credit_risk_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "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\n",
    "# Import the train_test_learn module\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Import the LogisticRegression module from SKLearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# Import the RandomOverSampler module form imbalanced-learn\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "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": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <td>53000</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\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  loan_status  \n",
       "0                 1       22800            0  \n",
       "1                 0       13600            0  \n",
       "2                 0       16100            0  \n",
       "3                 1       22700            0  \n",
       "4                 1       23000            0  "
      ]
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     "output_type": "display_data"
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       "      <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",
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       "      <td>19100.0</td>\n",
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       "      <td>9</td>\n",
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      "text/plain": [
       "       loan_size  interest_rate  borrower_income  debt_to_income  \\\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",
       "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  "
      ]
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Read the CSV file from the Resources folder into a Pandas DataFrame\n",
    "data = Path('Resources/lending_data.csv')\n",
    "lending_df = pd.read_csv(data)\n",
    "\n",
    "# Review the DataFrame (displayed both head and tail to see if 'loan_status' have both 0's and 1's)\n",
    "display(lending_df.head())\n",
    "display(lending_df.tail())"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the data into labels and features\n",
    "# Separate the y variable, the labels\n",
    "y = lending_df['loan_status']\n",
    "\n",
    "# Separate the X variable, the features\n",
    "X = lending_df.drop(columns=['loan_status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
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     "execution_count": 4,
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   "source": [
    "# Review the y variable Series\n",
    "y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <td>52700</td>\n",
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       "      <td>5</td>\n",
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       "      <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",
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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": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    75036\n",
       "1     2500\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data using train_test_split\n",
    "# Assign a random_state of 1 to the function\n",
    "# Add 'stratify=y' so we have an even distribution of 1's and 0's within our model, so we have a representation of all of our data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y,\n",
    "                                                    stratify=y,\n",
    "                                                    random_state=1, \n",
    "                                                    )"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the Logistic Regression model\n",
    "# Assign a random_state parameter of 1 to the model\n",
    "# Add 'max_iter = 200', so the model runs this 200 times. This is part of the learning aspect of it\n",
    "# This gives you an idea on how well the model is predicting\n",
    "classifier = LogisticRegression(solver='lbfgs',\n",
    "                                max_iter = 200,\n",
    "                                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": 9,
   "metadata": {},
   "outputs": [
    {
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       "<p>19384 rows × 2 columns</p>\n",
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      "text/plain": [
       "       Prediction  Actual\n",
       "36831           0       0\n",
       "75818           0       1\n",
       "36563           0       0\n",
       "13237           0       0\n",
       "43292           0       0\n",
       "...           ...     ...\n",
       "38069           0       0\n",
       "36892           0       0\n",
       "5035            0       0\n",
       "40821           0       0\n",
       "35030           0       0\n",
       "\n",
       "[19384 rows x 2 columns]"
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "# Make a prediction using the testing data\n",
    "predictions = classifier.predict(X_test)\n",
    "pd.DataFrame({\"Prediction\": predictions, \"Actual\": y_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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9442676901753825"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the balanced_accuracy score of the model\n",
    "balanced_accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>18679</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>67</td>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0        18679           80\n",
       "Actual 1           67          558"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a confusion matrix for the model\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"]\n",
    ")\n",
    "display(cm_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "  healthy loan       1.00      1.00      1.00     18759\n",
      "high-risk loan       0.87      0.89      0.88       625\n",
      "\n",
      "      accuracy                           0.99     19384\n",
      "     macro avg       0.94      0.94      0.94     19384\n",
      "  weighted avg       0.99      0.99      0.99     19384\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report for the model\n",
    "print(\"Classification Report\")\n",
    "target_names = [\"healthy loan\",\"high-risk loan\"]\n",
    "print(classification_report(y_test, predictions, target_names = target_names))"
   ]
  },
  {
   "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",
    "- Precision measures the proportion of correctly predicted instances out of the total predicted instances for a specific label. For the \"healthy loan\" class, the precision is 1.00, indicating that the model predicts this label with perfect precision. For the \"high-risk loan\" class, the precision is 0.87, meaning that 87% of the predicted high-risk loans are correct.\n",
    "\n",
    "- Recall, also known as sensitivity or true positive rate, measures the proportion of correctly predicted instances out of the total actual instances for a specific label. The recall for the \"healthy loan\" class is 1.00, indicating that the model captures all the actual healthy loans. The recall for the \"high-risk loan\" class is 0.89, meaning that the model identifies 89% of the high-risk loans correctly.\n",
    "\n",
    "- The F1-score is the mean of precision and recall and provides a balanced measure of the model's performance. For the \"healthy loan\" class, the F1-score is 1.00, indicating perfect performance. The F1-score for the \"high-risk loan\" class is 0.88, representing a good balance between precision and recall for this label.\n",
    "\n",
    "- The accuracy score is 99%, this model performs exceptionally well.\n",
    "\n",
    "- One improvement would be, to get the 'False Negative' value (67) lower, as we would want to migtigate away from it as much as possible. In certain situations, such as identifying potential high-risk loans or detecting fraudulent activities, false negatives can result in missed opportunities. By minimising false negatives, we increase the chances of correctly identifying positive instances, enabling us to take appropriate actions and capitalise on those opportunities. "
