support_vector_machine.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "support_vector_machine.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/antonio2505/SuperDataScience_Projects_ML/blob/main/support_vector_machine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MRC0e0KhQ0S"
      },
      "source": [
        "# Support Vector Machine (SVM)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWd1UlMnhT2s"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvGPUQaHhXfL"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1VMqkGvhc3-"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M52QDmyzhh9s"
      },
      "source": [
        "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
        "X = dataset.iloc[:, [2, 3]].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvxIPVyMhmKp"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVzJWAXIhxoC"
      },
      "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 = 0.25, random_state = 0)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kW3c7UYih0hT"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fQlDPKCh8sc"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bb6jCOCQiAmP"
      },
      "source": [
        "## Training the SVM model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cbyP8QKcMxlD",
        "outputId": "147c065e-1ae2-4e5b-e58a-78d2a4713bc9"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "classifier = SVC(kernel = 'linear', random_state = 0)\n",
        "\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
              "    max_iter=-1, probability=False, random_state=0, shrinking=True, tol=0.001,\n",
              "    verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmePIEiiiKyw"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWK4AcCgiNwF"
      },
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4Hwj34ziWQW"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6bpZwUiiXic",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f04f6e33-424c-4c28-c8b8-810533ca3fdd"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[66  2]\n",
            " [ 8 24]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OMC_P0diaoD"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NOjKvZRid5l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "bb6dbe26-1668-4551-a7ce-dd5a0baa40d4"
