bayes\n",
    "***Prediction in real time***\n",
    "- It is an eager learner and it learns fast.\n",
    "- It is used for making predictions in real time.\n",
    "\n",
    "***Multi class prediction***\n",
    "- It is known for multi class prediction feature.\n",
    "- We can predict the probability of multiple classes of target variable.\n",
    "\n",
    "***Text Classification/ Spam Filtering***\n",
    "- It is mostly used in text classification because it is good at solving multi-class problems.\n",
    "- It is used in spam filtering(classifying spam email) and sentiment analysis(to identify positive and negative customer sentiments).\n",
    "\n",
    "***Recommendation System***\n",
    "- Naive Bayes filter builds a recommendation system that uses machine learning and data mining techniques to filter unsen information.\n",
    "- It predicts if the user likes a given resource or not."
   ]
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   "cell_type": "markdown",
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   "source": [
    "# Objective of Fake News Detection\n",
    "The main objective is to detect the fake news which is a classic text classification problem. With a straight forward proposition. It is needed to build a model that can differentiate between \"Real\" news and \"Fake\" news.\n",
    "\n",
    "\n",
    "# System Architecture\n",
    "\n",
    "Extracting the Training Data ---------> Generate TFIDF and countvectorizer ---------> Test train split ---------> Train Models(Passive aggresive algorithm, Multinomial Naive Bayes) ---------> Test on holdout set ---------> Results and pickled models\n",
    "\n",
    "\n",
    "# Modules\n",
    "\n",
    "### Data Pre-processing\n",
    "This file contains all the pre- processing functions needed to process all input documents and texts. First we read the train, tests and validation data files then performed some pre-processing like response variable distribution and data quality checks like null or missing values etc.\n",
    "\n",
    "### Feature Extraction\n",
    "\n",
    "### Classification\n",
    "\n",
    "### Prediction"
   ]
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  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "painted-anniversary",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "minute-flesh",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('news.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "passive-portfolio",
   "metadata": {},
   "outputs": [
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   "source": [
    "df.head()"
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   "cell_type": "code",
   "execution_count": 4,
   "id": "compatible-brother",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop('label', axis=1) # features\n",
    "y = df['label'] # target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "regulated-crowd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
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   "cell_type": "code",
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   "id": "speaking-constraint",
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       "1      1   1  FLYNN: Hillary Clinton, Big Woman on Campus - ...   \n",
       "2      2   2                  Why the Truth Might Get You Fired   \n",
       "3      3   3  15 Civilians Killed In Single US Airstrike Hav...   \n",
       "4      4   4  Iranian woman jailed for fictional unpublished...   \n",
       "5      5   5  Jackie Mason: Hollywood Would Love Trump if He...   \n",
       "6      7   7  Benoît Hamon Wins French Socialist Party’s Pre...   \n",
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       "8     10  10  Obama’s Organizing for Action Partners with So...   \n",
       "9     11  11  BBC Comedy Sketch \"Real Housewives of ISIS\" Ca...   \n",
       "\n",
       "                         author  \\\n",
       "0                 Darrell Lucus   \n",
       "1               Daniel J. Flynn   \n",
       "2            Consortiumnews.com   \n",
       "3               Jessica Purkiss   \n",
       "4                Howard Portnoy   \n",
       "5               Daniel Nussbaum   \n",
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       "9               Chris Tomlinson   \n",
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       "3  Videos 15 Civilians Killed In Single US Airstr...      1  \n",
       "4  Print \\nAn Iranian woman has been sentenced to...      1  \n",
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    "messages = df.copy()\n",
    "messages.reset_index(inplace=True)\n",
    "messages.head(10)"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ceramic-costs",
