Individual Model (Train Test same dataset).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9d50064",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "#Preprocessing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import string\n",
    "import re\n",
    "import spacy\n",
    "import nltk\n",
    "from textblob import TextBlob\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.corpus import wordnet\n",
    "from cleantext import clean\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#Model Algorithms\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import VotingClassifier,StackingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import model_selection\n",
    "\n",
    "import time\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf4db763",
   "metadata": {},
   "source": [
    "### Read Training Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1bbb20f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial :\n",
      "Train\n",
      "------------------\n",
      "       textID                                               text  \\\n",
      "0  cb774db0d1                I`d have responded, if I were going   \n",
      "1  549e992a42      Sooo SAD I will miss you here in San Diego!!!   \n",
      "2  088c60f138                          my boss is bullying me...   \n",
      "3  9642c003ef                     what interview! leave me alone   \n",
      "4  358bd9e861   Sons of ****, why couldn`t they put them on t...   \n",
      "\n",
      "                         selected_text sentiment Time of Tweet Age of User  \\\n",
      "0  I`d have responded, if I were going   neutral       morning        0-20   \n",
      "1                             Sooo SAD  negative          noon       21-30   \n",
      "2                          bullying me  negative         night       31-45   \n",
      "3                       leave me alone  negative       morning       46-60   \n",
      "4                        Sons of ****,  negative          noon       60-70   \n",
      "\n",
      "       Country  Population -2020  Land Area (Km²)  Density (P/Km²)  \n",
      "0  Afghanistan          38928346         652860.0               60  \n",
      "1      Albania           2877797          27400.0              105  \n",
      "2      Algeria          43851044        2381740.0               18  \n",
      "3      Andorra             77265            470.0              164  \n",
      "4       Angola          32866272        1246700.0               26  \n"
     ]
    }
   ],
   "source": [
    "train=pd.read_csv(\"train.csv\",encoding=\"unicode_escape\")\n",
    "print(\"Initial :\")\n",
    "\n",
    "print('Train')\n",
    "print('------------------')\n",
    "print(train.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec4cf45",
   "metadata": {},
   "source": [
    "### Attribute Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fea17548",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attribute Selection :\n",
      "\n",
      "Train\n",
      "------------------\n",
      "                                                text sentiment\n",
      "0                I`d have responded, if I were going   neutral\n",
      "1      Sooo SAD I will miss you here in San Diego!!!  negative\n",
      "2                          my boss is bullying me...  negative\n",
      "3                     what interview! leave me alone  negative\n",
      "4   Sons of ****, why couldn`t they put them on t...  negative\n"
     ]
    }
   ],
   "source": [
    "print('Attribute Selection :')\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train=train[['text','sentiment']]\n",
    "\n",
    "print(train.head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36c5de5",
   "metadata": {},
   "source": [
    "### Drop rows with null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5d6dbd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.isnull().sum()\n",
    "train=train.dropna(axis=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca0e90c",
   "metadata": {},
   "source": [
    "### Convert to lowercase "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a3aca1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowercase\n",
      "\n",
      "Train\n",
      "------------------\n",
      "0                  i`d have responded, if i were going\n",
      "1        sooo sad i will miss you here in san diego!!!\n",
      "2                            my boss is bullying me...\n",
      "3                       what interview! leave me alone\n",
      "4     sons of ****, why couldn`t they put them on t...\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('Lowercase')\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text']=train['text'].str.lower()\n",
    "print(train['text'].head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f9d156",
   "metadata": {},
   "source": [
    "### Remove URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb4ab375",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove URL\n",
      "\n",
      "Train\n",
      "------------------\n",
      "Remove URL: \n",
