Kernel_SVM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Kernel SVM.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN9XDVZvLoonzO9K60m3GQ3"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the libraries**"
      ],
      "metadata": {
        "id": "jlYq5Zt0IZj8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "9nczS44sEmND"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the dataset**"
      ],
      "metadata": {
        "id": "UluafnVZIW1R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "8wLw5joXG3Oz"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting the dataset into the Training set and Test set**"
      ],
      "metadata": {
        "id": "wlShZyBHITVp"
      }
    },
    {
      "cell_type": "code",
      "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)\n",
        "print(X_train)\n",
        "print(y_train)\n",
        "print(X_test)\n",
        "print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wa9XZbpuG4XK",
        "outputId": "2ec6bfdd-180a-48a0-d4fa-969612b1539d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Scaling**"
      ],
      "metadata": {
        "id": "8vi5c-6zIPQZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)\n",
        "print(X_train)\n",
        "print(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ilk_fRawG5t6",
        "outputId": "e14342fb-01b2-4aaa-b43d-7147b0c86cc7"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            " [-1.10189888  0.59194336]\n",
            " [ 0.08648817 -0.79972756]\n",
            " [-1.00286662  1.54871711]\n",
            " [-0.70576986  1.40375139]\n",
            " [-1.29996338  0.50496393]\n",
            " [-0.30964085  0.04107362]\n",
            " [-0.11157634  0.01208048]\n",
            " [-0.30964085 -0.88670699]\n",
            " [ 0.8787462  -1.3505973 ]\n",
            " [-0.30964085  2.24455257]\n",
            " [ 0.97777845  1.98361427]\n",
            " [-1.20093113  0.47597078]\n",
            " [-1.29996338  0.27301877]\n",
            " [ 1.37390747  1.98361427]\n",
            " [ 1.27487521 -1.3505973 ]\n",
            " [-0.30964085 -0.27785096]\n",
            " [-0.50770535  1.25878567]\n",
            " [-0.80480212  1.08482681]\n",
            " [ 0.97777845 -1.06066585]\n",
            " [ 0.28455268  0.30201192]\n",
            " [ 0.97777845  0.76590222]\n",
            " [-0.70576986 -1.49556302]\n",
            " [-0.70576986  0.04107362]\n",
            " [ 0.48261718  1.72267598]\n",
            " [ 2.06713324  0.18603934]\n",
            " [-1.99318916 -0.74174127]\n",
            " [-0.21060859  1.40375139]\n",
            " [ 0.38358493  0.59194336]\n",
            " [ 0.8787462  -1.14764529]\n",
            " [-1.20093113 -0.77073441]\n",
            " [ 0.18552042  0.24402563]\n",
            " [ 0.77971394 -0.30684411]\n",
            " [ 2.06713324 -0.79972756]\n",
            " [ 0.77971394  0.12805305]\n",
            " [-0.30964085  0.6209365 ]\n",
            " [-1.00286662 -0.30684411]\n",
            " [ 0.18552042 -0.3648304 ]\n",
            " [ 2.06713324  2.12857999]\n",
            " [ 1.86906873 -1.26361786]\n",
            " [ 1.37390747 -0.91570013]\n",
            " [ 0.8787462   1.25878567]\n",
            " [ 1.47293972  2.12857999]\n",
            " [-0.30964085 -1.23462472]\n",
            " [ 1.96810099  0.91086794]\n",
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            " [-0.01254409 -0.30684411]\n",
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            " [-0.01254409 -0.13288524]\n",
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            " [ 0.77971394 -1.20563157]\n",
            " [ 0.97777845  2.07059371]\n",
            " [-1.20093113 -1.52455616]\n",
            " [-0.30964085  0.79489537]\n",
            " [ 0.08648817 -0.30684411]\n",
            " [-1.39899564 -1.23462472]\n",
            " [-0.60673761 -1.49556302]\n",
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            " [-1.29996338  0.59194336]\n",
            " [-0.30964085  0.53395707]\n",
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            " [-0.30964085  0.07006676]\n",
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            " [-1.10189888 -0.3648304 ]\n",
            " [-0.01254409  0.04107362]\n",
            " [ 0.08648817 -0.24885782]\n",
            " [-1.59706014 -1.23462472]\n",
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            " [ 0.97777845  0.12805305]\n",
            " [ 1.96810099 -1.3505973 ]\n",
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            " [-0.80480212  0.30201192]\n",
            " [ 1.96810099  0.73690908]\n",
            " [-1.20093113 -0.50979612]\n",
            " [ 0.68068169  0.27301877]\n",
            " [-1.39899564 -0.42281668]\n",
            " [ 0.18552042  0.1570462 ]\n",
            " [-0.50770535 -1.20563157]\n",
