From 9b8fbfa70435c1a1b292122d965c073e4daa452b Mon Sep 17 00:00:00 2001
From: Jeanlebaptist <jeanlebaptist@yahoo.co.uk>
Date: Wed, 30 Jul 2025 16:35:12 +0200
Subject: [PATCH] logistic regression with pathon

---
 ChurnData.csv                             |  201 +++
 lab-logistic-regression-with-python.ipynb | 1391 ++++++++++++++++++++-
 2 files changed, 1536 insertions(+), 56 deletions(-)
 create mode 100644 ChurnData.csv

diff --git a/ChurnData.csv b/ChurnData.csv
new file mode 100644
index 0000000..179441c
--- /dev/null
+++ b/ChurnData.csv
@@ -0,0 +1,201 @@
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diff --git a/lab-logistic-regression-with-python.ipynb b/lab-logistic-regression-with-python.ipynb
index 05ead5e..8557100 100644
--- a/lab-logistic-regression-with-python.ipynb
+++ b/lab-logistic-regression-with-python.ipynb
@@ -123,16 +123,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import piplite\n",
-    "await piplite.install(['pandas'])\n",
-    "await piplite.install(['matplotlib'])\n",
-    "await piplite.install(['numpy'])\n",
-    "await piplite.install(['scikit-learn'])\n",
-    "await piplite.install(['scipy'])\n"
+    "# import piplite\n",
+    "# await piplite.install(['pandas'])\n",
+    "# await piplite.install(['matplotlib'])\n",
+    "# await piplite.install(['numpy'])\n",
+    "# await piplite.install(['scikit-learn'])\n",
+    "# await piplite.install(['scipy'])\n"
    ]
   },
   {
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "button": false,
     "new_sheet": false,
@@ -171,17 +171,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pyodide.http import pyfetch\n",
+    "# from pyodide.http import pyfetch\n",
     "\n",
-    "async def download(url, filename):\n",
-    "    response = await pyfetch(url)\n",
-    "    if response.status == 200:\n",
-    "        with open(filename, \"wb\") as f:\n",
-    "            f.write(await response.bytes())\n"
+    "# async def download(url, filename):\n",
+    "#     response = await pyfetch(url)\n",
+    "#     if response.status == 200:\n",
+    "#         with open(filename, \"wb\") as f:\n",
+    "#             f.write(await response.bytes())\n"
    ]
   },
   {
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "button": false,
     "new_sheet": false,
@@ -262,18 +262,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "\n",
-    "await download(path, \"ChurnData.csv\")\n",
-    "path=\"ChurnData.csv\"\n"
+    "# await download(path, \"ChurnData.csv\")\n",
+    "# path=\"ChurnData.csv\"\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "button": false,
     "new_sheet": false,
@@ -281,7 +281,207 @@
      "read_only": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>age</th>\n",
+       "      <th>address</th>\n",
+       "      <th>income</th>\n",
+       "      <th>ed</th>\n",
+       "      <th>employ</th>\n",
+       "      <th>equip</th>\n",
+       "      <th>callcard</th>\n",
+       "      <th>wireless</th>\n",
+       "      <th>longmon</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pager</th>\n",
+       "      <th>internet</th>\n",
+       "      <th>callwait</th>\n",
+       "      <th>confer</th>\n",
+       "      <th>ebill</th>\n",
+       "      <th>loglong</th>\n",
+       "      <th>logtoll</th>\n",
+       "      <th>lninc</th>\n",
+       "      <th>custcat</th>\n",
+       "      <th>churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>136.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.40</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.482</td>\n",
+       "      <td>3.033</td>\n",
+       "      <td>4.913</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.45</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.246</td>\n",
+       "      <td>3.240</td>\n",
+       "      <td>3.497</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.30</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.841</td>\n",
+       "      <td>3.240</td>\n",
+       "      <td>3.401</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.05</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.800</td>\n",
+       "      <td>3.807</td>\n",
+       "      <td>4.331</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.10</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.960</td>\n",
+       "      <td>3.091</td>\n",
+       "      <td>4.382</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   tenure   age  address  income   ed  employ  equip  callcard  wireless  \\\n",
+       "0    11.0  33.0      7.0   136.0  5.0     5.0    0.0       1.0       1.0   \n",
+       "1    33.0  33.0     12.0    33.0  2.0     0.0    0.0       0.0       0.0   \n",
+       "2    23.0  30.0      9.0    30.0  1.0     2.0    0.0       0.0       0.0   \n",
+       "3    38.0  35.0      5.0    76.0  2.0    10.0    1.0       1.0       1.0   \n",
+       "4     7.0  35.0     14.0    80.0  2.0    15.0    0.0       1.0       0.0   \n",
+       "\n",
+       "   longmon  ...  pager  internet  callwait  confer  ebill  loglong  logtoll  \\\n",
+       "0     4.40  ...    1.0       0.0       1.0     1.0    0.0    1.482    3.033   \n",
+       "1     9.45  ...    0.0       0.0       0.0     0.0    0.0    2.246    3.240   \n",
+       "2     6.30  ...    0.0       0.0       0.0     1.0    0.0    1.841    3.240   \n",
