3. Model Building Resumes Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37dc384d",
   "metadata": {},
   "source": [
    "## 1. Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "92cac3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import f1_score, classification_report, precision_score, recall_score\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9eaca50",
   "metadata": {},
   "source": [
    "## 2. Import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c85c7638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Category</th>\n",
       "      <th>Resume_Details</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PeopleSoft</td>\n",
       "      <td>anubhav kumar singh work globally competitive ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PeopleSoft</td>\n",
       "      <td>ananda rayudu profile summary years experience...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PeopleSoft</td>\n",
       "      <td>peoplesoft database administrator gangareddy p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PeopleSoft</td>\n",
       "      <td>classification internal murali experience summ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PeopleSoft</td>\n",
       "      <td>priyanka ramadoss mountpleasant coonoor nilgir...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category                                     Resume_Details\n",
       "0  PeopleSoft  anubhav kumar singh work globally competitive ...\n",
       "1  PeopleSoft  ananda rayudu profile summary years experience...\n",
       "2  PeopleSoft  peoplesoft database administrator gangareddy p...\n",
       "3  PeopleSoft  classification internal murali experience summ...\n",
       "4  PeopleSoft  priyanka ramadoss mountpleasant coonoor nilgir..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resume_data = pd.read_csv('Cleaned_Resumes.csv')\n",
    "resume_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef25b879",
   "metadata": {},
   "source": [
    "## 3. Data Preprocessing\n",
    "### 3.1 LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4771083a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Category</th>\n",
       "      <th>Resume_Details</th>\n",
       "      <th>LebelEncoding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PeopleSoft</td>\n",
       "      <td>anubhav kumar singh work globally competitive ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PeopleSoft</td>\n",
       "      <td>ananda rayudu profile summary years experience...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PeopleSoft</td>\n",
       "      <td>peoplesoft database administrator gangareddy p...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PeopleSoft</td>\n",
       "      <td>classification internal murali experience summ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PeopleSoft</td>\n",
       "      <td>priyanka ramadoss mountpleasant coonoor nilgir...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Workday</td>\n",
       "      <td>workday integration consultant name sri krishn...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Workday</td>\n",
       "      <td>seeking suitable positions workday hcm techno ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Workday</td>\n",
       "      <td>workday hcm fcm name role kumar workday consul...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Workday</td>\n",
       "      <td>venkateswarlu workday consultant professional ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Workday</td>\n",
       "      <td>vinay kumar workday functional consultant expe...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Category                                     Resume_Details  \\\n",
       "0   PeopleSoft  anubhav kumar singh work globally competitive ...   \n",
       "1   PeopleSoft  ananda rayudu profile summary years experience...   \n",
       "2   PeopleSoft  peoplesoft database administrator gangareddy p...   \n",
       "3   PeopleSoft  classification internal murali experience summ...   \n",
       "4   PeopleSoft  priyanka ramadoss mountpleasant coonoor nilgir...   \n",
       "..         ...                                                ...   \n",
       "74     Workday  workday integration consultant name sri krishn...   \n",
       "75     Workday  seeking suitable positions workday hcm techno ...   \n",
       "76     Workday  workday hcm fcm name role kumar workday consul...   \n",
       "77     Workday  venkateswarlu workday consultant professional ...   \n",
       "78     Workday  vinay kumar workday functional consultant expe...   \n",
       "\n",
       "    LebelEncoding  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "..            ...  \n",
       "74              3  \n",
