Kernel Support Vector Machine.ipynb

{
 "cells": [
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kernel Support Vector Machine\n",
    "- The Gaussian RBF Kernel\n",
    "![image.png](attachment:image.png)\n",
    "    - k stand to Kernel, x is datasets, y is serveral landmakrs\n",
    "- Types of Kernel Function;\n",
    "    1. Gausssian RBF Kernel\n",
    "    2. Sigmoid Kernel\n",
    "    3. Polynomial Kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numpy allows us to work with array.\n",
    "import numpy as np\n",
    "\n",
    "# Maptplotlib which allows us to plot some chart.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Pandas allows us to not only import the datasets but also create the matrix of features(independent) and \n",
    "# dependent variable.\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The independent variable usally in the first columns of dataset and dependent variable usally in the last columns of the data sets.\n",
    "- X is Independent Variable.\n",
    "- Y is Dependent Variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [    49  36000]]\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "x = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, -1].values\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 1 0 0 0 1 0 1\n",
      " 1 1 0 0 1 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 1 0 1 0 0 1\n",
      " 1 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 0 1 0 1 1 1 1 0 0 0\n",
      " 1 1 0 1 1 1 1 1 0 0 0 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1 0 1 1 0 0 0 1 1 0 1 0\n",
      " 0 1 0 1 0 0 1 1 0 0 1 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 1 1 0 1\n",
      " 1 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WemVnqgeA70k"
   },
   "source": [
    "**Splitting the dataset into the Training set and Test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [    36  52000]\n",
      " [    27  54000]\n",
      " [    26 118000]]\n"
     ]
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Dividing training and test set.\n",
    "# The best ratio is 80 - 20 for trainging and testing respectively.\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 0)\n",
    "\n",
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "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",
      " 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0\n",
      " 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"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YS8FeLHYS-nI"
   },
   "source": [
    "**Feature Scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Package\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "\n",
    "# Fitting and Transforming\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    }
   ],
   "source": [
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    }
   ],
   "source": [
    "print(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eiU6D2QFRjxY"
   },
   "source": [
    "**Training the Kernel KVM model on the Training set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(random_state=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "classifier = SVC(kernel = \"rbf\", random_state = 0)\n",
    "\n",
    "# Fitting\n",
    "classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicting a new result**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(classifier.predict(sc.transform([[30, 87000]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aPYA5W1pDBOE"
   },
   "source": [
