11_kernel_svm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0MRC0e0KhQ0S"
   },
   "source": [
    "# Kernel SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LWd1UlMnhT2s"
   },
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YvGPUQaHhXfL"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K1VMqkGvhc3-"
   },
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M52QDmyzhh9s"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YvxIPVyMhmKp"
   },
   "source": [
    "## Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AVzJWAXIhxoC"
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1501,
     "status": "ok",
     "timestamp": 1588268004246,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "P3nS3-6r1i2B",
    "outputId": "000cce2c-0721-441e-d7b6-36a07ad0ed20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 171
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1496,
     "status": "ok",
     "timestamp": 1588268004246,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "8dpDLojm1mVG",
    "outputId": "f889b621-535f-4bbf-f476-eda5e4221313"
   },
   "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",
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      " 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1492,
     "status": "ok",
     "timestamp": 1588268004246,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "qbb7i0DH1qui",
    "outputId": "a3f11154-8674-4902-ca81-e296d2a6fca9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1489,
     "status": "ok",
     "timestamp": 1588268004247,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "kj1hnFAR1s5w",
    "outputId": "f636a0fb-dc1f-43b6-eda0-3e0b4d2f6053"
   },
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kW3c7UYih0hT"
   },
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9fQlDPKCh8sc"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
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      " [-0.60673761  1.89663484]\n",
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      " [ 1.96810099 -1.3505973 ]\n",
      " [ 1.47293972  0.07006676]\n",
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      " [-0.80480212  0.30201192]\n",
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      " [ 0.68068169  0.27301877]\n",
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      " [-1.59706014 -1.49556302]\n",
      " [-0.50770535 -0.53878926]\n",
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      " [-1.39899564 -1.089659  ]\n",
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      " [-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",
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      " [-0.11157634  1.95462113]\n",
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      " [-0.50770535 -0.8287207 ]\n",
      " [ 0.68068169 -1.37959044]\n",
      " [-0.80480212 -1.58254245]\n",
      " [-1.89415691 -1.46656987]\n",
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      " [-0.30964085  0.09905991]\n",
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      " [-0.11157634  0.1570462 ]\n",
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      " [-0.80480212  1.89663484]\n",
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      " [ 1.77003648  1.83864855]\n",
      " [-0.90383437 -0.24885782]\n",
      " [-0.80480212  0.56295021]\n",
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      " [ 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": 10,
   "metadata": {
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     "height": 1000
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    "colab_type": "code",
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1588268004638,
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    "id": "JUd6iBRp2C3L",
    "outputId": "d5c1422d-0160-42ee-d242-7600b7897307"
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   "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",
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      " [ 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",
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      " [-1.10189888 -0.33583725]\n",
      " [ 0.18552042 -0.65476184]\n",
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      " [ 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": "bb6jCOCQiAmP"
   },
   "source": [
    "## Training the Kernel SVM model on the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1868,
     "status": "ok",
     "timestamp": 1588268004639,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "e0pFVAmciHQs",
    "outputId": "4dcb0795-fcfa-4753-c3a4-13c10d43015a"
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "SVC(random_state=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yyxW5b395mR2"
   },
   "source": [
    "## Predicting a new result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1864,
     "status": "ok",
     "timestamp": 1588268004639,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "f8YOXsQy58rP",
    "outputId": "6525f27b-9adf-48a6-cea4-cb15972e928d"
   },
   "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": "vKYVQH-l5NpE"
   },
   "source": [
    "## Predicting the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1861,
     "status": "ok",
     "timestamp": 1588268004640,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "p6VMTb2O4hwM",
    "outputId": "ec76af11-3625-4b2a-c1da-3bd7e77e8ade"
   },
   "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",
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      " [1 0]\n",
      " [0 0]\n",
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      " [1 1]\n",
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      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
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      " [0 0]\n",
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      " [1 1]\n",
      " [0 0]\n",
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      " [1 1]\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",
      " [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": [
    "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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h4Hwj34ziWQW"
   },
   "source": [
    "## Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1858,
     "status": "ok",
     "timestamp": 1588268004640,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "D6bpZwUiiXic",
    "outputId": "964dd5af-7158-455f-eba2-c205e1f05a19"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[64  4]\n",
      " [ 3 29]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.93"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6OMC_P0diaoD"
   },
   "source": [
    "## Visualising the Training set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 326556,
     "status": "ok",
     "timestamp": 1588268329342,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "_NOjKvZRid5l",
    "outputId": "9c21c6b4-ac96-4683-972c-224c1a840993"
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = sc.inverse_transform(X_train), y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
    "plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Kernel SVM (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SZ-j28aPihZx"
   },
   "source": [
    "## Visualising the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 647842,
     "status": "ok",
     "timestamp": 1588268650632,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "qeTjz2vDilAC",
    "outputId": "ef991d23-2b7f-4046-88f2-dbfc639d73cc"
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = sc.inverse_transform(X_test), y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
    "plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Kernel SVM (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyMARUU4AeVdXSZH1V+VybNB",
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "kernel_svm.ipynb",
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   "toc_visible": true
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  "kernelspec": {
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   "language": "python",
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  "language_info": {
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