support_vector_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "support_vector_regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3PAEPRDRLA3",
        "colab_type": "text"
      },
      "source": [
        "# Support Vector Regression (SVR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VCUAVIjRdzZ",
        "colab_type": "text"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56oRF-QfSDzC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXVXoFWtSF4_",
        "colab_type": "text"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfoa8OSORfHQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv('Position_Salaries.csv')\n",
        "X = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fAWJV6gpiTYM",
        "colab_type": "code",
        "outputId": "34d608d9-9699-438f-c21d-e7eb137a20d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1]\n",
            " [ 2]\n",
            " [ 3]\n",
            " [ 4]\n",
            " [ 5]\n",
            " [ 6]\n",
            " [ 7]\n",
            " [ 8]\n",
            " [ 9]\n",
            " [10]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P1CzeAyRiU3c",
        "colab_type": "code",
        "outputId": "760b4b2d-6827-4c54-a56d-dbdce814d76a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[  45000   50000   60000   80000  110000  150000  200000  300000  500000\n",
            " 1000000]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8Ny1GfPiV3m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = y.reshape(len(y),1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4aFLlbziaV9",
        "colab_type": "code",
        "outputId": "74ae1dab-4aa4-4cf9-a8c8-bb48edc1b16d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  45000]\n",
            " [  50000]\n",
            " [  60000]\n",
            " [  80000]\n",
            " [ 110000]\n",
            " [ 150000]\n",
            " [ 200000]\n",
            " [ 300000]\n",
            " [ 500000]\n",
            " [1000000]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YS8FeLHYS-nI",
        "colab_type": "text"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGeAlD1HTDI1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_X = StandardScaler()\n",
        "sc_y = StandardScaler()\n",
        "X = sc_X.fit_transform(X)\n",
        "y = sc_y.fit_transform(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXa8Z9FgjFTQ",
        "colab_type": "code",
        "outputId": "2b1cdd7f-db8d-47dd-bb72-0b571694a1b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-1.5666989 ]\n",
