Twitter_Sentiment_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "68084067-653a-4c4f-8a39-3cfe9fa8f302",
   "metadata": {},
   "source": [
    "# Downloading data from kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ff90d25-4323-4cac-810d-f4e1bdad6a97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset URL: https://www.kaggle.com/datasets/kazanova/sentiment140\n",
      "License(s): other\n",
      "sentiment140.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
     ]
    }
   ],
   "source": [
    "!kaggle datasets download -d kazanova/sentiment140"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d60da783-8a84-4c9b-b6ad-f371450cb848",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset is extracted!\n"
     ]
    }
   ],
   "source": [
    "# Extracting the zip file\n",
    "from zipfile import ZipFile\n",
    "\n",
    "dataset = '...\\Twitter Sentiment Analysis\\\\sentiment140.zip'\n",
    "\n",
    "with ZipFile(dataset, 'r') as zip:\n",
    "    zip.extractall()\n",
    "    print('The dataset is extracted!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba52a9fe-a556-4466-8177-f814fbe06e26",
   "metadata": {},
   "source": [
    "# Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d44976b3-fe48-4245-93e7-cfda543e9101",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import (accuracy_score, ConfusionMatrixDisplay, confusion_matrix, classification_report,\n",
    "roc_curve, roc_auc_score, RocCurveDisplay)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c19ae6-de7d-4462-ab59-2b817cfdce4c",
   "metadata": {},
   "source": [
    "# Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ebbae0f-6cf0-4009-9d3d-9663888d2dab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "file = 'Twitter_data.csv'\n",
    "column_names = ['target', 'id', 'date', 'flag', 'user', 'text']\n",
    "data = pd.read_csv(file, sep=',', names=column_names, encoding='ISO-8859-1')\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d6b7a136-8955-4095-bae8-4860c3d6db55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1600000, 6)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "80db148e-d1df-402a-b51c-61335e538aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>flag</th>\n",
       "      <th>user</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810369</td>\n",
       "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>_TheSpecialOne_</td>\n",
       "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810672</td>\n",
       "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>scotthamilton</td>\n",
       "      <td>is upset that he can't update his Facebook by ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810917</td>\n",
       "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>mattycus</td>\n",
       "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1467811184</td>\n",
       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>ElleCTF</td>\n",
       "      <td>my whole body feels itchy and like its on fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1467811193</td>\n",
       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>Karoli</td>\n",
       "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target          id                          date      flag  \\\n",
       "0       0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n",
       "1       0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
       "2       0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
       "3       0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
       "4       0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
       "\n",
       "              user                                               text  \n",
       "0  _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...  \n",
       "1    scotthamilton  is upset that he can't update his Facebook by ...  \n",
       "2         mattycus  @Kenichan I dived many times for the ball. Man...  \n",
       "3          ElleCTF    my whole body feels itchy and like its on fire   \n",
       "4           Karoli  @nationwideclass no, it's not behaving at all....  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6e6ddb23-36cc-448d-bd19-1415dc37e66a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    800000\n",
       "4    800000\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a6ae5a8e-7547-408b-8a90-6186cc4e5edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target    0\n",
       "id        0\n",
       "date      0\n",
       "flag      0\n",
       "user      0\n",
       "text      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4aadd2ce-1149-4e68-977f-330a935d0c86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9556279f-00aa-44f6-a606-104d700dcca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'].replace(4, 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5d0cfb98-cfee-4339-8fd3-7dcf3548a137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    800000\n",
       "1    800000\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1828f016-094c-4062-bf82-55f70013f5f3",
   "metadata": {},
