Support_Vector_Regression_(SVR)_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Support Vector Regression (SVR) model",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOyid1fXIj4eLOFOfxGAAI1"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yysOcrlPd7vZ"
      },
      "source": [
        "# SVR MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bP_hlIOfd0H3",
        "outputId": "1af11b9c-d43a-4f69-8983-6d484b1671fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        }
      },
      "source": [
        "from IPython.display import Image\n",
        "Image(\"svr.JPG\", width=780)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/jpeg": {
              "width": 780
            }
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o3-y9nZwhNH3"
      },
      "source": [
        "## lib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2TthkwBclpol"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1zxzZSLhPW6"
      },
      "source": [
        "## dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fq8mX2aYlsJH",
        "outputId": "a48b2818-90b4-4d71-a513-68fdbb2cb91e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df = pd.read_csv('Position_Salaries.csv')\n",
        "df.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Position</th>\n",
              "      <th>Level</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Business Analyst</td>\n",
              "      <td>1</td>\n",
              "      <td>45000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Junior Consultant</td>\n",
              "      <td>2</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Senior Consultant</td>\n",
              "      <td>3</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Manager</td>\n",
              "      <td>4</td>\n",
              "      <td>80000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Country Manager</td>\n",
              "      <td>5</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Position  Level  Salary\n",
              "0   Business Analyst      1   45000\n",
              "1  Junior Consultant      2   50000\n",
              "2  Senior Consultant      3   60000\n",
              "3            Manager      4   80000\n",
              "4    Country Manager      5  110000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xpz7dNfUlvgi"
      },
      "source": [
        "x = df.iloc[:, 1:-1].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ea0-pGzXl1eu",
        "outputId": "433f8ea0-28ac-4dcc-c0f8-fcd3fd86efaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 19,
      "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": "5u4c_DY4rqsH",
        "outputId": "4bb00a47-c194-496b-fd8c-0f507f2f4bb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[  45000   50000   60000   80000  110000  150000  200000  300000  500000\n",
            " 1000000]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxZRerNXrsRV"
      },
      "source": [
        "y = y.reshape(len(y),1)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sNnhViEryjr",
        "outputId": "66859ae3-28e9-4887-9401-b436506a9153",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 22,
      "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": "9dFJ4MYDhdgu"
      },
      "source": [
        "## feature scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tIT0KplHmHQU"
      },
      "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": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXxMKQ_yus_G",
        "outputId": "6fbd52a6-2d01-407e-c17d-d28c586acc52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 25,
      "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": "kj77tyF2uvV4",
        "outputId": "eeb091d9-bba1-46dd-c688-d1094e669745",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 26,
      "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": "CZONOWM7hggI"
      },
      "source": [
        "## training svr on whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LT1xIAUZu3sJ",
        "outputId": "5dbf9d1c-3056-4e93-b5b7-a44bd2ba4eb9",
        "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": 27,
      "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": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eG3diI_LlK1B"
      },
      "source": [
        "## predicting new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OqHqh8xDvxRK",
        "outputId": "2025202a-ac3e-4f51-a6ff-7496edbb3a4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "regressor.predict(sc_x.transform([[6.5]]))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.27861589])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3NrUMGGuwapx"
      },
      "source": [
        "## reverse one hot encoding - inverse transform method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-JjPFHjcwj_Y",
        "outputId": "5ce7db75-79d6-4811-927d-430b12cf8af9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sc_y.inverse_transform(regressor.predict(sc_x.transform([[6.5]])))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([170370.0204065])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iC1vRLL8lKYB"
      },
      "source": [
        "## visualize svr result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDI0u3iGcklN",
        "outputId": "bac19f87-8765-435a-c5c4-f8e23370d225",
        "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": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHW4XfO3lUea"
      },
      "source": [
        "smooth curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOOolv7wdK5_",
        "outputId": "54be73cc-3cfc-4235-a94b-454875eb72bf",
        "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": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8g_RAjn2y8Kk"
      },
      "source": [
        "## polynomial is better in this case|"
      ]
    }
  ]
}