            " [-1.21854359]\n",
            " [-0.87038828]\n",
            " [-0.52223297]\n",
            " [-0.17407766]\n",
            " [ 0.17407766]\n",
            " [ 0.52223297]\n",
            " [ 0.87038828]\n",
            " [ 1.21854359]\n",
            " [ 1.5666989 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7Oo2kybjGr2",
        "colab_type": "code",
        "outputId": "b2e276ad-7622-4c70-8782-841be777763e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-0.72004253]\n",
            " [-0.70243757]\n",
            " [-0.66722767]\n",
            " [-0.59680786]\n",
            " [-0.49117815]\n",
            " [-0.35033854]\n",
            " [-0.17428902]\n",
            " [ 0.17781001]\n",
            " [ 0.88200808]\n",
            " [ 2.64250325]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eiU6D2QFRjxY",
        "colab_type": "text"
      },
      "source": [
        "## Training the SVR model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6R4rt_GRz15",
        "colab_type": "code",
        "outputId": "4b0c1e6b-d4b8-417e-d7d0-4c9dd7bc2b12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "regressor = SVR(kernel = 'rbf')\n",
        "regressor.fit(X, y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "deDnDr8UR5vq",
        "colab_type": "text"
      },
      "source": [
        "## Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ib89-Fq8R8v-",
        "colab_type": "code",
        "outputId": "0cbe0d2a-4d4f-402e-f811-0b323ce76ecc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sc_y.inverse_transform(regressor.predict(sc_X.transform([[6.5]])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([170370.0204065])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zzedFlUISSu_",
        "colab_type": "text"
      },
      "source": [
        "## Visualising the SVR results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWPRGsKpSW9U",
        "colab_type": "code",
        "outputId": "d98957c5-e6c8-4a85-8959-fc7ccd59021a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(sc_X.inverse_transform(X), sc_y.inverse_transform(y), color = 'red')\n",
        "plt.plot(sc_X.inverse_transform(X), sc_y.inverse_transform(regressor.predict(X)), color = 'blue')\n",
