From 0dec9fb5f6c23f08d762e972d392e910f21ee8b5 Mon Sep 17 00:00:00 2001
From: Jeanlebaptist <jeanlebaptist@yahoo.co.uk>
Date: Fri, 1 Aug 2025 17:49:14 +0200
Subject: [PATCH] lab support vector machines(svn)

---
 cell_samples.csv |  700 +++++++++++++++++++++++++++
 lab-svm.ipynb    | 1201 ++++++++++++++++++++++++++++++++++++++++++++--
 2 files changed, 1849 insertions(+), 52 deletions(-)
 create mode 100644 cell_samples.csv

diff --git a/cell_samples.csv b/cell_samples.csv
new file mode 100644
index 0000000..66a12f1
--- /dev/null
+++ b/cell_samples.csv
@@ -0,0 +1,700 @@
+ID,Clump,UnifSize,UnifShape,MargAdh,SingEpiSize,BareNuc,BlandChrom,NormNucl,Mit,Class
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+1096352,6,3,3,3,3,2,6,1,1,2
+1140597,7,1,2,3,2,1,2,1,1,2
+1149548,1,1,1,1,2,1,1,1,1,2
+1174009,5,1,1,2,1,1,2,1,1,2
+1183596,3,1,3,1,3,4,1,1,1,2
+1190386,4,6,6,5,7,6,7,7,3,4
+1190546,2,1,1,1,2,5,1,1,1,2
+1213273,2,1,1,1,2,1,1,1,1,2
+1218982,4,1,1,1,2,1,1,1,1,2
+1225382,6,2,3,1,2,1,1,1,1,2
+1235807,5,1,1,1,2,1,2,1,1,2
+1238777,1,1,1,1,2,1,1,1,1,2
+1253955,8,7,4,4,5,3,5,10,1,4
+1257366,3,1,1,1,2,1,1,1,1,2
+1260659,3,1,4,1,2,1,1,1,1,2
+1268952,10,10,7,8,7,1,10,10,3,4
+1275807,4,2,4,3,2,2,2,1,1,2
+1277792,4,1,1,1,2,1,1,1,1,2
+1277792,5,1,1,3,2,1,1,1,1,2
+1285722,4,1,1,3,2,1,1,1,1,2
+1288608,3,1,1,1,2,1,2,1,1,2
+1290203,3,1,1,1,2,1,2,1,1,2
+1294413,1,1,1,1,2,1,1,1,1,2
+1299596,2,1,1,1,2,1,1,1,1,2
+1303489,3,1,1,1,2,1,2,1,1,2
+1311033,1,2,2,1,2,1,1,1,1,2
+1311108,1,1,1,3,2,1,1,1,1,2
+1315807,5,10,10,10,10,2,10,10,10,4
+1318671,3,1,1,1,2,1,2,1,1,2
+1319609,3,1,1,2,3,4,1,1,1,2
+1323477,1,2,1,3,2,1,2,1,1,2
+1324572,5,1,1,1,2,1,2,2,1,2
+1324681,4,1,1,1,2,1,2,1,1,2
+1325159,3,1,1,1,2,1,3,1,1,2
+1326892,3,1,1,1,2,1,2,1,1,2
+1330361,5,1,1,1,2,1,2,1,1,2
+1333877,5,4,5,1,8,1,3,6,1,2
+1334015,7,8,8,7,3,10,7,2,3,4
+1334667,1,1,1,1,2,1,1,1,1,2
+1339781,1,1,1,1,2,1,2,1,1,2
+1339781,4,1,1,1,2,1,3,1,1,2
+13454352,1,1,3,1,2,1,2,1,1,2
+1345452,1,1,3,1,2,1,2,1,1,2
+1345593,3,1,1,3,2,1,2,1,1,2
+1347749,1,1,1,1,2,1,1,1,1,2
+1347943,5,2,2,2,2,1,1,1,2,2
+1348851,3,1,1,1,2,1,3,1,1,2
+1350319,5,7,4,1,6,1,7,10,3,4
+1350423,5,10,10,8,5,5,7,10,1,4
+1352848,3,10,7,8,5,8,7,4,1,4
+1353092,3,2,1,2,2,1,3,1,1,2
+1354840,2,1,1,1,2,1,3,1,1,2
+1354840,5,3,2,1,3,1,1,1,1,2
+1355260,1,1,1,1,2,1,2,1,1,2
+1365075,4,1,4,1,2,1,1,1,1,2
+1365328,1,1,2,1,2,1,2,1,1,2
+1368267,5,1,1,1,2,1,1,1,1,2
+1368273,1,1,1,1,2,1,1,1,1,2
+1368882,2,1,1,1,2,1,1,1,1,2
+1369821,10,10,10,10,5,10,10,10,7,4
+1371026,5,10,10,10,4,10,5,6,3,4
+1371920,5,1,1,1,2,1,3,2,1,2
+466906,1,1,1,1,2,1,1,1,1,2
+466906,1,1,1,1,2,1,1,1,1,2
+534555,1,1,1,1,2,1,1,1,1,2
+536708,1,1,1,1,2,1,1,1,1,2
+566346,3,1,1,1,2,1,2,3,1,2
+603148,4,1,1,1,2,1,1,1,1,2
+654546,1,1,1,1,2,1,1,1,8,2
+654546,1,1,1,3,2,1,1,1,1,2
+695091,5,10,10,5,4,5,4,4,1,4
+714039,3,1,1,1,2,1,1,1,1,2
+763235,3,1,1,1,2,1,2,1,2,2
+776715,3,1,1,1,3,2,1,1,1,2
+841769,2,1,1,1,2,1,1,1,1,2
+888820,5,10,10,3,7,3,8,10,2,4
+897471,4,8,6,4,3,4,10,6,1,4
+897471,4,8,8,5,4,5,10,4,1,4
diff --git a/lab-svm.ipynb b/lab-svm.ipynb
index bbbfd6b..695f6a5 100644
--- a/lab-svm.ipynb
+++ b/lab-svm.ipynb
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,21 +53,21 @@
   },
   {
    "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"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,17 +83,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "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"
    ]
   },
   {
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "button": false,
     "new_sheet": false,
@@ -141,8 +141,36 @@
    },
    "outputs": [],
    "source": [
-    "#Click here and press Shift+Enter\n",
-    "path=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv\""
+    "# #Click here and press Shift+Enter\n",
+    "# path=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Download completed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv\"\n",
+    "filename = \"cell_samples.csv\"\n",
+    "\n",
+    "response = requests.get(url)\n",
+    "\n",
+    "# Save the file\n",
+    "with open(filename, \"wb\") as file:\n",
+    "    file.write(response.content)\n",
+    "\n",
+    "print(\"Download completed.\")\n"
    ]
   },
   {
@@ -160,16 +188,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "await download(path, \"cell_samples.csv\")"
+    "# await download(path,\"cell_samples.csv\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "button": false,
     "new_sheet": false,
@@ -177,7 +205,137 @@
      "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>ID</th>\n",
+       "      <th>Clump</th>\n",
+       "      <th>UnifSize</th>\n",
+       "      <th>UnifShape</th>\n",
+       "      <th>MargAdh</th>\n",
+       "      <th>SingEpiSize</th>\n",
+       "      <th>BareNuc</th>\n",
+       "      <th>BlandChrom</th>\n",
+       "      <th>NormNucl</th>\n",
+       "      <th>Mit</th>\n",
