svm_tutorial.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPqdowGNY3VgVO/6vHXLbxK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/swagata87/IITKanpurPhy654/blob/main/svm_tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Zpu751zmOJO",
        "outputId": "e6a35abe-14c2-4dae-d5f7-19b6a346c8b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     x1   x2  y\n",
            "0   3.9  2.9  1\n",
            "1   4.0  3.0  1\n",
            "2   3.1  2.0  1\n",
            "3   3.5  2.1  1\n",
            "4   4.1  2.2  1\n",
            "5   3.8  2.3  1\n",
            "6   3.2  2.4  1\n",
            "7   4.5  2.5  1\n",
            "8   1.0  1.9  0\n",
            "9   1.1  1.7  0\n",
            "10  1.3  1.6  0\n",
            "11  1.5  1.3  0\n",
            "12  1.6  1.2  0\n",
            "13  1.7  0.9  0\n",
            "14  0.8  1.1  0\n",
            "15  1.1  1.2  0\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = {\n",
        "  \"x1\": [3.9, 4.0, 3.1, 3.5, 4.1, 3.8, 3.2, 4.5, 1.0, 1.1, 1.3, 1.5, 1.6, 1.7, 0.8, 1.1], #  3.8],\n",
        "  \"x2\": [2.9, 3.0, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 1.9, 1.7, 1.6, 1.3, 1.2, 0.9, 1.1, 1.2], #  2.3],\n",
        "  \"y\" : [1,   1,   1,   1,   1,   1,   1,   1,   0,   0,   0,   0,   0,   0,   0,   0, ]  #0]\n",
        "}\n",
        "\n",
        "#load data into a DataFrame object:\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "print(df)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['y'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "ndMmh7q7xkJS",
        "outputId": "dbf9c76f-7e10-4152-bf62-434fc17cc4d0"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "y\n",
              "1    8\n",
              "0    8\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# define custom color map\n",
        "from matplotlib.colors import ListedColormap\n",
        "cmap = ListedColormap([[1,0,0], [0,1,0]])\n",
        "\n",
        "# use plt.scatter for color bar customization\n",
        "plt.scatter(df['x1'], df['x2'], c=df['y'], cmap=cmap)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "EeqTpsaxxqA2",
