Fake Reviews Detection Part-2 Continued.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1ddc23d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings, string\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46e09e82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>category</th>\n",
       "      <th>rating</th>\n",
       "      <th>label</th>\n",
       "      <th>text_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Home_and_Kitchen_5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CG</td>\n",
       "      <td>love well made sturdi comfort i love veri pretti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Home_and_Kitchen_5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CG</td>\n",
       "      <td>love great upgrad origin i 've mine coupl year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Home_and_Kitchen_5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CG</td>\n",
       "      <td>thi pillow save back i love look feel pillow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Home_and_Kitchen_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CG</td>\n",
       "      <td>miss inform use great product price i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Home_and_Kitchen_5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CG</td>\n",
       "      <td>veri nice set good qualiti we set two month</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0            category  rating label  \\\n",
       "0           0  Home_and_Kitchen_5     5.0    CG   \n",
       "1           1  Home_and_Kitchen_5     5.0    CG   \n",
       "2           2  Home_and_Kitchen_5     5.0    CG   \n",
       "3           3  Home_and_Kitchen_5     1.0    CG   \n",
       "4           4  Home_and_Kitchen_5     5.0    CG   \n",
       "\n",
       "                                              text_  \n",
       "0  love well made sturdi comfort i love veri pretti  \n",
       "1    love great upgrad origin i 've mine coupl year  \n",
       "2      thi pillow save back i love look feel pillow  \n",
       "3             miss inform use great product price i  \n",
       "4       veri nice set good qualiti we set two month  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Preprocessed Fake Reviews Detection Dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ec807090",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Unnamed: 0',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "210251cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>category</th>\n",
       "      <th>rating</th>\n",
       "      <th>label</th>\n",
       "      <th>text_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Home_and_Kitchen_5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CG</td>\n",
       "      <td>love well made sturdi comfort i love veri pretti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Home_and_Kitchen_5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CG</td>\n",
       "      <td>love great upgrad origin i 've mine coupl year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Home_and_Kitchen_5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CG</td>\n",
       "      <td>thi pillow save back i love look feel pillow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Home_and_Kitchen_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CG</td>\n",
       "      <td>miss inform use great product price i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Home_and_Kitchen_5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CG</td>\n",
       "      <td>veri nice set good qualiti we set two month</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             category  rating label  \\\n",
       "0  Home_and_Kitchen_5     5.0    CG   \n",
       "1  Home_and_Kitchen_5     5.0    CG   \n",
       "2  Home_and_Kitchen_5     5.0    CG   \n",
       "3  Home_and_Kitchen_5     1.0    CG   \n",
       "4  Home_and_Kitchen_5     5.0    CG   \n",
       "\n",
       "                                              text_  \n",
       "0  love well made sturdi comfort i love veri pretti  \n",
       "1    love great upgrad origin i 've mine coupl year  \n",
       "2      thi pillow save back i love look feel pillow  \n",
       "3             miss inform use great product price i  \n",
       "4       veri nice set good qualiti we set two month  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5763dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "304aa467",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['length'] = df['text_'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0c47197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 40431 entries, 0 to 40431\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   category  40431 non-null  object \n",
      " 1   rating    40431 non-null  float64\n",
      " 2   label     40431 non-null  object \n",
      " 3   text_     40431 non-null  object \n",
      " 4   length    40431 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6148dffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['length'],bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78e8500d",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">rating</th>\n",
       "      <th colspan=\"8\" halign=\"left\">length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CG</th>\n",
       "      <td>20215.0</td>\n",
       "      <td>4.259906</td>\n",
       "      <td>1.141092</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20215.0</td>\n",
       "      <td>173.639030</td>\n",
       "      <td>173.955252</td>\n",
       "      <td>13.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>1300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>20216.0</td>\n",
       "      <td>4.253265</td>\n",
       "      <td>1.147652</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20216.0</td>\n",
       "      <td>237.893599</td>\n",
       "      <td>252.920950</td>\n",
       "      <td>8.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>2232.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rating                                                length  \\\n",
       "         count      mean       std  min  25%  50%  75%  max    count   \n",
       "label                                                                  \n",
       "CG     20215.0  4.259906  1.141092  1.0  4.0  5.0  5.0  5.0  20215.0   \n",
       "OR     20216.0  4.253265  1.147652  1.0  4.0  5.0  5.0  5.0  20216.0   \n",
       "\n",
       "                                                                 \n",
       "             mean         std   min   25%    50%    75%     max  \n",
       "label                                                            \n",
       "CG     173.639030  173.955252  13.0  57.0   99.0  217.0  1300.0  \n",
       "OR     237.893599  252.920950   8.0  74.0  134.0  296.0  2232.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('label').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e466777e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='length',by='label',bins=50,color='blue',figsize=(12,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "092b8ad0",
   "metadata": {},
   "source": [
    "Let's extract the largest review..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e20b02e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"weak on current scienc after see twice i agre much posit five star review out respect read review i 'll repeat everyth i like present i found goofi over ear hairdo facial hair arrang daniel vitali describ `` wild food expert '' distract ugh ditto david wolf extrem goofi wild hairdo on hand jon gabriel describ `` author weight loss expert '' nice groom good present hi stori person transform fellow pound whew becom jock normal weight inspir christian northrup preserv rank one america 's cutest doctor a realli nice look woman present dr. mercola jason vale kri carr alejandro junger fine it disappoint jami oliv popular uk give babi cow growth fluid pas unscientif popular idea milk none present anyth zilch say work doctor t. colin campbel milk bodi bad it good see present take stand sugar they agre evil sugar refin carbohydr with respect dr. northrup `` it 's fat make fat 's sugar '' statement pas muster commun expert recogn evil sugar not mutual exclus recogn proven danger fat particularli fat dead anim extract fat all kind oliv oil not health food data-hook= '' product-link-link '' class= '' a-link-norm '' href= '' /the-china-study-the-most-comprehensive-study-of-nutrition-ever-conducted-and-the-startling-implications-for-diet-weight-loss-and-long-term-health/dp/1932100660/ref=cm_cr_arp_d_rvw_txt ie=utf8 '' the china studi the most comprehens studi nutrit ever conduct and startl implic diet weight loss and long-term health /a data-hook= '' product-link-link '' class= '' a-link-norm '' href= '' /forks-over-knives/dp/b0053zhzi2/ref=cm_cr_arp_d_rvw_txt ie=utf8 '' fork over knive /a data-hook= '' product-link-link '' class= '' a-link-norm '' href= '' /prevent-and-reverse-heart-disease-the-revolutionary-scientifically-proven-nutrition-based-cure/dp/1583333002/ref=cm_cr_arp_d_rvw_txt ie=utf8 '' prevent revers heart diseas the revolutionari scientif proven nutrition-bas cure /a data-hook= '' product-link-link '' class= '' a-link-norm '' href= '' /the-plant-based-journey-a-step-by-step-guide-for-transitioning-to-a-healthy-lifestyle-and-achieving-your-ideal-weight/dp/1941631363/ref=cm_cr_arp_d_rvw_txt ie=utf8 '' the plant-bas journey a step-by-step guid transit healthi lifestyl achiev your ideal weight /a\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['label']=='OR'][['text_','length']].sort_values(by='length',ascending=False).head().iloc[0].text_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c9d5a480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    40431.000000\n",
       "mean       205.767109\n",
       "std        219.422327\n",
       "min          8.000000\n",
       "25%         64.000000\n",
       "50%        116.000000\n",
       "75%        253.000000\n",
       "max       2232.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.length.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "81649454",
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_process(review):\n",
    "    nopunc = [char for char in review if char not in string.punctuation]\n",
    "    nopunc = ''.join(nopunc)\n",
    "    return [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d3fc209c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer(analyzer=<function text_process at 0x0000021F7471D940>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow_transformer = CountVectorizer(analyzer=text_process)\n",
    "bow_transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bdb678ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Vocabulary: 34452\n"
     ]
    }
   ],
   "source": [
    "bow_transformer.fit(df['text_'])\n",
