Final_Naive_Bayes_Logistic_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f21c86ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#for text pre-processing\n",
    "import re, string\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import SnowballStemmer\n",
    "from nltk.corpus import wordnet\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "#nltk.download('punkt')\n",
    "#nltk.download('averaged_perceptron_tagger')\n",
    "#nltk.download('wordnet')\n",
    "#nltk.download('omw-1.4')\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "from sklearn.calibration import calibration_curve, CalibrationDisplay\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72defce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stopwords_list = set(stopwords.words('english'))\n",
    "#stopwords_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a25d2018",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"train_data.csv\")\n",
    "train_results = pd.read_csv(\"train_results.csv\")\n",
    "test_data = pd.read_csv(\"test_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7689a2f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1040323\n",
      "560175\n"
     ]
    }
   ],
   "source": [
    "print(len(train_data))\n",
    "print(len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2071ced1",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Anyway Im getting of for a while</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>My red, Apache isn't feelin too well this morn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>@danyelljoy you should be  its great. friday w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>its 11:30pm and i dont wanna sleep; so i debat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Why does twitter eat my DM's?  Not happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040318</th>\n",
       "      <td>1040318</td>\n",
       "      <td>getting ready 2 watch mental</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040319</th>\n",
       "      <td>1040319</td>\n",
       "      <td>Wristcutters and Half Nelson are on Sundance F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040320</th>\n",
       "      <td>1040320</td>\n",
       "      <td>@t_isfortammy Going out in Northbridge makes m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040321</th>\n",
       "      <td>1040321</td>\n",
       "      <td>@iModel_ lol.... Gorgeous...lol! U look sooo m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040322</th>\n",
       "      <td>1040322</td>\n",
       "      <td>@iamjonathancook why?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1040323 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                                               text\n",
       "0              0                  Anyway Im getting of for a while \n",
       "1              1  My red, Apache isn't feelin too well this morn...\n",
       "2              2  @danyelljoy you should be  its great. friday w...\n",
       "3              3  its 11:30pm and i dont wanna sleep; so i debat...\n",
       "4              4          Why does twitter eat my DM's?  Not happy \n",
       "...          ...                                                ...\n",
       "1040318  1040318                      getting ready 2 watch mental \n",
       "1040319  1040319  Wristcutters and Half Nelson are on Sundance F...\n",
       "1040320  1040320  @t_isfortammy Going out in Northbridge makes m...\n",
       "1040321  1040321  @iModel_ lol.... Gorgeous...lol! U look sooo m...\n",
       "1040322  1040322                             @iamjonathancook why? \n",
       "\n",
       "[1040323 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0c19f81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id      0\n",
       "text    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fba645e7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>so cut... missed out on all the TCE EXTREME ME...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Thank you very much Wasin  this is awesome lol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Squeezing in some internet time before Dustin ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>@fanoou; Oh i do have moral but i'm gonna be b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>@NSuissa   I just bought Sammy some joint/hip ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560170</th>\n",
       "      <td>560170</td>\n",
       "      <td>@meganintime So is mine and I've only got abou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560171</th>\n",
       "      <td>560171</td>\n",
       "      <td>@FairyForest Thanks cutie ) Wishing u a wonder...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560172</th>\n",
       "      <td>560172</td>\n",
       "      <td>ok know i just got home my family just drop me...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560173</th>\n",
       "      <td>560173</td>\n",
       "      <td>long run status: complete. Now time for church...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560174</th>\n",
       "      <td>560174</td>\n",
       "      <td>@textualoffender Doestovesky yes,remaining 3 w...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560175 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                                               text\n",
       "0            0  so cut... missed out on all the TCE EXTREME ME...\n",
       "1            1     Thank you very much Wasin  this is awesome lol\n",
       "2            2  Squeezing in some internet time before Dustin ...\n",
       "3            3  @fanoou; Oh i do have moral but i'm gonna be b...\n",
       "4            4  @NSuissa   I just bought Sammy some joint/hip ...\n",
       "...        ...                                                ...\n",
       "560170  560170  @meganintime So is mine and I've only got abou...\n",
       "560171  560171  @FairyForest Thanks cutie ) Wishing u a wonder...\n",
       "560172  560172  ok know i just got home my family just drop me...\n",
       "560173  560173  long run status: complete. Now time for church...\n",
       "560174  560174  @textualoffender Doestovesky yes,remaining 3 w...\n",
       "\n",
       "[560175 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1a28d148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         so cut... missed out on all the TCE EXTREME ME...\n",
       "1            Thank you very much Wasin  this is awesome lol\n",
       "2         Squeezing in some internet time before Dustin ...\n",
       "3         @fanoou; Oh i do have moral but i'm gonna be b...\n",
       "4         @NSuissa   I just bought Sammy some joint/hip ...\n",
       "                                ...                        \n",
       "560170    @meganintime So is mine and I've only got abou...\n",
       "560171    @FairyForest Thanks cutie ) Wishing u a wonder...\n",
       "560172    ok know i just got home my family just drop me...\n",
       "560173    long run status: complete. Now time for church...\n",
       "560174    @textualoffender Doestovesky yes,remaining 3 w...\n",