        "outputId": "a3bdb3ef-4f1f-4ca2-ae55-5acf8a5cdfbe"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x79e460d27160>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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lXEIppWGCKaVZRiRJAOxkJ/dyb4PLaqhhP/u5kzsTnEqdgWVEkgTAYhY3+Ubaaqp5juf4O39PYCp1BpYRSRIAZZSRTnqT60REfMRHCUqkzsIyIkkCoA99qKa6yXXSSKMnPROUSJ2FZUSSBMA3+SZpTXwspJPOV/kqPeiRwFTqDCwjkiQAetKTu7irwWXppJNNNj/jZwlOpc7AMiJJqvMf/AcP8dARRz+GM5xXeZWzOCtQMqWyWBRFjV863UFUVFSQm5tLeXk5OTk5oeNIUsrbz35WsYoKKjid0xnEoNCRlISa+/ntE1glSUfIJJOxjA0dQ52Ep2kkSVJQlhFJkhSUZUSSJAVlGZEkSUF5AasktbFqqimmmB3s4EROZCxjySQzdCypw4rryEhhYSHnn38+3bp1o2fPnkyaNIktW7Ycdbtnn32WM844g+zsbM455xyWL1/e4sCS1JEtYxkDGMClXMpUpjKBCfShDwtYEDqa1GHFVUaKi4uZMWMGr7/+OkVFRRw4cIDLL7+cysrKRrdZvXo1V199NdOmTWPDhg1MmjSJSZMmsWnTplaHl6SO5AVe4Kt8lb/xt3rz/87fmcY0fs2vAyWTOrZWPfTso48+omfPnhQXF3PxxRc3uM5VV11FZWUly5Ytq5t34YUXMnToUObNm9esn+NDzyR1dBERZ3EWW9hCRMP/rB7P8exiF9lkJzidFEZzP79bdQFreXk5AD16NP7SpJKSEsaOrf/gnHHjxlFSUtLoNlVVVVRUVNSbJKkjK6WUt3m70SIC8A/+wfM8n8BUUnJocRmpqalh1qxZjBw5kkGDGn9McFlZGXl5efXm5eXlUVZW1ug2hYWF5Obm1k35+fktjSlJCVFG4/+mtWQ9qTNpcRmZMWMGmzZtYvHixW2ZB4DZs2dTXl5eN+3YsaPNf4YktaU+9GnWen3p285JpOTTolt7Z86cybJly1i1ahX9+vVrct1evXqxe/fuevN2795Nr169Gt0mKyuLrKyslkSTpCAGM5hBDOJN3qSGmgbX6UEPvsJXEpxM6vjiOjISRREzZ85kyZIlvPLKKwwYMOCo2xQUFPDyyy/Xm1dUVERBQUF8SSWpA4sR45f8krR//mrIgzzo80akBsRVRmbMmMFTTz3F008/Tbdu3SgrK6OsrIy9e/fWrTN58mRmz55d9/WNN97ICy+8wP3338/bb7/Nj3/8Y9auXcvMmTPbbhSS1AGMYQwv8AKncVq9+f3ox2IWcy3XBkomdWxx3dobi8UanL9w4UKmTp0KwOjRozn55JNZtGhR3fJnn32WO+64g7/+9a+cdtpp3HfffVxxxRXNDumtvZKSSUTEGtbUPYF1JCNJJz10LCnhmvv53arnjCSKZUSSpOSTkOeMSJIktZZlRJIkBWUZkSRJQVlGJElSUC166JmaIYrgtdfg3Xehe3e47DI45pjQqSRJ6nAsI+2huBimT4etWz+fl5sLd90Fs2ZBI7dIS5LUGVlG2lpJSe1RkOrq+vPLy+Hmm6GqCm6/PUw2SZI6IK8ZaWu33VZbRGoafjcFP/4xfPJJQiNJktSRWUba0vvvw5/+1HgRAdi/H373u8RlkiSpg7OMtKXD3k7coPR0KCtr/yySJCUJy0hb6t376OtUV0Pfvu2fRZKkJGEZaUv9+sEll9Qe/WhMVhZ87WuJyyRJUgdnGWlr990HXbpAWiN/tIWFtbf5SpIkwDLS9s49F1auhHPOqT+/Z0/41a9qnzMiSZLq+JyR9nDBBVBaWjtt2wbHHw8XXVR7xESSJNVjGWlPQ4fWTpIkqVGeppEkSUFZRiRJUlCWEUmSFJRlRJIkBWUZkSRJQVlGJElSUJYRSZIUlGVEkiQFZRmRJElBWUYkSVJQlhFJkhSUZUSSJAVlGZEkSUFZRiRJUlCWEUmSFJRlRJIkBWUZkSRJQVlGJElSUJYRSZIUlGVEkiQFlRE6gL5g40YoLYWsLLj0UjjhhNCJJElqd5aRjmDrVpgyBVav/nxeZiZ873swZ07t7yVJSlGWkdA++ABGjICPP64/f/9+mDsXdu+GxYshFguTT5KkduY1I6HNmVNbRKqrj1xWUwPPPANr1iQ+lyRJCWIZCSmKYOHChovIIRkZ8PjjicskSVKCWUZCOngQysubXqe6GsrKEpNHkqQALCMhdekC3bs3vU56OvTtm5g8kiQFYBkJbdq02sLRmIMHYerUhMWRJCnRLCOh/eAHkJfXcCGJxWpv+R0+PPG5JElKEMtIaHl5UFJS+5CzLzr2WJg9Gx57LEwuSZISxOeMdAT9+8OKFbBtG7zxBmRnw0UXwXHHhU4mSVK7s4x0JF/6Uu0kSVIn4mkaSZIUlGVEkiQFZRmRJElBWUYkSVJQXsDamXzyCbz0EuzdC0OG1E6SJAVmGekMDhyA226Dhx+GqqrP559/PjzxBJxxRrhskqROz9M0ncG0afDgg/WLCMD69TByJGzfHiSWJElgGUl9paXw5JMQRUcuq66Gigq4776Ex5Ik6RDLSKp74gnIaOJs3MGDsGgR1NQkLJIkSV9kGUl1ZWUNHxX5os8+g8rKxOSRJOkwlpFU17dv7dt/m3LccdC1a2LySJJ0GMtIqpsypfZUTGMyMuC66yDNvwqSpDD8BEp1gwbBd7/b8NGRjAzo0QNuvTXxuSRJ+ifLSGfw8MNw5521p2O+6OKL4fXXa0/lSJIUSCyKjnZ1Y3gVFRXk5uZSXl5OTk5O6DjJq7ISVq2CffvgnHPg1FNDJ5IkpbDmfn77BNbOpGtXGD8+dApJkurxNI0kSQrKMiJJkoKyjEiSpKAsI5IkKSjLiCRJCiruMrJq1SomTpxInz59iMViLF26tMn1V65cSSwWO2IqKytraWZJkpRC4i4jlZWVDBkyhLlz58a13ZYtW9i1a1fd1LNnz3h/tCRJSkFxP2dk/PjxjG/Bsyp69uzJ8ccfH/d2kiQptSXsmpGhQ4fSu3dvLrvsMl577bUm162qqqKioqLeJEmSUlO7l5HevXszb948fv/73/P73/+e/Px8Ro8ezfr16xvdprCwkNzc3LopPz+/vWNKkqRAWvVumlgsxpIlS5g0aVJc240aNYr+/fvz5JNPNri8qqqKqqqquq8rKirIz8/33TSSJCWRDv1umi9/+cu8+uqrjS7PysoiKysrgYkkSVIoQZ4zUlpaSu/evUP8aEmS1MHEfWTk008/ZevWrXVfv/fee5SWltKjRw/69+/P7Nmz+eCDD3jiiScAePDBBxkwYABnn302+/btY/78+bzyyiu8+OKLbTcKSZKUtOIuI2vXrmXMmDF1X998880ATJkyhUWLFrFr1y7ef//9uuX79+/nlltu4YMPPuDYY49l8ODBvPTSS/W+hyRJ6rxadQFrojT3AhhJktRxNPfz23fTSJKkoCwjkiQpKMuIJEkKyjIiSZKCsoxIkqSgLCOSJCkoy4gkSQrKMiJJkoKyjEiSpKAsI5IkKSjLiCRJCsoyIkmSgrKMSJKkoCwjkiQpKMuIJEkKyjIiSZKCsoxIkqSgLCOSJCkoy4gkSQrKMiJJkoLKCB1AKaSmBoqL4f334V/+BS67DDIzQ6eSJHVwlhG1jeXL4YYbaovIIT16wD33wHe+Ey6XJKnDs4yo9V58ESZOhCiqP//jj+G734Xqarj++jDZJEkdnteMqHWiCG666fPfN+T222Hv3sRlkiQlFcuIWmfjRnjzzdrrRRpTXg7PPZe4TJKkpGIZUeuUlR19nViseetJkjoly4hap0+fo68TRdC3b/tnkSQlJcuIWmfQIBg6FNKa+KvUvTtccUXCIkmSkotlRK334IO1ZaSxQvLAA5CVldBIkqTkYRlR640aVXt77+mn15/fty/8z//AlClhckmSkoLPGVHbGDOm9q6atWtrH3x24okwciSkp4dOJknq4CwjajuxGJx/fu0kSVIzeZpGkiQFZRmRJElBWUYkSVJQlhFJkhSUF7B2JBs3Qmlp7TM5Lr0UTjghdKL2t349bNoEXbvC2LGQmxs6kSQpwSwjHcHWrbXP4li9+vN5mZnwve/BnDm1v081GzfWjnnDhs/nZWfXvgH47ru9JViSOhHLSGgffAAjRsDHH9efv38/zJ0Lu3fD4sW1t82miq1b4V//FSor68/ftw/uuaf2z2LevDDZJEkJ5zUjoc2ZU/vhW1195LKaGnjmGVizJvG52tPdd8NnnzU85iiCRx+FLVsSn0uSFIRlJKQogoULG/5QPiQjAx5/PHGZ2ltVFfzmN3DwYOPrpKfDk08mLpMkKSjLSEgHD0J5edPrVFdDWVli8iRCeTkcOND0OrFYao1ZktQky0hIXbpA9+5Nr5OeXvvCuVRx/PFHf4NvFKXWmCVJTbKMhDZtWtN3jhw8CFOnJixOu8vMhGuuqT391Jjqapg8OXGZJElBWUZC+8EPIC+v4