credit_risk_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from sklearn.metrics import balanced_accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the Data into Training and Testing Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Read the `lending_data.csv` data from the `Resources` folder into a Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>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>52800</td>\n",
       "      <td>0.431818</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>22800</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>8400.0</td>\n",
       "      <td>6.692</td>\n",
       "      <td>43600</td>\n",
       "      <td>0.311927</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13600</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9000.0</td>\n",
       "      <td>6.963</td>\n",
       "      <td>46100</td>\n",
       "      <td>0.349241</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>16100</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10700.0</td>\n",
       "      <td>7.664</td>\n",
       "      <td>52700</td>\n",
       "      <td>0.430740</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>22700</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10800.0</td>\n",
       "      <td>7.698</td>\n",
       "      <td>53000</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>23000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>77531</th>\n",
       "      <td>19100.0</td>\n",
       "      <td>11.261</td>\n",
       "      <td>86600</td>\n",
       "      <td>0.653580</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>56600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77532</th>\n",
       "      <td>17700.0</td>\n",
       "      <td>10.662</td>\n",
       "      <td>80900</td>\n",
       "      <td>0.629172</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>50900</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77533</th>\n",
       "      <td>17600.0</td>\n",
       "      <td>10.595</td>\n",
       "      <td>80300</td>\n",
       "      <td>0.626401</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>50300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77534</th>\n",
       "      <td>16300.0</td>\n",
       "      <td>10.068</td>\n",
       "      <td>75300</td>\n",
       "      <td>0.601594</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>45300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77535</th>\n",
       "      <td>15600.0</td>\n",
       "      <td>9.742</td>\n",
       "      <td>72300</td>\n",
       "      <td>0.585062</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>42300</td>\n",
       "      <td>1</td>\n",
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       "<p>77536 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_size  interest_rate  borrower_income  debt_to_income   \n",
       "0        10700.0          7.672            52800        0.431818  \\\n",
       "1         8400.0          6.692            43600        0.311927   \n",
       "2         9000.0          6.963            46100        0.349241   \n",
       "3        10700.0          7.664            52700        0.430740   \n",
       "4        10800.0          7.698            53000        0.433962   \n",
       "...          ...            ...              ...             ...   \n",
       "77531    19100.0         11.261            86600        0.653580   \n",
       "77532    17700.0         10.662            80900        0.629172   \n",
       "77533    17600.0         10.595            80300        0.626401   \n",
       "77534    16300.0         10.068            75300        0.601594   \n",
       "77535    15600.0          9.742            72300        0.585062   \n",
       "\n",
       "       num_of_accounts  derogatory_marks  total_debt  loan_status  \n",
       "0                    5                 1       22800            0  \n",
       "1                    3                 0       13600            0  \n",
       "2                    3                 0       16100            0  \n",
       "3                    5                 1       22700            0  \n",
       "4                    5                 1       23000            0  \n",
       "...                ...               ...         ...          ...  \n",
       "77531               12                 2       56600            1  \n",
       "77532               11                 2       50900            1  \n",
       "77533               11                 2       50300            1  \n",
       "77534               10                 2       45300            1  \n",
       "77535                9                 2       42300            1  \n",
       "\n",
       "[77536 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV file from the Resources folder into a Pandas DataFrame\n",
    "df_lending_data = pd.read_csv(Path(\"data/lending_data.csv\"))\n",
    "\n",
    "# Review the DataFrame\n",
    "df_lending_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Create the labels set (`y`)  from the “loan_status” column, and then create the features (`X`) DataFrame from the remaining columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the data into labels and features\n",
    "\n",
    "# Separate the y variable, the labels\n",
    "y = df_lending_data[\"loan_status\"]\n",