            " [ 0.58164944  2.01260742]\n",
            " [-1.59706014 -1.49556302]\n",
            " [-0.50770535 -0.53878926]\n",
            " [ 0.48261718  1.83864855]\n",
            " [-1.39899564 -1.089659  ]\n",
            " [ 0.77971394 -1.37959044]\n",
            " [-0.30964085 -0.42281668]\n",
            " [ 1.57197197  0.99784738]\n",
            " [ 0.97777845  1.43274454]\n",
            " [-0.30964085 -0.48080297]\n",
            " [-0.11157634  2.15757314]\n",
            " [-1.49802789 -0.1038921 ]\n",
            " [-0.11157634  1.95462113]\n",
            " [-0.70576986 -0.33583725]\n",
            " [-0.50770535 -0.8287207 ]\n",
            " [ 0.68068169 -1.37959044]\n",
            " [-0.80480212 -1.58254245]\n",
            " [-1.89415691 -1.46656987]\n",
            " [ 1.07681071  0.12805305]\n",
            " [ 0.08648817  1.51972397]\n",
            " [-0.30964085  0.09905991]\n",
            " [ 0.08648817  0.04107362]\n",
            " [-1.39899564 -1.3505973 ]\n",
            " [ 0.28455268  0.07006676]\n",
            " [-0.90383437  0.38899135]\n",
            " [ 1.57197197 -1.26361786]\n",
            " [-0.30964085 -0.74174127]\n",
            " [-0.11157634  0.1570462 ]\n",
            " [-0.90383437 -0.65476184]\n",
            " [-0.70576986 -0.04590581]\n",
            " [ 0.38358493 -0.45180983]\n",
            " [-0.80480212  1.89663484]\n",
            " [ 1.37390747  1.28777882]\n",
            " [ 1.17584296 -0.97368642]\n",
            " [ 1.77003648  1.83864855]\n",
            " [-0.90383437 -0.24885782]\n",
            " [-0.80480212  0.56295021]\n",
            " [-1.20093113 -1.5535493 ]\n",
            " [-0.50770535 -1.11865214]\n",
            " [ 0.28455268  0.07006676]\n",
            " [-0.21060859 -1.06066585]\n",
            " [ 1.67100423  1.6067034 ]\n",
            " [ 0.97777845  1.78066227]\n",
            " [ 0.28455268  0.04107362]\n",
            " [-0.80480212 -0.21986468]\n",
            " [-0.11157634  0.07006676]\n",
            " [ 0.28455268 -0.19087153]\n",
            " [ 1.96810099 -0.65476184]\n",
            " [-0.80480212  1.3457651 ]\n",
            " [-1.79512465 -0.59677555]\n",
            " [-0.11157634  0.12805305]\n",
            " [ 0.28455268 -0.30684411]\n",
            " [ 1.07681071  0.56295021]\n",
            " [-1.00286662  0.27301877]\n",
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            " [ 0.18552042 -0.3648304 ]\n",
            " [ 2.1661655  -1.03167271]\n",
            " [-0.30964085  1.11381995]\n",
            " [-1.6960924   0.07006676]\n",
            " [-0.01254409  0.04107362]\n",
            " [ 0.08648817  1.05583366]\n",
            " [-0.11157634 -0.3648304 ]\n",
            " [-1.20093113  0.07006676]\n",
            " [-0.30964085 -1.3505973 ]\n",
            " [ 1.57197197  1.11381995]\n",
            " [-0.80480212 -1.52455616]\n",
            " [ 0.08648817  1.8676417 ]\n",
            " [-0.90383437 -0.77073441]\n",
            " [-0.50770535 -0.77073441]\n",
            " [-0.30964085 -0.91570013]\n",
            " [ 0.28455268 -0.71274813]\n",
            " [ 0.28455268  0.07006676]\n",
            " [ 0.08648817  1.8676417 ]\n",
            " [-1.10189888  1.95462113]\n",
            " [-1.6960924  -1.5535493 ]\n",
            " [-1.20093113 -1.089659  ]\n",
            " [-0.70576986 -0.1038921 ]\n",
            " [ 0.08648817  0.09905991]\n",
            " [ 0.28455268  0.27301877]\n",
            " [ 0.8787462  -0.5677824 ]\n",
            " [ 0.28455268 -1.14764529]\n",
            " [-0.11157634  0.67892279]\n",
            " [ 2.1661655  -0.68375498]\n",
            " [-1.29996338 -1.37959044]\n",
            " [-1.00286662 -0.94469328]\n",
            " [-0.01254409 -0.42281668]\n",
            " [-0.21060859 -0.45180983]\n",
            " [-1.79512465 -0.97368642]\n",
            " [ 1.77003648  0.99784738]\n",
            " [ 0.18552042 -0.3648304 ]\n",
            " [ 0.38358493  1.11381995]\n",
            " [-1.79512465 -1.3505973 ]\n",
            " [ 0.18552042 -0.13288524]\n",
            " [ 0.8787462  -1.43757673]\n",
            " [-1.99318916  0.47597078]\n",
            " [-0.30964085  0.27301877]\n",
            " [ 1.86906873 -1.06066585]\n",
            " [-0.4086731   0.07006676]\n",
            " [ 1.07681071 -0.88670699]\n",
            " [-1.10189888 -1.11865214]\n",
            " [-1.89415691  0.01208048]\n",
            " [ 0.08648817  0.27301877]\n",
            " [-1.20093113  0.33100506]\n",
            " [-1.29996338  0.30201192]\n",
            " [-1.00286662  0.44697764]\n",
            " [ 1.67100423 -0.88670699]\n",
            " [ 1.17584296  0.53395707]\n",
            " [ 1.07681071  0.53395707]\n",
            " [ 1.37390747  2.331532  ]\n",
            " [-0.30964085 -0.13288524]\n",
            " [ 0.38358493 -0.45180983]\n",