UISi9Xe/jp8eOJztac774ScnMZL2KxZcMopCY0kSQrHMhJaXh6UlNQ+5OyLjj0WZs+Gxx4Lk6s9nXxy7ZhHjqw/PycHfvYzuP/+ILEkSWHEoiiKQoc4moqKCnJzcykvLycnJyd0nPazbRu88Ubtw78uugiOOy50ova3ZQu8+WZt+br4YjjmmNCJJEltpLmf3z70rCP50pdqp85k4MDaSZLUaXmaRpIkBWUZkSRJQVlGJElSUJYRSZIUVOe9gHXPHigqgk8/hTPPhPPOS60340qSlCQ6XxmpqYEf/7j2bbl7934+f/BgWLQIhg0LlUySpE6p852muekm+H//r34RAdi8ufbZHm+9FSaXJEmdVOcqI++9B//1X7UvYjtcdXXt6+1/+tPE55IkqRPrXGXkqacgrYkhHzwIv/td7XUkkiQpITpXGSkra7qMQG0h+fjjxOSRJEmdrIz07Vt7AWtTunSBE05ITB5JktTJysi3vtV0GcnIgKuugq5dE5dJkqROrnOVkf794bbbGl6Wnl5bQu66K7GZJEnq5DpXGQH4+c/h3nshN7f+/AsugNWr4dRTw+SSJKmTikVRQ/e5diwVFRXk5uZSXl5OTk5O23zTfftg1arPn8B65plt830lSRLQ/M/vzvcE1kOys+Hyy0OnkCSp0+t8p2kkSVKHYhmRJElBWUYkSVJQlhFJkhSUZUSSJAUVdxlZtWoVEydOpE+fPsRiMZYuXXrUbVauXMnw4cPJysri1FNPZdGiRS2IKkmSUlHcZaSyspIhQ4Ywd+7cZq3/3nvvMWHCBMaMGUNpaSmzZs1i+vTprFixIu6wkiQp9cT9nJHx48czfvz4Zq8/b948BgwYwP333w/AmWeeyauvvsoDDzzAuHHj4v3xkiQpxbT7NSMlJSWMHTu23rxx48ZRUlLS6DZVVVVUVFTUmyRJUmpq9zJSVlZGXl5evXl5eXlUVFSwd+/eBrcpLCwkNze3bsrPz2/vmJIkKZAOeTfN7NmzKS8vr5t27NgROpIkSWon7f5uml69erF79+5683bv3k1OTg7HHHNMg9tkZWWRlZXV3tEkSVIH0O5HRgoKCnj55ZfrzSsqKqKgoKC9f7QkSUoCcZeRTz/9lNLSUkpLS4HaW3dLS0t5//33gdpTLJMnT65b//rrr2fbtm3ceuutvP322zz88MM888wz3HTTTW0zAkmSlNTiLiNr165l2LBhDBs2DICbb76ZYcOG8aMf/QiAXbt21RUTgAEDBvDcc89RVFTEkCFDuP/++5k/f7639UqSJABiURRFoUMcTUVFBbm5uZSXl5OTkxM6jiRJaobmfn53yLtpJElS52EZkSRJQVlGJElSUJYRSZIUVLs/9ExqU+++C6+/DmlpcPHF0Ldv6ESSpFayjCg57N4N110Hy5d/Pi8tDa66CubNA++ykqSkZRlRx7dnT+1RkG3b6s+vqYFnnoG//hWKi6FLlyDxJEmt4zUj6vgeewzeeQcOHjxyWXU1lJTA0qUJjyVJahuWEXV8CxY0vTw9HRYuTEwWSVKbs4yo49u1C5p6UHB1NXzwQeLySJLalGVEHV/fvhCLNb48PR36909cHklSm7KMqOObPr3p5dXVMG1aYrJIktqcZUQd37e/DYMG1R4BOVxaGowZAxMnJj6XJKlNWEbU8XXtCitXwte/Xls+DunSpfaoybJlDRcVSVJSiEVRU1cGdgzNfQWxOoGdO2HNmtpSMmIEnHBC6ESSpEY09/Pbh54pufTpA1deGTqFJKkNeZpGkiQFZRmRJElBWUYkSVJQlhFJkhSUZUSSJAVlGZEkSUFZRiRJUlCWEUmSFJRlRJIkBZUUT2A99MT6ioqKwEkkSVJzHfrcPtqbZ5KijOzZsweA/Pz8wEkkSVK89uzZQ25ubqPLk+JFeTU1NezcuZNu3boRi8VCx4lLRUUF+fn57Nixo9O85M8xp/6YO9t4wTE75tTVnmOOoog9e/bQp08f0tIavzIkKY6MpKWl0a9fv9AxWiUnJ6fT/MU+xDGnvs42XnDMnYVjbjtNHRE5xAtYJUlSUJYRSZIUlGWknWVlZXHXXXeRlZUVOkrCOObU19nGC465s3DMYSTFBaySJCl1eWREkiQFZRmRJElBWUYkSVJQlhFJkhSUZaQVVq1axcSJE+nTpw+xWIylS5cedZuVK1cyfPhwsrKyOPXUU1m0aFG752xL8Y555cqVxGKxI6aysrLEBG4DhYWFnH/++XTr1o2ePXsyadIktmzZctTtnn32Wc444wyys7M555xzWL58eQLSto2WjHnRokVH7Ofs7OwEJW69Rx55hMGDB9c9+KmgoIDnn3++yW2SeR/HO95k378Nueeee4jFYsyaNavJ9ZJ5Px+uOWMOsa8tI61QWVnJkCFDmDt3brPWf++995gwYQJjxoyhtLSUWbNmMX36dFasWNHOSdtOvGM+ZMuWLezatatu6tmzZzslbHvFxcXMmDGD119/naKiIg4cOMDll19OZWVlo9usXr2aq6++mmnTprFhwwYmTZrEpEmT2LRpUwKTt1xLxgy1T3D84n7evn17ghK3Xr9+/bjnnntYt24da9eu5ZJLLuHKK69k8+bNDa6f7Ps43vFCcu/fw61Zs4ZHH32UwYMHN7lesu/nL2rumCHAvo7UJoBoyZIlTa5z6623RmeffXa9eVdddVU0bty4dkzWfpoz5j/+8Y8REH3yyScJyZQIH374YQRExcXFja7zjW98I5owYUK9eRdccEH0ve99r73jtYvmjHnhwoVRbm5u4kIlQPfu3aP58+c3uCzV9nEUNT3eVNq/e/bsiU477bSoqKgoGjVqVHTjjTc2um6q7Od4xhxiX3tkJIFKSkoYO3ZsvXnjxo2jpKQkUKLEGTp0KL179+ayyy7jtddeCx2nVcrLywHo0aNHo+uk2r5uzpgBPv30U0466STy8/OP+r/sjqy6uprFixdTWVlJQUFBg+uk0j5uznghdfbvjBkzmDBhwhH7ryGpsp/jGTMkfl8nxYvyUkVZWRl5eXn15uXl5VFRUcHevXs55phjAiVrP71792bevHmcd955VFVVMX/+fEaPHs2f//xnhg8fHjpe3Gpqapg1axYjR45k0KBBja7X2L5OpmtlDmnumAcOHMiCBQsYPHgw5eXlzJkzhxEjRrB58+akedHlxo0bKSgoYN++fRx33HEsWbKEs846q8F1U2EfxzPeVNi/AIsXL2b9+vWsWbOmWeunwn6Od8wh9rVlRO1q4MCBDBw4sO7rESNG8O677/LAAw/w5JNPBkzWMjNmzGDTpk28+uqroaMkTHPHXFBQUO9/1SNGjODMM8/k0Ucf5e67727vmG1i4MCBlJaWUl5ezu9+9zumTJlCcXFxox/QyS6e8abC/t2xYwc33ngjRUVFSX/xbXO1ZMwh9rVlJIF69erF7t27683bvXs3OTk5KXlUpDFf/vKXk/LDfObMmSxbtoxVq1Yd9X8Hje3rXr16tWfENhfPmA/XpUsXhg0bxtatW9spXdvLzMzk1FNPBeDcc89lzZo1PPTQQzz66KNHrJsK+zie8R4uGffvunXr+PDDD+sdla2urmbVqlX893//N1VVVaSnp9fbJtn3c0vGfLhE7GuvGUmggoICXn755XrzioqKmjxHm4pKS0vp3bt36BjNFkURM2fOZMmSJbzyyisMGDDgqNsk+75uyZgPV11dzcaNG5NqXx+upqaGqqqqBpcl+z5uSFPjPVwy7t9LL72UjRs3UlpaWjedd955XHPNNZSWljb4oZzs+7klYz5cQvZ1Qi+XTTF79uyJNmzYEG3YsCECov/8z/+MNmzYEG3fvj2Koii6/fbbo2uvvbZu/W3btkXHHnts9O///u/RW2+9Fc2dOzdKT0+PXnjhhVBDiFu8Y37ggQeipUuXRu+88060cePG6MYbb4zS0tKil156KdQQ4nbDDTdEubm50cqVK6Ndu3bVTZ999lndOtdee210++2313392muvRRkZGdGcOXOit956K7rrrruiLl26RBs3bgwxhLi1ZMw/+clPohUrVkTvvvtutG7duujf/u3fouzs7Gjz5s0hhhC322+/PSouLo7ee++96C9/+Ut0++23R7FYLHrxxRejKEq9fRzveJN9/zbm8DtLUm0/N+RoYw6xry0jrXDottXDpylTpkRRFEVTpkyJRo0adcQ2Q4cOjTIzM6MvfelL0cKFCxOeuzXiHfO9994bnXLKKVF2dnbUo0ePaPTo0dErr7wSJnwLNTReoN6+GzVqVN2fwSHPPPNMdPrpp0eZmZnR2WefHT333HOJDd4KLRnzrFmzov79+0eZmZlRXl5edMUVV0Tr169PfPgWuu6666KTTjopyszMjE488cTo0ksvrftgjqLU28fxjjfZ929jDv9gTrX93JCjjTnEvo5FURS133EXSZKkpnnNiCRJCsoyIkmSgrKMSJKkoCwjkiQpKMuIJEkKyjIiSZKCsoxIkqSgLCOSJCkoy4gkSQrKMiJJkoKyjEiSpKAsI5IkKaj/DyLk5JSzlnIjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = df[['x1','x2']]\n",