       "Name: text, Length: 560175, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "test_data['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e165619d",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
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      "text/plain": [
       "         target\n",
       "0             2\n",
       "1             0\n",
       "2             2\n",
       "3             2\n",
       "4             0\n",
       "...         ...\n",
       "1040318       2\n",
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       "1040320       0\n",
       "1040321       2\n",
       "1040322       0\n",
       "\n",
       "[1040323 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_label = train_results['target'].replace(['negative' , 'neutral', 'positive'] , [0,1,2])\n",
    "train_target = pd.DataFrame(full_train_label)\n",
    "train_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eaca144f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    520436\n",
       "0    519803\n",
       "1        84\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f41823c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "positive    520436\n",
       "negative    519803\n",
       "neutral         84\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_results['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "42b39dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert to lowercase, strip and remove punctuations\n",
    "def preprocess(text):\n",
    "    text = text.lower() \n",
    "    text=  text.strip()  \n",
    "    text=  re.compile('<.*?>').sub('', text) \n",
    "    text = re.compile('[%s]' % re.escape(string.punctuation)).sub(' ', text)  \n",
    "    text = re.sub('\\s+', ' ', text)  \n",
    "    text = re.sub(r'\\[[0-9]*\\]',' ',text) \n",
    "    text= re.sub(r'[^\\w\\s]', '', str(text).lower().strip())\n",
    "    text = re.sub(r'\\d',' ',text) \n",
    "    text = re.sub(r'\\s+',' ',text) \n",
    "    text = re.sub(r'https?://\\S+|www\\.\\S+', '', text) # Removes url\n",
    "    text = re.compile(r'<[^>]+>').sub('', text) #Removes HTML tags:\n",
    "    text = re.sub(r\"\\s+[a-zA-Z]\\s+\", ' ', text) # Single character removal\n",
    "    text = re.sub(r'\\s+', ' ', text) # Remove multiple spaces\n",
    "    return text\n",
    "\n",
    "# STOPWORD REMOVAL\n",
    "def stopword(string):\n",
    "    a= [i for i in string.split() if i not in stopwords.words('english')]\n",
    "    return ' '.join(a)\n",
    "\n",
    "#LEMMATIZATION\n",
    "# Initialize the lemmatizer\n",
    "wl = WordNetLemmatizer()\n",
    " \n",
    "# This is a helper function to map NTLK position tags\n",
    "def get_wordnet_pos(tag):\n",
    "    if tag.startswith('J'):\n",
    "        return wordnet.ADJ\n",
    "    elif tag.startswith('V'):\n",
    "        return wordnet.VERB\n",
    "    elif tag.startswith('N'):\n",
    "        return wordnet.NOUN\n",
    "    elif tag.startswith('R'):\n",
    "        return wordnet.ADV\n",
    "    else:\n",
    "        return wordnet.NOUN\n",
    "    \n",
    "# Tokenize the sentence\n",
    "def lemmatizer(string):\n",
    "    word_pos_tags = nltk.pos_tag(word_tokenize(string)) # Get position tags\n",
    "    a=[wl.lemmatize(tag[0], get_wordnet_pos(tag[1])) for idx, tag in enumerate(word_pos_tags)] # Map the position tag and lemmatize the word/token\n",
    "    return \" \".join(a)\n",
    "\n",
    "def finalpreprocess(string):\n",
    "    #return lemmatizer(stopword(preprocess(string)))\n",
    "    return lemmatizer(preprocess(string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "87975895",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Anyway Im getting of for a while</td>\n",
       "      <td>anyway im getting of for while</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>My red, Apache isn't feelin too well this morn...</td>\n",
       "      <td>my red apache isn feelin too well this morning...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>@danyelljoy you should be  its great. friday w...</td>\n",
       "      <td>danyelljoy you should be its great friday will...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>its 11:30pm and i dont wanna sleep; so i debat...</td>\n",
       "      <td>its pm and dont wanna sleep so debated with my...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Why does twitter eat my DM's?  Not happy</td>\n",
       "      <td>why does twitter eat my dm not happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>1040318</td>\n",
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       "      <td>1040320</td>\n",
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       "      <td>1040322</td>\n",
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      "text/plain": [
       "              id                                               text  \\\n",
       "0              0                  Anyway Im getting of for a while    \n",
       "1              1  My red, Apache isn't feelin too well this morn...   \n",
       "2              2  @danyelljoy you should be  its great. friday w...   \n",
       "3              3  its 11:30pm and i dont wanna sleep; so i debat...   \n",
       "4              4          Why does twitter eat my DM's?  Not happy    \n",
       "...          ...                                                ...   \n",
       "1040318  1040318                      getting ready 2 watch mental    \n",
       "1040319  1040319  Wristcutters and Half Nelson are on Sundance F...   \n",
       "1040320  1040320  @t_isfortammy Going out in Northbridge makes m...   \n",
       "1040321  1040321  @iModel_ lol.... Gorgeous...lol! U look sooo m...   \n",
       "1040322  1040322                             @iamjonathancook why?    \n",
       "\n",
       "                                                clean_text  \n",
       "0                           anyway im getting of for while  \n",
       "1        my red apache isn feelin too well this morning...  \n",
       "2        danyelljoy you should be its great friday will...  \n",
       "3        its pm and dont wanna sleep so debated with my...  \n",
       "4                     why does twitter eat my dm not happy  \n",
       "...                                                    ...  \n",
       "1040318                         getting ready watch mental  \n",
       "1040319  wristcutters and half nelson are on sundance f...  \n",
       "1040320  t isfortammy going out in northbridge makes me...  \n",
       "1040321  imodel lol gorgeous lol look sooo much like th...  \n",
       "1040322                                iamjonathancook why  \n",
       "\n",
       "[1040323 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['clean_text']=train_data['text'].map(lambda s:preprocess(s)) \n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "33adcc08",
   "metadata": {},
   "outputs": [
    {
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       "                                                clean_text\n",
       "0                           anyway im getting of for while\n",