            " [-0.4086731  -0.77073441]\n",
            " [-0.11157634 -0.50979612]\n",
            " [ 0.97777845 -1.14764529]\n",
            " [-0.90383437 -0.77073441]\n",
            " [-0.21060859 -0.50979612]\n",
            " [-1.10189888 -0.45180983]\n",
            " [-1.20093113  1.40375139]]\n",
            "[[-0.80480212  0.50496393]\n",
            " [-0.01254409 -0.5677824 ]\n",
            " [-0.30964085  0.1570462 ]\n",
            " [-0.80480212  0.27301877]\n",
            " [-0.30964085 -0.5677824 ]\n",
            " [-1.10189888 -1.43757673]\n",
            " [-0.70576986 -1.58254245]\n",
            " [-0.21060859  2.15757314]\n",
            " [-1.99318916 -0.04590581]\n",
            " [ 0.8787462  -0.77073441]\n",
            " [-0.80480212 -0.59677555]\n",
            " [-1.00286662 -0.42281668]\n",
            " [-0.11157634 -0.42281668]\n",
            " [ 0.08648817  0.21503249]\n",
            " [-1.79512465  0.47597078]\n",
            " [-0.60673761  1.37475825]\n",
            " [-0.11157634  0.21503249]\n",
            " [-1.89415691  0.44697764]\n",
            " [ 1.67100423  1.75166912]\n",
            " [-0.30964085 -1.37959044]\n",
            " [-0.30964085 -0.65476184]\n",
            " [ 0.8787462   2.15757314]\n",
            " [ 0.28455268 -0.53878926]\n",
            " [ 0.8787462   1.02684052]\n",
            " [-1.49802789 -1.20563157]\n",
            " [ 1.07681071  2.07059371]\n",
            " [-1.00286662  0.50496393]\n",
            " [-0.90383437  0.30201192]\n",
            " [-0.11157634 -0.21986468]\n",
            " [-0.60673761  0.47597078]\n",
            " [-1.6960924   0.53395707]\n",
            " [-0.11157634  0.27301877]\n",
            " [ 1.86906873 -0.27785096]\n",
            " [-0.11157634 -0.48080297]\n",
            " [-1.39899564 -0.33583725]\n",
            " [-1.99318916 -0.50979612]\n",
            " [-1.59706014  0.33100506]\n",
            " [-0.4086731  -0.77073441]\n",
            " [-0.70576986 -1.03167271]\n",
            " [ 1.07681071 -0.97368642]\n",
            " [-1.10189888  0.53395707]\n",
            " [ 0.28455268 -0.50979612]\n",
            " [-1.10189888  0.41798449]\n",
            " [-0.30964085 -1.43757673]\n",
            " [ 0.48261718  1.22979253]\n",
            " [-1.10189888 -0.33583725]\n",
            " [-0.11157634  0.30201192]\n",
            " [ 1.37390747  0.59194336]\n",
            " [-1.20093113 -1.14764529]\n",
            " [ 1.07681071  0.47597078]\n",
            " [ 1.86906873  1.51972397]\n",
            " [-0.4086731  -1.29261101]\n",
            " [-0.30964085 -0.3648304 ]\n",
            " [-0.4086731   1.31677196]\n",
            " [ 2.06713324  0.53395707]\n",
            " [ 0.68068169 -1.089659  ]\n",
            " [-0.90383437  0.38899135]\n",
            " [-1.20093113  0.30201192]\n",
            " [ 1.07681071 -1.20563157]\n",
            " [-1.49802789 -1.43757673]\n",
            " [-0.60673761 -1.49556302]\n",
            " [ 2.1661655  -0.79972756]\n",
            " [-1.89415691  0.18603934]\n",
            " [-0.21060859  0.85288166]\n",
            " [-1.89415691 -1.26361786]\n",
            " [ 2.1661655   0.38899135]\n",
            " [-1.39899564  0.56295021]\n",
            " [-1.10189888 -0.33583725]\n",
            " [ 0.18552042 -0.65476184]\n",
            " [ 0.38358493  0.01208048]\n",
            " [-0.60673761  2.331532  ]\n",
            " [-0.30964085  0.21503249]\n",
            " [-1.59706014 -0.19087153]\n",
            " [ 0.68068169 -1.37959044]\n",
            " [-1.10189888  0.56295021]\n",
            " [-1.99318916  0.35999821]\n",
            " [ 0.38358493  0.27301877]\n",
            " [ 0.18552042 -0.27785096]\n",
            " [ 1.47293972 -1.03167271]\n",
            " [ 0.8787462   1.08482681]\n",
            " [ 1.96810099  2.15757314]\n",
            " [ 2.06713324  0.38899135]\n",
            " [-1.39899564 -0.42281668]\n",
            " [-1.20093113 -1.00267957]\n",
            " [ 1.96810099 -0.91570013]\n",
            " [ 0.38358493  0.30201192]\n",
            " [ 0.18552042  0.1570462 ]\n",
            " [ 2.06713324  1.75166912]\n",
            " [ 0.77971394 -0.8287207 ]\n",
            " [ 0.28455268 -0.27785096]\n",
            " [ 0.38358493 -0.16187839]\n",
            " [-0.11157634  2.21555943]\n",
            " [-1.49802789 -0.62576869]\n",
            " [-1.29996338 -1.06066585]\n",
            " [-1.39899564  0.41798449]\n",
            " [-1.10189888  0.76590222]\n",
            " [-1.49802789 -0.19087153]\n",
            " [ 0.97777845 -1.06066585]\n",
            " [ 0.97777845  0.59194336]\n",
            " [ 0.38358493  0.99784738]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training the Kernel SVM model on the Training set**"
      ],
      "metadata": {
        "id": "Uh0rIJsEIKgg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KuddS5XlG8Ha",