   "source": [
    "* **0 is negative sentiment**\n",
    "* **1 is positive sentiment**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29494397-0592-4275-89db-09da83e7dd0c",
   "metadata": {},
   "source": [
    "# Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8f90ead8-1849-4efb-968c-4af11b24e3d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2c3a7fb7-522b-48ac-ad1e-b1be9fc36eae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stemming(content):\n",
    "    \n",
    "    stemmed_content = re.sub('[^a-zA-Z]', ' ', content)\n",
    "    stemmed_content = stemmed_content.lower()\n",
    "    stemmed_content = stemmed_content.split()\n",
    "    stemmed_content = [ps.stem(word) for word in stemmed_content if not word in stopwords.words('english')]\n",
    "    stemmed_content = ' '.join(stemmed_content)\n",
    "\n",
    "    return stemmed_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5f83991e-8d59-494a-9c8a-d18baf546656",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1h 54min 53s\n",
      "Wall time: 2h 31s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['stemmed_content'] = df['text'].apply(stemming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "feed7395-8b1d-4579-8910-ba77a5b2c718",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>flag</th>\n",
       "      <th>user</th>\n",
       "      <th>text</th>\n",
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       "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>_TheSpecialOne_</td>\n",
       "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
       "      <td>switchfoot http twitpic com zl awww bummer sho...</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810672</td>\n",
       "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>scotthamilton</td>\n",
       "      <td>is upset that he can't update his Facebook by ...</td>\n",
       "      <td>upset updat facebook text might cri result sch...</td>\n",
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       "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>mattycus</td>\n",
       "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
       "      <td>kenichan dive mani time ball manag save rest g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1467811184</td>\n",
       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>ElleCTF</td>\n",
       "      <td>my whole body feels itchy and like its on fire</td>\n",
       "      <td>whole bodi feel itchi like fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1467811193</td>\n",
       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>Karoli</td>\n",
       "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
       "      <td>nationwideclass behav mad see</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target          id                          date      flag  \\\n",
       "0       0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n",
       "1       0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
       "2       0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
       "3       0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
       "4       0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
       "\n",
       "              user                                               text  \\\n",
       "0  _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...   \n",
       "1    scotthamilton  is upset that he can't update his Facebook by ...   \n",
       "2         mattycus  @Kenichan I dived many times for the ball. Man...   \n",
       "3          ElleCTF    my whole body feels itchy and like its on fire    \n",
       "4           Karoli  @nationwideclass no, it's not behaving at all....   \n",
       "\n",
       "                                     stemmed_content  \n",
       "0  switchfoot http twitpic com zl awww bummer sho...  \n",
       "1  upset updat facebook text might cri result sch...  \n",
       "2  kenichan dive mani time ball manag save rest g...  \n",
       "3                    whole bodi feel itchi like fire  \n",
       "4                      nationwideclass behav mad see  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "23ce7ec3-e3ed-4e4c-851d-a5a067d0afde",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_csv('Twitter_data_stemmed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b9a6e27b-18d8-4458-948e-829624309d34",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'Twitter_data_stemmed.csv'\n",
    "data1 = pd.read_csv(file, sep=',')\n",
    "df1 = pd.DataFrame(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "127dcd94-1ef6-4602-8de7-ee842ea069aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>id</th>\n",
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       "      <th>flag</th>\n",
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       "      <td>1467810369</td>\n",
       "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>_TheSpecialOne_</td>\n",
       "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
       "      <td>switchfoot http twitpic com zl awww bummer sho...</td>\n",
