credit_risk_classification.ipynb

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   "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": 2,
   "metadata": {},
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       "    <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",
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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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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Read the CSV file from the Resources folder into a Pandas DataFrame\n",
    "df = pd.read_csv(\"Resources/lending_data.csv\")\n",
    "\n",
    "# Review the DataFrame\n",
    "df.head()"
   ]
  },
  {
   "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",
    "\n",
    "# Separate the y variable, the labels\n",
    "y = df['loan_status']\n",
    "\n",
    "# Separate the X variable, the features\n",
    "X = 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,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Review the y variable Series\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "    <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",
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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": [
    "# 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": 8,
   "metadata": {},
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       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000, random_state=1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, random_state=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "model = LogisticRegression(solver = 'lbfgs', random_state = 1, max_iter = 1000)\n",
    "\n",
    "# Fit the model using training data\n",
    "model.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": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a prediction using the testing data\n",
    "prediction = model.predict(X_test)\n",
    "prediction"
   ]
  },
  {
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9672620331306306\n"
     ]
    }
   ],
   "source": [
    "# Print the balanced_accuracy score of the model\n",
    "print(balanced_accuracy_score(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18658,   107],\n",
       "       [   37,   582]], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a confusion matrix for the model\n",
    "confusion_matrix(y_test, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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      0.94      0.89       619\n",
      "\n",
      "    accuracy                           0.99     19384\n",
      "   macro avg       0.92      0.97      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(y_test, prediction))"
   ]
  },
  {
   "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:**  Of 19,384 records tested, our first model correctly identified 18,658 of the 18,695 \"0\", mislabeling 37 and correctly labeling pertneer 100% of the healthy loans, correctly identifying 582 of the 689 \"1\", mislabeling 107 and correctly labeling 84% of the risky loans (our `Precision Score`[^1]). This result makes sense because the original dataset only has a fraction as many \"1\" records as \"0\" records: y.value_counts() = 75036 \"0\" and 2500 \"1\". Despite mislabeling a higher percentage of \"1\" than \"0\", 582 of the 619 records labeled \"1\" did belong to that category, giving us a `Recall Score` of 94%[^1].  When class distributions are imbalanced, our `F1 Score` of 89%[^1] best describes the overall accuracy of this model.[^2] **This model can be trusted to predict which loans will be healthy vs risky with 89% accuracy.** \n",
    "\n",
    "[^1] https://medium.com/@kennymiyasato/classification-report-precision-recall-f1-score-accuracy-16a245a437a5 <br>\n",
    "[^2] https://medium.com/analytics-vidhya/accuracy-vs-f1-score-6258237beca2#:~:text=Accuracy%20can%20be%20used%20when,to%20evaluate%20our%20model%20on"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting imbalanced-learn\n",
      "  Downloading imbalanced_learn-0.12.0-py3-none-any.whl.metadata (8.2 kB)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\dale\\.conda\\envs\\hvplot\\lib\\site-packages (from imbalanced-learn) (1.23.5)\n",
      "Requirement already satisfied: scipy>=1.5.0 in c:\\users\\dale\\.conda\\envs\\hvplot\\lib\\site-packages (from imbalanced-learn) (1.11.3)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\dale\\.conda\\envs\\hvplot\\lib\\site-packages (from imbalanced-learn) (1.4.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\dale\\.conda\\envs\\hvplot\\lib\\site-packages (from imbalanced-learn) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\dale\\.conda\\envs\\hvplot\\lib\\site-packages (from imbalanced-learn) (3.2.0)\n",
      "Downloading imbalanced_learn-0.12.0-py3-none-any.whl (257 kB)\n",
      "   ---------------------------------------- 257.7/257.7 kB 1.8 MB/s eta 0:00:00\n",
      "Installing collected packages: imbalanced-learn\n",
      "Successfully installed imbalanced-learn-0.12.0\n"
     ]
    }
   ],
   "source": [
    "# First install imbalanced-learn\n",
    "!pip install -U imbalanced-learn \n",
    "\n",
    "# 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",
    "model = RandomOverSampler(random_state=1)\n",
    "\n",
    "# Fit the original training data to the random_oversampler model\n",
    "X_resampled, y_resampled = model.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    56271\n",
       "1    56271\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the distinct values of the resampled labels data\n",
    "y_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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the Logistic Regression model\n",
    "# Assign a random_state parameter of 1 to the model\n",
    "model = LogisticRegression(random_state=1)\n",
    "\n",
    "# Fit the model using the resampled training data\n",
    "model.fit(X_resampled, y_resampled)\n",
    "\n",
    "# Make a prediction using the testing data\n",
    "prediction = model.predict(X_resampled)\n",
    "prediction"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9946686570347071\n"
     ]
    }
   ],
   "source": [
    "# Print the balanced_accuracy score of the model \n",
    "print(balanced_accuracy_score(y_resampled, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[55957   314]\n",
      " [  286 55985]]\n"
     ]
    }
   ],
   "source": [
    "# Generate a confusion matrix for the model\n",
    "print(confusion_matrix(y_resampled, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99     56271\n",
      "           1       0.99      0.99      0.99     56271\n",
      "\n",
      "    accuracy                           0.99    112542\n",
      "   macro avg       0.99      0.99      0.99    112542\n",
      "weighted avg       0.99      0.99      0.99    112542\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report for the model\n",
    "print(classification_report(y_resampled, prediction))"
   ]
  },
  {
   "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:**  Of 112542 records tested, our second model \"correctly\" identified 55,957 of the 56,271 \"0\", mislabeling 286 and \"correctly\" labeling pertneer 99% of the healthy loans and \"correctly\" identified 55,985 of the 56,299 \"1\", mislabeling 314 and \"correctly\" labeling 99% of the risky loans (our `Precision Score`[^1]). 55,985 of the 56,271 records labeled \"1\" did belong to that category, giving us a 99% `Recall Score`[^1].  Now that class distributions are artificially balanced, our 99% `Accuracy Score`[^1] best describes the overall \"accuracy\" of this model.[^2] The original dataset of 75,536 records was imbalanced with a mere 2,500 records labeled \"1\" (risky). With only 3% of the records available for the `test-train split` (2,500 / 75,536 = 0.0330968), the fact that the original model could be trusted to predict which loans would be risky with an 89% accuracy rate is a testament to the power of the `LogisticRegression` model. To correct the imbalance, our `RandomOverSampler` bloated the file by 45% ((112,542 - 75,536) / 75,536 = 0.451481) because it \"duplicates examples from the minority class in the training dataset and can result in overfitting for some models.\"[^3] The 37,0006 clones represent over 14 times the number of actual risky loans (112,542 - 75,536 = 37,006 / 2,500 = 14.8024), rendering this model a self-congratulating exercise with no practical use. **This model should NOT be trusted to predict any real world activity.**\n",
    "\n",
    "[^1] https://medium.com/@kennymiyasato/classification-report-precision-recall-f1-score-accuracy-16a245a437a5 <br>\n",
    "[^2] https://medium.com/analytics-vidhya/accuracy-vs-f1-score-6258237beca2#:~:text=Accuracy%20can%20be%20used%20when,to%20evaluate%20our%20model%20on <br>\n",
    "[^3] https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/"
   ]
  }
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