Sentiment_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import warnings\n",
    "import csv\n",
    "import langid\n",
    "from langdetect import detect\n",
    "import os \n",
    "import spacy\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import matplotlib.pyplot as plt\n",
    "from spacy.lang.ar import Arabic\n",
    "from sklearn import preprocessing\n",
    "import seaborn as sns \n",
    "from sklearn.model_selection import train_test_split ,GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import accuracy_score, classification_report , confusion_matrix, f1_score, precision_score, recall_score\n",
    "import nltk \n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize, RegexpTokenizer\n",
    "from collections import Counter \n",
    "import re\n",
    "import string\n",
    "import matplotlib.cm as cm\n",
    "import imblearn\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier\n",
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_colwidth', 1)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Labeled Data/combined_file.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-05-27 04:41:01</td>\n",
       "      <td>ياخي ارسلت اكثر مره احد خدمني عندي شريحة مسبقة الدفع ابي افعل الجيل الخامس محدود يقبل معي ماي اس سي ليه</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-27 03:47:08</td>\n",
       "      <td>ابي افعل انترنت الجيل الخامس لمده شهر محدود لشريحه كويك نت و رصيد يغطي جربت عشرين مره ماي اس سي و لاضبط امل حل مشكلتي و شكرا والا فعلوها عندكم و خلصنا تو ارسلت بالخاص قالي الموظف حاول مره اخرى سلامات حل اسبوع احاول</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-05-27 03:45:29</td>\n",
       "      <td>اخوي شحنت رصيد بفعل خدمه مو راضي طريق ماي اس سي</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-05-27 03:07:36</td>\n",
       "      <td>سلام علكيم عام وانتو بخير انا جيت تطبيق ماي اس سي مالقيت ايفون اكس اس ماكس متوفره</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-27 01:45:20</td>\n",
       "      <td>الله يحيك تم طلبها طريق ماي اس سي يوم ويجيني النت وتتفعل الخدمه</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-05-27 00:58:13</td>\n",
       "      <td>طريق ماي اس سي</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-05-26 22:49:22</td>\n",
       "      <td>طريقة استخراج بدل فاقد للشريحه ماي اس سي</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-05-26 18:56:54</td>\n",
       "      <td>اريد التواصل موظف خدمة العملاء بتصل ب بدون اي فائدة رد مفهوم مرة يقول حمل تطبيق ماي اس سي خلال التطبيق ستنجز معاملاتك للاسف</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-05-26 12:55:41</td>\n",
       "      <td>السلام عليكم ، عندي رقم اس سي ماتوصل رسايل اتصال و انحذف تطبيق ماي اس سي مايظهر عندي</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-05-26 12:13:20</td>\n",
       "      <td>السلام عليكم فقدت شريحتي، وطلبته طريق ماي اس سي ورفض بحجة المكان مغطى ماالعمل</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  \\\n",
       "0  2020-05-27 04:41:01   \n",
       "1  2020-05-27 03:47:08   \n",
       "2  2020-05-27 03:45:29   \n",
       "3  2020-05-27 03:07:36   \n",
       "4  2020-05-27 01:45:20   \n",
       "5  2020-05-27 00:58:13   \n",
       "6  2020-05-26 22:49:22   \n",
       "7  2020-05-26 18:56:54   \n",
       "8  2020-05-26 12:55:41   \n",
       "9  2020-05-26 12:13:20   \n",
       "\n",
       "                                                                                                                                                                                                                    Tweet  \\\n",
       "0  ياخي ارسلت اكثر مره احد خدمني عندي شريحة مسبقة الدفع ابي افعل الجيل الخامس محدود يقبل معي ماي اس سي ليه                                                                                                                  \n",
       "1  ابي افعل انترنت الجيل الخامس لمده شهر محدود لشريحه كويك نت و رصيد يغطي جربت عشرين مره ماي اس سي و لاضبط امل حل مشكلتي و شكرا والا فعلوها عندكم و خلصنا تو ارسلت بالخاص قالي الموظف حاول مره اخرى سلامات حل اسبوع احاول   \n",
       "2  اخوي شحنت رصيد بفعل خدمه مو راضي طريق ماي اس سي                                                                                                                                                                          \n",
       "3  سلام علكيم عام وانتو بخير انا جيت تطبيق ماي اس سي مالقيت ايفون اكس اس ماكس متوفره                                                                                                                                        \n",
       "4  الله يحيك تم طلبها طريق ماي اس سي يوم ويجيني النت وتتفعل الخدمه                                                                                                                                                          \n",
       "5  طريق ماي اس سي                                                                                                                                                                                                           \n",
       "6  طريقة استخراج بدل فاقد للشريحه ماي اس سي                                                                                                                                                                                 \n",
       "7  اريد التواصل موظف خدمة العملاء بتصل ب بدون اي فائدة رد مفهوم مرة يقول حمل تطبيق ماي اس سي خلال التطبيق ستنجز معاملاتك للاسف                                                                                              \n",
       "8  السلام عليكم ، عندي رقم اس سي ماتوصل رسايل اتصال و انحذف تطبيق ماي اس سي مايظهر عندي                                                                                                                                     \n",
       "9  السلام عليكم فقدت شريحتي، وطلبته طريق ماي اس سي ورفض بحجة المكان مغطى ماالعمل                                                                                                                                            \n",
       "\n",
       "   label  \n",
       "0  0.0    \n",
       "1 -1.0    \n",
       "2 -1.0    \n",
       "3  0.0    \n",
       "4  1.0    \n",
       "5  0.0    \n",
       "6  0.0    \n",
       "7 -1.0    \n",
       "8 -1.0    \n",
       "9 -1.0    "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'label': 'Sentiment'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-05-27 04:41:01</td>\n",
       "      <td>ياخي ارسلت اكثر مره احد خدمني عندي شريحة مسبقة الدفع ابي افعل الجيل الخامس محدود يقبل معي ماي اس سي ليه</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-27 03:47:08</td>\n",
       "      <td>ابي افعل انترنت الجيل الخامس لمده شهر محدود لشريحه كويك نت و رصيد يغطي جربت عشرين مره ماي اس سي و لاضبط امل حل مشكلتي و شكرا والا فعلوها عندكم و خلصنا تو ارسلت بالخاص قالي الموظف حاول مره اخرى سلامات حل اسبوع احاول</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-05-27 03:45:29</td>\n",
       "      <td>اخوي شحنت رصيد بفعل خدمه مو راضي طريق ماي اس سي</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-05-27 03:07:36</td>\n",
       "      <td>سلام علكيم عام وانتو بخير انا جيت تطبيق ماي اس سي مالقيت ايفون اكس اس ماكس متوفره</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-27 01:45:20</td>\n",
       "      <td>الله يحيك تم طلبها طريق ماي اس سي يوم ويجيني النت وتتفعل الخدمه</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  \\\n",
       "0  2020-05-27 04:41:01   \n",
       "1  2020-05-27 03:47:08   \n",
       "2  2020-05-27 03:45:29   \n",
       "3  2020-05-27 03:07:36   \n",
       "4  2020-05-27 01:45:20   \n",
       "\n",
       "                                                                                                                                                                                                                    Tweet  \\\n",
       "0  ياخي ارسلت اكثر مره احد خدمني عندي شريحة مسبقة الدفع ابي افعل الجيل الخامس محدود يقبل معي ماي اس سي ليه                                                                                                                  \n",
       "1  ابي افعل انترنت الجيل الخامس لمده شهر محدود لشريحه كويك نت و رصيد يغطي جربت عشرين مره ماي اس سي و لاضبط امل حل مشكلتي و شكرا والا فعلوها عندكم و خلصنا تو ارسلت بالخاص قالي الموظف حاول مره اخرى سلامات حل اسبوع احاول   \n",
       "2  اخوي شحنت رصيد بفعل خدمه مو راضي طريق ماي اس سي                                                                                                                                                                          \n",