       "75              3  \n",
       "76              3  \n",
       "77              3  \n",
       "78              3  \n",
       "\n",
       "[79 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "Encoder=LabelEncoder()\n",
    "resume_data[\"LebelEncoding\"]=Encoder.fit_transform(resume_data [\"Category\"])\n",
    "\n",
    "resume_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "794458f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>LebelEncoding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.455696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.141271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LebelEncoding\n",
       "count      79.000000\n",
       "mean        1.455696\n",
       "std         1.141271\n",
       "min         0.000000\n",
       "25%         0.500000\n",
       "50%         1.000000\n",
       "75%         3.000000\n",
       "max         3.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resume_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc7ce797",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category          0\n",
       "Resume_Details    0\n",
       "LebelEncoding     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resume_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca6fcec",
   "metadata": {},
   "source": [
    "## 4. Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f672abd",
   "metadata": {},
   "source": [
    "### 4.1 Train - Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "387f920d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = resume_data['Resume_Details'].values\n",
    "y = resume_data['Category'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "145860f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((59,), (20,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, random_state=45, test_size=0.25,stratify=y)\n",
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dfca181",
   "metadata": {},
   "source": [
    "### 4.2 TF-IDF Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b7bdce45",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vector = TfidfVectorizer(sublinear_tf=True,stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba2bd821",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = tfidf_vector.fit_transform(x_train)\n",
    "x_test = tfidf_vector.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a48b815f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((59, 3798), (20, 3798))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a141083f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PeopleSoft' 'React JS Developer' 'PeopleSoft' 'Workday' 'SQL Developer'\n",
      " 'SQL Developer' 'React JS Developer' 'PeopleSoft' 'React JS Developer'\n",
      " 'PeopleSoft' 'React JS Developer' 'SQL Developer' 'React JS Developer'\n",
      " 'React JS Developer' 'Workday' 'React JS Developer' 'SQL Developer'\n",
      " 'React JS Developer' 'PeopleSoft' 'React JS Developer'\n",
      " 'React JS Developer' 'Workday' 'PeopleSoft' 'PeopleSoft' 'PeopleSoft'\n",
      " 'SQL Developer' 'Workday' 'Workday' 'Workday' 'React JS Developer'\n",
      " 'Workday' 'React JS Developer' 'SQL Developer' 'Workday'\n",
      " 'React JS Developer' 'Workday' 'SQL Developer' 'Workday' 'PeopleSoft'\n",
      " 'PeopleSoft' 'SQL Developer' 'PeopleSoft' 'Workday' 'SQL Developer'\n",
      " 'PeopleSoft' 'Workday' 'PeopleSoft' 'SQL Developer' 'Workday' 'Workday'\n",
      " 'Workday' 'PeopleSoft' 'React JS Developer' 'React JS Developer'\n",
      " 'React JS Developer' 'PeopleSoft' 'React JS Developer'\n",
      " 'React JS Developer' 'Workday']\n",
      "['PeopleSoft' 'PeopleSoft' 'React JS Developer' 'Workday' 'Workday'\n",
      " 'React JS Developer' 'React JS Developer' 'React JS Developer'\n",
      " 'PeopleSoft' 'React JS Developer' 'SQL Developer' 'SQL Developer'\n",
      " 'SQL Developer' 'SQL Developer' 'Workday' 'Workday' 'PeopleSoft'\n",
      " 'Workday' 'PeopleSoft' 'React JS Developer']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(y_train),print(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a77df8c",
   "metadata": {},
   "source": [
    "# 5. Classification Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d1ee97b",
   "metadata": {},
   "source": [
    "## 5.1 KNN Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "80046a1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of training set : 0.66\n",
      "Accuracy of  test set    : 0.60\n",
      "Classification report for classifier KNeighborsClassifier(n_neighbors=41):\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "        PeopleSoft       0.33      0.20      0.25         5\n",
      "React JS Developer       0.86      1.00      0.92         6\n",