      "0                  i`d have responded, if i were going\n",
      "1        sooo sad i will miss you here in san diego!!!\n",
      "2                            my boss is bullying me...\n",
      "3                       what interview! leave me alone\n",
      "4     sons of ****, why couldn`t they put them on t...\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('Remove URL')\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text'] = train['text'].replace('((www.[^\\s]+)|(https?://[^\\s]+))', '', regex = True)\n",
    "print(\"Remove URL: \")\n",
    "print(train['text'].head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962faba2",
   "metadata": {},
   "source": [
    "### Remove Emoji "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff07ca6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove Emoji\n",
      "\n",
      "Train\n",
      "------------------\n",
      "0                  i`d have responded, if i were going\n",
      "1        sooo sad i will miss you here in san diego!!!\n",
      "2                            my boss is bullying me...\n",
      "3                       what interview! leave me alone\n",
      "4     sons of ****, why couldn`t they put them on t...\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "def remove_emojis(data):\n",
    "    emoj = re.compile(\"[\"\n",
    "        u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "        u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "        u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "        u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "        u\"\\U00002500-\\U00002BEF\"  # chinese char\n",
    "        u\"\\U00002702-\\U000027B0\"\n",
    "        u\"\\U00002702-\\U000027B0\"\n",
    "        u\"\\U000024C2-\\U0001F251\"\n",
    "        u\"\\U0001f926-\\U0001f937\"\n",
    "        u\"\\U00010000-\\U0010ffff\"\n",
    "        u\"\\u2640-\\u2642\" \n",
    "        u\"\\u2600-\\u2B55\"\n",
    "        u\"\\u200d\"\n",
    "        u\"\\u23cf\"\n",
    "        u\"\\u23e9\"\n",
    "        u\"\\u231a\"\n",
    "        u\"\\ufe0f\"  # dingbats\n",
    "        u\"\\u3030\"\n",
    "                      \"]+\", re.UNICODE)\n",
    "    return re.sub(emoj, '', data)\n",
    "\n",
    "print('Remove Emoji')\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text'] = train['text'].apply(lambda text: remove_emojis(text))\n",
    "print(train['text'].head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5fa10b9",
   "metadata": {},
   "source": [
    "### Remove @user "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "92c4583b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove @ User\n",
      "\n",
      "Train\n",
      "------------------\n",
      "Remove @user: \n",
      "0                  i`d have responded, if i were going\n",
      "1        sooo sad i will miss you here in san diego!!!\n",
      "2                            my boss is bullying me...\n",
      "3                       what interview! leave me alone\n",
      "4     sons of ****, why couldn`t they put them on t...\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('Remove @ User')\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text'] = train['text'].replace('(@[^\\s]+)', '', regex = True)\n",
    "print(\"Remove @user: \")\n",
    "print(train['text'].head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a1902f2",
   "metadata": {},
   "source": [
    "### Remove # keyword "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d10c4302",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove #\n",
      "\n",
      "Train\n",
      "------------------\n",
      "Remove #keyword: \n",
      "0                  i`d have responded, if i were going\n",
      "1        sooo sad i will miss you here in san diego!!!\n",
      "2                            my boss is bullying me...\n",
      "3                       what interview! leave me alone\n",
      "4     sons of ****, why couldn`t they put them on t...\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('Remove #')\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text'] = train['text'].replace('(#[^\\s]+)', '', regex = True)\n",
    "print(\"Remove #keyword: \")\n",
    "print(train['text'].head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65d6bb81",
   "metadata": {},
   "source": [
    "### Remove numerical characters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d1a1f4f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove numerical characters\n",
      "\n",
      "Train\n",
      "------------------\n",
      "0                  i`d have responded, if i were going\n",
      "1        sooo sad i will miss you here in san diego!!!\n",
      "2                            my boss is bullying me...\n",
      "3                       what interview! leave me alone\n",
      "4     sons of ****, why couldn`t they put them on t...\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('Remove numerical characters')\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text'] = train['text'].replace('[0-9]+', '', regex = True)\n",
