diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb
index ea7d324..dea670b 100644
--- a/logistic_regression.ipynb
+++ b/logistic_regression.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 1,
    "id": "096082cc",
    "metadata": {},
    "outputs": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "d58417ad",
    "metadata": {},
    "outputs": [
@@ -127,18 +127,6 @@
       "weighted avg       0.76      0.75      0.75        24\n",
       "\n"
      ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[132], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Initialize and train the model without explicit regularization\u001b[39;00m\n\u001b[1;32m      5\u001b[0m logreg_simple \u001b[38;5;241m=\u001b[39m LogisticRegression(random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mlogreg_simple\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train_scaled\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Predictions on the test set\u001b[39;00m\n\u001b[1;32m      9\u001b[0m y_pred_simple \u001b[38;5;241m=\u001b[39m logreg_simple\u001b[38;5;241m.\u001b[39mpredict(X_test_scaled)\n",
-      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1471\u001b[0m     )\n\u001b[1;32m   1472\u001b[0m ):\n\u001b[0;32m-> 1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
     }
    ],
    "source": [
@@ -215,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "d8804f23",
    "metadata": {},
    "outputs": [
@@ -336,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "ba6b4fd9",
    "metadata": {},
    "outputs": [
@@ -436,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "bd749019",
    "metadata": {},
    "outputs": [
@@ -615,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "e9c2a95f",
    "metadata": {},
    "outputs": [
@@ -623,9 +611,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                          Model  Recall  F1-score    AUC\n",
-      "0                 Simple LogReg   0.692      0.75  0.783\n",
-      "1  Optimized LogReg (L1, C=100)   0.692      0.75  0.790\n"
+      "                           Model  Recall  F1-score    AUC\n",
+      "0                  Simple LogReg   0.692      0.75  0.783\n",
+      "1  Optimized LogReg (L2, C≈4.64)   0.692      0.75  0.790\n"
      ]
     },
     {
@@ -649,7 +637,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -749,7 +737,9 @@
     }
    ],
    "source": [
-    "from sklearn.metrics import roc_auc_score\n",
+    "from sklearn.metrics import roc_auc_score, roc_curve, recall_score, f1_score\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
     "\n",
     "# 1. Compute predicted probabilities\n",
     "y_proba_simple = logreg_simple.predict_proba(X_test_scaled)[:, 1]\n",
@@ -757,7 +747,7 @@
     "\n",
     "# 2. Summary of scores\n",
     "results = pd.DataFrame({\n",
-    "    'Model': ['Simple LogReg', 'Optimized LogReg (L1, C=100)'],\n",
+    "    'Model': ['Simple LogReg', 'Optimized LogReg (L2, C≈4.64)'],\n",
     "    'Recall': [\n",
     "        recall_score(y_test, y_pred_simple),\n",
     "        recall_score(y_test, y_pred_best)\n",
@@ -793,7 +783,7 @@
     "coef_df = pd.DataFrame({\n",
     "    'Feature': X_train_scaled.columns,\n",
     "    'Simple LogReg': logreg_simple.coef_[0],\n",
-    "    'Optimized LogReg (L1)': best_logreg.coef_[0]\n",
+    "    'Optimized LogReg (L2)': best_logreg.coef_[0]\n",
     "}).set_index('Feature')\n",
     "\n",
     "plt.figure(figsize=(10, 6))\n",
@@ -834,67 +824,59 @@
    "id": "a30c65a7",
    "metadata": {},
    "source": [
+    "### Comparison of Logistic Regression Variants\n",
     "\n",
     "We now compare the two logistic regression models trained earlier:\n",
     "\n",
-    "- A **simple logistic regression model**, trained without regularization.\n",
-    "- An **optimized logistic regression model**, selected via cross-validation using **L1 regularization** and **C = 100**.\n",
+    "- A **simple logistic regression model**, trained without explicit regularization.\n",
+    "- An **optimized logistic regression model**, selected via cross-validation using **L2 regularization** and **C = 4.64**.\n",
     "\n",
-    "The comparison is performed using multiple criteria: prediction scores, ROC curves, learned coefficients, predicted probabilities, and error analysis.\n",
+    "The comparison is performed across several dimensions: classification metrics, ROC curves, model coefficients, predicted probability distributions, and individual error analysis.\n",
     "\n",
     "---\n",
     "\n",
     "### Overall Performance on Test Set\n",
     "\n",
-    "We summarize the classification results of both models on the test set:\n",
+    "| Model                         | Recall | F1-score | AUC   |\n",
+    "|------------------------------|--------|----------|-------|\n",