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_train, y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('SVM (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eO3OzxJY93Sw8mWwzmcLkNdO6RWFNSF7EYVr62FUyOfkXOD7bGQ9DvdY4qjcSdwKLyHzgHuD9qvpc6fuqugHYAHDpggXprcTUgPS1D09N7AKON+Xoax/2vQtw20E8+3APZ72qPDy0WqGVUay041hpxmFaOtI8HmjcLxZxUxskqgBEZBbO5L9RVb+ZpCxGcLzq+3iNl1LYQRSUSGEHAfDswz3MuuKMEqhmaGXQyWvXoV3sP7Z/8vXS+UtZdfaqSGWCeExLUSgVtzDSbcss4qYWSEwBiFNY+0vAkKpG2+fMqArLsy3snVP+T+5V96eU6XYQpx8400xmdnNL1UIrg0yKpZM/MPk6jBKoFJsf5S6odW5rmfyFcb9yuoWR/urdi/nmi6IvBtcoCXbVIkkfwKuBtwNXisj2/NdrE5THCEj/cDvzJqb+Cc2byNA/7O+f3M8OotBgPjtePZNCkHBBt8mz0rgfCpPq4rEsGc5MqmsfHKVtfhtrlq2hZ2UPa5atCT35jZ0YCzReilcY6efujL4YXFjfjFFOklFAWymrEG/UEm71fYJEAfndQeQGe5DuAddrxGFSSDpcsFJsvltyVphVcVhb/blj7sedO+ZeDC4MtZRgVysk7gQ2ahu3+j5+casQ6rWDuPP7c3nbNSemLBniTOJJMlyw0qRaSljfSJM0MaETruN+ONjawmIXuQ62Rq+YzbEcPakIAzUak96DbWzY2cGKky2IwoqTLWzY2eGqUHrnvoo7h1ZDPg4sLfXll85fGmjcDwdbW9h4Max8P2Q+7nzfeLH7pBo23NKrx63f3re339DOydlTp5GTszPcfkP0itlrt2eO5ZljOwAjUYLsIHoPtvGeFw9xZA7MnTU38ckfzjh6o4wC+qu3tXLvC/aXJWe99vlyx2zYVfF4zj3c02u8lIJJaqbF5IIQV4JdI2MKwKgpCjkCR0hHyQhwlECUYZ+bzh4jW2KVOT4bNs0dozSHNmwYZxRhoJsvb4tlwi8lad9MPWImIKPmePbhHiDZbOE4CbKqD1vgrNYKpEUdBdXomAIwapJCeGhYJVCt3r9BCGLrDlt2Iq6yFUZtYCYgo2aZ7CMww45io8dG2XFoB5r3LGcnsuw4tANItnFLUFt32IiltBZIs6Sv+LEdgFHThNkJPPHME5OTfwFFeeKZJyKQbObYqtySvqqF7QCMyIm0RLQPZroTCBsBEydpXZVXC0v6qg62AzAiJY4S0X6IyicwE9LoR6h1LOmrOpgCMCKlUoG3uAmqBLyyXf1mwYKZKuLCkr6qgykAI1LCloiuxMZzR1l52TYy3QOsvGyb665iUgnsGZz2eqta3WP3vcbdsMYn8VBr4am1ivkAjEgJWyLai0q9A0r9C7n+ZjJ942zdt5Wu5V2e14wisageTBVpjLaxpK/qMK0CEJG/Au5U1WerII+RZkZHYXgYslloaYH2dmib+g8ZpMCbF25O5EDdx7q6yK3fTuZ98WcLx9UAvlpUs9FOUBrdEV4N/JiA2oCficjXReQa8VslyqgvRkdh505n8gfn+86dzngRQQq8ueHlRN4b1LTU2QlU9gdEYb9Pq6nCr2PaTFiNzbQ7AFX9axG5DXgN8A7g8yLydeBLqvrbuAU0UsLwMOSmThTkcs54yS4gUInokl1F33snXFf6kgN1WXq86LS3w3a68NCoev8WrpUWU0WQVX09mLCMmePLB6CqKiIjwAgwDpwF3C0i96vqh+MU0EgJWY8JwWvcD4VdRUGxZLPsm+d+qNvk71B5Q1pJCUQ1+aXNVBFEsdW6CcsIx7QmIBF5n4j8HPh74CfAxar6buCVwA0xy2ekhRaPCcFr3A8uu4rlR4Jd4plZ0ydteYWH1muoYTWLyVVi7YOjfO2D29h88wBf++A21j5oobFpw88O4Czgzaq6t3hQVXMi8vp4xDJSR3v71NU6QCbjjM8Ul91D/2an9n2hFj44TuS5ExnGZpdP9suPN8O2bRUd01C0E9gzSPfKbueRAtbcSWO0jBuVVvVuz9DR2hH5c3k1iweqUjra8EfFHYCINAE3lk7+BVR1KBapjPTR1gYdHWdW/C0tzmuXydY3LruH3sdgw31NZU7k9U9eUN6Aflzo//74tI7pArnBHlCd3AkEqblTSwlfXqv61rmtrs8ARF5iuVJfYyM9VNwBqOqEiOwUkeWquq9aQhkppa0t3IRfiseuovfkKnofcr/PlPDQH4zT+1hJ55S8Y3rjxe7N6nPrF5F53+HJHAG/9vtaqk3j5Ziu5jME6WtsJIdfE9DjIvJT4PnCoKq+ITapjMagoEymyS0oUBZd9MsB1+M2rsp6J411dpLr30qmb5ztI9vpXNzpS9TseNbV35wdT+eE5qbYhg65b9jjiPipZrN4Y+b4UQC3