+       "3     6.05  ...    1.0       1.0       1.0     1.0    1.0    1.800    3.807   \n",
+       "4     7.10  ...    0.0       0.0       1.0     1.0    0.0    1.960    3.091   \n",
+       "\n",
+       "   lninc  custcat  churn  \n",
+       "0  4.913      4.0    1.0  \n",
+       "1  3.497      1.0    1.0  \n",
+       "2  3.401      3.0    0.0  \n",
+       "3  4.331      4.0    0.0  \n",
+       "4  4.382      3.0    0.0  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "churn_df = pd.read_csv(path)\n",
     "churn_df.head()"
@@ -303,9 +503,133 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>age</th>\n",
+       "      <th>address</th>\n",
+       "      <th>income</th>\n",
+       "      <th>ed</th>\n",
+       "      <th>employ</th>\n",
+       "      <th>equip</th>\n",
+       "      <th>callcard</th>\n",
+       "      <th>wireless</th>\n",
+       "      <th>churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>136.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   tenure   age  address  income   ed  employ  equip  callcard  wireless  \\\n",
+       "0    11.0  33.0      7.0   136.0  5.0     5.0    0.0       1.0       1.0   \n",
+       "1    33.0  33.0     12.0    33.0  2.0     0.0    0.0       0.0       0.0   \n",
+       "2    23.0  30.0      9.0    30.0  1.0     2.0    0.0       0.0       0.0   \n",
+       "3    38.0  35.0      5.0    76.0  2.0    10.0    1.0       1.0       1.0   \n",
+       "4     7.0  35.0     14.0    80.0  2.0    15.0    0.0       1.0       0.0   \n",
+       "\n",
+       "   churn  \n",
+       "0      1  \n",
+       "1      1  \n",
+       "2      0  \n",
+       "3      0  \n",
+       "4      0  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "churn_df = churn_df[['tenure', 'age', 'address', 'income', 'ed', 'employ', 'equip',   'callcard', 'wireless','churn']]\n",
     "churn_df['churn'] = churn_df['churn'].astype('int')\n",
@@ -329,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "button": false,
     "new_sheet": false,
@@ -337,9 +661,76 @@
      "read_only": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 The dataset contains 200 rows and 28 columns.\n",
+      "\n",
+      "🧾 Column names:\n",
+      "- tenure\n",
+      "- age\n",
+      "- address\n",
+      "- income\n",
+      "- ed\n",
+      "- employ\n",
+      "- equip\n",
+      "- callcard\n",
+      "- wireless\n",
+      "- longmon\n",
+      "- tollmon\n",
+      "- equipmon\n",
+      "- cardmon\n",
+      "- wiremon\n",
+      "- longten\n",
+      "- tollten\n",
+      "- cardten\n",
+      "- voice\n",
+      "- pager\n",
+      "- internet\n",
+      "- callwait\n",
+      "- confer\n",
+      "- ebill\n",
+      "- loglong\n",
+      "- logtoll\n",
+      "- lninc\n",
+      "- custcat\n",
+      "- churn\n"
+     ]
+    }
+   ],
    "source": [
-    "# write your code here\n"
+    "# write your code here\n",
+    "df = pd.read_csv(r'C:\\Users\\User\\Desktop\\AI_Lab\\WK2-Labs\\lab-logistic-regression-with-python\\ChurnData.csv') \n",
+    "# Check shape of the DataFrame\n",
+    "num_rows, num_columns = df.shape\n",
+    "print(f\"📊 The dataset contains {num_rows} rows and {num_columns} columns.\\n\")\n",
+    "\n",
+    "# Display column names\n",
+    "print(\"🧾 Column names:\")\n",
+    "for col in df.columns:\n",
+    "    print(f\"- {col}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(200, 28)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churn_df.shape\n"
    ]
   },
   {
@@ -365,9 +756,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 11.,  33.,   7., 136.,   5.,   5.,   0.],\n",
+       "       [ 33.,  33.,  12.,  33.,   2.,   0.,   0.],\n",
+       "       [ 23.,  30.,   9.,  30.,   1.,   2.,   0.],\n",
+       "       [ 38.,  35.,   5.,  76.,   2.,  10.,   1.],\n",
+       "       [  7.,  35.,  14.,  80.,   2.,  15.,   0.]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "X = np.asarray(churn_df[['tenure', 'age', 'address', 'income', 'ed', 'employ', 'equip']])\n",
     "X[0:5]"
@@ -375,9 +781,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 1., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "y = np.asarray(churn_df['churn'])\n",
     "y [0:5]"
@@ -392,9 +809,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.13518441, -0.62595491, -0.4588971 ,  0.4751423 ,  1.6961288 ,\n",
+       "        -0.58477841, -0.85972695],\n",
+       "       [-0.11604313, -0.62595491,  0.03454064, -0.32886061, -0.6433592 ,\n",
+       "        -1.14437497, -0.85972695],\n",
+       "       [-0.57928917, -0.85594447, -0.261522  , -0.35227817, -1.42318853,\n",
+       "        -0.92053635, -0.85972695],\n",
+       "       [ 0.11557989, -0.47262854, -0.65627219,  0.00679109, -0.6433592 ,\n",
+       "        -0.02518185,  1.16316   ],\n",
+       "       [-1.32048283, -0.47262854,  0.23191574,  0.03801451, -0.6433592 ,\n",
+       "         0.53441472, -0.85972695]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn import preprocessing\n",
     "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
@@ -417,9 +854,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set: (160, 7) (160,)\n",
+      "Test set: (40, 7) (40,)\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
@@ -447,9 +893,702 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\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: 0.5em;\n",