      "     SQL Developer       0.00      0.00      0.00         4\n",
      "           Workday       0.50      1.00      0.67         5\n",
      "\n",
      "          accuracy                           0.60        20\n",
      "         macro avg       0.42      0.55      0.46        20\n",
      "      weighted avg       0.47      0.60      0.51        20\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_knn = KNeighborsClassifier(n_neighbors=41)\n",
    "model_knn.fit(x_train, y_train)\n",
    "y_pred = model_knn.predict(x_test)\n",
    "accuracy_knn = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy of training set : {:.2f}'.format(model_knn.score(x_train, y_train)))\n",
    "print('Accuracy of  test set    : {:.2f}'.format(model_knn.score(x_test, y_test)))\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\" % (model_knn,classification_report(y_test, y_pred)))\n",
    "nb_score = model_knn.score(x_test, y_test)\n",
    "nb_cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "precision_knn = round(precision_score(y_test,y_pred,average = 'macro'),2)\n",
    "recall_knn = round(recall_score(y_test,y_pred, average = 'macro'),2)\n",
    "f1_knn = round(f1_score(y_test,y_pred, average = 'macro'),2)\n",
    "accuracy_knn = round(accuracy_score(y_test,y_pred),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e78e6eb5",
   "metadata": {},
   "source": [
    "## 5.2 Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8ecee7f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of training set : 1.00\n",
      "Accuracy of  test set    : 1.00\n",
      "Classification report for classifier DecisionTreeClassifier():\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "        PeopleSoft       1.00      1.00      1.00         5\n",
      "React JS Developer       1.00      1.00      1.00         6\n",
      "     SQL Developer       1.00      1.00      1.00         4\n",
      "           Workday       1.00      1.00      1.00         5\n",
      "\n",
      "          accuracy                           1.00        20\n",
      "         macro avg       1.00      1.00      1.00        20\n",
      "      weighted avg       1.00      1.00      1.00        20\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_DT = DecisionTreeClassifier(criterion='gini')\n",
    "model_DT.fit(x_train, y_train)\n",
    "y_pred = model_DT.predict(x_test)\n",
    "accuracy_DT = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy of training set : {:.2f}'.format(model_DT.score(x_train, y_train)))\n",
    "print('Accuracy of  test set    : {:.2f}'.format(model_DT.score(x_test, y_test)))\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\" % (model_DT,classification_report(y_test, y_pred)))\n",
    "nb_score = model_DT.score(x_test, y_test)\n",
    "nb_cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "precision_DT = round(precision_score(y_test,y_pred,average = 'macro'),2)\n",
    "recall_DT= round(recall_score(y_test,y_pred, average = 'macro'),2)\n",
    "f1_DT = round(f1_score(y_test,y_pred, average = 'macro'),2)\n",
    "accuracy_DT = round(accuracy_score(y_test,y_pred),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d124315",
   "metadata": {},
   "source": [
    "## 5.3 Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b46ada3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of training set : 1.00\n",
      "Accuracy of  test set    : 0.95\n",
      "Classification report for classifier RandomForestClassifier(n_estimators=200):\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "        PeopleSoft       1.00      0.80      0.89         5\n",
      "React JS Developer       0.86      1.00      0.92         6\n",
      "     SQL Developer       1.00      1.00      1.00         4\n",
      "           Workday       1.00      1.00      1.00         5\n",
      "\n",
      "          accuracy                           0.95        20\n",
      "         macro avg       0.96      0.95      0.95        20\n",
      "      weighted avg       0.96      0.95      0.95        20\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_RF = RandomForestClassifier(n_estimators=200)\n",
    "model_RF.fit(x_train, y_train)\n",
    "y_pred = model_RF.predict(x_test)\n",
    "accuracy_RF = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy of training set : {:.2f}'.format(model_RF.score(x_train, y_train)))\n",
    "print('Accuracy of  test set    : {:.2f}'.format(model_RF.score(x_test, y_test)))\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\" % (model_RF,classification_report(y_test, y_pred)))\n",
    "nb_score = model_RF.score(x_test, y_test)\n",
    "nb_cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "\n",
    "precision_RF = round(precision_score(y_test,y_pred,average = 'macro'),2)\n",
