support_vector_regression.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3PAEPRDRLA3"
      },
      "source": [
        "# Support Vector Regression (SVR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VCUAVIjRdzZ"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56oRF-QfSDzC"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXVXoFWtSF4_"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfoa8OSORfHQ"
      },
      "source": [
        "dataset = pd.read_csv('Data.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8Ny1GfPiV3m"
      },
      "source": [
        "y = y.reshape(len(y),1)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Vk3nNlrCVCN"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_fXqrziCV3_"
      },
      "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 = 0.2, random_state = 0)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YS8FeLHYS-nI"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGeAlD1HTDI1"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_X = StandardScaler()\n",
        "sc_y = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "y_train = sc_y.fit_transform(y_train)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eiU6D2QFRjxY"
      },
      "source": [
        "## Training the SVR model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6R4rt_GRz15",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "101e27f8-4437-4c06-e1a9-66d1f6ceb313"
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "regressor = SVR(kernel = 'rbf')\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: 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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR()"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPYA5W1pDBOE"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSqFVDYrDROW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c536ab2a-e7f5-4eab-c99c-0231eb1d7743"
      },
      "source": [
        "y_pred = sc_y.inverse_transform(regressor.predict(sc_X.transform(X_test)).reshape(-1,1))\n",
        "np.set_printoptions(precision=2)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[434.05 431.23]\n",
            " [457.94 460.01]\n",
            " [461.03 461.14]\n",
            " ...\n",
            " [470.6  473.26]\n",
            " [439.42 438.  ]\n",
            " [460.92 463.28]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CMsYlps2DX1d"
      },
      "source": [
        "## Evaluating the Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxsRWlURDr6S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "926a31e5-5cbc-4b56-fac3-4971581af8b8"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_test, y_pred)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9480784049986258"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    }
  ]
}