Machine Learning on NLP Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80eed180",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4786ea35",
   "metadata": {},
   "outputs": [],
   "source": [
    "mmt = pd.read_csv(r\"C:\\Users\\mohds\\Downloads\\Sem -3\\NLP Project\\Project\\Sentiment Dataset\\MakeMyTrip.csv\")\n",
    "yatra = pd.read_csv(r\"C:\\Users\\mohds\\Downloads\\Sem -3\\NLP Project\\Project\\Sentiment Dataset\\yatra.csv\")\n",
    "booking = pd.read_csv(r\"C:\\Users\\mohds\\Downloads\\Sem -3\\NLP Project\\Project\\Sentiment Dataset\\booking.csv\")\n",
    "goibibo = pd.read_csv(r\"C:\\Users\\mohds\\Downloads\\Sem -3\\NLP Project\\Project\\Sentiment Dataset\\Goibibo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd02e4ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_description    0\n",
       "polarity              0\n",
       "Sentiment             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([mmt, yatra, booking, goibibo], axis = 0)\n",
    "data.reset_index(inplace=True)\n",
    "data.drop('index', axis=1, inplace=True)\n",
    "data.dropna(inplace=True)\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3c2909",
   "metadata": {},
   "source": [
    "- 8 duplicate instances were removed, while no null values are present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27ef7970",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Data: (7992, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_description</th>\n",
       "      <th>polarity</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>different another try connect service call dis...</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hope review someone bad mood trip price higher...</td>\n",
       "      <td>0.135714</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abysmally poor service would mistake rely make...</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>many lack try senior able time senior citizen ...</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>immediate travel encounter technical issue tra...</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7989</th>\n",
       "      <td>cash scam waste keep open time answer earn tra...</td>\n",
       "      <td>-0.078571</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7990</th>\n",
       "      <td>highly worst service name covid care fool arou...</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7991</th>\n",
       "      <td>fact try level best get money back unfortunate...</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7992</th>\n",
       "      <td>actively last last week first time raise suppo...</td>\n",
       "      <td>0.088889</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7993</th>\n",
       "      <td>awful yet show service also respond bad experi...</td>\n",
       "      <td>-0.850000</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7992 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     review_description  polarity Sentiment\n",
       "0     different another try connect service call dis... -0.250000  Negative\n",
       "1     hope review someone bad mood trip price higher...  0.135714  Positive\n",
       "2     abysmally poor service would mistake rely make... -0.133333  Negative\n",
       "3     many lack try senior able time senior citizen ...  0.218750  Positive\n",
       "4     immediate travel encounter technical issue tra...  0.300000  Positive\n",
       "...                                                 ...       ...       ...\n",
       "7989  cash scam waste keep open time answer earn tra... -0.078571  Negative\n",
       "7990  highly worst service name covid care fool arou... -0.400000  Negative\n",
       "7991  fact try level best get money back unfortunate...  0.150000  Positive\n",
       "7992  actively last last week first time raise suppo...  0.088889  Positive\n",
       "7993  awful yet show service also respond bad experi... -0.850000  Negative\n",
       "\n",
       "[7992 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Shape of Data: {data.shape}\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af4ec0a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data['review_description'], data['Sentiment'], test_size=0.2, random_state=42)\n",
    "\n",
    "# Convert text data to TF-IDF features\n",
    "tfidf_vectorizer = TfidfVectorizer(use_idf=True, \n",
    "                                   stop_words='english', \n",
    "                                   analyzer='word')\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a33d36",
   "metadata": {},
   "source": [
    "- IF-IDF is a numerical statistic that reflects the importance of a word in a document relative to collection of document.\n",
    "- It is used to convert text data into a numerical format, enabling machine learning algorithm to process and analyse textual information.\n",
    "- It helps in retrieving relevant documents from in large document based on the importance of terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6162ca5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<6393x3631 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 82890 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_tfidf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "034c859b",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "473cece4",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'max_depth': [2,3,4,5], \n",
    "          'min_samples_split': [100, 150, 200, 250, 225],\n",
    "          'min_samples_leaf': [50, 75, 115, 100]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b6b0667f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 80 candidates, totalling 800 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "             param_grid={&#x27;max_depth&#x27;: [2, 3, 4, 5],\n",