        "outputId": "738370db-b312-4564-f2f9-6d862011b3b2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting a new result**"
      ],
      "metadata": {
        "id": "hGh7uuREIG84"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classifier.predict(sc.transform([[30,87000]])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mWduWpn3G-Qa",
        "outputId": "00508b8c-4146-455f-8ee6-a9c86edca567"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting the Test set results**"
      ],
      "metadata": {
        "id": "q_kRsobKIDtC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VQwv8ByHLUx",
        "outputId": "6a6fc53a-a939-40ac-92ab-5078d46b2fe3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 0]\n",
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            " [0 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [1 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [1 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Making the Confusion Matrix**"
      ],
      "metadata": {
        "id": "0va09mCJH_aa"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S7RrCgfTHVmK",
        "outputId": "9a4c9b59-3ac0-49fe-c934-db0be7930803"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[64  4]\n",
            " [ 3 29]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.93"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualising the Training set results**"
      ],
      "metadata": {
        "id": "1QrWm9alH7YJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = sc.inverse_transform(X_train), y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
        "plt.contourf(X1, X2, classifier.predict(sc.transform(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], c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Kernel SVM (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "JoKSS-fJHX8R",
        "outputId": "71268499-2b95-4dec-d816-dc10df726877"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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40me+RNvKNhqOauCBRx+I5ZxWW8xIJVF9AGHMT6U0VYXZV75gGWk8DAsf6+JHV7ez6vI2fnR1eyK1viC6j6mU5WMgmSoFlcCfLvpT/n3Fv8d6TtNcjNQSxQcQxvxU6m6H5c4iH0kbiJOogruU5WMqJXfmgY0PcHP7zezo3cHUCVO5at5VXHhCtLyXd5z5DrZu3jryxBCY5mJUJWFCa8td8LDUlFIbiBrV5VcmJonyMWmN5MvmgY0P8OVHvsz23u0oyvbe7Xz5kS/zwMZ4TFlxYsLFqErCmJ/KXfDQj2kTpoUaD0pYbSCKqchPQAcV3Msvbmb/2Nzb1P6xNSy/OH7Bn+ZIvgw3t9/M/v7ckvv7+/dzc3v6Su6bWcyoWsKYn8ptqvIi47SPO1osTDHJOExFgqBozvugZMx0pWg2VmrzaDHs6PUure83Xk5MuBhGijnhqBNijwxbfnFzjs8F/LWBqJF0Hbs7cgQLgBKu/MuqMxsTESb5VELuzNQJUz0DOqZOiFZyPwlMuBjGKGPVmY38bnwPa2u3s20iTH8dzhho4tVTht/Ao5qKKsHUlCGpitNxctW8q/jyI1/OMY2NqxvHVfOildz/3JLP8eT/PMnuXbtZ8LYFfPrzn+aSD18S6ZwmXAxjlNHV28Wvj+xk0FUoth4BP5NOWnqH555ENRVVgqkpmzSaR7PJRIXFHS32jVu/EcfycjDhYhijjDCmrqimokowNVUaF55wYWRhUgpMuBjGKCOMqSqqqagSTE1GMphwMYxRRlhTVVRTUdpNTeVmkEFUlbzuvKlDVRlkcOSJLiZcDCNB0tgYzExV6WLHvh00vN5A/cT61AoYVaXv9T527Ase8mzCxTASIq3lRMxUlS5WbF7BIhYxdfxUalKa1z7IIDv27WDF5hWBjzHhYhgJkeZWvGaqSo9W2TvQy/KXlpf8ukljwsUwEqKScjxGG2nVKquJdOpghmEYCVIJRSorHdNcjIoiLaYMo7IxrTJ5TLgYFUOlmTLiyE43YZoMlVY5oBIx4VIGVr/UBkDtIAy4hsnaQZhwAObsn8y6cXvoHZv7Web1guNaS77etJBmB7kXUUN+K02YVhIWjp08JlwSZF3nOnrf2OMpJAZvmQxz5oQ42TpqrtzDmj+0AbmChzF1zJ85P7Z1p5VKM2VEDfmtNGFaSVg4dvIkJlxE5HbgAmCnqp7sjv0/4ELgAPAH4GOqusf97Frg48AA8BlVfdgdPx+4BagFlqvq193x44AVQAPwG+AjqnpAROqBu4BTgW7gQ6q6Kal9epHRTAAmHYDdj7cOnxRCrjjz5zC42uezNWuoWepcs9a9Fw3UCgtmLQh5kXRTiaaMKCG/lSZMKw0Lx06WJDWXO4Bv49zoM6wErlXVfhG5EbgW+IKInAQsAv4ImAb8UkQyTSy+A5wLbAWeFJH7VfU54EbgZlVdISL/iiOYvuv+3K2qx4vIInfehxLcJ+AIlMyNnZoiNJMozJ+fK3jWrKFmaf+Q+a22OjSbJE0ZafRtVKIwNYwMiQkXVf21iMzKG/vvrLdrgUzDgIuAFaraB7wkIi8Cp7ufvaiqHQAisgK4SETWA+cAf+7OuRO4Hke4XOS+BrgH+LaIiKrmdiyKgTWb18DB/kMmqoP/UAfz3Zt4ieSKJ1nC5uxZq1kzY0jQTBo/mTlN5Vxc8SRlykirb6Na/AJpFNxG8pTT5/IXwI/d19NxhE2Gre4YwJa88bk4prA9qtrvMX965hhXQ+px578a5+IzmsqEbLNXCpWDRzYtgE3O6zFntdH7xp5DgqYSgwOSMGWk1bdRDX6BtApuI3nKIlxEZCnQD9xdjutnrWMJsASg3qN/uBfrOtfRs28PkKepVAAHH211XrjBARkBmQb/zMLHukrSJx2GP0kn6duI+tRe6X6BtApuI3lKLlxE5HIcR//CLFPVNmBG1rRj3DF8xruBySJS52ov2fMz59oqInXAJHf+MFT1VuBWgInHTRzRbLZ602pqB5Ra3Bt15ciVXLKCA86etZrVx+qQz6gMkWcLH+vK6ene1N3HNXc4T7dxCxivJ2k/ovo2qvmpPajQtKCE0UtJhYsb+fV5YIGq7s366H7ghyLyDRyH/mzgCUCA2W5k2DYcp/+fq6qKyCM4PpsVwGXAfVnnugxodz//VRR/S7amMrissjSVIGSbzabMbaOnpr/kZrMr7u04JFgyjDswyBX3dsQuXLyepP1oGN8Q+7Wq4ak9jNC0oITRS5KhyD8CWoGjRGQrcB1OdFg9sNLtW7BWVf9KVZ8VkZ8Az+GYyz6pqgPueT4FPIwTiny7qj7rXuILwAoR+RrwNHCbO34b8H03KGAXjkAqiozDvuI1lYAc8h1l59SUQJM5utv7KdZvPChepra2acHP2b3PU+ENTLU+tZeyTbJRuSQZLXapx/BtHmOZ+cuAZR7jDwIPeox3MBRRlj2+H/hAqMX6MDA4wIIt4jzdjyZcs9nZs1azrjF5TWZnQz1NHoJkZ0A/mBd+prabPl9HT13/CEc7RBUC1frUXso2yUblYhn6PgwlQqazM1wpyI80y64OEKegWX5xc44gANg/toblFxf/dOtnavv7Xwqffk9NINNYVCFQrU/tpW6TbFQmJlzyyJRsKXkiZMo5FGmWlaAZV85Mxq8SZ7SYn0ntr9YO8O0Pn5jzJN0wvoHONzpjFwLV+tSeFqFZyghDIzySQG5hRTLxuIn6xuVvUDugubkrhidjzmoD0ltQ80dXt3ua2job6rn0n+YNG7dEv3CU+/vKN3uCo+3edHmLCZgS03Z5229U9bT8cdNcXHr7ehGF+aPRx1IESWoycRDW1OZluin1k3G5b9hh8Pq+Srn+UkYYGsVhwiULM4MVgVtq5lD2/6bVZU/IhOimtkK5Nz98WzIlaF7c+TwHaxxLQt9AHy/ufB6ojJyYUuf0JBVhaMSHCRcjFg4+2gpr1jDm2v6h0jiHl1eTWXVmY9FPsX5Pxkf+fiMbpmvsN9HtnS9wsC7XRH2wRtne+QKNx6dfuJQ6pyeJCEMjXmrKvYC0cOrrE0xricr8+Rx8tJXB1a1MOMAhTaYS8XsCvvGMgUR6r79W6x0e7TeeNkqd07P84mb2j829fUWNMDTixYSLkQi7H29l/hYBdUrLrPlDm5OUWiH4PQFvmeQ9P+pNdEZPuPG04ReGnFROz6ozG7np8hY6G+oZxAnUMGd+ujCzmJEY+aVlenFMZmmLLPPCLyDgiIEazyTMqDfRL6yt5W/PGWDv2KGxww444z+tAIW6HOHJUcyeRvKY5mKUhN2PtzpVpMnt1JlW/J6MpzXNpkZy/9vEcRPd9bYT+M6DcOweEHV+fudBZ7wSaJzQSEtDyyEhW19bT0tDS0UEIxjJYJqLUTqympiNGWwD4s/2jxOvJ+PMu7ijxVad2chCYO33Kzcp0DLxjWwsidLltIkT9anThuUBGUni5shAZXfIhPRmi1dS7kxSpOE7SMMaksKSKI30kZcjs+YPbbFqMqX6D13KfjQQfF/V3E8mKGn4DtKwhnIwos9FRD4tIlNKsRhjdHLw0VbnX5ZPZl3nukjnzPyHzkRxZf5Dd/V2RV5vPoWyxeMmzL4K5Z6MFtLwHaRhDeUgiObSCDwpIr8FbgcejtJ8yzB8yc/2j1Dqv5RJfUlmi+drKf2D/YH3Va39ZMKQhu8gDWsoByNqLqr6f3E6Q94GXA68ICJ/LyJvTnhtxiglo8kM3jIZKE6TKeV/aL+cmKjZ4l5ayoDTQ28YYUrgV3o/mTCk4TtIwxrKQaBQZFdT6XT/9QNTgHtE5B8TXJsx2pkzh8HVrdQOhs/2L+V/6KSyxcO0ZPbaV/OU5kTCpiuJNHwHaVhDORjRLCYiVwIfBV4FlgN/q6oHRaQGeAH4fLJLNEY7hyowr1tHzZVO3bKR2i+XMqkviX40EFzL8ttXtfaTKYRXsEPT4U1s791+aE7T4U2+30ESQSCj8fcAAUKRReR64Huq+rLHZyeq6vqE1lZSLBS5cpgyt43esSP3kqn08M/2Le2eAqaupo5aqa3YfSVFflRWBkFQhu5zNVLjmeDpdbzfXGOIokKRRaQWWKSq13t9Xi2CxagsDjVyW7eOmiudEGYvTabSk/r8tK/ZR86u6H0lhZ8ZMVuwgH8ARKkrO1c7BYWLqg6IyAYRmamqm0u1KMMIxJw5DK7OrVsG6c34D8toNacUS5hgDa+5SQaBVLoWXQxBQpGnAM+KyBPAG5lBVf2TxFZlGCHIbkk95qw2p59MbWGfTKVQ6dpXKamvrQ8sCLwCIPyOjxoEMlqTKIMIly8nvgrDiImDj7Zy9qzVrJkxpMlUemkZIxheZkTw9rl4BUCEDQIJqo2MVnPbiMJFVYvq9iQitwMXADtV9WR37Ejgx8AsnGLsH1TV3SIiwC3Ae4G9wOWq+lv3mMuA/+ue9muqeqc7fipwBzAeeBC4UlXV7xrF7MGoTLJL/QdNyByNZotqw8+M6DXm9bsNY4YMo42M1iTKINFiZwDfAk4ExgK1wBuqesQIx/0foBe4K0u4/COwS1W/LiJfBKao6hdE5L3Ap3GEy1zgFlWd6wqKp4DTAAV+A5zqCqQngM8Aj+MIl2+q6kN+1xjpi7BosSrHdf4D1A7CQK2wYNYCwKKEjPD4RfLV19Yzb8a8oudWIn7RYkGSKL8NXIqT0zIeuAL4zkgHqeqvgV15wxcBd7qv7wTenzV+lzqsBSaLyFTg3cBKVd3lah8rgfPdz45Q1bVuguddeefyuoYxmnETMgdX53bIhNFb+8konjDaiCVRFkBVXxSRWlUdAL4nIk8D1xZxvUZV3eG+7mSoPcZ0YEvWvK3uWKHxrR7jha4xDBFZAiwBmFlf3aUYjCFyzGaDbfTXADJ8XrWbLYziCeP8H61Rf0GEy14RGQusc01OO4ihg6XrH0m0AOZI11DVW4FbwTGLJbkWI50cfLSVWXPaeHny8M+qvfaTUTxhnf+jMeoviJD4CI6f5VM4ocgzgIuLvF6Xa9LC/bnTHd/mnjfDMe5YofFjPMYLXcMwPFm2/UQOG8j7r6DQ19/HuvVtrNm8ZsRzdPV20b6lnbZNbbRvaU+ktL+RHqyt88gEiRbLlH3ZB3wl4vXuBy4Dvu7+vC9r/FMisgLHod+jqjtE5GHg77P6yZwHXKuqu0TkNTfY4HGc2mffGuEahuHJ4p3ODWFpcweb6/uY2VfPso5mFu9sZMrcNjhYOKx5tOYxjHZGozYSBt9oMRF5BihkUnpbwROL/AhoBY4CuoDrgJ8DPwFmAi/jhAnvckORvw2cjxOK/DFVfco9z18AX3JPu0xVv+eOn8ZQKPJDwKddM1iD1zUKfgtYtJgxMmPOagOG1zRLMhoorSHSYdo6p3UPRjz4RYsVEi7HFjqhVyHLSsaEixGYNWuoWdoPOGHN/bX+U1tntRZ9mbSGSOe3dQanxcBNl7cMEzBp3UMcmNB0CF24stqEh2HEhtsxE+DsWatpO1Y9o82iBgSkNbO7UFvnfOGS1j1ExUyhIzOiQ19EzhCRJ0WkV0QOiMiAiLxWisUZRtp5ZNMCfrDeOyDgwMG+QMEAfqQ1sztMW+e07iEqlhs1MkFCkb8NLAJ+ipMp/1HghCQXZRiVhF9AwKeOW0+vDgUDZJqczT5ydiBzSlKFFKOys6GeJg9B4tXWOa17iEq1Cs04KXUSpWFUJYt3Nh4SMtlj2UyZ20aP9LP+lfWHzGiFzCml7KYZhuUXN3v6XLzaOqd1D1GpVqEZJ0HyXHKSKEXkqoDHGYaRxe7HW5l5oH6Yf2ZQB1n/ynrWda7LGU9rLsWqMxu56fIWOhvqGQQ6G+o9nfmQ3j1EZbSWdAlDkMKVx+KEEo8FrgImAf+iqi8mv7zSYdFiRimoWdCGejj/UagbHLl1s5EeLFrMoag2xzAUNSYiAzgJittU1bLeDSOLu4/u8kzCzGdmXz0vjxtuTjm2r55Na+cdqt68+qW2YdWbjXRhSZSF8RUuIvKvwLdU9VkRmQS0AwPAkSJyjar+qFSLNKqIri7o6IC+Pqivh+ZmaPT5Dxpmbhm5++gulrRsYG+t41d4eVwfS1ocP0q+gFnW0ZwzF+CwgRqWdbjmFLd1MwBr1jDm2pSdINEAACAASURBVP5DggbCCxt7ujbKRaEkymdV9Y/c158FWlX1/SLSBDykqqeUcJ2JY2axEtDVBRs2wGBWCGdNDbS0DBcaYeaWmVlntHtrI/tdbSSPoFrOMPKSNxlTuJVzNScwGumhGLPYgazX5+KEIqOqnU61FsMISUdHrrAA531Hx3CB4Tf3hReiaz4xa0Sb673DT/3GvSLLApGVvDllbhu99BfssJnmBEbTqKqfQsJlj4hcgFNt+J3AxwFEpA6nnpdhhKPPJwfAa9xvbn+/8y8zZ4NjfhpR88nM7emBzs7h417nCIifH2VmX3Jhqbsfbz30esxZbY7prDZXk0kyFyOKcOjq7WL9q+tz1pP93oROdVAopPgvccrsfw/4rKp2uuMLgf9MemFGFeLXkM1rPGjztozmk4+f5rN9u7/2VCTLnm7gsAO5Y4cdcMZLwcFHW1nwshyq3rz6pTbWda7zzbmImouRMbdlhFQmVydom4GN3Rs9x59/9flI5zXSha9wUdWNqnq+qs5R1Tuyxh9W1atLsjqjumhuhnyTqogz7jW3JmA6VRjNJ8w5ArK4rZtbH4Bj94Co8/PWB5zxUvHIpgXcseFEZvbVo8CefXvo6+8bVtc8jlyMqKVPBnTAc1xRK6lSRQTK0DeM2MgPIPHLs8qYqLJ9I/39MOBxY/LTfMIIjChtrvv6WPwMLH5m2AfFnzMk+RFr4GhPe8fgCBghNjNTqUufWEmVysSES7WSxjBeP9NTZtxrvdlr9osg89N8vOY2NeX6XAqdIyh+giyKwArJ0uaOHMECsHesE7G2bYyztgMciMV/EbX0SV1NHf2D/aGuZ1QeVsalGsnchDM3vIzTuqvMtutCDv0g621sdIRDNk1N3kKzsdEJW87c4OvrnfcnnOA9HkXwepnwogqskBSKWDv4aCvztwiosvqlNtb8oW1YqZkwRC19MvvI2UheDRxBmDZhmpVUqSIKJVF+rtCBqvqN+JdjxEKYkN9SUshUFWS9XV2O1pFNZydMmuQvYMKMF4uXCa/EmuJIEWuPbFoAm5yxKXPb6KnZw+pNq4vK/s9oP8VGdRU6ftK4SRYtViUUMotNdH+2AO/AKf0CcCHwRJKLMiISJuS3lPiZqvIFS4b89aZVaEL8AiskI2b+Z7H78VbOnrWa1cc6mkwxdcyilj7xO95KqlQPhTpRfgVARH4NvF1VX3ffX4+FIqebFPgAPPF7wt+40dtRX5vXP7iQ0Eyjj6mE+PWU8UvWfGTTAvi5U8dszR/aRsz2N4ywBHHoN5KbrX/AHTPSip+GUEIfgC9eT/gvvOA9N0wlCK+Eycz1RgmhM//dOmbZ2f5WjdmIiyDC5S7gCRH5D/f9+4E7k1uSEZmwPoCNG53kwgzTpjmO76BE1Rr6fSKH/Ma9SKu5rALY/XjrobplQQXMwse6uOLeDo7u7mNnQz3LL2727OdSaqysTHoIUnJ/mYg8BJzlDn1MVZ9OdllGZIL6APIFCwy9DyJg/MqsZNYQhKBmvLC5K+X2MVUS8+czeMs6xnx6ZDPZwse6cjpRNnX3cc0dzu+8nAImv1BnoS6fRvIEDUU+DHhNVW8BtorIcQmuySgl+YJlpPF8CjnZg9LgUyYlf9wv5LfO5xmp3D6mSmPOHA4+2sqEAxwqJePFFfd25LQ4Bhh3YJAr7i1vJn3UygFGvIyouYjIdcBpOFFj3wPGAD/AKWZZFG6r5CtwcoefAT4GTAVWAA3Ab4CPqOoBEanHMc2dCnQDH1LVTe55rsUpqDkAfEZVH3bHzwduAWqB5ar69WLXaoxA2Mg0LxNat0+ZlPxxP3MfePuYGhqgvT19Tv6kqjV7HQ+hz5ltJvPi6G7v363feKkodeUAozBBfC5/CpwC/BZAVbeLyMTCh/gjItOBzwAnqeo+EfkJsAh4L3Czqq5wG5V9HPiu+3O3qh4vIouAG4EPichJ7nF/BEwDfikiGTvOd3DaBGwFnhSR+1X1uWLXbBQgTGSanwktaCgyFDb3Zd9EGxpir34cC0lVa/Y67/PP55bXKeI7WP1SG5PGT2ZO05xDYzsb6mnyECQ7G8qrKUatHGDESxDhckBVVUQUQEQOj+m640XkII7JbQdwDvDn7ud3AtfjCJeL3NcA9wDfFqehzEXAClXtA14SkReB0915L6pqh7veFe5cEy5eTJvmbQKbNi3Y8WEi0/xMaGHwe7rPFzrt7aV18gfVOgpVa85ncNDxiRV7Xq+6bUG/A7d3zJiz2rhg7R6++kgbM3scAdL+tgZ6dm/n+lbYPAlm9sD1bbD19PJGIzZPafZsjmYZ/uUhiHD5iYj8GzBZRD4B/AWwvNgLquo2EbkJ2AzsA/4bxwy2R1UzevhWYLr7ejqwxT22X0R6cExn04G1WafOPmZL3vhcr7WIyBJgCcDM0Wqfzzjti40WCxOZFtXBHiZ4oJSJpCHXdfdbYenCoRvzslVeRS9dBgaGcoCytZzu7tzvO6FAh4P3nMj+jesZ5y6hqbuPnt3b+asLhb5aR3i9PBmW/Ilw/NHlzVGIWjnAiJcg0WI3ici5wGs4fpe/U9WVxV5QRKbgaBLHAXtwOlyeX+z5oqCqtwK3gtPmuBxrSAUnnBAu9DifpLLT8wV+mAz9UiaShljX3afUsuQ9A+wd67x/eTIsudB57Stg8s+b/SCQLciCEuY76Og4JFgyXN/KIcGS4WCNpqLDpWX4p4cRo8VE5EZVXamqf6uq16jqShG5McI13wW8pKqvqOpB4Gc4wQGT3S6XAMfgdMDE/TnDXUsdMAnHsX9oPO8Yv3EjzQQp/BhGGyllMckQ61r6LjkkWDLsHQtLz6sN3r8mnzCmxbDfgcceNk/ymWqOcyOLIH/N53qMvSfCNTcDZ4jIYa7vZCGOP+QR4BJ3zmXAfe7r+933uJ//SlXVHV8kIvVuaPRsnJpnTwKzReQ4ERmL4/TP1EUzykmhTpRBKhWH6WTpVxU5E5XV3g5tbc7PqNWiQ6xr82HeEVibJwwMX69fiHUY6uqiVYD22MPMHp+p5jinq7eL9i3ttG1qo31L+6juolmoKvJfA38DNIvI77M+mgj8T7EXVNXHReQenOizfuBpHNPUfwIrRORr7tht7iG3Ad93Hfa7cIQFqvqsG2n2nHueT6o6Le5E5FPAwzihyLer6rPFrteIkULO/yCmtbBlbbzOGUfSZ4R1FaxeHKR/jR+1tY4DP38Ns2dHbyeQt4a/ewSWXAQDWY+m5ji3JM58Cj0a/RB4CPgH4ItZ46+r6q4oF1XV64Dr8oY7GIr2yp67H/iAz3mWAcs8xh8EHoyyRiMBopamL3R81GitKFFkjY2Okz3bF5LpM5O3rmX1DSx5xw721g35LA7rF8/qxTQ2cvfMHpaetJ3NR8DM12DZU5NZ/Oie4XMzPjOvPJcouT4e3/n2I/oYqBkK/TXHuUOhJM7R+N0UqorcA/QAlwKIyNHAOGCCiExQ1c2lWaJRVUR1/kfVRpKIIvPrM5P5mbWuxQ9vh+350WLK4n6GhVo5rYs72esWh355EixZ0AN78pz/mQKfI2k+xWppeeddNWs1osqB/j5ardDlISyJM5cgGfoXAt/ASVTcCRwLrMdJXjSqgUovV1/uKLIwuSs4gmFYZFj98LV6ti6uU5YuzDte1XuvCfW/eWTTAvjx42wau4+ZPW0jFq70Kib5578nlYUv/QhSENOSOHMJ4tD/GnAGsFFVj8NxwK8tfIhRMaS1JXIYwmgjQeuYxXH9iOfwbV3sFa3ltQY3p2bWZ6HmOufn3W/1mRuGri7Yt49ZPc4NJFO4cuFjw/9mMn6IzE23b6CPF3c+zzFPrKepu2/E49OA1x42dG8Y5qyP2v652ggiXA6qajdQIyI1qvoITq0xoxqIo/BkuQkTRRa0jlkY8puaxcTMvd6GBc9oLY+93n1KLUsudHJpVIZyau4+JeJ6Pf42/ApXevkhDtYo17cGOz4NBC2I2TihkZaGlkOaSn1tPS0NLaPS3wLBMvT3iMgE4NfA3SKyE3gj2WUZJaPQU38piz5GMc2FiSKLw+eSv1a/aC6vCK4QLPulsuQ95OTFHHbAyejPQcTZ6+OPw759h4aXfhbvnJp3CYufLGpJDj7flVfhSj9/g5f2FUfhyyT6uYTxpVgS5xBBhMtFwH7gKmAxThLjDUkuyighhXqk5JvKILm6XFEcz2Gi0KL6XLzW6sfAAJx4Yu66+vu9WzrX1Q3rrbMYoH94qRhwTFw5AQHPvwgHD+ac0i/Z0S/XJjA+36FX4Uo/P4SX9hW18GVSocDmSymOIOVf3gAQkSOABxJfkVFavJ76vUiy6GMcjuegUWgNDd6O9qA+F6+1hlnX6tXe8/r7PdeV7/y/+62OaWt4+ZiDw4IEZvY4n+czsy/iTdHjb+aNMbD84uGaolcxyTGDwvVtueVj9o+t8Tw+DEmFAltBzOIIEi32l8BXcLSXQUBw+rDYN1uJeJmfWlpyx0pZ9LHQeZO4XlSfS9Q1qYYrXJnH0oU+pq78CDKc82YLIoDDBmq8c2rCkKcpdh4OV78btntEe3kWkzyqma2nQ+eWeKPFCpmv2re0F20qs4KYxRHELHYNcLKqvpr0YoyE8TM/tbTAvHlD8zK+lnySqhxdyiKTUQVZ2FbLefhrHsEEjK+py2M8c76l76tnc30fM/vqWdbRzOKdMdwUszSyS2etZvUsWOA31cMPserM+Fsi+5mvgGGRXpl1BcV8KeEJEi32B2Bv0gsxSkDQyLBSFn0s9fXCRJZ51SDzWqsfmeTGLAppHkHwq+vlN774Gdi0dh6Dq1vZtHZePIIlpXiFAnthrY9LQ5D/JdcCj4nIv4nINzP/kl6YkQBBn9obG53yJdlkypkkQaEik3HjJ7Dyx/3yf2D4Wv0aq73lLcOGwmgeXixbBYfl+u2d8jHrfdZw4onBThwVVVa/1Faaa/ngFQrsx2jNmi8lQcxi/wb8CqfXfXExlUY6CGp+8itnMmlSsgKmVFUBRHK7NHpoGAW1vHnzhq910qRA0WoFC1dOaxjetA1yI8i6p8ELk1ja3JFr6uprhBODrSFuHtm0AH6+jporPWqelZh881XG15KPRXolTxDhMkZVP5f4SozkCZoPklDZkFTQ0TG8/a9X+ZSwvpmAwnFZRzNLWjbklHU55GQ/odG7aVve2OKdeJu3SimgKwSL9CofQYTLQ2474AeAQ/+zolZGNspA0HyQsDfWSqpNFnRvCQUZZITCMM2jin0h5cQivcpHEOFyqfvz2qwxC0WuVII83Ya5sSbRHyVJamu9kxjzS7iE7R0TgsU7G02YlBCL9CoPQZIojyvFQowUEebGWmkmNC//itd42N4zcWhvQc8R5lp5Wf9Mm+ZteisxCx/rqqiqyEZ4CnWiPEdVfyUif+b1uar+LLllGWUlzI211AmXUen3KX3iNR7UhxGH9hb0HGGulS9YYOh9GQXMwse6uOaODYw74OwhUxUZ4s99McpHIc1lAU6U2IUenylgwqWaCXpjLWUCZByUsp9LGO0t6DnCXMunnwzbt5dVuFxxb8chwZIhUxXZhEv1UKgTZaYN8Q2q+lL2ZyJipjLDIUHfRCIksd5C2ltQE1ZQDTDMtVKKX/XjOKoiG+khSBLlvR5j98S9EKNCKWUCZBwksV6/G3ldXfBGbEErB4S5Vkrxq34ctSqykS4K+VzegtPKeFKe3+UIYFzSCzMqiLTmV/hpDXGv108b8url4mfCCqpRhbmWH34VBUrE8oubc3wuEE9VZCNdFPK5tAAXAJPJ9bu8DnwiyUUZRmjyBUlDg1NVoBQh0o2N0NOT6+NoavL3eXhpFY2NsGMH7MnKcj/iCOdnftO2/HMXulY+KYgWy/hVLFqsuinkc7kPuE9E5qlqewnXZBjh8Iqg8rrZJhUi7Vcuxy+nxsu0tXFjrmAB531Pz1BFgb4+eP754RUGOjsds5hXxFt9fW7F65Sw6sxGEyZVThCfy5+KyBEiMkZEVonIKyLy4SgXFZHJInKPiDwvIutFZJ6IHCkiK0XkBffnFHeuuMUyXxSR34vI27POc5k7/wURuSxr/FQRecY95psifskNRlUQpoFX2HbG+VWRg15/cNA/p8YreMBP8/AqVZPP4KAzXspK1lVKV28X7VvaadvURvuWdrp6fX7nxogEydA/T1U/LyJ/CmwC/gz4NfCDCNe9BfgvVb1ERMYChwFfAlap6tdF5IvAF4EvAO8BZrv/5gLfBeaKyJHAdcBpOKHRvxGR+1V1tzvnE8DjwIPA+cBDEdZrpAWvpMAwAiNKO2M/s5rf9fv7gxXJjIOBAee7yDeXJeULyzdDBm1DkGKSapM8WgnyFzHG/fk+4Keq6tM5IhgiMgn4P8BtAKp6QFX3ABcBd7rT7gTe776+CLhLHdYCk0VkKvBuYKWq7nIFykrgfPezI1R1raoqcFfWuYxKxi8pML90ix9hnuSD9r4ZCb8imXEj4m2a89O2ouDVjmDfPi4N2E0zrRRqk2