support_vector_machine.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "support_vector_machine",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "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": 1,
      "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[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 2,
      "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": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3nS3-6r1i2B",
        "outputId": "9688238c-a6bd-4ba4-add7-9cb18a9f6e32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dpDLojm1mVG",
        "outputId": "90a24d61-a927-4117-cb5e-7362f4e041ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(y_train)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 0 1 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 1\n",
            " 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 1\n",
            " 1 1 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0\n",
            " 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0\n",
            " 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0\n",
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            " 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0\n",
            " 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1\n",
            " 0 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbb7i0DH1qui",
        "outputId": "31e7d515-9909-42e2-9b54-f9fb6ba277a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(X_test)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[    30  87000]\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kj1hnFAR1s5w",
        "outputId": "97632e41-4ffe-48ff-934f-c7697b52e93e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "print(y_test)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0\n",
            " 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1\n",
            " 0 0 0 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 1 0 1 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syrnD1Op2BSR",
        "outputId": "085370cd-bb70-4e5a-b2d6-7f0e18d46a0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.58164944 -0.88670699]\n",
            " [-0.60673761  1.46173768]\n",
            " [-0.01254409 -0.5677824 ]\n",
            " [-0.60673761  1.89663484]\n",
            " [ 1.37390747 -1.40858358]\n",
            " [ 1.47293972  0.99784738]\n",
            " [ 0.08648817 -0.79972756]\n",
            " [-0.01254409 -0.24885782]\n",
            " [-0.21060859 -0.5677824 ]\n",
            " [-0.21060859 -0.19087153]\n",
            " [-0.30964085 -1.29261101]\n",
            " [-0.30964085 -0.5677824 ]\n",
            " [ 0.38358493  0.09905991]\n",
            " [ 0.8787462  -0.59677555]\n",
            " [ 2.06713324 -1.17663843]\n",
            " [ 1.07681071 -0.13288524]\n",
            " [ 0.68068169  1.78066227]\n",
            " [-0.70576986  0.56295021]\n",
            " [ 0.77971394  0.35999821]\n",
            " [ 0.8787462  -0.53878926]\n",
            " [-1.20093113 -1.58254245]\n",
            " [ 2.1661655   0.93986109]\n",
            " [-0.01254409  1.22979253]\n",
            " [ 0.18552042  1.08482681]\n",