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       "'PARIS  —   France chose an idealistic, traditional   candidate in Sunday’s primary to represent the Socialist and   parties in the presidential election this spring. The candidate, Benoît Hamon, 49, who ran on the slogan that he would “make France’s heart beat,” bested Manuel Valls, the former prime minister, whose campaign has promoted more   policies and who has a strong    background. Mr. Hamon appeared to have won by a wide margin, with incomplete returns showing him with an estimated 58 percent of the vote to Mr. Valls’s 41 percent. “Tonight the left holds its head up high again it is looking to the future,” Mr. Hamon said, addressing his supporters. “Our country needs the left, but a modern, innovative left,” he said. Mr. Hamon’s victory was the clearest sign yet that voters on the left want a break with the policies of President François Hollande, who in December announced that he would not seek  . However, Mr. Hamon’s strong showing is unlikely to change widespread assessments that   candidates have little chance of making it into the second round of voting in the general election. The first round of the general election is set for April 23 and the runoff for May 7. The Socialist Party is deeply divided, and one measure of its lack of popular enthusiasm was the relatively low number of people voting. About two million people voted in the second round of the primary on Sunday, in contrast with about 2. 9 million in the second round of the last presidential primary on the left, in 2011. However, much of the conventional wisdom over how the elections will go has been thrown into question over the past week, because the leading candidate, François Fillon, who represents the main   party, the Republicans, was accused of paying his wife large sums of money to work as his parliamentary aide. While nepotism is legal in the French political system, it is not clear that she actually did any work. Prosecutors who specialize in financial malfeasance are reviewing the case. France’s electoral system allows multiple candidates to run for president in the first round of voting, but only the top two   go on to a second round. Mr. Hamon is entering a race that is already crowded on the left, with candidates who include   Mélenchon on the far left, and Emmanuel Macron, an independent who served as economy minister in Mr. Hollande’s government and who embraces more   policies. Unless he decides to withdraw, Mr. Fillon, the mainstream right candidate, will also run, as will the extreme right candidate Marine Le Pen. The two have been expected to go to the runoff. Mr. Hamon’s victory can be attributed at least in part to his image as an idealist and traditional leftist candidate who appeals to union voters as well as more environmentally concerned and socially liberal young people. Unlike Mr. Valls, he also clearly distanced himself from some of Mr. Hollande’s more unpopular policies, especially the economic ones. Thomas Kekenbosch, 22, a student and one of the leaders of the group the Youth With Benoît Hamon, said Mr. Hamon embodied a new hope for those on the left. “We have a perspective we have something to do, to build,” Mr. Kekenbosch said. Mr. Hollande had disappointed many young people because under him the party abandoned ideals, such as support for workers, that many   voters believe in, according to Mr. Kekenbosch. Mr. Hollande’s government, under pressure from the European Union to meet budget restraints, struggled to pass labor code reforms to make the market more attractive to foreign investors and also to encourage French businesses to expand in France. The measures ultimately passed after weeks of strikes, but they were watered down and generated little concrete progress in improving France’s roughly 10 percent unemployment rate and its nearly 25 percent youth joblessness rate. Mr. Hamon strongly endorses a stimulus approach to improving the economy and has promised to phase in a universal income, which would especially help young people looking for work, but would also supplement the livelihood of   French workers. The end goal would be to have everyone receive 750 euros per month (about $840). “We have someone that trusts us,” Mr. Kekenbosch said, “who says: ‘I give you enough to pay for your studies. You can have a scholarship which spares you from working at McDonald’s on provisional contracts for 4 years. ” Mr. Hamon advocates phasing out diesel fuel and encouraging drivers to replace vehicles that use petroleum products with electrical ones. His leftist pedigree began early. His father worked at an arsenal in Brest, a city in the far west of Brittany, and his mother worked off and on as a secretary. He was an early member of the Movement of Young Socialists, and he has continued to work closely with them through his political life. He also worked for Martine Aubry, now the mayor of Lille and a former Socialist Party leader.'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages['text'][6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "blocked-argument",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps = PorterStemmer()\n",
    "corpus = [] \n",
    "for i in range(0, len(messages)):\n",
    "    review = re.sub('[^a-zA-Z]', ' ', messages['text'][i])\n",
    "    review = review.lower()\n",
    "    review = review.split()\n",
    "    review = [ps.stem(word) for word in review if not word in stopwords.words('english')] \n",
    "    review = ' '.join(review)\n",