    "print(train['text'].head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3144ac82",
   "metadata": {},
   "source": [
    "### Remove punctuation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af5c38e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove punctuation\n",
      "\n",
      "Train\n",
      "------------------\n",
      "Remove punctuation: \n",
      "0                    id have responded if i were going\n",
      "1           sooo sad i will miss you here in san diego\n",
      "2                               my boss is bullying me\n",
      "3                        what interview leave me alone\n",
      "4     sons of  why couldnt they put them on the rel...\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('Remove punctuation')\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text'] = train['text'].str.translate(str.maketrans('', '', string.punctuation))\n",
    "print(\"Remove punctuation: \")\n",
    "print(train['text'].head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca1b07e6",
   "metadata": {},
   "source": [
    "### Remove repeated keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0c43e663",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove repeated keywords :\n",
      "\n",
      "Train\n",
      "------------------\n",
      "0                    id have responded if i were going\n",
      "1            soo sad i will miss you here in san diego\n",
      "2                               my boss is bullying me\n",
      "3                        what interview leave me alone\n",
      "4     sons of  why couldnt they put them on the rel...\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('Remove repeated keywords :')\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text'] = train['text'].replace(r'(.)\\1+', r'\\1\\1', regex = True)\n",
    "print(train['text'].head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0883cc7a",
   "metadata": {},
   "source": [
    "###  Remove Stopwords "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6bc7d6d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove stopwords: \n",
      "\n",
      "Train\n",
      "------------------\n",
      "0                          id responded going\n",
      "1                      soo sad miss san diego\n",
      "2                               boss bullying\n",
      "3                       interview leave alone\n",
      "4    sons couldnt put releases already bought\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "def cleaning_stopwords(text):\n",
    "    return \" \".join([word for word in str(text).split() if word not in stop_words])\n",
    "\n",
    "print(\"Remove stopwords: \")\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text'] = train['text'].apply(lambda text: cleaning_stopwords(text))\n",
    "print(train['text'].head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4714f948",
   "metadata": {},
   "source": [
    "### Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "efb095b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenization: \n",
      "\n",
      "Train\n",
      "------------------\n",
      "Tokenization: \n",
      "0                             [id, responded, going]\n",
      "1                       [soo, sad, miss, san, diego]\n",
      "2                                   [boss, bullying]\n",
      "3                          [interview, leave, alone]\n",
      "4    [sons, couldnt, put, releases, already, bought]\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"Tokenization: \")\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text'] = train['text'].apply(nltk.word_tokenize)\n",
    "print(\"Tokenization: \")\n",
    "print(train['text'].head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de8d323f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train\n",
      "------------------\n",
      "                                              text sentiment\n",
      "0                           [id, responded, going]   neutral\n",
      "1                     [soo, sad, miss, san, diego]  negative\n",
      "2                                 [boss, bullying]  negative\n",
      "3                        [interview, leave, alone]  negative\n",
      "4  [sons, couldnt, put, releases, already, bought]  negative\n"
     ]
    }
   ],
   "source": [
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "print(train.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cc5236d",
   "metadata": {},
   "source": [
    "### Label Encoding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "021e4e6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train\n",
      "------------------\n",
      "                                              text  sentiment\n",
      "0                           [id, responded, going]          0\n",
      "1                     [soo, sad, miss, san, diego]         -1\n",
      "2                                 [boss, bullying]         -1\n",
      "3                        [interview, leave, alone]         -1\n",
      "4  [sons, couldnt, put, releases, already, bought]         -1\n"
     ]
    }
   ],
   "source": [
    "train['sentiment'] = train['sentiment'].map({\"positive\": 1, \"neutral\": 0, \"negative\": -1})\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "print(train.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "045ff26a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train\n",
      "------------------\n",
      " 0    11117\n",
      " 1     8582\n",
      "-1     7781\n",
      "Name: sentiment, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "print(train['sentiment'].value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fe05071",
   "metadata": {},
   "source": [
    "### POS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "55c328fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POS : \n",
      "\n",
      "Train\n",
      "------------------\n",
      "POS tagging: \n",
      "0           [(id, NN), (responded, VBD), (going, VBG)]\n",
      "1    [(soo, JJ), (sad, JJ), (miss, NN), (san, NN), ...\n",
      "2                        [(boss, IN), (bullying, VBG)]\n",
      "3         [(interview, NN), (leave, VBP), (alone, RB)]\n",
      "4    [(sons, NNS), (couldnt, VBP), (put, VBN), (rel...\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"POS : \")\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text'] = train['text'].apply(nltk.pos_tag)\n",
    "print(\"POS tagging: \")\n",
    "print(train['text'].head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ee368aa",
   "metadata": {},
   "source": [
    "### Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e2ae3553",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lemmatization : \n",
      "\n",
      "Train\n",
      "------------------\n",
      "0                          id respond go\n",
      "1                 soo sad miss san diego\n",
      "2                              bos bully\n",
      "3                  interview leave alone\n",
      "4    son couldnt put release already buy\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "def get_wordnet_pos(treebank_pos):\n",
    "    if treebank_pos.startswith('J'):\n",
    "        return wordnet.ADJ\n",
    "    elif treebank_pos.startswith('V'):\n",
    "        return wordnet.VERB\n",
    "    elif treebank_pos.startswith('N'):\n",
    "        return wordnet.NOUN\n",
    "    elif treebank_pos.startswith('R'):\n",
    "        return wordnet.ADV\n",
    "    else:\n",
    "        return wordnet.NOUN\n",
    "\n",
    "def lemmatization(text):\n",
    "    lemmatized_tokens = []\n",
    "\n",
    "    for i in range(len(text)):\n",
    "        word, pos = text[i]\n",
    "        \n",
    "        lemmatized_tokens.append(nltk.WordNetLemmatizer().lemmatize(word, get_wordnet_pos(pos)))\n",
    "        \n",
    "    return \" \".join(lemmatized_tokens)\n",
    "\n",
    "print(\"Lemmatization : \")\n",
    "print('\\nTrain')\n",
    "print('------------------')\n",
    "train['text'] = train['text'].apply(lambda text: lemmatization(text))\n",
    "print(train['text'].head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daefa7a9",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "483b3029",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X=train['text']\n",
    "y=train['sentiment']\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "50c0db16",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vectorizer= TfidfVectorizer(max_df=0.90, min_df=2,max_features =1000)\n",
    "X_train = tfidf_vectorizer.fit_transform(X_train) #Tokenize and build vocabulary\n",
    "X_test = tfidf_vectorizer.transform(X_test) #Tokenize and build vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0372f97e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21984, 1000)\n",
      "(21984,)\n",
      "(5496, 1000)\n",
      "(5496,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "357e5384",
   "metadata": {},
   "source": [
    "## Individual Model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4713429",
   "metadata": {},
   "source": [
    "#### Confusion Matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "20f88a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_matrix_evaluation(classifier,y_predicted):\n",
    "    mat=confusion_matrix(y_test,y_predicted)\n",
    "    sns.heatmap(mat.T, xticklabels=['Predicted 1','Predicted 0','Predicted -1'],yticklabels=['Actual 1','Actual 0','Actual -1'],\n",
    "                annot=True, fmt='d', cbar=False,cmap=\"YlGnBu\")\n",
    "    plt.title('Confusion Matrix for '+classifier)\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.savefig(\"confusion_matrix.png\")\n",
    "    plt.show()\n",
    "    print(classifier+' Classification Report ')\n",
    "    print('======================================================')\n",
    "    print(classification_report(y_test,y_predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae1ee37",
   "metadata": {},
   "source": [
    "### Individual Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dd9df31e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\t Decision Tree\n",