            " [ 0.38358493 -0.48080297]\n",
            " [-0.30964085 -0.30684411]\n",
            " [ 0.97777845 -0.8287207 ]\n",
            " [ 0.97777845  1.8676417 ]\n",
            " [-0.01254409  1.25878567]\n",
            " [-0.90383437  2.27354572]\n",
            " [-1.20093113 -1.58254245]\n",
            " [ 2.1661655  -0.79972756]\n",
            " [-1.39899564 -1.46656987]\n",
            " [ 0.38358493  2.30253886]\n",
            " [ 0.77971394  0.76590222]\n",
            " [-1.00286662 -0.30684411]\n",
            " [ 0.08648817  0.76590222]\n",
            " [-1.00286662  0.56295021]\n",
            " [ 0.28455268  0.07006676]\n",
            " [ 0.68068169 -1.26361786]\n",
            " [-0.50770535 -0.01691267]\n",
            " [-1.79512465  0.35999821]\n",
            " [-0.70576986  0.12805305]\n",
            " [ 0.38358493  0.30201192]\n",
            " [-0.30964085  0.07006676]\n",
            " [-0.50770535  2.30253886]\n",
            " [ 0.18552042  0.04107362]\n",
            " [ 1.27487521  2.21555943]\n",
            " [ 0.77971394  0.27301877]\n",
            " [-0.30964085  0.1570462 ]\n",
            " [-0.01254409 -0.53878926]\n",
            " [-0.21060859  0.1570462 ]\n",
            " [-0.11157634  0.24402563]\n",
            " [-0.01254409 -0.24885782]\n",
            " [ 2.1661655   1.11381995]\n",
            " [-1.79512465  0.35999821]\n",
            " [ 1.86906873  0.12805305]\n",
            " [ 0.38358493 -0.13288524]\n",
            " [-1.20093113  0.30201192]\n",
            " [ 0.77971394  1.37475825]\n",
            " [-0.30964085 -0.24885782]\n",
            " [-1.6960924  -0.04590581]\n",
            " [-1.00286662 -0.74174127]\n",
            " [ 0.28455268  0.50496393]\n",
            " [-0.11157634 -1.06066585]\n",
            " [-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",
            " [ 0.68068169 -0.71274813]\n",
            " [-1.49802789  0.35999821]\n",
            " [ 0.77971394 -1.3505973 ]\n",
            " [ 0.38358493 -0.13288524]\n",
            " [-1.00286662  0.41798449]\n",
            " [-0.01254409 -0.30684411]\n",
            " [-1.20093113  0.41798449]\n",
            " [-0.90383437 -1.20563157]\n",
            " [-0.11157634  0.04107362]\n",
            " [-1.59706014 -0.42281668]\n",
            " [ 0.97777845 -1.00267957]\n",
            " [ 1.07681071 -1.20563157]\n",
            " [-0.01254409 -0.13288524]\n",
            " [-1.10189888 -1.52455616]\n",
            " [ 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",
            " [ 0.77971394  0.53395707]\n",
            " [-0.30964085 -0.33583725]\n",
            " [ 1.77003648 -0.27785096]\n",
            " [ 0.8787462  -1.03167271]\n",
            " [ 0.18552042  0.07006676]\n",
            " [-0.60673761  0.8818748 ]\n",
            " [-1.89415691 -1.40858358]\n",
            " [-1.29996338  0.59194336]\n",
            " [-0.30964085  0.53395707]\n",
            " [-1.00286662 -1.089659  ]\n",
            " [ 1.17584296 -1.43757673]\n",
            " [ 0.18552042 -0.30684411]\n",
            " [ 1.17584296 -0.74174127]\n",
            " [-0.30964085  0.07006676]\n",
            " [ 0.18552042  2.09958685]\n",
