Support Vectore Machine.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SUPPORT VECTOR MACHINES USING SCIKIT LEARN LIBRARY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>In this below code snippets, we shall see how to use scikit learn library to develope a Support Vector Machine to identify the type of a flower.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:red\">We begin by importing some essential libraries</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'filename', 'target', 'target_names']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "      <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",
       "  </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  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'] = iris.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df[df.target == 0]\n",
    "df1 = df[df.target == 1]\n",
    "df2 = df[df.target == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1810b978>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a18039c18>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEKCAYAAAAB0GKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGs9JREFUeJzt3X20XXV95/H3ZwIIIk+a6DiEEFTqiA883YlGqkZRHloFFLsa1C4csUGXtLZdLYJOwdJVwTpTO1Ycc1tTtGOhIwGbYZRAgQiWINwoIATBFHG4xtUEeR4okPCZP/a+crjce86+99x99745n9daZ53924/fs9dKvvf323t/t2wTERHRy79rOoCIiJgbkjAiIqKSJIyIiKgkCSMiIipJwoiIiEqSMCIiopIkjIiIqCQJIyIiKknCiIiISnZqOoCZNH/+fC9evLjpMCIi5owNGzbcZ3tBlXV3qISxePFiRkZGmg4jImLOkPTTqutmSCoiIipJwoiIiEqSMCIiopIkjIiIqCQJIyIiKknCiIiISpIwIiKikiSMiIhZsH49nHtu8T1X7VAP7kVEtNH69XDkkfDkk7DLLnDVVbB0adNRTV16GBERNVu3rkgW27cX3+vWNR3R9CRhRETUbNmyomcxb17xvWxZ0xFNT21DUpJWAe8Etth+zQTL/wh4f0ccrwIW2L5f0j3AI8B2YJvtobrijIio29KlxTDUunVFspiLw1EAsl3PjqU3A48CX5soYYxb913A79t+W9m+Bxiyfd9Ujjk0NOQUH4yIqE7Shqp/lNc2JGX7WuD+iqufBFxYVywREdG/xq9hSHo+cAywumO2gSskbZC0osf2KySNSBrZunVrnaFGRAy0xhMG8C7gn2139kaOsH0YcCzwsXJ4a0K2h20P2R5asKDSO0AiImIa2pAwljNuOMr25vJ7C3ApsKSBuCIiokOjCUPSXsBbgH/smLe7pD3GpoGjgNuaiTAiIsbUeVvthcAyYL6kUeBsYGcA218uV3s3cIXt/9ex6UuASyWNxff3ti+vK86IiDZbv749t+PWljBsn1RhnQuAC8bNuxs4uJ6oIiLmjraVFGnDNYyIiJhA20qKJGFERLRU20qKpFptRERLta2kSBJGRESLLV3afKIYkyGpiIioJAkjIiIqScKIiIhKkjAiIqKSJIyIiKgkCSMiIipJwoiIKK1fD+eeW3zP5rb9mM3j5jmMiAj6q9vUVM2n2T5uehgREfRXt6mpmk+zfdwkjIgI+qvb1FTNp9k+boakIiLor25TUzWfZvu4sl3vEWbR0NCQR0ZGmg4jImLOkLTB9lCVdTMkFRERlSRhREREJUkYERFRSRJGRERUUlvCkLRK0hZJt02yfJmkhyTdXH7O6lh2jKQ7JW2SdEZdMUZERHV19jAuAI7psc51tg8pP+cASJoHnA8cCxwEnCTpoBrjjIg5pq5yGEcfDc9/fvE91eP2E9PwcHHM4eGpbzubansOw/a1khZPY9MlwCbbdwNIugg4Htg4c9FFxFxVVzmMo4+GK64opq+4omivXVvtuP3ENDwMp576zHEBVqzo//fUoelrGEsl3SLp25JeXc7bF7i3Y53Rct6EJK2QNCJpZOvWrXXGGhEtUFc5jOuu697udtx+Ylq9unu7TZpMGN8H9rd9MPBXwDfL+Zpg3UmfLrQ9bHvI9tCCBQtqCDMi2qSuchhvelP3drfj9hPTiSd2b7dJY6VBbD/cMf0tSV+SNJ+iR7Ffx6oLgc2zHV9EtFNd5TDWri2Goa67rkgWncNRvY7bT0xjw0+rVxfJoq3DUVBzaZDyGsZltl8zwbJ/D/yrbUtaAlwM7A/MA+4CjgR+BtwEvM/27b2Ol9IgERFTM5XSILX1MCRdCCwD5ksaBc4Gdgaw/WXgvcBHJW0DHgeWu8he2ySdBqylSB6rqiSLiIioV4oPRkQMsBQfjIiIGZeEERERlSRhREREJUkYERFRSRJGRNSmrppP/einblO339Nrv208F1OVd3pHRC3qqvnUj37qNnX7Pb3228ZzMR3pYURELeqq+dSPfuo2dfs9vfbbxnMxHUkYEVGLumo+9aOfuk3dfk+v/bbxXExHhqQiohZ11XzqRz91m7r9nl77beO5mI486R0RMcDypHdERMy4JIyIiKgkCSMiIipJwoiIiEqSMCIiopIkjIhopGzFJz4BBx5YfE+kW6mNXmU4ui3v9lt7nYcdobxHX2zvMJ/DDz/cETE1119v77abPW9e8X399fUf8/TTbXjmc/rpz16+cuWzl69cWW1Zr+Xdfmuv89DEeZoNwIgr/h+bHkbEgGuibMUll3Rvdyu10asMR7d2t9/a6zzsKOU9+pGEETHgmihb8Z73dG93K7XRqwxHt3a339rrPOwo5T36UduT3pJWAe8Etth+zQTL3w+MjV4+CnzU9i3lsnuAR4DtwDZXfAoxT3pHTM/69bNftuITnyh6Fu95D3z2s89dPjw8eamNbst6Le/2W3udhybOU92m8qR3nQnjzRSJ4GuTJIw3AnfYfkDSscCnbb++XHYPMGT7vqkcMwkjImJqppIwais+aPtaSYu7LL++o3kDsLCuWCIion9tuYZxCvDtjraBKyRtkDSFepIREVGXxsubS3orRcL41Y7ZR9jeLOnFwJWSfmT72km2XwGsAFi0aFHt8UZEDKpGexiSXgf8DXC87V+Mzbe9ufzeAlwKLJlsH7aHbQ/ZHlqwYEHdIUdEDKzGEoakRcAlwG/Zvqtj/u6S9hibBo4CbmsmyoiIGFPbkJSkC4FlwHxJo8DZwM4Atr8MnAW8CPiSJHjm9tmXAJeW83YC/t725XXFGRER1dR5l9RJPZZ/GPjwBPPvBg6uK66ImD39PPMw3f3WuW0T+22Txi96R8SOaf16OPLIoozGLrsU77Qe+4+027J+9lvntk3st23aclttROxg+qnbNN391rltE/ttmySMiKhFP3WbprvfOrdtYr9t07M0iKQFwG8Di+kYwrL9oVojm4aUBolol1zDaL8ZrSUl6XrgOmADRTFAAGyvnnSjhiRhRERMzUzXknq+7UneiRUREYOiyjWMyyT9Wu2RREREq03aw5D0CEURQAGflPQE8FTZtu09ZyfEiIhog0kThu09ZjOQiIhot55DUpKuqjIvIiJ2bN2GpHYFdqeoBbUPxVAUwJ7Af5iF2CLmpLl6e+V01XXrbLRPt7ukTgV+jyI5fL9j/sPA+XUGFTFXDUqJiDF1lf+Idpp0SMr2f7d9APCHtg/o+Bxs+4uzGGPEnDEoJSLG1FX+I9qp25DUe8rJn3VM/5LtS2qLKmKOGisRMfZX9Y5aImJMt987aOdiEHQbknpX+f1i4I3A1WX7rcA6ipcfRUSHpUuLoZdBGbfv9nsH7VwMgiqlQS4Dftv2z8v2S4HzbT+n19G0lAaJiJiaqZQGqfKk9+KxZFH6V+BXphVZRETMWVVqSa2TtBa4kOLJ7+XANbVGFRERrdMzYdg+rbzo/aZy1rDtS+sNKyIi2qbSK1rLO6JykTsiYoBNeg1D0nfL70ckPdzxeUTSw1V2LmmVpC2SbptkuSR9QdImSbdKOqxj2cmSflx+Tp7qD4uIiJnV7cG9Xy2/97C9Z8dnjylUqr0AOKbL8mOBA8vPCuB/AEh6IXA28HpgCXB2WZ4kIiIa0q2H8ZeSfkPStOtG2b4WuL/LKscDX3PhBmDv8rbdo4Erbd9v+wHgSronnoiBMDwMRx9dfM/GdlCU+Dj33OJ7JvXab13Hjenrdg1jE/Bu4HOSAK4H/rn8vsX20zNw/H2Bezvao+W8yeZHDKzhYTj11GL6iiuK7xUr6tsO6qsH1Wu/qUPVTt2GpL5o+322FwNLKS56vxz4BvDgDB1fE8xzl/nP3YG0QtKIpJGtW7fOUFgR7bN6dff2TG8H9dWD6rXf1KFqp64P7pUXpV9HMXR0PPAWip7Hf5uh448C+3W0FwKbu8x/DtvDtodsDy1YsGCGwoponxNP7N6e6e3gmXpQ8+bNbD2oXvut67jRn27FB6+kePfFzcANwGds3zHDx18DnCbpIooL3A/Z/nn5oOBnOi50HwWcOcPHjphTxoaRVq8u/tOvOqw03e2gvnpQvfabOlTtNGktKUkrgYOBxygSxnpgve37Ku9cuhBYBsynKClyNrAzgO0vq7g48kWKC9qPAf/Z9ki57YeAT5a7+jPbf9vreKklFRExNVOpJVWl+OCewBsoKta+AVgA3Ga7dc9GJGFEREzNVBJGlSe9n6D46//xcnohsMv0w4uIiLmo23MYn5f0PeDnwDnAHsBK4JW2XztL8UVEREt062H8BPg68APb22cpnoiIaKlJE4btL8xmIBER0W5VXqAUERGRhBEREdV0e3Dvhd02tN2tqGBEROxgul303kD3uk4vqyWiiIhopW4XvQ+YzUAiIqLdKr2itazpdCCw69i88l0XERExIHomDEkfBj5O8YT3zRTlQdYDb6s3tIiIaJMqd0l9HPhPwE9tvxU4FMiLJyIiBkyVhPFvtv8NQNLzbP8IeGW9YUVERNtUuYYxKmlv4JvAlZIeYJKXGUVExI6rZ8Kw/e5y8tOSrgH2Ar5da1QREdE6PYekJP3d2LTt79heA6yqNaqIiGidKtcwXt3ZkDQPOLyecCIioq26vQ/jTEmPAK+T9LCkR8r2FuAfZy3CiIhohUkThu1zbe8BfM72nrb3KD8vsn3mLMYYEREtUGVI6lOSPiDpjwEk7SdpSc1xRUREy1RJGOcDS4H3le1Hy3k9STpG0p2SNkk6Y4Lln5d0c/m5S9KDHcu2dyxbU+V4ERFRnyrPYbze9mGSfgBg+wFJu/TaqLw4fj7wDmAUuEnSGtsbx9ax/fsd6/8OxVPkYx63fUjF3xERETWr0sN4qvzP3wCSFgBPV9huCbDJ9t22nwQuAo7vsv5JwIUV9hsREQ2okjC+AFwKvFjSnwHfBT5TYbt9gXs72qPlvOeQtD9wAHB1x+xdJY1IukHSCRWOFxERNarypPfXJW0AjqR4mdIJtu+osO/JXrw0keXAxba3d8xbZHuzpJcBV0v6oe1/ec5BpBXACoBFixZVCCsiIqaj2ytadwU+ArwC+CGw0va2Kex7FNivo72QyWtQLQc+1jnD9uby+25J6yiubzwnYdgeBoYBhoaGJktIERHRp25DUl8FhiiSxbHAf53ivm8CDpR0QHmRfDnwnLudJL0S2IfiHRtj8/aR9Lxyej5wBLBx/LYRETF7ug1JHWT7tQCSvgLcOJUd294m6TRgLTAPWGX7dknnACNlTSooLnZfZLuzd/AqYKWkpymS2nmdd1dFRMTs65YwnhqbKP/zn/LObX8L+Na4eWeNa396gu2uB1475QNGRERtuiWMgyU9XE4L2K1sC7DtPWuPLiIiWmPShGF73mwGEhER7VblOYyIiIgkjIiIqCYJIyIiKknCiIiISpIwIiKikiSMiIioJAkjIiIqScKYo5ZdsIxlFyxrOoyIGCBJGBERUUmVV7RGi4z1Kr7z0+88q73ug+uaCSgiBkZ6GBERUUl6GHPMWE8iPYuImG3pYURERCXpYcxR6VlExGxLDyMiIipJwoiIiEqSMCIiopIkjIiIqKTWhCHpGEl3Stok6YwJln9Q0lZJN5efD3csO1nSj8vPyXXGuaNJ2ZCIqENtd0lJmgecD7wDGAVukrTG9sZxq/6D7dPGbftC4GxgCDCwodz2gbrijYiI7uq8rXYJsMn23QCSLgKOB8YnjIkcDVxp+/5y2yuBY4ALa4p1h5CyIRFRpzqHpPYF7u1oj5bzxjtR0q2SLpa03xS3RdIKSSOSRrZu3ToTcUdExATq7GFognke1/7fwIW2n5D0EeCrwNsqblvMtIeBYYChoaEJ1xkUKRsSEXWqs4cxCuzX0V4IbO5cwfYvbD9RNv8aOLzqthERMbvq7GHcBBwo6QDgZ8By4H2dK0h6qe2fl83jgDvK6bXAZyTtU7aPAs6sMdYdSnoWEVGH2hKG7W2STqP4z38esMr27ZLOAUZsrwF+V9JxwDbgfuCD5bb3S/pTiqQDcM7YBfCIiGiG7B1n2H9oaMgjIyNNhxERMWdI2mB7qMq6edI7IiIqScKIiIhKkjAiIqKSJIyaTbeu007n7MRO50x+T0K3/fZTSyp1qCJiMkkYERFRSV7RWpPp1nUa61Vs9/Zntbedta3nfvupJZU6VBHRS3oYERFRSZ7DqNl0/1If37OYyn776R2kZxExWPIcRkREzLj0MCIiBlh6GBERMeOSMCIiopIkjIiIqCQJIyIiKknCqNne5+3N3uftPeGybuU/Ut4jItomCSMiIipJaZCajPUqHnrioWe1Hzzjwa7lP1LeIyLaKj2MiIioJA/u1ayzZzFet/IfKe8REbOhNQ/uSTpG0p2SNkk6Y4LlfyBpo6RbJV0laf+OZdsl3Vx+1tQZZ0RE9FZbD0PSPOAu4B3AKHATcJLtjR3rvBX4nu3HJH0UWGb7N8tlj9p+wVSO2cYeRkREm7Wlh7EE2GT7bttPAhcBx3euYPsa24+VzRuAhTXGExERfagzYewL3NvRHi3nTeYU4Nsd7V0ljUi6QdIJdQQYERHV1XlbrSaYN+H4l6QPAEPAWzpmL7K9WdLLgKsl/dD2v0yw7QpgBcCiRYv6jzoiIiZUZw9jFNivo70Q2Dx+JUlvBz4FHGf7ibH5tjeX33cD64BDJzqI7WHbQ7aHFixYMHPRR0TEs9SZMG4CDpR0gKRdgOXAs+52knQosJIiWWzpmL+PpOeV0/OBI4CNREREY2obkrK9TdJpwFpgHrDK9u2SzgFGbK8BPge8APiGJID/a/s44FXASklPUyS18zrvrppp/Ty30O05CwD9STEy57OfOxo33WW9lucZjoioQ62lQWx/C/jWuHlndUy/fZLtrgdeW2dsERExNQP9pPf42ktv2b+45l7lr+vxtaL2et5ewDM9jbEewHg+29Ne1mu//fyefraNiLmrLc9hRETEDmSgexhjcg1jZraNiLknPYyIiJhx6WFERAyw9DAiImLGJWFEREQlSRgREVFJEkZERFSShFHBsguW/fJ205m093l7//K23Kksi4hoQhJGRERUUmstqblufLmMmXqobXxZkc6H/7oti4hoUnoYERFRSR7cq6Cuchndeg/pWUTEbMiDexERMePSw4iIGGDpYURExIxLwoiIiEqSMCIiopIkjIiIqKTWhCHpGEl3Stok6YwJlj9P0j+Uy78naXHHsjPL+XdKOrrOOCMiorfaEoakecD5wLHAQcBJkg4at9opwAO2XwF8Hvhsue1BwHLg1cAxwJfK/UVEREPq7GEsATbZvtv2k8BFwPHj1jke+Go5fTFwpCSV8y+y/YTtnwCbyv1FRERD6kwY+wL3drRHy3kTrmN7G/AQ8KKK20ZExCyqs/igJpg3/inBydapsm2xA2kFsKJsPirpzsoRPtt84L5pbjtIcp6qyXmqJuepmjrP0/5VV6wzYYwC+3W0FwKbJ1lnVNJOwF7A/RW3BcD2MDDcb7CSRqo+7TjIcp6qyXmqJuepmracpzqHpG4CDpR0gKRdKC5irxm3zhrg5HL6vcDVLmqVrAGWl3dRHQAcCNxYY6wREdFDbT0M29sknQasBeYBq2zfLukcYMT2GuArwN9J2kTRs1hebnu7pP8FbAS2AR+zvb2uWCMiorcdqvhgPyStKIe3ooucp2pynqrJeaqmLecpCSMiIipJaZCIiKhk4BOGpFWStki6relY2kzSfpKukXSHpNslfbzpmNpI0q6SbpR0S3me/qTpmNpM0jxJP5B0WdOxtJWkeyT9UNLNkhp94c/AD0lJejPwKPA1269pOp62kvRS4KW2vy9pD2ADcILtjQ2H1iplpYLdbT8qaWfgu8DHbd/QcGitJOkPgCFgT9vvbDqeNpJ0DzBku/HnVQa+h2H7Woo7tKIL2z+3/f1y+hHgDvL0/XO48GjZ3Ln8DPZfZZOQtBD4deBvmo4lqhn4hBFTV1YVPhT4XrORtFM5zHIzsAW40nbO08T+EjgdeLrpQFrOwBWSNpSVLRqThBFTIukFwGrg92w/3HQ8bWR7u+1DKCoULJGUoc5xJL0T2GJ7Q9OxzAFH2D6MovL3x8ph9EYkYURl5Zj8auDrti9pOp62s/0gsI6iRH882xHAceX4/EXA2yT9z2ZDaifbm8vvLcClNFi5OwkjKikv5n4FuMP2XzQdT1tJWiBp73J6N+DtwI+ajap9bJ9pe6HtxRQVHq62/YGGw2odSbuXN5kgaXfgKKCxOzoHPmFIuhBYD7xS0qikU5qOqaWOAH6L4i/Bm8vPrzUdVAu9FLhG0q0U9dSutJ1bRmO6XgJ8V9ItFPX0/o/ty5sKZuBvq42IiGoGvocRERHVJGFEREQlSRgREVFJEkZERFSShBEREZUkYcRAkbS9vCX4NknfkPT8Hut/suJ+75E0v+r8mSLpBEkHdbTXSWr83c+xY0rCiEHzuO1DysrETwIf6bF+pYTRoBOAg3quFTEDkjBikF0HvAJA0gfK91jcLGllWUDwPGC3ct7Xy/W+WRaBu326heDKp3dXSbqpfBfE8eX8D0q6RNLlkn4s6c87tjlF0l1lD+KvJX1R0huB44DPlTG+vFz9N8rfcpekN/VxfiKeZaemA4hogqSdKIq5XS7pVcBvUhR5e0rSl4D32z5D0mllIcExH7J9f1n24yZJq23/YoqH/xRFKYwPlWVEbpT0T+WyQygqAT8B3Cnpr4DtwB8DhwGPAFcDt9i+XtIa4DLbF5e/C2An20vKJ/HPpihPEtG3JIwYNLuVpceh6GF8BVgBHE6RAAB2oyhNPpHflfTucno/4EBgqgnjKIrCe39YtncFFpXTV9l+CEDSRmB/YD7wHdv3l/O/AfxKl/2PFYbcACyeYmwRk0rCiEHz+Lgew1hhxa/aPrPbhpKWUfy1vtT2Y5LWUfxnP1UCTrR957j9v56iZzFmO8W/UU1x/2P7GNs+YkbkGkYEXAW8V9KLASS9UNL+5bKnyrLuAHsBD5TJ4j8Cb5jm8dYCv1MmKiQd2mP9G4G3SNqnHEo7sWPZI8Ae04wjYkqSMGLgle8l/y8UbzW7FbiSouoswDBwa3nR+3Jgp3KdPwWqvqf71rIS8qikvyi33bmcf1vZ7hbfz4DPULzh8J+AjcBD5eKLgD8qL56/fJJdRMyIVKuNmAMkvcD2o2UP41Jgle1Lm44rBkt6GBFzw6fLi/W3AT8BvtlwPDGA0sOIiIhK0sOIiIhKkjAiIqKSJIyIiKgkCSMiIipJwoiIiEqSMCIiopL/DySx+bu7iG8MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['target'], axis='columns')\n",
    "y = df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC(gamma='scale')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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