Breast Cancer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = load_breast_cancer()\n",
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_feat = pd.DataFrame(data['data'], columns=data['feature_names'])\n",
    "data_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data_feat.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['target_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_feat\n",
    "y = data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\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",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\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",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\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: 1ex;\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",
       "</style><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 fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(X_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 56  10]\n",
      " [  3 102]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.85      0.90        66\n",
      "           1       0.91      0.97      0.94       105\n",
      "\n",
      "    accuracy                           0.92       171\n",
      "   macro avg       0.93      0.91      0.92       171\n",
      "weighted avg       0.93      0.92      0.92       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, pred))\n",
    "print('\\n')\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C': [0.1,1,10,100,1000], 'gamma': [1,0.1,0.01,0.001,0.0001], 'kernel': ('linear', 'rbf', 'poly', 'sigmoid'), }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(SVC(), param_grid, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
      "[CV 1/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.950 total time=   0.1s\n",
      "[CV 2/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.925 total time=   0.1s\n",
      "[CV 3/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.937 total time=   0.0s\n",
      "[CV 5/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.962 total time=   0.1s\n",
      "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.625 total time=   0.0s\n",
      "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.938 total time=  29.3s\n",
      "[CV 2/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.938 total time=   7.9s\n",
      "[CV 3/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.950 total time=  31.1s\n",
      "[CV 4/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.924 total time=10.2min\n"
     ]
    }
   ],
   "source": [
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 100, 'gamma': 1, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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: 1ex;\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-4 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-4 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-4 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-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=100, gamma=1, kernel=&#x27;linear&#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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=100, gamma=1, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=100, gamma=1, kernel='linear')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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: 1ex;\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-3 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-3 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-3 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-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=100, gamma=1, kernel=&#x27;linear&#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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=100, gamma=1, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=100, gamma=1, kernel='linear')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC(C=100, gamma=1, kernel='linear')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(X_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 60   6]\n",
      " [  2 103]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.91      0.94        66\n",
      "           1       0.94      0.98      0.96       105\n",
      "\n",
      "    accuracy                           0.95       171\n",
      "   macro avg       0.96      0.95      0.95       171\n",
      "weighted avg       0.95      0.95      0.95       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, pred))\n",
    "print('\\n')\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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