        "plt.title('Truth or Bluff (SVR)')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vBmiuRnDEOrYF4ERgZp3TrFnNro+AyZNTbnjySdhllzQUxOGHp5vCHcU6E0FEnNhegZiZ1ZQBA1JzUKX1wF//mjqD/e1v6V5AQwOMHNl+vYHbkjuUmZlVMn78mvcIALp354mxP+dbh6VxgPr2hV/8AsaMgY03rl6oG8odyszMKmm8DzBuHMyaxT/77s//G3ANk8bV8YEPwIUXwsknp45hHV1LKzH7RMTngTci4jxgb2CH/MIyM6sBo0cz54GXGPvFVXzo1fu5/ak6xo2DGTPgjDM6RxKA1ncoex13KDOzTu6ee+A//zO1Dn35y6ly0KdPtaNqey1NBI0dyn4IPJqtuzKfkMzMqu/qq9Mtgh13hN/9DrbfvtoR5WedTUOS9pC0dUR8NyLeBHoATwO/AS5pjwDNzNpTRBoZ9AtfgAMOgAce6NxJAJq/R/BLYBmApP2BH2TrFgH1+YZmZta+3nsv3SMePz49CTR5Mmy+ebWjyl9zTUMbRcTr2edjgfqI+C1pqIkn8g3NzKz9LFwIRx2V+gVccAGcdVbH6hS2IZpNBJK6RsQKYDgwdj2+a2bWIUyfDocdljoNT5yYOoYVSXMn84nAnyW9Rnpy6K8AkgaTmofMzDq0Bx6AI49Mn++5J/+J4mtRc0NMjJd0D+lR0T9GRGSbugCn5B2cmVmeJk2C449Po0b84Q8wZEi1I6qOZpt3IuLhCuv+kU84Zmb5i0g9g88+G/bbD269Nc0dXFQdcHgkM7PWW7489Q84+2wYNQr+9KdiJwFwIjCzAlm0KA0RfeWVqZfwr38Nm27a/Pc6Oz/5Y2aFMGtWSgJ//ztcdVXqMGZJrjUCSYdIel7SdElnraPcf0oKScPyjMfMiunRR2GvvVIyuOMOJ4FyuSUCSRsBlwGHAkOBUZKGVii3GXAq8EhesZhZcd1+e5oycpNN4MEH4eCDqx1R7cmzRrAnMD0iZkTEMuAG4MgK5b4LXAgszTEWMyugn/409RYeOhQeeQQ+/OFqR1Sb8kwE/YCXS5ZnZ+v+RdLuwLYR8Yd17UjSWElTJU1dsGBB20dqZp3KypVw2mnw1a/CEUfA/ffD1ltXO6raVbWnhiR1AS4GvtFc2Yioj4hhETGsd+/e+QdnZh3Wu++mOQR+8pOUDH77W3j/+6sdVW3L86mhOcC2Jcv9s3WNNgN2Bu5XGtlpa+A2SSMiYmqOcZlZJ/XKK6kG8NhjcOmlcIrHP2iRPBPBFGCIpEGkBDASOK5xY0QsAno1Lku6HzjdScDMWmPatDRw3GuvpZ7CRxxR7Yg6jtyahrIRS08G7gKeA26MiGmSzpc0Iq/jmlnx3H037LMPLFsGf/mLk8D6yrVDWURMBiaXrTunibIH5BmLmXVOEybAl74EO+2UBo4bMKDaEXU8HmLCzDqkVavSMBFjxsCBB6YJZZwEWsdDTJhZh7N0KZx4ItxwA3zxi/Dzn8PGG1c7qo7LicDMOpTSKSW//30488ziTCmZFycCM+swSqeUvOEGOPbYakfUOTgRmFmH4Ckl8+ObxWZW8yZNguHD4YMfhIcfdhJoa04EZlazItJ9gJEjYc894aGHYPDgakfV+bhpyMxq0vLl8D//kyaROe641F+gW7dqR9U5uUZgZjVn3jz45CdTEvj2t9OUkk4C+XGNwMxqyl13wec+l0YRve669Nny5RqBmdWE5cvh7LPhkEOgTx+YMsVJoL24RmBmVTdrFowalaaSPOkk+PGPoXv3akdVHK4RmFlV3XYb7LorPP00TJwI9fXQ/ZYGqKuDLl3Se0NDtcPs1JwIzKwqli2Dr30tdRIbNChNJjNyJOmkP3YszJyZnh+dOTMtOxnkxonAzNrdCy+kTmE//nGaV/jBB0v6B4wbB4sXr/mFxYvTesuF7xGYWbu68cZ0H6BLF7jlljSA3Bpmzar8xabW2wZzjcDM2sWSJfDf/50Gihs6FJ54okISgKYnFfBkA7lxIjCz3P3977DXXvDLX8IZZ6TpJAcObKLw+PFrPzLUvXtab7lwIjCzXF13HXz0o6m38OTJcOGFzUwiM3p0enRo4MA00cDAgWl59Oh2i7lofI/AzHLxzjtw8slw7bWw//5w/fXQr18Lvzx6tE/87cg1AjNrc089BXvskWoD55yT5g9ocRKwducagZm1mQi44go49VTYYgu4+2446KBqR2XNcY3AzNrEW2+lYSK+9KXUFPTkk04CHYUTgZltsEcfhd13h5tuShPJ3HEHbLVVtaOylnIiMLNWi4Cf/AT23jsNGfHnP8NZZ6XOYtZx+B6BmbXK66/DiSemQeOOOAKuvhq23LLaUVlrOG+b2Xp78ME0Yugdd8All8Dvfuck0JE5EZhZi61alTqE7b9/6hT24INw2mmp35d1XG4aMrMWmT8fPv/5NJXkZz+bHhPdfPNqR2VtwYnAzJp1//1w3HHpvsDll6fpAVwL6DzcNGRmTVq5Es47D4YPh5494f/+L/UTcBLoXFwjMLOK5s5Nw/3cf39qErrsMujRo9pRWR6cCMxsLXfeCZ/7XJoY7Jpr4Pjjqx2R5SnXpiFJh0h6XtJ0SWdV2P51Sc9KekrSPZKaGqHczHK2ahXc+627GfX+2zj0UOi76Dmmfud2J4ECyC0RSNoIuAw4FBgKjJI0tKzY48CwiNgFuAn4YV7xmFllc+fCBRfAkL5vM/z7B3Pn4o9zOhfxyPLd+dB5Iz1pfAHkWSPYE5geETMiYhlwA3BkaYGIuC8iGmepfhjon2