+       "      <th>Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1000025</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1002945</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1015425</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1016277</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1017023</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        ID  Clump  UnifSize  UnifShape  MargAdh  SingEpiSize BareNuc  \\\n",
+       "0  1000025      5         1          1        1            2       1   \n",
+       "1  1002945      5         4          4        5            7      10   \n",
+       "2  1015425      3         1          1        1            2       2   \n",
+       "3  1016277      6         8          8        1            3       4   \n",
+       "4  1017023      4         1          1        3            2       1   \n",
+       "\n",
+       "   BlandChrom  NormNucl  Mit  Class  \n",
+       "0           3         1    1      2  \n",
+       "1           3         2    1      2  \n",
+       "2           3         1    1      2  \n",
+       "3           3         7    1      2  \n",
+       "4           3         1    1      2  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cell_df = pd.read_csv(\"cell_samples.csv\")\n",
     "cell_df.head()"
@@ -196,9 +354,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ax = cell_df[cell_df['Class'] == 4][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='DarkBlue', label='malignant');\n",
     "cell_df[cell_df['Class'] == 2][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='Yellow', label='benign', ax=ax);\n",
@@ -221,9 +390,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID              int64\n",
+       "Clump           int64\n",
+       "UnifSize        int64\n",
+       "UnifShape       int64\n",
+       "MargAdh         int64\n",
+       "SingEpiSize     int64\n",
+       "BareNuc        object\n",
+       "BlandChrom      int64\n",
+       "NormNucl        int64\n",
+       "Mit             int64\n",
+       "Class           int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cell_df.dtypes"
    ]
@@ -237,9 +428,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID             int64\n",
+       "Clump          int64\n",
+       "UnifSize       int64\n",
+       "UnifShape      int64\n",
+       "MargAdh        int64\n",
+       "SingEpiSize    int64\n",
+       "BareNuc        int64\n",
+       "BlandChrom     int64\n",
+       "NormNucl       int64\n",
+       "Mit            int64\n",
+       "Class          int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cell_df = cell_df[pd.to_numeric(cell_df['BareNuc'], errors='coerce').notnull()]\n",
     "cell_df['BareNuc'] = cell_df['BareNuc'].astype('int')\n",
@@ -248,9 +461,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 5,  1,  1,  1,  2,  1,  3,  1,  1],\n",
+       "       [ 5,  4,  4,  5,  7, 10,  3,  2,  1],\n",
+       "       [ 3,  1,  1,  1,  2,  2,  3,  1,  1],\n",
+       "       [ 6,  8,  8,  1,  3,  4,  3,  7,  1],\n",
+       "       [ 4,  1,  1,  3,  2,  1,  3,  1,  1]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "feature_df = cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']]\n",
     "X = np.asarray(feature_df)\n",
@@ -266,9 +494,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 2, 2, 2])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cell_df['Class'] = cell_df['Class'].astype('int')\n",
     "y = np.asarray(cell_df['Class'])\n",
@@ -291,9 +530,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set: (546, 9) (546,)\n",
+      "Test set: (137, 9) (137,)\n"
+     ]
+    }
+   ],
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
     "print ('Train set:', X_train.shape,  y_train.shape)\n",
@@ -325,15 +573,729 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "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,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);\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>SVC()</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>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</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('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kernel',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kernel&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('degree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">degree&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">gamma&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('coef0',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">coef0&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shrinking',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">shrinking&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('probability',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">probability&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\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cache_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cache_size&nbsp;</td>\n",
