fake_news_classificaton using spacy word vector .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3733391e",
   "metadata": {},
   "source": [
    "# Text Classification using word vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8e86f99",
   "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>Text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Top Trump Surrogate BRUTALLY Stabs Him In The...</td>\n",
       "      <td>Fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U.S. conservative leader optimistic of common ...</td>\n",
       "      <td>Real</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Trump proposes U.S. tax overhaul, stirs concer...</td>\n",
       "      <td>Real</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Court Forces Ohio To Allow Millions Of Illega...</td>\n",
       "      <td>Fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Democrats say Trump agrees to work on immigrat...</td>\n",
       "      <td>Real</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text label\n",
       "0   Top Trump Surrogate BRUTALLY Stabs Him In The...  Fake\n",
       "1  U.S. conservative leader optimistic of common ...  Real\n",
       "2  Trump proposes U.S. tax overhaul, stirs concer...  Real\n",
       "3   Court Forces Ohio To Allow Millions Of Illega...  Fake\n",
       "4  Democrats say Trump agrees to work on immigrat...  Real"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "df = pd.read_csv(\"Fake_Real_Data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1606c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9900, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b3ab7b26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19800"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13b33303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fake    5000\n",
       "Real    4900\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d7629ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"label_num\"] = df[\"label\"].map({\n",
    "    'Fake':0,\n",
    "    'Real':1\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2499ab10",
   "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>Text</th>\n",
       "      <th>label</th>\n",
       "      <th>label_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Top Trump Surrogate BRUTALLY Stabs Him In The...</td>\n",
       "      <td>Fake</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U.S. conservative leader optimistic of common ...</td>\n",
       "      <td>Real</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Trump proposes U.S. tax overhaul, stirs concer...</td>\n",
       "      <td>Real</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Court Forces Ohio To Allow Millions Of Illega...</td>\n",
       "      <td>Fake</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Democrats say Trump agrees to work on immigrat...</td>\n",
       "      <td>Real</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text label  label_num\n",
       "0   Top Trump Surrogate BRUTALLY Stabs Him In The...  Fake          0\n",
       "1  U.S. conservative leader optimistic of common ...  Real          1\n",
       "2  Trump proposes U.S. tax overhaul, stirs concer...  Real          1\n",
       "3   Court Forces Ohio To Allow Millions Of Illega...  Fake          0\n",
       "4  Democrats say Trump agrees to work on immigrat...  Real          1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38a6c7f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "nlp=spacy.load(\"en_core_web_lg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7b7717ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc=nlp(\"top companies of tech is in california\")\n",
    "doc.vector.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ccacf6c",
   "metadata": {},
   "source": [
    "##### simillarly we need to generate vector for all rows of text present if dataframe \n",
    "##### we will add one new column which will store the vector of that particular row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "31174e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['vector']=df['Text'].apply(lambda x: nlp(x).vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72e11e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Text</th>\n",