       "3  سلام علكيم عام وانتو بخير انا جيت تطبيق ماي اس سي مالقيت ايفون اكس اس ماكس متوفره                                                                                                                                        \n",
       "4  الله يحيك تم طلبها طريق ماي اس سي يوم ويجيني النت وتتفعل الخدمه                                                                                                                                                          \n",
       "\n",
       "   Sentiment  \n",
       "0  0.0        \n",
       "1 -1.0        \n",
       "2 -1.0        \n",
       "3  0.0        \n",
       "4  1.0        "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1831 entries, 0 to 1830\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   date       1831 non-null   object \n",
      " 1   Tweet      1831 non-null   object \n",
      " 2   Sentiment  1822 non-null   float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 43.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date         0\n",
       "Tweet        0\n",
       "Sentiment    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(df.isnull(), cbar=False, cmap='viridis', yticklabels=False)\n",
    "plt.title('Null Values in Dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(subset=['Sentiment', 'Tweet', 'date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date         1822\n",
       "Tweet        1822\n",
       "Sentiment    1822\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop duplicates and Non-arabic tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates(subset='Tweet', keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date         1719\n",
       "Tweet        1719\n",
       "Sentiment    1719\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_farsi_tweets(df):\n",
    "    # Create a new column to store language detection results\n",
    "    df['language'] = df['Tweet'].apply(detect)\n",
    "\n",
    "    # Filter the DataFrame to keep only rows with Arabic language\n",
    "    filtered_df = df[df['language'] == 'ar']\n",
    "\n",
    "    # Drop the 'language' column since it's no longer needed\n",
    "    filtered_df.drop('language', axis=1, inplace=True)\n",
    "\n",
    "    return filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = filter_farsi_tweets(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = filtered_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date         1555\n",
       "Tweet        1555\n",
       "Sentiment    1555\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PreProccessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "punctuations = '''`÷×؛<>_()*&^%][ـ،/:\"؟.,'{}~¦+|!”…“–ـ''' + string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\iraha\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# Arabic stop words with nltk\n",
    "nltk.download('stopwords')\n",
    "stop_words = stopwords.words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_diacritics = re.compile(\"\"\"\n",
    "                             ّ    | # Shadda\n",
    "                             َ    | # Fatha\n",
    "                             ً    | # Tanwin Fath\n",
    "                             ُ    | # Damma\n",
    "                             ٌ    | # Tanwin Damm\n",
    "                             ِ    | # Kasra\n",
    "                             ٍ    | # Tanwin Kasr\n",
    "                             ْ    | # Sukun\n",
    "                             ـ     # Tatwil/Kashida\n",
    "                         \"\"\", re.VERBOSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(text):\n",
    "    \n",
    "    #remove punctuations\n",
    "    translator = str.maketrans('', '', punctuations)\n",
    "    text = text.translate(translator)\n",
    "    \n",
    "    # remove Tashkeel\n",
    "    text = re.sub(arabic_diacritics, '', text)\n",
    "    \n",
    "    #remove longation\n",
    "    text = re.sub(\"[إأآا]\", \"ا\", text)\n",
    "    text = re.sub(\"ى\", \"ي\", text)\n",
    "    text = re.sub(\"ؤ\", \"ء\", text)\n",
    "    text = re.sub(\"ئ\", \"ء\", text)\n",
    "    text = re.sub(\"ة\", \"ه\", text)\n",
    "    text = re.sub(\"گ\", \"ك\", text)\n",
    "\n",
    "    #remove opreating char\n",
    "    text =re.sub(r'(.)\\1+', r'\\1',text)\n",
    "\n",
    "    #Replace #word with word\n",
    "    text = re.sub(r'#([^\\s]+)', r'\\1', text)\n",
    "\n",
    "    #text = ' '.join(word for word in text.split() if word not in stop_words)\n",
    "\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  date  \\\n",
      "0  2020-05-27 04:41:01   \n",
      "1  2020-05-27 03:47:08   \n",
      "3  2020-05-27 03:07:36   \n",
      "4  2020-05-27 01:45:20   \n",
      "6  2020-05-26 22:49:22   \n",
      "\n",
      "                                                                                                                                                                                                                    Tweet  \\\n",
      "0  ياخي ارسلت اكثر مره احد خدمني عندي شريحه مسبقه الدفع ابي افعل الجيل الخامس محدود يقبل معي ماي اس سي ليه                                                                                                                  \n",
      "1  ابي افعل انترنت الجيل الخامس لمده شهر محدود لشريحه كويك نت و رصيد يغطي جربت عشرين مره ماي اس سي و لاضبط امل حل مشكلتي و شكرا والا فعلوها عندكم و خلصنا تو ارسلت بالخاص قالي الموظف حاول مره اخري سلامات حل اسبوع احاول   \n",
      "3  سلام علكيم عام وانتو بخير انا جيت تطبيق ماي اس سي مالقيت ايفون اكس اس ماكس متوفره                                                                                                                                        \n",
      "4  اله يحيك تم طلبها طريق ماي اس سي يوم ويجيني النت وتفعل الخدمه                                                                                                                                                            \n",
      "6  طريقه استخراج بدل فاقد لشريحه ماي اس سي                                                                                                                                                                                  \n",
      "\n",
      "   Sentiment  \n",
      "0  0.0        \n",
      "1 -1.0        \n",
      "3  0.0        \n",
      "4  1.0        \n",
      "6  0.0        \n"
     ]
    }
   ],
   "source": [
    "df['Tweet'] = df['Tweet'].apply(preprocess)\n",
    "print(df.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "df[\"Tweet\"] = df[\"Tweet\"].apply(tokenizer.tokenize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     [ياخي, ارسلت, اكثر, مره, احد, خدمني, عندي, شريحه, مسبقه, الدفع, ابي, افعل, الجيل, الخامس, محدود, يقبل, معي, ماي, اس, سي, ليه]                                                                                                                                      \n",
       "1     [ابي, افعل, انترنت, الجيل, الخامس, لمده, شهر, محدود, لشريحه, كويك, نت, و, رصيد, يغطي, جربت, عشرين, مره, ماي, اس, سي, و, لاضبط, امل, حل, مشكلتي, و, شكرا, والا, فعلوها, عندكم, و, خلصنا, تو, ارسلت, بالخاص, قالي, الموظف, حاول, مره, اخري, سلامات, حل, اسبوع, احاول]\n",
       "3     [سلام, علكيم, عام, وانتو, بخير, انا, جيت, تطبيق, ماي, اس, سي, مالقيت, ايفون, اكس, اس, ماكس, متوفره]                                                                                                                                                                \n",
       "4     [اله, يحيك, تم, طلبها, طريق, ماي, اس, سي, يوم, ويجيني, النت, وتفعل, الخدمه]                                                                                                                                                                                        \n",
       "6     [طريقه, استخراج, بدل, فاقد, لشريحه, ماي, اس, سي]                                                                                                                                                                                                                   \n",
       "7     [اريد, التواصل, موظف, خدمه, العملاء, بتصل, ب, بدون, اي, فاءده, رد, مفهوم, مره, يقول, حمل, تطبيق, ماي, اس, سي, خلال, التطبيق, ستنجز, معاملاتك, لاسف]                                                                                                                \n",
