Support Vector Machine.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "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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     ]
    }
   ],
   "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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      " [    52 150000]\n",
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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 KVM model on the Training set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear', 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 = \"linear\", 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",
      " [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",
      " [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",
      " [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"
     ]
    }
   ],
   "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": [
      "[[66  2]\n",
      " [ 8 24]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "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": 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_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": 15,
   "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
}