M8ZpZZsQJuvx1GjEgzQI4bBwMWPcOvGc1ctuEizuB9LPWk8QWR5z2CfsDLJcuzgb3WUX4McEelDZLGAmMBBnjeUrNWe+EFmDAhDQcxbx706QOnnw5jxsCQHfcFYu0vedL4Tq8mbhZL+i9gGPCJStsjoh6oBxg2bFiFf6lm1pSlS+GWW+DKK+Hee9OAcIceCl/8Ihx+eMm0kQMGwMyZa+/AF1+dXp5NQ3OAbUuW+2fr1iDpYGAcMCIi3ssxHrNCefrpNPxDv36pM9iMGfDd76Zz/e9/D0cdVTZ3sCeNL6w8awRTgCGSBpESwEjguNICknYDfgkcEhHzc4zFrBDefhsmTUpX/488AptsAp/+dLr6P+igZoaHbpwjeNy41Bw0YEBKAp47uNPLLRFExApJJwN3ARsBEyJimqTzgakRcRtwEdAD+I1SV8VZETEir5jMOqOIdNK/8kq44QZ4910YOhQuvjj1BejVaz125knjCynXewQRMRmYXLbunJLPB+d5fLPObOFC+PWvUwJ45pnUijNyZLr6/9jHPAyEtVxN3Cw2s5ZZtQruuy+d/G++Oc0KtsceUF8Pxx6bxgMyW18edM6sFjQ0QF1dasSvq1urE9ecOam5fvBgOPjgNATEl74ETzyRBoI76SQnAWs91wjMqq2hIY3rvDjrWzlzJowdy4qVYvIWx3HllfCHP6TawIEHwve+l24Av+991Q3bOg8nArNqGzdudRIAXmA7rlo8hmu+MJx5K2HrreHMM+ELX0g1ArO25kRgViURqXfv9JkDmc5BTGcwD7E393MgXVjJYSsnc9LvjuDQQ8ue9zdrY04EZjlauRJmz05DO0yfvubrhRcaKwJ/BqAry9mBf/A9xnEC19Bv4MYw4oiqxm/F4ERgtoFWrEjN+uUn+unTU2/eZctWl+3WDbbbbvVN38GDYfCsexj8k68yYOnzdGVlKti9O4yvr84PssJxIjBrgffegxdfrHxl/9JLKRk06t49neCHDk2jew4evPrVr1+l3r3D4d++5R69VjWK6FhjuA0bNiymTp1a7TCsE1q8OF3BV7qynzUrtek36tlzzRN86Wvrrd2Zy2qPpEcjYlilba4RWKe2fDksWACvvgrz56dX4+d/vT/3Oq/MXs68lX3W+O6WW6YT+377rT7Jb799eu/Vyyd76zycCKxDiYB33ql8Qq+07vXXK++nW7c0Fv9WGy2k78tT2HXVXLZjBoOZzuBus9n+0lP5wNjPtu+PM6sSJwKrupUr07g5Fa/WK5zklyypvJ8ttshO7lvBzjun98bl0s99+sBmm2VX9HUfhVVlY/C/B1wwG5wIrCCcCCx377yTnqp56aX0Xv55/vzUa7Zc166rT+JbbQU77rj2Cb10+yabtCK4pmbf8qxcViBOBLZBIuDNN9c+wZee6BcuXPM7m2ySHoypq0szZPXtu/bJvU+fdIW/zvHz24Jn5TJzIrB1i4DXXqt8gm/8/NZba36ne3cYODC99twzvdfVrV639dbtcIJvqfHj1xznBzwrlxWOE0HBrVoFr7xS+QTf+F7eJt+zZzqx19XBJz6x+iTf+N6hnqjxrFxm7kfQma1cmW6wzpmTXrNnr/48Z046782atWbPV0iPTZae2Euv5uvqUpNNm2ho8AnYrJ24H0EntGRJ0yf4xuV581IyKNW1K2yzTerhuvvu8JnPrH2y79GjHX5AE0MvA04GZu3MNYIaE5Gefa90ki/9XOn5+M02Syf4/v3Te+OrdHmrrWqkfb6urvJN2oEDU5uUmbUp1wiAG29M0/ttvHG6Ki59tfe6xYsrX8E3vpYuXTN2KZ3A+/eHQYNST9fyk32/fh1shio/tmlWMwqTCJYvh7ffToODlb6WL29+XaVn3NtKt26rT+R77JFmniq/mu/bt43Ho6+Ftnk/tmlWMwqTCEaPbv25btWqtZNFS5NIpXWlJ/8tt2znJ2xqpW3ej22a1QzfI2hPtXAlXktt87Xw9zAriHXdI6iF24b5a2hIJ8AuXdJ7Q0N1Yhg7Np2EI1Zfibd3LLXUNj96dEo+q1aldycBs6ro/ImgVk7AZROUA2l53Lj2jaOpNni3zZsVVudPBLVyAq6VK/Hx41NbfCm3zZsVWudPBLVyAq6VK/HRo6G+Pt0TkNJ7fb2bZcwKrPMnglo5AdfSlbjb5s2sROdPBLVyAvaVuJnVqM7fj6CWRpfckM4MZmY56fyJAHwCNjNbh87fNGRmZuuUayKQdIik5yVNl3RWhe3dJE3Ktj8iqS7PeMzMbG25JQJJGwGXAYcCQ4FRkoaWFRsDvBERg4FLgAvzisfMzCrLs0awJzA9ImZExDLgBuDIsjJHAtdmn28ChksdZpJDM7NOIc9E0A94uWR5drauYpmIWAEsArYs35GksZKmSpq6YMGCnMI1MyumDvHUUETUA/UAkhZIqjB8ZofSC3it2kHUEP89VvPfYk3+e6xpQ/4eA5vakGcimANsW7LcP1tXqcxsSV2BzYGF69ppRPRuyyCrQdLUpoaDLSL/PVbz32JN/nusKa+/R55NQ1OAIZIGSdoEGAncVlbmNuD47PPRwL3R0SZIMDPr4HKrEUTECkknA3cBGwETImKapPOBqRFxG3AV8CtJ04HXScnCzMzaUa73CCJiMjC5bN05JZ+XAp/NM4YaVV/tAGqM/x6r+W+xJv891pTL36PDTVVpZmZty0NMmJkVnBOBmVnBORG0I0nbSrpP0rOSpkk6tdoxVZukjSQ9Lun31Y6l2iRtIekmSX+X9JykvasdUzVJ+lr2/+QZSRMlbVrtmNqLpAmS5kt6pmTdByX9SdI/s/cPtNXxnAja1wrgGxExFPgY8JUK4y8VzanAc9UOokb8BLgzInYCPkKB/y6S+gFfBYZFxM6kJw+L9FThNcAhZevOAu6JiCHAPdlym3AiaEcRMS8iHss+v036j14+7EZhSOoPHA5cWe1Yqk3S5sD+pEeqiYhlEfFmdaOquq7A+7LOpt2BuVWOp91ExF9Ij9SXKh2b7VrgqLY6nhNBlWRDbu8GPFLdSKrqx8AZwKpqB1IDBgELgKuzprIrJb2/2kFVS0TMAf4XmAXMAxZFxB+rG1XV9YmIednnV4A+bbVjJ4IqkNQD+C1wWkS8Ve14qkHSp4D5EfFotWOpEV2B3YFfRMRuwLu0YdW/o8nav48kJchtgPdL+q/qRlU7shEY2uzZfyeCdiZpY1ISaIiIm6sdTxXtC4yQ9BJpiPKDJP26uiFV1WxgdkQ01hBvIiWGojoYeDEiFkTEcuBmYJ8qx1Rtr0rqC5C9z2+rHTsRtKNsroWrgOci4uJqx1NNEXF2RPSPiDrSTcB7I6KwV3wR8QrwsqQds1XDgWerGJg3iWwAAAL9SURBVFK1zQI+Jql79v9mOAW+eZ4pHZvteOB3bbVjJ4L2tS/wOdLV7xPZ67BqB2U14xSgQdJTwK7ABVWOp2qymtFNwGPA06RzVWGGm5A0EXgI2FHSbEljgB8A/y7pn6Qa0w/a7HgeYsLMrNhcIzAzKzgnAjOzgnMiMDMrOCcCM7OCcyIwMys4JwLrVCStzB7LfUbSbyR1X8/vbyPppuzzrqWP90oaIalNevtKeqct9pP3Pq0Y/PiodSqS3omIHtnnBuDR1nbek3QCafTLk9swxMZ9/yvOWt6nFYNrBNaZ/RUYnI3jfqukpyQ9LGkXAEmfKOnY97ikzSTVZbWJTYDzgWOz7cdKOkHSz7Lv1km6N9vnPZIGZOuvkXSppAclzZB0dHNBSvqmpCnZvs7L1v1A0ldKypwr6fSmypttCCcC65SyoYsPJfVKPQ94PCJ2Ab4FXJcVOx34SkTsCnwcWNL4/YhYBpwDTIqIXSNiUtkhfgpcm+2zAbi0ZFtfYD/gUzTT+1PSJ4EhwJ6k3sQflbQ/MAk4pqToMcCkdZQ3azUnAuts3ifpCWAqabyaq0gn5V8BRMS9wJaSegIPABdL+iqwRUSsWI/j7A1cn33+VXaMRrdGxKqIeJbmhwr+ZPZ6nDScwk7AkIh4HNgqu2fxEeCNiHi5qfLrEbfZWrpWOwCzNrYku8L/lzRm2doi4geS/gAcBjwg6T+ApW0Qw3ulh2+mrIDvR8QvK2z7DXA0sDWphtBcebNWcY3AiuCvwGgASQcAr0XEW5K2j4inI+JCYArp6rrU28BmTezzQVZPnTg6O0Zr3AV8IZujAkn9JG2VbZuUHeNoUlJorrxZq7hGYEVwLjAhG9VzMauH8j1N0oGkGdKmAXeQ2vcb3QeclTU1fb9sn6eQZhP7JmlmsRNbE1hE/FHSh4CHsprLO8B/kSbtmSZpM2BO48xU6yrfmuObgR8fNTMrPDcNmZkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkV3P8HgE+ve/Y7x1cAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UahPVNlJSZ-K",
        "colab_type": "text"
      },
      "source": [
        "## Visualising the SVR results (for higher resolution and smoother curve)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fkhPL7RSd2X",
        "colab_type": "code",
        "outputId": "95970aed-f5a3-4047-fad7-61e2f998aff1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "X_grid = np.arange(min(sc_X.inverse_transform(X)), max(sc_X.inverse_transform(X)), 0.1)\n",
        "X_grid = X_grid.reshape((len(X_grid), 1))\n",
        "plt.scatter(sc_X.inverse_transform(X), sc_y.inverse_transform(y), color = 'red')\n",