       "1        my red apache isn feelin too well this morning...\n",
       "2        danyelljoy you should be its great friday will...\n",
       "3        its pm and dont wanna sleep so debated with my...\n",
       "4                     why does twitter eat my dm not happy\n",
       "...                                                    ...\n",
       "1040318                         getting ready watch mental\n",
       "1040319  wristcutters and half nelson are on sundance f...\n",
       "1040320  t isfortammy going out in northbridge makes me...\n",
       "1040321  imodel lol gorgeous lol look sooo much like th...\n",
       "1040322                                iamjonathancook why\n",
       "\n",
       "[1040323 rows x 1 columns]"
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     "execution_count": 14,
     "metadata": {},
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   "source": [
    "df_clean = pd.DataFrame(train_data['clean_text'])\n",
    "df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "967612f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = pd.concat([df_clean, train_target], axis=1)\n",
    "\n",
    "df_clean.to_csv('train_cleaned_text_target_final_rani.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e947083b",
   "metadata": {},
   "outputs": [
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       "                                                clean_text  target\n",
       "0                           anyway im getting of for while       2\n",
       "1        my red apache isn feelin too well this morning...       0\n",
       "2        danyelljoy you should be its great friday will...       2\n",
       "3        its pm and dont wanna sleep so debated with my...       2\n",
       "4                     why does twitter eat my dm not happy       0\n",
       "...                                                    ...     ...\n",
       "1040318                         getting ready watch mental       2\n",
       "1040319  wristcutters and half nelson are on sundance f...       0\n",
       "1040320  t isfortammy going out in northbridge makes me...       0\n",
       "1040321  imodel lol gorgeous lol look sooo much like th...       2\n",
       "1040322                                iamjonathancook why       0\n",
       "\n",
       "[1040323 rows x 2 columns]"
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_clean"
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04f7ccac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SPLITTING THE TRAINING DATASET INTO TRAIN AND TEST\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_clean[\"clean_text\"],df_clean[\"target\"],test_size=0.2,shuffle=True, random_state=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2869aba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "198662    jlneveloff want to go too quinto too bad we re...\n",
       "198588    what do you guys think of this laptop http is ...\n",
       "497233    thecoveted boo their name seemed so familiar w...\n",
       "850715    staying in because a dicky tummy and feel bit ...\n",
       "592604    listing to my pod and slept my neck wrong so i...\n",
       "                                ...                        \n",
       "231557      mrcakey hope you have beer to keep you cool too\n",
       "780649    envisioning photo campaign ugly organic food t...\n",
       "149489    chillin in la still dont know when m goin home...\n",
       "550228              tonesa we re tryina find somethin to do\n",
       "594371                              watta so busy right now\n",
       "Name: clean_text, Length: 832258, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b4dc6647",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.81      0.80    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.80      0.79      0.80    104407\n",
      "\n",
      "    accuracy                           0.80    208065\n",
      "   macro avg       0.53      0.53      0.53    208065\n",
      "weighted avg       0.80      0.80      0.80    208065\n",
      "\n",
      "Confusion Matrix: [[83511     0 20136]\n",
      " [    1     0    10]\n",
      " [21882     0 82525]]\n",
      "accuracy: 0.7980006248047485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27344e7c9d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# vectorization using Bag-of-Words (with Tf-Idf ) and Word2Vec\n",
    "#Tf-Idf\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_features=150000, ngram_range=(1,3))\n",
    "X_train_vectors_tfidf = tfidf_vectorizer.fit_transform(X_train) \n",
    "X_test_vectors_tfidf = tfidf_vectorizer.transform(X_test)\n",
    "\n",
    "#FITTING THE CLASSIFICATION MODEL using Naive Bayes(tf-idf)\n",
    "\n",
    "print(\"Naive Bayes\")\n",
    "\n",
    "nb_tfidf = MultinomialNB()\n",
    "nb_tfidf.fit(X_train_vectors_tfidf, y_train)  \n",
    "\n",
    "#Predict y value for test dataset\n",
    "y_predict = nb_tfidf.predict(X_test_vectors_tfidf)\n",
    "y_prob = nb_tfidf.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "885c42fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.81      0.82    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.82      0.83      0.82    104407\n",
      "\n",
      "    accuracy                           0.82    208065\n",
      "   macro avg       0.55      0.55      0.55    208065\n",
      "weighted avg       0.82      0.82      0.82    208065\n",
      "\n",
      "Confusion Matrix: [[84069     0 19578]\n",
      " [    1     0    10]\n",
      " [17880     0 86527]]\n",
      "accuracy: 0.8199168529065436\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2735620e940>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "lr  = LogisticRegression(C=1.0, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "191284d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.81      0.80    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.81      0.79      0.80    104407\n",
      "\n",
      "    accuracy                           0.80    208065\n",
      "   macro avg       0.53      0.53      0.53    208065\n",
      "weighted avg       0.80      0.80      0.80    208065\n",
      "\n",
      "Confusion Matrix: [[83722     0 19925]\n",
      " [    1     0    10]\n",
      " [21767     0 82640]]\n",
      "accuracy: 0.7995674428664119\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2730771ba00>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# vectorization using Bag-of-Words (with Tf-Idf ) \n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_features=200000, ngram_range=(1,3))\n",
    "X_train_vectors_tfidf = tfidf_vectorizer.fit_transform(X_train) \n",
    "X_test_vectors_tfidf = tfidf_vectorizer.transform(X_test)\n",
    "\n",
    "#FITTING THE CLASSIFICATION MODEL using Naive Bayes(tf-idf)\n",
    "\n",
    "print(\"Naive Bayes\")\n",
    "\n",
    "nb_tfidf = MultinomialNB()\n",
    "nb_tfidf.fit(X_train_vectors_tfidf, y_train)  \n",
    "#Predict y value for test dataset\n",
    "y_predict = nb_tfidf.predict(X_test_vectors_tfidf)\n",
