{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b14f682-fd25-4071-ab2d-0405878bdada",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating synthetic loan book...\n",
      "  8,000 loans | default rate: 17.3%\n",
      "\n",
      "Training Logistic Regression...\n",
      "Training Decision Tree...\n",
      "Training Random Forest...\n",
      "Training Gradient Boosting...\n",
      "Training XGBoost...\n",
      "\n",
      "====================================================================================================\n",
      "MODEL COMPARISON (test set, 25% holdout)\n",
      "====================================================================================================\n",
      "              Model  CV AUC (train)  Test AUC  Gini    KS  PR-AUC  Recall (defaults caught)  Precision    F1  Brier (calibration)  Business Cost  Threshold\n",
      "Logistic Regression           0.633     0.645 0.290 0.229   0.259                     0.723      0.226 0.344               0.2316           1338       0.45\n",
      "      Random Forest           0.618     0.618 0.236 0.197   0.237                     0.445      0.230 0.303               0.2043           1476       0.47\n",
      "  Gradient Boosting           0.615     0.611 0.222 0.170   0.227                     0.405      0.227 0.291               0.1420           1506       0.20\n",
      "            XGBoost           0.600     0.592 0.185 0.149   0.230                     0.327      0.225 0.266               0.1964           1555       0.50\n",
      "      Decision Tree           0.562     0.566 0.132 0.121   0.213                     0.587      0.209 0.308               0.2437           1485       0.46\n",
      "\n",
      "Saved: credit_model_comparison.csv\n",
      "Saved: credit_model_comparison.png\n",
      "\n",
      "Top risk drivers (Gradient Boosting):\n",
      "  interest_rate             0.135\n",
      "  annual_income             0.127\n",
      "  credit_utilization        0.114\n",
      "  loan_amount               0.102\n",
      "  employment_years          0.098\n",
      "  credit_history_years      0.097\n",
      "  debt_to_income            0.085\n",
      "  age                       0.065\n",
      "  delinquencies_2yrs        0.049\n",
      "  inquiries_6mths           0.046\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Credit Risk Model Comparison for a Small Bank\n",
    "=============================================\n",
    "Builds and benchmarks several default-prediction models on synthetic\n",
    "loan data, then ranks them by predictive power and business cost.\n",
    "\n",
    "Models compared:\n",
    "  1. Logistic Regression   (interpretable baseline, regulator-friendly)\n",
    "  2. Decision Tree         (simple rules, easy to explain)\n",
    "  3. Random Forest         (bagged ensemble, robust)\n",
    "  4. Gradient Boosting     (usually the strongest tabular performer)\n",
    "  5. XGBoost               (optional; used if the xgboost package is installed)\n",
    "\n",
    "Run:  pip install pandas numpy scikit-learn matplotlib   (xgboost optional)\n",
    "      python credit_risk_model.py\n",
    "\"\"\"\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import (roc_auc_score, roc_curve, precision_recall_curve,\n",
    "                             auc, precision_score, recall_score, f1_score,\n",
    "                             confusion_matrix, brier_score_loss)\n",
    "\n",
    "RANDOM_STATE = 42\n",
    "COST_FN = 5.0   # business cost of missing a defaulter (lost principal) - relative weight\n",
    "COST_FP = 1.0   # business cost of rejecting a good customer (lost interest/relationship)\n",
    "\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "# 1. Synthetic loan portfolio (swap in your real data at this step)\n",
    "# --------------------------------------------------------------------------\n",