    "    corpus.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "laughing-promise",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'video civilian kill singl us airstrik identifi rate civilian kill american airstrik afghanistan higher us engag activ combat oper photo hellfir missil load onto us militari reaper drone afghanistan staff sgt brian ferguson u air forc bureau abl identifi civilian kill singl us drone strike afghanistan last month biggest loss civilian life one strike sinc attack medecin san frontier hospit msf last octob us claim conduct counter terror strike islam state fighter hit nangarhar provinc missil septemb next day unit nation issu unusu rapid strong statement say strike kill civilian injur other gather hous celebr tribal elder return pilgrimag mecca bureau spoke man name haji rai said owner hous target said peopl kill other injur provid name list bureau abl independ verifi ident die rai son headmast local school among anoth man abdul hakim lost three son attack rai said involv deni us claim member visit hous strike said even speak sort peopl phone let alon receiv hous death amount biggest confirm loss civilian life singl american strike afghanistan sinc attack msf hospit kunduz last octob kill least peopl nangarhar strike us attack kill civilian septemb bureau data indic mani civilian alli soldier kill four american strike afghanistan somalia month septemb pair strike kill eight afghan policemen tarinkot capit urozgan provic us jet reportedli hit polic checkpoint kill one offic return target first respond use tactic known doubl tap strike controversi often hit civilian rescuer us told bureau conduct strike individu fire pose threat afghan forc email directli address alleg afghan policemen kill end month somalia citizen burnt us flag street north central citi galcayo emerg drone attack may unintent kill somali soldier civilian strike occur day one nangarhar somali afghan incid us first deni non combat kill investig strike nangarhar galcayo rate civilian kill american airstrik afghanistan higher us engag activ combat oper name'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "basic-roommate",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TFidf Vectorizer\n",
    "tfidf_v = TfidfVectorizer(max_features=5000,ngram_range=(1,3))\n",
    "x = tfidf_v.fit_transform(corpus).toarray()\n",
    "y = messages['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "unlimited-sight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aaron',\n",
       " 'abandon',\n",
       " 'abc',\n",
       " 'abc news',\n",
       " 'abe',\n",
       " 'abedin',\n",
       " 'abid',\n",
       " 'abil',\n",
       " 'abl',\n",
       " 'abort',\n",
       " 'abroad',\n",
       " 'absenc',\n",
       " 'absolut',\n",
       " 'absorb',\n",
       " 'absurd',\n",
       " 'abu',\n",
       " 'abus',\n",
       " 'academ',\n",
       " 'academi',\n",
       " 'acceler']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_v.get_feature_names()[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "elder-prospect",
   "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>aaron</th>\n",
       "      <th>abandon</th>\n",
       "      <th>abc</th>\n",
       "      <th>abc news</th>\n",
       "      <th>abe</th>\n",
       "      <th>abedin</th>\n",
       "      <th>abid</th>\n",
       "      <th>abil</th>\n",
       "      <th>abl</th>\n",
       "      <th>abort</th>\n",
       "      <th>...</th>\n",
       "      <th>young man</th>\n",
       "      <th>young peopl</th>\n",
       "      <th>younger</th>\n",
       "      <th>youth</th>\n",
       "      <th>youtub</th>\n",
       "      <th>zero</th>\n",
       "      <th>zika</th>\n",
       "      <th>zionist</th>\n",
       "      <th>zone</th>\n",
       "      <th>zu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.022379</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018296</td>\n",
       "      <td>0.014722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 5000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   aaron  abandon       abc  abc news  abe    abedin  abid      abil  \\\n",
       "0    0.0      0.0  0.000000       0.0  0.0  0.000000   0.0  0.000000   \n",
       "1    0.0      0.0  0.000000       0.0  0.0  0.022379   0.0  0.000000   \n",
       "2    0.0      0.0  0.000000       0.0  0.0  0.000000   0.0  0.000000   \n",
       "3    0.0      0.0  0.000000       0.0  0.0  0.000000   0.0  0.018296   \n",
       "4    0.0      0.0  0.050957       0.0  0.0  0.000000   0.0  0.000000   \n",
       "\n",
       "        abl  abort  ...  young man  young peopl  younger  youth  youtub  zero  \\\n",
       "0  0.000000    0.0  ...   0.000000          0.0      0.0    0.0     0.0   0.0   \n",
       "1  0.000000    0.0  ...   0.000000          0.0      0.0    0.0     0.0   0.0   \n",
       "2  0.000000    0.0  ...   0.000000          0.0      0.0    0.0     0.0   0.0   \n",
       "3  0.014722    0.0  ...   0.026633          0.0      0.0    0.0     0.0   0.0   \n",
       "4  0.000000    0.0  ...   0.000000          0.0      0.0    0.0     0.0   0.0   \n",