            " [ 0.77971394 -1.089659  ]\n",
            " [ 0.08648817  0.04107362]\n",
            " [-1.79512465  0.12805305]\n",
            " [-0.90383437  0.1570462 ]\n",
            " [-0.70576986  0.18603934]\n",
            " [ 0.8787462  -1.29261101]\n",
            " [ 0.18552042 -0.24885782]\n",
            " [-0.4086731   1.22979253]\n",
            " [-0.01254409  0.30201192]\n",
            " [ 0.38358493  0.1570462 ]\n",
            " [ 0.8787462  -0.65476184]\n",
            " [ 0.08648817  0.1570462 ]\n",
            " [-1.89415691 -1.29261101]\n",
            " [-0.11157634  0.30201192]\n",
            " [-0.21060859 -0.27785096]\n",
            " [ 0.28455268 -0.50979612]\n",
            " [-0.21060859  1.6067034 ]\n",
            " [ 0.97777845 -1.17663843]\n",
            " [-0.21060859  1.63569655]\n",
            " [ 1.27487521  1.8676417 ]\n",
            " [-1.10189888 -0.3648304 ]\n",
            " [-0.01254409  0.04107362]\n",
            " [ 0.08648817 -0.24885782]\n",
            " [-1.59706014 -1.23462472]\n",
            " [-0.50770535 -0.27785096]\n",
            " [ 0.97777845  0.12805305]\n",
            " [ 1.96810099 -1.3505973 ]\n",
            " [ 1.47293972  0.07006676]\n",
            " [-0.60673761  1.37475825]\n",
            " [ 1.57197197  0.01208048]\n",
            " [-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",
            " [ 1.47293972  0.35999821]\n",
            " [ 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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JUd6iBRp2C3L",
        "outputId": "244e5d9e-1e79-4ffe-ad06-85b2dec7bf00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(X_test)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bb6jCOCQiAmP"
      },
      "source": [
        "## Training the SVM model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0pFVAmciHQs",
        "outputId": "5eae907e-3fee-4e59-aae6-b34b740d99dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'linear', random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 11,
      "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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yyxW5b395mR2"
      },
      "source": [
        "## Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8YOXsQy58rP",
        "outputId": "cf81d09f-00a7-495a-e82f-18d3485d4031",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(classifier.predict(sc.transform([[30,87000]])))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKYVQH-l5NpE"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6VMTb2O4hwM",
        "outputId": "56a4efc9-12cb-45ba-b0ce-ee7d0acfa3ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "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))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\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",
            " [1 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 1]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
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            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 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",