    "def generate_loan_data(n_customers: int = 8000, default_rate: float = 0.18) -> pd.DataFrame:\n",
    "    \"\"\"Simulate a small-bank loan book with realistic borrower features.\n",
    "\n",
    "    Replace this function with pd.read_csv('your_loans.csv') when real data\n",
    "    is available; keep the same column names.\n",
    "    \"\"\"\n",
    "    rng = np.random.default_rng(RANDOM_STATE)\n",
    "\n",
    "    df = pd.DataFrame({\n",
    "        \"age\": rng.normal(42, 12, n_customers).clip(21, 75).round(),\n",
    "        \"annual_income\": rng.lognormal(10.9, 0.45, n_customers).round(),      # ~ $55k median\n",
    "        \"loan_amount\": rng.lognormal(9.3, 0.6, n_customers).round(),          # ~ $11k median\n",
    "        \"loan_term_months\": rng.choice([12, 24, 36, 48, 60], n_customers,\n",
    "                                       p=[0.15, 0.2, 0.35, 0.15, 0.15]),\n",
    "        \"interest_rate\": rng.normal(11, 4, n_customers).clip(4, 29).round(2),\n",
    "        \"credit_utilization\": rng.beta(2, 5, n_customers).round(3),           # 0-1 of limit used\n",
    "        \"delinquencies_2yrs\": rng.poisson(0.4, n_customers),\n",
    "        \"credit_history_years\": rng.gamma(3, 4, n_customers).clip(0, 45).round(1),\n",
    "        \"employment_years\": rng.gamma(2.5, 3, n_customers).clip(0, 40).round(1),\n",
    "        \"num_open_accounts\": rng.poisson(5, n_customers).clip(1, 25),\n",
    "        \"inquiries_6mths\": rng.poisson(0.8, n_customers),\n",
    "    })\n",
    "    df[\"debt_to_income\"] = ((df[\"loan_amount\"] * 1.15 / df[\"loan_term_months\"] * 12)\n",
    "                            / (df[\"annual_income\"] + 1)).clip(0, 1).round(3)\n",
    "    df[\"home_ownership\"] = rng.choice([\"RENT\", \"OWN\", \"MORTGAGE\"], n_customers, p=[0.4, 0.25, 0.35])\n",
    "    df[\"purpose\"] = rng.choice([\"DEBT_CONSOLIDATION\", \"HOME_IMPROVEMENT\", \"AUTO\",\n",
    "                                \"MEDICAL\", \"BUSINESS\", \"OTHER\"],\n",
    "                               n_customers, p=[0.35, 0.15, 0.2, 0.1, 0.1, 0.1])\n",
    "\n",
    "    # Latent creditworthiness drives the true default probability\n",
    "    z = (\n",
    "        0.9\n",
    "        - 2.6 * df[\"debt_to_income\"]\n",
    "        - 1.8 * df[\"credit_utilization\"]\n",
    "        + 0.55 * df[\"delinquencies_2yrs\"]\n",
    "        - 0.045 * df[\"credit_history_years\"]\n",
    "        - 0.06 * df[\"employment_years\"]\n",
    "        + 0.35 * df[\"inquiries_6mths\"]\n",
    "        + 0.05 * (df[\"interest_rate\"] - 11)\n",
    "        - 0.25 * (df[\"home_ownership\"] == \"OWN\")\n",
    "        + 0.15 * (df[\"home_ownership\"] == \"RENT\")\n",
    "        + 0.3 * (df[\"purpose\"] == \"DEBT_CONSOLIDATION\")\n",
    "    )\n",
    "    p_default = 1 / (1 + np.exp(-z))\n",
    "    # Rescale so the book matches the target default rate\n",
    "    p_default = p_default / p_default.mean() * default_rate\n",
    "    df[\"default\"] = (rng.random(n_customers) < p_default).astype(int)\n",
    "    return df\n",
    "\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "# 2. Preprocessing\n",
    "# --------------------------------------------------------------------------\n",
    "def preprocess(df: pd.DataFrame):\n",
    "    df = df.copy()\n",
    "    df = pd.get_dummies(df, columns=[\"home_ownership\", \"purpose\"], drop_first=True)\n",
    "\n",
    "    X = df.drop(columns=[\"default\"])\n",
    "    y = df[\"default\"]\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=0.25, stratify=y, random_state=RANDOM_STATE\n",
    "    )\n",
    "\n",
    "    scaler = StandardScaler()\n",
    "    num_cols = [\"age\", \"annual_income\", \"loan_amount\", \"loan_term_months\",\n",
    "                \"interest_rate\", \"credit_utilization\", \"delinquencies_2yrs\",\n",
    "                \"credit_history_years\", \"employment_years\", \"num_open_accounts\",\n",
    "                \"inquiries_6mths\", \"debt_to_income\"]\n",