+       "  text-align: center;\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",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.01, solver=&#x27;liblinear&#x27;)</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\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/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></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">penalty&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">dual&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</td>\n",
+       "            <td class=\"value\">0.01</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">solver&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;liblinear&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('multi_class',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">multi_class&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "LogisticRegression(C=0.01, solver='liblinear')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "from sklearn.metrics import confusion_matrix\n",
@@ -466,9 +1605,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 1., 0., 0., 0.,\n",
+       "       1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n",
+       "       0., 0., 1., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "yhat = LR.predict(X_test)\n",
     "yhat"
@@ -483,9 +1635,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.54132919, 0.45867081],\n",
+       "       [0.60593357, 0.39406643],\n",
+       "       [0.56277713, 0.43722287],\n",
+       "       [0.63432489, 0.36567511],\n",
+       "       [0.56431839, 0.43568161],\n",
+       "       [0.55386646, 0.44613354],\n",
+       "       [0.52237207, 0.47762793],\n",
+       "       [0.60514349, 0.39485651],\n",
+       "       [0.41069572, 0.58930428],\n",
+       "       [0.6333873 , 0.3666127 ],\n",
+       "       [0.58068791, 0.41931209],\n",
+       "       [0.62768628, 0.37231372],\n",
+       "       [0.47559883, 0.52440117],\n",
+       "       [0.4267593 , 0.5732407 ],\n",
+       "       [0.66172417, 0.33827583],\n",
+       "       [0.55092315, 0.44907685],\n",
+       "       [0.51749946, 0.48250054],\n",
+       "       [0.485743  , 0.514257  ],\n",
+       "       [0.49011451, 0.50988549],\n",
+       "       [0.52423349, 0.47576651],\n",
+       "       [0.61619519, 0.38380481],\n",
+       "       [0.52696302, 0.47303698],\n",
+       "       [0.63957168, 0.36042832],\n",
+       "       [0.52205164, 0.47794836],\n",
+       "       [0.50572852, 0.49427148],\n",
+       "       [0.70706202, 0.29293798],\n",
+       "       [0.55266286, 0.44733714],\n",
+       "       [0.52271594, 0.47728406],\n",
+       "       [0.51638863, 0.48361137],\n",
+       "       [0.71331391, 0.28668609],\n",
+       "       [0.67862111, 0.32137889],\n",
+       "       [0.50896403, 0.49103597],\n",
+       "       [0.42348082, 0.57651918],\n",
+       "       [0.71495838, 0.28504162],\n",
+       "       [0.59711064, 0.40288936],\n",
+       "       [0.63808839, 0.36191161],\n",
+       "       [0.39957895, 0.60042105],\n",
+       "       [0.52127638, 0.47872362],\n",
+       "       [0.65975464, 0.34024536],\n",
+       "       [0.5114172 , 0.4885828 ]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "yhat_prob = LR.predict_proba(X_test)\n",
     "yhat_prob"
@@ -509,9 +1711,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7058823529411765"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.metrics import jaccard_score\n",
     "jaccard_score(y_test, yhat,pos_label=0)"
@@ -528,9 +1741,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 6  9]\n",
+      " [ 1 24]]\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import classification_report, confusion_matrix\n",
     "import itertools\n",
@@ -572,9 +1794,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion matrix, without normalization\n",
+      "[[ 6  9]\n",
+      " [ 1 24]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Compute confusion matrix\n",
     "cnf_matrix = confusion_matrix(y_test, yhat, labels=[1,0])\n",
@@ -604,9 +1846,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.73      0.96      0.83        25\n",
+      "         1.0       0.86      0.40      0.55        15\n",
+      "\n",
+      "    accuracy                           0.75        40\n",
+      "   macro avg       0.79      0.68      0.69        40\n",
+      "weighted avg       0.78      0.75      0.72        40\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "print (classification_report(y_test, yhat))\n"
    ]
@@ -643,9 +1901,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6017092478101186"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.metrics import log_loss\n",
     "log_loss(y_test, yhat_prob)"
@@ -661,12 +1930,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogLoss: : 0.61\n"
+     ]
+    }
+   ],
    "source": [
-    "# write your code here\n",
-    "\n"
+    "#write your code here\n",
+    "LR2 = LogisticRegression(C=0.01, solver='sag').fit(X_train,y_train)\n",
+    "yhat_prob2 = LR2.predict_proba(X_test)\n",
+    "print (\"LogLoss: : %.2f\" % log_loss(y_test, yhat_prob2))\n"
    ]
   },
   {
@@ -695,7 +1974,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -709,7 +1988,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.13.5"
   },
   "widgets": {
    "state": {},