       "      <th>label</th>\n",
       "      <th>label_num</th>\n",
       "      <th>vector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Top Trump Surrogate BRUTALLY Stabs Him In The...</td>\n",
       "      <td>Fake</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.6759837, 1.4263071, -2.318466, -0.451093, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U.S. conservative leader optimistic of common ...</td>\n",
       "      <td>Real</td>\n",
       "      <td>1</td>\n",
       "      <td>[-1.8355803, 1.3101058, -2.4919677, 1.0268308,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Trump proposes U.S. tax overhaul, stirs concer...</td>\n",
       "      <td>Real</td>\n",
       "      <td>1</td>\n",
       "      <td>[-1.9851209, 0.14389805, -2.4221718, 0.9133005...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Court Forces Ohio To Allow Millions Of Illega...</td>\n",
       "      <td>Fake</td>\n",
       "      <td>0</td>\n",
       "      <td>[-2.7812982, -0.16120885, -1.609772, 1.3624227...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Democrats say Trump agrees to work on immigrat...</td>\n",
       "      <td>Real</td>\n",
       "      <td>1</td>\n",
       "      <td>[-2.2010763, 0.9961637, -2.4088492, 1.128273, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                Text label  label_num  \\\n",
       "0   Top Trump Surrogate BRUTALLY Stabs Him In The...  Fake          0   \n",
       "1  U.S. conservative leader optimistic of common ...  Real          1   \n",
       "2  Trump proposes U.S. tax overhaul, stirs concer...  Real          1   \n",
       "3   Court Forces Ohio To Allow Millions Of Illega...  Fake          0   \n",
       "4  Democrats say Trump agrees to work on immigrat...  Real          1   \n",
       "\n",
       "                                              vector  \n",
       "0  [-0.6759837, 1.4263071, -2.318466, -0.451093, ...  \n",
       "1  [-1.8355803, 1.3101058, -2.4919677, 1.0268308,...  \n",
       "2  [-1.9851209, 0.14389805, -2.4221718, 0.9133005...  \n",
       "3  [-2.7812982, -0.16120885, -1.609772, 1.3624227...  \n",
       "4  [-2.2010763, 0.9961637, -2.4088492, 1.128273, ...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3548c672",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(\n",
    "df.vector.values,\n",
    "df.label_num, \n",
    "test_size=0.2,\n",
    "# stratify=df.label_num,\n",
    "random_state=2023)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "58f9e27a",
   "metadata": {},
   "outputs": [
    {
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       "             dtype=float32)                                                    ,\n",
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       "             dtype=float32)                                                       ,\n",
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       "             dtype=float32)                                                    ,\n",
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       "             dtype=float32)                                                    ,\n",
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       "               0.17860104,  0.34519938,  2.8179116 ,  0.12645574, -0.66419816,\n",
       "               3.4680882 ,  0.85765606, -2.2323873 ,  0.67532855,  0.6968741 ,\n",
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       "              -1.2029682 ,  0.72317886,  0.19667734, -2.619549  , -0.209046  ,\n",
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       "               0.23251887, -0.91881347, -0.4411238 , -0.97452974,  2.1072125 ,\n",
       "               0.9730096 , -1.620316  , -2.2801905 ,  0.35606772,  1.4298155 ,\n",
       "               0.08233584, -1.1386691 , -0.8128223 ,  0.13352625,  0.00918002,\n",
       "               0.78767943, -2.1205242 ,  1.5998834 , -1.8719664 ,  0.26627824,\n",
       "              -1.8895781 , -0.6600771 ,  0.5677939 ,  0.7495608 ,  1.2365373 ,\n",