    "print(\"Total Vocabulary:\",len(bow_transformer.vocabulary_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07e1d8e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'miss inform use great product price i'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review4 = df['text_'][3]\n",
    "review4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d6fdd2b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 13599)\t1\n",
      "  (0, 15841)\t1\n",
      "  (0, 19849)\t1\n",
      "  (0, 23848)\t1\n",
      "  (0, 23978)\t1\n",
      "  (0, 32184)\t1\n",
      "(1, 34452)\n"
     ]
    }
   ],
   "source": [
    "bow_msg4 = bow_transformer.transform([review4])\n",
    "print(bow_msg4)\n",
    "print(bow_msg4.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61fa8d1a",
   "metadata": {},
   "source": [
    "There are 6 unique words in the 4th review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "600e72bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inform\n",
      "price\n"
     ]
    }
   ],
   "source": [
    "print(bow_transformer.get_feature_names()[15841])\n",
    "print(bow_transformer.get_feature_names()[23848])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b00e702c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bow_reviews = bow_transformer.transform(df['text_'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87b42dd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Bag of Words Transformer for the entire reviews corpus: (40431, 34452)\n",
      "Amount of non zero values in the bag of words model: 1013899\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of Bag of Words Transformer for the entire reviews corpus:\",bow_reviews.shape)\n",
    "print(\"Amount of non zero values in the bag of words model:\",bow_reviews.nnz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bb8dd9b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sparsity: 0.07\n"
     ]
    }
   ],
   "source": [
    "print(\"Sparsity:\",np.round((bow_reviews.nnz/(bow_reviews.shape[0]*bow_reviews.shape[1]))*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9d84aa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 13599)\t1\n",
      "  (0, 15841)\t1\n",
      "  (0, 19849)\t1\n",
      "  (0, 23848)\t1\n",
      "  (0, 23978)\t1\n",
      "  (0, 32184)\t1\n"
     ]
    }
   ],
   "source": [
    "tfidf_transformer = TfidfTransformer().fit(bow_reviews)\n",
    "tfidf_rev4 = tfidf_transformer.transform(bow_msg4)\n",
    "print(bow_msg4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9dc5bf62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.91422964906803\n",
      "2.821684385176731\n"
     ]
    }
   ],
   "source": [
    "print(tfidf_transformer.idf_[bow_transformer.vocabulary_['mango']])\n",
    "print(tfidf_transformer.idf_[bow_transformer.vocabulary_['book']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "144873ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (40431, 34452)\n",
      "No. of Dimensions: 2\n"
     ]
    }
   ],
   "source": [
    "tfidf_reviews = tfidf_transformer.transform(bow_reviews)\n",
    "print(\"Shape:\",tfidf_reviews.shape)\n",
    "print(\"No. of Dimensions:\",tfidf_reviews.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a11fecb",
   "metadata": {},
   "source": [
    "## Creating training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "501767f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "review_train, review_test, label_train, label_test = train_test_split(df['text_'],df['label'],test_size=0.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca4043a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('bow',CountVectorizer(analyzer=text_process)),\n",
    "    ('tfidf',TfidfTransformer()),\n",
    "    ('classifier',MultinomialNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549f1e01",
   "metadata": {},
   "source": [
    "## Training and testing Multinomial Naive Bayes Algorithm on the preprocessed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dd5c82d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('bow',\n",
       "                 CountVectorizer(analyzer=<function text_process at 0x0000021F7471D940>)),\n",
       "                ('tfidf', TfidfTransformer()),\n",
       "                ('classifier', MultinomialNB())])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(review_train,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c5d2587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CG', 'CG', 'CG', ..., 'OR', 'OR', 'OR'], dtype='<U2')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = pipeline.predict(review_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f921c833",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "          CG       0.81      0.89      0.85      7032\n",
      "          OR       0.88      0.80      0.84      7119\n",
      "\n",
      "    accuracy                           0.84     14151\n",
      "   macro avg       0.85      0.84      0.84     14151\n",
      "weighted avg       0.85      0.84      0.84     14151\n",
      "\n",
      "Confusion Matrix: [[6237  795]\n",
      " [1420 5699]]\n",
      "Accuracy Score: 0.8434739594374956\n"
     ]
    }
   ],
   "source": [
    "print('Classification Report:',classification_report(label_test,predictions))\n",
    "print('Confusion Matrix:',confusion_matrix(label_test,predictions))\n",
    "print('Accuracy Score:',accuracy_score(label_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f9a1b893",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Prediction Accuracy: 84.35%\n"
     ]
    }
   ],
   "source": [