    "\n",
    "# Separate the X variable, the features\n",
    "x = df_lending_data.drop(columns=\"loan_status\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the y variable Series\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10700.0</td>\n",
       "      <td>7.672</td>\n",
       "      <td>52800</td>\n",
       "      <td>0.431818</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>22800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8400.0</td>\n",
       "      <td>6.692</td>\n",
       "      <td>43600</td>\n",
       "      <td>0.311927</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13600</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9000.0</td>\n",
       "      <td>6.963</td>\n",
       "      <td>46100</td>\n",
       "      <td>0.349241</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>16100</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10700.0</td>\n",
       "      <td>7.664</td>\n",
       "      <td>52700</td>\n",
       "      <td>0.430740</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>22700</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10800.0</td>\n",
       "      <td>7.698</td>\n",
       "      <td>53000</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   loan_size  interest_rate  borrower_income  debt_to_income  num_of_accounts   \n",
       "0    10700.0          7.672            52800        0.431818                5  \\\n",
       "1     8400.0          6.692            43600        0.311927                3   \n",
       "2     9000.0          6.963            46100        0.349241                3   \n",
       "3    10700.0          7.664            52700        0.430740                5   \n",
       "4    10800.0          7.698            53000        0.433962                5   \n",
       "\n",
       "   derogatory_marks  total_debt  \n",
       "0                 1       22800  \n",
       "1                 0       13600  \n",
       "2                 0       16100  \n",
       "3                 1       22700  \n",
       "4                 1       23000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the X variable DataFrame\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Check the balance of the labels variable (`y`) by using the `value_counts` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_status\n",
       "0    75036\n",
       "1     2500\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Split the data into training and testing datasets by using `train_test_split`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the train_test_learn module\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data using train_test_split\n",
    "# Assign a random_state of 1 to the function\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Logistic Regression Model with the Original Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Step 1: Fit a logistic regression model by using the training data (`X_train` and `y_train`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the LogisticRegression module from SKLearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Instantiate the Logistic Regression model\n",
    "# Assign a random_state parameter of 1 to the model\n",
    "classifier = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "\n",
    "# Fit the model using training data\n",
    "lr_model = classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Save the predictions on the testing data labels by using the testing feature data (`X_test`) and the fitted model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a prediction using the testing data\n",
    "predictions = lr_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Evaluate the model’s performance by doing the following:\n",
    "\n",
    "* Calculate the accuracy score of the model.\n",
    "\n",
    "* Generate a confusion matrix.\n",
    "\n",
    "* Print the classification report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9520479254722232\n"
     ]
    }
   ],
   "source": [
    "# Print the balanced_accuracy score of the model\n",
    "lr_accuracy_score = balanced_accuracy_score(y_test, predictions)\n",
    "print(lr_accuracy_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[18663   102]\n",
      " [   56   563]]\n"
     ]
    }
   ],
   "source": [
    "# Generate a confusion matrix for the model\n",
    "lr_matrix = confusion_matrix(y_test, predictions)\n",
    "print(lr_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00     18765\n",
      "           1       0.85      0.91      0.88       619\n",
      "\n",
      "    accuracy                           0.99     19384\n",
      "   macro avg       0.92      0.95      0.94     19384\n",
      "weighted avg       0.99      0.99      0.99     19384\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report for the model\n",
    "lr_class_report = classification_report(y_test, predictions)\n",
    "print(lr_class_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Answer the following question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** How well does the logistic regression model predict both the `0` (healthy loan) and `1` (high-risk loan) labels?\n",
    "\n",
    "**Answer:** \n",
    "\n",