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the random oversampler model\n",
    "# # Assign a random_state parameter of 1 to the model\n",
    "model_ros = RandomOverSampler(random_state=1)\n",
    "\n",
    "# Fit the original training data to the random_oversampler model\n",
    "X_resampled, y_resampled = model_ros.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    56277\n",
       "1    56277\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "loan_size  interest_rate  borrower_income  debt_to_income  num_of_accounts  derogatory_marks  total_debt\n",
       "19900.0    11.596         89700            0.665552        13               2                 59700         185\n",
       "19100.0    11.246         86400            0.652778        12               2                 56400         163\n",
       "19300.0    11.316         87100            0.655568        12               2                 57100         162\n",
       "18500.0    10.976         83900            0.642431        12               2                 53900         162\n",
       "19300.0    11.310         87000            0.655172        12               2                 57000         156\n",
       "                                                                                                           ... \n",
       "17800.0    10.696         81300            0.630996        11               2                 51300           1\n",
       "12300.0    8.371          59400            0.494949        6                1                 29400           1\n",
       "           8.369          59400            0.494949        6                1                 29400           1\n",
       "           8.368          59300            0.494098        6                1                 29300           1\n",
       "5000.0     5.250          30000            0.000000        0                0                 0               1\n",
       "Length: 4624, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the distinct values of the resampled labels data\n",
    "display(y_resampled.value_counts())\n",
    "print('-'*115 + '\\n')\n",
    "display(X_resampled.value_counts())"
   ]
  },
  {
   "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": 25,
   "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>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36831</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75818</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36563</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13237</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43292</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38069</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36892</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5035</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40821</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35030</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19384 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prediction  Actual\n",
       "36831           0       0\n",
       "75818           1       1\n",
       "36563           0       0\n",
       "13237           0       0\n",
       "43292           0       0\n",
       "...           ...     ...\n",
       "38069           0       0\n",
       "36892           0       0\n",
       "5035            0       0\n",
       "40821           0       0\n",
       "35030           0       0\n",
       "\n",
       "[19384 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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_resampled, y_resampled)\n",
    "\n",
    "# Make a prediction using the testing data\n",
    "predictions_ros = classifier_ros.predict(X_test)\n",
    "pd.DataFrame({\"Prediction\": predictions_ros, \"Actual\": y_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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9959744975744975"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the balanced_accuracy score of the model \n",
    "balanced_accuracy_score(y_test, predictions_ros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>18668</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>2</td>\n",
       "      <td>623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0        18668           91\n",
       "Actual 1            2          623"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a confusion matrix for the model\n",
    "matrix = confusion_matrix(y_test, predictions_ros)\n",
    "matrix_df = pd.DataFrame(\n",
    "   matrix, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"]\n",
    ")\n",
    "display(matrix_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROS Classification Report\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "  healthy loan       1.00      1.00      1.00     18759\n",
      "high-risk loan       0.87      1.00      0.93       625\n",
      "\n",
      "      accuracy                           1.00     19384\n",
      "     macro avg       0.94      1.00      0.96     19384\n",
      "  weighted avg       1.00      1.00      1.00     19384\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report for the model\n",
    "print(\"ROS Classification Report\")\n",
    "target_names = [\"healthy loan\",\"high-risk loan\"]\n",
    "print(classification_report(y_test, predictions_ros, target_names = target_names))"
   ]
  },
  {
   "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",
    "- For the \"healthy loan\" class, the precision is 1.00, indicating that the model predicts this label with perfect precision. For the \"high-risk loan\" class, the precision is 0.87, meaning that 87% of the predicted high-risk loans are correct.\n",
    "-  The recall for both the \"healthy loan\" and \"high-risk loan\" classes is 1.00, indicating that the model captures all the actual instances of both classes.\n",
    "- For the \"healthy loan\" class, the F1-score is 1.00, indicating excellent performance. The F1-score for the \"high-risk loan\" class is 0.93, representing a good balance between precision and recall for this label.\n",
    "- The accuracy score is 100%, this model performs exceptionally well.\n",
    "- This model was able to reduce the value of False Negative all the way from 67 down to 2, which I am extremely happy with. This increase the chances of correctly identifying positive instances, enabling us to take appropriate actions and capitalise on those opportunities of identifying potential high-risk loans or detecting fraudulent activities. False negatives can lead to wasted resources when subsequent actions, investigations, or interventions are not performed when they should have been. By minimising false negatives, we can optimise resource allocation, focusing efforts on instances that truly require attention. Lastly, false negatives can erode trust in the predictive model or system. If users consistently encounter false negatives, they may lose confidence in the model's ability to accurately identify positive instances. This loss of trust can have a detrimental impact on the reputation and adoption of the model or system. By striving to minimise false negatives, we can enhance the reliability and credibility of the predictions, fostering trust among users. \n",
    "- Based on the above factors, the \"logistic regression model fit with oversampled data\" would be preferred over the \"logistic regression model fit with the original data.\" The model trained with oversampled data shows improved performance in correctly identifying instances of the \"high-risk loan\" class, resulting in higher recall and F1-score. Additionally, the reduction in false negatives is crucial for effectively identifying high-risk loans and mitigating potential risks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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