       "    </tr>\n",
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       "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>scotthamilton</td>\n",
       "      <td>is upset that he can't update his Facebook by ...</td>\n",
       "      <td>upset updat facebook text might cri result sch...</td>\n",
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       "      <td>1467810917</td>\n",
       "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>mattycus</td>\n",
       "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
       "      <td>kenichan dive mani time ball manag save rest g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>1467811184</td>\n",
       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>ElleCTF</td>\n",
       "      <td>my whole body feels itchy and like its on fire</td>\n",
       "      <td>whole bodi feel itchi like fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1467811193</td>\n",
       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>Karoli</td>\n",
       "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
       "      <td>nationwideclass behav mad see</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target          id                          date      flag  \\\n",
       "0       0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n",
       "1       0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
       "2       0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
       "3       0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
       "4       0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
       "\n",
       "              user                                               text  \\\n",
       "0  _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...   \n",
       "1    scotthamilton  is upset that he can't update his Facebook by ...   \n",
       "2         mattycus  @Kenichan I dived many times for the ball. Man...   \n",
       "3          ElleCTF    my whole body feels itchy and like its on fire    \n",
       "4           Karoli  @nationwideclass no, it's not behaving at all....   \n",
       "\n",
       "                                     stemmed_content  \n",
       "0  switchfoot http twitpic com zl awww bummer sho...  \n",
       "1  upset updat facebook text might cri result sch...  \n",
       "2  kenichan dive mani time ball manag save rest g...  \n",
       "3                    whole bodi feel itchi like fire  \n",
       "4                      nationwideclass behav mad see  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "918ddd39-0737-4e05-9550-1059a259225c",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fcbfa8f8-f003-46fd-80ce-1b95a61c93c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df1['stemmed_content'].values\n",
    "Y = df1['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "71718fff-f0f5-4da9-947f-01a8613e45d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['switchfoot http twitpic com zl awww bummer shoulda got david carr third day'\n",
      " 'upset updat facebook text might cri result school today also blah'\n",
      " 'kenichan dive mani time ball manag save rest go bound' ...\n",
      " 'readi mojo makeov ask detail'\n",
      " 'happi th birthday boo alll time tupac amaru shakur'\n",
      " 'happi charitytuesday thenspcc sparkschar speakinguph h']\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d0d7525f-a26b-46e1-8596-3189b2712bc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fc0aacdd-05b0-4fbd-9ae1-a795f9d9aea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, x_test, Y_train, y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1ea49ef4-ae61-4623-beb4-f070d56d351a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1280000,), (320000,), (1280000,), (320000,))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, x_test.shape, Y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "36e15b17-a021-4c1e-b525-ecf02b58b057",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['im come back mum friend hous met cute littl boy year old guess'\n",
      " 'samicappola come get yearbook owe chill oh im leav present' 'tire' ...\n",
      " 'updat site style' 'jonathanrknight guess'\n",
      " 'peopl say fond read say found right book']\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "97ceede6-034c-4419-a5be-c4e2c6569f57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['oh forgot import birthday today mrsrestrictor redact year old today'\n",
      " 'tire yawn best work'\n",
      " 'wonder time tonight stinkin awesom famili even chelsey bo alright' ...\n",
      " 'eat sushi' 'hotnsaucey day good friend' 'yay oral final exam']\n"
     ]
    }
   ],
   "source": [
    "print(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d241354b-4a76-46dd-9d73-0e3b31507ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "402"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pd.isna(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3f7a61ac-a682-4c1a-97e6-0ee66fdb781f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pd.isna(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d6bea716-2565-4da6-8681-9e44f409d281",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.where(pd.isna(X_train), '', X_train)\n",
    "x_test = np.where(pd.isna(x_test), '', x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ead86da8-868e-440d-9604-3a26b1654638",
   "metadata": {},
   "source": [
    "# Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "081d4093-63bc-43a6-924e-99763b915e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "de88c746-0454-41fe-a36f-64b587c3653b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = vectorizer.fit_transform(X_train)\n",
    "x_test = vectorizer.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "aea44341-2ea4-4a4c-9258-33f27c215730",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 155372)\t0.28025612403710826\n",
      "  (0, 300402)\t0.28164691480372833\n",
      "  (0, 453293)\t0.26730987929913863\n",
      "  (0, 49383)\t0.2981972422694594\n",
      "  (0, 237192)\t0.26537306011695083\n",
      "  (0, 86882)\t0.30159464794998875\n",
      "  (0, 262111)\t0.3555114768058745\n",
      "  (0, 170258)\t0.2762802934137004\n",
      "  (0, 138742)\t0.24513704938905198\n",
      "  (0, 279423)\t0.3428184401343486\n",
      "  (0, 31110)\t0.20773704112163793\n",
      "  (0, 78681)\t0.22576199333749816\n",
      "  (0, 177827)\t0.21427584629824487\n",
      "  (1, 322661)\t0.31356968603824703\n",
      "  (1, 230543)\t0.24061578831492064\n",
      "  (1, 299033)\t0.1972545806518825\n",
      "  (1, 70180)\t0.30992185225350954\n",
      "  (1, 305640)\t0.3826922105576465\n",
      "  (1, 453295)\t0.4089354835572553\n",
      "  (1, 145799)\t0.1556199416062376\n",
      "  (1, 350909)\t0.5440254661314423\n",
      "  (1, 78681)\t0.19857578617690264\n",
      "  (1, 177827)\t0.1884727983145768\n",
      "  (2, 409653)\t1.0\n",
      "  (3, 336514)\t0.3435788672894467\n",
      "  :\t:\n",
      "  (1279994, 375510)\t0.24963676833883203\n",
      "  (1279994, 371210)\t0.21239382765987702\n",
      "  (1279994, 129214)\t0.29611126482529604\n",
      "  (1279994, 290816)\t0.1980705533534787\n",
      "  (1279994, 150667)\t0.16714882508999246\n",
      "  (1279994, 149674)\t0.15066947618460688\n",
      "  (1279995, 273119)\t0.7400785060754831\n",
      "  (1279995, 425028)\t0.5153596584238539\n",
      "  (1279995, 84051)\t0.43207433077461305\n",
      "  (1279996, 381680)\t0.5914853465244185\n",
      "  (1279996, 93728)\t0.41719349906742353\n",
      "  (1279996, 274505)\t0.5373345200323607\n",
      "  (1279996, 150667)\t0.4328582709898943\n",
      "  (1279997, 386516)\t0.6631262753804682\n",
      "  (1279997, 369378)\t0.5539597149623109\n",
      "  (1279997, 425929)\t0.5033807476442684\n",
      "  (1279998, 201384)\t0.8100829430757077\n",
      "  (1279998, 155372)\t0.5863152951593534\n",
      "  (1279999, 135536)\t0.5355826518736707\n",
      "  (1279999, 334274)\t0.2953029041808539\n",
      "  (1279999, 47880)\t0.3208399335519647\n",
      "  (1279999, 136605)\t0.3187482401352458\n",
      "  (1279999, 312373)\t0.2748678667415516\n",
      "  (1279999, 340668)\t0.25867767637725464\n",
      "  (1279999, 354370)\t0.5281498175900352\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bb92400f-2282-4952-8584-87a561a59728",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 453293)\t0.2147088227361715\n",
      "  (0, 411033)\t0.3190359682563617\n",
      "  (0, 335523)\t0.5019062696098175\n",
      "  (0, 300402)\t0.2262246261280556\n",
      "  (0, 299033)\t0.18013019468696764\n",
      "  (0, 277123)\t0.5498437106140087\n",
      "  (0, 178709)\t0.30763458557423734\n",
      "  (0, 136191)\t0.25595893278772636\n",
      "  (0, 42799)\t0.23259338082271522\n",
      "  (1, 452765)\t0.7131030172984495\n",
      "  (1, 445317)\t0.314363397907563\n",
      "  (1, 409653)\t0.4414973538142055\n",
      "  (1, 39459)\t0.44467946585255413\n",
      "  (2, 444429)\t0.2508912564017652\n",
      "  (2, 412343)\t0.21181427109967296\n",
      "  (2, 408641)\t0.17446103046604197\n",
      "  (2, 384351)\t0.4489961539402112\n",
      "  (2, 127912)\t0.2603631559671138\n",
      "  (2, 124343)\t0.21757689112411835\n",
      "  (2, 68871)\t0.4881406163788745\n",
      "  (2, 46494)\t0.3964047789290299\n",
      "  (2, 28897)\t0.22575268477323093\n",
      "  (2, 12453)\t0.3140750440261823\n",
      "  (3, 445317)\t0.2480083789348721\n",
      "  (3, 412035)\t0.30499991663853565\n",
      "  :\t:\n",
      "  (319994, 349338)\t0.2736536233759103\n",
      "  (319994, 131790)\t0.444678567420096\n",
      "  (319994, 102965)\t0.499039675674248\n",
      "  (319994, 18696)\t0.6916169953040396\n",
      "  (319995, 459380)\t0.5053000935625689\n",
      "  (319995, 413838)\t0.29367217889043845\n",
      "  (319995, 290816)\t0.19140924827019137\n",
      "  (319995, 237592)\t0.2376291103926011\n",
      "  (319995, 208633)\t0.45893131854293306\n",
      "  (319995, 153133)\t0.20317508606498\n",
      "  (319995, 89721)\t0.28125665971936886\n",
      "  (319995, 89419)\t0.2457983840990573\n",
      "  (319995, 78297)\t0.303678492228808\n",
      "  (319995, 25906)\t0.28580154079435766\n",
      "  (319996, 426714)\t1.0\n",
      "  (319997, 390167)\t0.8203596439334687\n",
      "  (319997, 113000)\t0.5718479296153416\n",
      "  (319998, 170050)\t0.8819690758035346\n",
      "  (319998, 150667)\t0.24575926132814258\n",
      "  (319998, 138742)\t0.32500414867837873\n",
      "  (319998, 93728)\t0.23686544310968435\n",
      "  (319999, 452798)\t0.4111091587065748\n",
      "  (319999, 303438)\t0.7000954934210488\n",
      "  (319999, 132032)\t0.38777114306345634\n",
      "  (319999, 125349)\t0.436450570312803\n"
     ]
    }
   ],
   "source": [
    "print(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caadc516-b219-4707-87e1-83d192cb73f9",
   "metadata": {},
   "source": [
    "# Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bc929630-6e8b-4215-97ed-518b9d3424ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3a744b83-d3fc-4b72-b20a-24b7c670cc0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "54631019-f291-4100-855a-3f34539dc6c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_predict_train = model.predict(X_train)\n",
    "model_predict_test = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0bd201bf-701b-457d-a36b-0c23ed634c6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc = accuracy_score(Y_train, model_predict_train)\n",
    "test_acc = accuracy_score(y_test, model_predict_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "31b9b71e-337f-4819-8665-e0f1b6493da3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.008203125\n"
     ]
    }
   ],
   "source": [
    "print(train_acc*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "8eb254eb-f19a-43b0-80dd-6ce9f35bfc92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77.93875\n"
     ]
    }
   ],
   "source": [
    "print(test_acc*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "632601f4-a438-4de8-a18c-3793f32c3f09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[121484  38516]\n",
      " [ 32080 127920]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, model_predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3f1aeff9-5025-4b5c-ad21-71e11b1bb770",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[505254 134746]\n",
      " [108349 531651]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(Y_train, model_predict_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "59d156b4-285b-4f0c-8cd8-280fe0d02352",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.76      0.77    160000\n",
      "           1       0.77      0.80      0.78    160000\n",
      "\n",
      "    accuracy                           0.78    320000\n",
      "   macro avg       0.78      0.78      0.78    320000\n",
      "weighted avg       0.78      0.78      0.78    320000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, model_predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1ce61781-eb41-4387-b72b-65bea602e4f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.79      0.81    640000\n",
      "           1       0.80      0.83      0.81    640000\n",
      "\n",
      "    accuracy                           0.81   1280000\n",
      "   macro avg       0.81      0.81      0.81   1280000\n",
      "weighted avg       0.81      0.81      0.81   1280000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_train, model_predict_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "57c04a28-ff28-4c11-8455-9f3d0865ea39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test data\n",
    "conf_matrix = confusion_matrix(y_test, model_predict_test)\n",
    "ConfusionMatrixDisplay(conf_matrix).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3a51c8cc-3237-4dca-93dc-da7e21c07e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train data\n",
    "conf_matrix = confusion_matrix(Y_train, model_predict_train)\n",
    "ConfusionMatrixDisplay(conf_matrix).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a706bccb-e221-437b-b817-faeafea92318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#AUC\n",
    "score = model.decision_function(x_test)\n",
    "fpr, tpr, _ = roc_curve(y_test, score, pos_label=model.classes_[1])\n",
    "auc = roc_auc_score(y_test, score)\n",
    "\n",
    "roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=auc).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "da1e60aa-a623-4560-ab1c-a2e7223f4923",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: [0]\n"
     ]
    }
   ],
   "source": [
    "input_text ='''Rahul is a bad example of a leader for a country which is the fifth largest economy in the world!\n",
    "                People dont want to see him as a prime minister because they believe Rahul will be a devastating\n",
    "                    minister & ruin the country!'''\n",
    "\n",
    "input_vec = vectorizer.transform([input_text])\n",
    "\n",
    "input_vec = input_vec.reshape(1, -1)\n",
    "\n",
    "predict = model.predict(input_vec)\n",
    "\n",
    "print(\"Prediction:\", predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2f4b1a6-141d-49e7-9bb7-67e389483b3f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}