            " [0 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 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",
            " [1 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 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",
            " [0 1]\n",
            " [1 1]\n",
            " [1 1]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4Hwj34ziWQW"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6bpZwUiiXic",
        "outputId": "66760e72-28c2-4c28-d9d1-5cd1b8b22ab8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "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": 14,
      "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": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OMC_P0diaoD"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NOjKvZRid5l",
        "outputId": "ec376d1e-f3b5-4e89-ab13-0f0c6e633fe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "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('SVM (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZ-j28aPihZx"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeTjz2vDilAC",
        "outputId": "260c92ad-ab45-4ac0-e8b4-a5ede88a9658",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "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('SVM (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "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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Xfrc7zpi8qRyyXSvLia0ZNjahBRdV/SVwIKXs31U1sdzsM8BM9/hqYJWq9qvqNmALcKH72qKqnap6FFgFXC0iAlwCJDYdWAFck3StFe7xo8Cl7nhjcrauq9lSk8tMGOuQlYMox1z+B/CUezwD2Jn02i5Xlq58GtCbFKgS5SOu5V5/0x1vTF5ZanJ5CGMdsnIQyUcwEVkKDAIro3j/pHosAZYAzK6yJq4JyFKTy04Y65DFXcGDi4jcAFwJXKrHlwHdDcxKOmymKyNN+X5gqoiMc62T5OMT19olIuOAKe74E6jqcmA5wNzJk4t3SVITvu5u6OyE/n6oqoL6eqgd/Y9J36Fe2rraaK5rLkAljSkdBe0WE5EFwK3AJ1X17aSXngAWukyvOcDZwK+AZ4GzXWbYBLxB/ydcUFoHXOvOXww8nnStxe7xtcAvtJjXsjbR6+6Gjg4vsID3vaPDK88gkZpsjDlRmKnIDwMbgAYR2SUiNwL/CEwG1ojIJhH5ZwBVfQl4BHgZ+DnwBVUdcq2SLwJPA5uBR9yxAF8BbhGRLXhjKve78vuBaa78FuBY+rIxvjo7YThlE6ThYa88CLXsMWNShdYtpqrX+RTf71OWOH4ZsMynfDWw2qe8Ey+bLLX8CPDHWVXWlLf+NPMV0pUndaGtq6qCigoqbj0cXv2MKUE2Q9+YdMkcfuV+XWiHD3Pdi+FVz5hSZMHFmPp6qEj5r1BR4ZWn8utCA5athfatlppsTILNBjMmkRUWJFssTVdZ3ZtQfRQ4ObxqGlNKLLgYA14gCZB6TFWVf4CpqgL6LTXZGMeCizHZqK/3xlySu8ZcF1rPxlo+UtdGW11UlQvO9icxYbMxF2OyUVsLDQ3HB/urqrznya2eIk9Ntv1JTCFYy8WYbGXoQlvX1QyPbaLipt4CVyq4TPuTWOvF5Iu1XIwJSfvW1qLctdL2JzGFYMHFmHxrbGS4rcXLHitCtj+JKQQLLsaUGdufxBSCBRdjQtKzsYWBOym6wX3bn8QUgg3oG1OGbH8SEzYLLsYUQNu2VprntERdjVDZ3BmTbNRuMRH5CxGpKURljImdpiaG75kadS1CZ3NnTKogYy61wLMi8oiILBAR2x3JmDEo1tTkfMg0d8aUp1GDi6r+Nd7OkPcDNwCvicj/FpE/CLluxsRDkacm54PNnTGpAmWLuW2C97qvQaAGeFRE/k+IdTMmfgYGiy57LB/GVfgP36YrN/E36k9eRG4Crgd+D9wH/E9VHRCRCuA14NZwq2hMPPRsbIH2diqWDhbsPQs1yO59/gxebuIvSMulBvgjVf24qv6bqg4AqOowcGWotTMmpgrReinkIPuQDmVVbuIvY3ARkUpgoapu93tdVTeHUitj4qqpieG2FiqHCX3XykIOstuSMiZVxuCiqkNAh4jMLlB9jDF5UshBdltSxqQKMtpWA7wkIr8CDiUKVfWTodXKmDLQd6iX9h3tNM1uCuX6VZVVvoEkjNZEYhzHJlGahCDB5Wuh18KYMjOwvoWai1rpGx/ee9TX1NOxv2NE11iYrQlbUsYkCzLPpc3va7TzROQBEdknIr9LKjtVRNaIyGvue40rFxH5exHZIiIviMgFSecsdse/JiKLk8o/ICIvunP+PjG5M917GFNsGrsl1NRkW6DSRCnI8i/zRORZEekTkaMiMiQiBwNc+0FgQUrZbcBaVT0bWOueA1yON1HzbGAJcK9771OBO4CLgAuBO5KCxb3A55LOWzDKexhTVNZ1NTNwV7jzQGqra5k/az