    "recall_RF = round(recall_score(y_test,y_pred, average = 'macro'),2)\n",
    "f1_RF = round(f1_score(y_test,y_pred, average = 'macro'),2)\n",
    "accuracy_RF = round(accuracy_score(y_test,y_pred),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "715565fe",
   "metadata": {},
   "source": [
    "## 5.4 Support Vectore Machine (SVM) Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77925379",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of training set : 1.00\n",
      "Accuracy of  test set    : 0.95\n",
      "Classification report for classifier SVC():\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "        PeopleSoft       1.00      0.80      0.89         5\n",
      "React JS Developer       0.86      1.00      0.92         6\n",
      "     SQL Developer       1.00      1.00      1.00         4\n",
      "           Workday       1.00      1.00      1.00         5\n",
      "\n",
      "          accuracy                           0.95        20\n",
      "         macro avg       0.96      0.95      0.95        20\n",
      "      weighted avg       0.96      0.95      0.95        20\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_svm = SVC()\n",
    "model_svm.fit(x_train, y_train)\n",
    "y_pred = model_svm.predict(x_test)\n",
    "accuracy_svm = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy of training set : {:.2f}'.format(model_svm.score(x_train, y_train)))\n",
    "print('Accuracy of  test set    : {:.2f}'.format(model_svm.score(x_test, y_test)))\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\" % (model_svm,classification_report(y_test, y_pred)))\n",
    "nb_score = model_svm.score(x_test, y_test)\n",
    "nb_cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "precision_svm = round(precision_score(y_test,y_pred,average = 'macro'),2)\n",
    "recall_svm = round(recall_score(y_test,y_pred, average = 'macro'),2)\n",
    "f1_svm = round(f1_score(y_test,y_pred, average = 'macro'),2)\n",
    "accuracy_svm = round(accuracy_score(y_test,y_pred),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea60abff",
   "metadata": {},
   "source": [
    "## 5.5 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6818c154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of training set : 1.00\n",
      "Accuracy of  test set    : 0.95\n",
      "Classification report for classifier LogisticRegression():\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "        PeopleSoft       1.00      0.80      0.89         5\n",
      "React JS Developer       0.86      1.00      0.92         6\n",
      "     SQL Developer       1.00      1.00      1.00         4\n",
      "           Workday       1.00      1.00      1.00         5\n",
      "\n",
      "          accuracy                           0.95        20\n",
      "         macro avg       0.96      0.95      0.95        20\n",
      "      weighted avg       0.96      0.95      0.95        20\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_lgr = LogisticRegression()\n",
    "model_lgr.fit(x_train, y_train)\n",
    "y_pred = model_lgr.predict(x_test)\n",
    "accuracy_lgr = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy of training set : {:.2f}'.format(model_lgr.score(x_train, y_train)))\n",
    "print('Accuracy of  test set    : {:.2f}'.format(model_lgr.score(x_test, y_test)))\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\" % (model_lgr,classification_report(y_test, y_pred)))\n",
    "nb_score = model_lgr.score(x_test, y_test)\n",
    "nb_cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "\n",
    "precision_lgr = round(precision_score(y_test,y_pred,average = 'macro'),2)\n",
    "recall_lgr = round(recall_score(y_test,y_pred, average = 'macro'),2)\n",
    "f1_lgr = round(f1_score(y_test,y_pred, average = 'macro'),2)\n",
    "accuracy_lgr = round(accuracy_score(y_test,y_pred),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565cfd69",
   "metadata": {},
   "source": [
    "## 5.6 Bagging Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "047bc8dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of training set : 0.98\n",
      "Accuracy of  test set    : 1.00\n",
      "Classification report for classifier BaggingClassifier(base_estimator=KNeighborsClassifier(), max_features=0.5,\n",
      "                  max_samples=0.5):\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "        PeopleSoft       1.00      1.00      1.00         5\n",
      "React JS Developer       1.00      1.00      1.00         6\n",
      "     SQL Developer       1.00      1.00      1.00         4\n",
      "           Workday       1.00      1.00      1.00         5\n",
      "\n",
      "          accuracy                           1.00        20\n",
