credit_risk_resampling (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Risk Resampling Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV into DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>loan_size</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>homeowner</th>\n",
       "      <th>borrower_income</th>\n",
       "      <th>debt_to_income</th>\n",
       "      <th>num_of_accounts</th>\n",
       "      <th>derogatory_marks</th>\n",
       "      <th>total_debt</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10700.0</td>\n",
       "      <td>7.672</td>\n",
       "      <td>own</td>\n",
       "      <td>52800</td>\n",
       "      <td>0.431818</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>22800</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8400.0</td>\n",
       "      <td>6.692</td>\n",
       "      <td>own</td>\n",
       "      <td>43600</td>\n",
       "      <td>0.311927</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13600</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9000.0</td>\n",
       "      <td>6.963</td>\n",
       "      <td>rent</td>\n",
       "      <td>46100</td>\n",
       "      <td>0.349241</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>16100</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10700.0</td>\n",
       "      <td>7.664</td>\n",
       "      <td>own</td>\n",
       "      <td>52700</td>\n",
       "      <td>0.430740</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>22700</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10800.0</td>\n",
       "      <td>7.698</td>\n",
       "      <td>mortgage</td>\n",
       "      <td>53000</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>23000</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_size  interest_rate homeowner  borrower_income  debt_to_income  \\\n",
       "0    10700.0          7.672       own            52800        0.431818   \n",
       "1     8400.0          6.692       own            43600        0.311927   \n",
       "2     9000.0          6.963      rent            46100        0.349241   \n",
       "3    10700.0          7.664       own            52700        0.430740   \n",
       "4    10800.0          7.698  mortgage            53000        0.433962   \n",
       "\n",
       "   num_of_accounts  derogatory_marks  total_debt loan_status  \n",
       "0                5                 1       22800    low_risk  \n",
       "1                3                 0       13600    low_risk  \n",
       "2                3                 0       16100    low_risk  \n",
       "3                5                 1       22700    low_risk  \n",
       "4                5                 1       23000    low_risk  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "file_path = Path('Resources/lending_data.csv')\n",
    "df = pd.read_csv(file_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our features\n",
    "X = df.drop(\"loan_status\", axis=1)\n",
    "X = pd.get_dummies(X)\n",
    "# Create our target\n",
    "y = df[\"loan_status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_size</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>borrower_income</th>\n",
       "      <th>debt_to_income</th>\n",
       "      <th>num_of_accounts</th>\n",
       "      <th>derogatory_marks</th>\n",
       "      <th>total_debt</th>\n",
       "      <th>homeowner_mortgage</th>\n",
       "      <th>homeowner_own</th>\n",
       "      <th>homeowner_rent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>77536.000000</td>\n",
       "      <td>77536.000000</td>\n",
       "      <td>77536.000000</td>\n",
       "      <td>77536.000000</td>\n",
       "      <td>77536.000000</td>\n",
       "      <td>77536.000000</td>\n",
       "      <td>77536.000000</td>\n",
       "      <td>77536.000000</td>\n",
       "      <td>77536.000000</td>\n",
       "      <td>77536.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9805.562577</td>\n",
       "      <td>7.292333</td>\n",
       "      <td>49221.949804</td>\n",
       "      <td>0.377318</td>\n",
       "      <td>3.826610</td>\n",
       "      <td>0.392308</td>\n",
       "      <td>19221.949804</td>\n",
       "      <td>0.497472</td>\n",
       "      <td>0.398911</td>\n",
       "      <td>0.103616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2093.223153</td>\n",
       "      <td>0.889495</td>\n",
       "      <td>8371.635077</td>\n",
       "      <td>0.081519</td>\n",
       "      <td>1.904426</td>\n",
       "      <td>0.582086</td>\n",
       "      <td>8371.635077</td>\n",
       "      <td>0.499997</td>\n",
       "      <td>0.489678</td>\n",
       "      <td>0.304764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8700.000000</td>\n",
       "      <td>6.825000</td>\n",
       "      <td>44800.000000</td>\n",
       "      <td>0.330357</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14800.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9500.000000</td>\n",
       "      <td>7.172000</td>\n",
       "      <td>48100.000000</td>\n",
       "      <td>0.376299</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10400.000000</td>\n",
       "      <td>7.528000</td>\n",
       "      <td>51400.000000</td>\n",
       "      <td>0.416342</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21400.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23800.000000</td>\n",
       "      <td>13.235000</td>\n",
       "      <td>105200.000000</td>\n",
       "      <td>0.714829</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>75200.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_size  interest_rate  borrower_income  debt_to_income  \\\n",
       "count  77536.000000   77536.000000     77536.000000    77536.000000   \n",
       "mean    9805.562577       7.292333     49221.949804        0.377318   \n",
       "std     2093.223153       0.889495      8371.635077        0.081519   \n",
       "min     5000.000000       5.250000     30000.000000        0.000000   \n",
       "25%     8700.000000       6.825000     44800.000000        0.330357   \n",
       "50%     9500.000000       7.172000     48100.000000        0.376299   \n",
       "75%    10400.000000       7.528000     51400.000000        0.416342   \n",
       "max    23800.000000      13.235000    105200.000000        0.714829   \n",
       "\n",
       "       num_of_accounts  derogatory_marks    total_debt  homeowner_mortgage  \\\n",
       "count     77536.000000      77536.000000  77536.000000        77536.000000   \n",
       "mean          3.826610          0.392308  19221.949804            0.497472   \n",