yEJ4hweUBEngdOBVaJyJtwWh4Xy3HAK8D3RORpEVkuIocDjaq6w53TCWQeFaYDW7KO3+qOFRrf6jE+DBFZIiJPichTrxw86DXFSBN+pqOBAW+T0LRp3iHHQcxdSVYe8DpHUAHpR6HItLjxMUMuWxX/pUrJSF0sjXAEqS32RRH5R6BHVQdEZC+ONhHlmm8HPq2qj4vILTgmsOxrqoh4GJfjRVVvBW4FOG3ixMSvZyRIS0vwulxBzF1JVh7wOoeX4z8Oksh38TmnXzfMSsGvTbL1fimOQnkun1fVf3TfLlTVnwKo6hsishTHR1IMW4Gtqvq4+/4eHOHSJSJTVXWHa9ra6X6+DZiRdfwx7tg2oDVvvM0dP8ZjvlFphMk4D5q7ErR8SlyZ/DU1w8/R0DA8vNhPmEUliUx9n7UG7aaZVqz3S7wUMostynp9bd5n5xd7QVXtBLaISIs7tBB4DrgfyER8XQbc576+H/ioGzV2Bo4GtQN4GDhPRKa4kWXnAQ+7n70mIme4UWIfzTqXUSl42fX9nOFhkgLDtHqOI5M//xxNTY4vJD9rv6HB2ykeNABgzJjSRYs1N3uudenC+C9VSrzaJLc0tJgzv0gKmcXE57XX+7B8GrjbjRTrAD6GI+h+IiIfB14GPujOfRB4L/AisNedi6ruEpGvAk+6827IamD2N8AdwHicKDGLFKs0vDQM1eG5I2GTAsOYu+Io3pl/jvZ2b82pu9vbtAe5Y37C8eBBOPHE0jRty6ua3Xk4XP1u+NFbnWq3lYz1fomPQsJFfV57vQ+Fqq7DCSHOZ9izjxvx9Umf89wO3O4x/hRwcpQ1Gj6E6SUSpceJ3010YABaW4taOpBM4crmZli/3ns8n6jtk9va/NdRyjI8Wde6dNZqVs+CBbMqXbQYcVJIuPyxiLyGo6WMd1/jvrfaYqORMLkfUXucJOVQD9sELCh++Sz5ArZQJ8wkOnqmtFmYUf0UKv8SMTbSqDrC9BKJ2uOkocE79LihIdyavYj7Cb+jwzufZePG3BBhP79RTY0zni90vL6vyZOHl4nJjOeT0mZhxuig8tNqjdIRxqQTNU+kuzvceDkpZMLz8hvV1Q0PFPDrkJl/7jlzhguSyZOd8XwKNQszjIQJUv7FMBzCmKrCmrXyzUeV1D7Zz9TlR3//8KTJMN+XlyCBsjQLmzK3jd6xED3Gx6g2THMxguMVgurnDA8z1yvs2I80dlgspl5YkFDkMIEGYb7DGOkdCxMOn2zOfGMYprkYwQnjDA8zN2hV41JX+Q0a7eZn0gpKJhTZq09LUN9QmMrQCdHV20XH7g76Bvqor62neUqzhfWOYky4GOEI4wwPOnckTSXpvA0vwkS7xZFd39fnXXhy0qTo32E+cWh/69ZRc+Ueat2vxyoKG/mYcDHKTyF/Q7kaXYWJdmtuHt7ES8Txq4TRaqJE1wUVcDFrf/Pf3ApA+5Z234rCJlxGJ+ZzMYI3xUqKMP6ZUhE2oMArFHnChGDXKtQLJahG4vcd+lWGjhmrKGzkY5rLaCdqsmMcJJXYGIUw0Vt+Ze298lHA0Wjq6nL36pXhH4YSfodnz1rN6gVWRNwojAmX0U7UZMe4KGXpkiCEKRMT1t8yMABnnZU7FlW4QGm/QxGLEDMKYsJltFNJ+SSFiFLHzIswmkBYh34ceUFG2bHouMKYcBntVMNNLSnTXlBNwE/LyZTXD6L9JFFQMwHGnNXmvsrN7Rk/IOyrHW4qGz9QncmVFh03MubQH+2k0Zkelrj63ReLX++XE04I3hMmrv4xJcArafKmlTWMyQuMG9PvjFcjHbs7fKPjvOjq7aJ9Szttm9po39JOV2+Jg2bKgGkuo500OtPDkgbTnp+Wk0ReUAr5q7UDTHrDaRi2eZLT8njZKrj0mQF++lflXl38hImOG61ajgkXo6JvakB1mPbiIG6/k8vdR3exdNp6XnbbGPf1D/+udzbUs/iZPhY/kzve2VCdv4P62npPQZLpYplNIS3HhItReSR0oyk7XvtKg7+i3N93Qn6nu4/uYknLBvZm1dnsG+ijq7cr58a4/OJmrrljA+MODP0O9o+tYfnF4X4HCx/r4op7Ozi6u4+dDfUsv7iZVWem7++2eUpzjjYCUCM1NE8Zvt/RmgNUnQbR0Y5XEcMNG0qfHBk3fvuC8vor0vB9J+R3Wtrcwd7akX0Lq85s5KbLW+hsqGcQR2O56fKWUIJh4WNdXHPHBpq6+6gBmrr7uOaODSx8LH1/t40TGmlpaDmkqdTX1tPS0OKpiXhpM4XGqwXTXKqRtOSuhCHIk3+hfTU05N7ce3qi79VvTfnj/f3l/74T8jttrg/+1L3qzMZIWsYV93bkaD4A4w4McsW9HanUXhonNAYya4XRcqoJEy7VSBoc3GEIatIptK+4Oy76ramnJze8uNB3Gsf3HbRNcUJ+J788/CSeuo/u9v6+/MbTiJ9Zr2d/D9t7h36PTYc3VbW/BcwsVp343VDS6uAOatIJu/4oHRf91rR9e/DS9lG/b782xRs3Dp+bUEi5eEiXpJ66d/o4//3G04afWe+opzfS+UZuxevONzqrPhzZhEs1Umm5K0E1Lb99lXJNQYnj+w7TpjjuPJk1a6hZ0AYC4+vGB/It+BE0x2P5xc3sH5v7+ywmKKBc+Jn11tZuD5UTUy2UzSwmIrXAU8A2Vb1ARI4DVgANwG+Aj6jqARGpB+4CTgW6gQ+p6ib3HNcCHwcGgM+o6sPu+PnALUAtsFxVv17SzZWbSstdCWrS8dtXHHW58qmrC1cuv67OKUhZzu87gZDyBce1Rjo+TI5Hxq9SCdFiXviZ77ZN9J5f7dFi5fS5XAmsB45w398I3KyqK0TkX3GExnfdn7tV9XgRWeTO+5CInAQsAv4ImAb8UkQyxujvAOcCW4EnReR+VX2uVBtLBZWUuxImlNhrXz093k/z06YVv6b8EvoZRJx/+WudPbtyvu8AnP3hgVjOEzbHI2pQQDnZ2VBPk4eAmf46bD1i+PxqjxYri1lMRI4B3gcsd98LcA5wjzvlTuD97uuL3Pe4ny90518ErFDVPlV9CXgRON3996KqdqjqARxt6KLkd2UUTVSTzgknDBckfo7voAz43FxVvdcK8ffE8ROOUYRmIdy+PoNtbdzxH8oNO06MfMpCOR7lLocSd0kWP7PeGQPTqJHccYsWS45/Bj4PZBTGBmCPqmbsEFuB6e7r6cAWAFXtF5Eed/50YG3WObOP2ZI3PjfuDRgxE1XTOuGEaMIkn0Kmuvy1JlU4M7OfINFiUcnaQw1wbA9cc4ezhyiahF8mOwwJnnKUQ0miJIufWe/VUxpp6Z006iool1y4iMgFwE5V/Y2ItJb6+nlrWQIsAZiZ1kgqozyEMdUlmVcUt9D0w2MPceSYeOV4eFFMOZQoJe+TKsniZ9YLmhNTTZRDc3kn8Cci8l5gHI7P5RZgsojUudrLMcA2d/42YAawVUTqgEk4jv3MeIbsY/zGc1DVW4FbAU6bONFa61UCpSqzEiYootLyirzwWWvUHJPMDTVbCMRRDiWq5jFaS7KUkpILF1W9FrgWwNVcrlHVxSLyU+ASHB/JZcB97iH3u+/b3c9/paoqIvcDPxSRb+A49GcDT+A0mpjtRp9tw3H6/3mJtmckSalbMldSUERCxJFjkv/U3r6lPXDRRz+iah5hCk8axZGmDP0vACtE5GvA08Bt7vhtwPdF5EVgF46wQFWfFZGfAM8B/cAnVXUAQEQ+BTyME4p8u6o+W9KdVBrlLroYlEosa1MBTJnbxgWT4V//Ew4/ODSeVI5JoXIoQU1dUTWP0VqSpZSUVbioahvQ5r7uwIn0yp+zH/iAz/HLgGUe4w8CD8a41Oql1NpAFNJqfqqCkv8r3l5Hy9GzE8kx8RIYLQ0tw8aAwKauqJqHl7luNDjZS0maNBejHFSSNpDWm3gaSv4Xw7p11Fy5h0zR4yRyTPx8Iy0NLcybMS9nbvuW9sCmrjg0j9HoZC8lJlxGO2nVBrxI60280ioi5DH/za2xnMdLQwnjGwlj6qo0zSNKZFulYsJltJNWbcCLNN/EK8z5f/as1axeEF+ApJ+G4heC7GfSCmPqqhTNw9ocG6OTtGoDflTYTdwzWALSISBFWDBrQSyn8tNQ/PASGGl2sqcxpybtmHAZ7SSpDYSJQgs6t1Ii28A7WOL553PrlpUhgGLK3DZ6xoETtV8c+TfbsPkhDeMbho2l1dRlOTXFYcLFSEYbCBOFFnRuJUW2gXewhFdBzDIEUNTW1jF/5vyijvW62Yale1+353gaTV2WU1McJlyMZAgTheY3d+PG8rYTjqolhQmKKEUARV50WLF43Wy9qJGaUD6XtGI5NcVhwsVIhjBRaH5zBwaGqhMn3U44nzi0JL9gCb+5UQkoDKNGhwW9qTYd3kT3vu6Kf2q3nJriMOFivsfspgAADb1JREFUJEOYKLQwN2G/a8VNHPk/XsESIsNNY3EEUIwgDOOMDgvqY+l8o5Mjxh7hOdfL55JWLKemOEy4GMkQJgrNa25QkopsiyP/xy9YwmssqlkviDCMKTosTKXjPX17PD/z87mkkTCax2jMZ/HDhIuRDGGi0LzmDgx4txkuVTvhuPJ//IIl4l5zAWE45qw2903x0WHZhKl07Ecl+VwgmOYxWvNZ/DDhYiRHmCi0kRpwQeF2wnGHKDc3Dw8bFklv/k9BYdjHhMMnM6dpTmKXr5VaBjRca+RMdeRqecIfrfksfphwMcKRRO6KF2E0n6RClPN9I15hxNlrKGf+jY9pcfH7+hiIuZm51xO6eGhFhaLFMsdlflbDE/5ozWfxw4SLEZwkclcKEVTzSaL4ZkeH/3gSe42KlzA+cIAfvVVZcFxrrJfyekJXlLqaOmqldlhtsSA312p