    "y_prob = nb_tfidf.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cf382667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.81      0.82    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.82      0.83      0.82    104407\n",
      "\n",
      "    accuracy                           0.82    208065\n",
      "   macro avg       0.55      0.55      0.55    208065\n",
      "weighted avg       0.82      0.82      0.82    208065\n",
      "\n",
      "Confusion Matrix: [[84125     0 19522]\n",
      " [    1     0    10]\n",
      " [17843     0 86564]]\n",
      "accuracy: 0.8203638286112513\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27366249040>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "lr  = LogisticRegression(C=1.0, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99cd2687",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.81      0.80    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.81      0.79      0.80    104407\n",
      "\n",
      "    accuracy                           0.80    208065\n",
      "   macro avg       0.53      0.53      0.53    208065\n",
      "weighted avg       0.80      0.80      0.80    208065\n",
      "\n",
      "Confusion Matrix: [[83961     0 19686]\n",
      " [    1     0    10]\n",
      " [22200     0 82207]]\n",
      "accuracy: 0.798635041934011\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27349dc0e20>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# vectorization using Bag-of-Words (with Tf-Idf ) \n",
    "\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_features=150000, ngram_range=(1,2))\n",
    "X_train_vectors_tfidf = tfidf_vectorizer.fit_transform(X_train) \n",
    "X_test_vectors_tfidf = tfidf_vectorizer.transform(X_test)\n",
    "\n",
    "#FITTING THE CLASSIFICATION MODEL using Naive Bayes(tf-idf)\n",
    "\n",
    "print(\"Naive Bayes\")\n",
    "\n",
    "nb_tfidf = MultinomialNB()\n",
    "nb_tfidf.fit(X_train_vectors_tfidf, y_train)  \n",
    "#Predict y value for test dataset\n",
    "y_predict = nb_tfidf.predict(X_test_vectors_tfidf)\n",
    "y_prob = nb_tfidf.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fb44b6be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.81      0.82    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.81      0.83      0.82    104407\n",
      "\n",
      "    accuracy                           0.82    208065\n",
      "   macro avg       0.55      0.55      0.55    208065\n",
      "weighted avg       0.82      0.82      0.82    208065\n",
      "\n",
      "Confusion Matrix: [[83999     0 19648]\n",
      " [    1     0    10]\n",
      " [17914     0 86493]]\n",
      "accuracy: 0.8194170091077307\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2730eb74a00>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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201W1SlXXAaXAEJfxr6OqLnRJqaY2qhNo63lgRGAUGYoFRWOML4pQW5cW1QYUikhJ0DYmqKnjgG3A30RkmYg8ISLtgK4uQx/uZxd3fH1ieyeQ9L7IvW5c3qCOqtYAu2mUE6Yxm2gxxvjmY6KlQlUHh9iXAZwG/FhVF4nIg7hT5RBCJbYPl/A+3L4m2UjRGOOLxm6ipQwoU9VF7v3zeEFyizslxv3cGnR8z6D6gaT3Ze514/IGdUQkA8jFS3UakgVFY4xvqhLVFr4N3QxsEJH+rmgEXk7n4AT2o2iY2L7YzSj3wZtQWexOsStFZKi7XnhtozqBtq4A3nDXHUOy02djjE8xXRDix8CzIpIFrAV+gDdYmykio4H1uLzNqrpCRGbiBc4aYLyq1rp2xgFPAW2B2W4DbxLnaREpxRshFkfqkAVFY4xvkUaB0bejy4GmrjmOCHH8JGBSE+UlwMAmyg/igmq0Eioorv2wPcU9z4x3N0wSy571fry7kNBE9x91G6pQW5e6T7QkVFA0xiSHVH7Mz4KiMcYXJXanz4nIgqIxxidbedsYYxoIf1NLcrOgaIzxzU6fjTHG8WafU/e5DwuKxhjf7PTZGGOC2OmzMcY4SuTnmpOZBUVjjG8pfPZsQdEY45OC2mN+xhhzmJ0+G2NMkFY5+ywiDxPm0oGq3tQsPTLGJLTW/OxzSYv1whiTPBRojUFRVacEvxeRdqq6r/m7ZIxJdLE6fRaRz4BKoBaoUdXBIpIPzAB6A58B31XVne74iXi5nGuBm1R1jisfxOGVt2cBN6uqikg2XsrTQcB24EpV/SxcnyI+qyMiw0RkJV6SakTkZBGZ7OeLG2NSiaB10W1ROldVTwnK+jcBmK+qfYH57j0iMgAvncBJeMnuJ4tIuqvzKDAGL29LX7cfvAC6U1WPB+4H7o3UmWgeYHwAGIkXZVHVD/ASWBtjWiuNcvtighPYT6FhYvvpqlqlquuAUmCIy/jXUVUXuqRUUxvVCbT1PDDCJbcKKaqnulV1Q6Oi2iYPNMakPvWVza9QREqCtjFHtsZcEVkStK+ry9CH+9nFldcntncCSe+L3OvG5Q3qqGoNsBsoCPf1orklZ4OInAmoy7h1E+5U2hjTSkU/CqwIOi1uylmquklEugDzROQ/YY4Nldg+XML7cPuaFM1IcSwwHi/ibgROce+NMa2WRLmFp6qb3M+twIvAEGCLOyXG/dzqDq9PbO8Ekt6XudeNyxvUEZEMIBcv1WlIEYOiqlao6tWq2lVVO6vqNaq6PVI9Y0wKq4tyC0NE2olIh8Br4BvAxzRMYD+Khonti0UkW0T64E2oLHan2JUiMtRdL7y2UZ1AW1cAb7jrjiFFPH0WkeOAB4GheMPOhcBPVXVtpLrGmBQUu/sUuwIvunmPDGCaqr4mIu8DM0VkNLAel7dZVVeIyExgJVADjFfVwPzGOA7fkjPbbQBPAk+LSCneCLE4UqeiuaY4DfgTcJl7Xww8B5wRRV1jTAqKxX2KbmB1chPl24ERIepMAiY1UV4CDGyi/CAuqEYrmmuKoqpPq2qN254htVcOMsZE0ry35MRVuGef893LN0VkAjAd72teCfyrBfpmjElUrfExP2AJDae7rw/ap8BdzdUpY0xikyQdBUYj3LPPfVqyI8aYJKECrX2RWREZCAwA2gTKVHVqc3XKGJPgWuNIMUBE7gCG4wXFWcCFwAK85wuNMa1RCgfFaGafr8CbHt+sqj/Am0LPbtZeGWMSW2ucfQ5yQFXrRKRGRDriPXJzXDP3K65+dt96zvh6JbsqMrj+vP7x7k5CGjx8D2Pv2kR6mjL7uXxmPtI13l2Ku6b+3nTIq+GXj31O1x6H2FKWxaTrj2Xv7iTPApLii8xGM1IsEZE84C94M9JLgcWRKonIX0Vkq4h8fHRdbHlzZ+Rz69U2zxRKWpoy/u6N3HZ1H340vD/nXrKLXn0PxrtbcdfU35vv3riVZQva88OzT2TZgvZceePWELWTi2h0WzKK5tnnG1R1l6o+BpwPjHKn0ZE8xeGFHpPKx4vaU7kzyf83b0b9T93Pps+y2Lw+m5rqNN56OY9hI3fHu1tx19Tfm2Ej9/D6TO+W39dn5jPsgj3x6FrstcbTZxE5Ldw+VV0armFVfVtEeh9F30yCKuhWzbZNWfXvK8ozOeG0/XHsUeLqVFjNjq2ZAOzYmkleQU2cexQbyToKjEa44dAfw+xT4LxYdMAtLDkGoA05sWjSNLOm1i1O5ZSXpgkpfE0x3M3b57ZEB1T1ceBxgI6Sb/+0kkBFeSadjzlU/76wezXbN2fGsUeJa2dFJvldvNFifpdqdm1PgcsySXxqHI2o0hEYE2z18hyK+hyia88qMjLrGH7JLt6bmxvvbiWk9+Z25Ovf9dY0/fp3d7BwTsc49yhGWuM1xdZswuTP+cqwveTm1/BMyUqe/mNX5jwXNq1Dq1JXK/zp1iLunraWtHSYOz2fzz9pE7liimvq782MR7pw62Ofc0HxDrZu9G7JSQUSYQHZZCYRFqH94g2LPIf3JEwhsAW4Q1WfDFeno+TrGdLkMmrGmBhYpPPZozuO6oJgds+e2uPmn0Z17Npf3LIkQo6WhBNN3mcRkWtE5FfufS8RGRKpnqpepardVTVTVXtECojGmOQQ7T2K0c5Qi0i6iCwTkVfd+3wRmScia9zPTkHHThSRUhFZLSIjg8oHichHbt9DgTSmLnXBDFe+KJo7YqK5pjgZGAZc5d5X4q3EbYxprVSi26JzMw0zhE4A5qtqX2C+e4+IDMBb+f8kvHugJ4tIuqvzKN5dLH3dFrhHejSwU1WPB+4H7o3UmWiC4hmqOh44CKCqO4Gs8FWMMSktRhMtItIDuBh4Iqg4OIH9FBomtp+uqlWqug4oBYa4jH8dVXWhS0o1tVGdQFvPAyMCo8hQogmK1S4aq/sSnYmYp8sYk8p8nD4XikhJ0DamUVMPAP9Nw5jS1WXow/3s4srrE9s7gaT3Re514/IGdVS1BtgNhJ01jWb2+SG8fKxdRGQS3qo5t0VRzxiTitTX7HNFqIkWEfkmsFVVl4jI8CjaCpXYPlzC+3D7mhQxKKrqsyKyBG/5MAEuVdVVEaoZY1JZbG5aOQv4lohchLeAdUcReQbYIiLdVbXcnRoHVtGoT2zvBJLel7nXjcuD65SJSAaQi5fqNKRoZp97AfuBf+Illt7nyowxrVUMrimq6kR3Z0pvvAmUN1T1GhomsB9Fw8T2xW5GuQ/ehMpid4pdKSJD3fXCaxvVCbR1hfuMoxsp4mXuCwxR2wB9gNV4M0DGmFaomReEuAeYKSKjgfW4vM2qukJEZgIrgRpgvKrWujrj8FbmagvMdhvAk8DTIlKKN0IsjvTh0Zw+fzn4vVs95/oQhxtjjG+q+hbwlnu9He9yXVPHTQImNVFeAgxsovwgLqhGy/djfqq6VERO91vPGJNCkvS55mhEk7jqZ0Fv04DTgG3N1iNjTGLzN/ucdKIZKXYIel2Dd43xH83THWNMUmitI0V303Z7Vf1FC/XHGJPghFa68raIZKhqTbi0BMaYVqo1BkW8jH2nActF5BXg78C+wE5VfaGZ+2aMSURJnKkvGtFcU8wHtuPlZAncr6iABUVjWqtWOtHSxc08f8yRzxem8P8TxphIWutIMR1ozxd4oNoYk+JSOAKEC4rlqvqbFuuJMSY5JHFSqmiEC4qpm9jVGHNUWuvps2WQMsY0rTUGRVUNu+aYMab1au2P+RljzGGt+JqiMcYcQUjtCQcLisYY/1J4pBhNNj9jjGnARza/0G2ItBGRxSLygYisEJFfu/J8EZknImvcz05BdSa6xParRWRkUPkgEfnI7XsokMbUpS6Y4coXiUjvSN/NgqIxxr/Y5H2uAs5T1ZOBU4ALRGQoMAGYr6p9gfnuPSIyAC+dwEl4ye4nu5W8AB4FxuDlbenr9gOMBnaq6vHA/cC9kTplQdEY449bZDaaLWwznr3ubabblIYJ7KfQMLH9dFWtUtV1QCkwxGX866iqC11SqqmN6gTaeh4YERhFhmJB0RjjX/QjxUIRKQnaxgQ3IyLpIrIcL43pPFVdBHR1GfpwP7u4w+sT2zuBpPdF7nXj8gZ1VLUG2A0UhPtqNtFijPHNxxMtFao6ONROl43vFBHJA14UkSOSTwV/bFNNhCkPVyckGykaY/yLzTXFw82p7sLL5ncBsMWdEuN+bnWHBRLbBwSS3pe5143LG9QRkQwgFy/VaUgJNVLUDjlUDx0U724krMzXl8S7Cwlvzqbl8e5CQhsycn9M2onFs88i0hmoVtVdItIW+DreREgggf097mdwYvtpInIfcAzehMpiVa0VkUo3SbMIuBZ4OKjOKGAhcAXwhrvuGFJCBUVjTBJQYrXIbHdgiptBTgNmquqrIrIQmCkio4H1uLzNqrpCRGYCK/GS6I13p98A44CngLbAbLcBPAk8LSKleCPE4kidsqBojPElVomrVPVD4NQmyrcTYkEaVZ0ETGqivAQ44nqkqh7EBdVoWVA0xviXwk+0WFA0xvgm4S/LJTULisYYf2yVHGOMaai1rrxtjDFNskVmjTEmmI0UjTHGiWJZsGRmQdEY458FRWOM8cTq5u1EZUHRGOOb1KVuVLSgaIzxx+5TNMaYhuyWHGOMCWYjRWOMOcwmWowxJkABWxDCGGMOS+VripajxRjjS+A+xWi2sO2I9BSRN0VklYisEJGbXXm+iMwTkTXuZ6egOhNdYvvVIjIyqHyQiHzk9j0USGMqItkiMsOVLxKR3pG+nwVFY4w/qtFv4dUAt6jqicBQYLxLeD8BmK+qfYH57j1uXzFwEl6Cq8kulQHAo8AYvLwtfd1+gNHATlU9HrgfLwdMWBYUjTG+xWKkqKrlqrrUva4EVuHlaQ5OYD+Fhontp6tqlaquA0qBIS7jX0dVXeiSUk1tVCfQ1vPAiMAoMhQLisYY/2Kc4tSd1p6Kl42vq6qWgxc4gS7usPrE9k4g6X2Re924vEEdVa0BdgMF4fpiEy3GGN983JJTKCIlQe8fV9XHG7Ql0h74B/ATVd0TZiAXKrF9uIT34fY1yYKiMcYfBWqjjooVqjo41E4RycQLiM+q6guueIuIdFfVcndqvNWV1ye2dwJJ78vc68blwXXKRCQDyMVLdRqSnT4bY3yL0eyz4OVlXqWq9wXtCiSwx/18Oai82M0o98GbUFnsTrErRWSoa/PaRnUCbV0BvOGuO4ZkI0VjjH+xuXn7LOD7wEcistyV/RK4B5gpIqOB9bi8zaq6QkRmAivxZq7Hq2qtqzcOeApoC8x2G3hB92kRKcUbIRZH6pQFRWOMb7F4zE9VF9D0NT+AESHqTAImNVFeAgxsovwgLqhGy4KiMcYfWzrMGGMOE0Cin2hJOhYUjTG+iS0IYYwxjp0+J7dfXPcOQ0/ZwK49bRj9y8sBuH38m/TsthuA9jmH2Ls/izG3X0pGei0/+8G79OtTgSo88sxQPvhPdwB+eEUJ3zjrUzq0q+LiMdce8TnnnL6OO3/8JmPv+BafrCtsuS8YJ4OH72HsXZtIT1NmP5fPzEe6xrtLzeaFxzsze1o+ItDnhIPccv96stooLz9ZyCt/KyQtQzljxB6uu728vs7Wskx+NPwErrllM98Ztw2At17OY/pDXamtpcHxr04t4J9PFZKWBm3b1XLz7zdwbL+quHzX6ET1XHPSaragKCI98Z5B7AbU4d3J/mBzfV4oc97py0vzTmTC9W/Xl931p3PrX4+9ahH79mcBcPHw1QBcd+tl5HU4wD0/n8u4O7+FqrBwWS9emjeAp3///BGf0bZNNZefv5KVpZ2b+dskhrQ0ZfzdG5lYfBwV5Zk8PGsN783JZf2aNvHuWsxVlGfy0pOF/OWt/5DdVvnt9cfy1sud6NLjEO/OyeXR+avJylZ2VTT8p/TYnUWcfl5l/fs9O9J54q5jeGTOavIKavn9zb1Y9k57Tv3qXs69bCffvHY7AAvndOTPdxZx97S1Lfo9/UrlRWab8+btUCtgtKgPV3djz77sEHuV4UM+4433jgPg2KJdLF3pjQx3VbZl7/4s+vepAGDVp13YsTunyVZ++O0lTJ/1ZQ5Vpze5P9X0P3U/mz7LYvP6bGqq03jr5TyGjdwd7241m9oaoepgGrU1UHUgjYKu1bw6tYArb9xCVrYXHfIKa+qPf3d2Lt17HeLYfgfry8rXZ1F0XBV5Bd5tdad+tZIFs/IAaNfh8OKEB/enEX65ggQRm1VyElKzBcUwK2AkjK/038LOPW3YuCUXgE/X53PWaetJS6ujW2El/Xpvp3P+vrBtHH+sd8x7y3u1RJcTQkG3arZtyqp/X1GeSWH36jj2qPkUdq/minFb+f7pA7jqlIG061DLoOGVbPy0DR8vas9NF/fl55cfz+rlbQEvqM2c3IVrbtncoJ1jeh+i7NNsNm/IorYG3n0tl20bM+v3v/K3Qv7fsBN54rfHcMNdZSQ09Wafo9mSUYs85tdoBYyEcd7Qtbyx8Lj697Pf7se2He147NevMP6aRawo7UJtXej/tkWUG763iEefG9IS3U0YTY1kknRQEFHlrnQWzsllyqKVTFv2MQf3pzP/H52orYW9u9N58NU1XHf7JiZd3xtVmPr7blz2o220bddwaeoOebX8+H/LuHvssdxyWV+69jxEesbhX9q3flDBUwtXMfrWTUx7sFtLf03/YrxKTiJp9omWxitgNLF/DN7ikGS3yWvu7tRLS6vj7MGfMfZXl9SX1dWlMXnaGfXvH779VTZu7hiyjZw21fTpsZP7J3pPFOXnHuC3P5nHbQ+cn9KTLRXlmXQ+5lD9+8Lu1WzfnBmmRvJa9k57uvU8VH/ae9ZFu1hZ0o7C7tWcddFuROCEU/eTlga7d6Tzn2U5LPhXHk/+9hj27klH0pSsbOWSH1Yw9Bt7GPoN75/ArGcKSE87MmoMv3QXD0/seUR5orFbcr6gECtgNOCWEXocoEPHHi32mx500iY2lOdRsbNdfVl2Vg2CcvBQJoNO2khtrfD5pk4h29h3IIvLxl9d//6+ibN4bPqQlA6IAKuX51DU5xBde1axfXMmwy/ZxT3jj413t5pFl6JqVi3N4eB+IbutsnxBB/p9ZT99TjzA8gXtOfnMvZR9mk31ISE3v5b7Xiqtr/v0H7rRpl0tl/zQuy69qyKDvMIaKnel88+nCrn1z58BsHFtFkXHef/JLH69I0V9Ennm2bGg6F+YFTBa1G3j3uTkEzeT2/4gMx6YzlMvnMbst/txbqNTZ4C8jgf43S/mUKdCxc4c/vfPX6vfN+bK9xkx7FOys2qY8cB0Zv1fP6a8eFpLf52EUFcr/OlWb4Y0LR3mTs/n809Sb+YZ4ITT9vPVi3czfmR/0jOU4wce4MJrtiMC9/2sJ2PO7U9mpvKLB9dHnCB59PYi1q70rj1e/dPN9PiSF/xe+Vtnlr7TnowMaJ9Xw88fXN/cX+voKN79JClKIqyi88UbFjkbeAf4iMO/wl+q6qxQdTp07KGDht7YLP1JBZmvL4l3FxLenE3L492FhDZk5AZKPjh4VPPbue2O0aEDro/q2Lkldy4Jt55iImq2kWKEFTCMMcmsLnWHiin/RIsxJsZS/PTZgqIxxjebfTbGmGApHBQtR4sxxqcoH/GLInCKyF9FZKuIfBxUli8i80RkjfvZKWjfRBEpFZHVIjIyqHyQiHzk9j0UyO3s8rnMcOWL3IMkYVlQNMb4E8jmF80W2VPABY3KJgDzVbUvMN+9x62dUAyc5OpMFpHAggOP4j0E0tdtgTZHAztV9XjgfuDeSB2yoGiM8U1Uo9oiUdW3OTLl6CXAFPd6CnBpUPl0Va1S1XVAKTDEpUHtqKoLXaa+qY3qBNp6HhgRGEWGYkHRGONf9KfPhSJSErSNiaL1ri5tKe5nF1deBGwIOq7MlRW5143LG9RR1RpgN1AQ7sNtosUY448CdVFPtFTE8ObtpkZ4GqY8XJ2QbKRojPEpdhMtIWxxp8S4n1tdeRkQvFpGD2CTK+/RRHmDOiKSAeRy5Ol6AxYUjTH+NW9QfAUY5V6PAl4OKi92M8p98CZUFrtT7EoRGequF17bqE6grSuANzTCs812+myM8UeB2tg80iIizwHD8a49lgF3APcAM0VkNLAel8xeVVeIyExgJd7K/uNVtdY1NQ5vJrstMNtt4C1K87SIlOKNEIsj9cmCojHGJwWNTVBU1atC7BoR4vhJwKQmykuAgU2UH8QF1WhZUDTG+JfCT7RYUDTG+ONv9jnpWFA0xvhnI0VjjAliQdEYYxxVqK2NfFySsqBojPHPRorGGBPEgqIxxgSozT4bY0w9BY3RzduJyIKiMca/GD3ml4gsKBpj/FG1FKfGGNOATbQYY8xhaiNFY4wJOKq1EhOeBUVjjD+2IIQxxhymgKbwY36WjsAY44+6RWaj2SIQkQtcYvtSEZnQAr2PyEaKxhjfNAanzy6R/Z+A8/ESTL0vIq+o6sqjbvwo2EjRGONfbEaKQ4BSVV2rqoeA6XjJ6+NKIiS2alEisg34PN79CFIIVMS7EwnMfj+RJdrv6FhV7Xw0DYjIa3jfKxptgINB7x9X1cddO1cAF6jqde7994EzVPXGo+nf0Uqo0+ej/cOKNREpiWEi75Rjv5/IUvF3pKoXxKgp34nqW4KdPhtj4iVUcvu4sqBojImX94G+ItJHRLLwcjK/Euc+JdbpcwJ6PN4dSHD2+4nMfkchqGqNiNwIzAHSgb+q6oo4dyuxJlqMMSbe7PTZGGOCWFA0xpggFhSbkIiPHiUSEfmriGwVkY/j3ZdEJCI9ReRNEVklIitE5OZ498lEz64pNuIePfqEoEePgKvi/ehRIhGRc4C9wFRVHRjv/iQaEekOdFfVpSLSAVgCXGp/h5KDjRSPlJCPHiUSVX0b2BHvfiQqVS1X1aXudSWwCiiKb69MtCwoHqkI2BD0vgz7C22+IBHpDZwKLIpzV0yULCgeKSEfPTLJR0TaA/8AfqKqe+LdHxMdC4pHSshHj0xyEZFMvID4rKq+EO/+mOhZUDxSQj56ZJKHiAjwJLBKVe+Ld3+MPxYUG1HVGiDw6NEqYGYiPHqUSETkOWAh0F9EykRkdLz7lGDOAr4PnCciy912Ubw7ZaJjt+QYY0wQGykaY0wQC4rGGBPEgqIxxgSxoGiMMUEsKBpjTBALiklERGrd7R0fi8jfRSTnKNp6ymVTQ0SeEJEBYY4dLiJnfoHP+ExEjsj6Fqq80TF7fX7WnSLyc799NKYxC4rJ5YCqnuJWpjkEjA3e6Vb48U1Vr4uwgstwwHdQNCYZWVBMXu8Ax7tR3JsiMg34SETSReT3IvK+iHwoIteD95SFiDwiIitF5F9Al0BDIvKWiAx2ry8QkaUi8oGIzHcLGowFfupGqV8Vkc4i8g/3Ge+LyFmuboGIzBWRZSLyZ5p+jrwBEXlJRJa4dQfHNNr3R9eX+SLS2ZV9SURec3XeEZETYvLbNMaxxFVJSEQygAuB11zREGCgqq5zgWW3qp4uItnAv0VkLt5KLf2BLwNdgZXAXxu12xn4C3COaytfVXeIyGPAXlX9gztuGnC/qi4QkV54T/+cCNwBLFDV34jIxUCDIBfCD91ntAXeF5F/qOp2oB2wVFVvEZFfubZvxEsENVZV14jIGcBk4Lwv8Gs0pkkWFJNLWxFZ7l6/g/d87ZnAYlVd58q/AXwlcL0QyAX6AucAz6lqLbBJRN5oov2hwNuBtlQ11JqJXwcGeI/4AtDRLaZ6DnC5q/svEdkZxXe6SUQuc697ur5uB+qAGa78GeAFt+rMmcDfgz47O4rPMCZqFhSTywFVPSW4wAWHfcFFwI9VdU6j4y4i8hJoEsUx4F12GaaqB5roS9TPjYrIcLwAO0xV94vIW0CbEIer+9xdjX8HxsSSXVNMPXOAcW7pKkSkn4i0A94Git01x+7AuU3UXQh8TUT6uLr5rrwS6BB03Fy8U1nccae4l28DV7uyC4FOEfqaC+x0AfEEvJFqQBoQGO1+D++0fA+wTkS+4z5DROTkCJ9hjC8WFFPPE3jXC5eKl1jqz3hnBC8Ca4CPgEeB/2tcUVW34V0HfEFEPuDw6es/gcsCEy3ATcBgN5GzksOz4L8GzhGRpXin8esj9PU1IENEPgTuAt4L2rcPOElEluBdM/yNK78aGO36twJLFWFizFbJMcaYIDZSNMaYIBYUjTEmiAVFY4wJYkHRGGOCWFA0xpggFhSNMSaIBUVjjAny/wHh+zw9s82ZUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "lr  = LogisticRegression(C=1.0, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "881b3b1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.81      0.80    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.81      0.79      0.80    104407\n",
      "\n",
      "    accuracy                           0.80    208065\n",
      "   macro avg       0.53      0.53      0.53    208065\n",
      "weighted avg       0.80      0.80      0.80    208065\n",
      "\n",
      "Confusion Matrix: [[84156     0 19491]\n",
      " [    1     0    10]\n",
      " [22162     0 82245]]\n",
      "accuracy: 0.7997548842909667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2734963dfd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# vectorization using Bag-of-Words (with Tf-Idf ) \n",
    "\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_features=200000, ngram_range=(1,2))\n",
    "X_train_vectors_tfidf = tfidf_vectorizer.fit_transform(X_train) \n",
    "X_test_vectors_tfidf = tfidf_vectorizer.transform(X_test)\n",
    "\n",
    "#FITTING THE CLASSIFICATION MODEL using Naive Bayes(tf-idf)\n",
    "\n",
    "print(\"Naive Bayes\")\n",
    "\n",
    "nb_tfidf = MultinomialNB()\n",
    "nb_tfidf.fit(X_train_vectors_tfidf, y_train)  \n",
    "#Predict y value for test dataset\n",
    "y_predict = nb_tfidf.predict(X_test_vectors_tfidf)\n",
    "y_prob = nb_tfidf.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c81296a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.81      0.82    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.82      0.83      0.82    104407\n",
      "\n",
      "    accuracy                           0.82    208065\n",
      "   macro avg       0.55      0.55      0.55    208065\n",
      "weighted avg       0.82      0.82      0.82    208065\n",
      "\n",
      "Confusion Matrix: [[84045     0 19602]\n",
      " [    1     0    10]\n",
      " [17845     0 86562]]\n",
      "accuracy: 0.8199697210006488\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27359097700>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "lr  = LogisticRegression(C=1.0, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "\n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "74931bd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.81      0.80    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.81      0.79      0.80    104407\n",
      "\n",
      "    accuracy                           0.80    208065\n",
      "   macro avg       0.53      0.53      0.53    208065\n",
      "weighted avg       0.80      0.80      0.80    208065\n",
      "\n",
      "Confusion Matrix: [[83943     0 19704]\n",
      " [    1     0    10]\n",
      " [21734     0 82673]]\n",
      "accuracy: 0.8007882152212049\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27363daa880>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# vectorization using Bag-of-Words (with Tf-Idf ) \n",
    "\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_features=300000, ngram_range=(1,3))\n",
    "X_train_vectors_tfidf = tfidf_vectorizer.fit_transform(X_train) \n",
    "X_test_vectors_tfidf = tfidf_vectorizer.transform(X_test)\n",
    "\n",
    "#FITTING THE CLASSIFICATION MODEL using Naive Bayes(tf-idf)\n",
    "\n",
    "print(\"Naive Bayes\")\n",
    "\n",
    "nb_tfidf = MultinomialNB()\n",
    "nb_tfidf.fit(X_train_vectors_tfidf, y_train)  \n",
    "#Predict y value for test dataset\n",
    "y_predict = nb_tfidf.predict(X_test_vectors_tfidf)\n",
    "y_prob = nb_tfidf.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fd2df2c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.81      0.82    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.82      0.83      0.82    104407\n",
      "\n",
      "    accuracy                           0.82    208065\n",
      "   macro avg       0.55      0.55      0.55    208065\n",
      "weighted avg       0.82      0.82      0.82    208065\n",
      "\n",
      "Confusion Matrix: [[84241     0 19406]\n",
      " [    1     0    10]\n",
      " [17799     0 86608]]\n",
      "accuracy: 0.8211328190709634\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2732b26a220>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "lr  = LogisticRegression(C=1.0, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "\n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "99a2622b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.81      0.80    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.81      0.79      0.80    104407\n",
      "\n",
      "    accuracy                           0.80    208065\n",
      "   macro avg       0.53      0.53      0.53    208065\n",
      "weighted avg       0.80      0.80      0.80    208065\n",
      "\n",
      "Confusion Matrix: [[84044     0 19603]\n",
      " [    1     0    10]\n",
      " [21703     0 82704]]\n",
      "accuracy: 0.8014226323504674\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27310940ac0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# vectorization using Bag-of-Words (with Tf-Idf ) \n",
    "\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_features=400000, ngram_range=(1,3))\n",
    "X_train_vectors_tfidf = tfidf_vectorizer.fit_transform(X_train) \n",
    "X_test_vectors_tfidf = tfidf_vectorizer.transform(X_test)\n",
    "\n",
    "#FITTING THE CLASSIFICATION MODEL using Naive Bayes(tf-idf)\n",
    "\n",
    "print(\"Naive Bayes\")\n",
    "\n",
    "nb_tfidf = MultinomialNB()\n",
    "nb_tfidf.fit(X_train_vectors_tfidf, y_train)  \n",
    "#Predict y value for test dataset\n",
    "y_predict = nb_tfidf.predict(X_test_vectors_tfidf)\n",
    "y_prob = nb_tfidf.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7ae0ff4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.81      0.82    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.82      0.83      0.82    104407\n",
      "\n",
      "    accuracy                           0.82    208065\n",
      "   macro avg       0.55      0.55      0.55    208065\n",
      "weighted avg       0.82      0.82      0.82    208065\n",
      "\n",
      "Confusion Matrix: [[84308     0 19339]\n",
      " [    1     0    10]\n",
      " [17799     0 86608]]\n",
      "accuracy: 0.8214548338259678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x273521a9310>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "lr  = LogisticRegression(C=1.0, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "\n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3475d875",
   "metadata": {},
   "source": [
    "\n",
    "## Hyperparamter Search C values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d1dafdac",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_features=200000, ngram_range=(1,3))\n",
    "X_train_vectors_tfidf = tfidf_vectorizer.fit_transform(X_train) \n",
    "X_test_vectors_tfidf = tfidf_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4a0500e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.77      0.75    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.76      0.72      0.74    104407\n",
      "\n",
      "    accuracy                           0.74    208065\n",
      "   macro avg       0.50      0.50      0.50    208065\n",
      "weighted avg       0.74      0.74      0.74    208065\n",
      "\n",
      "Confusion Matrix: [[79294     0 24353]\n",
      " [    1     0    10]\n",
      " [29053     0 75354]]\n",
      "accuracy: 0.7432677288347391\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2735644d400>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "lr  = LogisticRegression(C=0.001, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "\n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "350d1764",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.78      0.78    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.78      0.78      0.78    104407\n",
      "\n",
      "    accuracy                           0.78    208065\n",
      "   macro avg       0.52      0.52      0.52    208065\n",
      "weighted avg       0.78      0.78      0.78    208065\n",
      "\n",
      "Confusion Matrix: [[80751     0 22896]\n",
      " [    1     0    10]\n",
      " [22835     0 81572]]\n",