    "**Predicting the Test set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "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",
      " [1 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 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",
      " [0 0]\n",
      " [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",
      " [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"
     ]
    }
   ],
   "source": [
    "# Predicting \n",
    "y_pred = classifier.predict(x_test)\n",
    "\n",
    "# Concatenating and Reshaping\n",
    "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\n",
    "\n",
    "# First column is the predicted value and Second column is the real value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Making the confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[64  4]\n",
      " [ 3 29]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.93"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "# Confusion Maxtrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "\n",
    "# Accuracy Score\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualising the Training Set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "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"
     ]
    },
    {
     "data": {
      "image/png": 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g2oq1b3BpB2hR1ax+1wN4/mVFZAmOpkRDjF9yiyUO4qpKEEeU07IFbUx/fDXXzIN1TTCzH67pgA3HhTdT1VLEXNZpH3W0GMBJHzqJkz50UuhxspRMuIjIBOArOCaxkqCqKiJa5PjNwM0AEw+Z6NnPYkkqcTydx+HL+cm74eVWYXed82/26iRY8jHh0AOLPP35oNIi5qLgzbPPiESYxE0pNZd3AIcAWa1lOvB7ETkO2AjMyOk73bRtZK+JK9veYdqnu/QH6BWRKaq62ZjWtkS+EkvJqOan0qSuLY4yK91bu/cIliy76zR0zkcS8lks7pQsFFlVn1XVA1X1YFU9GMeU9V5V7QHuB84zUWMnAP3GtPUwcKqITDaO/FOBh82x7SJygokSO4+9Ppz7gWxU2WLyfTuWCiKu3SWTQJLXFkeUU1yRbZUUMVdrxBmKfBfQCbSLyAYRuaBI9weBbuBl4D+AvwUwjvyvA0+Yn2uzzn3TZ5k55084znyA64EPichLwAfNe0sFUkl5HEFJ8triiHKKK7ItCfksFnfijBY7Z4TjB+e8VuAij363Are6tD8JHOXS3gfMDzhdSwKJ6qk0iPmpVKaqpD9xR+3LiSuyrRo2QatWbPkXS2KJwvYfxOFbSudw0LWteX0Nm9Kb9ryf2jiVw/Y/LNI5xUlLYwv9O/vz1tC6T2vgz9WthMxP3l3Z2xxXK7b8iyWxRGH7D2J+KqWpKsjaCgULwKb0Jta8vibyecVFb7qXnrd68tp63uoJ5GPyKiHzyWcIVenAAl/5/Fc48YgTOeMD0UWhWeFiSSxR2P6DmJ9KaaoKsrZCwTJSexDmP9bLXZd2svL8Du66tDO2Wl9RCO5Sl5CJukpBkvmLhX/Bfyz/j0jHtGYxS6IJa/sPYn4q9U6H5c4gz2oC2Rt2VhMAIq9WHIXgLmUJmSTnzzyw5gFu6LyBzenNTGmcwiVzLuGMw8JpHO878X1sWLdh5I4BsJqLpaoJYn5KQrHDUlJKTSCKqC6vUjFxlJBJajTfA2se4KpHrmJTehOKsim9iaseuYoH1jxQ1nm5YYWLpaoJYn5KQrFDN6Y2Tg3U7pegmkAYM5GXgA4iuJctaGPn2Pxb1s6xdSxbEL3wT2o03w2dN7BzML/k/s7BndzQmbyS+9YsZql6gpifym2qciMbFRZ1tFiQYpJRmIkEQdG890HImupKseFYqU2kftmcdi+t79VeTqxwsVgqgMP2Pyzy0ONlC9ryfC7grQmELbPSvbU7T7AAKMHLv6w8sSW23StzSWr+zJTGKa6BHFMaw5XcjwMrXCyWGmXliS38YXw/q1Kb2DgRpr0JJ2Raef3o4TfvsGaipJqZvIir4nRYLplzCVc9clWeaWxc/TgumROu5P4Xl3yRJ377BFvf2MpJ7z6Jz33pc5z9qbNDjWmFi8VSo/Sme/nNfj0MGYViw77wc+mhPd007CYa1kyUVDNTMZJoIs1GhUUdLfatm78VxfTysMLFYqlRgpi6wpqJkmpmqkTOOOyM0MKkFFjhYrHUKEFMVWHNREk1M1niwwoXi6VGCWqqCmsmSqKZKQkMMYSqUrA7b+JQVYYYGrmjwQoXi6UEJHFjMGuqSgabd2ym+c1mGiY2JFbAqCoDbw6weYf/kGcrXCyWmElqKRFrqkoGy9ctZyELmTJ+CnUJzWsfYojNOzazfN1y3+dY4WKxxEySt+K1piqHcmqW6UyaZa8sK8m1SokVLhZLzFRajketkVTNstJJpg5msVgsJSKpRSorHau5WCqSJDrILZWJ1SzjwQoXS8VRaWaMqLZrtsI0HiqxekAlYIVLGejq6SK9Kw1A4wDMzt39NZOhqxXSY6Exk2J2uhHSaboOyJCekHLOGdvI7NbZpZ94Qkiyg9yNsCG/lSZMKw0bkh0PVrjEzB5BMphh7kZHONAKjeb47B545HagsRFmz3Ya1xYO0sXJZ/XTNc28H0jDS486h1qhf0yGpnFNNSNwKs2METbkt9KEaaVhQ7LjITbhIiK3AqcDW1T1KNP2L8AZwC7gT8CnVXWbOXYFcAGQAT6vqg+b9tOAbwMpYJmqXm/aDwGWA83AU8C5qrpLRBqAO4BjgD7gE6q6Nq51ZsnVRhjM0LgLZr+W2itIMi5CJMvcEQafPZtH1pIvdLq6IJ3m5MXw6DTgzTRs7oL+frqmpUgbjb4atZxKNGOECfmtNGFaidiQ7OiJU3O5Dfguzo0+ywrgClUdFJFvAFcAXxaRI4GFwDuBqcAvRSS7ecX3gA8BG4AnROR+VX0e+AZwg6ouF5Hv4wimm8zvrap6qIgsNP0+EeM699A41tFHZvcZQQL5gmQkIRIEM+4jf8IR01kefZSTFzsaDVCVWk6cZowk+jYqUZhaLLEJF1X9jYgcXND2i5y3q4DshgFnAstVdQB4RUReBo4zx15W1W4AEVkOnCkiq4FTgE+aPrcD1+AIlzPNa4B7gO+KiKhq/k5FIfAyde0hbX4aG13Ojpm5c4cLnK4uAEfLaU6Xfk4RE5cZI6m+jWrxCSRRcFvio5w+l78GfmpeT8MRNlk2mDaA9QXtx+OYwrap6qBL/2nZc4yG1G/6vx7l5BvHNsJAmkfuNQIkqKmr1KTTjsBryjjaTIWb0OIwYyTVt1ENPoGkCm5LfJRFuIjIlcAgcGc5rp8zjyXAEoAGl33Dq4ZiJrSLGumaWI5JDWf+Y70l2R8d3J+i4/RthH1qr3SfQFIFtyU+Si5cROR8HEf//BxT1UZgRk636aYNj/Y+YJKI1BvtJbd/dqwNIlIPNJn+w1DVm4GbASYeMnGY2cwzZDhrAstkHI2gHOavqEin4c2c92Xyz8x/rDdvP/fWvgEuu815so1awHg9RackRUYzw/qH9W1U+1O7H8FpgxJqj5IKFxP59SXgJFV9O+fQ/cBPRORbOA79WcDjgACzTGTYRhyn/ydVVUXkERyfzXJgMXBfzliLgU5z/Fdh/C15Tvp7TWNcTvpSkyD/zIX3du8RLFnG7Rriwnu7IxcuXk/Rgnu58+bxzbFcrxqe2v0KThuUUHvEGYp8FzAP2F9ENgBX40SHNQArzL4Fq1T1s6r6nIjcDTyPYy67SNV5hBSRi4GHcUKRb1XV58wlvgwsF5HrgKeBW0z7LcCPTFDAGzgCyZJwDuxzf4L1ag9CobltysUDuMkRxf0ZpG+Hq+Lrm2p+avcrOKslKMHinzijxc5xab7FpS3bfymw1KX9QeBBl/Zu9kaU5bbvBP4q0GQt+Rjn/1wa6WosjfN/S3MDrS6CZEtIX5ibuW3Gdljf5H+MsEKgmp/a/QrOaghKsATDZugbduzewaPrHt2TAAkwe0eTc3OF8oYXl5Jc538uMTv/ly1oyxMCADvH1rFsQbgnWzdz2z/9Ej7zMdgxZm9bndRRJ3UMDg1SSFghUM1P7UEEZ6UHJViCYYVLDtnw4q3fr5Dw4lITo/M/61eJOlrMzay26FlQ4MKPN+Q9RQOxCIFqfmpPguAsZZShxT9WuFj8UQLn/8oTWyK/KXiZ2z64qYE5M+a4nhOHEKjWp/ZyC85SRhlagmGFi6WqCWpucxMCpX4yrrRMdrfPrFRrKGWUoSUYVrhYwpHwzP+w5rZiT8Y/eXc8JWhe3vICu+ucyLWBzAAvb3kBqJycmFLm9cQZZWgJhxUuWTIZp7JwNSRGlooKyfwPY27zejLe75k1vDhNI7+Bbup5id31+SHRu+uUTT0v0XJoZQiXUub1xBVlaAlPXbknkBRmpxvZ+tu5bF11EsydO9yZb6lJvJ6Av3FCJpZ917enhkerFWtPIqXM61m2oI2dY/NvY1FEGVrCYzUXSzwkpKxMWLyejL3yZMLeQGf0w7pJ7u2VQinzeuKKMrSExwoXS/QkqKxMWLwCAvbN1NFfH31OzJdXpfj7UzK8PXZv24RdTvvPZocaumSUOjw5jihDS3isWcxiKcLKE1v45vnt9DQ3MAT0NDfwzfPbmdo6izrJ//eJ4gb6xrsP43sPwkHbQNT5/b0HnfZKoaWxhfbm9j2CtiHVQHtze8UEJFiiQSLcQ6uiOXbiRH3y2GPLPY2a4uT3dCUqsiwocYXb2qRASyXRcX7HU6o67OZpzWKWsvHIH2YnLrIsCHHlxMRl5qm0/Jk4SMJnkIQ5lAIrXCzlJ2bnf6n+mcuRLe53bdW+p4wfkvAZJGEOpWJE4SIinwN+rKpbSzAfS60Rs/O/lP/Mpc4WD7K2at5Txi9J+AySMIdS4UdzaQGeEJHfA7cCD4fZfMtiKSWl/GeOM1vcTUMJsrZq3lPGL0n4DJIwh1IxonBR1a+KyFXAqcCnge+ajb1uUdXCwuwWSzREVFamlP/McWWLe2kohYIli1eOSbXuKeOXJHwGSZhDqfAVimw0lR7zMwhMBu4RkX+OcW6WWmX2bGMum8vu35zE1t/O5ZE/zOaRH6eYnW6kcWzjnu2nR8LrnzaOf+a4ssW9NBQv3NbWNrktltDpSiIJn0ES5lAq/PhcvgCcB7wOLAP+XlV3i0gd8BLwpXinaLGMnlIm9MWVLV5My6qTOl9rK3dp/HLgZkps3aeVTelNe/q07tPq+RnEEQhSS38HPz6XycBfquqruY2qOiQip8czLYvFg4LIsq5tv3aax0KmjmGRZaX+Z44jjLiYKSXre/GztmrdU8YNN1Pi6tdXI0hev563emga1+S6ZUBcgSC18ncoKlxEJAUsVNVr3I6r6uo4JmWxuDLKyLJK/2cupn1V+triws2UCKDkxyJ5BUDUUlRXXBQVLqqaEZEXRWSmqq4r1aQslkC4Of/TabpaqcjM/0JqyZQSFUECNtz6xhkIYpMo9zIZeE5EHgfeyjaq6sdim5XF4pcK2VMmLFZDCYaXKdGrr9/zwwaC2CTKfK6KfRYWi8USIW6mRABB8kxjXgEQQQNB/GojtWRu85Pn8uvRDCwitwKnA1tU9SjTth/wU+BgYC3wcVXdKiICfBv4KPA2cL6q/t6csxj4qhn2OlW93bQfA9wGjAceBL6gqup1jdGswVLhFHH+U59yNZXVismi2vEyJbq1uf19g5gig2gjNokyBxE5AfgOcAQwFkgBb6nqviOcehvwXeCOnLbLgZWqer2IXG7efxn4CDDL/BwP3AQcbwTF1cCxgAJPicj9RljcBHwG+B2OcDkNeKjINSy1RBHn/+SL0jBheJ5MLZksagEvU6Lfv6VfU2QQbcQmUebzXeAcnJyW8cCFwPdGOklVfwO8UdB8JnC7eX07cFZO+x3qsAqYJCJTgA8DK1T1DSNQVgCnmWP7quoqk+B5R8FYbtewWCCdZnYP8Gba+Xm6i67VHXS99CirX18dy9bFluomiDZikygLUNWXRSSlqhnghyLyNHDFKK7XoqqbzesenLplANOA9Tn9Npi2Yu0bXNqLXWMYIrIEWAIws6H6nhwsBYzg/O+YvM31tGo0WViiI4g2UkuRf36Ey9siMhboMuVeNhPBDpbGPxJrAcyRrqGqNwM3g7NZWJxzsSScdBqZBCruh7t6uio2lNkSL0Gd/7US+edHSJyL42e5GCcUeQawYJTX6zUmLczvLaZ9oxk3y3TTVqx9ukt7sWtYLO6YOmY/Wn0EEzIF/xIK4+vH+x6qN91L5/pOOtZ20Lm+k950b8STtSQNu62zO36ixbJlX3YAXwt5vfuBxcD15vd9Oe0Xi8hyHId+v6puFpGHgX8Ukcmm36nAFar6hohsN8EGv8OpffadEa5hsRRl0RbnZnBlWzfrGgaY2Q9bJkDD4G5gd94mZm7JmTYgoHapFW0kCJ7CRUSeBYqZlN5dbGARuQuYB+wvIhtwor6uB+4WkQuAV4GPm+4P4oQhv4wTivxpc403ROTrwBOm37Wqmg0S+Fv2hiI/ZH4ocg2LZUQWbWnZI2SGkVNqxi05M84chiSHSPvd2jnJa7BETzHNJVRRSlU9x+PQfJe+ClzkMc6tOJuUFbY/CRzl0t7ndg2LJTSzZ8OjjzqlZly2ZR6YPgguPpuwAQFJ1oj8bu2c5DVEgRWcw/EULoVVkC0WC975M+k0dV/xDgh4dN2jo65vluSsbr9bOyd5DWGpdsE5WkZ06IvICSLyhIikRWSXiGREZHspJmexVARmc7MfvTA8IKDOBAT43dzMjSRndfvd2jnJawhLMcFZy/gJRf4usBD4GU6m/HnAYXFOymKpRNwCAiZkhM37moAAgBd+DakUj07LkJVDihY1pSQ5q9vv1s5JXkNYqllwhqHUSZQWS1VTNCAA9gQFTLi4n8G6vfEyxUwppdxNMyjLFrTl+VzAfWvnJK8hLNUsOMNQtiRKi6VmSafZWTc8EHNIh1j9+mpeeuOlPP9MkrO6/W7tnOQ1hKWaBWcYxAnUKtJB5CCgF6do5SVAE/Dvqvpy/NMrHcdOnKhPHntsuadhqRHqTupwd/4rTBp0nvlm9zhNj07LeFZxtiSDWo4W6zi/4ylVHXbz9J1EKSIZnATFjapqs94tFhfuPLB3r89loIGl3W2uZrKZAw28Om64KeWggQbWrprjvBmhirMlOdgkyuEUS6L8PvAdVX1ORJqATiAD7Ccil6nqXaWapKXK6O2F7m4YGICGBmhrgxaPf8wgfcvMnQf2sqT9Rd5OOeaRV8cNsKTd8aMUCpil3W15fQEmZOpY2l1gSkmnIZNxKjgDvPQoXQdk9uxJA/63cK7lp2tL6SmmuXxAVT9rXn8aWKOqZ4lIK042vBUuluD09sKLL8KQuakODDjvYbjQCNI3AVzZ1p0nLADeTg1xZVv3MOEyLLLMTcsxVZy3rso5MbdKwBT/c7O5GJZSU0y47Mp5/SGcUGRUtcfZONJiGQXd3XuFRZahIae9UGAU65s9PlrtJ8j5PlnX4B566tU+YmSZF+m0Z5UAL/9MkpMYrUZVnRQTLttE5HScasPvBy4AEJF6nHpeFktwBjxi/93ai/UNo/288ALkBrJEpBF5+VFmDkQYkuq1J80I/pk4czHCCIfedC+rX1+dN5/Vr6+mf2c/fTv6rMCpYIoJl78BbgRagb9TVRO7wnzgf+KemKVKaWhwFxpum7V59YVw2o9bhKTX+QFY+nQzS47ZxNtj97ZN2OW0lwQX/ww4VZwZ435K2FyMsOa2NX1rXNs3pTfteW1NeJWJZ76Kqq5R1dNUdbaq3pbT/rCqXlqS2Vmqj7Y2KDSriuw1VRX2rSv4iha+zyWI9uP3/AAs6ujj5gfgoG0g6vy++QGnPXZMCZqtq07iuy/NoimT4tfTB3mlKcOUgTGue9JEkYsRtvRJRjO++tlyKpWHrwx9iyVSCjUHr1yrrBZR6BvJvi8kqPbj5/wgDAyw6FlY9OywA+HGDcCwiLV9lTrdQQPjGV8/np2DO0csNxOEUpY+qfVyKpWGFS7VTBLDeLs9nj6LOend5pzrRwFHo/HSfgr7igwXaF7nByGIyS8m3CLWhgR27t5B02A9x6ebHPMZGR7d/cKwagBBCVv6pL6unsGhQd/XslQOVrhUK0kN443CSd/SAv39sGmvXZ7WVvd1eWk/bm1hPxc3QRaF0AqAV2QawNbfznVeRJicGbb0yaz9ZvHC6y+gOfsSCpL3PuiYlmRQLInyi8VOVNVvRT8dS2QECfktJVE46Xt7oacnv62nB5qavAVMMcETFV6CrISfd0ki1nIIWzPM6/wwY1qSQTHNJbuRazvwPpzSLwBnAI/HOSlLBAQJ+S0lXk/3hYIli9t8kyo4wVuQlYgwmf/gRJaljRyaO3Our2uGLX3idb4VJpVNsZ0ovwYgIr8B3quqb5r312BDkZNPAuz/rng93a9Z49zsCkmlhrcVE5ydncnyMZWYSDL/J0J6V7o0E7ZULX58Li3kZ+vvMm2WJJMA+78nbk/3L73k3jdoNYis4EmKj6kMjDrz32KJED/C5Q7gcRH5T/P+LOD22GZkiYag9v81a/Id5FOnwmEBNhwNG5k26BEx5NXuh6SYyqqc+Y/1jrifSzmwZWXKi5+S+0tF5CHgA6bp06r6dLzTskSCX/t/oWCBve/9CJgoItOiytwvpNw+pkoit2ZZKgNPd5mwZXj0IKdLYdjy/Md683aibO0b4LLbnL99OQWMLdRZfvyGIk8AtqvqD0XkABE5RFVfiXNilhJSKFhy2/0Ilygc7M3N7vNodimd4mby86LcPqZKwU/NsonDw5YvvLc7b4tjgHG7hrjw3u6yCpckF+qsFUYULiJyNXAsTtTYD3GqFP0Yp5jlqBCRS4ALAQWexSnpPwVYDjQDTwHnquouEWnAMc0dA/QBn1DVtWacK3AKamaAz6vqw6b9NODbQApYpqrXj3auFh8EjUxzM6H1eZRIcWt3M/k1NzvhyG4+piQmk4L3vMLON65xXTiwz/1v7NVeKkpZOcDijh/N5S+Ao4HfA6jqJhGZWPwUb0RkGvB54EhV3SEidwMLgY8CN6jqcrNR2QXATeb3VlU9VEQWAt8APiEiR5rz3glMBX4pItnH7O/hbBOwAXhCRO5X1edHO2fLCAQxaXmZ0IKEIoO7ya+pyT1ZMonJpF6fQ39/vpAMOt+4xvVgS3MDrS6CZEtzeTXGsJUDLOHxI1x2qaqKiAKIyD4RXXe8iOzGMbltBk4BPmmO3w5cgyNczjSvAe4BvivOhjJnAstVdQB4RUReBo4z/V5W1W4z3+WmrxUuXkyd6m6SmjrV3/lBItO8TGhB8XoKL7xRdnaWNifGr3bg9Tm4/R2Ghhy/WBzjBvgcFjw9wD88vJPp2zr2OO6XLWhj+uOruWYerGuCmf1wTQdsOK68UYlhKwdYwuNHuNwtIj8AJonIZ4C/BpaN9oKqulFEvgmsA3YAv8Axg21T1Wxo0AZgmnk9DVhvzh0UkX4c09k0IDdSP/ec9QXtx7vNRUSWAEsAZtaybT7rVxlttFiQyLQoHOxBAghKmUwacF53vguunL/3prx0pVvRS0MmszcPKHdcyP/cg67LZ/8Ff8xw44ODTNjtvM867s/9f61c+zFhd51TruXVSbDkY8KhB5Y3XyFs5QBLePxEi31TRD4EbMfxu/yDqq4Y7QVFZDKOJnEIsA1nh8vTRjteGFT1ZuBmgGMnTvQozVsjHHZYsNDjQuLKTHcT+kECCEqZTBpgXncenWLJRzJ79n55dRIsOcN57SlgCsd96SXnd64wC4qfzyGd5h8e0T2CJcu4XUN0pjaxu2AXhN11mgjHedjKAZZwFNkcw0FEvqGqK1T171X1MlVdISLfCHHNDwKvqOprqrob+DlOcMAks8slwHScHTAxv2eYudQDTTiO/T3tBed4tVuSjtveLW6mtSDaiNeeMHEkkwaY15UflLxNxQDeHgtXnpoqvmdNLoODwUyKo/kczD4xM7e7H97k4X21jnOLn2/xh1zaPhLimuuAE0RkgvGdzMfxhzwCnG36LAbuM6/vN+8xx