xS6F0biE2VW0tLj6EfquonLSWFdXXgn4TxQ7/yg89UL38VqhmhE/t9/QPsUHAPE1izdmzrRRQKo66PZVDeEMoyLt7Y4juphMhn0uEzWUJI11ddG9V3wXjvvb+2Heqalj804547VCNZPWNl/exqdv7mCktYUcMNLawqdv7jAHcMrwUwriMuBzwGpgNtAEPK+qHv9mhlElPExIy7PD7vWISiKGtoxeyFltQ776CFy1v4UNm7L0rYV9C51w1f7NsHZ/C18K+RjVii6qdtKa32bxaYiuSoMMSeDHBPR54EbgG8ClwJ8B/sslGkYFQjePcTEh9Q9TXo9oMmIoH0qajxh69jurmfWW6ZVAwaTR+9hUk8anbw4XMlrtWjxpS1pLQy2iNMiQFL7KQavqk0CTqk6o6peBa+IVy2gE4moe41qP6HsZeh8rOTAfMXT6gR6gct2gSiaN0ro7uw7t8h0y2ui1eNLw/GmQISn87ACOi8hsYLuI/D1wAOsjYERAoAqfAfEbMVRwIvtpK+lm0nBbPe4/tr/sXK9wy0avxZOG50+DDEnhRwG8Hcfu/x7gVmAZEZWAEJF/AV4PHFTVi6K4plE7xNk8pgyPiKHiZLSZ9BZ2Wz164RWB00i1eEpNY82ZZtcezF7PH4etvtF+B8X4iQLaq6onVPU5Vf2kqn4gbxKKgq9g5qSGxatJTNjmMa54RAyVlrII2lYyyCrRbUJJaznpOHDLpnab/AFa57b6Oj+KbOxG+h2U4qkAROQxEXnU6yuKm6vqj4FnoriWUXv0D7eXl3cI2DzGNwFKWRSUwNZ9W6e9rN9VoteEEqQcRa0TZLc0dmLM1/lR2Orb5rex+AWLp4wtfsHiuvwdlFLJBJSKQm8isg5YB7A8TOVJI3UUbPShooCCECDprHuvMLhifErxODe8CsotfsFixk6M+TJVpC0yJy6C7Jbcjg1qqw8SiTXy/MiUsZHnR1g4Z2Hd/148FYBXAbhqo6obgA0Aly5YoAmLY0RMkOYxoUNGA7BlTzfc6ZSMqEQaG8KkFS9bu9exfs93OzZIaGct1XmKGj/9AC4TkZ+JyDEROSUiEyLyXDWEM4wCcYWMVqTLaSg/nT+gbX5b5NU06xE3WzuAlBRZ8jKXBbHVBzEXNXIUkJ9wzs8DNwFPAHOBW4B/ilMowyilUshonAR1ChveuPk7Vp+9mgvPvtCXDySIvyTIpF6vjYH84Lcl5JMi0qSqE8CXReSXwEfD3lxEvgb0AGeLyFPAx1U1bGa9UYdUNWS0hJmEhxruePk7gvRg9nNsEHNR0MZA9YSfHcCURDARudXnedOiqjep6hJVnaWq59vkb3gRRcjoxnNHWXnZNjLdA6y8bFsg85HtBGqLIOaiRorEKsVvIliGGBLBDMMv/cPt5fV9AoSMFnwIrj0CfDqSvXYCaSwktvbBUW65Z5hzx7IcbG3h9hvaPQuzpVH+sAR1zjdKJFYpfhPBTgIngO8A/1+EiWCG4QvX+j47O3xP3lH5EEp3AmlsFVnox7t4LEuGM/143Zqyp1F+o3pUSgT7goi8LP/zQuBXwL8CvxSRm6okn2FM0nuwjT0PrSE32MOeh9YECgGN0odQrATSWEgsSD/eNMofBabY/FFpB3CFqj6e//kdwC5VvRh4JfDh2CUzjAgJ6kOYzl+Q63esp14tIZMMIQzSj7deQyDrVbFFTSUFUNz/6Grg3wFUdcT9cMNIL0HKTvjKOejqIrd+kef9kgwh9Oq76zZeryGQ9arYoqaSAjgsIq8XkUuAVwPfBxCRZpx8AMOoGXoPtvHnBxbTlAMUmnLw5wcWu5qRfPsLOju5c2g1lOSnJx1CePsN7ZycPfVf26sfb70WQqtXxRY1laKA3gn8I7AYeH/Ryn8t8L24BTMSZHS0rM2iZw2dIMcmyMZzR7ljyQiFTcCEwB1LRnj1cwvLlEAQf0HvwTbefuGQowOEGUXRRB2FU4j28RMFVK+lLBo5tj8Iolo75XUuXbBAH7n00qTFqG9GR2HnTqdbVoFMxr1yptexixfD2NjMFQhErlRWXrbNtU/wipMt7HlozYyPLXDWqwY4MofAiWKlNWvAmajSEIde6+GhtS5/lAzcPPBzVS2bPH1lAhsNxPDw1AkdJlsnlk3CXsfuL+qIle+9C0yvQLJZ2LEDihcllc4PQJBV/UxyDp59eGbZwnEVIgs7+Y0eG2Xo0NDk6+xElqFDQxw5ecR3ldOkadTY/iBYa0djKm5ds7zGvY4tpaBASnFTIG47Uq/zA7D8uPtax218pjkH02ULl/YOHj02GouzMooQyF1ju1zH9x/bb6GVdYTtAJIkjfbz5mYYdymB3NxcLm9TE0xM+LtuGAUS9FgX+n+krLsWjs8+MzbvlDPOwvLjg5SpLqZStrBbeeKgLRH9EMWuYkL9/V4bpWxyveKpAETkA5VOVNV/iF6cBsLN