       "                         &#x27;min_samples_leaf&#x27;: [50, 75, 115, 100],\n",
       "                         &#x27;min_samples_split&#x27;: [100, 150, 200, 250, 225]},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 3, 4, 5],\n",
       "                         &#x27;min_samples_leaf&#x27;: [50, 75, 115, 100],\n",
       "                         &#x27;min_samples_split&#x27;: [100, 150, 200, 250, 225]},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'max_depth': [2, 3, 4, 5],\n",
       "                         'min_samples_leaf': [50, 75, 115, 100],\n",
       "                         'min_samples_split': [100, 150, 200, 250, 225]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "gridcv = GridSearchCV(DecisionTreeClassifier(), \n",
    "                      params, \n",
    "                      verbose = 1,\n",
    "                      cv = 10)\n",
    "gridcv.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "29ea3837",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "ModelDT = DecisionTreeClassifier(**gridcv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9b9d193d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5, min_samples_leaf=50, min_samples_split=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5, min_samples_leaf=50, min_samples_split=100)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5, min_samples_leaf=50, min_samples_split=100)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelDT.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "86e3e01b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train_DT = ModelDT.predict(X_train_tfidf)\n",
    "y_pred_test_DT = ModelDT.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4cb7c376",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report on Train (Decision Tree):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.92      0.73      0.81      3059\n",
      "    Positive       0.79      0.95      0.86      3334\n",
      "\n",
      "    accuracy                           0.84      6393\n",
      "   macro avg       0.86      0.84      0.84      6393\n",
      "weighted avg       0.85      0.84      0.84      6393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report on Train (Decision Tree):\\n\", classification_report(y_train, y_pred_train_DT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "81e2247e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report on Test (Decision Tree):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.92      0.71      0.80       756\n",
      "    Positive       0.78      0.94      0.86       843\n",
      "\n",
      "    accuracy                           0.83      1599\n",
      "   macro avg       0.85      0.83      0.83      1599\n",
      "weighted avg       0.85      0.83      0.83      1599\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report on Test (Decision Tree):\\n\", classification_report(y_test, y_pred_test_DT))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "160802b5",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0e713403",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 80 candidates, totalling 800 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 3, 4, 5],\n",
       "                         &#x27;min_samples_leaf&#x27;: [50, 75, 115, 100],\n",
       "                         &#x27;min_samples_split&#x27;: [100, 150, 200, 250, 225]},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 3, 4, 5],\n",
       "                         &#x27;min_samples_leaf&#x27;: [50, 75, 115, 100],\n",
       "                         &#x27;min_samples_split&#x27;: [100, 150, 200, 250, 225]},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestClassifier(),\n",
       "             param_grid={'max_depth': [2, 3, 4, 5],\n",
       "                         'min_samples_leaf': [50, 75, 115, 100],\n",
       "                         'min_samples_split': [100, 150, 200, 250, 225]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "gridcv = GridSearchCV(RandomForestClassifier(), \n",
    "                      params, \n",
    "                      verbose = 1,\n",
    "                      cv = 10)\n",
    "gridcv.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d3310e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "ModelRF = RandomForestClassifier(**gridcv.best_params_, \n",
    "                                 n_estimators = 25, \n",
    "                                 criterion = 'gini', \n",
    "                                 max_features='sqrt'\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6acafdb1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, min_samples_leaf=50, min_samples_split=100,\n",
       "                       n_estimators=25)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5, min_samples_leaf=50, min_samples_split=100,\n",
       "                       n_estimators=25)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=5, min_samples_leaf=50, min_samples_split=100,\n",
       "                       n_estimators=25)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelRF.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7678c34a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train_RF = ModelRF.predict(X_train_tfidf)\n",
    "y_pred_test_RF = ModelRF.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a939e408",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report on Train (Random Forest):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.85      0.65      0.74      3059\n",
      "    Positive       0.74      0.89      0.81      3334\n",
      "\n",
      "    accuracy                           0.78      6393\n",
