accuracy_run.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the Accuracy"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Analysis Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Data Viz Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#Data Processing\n",
    "from sklearn import preprocessing\n",
    "\n",
    "#Data Splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#ML Libraries\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier ,GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = pd.read_csv(\"results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed   \n",
       "0  LP001015   Male     Yes          0      Graduate            No  \\\n",
       "1  LP001022   Male     Yes          1      Graduate            No   \n",
       "2  LP001031   Male     Yes          2      Graduate            No   \n",
       "3  LP001035   Male     Yes          2      Graduate            No   \n",
       "4  LP001051   Male      No          0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term   \n",
       "0             5720                  0       110.0             360.0  \\\n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "3             2340               2546       100.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Urban           Y  \n",
       "2             1.0         Urban           Y  \n",
       "3             NaN         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_path.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 367 entries, 0 to 366\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            367 non-null    object \n",
      " 1   Gender             356 non-null    object \n",
      " 2   Married            367 non-null    object \n",
      " 3   Dependents         357 non-null    object \n",
      " 4   Education          367 non-null    object \n",
      " 5   Self_Employed      344 non-null    object \n",
      " 6   ApplicantIncome    367 non-null    int64  \n",
      " 7   CoapplicantIncome  367 non-null    int64  \n",
      " 8   LoanAmount         362 non-null    float64\n",
      " 9   Loan_Amount_Term   361 non-null    float64\n",
      " 10  Credit_History     338 non-null    float64\n",
      " 11  Property_Area      367 non-null    object \n",
      " 12  Loan_Status        367 non-null    object \n",
      "dtypes: float64(3), int64(2), object(8)\n",
      "memory usage: 37.4+ KB\n"
     ]
    }
   ],
   "source": [
    "train_path.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>361.000000</td>\n",
       "      <td>338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4805.599455</td>\n",
       "      <td>1569.577657</td>\n",
       "      <td>136.132597</td>\n",
       "      <td>342.537396</td>\n",
       "      <td>0.825444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4910.685399</td>\n",
       "      <td>2334.232099</td>\n",
       "      <td>61.366652</td>\n",
       "      <td>65.156643</td>\n",
       "      <td>0.380150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2864.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.250000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3786.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5060.000000</td>\n",
       "      <td>2430.500000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72529.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term   \n",
       "count       367.000000         367.000000  362.000000        361.000000  \\\n",
       "mean       4805.599455        1569.577657  136.132597        342.537396   \n",
       "std        4910.685399        2334.232099   61.366652         65.156643   \n",
       "min           0.000000           0.000000   28.000000          6.000000   \n",
       "25%        2864.000000           0.000000  100.250000        360.000000   \n",
       "50%        3786.000000        1025.000000  125.000000        360.000000   \n",
       "75%        5060.000000        2430.500000  158.000000        360.000000   \n",
       "max       72529.000000       24000.000000  550.000000        480.000000   \n",
       "\n",
       "       Credit_History  \n",
       "count      338.000000  \n",
       "mean         0.825444  \n",
       "std          0.380150  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_path.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Pre-Processing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               11\n",
       "Married               0\n",
       "Dependents           10\n",
       "Education             0\n",
       "Self_Employed        23\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            5\n",
       "Loan_Amount_Term      6\n",
       "Credit_History       29\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_path.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train_path.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path.drop(['Loan_ID'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path ['Gender'] = train_path ['Gender'].fillna(train_path['Gender'].mode()[0])\n",