       "\n",
       "   zika  zionist  zone   zu  \n",
       "0   0.0      0.0   0.0  0.0  \n",
       "1   0.0      0.0   0.0  0.0  \n",
       "2   0.0      0.0   0.0  0.0  \n",
       "3   0.0      0.0   0.0  0.0  \n",
       "4   0.0      0.0   0.0  0.0  \n",
       "\n",
       "[5 rows x 5000 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Divide the dataset into Train and Test\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=0)\n",
    "\n",
    "count_df = pd.DataFrame(x_train, columns=tfidf_v.get_feature_names())\n",
    "count_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "boring-northwest",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "general-exercise",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.831\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "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": [
    "classifier = MultinomialNB()\n",
    "classifier.fit(x_train, y_train)\n",
    "pred = classifier.predict(x_test)\n",
    "score = metrics.accuracy_score(y_test, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)\n",
    "cm = metrics.confusion_matrix(y_test, pred)\n",
    "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "improved-knowing",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aditi Patil\\anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:509: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.0, Score : 0.8611111111111112\n",
      "Alpha: 0.1, Score : 0.8694444444444445\n",
      "Alpha: 0.2, Score : 0.8666666666666667\n",
      "Alpha: 0.30000000000000004, Score : 0.8597222222222223\n",
      "Alpha: 0.4, Score : 0.8555555555555555\n",
      "Alpha: 0.5, Score : 0.8527777777777777\n",
      "Alpha: 0.6000000000000001, Score : 0.8513888888888889\n",
      "Alpha: 0.7000000000000001, Score : 0.8444444444444444\n",
      "Alpha: 0.8, Score : 0.8375\n",
      "Alpha: 0.9, Score : 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "previous_score = 0\n",
    "for alpha in np.arange(0,1,0.1):\n",
    "    sub_classifier = MultinomialNB(alpha=alpha)\n",
    "    sub_classifier.fit(x_train,y_train)\n",
    "    y_pred = sub_classifier.predict(x_test)\n",
    "    score = metrics.accuracy_score(y_test, y_pred)\n",
    "    if score > previous_score:\n",
    "        classifier = sub_classifier\n",
    "    print(\"Alpha: {}, Score : {}\".format(alpha,score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "another-bridge",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Features names\n",
    "feature_names = tfidf_v.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "existing-jimmy",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aditi Patil\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:101: FutureWarning: Attribute coef_ was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26).\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(-5.896986850553509, 'trump'),\n",
       " (-5.907970944578581, 'clinton'),\n",
       " (-6.144631348239059, 'hillari'),\n",
       " (-6.298335500575183, 'elect'),\n",
       " (-6.397926987608068, 'us'),\n",
       " (-6.591945337846607, 'peopl'),\n",
       " (-6.628204625877579, 'state'),\n",
       " (-6.629704158068672, 'email'),\n",
       " (-6.644412249730442, 'vote'),\n",
       " (-6.692814058898546, 'one'),\n",
       " (-6.746882378757433, 'hillari clinton'),\n",
       " (-6.7565666490231315, 'fbi'),\n",
       " (-6.757997840017799, 'american'),\n",
       " (-6.7629873533684055, 'would'),\n",
       " (-6.790123860243235, 'like'),\n",
       " (-6.829833741697983, 'go'),\n",
       " (-6.848338893321211, 'octob'),\n",
       " (-6.883842753543826, 'war'),\n",
       " (-6.896665336516886, 'obama'),\n",
       " (-6.897522678051416, 'new')]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most real\n",
    "sorted(zip(classifier.coef_[0], feature_names), reverse=True)[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "living-christmas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-9.320369918130927, 'abe'),\n",
       " (-9.320369918130927, 'actress'),\n",
       " (-9.320369918130927, 'ask mr'),\n",
       " (-9.320369918130927, 'barbara'),\n",
       " (-9.320369918130927, 'basketbal'),\n",
       " (-9.320369918130927, 'bay area'),\n",
       " (-9.320369918130927, 'belgium'),\n",
       " (-9.320369918130927, 'bentley'),\n",
       " (-9.320369918130927, 'berggruen'),\n",
       " (-9.320369918130927, 'borman'),\n",
       " (-9.320369918130927, 'branca'),\n",
       " (-9.320369918130927, 'breitbart com'),\n",
       " (-9.320369918130927, 'breitbart texa'),\n",
       " (-9.320369918130927, 'brief post'),\n",
       " (-9.320369918130927, 'calif'),\n",
       " (-9.320369918130927, 'call mr'),\n",
       " (-9.320369918130927, 'campaign mr'),\n",
       " (-9.320369918130927, 'casino'),\n",
       " (-9.320369918130927, 'championship'),\n",
       " (-9.320369918130927, 'commut')]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most real\n",
    "sorted(zip(classifier.coef_[0], feature_names))[:20]"
   ]
  }
 ],
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