       "8     [السلام, عليكم, عندي, رقم, اس, سي, ماتوصل, رسايل, اتصال, و, انحذف, تطبيق, ماي, اس, سي, مايظهر, عندي]                                                                                                                                                               \n",
       "9     [السلام, عليكم, فقدت, شريحتي, وطلبته, طريق, ماي, اس, سي, ورفض, بحجه, المكان, مغطي, مالعمل]                                                                                                                                                                         \n",
       "10    [السلام, عليكم, الطريقه, لطلب, جهاز, جوال, بنظام, الاقساط, طريق, ماي, اس, سي]                                                                                                                                                                                      \n",
       "11    [اقسم, باله, عمركم, سويتوا, شي, كويس, امس, تطبيقاتكم, معطله, وفيها, مشكله, عروض, سوا, الي, ماي, اس, سي, شلتوها]                                                                                                                                                    \n",
       "Name: Tweet, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Tweet\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords_list = stopwords.words('arabic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['إذ',\n",
       " 'إذا',\n",
       " 'إذما',\n",
       " 'إذن',\n",
       " 'أف',\n",
       " 'أقل',\n",
       " 'أكثر',\n",
       " 'ألا',\n",
       " 'إلا',\n",
       " 'التي',\n",
       " 'الذي',\n",
       " 'الذين',\n",
       " 'اللاتي',\n",
       " 'اللائي',\n",
       " 'اللتان',\n",
       " 'اللتيا',\n",
       " 'اللتين',\n",
       " 'اللذان',\n",
       " 'اللذين',\n",
       " 'اللواتي',\n",
       " 'إلى',\n",
       " 'إليك',\n",
       " 'إليكم',\n",
       " 'إليكما',\n",
       " 'إليكن',\n",
       " 'أم',\n",
       " 'أما',\n",
       " 'أما',\n",
       " 'إما',\n",
       " 'أن',\n",
       " 'إن',\n",
       " 'إنا',\n",
       " 'أنا',\n",
       " 'أنت',\n",
       " 'أنتم',\n",
       " 'أنتما',\n",
       " 'أنتن',\n",
       " 'إنما',\n",
       " 'إنه',\n",
       " 'أنى',\n",
       " 'أنى',\n",
       " 'آه',\n",
       " 'آها',\n",
       " 'أو',\n",
       " 'أولاء',\n",
       " 'أولئك',\n",
       " 'أوه',\n",
       " 'آي',\n",
       " 'أي',\n",
       " 'أيها',\n",
       " 'إي',\n",
       " 'أين',\n",
       " 'أين',\n",
       " 'أينما',\n",
       " 'إيه',\n",
       " 'بخ',\n",
       " 'بس',\n",
       " 'بعد',\n",
       " 'بعض',\n",
       " 'بك',\n",
       " 'بكم',\n",
       " 'بكم',\n",
       " 'بكما',\n",
       " 'بكن',\n",
       " 'بل',\n",
       " 'بلى',\n",
       " 'بما',\n",
       " 'بماذا',\n",
       " 'بمن',\n",
       " 'بنا',\n",
       " 'به',\n",
       " 'بها',\n",
       " 'بهم',\n",
       " 'بهما',\n",
       " 'بهن',\n",
       " 'بي',\n",
       " 'بين',\n",
       " 'بيد',\n",
       " 'تلك',\n",
       " 'تلكم',\n",
       " 'تلكما',\n",
       " 'ته',\n",
       " 'تي',\n",
       " 'تين',\n",
       " 'تينك',\n",
       " 'ثم',\n",
       " 'ثمة',\n",
       " 'حاشا',\n",
       " 'حبذا',\n",
       " 'حتى',\n",
       " 'حيث',\n",
       " 'حيثما',\n",
       " 'حين',\n",
       " 'خلا',\n",
       " 'دون',\n",
       " 'ذا',\n",
       " 'ذات',\n",
       " 'ذاك',\n",
       " 'ذان',\n",
       " 'ذانك',\n",
       " 'ذلك',\n",
       " 'ذلكم',\n",
       " 'ذلكما',\n",
       " 'ذلكن',\n",
       " 'ذه',\n",
       " 'ذو',\n",
       " 'ذوا',\n",
       " 'ذواتا',\n",
       " 'ذواتي',\n",
       " 'ذي',\n",
       " 'ذين',\n",
       " 'ذينك',\n",
       " 'ريث',\n",
       " 'سوف',\n",
       " 'سوى',\n",
       " 'شتان',\n",
       " 'عدا',\n",
       " 'عسى',\n",
       " 'عل',\n",
       " 'على',\n",
       " 'عليك',\n",
       " 'عليه',\n",
       " 'عما',\n",
       " 'عن',\n",
       " 'عند',\n",
       " 'غير',\n",
       " 'فإذا',\n",
       " 'فإن',\n",
       " 'فلا',\n",
       " 'فمن',\n",
       " 'في',\n",
       " 'فيم',\n",
       " 'فيما',\n",
       " 'فيه',\n",
       " 'فيها',\n",
       " 'قد',\n",
       " 'كأن',\n",
       " 'كأنما',\n",
       " 'كأي',\n",
       " 'كأين',\n",
       " 'كذا',\n",
       " 'كذلك',\n",
       " 'كل',\n",
       " 'كلا',\n",
       " 'كلاهما',\n",
       " 'كلتا',\n",
       " 'كلما',\n",
       " 'كليكما',\n",
       " 'كليهما',\n",
       " 'كم',\n",
       " 'كم',\n",
       " 'كما',\n",
       " 'كي',\n",
       " 'كيت',\n",
       " 'كيف',\n",
       " 'كيفما',\n",
       " 'لا',\n",
       " 'لاسيما',\n",
       " 'لدى',\n",
       " 'لست',\n",
       " 'لستم',\n",
       " 'لستما',\n",
       " 'لستن',\n",
       " 'لسن',\n",
       " 'لسنا',\n",
       " 'لعل',\n",
       " 'لك',\n",
       " 'لكم',\n",
       " 'لكما',\n",
       " 'لكن',\n",
       " 'لكنما',\n",
       " 'لكي',\n",
       " 'لكيلا',\n",
       " 'لم',\n",
       " 'لما',\n",
       " 'لن',\n",
       " 'لنا',\n",
       " 'له',\n",
       " 'لها',\n",
       " 'لهم',\n",
       " 'لهما',\n",
       " 'لهن',\n",
       " 'لو',\n",
       " 'لولا',\n",
       " 'لوما',\n",
       " 'لي',\n",
       " 'لئن',\n",
       " 'ليت',\n",
       " 'ليس',\n",
       " 'ليسا',\n",
       " 'ليست',\n",
       " 'ليستا',\n",
       " 'ليسوا',\n",
       " 'ما',\n",
       " 'ماذا',\n",
       " 'متى',\n",
       " 'مذ',\n",
       " 'مع',\n",
       " 'مما',\n",
       " 'ممن',\n",
       " 'من',\n",
       " 'منه',\n",
       " 'منها',\n",
       " 'منذ',\n",
       " 'مه',\n",
       " 'مهما',\n",
       " 'نحن',\n",
       " 'نحو',\n",
       " 'نعم',\n",
       " 'ها',\n",
       " 'هاتان',\n",
       " 'هاته',\n",
       " 'هاتي',\n",
       " 'هاتين',\n",
       " 'هاك',\n",
       " 'هاهنا',\n",
       " 'هذا',\n",
       " 'هذان',\n",
       " 'هذه',\n",
       " 'هذي',\n",
       " 'هذين',\n",
       " 'هكذا',\n",
       " 'هل',\n",
       " 'هلا',\n",
       " 'هم',\n",
       " 'هما',\n",
       " 'هن',\n",
       " 'هنا',\n",
       " 'هناك',\n",
       " 'هنالك',\n",
       " 'هو',\n",
       " 'هؤلاء',\n",
       " 'هي',\n",
       " 'هيا',\n",
       " 'هيت',\n",
       " 'هيهات',\n",
       " 'والذي',\n",
       " 'والذين',\n",
       " 'وإذ',\n",
       " 'وإذا',\n",
       " 'وإن',\n",
       " 'ولا',\n",
       " 'ولكن',\n",
       " 'ولو',\n",
       " 'وما',\n",
       " 'ومن',\n",
       " 'وهو',\n",
       " 'يا',\n",
       " 'أبٌ',\n",
       " 'أخٌ',\n",
       " 'حمٌ',\n",
       " 'فو',\n",
       " 'أنتِ',\n",
       " 'يناير',\n",
       " 'فبراير',\n",
       " 'مارس',\n",
       " 'أبريل',\n",
       " 'مايو',\n",
       " 'يونيو',\n",
       " 'يوليو',\n",
       " 'أغسطس',\n",
       " 'سبتمبر',\n",
       " 'أكتوبر',\n",
       " 'نوفمبر',\n",
       " 'ديسمبر',\n",
       " 'جانفي',\n",
       " 'فيفري',\n",
       " 'مارس',\n",
       " 'أفريل',\n",
       " 'ماي',\n",
       " 'جوان',\n",
       " 'جويلية',\n",
       " 'أوت',\n",
       " 'كانون',\n",
       " 'شباط',\n",
       " 'آذار',\n",
       " 'نيسان',\n",
       " 'أيار',\n",
       " 'حزيران',\n",
       " 'تموز',\n",
       " 'آب',\n",
       " 'أيلول',\n",
       " 'تشرين',\n",
       " 'دولار',\n",
       " 'دينار',\n",
       " 'ريال',\n",
       " 'درهم',\n",
       " 'ليرة',\n",
       " 'جنيه',\n",
       " 'قرش',\n",
       " 'مليم',\n",
       " 'فلس',\n",
       " 'هللة',\n",
       " 'سنتيم',\n",
       " 'يورو',\n",
       " 'ين',\n",
       " 'يوان',\n",
       " 'شيكل',\n",
       " 'واحد',\n",
       " 'اثنان',\n",
       " 'ثلاثة',\n",
       " 'أربعة',\n",
       " 'خمسة',\n",
       " 'ستة',\n",
       " 'سبعة',\n",
       " 'ثمانية',\n",
       " 'تسعة',\n",
       " 'عشرة',\n",
       " 'أحد',\n",
       " 'اثنا',\n",
       " 'اثني',\n",
       " 'إحدى',\n",
       " 'ثلاث',\n",
       " 'أربع',\n",
       " 'خمس',\n",
       " 'ست',\n",
       " 'سبع',\n",
       " 'ثماني',\n",
       " 'تسع',\n",
       " 'عشر',\n",
       " 'ثمان',\n",
       " 'سبت',\n",
       " 'أحد',\n",
       " 'اثنين',\n",
       " 'ثلاثاء',\n",
       " 'أربعاء',\n",
       " 'خميس',\n",
       " 'جمعة',\n",
       " 'أول',\n",
       " 'ثان',\n",
       " 'ثاني',\n",
       " 'ثالث',\n",
       " 'رابع',\n",
       " 'خامس',\n",
       " 'سادس',\n",
       " 'سابع',\n",
       " 'ثامن',\n",
       " 'تاسع',\n",
       " 'عاشر',\n",
       " 'حادي',\n",
       " 'أ',\n",
       " 'ب',\n",
       " 'ت',\n",
       " 'ث',\n",
