sentiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "401 - Unauthorized - Unauthenticated\n"
     ]
    }
   ],
   "source": [
    "import kaggle\n",
    "\n",
    "# Verify authentication by listing available datasets\n",
    "!kaggle datasets list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "#API to fetch Twitter dataset from Kaggle\n",
    "#!/bin/bash\n",
    "!kaggle datasets download kazanova/sentiment140"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extraction complete.\n"
     ]
    }
   ],
   "source": [
    "from zipfile import ZipFile\n",
    "\n",
    "# Path to the ZIP file (Make sure this is correct)\n",
    "zip_file_path = \"sentiment140.zip\"  # Update this if necessary\n",
    "\n",
    "# Extract the files\n",
    "with ZipFile(zip_file_path, 'r') as zip_ref:\n",
    "    zip_ref.extractall(\"sentiment140_extracted\")  # Extract to a folder\n",
    "    print(\"Extraction complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing dependencies\n",
    "import pandas as pd #dataframe\n",
    "import numpy as np\n",
    "import re #pattern matching search etc.\n",
    "from nltk.corpus import stopwords \n",
    "from nltk.stem.porter import PorterStemmer #nlp libraries\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 #calculate model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Rohit\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stopwords - Ignore common words like 'i', 'the', 'a'\n",
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stopwords are like filler words, they do have meaning for formation but not influential for the sentence context.\n",
    "# The model would not consider these words as part of context during preprocessing.\n",
    "# We will remove these types of words from the dataset and keep only important words.\n",
    "# Shortening the huge dataset extracting only important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
     ]
    }
   ],
   "source": [
    "print(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('sentiment140_extracted/training.1600000.processed.noemoticon.csv', encoding='ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1467810369</th>\n",
       "      <th>Mon Apr 06 22:19:45 PDT 2009</th>\n",
       "      <th>NO_QUERY</th>\n",
       "      <th>_TheSpecialOne_</th>\n",
       "      <th>@switchfoot http://twitpic.com/2y1zl - Awww, that's a bummer.  You shoulda got David Carr of Third Day to do it. ;D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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>1</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>2</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>3</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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1467811372</td>\n",
       "      <td>Mon Apr 06 22:20:00 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>joy_wolf</td>\n",
       "      <td>@Kwesidei not the whole crew</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY _TheSpecialOne_  \\\n",
       "0  0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   scotthamilton   \n",
       "1  0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY        mattycus   \n",
       "2  0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY         ElleCTF   \n",
       "3  0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY          Karoli   \n",
       "4  0  1467811372  Mon Apr 06 22:20:00 PDT 2009  NO_QUERY        joy_wolf   \n",
       "\n",
       "  @switchfoot http://twitpic.com/2y1zl - Awww, that's a bummer.  You shoulda got David Carr of Third Day to do it. ;D  \n",
       "0  is upset that he can't update his Facebook by ...                                                                   \n",
       "1  @Kenichan I dived many times for the ball. Man...                                                                   \n",
       "2    my whole body feels itchy and like its on fire                                                                    \n",
       "3  @nationwideclass no, it's not behaving at all....                                                                   \n",
       "4                      @Kwesidei not the whole crew                                                                    "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#naming the columns\n",
    "col = ['target', 'ids', 'date', 'flag', 'user', 'text']\n",
    "data = pd.read_csv('sentiment140_extracted/training.1600000.processed.noemoticon.csv', encoding='ISO-8859-1', names=col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1600000, 6)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>target</th>\n",