        "plt.plot(X_grid, sc_y.inverse_transform(regressor.predict(sc_X.transform(X_grid))), color = 'blue')\n",
        "plt.title('Truth or Bluff (SVR)')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DbL6NDUPdVIngmI18FkCrJwIzs1Yxd26j29esSUNCXHwx7LQTTJoERx3VuuG1pI0mgog4rbUCMTOrKP37N1gieG3n4Zx8CDz+eOoM9qtfQffurR9eS3KHMjOzhlx66frPCIBbO5/C2CW/h+XpEcJXv1rG+FqQO5SZmTVk9Og0BsSAAXzA1vxz1xs5cdUN7LNfZ559tv0kAXCHMjOzxo0ezaz7X+PAoe/zu3dP5jvfgYcfTs+R25PmVg3V71C2BHcoM7N2buJEOOUU6NQJ/vxn+EI7nZllUzuUXQ5MBV7FHcrMrJ1auxb+8z/TvAC77w5PP91+kwA0USKQ9AlgXm2HMkldgeeBl4Ar8g/PzKx1LVmS6v/vvRfGjEmzhG29ddPfa8uaKhFcBawCyCap/3G2bRkwPt/QzMxa14wZaZiIBx9Mz4l///v2nwSg6WcEHSNiSfb+BGB8RPyBNNTEM/mGZmbWeu6+O5UEunRJD4SHDy93RK2nqRJBR0m1yeIw4MGSz5rdB8HMrFJFwGWXpYlidt89DRtRpCQATV/MbwYekbSY1HLoMQBJg0jVQ2ZmbdaqVfDNb8KECWnymGuvTSWComlqiIlLJT1Aaip6X9SNUNeB1LnMzKxNWrw4TRTz6KNp9NALLmhbI4a2pCardyJicgPbXs4nHDOz/M2cCZ//PMyfnyaUP+mkckdUXq7nN7NCefTRNGdwp07w0ENpOsmiq8DZM83M8lFdDZ/9LPTqlSaWdxJInAjMrN2LSIOJnnxyuvg/8QTsumu5o6ocuSYCSUdKmilplqTzN7LflyWFpAZnzzEz21xr1qSWQd//fhpQ9N57YYcdyh1VZcktEUjqCFwJHAUMAU6SNKSB/bYD/gV4Kq9YzKyY3n039Q+4+mr43vfgxhthq63KHVXlybNEsD8wKyJmR8Qq4BZgVAP7/RdwGfBBjrGYWcG89RYcckgqAVx1VaoaKmrz0KbkmQj6AvNK1udn2/5B0seAXSLiLxs7kKSxkmok1SxatKjlIzWzduXll9OzgBdfhD/9KU00Zo0r28NiSR2AnwHnNbVvRIyPiGERMaxnz575B2dmbdbkyWmIiOXLU/PQ9jx8dEvJMxEsAHYpWe+Xbau1HbAP8LCk14ADgIl+YGxmm2viRDj00DSZ/JNPppFErWl5JoIpwGBJAyV1Bk4EJtZ+GBHLIqJHRFRFRBUwGRgZETU5xmRm7dTVV8OXvgR7752ahw4aVO6I2o7cEkFErAHOBu4FXgRui4jpki6RNDKv85pZsUSksYLGjoUjjkjVQb16lTuqtiXXISYiYhIwqd62CxrZ95A8YzGz9mfNGjjzzFQaOO201DroQx8qd1Rtj3sWm1mbtGJFqgq6+moYNw6uucZJYHN50Dkza3MWL06tgaZMgd/+Fv7pn8odUdvmRGBmbcrs2XDUUTB3LvzhD2kkUdsyTgRm1mZMnQpHHw2rV8P998NBB5U7ovbBzwjMrE245x44+GDYZht4/HEngZbkRGBmFe+669IzgcGDU0exvfYqd0TtixOBmVWs2j4Cp5+eegw/8gj06VPuqNofPyMws4q0enXqJDZhgvsI5M0lAjOrOMuWpcnlJ0xIJQL3EciXSwRmVlHmzElJYObM9Gzg1FPLHVH750RgZhVj6tT0UPj999OEMoceWu6IisFVQ2ZWEe66C0aMSFNJPvGEk0BrciIws7KKgMsvh2OPhX33TRPLDJlWDVVV0KFDeq2uLneY7ZqrhsysbFatSuMEXXcdnHBCet3mzurUXGjFirTTnDl1c02OHl2+YNsxlwjMrCwWLYLDD08X/wsugJtuSr2GGTeuLgnUWrEibbdcuERgZq3u2Wdh1Ch4662UAE46qeTDuXMb/lJj222LuURgZq3qzjvT5PJr1sBjj9VLAgD9+zf8xca22xZzIjCzVrF2LXz/+/DlL6eHwlOmwLBhDex46aXQpcv627p0SdstF04EZpa7pUvhmGPStXzMGHj44Y2MGTR6NIwfDwMGgJRex4/3g+Ic+RmBmeXquedS09C5c+F3v0sNgKQmvjR6tC/8rcglAjPLzYQJ8MlPpkY/Dz8M3/xmM5KAtTonAjNrce+/D9/4Rho1dPhwmDYtvVplciIwsxb10ktwwAFpxNBx4+C++6B373JHZRvjZwRm1iIi4Prr4ayzUiOfv/wlzS9slc8lAjPbYsuWwde+lqqC9t8fnnnGSaAtcSIwsy3y2GPw0Y+mHsIXXwz33w99+5Y7KtsUTgRmtllWrYLvfQ8OPhg6dYK//jWNGdSxY7kjs03lZwRmtsmmTUszhz33XGoddMUV0LVruaOyzeUSgZk126pVcOGF6TnAwoUwcSJcfbWTQFvnEoGZNcuTT6ZewS+8AKecAj//OeywQ7mjspbgEoGZbdSyZXDmmXDQQfD226kUcMMNTgLtiROBmTUoIrUE2msvuOoq+Na3YMaMNHictS+uGjKzDTz3HJx9dmoa+vGPw5/+BJ/4RLmjsrzkWiKQdKSkmZJmSTq/gc//TdIMSc9JekDSgDzjMbONW/ibOzhzuxvZ76NrmfH4EsaPmcxTTzkJtHe5JQJJHYErgaOAIcBJkobU220aMCwiPgLcAVyeVzxm1rgVK+CHxz/DoLM+x9XvnsiZ/IaX1w3ijJsPo+Mt1eUOz3KWZ4lgf2BWRMyOiFXALcCo0h0i4qGIqJ2lejLQL8d4zKyeVavSHAG77w7jbh/KYTzAC+zDr/gWO7DUk8YXRJ6JoC8wr2R9fratMWOA/2noA0ljJdVIqlm0aFELhmhWTKtXw3XXwR57wD//M1RVwSMczB85lj14ef2dPWl8u1cRrYYknQwMA37S0OcRMT4ihkXEsJ49e7ZucGbtyAcfwG9/m0oAp58OPXrAPfekh8IjBsxp+EueNL7dyzMRLAB2KVnvl21bj6TDgXHAyIhYmWM8ZoW1dClcdhkMHJj6BPTunfoD/N//wRFHZLOGedL4wsqz+egUYLCkgaQEcCLw1dIdJO0HXAUcGRELc4zFrJBmzoRf/CLNE7BiBRx2GFRXw2c+08CUkbVzBI8bl6qD+vdPScBzB7d7uSWCiFgj6WzgXqAjcG1ETJd0CVATERNJVUFdgduV/lXOjYiRecVkVgSrVsFdd6VOYA8+CJ07p2v5uefCRz7SxJc9aXwh5dqhLCImAZPqbbug5P3heZ7frEiefz4N/XDDDWlAuAED4Ac/SKODeqpI2xj3LDZrw+bOhTvugBtvTLOCdeoEn/98GhzuiCM8N4A1T0W0GjIrvOrq1IazQ4f0Wt14J67Zs9P4/8OHp7v+885LCeBXv4LXX0/VQkcf7SRgzecSgVm5VVenW/gVWd/KOXPSOsDo0axeDZMnw6RJqaXPjBnpo6FD4Yc/hK98BQYNKk/o1j44EZiV27hxdUkAWIeYvmJXHjlnJv97Gzz0ECxfnu76R4yAM85II4DutlsZY7Z2xYnArMzem7OYGkYwmQN4guE8xqdZyg6wFHZ9ITXi+dznUpPP7t3LHa21R04EZq3o7bfTDF/TpsHTT6dlOm+zNvuvOIhX+BJ/5GAeYUTf2VT97a9ljtiKwInArIVFwKJFqTPXzJnw0kupXv+FF2BeyehbvXqlsf5HDnyRA++5kP1XPkoP/p4+7NIFLhtfnh9gheNEYLaJItKd/fz5qfnma6/VLbNmwd/+lur0a229dRrbZ8QI2Gcf2Hdf2G8/6NOntnfvvlD9ZRj3NMxd4h691uqcCMwytRf4t96CN9+sW954IzXLrF3mzYP33lv/u1ttlVp97rYbfPrT6XWPPdLSv38zmnK6R6+VkROBtVu1F/bFi1NVTWPLwpeWsHD+Shau3ZHVdN7gOJ07w847p2XvvVNHrV12SUv//ikB9OqVugCYtUVOBNZmRMA776Q79oUL118ausAvXgxr1jR8rC5doGdP6N1pMX3n/R9D171Jb96iN2/Rq/Myev/baPqccjh9+qSWOhsM0GbWjjgRWNmtXp0u7rVVL2+8kZbaqpnaqpq33oKVjQxU3r17urD37Am77gqf/GR636NH3fbS9/8YbblqGKyrNw7/KuDmh+BHr+X3o80qiBOB5eqDD1Kd+rx56eHq/Pnp/YIFdcvCheluv5SUqlt22ikNmLbHHum1dunVq+61R49UfbNZGpt9y7NyWYE4Edhmi0gTnsyZk1rMzJlTt8ydm5aGZhbdYQfo1w/69oWPfSy91tbB9+mTll69Uk/a3PXvnwJuaLtZQTgR2EYtXw6vvrr+UttU8tVX128mCbDttmkgtP79Uxv5/v3rHqzusku66G+zTTl+SSMuvXT9cX7As3JZ4TgRFNyaNamq5tVX06iWs2ev/37x4vX379o1TXdYVQUHH5xea5cBA9Ldfpt6sOpZucxQ1K+crXDDhg2LmpqacofRZkSki3npHX3pxX7OHFi7tm7/Tp3StXDXXeuWgQPrlh13bMELfXW1L8BmrUTS1IgY1tBnLhG0cRHpYWtp/XxtnX3t8u6763+ntmXN/vvDiSfWXex33TXV3bdK3XwTQy+bWetxiaCCrVlT16zyjTfqWt0sWJCqc+bOTev1m1R267Z+lU3pHf3Agal6p+yqqhp+SDtgQMpeZtaiXCIAbr8drr02NUfs0ye91rYt33HHVLfdrRtsv33Lz+wUkS7W77yTlmXLYMmS1OJmyZK6nq+LF6cLf+2yePGGzSo7dUqta/r2hU98Ao49Nj2EHTCgbmkTQxW72aZZxShMIli5Ml1Yn38+XWQb63EKqeXLttumxiPbbpvGkencGT70obRIaTgBCdatS3Xsa9emjlGrVqVzffBBqvV47720bOx8kBJQjx6p2eSgQXDQQamdfGmTyn790udbnKgqoW7ezTbNKkZhEsHJJ6cF0sW79k68dlm6NN2p1y4rVtRdyFeuXP8iH5GWdevSRblDh/S67bbw4Q+nxLHVVusnlO23r1u6dUslkA9/OC1b1CFqU1VK3bybbZpVDD8jaE2VcCdeSXXzlfD3MCuIjT0jKMZ4idXV6QLYoUN6ra4uTwxjx6aLcETdnXhrx1JJdfOjR6fks25denUSMCuL9p8IKuUCXG+CciCtjxvXunE0Vgfvunmzwmr/iaBSLsCVcid+6aUlQ29mXDdvVmjtPxFUygW4Uu7ER4+G8ePTMwEpvY4f72oZswJr/4mgUi7AlXQn7rp5MyvR/hNBpVyAfSduZhWq/fcjqKTRJT1BuZlVoPafCMAXYDOzjWj/VUNmZrZRuSYCSUdKmilplqTzG/h8K0m3Zp8/Jakqz3jMzGxDuSUCSR2BK4GjgCHASZKG1NttDLA0IgYBVwCX5RWPmZk1LM8Swf7ArIiYHRGrgFuAUfX2GQVcn72/AzhMalMTHZqZtXl5JoK+wLyS9fnZtgb3iYg1wDJgx/oHkjRWUo2kmkWLFuUUrplZMbWJVkMRMR4YDyBpkaQGhs9sU3oAi5vcqzj896jjv8X6/PdY35b8PQY09kGeiWABsEvJer9sW0P7zJfUCegG/H1jB42Ini0ZZDlIqmlsONgi8t+jjv8W6/PfY315/T3yrBqaAgyWNFBSZ+BEYGK9fSYCX8/eHwc8GG1tggQzszYutxJBRKyRdDZwL9ARuDYipku6BKiJiInANcCNkmYBS0jJwszMWlGuzwgiYhIwqd62C0refwB8Jc8YKtT4cgdQYfz3qOO/xfr891hfLn+PNjdVpZmZtSwPMWFmVnBOBGZmBedE0Iok7SLpIUkzJE2X9C/ljqncJHWUNE3S3eWOpdwkdZd0h6SXJL0o6cByx1ROkv41+3/ygqSbJW1d7phai6RrJS2U9ELJth0k/a+kV7LXD7fU+ZwIWtca4LyIGAIcAJzVwPhLRfMvwIvlDqJC/AK4JyL2BD5Kgf8ukvoC3wKGRcQ+pJaHRWpVOAE4st6284EHImIw8EC23iKcCFpRRLwREU9n75eT/qPXH3ajMCT1Az4P/L7csZSbpG7ACFKTaiJiVUS8Xd6oyq4TsE3W2bQL8HqZ42k1EfEoqUl9qdKx2a4HvthS53MiKJNsyO39gKfKG0lZ/Rz4DrCu3IFUgIHAIuC6rKrs95K2LXdQ5RIRC4CfAnOBN4BlERlnjqgAAAPJSURBVHFfeaMqu94R8Ub2/k2gd0sd2ImgDCR1Bf4AnBsR75Q7nnKQ9AVgYURMLXcsFaIT8DHgtxGxH/AeLVj0b2uy+u9RpAS5M7CtpJPLG1XlyEZgaLG2/04ErUzSh0hJoDoi7ix3PGV0EDBS0mukIcoPlfT/yhtSWc0H5kdEbQnxDlJiKKrDgVcjYlFErAbuBIaXOaZye0tSH4DsdWFLHdiJoBVlcy1cA7wYET8rdzzlFBHfjYh+EVFFegj4YEQU9o4vIt4E5knaI9t0GDCjjCGV21zgAEldsv83h1Hgh+eZ0rHZvg78qaUO7ETQug4CTiHd/T6TLUeXOyirGOcA1ZKeA4YCPyxzPGWTlYzuAJ4Gniddqwoz3ISkm4EngT0kzZc0Bvgx8FlJr5BKTD9usfN5iAkzs2JzicDMrOCcCMzMCs6JwMys4JwIzMwKzonAzKzgnAisXZG0NmuW+4Kk2yV12cTv7yzpjuz90NLmvZJGSmqR3r6S3m2J4+R9TCsGNx+1dkXSuxHRNXtfDUzd3M57kk4ljX55dguGWHvsf8RZyce0YnCJwNqzx4BB2Tjud0l6TtJkSR8BkHRwSce+aZK2k1SVlSY6A5cAJ2SfnyDpVEm/zr5bJenB7JgPSOqfbZ8g6ZeSnpA0W9JxTQUp6duSpmTHujjb9mNJZ5Xsc5Gkf29sf7Mt4URg7VI2dPFRpF6pFwPTIuIjwPeAG7Ld/h04KyKGAp8G3q/9fkSsAi4Abo2IoRFxa71T/Aq4PjtmNfDLks/6AJ8CvkATvT8lfQ4YDOxP6k38cUkjgFuB40t2PR64dSP7m202JwJrb7aR9AxQQxqv5hrSRflGgIh4ENhR0vbA48DPJH0L6B4RazbhPAcCN2Xvb8zOUeuuiFgXETNoeqjgz2XLNNJwCnsCgyNiGtAre2bxUWBpRMxrbP9NiNtsA53KHYBZC3s/u8P/hzRm2YYi4seS/gIcDTwu6QjggxaIYWXp6ZvYV8CPIuKqBj67HTgO2IlUQmhqf7PN4hKBFcFjwGgASYcAiyPiHUm7RcTzEXEZMIV0d11qObBdI8d8grqpE0dn59gc9wKnZ3NUIKmvpF7ZZ7dm5ziOlBSa2t9ss7hEYEVwEXBtNqrnCuqG8j1X0mdIM6RNB/6HVL9f6yHg/Kyq6Uf1jnkOaTaxb5NmFjttcwKLiPsk7QU8mZVc3gVOJk3aM13SdsCC2pmpNrb/5pzfDNx81Mys8Fw1ZGZWcE4EZmYF50RgZlZwTgRmZgXnRGBmVnBOBGZmBedEYGZWcP8fcQasYi2sGx8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}