    "train_path ['Married'] = train_path ['Married'].fillna(train_path['Married'].mode()[0])\n",
    "train_path ['Dependents'] = train_path ['Dependents'].fillna(train_path['Dependents'].mode()[0])\n",
    "train_path ['Self_Employed'] = train_path ['Self_Employed'].fillna(train_path['Self_Employed'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path ['LoanAmount'] = train_path ['LoanAmount'].fillna(train_path['LoanAmount'].mean())\n",
    "train_path ['Loan_Amount_Term'] = train_path ['Loan_Amount_Term'].fillna(train_path['Loan_Amount_Term'].mean())\n",
    "train_path ['Credit_History'] = train_path ['Credit_History'].fillna(train_path['Credit_History'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_path.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical variables: 7\n"
     ]
    }
   ],
   "source": [
    "obj = (train_path.dtypes == 'object')\n",
    "print(\"Categorical variables:\",len(list(obj[obj].index)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = preprocessing.LabelEncoder()\n",
    "cols = ['Gender','Married','Education','Self_Employed','Property_Area','Loan_Status','Dependents']\n",
    "for col in cols:\n",
    "  train_path[col] = label_encoder.fit_transform(train_path[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical variables: 0\n"
     ]
    }
   ],
   "source": [
    "obj = (train_path.dtypes == 'object')\n",
    "print(\"Categorical variables:\",len(list(obj[obj].index)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((220, 11), (147, 11), (220,), (147,))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "  \n",
    "x = train_path.drop(['Loan_Status'],axis=1)\n",
    "y = train_path['Loan_Status']\n",
    "x.shape,y.shape\n",
    "  \n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4,random_state=42 )\n",
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of  RandomForestClassifier = 98.18181818181819\n",
      "r2 score of  RandomForestClassifier = 0.8700339683946241 \n",
      "\n",
      "Accuracy score of  KNeighborsClassifier = 85.0\n",
      "r2 score of  KNeighborsClassifier = -0.0722197607443511 \n",
      "\n",
      "Accuracy score of  LogisticRegression = 94.0909090909091\n",
      "r2 score of  LogisticRegression = 0.5776103972825284 \n",
      "\n",
      "Accuracy score of  SVC = 83.18181818181817\n",
      "r2 score of  SVC = -0.202185792349727 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\keith\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of  GradientBoostingClassifier = 100.0\n",
      "r2 score of  GradientBoostingClassifier = 1.0 \n",
      "\n",
      "Accuracy score of  GaussianNB = 94.0909090909091\n",
      "r2 score of  GaussianNB = 0.5776103972825284 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "rfc = RandomForestClassifier(n_estimators = 7)\n",
    "logr = LogisticRegression()\n",
    "#linr = LinearRegression()\n",
    "svc = SVC()\n",
    "gbc = GradientBoostingClassifier()\n",
    "gaussian = GaussianNB()\n",
    "\n",
    "for clf in (rfc, knn,logr,svc,gbc,gaussian):\n",
    "    clf.fit(x_train, y_train)\n",
    "    y_pred = clf.predict(x_train)\n",
    "    print(\"Accuracy score of \", clf.__class__.__name__, \"=\",100*metrics.accuracy_score(y_train, y_pred))\n",
    "    print(\"r2 score of \", clf.__class__.__name__, \"=\",metrics.r2_score(y_train, y_pred),\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of  RandomForestClassifier = 91.83673469387756\n",
      "r2 score of  RandomForestClassifier = 0.6107678729037953 \n",
      "\n",
      "Accuracy score of  KNeighborsClassifier = 66.66666666666666\n",
      "r2 score of  KNeighborsClassifier = -0.5893645189761694 \n",
      "\n",
      "Accuracy score of  LogisticRegression = 91.83673469387756\n",
      "r2 score of  LogisticRegression = 0.6107678729037953 \n",
      "\n",
      "Accuracy score of  SVC = 70.06802721088435\n",
      "r2 score of  SVC = -0.4271844660194173 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\keith\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of  GradientBoostingClassifier = 93.19727891156462\n",
      "r2 score of  GradientBoostingClassifier = 0.675639894086496 \n",
      "\n",
      "Accuracy score of  GaussianNB = 94.5578231292517\n",
      "r2 score of  GaussianNB = 0.7405119152691968 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for clf in (rfc, knn,logr,svc,gbc,gaussian):\n",
    "    clf.fit(x_train, y_train)\n",
    "    y_pred = clf.predict(x_test)\n",
    "    print(\"Accuracy score of \", clf.__class__.__name__, \"=\",100*metrics.accuracy_score(y_test, y_pred))\n",
    "    print(\"r2 score of \", clf.__class__.__name__, \"=\",metrics.r2_score(y_test, y_pred),\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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