      "         macro avg       1.00      1.00      1.00        20\n",
      "      weighted avg       1.00      1.00      1.00        20\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_bagg = BaggingClassifier(KNeighborsClassifier(), max_samples=0.5, max_features=0.5)\n",
    "model_bagg.fit(x_train, y_train)\n",
    "y_pred = model_bagg.predict(x_test)\n",
    "accuracy_bagg = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy of training set : {:.2f}'.format(model_bagg.score(x_train, y_train)))\n",
    "print('Accuracy of  test set    : {:.2f}'.format(model_bagg.score(x_test, y_test)))\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\" % (model_bagg,classification_report(y_test, y_pred)))\n",
    "nb_score = model_bagg.score(x_test, y_test)\n",
    "nb_cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "precision_bagg = round(precision_score(y_test,y_pred,average = 'macro'),2)\n",
    "recall_bagg = round(recall_score(y_test,y_pred, average = 'macro'),2)\n",
    "f1_bagg = round(f1_score(y_test,y_pred, average = 'macro'),2)\n",
    "accuracy_bagg = round(accuracy_score(y_test,y_pred),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bb1008",
   "metadata": {},
   "source": [
    "## 5.7 AdaBoost Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "268a8ab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of training set : 0.75\n",
      "Accuracy of  test set    : 0.75\n",
      "Classification report for classifier AdaBoostClassifier(n_estimators=100):\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "        PeopleSoft       0.00      0.00      0.00         5\n",
      "React JS Developer       0.55      1.00      0.71         6\n",
      "     SQL Developer       1.00      1.00      1.00         4\n",
      "           Workday       1.00      1.00      1.00         5\n",
      "\n",
      "          accuracy                           0.75        20\n",
      "         macro avg       0.64      0.75      0.68        20\n",
      "      weighted avg       0.61      0.75      0.66        20\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_Adaboost = AdaBoostClassifier(n_estimators=100)\n",
    "model_Adaboost.fit(x_train, y_train)\n",
    "y_pred = model_Adaboost.predict(x_test)\n",
    "accuracy_Adaboost = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy of training set : {:.2f}'.format(model_Adaboost.score(x_train, y_train)))\n",
    "print('Accuracy of  test set    : {:.2f}'.format(model_Adaboost.score(x_test, y_test)))\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\" % (model_Adaboost,classification_report(y_test, y_pred)))\n",
    "nb_score = model_Adaboost.score(x_test, y_test)\n",
    "nb_cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "precision_Adaboost = round(precision_score(y_test,y_pred,average = 'macro'),2)\n",
    "recall_Adaboost = round(recall_score(y_test,y_pred, average = 'macro'),2)\n",
    "f1_Adaboost = round(f1_score(y_test,y_pred, average = 'macro'),2)\n",
    "accuracy_Adaboost = round(accuracy_score(y_test,y_pred),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d78406",
   "metadata": {},
   "source": [
    "## 5.8 Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aec60b97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of training set : 1.00\n",
      "Accuracy of  test set    : 1.00\n",
      "Classification report for classifier GradientBoostingClassifier(learning_rate=1.0, max_depth=1, random_state=0):\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "        PeopleSoft       1.00      1.00      1.00         5\n",
      "React JS Developer       1.00      1.00      1.00         6\n",
      "     SQL Developer       1.00      1.00      1.00         4\n",
      "           Workday       1.00      1.00      1.00         5\n",
      "\n",
      "          accuracy                           1.00        20\n",
      "         macro avg       1.00      1.00      1.00        20\n",
      "      weighted avg       1.00      1.00      1.00        20\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_GradientBoost = GradientBoostingClassifier(n_estimators=100,learning_rate=1.0,max_depth=1, random_state=0)\n",
    "model_GradientBoost.fit(x_train, y_train)\n",
    "y_pred = model_GradientBoost.predict(x_test)\n",
    "accuracy_GradientBoost = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy of training set : {:.2f}'.format(model_GradientBoost.score(x_train, y_train)))\n",
    "print('Accuracy of  test set    : {:.2f}'.format(model_GradientBoost.score(x_test, y_test)))\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\" % (model_GradientBoost,classification_report(y_test, y_pred)))\n",
    "nb_score = model_GradientBoost.score(x_test, y_test)\n",
    "nb_cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "precision_Gradientboost = round(precision_score(y_test,y_pred,average = 'macro'),2)\n",