    "    X_train[num_cols] = scaler.fit_transform(X_train[num_cols])\n",
    "    X_test[num_cols] = scaler.transform(X_test[num_cols])\n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "# 3. Candidate models\n",
    "# --------------------------------------------------------------------------\n",
    "def get_models():\n",
    "    models = {\n",
    "        \"Logistic Regression\": LogisticRegression(\n",
    "            max_iter=2000, class_weight=\"balanced\", C=0.5),\n",
    "        \"Decision Tree\": DecisionTreeClassifier(\n",
    "            max_depth=5, min_samples_leaf=50, class_weight=\"balanced\",\n",
    "            random_state=RANDOM_STATE),\n",
    "        \"Random Forest\": RandomForestClassifier(\n",
    "            n_estimators=400, max_depth=10, min_samples_leaf=20,\n",
    "            class_weight=\"balanced\", n_jobs=-1, random_state=RANDOM_STATE),\n",
    "        \"Gradient Boosting\": GradientBoostingClassifier(\n",
    "            n_estimators=300, learning_rate=0.05, max_depth=3,\n",
    "            subsample=0.8, random_state=RANDOM_STATE),\n",
    "    }\n",
    "    try:\n",
    "        from xgboost import XGBClassifier\n",
    "        models[\"XGBoost\"] = XGBClassifier(\n",
    "            n_estimators=400, learning_rate=0.05, max_depth=4,\n",
    "            subsample=0.8, colsample_bytree=0.8,\n",
    "            scale_pos_weight=(1 / 0.18) - 1,     # inverse default rate\n",
    "            eval_metric=\"auc\", n_jobs=-1, random_state=RANDOM_STATE,\n",
    "            verbosity=0)\n",
    "    except ImportError:\n",
    "        print(\"  (xgboost not installed - skipping XGBoost; \"\n",
    "              \"pip install xgboost to include it)\")\n",
    "    return models\n",
    "\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "# 4. Credit-risk evaluation metrics\n",
    "# --------------------------------------------------------------------------\n",
    "def ks_statistic(y_true, y_prob):\n",
    "    \"\"\"Kolmogorov-Smirnov: max separation between good/bad score distributions.\n",
    "    A classic credit-scoring metric; banks often target KS > 0.3.\"\"\"\n",
    "    fpr, tpr, _ = roc_curve(y_true, y_prob)\n",
    "    return np.max(tpr - fpr)\n",
    "\n",
    "\n",
    "def business_cost(y_true, y_pred):\n",
    "    \"\"\"Weighted cost of decisions: missing a defaulter costs more than\n",
    "    declining a good borrower.\"\"\"\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
    "    return COST_FN * fn + COST_FP * fp\n",
    "\n",
    "\n",
    "def best_threshold_by_cost(y_true, y_prob):\n",
    "    thresholds = np.linspace(0.05, 0.95, 181)\n",
    "    costs = [business_cost(y_true, (y_prob >= t).astype(int)) for t in thresholds]\n",
    "    return thresholds[int(np.argmin(costs))]\n",
    "\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "# 5. Train, evaluate, compare\n",
    "# --------------------------------------------------------------------------\n",
    "def run_pipeline():\n",
    "    print(\"Generating synthetic loan book...\")\n",
    "    df = generate_loan_data()\n",
    "    print(f\"  {len(df):,} loans | default rate: {df['default'].mean():.1%}\\n\")\n",
    "\n",
    "    X_train, X_test, y_train, y_test = preprocess(df)\n",
    "    models = get_models()\n",
    "\n",
    "    cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n",
    "    results, curves = [], {}\n",
    "\n",
    "    for name, model in models.items():\n",
    "        print(f\"Training {name}...\")\n",
    "        cv_auc = cross_val_score(model, X_train, y_train, cv=cv,\n",
    "                                 scoring=\"roc_auc\", n_jobs=-1).mean()\n",
    "        model.fit(X_train, y_train)\n",
    "        prob = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "        thr = best_threshold_by_cost(y_train, model.predict_proba(X_train)[:, 1])\n",
    "        pred = (prob >= thr).astype(int)\n",
    "\n",