       " 'ج',\n",
       " 'ح',\n",
       " 'خ',\n",
       " 'د',\n",
       " 'ذ',\n",
       " 'ر',\n",
       " 'ز',\n",
       " 'س',\n",
       " 'ش',\n",
       " 'ص',\n",
       " 'ض',\n",
       " 'ط',\n",
       " 'ظ',\n",
       " 'ع',\n",
       " 'غ',\n",
       " 'ف',\n",
       " 'ق',\n",
       " 'ك',\n",
       " 'ل',\n",
       " 'م',\n",
       " 'ن',\n",
       " 'ه',\n",
       " 'و',\n",
       " 'ي',\n",
       " 'ء',\n",
       " 'ى',\n",
       " 'آ',\n",
       " 'ؤ',\n",
       " 'ئ',\n",
       " 'أ',\n",
       " 'ة',\n",
       " 'ألف',\n",
       " 'باء',\n",
       " 'تاء',\n",
       " 'ثاء',\n",
       " 'جيم',\n",
       " 'حاء',\n",
       " 'خاء',\n",
       " 'دال',\n",
       " 'ذال',\n",
       " 'راء',\n",
       " 'زاي',\n",
       " 'سين',\n",
       " 'شين',\n",
       " 'صاد',\n",
       " 'ضاد',\n",
       " 'طاء',\n",
       " 'ظاء',\n",
       " 'عين',\n",
       " 'غين',\n",
       " 'فاء',\n",
       " 'قاف',\n",
       " 'كاف',\n",
       " 'لام',\n",
       " 'ميم',\n",
       " 'نون',\n",
       " 'هاء',\n",
       " 'واو',\n",
       " 'ياء',\n",
       " 'همزة',\n",
       " 'ي',\n",
       " 'نا',\n",
       " 'ك',\n",
       " 'كن',\n",
       " 'ه',\n",
       " 'إياه',\n",
       " 'إياها',\n",
       " 'إياهما',\n",
       " 'إياهم',\n",
       " 'إياهن',\n",
       " 'إياك',\n",
       " 'إياكما',\n",
       " 'إياكم',\n",
       " 'إياك',\n",
       " 'إياكن',\n",
       " 'إياي',\n",
       " 'إيانا',\n",
       " 'أولالك',\n",
       " 'تانِ',\n",
       " 'تانِك',\n",
       " 'تِه',\n",
       " 'تِي',\n",
       " 'تَيْنِ',\n",
       " 'ثمّ',\n",
       " 'ثمّة',\n",
       " 'ذانِ',\n",
       " 'ذِه',\n",
       " 'ذِي',\n",
       " 'ذَيْنِ',\n",
       " 'هَؤلاء',\n",
       " 'هَاتانِ',\n",
       " 'هَاتِه',\n",
       " 'هَاتِي',\n",
       " 'هَاتَيْنِ',\n",
       " 'هَذا',\n",
       " 'هَذانِ',\n",
       " 'هَذِه',\n",
       " 'هَذِي',\n",
       " 'هَذَيْنِ',\n",
       " 'الألى',\n",
       " 'الألاء',\n",
       " 'أل',\n",
       " 'أنّى',\n",
       " 'أيّ',\n",
       " 'ّأيّان',\n",
       " 'أنّى',\n",
       " 'أيّ',\n",
       " 'ّأيّان',\n",
       " 'ذيت',\n",
       " 'كأيّ',\n",
       " 'كأيّن',\n",
       " 'بضع',\n",
       " 'فلان',\n",
       " 'وا',\n",
       " 'آمينَ',\n",
       " 'آهِ',\n",
       " 'آهٍ',\n",
       " 'آهاً',\n",
       " 'أُفٍّ',\n",
       " 'أُفٍّ',\n",
       " 'أفٍّ',\n",
       " 'أمامك',\n",
       " 'أمامكَ',\n",
       " 'أوّهْ',\n",
       " 'إلَيْكَ',\n",
       " 'إلَيْكَ',\n",
       " 'إليكَ',\n",
       " 'إليكنّ',\n",
       " 'إيهٍ',\n",
       " 'بخٍ',\n",
       " 'بسّ',\n",
       " 'بَسْ',\n",
       " 'بطآن',\n",
       " 'بَلْهَ',\n",
       " 'حاي',\n",
       " 'حَذارِ',\n",
       " 'حيَّ',\n",
       " 'حيَّ',\n",
       " 'دونك',\n",
       " 'رويدك',\n",
       " 'سرعان',\n",
       " 'شتانَ',\n",
       " 'شَتَّانَ',\n",
       " 'صهْ',\n",
       " 'صهٍ',\n",
       " 'طاق',\n",
       " 'طَق',\n",
       " 'عَدَسْ',\n",
       " 'كِخ',\n",
       " 'مكانَك',\n",
       " 'مكانَك',\n",
       " 'مكانَك',\n",
       " 'مكانكم',\n",
       " 'مكانكما',\n",
       " 'مكانكنّ',\n",
       " 'نَخْ',\n",
       " 'هاكَ',\n",
       " 'هَجْ',\n",
       " 'هلم',\n",
       " 'هيّا',\n",
       " 'هَيْهات',\n",
       " 'وا',\n",
       " 'واهاً',\n",
       " 'وراءَك',\n",
       " 'وُشْكَانَ',\n",
       " 'وَيْ',\n",
       " 'يفعلان',\n",
       " 'تفعلان',\n",
       " 'يفعلون',\n",
       " 'تفعلون',\n",
       " 'تفعلين',\n",
       " 'اتخذ',\n",
       " 'ألفى',\n",
       " 'تخذ',\n",
       " 'ترك',\n",
       " 'تعلَّم',\n",
       " 'جعل',\n",
       " 'حجا',\n",
       " 'حبيب',\n",
       " 'خال',\n",
       " 'حسب',\n",
       " 'خال',\n",
       " 'درى',\n",
       " 'رأى',\n",
       " 'زعم',\n",
       " 'صبر',\n",
       " 'ظنَّ',\n",
       " 'عدَّ',\n",
       " 'علم',\n",
       " 'غادر',\n",
       " 'ذهب',\n",
       " 'وجد',\n",
       " 'ورد',\n",
       " 'وهب',\n",
       " 'أسكن',\n",
       " 'أطعم',\n",
       " 'أعطى',\n",
       " 'رزق',\n",
       " 'زود',\n",
       " 'سقى',\n",
       " 'كسا',\n",
       " 'أخبر',\n",
       " 'أرى',\n",
       " 'أعلم',\n",
       " 'أنبأ',\n",
       " 'حدَث',\n",
       " 'خبَّر',\n",
       " 'نبَّا',\n",
       " 'أفعل به',\n",
       " 'ما أفعله',\n",
       " 'بئس',\n",
       " 'ساء',\n",
       " 'طالما',\n",
       " 'قلما',\n",
       " 'لات',\n",
       " 'لكنَّ',\n",
       " 'ءَ',\n",
       " 'أجل',\n",
       " 'إذاً',\n",
       " 'أمّا',\n",
       " 'إمّا',\n",
       " 'إنَّ',\n",
       " 'أنًّ',\n",
       " 'أى',\n",
       " 'إى',\n",
       " 'أيا',\n",
       " 'ب',\n",
       " 'ثمَّ',\n",
       " 'جلل',\n",
       " 'جير',\n",
       " 'رُبَّ',\n",
       " 'س',\n",
       " 'علًّ',\n",
       " 'ف',\n",
       " 'كأنّ',\n",
       " 'كلَّا',\n",
       " 'كى',\n",
       " 'ل',\n",
       " 'لات',\n",
       " 'لعلَّ',\n",
       " 'لكنَّ',\n",
       " 'لكنَّ',\n",
       " 'م',\n",
       " 'نَّ',\n",
       " 'هلّا',\n",
       " 'وا',\n",
       " 'أل',\n",
       " 'إلّا',\n",
       " 'ت',\n",
       " 'ك',\n",
       " 'لمّا',\n",
       " 'ن',\n",
       " 'ه',\n",
       " 'و',\n",
       " 'ا',\n",
       " 'ي',\n",
       " 'تجاه',\n",
       " 'تلقاء',\n",
       " 'جميع',\n",
       " 'حسب',\n",
       " 'سبحان',\n",
       " 'شبه',\n",
       " 'لعمر',\n",
       " 'مثل',\n",
       " 'معاذ',\n",
       " 'أبو',\n",
       " 'أخو',\n",
       " 'حمو',\n",
       " 'فو',\n",
       " 'مئة',\n",
       " 'مئتان',\n",
       " 'ثلاثمئة',\n",
       " 'أربعمئة',\n",
       " 'خمسمئة',\n",
       " 'ستمئة',\n",
       " 'سبعمئة',\n",
       " 'ثمنمئة',\n",
       " 'تسعمئة',\n",
       " 'مائة',\n",
       " 'ثلاثمائة',\n",
       " 'أربعمائة',\n",
       " 'خمسمائة',\n",
       " 'ستمائة',\n",
       " 'سبعمائة',\n",
       " 'ثمانمئة',\n",
       " 'تسعمائة',\n",
       " 'عشرون',\n",
       " 'ثلاثون',\n",
       " 'اربعون',\n",
       " 'خمسون',\n",
       " 'ستون',\n",
       " 'سبعون',\n",
       " 'ثمانون',\n",
       " 'تسعون',\n",
       " 'عشرين',\n",
       " 'ثلاثين',\n",
       " 'اربعين',\n",
       " 'خمسين',\n",
       " 'ستين',\n",
       " 'سبعين',\n",
       " 'ثمانين',\n",
       " 'تسعين',\n",
       " 'بضع',\n",
       " 'نيف',\n",
       " 'أجمع',\n",
       " 'جميع',\n",
       " 'عامة',\n",
       " 'عين',\n",
       " 'نفس',\n",
       " 'لا سيما',\n",
       " 'أصلا',\n",
       " 'أهلا',\n",
       " 'أيضا',\n",
       " 'بؤسا',\n",
       " 'بعدا',\n",
       " 'بغتة',\n",
       " 'تعسا',\n",
       " 'حقا',\n",
       " 'حمدا',\n",
       " 'خلافا',\n",
       " 'خاصة',\n",
       " 'دواليك',\n",
       " 'سحقا',\n",
       " 'سرا',\n",
       " 'سمعا',\n",
       " 'صبرا',\n",
       " 'صدقا',\n",
       " 'صراحة',\n",
       " 'طرا',\n",
       " 'عجبا',\n",
       " 'عيانا',\n",
       " 'غالبا',\n",
       " 'فرادى',\n",
       " 'فضلا',\n",
       " 'قاطبة',\n",
       " 'كثيرا',\n",
       " 'لبيك',\n",
       " 'معاذ',\n",
       " 'أبدا',\n",
       " 'إزاء',\n",
       " 'أصلا',\n",
       " 'الآن',\n",
       " 'أمد',\n",
       " 'أمس',\n",
       " 'آنفا',\n",
       " 'آناء',\n",
       " 'أنّى',\n",
       " 'أول',\n",
       " 'أيّان',\n",
       " 'تارة',\n",
       " 'ثمّ',\n",
       " 'ثمّة',\n",
       " 'حقا',\n",
       " 'صباح',\n",
       " 'مساء',\n",
       " 'ضحوة',\n",
       " 'عوض',\n",
       " 'غدا',\n",
       " 'غداة',\n",
       " 'قطّ',\n",
       " 'كلّما',\n",
       " 'لدن',\n",
       " 'لمّا',\n",
       " 'مرّة',\n",
       " 'قبل',\n",
       " 'خلف',\n",
       " 'أمام',\n",
       " 'فوق',\n",
       " 'تحت',\n",
       " 'يمين',\n",
       " 'شمال',\n",
       " 'ارتدّ',\n",
       " 'استحال',\n",
       " 'أصبح',\n",
       " 'أضحى',\n",
       " 'آض',\n",
       " 'أمسى',\n",
       " 'انقلب',\n",
       " 'بات',\n",
       " 'تبدّل',\n",
       " 'تحوّل',\n",
       " 'حار',\n",
       " 'رجع',\n",
       " 'راح',\n",
       " 'صار',\n",
       " 'ظلّ',\n",
       " 'عاد',\n",
       " 'غدا',\n",
       " 'كان',\n",
       " 'ما انفك',\n",
       " 'ما برح',\n",
       " 'مادام',\n",
       " 'مازال',\n",
       " 'مافتئ',\n",
       " 'ابتدأ',\n",
       " 'أخذ',\n",
       " 'اخلولق',\n",
       " 'أقبل',\n",
       " 'انبرى',\n",
       " 'أنشأ',\n",
       " 'أوشك',\n",