       "              -0.4630507 , -1.1475573 , -1.9687543 ,  1.1496147 , -0.7804937 ,\n",
       "              -0.5155105 ,  0.3976441 ,  1.9134773 , -2.5079648 ,  0.29956654,\n",
       "              -0.4482968 , -0.0172788 , -0.47380346,  1.2906734 ,  0.9361669 ,\n",
       "               1.7763321 ,  1.1869203 ,  1.5454189 ,  2.4320638 , -0.17589834,\n",
       "               2.3810751 , -0.23252271, -1.31863   , -0.32955396, -2.4155638 ,\n",
       "               0.56816554,  0.03069765, -0.24793571,  0.5276278 ,  0.28708595,\n",
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       "              -0.1998662 , -2.4905932 ,  0.7231237 ,  0.49098432, -0.512323  ,\n",
       "              -1.7452214 ,  1.1956474 , -1.4403654 ,  2.065839  , -0.9231132 ,\n",
       "              -1.6063151 ,  0.07468359,  2.7118936 , -0.60844857,  0.3292668 ,\n",
       "               0.2715887 , -0.9866037 , -0.7739552 ,  2.016852  , -1.4833834 ,\n",
       "              -1.2547569 , -0.5573537 , -0.06793823,  1.1523033 ,  0.14327526,\n",
       "               0.07436188, -2.487007  , -0.15691853,  1.2979854 ,  0.12693949,\n",
       "              -0.46195808,  1.4036356 ,  0.31853384,  1.9084736 , -0.7569246 ,\n",
       "               0.64774734,  1.9024103 , -0.45403796, -1.5755203 , -1.1969688 ,\n",
       "               0.02303499, -2.2914762 , -0.22978161,  1.593286  , -1.9621974 ,\n",
       "              -1.4654833 , -2.690437  ,  1.0312057 , -0.51552325,  0.80544806,\n",
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       "              -0.61304784, -0.4535892 ,  0.8790297 , -0.23452826, -0.6199543 ,\n",
       "               1.2225626 , -0.99786526, -0.33156916,  0.58316404,  1.429968  ,\n",
       "              -0.08324044, -0.8226428 , -0.7701076 , -0.5583293 ,  0.17230023,\n",
       "               0.04939278, -2.2589946 , -0.38803726,  0.00729149,  0.8145592 ,\n",
       "              -0.78896904, -0.8710954 ,  0.20747435, -1.6625717 ,  1.0875907 ,\n",
       "               0.36266026, -1.7201412 ,  0.7166491 , -0.7825129 , -0.6315713 ,\n",
       "               0.43132314,  0.18127726, -0.60168386,  1.3778659 ,  0.6781928 ,\n",
       "               1.6300572 ,  0.3389674 , -1.27081   ,  0.36620778,  0.31086195,\n",
       "              -1.148578  ,  0.45595855, -0.069532  ,  0.8577048 , -1.2538477 ,\n",
       "              -1.0236955 ,  0.3192053 ,  1.7779423 ,  0.9585917 ,  0.18286197,\n",
       "               0.9789661 , -1.9692153 , -0.7362407 ,  0.86051726,  1.2838993 ,\n",
       "               0.801096  , -0.8132346 ,  0.8939821 , -0.4524129 , -0.24043962,\n",
       "               0.26521873,  0.2974538 ,  1.2601694 , -0.2268594 , -0.45664594,\n",
       "               0.3822706 , -1.2630211 ,  0.00980975,  0.49496913,  1.6002176 ,\n",
       "               0.5750037 , -1.5725986 , -3.2360544 , -0.29936007,  0.6245779 ,\n",
       "              -1.2593619 ,  1.1095674 , -0.2835182 ,  0.5627805 ,  0.84909075,\n",
       "              -0.14175124,  2.5248923 ,  1.704249  ,  1.399829  ,  0.80422986,\n",
       "               0.07848979,  0.54628223,  1.5562987 , -2.586204  , -0.3973308 ,\n",
       "               0.2019997 , -0.09798364, -0.02191811, -0.92236733,  0.1107214 ,\n",
       "               0.06850091,  1.0517887 , -1.6558472 , -0.70022964,  1.5499352 ,\n",
       "               0.95869744,  0.16042969,  0.699771  ,  0.68076897,  2.039266  ,\n",
       "              -0.41601992,  0.25016868,  1.2113277 , -1.7546299 , -0.07757178,\n",
       "               0.7463889 , -0.587091  ,  0.0280543 , -0.52921635, -0.8916567 ,\n",
       "               1.1651592 ,  1.3611516 , -0.26167375, -1.6082795 ,  0.9961052 ],\n",
       "             dtype=float32)                                                    ,\n",
       "       array([-1.34232819e+00,  1.39684284e+00, -2.85336375e+00,  2.28004500e-01,\n",
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       "               5.14096022e-01, -3.97596622e+00, -7.61753023e-01,  6.30353451e-01,\n",