       "std           1.904426          0.582086   8371.635077            0.499997   \n",
       "min           0.000000          0.000000      0.000000            0.000000   \n",
       "25%           3.000000          0.000000  14800.000000            0.000000   \n",
       "50%           4.000000          0.000000  18100.000000            0.000000   \n",
       "75%           4.000000          1.000000  21400.000000            1.000000   \n",
       "max          16.000000          3.000000  75200.000000            1.000000   \n",
       "\n",
       "       homeowner_own  homeowner_rent  \n",
       "count   77536.000000    77536.000000  \n",
       "mean        0.398911        0.103616  \n",
       "std         0.489678        0.304764  \n",
       "min         0.000000        0.000000  \n",
       "25%         0.000000        0.000000  \n",
       "50%         0.000000        0.000000  \n",
       "75%         1.000000        0.000000  \n",
       "max         1.000000        1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     75036\n",
       "high_risk     2500\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create X_train, X_test, y_train, y_test\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the StandardScaler instance\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the Standard Scaler with the training data\n",
    "X_scaler = scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the training and testing data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_tested = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9543211898288821"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "y_pred = model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  571,    54],\n",
       "       [   93, 18666]], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.86      0.91      1.00      0.89      0.95      0.90       625\n",
      "   low_risk       1.00      1.00      0.91      1.00      0.95      0.92     18759\n",
      "\n",
      "avg / total       0.99      0.99      0.92      0.99      0.95      0.92     19384\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oversampling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Random Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'low_risk': 56277, 'high_risk': 56277})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with the RandomOversampler\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# View the count of target classes with Counter\n",
    "ros = RandomOverSampler(random_state=1)\n",
    "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(solver= \"lbfgs\", random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9543211898288821"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  622,     3],\n",
       "       [  106, 18653]], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9947746894823818"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'low_risk': 56277, 'high_risk': 56277})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with SMOTE\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "X_resampled, y_resampled = SMOTE(random_state=1, sampling_strategy=1.0).fit_resample(\n",
    "    X_train, y_train\n",
    ")\n",
    "from collections import Counter\n",
    "# View the count of target classes with Counter\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "model = LogisticRegression(solver=\"lbfgs\", random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9948279972279972"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  622,     3],\n",
       "       [  104, 18655]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.86      1.00      0.99      0.92      0.99      0.99       625\n",
      "   low_risk       1.00      0.99      1.00      1.00      0.99      0.99     18759\n",
      "\n",
      "avg / total       1.00      0.99      1.00      0.99      0.99      0.99     19384\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Undersampling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'high_risk': 1875, 'low_risk': 1875})"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the data using the ClusterCentroids resampler\n",
    "from imblearn.under_sampling import ClusterCentroids\n",
    "\n",
    "cc = ClusterCentroids(random_state=1)\n",
    "X_resampled, y_resampled = cc.fit_resample(X_train_scaled, y_train)\n",
    "\n",
    "# View the count of target classes with Counter\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(solver=\"lbfgs\", random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the balanced accuracy score\n",
    "y_pred = model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0,   625],\n",
       "       [    0, 18759]], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.00      0.00      1.00      0.00      0.00      0.00       625\n",
      "   low_risk       0.97      1.00      0.00      0.98      0.00      0.00     18759\n",
      "\n",
      "avg / total       0.94      0.97      0.03      0.95      0.00      0.00     19384\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combination (Over and Under) Sampling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'high_risk': 55513, 'low_risk': 55928})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with SMOTEENN\n",
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "sm = SMOTEENN(random_state=1)\n",
    "X_resampled, y_resampled = sm.fit_resample(X_train, y_train)\n",
    "\n",
    "# View the count of target classes with Counter\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(solver=\"lbfgs\", random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.994748035609574"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the balanced accuracy score\n",
    "y_pred = model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  622,     3],\n",
       "       [  107, 18652]], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.85      1.00      0.99      0.92      0.99      0.99       625\n",
      "   low_risk       1.00      0.99      1.00      1.00      0.99      0.99     18759\n",
      "\n",
      "avg / total       1.00      0.99      1.00      0.99      0.99      0.99     19384\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Questions\n",
    "\n",
    "1. Which model had the best balanced accuracy score?\n",
    "\n",
    "   Logistic Regression using SMOTEEN\n",
    "\n",
    "2. Which model had the best recall score?\n",
    "\n",
    "    Naive Random Sampling\n",
    "\n",
    "3. Which model had the best geometric mean score?\n",
    "\n",
    "    Logistic Regression using SMOTEEN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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