11[svm].ipynb

{
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   "execution_count": 4,
   "id": "decea6d8-2255-4dae-8339-0c162ad2d972",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f297a051-751a-464b-b8ee-0ef4d810aba5",
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ddde434d-2606-4812-9263-4047c9ac46fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b682a152-f7af-4fa2-86d8-28b54ce4916e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type([iris.data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bff2bfe9-11cb-402c-ae67-2d2136249ac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris.data)"
   ]
  },
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   "execution_count": 34,
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\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",
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       "    <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",
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      ],
      "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(iris.data, columns = [iris.feature_names])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "00016530-2ba8-4737-938c-828f6d058273",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\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",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\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",
       "    </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",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
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       "      <td>2</td>\n",
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       "    <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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'] = iris.target\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fb9e6499-953b-4557-b644-f468e6512b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "28e0c5b3-acbd-492a-b7e2-07d93d3a95d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)      0.0\n",
       "sepal width (cm)       0.0\n",
       "petal length (cm)      0.0\n",
       "petal width (cm)       0.0\n",
       "target               100.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.target==2].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b1cc5baf-cbc6-4b18-bd50-6871e732d2e4",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\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",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <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",
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       "    <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <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",
       "  </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   \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  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flower_name'] = df.target.apply(lambda x: iris.target_names[x])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a26e2376-b99f-4be4-a5f5-50238f71f8c1",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <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",
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       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
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       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
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       "    <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",
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       "    <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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0d7f38ca-e29b-45b1-b1fe-4e3798cd7f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df[:50]\n",
    "df1 = df[50:100]\n",
    "df2 = df[100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "df72010b-e8f1-42bf-8c6a-b3a5fe95645a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x28afaa65450>"
      ]
     },
     "execution_count": 44,
     "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')\n",
    "plt.ylabel('Sepal Width')\n",
    "plt.scatter(df0['sepal length (cm)'], df0['sepal width (cm)'],color=\"green\",marker='+')\n",
    "plt.scatter(df1['sepal length (cm)'], df1['sepal width (cm)'],color=\"blue\",marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "61bab0d4-8859-4348-b453-bff927a85c90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x28afa3b6410>"
      ]
     },
     "execution_count": 45,
     "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')\n",
    "plt.ylabel('Petal Width')\n",
    "plt.scatter(df0['petal length (cm)'], df0['petal width (cm)'],color=\"green\",marker='+')\n",
    "plt.scatter(df1['petal length (cm)'], df1['petal width (cm)'],color=\"blue\",marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1ae6953b-3d38-4026-a498-9f294a8f8a9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jlhb83\\AppData\\Local\\Temp\\ipykernel_1900\\3564066704.py:2: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
      "  X = df_new.drop(['target','flower_name'], axis='columns')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df_new.drop(['target','flower_name'], axis='columns')\n",
    "y = df_new.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b03c7749-a2a7-490d-9652-8a13bdaf3e68",
   "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": 55,
   "id": "42599779-ccc3-4e1a-bf23-b599c43b531d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC(C=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "088e595e-e0b3-46fa-9b71-383227055a0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "SVC(C=10)"
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     "execution_count": 56,
     "metadata": {},
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    "model.fit(X_train, y_train)"
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   "cell_type": "code",
   "execution_count": 57,
   "id": "04f7790b-25fb-47a2-befd-9916f193fa14",
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    {
     "data": {
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       "1.0"
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     "execution_count": 57,
     "metadata": {},
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    "model.score(X_test, y_test)"
   ]
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   "cell_type": "code",
   "execution_count": 53,
   "id": "c563559a-535d-48fc-b3cd-70b1d7eb0139",
   "metadata": {},
   "outputs": [
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     "data": {
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       "array([0])"
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     "execution_count": 53,
     "metadata": {},
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    "model.predict([[4.8,3.0,1.5,0.3]])"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "265616a5-472d-4dba-b441-8aa48958464e",
   "metadata": {},
   "outputs": [],
   "source": []
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