4tdS3MnzXfAospmCCpyP8IXIc3p2US8CfAd0Y7SVV/CRxIKb4aWOEerwCuSSp/SD3PAFNFZDrwcWCNqh5Q1R5gDbDAvXaKqj7jJng+lHItv/cwpuBWnt5N3bwNVDS3UjdvAytP908DbtvWGnr2mDGFFHSG/hagUlWHVPV7nNgiCapWVfe4x3vx1i0DmAHsTDpulyvLVL7LpzzTe5xARJaIyHMi8twbAwNjuB1j0lt5ejdLGjrYPrEfFdg+sZ8lDR0jA0xSarIxcRIkuLwtIhOATSLyf0Tk5oDnZeRaHKFO3x3tPVR1uarOVdW5p40PcWTVlKWl9Z28nRI13q4cZmm9/zyTvkO9tHWNOpxpTEkIEiQ+A1QCX8RLRZ4FfGqM79fturRw3/e58t3uugkzXVmm8pk+5Znew5iC2lHlP5/Er3xgfQtNO23BcRMfQbLFtqvqYVU9qKpfV9VbXDfZWDwBJDK+FgOPJ5Vf77LG5gFvuq6tp4HLRKTGDeRfBjztXjvokg0Eb+2zx0d5D2MKana//3ySdOXGxEna4OLSfF9I9zXahUXkYWAD0CAiu0TkRuBvgI+JyGvAR91zgNVAJ7AF+C7weQBVPQB8A3jWfd3pynDH3OfO2Qo85crTvYcpMUEHw4vVss56Thoa+V/spKEKlnVmmGeiGstVk035kXSrlorIWZlOTLfeWKmaO3myPjd3btTVME5iMDx5zOKkoQqWdzSwaF/ppNOuPL2bpfWd7KjqZ3Z/Fcs66zPXf9MmKm7qjf2WyCY+Wm9o/bWqnvDHM22SfdyChyktmQbDSym4LNpXO6b6tm1rZcqkqTSe0RhCrYwJX5iTKI0Zs2wGw2PF7Vppqcmm1IU2idKYXNhguKUmm9JW6EmUxgQypsHwGLHUZFPqIptEaUwmi/bVsryjgbOOVCEKZx2pKrnB/Lyw7DFTooKsmvcZvGDyReBmcptEaUxgYx0Mj4t1Xc3wmJc9ZkypGTW4JLLGRGQIb4LiblW1We/GGGPSShtcROSfgX9Q1ZdEZArehMgh4FQR+bKqPlyoShpT7tq3tsL4caHtWhmm7r7uUHaoDOu6Jj8yjZ1crKovucefBV5V1fcCHwBuDb1mxphjqcnVR6OuyNh093XTsb/j2HbL/UP9dOzvoLsvt9UWwrquyZ9M3WLJv84fA/4NQFX3uk0fjcmsuxs6O6G/H6qqoL4earP4ZJnr+TEzNDRIW1cbzXXNUVclsM6ezhHbLAMM6zCdPZ05tTLCuq7Jn0zBpVdErsRbbfjDwI0AIjIOb76LibN8BIaODhh2fwD6+73nEOw63d3wyiuQWJ6ov997HvT8mOnZ2MJH6tpoq4u6JtlJtCyClkd93VxZV91xmbrF/hQvQ+x7wF+p6l5Xfinws7ArZiKUCAz97j9qIjB0Z9Hl0Nl5PLAkDA975UG89trxwJKg6pWXsxJLTa6q9J/0mq486uvmwrrqRsq0ttir+EyWVNWn8ZbCN3GVKTAEbTX0p/kEma481eBg+vING4K1qGLWrVaKqcn1NfV07O8Y0YVVIRXU1+Q2GTas6+bCuupGCjLPxZSbXAMDeH/M/Y6vysMny9QWFZwYNHLtlitypZI9lvijmu+uorCum4ti7aqLigUXc+In/HHj/FsO2QSG+vqRf9wBKiq88iAqK2FoaPTj0rWosm19lUorp7GR4TaouaiVvhLZmbu2uvaEP/r5GJvwu26UqiqrfANJlF11UbLgUm5S/4hOmwZ79478hA8gMnLMI5vAAMf/MI/1D/Y558DmzcGO9WshZdP6inkrp9gkxiYSXUiJsQmgqIJFtoqxqy5KmSZR3pLpRFX9dv6rY0Ll90f09df9j62o8FowuXySr60d+x9nv+A0OOjfmvFrUWXTLZePMaYCa+wWNtUO0ratteQ2Fovr2EQxdtVFKVPLZbL73gB8EG/pF4CrgF+FWSkTEr8/oukMDcHFF+f2fvnuaqqtHdnKgvQtqmy65fIxxlRg67qa4QftVCxNk/hQxOI8NlFsXXVRypQt9nUAEfklcIGqvuWe/y8sFbk0ZTsgn4t8zHNJPX/vXjjjDNi//8SA5RfIGhqCBbcwkw8KoNR2rYzz2MSl/9XNn/yok9P397NvWhX3faqetR+qLcv5L0HGXGoZOVv/qCszpSbdH9FU2Y6v+Mm1qynd+fv3w/z5I8vTBbKGhhOP9ZNr8kFUmpoYboPxF7dGXZOsxHVs4tL/6ubLD3Yw8ah3X2fs7+fLD3bw20lv8stT98ZujGk0QYLLQ8CvROQn7vk1wIrwqmTywu+TfLo/oulaA7nItaspm/NzDWS5Jh8UgTcP99K+o73oU5MhvmMTf/KjzmOBJWHi0WGeqXyd4ZT5wHEYYxpNkCX3l4nIU0CiA/6zqvp8uNUyOcn0ST5oV1Gmaxeiqymb8/MxZpJL8kHEBta3eKnJJ0ddk+DiODZx+n7/37fdk32LYzHGlEnQHSVPAg6q6j3ALhGZE2KdTK5G+yQ/fz60tHjfx7JeWJBlYerrvVZRsmy6mrI5P13AKpExk7wZGCyppWHiZt80/9+3GW/5Hx+HMaZMRg0uInIH8BXgdlc0HvhBLm8qIjeLyEsi8jsReVhEJorIHBHZKCJbROSHbmtlRKTKPd/iXq9Lus7trrxDRD6eVL7AlW0RkdtyqWtJCiv7KZv1wmprvVZS4g98VZX3PNNSLRs2QGur9x2Cn19f783LSSZSvGMmqfeazZptafRsbGHgrhM7Irr7utmwcwOtXa1s2LmhbNe5KoT7PlXPkQkj/6QemVDBvKEzqZCR5XEYYxpNkDGXPwTOB34DoKqvi0iaht7oRGQG8JfAuap6WEQeARYCVwB3q+oqt1HZjcC97nuPqr5TRBYC3wL+HxE51533HuBM4D9E5Bz3Nt/B2yZgF/CsiDyhqi+Ptc4lJ6zsp2yDVtCuplwH5MF/kctiVIAJm+1bW2n6g5bYTlYsVms/5P2bpmaL/f78Whr6psRujGk0QYLLUVVVEVEAEclHz+44YJKIDOB1ue0BLgH+u3t9BfC/8ILL1e4xwKPAP4q3oczVwCpV7Qe2icgW4EJ33BZV7XT1XeWOLZ/gElb2U1hBKx+ZZX5ee634Buk7O1n5nmGWXgo7psDsN2HZ2mEWvZqHCZtJ2WOb9m7i8MDhWE5WLGZrP1R7LMgki+MY02iCjLk8IiL/AkwVkc8B