6_Support_Vector_Machine.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'data_module',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "..      ...  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "iris_df[\"target\"] = iris.target\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names\n",
    "\n",
    "# 0 -> setosa \n",
    "# 1 -> versicolor\n",
    "# 2 -> virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>flower_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target flower_name  \n",
       "0         0      setosa  \n",
       "1         0      setosa  \n",
       "2         0      setosa  \n",
       "3         0      setosa  \n",
       "4         0      setosa  \n",
       "..      ...         ...  \n",
       "145       2   virginica  \n",
       "146       2   virginica  \n",
       "147       2   virginica  \n",
       "148       2   virginica  \n",
       "149       2   virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df[\"flower_name\"] = iris_df.target.apply(lambda x : iris.target_names[x])\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = iris_df[iris_df.target == 0] # setosa df\n",
    "df1 = iris_df[iris_df.target == 1] # versicolor df\n",
    "df2 = iris_df[iris_df.target == 2] # virginica df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211f5d3ebc0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"sepal length (cm)\")\n",
    "plt.ylabel(\"sepal width (cm)\")\n",
    "plt.scatter(df0[\"sepal length (cm)\"], df0[\"sepal width (cm)\"], color=\"green\")\n",
    "plt.scatter(df1[\"sepal length (cm)\"], df1[\"sepal width (cm)\"], color=\"blue\")\n",
    "plt.scatter(df2[\"sepal length (cm)\"], df2[\"sepal width (cm)\"], color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x211f5d03970>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"petal length (cm)\")\n",
    "plt.ylabel(\"petal width (cm)\")\n",
    "plt.scatter(df0[\"petal length (cm)\"], df0[\"petal width (cm)\"], color=\"green\")\n",
    "plt.scatter(df1[\"petal length (cm)\"], df1[\"petal width (cm)\"], color=\"blue\")\n",
    "plt.scatter(df2[\"petal length (cm)\"], df2[\"petal width (cm)\"], color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = iris_df.drop([\"target\", \"flower_name\"], axis=\"columns\")\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = iris_df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\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-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5\" 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-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()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\utkarsh.alpuria\\Desktop\\My Work\\Python\\Python ML\\Python ML Algorithm\\.ml_algo_py_venv\\lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[6.7,3.0,5.2,2.3]])"
   ]
  }
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  "kernelspec": {
   "display_name": ".ml_algo_py_venv",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "mimetype": "text/x-python",
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