       " 'جعل',\n",
       " 'حرى',\n",
       " 'شرع',\n",
       " 'طفق',\n",
       " 'علق',\n",
       " 'قام',\n",
       " 'كرب',\n",
       " 'كاد',\n",
       " 'هبّ']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_To_Str = ' '.join([str(elem) for elem in stopwords_list]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'إذ إذا إذما إذن أف أقل أكثر ألا إلا التي الذي الذين اللاتي اللائي اللتان اللتيا اللتين اللذان اللذين اللواتي إلى إليك إليكم إليكما إليكن أم أما أما إما أن إن إنا أنا أنت أنتم أنتما أنتن إنما إنه أنى أنى آه آها أو أولاء أولئك أوه آي أي أيها إي أين أين أينما إيه بخ بس بعد بعض بك بكم بكم بكما بكن بل بلى بما بماذا بمن بنا به بها بهم بهما بهن بي بين بيد تلك تلكم تلكما ته تي تين تينك ثم ثمة حاشا حبذا حتى حيث حيثما حين خلا دون ذا ذات ذاك ذان ذانك ذلك ذلكم ذلكما ذلكن ذه ذو ذوا ذواتا ذواتي ذي ذين ذينك ريث سوف سوى شتان عدا عسى عل على عليك عليه عما عن عند غير فإذا فإن فلا فمن في فيم فيما فيه فيها قد كأن كأنما كأي كأين كذا كذلك كل كلا كلاهما كلتا كلما كليكما كليهما كم كم كما كي كيت كيف كيفما لا لاسيما لدى لست لستم لستما لستن لسن لسنا لعل لك لكم لكما لكن لكنما لكي لكيلا لم لما لن لنا له لها لهم لهما لهن لو لولا لوما لي لئن ليت ليس ليسا ليست ليستا ليسوا ما ماذا متى مذ مع مما ممن من منه منها منذ مه مهما نحن نحو نعم ها هاتان هاته هاتي هاتين هاك هاهنا هذا هذان هذه هذي هذين هكذا هل هلا هم هما هن هنا هناك هنالك هو هؤلاء هي هيا هيت هيهات والذي والذين وإذ وإذا وإن ولا ولكن ولو وما ومن وهو يا أبٌ أخٌ حمٌ فو أنتِ يناير فبراير مارس أبريل مايو يونيو يوليو أغسطس سبتمبر أكتوبر نوفمبر ديسمبر جانفي فيفري مارس أفريل ماي جوان جويلية أوت كانون شباط آذار نيسان أيار حزيران تموز آب أيلول تشرين دولار دينار ريال درهم ليرة جنيه قرش مليم فلس هللة سنتيم يورو ين يوان شيكل واحد اثنان ثلاثة أربعة خمسة ستة سبعة ثمانية تسعة عشرة أحد اثنا اثني إحدى ثلاث أربع خمس ست سبع ثماني تسع عشر ثمان سبت أحد اثنين ثلاثاء أربعاء خميس جمعة أول ثان ثاني ثالث رابع خامس سادس سابع ثامن تاسع عاشر حادي أ ب ت ث ج ح خ د ذ ر ز س ش ص ض ط ظ ع غ ف ق ك ل م ن ه و ي ء ى آ ؤ ئ أ ة ألف باء تاء ثاء جيم حاء خاء دال ذال راء زاي سين شين صاد ضاد طاء ظاء عين غين فاء قاف كاف لام ميم نون هاء واو ياء همزة ي نا ك كن ه إياه إياها إياهما إياهم إياهن إياك إياكما إياكم إياك إياكن إياي إيانا أولالك تانِ تانِك تِه تِي تَيْنِ ثمّ ثمّة ذانِ ذِه ذِي ذَيْنِ هَؤلاء هَاتانِ هَاتِه هَاتِي هَاتَيْنِ هَذا هَذانِ هَذِه هَذِي هَذَيْنِ الألى الألاء أل أنّى أيّ ّأيّان أنّى أيّ ّأيّان ذيت كأيّ كأيّن بضع فلان وا آمينَ آهِ آهٍ آهاً أُفٍّ أُفٍّ أفٍّ أمامك أمامكَ أوّهْ إلَيْكَ إلَيْكَ إليكَ إليكنّ إيهٍ بخٍ بسّ بَسْ بطآن بَلْهَ حاي حَذارِ حيَّ حيَّ دونك رويدك سرعان شتانَ شَتَّانَ صهْ صهٍ طاق طَق عَدَسْ كِخ مكانَك مكانَك مكانَك مكانكم مكانكما مكانكنّ نَخْ هاكَ هَجْ هلم هيّا هَيْهات وا واهاً وراءَك وُشْكَانَ وَيْ يفعلان تفعلان يفعلون تفعلون تفعلين اتخذ ألفى تخذ ترك تعلَّم جعل حجا حبيب خال حسب خال درى رأى زعم صبر ظنَّ عدَّ علم غادر ذهب وجد ورد وهب أسكن أطعم أعطى رزق زود سقى كسا أخبر أرى أعلم أنبأ حدَث خبَّر نبَّا أفعل به ما أفعله بئس ساء طالما قلما لات لكنَّ ءَ أجل إذاً أمّا إمّا إنَّ أنًّ أى إى أيا ب ثمَّ جلل جير رُبَّ س علًّ ف كأنّ كلَّا كى ل لات لعلَّ لكنَّ لكنَّ م نَّ هلّا وا أل إلّا ت ك لمّا ن ه و ا ي تجاه تلقاء جميع حسب سبحان شبه لعمر مثل معاذ أبو أخو حمو فو مئة مئتان ثلاثمئة أربعمئة خمسمئة ستمئة سبعمئة ثمنمئة تسعمئة مائة ثلاثمائة أربعمائة خمسمائة ستمائة سبعمائة ثمانمئة تسعمائة عشرون ثلاثون اربعون خمسون ستون سبعون ثمانون تسعون عشرين ثلاثين اربعين خمسين ستين سبعين ثمانين تسعين بضع نيف أجمع جميع عامة عين نفس لا سيما أصلا أهلا أيضا بؤسا بعدا بغتة تعسا حقا حمدا خلافا خاصة دواليك سحقا سرا سمعا صبرا صدقا صراحة طرا عجبا عيانا غالبا فرادى فضلا قاطبة كثيرا لبيك معاذ أبدا إزاء أصلا الآن أمد أمس آنفا آناء أنّى أول أيّان تارة ثمّ ثمّة حقا صباح مساء ضحوة عوض غدا غداة قطّ كلّما لدن لمّا مرّة قبل خلف أمام فوق تحت يمين شمال ارتدّ استحال أصبح أضحى آض أمسى انقلب بات تبدّل تحوّل حار رجع راح صار ظلّ عاد غدا كان ما انفك ما برح مادام مازال مافتئ ابتدأ أخذ اخلولق أقبل انبرى أنشأ أوشك جعل حرى شرع طفق علق قام كرب كاد هبّ'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_To_Str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Sentiment']\n",
    "counter=y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QW8cVdoYNGyZJOnToUJ80AwAAEGrHFXa+6W9/+5u2bNmixsbGbuHngQceOOHGAAAAQqFXYefxxx/Xf/zHfyg5OVkej0cOh8M+5nA4CDsAACBi9Oqj54888oh+9rOfye/3a/v27dq2bZu9vfXWW71qpKysTA6HQ4WFhfaYZVkqKSmR1+tVXFycsrOztWvXrqDzAoGAZs2apeTkZPXv318TJ07Uvn37etUDAAAwT6/CTlNTk2666aaQNVFbW6sVK1bowgsvDBpfuHChFi1apGXLlqm2tlYej0fjxo1Ta2urXVNYWKiKigqVl5dr69atamtr04QJE9TV1RWy/gAAwKmrV2HnpptuUmVlZUgaaGtr06233qrHH39cAwcOtMcty9KSJUs0f/58TZo0SWlpaVqzZo2++OILrV+/XpLU3NyslStX6rHHHlNOTo5GjRqldevWaceOHdq8eXNI+gMAAKe2Xj2zc+655+r+++/X66+/rvT0dMXExAQdv+eee475WnfddZeuvfZa5eTk6JFHHrHH6+vr5ff7lZuba485nU6NGTNGNTU1KigoUF1dnTo7O4NqvF6v0tLSVFNTo7y8vKO+ZiAQUCAQsPdbWlqOuV8AAHBq6VXYWbFihQYMGKDq6mpVV1cHHXM4HMccdsrLy/XWW2+ptra227HDv53Z7XYHjbvdbn388cd2TWxsbNCK0OGab/525yOVlZXpoYceOqYeAQDAqa1XYae+vv6EX3jv3r269957VVlZqX79+vVY981Peklfv7115NiRvqumuLhYs2fPtvdbWlrk8/mOsXMAAHAq6dUzO6FQV1enxsZGZWRkKDo6WtHR0aqurtYvf/lLRUdH2ys6R67QNDY22sc8Ho86OjrU1NTUY83ROJ1OJSYmBm0AAMBMvVrZueOOO771+BNPPPGd1xg7dqx27NgRNPbjH/9Y559/vubNm6ezzz5bHo9HVVVVGjVqlCSpo6ND1dXVWrBggSQpIyNDMTExqqqqUn5+viSpoaFBO3fu1MKFC3szNQAAYJhehZ0jV1I6Ozu1c+dOHThw4KhfEHo0CQkJSktLCxrr37+/kpKS7PHCwkKVlpYqNTVVqampKi0tVXx8vCZPnixJcrlcmjZtmubMmaOkpCQNGjRIRUVFSk9PV05OTm+mBgAADNOrsFNRUdFt7NChQ5o5c6bOPvvsE27qsLlz56q9vV0zZ85UU1OTMjMzVVlZqYSEBLtm8eLFio6OVn5+vtrb2zV27FitXr2aLyQFAACSJIdlWVaoLrZ7925lZ2eroaEhVJc8KVpaWuRyudTc3Nzr53cy7nsyxF3hVFf38ynhbkF7Hk4PdwuIIGc9sOO7i4BTyLH+/A7pA8offvihvvrqq1BeEgAA4IT06m2sb35sW/r6o94NDQ363//9X02dOjUkjQEAAIRCr8LOtm3bgva/973vafDgwXrssce+85NaAAAAJ1Ovws5LL70U6j4AAAD6RK/CzmGffvqpdu/eLYfDoREjRmjw4MGh6gsAACAkevWA8sGDB3XHHXdo6NChuvLKK3XFFVfI6/Vq2rRp+uKLL0LdIwAAQK/1KuzMnj1b1dXV+vOf/6wDBw7owIED+tOf/qTq6mrNmTMn1D0CAAD0Wq/exnr66af11FNPKTs72x675pprFBcXp/z8fC1fvjxU/QEAAJyQXq3sfPHFF0f9os0hQ4bwNhYAAIgovQo7o0eP1oMPPqgvv/zSHmtvb9dDDz2k0aNHh6w5AACAE9Wrt7GWLFmi8ePH68wzz9RFF10kh8Oh7du3y+l0qrKyMtQ9AgAA9Fqvwk56erref/99rVu3Tn/9619lWZZuueUW3XrrrYqLiwt1jwAAAL3Wq7BTVlYmt9ut6dOnB40/8cQT+vTTTzVv3ryQNAcAAHCievXMzm9/+1udf/753cZ/8IMf6De/+c0JNwUAABAqvQo7fr9fQ4cO7TY+ePBgNTQ0nHBTAAAAodKrsOPz+fTqq692G3/11Vfl9XpPuCkAAIBQ6dUzO3feeacKCwvV2dmpq666SpL04osvau7cufwGZQAAEFF6FXbmzp2rzz//XDNnzlRHR4ckqV+/fpo3b56Ki4tD2iAAAMCJ6FXYcTgcWrBgge6//3699957iouLU2pqqpxOZ6j7AwAAOCG9CjuHDRgwQJdddlmoegEAAAi5Xj2gDAAAcKog7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGC2vYWb58uS688EIlJiYqMTFRo0eP1vPPP28ftyxLJSUl8nq9iouLU3Z2tnbt2hV0jUAgoFmzZik5OVn9+/fXxIkTtW/fvpM9FQAAEKHCGnbOPPNMPfroo3rzzTf15ptv6qqrrtL1119vB5qFCxdq0aJFWrZsmWpra+XxeDRu3Di1trba1ygsLFRFRYXKy8u1detWtbW1acKECerq6grXtAAAQAQJa9i57rrrdM0112jEiBEaMWKEfvazn2nAgAF6/fXXZVmWlixZovnz52vSpElKS0vTmjVr9MUXX2j9+vWSpObmZq1cuVKPPfaYcnJyNGrUKK1bt047duzQ5s2be3zdQCCglpaWoA0AAJgpYp7Z6erqUnl5uQ4ePKjRo0ervr5efr9fubm5do3T6dSYMWNUU1MjSaqrq1NnZ2dQjdfrVVpaml1zNGVlZXK5XPbm8/n6bmIAACCswh52duzYoQEDBsjpdGrGjBmqqKjQyJEj5ff7JUlutzuo3u1228f8fr9iY2M1cODAHmuOpri4WM3Nzfa2d+/eEM8KAABEiuhwN3Deeedp+/btOnDggJ5++mlNnTpV1dXV9nGHwxFUb1lWt7EjfVeN0+mU0+k8scYBAMApIewrO7GxsTr33HN16aWXqqysTBdddJF+8YtfyOPxSFK3FZrGxkZ7tcfj8aijo0NNTU091gAAgNNb2MPOkSzLUiAQUEpKijwej6qqquxjHR0dqq6uVlZWliQpIyNDMTExQTUNDQ3auXOnXQMAAE5vYX0b66c//anGjx8vn8+n1tZWlZeXa8uWLdq0aZMcDocKCwtVWlqq1NRUpaamqrS0VPHx8Zo8ebIkyeVyadq0aZozZ46SkpI0aNAgFRUVKT09XTk5OeGcGgAAiBBhDTv//Oc/ddttt6mhoUEul0sXXnihNm3apHHjxkmS5s6dq/b2ds2cOVNNTU3KzMxUZWWlEhIS7GssXrxY0dHRys/PV3t7u8aOHavVq1crKioqXNMCAAARxGFZlhXuJsKtpaVFLpdLzc3NSkxM7NU1Mu57MsRd4VRX9/Mp4W5Bex5OD3cLiCBnPbAj3C0AIXWsP78j7pkdAACAUCLsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBoYQ07ZWVluuyyy5SQkKAhQ4bohhtu0O7du4NqLMtSSUmJvF6v4uLilJ2drV27dgXVBAIBzZo1S8nJyerfv78mTpyoffv2ncypAACACBXWsFNdXa277rpLr7/+uqqqqvTVV18pNzdXBw8etGsWLlyoRYsWadmyZaqtrZXH49G4cePU2tpq1xQWFqqiokLl5eXaunWr2traNGHCBHV1dYVjWgAAIIJEh/PFN23aFLS/atUqDRkyRHV1dbryyitlWZaWLFmi+fPna9KkSZKkNWvWyO12a/369SooKFBzc7NWrlyptWvXKicnR5K0bt06+Xw+bd68WXl5eSd9XgAAIHKENewcqbm5WZI0aNAgSVJ9fb38fr9yc3PtGqfTqTFjxqimpkYFBQWqq6tTZ2dnUI3X61VaWppqamqOGnYCgYACgYC939LS0ldTAgB8w+VLLw93C4ggr8569aS8TsQ8oGxZlmbPnq1//dd/VVpamiTJ7/dLktxud1Ct2+22j/n9fsXGxmrgwIE91hyprKxMLpfL3nw+X6inAwAAIkTEhJ27775b77zzjn7/+993O+ZwOIL2LcvqNnakb6spLi5Wc3Ozve3du7f3jQMAgIgWEWFn1qxZevbZZ/XSSy/pzDPPtMc9Ho8kdVuhaWxstFd7PB6POjo61NTU1GPNkZxOpxITE4M2AABgprCGHcuydPfdd+uZZ57R//3f/yklJSXoeEpKijwej6qqquyxjo4OVVdXKysrS5KUkZGhmJiYoJqGhgbt3LnTrgEAAKevsD6gfNddd2n9+vX605/+pISEBHsFx+VyKS4uTg6HQ4WFhSotLVVqaqpSU1NVWlqq+Ph4TZ482a6dNm2a5syZo6SkJA0aNEhFRUVKT0+3P50FAABOX2ENO8uXL5ckZWdnB42vWrVKt99+uyRp7ty5am9v18yZM9XU1KTMzExVVlYqISHBrl+8eLGio6OVn5+v9vZ2jR07VqtXr1ZUVNTJmgoAAIhQYQ07lmV9Z43D4VBJSYlKSkp6rOnXr5+WLl2qpUuXhrA7AABggoh4QBkAAKCvEHYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMFpYw87LL7+s6667Tl6vVw6HQxs2bAg6blmWSkpK5PV6FRcXp+zsbO3atSuoJhAIaNasWUpOTlb//v01ceJE7du37yTOAgAARLKwhp2DBw/qoosu0rJly456fOHChVq0aJGWLVum2tpaeTwejRs3Tq2trXZNYWGhKioqVF5erq1bt6qtrU0TJkxQV1fXyZoGAACIYNHhfPHx48dr/PjxRz1mWZaWLFmi+fPna9KkSZKkNWvWyO12a/369SooKFBzc7NWrlyptWvXKicnR5K0bt06+Xw+bd68WXl5eUe9diAQUCAQsPdbWlpCPDMAABApIvaZnfr6evn9fuXm5tpjTqdTY8aMUU1NjSSprq5OnZ2dQTVer1dpaWl2zdGUlZXJ5XLZm8/n67uJAACAsIrYsOP3+yVJbrc7aNztdtvH/H6/YmNjNXDgwB5rjqa4uFjNzc32tnfv3hB3DwAAIkVY38Y6Fg6HI2jfsqxuY0f6rhqn0ymn0xmS/gAAQGSL2JUdj8cjSd1WaBobG+3VHo/Ho46ODjU1NfVYAwAATm8RG3ZSUlLk8XhUVVVlj3V0dKi6ulpZWVmSpIyMDMXExATVNDQ0aOfOnXYNAAA4vYX1bay2tjZ98MEH9n59fb22b9+uQYMG6ayzzlJhYaFKS0uVmpqq1NRUlZaWKj4+XpMnT5YkuVwuTZs2TXPmzFFSUpIGDRqkoqIipaen25/OAgAAp7ewhp0333xTP/rRj+z92bNnS5KmTp2q1atXa+7cuWpvb9fMmTPV1NSkzMxMVVZWKiEhwT5n8eLFio6OVn5+vtrb2zV27FitXr1aUVFRJ30+AAAg8oQ17GRnZ8uyrB6POxwOlZSUqKSkpMeafv36aenSpVq6dGkfdAgAAE51EfvMDgAAQCgQdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNGPCzq9//WulpKSoX79+ysjI0CuvvBLulgAAQAQwIuz84Q9/UGFhoebPn69t27bpiiuu0Pjx47Vnz55wtwYAAMLMiLCzaNEiTZs2TXfeeacuuOACLVmyRD6fT8uXLw93awAAIMyiw93Aiero6FBdXZ3+8z//M2g8NzdXNTU1Rz0nEAgoEAjY+83NzZKklpaWXvfRFWjv9bkw04ncT6HS+mVXuFtABImEe/Kr9q/C3QIiyInek4fPtyzrW+tO+bCzf/9+dXV1ye12B4273W75/f6jnlNWVqaHHnqo27jP5+uTHnF6ci2dEe4WgGBlrnB3AARxzQvNPdna2iqXq+drnfJh5zCHwxG0b1lWt7HDiouLNXv2bHv/0KFD+vzzz5WUlNTjOTg2LS0t8vl82rt3rxITE8PdDsA9iYjDPRk6lmWptbVVXq/3W+tO+bCTnJysqKiobqs4jY2N3VZ7DnM6nXI6nUFjZ5xxRl+1eFpKTEzkP2JEFO5JRBruydD4thWdw075B5RjY2OVkZGhqqqqoPGqqiplZWWFqSsAABApTvmVHUmaPXu2brvtNl166aUaPXq0VqxYoT179mjGDJ6ZAADgdGdE2Ln55pv12Wef6eGHH1ZDQ4PS0tK0ceNGDRs2LNytnXacTqcefPDBbm8TAuHCPYlIwz158jms7/q8FgAAwCnslH9mBwAA4NsQdgAAgNEIOwAAwGiEHQAAYDTCDo7bM888o7y8PCUnJ8vhcGj79u3HdN7TTz+tkSNHyul0auTIkaqoqOjbRnHa+PWvf62UlBT169dPGRkZeuWVV761vrq6WhkZGerXr5/OPvts/eY3vzlJneJ08PLLL+u6666T1+uVw+HQhg0bvvMc7sm+RdjBcTt48KAuv/xyPfroo8d8zmuvvaabb75Zt912m95++23ddtttys/P1xtvvNGHneJ08Ic//EGFhYWaP3++tm3bpiuuuELjx4/Xnj17jlpfX1+va665RldccYW2bdumn/70p7rnnnv09NNPn+TOYaqDBw/qoosu0rJly46pnnuy7/HRc/TaRx99pJSUFG3btk0XX3zxt9befPPNamlp0fPPP2+PXX311Ro4cKB+//vf93GnMFlmZqYuueQSLV++3B674IILdMMNN6isrKxb/bx58/Tss8/qvffes8dmzJiht99+W6+99tpJ6RmnD4fDoYqKCt1www091nBP9j1WdnBSvPbaa8rNzQ0ay8vLU01NTZg6ggk6OjpUV1fX7d7Kzc3t8d7q6V5888031dnZ2We9Aj3hnux7hB2cFH6/v9sXs7rd7m5f4Aocj/3796urq+u47q2e7sWvvvpK+/fv77NegZ5wT/Y9wg6+1f/8z/9owIAB9vZdD35+G4fDEbRvWVa3MaA3jvfeOlr90caBk4V7sm8Z8d1Y6DsTJ05UZmamvf/973+/V9fxeDzd/qXd2NjY7V8zwPFITk5WVFTUcd1bPd2L0dHRSkpK6rNegZ5wT/Y9VnbwrRISEnTuuefaW1xcXK+uM3r0aFVVVQWNVVZWKisrKxRt4jQVGxurjIyMbvdWVVVVj/dWT/fipZdeqpiYmD7rFegJ92TfI+zguH3++efavn273n33XUnS7t27tX379qB/mUyZMkXFxcX2/r333qvKykotWLBAf/3rX7VgwQJt3rxZhYWFJ7t9GGb27Nn63e9+pyeeeELvvfeefvKTn2jPnj2aMWOGJKm4uFhTpkyx62fMmKGPP/5Ys2fP1nvvvacnnnhCK1euVFFRUbimAMO0tbVp+/bt9u8gq6+v1/bt2+1fh8A9GQYWcJxWrVplSeq2Pfjgg3bNmDFjrKlTpwad98c//tE677zzrJiYGOv888+3nn766ZPbOIz1q1/9yho2bJgVGxtrXXLJJVZ1dbV9bOrUqdaYMWOC6rds2WKNGjXKio2NtYYPH24tX778JHcMk7300ktH/X/k4f8nck+efPyeHQAAYDTexgIAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAWCcLVu2yOFw6MCBA+FuBUAEIOwA6DONjY0qKCjQWWedJafTKY/Ho7y8PL322mshe43s7Oxu37GWlZWlhoYGuVyukL1Ob91+++264YYbwt0GcFqLDncDAMx14403qrOzU2vWrNHZZ5+tf/7zn3rxxRf1+eef9+nrxsbGyuPx9OlrADiFhPvLuQCYqampyZJkbdmypceaAwcOWNOnT7cGDx5sJSQkWD/60Y+s7du328cffPBB66KLLrKefPJJa9iwYVZiYqJ18803Wy0tLZZlff2Fijriyxbr6+vtL2JsamqyLOvrL691uVzWn//8Z2vEiBFWXFycdeONN1ptbW3W6tWrrWHDhllnnHGGdffdd1tfffWV/fqBQMC67777LK/Xa8XHx1v/8i//Yr300kv28cPX3bRpk3X++edb/fv3t/Ly8qxPPvnE7v/I/r55PoCTg7exAPSJAQMGaMCAAdqwYYMCgUC345Zl6dprr5Xf79fGjRtVV1enSy65RGPHjg1a+fnwww+1YcMGPffcc3ruuedUXV2tRx99VJL0i1/8QqNHj9b06dPV0NCghoYG+Xy+o/bzxRdf6Je//KXKy8u1adMmbdmyRZMmTdLGjRu1ceNGrV27VitWrNBTTz1ln/PjH/9Yr776qsrLy/XOO+/opptu0tVXX633338/6Lr//d//rbVr1+rll1/Wnj17VFRUJEkqKipSfn6+rr76aru/rKyskPz9AjgO4U5bAMz11FNPWQMHDrT69etnZWVlWcXFxdbbb79tWZZlvfjii1ZiYqL15ZdfBp1zzjnnWL/97W8ty/p6ZSQ+Pt5eybEsy7rvvvuszMxMe3/MmDHWvffeG3SNo63sSLI++OADu6agoMCKj4+3Wltb7bG8vDyroKDAsizL+uCDDyyHw2H94x//CLr22LFjreLi4h6v+6tf/cpyu932/tSpU63rr7/+mP6+APQNntkB0GduvPFGXXvttXrllVf02muvadOmTVq4cKF+97vf6dNPP1VbW5uSkpKCzmlvb9eHH35o7w8fPlwJCQn2/tChQ9XY2HjcvcTHx+ucc86x991ut4YPH64BAwYEjR2+9ltvvSXLsjRixIig6wQCgaCej7xub/sD0HcIOwD6VL9+/TRu3DiNGzdODzzwgO688049+OCDmjlzpoYOHaotW7Z0O+eMM86w/xwTExN0zOFw6NChQ8fdx9Gu823XPnTokKKiolRXV6eoqKigum8GpKNdw7Ks4+4PQN8h7AA4qUaOHKkNGzbokksukd/vV3R0tIYPH97r68XGxqqrqyt0Df7/Ro0apa6uLjU2NuqKK67o9XX6qj8Ax44HlAH0ic8++0xXXXWV1q1bp3feeUf19fX64x//qIULF+r6669XTk6ORo8erRtuuEEvvPCCPvroI9XU1Oi//uu/9Oabbx7z6wwfPlxvvPGGPvroI+3fv79Xqz5HM2LECN16662aMmWKnnnmGdXX16u2tlYLFizQxo0bj6u/d955R7t379b+/fvV2dkZkv4AHDvCDoA+MWDAAGVmZmrx4sW68sorlZaWpvvvv1/Tp0/XsmXL5HA4tHHjRl155ZW64447NGLECN1yyy366KOP5Ha7j/l1ioqKFBUVpZEjR2rw4MHas2dPyOawatUqTZkyRXPmzNF5552niRMn6o033ujxE19HM336dJ133nm69NJLNXjwYL366qsh6w/AsXFYvLkMAAAMxsoOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIz2/wHWgd9v/KAzaQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data= df, x = \"Sentiment\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the TfidfVectorizer\n",
    "word_vectorizer = TfidfVectorizer(\n",
    "    sublinear_tf=True,\n",
    "    strip_accents='unicode',\n",
    "    analyzer='word',\n",
    "    ngram_range=(1, 1),\n",
    "    max_features=10000\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "unigramdataGet = word_vectorizer.fit_transform(df['Tweet'].astype('str'))\n",
    "unigramdataGet =unigramdataGet.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = word_vectorizer.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "unigramdata_features = pd.DataFrame(np.round(unigramdataGet, 1), columns=vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "unigramdata_features[unigramdata_features > 0] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Sentiment']\n",
    "X=unigramdata_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Balancing The data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## using SMOTE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=333)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Sampling: Sentiment\n",
      " 0.0    741\n",
      "-1.0    613\n",
      " 1.0    201\n",
      "Name: count, dtype: int64\n",
      "After Sampling: Sentiment\n",
      " 0.0    523\n",
      "-1.0    523\n",
      " 1.0    523\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "smt = SMOTE(sampling_strategy='all')\n",
    "print(\"Before Sampling:\", counter)\n",
    "X_train_sm,y_train_sm= smt.fit_resample(X_train,y_train)\n",
    "\n",
    "c=y_train_sm.value_counts()\n",
    "print(\"After Sampling:\", c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree (DT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_train_sm, y_train_sm, test_size=0.30, random_state=333)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape:  (1098, 5933)\n",
      "x_test shape:  (471, 5933)\n",
      "y_train shape:  (1098,)\n",
      "y_test shape:  (471,)\n"
     ]
    }
   ],
   "source": [
    "print(\"x_train shape: \", X_train.shape)\n",
    "print(\"x_test shape: \", X_test.shape)\n",
    "\n",
    "print(\"y_train shape: \", y_train.shape)\n",
    "print(\"y_test shape: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ds=DecisionTreeClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the hyperparameter grid to search\n",
    "param_grid = {\n",
    "    'max_depth': [None, 10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'criterion': ['gini', 'entropy'] }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [None, 10, 20, 30],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [None, 10, 20, 30],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [None, 10, 20, 30],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use GridSearchCV for hyperparameter tuning\n",
    "grid_search = GridSearchCV(Ds, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "grid_search.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5}\n"
     ]
    }
   ],
   "source": [
    "# Print the best hyperparameters found\n",
    "print(\"Best Hyperparameters:\", grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the best model for predictions\n",
    "best_ds_classifier = grid_search.best_estimator_\n",