    "print('Model Prediction Accuracy:',str(np.round(accuracy_score(label_test,predictions)*100,2)) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ce7083df",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('bow',CountVectorizer(analyzer=text_process)),\n",
    "    ('tfidf',TfidfTransformer()),\n",
    "    ('classifier',RandomForestClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7e5aad6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('bow',\n",
       "                 CountVectorizer(analyzer=<function text_process at 0x0000021F7471D940>)),\n",
       "                ('tfidf', TfidfTransformer()),\n",
       "                ('classifier', RandomForestClassifier())])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(review_train,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0ab07e65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CG', 'CG', 'CG', ..., 'CG', 'CG', 'OR'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_pred = pipeline.predict(review_test)\n",
    "rfc_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "68e19966",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "          CG       0.80      0.89      0.84      7032\n",
      "          OR       0.88      0.78      0.83      7119\n",
      "\n",
      "    accuracy                           0.84     14151\n",
      "   macro avg       0.84      0.84      0.84     14151\n",
      "weighted avg       0.84      0.84      0.84     14151\n",
      "\n",
      "Confusion Matrix: [[6244  788]\n",
      " [1539 5580]]\n",
      "Accuracy Score: 0.835559324429369\n",
      "Model Prediction Accuracy: 83.56%\n"
     ]
    }
   ],
   "source": [
    "print('Classification Report:',classification_report(label_test,rfc_pred))\n",
    "print('Confusion Matrix:',confusion_matrix(label_test,rfc_pred))\n",
    "print('Accuracy Score:',accuracy_score(label_test,rfc_pred))\n",
    "print('Model Prediction Accuracy:',str(np.round(accuracy_score(label_test,rfc_pred)*100,2)) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0c018b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('bow',CountVectorizer(analyzer=text_process)),\n",
    "    ('tfidf',TfidfTransformer()),\n",
    "    ('classifier',DecisionTreeClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9e33916b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('bow',\n",
       "                 CountVectorizer(analyzer=<function text_process at 0x0000021F7471D940>)),\n",
       "                ('tfidf', TfidfTransformer()),\n",
       "                ('classifier', DecisionTreeClassifier())])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(review_train,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5e483f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['OR', 'CG', 'CG', ..., 'OR', 'OR', 'CG'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree_pred = pipeline.predict(review_test)\n",
    "dtree_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c775e38e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "          CG       0.72      0.75      0.74      7032\n",
      "          OR       0.75      0.71      0.73      7119\n",
      "\n",
      "    accuracy                           0.73     14151\n",
      "   macro avg       0.73      0.73      0.73     14151\n",
      "weighted avg       0.73      0.73      0.73     14151\n",
      "\n",
      "Confusion Matrix: [[5305 1727]\n",
      " [2046 5073]]\n",
      "Accuracy Score: 0.7333757331637341\n",
      "Model Prediction Accuracy: 73.34%\n"
     ]
    }
   ],
   "source": [
    "print('Classification Report:',classification_report(label_test,dtree_pred))\n",
    "print('Confusion Matrix:',confusion_matrix(label_test,dtree_pred))\n",
    "print('Accuracy Score:',accuracy_score(label_test,dtree_pred))\n",
    "print('Model Prediction Accuracy:',str(np.round(accuracy_score(label_test,dtree_pred)*100,2)) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d2e130f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('bow',CountVectorizer(analyzer=text_process)),\n",
    "    ('tfidf',TfidfTransformer()),\n",
    "    ('classifier',KNeighborsClassifier(n_neighbors=2))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b014bfd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('bow',\n",
       "                 CountVectorizer(analyzer=<function text_process at 0x0000021F7471D940>)),\n",
       "                ('tfidf', TfidfTransformer()),\n",
       "                ('classifier', KNeighborsClassifier(n_neighbors=2))])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(review_train,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "af69506a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CG', 'CG', 'CG', ..., 'OR', 'CG', 'CG'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_pred = pipeline.predict(review_test)\n",
    "knn_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f9d23318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "          CG       0.54      0.97      0.69      7032\n",
      "          OR       0.86      0.19      0.31      7119\n",
      "\n",
      "    accuracy                           0.58     14151\n",
      "   macro avg       0.70      0.58      0.50     14151\n",
      "weighted avg       0.70      0.58      0.50     14151\n",
      "\n",
      "Confusion Matrix: [[6818  214]\n",
      " [5777 1342]]\n",
      "Accuracy Score: 0.5766376934492262\n",
      "Model Prediction Accuracy: 57.66%\n"
     ]
    }
   ],
   "source": [
    "print('Classification Report:',classification_report(label_test,knn_pred))\n",