/wHcN9Y3VNXdwN8CO/CCypvAr4FeVU3MCNsFzHCPZwA73bmD7vhpyeUp56QrP4GILBGR50TkuTcGBsZ6S8Un2y6poHIdR0knrMyywcHctg0Iwcpz+llyFWyfCire9yVXeeX51j+YZrJimnJj8ilIttjfisjHgIN4s/X/P1VdM9Y3FJEavJbEHKAXb4fLE5b2LwRVXQ4sB5g7eXKR9qOMURjZT2Gl7IaVWZaqCJZ0WfpReHvCyLK3J3jli/KYg9l3qJcZh2D3KSe+NjPNALMx+TRqcBGRb6nqV4A1PmVj8VFgm6q+4a71Y7ydLqeKyDjXOpmJtwMm7vssvCy1ccAUYH9SeULyOenKTa7CCFq5duP5nZ9OWEu6BEzR3uHzxz5T+ViW0BlY7+1aeeOvlT+9amQwO+ko3LUG7n9fwPsyZoyCdIt9zKfs8hzecwcwT0ROcmMnl+KNh6wDrnXHLAYed4+fcM9xr/9CVdWVL3TZZHOAs/HWPHsWONtln03AG/RPrItmQshUylmu3Xh+51dW+h8bRnpyFinas9/2/zznW57jjqBNO2D5k3BWL4h635c/CR99Pd4psKY4ZFoV+c+BzwP1IvJC0kuTgf8c6xuq6kYReRQv+2wQeB6va+pnwCoR+aYru9+dcj/wfTdgfwAvWKCqL7lMs5fddb6gqkOu7l/E2y2zEnhAVV8aa31jpZiXls+1RZR6fuq9QnhLumSRkLDsP5Qll5/Ymlj2H+q1ycd43VTruprhYBunvaIsevF4+ZEJFfztDfFOgTXFIVO32L8CTwF3AclzRd5S1QO5vKmq3gHckVLcyfFsr+RjjwB/nOY6y4BlPuWrgdW51DGWSnBp+TEr5JIuWSQkLHp+CAZJyRaDRS8OQUsW1w2y1XNlJUuuGmTZWu99khdSNCZsmVZFfhMvM+s6ABE5HZgIVItItaruKEwVTd6U4NLyOSnUki7pdu4c5/Pfq6qKRS/2j2hNJMr9jh31Z5ap9dnUxMoDMP4vW6k+uXRWTTbxEGSG/lUi8hqwDWgDuvBaNKbU2DIp4Ug3YdOvPJt0br9j/aRbJSFJ36Fe2rraRr+WMXkSZED/m8A84FVVnYM3AP9MqLUy4Qhrnkq589sdM115NskLfsemk5ZLpvQAABihSURBVKH1ObC+haadkvZ1Y8IQZIb+gKruF5EKEalQ1XUi8neh18zkXwyWli9K2c7Tyaa7LvXYxFhL0PcyJiJBgkuviFQDvwRWisg+4FC41TKhKeGl5YtWITcby+W9VGnb1krznJb818uYFEG6xa4GDgM3Az8HtgJXhVkpY0pKWMvt5PG91nU1M3zP1PzXx5g0giz/cghARE4Bngy9RsaUokK2CHN8r/atrTB+XEnsWmlKV5DlX/4U+DpwBBgGBFDARoGNKXYpy8cMP/Juaj65mb7xUVfMxF2QbrEvA+epap2q1qvqHFW1wGJMsUuzfMynXoKhoUFLTTahChJctgJvh10RY0yepVmR4b41VTRvt9RkE64g2WK3A/8lIhuBYzmQqvqXodXK5G4Mq+mamMk4u18se8yEKkhw+RfgF8CLeGMuptgV8wKVpnAyzL9Z1zUfHttExU29ha+XKQtBgst4Vb0l9JqY/CmnBSpNeoWcf2NMiiBjLk+57YCni8ipia/Qa2bGrtwWqDT+As6Jad/aSvuO9ggqaOIsSMvlOvf99qQyS0UuZrluG2ziI9OcmMZGhtug5qJWS002eTdqy8WlHqd+WWApZrZApcnWwCBt21qjroWJkUw7UV6iqr8QkT/ye11VfxxetUxObIFKkxAga7BnYwu0t1Ox1GdPGmPGKFO3WDNelpjfOmIKWHApZrZApRlD1qClJpt8ybQTZWIb4jtVdVvyayIyJ9RameLl90kYrJVUjLLJGmxqYvieTYz/C0tNNvkRJFvsRz5lj+a7IqYE+C0nsnkzvPLKidvudndHV0/jGWPWoGWPmXzINObyLuA9wJSUcZdTgIlhV8wUIb9PwnDidr42pyazQq2ekG3WYGMjA+ste8zkR6YxlwbgSmAqI8dd3gI+F2alTJHKZp6MzanxV8jVE2wSpYlQpjGXx4HHRWS+qm4oYJ1MsUr3STjdseZEhVw9YYxZg43dQvusQRvcNzkJMonyD0XkJbzdKH8OvA+4WVV/MNY3FZGpwH3AeXiZZ/8D6AB+CNQBXcCnVbVHRAS4B7gCb3XmG1T1N+46i4G/dpf9pqqucOUfAB4EJgGrgZtUU/tuTNb8PgkDiIzsGrNPx+nlY/WEdN1q6cqzDFrruprhB5aanKy7r5vOnk76h/qpqqyivqae2mrr9s0kyID+Zap6EK+LrAt4J/A/c3zfe4Cfq+q7gPcDm4HbgLWqejaw1j0HuBw4230tAe4FcEvQ3AFcBFwI3CEiNe6ce/G67hLnLcixvgb8lxN597vhXe8qzBa/cZCuRRe0pZdmjxZefdW/PMfEirZtrWzauymna5S67r5uOvZ30D/k/dv2D/XTsb+D7j5LWskk0MKV7vsngH9T1Te9xsTYiMgU4L8BNwCo6lHgqIhcDbS4w1YArcBXgKuBh1zL4xkRmSoi092xa1T1gLvuGmCBiLQCp6jqM678IeAa4KkxV9ocl+6TsAWTYHIdB0nXrfb66ycem0t3W1MTw20w/uLW7M+Nmc6eToZ15L/5sA7T2dNprZcMggSXJ0XkFbxusT8XkdPwtjweqznAG8D3ROT9wK+Bm4BaVd3jjtkLJH5qM4CdSefvcmWZynf5lJ9ARJbgtYaYbWME+ZdrVtSrr478o3nmmXDOOfmvZyHlunpCtokSeUis6DvUS1tXG811zTlfqxQlWixBy40nyNpitwEfAuaq6gDeuMfVObznOOAC4F5VPR84xPEusMR7Kt5YTKhUdbmqzlXVuaeNt9zLvErXfRO0myY1sID3/NVX81vPKNTWwvz50NLifc8m4Gb7ISjHD00D61to2lneu1ZWVfr/G6YrN560wUVEbk16eqmqDgGo6iEgl10odwG7VHWje/4oXrDpdt1duO/73Ou7gVlJ5890ZZnKZ/qUm0LKlBUVhF83T6byqHV3w4YN0NrqfQ9rEmm6RUnPPDPcxUrdrpXlqL6mngoZ+W9bIRXU11jSSiaZWi4Lkx7fnvLamAfIVXUvsFNEGlzRpcDLwBPAYle2GHjcPX4CuF4884A3XffZ08BlIlLjBvIvA552rx0UkXku0+z6pGuZQimnPWVybaVlI90eLeecE2jvlrFY19XM8LIgPejxVFtdS8O0hmMtlarKKhqmNdh4yygy/cZImsd+z7P1F8BKEZkAdAKfxQt0j4jIjcB24NPu2NV4achb8LrkPgugqgdE5BvAs+64OxOD+8DnOZ6K/BQ2mF945bSnTKF3/syUVGGJFaGora61YJKlTMFF0zz2e54VVd0EzPV56VKfYxX4QprrPAA84FP+HN4cGhOV+npv3TG/8iDOPNO/C2zqVK/bqZgWycy2lVao5V/yKZE9NtxK9clTaTyjMeoamSKXKbi8X0QO4rVSJrnHuOe2tpgZXerkymxS2BNZYckBZupUOHiwMEunZCObVlohl38xJkJpx1xUtVJVT1HVyao6zj1OPLfUKpNZZ+eJC1qqBh/QBy/AtLQc/zp8OLckgbBks/NnrokORSCRmmxMJkFm6BuTvTAG9Is1SSDdILtfS6RY7yEgS002QZVvCkjcRd2vH8aAfjEnCQQdTK+shKEh//JcFfJn7lKTbWFLk461XOKokKmx6WTTVRTlNQst3bhTDksqAQX9ma/ramb4nql5v66JFwsucVQM/frZdBVFec1CG0yz0nC68qAi+pm3b7WFLY0/6xaLo2Lp1w9j3kWpz+UIq2uv0D/zxkZb2NJkZMEljop5bCKugo53ZFoVOepxsjGoPgoc7aXtSPkubGn8WbdYHMVhbCIfCrXeVzbjHem69iD6cbIx6NnYQmO3ZY+ZE1nLJY5yXdY9Dgo5WTHb5V/8uvY2bMhtCRlrrZoiY8Elrgo5NlGM3TmFXO8rH+MduV4j103IcmWpySaFBReTm2JdzqSQA9z5aDVku4RMumAeQZBf19UMj22i4qbe0N/LlA4LLiY3hV4ROKhCdhNNm+a/yOa0acGvEbTlMVowj7jF2L61FcaPo2l2U6T1MNGzAX2Tm2JJe05VyKSG/fuzK/cTdA5PMcxh8tPYyHBbi5c9ZgzWcjG5KtaB5EJ2E+UrwAZpeRRrME82MGjjL8aCi8lR1APJmRSqm6iQAbZYg7nTs7EF2tupWJrjigOm5Fm3mMlNHJZkyVUhu+BKaA5T27bWqKtgImQtF5O7IhhIjlQhu+BKYQ5TUxPD91j2WLmz4GJMttKlAhfqD3yxBvPkfxcRrnsRXp8TdaVMVCy4GJONYp3XE7XUfxdVlj8JfzbcyqoLLDW5u6+bzp5O+of6qaqsor6mntrqeP++2JiLMdko1lTgqPn8u5w8AMt+EVF9ikh3Xzcd+zvoH/ISMfqH+unY30F3X3GvG5crCy7GZKMUUoELYOXp3dTN20BFcyt18zaw8hz/+595kGOpyeWqs6eTYR0ZeId1mM6eeH8giSy4iEiliDwvIj91z+eIyEYR2SIiPxSRCa68yj3f4l6vS7rG7a68Q0Q+nlS+wJVtEZHbCn1vJsbSpfwWSSpwIaw8vZslDR1sn9iPCmyf2M+Sq2Dle088tqKqioG7yrv3PdFiCVoeF1G2XG4CNic9/xZwt6q+E+gBbnTlNwI9rvxudxwici6wEHgPsAD4JxewKoHvAJcD5wLXuWONyV0JpQKHZWl9J29Xjvwk/vYEWHppyoEp/y5t28pz18qqSv8PHunK4yKS4CIiM4FPAPe55wJcAjzqDlkBXOMeX+2e416/1B1/NbBKVftVdRuwBbjQfW1R1U5VPQqscscakzub18OOKv9P3Dum4P/v0tTEcFsLKfGobNTX1FMhI//UVkgF9TXx/kASVXv174Bbgcnu+TSgV1UT03p3ATPc4xnATgBVHRSRN93xM4Bnkq6ZfM7OlPKL8n0DpowVaypwgczur2L7xBMDzOz+Kpg/P+O5fYd6ad/RXlbZY4mssHLLFit4cBGRK4F9qvprEWkp9Pun1GUJsARgdhn1mRuTi2Wd9Sxp6BjRNXbSUAXLOjN/Eh9Y30LNRa30jQ+7hoWRTXpxbXVt7INJqihaLh8GPikiVwATgVOAe4CpIjLOtV5mArvd8buBWcAuERkHTAH2J5UnJJ+TrnwEVV0OLAeYO3my5n5rxsTfon3eH8ml9Z3sqOpndn8Vyzrrj5WXg0R6cSILLJFeDJRdEEmn4GMuqnq7qs5U1Tq8AflfqOoiYB1wrTtsMfC4e/yEe457/Reqqq58ocsmmwOcDfwKeBY422WfTXDv8UQBbs2YsrFoXy1dz8xnuK2FrmfmBw4sjd0Si9Tkck0vzkYx5Qh+BVglIt8Engfud+X3A98XkS3AAbxggaq+JCKPAC8Dg8AXVHUIQES+CDwNVAIPqOpLBb0TY4yvdV3N8IN2ZOkgG3ZuKNkxiHJNL85GpMFFVVuBVve4Ey/TK/WYI8Afpzl/GbDMp3w1sDqPVTVhy7R1r4mVle8ZAjhhxjqUTpdSVWWVbyCJe3pxNmyGvoleYl2qxCz3xHpd3fFeHqNcLb18AsjIslLrUirX9OJsFFO3mClXmdbrCqP1Yq2kSKWbJ5OPLqVCLRBZrunF2bDgYqJXyPW6bFXjyKWbJ5Nrl1KhM7jKMb04GxZcTGH5tRoKuXVvoVtJhpWnd49IW77i99NYMX3vyCVklJy7lDJlcFkQKDwbczGFk25sZdq0wq3XZasaZ+2EFZBPDz4W5rfI5Yrpe1m85wzOOlKFKJx10BuAyTUAWAZXcbHgYgonXath//7CrddlqxpnxXcF5IaOwAHGd5HLymFWv2P/8XkyD0xJHd8fk3JdILJYWbeYKZxMrYZCrddVXz9yzAXKblXjbKQLDkvrOwNNnEy7yGVyeWMjw21QQStTJk2l8YzGMdW1vqZ+xJgLWAZXlCy4mMLJNLZSqAyuxDUtWyyQQMEhg4yLXKbIddVky+AqLhZcTOGkazVMm1bYDK4yX9U4G9kEBz/ZLnLZd6iXtq42muuax1Rfy+AqHjbmYgon3V4o+/fbvvRFallnPScNjfwzEWQF5IRF+2pZ3tFwfPD+SBXLOxp8u9QG1rfQtDMfoy+mGFjLxRSWX6th82b/Yy2DK3L5WAF50b7a7FZMVqVtWyvNc1qyrK0pJhZcTO5yHS8p5DwXk7Wsg0MAqXNfEgFrXVczPLaJipt68/p+pvCsW8zkJh/rgtm+9GUlaHpz+9ZW2ne0R1RLkysLLiY3mWa8B2X70peVTOnNgEtNbqH6aASVM3lj3WLlJt8pv/ma8W4ZXGUjq/TmgcGcssdMdKzlUk7CWNq+HGe8d3fDhg3Q2up9t60BspIujTm1vGejZY+VMgsu5SQfXVipym28xPaeyVmu6c2mNFhwKSdhLNpYbuMlYQToMpPN3Jd1P6g8lppsSouNuZSTsFJ+y2m8xFZVzovA6c1NTQzfY6nJpchaLuWk3LqwwlCOY0xFwlKTS4sFl3JSbl1YYUgXoKdNi+0gfy77ueSFpSaXJOsWKzfl1IUVBr9VladNg717Y7l1cmLCY2JeSmLCI5D3WfuBDAza0jAlwlouxmSrthbmz4eWFu97jBfeHHXCYwH1bGxh4C77PFwqCh5cRGSWiKwTkZdF5CURucmVnyoia0TkNfe9xpWLiPy9iGwRkRdE5IKkay12x78mIouTyj8gIi+6c/5eRCxZPkxhzfsolfkkMR7kz3U/l7BY9ljxi6LlMgh8SVXPBeYBXxCRc4HbgLWqejaw1j0HuBw4230tAe4FLxgBdwAXARcCdyQCkjvmc0nnLSjAfZWnsOZ9lNJ8ksrK7MpLSNAJjwXT1MRwWwuVw7Bp76Zo6mACKXhwUdU9qvob9/gtYDMwA7gaWOEOWwFc4x5fDTyknmeAqSIyHfg4sEZVD6hqD7AGWOBeO0VVn1FVBR5KupbJt7DmfZTSfJJ0DeMYNJhtwqMZq0g7MEWkDjgf2AjUquoe99JeIDFaOAPYmXTaLleWqXyXT7nf+y/Baw0x21JJxyasLqFCdzXlsuba4GB25SUkH/u5hKXvUC/tO9ppmt0UdVWMj8iCi4hUAz8C/kpVDyYPi6iqioiGXQdVXQ4sB5g7eXLo7xdLYU3MLOQeL4kuuLFme8V8P5ow9nPJ1cD6FmouaqVvfNQ1MelEki0mIuPxAstKVf2xK+52XVq47/tc+W5gVtLpM11ZpvKZPuUmDGFNzCzkhM9cu+Bscmp0XGqyKT5RZIsJcD+wWVW/nfTSE0Ai42sx8HhS+fUua2we8KbrPnsauExEatxA/mXA0+61gyIyz73X9UnXMvkW1sTMQk74zLULzianRsJSk4tbFD+ZDwOfAV4UkUS6x1eBvwEeEZEbge3Ap91rq4ErgC3A28BnAVT1gIh8A3jWHXenqh5wjz8PPAhMAp5yXyYsYU3MLNSEz3x0a9nk1Ei1bWtlyqSpNJ7RGHVVjFPw4KKq7UC6NJpLfY5X4AtprvUA8IBP+XPAeTlU08RZ6uB96gx7sG6tUtHUxHAbjL+4NeqamBQ2Q9+UF7/5M3v3whlnWLdWies71EtbV1vU1TCOdVia8pJu8H7/fm8pF1OSBta38JG6Ntrqoq6JSbCWiykvMV6qxZhiYsHFlBfbjyXWKods18piId54uRGRN/Cy1IJ4B/D7EKsTldjf1zvg1NlwliR9sFIY3gHbfw8H0l6hOMX15wXxvbc43tdZqnpaaqEFlzEQkedUdW7U9cg3u6/SEtf7gvjeW1zvy491ixljjMk7Cy7GGGPyzoLL2CyPugIhsfsqLXG9L4jvvcX1vk5gYy7GGGPyzlouxhhj8s6CizHGmLyz4DIKEXlARPaJyO+Syk4VkTUi8pr7XhNlHbMlIrNEZJ2IvCwiL4nITa68pO8LQEQmisivROS37t6+7srniMhGEdkiIj8UkQlR13UsRKRSRJ4XkZ+65yV/XyLSJSIvisgmEXnOlcXhd3GqiDwqIq+IyGYRmR+H+wrKgsvoHgQWpJTdBqxV1bOBte55KRkEvqSq5wLzgC+IyLmU/n0B9AOXqOr7gUZggdsH6FvA3ar6TqAHuDHCOubiJmBz0vO43NdHVLUxaQ5IHH4X7wF+rqrvAt6P93OLw30Fo6r2NcoXUAf8Lul5BzDdPZ4OdERdxxzv73HgYzG8r5OA3wAX4c2KHufK5+NtLBd5HbO8n5l4f5AuAX6Kt3VFHO6rC3hHSllJ/y4CU4BtuKSpuNxXNl/WchmbWvV2vATYC5Ts2uwiUgecD2wkJvfluo424W2VvQbYCvSq6qA7ZBcwI6r65eDvgFuBxLLO04jHfSnw7yLyaxFZ4spK/XdxDvAG8D3XjXmfiJxM6d9XYBZccqTeR5CSzOcWkWrgR8BfqerB5NdK+b5UdUhVG/E+6V8IvCviKuVMRK4E9qnqr6OuSwiaVPUC4HK8Ltr/lvxiif4ujgMuAO5V1fOBQ6R0gZXofQVmwWVsukVkOoD7vi/i+mRNRMbjBZaVqvpjV1zy95VMVXuBdXjdRVNFJLF/0Uxgd2QVG5sPA58UkS5gFV7X2D2U/n2hqrvd933AT/A+EJT67+IuYJeqbnTPH8ULNqV+X4FZcBmbJ4DF7vFivDGLkiEiAtwPbFbVbye9VNL3BSAip4nIVPd4Et5Y0ma8IHOtO6zk7k1Vb1fVmapaBywEfqGqiyjx+xKRk0VkcuIxcBnwO0r8d1FV9wI7RaTBFV0KvEyJ31c2bIb+KETkYaAFb6nsbuAO4DHgEWA23jL9n1bVklmuXUSagPXAixzvv/8q3rhLyd4XgIi8D1gBVOJ9eHpEVe8UkXq8T/ynAs8D/6+qluQOYSLSAnxZVa8s9fty9f+JezoO+FdVXSYi0yj938VG4D5gAtAJfBb3O0kJ31dQFlyMMcbknXWLGWOMyTsLLsYYY/LOgosxxpi8s+BijDEm7yy4GGOMyTsLLsYUARG5RkRUREp+NQFjwIKLMcXiOqDdfTem5FlwMSZibo23Jrzl8he6sgoR+Se3F8gaEVktIte61z4gIm1uocenE8uJGFNMLLgYE72r8fb9eBXYLyIfAP4Ib6uHc4HP4K2PllgT7h+Aa1X1A8ADwLIoKm1MJuNGP8QYE7Lr8BahBG8pl+vw/m/+m6oOA3tFZJ17vQE4D1jjLRFHJbAHY4qMBRdjIiQip+KtcPxeEVG8YKEcX2/rhFOAl1R1foGqaMyYWLeYMdG6Fvi+qp6lqnWqOgtvB8MDwKfc2Est3uKp4O1keJqIHOsmE5H3RFFxYzKx4GJMtK7jxFbKj4Az8PYEeRn4Ad52zW+q6lG8gPQtEfktsAn4UOGqa0wwtiqyMUVKRKpVtc8tP/8r4MNunxBjip6NuRhTvH7qNj6bAHzDAospJdZyMcYYk3c25mKMMSbvLLgYY4zJOwsuxhhj8s6CizHGmLyz4GKMMSbv/i+2xRRA+EfChAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}