    "recall_Gradientboost = round(recall_score(y_test,y_pred, average = 'macro'),2)\n",
    "f1_Gradientboost = round(f1_score(y_test,y_pred, average = 'macro'),2)\n",
    "accuracy_Gradientboost = round(accuracy_score(y_test,y_pred),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "201d7e29",
   "metadata": {},
   "source": [
    "## 5.9 Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3fdc2fa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of training set : 1.00\n",
      "Accuracy of  test set    : 0.95\n",
      "Classification report for classifier MultinomialNB(alpha=1, fit_prior=False):\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "        PeopleSoft       1.00      0.80      0.89         5\n",
      "React JS Developer       0.86      1.00      0.92         6\n",
      "     SQL Developer       1.00      1.00      1.00         4\n",
      "           Workday       1.00      1.00      1.00         5\n",
      "\n",
      "          accuracy                           0.95        20\n",
      "         macro avg       0.96      0.95      0.95        20\n",
      "      weighted avg       0.96      0.95      0.95        20\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_NB =MultinomialNB(alpha=1, fit_prior=False, class_prior=None)\n",
    "model_NB.fit(x_train, y_train)\n",
    "y_pred = model_NB.predict(x_test)\n",
    "accuracy_NB = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy of training set : {:.2f}'.format(model_NB.score(x_train, y_train)))\n",
    "print('Accuracy of  test set    : {:.2f}'.format(model_NB.score(x_test, y_test)))\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\" % (model_NB,classification_report(y_test, y_pred)))\n",
    "nb_score = model_NB.score(x_test, y_test)\n",
    "nb_cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "precision_NB = round(precision_score(y_test,y_pred,average = 'macro'),2)\n",
    "recall_NB = round(recall_score(y_test,y_pred, average = 'macro'),2)\n",
    "f1_NB = round(f1_score(y_test,y_pred, average = 'macro'),2)\n",
    "accuracy_NB = round(accuracy_score(y_test,y_pred),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6178e74d",
   "metadata": {},
   "source": [
    "## 6. Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7e59b26",
   "metadata": {},
   "outputs": [
    {
     "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>Models</th>\n",
       "      <th>Train_Accuracy(%)</th>\n",
       "      <th>Test_Accuracy(%)</th>\n",
       "      <th>Precision(%)</th>\n",
       "      <th>Recall(%)</th>\n",
       "      <th>F1-Score(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.661017</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTree Classifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest Classifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVM Classifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bagging Classifier</td>\n",
       "      <td>0.983051</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost Classifier</td>\n",
       "      <td>0.745763</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Naive Bayes Classifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Models  Train_Accuracy(%)  Test_Accuracy(%)  \\\n",
       "0                KNN Classifier           0.661017              0.60   \n",
       "1       DecisionTree Classifier           1.000000              1.00   \n",
       "2       RandomForest Classifier           1.000000              0.95   \n",
       "3                SVM Classifier           1.000000              0.95   \n",
       "4           Logistic Regression           1.000000              0.95   \n",
       "5            Bagging Classifier           0.983051              1.00   \n",
       "6           AdaBoost Classifier           0.745763              0.75   \n",
       "7  Gradient Boosting Classifier           1.000000              1.00   \n",
       "8        Naive Bayes Classifier           1.000000              0.95   \n",
       "\n",
       "   Precision(%)  Recall(%)  F1-Score(%)  \n",
       "0          0.42       0.55         0.46  \n",
       "1          1.00       1.00         1.00  \n",
       "2          0.96       0.95         0.95  \n",
       "3          0.96       0.95         0.95  \n",
       "4          0.96       0.95         0.95  \n",
       "5          1.00       1.00         1.00  \n",
       "6          0.64       0.75         0.68  \n",
       "7          1.00       1.00         1.00  \n",
       "8          0.96       0.95         0.95  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Evaluation = {\"Models\":[\"KNN Classifier\",\"DecisionTree Classifier\",\"RandomForest Classifier\",\"SVM Classifier\",\n",