    "        prec, rec, _ = precision_recall_curve(y_test, prob)\n",
    "        results.append({\n",
    "            \"Model\": name,\n",
    "            \"CV AUC (train)\": round(cv_auc, 3),\n",
    "            \"Test AUC\": round(roc_auc_score(y_test, prob), 3),\n",
    "            \"Gini\": round(2 * roc_auc_score(y_test, prob) - 1, 3),\n",
    "            \"KS\": round(ks_statistic(y_test, prob), 3),\n",
    "            \"PR-AUC\": round(auc(rec, prec), 3),\n",
    "            \"Recall (defaults caught)\": round(recall_score(y_test, pred), 3),\n",
    "            \"Precision\": round(precision_score(y_test, pred), 3),\n",
    "            \"F1\": round(f1_score(y_test, pred), 3),\n",
    "            \"Brier (calibration)\": round(brier_score_loss(y_test, prob), 4),\n",
    "            \"Business Cost\": int(business_cost(y_test, pred)),\n",
    "            \"Threshold\": round(thr, 2),\n",
    "        })\n",
    "        curves[name] = roc_curve(y_test, prob)[:2] + (prob,)\n",
    "\n",
    "    results_df = pd.DataFrame(results).sort_values(\"Test AUC\", ascending=False)\n",
    "    print(\"\\n\" + \"=\" * 100)\n",
    "    print(\"MODEL COMPARISON (test set, 25% holdout)\")\n",
    "    print(\"=\" * 100)\n",
    "    print(results_df.to_string(index=False))\n",
    "\n",
    "    results_df.to_csv(\"credit_model_comparison.csv\", index=False)\n",
    "    print(\"\\nSaved: credit_model_comparison.csv\")\n",
    "\n",
    "    plot_results(results_df, curves, y_test)\n",
    "    show_feature_importance(models, X_train.columns)\n",
    "    return results_df\n",
    "\n",
    "\n",
    "def plot_results(results_df, curves, y_test):\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "    # ROC curves\n",
    "    ax = axes[0]\n",
    "    for name, (fpr, tpr, _) in curves.items():\n",
    "        ax.plot(fpr, tpr, label=f\"{name} (AUC {results_df.set_index('Model').loc[name, 'Test AUC']:.3f})\")\n",
    "    ax.plot([0, 1], [0, 1], \"k--\", lw=0.8)\n",
    "    ax.set(title=\"ROC Curves\", xlabel=\"False Positive Rate\", ylabel=\"True Positive Rate\")\n",
    "    ax.legend(fontsize=8)\n",
    "\n",
    "    # Ranked AUC bar chart\n",
    "    ax = axes[1]\n",
    "    ax.barh(results_df[\"Model\"][::-1], results_df[\"Test AUC\"][::-1], color=\"#01A982\")\n",
    "    ax.set(title=\"Test AUC by Model\", xlabel=\"ROC AUC\")\n",
    "    ax.set_xlim(0.5, 1.0)\n",
    "\n",
    "    # Score distribution separation (best model)\n",
    "    best_name = results_df.iloc[0][\"Model\"]\n",
    "    prob = curves[best_name][2]\n",
    "    ax = axes[2]\n",
    "    for label, color in [(0, \"#2E7D32\"), (1, \"#C62828\")]:\n",
    "        subset = prob[y_test == label]\n",
    "        ax.hist(subset, bins=40, alpha=0.6, density=True,\n",
    "                label=f\"{'Default' if label else 'Good'}\", color=color)\n",
    "    ax.set(title=f\"Score Separation - {best_name}\", xlabel=\"Predicted Default Probability\")\n",
    "    ax.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"credit_model_comparison.png\", dpi=150)\n",
    "    print(\"Saved: credit_model_comparison.png\")\n",
    "\n",
    "\n",
    "def show_feature_importance(models, columns):\n",
    "    best_name = \"Gradient Boosting\" if \"Gradient Boosting\" in models else list(models)[0]\n",
    "    model = models[best_name]\n",
    "    if hasattr(model, \"feature_importances_\"):\n",
    "        imp = pd.Series(model.feature_importances_, index=columns).sort_values(ascending=False)[:10]\n",
    "        print(f\"\\nTop risk drivers ({best_name}):\")\n",
    "        for feat, val in imp.items():\n",
    "            print(f\"  {feat:<25} {val:.3f}\")\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    run_pipeline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "202bc4a1-c167-45ac-bc66-b7c42af42341",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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