        "y_train = df['y']"
      ],
      "metadata": {
        "id": "JWYeuRcWy69U"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Import svm model\n",
        "from sklearn import svm\n",
        "\n",
        "#Create a svm Classifier\n",
        "clf = svm.SVC(kernel='linear') # Linear Kernel\n",
        "\n",
        "#Train the model using the training sets\n",
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "id": "JU-HSijPyz9a",
        "outputId": "36ec5b77-8b3e-42fd-8aa5-31ca73166119"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ],
            "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(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(X_train)"
      ],
      "metadata": {
        "id": "yw1Sp37t2I94"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "# Model Accuracy: how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_train, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T_7LdzRI2QfR",
        "outputId": "387661f9-1a9e-4731-921c-f6c271b056fc"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.inspection import DecisionBoundaryDisplay\n",
        "\n",
        "disp = DecisionBoundaryDisplay.from_estimator(\n",
        "    clf, X_train, response_method=\"predict\",\n",
        "    xlabel=\"x1\", ylabel=\"x2\",\n",
        "    alpha=0.5,\n",
        ")\n",
        "disp.ax_.scatter(df['x1'], df['x2'], c=df['y'], edgecolor=\"k\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "GIl0yNqTzH3c",
        "outputId": "289e4c45-acd8-41a6-e065-979785e4363e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_circles\n",
        "X,Y=make_circles(n_samples=500,noise=0.02)\n",
        "print(X.shape,Y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fQau-BMq4Wd0",
        "outputId": "e4f2d848-67c6-4072-a569-14a38e904db6"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(500, 2) (500,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\n",
        "  \"x1\": X[:,0],\n",
        "  \"x2\": X[:,1],\n",
        "  \"y\" : Y,\n",
        "}\n",
        "\n",
        "#load data into a DataFrame object:\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GEip_rLJ650r",