3+lqmraF4pIg4gcAszCqXn2BDBLRA4RkbE4Tv9sXTRLufF6Um5ogPb2vcez7920oWJjFNLS4j4uOP6Yjg7nd29voGX4vr5H+7oJ7smh6xozw+dbH0Hx8iCfr9f5Lkx906O7dZwDTr5N5/pOOtZ20Lm+k950BN+zCqFYVeT/B/wt0CYiz+Qcmgj8drQXVNXficg9ONFng8DTOKap/wGWi8h1pu0Wc8otwI+Mw/4NHGGBqj5nIs2eN+NcpOpsayciFwMP44Qi36qqz412vpaIKeb892taC1rapnDcuLYjCDCvotWLR5pvdlwR93pshQT9fN1wWdvOejj0zTFs3DffXmYd5w61nsgp6rELoIg0AZOBfwIuzzn0pqq+UYK5lZRjJ07UJ489ttzTqA2iyLdwGwP8jZstbllIQwPMmRN8Pbl4ldEpmO+d85pZ8r7NvF2/9/9vwqBw85rDXeuC3dmwhiuP3MS6fWHmdlj6/FQWrWuC1auHz2HqVCc/KM78GRGuO3Uc/3pyA/07+xmbGmsd5wV0ru/0DIeeMyPk9yxBdJzf8ZSqDrt5FquK3A/0A+cAiMiBwDigUUQaVXVdXJO1VDlROP/DaCNxRZB57TOT/Z0zt0UPb4JNhdFiyqJBhoVZOVsX9/C2KRD9ahMsOc4Zd9ELBbtqijj5PoXBGVFoa9nP3GTtrzzcaU7VparqZhkVtZ7I6SdD/wzgWziJiluAg4DVOMmLlmohqVnsfklCBFmQHBOcqLBhkWENw+frtnXx26khrjxyE4seLjhf1X3NMe6Bs/APGf7l250jFq70KiSZ1MKXXvgtiFnriZx+HPrXAScAa1T1EBwH/Krip1gqiuxTbWG5+iic3KUiiDbiVq+sWHvYOYQcw2vr4nX7BpiHyas5+O+g7mrn953v8ugbgAVPD/D9+5XWvgHq2Jv/Mv+x/O9O1v+Qvdlm/Q/7P72Gy257ccTzk4LXOtwc9W2T26iT/FtsLfmj/AiX3araB9SJSJ2qPoJTa8xSLRR7qq0UgkSQBaljFgS3jc0iYObb7gaGmf0eJ7is+c6jUyw5w8mnUdmbV3Pn0aOcczrt5L88tIN9XPJfzv3parp6uva0eRWSXJXa5Fn4MokUK4hZSEtjC+3N7Xs0lYZUA+3N7TXjj/IT47hNRBqB3wB3isgW4K14p2UpKUnZ2TGMaS5IBFlUPpfC+XrlnKRSjrlqNGVugKW/VJZ8hLy8mAm7YOkjAsJwn8vQkBNinWX8eK682COv5oPCoicCTii3gvL2DtcuMwoEn5efYaNHnkwUhS/j2M8lqB+llhM5/QiXM4GdwCXAIpwkxmvjnJSlxBQrG1KqnR3DOpyDlKCJwufiNl8vMhk44oj8uQ0OuocR19cPizhbBDDoVipGXUrIKIueLVAlduxg3QT3qXnl2/jG47PcMCl/B1Ev/8M0jzyZsIUv4woDrnU/ShD8lH95C0BE9gUeiH1GltLjd3+UOAs+RuFw9huFFmTvGC/c5htkbr/+tXu/wUHXubk5/+98l2Pa8lNCZma/c7yQmQMhb4ou3523x8C1Hx6X382jkOQJmVZ2ju3JM43tHFvHsgXh/BJx7ediC2L6x0+02N8AX8PRXoYwijhgP81KpJjpyU8BxLh2dizl9aLwuYSdl7ppHT7rihmunI+7qWv+8HGWroQlZ9XlRZ1NyNSxtDvkv3HBd6dnH/jq6eO59+h8oeVVSPL1g1r45vlNkUeLFTNfZfNPRmMqswUx/ePHLHYZcJSqvh73ZCwxM5LpKffJuliiYRyUssBkFIJsNBWIcwiidXixrsl/+6JngSPaubKtm3UNA8wcaGBpd5trwmZgcvJfzlmQpmuW+9/My/+w8sSWyEOPvcxXwLBIr+zc/FLLfpQg+BEufwLejnsilhIQxPQUtMRKWEp5vaCCzE3bC7LVssiwpiBahxeepi6PKLJFW1qiESYVgJv5yg279XF8+AlFvgJ4TER+ICI3Zn/inpglBoI8sbe0QGtrfltra3zRYl4FJuO4npfAcmv3ygGC4fP12lzt8MOHNQXROrxY+tQkJgzmC64Ju4WlK106H3GE/4GrALcwYC9qJWO+1PjRXH4A/Apnr/vRxVJakkHQ7YjdSpk0NcUrYEpVFUBcyqa4UUzbmzPH31bLLmsqWrhyavPw+mQwrG1R6jBY0zvc1DWIk+lfqdUWIqLQfFWs1pclevwIlzGq+sXYZ2KJnyi2I44rWqyUdHfnCxbwLpsS1D/jU0Au7W5jSfuL7g72w1rcN25zaXM1dbVQ+X+jGLCRXqXFj3B5yGwH/ACw5z+qGisjVz1RbEfs1V5JtcmCrC2mQIOsQIjFwW5xxUZ6lRY/wuUc8/uKnDYbilyp+DU9BTWhxbE/SlykUu4JjG7lW2IMNKglB3tSsJFepcNPEuUhpZiIJWFUswnNy7/i1h5E24P49qqJ4npee82UmUqrimzxR7GdKE9R1V+JyF+6HVfVn8c3LUvZidOEVm4GPUqeeLX71fai0OCCjBGkb6Fggb3vyyhg5j/Wy2W3vbgnQz9bFRmwAqbCKaa5nIQTJXaGyzEFrHCpduIwoSWBUu/nEkSDCzJGkL4ee8qwaVNZhcuF93Z7VkW2wqWyKbYT5dXm5bWq+kruMRGxpjLLXkqdcBmWuOZbTIPza74KogUGqWadULyqH0dRFdlSXvwkUd7r0nZP1BOxVDClTICMgrjm66X51Nf734wtyL40xTQtt6TPBOJV/ThsVWRL+SnmczkcZyvjpgK/y77AOPezLDVLKRMgg+KlNUQ9Xy+NyG0vlyjK7gSpZu0VIedVVaBELFvQludzgWiqIlvKTzGfSztwOjCJfL/Lm8BnYpyTxTI63IQIlC5MuqUF+vvz/Rutrd7+Dq+yO5s3w7Zte9v2NfsZu23cVng9LzIZR5AkLFos61ex0WLVRzGfy33AfSIyR1U7SzgniyU4XpFTdXWlC5P2KpnjpTW4mbXWrMkXLOC87+/fW1Ugu7b+/uHX86KhwREkCQg9LiSOqsiW8uPH5/IXIrKviIwRkZUi8pqIfCrMRUVkkojcIyIviMhqEZkjIvuJyAoRecn8nmz6iimW+bKIPCMi780ZZ7Hp/5KILM5pP0ZEnjXn3CjildhgqRq8Iqe8wouDhEn39jpaQ0eH89vNV1JsDl5fPzdTl5cWUliuZmjI6eunKnOSgysSTG+6l871nXSs7aBzfSe9aY+/u8UVP8LlVFXdjmMiWwscCvx9yOt+G/hfVT0ceA+wGrgcWKmqs4CV5j3AR4BZ5mcJcBOAiOwHXA0cDxwHXJ0VSKbPZ3LOOy3kfC1JY80a52af/QmaU+M37NirKrKbgPGaw+DgcAFTyuedOKtZw17hu20bdy0fZMHTlR/pld0muXDvFytg/ONHuIwxv/8c+JmqeuwW4Q8RaQL+DLgFQFV3qeo24EzgdtPtduAs8/pM4A51WAVMEpEpwIeBFar6hqpuBVYAp5lj+6rqKlVV4I6csSzVgFtCoBeplPPknkuQJ/liuSRB8CqUWQp6ery1rbAUCN/Wt+DGn++oeAFTbJtkiz/8CJcHROQF4BhgpYgcgLPl8Wg5BHgN+KGIPC0iy0RkH6BFVTebPj04tV0BpgHrc87fYNqKtW9waR+GiCwRkSdF5MnXdu8OsSRLSfErWOrqHB+DW9gx+DN1xVl9wG0Mt/pmYRmNMPSLi/CdsBv+4eEwt4jyM9IulpaR8VNb7HIR+WegX1UzIvI2jjYR5prvBT6nqr8TkW+z1wSWvaaKiLqeHSGqejNwM8CxEyfGfj1LCchm3xcmKuaahYKUTYmz+oDbGG6O/yiIqxSPx7jTt1X2v5PXNsl27xf/eGouIvKlnLfzVTUDoKpvAZ8Pcc0NwAZV/Z15fw+OsOk1Ji3M7y3m+EZgRs75001bsfbpLu2WSsSvMz3LnDkwb577Rl5Zgpi62trCmdVyzyl839w8fG1xlcwp8bgbJlV2DE3b5DbqJP9vZvd+CUYxs9jCnNdXFBwbtYNcVXuA9SJibBPMB54H7geyEV+LgfvM6/uB80zU2Ak4GtRm4GHgVBGZbBz5pwIPm2PbReQEEyV2Xs5YlkrCy5k+aZJ7f78JgUG3e44im79wjNZWxxdSuLbm5uGCCIYHANTVwZgxw/u5EWe0mIvwfXsMXPvhys6zdtsmub253ZbrD0Axs5h4vHZ7H5TPAXeKyFigG/g0jqC7W0QuAF4FPm76Pgh8FHgZeNv0RVXfEJGvA0+YftfmbGD2t8BtwHjgIfNjqTS8NIwdO8IlBAY1dUVRwLNwjM5O97X19TmCyC0ZtLBt9eqR5xL3xm0F1bN79oGvnj6ee4+ufPOR3fslHMWEi3q8dnsfCFXtAo51OTTfpa8CF3mMcytwq0v7k8BRYeZoKUJc+40UUkzDCJMQGFfhSq8bvtu4UWyfXEy4zJnjfSxqsnPr6uKcBWm6ZlW+YLGEp5hweY+IbMfRUsab15j3la3zWkZPXPuNuBGXMz3oBmBBEMkPO86aswqFbLHdMOMqV5PQzcIs1Umx8i8xxERaKp649htxo7nZPey4uTnYnN2Io3Bld7d7PsuaNfnFKwcG3JMo6+qc9kKh4/WZTZo0vFRMtr2QhG4WZqle/OS5WCx7iWq/ET/09QVrLzde68pkhgtZVacUf2GgQJByNbNnDxckkyY57YUU2yzMYomBEfNcLJY8gpiqgpq1Ck1HlbZ9spepy4vBweFJk0E/MzdB4lUd2mIpIVZzsQQjSN5HkL5uYcdeJHX75NHUC/MTihwk2MArfNtiKTFWc7EEI4gzPEhfN/+MG+Wo8Os34s3LpOWXbChy4R4wQQpPevm5SszA4AA7B3fSsbaDhlQDbZPbbFhvjWGFiyU4QZzhfvuOpKmUImfDjShKxQRhYMB9T5impvCfoxuj0QK7uiCd5uTF0NUK6VSGuf1N8I40XQdk6N/Zj+ZkK2QrCgNWwNQQVrhYkkExX0MpczYKCRLx1tYGL7wwPBQ5lQqm1YSJsPP6HOvrnXGiyu1pbIRGYCKwKw1HzwZgNtC5vnNYXa5sRWErXGoHK1wsDmGSHaMgrsTGsAQNKnALRW5sdA8ZLsRt18yRrleI1+c4a5bzugR/Y1tR2AJWuFggfLJjFMSZ2BiGINFbXmXtvQRLKuVoFH5LuvhhpM+x3J+npWawwsUSPtkxKuJIbAxLEI0qqL8jk4EPfCC/LaxwgWR+jpaawwoXS+Xlk3gRh2kviEYV1KEfRW6QJRH0pnvp3trNQGbARscZrHCxVMcNLU7Tnl9NwEvLyZbX96P9JNX3FIDxGWFHanht2/GZyt7jxYvedC8v9r24Z1tkGx3nYJMoLdFtiFVOotrrPgxee794bbXslRsUxf4xZeSbK+oYUxAcN2bQaa9Gurd27xEsWbLRcW70pnvpXN9Jx9oOOtd30pseYQO8CsVqLpbkOtODkBTTnpeWE0duUEL57KoMTW/BlfNhXRPM7IelK+GcZzP87LPlnl30BImOqyUtxwoXi0OF39CqwrQXFTGFld95YC9XtnWz7qQBZr4pTKgbBwz/fLc0N7Do2QEWPZvf3tNcnX+LhlSDqyDJ7mKZSzEtxwoXS+VQ7tyVOClcW3Ozf79GKeeV5IoCAbjzwF6WtL/I2yln3Ff3Vep0Bw0uuaHLFrRx2W0vMm7X3r/FzrF1LFvg/28x/7FeLry3mwP7BtjS3MCyBW2sPDGZ3922yW152ghAndTRNnn4emspB8gKl2olCbkrceG2tp4ex3He11d1N/ZARB1Wbkq9nPuVQbTAZTIksGNwB/V1+beRrBAYrXCY/1hvnnBq7RvgsttezBs7SWQ1Dj/RYkG0nErHCpdqJSm5K0Hw+9TvtbbNm/dmyA8MQH9/NGv1mldh++Bg+T/zOHxPjY2obPM8PHfm3GFtK09sGbUguPDe7jytB2DcriEuvLc7kcIFHAHjx6wVRMupdKxwqVaS4uD2S5Cnfr+lV6LYadFrXv39+Wa4Yp9rFJ+53y2KY/I9CTA8uDieJ+4D+9w/L6/2JONm3rvpsFY2pff+LVv3aa06fwvYUOTqxetmklQHd5BQ4iBrCLvTote8Nm3yX8o+7GfutUXxmjXD+8YUVj7OJUclrifuLR6Of6/2pJI177X2DVCHY96b/vhqXtu+Oa9fz1s9VRmObIVLtVJpuStBNC23tcVFWK0jis88yBbFMeXJNGiK8fXjERwh05BqoL25PdATt9/8jmUL2tg5Nv/vGzQgIAm4mfeumQe76/J1wGI5MZVM2cxiIpICngQ2qurpInIIsBxoBp4CzlXVXSLSANwBHAP0AZ9Q1bVmjCuAC4AM8HlVfdi0nwZ8G0gBy1T1+pIuLglUWu5KEHOO29riMvfV1wcrl19f7xSkLOdnHlNYeUN9Aw31DcxunR343CD5HWEDApKCmxlvXZN7XxstFi1fAFYD+5r33wBuUNXlIvJ9HKFxk/m9VVUPFZGFpt8nRORIYCHwTmAq8EsRyRqhvwd8CNgAPCEi96vq86VaWGKopNyVoGVPCtfmZjoCxzcRhkI/ThYR58ettH2lfOYlJGh+R5iAgKSwpbmB1gIBM7MfXp00vG81RouVxSwmItOBPweWmfcCnALcY7rcDpxlXp9p3mOOzzf9zwSWq+qAqr4CvAwcZ35eVtVuVd2Fow2dGfuiLOEIa8457LDhgsTL6R2ETMa9XdV9vgCdndDR4fzujcCW7iUgwwrOYvT2OvPftg36+1nwR4/PwSfF8juSUA4ljjm4mfeu6YAxQ/n+KxstFi3/BnwJZx87cExh21Q1a3/YAEwzr6cB6wFUdVBE+k3/acCqnDFzz1lf0H58xPO3xEFYTeuww8ILk0KKmesK5xtXnkt2TX6ixaKgcB2q3Pg/wLgB7j16dE/YXvkdKUmVvRxKXCVZ3Mx7G45r49AD/eXEVDolFy4icjqwRVWfEpF5pb5+wVyWAEsAZiY1ispSXoKY6+LMLYpDcOZikiVPXgy3/36QmQXLmDAIV/3vjlELF6/8DhEhM5SvFQUthxK23H2cJVnczHstVF8dMTfKobm8H/iYiHwUGIfjc/k2MElE6o32Mh3YaPpvBGYAG0SkHmjCcexn27PknuPVnoeq3gzcDHDsxIkexnVLIilVmZUggRGVlltUSGMjNML07dtcD8/oZ1TOfPDOYl/9+mrX/n4d3FFoHbVUkqWUlFy4qOoVwBUARnO5TFUXicjPgLNxfCSLgfvMKfeb953m+K9UVUXkfuAnIvItHIf+LOBxnHyvWSb6bCOO0/+TpVmdpSSUusxKJQVGRMCGScLMbcOftcLmmbhlsWeFTSF+HdxRaB21VJKllCQpz+XLwBdF5GUcn8otpv0WoNm0fxG4HEBVnwPuBp4H/he4SFUzRvO5GHgYJxrtbtPXUoysAzdKR3RcJGHvlirm2g+P460x+W1x5Zm0TW6jTvJvQ3VSR/P4Zl8O9ii0Dq85VKOTvZSUtfyLqnYAHeZ1N06kV2GfncBfeZy/FFjq0v4g8GCEU61uklBwMQhJNT9VSdn/e49uYGBwJ//SMTbSPJNivpHc9ubxzfS81ePL1BWF1hGk8KTFP7a2mKXyilwm9SZeBVsUZ1n+nhQbzpgT2Xgj+UZyb+Sd6zt9m7qiKgTpt/CkxT9WuFiSqwl4kdSbeKVVRYiRQi1lcGjQt8AIYuqqRK0jbHRbpWCFiyW5moAXSb6JV4Lz34QdT74sA6kU6VSGue9IOYcaIb0r3PBuWooXXiatIKauStI67DbHltoiqZpAMSrhJp6LW+g0lE9ANjZCKg0TG2kE+MBsAGZHMLRbBJcXbgIj6XuehNE87DbHltoiTk0gSD5KXH3LjVvAxAsv5NctS3oQRREKb7ZBIrWaxzcPa0uyqSus5lFLOTVWuFgc4tAEgkShxdU3CbgFTLgVxExyEIUHQUxgbvTt6HNtT6qpK6zmUUs5NVa4WOIjSBSaV981a4ZrKKWObgurJQUJjEhqEIUHQUxgblTaE3tYzSPpJr8oscLFEh9BotC8+mYyeysTZzUUrx0g47gxR6ElBdlvJoogihKaDP3eVAVBXTZKrrQn9rCaR5JNflFjhYslPoJEofm9ARfbWjiO6LYotCS3gAmR4aaxKIIoSmwy9OtjcRMs4O5zSTJRaB5JNflFjRUulvgIEoXm1rcYdXWliW6LIgfIK2DCrS2sACixydDtZhsEL59LUgmqedRKTosbVrhY4iNIFJpb30zGfYvhXN9L3KafqHKAvAImop6zh9AbGhig+f2PAjD7HU5bOpWBXWkaxzaO+nJuN9sgfpRK87mAf82jlnJa3LDCxRIvQaLQRtp8C/ZqKG7jxuFraGsbHjYsktwcIA9huGGSwEQjRGbNBmBuTFNISYqM+t+5slqf7mspp8UNK1wswSlVPkoQzSdOX0Ohb8QtjDh3HuXMv3ExL+6sdyodx4Hb07kgI5yVT7U+3ddSTosbVrhYglHqfBS/mk9cvgavMv5u4yYh/6ZQIIvwzfnjRr2D5Ei4PZ0rSn1dPSlJ7dFGMpphcMjFxAlV+3RfSzktbljhYglGFLkrcTiX4yq+GWTcpFSXzgrkri4AVh4ezbBu5iuvp/DBoUHmHjw371y3KCuvQIC4nu6jMMH5HaOWclrcsMKl1ogrITBI7koc+ShxFd8MMm6lVZcOgJdzur6u3lUjSUmKzvWdeTfg9ub2YTflsDtRRrEG8G+CCzJGLeW0uGGFSy0RZ0JgkNyVOPJR4iq+GWTc+nr36Lb6yv8383JOC+KqgQzp0B6hkb0Btze3M2fGnGFjl+rpPgoHe9AxaiWnxY3K/9Zb/BNXQmCQ3JW48lHiKr4ZZFwvR3+xAICoKCijD3tDjnPL6I827NjLTJXRDEfsf0Te07mbf8XrBlzKp/soHOy17qQPghUutUScCYF+c1fijJ6Kqwy/33EzHuG3Xu1Rk1NGH9gTcjw7gqGLOacLn8471na4juF1Ay7V030UDvZad9IHwQqXWqKYmSqIL6bS9lIpFZW26VoAijmnCx3cXn6YKG7AYRzyUTjYvcZoHt88zMdUq+awLFa41BJeZqrm5nhCaL18PP390NdXGXuxBKG5GTZtcm+vcFoaW+jf2c+m9N71te7TCsDLW15gd51j+hvIDCAKbqkuYeuIhXXIR2GCcxujeXwzPW/1VGWuThiscKklvMxUceaIuI2bewNO+l4sQejzqJPl1V5B9KZ76XmrJ6+t560etvZvZncq36ekHjmUYeuIReGQj8IEVzhG5/rOqs3VCYMVLpbS54gUUoGbZLlSxaHIXjf2HXX+xwjr9C7mTC+nSco6+d0J8NWIBhGZISKPiMjzIvKciHzBtO8nIitE5CXze7JpFxG5UUReFpFnROS9OWMtNv1fEpHFOe3HiMiz5pwbRSRYPYpqJWumyt7sslqDV6hsFDkifqmCG7DneqvA5xLFjTKIz6U33Uvn+k461nbQub6T3nRv0fMLw557072h5+sXr3nVupO/5MIFGAQuVdUjgROAi0TkSOByYKWqzgJWmvcAHwFmmZ8lwE3gCCPgauB44Djg6qxAMn0+k3PeaSVYV/LxMlOpOr6XXKLKESkc14squAG7rjeu0OsS43WjnLwTJuzKb0tlwG37Fr8+l6xvpVBgNI9vpk5G/j5lTVKlom1y27B51VImvhclN4up6mZgs3n9poisBqYBZwLzTLfbgQ7gy6b9DlVVYJWITBKRKabvClV9A0BEVgCniUgHsK+qrjLtdwBnAQ+VYHnJpthuj0ccEU+OSH9/vo9l0iTYvr00uS+lphSh1yaf5eTF0NW6tznKMvpueEVJzd/Zyp//ehPXzIN1TTCzH9JjoW/C8DH8+ly8THB9O/qGZfknwSRV65n4XpTV5yIiBwNHA78DWozgAegBsn+ZacD6nNM2mLZi7Rtc2t2uvwRHG2JmNTw5j0Qq5Z5zkUoFCy/2G7bc2ws9+U5gtm+H1tbyR4t5rSFseZxShGk3NkIjMDGnLeYy+l430NcPamHDjiZW/aibA/sG2NLcwJSLw93wiwkMN2d6EvJOajkT34uyCRcRaQTuBf5OVbfnukVUVUUk9rRmVb0ZuBng2IkTS5BGXWa8XE9BXFJBSsh4meH6+mDO8DIgJaNYiHRPT3mrGicYrxvoyhNbWHni3vaGkDf8IImKtV4cMsmURbiIyBgcwXKnqv7cNPeKyBRV3WzMXltM+0ZgRs7p003bRvaa0bLtHaZ9ukt/i1vdq2LtbgQJWw4aPeWmNWSvGUbLKRx3cHDkEOmR1mbxJOwNP8j51iSVXEouXEzk1i3AalX9Vs6h+4HFwPXm93057ReLyHIc532/EUAPA/+Y48Q/FbhCVd8Qke0icgKOue084DuxL6wSiCKDPIjACHI9N22icAfI0WgSbuMGpRoi2UpI2Bt+0POtSSqZlENzeT9wLvCsiHSZtq/gCJW7ReQC4FXg4+bYg8BHgZeBt4FPAxgh8nXgCdPv2qxzH/hb4DZgPI4j3zrzIXghSTdNIojACHI9N43IreBjUE3Cbdyg1II/LmLC3vCtwKh8yhEt9iiuxSEAmO/SX4GLPMa6FbjVpf1J4KgQ06xOotg2uLU13y8B3gIjyPWCaAdx9K2rC7Y2i8VSFJuhX2uE3Ta4rw/a28tb5DKIJuG1x0oq5RwrXENTU+mqOFssVYwVLhZ3ivlWylkVOagmUWwvFbeItSRVfI55jxaLJU6scLG4k6Ty8dm5jEaTKPceK2GJcY8WiyVOrHCxuFPKXSRHIkxOTJKEpMVSQ4iWYgvWCkBEXsOJUvPD/sDrMU6nnOxZ2/6w31SYNgbG7oZdm2Dj6/DGCOePioNhZjMcUNjeB6+thXWjHXd/2G8mHCRQ9xrOBRSG1sGrca2lTFTrd7Ja1wXVs7aDVHXY/64VLqNARJ5U1WPLPY84sGurTKp1bdW6LqjutUF5qiJbLBaLpcqxwsVisVgskWOFy+i4udwTiBG7tsqkWtdWreuC6l6b9blYLBaLJXqs5mKxWCyWyLHCxWKxWCyRY4XLCIjIrSKyRUT+mNO2n4isEJGXzO/JxcZIIiIyQ0QeEZHnReQ5EfmCaa+GtY0TkcdF5A9mbV8z7YeIyO9E5GUR+amIjC33XEeLiKRE5GkR+W/zvirWJiJrReRZEekSkSdNW8V/JwHMFu33iMgLIrJaROZUy9rcsMJlZG4DTitouxxYqaqzgJXmfaUxCFyqqkcCJwAXiciRVMfaBoBTVPU9OJVSTjP7+3wDuEFVDwW2AheUb4qh+QKwOud9Na3tZFWdnZMDUg3fSYBvA/+rqocD78H5+1XL2oajqvZnhB/gYOCPOe9fBKaY11OAF8s9xwjWeB/woWpbGzAB+D3ORnOvA/WmfQ7wcLnnN8o1Tce5EZ0C/DfOFhbVsra1wP4FbRX/nQSagFcwQVTVtDavH6u5jI4WVd1sXvcACSmjOzpE5GDgaJydO6tibcZs1IWzXfYK4E/ANlXN1t/fAEwr0/TC8m/Al4Bs4bdmqmdtCvxCRJ4SkSWmrRq+k4cArwE/NObMZSKyD9WxNlescAmJOo8cFRvPLSKNwL3A36nq9txjlbw2Vc2o6mycp/zjgMPLO6NoEJHTgS2q+lS55xITc1X1vcBHcEy1f5Z7sIK/k/XAe4GbVPVo4C0KTGAVvDZXrHAZHb0iMgXA/N5S5vmMChEZgyNY7lTVn5vmqlhbFlXdBjyCYyqaJCLZSuDTgY3lmlcI3g98TETWAstxTGPfpjrWhqpuNL+3AP+J82BQDd/JDcAGVf2deX8PjrCphrW5YoXL6LgfWGxeL8bxV1QUIiLALcBqVf1WzqFqWNsBIjLJvB6P40tajSNkzjbdKnJtqnqFqk5X1YOBhcCvVHURVbA2EdlHRCZmXwOnAn+kCr6TqtoDrBeRdtM0H3ieKlibFzZDfwRE5C5gHk557F7gauC/gLuBmThl+j+uqhVVvl1E5gL/BzzLXtv9V3D8LpW+tncDtwMpnAeou1X1WhFpw3na3w94GviUqnpsuZl8RGQecJmqnl4NazNr+E/zth74iaouFZFmKvw7CSAis4FlwFigG/g05vtJha/NDStcLBaLxRI51ixmsVgslsixwsVisVgskWOFi8VisVgixwoXi8VisUSOFS4Wi8ViiRwrXCyWBCAiZ4mIikhVVBKwWKxwsViSwTnAo+a3xVLxWOFisZQZU99tLk6Z/IWmrU5E/t3s/bFCRB4UkbPNsWNE5NemuOPD2fIhFkuSsMLFYik/Z+Ls87EG6BORY4C/xNnq4UjgXJzaaNl6cN8BzlbVY4BbgaXlmLTFUoz6kbtYLJaYOQen+CQ4JVzOwfnf/JmqDgE9IvKIOd4OHAWscMrDkQI2Y7EkDCtcLJYyIiL74VQ2fpeIKI6wUPbW2Bp2CvCcqs4p0RQtllFhzWIWS3k5G/iRqh6kqger6gycHQvfABYY30sLTvFUcHYuPEBE9pjJROSd5Zi4xVIMK1wslvJyDsO1lHuBVpw9QJ4HfoyzVXO/qu7CEUjfEJE/AF3AiSWbrcXiE1sV2WJJKCLSqKppU3L+ceD9Zl8QiyXxWJ+LxZJc/ttsejYW+LoVLJZKwmouFovFYokc63OxWCwWS+RY4WKxWCyWyLHCxWKxWCyRY4WLxWKxWCLHCheLxWKxRM7/B8ni2wwcTVJiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing Package\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "x_set, y_set = sc.inverse_transform(x_train), y_train\n",
    "\n",
    "x1, x2 = np.meshgrid(np.arange(start = x_set[:, 0].min() - 10, stop = x_set[:, 0].max() + 10, step = 1),\n",
    "                     np.arange(start = x_set[:, 1].min() - 1000, stop = x_set[:, 1].max() + 1000, step = 1))\n",
    "\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",
    "\n",
    "plt.xlim(x1.min(), x1.max())\n",
    "plt.ylim(x2.min(), x2.max())\n",
    "\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",
    "\n",
    "plt.title('Support Vector Machine (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualising the Test Set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "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"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing Package\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "x_set, y_set = sc.inverse_transform(x_test), y_test\n",
    "\n",
    "x1, x2 = np.meshgrid(np.arange(start = x_set[:, 0].min() - 10, stop = x_set[:, 0].max() + 10, step = 1),\n",
    "                     np.arange(start = x_set[:, 1].min() - 1000, stop = x_set[:, 1].max() + 1000, step = 1))\n",
    "\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",
    "\n",
    "plt.xlim(x1.min(), x1.max())\n",
    "plt.ylim(x2.min(), x2.max())\n",
    "\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",
    "\n",
    "plt.title('Support Vector Machine (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}