+    "| Simple LogReg                | 0.692  | 0.750    | 0.783 |\n",
+    "| Optimized LogReg (L2, C=4.64)| 0.692  | 0.750    | 0.790 |\n",
     "\n",
-    "| Model                        | Recall | F1-score | AUC   |\n",
-    "|-----------------------------|--------|----------|-------|\n",
-    "| Simple LogReg               | 0.692  | 0.750    | 0.783 |\n",
-    "| Optimized LogReg (L1, C=100)| 0.692  | 0.750    | 0.790 |\n",
-    "\n",
-    "- Both models show **identical recall and F1-score**.\n",
-    "- The **optimized model** yields a **slightly higher AUC** (+0.007), suggesting a marginal improvement in discriminative ability.\n",
+    "- Both models achieve **identical recall and F1-score**.\n",
+    "- The **optimized model** achieves a **slightly better AUC** (+0.007), indicating a marginal improvement in discriminative capacity.\n",
     "\n",
     "---\n",
     "\n",
     "### ROC Curve Comparison\n",
     "\n",
-    "We plotted the ROC curves of both models:\n",
-    "\n",
-    "- The **ROC curves are nearly identical**, confirming comparable discriminative performance.\n",
-    "- The optimized model achieves a **slightly better AUC** (0.790 vs. 0.783), but the improvement is modest.\n",
+    "- The ROC curves are **almost superimposed**, confirming similar discriminative performance.\n",
+    "- The optimized model achieves a **slightly higher AUC** (0.790 vs. 0.783), but the gain remains modest.\n",
     "\n",
     "---\n",
     "\n",
     "### Coefficient Comparison\n",
     "\n",
-    "We compared the learned coefficients of both models:\n",
-    "\n",
-    "- The **L1-regularized model** produces **sparser coefficients**, nullifying several features entirely.\n",
-    "- Notably:\n",
-    "  - `Insulin_log` and `HOMA_log` are downweighted or removed.\n",
-    "  - `Glucose` and `BMI` remain strong contributors in both models.\n",
-    "- This sparsity improves **interpretability** and suggests better **feature selection** under L1 regularization.\n",
+    "- The **L2-regularized model** produces **dense coefficients** (no exact zeros), but with slightly more constrained magnitudes.\n",
+    "- Notable changes:\n",
+    "  - Features like `Insulin_log` and `HOMA_log` are **slightly downweighted**.\n",
+    "  - `Glucose` and `BMI` remain major contributors in both models.\n",
+    "- This confirms that **L2 regularization stabilizes weights** without enforcing sparsity.\n",
     "\n",
     "---\n",
     "\n",
     "### Predicted Probabilities\n",
     "\n",
-    "We visualized the distribution of predicted probabilities for the positive class:\n",
-    "\n",
-    "- Both models produce similar overall distributions.\n",
-    "- The optimized model yields **more extreme probabilities** (closer to 0 or 1), which can reflect **greater decision confidence** and separation of classes.\n",
+    "- Both models produce **comparable probability distributions** for the positive class.\n",
+    "- The optimized model yields **more confident predictions** (closer to 0 or 1), suggesting **enhanced separation** and **greater decision certainty**.\n",
     "\n",
     "---\n",
     "\n",
     "### Error Analysis\n",
     "\n",
-    "We identified individuals **misclassified by at least one model**:\n",
+    "We examined the predictions for cases **misclassified by at least one model**:\n",
     "\n",
-    "- Most predictions agree between models.\n",
-    "- A few **borderline cases** are treated differently, which may reflect **structural differences in the decision boundary**.\n",
-    "- This type of analysis helps reveal how regularization affects decision behavior.\n",
+    "- Most predictions match across models.\n",
+    "- A few **borderline examples** are classified differently, possibly reflecting subtle shifts in the **decision boundary** induced by regularization.\n",
     "\n",
     "---\n",
     "\n",
@@ -903,11 +885,10 @@
     "| Model                   | Key Strength                         |\n",
     "|-------------------------|---------------------------------------|\n",
     "| Simple LogReg           | Simpler, marginally faster            |\n",
-    "| Optimized LogReg (L1)   | Parsimony, better interpretability    |\n",
+    "| Optimized LogReg (L2)   | More robust, better generalization    |\n",
     "\n",
-    "- Both models are **viable** and yield **equivalent performance**.\n",
-    "- The **L1-regularized model** is preferred when **feature selection**, **interpretability**, or **generalization** are priorities.\n",
-    "\n"
+    "- Both models are **viable** and offer **equivalent predictive performance** on this dataset.\n",
+    "- The **L2-regularized model** is preferable when **stability**, **generalization**, or **coefficient shrinkage** are desired — even if no sparsity is enforced.\n"
    ]
   }
  ],