/BGBqSM0Xj6hiYlyeUXcj3XDrZmPV59ev+cHoPeXEzAOfWth30JYfgT6N0PvYxNOOcIImVQCRc1kvCZlQchIJlJnZbVDIC20snapZAJakP+6FHg3cF7+613AH8QvWp1TydaeJF4relV3c01z89Q2i4s8YuNby3u8uvbpdVMqLr17A9PSQu9jsOezkPuk8733MUIrFi9ygz2gOtlW0muSnNCJyAuRRREC2Zzxbxyw0MrapVJHsE8CiMiPgT9Q1aP515/AwkDDE8TWXk2CrMrBMRflG6cAsNWjj+7Bg7BwYbnJq6OjKlFAtLe7RyyFVSwVyK1fROZ9h9k+sr1ieeKonZVRhEBe8KIL2HFoB1qU5CDIlNczua6RLvyo+TamZgWfyo8ZYfCaaJPue+w1UZau/r1w8x8Uxt1MXh0dsMYltDJqM1jhetX0uXR2kuvfSqbvMBees7pqcelRxPZ7XSPsdY104UcB/CvwUxH5Vv71G4E74hOpQQi6It21a2p45dKlsGqV//v5dTh7TZRDQ+XHBsVveGlcBGgKHxldXXTvHWSQIS48Z3XVJs+3Pgq33APnjsHBVrj9Bth8ebBrhG3e4obF5qeLaRWAqvaLyH3AFfmhd6jqL+MVqwEIsiItnfzhzGs/SiCow9ltoizIWUrpjiVIZFBBljpny55uzmobYMfTQ1XpKFYoB12oCFooBw149gSoBkEatRvVwa+nZx7wnKp+WUTOEZEXq+ruOAVrCPyuSEsn/+JxPwogSHIXuO8WWlvd5Sh17q5a5b5bqBReum1bukJhIfIM5Wcf7uGW+QP89WcHWH6EKeUZgjRv8UOlctBJKoC4kt6MmTOtAhCRj+NEAnUAXwZmAXfiFIgzaoEgDufR0anZuIXsXC8OHpyqhNra4MiRcnPVwoXlJi8RRykUFEPcobB+zWBBM5Rh+uuOjnL7wJmXi8eyfPhLQ7zsiSNc+5ORUKv1UgVy7liWjReXh7ze9Fiyuy2r0Jk+/OwA3gRcAvwCQFX3i8iCWKUyoiWIw/mJJ8pzASrViypd1Y+OwkhJxfCREUcBlEb8jI+Xm4vi8gsEMYMND7PxZbmSCVSdsNFSWXftmhoiW7jukSNT6yGdOkUpsyfgDVv201QyHmS17mbuufNieOd1Z5Le9i6CddfBkReU3qm6BGnUblQHP6UgTqlTMc4peCjygnhFMspYujTYeClu8fZeDmevKB6/TGduWrMGenqc716+gjj8AgHyLjauyrLuOmfiVDkzgW682OW6ExPe9Ulbv54AAB7mSURBVJAKz5HNeipRr39Ar6YupbiZe/567dSMZ3Bef+yqAIl7MVCvpadrGT87gK+LyD8Di0TkvwB/Adwer1jGFAomlplGAcUZAtlUsqoMYm7y2pnE4RcIIFffVe4TaN9ayncBMeHV1KUUN0Wxz6W0BcCR5pDKPSRpKD1tUUhT8RMF9GkRuRp4DscP8Deqen/skhlTWbUqWNhnKX4dzl5RPCLuq9hSmSqZm0pt8K2tjnmoGn6BAMpm3wvdL1E2sWYykMmwcfW4S4kJf2IdnQ2zx2FO0UdwqgnX5i1uHGxtYXGJElh+xNm1lJIGU0uSFTotCqkcP07gv1PV/xu432XMqBbVKhznFcVz4YXO9+lk8MpvaG0tt8GPjJT3DojLL+AVxeSibJYfa2LvgnIluPxYE7Q0T3n+jcuPsO6V+8vs7eBDCYjwwlNKttQ0H6DG0u03tE/xAQB8YgDedZ2QbTqjsGflhPaz3ZWK16o46uikOPGzsrcopHL8+ACudhm7NmpBjAoUHJjFNuWdO53xqGlrg9Wrp9b3Wb36zA6i2IbvlUPQ0TH1/I4OZ5J3s5WPjVXHLzA25u+4XI7+H04wr8RnO+8U9N+fK3v+vkvGPM1FZYhM/VwuvBBaWrj7pbDy/ZD5uPP9G6uVW+7xVxNq8+VtfPrmDkZaW8gBI60tLDxrKV/YpKw4jFPO+jBs+I7y1kfLz/cqHX32L3fxoa/sZPFYlgxnopPWPpi+0s9+y19bFFI5laqBvhv4P4F2ESn+01kA/CRuwYwipnNgRr0z8DIXBckmLh33yiSOKxGsVNYA9yms3MvNOlpWOdSz0YybHV51qhLfs2fS4Vy6g/jnTf7l3Xx525SV+dc+uI3FY3Dz9qnHjfyuPLLIa1X8UNN+5pQowShyCeKwwftd2VsUUjmVTED/BtwH/C3wkaLxo6r6TKxSGVOp5MCsVknpsOWrq1n7yE3WgPQ+5mHCGRg48/PSpSy/BPa6TPYvOu6s5iv6BU6coM8jYuejV8NLAkvt4BVB5Dbutfr9vUegt9/oJDfissH7Xdlbn+ByKlUDPQIcAW4CEJFzgTnAfBGZr6r7qiNiAxKk8Uq16usEzSYuxW/tI69nLY02CiqrX/KF79wSqcom8P376f8RU1bw4Dh1n2uBsXzAdCW/gFfEzlMLZq4A3BzDhfFSvFbF5x31vvZM8Vqp7xrbFWpX4Hdln4YopLQxrQ9ARK4TkSeA3cAgsAdnZxAaEblGRHaKyJMi8pHpz2gA3Oz9bpNZaVx/MXGYVcKWr/byDZQqDy8HqNf46KgTxTMw4HwfHQ3+/CUybbwY33kAvY/Bhk1MsbcvyMLpkqWVl19g+ZFgovrh9hvaOTl76t/HydkZ18gir9j8yyaW+r6GXyr1RAjTZjJIfkHb/DbWLFtDz8oe1ixb09CTP/jLA/h/gcuAH6nqJSLyx8Dbwt5YRJqAf8JxMj8F/ExEvqOqvwl77ZrGq09uc7OzCi62v/st0BYFUZhw/ISiVionXYqXWcqr7pAXJeWo+zqHAuUBlJqLMh93v43bar9/M6x7Y6asAf3xTC5wg/kCBRu9nwger1XxoRVtfPrmhZFGAXmt1EsJGpljK/uZ40cBnFbVMRHJiEhGVbeIyGcjuPcfAU+q6jCAiNwFXA80tgLwWr2WNl6B8po7Bdy6b4WlWg1VgpiAvMxSIv57GLjsLLzMMl7jpXjF4but9nsfA1Z30Nc+zL6WLMuzLfQPt9N7sM21t7BfSh3DlfCKzQ9yDT+42eC9CBqZk2R+QS3jJwz0sIjMB34MbBSR9cDzEdz7POB3Ra+fyo9NQUTWicgjIvLI06dPR3DblOO1onYb9wpt9BvyGAS/JpywBDEBeSnLiYlyWb3KZhTyG4pYftx9XeRqrpk7t2yofzPMK/lTnXda6N/scv7q1fQebGPPQ2vIDfaw56E1k83oc4M9AAzuHnCXvcZom99W1v7Sq/VkI0fmVBM/O4DrgZPArUAvsBD4VJxCFaOqG4ANAJcuWFChKlmdEGSlHbTKZ9hw0Wo0VAliAqpklnKT1a0lpcvz9O+9gHWrdnC8+cyf27xxof/nC4HDZw4slOMo6dfQO7YUnlhYvqofB1qC/Q4mG8zPcCeQNkpX6qWRQWCROdXETymI5wFE5IXApgjv/XtgWdHr8/NjjU2Quj1+7fJhQzirSRATUExmqcIKvGwCPwS0nDjze1mYtwm5lOnofWyU3m8DWaAFaMdbgU7T7a2gBLaPbKdzcWeoZwtCNTKBzX6fLH5KQbwT+CTOLiAHCE5l0LAq+mfABSLyYpyJ/0bgrSGvWR/4XWn7nQDDhnBWkyAmoCDKMqAS7D3YNqkIAp8f5Fif3d4WnoQjHGbrvq10LS/xBcVANbuKmf0+OfyYgD4EXKSqh6K8saqOi8h7gB8ATcC/qOrjUd6j7vE7AYYN4awmQUxA4F9ZeinBJ57wp0CCKNEg9/LZ7e3Zh3v445WDDK6oTkXPtHYVM6LFjwL4LXA8jpur6r3AvXFcu2HwMwFWMws3KKW+Ca8QzrCyVoqu8lN5NIgSDXovn2zZ001mRXX8AUGyiY3axY8C+CjwoIg8jGPRBEBV3xubVEa0VCuEczqmKwddmDhLS09HIavfekBeq/ogZa795iHMIFu5Wk7hINnERu3iRwH8M/AfwGM4PgCj1oizIYxf3OziXuaPTMaZRP3I6je6yasctBtuE/3cue7jmUzoukOuVOj2Vg0l4FZmOmwmcBRYQ5do8aMAZqnqB2KXxIiXaoRwFnCLanErB+3FxARcccX0xwV1zPrFLeLo8OHyMYATJ/xf143CTihgt7fplEDYCJ4g2cTVwhq6RI8fBXCfiKzDCQEtNgFZRVCjnEpRLX7x2xIyiGPWq5ieGwEasoSmtXXG3d4mlcCeQbpXdk+ORxXBE3UmcFisoUv0+FEAN+W/f7RoLIowUKMeCFF335UgLSHjim4aHy9/rrgImbVdUALF4aH1GsFjDV2iZ9pSEKr6Ypcvm/wN98qllSitYJrJOOaO4pINblVOi5vfFBOkbEYQmpqCPVcYIrh2bv0iJibG2bpvK1C/ETxe5SGsbMTMqdQR7EpV/Q8RebPb+6r6zfjEMkIRpOxDmBIRQevud3RMf6/ihivFuE2UcUQ3ZTLOLiSIySgMUewuOjvJ9W8l0zfO9pHtdRvBYw1doqeSCagbJ/rnOpf3FDAFkEbiylh1I8jqdenS6HMWgkQ3lYaWerF4cTCfxaJF8NxzM2tAU1BW05SC8EVXV14JHOb2G1anMoInLFY2InoqdQQrVDX/lKruLn4vX77BSCNRZKz6LRHhVbendLINMqEFXdX7jW5qavIXmz8yUrkDWyknTpTvbCopxsL7BWXlVtLbpRSEL7q66N47yN+sGIKbV6cqgicqrGxEtPhxAt8D/EHJ2N3AK6MXxwhNFBmrflf2XtEyTU3lvQv8ElfOgt8GMbmcE4Xkt59ANluuhLZvdw8bXbQIOkuKuQ0NuV+3pBSEXwrZwn+zZIjN/6Mn8PlGY1HJB3Ah8DJgYYkf4IU4vYGNNBLEhBK0RESpvyBo3R6/VDNnwY3xcWfXUrwy9zIhuX1WnZ3lSmDRIliypDy8NQZygz3MuqJ+Skgb8VFpB9ABvB5YxFQ/wFHgv8QplBGCICaUIMe6+Qu8qGaNoSj6HJQi4piC/BznNYmXrvQr+Vti4PQD7olilklrFFPJB/Bt4NsiskZVt1VRJiMMQUwoQY71G/FTzRpDQZzYQXIUVMtX+26rfz9O5QJe/hYvIkhGK00UGz02ytChMyan7ER28rUpgcbEjw/gTSLyOHAC+D7wcuBWVb0zVsmMqQRZ6QYxofg9Nohjs1rmmyBO7CC1gILwxBPRR0yBa6vKmZDrbybTN87g7gEyGZcSF8CusV2mABoUPz2BX6Oqz+GYg/YALwH+a5xCGSW4JVzt3Bmsvk1YKiVdrVkDPT3O92ra7oM4sePokwz+/R2VPr/Vq6cmw61eHd3n2NVFbr3ToX5C3SObvMaN+sdXMbj899cB31DVI1LNWilGOjp6RZF0FbW9PogTO+gK3G8UkF8qfX5xO707O+neO8jACnX6+TUI5u+YHj8KYJOI7MAxAb1bRM7BaQ9pVIs0dPQKG54ZR1/iIEopiA+g8GzFz3rqlLvN361yqBsJl+TesqcbWTHg/mYAV0atELRyaKMqCz9N4T8iIn8PHFHVCRE5Dlwfv2jGJGnp6BVmpRrHLibIpOqmLNxCO71W5aOj7jH7M4jVj5qN546WN7A/WP4ZtJ6AsXnl558VsqJ1GglSObSRy0xXygP4sKr+ff7lWlX9BoCqPi8ifcDHqiFgXePXJJKWjl5+cXuuuHYxfpVSW1t51u2SJbBwYfQRU27EsQPCmfzXdezkeJNz3b1zsqzrcK5bqgTW3wfvuB5OF/3XzxqHf7wPvvRSf/cL22egWgSpHNrIZaYr7QBuBAoK4KPAN4reuwZTAOEIMiGkoaMX+FNYXs8VV69fL5m82k8WMzLiKIA1a/zdK207IKCvfXhy8i9wvClHX/twmQLo3dUC387Stxb2LYTlR6B/M6zd38KXfNwrqj4D1aClqcV1snerHNrIZaYrKQDx+NnttRGUoBNC0tmxfhWW13N5OVTnzo1epiNHynsNu4WARuVI91PMLaYd0L4W9/Ndx9vb6X18J72PnfldPD8LPvMOfzvJWuoz4FU59LpDrXzus9um7GC2LfOvLOqNSgpAPX52e20EJQ2O3SD4VVhB5fdqtRhGpiDx/mE/70od0IqVQEx+nOXZFvbOKb/u8qy/6qnrXpfl35YM0fK76R2gtdRnwK1y6HWHWvnq/xwp28Fseu8L+en88mdondtaVZmToJICeIWIPIez2p+b/5n8a6sFFJa0OHb94ldhRdEVzC9R3Cfs5+2lbEqLucXkx+kfbp/iAwCYN5Ghf9hf9dTXNo/ybwxNroArOUBrrc9AaeXQz312m+sOZn/OfREydiKm3JEUUakUhM/4NmNG1Jpj16/C8nquKGPqo6JSLZ+oicqPU+Lb6G1vBzp8RQG50dc+XGbQLThAYeoK+q/eNnUFDfH2GYg6NNNrp/L7Be7HN7oPwIiTtDh2/eJXYXk9l1vde3Ds5XFQqnTcQj6D1PKJgrB+HA+fRy8d9B706cguwcuHUNgJFIdGfvNFI/DuxXzuzjFfUUBhJvA4QjO9djDnHYWnXlh+fKP7AIy4SdqxG4SgRea8IpnCdr4qptKuxC2Ry41du8L9DkrLRhePR00MkURePgTANTRy09ljHPof0yubsBN4HKGZt9/Q7top7bKJxXxTRhqy1aQpAMM/fhWWV2jmqlXRJk4FKa/g1Ws4bO/fwvNEqdi8iCFwwM2HgOIZ5+fXLBJ2Ao8jNLOwUynNYzh0SRsdxxZaJnC1EJE/AT4BrAb+SFUfSUKOmiKOuvdxEFPCkytpMaNFrdi8iCFwoOArKPYh7G3JeiqB5kwz2363bdqJMuwEHiSOPwibL29zNVk1aqtJP9VA4+DXwJuBHyd0/9oiDdVA/VLJTBEHbW3+qpE2e6x1vMbTSHu7s8MpJobAgTuHVjs/lLhIBGE8N14WMTR6rPzv0Gui9juBt5/VTkamPmujmGWqSSJ//ao6BGBVRX2ShmqgfklrfsMFF8COHVMdvyLOeK0QwY6ntG7Qaw+1cseSkbJSEncOreZtq4cmdwItTS2M58bLSkd7mXW8ErH8TuBucfxxmmWsGJyRXtI6qbqR1vwGr8kTyvv0upWSSIvJLUTggFvdoC+ctx8tWYcVSklovqMYCmuWrWFgz4Drdd1MNVFM4EHMMmmLOKoVYlMAIvIjYLHLW335dpN+r7MOWAewPOlJJCninFSDTHR+jm1vd19ppyG/wa3Cp99SEnH5MWLCrUKoW92g0sm/wN58eOhkW8ndA7Q0B7PLV8uunsaIo1ohNgWgqldFdJ0NwAaASxcsaMwSFHEljQVx2FaaLMfGphZdq2a8fZiVepBSEmk1ubngVSH0eMZ/Ml5T0a+soASy41kymUzqwiXTGHFUKyTlBDaC0NYGHR1T2wZ2dISfjII4bCtNlsXOaa/SCHE4gcM6x4Oa0MKa3EZHHXPTwIDzPSYnvleF0KYAeniiNDt4sAfBmVgLK/6WphY6WjsSXyVHEXEUZLyeSCoM9E3A54BzgO+JyHZV/T+SkKVmiCNpLIhvIezkF4e/IqxzPGjdojAmtyqGx3pl904I5eGdHuGerafLK8EUdgITOkHPyp4IJI2GICGjbr6CsA7rWiaRHYCqfktVz1fVFlVts8k/ISo1Kvd7bJB7Rb0CDusc9wqrXLo0+nDLKobHulYCJW/W8VnYPZtxTEkrL9tGpnuAlZdtY+O5o+TWL2JiYpztI9sjlTkMfkNGC74Ct8J3Ha0dqdvZVAOLAqpHgnQa8+uwdfND+CWTcXwDUa+AvZrMeMX2u30uHR3un5XfTmF+qWIk12sPtfI/z9tfttIvNetU4ljThEensQ5y/cfI9IUo4x0xfiOOKvkK1ixb0xATfimmAOqNoKYGvw5btzDKQpetUuf04sVTHcOFujxR5zJ4yeo27vW5dHS4dwSL2uQWYyRXacTPscyE60q/KRdMCXh2GuvqoinnRAZ1v7gntPzVisFvZGevF6YA0kgckS1uE62X+SFIVzK/K2W3huoQbgXsVcfHbTzpZLqYIrncIn682jVNCMzKwelia0mFuj9uFPwLpx/o4axXhVcCUcTg+71GXOUlahmLAkobcUW2BHHsBpmU/ZZiCOJv8EuQayadTBdTJJdbxI/XhN56ugkpebNJnZ1BMbNy7k5gmOpfePbhHgAGdw8EEXkKlcwyUV/DykuUYzuAtOG1Ut21y99KO4ipoZpZu3GsgGutqU4MkVxeET+lK/vZOQGEU5mp24OJDLSeamJ+rnlK0hjgq9NYbrAH6R5gYPfAZMmIICacKMwyla5RWriuo7WjIUs+eGEKIG14rUgnJs6YNirZ9YNMiq2t7nH7rTH0Qo2jcmdaqoEmSKV6/sUoytgsF4c5MDZrgkODV7i+N12nsY3njjIvl5lUFEFNOFGYZbyuUZCnWK6O1g7WLHPx+TQopgDSht/YdC/7dZBJccyj5+noaLkTN4pJNY5chlpqqhMD/cPtvOPCoWnt+qczePoGvBLEeg+2Tdta0s0EFSQLN4oYfLdruNEo5R2CYAogbbiFZnrhpSj8TophdxtGKnDs+jMvtxEkMqiUSi0l/UT3RFU0rvQaFvHjD1MAacRv7ZwokrPC7DZqibRWKQ1JX/twmV3fywnc5JELsMIjccyN0pDTF51uYmx2edSVIL6je6IoGld6jYLtv5RGjvhxw6KA0obfzNAonJ3t7U7ilx/SWHo6CFVqplJtKjqBi5h3CnqGy8dRJ3HMD4WQ071zsqg4IadHm3PMKrW8KKhq6OieMFjEjz9sB5A2Kk20hVVsFGWbC1RrtxEEL/nD5EfUqcPYywncehzmn4Z9C2H5EejfDH1X4Zogdu/ZY/Dk9Pdys/efyiitp5qZn2ua4ix+22r3vI9qmWCq3VCmVjEFUEu4ZayWEiQTOOhuw20CLlxnppNq6TVLs4ujrNFfhw5jt6bu88aF9d9Xeh8rOjCT4e1vdneSeu4ifB73zKxxDg12TRnrax92VUzVNME0ap/fIJgCqHVKJ9Dxcf8Zr0F2GzA1mzebLc/uDTopuykrr1r8NV6jPy7cmrr3D7fTOw60TFXMy7Puk7JX8Ti34/ye76aYzASTPkwBpI0gzkq3CdQLr2t6jZfuNh54wPvaxQSZlN2S3oJS676JCHAN12yj7HfQP+wvucsL192Gx/nFiqnQXazj7MaosFlLmBM4bQRxVgaZQN0USJB7edXdccPvpBzF5F3jUTzVpPdgGxt2drDiZAuisOJkCxt2dkwb6z/T83sPtrHnoTVovpnMjqc96kEZiWE7gLQRxFnpdwL1mtTjcoxGPSkXKoy6VR6t8SieauMnuSuO84t7C0dRQdSIBlMAacSvs9LLhNPcDE1N/ib1qB2jUU3KbhFPUdfoN6pKQQls3beVruVd059gxI4pgFrGq+7PBRdUd2L0G57qdZ7beDVq9BtVp3uvMLhinME9g3Sv7E5anIbHfAC1TFzN4t1YutR73E85aDfqNDnL8GbLnm5y/c2gGqqMtBENtgOodaq1Kl61yvleHI65dOmZ8ZlQp8lZxjR0dZFbv53M+9LTVrJRMQVg+GfVqnATvhtm1mlMOjsBcwonjZmADMNIhNxgDxCuo5gRDlMAhmEkhimBZDEFYBhGopgSSA5TAIZhJI4pgWQwBWAYRiowJVB9TAEYhpEaJpXAnsFkBWkQTAEYhpEqcoM9oMrWfVuTFqXuSUQBiMh/F5EdIvKoiHxLRBYlIYdhGOkkt34RExPjbB/ZnrQodU1SO4D7gYtU9eXALuCjCclhGEYa6eykKQdHThw2JRAjiSgAVf2hqo7nXz4EnJ+EHIZhpJfTD/Sw8KSjBIx4SIMP4C+A+7zeFJF1IvKIiDzy9OnTVRTLMIykefbhHqeCqEUGxUJsCkBEfiQiv3b5ur7omD5gHNjodR1V3aCql6rqpefMmhWXuIZhpJQte5yy0aYEoie2YnCqelWl90XkZuD1wFpV1bjkMAyj9rGOYvGQVBTQNcCHgTeo6vEkZDAMo7awRLHoScoH8HlgAXC/iGwXkS8kJIdhGDVEQQlYjkA0JBUF9BJVXaaqnfmvdyUhh2EYtUeuv5mJiXFTAhGQhiggwzAM/3R1TSoBIxymAAzDqD26uiw8NAJMARiGUZNs2dPNwpPmFA6DKQDDMGqWZx/uoSlnSmCmmAIwDKOmOf1AD2BKYCaYAjAMo+axHIGZYQrAMIy6wJRAcEwBGIZRN5gSCIYpAMMw6gpTAv4xBWAYRt2R63fqXJoSqIwpAMMw6o+uLnLrrdPsdJgCMAyjPunsBGwXUAlTAIZh1C25QUsUq4QpAMMw6hpLFPPGFIBhGHWPRQa5YwrAMIyGwJRAOVJL7XhF5Glgb9JyxMDZwKGkhYiBen0uqN9nq9fngvp9Nj/PtUJVzykdrCkFUK+IyCOqemnSckRNvT4X1O+z1etzQf0+W5jnMhOQYRhGg2IKwDAMo0ExBZAONiQtQEzU63NB/T5bvT4X1O+zzfi5zAdgGIbRoNgOwDAMo0ExBWAYhtGgmAJICSLy30Vkh4g8KiLfEpG6KGUoIn8iIo+LSE5Eaj4ET0SuEZGdIvKkiHwkaXmiQkT+RUQOisivk5YlSkRkmYhsEZHf5P8O35e0TFEhInNE5Kci8qv8s30y6DVMAaSH+4GLVPXlwC7gownLExW/Bt4M/DhpQcIiIk3APwHXAi8FbhKRlyYrVWR8BbgmaSFiYBz4oKq+FLgM+L/q6HeWBa5U1VcAncA1InJZkAuYAkgJqvpDVR3Pv3wIOD9JeaJCVYdUdWfSckTEHwFPquqwqp4C7gKuT1imSFDVHwPPJC1H1KjqAVX9Rf7no8AQcF6yUkWDOhzLv5yV/woU1WMKIJ38BXBf0kIYZZwH/K7o9VPUyWTSCIjISuAS4OFkJYkOEWkSke3AQeB+VQ30bM3xiGW4ISI/Aha7vNWnqt/OH9OHs23dWE3ZwuDnuQwjSURkPnAP8H5VfS5peaJCVSeAzrzP8FsicpGq+vbjmAKoIqp6VaX3ReRm4PXAWq2hBI3pnquO+D2wrOj1+fkxI8WIyCycyX+jqn4zaXniQFUPi8gWHD+ObwVgJqCUICLXAB8G3qCqx5OWx3DlZ8AFIvJiEZkN3Ah8J2GZjAqIiABfAoZU9R+SlidKROScQrSgiMwFrgZ2BLmGKYD08HlgAXC/iGwXkS8kLVAUiMibROQpYA3wPRH5QdIyzZS8k/49wA9wnIlfV9XHk5UqGkTka8A2oENEnhKRv0xapoh4NfB24Mr8/9V2EXlt0kJFxBJgi4g8irM4uV9VvxvkAlYKwjAMo0GxHYBhGEaDYgrAMAyjQTEFYBiG0aCYAjAMw2hQTAEYhmE0KKYADMMnIvJGEVERuTBpWQwjCkwBGIZ/bgK25r8bRs1jCsAwfJCvJdMF/CVOBjAikhGR/z/fx+F+EblXRN6Sf++VIjIoIj8XkR+IyJIExTcMV0wBGIY/rge+r6q7gDEReSVOn4OVOL0B3o6T7VyoPfM54C2q+krgX4D+JIQ2jEpYMTjD8MdNwPr8z3flXzcD31DVHDCSL8YF0AFchFPWA6AJOFBdcQ1jekwBGMY0iMiLgCuBi0VEcSZ0Bb7ldQrwuKquqZKIhjEjzARkGNPzFuCrqrpCVVeq6jJgN04HrRvyvoA2oCd//E7gHBGZNAmJyMuSENwwKmEKwDCm5ybKV/v34DTBeQr4DXAn8AvgSL5d5FuAvxORXwHbgcurJ65h+MOqgRpGCERkvqoeE5FW4KfAq1V1JGm5DMMP5gMwjHB8N9+UYzbw/9jkb9QStgMwDMNoUMwHYBiG0aCYAjAMw2hQTAEYhmE0KKYADMMwGhRTAIZhGA3K/wZAgOhJoC87kwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZ-j28aPihZx"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeTjz2vDilAC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "31b29e85-0181-414e-a554-9073596051ad"
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_test, y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('SVM (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}