    "                        \"Logistic Regression\",\"Bagging Classifier\",\"AdaBoost Classifier\",\"Gradient Boosting Classifier\",\"Naive Bayes Classifier\"],\\\n",
    "            \"Train_Accuracy(%)\":[model_knn.score(x_train, y_train),model_DT.score(x_train, y_train),model_RF.score(x_train, y_train),model_svm.score(x_train, y_train),model_lgr.score(x_train, y_train),\n",
    "                                 model_bagg.score(x_train, y_train),model_Adaboost.score(x_train, y_train),model_GradientBoost.score(x_train, y_train), model_NB.score(x_train, y_train)],\n",
    "            \"Test_Accuracy(%)\":[accuracy_knn,accuracy_DT,accuracy_RF,accuracy_svm,accuracy_lgr,accuracy_bagg,accuracy_Adaboost,accuracy_GradientBoost,accuracy_NB],\\\n",
    "            \"Precision(%)\":[precision_knn,precision_DT,precision_RF,precision_svm,precision_lgr,precision_bagg,precision_Adaboost,precision_Gradientboost,precision_NB],\\\n",
    "            \"Recall(%)\":[recall_knn,recall_DT,recall_RF,recall_svm,recall_lgr,recall_bagg,recall_Adaboost,recall_Gradientboost,recall_NB],\\\n",
    "            \"F1-Score(%)\":[f1_knn,f1_DT,f1_RF,f1_svm,f1_lgr,f1_bagg,f1_Adaboost,f1_Gradientboost,f1_NB]}\n",
    "table = pd.DataFrame(Evaluation)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "31d04bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = [\"KNN\",\"DecisionTree\",\"RandomForest \",\"SVM \", \"Logistic Regression\",\n",
    "               \"Bagging\",\"AdaBoost\",\"Gradient Boosting \",\"Naive Bayes\"]\n",
    "Test_accuracies = [accuracy_knn*100,accuracy_DT*100,accuracy_RF*100,\n",
    "              accuracy_svm*100,accuracy_lgr*100,accuracy_bagg*100,\n",
    "              accuracy_Adaboost*100,accuracy_GradientBoost*100,accuracy_NB*100]\n",
    "Train_accuracies=[model_knn.score(x_train, y_train)*100,model_DT.score(x_train, y_train)*100,\n",
    "                  model_RF.score(x_train, y_train)*100,model_svm.score(x_train, y_train)*100,\n",
    "                  model_lgr.score(x_train, y_train)*100,model_bagg.score(x_train, y_train)*100,\n",
    "                  model_Adaboost.score(x_train, y_train)*100,model_GradientBoost.score(x_train, y_train)*100,\n",
    "                  model_NB.score(x_train, y_train)*100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0dd22fc",
   "metadata": {},
   "source": [
    "### 6.1 Train and Test Accuracy Plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f665a0e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 6000x3000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "rcParams = {'xtick.labelsize':'12','ytick.labelsize':'14','axes.labelsize':'16'}\n",
    "fig, axe = plt.subplots(1,1, figsize=(12,6), dpi=500)\n",
    "x_pos = np.arange(len(table))\n",
    "model_names = [\"KNN\", \"Decision T\", \"Random F\", \"SVM \", \"Logistic\", \"Bagging\", \"Ada Boost\", \"Grad Boost\",\"Naive Bayes\"]\n",
    "\n",
    "bar1 = plt.bar(x_pos - 0.2, table['Train_Accuracy(%)'], width=0.4, label='Train', color= \"Orange\")\n",
    "bar2 = plt.bar(x_pos + 0.2, table['Test_Accuracy(%)'], width=0.4, label='Test', color= \"b\")\n",
    "plt.xticks(x_pos, model_names)\n",
    "\n",
    "plt.xlabel(\"Classifiers\", fontsize = 16, fontweight = 'bold')\n",
    "plt.ylabel(\"Accuracy\", fontsize = 16, fontweight = 'bold')\n",
    "plt.title(\"Model Accuracy Scores\", fontsize = 18, fontweight = 'bold')\n",
    "plt.xticks(rotation = 10)\n",
    "plt.legend()\n",
    "\n",
    "for i, bar in enumerate(bar1):\n",
    "    plt.text(bar.get_x() + bar.get_width()/2 - 0.1, bar.get_height()*1.02, \n",
    "             s = '{:.2f}%'.format(Train_accuracies[i]), fontsize = 7)\n",
    "    \n",
    "for i, bar in enumerate(bar2):\n",
    "    plt.text(bar.get_x() + bar.get_width()/2 - 0.1, bar.get_height()*1.02, \n",
    "             s = '{:.2f}%'.format(Test_accuracies[i]), fontsize = 7)\n",
    "\n",
    "pylab.rcParams.update(rcParams)\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "fig.savefig('IMG\\Mod_Acc_Bar', dpi = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ba4135d",
   "metadata": {},
   "source": [
    "## 7. Model Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7369fcda",
   "metadata": {},
   "source": [
    "### 7.1 Pickle File "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eb67c2a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename = 'modelDT.pkl'\n",
    "pickle.dump(model_DT,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a6419f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename = 'vector.pkl'\n",
    "pickle.dump(tfidf_vector,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3699f71",
   "metadata": {},
   "source": [
    "# THE END !!"
   ]
  }
 ],
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