From 995dc7f3738eca8a53673ebe2ec5a3ec543c72ef Mon Sep 17 00:00:00 2001
From: samalki <sassmalki1@gmail.com>
Date: Mon, 3 Mar 2025 18:04:14 +0300
Subject: [PATCH] Add files via upload

---
 lab-logistic-regression-with-python.ipynb | 40 +++++++++++++++++++----
 1 file changed, 34 insertions(+), 6 deletions(-)

diff --git a/lab-logistic-regression-with-python.ipynb b/lab-logistic-regression-with-python.ipynb
index 05ead5e..b1c9539 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": 1,
+   "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[1], 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",
@@ -339,7 +351,14 @@
    },
    "outputs": [],
    "source": [
-    "# write your code here\n"
+    "\n",
+    "rows, cols = churn_df.shape\n",
+    "print(f\"Number of rows: {rows}\")\n",
+    "print(f\"Number of columns: {cols}\")\n",
+    "\n",
+    "\n",
+    "print(\"Column names:\")\n",
+    "print(churn_df.columns.tolist())"
    ]
   },
   {
@@ -665,7 +684,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# write your code here\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import log_loss\n",
+    "\n",
+    "LR_new = LogisticRegression(C=0.1, solver='saga').fit(X_train, y_train)\n",
+    "\n",
+    "yhat_prob_new = LR_new.predict_proba(X_test)\n",
+    "\n",
+    "log_loss_value = log_loss(y_test, yhat_prob_new)\n",
+    "\n",
+    "print(f\"New Log Loss value: {log_loss_value:.4f}\")\n",
     "\n"
    ]
   },
@@ -709,7 +737,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.11"
   },
   "widgets": {
    "state": {},