+       "            <td class=\"value\">200</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('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&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('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&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('decision_function_shape',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">decision_function_shape&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('break_ties',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">break_ties&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('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",
+       "                  </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": [
+       "SVC()"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn import svm\n",
     "clf = svm.SVC(kernel='rbf')\n",
     "clf.fit(X_train, y_train) "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔮 Predicted labels: [2 4 2 4 2 2 2 2 4 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions on the test set\n",
+    "y_pred = clf.predict(X_test)\n",
+    "\n",
+    "# Preview predictions\n",
+    "print(\"🔮 Predicted labels:\", y_pred[0:10])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -343,9 +1305,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 4, 2, 4, 2])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "yhat = clf.predict(X_test)\n",
     "yhat [0:5]"
@@ -360,7 +1333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -370,7 +1343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -411,9 +1384,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           2       1.00      0.94      0.97        90\n",
+      "           4       0.90      1.00      0.95        47\n",
+      "\n",
+      "    accuracy                           0.96       137\n",
+      "   macro avg       0.95      0.97      0.96       137\n",
+      "weighted avg       0.97      0.96      0.96       137\n",
+      "\n",
+      "Confusion matrix, without normalization\n",
+      "[[85  5]\n",
+      " [ 0 47]]\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=[2,4])\n",
@@ -435,9 +1437,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9639038982104676"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.metrics import f1_score\n",
     "f1_score(y_test, yhat, average='weighted') "
@@ -452,9 +1465,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9444444444444444"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.metrics import jaccard_score\n",
     "jaccard_score(y_test, yhat,pos_label=2)"
@@ -470,11 +1494,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📈 Evaluation: SVM with Linear Kernel\n",
+      "🔹 Accuracy:      0.9635036496350365\n",
+      "🔹 F1 Score:      0.9494949494949495\n",
+      "🔹 Jaccard Index: 0.9038461538461539\n",
+      "\n",
+      "📄 Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.94      0.97        90\n",
+      "           1       0.90      1.00      0.95        47\n",
+      "\n",
+      "    accuracy                           0.96       137\n",
+      "   macro avg       0.95      0.97      0.96       137\n",
+      "weighted avg       0.97      0.96      0.96       137\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "# write your code here\n"
+    "# write your code here\n",
+    "\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.metrics import accuracy_score, f1_score, jaccard_score, classification_report\n",
+    "\n",
+    "# 1️⃣ Prepare features and binary target\n",
+    "X = np.asarray(cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh',\n",
+    "                        'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']])\n",
+    "y = np.where(cell_df['Class'] == 2, 0, 1)  # 0 = benign, 1 = malignant\n",
+    "\n",
+    "# 2️⃣ Split data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\n",
+    "\n",
+    "# 3️⃣ Define and train linear SVM\n",
+    "linear_clf = SVC(kernel='linear')\n",
+    "linear_clf.fit(X_train, y_train)\n",
+    "\n",
+    "# 4️⃣ Predict\n",
+    "y_pred_linear = linear_clf.predict(X_test)\n",
+    "\n",
+    "# 5️⃣ Evaluate\n",
+    "print(\"📈 Evaluation: SVM with Linear Kernel\")\n",
+    "print(\"🔹 Accuracy:     \", accuracy_score(y_test, y_pred_linear))\n",
+    "print(\"🔹 F1 Score:     \", f1_score(y_test, y_pred_linear))\n",
+    "print(\"🔹 Jaccard Index:\", jaccard_score(y_test, y_pred_linear))\n",
+    "print(\"\\n📄 Classification Report:\")\n",
+    "print(classification_report(y_test, y_pred_linear))\n",
+    "\n"
    ]
   },
   {
@@ -495,6 +1570,28 @@
     "</details>\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Avg F1-score: 0.9639\n",
+      "Jaccard score: 0.9038\n"
+     ]
+    }
+   ],
+   "source": [
+    "clf2 = svm.SVC(kernel='linear')\n",
+    "clf2.fit(X_train, y_train) \n",
+    "yhat2 = clf2.predict(X_test)\n",
+    "print(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat2, average='weighted'))\n",
+    "print(\"Jaccard score: %.4f\" % jaccard_score(y_test, yhat2,pos_label=1))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -505,7 +1602,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -519,7 +1616,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,