Lab6-2(SVM).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7091f1d8-e888-4909-b560-764348cde31b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e9c64996-12c7-463c-ace7-5292fc18e832",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:/Users/baraa/PythonCodes/archive/data.csv\")      # Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ee67812f-abd1-47b1-a9f4-82bc968f3cd5",
   "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>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "76430b5b-1312-429c-80b3-8fc0fc63d933",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      " 32  Unnamed: 32              0 non-null      float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "30123bfe-0ac4-49c0-9c24-702896985c4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"Unnamed: 32\",\"id\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5da97128-2d3e-4cdf-ba3f-4bfeb0bdeb6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "M=data[data.diagnosis==\"M\"]\n",
    "B=data[data.diagnosis==\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a998cca4-55db-4eac-9d43-a875a4c3ffe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(M.radius_mean,M.texture_mean,color=\"red\",label=\"malignant\")\n",
    "plt.scatter(B.radius_mean,B.texture_mean,color=\"green\",label=\"benign\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"radius mean\")\n",
    "plt.ylabel(\"area mean\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "481646b1-88d1-4519-81c5-447d34a34b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.diagnosis=[1 if each==\"M\" else 0 for each in data.diagnosis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd6effa4-8e6b-4886-820e-1ad7f6ef7053",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data.diagnosis.values\n",
    "x_data=data.drop([\"diagnosis\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "507ffcf0-46f9-4c15-8ce5-97c15e63367c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=(x_data-np.min(x_data))/(np.max(x_data)-np.min(x_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dea8d08e-6720-46e9-8177-4825e5a9ad29",
   "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>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004229</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.028867</td>\n",
       "      <td>0.235308</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005966</td>\n",
       "      <td>0.004074</td>\n",
       "      <td>0.043394</td>\n",
       "      <td>0.474612</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.004835</td>\n",
       "      <td>0.004177</td>\n",
       "      <td>0.031241</td>\n",
       "      <td>0.311707</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005874</td>\n",
       "      <td>0.005503</td>\n",
       "      <td>0.037330</td>\n",
       "      <td>0.459803</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.004629</td>\n",
       "      <td>0.004995</td>\n",
       "      <td>0.030559</td>\n",
       "      <td>0.282793</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005541</td>\n",
       "      <td>0.006001</td>\n",
       "      <td>0.035849</td>\n",
       "      <td>0.401740</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002685</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.018237</td>\n",
       "      <td>0.090762</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003505</td>\n",
       "      <td>0.006229</td>\n",
       "      <td>0.023242</td>\n",
       "      <td>0.133451</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004770</td>\n",
       "      <td>0.003371</td>\n",
       "      <td>0.031758</td>\n",
       "      <td>0.304890</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005299</td>\n",
       "      <td>0.003919</td>\n",
       "      <td>0.035778</td>\n",
       "      <td>0.370240</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0     0.004229      0.002440        0.028867   0.235308         0.000028   \n",
       "1     0.004835      0.004177        0.031241   0.311707         0.000020   \n",
       "2     0.004629      0.004995        0.030559   0.282793         0.000026   \n",
       "3     0.002685      0.004791        0.018237   0.090762         0.000033   \n",
       "4     0.004770      0.003371        0.031758   0.304890         0.000024   \n",
       "\n",
       "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0          0.000065        0.000071             0.000035       0.000057   \n",
       "1          0.000018        0.000020             0.000016       0.000043   \n",
       "2          0.000038        0.000046             0.000030       0.000049   \n",
       "3          0.000067        0.000057             0.000025       0.000061   \n",
       "4          0.000031        0.000047             0.000025       0.000043   \n",
       "\n",
       "   fractal_dimension_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0                0.000019  ...      0.005966       0.004074         0.043394   \n",
       "1                0.000013  ...      0.005874       0.005503         0.037330   \n",
       "2                0.000014  ...      0.005541       0.006001         0.035849   \n",
       "3                0.000023  ...      0.003505       0.006229         0.023242   \n",
       "4                0.000014  ...      0.005299       0.003919         0.035778   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0    0.474612          0.000038           0.000156         0.000167   \n",
       "1    0.459803          0.000029           0.000044         0.000057   \n",
       "2    0.401740          0.000034           0.000100         0.000106   \n",
       "3    0.133451          0.000049           0.000204         0.000161   \n",
       "4    0.370240          0.000032           0.000048         0.000094   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0              0.000062        0.000108                 0.000028  \n",
       "1              0.000044        0.000065                 0.000021  \n",
       "2              0.000057        0.000085                 0.000021  \n",
       "3              0.000061        0.000156                 0.000041  \n",
       "4              0.000038        0.000056                 0.000018  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1b189751-67a5-462b-b42c-593e89f91b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "78a4cd9f-874d-471c-8d3d-58d242f4ed19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=42)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(random_state=42)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc=SVC(random_state=42)\n",
    "svc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cbb835c9-0b3f-400b-8b05-2bce17cece64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9181286549707602"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8ca9940b-0a51-40d7-b2e1-abfbd09ab98a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_accuracy=[]\n",
    "test_accuracy=[]\n",
    "for i in range(1,100):\n",
    "    svm=SVC(C=i)\n",
    "    svm.fit(x_train,y_train)\n",
    "    train_accuracy.append(svm.score(x_train,y_train))\n",
    "    test_accuracy.append(svm.score(x_test,y_test))\n",
    "plt.plot(range(1,100),train_accuracy,label=\"training accuracy\")\n",
    "plt.plot(range(1,100),test_accuracy,label=\"testing accuracy\")\n",
    "plt.xlabel(\"c values\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "19eef8d1-8714-4898-a40b-edb5f611df3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best accuracy is 0.935672514619883 when c =40\n"
     ]
    }
   ],
   "source": [
    "print(\"best accuracy is {} when c ={}\".format(np.max(test_accuracy),1+test_accuracy.index(np.max(test_accuracy))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d07bc1b-1246-44f1-91b2-f888f5c1b651",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}