       "               4.40906994e-02,  1.06118333e+00,  1.12217152e+00,  2.30021983e-01,\n",
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       "              -3.04591441e+00, -2.62740731e-01,  1.02241087e+00,  6.08474076e-01,\n",
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       "               1.33938408e+00, -7.18666136e-01, -9.20562208e-01, -2.25356698e-01,\n",
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       "               3.25964928e-01, -7.01264262e-01,  1.53912783e+00,  1.11674845e+00,\n",
       "               2.03138304e+00,  1.32829320e+00,  2.01235342e+00,  2.57692456e+00,\n",
       "              -6.35172546e-01,  2.63095045e+00, -4.44149673e-01, -1.62493324e+00,\n",
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       "              -9.92856741e-01,  1.40007234e+00,  5.06962121e-01,  6.85277164e-01,\n",
       "              -5.96622527e-01,  1.39881387e-01, -7.86895528e-02, -2.75540054e-01,\n",
       "              -1.31189775e+00, -3.21274304e+00,  4.67699051e-01,  5.40486395e-01,\n",
       "               4.64161001e-02, -2.35489774e+00,  1.58989704e+00, -1.52519095e+00,\n",
       "               2.41663027e+00, -1.35361958e+00, -2.23821115e+00, -1.02561736e+00,\n",
       "               3.35746098e+00, -8.68966937e-01, -2.49314785e-01,  3.86309236e-01,\n",
       "              -1.27116048e+00, -1.45804152e-01,  2.71795273e+00, -2.04603696e+00,\n",
       "              -2.47226286e+00, -1.05631936e+00,  7.20808506e-01,  1.57039881e+00,\n",
       "              -3.47311050e-02,  6.80778325e-01, -3.09190726e+00,  2.76910901e-01,\n",
       "               8.47181618e-01,  9.67476606e-01, -3.75026256e-01,  2.64654088e+00,\n",
       "               3.43102187e-01,  1.83040273e+00, -1.61459947e+00,  6.11818969e-01,\n",
       "               2.60604167e+00, -1.07739878e+00, -2.37806606e+00, -1.57108116e+00,\n",
       "              -7.91566610e-01, -2.86639047e+00, -4.77553070e-01,  1.78878653e+00,\n",
       "              -2.39171839e+00, -1.35243154e+00, -3.41059709e+00,  1.00799942e+00,\n",
       "              -3.58848244e-01,  5.77370286e-01,  9.55292225e-01,  9.94203389e-02,\n",
       "               1.38252783e+00,  5.66566288e-01,  1.87278914e+00,  6.55782521e-02,\n",
       "              -1.78900227e-01, -3.59158814e-01, -1.83220804e+00, -1.31477284e+00,\n",
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       "              -1.31083512e+00,  9.56757486e-01, -1.11115205e+00, -4.79201466e-01,\n",
       "               1.28070009e+00,  2.39532232e+00, -2.37357065e-01, -9.08362627e-01,\n",
       "              -9.18161750e-01, -8.98768067e-01, -6.78033456e-02, -1.40234515e-01,\n",
       "              -2.13852310e+00, -5.65673262e-02, -2.73066312e-01,  1.40573192e+00,\n",
       "              -1.55154383e+00, -1.05319846e+00, -7.90062487e-01, -1.80285740e+00,\n",
       "               1.54491425e+00,  5.79211414e-01, -2.38280439e+00,  8.22381318e-01,\n",
       "               1.19313985e-01, -7.36411989e-01,  1.41243589e+00,  4.32660103e-01,\n",
       "              -1.07353401e+00,  1.54746735e+00,  5.71603537e-01,  2.47087646e+00,\n",
       "               6.15679443e-01, -1.29407227e+00,  2.96990741e-02,  1.13163348e-02,\n",
       "              -2.09359956e+00,  4.35261130e-01,  1.92317426e-01,  6.43794984e-02,\n",
       "              -1.52774942e+00, -9.17136431e-01,  4.67685163e-01,  2.42757154e+00,\n",
       "               9.57675934e-01,  5.58837712e-01,  1.51509988e+00, -1.81811190e+00,\n",
       "              -6.58118546e-01,  1.49065888e+00,  1.82649350e+00,  1.62393129e+00,\n",
       "              -1.38239586e+00,  1.77032113e-01, -5.10510445e-01, -7.28392243e-01,\n",
       "              -9.83539045e-01,  6.66192293e-01,  1.82349098e+00,  4.70509939e-03,\n",
       "              -9.45598006e-01,  1.60485244e+00, -1.36237872e+00, -9.79090258e-02,\n",
       "               6.58181846e-01,  2.13826680e+00,  1.13026954e-01, -2.19582772e+00,\n",
       "              -4.82347918e+00, -6.35329247e-01,  1.34246618e-01, -1.92732966e+00,\n",
       "               1.67645776e+00, -2.49060154e-01,  1.67765450e-02,  9.54792500e-01,\n",