      "================================================================\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Classification Report \n",
      "======================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.75      0.49      0.59      1562\n",
      "           0       0.60      0.78      0.68      2193\n",
      "           1       0.75      0.71      0.73      1741\n",
      "\n",
      "    accuracy                           0.67      5496\n",
      "   macro avg       0.70      0.66      0.67      5496\n",
      "weighted avg       0.69      0.67      0.67      5496\n",
      "\n",
      "Training Accuracy : 0.6756       ||  0.67 seconds\n",
      "\t\t\t SVC\n",
      "================================================================\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC Classification Report \n",
      "======================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.79      0.41      0.54      1562\n",
      "           0       0.55      0.87      0.68      2193\n",
      "           1       0.82      0.58      0.68      1741\n",
      "\n",
      "    accuracy                           0.65      5496\n",
      "   macro avg       0.72      0.62      0.63      5496\n",
      "weighted avg       0.70      0.65      0.64      5496\n",
      "\n",
      "Training Accuracy : 0.6579       ||  40.07 seconds\n",
      "\t\t\t Logistic Regression\n",
      "================================================================\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Classification Report \n",
      "======================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.71      0.59      0.65      1562\n",
      "           0       0.62      0.76      0.69      2193\n",
      "           1       0.79      0.69      0.73      1741\n",
      "\n",
      "    accuracy                           0.69      5496\n",
      "   macro avg       0.71      0.68      0.69      5496\n",
      "weighted avg       0.70      0.69      0.69      5496\n",
      "\n",
      "Training Accuracy : 0.7223       ||  0.22 seconds\n",
      "\t\t\t Multinomail Naive Bayes\n",
      "================================================================\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomail Naive Bayes Classification Report \n",
      "======================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.84      0.30      0.45      1562\n",
      "           0       0.52      0.90      0.66      2193\n",
      "           1       0.81      0.52      0.64      1741\n",
      "\n",
      "    accuracy                           0.61      5496\n",
      "   macro avg       0.72      0.57      0.58      5496\n",
      "weighted avg       0.70      0.61      0.59      5496\n",
      "\n",
      "Training Accuracy : 0.6250       ||  0.01 seconds\n",
      "\t\t\t KNN\n",
      "================================================================\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Classification Report \n",
      "======================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.67      0.22      0.33      1562\n",
      "           0       0.45      0.90      0.60      2193\n",
      "           1       0.77      0.27      0.40      1741\n",
      "\n",
      "    accuracy                           0.51      5496\n",
      "   macro avg       0.63      0.46      0.45      5496\n",
      "weighted avg       0.61      0.51      0.46      5496\n",
      "\n",
      "Training Accuracy : 0.5451       ||  0.00 seconds\n",
      "\t\t\t Random Forest\n",
      "================================================================\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classification Report \n",
      "======================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.78      0.46      0.58      1562\n",
      "           0       0.59      0.81      0.68      2193\n",
      "           1       0.77      0.69      0.73      1741\n",
      "\n",
      "    accuracy                           0.67      5496\n",
      "   macro avg       0.71      0.65      0.66      5496\n",
      "weighted avg       0.70      0.67      0.67      5496\n",
      "\n",
      "Training Accuracy : 0.6787     ||  2.32 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Classifier Classification Report \n",
      "======================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.77      0.41      0.54      1562\n",
      "           0       0.55      0.85      0.67      2193\n",
      "           1       0.79      0.60      0.68      1741\n",
      "\n",
      "    accuracy                           0.64      5496\n",
      "   macro avg       0.71      0.62      0.63      5496\n",
      "weighted avg       0.69      0.64      0.64      5496\n",
      "\n",
      "Training Accuracy : 0.6618       ||  14.04 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extremely Randomized Classifier Classification Report \n",
      "======================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.77      0.48      0.59      1562\n",
      "           0       0.59      0.80      0.68      2193\n",