       "      <th>ids</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         ids                          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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target    0\n",
       "ids       0\n",
       "date      0\n",
       "flag      0\n",
       "user      0\n",
       "text      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for and counting missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    800000\n",
       "4    800000\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking distribution of target column (how many positive, negative, neutral tweets)\n",
    "data['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No neutral tweet, evenly distributed positive and negative tweets. Upsampling or downsampling not required.\n",
    "# Convert the target \"4\" to \"1\" and \"0\" to \"0\" for better understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace({'target':{4:1}}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    800000\n",
       "1    800000\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0 --> negative tweet\n",
    "# 1 --> positive tweet\n",
    "data['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Stemming\n",
    "portstem = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load stopwords once outside the loop\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "def stemming(content):\n",
    "    \"\"\"\n",
    "    Optimized stemming function\n",
    "    \"\"\"\n",
    "    stemmed_content = re.sub('[^a-zA-Z]', ' ', content)  # remove characters that are not a-z\n",
    "    stemmed_content = stemmed_content.lower()  # convert all the words to lowercase\n",
    "    stemmed_content = stemmed_content.split()  # splitting all words into a list\n",
    "    stemmed_content = [portstem.stem(word) for word in stemmed_content if word not in stop_words]  # reduced word form\n",
    "    stemmed_content = ' '.join(stemmed_content)  # joining the words back\n",
    "    return stemmed_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['stemmed_text'] = data['text'].apply(stemming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>target</th>\n",
       "      <th>ids</th>\n",
       "      <th>date</th>\n",
       "      <th>flag</th>\n",
       "      <th>user</th>\n",
       "      <th>text</th>\n",
       "      <th>stemmed_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",
       "      <td>switchfoot http twitpic com zl awww bummer sho...</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",
       "      <td>upset updat facebook text might cri result sch...</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",
       "      <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         ids                          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_text  \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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#separating data and model\n",
    "x = data['stemmed_text'].values\n",
    "y = data['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, stratify = y, random_state = 42)\n",
    "# 80% of the data is used for training and 20% for testing\n",
    "# stratify parameter makes sure that the distribution of target variable is even in both training and testing datasets\n",
    "# random state is used to reproduce the same results every time the code is run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1600000,) (1280000,) (320000,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape, x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['paisleypaisley lol get idea far advanc even june yet need third knitter summer group'\n",
      " 'worst headach ever'\n",
      " 'ewaniesciuszko sad wont see miss alreadi yeah perfect come back th' ...\n",
      " 'got home meet talk endlessli one coolest guy ever met smile'\n",
      " 'bought chocol bar quot win free bar quot label win either'\n",
      " 'misecia said hope dm email sunday']\n"
     ]
    }
   ],
   "source": [
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['stm denali ye black red fav color realli want color def look awesom jare'\n",
      " 'qu buy open hous weekend pm best valu one bedroom lic long island citi bd http tinyurl com pt nqd'\n",
      " 'ginoandfran fran greet air okay hahahaha thank' ...\n",
      " 'la brat follow also hope atleast get also wish get well soon'\n",
      " 'feel like decent swell sinc last fall hope wave myrtl beach week either least golf'\n",
      " 'relaxin busi day']\n"
     ]
    }
   ],
   "source": [
    "print(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Extraction\n",
    "\n",
    "# Convert text to numerical data using Vectorization.\n",