    "ds_y_pred = best_ds_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6751592356687898\n",
      "Precision: 0.6818291909127364\n",
      "Recall: 0.6743670375745848\n",
      "F1-Score: 0.6770456743436933\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "ds_accuracy = accuracy_score(y_test, ds_y_pred)\n",
    "print(f'Accuracy: {ds_accuracy}')\n",
    "\n",
    "\n",
    "ds_precision = precision_score(y_test, ds_y_pred, average=\"macro\")  # 'weighted' considers class imbalance\n",
    "ds_recall = recall_score(y_test, ds_y_pred, average=\"macro\")\n",
    "ds_f1 = f1_score(y_test, ds_y_pred, average=\"macro\")\n",
    "\n",
    "print(\"Precision:\", ds_precision)\n",
    "print(\"Recall:\", ds_recall)\n",
    "print(\"F1-Score:\", ds_f1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_cm = confusion_matrix(y_test, ds_y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(ds_cm, annot=True, fmt='d', cmap='Blues', xticklabels=np.unique(y), yticklabels=np.unique(y))\n",
    "plt.title('Confusion Matrix for DT')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.ylabel('Actual class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.56      0.63      0.59       156\n",
      "         0.0       0.62      0.60      0.61       156\n",
      "         1.0       0.86      0.80      0.83       159\n",
      "\n",
      "    accuracy                           0.68       471\n",
      "   macro avg       0.68      0.67      0.68       471\n",
      "weighted avg       0.68      0.68      0.68       471\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Display classification report\n",
    "print('\\nClassification Report:')\n",
    "print(classification_report(y_test, ds_y_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes (NB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment\n",
       "-1.0    367\n",
       " 0.0    367\n",
       " 1.0    364\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure 'Tweet' column contains strings\n",
    "df['Tweet'] = df['Tweet'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the Multinomial Naive Bayes classifier\n",
    "nb_classifier = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the hyperparameter grid\n",
    "param_grid = {\n",
    "    'alpha': [0.01, 0.1, 1.0, 10.0],  \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=MultinomialNB(), n_jobs=-1,\n",
       "             param_grid={&#x27;alpha&#x27;: [0.01, 0.1, 1.0, 10.0]}, scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=MultinomialNB(), n_jobs=-1,\n",
       "             param_grid={&#x27;alpha&#x27;: [0.01, 0.1, 1.0, 10.0]}, scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=MultinomialNB(), n_jobs=-1,\n",
       "             param_grid={'alpha': [0.01, 0.1, 1.0, 10.0]}, scoring='accuracy')"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use GridSearchCV for hyperparameter tuning\n",
    "nb_grid_search = GridSearchCV(estimator=nb_classifier, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "nb_grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha: 0.01\n"
     ]
    }
   ],
   "source": [
    "# Get the best hyperparameters\n",
    "best_alpha = nb_grid_search.best_params_['alpha']\n",
    "print(f'Best alpha: {best_alpha}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 78.1316\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.78      0.71      0.74       156\n",
      "         0.0       0.71      0.68      0.69       156\n",
      "         1.0       0.84      0.95      0.89       159\n",
      "\n",
      "    accuracy                           0.78       471\n",
      "   macro avg       0.78      0.78      0.78       471\n",
      "weighted avg       0.78      0.78      0.78       471\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train the model with the best hyperparameters\n",
    "nb_classifier = MultinomialNB(alpha=best_alpha)\n",
    "nb_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "nb_y_pred = nb_classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "nb_accuracy = accuracy_score(y_test, nb_y_pred)\n",
    "classification_report_str = classification_report(y_test, nb_y_pred)\n",
    "\n",
    "print(f'Accuracy: {nb_accuracy*100:.4f}')\n",
    "print('Classification Report:\\n', classification_report_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.7773107421687168\n",
      "Recall: 0.7802370585389453\n",
      "F1-Score: 0.7770893415765716\n"
     ]
    }
   ],
   "source": [
    "nb_precision = precision_score(y_test, nb_y_pred, average=\"macro\")  \n",
    "nb_recall = recall_score(y_test, nb_y_pred, average=\"macro\")\n",
    "nb_f1 = f1_score(y_test, nb_y_pred, average=\"macro\")\n",
    "\n",
    "print(\"Precision:\", nb_precision)\n",
    "print(\"Recall:\", nb_recall)\n",
    "print(\"F1-Score:\", nb_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, nb_y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=np.unique(y), yticklabels=np.unique(y))\n",
    "plt.title('Confusion Matrix for NB')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" checked><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_classifier = SVC(kernel='linear')\n",
    "svm_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "rbf = SVC(kernel='rbf', gamma=0.5, C=0.1).fit(X_train, y_train)\n",
    "poly = SVC(kernel='poly', degree=3, C=1).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_pred = poly.predict(X_test)\n",
    "rbf_pred = rbf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Polynomial Kernel):  58.17\n",
      "F1 (Polynomial Kernel):  55.48\n"
     ]
    }
   ],
   "source": [
    "poly_accuracy = accuracy_score(y_test, poly_pred)\n",
    "poly_f1 = f1_score(y_test, poly_pred, average='weighted')\n",
    "print('Accuracy (Polynomial Kernel): ', \"%.2f\" % (poly_accuracy*100))\n",
    "print('F1 (Polynomial Kernel): ', \"%.2f\" % (poly_f1*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (RBF Kernel):  50.96\n",
      "F1 (RBF Kernel):  47.18\n"
     ]
    }
   ],
   "source": [
    "rbf_accuracy = accuracy_score(y_test, rbf_pred)\n",
    "rbf_f1 = f1_score(y_test, rbf_pred, average='weighted')\n",
    "print('Accuracy (RBF Kernel): ', \"%.2f\" % (rbf_accuracy*100))\n",
    "print('F1 (RBF Kernel): ', \"%.2f\" % (rbf_f1*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training a linear SVM classifier \n",
    "svm_model_linear = SVC(kernel = 'linear', C = 1).fit(X_train, y_train) \n",
    "svm_predictions = svm_model_linear.predict(X_test) \n",
    "  \n",
    "# model accuracy for X_test   \n",
    "accuracy = svm_model_linear.score(X_test, y_test) \n",
    "  \n",
    "# creating a confusion matrix \n",
    "cm = confusion_matrix(y_test, svm_predictions) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" checked><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=1, kernel='linear')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model_linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 63,  50,   9],\n",
       "       [ 31, 116,  20],\n",
       "       [  1,   7, 144]], dtype=int64)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7324263038548753"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.66      0.52      0.58       122\n",
      "         0.0       0.67      0.69      0.68       167\n",
      "         1.0       0.83      0.95      0.89       152\n",
      "\n",
      "    accuracy                           0.73       441\n",
      "   macro avg       0.72      0.72      0.72       441\n",
      "weighted avg       0.72      0.73      0.72       441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,svm_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One-VS-One (OVO):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsOneClassifier(estimator=SVC(kernel=&#x27;linear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneVsOneClassifier</label><div class=\"sk-toggleable__content\"><pre>OneVsOneClassifier(estimator=SVC(kernel=&#x27;linear&#x27;))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneVsOneClassifier(estimator=SVC(kernel='linear'))"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an SVM classifier using One-vs-One strategy\n",
    "svm_model_ovo = OneVsOneClassifier(SVC(kernel='linear'))\n",
    "svm_model_ovo.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictions\n",
    "y_pred_ovo = svm_model_ovo.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (One-vs-One): 0.7961783439490446\n"