       "              -1.06700445e-02,  3.99647188e+00,  2.28127718e+00,  2.74270320e+00,\n",
       "               1.25526047e+00,  7.32449964e-02,  6.68949604e-01,  1.99819529e+00,\n",
       "              -3.35291195e+00, -2.55655617e-01, -1.37404963e-01, -2.33661115e-01,\n",
       "               1.10564522e-01, -2.08839822e+00,  4.09500808e-01, -1.54502499e+00,\n",
       "               1.35880864e+00, -8.17633033e-01, -1.20933270e+00,  1.74330747e+00,\n",
       "               1.01476860e+00,  4.53003526e-01, -3.53588276e-02,  1.07587421e+00,\n",
       "               3.53420997e+00, -5.32143235e-01,  3.89833301e-01,  1.71746910e+00,\n",
       "              -1.56279409e+00, -7.00677037e-01,  9.81865883e-01, -8.69272172e-01,\n",
       "               2.53838181e-01, -2.59256959e-01, -1.52332294e+00,  1.30409217e+00,\n",
       "               1.40672827e+00, -5.85784554e-01, -2.89412117e+00,  1.15011477e+00],\n",
       "             dtype=float32)                                                       ],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train\n",
    "#when we are giving it to our model for training it expecting 2D array but it is \n",
    "#not in 2d array here array inside array exist \n",
    "# we need to convert it to 2d arrray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "826eeec8",
   "metadata": {},
   "source": [
    "* converting to 2d numpy array\n",
    "* function np.stack convert it to 2d array when data is in array is it self an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5cdbf914",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X_train_2d = np.stack(X_train)\n",
    "X_test_2d = np.stack(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e5c0a5",
   "metadata": {},
   "source": [
    "#### applying naive_bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2f117977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# we  have some data in negative scale also so, model will not accept negative scale data\n",
    "# so , for that reason we are applying minmax scaler \n",
    "scaler =MinMaxScaler()\n",
    "\n",
    "scaled_train_embed = scaler.fit_transform(X_train_2d)\n",
    "scaled_test_embed = scaler.fit_transform(X_test_2d)\n",
    "\n",
    "clf = MultinomialNB()\n",
    "clf.fit(scaled_train_embed,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bac0b17b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict(scaled_test_embed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f4eb1221",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.97      0.95      1004\n",
      "           1       0.97      0.93      0.95       976\n",
      "\n",
      "    accuracy                           0.95      1980\n",
      "   macro avg       0.95      0.95      0.95      1980\n",
      "weighted avg       0.95      0.95      0.95      1980\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6f297d",
   "metadata": {},
   "source": [
    "#### applying KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8ae9add8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1004\n",
      "           1       0.99      0.99      0.99       976\n",
      "\n",
      "    accuracy                           0.99      1980\n",
      "   macro avg       0.99      0.99      0.99      1980\n",
      "weighted avg       0.99      0.99      0.99      1980\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "clf = KNeighborsClassifier()\n",
    "clf.fit(scaled_train_embed,y_train)\n",
    "y_pred=clf.predict(scaled_test_embed)\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ffe9665",
   "metadata": {},
   "source": [
    "**conclusion : with KNN classifier we are getting almost 1 precision and recall that is best performance \n",
    "    but earlier when we were trying on tf-idf or BOW or n-gram we were getting horible result \n",
    "    because KNN has problem with more dimensions here dimensions reduced and hence we are getting best result**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87371ffe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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