    "# using TfidfVectorizer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer()\n",
    "x_train = vectorizer.fit_transform(x_train) #fitting the model and transforming the text data to numerical data\n",
    "x_test = vectorizer.transform(x_test) #transforming the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# will consider each word in the text and assign a unique number to it. \n",
    "# The model will consider these numbers for prediction.\n",
    "# also the model will consider the frequency of the words in the text.\n",
    "# The model will consider the importance of the word in the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Compressed Sparse Row sparse matrix of dtype 'float64'\n",
      "\twith 9453607 stored elements and shape (1280000, 461280)>\n",
      "  Coords\tValues\n",
      "  (0, 307108)\t0.46206048815324474\n",
      "  (0, 239679)\t0.15130037108228483\n",
      "  (0, 146067)\t0.12929728405657018\n",
      "  (0, 175252)\t0.224070805470346\n",
      "  (0, 128605)\t0.22108856600702773\n",
      "  (0, 4832)\t0.317074267861159\n",
      "  (0, 124524)\t0.18318401951949756\n",
      "  (0, 205794)\t0.24140229063801746\n",
      "  (0, 454381)\t0.20169626473577715\n",
      "  (0, 286478)\t0.16123218610004272\n",
      "  (0, 406297)\t0.2978221095272138\n",
      "  (0, 220296)\t0.43015677907624866\n",
      "  (0, 388138)\t0.20555120011808467\n",
      "  (0, 154767)\t0.26976607043258233\n",
      "  (1, 445870)\t0.6361096685891185\n",
      "  (1, 161801)\t0.5778049407933611\n",
      "  (1, 124611)\t0.5113765148324884\n",
      "  (2, 125319)\t0.6383069130836649\n",
      "  (2, 349409)\t0.22232944888223494\n",
      "  (2, 444761)\t0.30331529032956345\n",
      "  (2, 358186)\t0.19837942712286838\n",
      "  (2, 267649)\t0.19309660201644555\n",
      "  (2, 12436)\t0.2529872032123258\n",
      "  (2, 453420)\t0.2347069337186747\n",
      "  (2, 312657)\t0.3154702974657607\n",
      "  :\t:\n",
      "  (1279997, 124611)\t0.2537781914144255\n",
      "  (1279997, 301683)\t0.1908678391932645\n",
      "  (1279997, 168646)\t0.20490659397970187\n",
      "  (1279997, 156392)\t0.23570311036994007\n",
      "  (1279997, 151848)\t0.18681351768822982\n",
      "  (1279997, 259065)\t0.26819595864353835\n",
      "  (1279997, 394120)\t0.24857410036759808\n",
      "  (1279997, 372797)\t0.3021907911926725\n",
      "  (1279997, 262076)\t0.3257051331036561\n",
      "  (1279997, 80839)\t0.4085075096529922\n",
      "  (1279997, 120136)\t0.5183864288132355\n",
      "  (1279998, 329551)\t0.3477852392627569\n",
      "  (1279998, 71116)\t0.26101247237184594\n",
      "  (1279998, 49119)\t0.24937088067546037\n",
      "  (1279998, 115954)\t0.24199212168201903\n",
      "  (1279998, 442745)\t0.46003281711454636\n",
      "  (1279998, 138188)\t0.22903437381896502\n",
      "  (1279998, 33019)\t0.5505296949726769\n",
      "  (1279998, 225235)\t0.3507164257239226\n",
      "  (1279999, 169469)\t0.24275491614294809\n",
      "  (1279999, 104826)\t0.39497658307784106\n",
      "  (1279999, 388491)\t0.3269499490733422\n",
      "  (1279999, 118242)\t0.3604832171863622\n",
      "  (1279999, 349900)\t0.3245817059299737\n",
      "  (1279999, 267426)\t0.6654816651208961\n"
     ]
    }
   ],
   "source": [
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Compressed Sparse Row sparse matrix of dtype 'float64'\n",
      "\twith 2288367 stored elements and shape (320000, 461280)>\n",
      "  Coords\tValues\n",
      "  (0, 28874)\t0.1778395103911245\n",
      "  (0, 43712)\t0.23562815302828183\n",
      "  (0, 78636)\t0.5158100011206617\n",
      "  (0, 96399)\t0.255967788489452\n",
      "  (0, 97585)\t0.4019235611854435\n",
      "  (0, 129417)\t0.25650960779862714\n",
      "  (0, 189057)\t0.31324918577405797\n",
      "  (0, 240451)\t0.15341308097014625\n",
      "  (0, 334643)\t0.14719329779308424\n",
      "  (0, 335577)\t0.22602158147814247\n",
      "  (0, 384697)\t0.3281164007446601\n",
      "  (0, 435956)\t0.14183025329879742\n",
      "  (0, 453357)\t0.1781708363247895\n",
      "  (1, 35118)\t0.3128685670821343\n",
      "  (1, 36669)\t0.2729958846742327\n",
      "  (1, 39445)\t0.16867093960211466\n",
      "  (1, 57115)\t0.19043301054662504\n",
      "  (1, 74274)\t0.21148120876702692\n",
      "  (1, 78790)\t0.13386322067407883\n",
      "  (1, 170374)\t0.17525273735418329\n",
      "  (1, 171245)\t0.12468774856570086\n",
      "  (1, 183279)\t0.24586158827112847\n",
      "  (1, 233854)\t0.3852709938491561\n",
      "  (1, 240223)\t0.1674195650536303\n",
      "  (1, 301683)\t0.13212235134015302\n",
      "  :\t:\n",
      "  (319997, 135536)\t0.21809964977532384\n",
      "  (319997, 146067)\t0.31485892246365776\n",
