diff --git a/lab-logistic-regression-with-python.ipynb b/lab-logistic-regression-with-python.ipynb
index 05ead5e..aacf544 100644
--- a/lab-logistic-regression-with-python.ipynb
+++ b/lab-logistic-regression-with-python.ipynb
@@ -123,9 +123,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'piplite'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpiplite\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m piplite\u001b[38;5;241m.\u001b[39minstall([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpandas\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m piplite\u001b[38;5;241m.\u001b[39minstall([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'piplite'"
+     ]
+    }
+   ],
    "source": [
     "import piplite\n",
     "await piplite.install(['pandas'])\n",
@@ -150,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {
     "button": false,
     "new_sheet": false,
@@ -226,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "button": false,
     "new_sheet": false,
@@ -661,12 +673,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'LogisticRegression' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[22], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# write your code here\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m LR2 \u001b[38;5;241m=\u001b[39m LogisticRegression(C\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.01\u001b[39m, solver\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msag\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mfit(X_train,y_train)\n\u001b[0;32m      3\u001b[0m yha2 \u001b[38;5;241m=\u001b[39m LR2\u001b[38;5;241m.\u001b[39mpredict_proba(X_test)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLogLoss: : \u001b[39m\u001b[38;5;132;01m%.2f\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m log_loss(y_test, yha2))\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'LogisticRegression' is not defined"
+     ]
+    }
+   ],
    "source": [
     "# write your code here\n",
-    "\n"
+    "LR2 = LogisticRegression(C=0.01, solver='sag').fit(X_train,y_train)\n",
+    "yha2 = LR2.predict_proba(X_test)\n",
+    "print (\"LogLoss: : %.2f\" % log_loss(y_test, yha2))"
    ]
   },
   {
@@ -695,9 +721,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:base] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-base-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -709,7 +735,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.12.3"
   },
   "widgets": {
    "state": {},