4wh+t+Sx+mHAxghNH7koSOSlJFN8Mc860VJbOEsZnz1rN6lmwYFZr5NMGzcbvH+xn/qzhiZlBnP+Q3BN+HE72IOcYrfksfphwGW1EMd/EkbuSRE5KEsU3w5wzZZWl43Tgh8nGr5VaXz9K9o25f7Df0z+TxBN+HE72oOcYrfksfphwGU1ENd/EkbuSRE5KEsU3w5yzrs4/sq1MxOXAD5qND45py8+Pkn2Dzb9ZQ3JP+HE42cOcYzTms/gRs6vPSDWFzDdBaG52brDZFMpdCTo3Ko2N0NIyJLjq6533UUxSYc7p5+gvFACQBGvWULOgLVYHfiFNJaNp1NfWU1dTh5K738wNOJ/GCY20NLTkHN/S0JLITTkOJ7s56ovDNJfRRFTzTdTclSQjqJIovhn0nAM+Ibh+4wkTpwO/kJM6u5Nk26Y2z+P9bsClesKPw8lujvriMOEymvAzVdXWQnt7MCFQaf1USkEKGq5NmdtG71nxn9fPSd0wviHHv1JXU0f/4HDTYBw34CgO+Tic7EG/g9HsX/HChMtowq/MyuDg0M0xrjBaP/9OTw90d1dGP5agNDTA9u3e46VkTB0Liiyj70fjhEZ69vewvXdof0eMPYJXXtvBwRrHDNY30IconvEDUWuIRXXIx+Fk9zpHw/gGOt/otGz8AphwGU0ELbMSRxitn38n+yac9n4sQen2qZPlNx4zY85y/Cy1CZy7q7eLzjc6c8b27N8zzFurPoFpUWuIxeGQj8MEl3+O9i3tlo0/AiZcRjteUU4QPYw26PHlyAeJmxSEIk8an0x5F89osRARzlGd3oWc6eUsEmlO/pEpebSYiMwQkUdE5DkReVZErnTHjxSRlSLygvtzijsuIvJNEXlRRH4vIm/POtdl7vwXROSyrPFTReQZ95hvikg8FfsqnYypKtsE5kdUf0GY48uUDxIbfntN2ueSQHRYPlFvlmF9Ll29XbRvaadtUxvtW9qpFW99rK6mjg3dG4aFPnf1dkVab1D89mVO/iHKEYrcD1ytqicBZwCfFJGTgC8Cq1R1NrDKfQ/wHmC2+28J8F1whBFwHTAXOB24LiOQ3DmfyDru/BLsK/14maq8iCNk2CsU2Y8SOr4ToZRh1x4sOK41saKUfjdLyYuyrh0APCKvw/hcMv6VbIHhlWNTIzWoqq9ZqhQ0T2mmRnJ/56M5G9+LkgsXVd2hqr91X78OrAemAxcBd7rT7gTe776+CLhLHdYCk0VkKvBuYKWq7lLV3cBK4Hz3syNUda2qKnBX1rlGN0E0lThyRMA5vqkpd2zy5LLehBMjiTybEZgyt40x1/qYNGPE6yY6ZlD4xFNw7B5HyBy7Byb34WkuC+NzKVSzLD8nxq8Cc6nMUqXM1alUyupzEZFZwCnA40Cjqu5wP+oEMr+l6cCWrMO2umOFxrd6jHtdfwmONsTMSn96DkJtrXfuRW0tzJs3fNyLoOVjurqgM9cRzGuvOQKn3NFiXnuAaDk55QjRTiA6LB/PaKujmpkxHdZ+v4Oju/vY2VDP1E8ll6zoVbPMryBmKc1Slo1fmLIJFxGZANwLfFZVX8t2i6iqiuQr3vGjqrcCtwKcNnFiidOpy4Cf6ymoSypM+Ri/aLHu7uCCLAm89rB+fW774TRHsa1bR82Ve6gtomlnsXjdRFedCavOHBqrd/M98kkqWdGKRKafsggXERmDI1juVtWfucNdIjJVVXe4pq2d7vg2YEbW4ce4Y9uA1rzxNnf8GI/5hl9kmN94PmGq/4aNoEpCm/A6p5/fKb9US8qj2Oa/ubXcS8ghyWRFr3NYkcj0U3Lh4kZu3QasV9VvZH10P3AZ8HX3531Z458SkRU4zvseVwA9DPx9lhP/POBaVd0lIq+JyBk45raPAt9KfGOVQNRM8jACI8y1vLSJ/C6QYbUJPy0rSEBD9jXTxJo11CxN3s9SDEklKxY6h5ml0k05NJd3Ah8BnhGRde7Yl3CEyk9E5OPAy8AH3c8eBN4LvAjsBT4G4AqRrwJPuvNuUNVd7uu/Ae4AxgMPuf+MMJV+vZ76wwiMMNfy0ia8ij6G0Sb8tKwwpMwPd/aHB0CEBbMWlHspniSRrGhULiUXLqq6Bv80rIUe8xX4pM+5bgdu9xh/Cjg5wjKrk6DFJP2e+puaHCd9EIERpnBlGA0h6NxC82pqhguabJ9LZk6KotimzG2jZxzE0aPFMEqBZeiPNoJENRVyxre0BPeDJBFBFVSbKNRjZfbs+P07JaC2to75CUeHGUZcmHAxhlPIt1LOqshhtIlCPVb89pAyYQKUJTrMMOLAhIsxnBSUkB+2lrDaRMp6rEQlbdFhhjESJlyM4STRNrhYis2JSZOANIxRiGipW7GmFBF5BSdKLQhHAa8muJxycWhfR8GR02D6GBh7EA5sh22vwq4Rji+KWTCzAd6UP94Nr2yCzcWc8yg4ciYcK1DzCs7JFQY3w8tJ7aMMVOvfIVTv3qpxX8eq6rD/vyZcikBEnlLV08q9jrixfVUW1bovqN69Veu+vChHVWTDMAyjyjHhYhiGYcSOCZfiuLXcC0gI21dlUa37gurdW7XuaxjmczEMwzBixzQXwzAMI3ZMuBiGYRixY8JlBETkdhHZKSL/mzV2pIisFJEX3J9TCp0jbYjIDBF5RESeE5FnReRKd7yi9wUgIuNE5AkR+Z27t6+448eJyOMi8qKI/FhExpZ7rcUgIrUi8rSI/MJ9X/H7EpFNIvKMiKwTkafcsWr4W5wsIveIyPMisl5E5lXDvoJiwmVk7gDOzxv7IrBKVWcDq9z3lUQ/cLWqngScAXxSRE6i8vcF0Aeco6p/DMwBznd7+9wI3KyqxwO7gY+XcY1RuBJYn/W+WvZ1tqrOycoBqYa/xVuA/1LVtwB/jPN7q4Z9BUNV7d8I/4BZwP9mvd8ATHVfTwU2lHuNEfd3H3BuFe7rMOC3OE3mXgXq3PF5wMPlXl8R+zkG54Z0DvALnPr71bCvTcBReWMV/bcITAJewg2aqpZ9hflnmktxNKrqDvd1J5DCcrrBEJFZwCk4XTurYl+u6WgdTqvslcAfgD2qmqnBvxWYXq71ReCfgc8DmaJvDVTHvhT4bxH5jYgscccq/W/xOOAV4HuuGXO5iBxO5e8rMCZcIqLOI0hFxnOLyATgXuCzqvpa9meVvC9VHVDVOThP+qcDbynzkiIjIhcAO1X1N+VeSwLMV9W3A+/BMdH+n+wPK/RvsQ54O/BdVT0FeIM8E1iF7iswJlyKo0tEpgK4P3eWeT2hEZExOILlblX9mTtc8fvKRlX3AI/gmIsmi0imCvgxwLayLaw43gn8iYhsAlbgmMZuofL3hapuc3/uBP4D54Gg0v8WtwJbVfVx9/09OMKm0vcVGBMuxXE/cJn7+jIcn0XFICIC3AasV9VvZH1U0fsCEJE3ichk9/V4HF/Sehwhc4k7reL2pqrXquoxqjoLWAT8SlUXU+H7EpHDRWRi5jVwHvC/VPjfoqp2AltEpMUdWgg8R4XvKwyWoT8CIvIjoBWnVHYXcB3wc+AnwEycMv0fVNWKKeMuIvOBR4FnGLLffwnH71Kx+wIQkbcBdwK1OA9PP1HVG0SkGeeJ/0jgaeDDqurTcjPdiEgrcI2qXlDp+3LX/x/u2zrgh6q6TEQaqPy/xTnAcmAs0AF8DPdvkgreV1BMuBiGYRixY2YxwzAMI3ZMuBiGYRixY8LFMAzDiB0TLoZhGEbsmHAxDMMwYseEi2GkABF5v4ioiFR8NQHDABMuhpEWLgXWuD8No+Ix4WIYZcat8TYfp1z+InesRkT+xe0FslJEHhSRS9zPThWR1W6hx4cz5UQMI02YcDGM8nMRTt+PjUC3iJwK/BlOq4eTgI/g1EfL1IT7FnCJqp4K3A4sK8eiDaMQdSNPMQwjYS7FKUIJTimXS3H+b/5UVQeBThF5xP28BTgZWOmUiKMW2IFhpAwTLoZRRkTkSJwKx28VEcURFspQva1hhwDPquq8Ei3RMIrCzGKGUV4uAb6vqseq6ixVnYHTwXAXcLHre2nEKZ4KTifDN4nIITOZiPxRORZuGIUw4WIY5eVShmsp9wJNOD1BngN+gNOuuUdVD+AIpBtF5HfAOuDM0i3XMIJhVZENI6WIyARV7XXLzz8BvNPtE2IYqcd8LoaRXn7hNj4bC3zVBItRSZjmYhiGYcSO+VwMwzCM2DHhYhiGYcSOCRfDMAwjdky4GIZhGLFjwsUwDMOInf8PEBve66fQl2MAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualising the Test set results**"
      ],
      "metadata": {
        "id": "811CFQH0HdIn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = sc.inverse_transform(X_test), y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
        "plt.contourf(X1, X2, classifier.predict(sc.transform(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], c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Kernel SVM (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "vUhgrdtxHZnR",
        "outputId": "13fcf726-bdf5-489f-c4c2-b74944cad6cc"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}