      "  (319997, 169469)\t0.1945044482645627\n",
      "  (319997, 225096)\t0.2933179603743362\n",
      "  (319997, 376036)\t0.2335941225019601\n",
      "  (319997, 438565)\t0.19669688299412047\n",
      "  (319997, 443410)\t0.20628884141115603\n",
      "  (319998, 35323)\t0.24243688403979866\n",
      "  (319998, 95798)\t0.31645368593596523\n",
      "  (319998, 115954)\t0.24587170807357303\n",
      "  (319998, 127979)\t0.24824116108565378\n",
      "  (319998, 130270)\t0.16408547784640734\n",
      "  (319998, 150603)\t0.3076966959712389\n",
      "  (319998, 169469)\t0.16828053521756142\n",
      "  (319998, 227862)\t0.17626972873530924\n",
      "  (319998, 230521)\t0.23127598680385447\n",
      "  (319998, 234585)\t0.14634570758159743\n",
      "  (319998, 281884)\t0.40416959301312766\n",
      "  (319998, 368628)\t0.22494548369604894\n",
      "  (319998, 391857)\t0.3533227149718125\n",
      "  (319998, 436860)\t0.2992074077395669\n",
      "  (319998, 438120)\t0.1865288234043992\n",
      "  (319999, 56596)\t0.4969436428888122\n",
      "  (319999, 93851)\t0.2958769159422991\n",
      "  (319999, 337075)\t0.8157842033312919\n"
     ]
    }
   ],
   "source": [
    "print(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training the ML model using Logistic Regression\n",
    "# Logistic Regression is a classification algorithm used to predict the probability of a target variable.\n",
    "# It is used to predict the probability of a target variable.\n",
    "# We will use the Logistic Regression model to predict the sentiment using probability of positive or negative tweet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(max_iter = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Evaluation\n",
    "# Accuracy Score\n",
    "# Accuracy is the ratio of correctly predicted instances to the total instances in the dataset.\n",
    "# It is used to measure the performance (how many correct predictions) of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_prediction = model.predict(x_train)\n",
    "training_data_accuracy = accuracy_score(y_train, x_train_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of the training data :  0.7999984375\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score of the training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking accuracy of the test data\n",
    "x_test_prediction = model.predict(x_test)\n",
    "test_data_accuracy = accuracy_score(y_test, x_test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of the test data :  0.776796875\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score of the test data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the vectorizer\n",
    "import pickle\n",
    "filename = 'vectorizer.pkl'\n",
    "pickle.dump(vectorizer, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the trained model to a file\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'model.pkl'\n",
    "pickle.dump(model, open(filename, 'wb'))\n",
    "# wb - write binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking and using saved model for future use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = pickle.load(open(filename, 'rb'))\n",
    "# rb - read binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "x_new = x_test[3]\n",
    "print(y_test[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "prediction = loaded_model.predict(x_new)\n",
    "print(prediction)\n",
    "# stored in list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive Tweet\n"
     ]
    }
   ],
   "source": [
    "if(prediction[0] == 0):\n",
    "  print('Negative Tweet')\n",
    "else:\n",
    "    print('Positive Tweet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recap\n",
    "# 1. Imported the Kaggle API (kaggle.json)\n",
    "# 2. Downloaded the Twitter dataset from Kaggle\n",
    "# 3. Extracted the dataset\n",
    "# 4. Imported the required libraries - pandas, numpy, re, nltk, TfidfVectorizer, train_test_split, LogisticRegression, accuracy_score\n",
    "# 5. Read the dataset using pandas\n",
    "# 6. Preprocessed the data - Removed missing values, converted the target column, performed stemming\n",
    "# 7. Split the data into training and testing datasets\n",
    "# 8. Converted the text data to numerical data using TfidfVectorizer\n",
    "# 9. Trained the Logistic Regression model (max_iter = 1000), supervised learning due to target variable\n",
    "# 10. Evaluated the model using accuracy score - Training and Testing data (both > 75%)\n",
    "# 11. Saved the trained model to a file using pickle\n",
    "# 12. Loaded the saved model and used it for prediction (Positive or Negative Tweet)"
   ]
  }
 ],
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