        "outputId": "701aea3b-fa10-4d5c-a147-da175ee9efa6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           x1        x2  y\n",
            "0    0.363255 -0.673310  1\n",
            "1   -0.670033  0.725677  0\n",
            "2   -0.038300  1.016382  0\n",
            "3    0.098753 -0.977237  0\n",
            "4   -0.149709  0.979836  0\n",
            "..        ...       ... ..\n",
            "495  0.483048  0.856993  0\n",
            "496 -0.807696  0.592096  0\n",
            "497  0.402116 -0.724355  1\n",
            "498 -0.294858 -0.922953  0\n",
            "499 -0.764541 -0.183461  1\n",
            "\n",
            "[500 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['x1'], df['x2'], c=df['y'], cmap=cmap)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "wjhfGU5Z4hlK",
        "outputId": "085ba285-11cc-4c2a-c8a1-cbb80d3d4b55"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x79e460bc4220>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = df[['x1','x2']]\n",
        "y_train = df['y']"
      ],
      "metadata": {
        "id": "gexvuIJk7bKj"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf2 = svm.SVC(kernel='rbf') # rbf Kernel, essentially gaussian\n",
        "clf2.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "id": "dEfQAus-5ZXl",
        "outputId": "bc177047-e209-49af-fffd-730cc95c3c83"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred2 = clf2.predict(X_train)"
      ],
      "metadata": {
        "id": "cfiYcrPa52O2"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy:\",metrics.accuracy_score(y_train, y_pred2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofAQL2uw6EF_",
        "outputId": "6f9b64c1-fe45-451b-df89-0870dd585596"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "disp = DecisionBoundaryDisplay.from_estimator(\n",
        "    clf2, X_train, response_method=\"predict\",\n",
        "    xlabel=\"x1\", ylabel=\"x2\",\n",
        "    alpha=0.5,\n",
        ")\n",
        "disp.ax_.scatter(df['x1'], df['x2'], c=df['y'], edgecolor=\"k\")\n",
        "plt.xlim(-1.2, 1.2)\n",
        "plt.ylim(-1.2, 1.2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "SfYyGe1k6H71",
        "outputId": "087767e6-c9c9-49b4-a58e-bb8bc135a7f6"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}