      "   macro avg       0.79      0.77      0.77      6393\n",
      "weighted avg       0.79      0.78      0.77      6393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report on Train (Random Forest):\\n\", classification_report(y_train, y_pred_train_RF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "763ed42d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report on Test (Random Forest):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.85      0.63      0.73       756\n",
      "    Positive       0.73      0.90      0.81       843\n",
      "\n",
      "    accuracy                           0.77      1599\n",
      "   macro avg       0.79      0.77      0.77      1599\n",
      "weighted avg       0.79      0.77      0.77      1599\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report on Test (Random Forest):\\n\", classification_report(y_test, y_pred_test_RF))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5def83bf",
   "metadata": {},
   "source": [
    "### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "310b78b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'max_depth': [2,3,4,5], \n",
    "          'min_samples_split': [100, 150, 200, 250, 225],\n",
    "          'min_samples_leaf': [50, 75, 115, 100], \n",
    "          'learning_rate':[0.7, 0.6], \n",
    "          'n_estimators': [25]\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af7ffd12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 160 candidates, totalling 1600 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10,\n",
       "             estimator=GradientBoostingClassifier(criterion=&#x27;squared_error&#x27;,\n",
       "                                                  random_state=42),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.7, 0.6], &#x27;max_depth&#x27;: [2, 3, 4, 5],\n",
       "                         &#x27;min_samples_leaf&#x27;: [50, 75, 115, 100],\n",
       "                         &#x27;min_samples_split&#x27;: [100, 150, 200, 250, 225],\n",
       "                         &#x27;n_estimators&#x27;: [25]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10,\n",
       "             estimator=GradientBoostingClassifier(criterion=&#x27;squared_error&#x27;,\n",
       "                                                  random_state=42),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.7, 0.6], &#x27;max_depth&#x27;: [2, 3, 4, 5],\n",
       "                         &#x27;min_samples_leaf&#x27;: [50, 75, 115, 100],\n",
       "                         &#x27;min_samples_split&#x27;: [100, 150, 200, 250, 225],\n",
       "                         &#x27;n_estimators&#x27;: [25]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(criterion=&#x27;squared_error&#x27;, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(criterion=&#x27;squared_error&#x27;, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=GradientBoostingClassifier(criterion='squared_error',\n",
       "                                                  random_state=42),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.7, 0.6], 'max_depth': [2, 3, 4, 5],\n",
       "                         'min_samples_leaf': [50, 75, 115, 100],\n",
       "                         'min_samples_split': [100, 150, 200, 250, 225],\n",
       "                         'n_estimators': [25]},\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "model = GradientBoostingClassifier(random_state=42,loss=\"log_loss\",criterion=\"squared_error\")\n",
    "\n",
    "gscv_GBM = GridSearchCV(estimator=model, \n",
    "                    param_grid=params,\n",
    "                    cv=10,\n",
    "                    verbose=1,\n",
    "                    n_jobs=-1,\n",
    "                    scoring='accuracy')\n",
    "\n",
    "gscv_GBM.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef5535eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.6, max_depth=4, min_samples_leaf=50,\n",
       "                           min_samples_split=150, n_estimators=25)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.6, max_depth=4, min_samples_leaf=50,\n",
       "                           min_samples_split=150, n_estimators=25)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.6, max_depth=4, min_samples_leaf=50,\n",
       "                           min_samples_split=150, n_estimators=25)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model_GB = GradientBoostingClassifier(**gscv_GBM.best_params_)\n",
    "Model_GB.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80bab13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train_GB = Model_GB.predict(X_train_tfidf)\n",
    "y_pred_test_GB = Model_GB.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e7d084c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report on Train (Gradient Boost):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.94      0.84      0.89      3059\n",
      "    Positive       0.87      0.95      0.91      3334\n",
      "\n",
      "    accuracy                           0.90      6393\n",
      "   macro avg       0.91      0.90      0.90      6393\n",
      "weighted avg       0.90      0.90      0.90      6393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report on Train (Gradient Boost):\\n\", classification_report(y_train, y_pred_train_GB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed5d428b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report on Test (Gradient Boost):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.93      0.83      0.88       756\n",
      "    Positive       0.86      0.94      0.90       843\n",
      "\n",
      "    accuracy                           0.89      1599\n",
      "   macro avg       0.89      0.89      0.89      1599\n",