     ]
    }
   ],
   "source": [
    "# Evaluate accuracy\n",
    "accuracy_ovo = accuracy_score(y_test, y_pred_ovo)\n",
    "print(\"Accuracy (One-vs-One):\", accuracy_ovo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.7922098100001982\n",
      "Recall: 0.7952346395742622\n",
      "F1-Score: 0.7927825575645753\n"
     ]
    }
   ],
   "source": [
    "ovo_precision = precision_score(y_test, y_pred_ovo, average=\"macro\")  # 'weighted' considers class imbalance\n",
    "ovo_recall = recall_score(y_test, y_pred_ovo, average=\"macro\")\n",
    "ovo_f1 = f1_score(y_test, y_pred_ovo, average='macro')\n",
    "\n",
    "print(\"Precision:\", ovo_precision)\n",
    "print(\"Recall:\", ovo_recall)\n",
    "print(\"F1-Score:\", ovo_f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One-Vs-All (OVA):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=SVC(kernel=&#x27;linear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneVsRestClassifier</label><div class=\"sk-toggleable__content\"><pre>OneVsRestClassifier(estimator=SVC(kernel=&#x27;linear&#x27;))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneVsRestClassifier(estimator=SVC(kernel='linear'))"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an SVM classifier using One-vs-All strategy\n",
    "svm_model_ova = OneVsRestClassifier(SVC(kernel='linear'))\n",
    "svm_model_ova.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictions\n",
    "y_pred_ova = svm_model_ova.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (One-vs-All): 0.7707006369426752\n",
      "Precision: 0.7653076997053706\n",
      "Recall: 0.7695936139332366\n",
      "F1-Score: 0.7658174857603933\n"
     ]
    }
   ],
   "source": [
    "# Evaluate accuracy\n",
    "accuracy_ova = accuracy_score(y_test, y_pred_ova)\n",
    "print(\"Accuracy (One-vs-All):\", accuracy_ova)\n",
    "\n",
    "precision_ova = precision_score(y_test, y_pred_ova, average='macro')  # 'weighted' considers class imbalance\n",
    "recall_ova = recall_score(y_test, y_pred_ova, average='macro')\n",
    "f1_ova = f1_score(y_test, y_pred_ova, average='macro')\n",
    "\n",
    "print(\"Precision:\", precision_ova)\n",
    "print(\"Recall:\", recall_ova)\n",
    "print(\"F1-Score:\", f1_ova)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Optimization:\n",
    "* (One-vs-All):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Tuning hyper-parameters for SVM: \n",
      "Best parameters set found on development set:\n",
      "{'estimator__C': 1, 'estimator__gamma': 0.001, 'estimator__kernel': 'linear'}\n"
     ]
    }
   ],
   "source": [
    "tuned_parameters = [{'estimator__kernel': [\"linear\"], 'estimator__gamma': [1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8],\n",
    "                     'estimator__C': [1, 10, 100, 1000]}]\n",
    "\n",
    "\n",
    "print(\"# Tuning hyper-parameters for SVM: \" )\n",
    "\n",
    "ova_clf = GridSearchCV(svm_model_ova, tuned_parameters, cv=5,\n",
    "                    scoring=\"accuracy\")\n",
    "ova_clf.fit(X_train, y_train)\n",
    "\n",
    "print(\"Best parameters set found on development set:\")\n",
    "print(ova_clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7650311332503115"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ova_clf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the best model for predictions\n",
    "ova_best_classifier = ova_clf.best_estimator_\n",
    "ova_svm_pred = best_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8492569002123143\n",
      "Precision: 0.8502886092641085\n",
      "Recall: 0.8493388163199483\n",
      "F1-Score: 0.8492340992340992\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "ova_accuracy = accuracy_score(y_test, ova_svm_pred)\n",
    "print(f'Accuracy: {ova_accuracy}')\n",
    "\n",
    "ova_precision = precision_score(y_test, ova_svm_pred, average=\"macro\")  # 'weighted' considers class imbalance\n",
    "ova_recall = recall_score(y_test, ova_svm_pred, average=\"macro\")\n",
    "ova_f1 = f1_score(y_test, ova_svm_pred, average=\"macro\")\n",
    "\n",
    "print(\"Precision:\", ova_precision)\n",
    "print(\"Recall:\", ova_recall)\n",
    "print(\"F1-Score:\", ova_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, ova_svm_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=np.unique(y), yticklabels=np.unique(y))\n",
    "plt.title('Confusion Matrix for SVM (One Vs All)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* (One-vs-One):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Tuning hyper-parameters for SVM: \n",
      "Best parameters set found on development set:\n",
      "{'estimator__C': 1, 'estimator__gamma': 0.001, 'estimator__kernel': 'linear'}\n"
     ]
    }
   ],
   "source": [
    "tuned_parameters = [{'estimator__kernel': [\"linear\"], 'estimator__gamma': [1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8],\n",
    "                     'estimator__C': [1, 10, 100, 1000]}]\n",
    "\n",
    "\n",
    "print(\"# Tuning hyper-parameters for SVM: \" )\n",
    "\n",
    "ovo_clf = GridSearchCV(svm_model_ovo, tuned_parameters, cv=5,\n",
    "                    scoring=\"accuracy\")\n",
    "ovo_clf.fit(X_train, y_train)\n",
    "\n",
    "print(\"Best parameters set found on development set:\")\n",
    "print(ovo_clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the best model for predictions\n",
    "ovo_best_classifier = ovo_clf.best_estimator_\n",
    "ovo_svm_pred = ovo_best_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7961783439490446\n",
      "Precision: 0.7922098100001982\n",
      "Recall: 0.7952346395742622\n",
      "F1-Score: 0.7927825575645753\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "ovo_accuracy = accuracy_score(y_test, ovo_svm_pred)\n",
    "print(f'Accuracy: {ovo_accuracy}')\n",
    "\n",
    "ovo_precision = precision_score(y_test, ovo_svm_pred, average='macro')  # 'weighted' considers class imbalance\n",
    "ovo_recall = recall_score(y_test, ovo_svm_pred, average='macro')\n",
    "ovo_f1 = f1_score(y_test, ovo_svm_pred, average='macro')\n",
    "\n",
    "print(\"Precision:\", ovo_precision)\n",
    "print(\"Recall:\", ovo_recall)\n",
    "print(\"F1-Score:\", ovo_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ovo_cm = confusion_matrix(y_test, ovo_svm_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(ovo_cm, annot=True, fmt='d', cmap='Blues', xticklabels=np.unique(y), yticklabels=np.unique(y))\n",
    "plt.title('Confusion Matrix for SVM (One Vs One)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Techniques:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Model Accuracy: 0.8110403397027601\n"
     ]
    }
   ],
   "source": [
    "# Create a voting classifier\n",
    "voting_classifier = VotingClassifier(\n",
    "    estimators=[('ds_classifier', best_ds_classifier), ('nb_classifier', nb_classifier), ('ovo_classifier', ovo_best_classifier), (\"ova_classifier\",ova_best_classifier)],\n",
    "    voting='hard'  \n",
    ")\n",
    "\n",
    "# Fit the ensemble model on the training data\n",
    "voting_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions using the ensemble model\n",
    "y_pred = voting_classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the ensemble model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Ensemble Model Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.8098607426729162\n",
      "Recall: 0.8101919045315272\n",
      "F1-Score: 0.807516409910027\n"
     ]
    }
   ],
   "source": [
    "en_precision = precision_score(y_test, y_pred, average='macro')  # 'weighted' considers class imbalance\n",
    "en_recall = recall_score(y_test, y_pred, average='macro')\n",
    "en_f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "print(\"Precision:\", en_precision)\n",
    "print(\"Recall:\", en_recall)\n",
    "print(\"F1-Score:\", en_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "en_cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(en_cm, annot=True, fmt='d', cmap='Blues', xticklabels=np.unique(y), yticklabels=np.unique(y))\n",
    "plt.title('Confusion Matrix for Ensemble (Majority Voting)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "c163f243c395656b1a27c24d02089d3db3bd2809d5932cd6c7da39d0eedf476a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}