      "accuracy: 0.7801552399490543\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27352b7cd60>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "lr  = LogisticRegression(C=0.01, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "\n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7f862830",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.80      0.81    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.81      0.82      0.81    104407\n",
      "\n",
      "    accuracy                           0.81    208065\n",
      "   macro avg       0.54      0.54      0.54    208065\n",
      "weighted avg       0.81      0.81      0.81    208065\n",
      "\n",
      "Confusion Matrix: [[83151     0 20496]\n",
      " [    1     0    10]\n",
      " [18933     0 85474]]\n",
      "accuracy: 0.8104438516809651\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x273518395b0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YKKdzfAL7EdRMbD88zCj3JppQmRmG2BslDQjXC8+vVSfW1hnAtHDdsV4+fHbOpcUso/cp/hR4RFIz4FPgh0Qna5MkjQSWEPI2m9lcSZOIAmcFMNrMKkM7FwEPAC2ByWGDaBLnIUkLic4QhyfrkAdF51zaLENB0czmAHVdcxxcz/FjgDF1lM8CDqyjfAshqKbKg6JzLk2+IIRzztWQqTPFXJRTQfGT99pwVo+B2e6Ga8RaPD8z213IabJNO9yGGVRWeVB0zrlqvnSYc84Fhg+fnXMujk+0OOdcDYlvf27cPCg659Lmw2fnnAui2eem+4SwB0XnXNp8+Oycc3F8+Oycc4EhD4rOORevCY+ePSg659JkYP6Yn3PObePDZ+eci7NLzj5LuoMElw7M7NIG6ZFzLqdl8tlnSYuBjUAlUGFmR0gqAiYCvYDFwFlmtjYcfzVRLudK4FIzmxLKD2fbytsvAJeZmUlqTpTy9HBgNXC2mS1O1KdEZ4qzvs6XdM41cQZkdvh8gpmVxu1fBUw1sxslXRX2r5TUjyidwAHA7sArkvYNKQnuBkYBbxIFxaFEKQlGAmvNbB9Jw4GbgLMTdabeoGhm4+P3JbU2s6/S+67OuaaogYfPw4BB4fV44FXgylA+wcy2AotC3pX+4WyznZlNB5D0IHAaUVAcBlwf2noCuFOSEiWvSvqsjqSBkuYB88P+wZLGpvUVnXNNiLCq1LYUGPCSpLcljQpl3UKGPsLPrqG8OrF9EEt6Xxxe1y6vUcfMKoD1QKdEHUplouU2YAhRqkDM7F1Jx6VQzznXVKV+pthZUvyluHFmNi5u/xgzWyapK/CypA8TtFVfYvtECe8TvVenlGafzWxplE61WmV9xzrnmjhLa6Kl1MzqytYXNWW2LPxcKelpoD+wQlJ3M1suqTuwMhxendg+iCW9Lwmva5fH1ymRVAC0J0p1Wq9UlrpYKulowCQ1k/QLwlDaObeLshS3BCS1ltQ29hr4NvABNRPYj6BmYvvhkppL6g30AWaGIfZGSQMUnb2dX6tOrK0zgGmJridCameKFwK3E43NPwemAKNTqOeca7IyMvvcDXg6jEILgEfN7EVJbwGTJI0ElhDyNpvZXEmTgHlABTA6zDwDXMS2W3Imhw3gPuChMCmzhmj2OqGkQTFMlZ+b4pd0zu0Kqna8CTP7FDi4jvLVwOB66owBxtRRPgs4sI7yLYSgmqpUZp/3kvS8pFWSVkp6VtJe6XyIc64Jid2nmMrWCKVyTfFRYBLQneiGyceBxxqyU8653GaW2tYYpRIUZWYPmVlF2B6maa8c5JxLJgMTLbkq0bPPReHlP8OjNhOIvubZwD92Qt+cc7mqkQ6NU5FoouVtat4YeUHcewbc0FCdcs7lNjXSs8BUJHr2uffO7IhzrpEwwa6+yKykA4F+QItYmZk92FCdcs7luF3xTDFG0nVEK1b0I1qS52TgDaI1ypxzu6ImHBRTmX0+g+hGyi/M7IdEN1s2b9BeOedy2644+xxns5lVSaqQ1I7o4ewmffP2Fbcs4aiTNrKutIALTuyb7e7kpCMGbeDCG5aRn2dMfqyISXd2y3aXsq6uv5u2HSr49V8+o1uPMlaUNGPMBXvy5fpGngUk84vM5pRUzhRnSeoA3EM0Iz0bmJmskqT7wxMwH+xYF3e+lyYWcc25Ps9Un7w8Y/QfPufac3vzk0F9OWHYOvbosyXb3cq6uv5uzrpkJe+80YYfHbs/77zRhrMvWVlP7cZFltrWGCUNimZ2sZmtM7O/AN8CRoRhdDIPEC0J3uh8MKMNG9c28n/NG1DfQzexbHEzvljSnIryPF59tgMDh6zPdreyrq6/m4FDNvDKpOiW31cmFTFw6IZsdC3zdsXhs6TDEr1nZrMTNWxmr0nqtQN9czmq027lrFrWrHq/dHkh+x22KYs9yl0dO5ezZmUhAGtWFtKhU0WWe5QZjfUsMBWJToduTvCeASdmogNhCfJRAC1olYkmXQNTHZeTGutzru5rasLXFBPdvH3CzuhAWJp8HEA7Ffn/Wo1A6fJCuuxeVr3fuXs5q78ozGKPctfa0kKKukZni0Vdy1m3uglclmnEQ+NUpDLR4lwNC+a0orh3Gd16bqWgsIpBw9bx5kvts92tnPTmS+046axo9fuTzlrD9CntstyjDNkVrynuyq4a+xkHDfyS9kUVPDxrHg/d3I0pjyVMALZLqaoUd11TzB8e/ZS8fHhpQhGffdQiecUmrq6/m4l3duWav3zG0OFrWPl5dEtOU6AMLDKbq5QkXcHXb1h6jOhJmM7ACuA6M7svUZ12KrKjVOeCu865DJhhU9lga3bogmDznj2tx2WXp3Tsp7/8+duJElcBSMoHZgGfm9mpYYWuiUAvYDFwlpmtDcdeTZTgvhK41MymhPLD2ZaO4AXgMjMzSc2Jnr47HFgNnG1mixP1J5WVtyXpPEm/Cft7SOqfrJ6ZnWNm3c2s0Mx6JAuIzrnGIdV7FNOYob6MmsnwrgKmmlkfYGrYR1I/ohwrBxDd7jc2BFSAu4kmbPuELXY74EhgrZntA9wK3JSsM6lcUxwLDATOCfsbgbtSqOeca6oylI5AUg/gv4B744qHAePD6/HAaXHlE8xsq5ktAhYC/UMa1HZmNj1k6nuwVp1YW08Ag6W67p/YJpWgeJSZjQa2AITT2GaJqzjnmrTUJ1o6S5oVt42q1dJtwK+omQqrW0hbSvjZNZQXA0vjjisJZcXhde3yGnXMrAJYDyScIEhloqU8nKIagKQuZCSXl3OusUpjaFxa3zVFSacCK83sbUmDUvnYOsosQXmiOvVKJSj+GXga6CppDNGqOdemUM851xRZxmafjwG+K+kUorVa20l6GFghqbuZLQ9D49gD4yVAz7j6PYBlobxHHeXxdUokFQDtifI/1yuVZ58fITq9/V9gOXCamT2erJ5zrgnLwH2KZnZ1mITtRTSBMs3MzgOeA0aEw0YAz4bXzwHDJTWX1JtoQmVmGGJvlDQgXC88v1adWFtnhM/YsTNFSXsAm4Dn48vMbEmyus65Jqphb8y+EZgkaSSwhJDM3szmSpoEzAMqgNFmVhnqXMS2W3Imhw3gPuAhSQuJzhCHJ/vwVIbP/2DbuL0F0BtYQDQt7pzbBWV6QQgzexV4NbxeTbSwdV3HjQHG1FE+CziwjvIthKCaqqRB0cy+Eb8fVs+5oJ7DnXOuUUv7MT8zmy3pyIbojHOukWikzzWnIpVrilfE7eYBhwGrGqxHzrnclrnZ55yUypli27jXFUTXGJ9smO445xqFXfVMMdy03cbMfrmT+uOcy3FiF115W1KBmVUkSkvgnNtF7YpBkShj32HAHEnPAY8DX8XeNLOnGrhvzrlc1Igz9aUilWuKRUTrkJ3ItvsVDfCg6NyuahedaOkaZp4/YPuHrpvwvxPOuWR21TPFfKANX2OVCedcE9eEI0CioLjczH6303rinGscGnFSqlQkCopNN7Grc26H7KrDZ88g5Zyr264YFM0s4UKMzrld167+mJ9zzm2zC19TdM657YimPeGQSjY/55yrKQPpCCS1kDRT0ruS5kr6bSgvkvSypI/Dz45xda6WtFDSAklD4soPl/R+eO/PsTSmIXXBxFA+Q1KvZF/Ng6JzLm11Jb6va0tiK3CimR0MHAIMlTQAuAqYamZ9gKlhH0n9iNIJHECU7H5sWLQG4G5gFFHelj7hfYCRwFoz2we4FbgpWac8KDrn0peZxFVmZl+G3cKwGTUT2I+nZmL7CWa21cwWAQuB/iHjXzszmx6SUj1Yq06srSeAwbGzyPp4UHTOpScsMpvKBnSWNCtuGxXflKR8SXOI0pi+bGYzgG4hQx/hZ9dweHVi+yCW9L44vK5dXqOOmVUA64FOib6eT7Q459KX+uxzqZkdUW8zUTa+QyR1AJ6WtF3yqTj1PXKc6FHktB9T9jNF51zaMnRNsZqZrSPK5jcUWBGGxISfK8NhscT2MbGk9yXhde3yGnUkFQDtiVKd1suDonMufZmZfe4SzhCR1BI4CfiQmgnsR1Azsf3wMKPcm2hCZWYYYm+UNCBcLzy/Vp1YW2cA08J1x3rl1PDZ2rWi7BhPFFifZi++le0u5Lwpy+Zkuws5rf+QTRlpJ0PPPncHxocZ5Dxgkpn9XdJ0YJKkkcASQt5mM5sraRIwjyhf1Ogw/Aa4CHgAaAlMDhvAfcBDkhYSnSEOT9apnAqKzrlGwMjIIrNm9h5waB3lq6ln7QUzGwOMqaN8FrDd9Ugz20IIqqnyoOicS8sum7jKOefq5UHROee2UeK5ikbNg6JzLj2+So5zztXk1xSdcy6OLzLrnHPx/EzROeeCNB/ha2w8KDrn0udB0TnnIn7ztnPO1aKqphsVPSg659Lj9yk651xNfkuOc87F8zNF55zbxidanHMuxgBfEMI557ZpytcUPUeLcy4tsfsUdzRxlaSekv4pab6kuZIuC+VFkl6W9HH42TGuztWSFkpaIGlIXPnhkt4P7/05lts55HOZGMpnSOqV7Pt5UHTOpccs9S2xCuDnZrY/MAAYLakfcBUw1cz6AFPDPuG94cABRFn/xob8LgB3A6OIkln1Ce8DjATWmtk+wK3ATck65UHROZe2TJwpmtlyM5sdXm8E5hMlrx8GjA+HjQdOC6+HARPMbKuZLQIWAv1DGtR2ZjY9ZOp7sFadWFtPAINjZ5H18aDonEtf6ilOO0uaFbeNqqu5MKw9FJgBdAtpSwk/u4bDioGlcdVKQllxeF27vEYdM6sA1gOdEn01n2hxzqUtjVtySs3siIRtSW2AJ4GfmdmGBCdydb1hCcoT1amXnyk659JjQKWltiUhqZAoID5iZk+F4hVhSEz4uTKUlwA946r3AJaF8h51lNeoI6kAaE+U/7leHhSdc2nL0OyziJLVzzezW+Leeg4YEV6PAJ6NKx8eZpR7E02ozAxD7I2SBoQ2z69VJ9bWGcC0cN2xXj58ds6lLzM3bx8D/AB4X9KcUPZr4EZgkqSRwBJCMnszmytpEjCPaOZ6tJlVhnoXAQ8ALYHJYYMo6D4kaSHRGeLwZJ3yoOicS1smHvMzszeo+5ofwOB66owBxtRRPgs4sI7yLYSgmioPis659PjSYc45t40ApTCJ0lh5UHTOpU2+IIRzzgU+fG7cfvXD1xhw8BLWbWjJj35zOgB791zNFee/QbPCSiqr8rjtoaP5cFFXCvIruWLEG/TtVYqZuOPRAby7YHcAbrr8RTp12ER+XhXvfbQbtz98NFWWx3cGzee0E+dRVSU2by3k5vHH8tmyjom61CQcMWgDF96wjPw8Y/JjRUy6s1u2u9RgnhrXhcmPFiFB7/228PNblzDxzm5MfrSI9kXR5OcPr15G/8Ebq+usLCnkJ4P247yff8GZF61i05d5/Py0PtXvly4v5MTT13LR7z6vLnv97+35/aje3DF5AfsevHnnfcG0pfRcc6PVYEFRUk+iZxB3A6qAcWZ2e0N9Xn1e/Hcfnp7aj6t//K/qsgvOnMn45w5j5vs9OeobS7ngzJlc/sdTOfX4BQCM/M3pdGi7mZsuf5ELbzgNM/Hbu09k05ZmgPHbi6dy/JGL+OfMvZn65t48/+r+ABx9yGdcfPYMrrx1aF1daTLy8ozRf/icq4fvRenyQu544WPenNKeJR+3yHbXMq50eSHP3NeZe179kOYtjd9fsCevPhv9o/e9n6zizItW1VnvL9cXc+SJ24JkqzZV3P3Kgur90UP25dhT1lXvb/oyj2fu68J+h33VMF8kw5ryIrMNefN2fStg7FTvfdSdDV813668dYuy6GerMlavaw3AnruvZfa86Mxw3caWfLmpOX17RX/0UUCE/HyjoKCyevgQKwdo0byiKY8qqvU9dBPLFjfjiyXNqSjP49VnOzBwyPpsd6vBVFaIrVvyqKyArZvz6NStPOHx/5ncnu57lLHnvlvqfP/zT5uxrrSAA4/aFgDH/7E7Z168kmbNG8lfUGZWyclJDRYUE6yAkXV3PjaAC86aycQ/PcaFZ83gniejRzM/WdqJYw5dQl5eFbt13si+vUrpWrTtD/ePV0zm6dseZvOWZvxrVu/q8tNOnMfDN07kgjNncscjA3f699nZOu1Wzqpl2/4xKF1eSOfuiQNFY9W5ezlnXLSSHxzZj3MOOZDWbSs5fFB0Bvj837pw4eC+3Hx5Tzaui1aw2rIpj0lju3Lez7+ot81/PtOR47+7jtgjvgvfb8mqZYUM+NaGBv8+GWHR7HMqW2O0Ux7zq7UCRtYNO2E+YycM4OxfnMPYCQP45Q9fB+CF1/dl1drW/PU3z3DJOdP5YGFXKiu3/Yp+dcvJnH759yksqOTQ/ZdVlz8zrR/nXXU24x4/kh98Z87O/jo7XV3P6zfSk4KkNq7LZ/qU9oyfMY9H3/mALZvymfpkR04dUcrfps9j7MsLKOpWzrjfRiOMB/9vN773k1W0bF3/0tT/erYjJ3xvLQBVVfDX64sZdd2yeo/PSamvktPoNHhQrL0CRh3vj4otK1RetnOup3z76I957e1eALz6Vm/26x0Nkauq8hg7YQA/uf6/ufaOb9OmVRklK9vVqFteUcB/5uzBMYcu2a7daTP35phDFzd097OudHkhXXYvq97v3L2c1V8UZrFHDeed19uwW88yOnSqpKAQjjllHfNmtaZjlwry8yEvD04+dw0L5rQC4MN3WnHf73fn/P79ePreLky4oxvP3t+5ur1P5ragshL6HBRNpGz+Mo/FH7bgV6fvw/n9+zF/diuu+5+9+Ojdlln5vqmSWUpbY9Sgs8/1rIBRg5mNA8YBtG3fY6f8Fleva8XBfZfz7oLdOWz/ZXy+Igp8zZtVIIwtZYUc3q+Eyso8PlvWkRbNy2nVopw161uRl1fFUQct5b2PdgOguOt6Pl/ZHoABBy2pft2ULZjTiuLeZXTruZXVXxQyaNg6bhy9Z7a71SC6Fpczf3YrtmwSzVsac95oy74HbWL1igI6dasAomuIvfpG1w9veWZhdd2H/rQbLVpXMuxHpdVlrz7TkUHD1lXvt25XxeNzP6je/+Xp+/CT33ye47PPNN2hAQ07+1zfChg71bUXTOOQvstp32YLk/70KA88ezh/Gv9NfnrOdPLzjbLyfG4e/00AOrTdzB9//iJWBaXrWvO/9x4PQMvm5Yy59CUKCyrJzzNmz9+d58KM8/cGz+Pwfp9TUZnHxq+ac2Oo05RVVYq7rinmD49+Sl4+vDShiM8+anozzwD7HbaJb/7XekYP6Ut+gbHPgZs5+bzV3PaLnnwytyUSdOtRxqV/XJq8MeC15ztww0OfNnCvG5gR3U/SRCnJKjpfv2HpWOB14H22/Qp/bWYv1FenbfsedtgxlzZIf5qCZi++le0u5Lwpy+Zkuws5rf+Qpcx6d0vC5fiTad96dxvQ74KUjn1p1vVvJ1tkNtc02JlikhUwnHONWVXTPVVs8k+0OOcyrIkPnz0oOufS1lhnllPh6Qicc+nL0BMtku6XtFLSB3FlRZJelvRx+Nkx7r2rQ2L7BZKGxJUfLun98N6fY2lMQ+qCiaF8RrhnOiEPis65NKUYEFM7m3yAbYnrY64CpppZH2Bq2Cc8JjwcOCDUGSspP9S5GxhFlLelT1ybI4G1ZrYPcCtwU7IOeVB0zqUng9n8zOw1ts+uF5/Afjw1E9tPMLOtZrYIWAj0Dxn/2pnZ9JCU6sFadWJtPQEMjp1F1sevKTrn0pbGNcXOkmbF7Y8LD2wk0i1k6MPMlkvqGsqLgTfjjoslvS8Pr2uXx+osDW1VSFoPdAJKqYcHRedc+lIPiqUZvE+xvsT2iRLeJ3qvTj58ds6lx4AqS237elaEITHh58pQXp3YPoglvS8Jr2uX16gjqQBoz/bD9Ro8KDrn0pTRiZa6xCewH0HNxPbDw4xyb6IJlZlhqL1R0oBwvfD8WnVibZ0BTLMkj/H58Nk5l74M3aco6TFgENG1xxLgOuBGYJKkkcASQt5mM5sraRIwj2gR69FmVhmauohoJrslMDlsEK2/8JCkhURniMOT9cmDonMuPQZUZuaRFjM7p563Btdz/BhgTB3ls4AD6yjfQgiqqfKg6JxLk4E13ef8PCg659LXhB/z86DonEtPbPa5ifKg6JxLn58pOudcHA+KzjkXmEFlZfLjGikPis659PmZonPOxfGg6JxzMTv0XHPO86DonEuPgfnN2845FydDj/nlIg+Kzrn0mHmKU+ecq8EnWpxzbhvzM0XnnIvZoQVkc54HRedcenxBCOec28YAa8KP+XmOFudceiwsMpvKloSkoZIWSFoo6aqd0Puk/EzROZc2y8DwWVI+cBfwLaKse29Jes7M5u1w4zvAzxSdc+nLzJlif2ChmX1qZmXABGBYg/c9CSXJ9rdTSVoFfJbtfsTpDJRmuxM5zH8/yeXa72hPM+uyIw1IepHoe6WiBbAlbn+cmY0L7ZwBDDWzH4f9HwBHmdklO9K/HZVTw+cd/Y+VaZJmmdkR2e5HrvLfT3JN8XdkZkMz1JTqaj5DbX9tPnx2zmVLCdAzbr8HsCxLfanmQdE5ly1vAX0k9ZbUjChR/XNZ7lNuDZ9z0LhsdyDH+e8nOf8d1cPMKiRdAkwB8oH7zWxulruVWxMtzjmXbT58ds65OB4UnXMujgfFOuTio0e5RNL9klZK+iDbfclFknpK+qek+ZLmSros231yqfNrirWER48+Iu7RI+CcbD96lEskHQd8CTxoZgdmuz+5RlJ3oLuZzZbUFngbOM3/hhoHP1PcXk4+epRLzOw1YE22+5GrzGy5mc0OrzcC84Hi7PbKpcqD4vaKgaVx+yX4H7T7miT1Ag4FZmS5Ky5FHhS3l5OPHrnGR1Ib4EngZ2a2Idv9canxoLi9nHz0yDUukgqJAuIjZvZUtvvjUudBcXs5+eiRazwkCbgPmG9mt2S7Py49HhRrMbMKIPbo0XxgUi48epRLJD0GTAf6SiqRNDLbfcoxxwA/AE6UNCdsp2S7Uy41fkuOc87F8TNF55yL40HROefieFB0zrk4HhSdcy6OB0XnnIvjQbERkVQZbu/4QNLjklrtQFsPhGxqSLpXUr8Exw6SdPTX+IzFkrbL+lZfea1jvkzzs66X9It0++hcbR4UG5fNZnZIWJmmDLgw/s2wwk/azOzHSVZwGQSkHRSda4w8KDZerwP7hLO4f0p6FHhfUr6k/5P0lqT3JF0A0VMWku6UNE/SP4CusYYkvSrpiPB6qKTZkt6VNDUsaHAhcHk4S/2mpC6Sngyf8ZakY0LdTpJekvSOpL9S93PkNUh6RtLbYd3BUbXeuzn0ZaqkLqFsb0kvhjqvS9ovI79N5wJPXNUISSoATgZeDEX9gQPNbFEILOvN7EhJzYF/S3qJaKWWvsA3gG7APOD+Wu12Ae4BjgttFZnZGkl/Ab40sz+F4x4FbjWzNyTtQfT0z/7AdcAbZvY7Sf8F1Ahy9fhR+IyWwFuSnjSz1UBrYLaZ/VzSb0LblxAlgrrQzD6WdBQwFjjxa/wanauTB8XGpaWkOeH160TP1x4NzDSzRaH828BBseuFQHugD3Ac8JiZVQLLJE2ro/0BwGuxtsysvjUTTwL6RY/4AtAuLKZ6HPDfoe4/JK1N4TtdKul74XXP0NfVQBUwMZQ/DDwVVp05Gng87rObp/AZzqXMg2LjstnMDokvCMHhq/gi4KdmNqXWcaeQfAk0pXAMRJddBprZ5jr6kvJzo5IGEQXYgWa2SdKrQIt6Drfwuetq/w6cyyS/ptj0TAEuCktXIWlfSa2B14Dh4Zpjd+CEOupOB46X1DvULQrlG4G2cce9RDSUJRx3SHj5GnBuKDsZ6Jikr+2BtSEg7kd0phqTB8TOdr9PNCzfACySdGb4DEk6OMlnOJcWD4pNz71E1wtnK0os9VeiEcHTwMfA+8DdwL9qVzSzVUTXAZ+S9C7bhq/PA9+LTbQAlwJHhImceWybBf8tcJyk2UTD+CVJ+voiUCDpPeAG4M24974CDpD0NtE1w9+F8nOBkaF/c/FUES7DfJUc55yL42eKzjkXx4Oic87F8aDonHNxPCg651wcD4rOORfHg6JzzsXxoOicc3H+P7t7xtvhPND6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "lr  = LogisticRegression(C=0.1, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "\n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ab3de1c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\rajes\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.81      0.82    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.82      0.83      0.82    104407\n",
      "\n",
      "    accuracy                           0.82    208065\n",
      "   macro avg       0.55      0.55      0.55    208065\n",
      "weighted avg       0.82      0.82      0.82    208065\n",
      "\n",
      "Confusion Matrix: [[84125     0 19522]\n",
      " [    1     0    10]\n",
      " [17843     0 86564]]\n",
      "accuracy: 0.8203638286112513\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27363164af0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "lr  = LogisticRegression(C=1.0, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "\n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d6c03b35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.80      0.80    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.80      0.81      0.81    104407\n",
      "\n",
      "    accuracy                           0.80    208065\n",
      "   macro avg       0.54      0.54      0.54    208065\n",
      "weighted avg       0.80      0.80      0.80    208065\n",
      "\n",
      "Confusion Matrix: [[82611     0 21036]\n",
      " [    1     0    10]\n",
      " [19667     1 84739]]\n",
      "accuracy: 0.8043159589551342\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27354d9bac0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "lr  = LogisticRegression(C=10.0, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "\n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5f86b7d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.78      0.78    103647\n",
      "           1       0.00      0.00      0.00        11\n",
      "           2       0.78      0.78      0.78    104407\n",
      "\n",
      "    accuracy                           0.78    208065\n",
      "   macro avg       0.52      0.52      0.52    208065\n",
      "weighted avg       0.78      0.78      0.78    208065\n",
      "\n",
      "Confusion Matrix: [[80330     0 23317]\n",
      " [    1     0    10]\n",
      " [22508     1 81898]]\n",
      "accuracy: 0.7796986518636003\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27305b131f0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Logistic Regression tf-idf)\n",
    "\n",
    "print(\"Logistic Regression \")\n",
    "\n",
    "lr  = LogisticRegression(C=100.0, max_iter=5000)\n",
    "lr.fit(X_train_vectors_tfidf, y_train)  \n",
    "\n",
    "#Predict y value for test dataset\n",
    "y_predict = lr.predict(X_test_vectors_tfidf)\n",
    "y_prob = lr.predict_proba(X_test_vectors_tfidf)[:,1]\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_predict)\n",
    "print(classification_report(y_test,y_predict))\n",
    "print('Confusion Matrix:',cf_matrix)\n",
    "\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_test, y_predict))\n",
    "\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix)\n",
    "disp.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a8b269b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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