      "weighted avg       0.89      0.89      0.89      1599\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report on Test (Gradient Boost):\\n\", classification_report(y_test, y_pred_test_GB))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b6797d",
   "metadata": {},
   "source": [
    "### Navie-Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "509d9d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BernoulliNB(alpha=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BernoulliNB</label><div class=\"sk-toggleable__content\"><pre>BernoulliNB(alpha=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BernoulliNB(alpha=1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "\n",
    "model = BernoulliNB(alpha=1)  # Laplace smoothing (alpha) = 1\n",
    "model.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0989343c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train_NB = model.predict(X_train_tfidf)\n",
    "y_pred_test_NB = model.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "62a95c16",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report on Train (Naive-Bayes):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.81      0.90      0.85      3059\n",
      "    Positive       0.90      0.80      0.85      3334\n",
      "\n",
      "    accuracy                           0.85      6393\n",
      "   macro avg       0.85      0.85      0.85      6393\n",
      "weighted avg       0.85      0.85      0.85      6393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report on Train (Naive-Bayes):\\n\", classification_report(y_train, y_pred_train_NB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b00a1149",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report on Test (Naive-Bayes):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.76      0.86      0.81       756\n",
      "    Positive       0.86      0.76      0.81       843\n",
      "\n",
      "    accuracy                           0.81      1599\n",
      "   macro avg       0.81      0.81      0.81      1599\n",
      "weighted avg       0.81      0.81      0.81      1599\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report on Test (Naive-Bayes):\\n\", classification_report(y_test, y_pred_test_NB))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25a54080",
   "metadata": {},
   "source": [
    "### Accuracy Comparision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "007c7af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "def model_evaluation(prediction, actual):\n",
    "    accuracy = accuracy_score(prediction, actual)\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "611fab86",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_train = model_evaluation(y_pred_train_DT, y_train)\n",
    "dt_test = model_evaluation(y_pred_test_DT, y_test)\n",
    "rf_train = model_evaluation(y_pred_train_RF, y_train)\n",
    "rf_test = model_evaluation(y_pred_test_RF, y_test)\n",
    "gb_train = model_evaluation(y_pred_train_GB, y_train)\n",
    "gb_test = model_evaluation(y_pred_test_GB, y_test)\n",
    "nb_train = model_evaluation(y_pred_train_NB, y_train)\n",
    "nb_test = model_evaluation(y_pred_test_NB, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "98982e21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the 'Decision Tree Train Model' is : 84.0%\n",
      "Accuracy of the 'Decision Tree Test Model' is : 83.0%\n",
      "\n",
      "Accuracy of the 'Random Forest Train Model' is : 78.0%\n",
      "Accuracy of the 'Random Forest Test Model' is : 77.0%\n",
      "\n",
      "Accuracy of the 'Gradient Boosting Train Model' is : 90.0%\n",
      "Accuracy of the 'Gradient Boosting Test Model' is : 89.0%\n",
      "\n",
      "Accuracy of the 'Naive-Bayes Train Model' is : 85.0%\n",
      "Accuracy of the 'Naive-Bayes Test Model' is : 81.0%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy of the 'Decision Tree Train Model' is : {np.round(dt_train*100)}%\")\n",
    "print(f\"Accuracy of the 'Decision Tree Test Model' is : {np.round(dt_test*100)}%\")\n",
    "print()\n",
    "print(f\"Accuracy of the 'Random Forest Train Model' is : {np.round(rf_train*100)}%\")\n",
    "print(f\"Accuracy of the 'Random Forest Test Model' is : {np.round(rf_test*100)}%\")\n",
    "print()\n",
    "print(f\"Accuracy of the 'Gradient Boosting Train Model' is : {np.round(gb_train*100)}%\")\n",
    "print(f\"Accuracy of the 'Gradient Boosting Test Model' is : {np.round(gb_test*100)}%\")\n",
    "print()\n",
    "print(f\"Accuracy of the 'Naive-Bayes Train Model' is : {np.round(nb_train*100)}%\")\n",
    "print(f\"Accuracy of the 'Naive-Bayes Test Model' is : {np.round(nb_test*100)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12e62279",
   "metadata": {},
   "source": [
    "- The models show a progression in performance from Decision Tree to Random Forest to Naive-Bayes and finally to Gradient Boosting.\n",
    "- `Gradient Boosting` with an accuracy of `90% on Train` and `89% on Test`, appears to be the most effective model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "253d5857",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b3eabe56",
   "metadata": {},
   "source": [
    "##### Model Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2049316e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(Model_GB, open(r\"C:\\Users\\mohds\\Downloads\\Sem -3\\NLP Project\\Project\\Deployment\\build.pkl\", 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5404ba83",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(tfidf_vectorizer, open(r\"C:\\Users\\mohds\\Downloads\\Sem -3\\NLP Project\\Project\\Deployment\\tfidf_vectorizer.pkl\", 'wb'))"
   ]
  }
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