      "           1       0.78      0.69      0.73      1741\n",
      "\n",
      "    accuracy                           0.68      5496\n",
      "   macro avg       0.71      0.66      0.67      5496\n",
      "weighted avg       0.70      0.68      0.67      5496\n",
      "\n",
      "Training Accuracy : 0.6853       ||  2.57 seconds\n"
     ]
    }
   ],
   "source": [
    "modelDT = DecisionTreeClassifier(random_state=1,max_leaf_nodes=65,max_depth=100)\n",
    "start_time = time.time()\n",
    "modelDT.fit(X_train, y_train)\n",
    "run_time = time.time() - start_time\n",
    "predicted_DT=modelDT.predict(X_test)\n",
    "score_DT=accuracy_score(predicted_DT,y_test)\n",
    "print('\\t\\t\\t Decision Tree')\n",
    "print('================================================================\\n')\n",
    "confusion_matrix_evaluation('Decision Tree',predicted_DT)\n",
    "print('Training Accuracy : %.4f       ||  %4.2f seconds' % (modelDT.score(X_train, y_train),run_time))\n",
    "\n",
    "\n",
    "modelSVC = SVC(C=0.1,kernel='linear',random_state=1)\n",
    "start_time = time.time()\n",
    "modelSVC.fit(X_train, y_train)\n",
    "run_time = time.time() - start_time\n",
    "predicted_SVC=modelSVC.predict(X_test)\n",
    "\n",
    "print('\\t\\t\\t SVC')\n",
    "print('================================================================\\n')\n",
    "confusion_matrix_evaluation('SVC',predicted_SVC)\n",
    "print('Training Accuracy : %.4f       ||  %4.2f seconds' % (modelSVC.score(X_train, y_train),run_time))\n",
    "\n",
    "\n",
    "modelLR = LogisticRegression(multi_class='multinomial',random_state=1,solver='saga')\n",
    "start_time = time.time()\n",
    "modelLR.fit(X_train, y_train)\n",
    "run_time = time.time() - start_time\n",
    "predicted_LR=modelLR.predict(X_test)\n",
    "score_LR=accuracy_score(predicted_LR,y_test)\n",
    "\n",
    "\n",
    "print('\\t\\t\\t Logistic Regression')\n",
    "print('================================================================\\n')\n",
    "confusion_matrix_evaluation('Logistic Regression',predicted_LR)\n",
    "print('Training Accuracy : %.4f       ||  %4.2f seconds' % (modelLR.score(X_train, y_train),run_time))\n",
    "\n",
    "\n",
    "modelMNB=MultinomialNB(alpha=20)\n",
    "start_time = time.time()\n",
    "modelMNB.fit(X_train, y_train)\n",
    "run_time = time.time() - start_time\n",
    "predicted_MNB=modelMNB.predict(X_test)\n",
    "score_MNB=accuracy_score(predicted_MNB,y_test)\n",
    "\n",
    "\n",
    "print('\\t\\t\\t Multinomail Naive Bayes')\n",
    "print('================================================================\\n')\n",
    "confusion_matrix_evaluation('Multinomail Naive Bayes',predicted_MNB)\n",
    "print('Training Accuracy : %.4f       ||  %4.2f seconds' % (modelMNB.score(X_train, y_train),run_time))\n",
    "\n",
    "\n",
    "modelKNN = KNeighborsClassifier(n_neighbors=13)\n",
    "start_time = time.time()\n",
    "modelKNN.fit(X_train, y_train)\n",
    "run_time = time.time() - start_time\n",
    "predictedKNN=modelKNN.predict(X_test)\n",
    "score_KNN=accuracy_score(predictedKNN,y_test)\n",
    "\n",
    "\n",
    "print('\\t\\t\\t KNN')\n",
    "print('================================================================\\n')\n",
    "confusion_matrix_evaluation('KNN',predictedKNN)\n",
    "print('Training Accuracy : %.4f       ||  %4.2f seconds' % (modelKNN.score(X_train, y_train),run_time))\n",
    "\n",
    "modelRF = RandomForestClassifier(random_state=1,max_leaf_nodes=65,max_depth=100)\n",
    "start_time = time.time()\n",
    "modelRF.fit(X_train, y_train)\n",
    "run_time = time.time() - start_time\n",
    "predictedRF=modelRF.predict(X_test)\n",
    "score_RF=accuracy_score(predictedRF,y_test)\n",
    "\n",
    "\n",
    "print('\\t\\t\\t Random Forest')\n",
    "print('================================================================\\n')\n",
    "confusion_matrix_evaluation('Random Forest',predictedRF)\n",
    "print('Training Accuracy : %.4f     ||  %4.2f seconds' % (modelRF.score(X_train, y_train),run_time))\n",
    "\n",
    "\n",
    "modelGBC = GradientBoostingClassifier(random_state=1,max_depth=3,max_leaf_nodes=40)\n",
    "start_time = time.time()\n",
    "modelGBC.fit(X_train, y_train)\n",
    "predictedGBC = modelGBC.predict(X_test)\n",
    "run_time = time.time() - start_time\n",
    "confusion_matrix_evaluation('Gradient Boosting Classifier',predictedGBC)\n",
    "print('Training Accuracy : %.4f       ||  %4.2f seconds' % (modelGBC.score(X_train, y_train),run_time))\n",
    "\n",
    "\n",
    "modelETC = ExtraTreesClassifier(random_state=1,max_depth=80,max_leaf_nodes=100)\n",
    "start_time = time.time()\n",
    "modelETC.fit(X_train, y_train)\n",
    "predictedETC = modelETC.predict(X_test)\n",
    "run_time = time.time() - start_time\n",
    "confusion_matrix_evaluation('Extremely Randomized Classifier',predictedETC)\n",
    "print('Training Accuracy : %.4f       ||  %4.2f seconds' % (modelETC.score(X_train, y_train),run_time))"
   ]
  }
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