    "print('Confusion Matrix:',confusion_matrix(label_test,knn_pred))\n",
    "print('Accuracy Score:',accuracy_score(label_test,knn_pred))\n",
    "print('Model Prediction Accuracy:',str(np.round(accuracy_score(label_test,knn_pred)*100,2)) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "411b3693",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('bow',CountVectorizer(analyzer=text_process)),\n",
    "    ('tfidf',TfidfTransformer()),\n",
    "    ('classifier',SVC())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d166fa0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('bow',\n",
       "                 CountVectorizer(analyzer=<function text_process at 0x0000021F7471D940>)),\n",
       "                ('tfidf', TfidfTransformer()), ('classifier', SVC())])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(review_train,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6b1224c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CG', 'CG', 'CG', ..., 'OR', 'OR', 'OR'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_pred = pipeline.predict(review_test)\n",
    "svc_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fb5086a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "          CG       0.89      0.87      0.88      7032\n",
      "          OR       0.87      0.89      0.88      7119\n",
      "\n",
      "    accuracy                           0.88     14151\n",
      "   macro avg       0.88      0.88      0.88     14151\n",
      "weighted avg       0.88      0.88      0.88     14151\n",
      "\n",
      "Confusion Matrix: [[6101  931]\n",
      " [ 752 6367]]\n",
      "Accuracy Score: 0.8810684757260971\n",
      "Model Prediction Accuracy: 88.11%\n"
     ]
    }
   ],
   "source": [
    "print('Classification Report:',classification_report(label_test,svc_pred))\n",
    "print('Confusion Matrix:',confusion_matrix(label_test,svc_pred))\n",
    "print('Accuracy Score:',accuracy_score(label_test,svc_pred))\n",
    "print('Model Prediction Accuracy:',str(np.round(accuracy_score(label_test,svc_pred)*100,2)) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fe899074",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('bow',CountVectorizer(analyzer=text_process)),\n",
    "    ('tfidf',TfidfTransformer()),\n",
    "    ('classifier',LogisticRegression())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c0daa08e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('bow',\n",
       "                 CountVectorizer(analyzer=<function text_process at 0x0000021F7471D940>)),\n",
       "                ('tfidf', TfidfTransformer()),\n",
       "                ('classifier', LogisticRegression())])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(review_train,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "087b741a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CG', 'CG', 'CG', ..., 'OR', 'OR', 'OR'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_pred = pipeline.predict(review_test)\n",
    "lr_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a992daa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "          CG       0.86      0.85      0.86      7032\n",
      "          OR       0.86      0.87      0.86      7119\n",
      "\n",
      "    accuracy                           0.86     14151\n",
      "   macro avg       0.86      0.86      0.86     14151\n",
      "weighted avg       0.86      0.86      0.86     14151\n",
      "\n",
      "Confusion Matrix: [[5996 1036]\n",
      " [ 938 6181]]\n",
      "Accuracy Score: 0.8605045579817681\n",
      "Model Prediction Accuracy: 86.05%\n"
     ]
    }
   ],
   "source": [
    "print('Classification Report:',classification_report(label_test,lr_pred))\n",
    "print('Confusion Matrix:',confusion_matrix(label_test,lr_pred))\n",
    "print('Accuracy Score:',accuracy_score(label_test,lr_pred))\n",
    "print('Model Prediction Accuracy:',str(np.round(accuracy_score(label_test,lr_pred)*100,2)) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6e0caeb",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d092c25e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance of various ML models:\n",
      "\n",
      "\n",
      "Logistic Regression Prediction Accuracy: 86.05%\n",
      "K Nearest Neighbors Prediction Accuracy: 57.66%\n",
      "Decision Tree Classifier Prediction Accuracy: 73.34%\n",
      "Random Forests Classifier Prediction Accuracy: 83.56%\n",
      "Support Vector Machines Prediction Accuracy: 88.11%\n",
      "Multinomial Naive Bayes Prediction Accuracy: 84.35%\n"
     ]
    }
   ],
   "source": [
    "print('Performance of various ML models:')\n",
    "print('\\n')\n",
    "print('Logistic Regression Prediction Accuracy:',str(np.round(accuracy_score(label_test,lr_pred)*100,2)) + '%')\n",
    "print('K Nearest Neighbors Prediction Accuracy:',str(np.round(accuracy_score(label_test,knn_pred)*100,2)) + '%')\n",
    "print('Decision Tree Classifier Prediction Accuracy:',str(np.round(accuracy_score(label_test,dtree_pred)*100,2)) + '%')\n",
    "print('Random Forests Classifier Prediction Accuracy:',str(np.round(accuracy_score(label_test,rfc_pred)*100,2)) + '%')\n",
    "print('Support Vector Machines Prediction Accuracy:',str(np.round(accuracy_score(label_test,svc_pred)*100,2)) + '%')\n",
    "print('Multinomial Naive Bayes Prediction Accuracy:',str(np.round(accuracy_score(label_test,predictions)*100,2)) + '%')"
   ]
  }
 ],
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