    "Our balanced accuracy score is 95%. The balanced accuracy score takes into account the sensitivity (true positive rate) and the specificity (true negative rate). The balanced accuracy score is measures the performance of our model, especially with imbalanced datasets. In our dataset, we have 75036 loans that were rejected, but only 2500 that were accepted. Since our data is imbalanced, the balanced accuracy score serves as a good metric for the performance of our model. \n",
    "\n",
    "Another measure of performance for our model is accuracy. The model's accuracy score is 99%, which means that the model correctly predicted 99% of all loans in the dataset. \n",
    "\n",
    "Based on the confusion matrix, the model accurately accepted 18663 healthy loans and rejected 563 high-risk loans. However, the model also incorrectly rejected 102 healthy loans and incorrectly accepted 56 high-risk loans. \n",
    "\n",
    "Precision looks at how predictions matched reality, while recall or sensitivity looks at reality matched the prediction. Our model's precision score is 85% for default loans and 100% for healthy loans, and the model's recall score is 91% for default loans and 99% for healthy loans.\n",
    "\n",
    "The F1 score is a weighted average of the precision and recall scores. Our model's F1 score is 88%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict a Logistic Regression Model with Resampled Training Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Use the `RandomOverSampler` module from the imbalanced-learn library to resample the data. Be sure to confirm that the labels have an equal number of data points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the RandomOverSampler module form imbalanced-learn\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# Instantiate the random oversampler model\n",
    "# # Assign a random_state parameter of 1 to the model\n",
    "ros = RandomOverSampler(random_state=1)\n",
    "\n",
    "# Fit the original training data to the random_oversampler model\n",
    "x_ros, y_ros = ros.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_status\n",
       "0    56271\n",
       "1    56271\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the distinct values of the resampled labels data\n",
    "y_ros.value_counts()\n",
    "\n",
    "# Labels have an equal number of data points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Use the `LogisticRegression` classifier and the resampled data to fit the model and make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the Logistic Regression model\n",
    "# Assign a random_state parameter of 1 to the model\n",
    "classifier_ros = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "\n",
    "# Fit the model using the resampled training data\n",
    "classifier_ros.fit(x_ros, y_ros)\n",
    "\n",
    "# Make a prediction using the testing data\n",
    "predictions_ros = classifier_ros.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Evaluate the model’s performance by doing the following:\n",
    "\n",
    "* Calculate the accuracy score of the model.\n",
    "\n",
    "* Generate a confusion matrix.\n",
    "\n",
    "* Print the classification report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9936781215845847\n"
     ]
    }
   ],
   "source": [
    "# Print the balanced_accuracy score of the model \n",
    "ros_accuracy_score = balanced_accuracy_score(y_test, predictions_ros)\n",
    "print(ros_accuracy_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[18649   116]\n",
      " [    4   615]]\n"
     ]
    }
   ],
   "source": [
    "# Generate a confusion matrix for the model\n",
    "ros_matrix = confusion_matrix(y_test, predictions_ros)\n",
    "print(ros_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00     18765\n",
      "           1       0.84      0.99      0.91       619\n",
      "\n",
      "    accuracy                           0.99     19384\n",
      "   macro avg       0.92      0.99      0.95     19384\n",
      "weighted avg       0.99      0.99      0.99     19384\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report for the model\n",
    "ros_class_report = classification_report(y_test, predictions_ros)\n",
    "print(ros_class_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Answer the following question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** How well does the logistic regression model, fit with oversampled data, predict both the `0` (healthy loan) and `1` (high-risk loan) labels?\n",
    "\n",
    "**Answer:** \n",
    "\n",
    "Our balanced accuracy score and our accuracy score are both 99%. The balanced accuracy score is measures the performance of our model when the dataset is imbalanced. In our dataset however, we have an equal number in each class, 56271. Hence the balanced accuracy score is the same as the accuracy score.\n",
    "\n",
    "Based on the confusion matrix, the model accurately accepted 18649 healthy loans and rejected 615 high-risk loans. However, the model also incorrectly rejected 116 healthy loans and incorrectly accepted 4 high-risk loans. \n",
    "\n",
    "Our model's precision score is 84% for default loans and 100% for healthy loans. The model's recall score is 99% for default loans and 99% for healthy loans.\n",
    "\n",
    "Our model's F1 score (the weighted average of precision and recall scores) is 91%."
   ]
  }
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