Support-Vector-Regressor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5e82185e",
   "metadata": {},
   "source": [
    "# **Support Vector Regressor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee9b3f3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f26421ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's create the data points\n",
    "from sklearn.datasets import make_regression\n",
    "\n",
    "#create the dependent and independent data\n",
    "X, y = make_regression(n_samples = 1000, n_features = 2, n_targets = 1, noise = 3.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a392f2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.35518632,  0.92501922],\n",
       "       [-0.72782582,  1.02526817],\n",
       "       [-0.91589724, -0.71273144],\n",
       "       ...,\n",
       "       [ 0.05478614, -0.09013722],\n",
       "       [ 0.07390849, -1.37252119],\n",
       "       [-3.77932808, -0.7589041 ]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af57acc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       -1.50657904e+01,  5.69133033e+01,  2.49235023e+02, -1.21219653e+02,\n",
       "        1.32021504e+02, -1.60175677e+00, -1.02838527e+02,  2.64014709e+01,\n",
       "       -2.07435044e+02,  2.62062246e+00, -1.12469966e+02, -7.43244558e+01,\n",
       "       -4.58174321e+01,  1.70333118e+02,  1.50145658e+02,  4.17667618e+01,\n",
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       "       -3.79366373e+01,  3.54500238e+01,  5.13259158e+01, -1.83834626e+00,\n",
       "        1.38944284e+02, -1.36581619e+02,  1.99504706e+02,  1.45076500e+02,\n",
       "       -5.59651248e+01, -5.56090450e+00, -2.02151485e+02, -7.22003213e+01,\n",
       "       -3.71669123e+01,  1.20427725e+02,  1.76934204e+02, -1.31892385e+02,\n",
       "        3.40412186e+01,  1.50825036e+02, -1.81255451e+02,  2.15559275e+02,\n",
       "       -8.15046608e+01, -4.90217062e+00, -1.19750294e+02, -2.73984189e+02])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b3042ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0', ylabel='1'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#visualize the data\n",
    "sns.scatterplot(x = pd.DataFrame(X)[0], y = pd.DataFrame(X)[1], hue = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf19d8d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the data into train and test data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#make train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 47)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ebae0314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((750, 2), (250, 2))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8513c4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the support vector regression\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "#define the model\n",
    "svr = SVR(kernel = \"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "36d6ceb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(kernel='linear')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit the model\n",
    "svr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50b63f08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53.9351413 , 92.11435409]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4dca6e04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-9.72244211e+01,  1.51988047e+01,  1.05573403e+02,  3.91163483e+01,\n",
       "        2.29227558e+01,  1.23593130e+02, -9.18109054e+01, -9.30694025e+01,\n",
       "        7.76935705e+01,  1.63620451e+02, -1.26211009e+02,  3.83164443e+01,\n",
       "       -3.52310894e+01,  4.50039359e+01, -1.17126200e+02, -1.05254997e+02,\n",
       "       -3.91224348e+01, -5.10249831e+01,  2.23009044e+01, -3.82527958e+01,\n",
       "        7.51329621e+01,  7.25009043e+01,  4.82565983e+01, -4.90302733e+01,\n",
       "       -1.39232648e+02, -1.41241019e+02,  6.58344723e+00, -8.53025283e+01,\n",
       "        1.13477650e+02,  2.56705428e+01,  4.35543476e+01,  1.19511261e+02,\n",
       "       -4.98556996e+01, -1.62031915e+02, -6.87490627e+01, -1.58571257e+02,\n",
       "       -2.16905865e+02, -1.09793368e+02,  1.21135404e+02, -1.04278899e+02,\n",
       "       -1.21862670e+02,  1.98756861e+01,  4.85013641e+00, -5.92232948e+01,\n",
       "       -9.61835186e+01,  5.25398005e+01,  1.36514162e+02, -9.09665051e+01,\n",
       "        9.33151064e+01, -1.12897062e+02, -5.97570613e+00,  3.83099962e+01,\n",
       "        4.13628507e+01,  1.04538458e+02, -2.40240629e+02,  2.09067486e+02,\n",
       "       -1.39664591e+01,  3.91166708e+00, -5.97015526e+01, -5.16746465e+01,\n",
       "       -1.42075638e+02,  3.41607914e+00,  5.85040454e+01, -2.44396816e+02,\n",
       "       -2.80421013e+00,  1.49278052e+02,  2.00941810e+02, -2.07457161e+02,\n",
       "       -4.17619922e+00,  2.64787983e+02, -5.13810475e+01,  9.50693756e+01,\n",
       "        4.34138703e+01, -1.02973967e+02, -6.56601643e+01,  7.86764121e+01,\n",
       "        8.82665444e+01, -1.05957861e+02, -8.70847708e+01,  8.33183470e+01,\n",
       "       -3.82013873e+01,  2.33892110e+02, -5.17674865e+01,  5.82053827e+01,\n",
       "        6.58280573e+01,  2.51144328e+00,  9.01272294e-01, -1.56063914e+02,\n",
       "       -7.20134918e+01, -2.34144929e+01,  5.06619232e+01, -2.01491001e+02,\n",
       "        7.94072150e+00,  1.29074148e+02,  1.33314555e+02,  2.62118895e+01,\n",
       "        1.25857736e+02,  1.44590593e+02, -1.16722596e+02,  1.37166279e+01,\n",
       "        1.41145048e+02, -2.24075440e+02, -1.74747068e+02, -5.56666705e+01,\n",
       "       -1.51723116e+02,  1.92545434e+02,  1.74078350e+02,  6.82372273e-02,\n",
       "       -4.17048546e+01, -1.80607971e+02,  1.46237881e+02, -1.00494237e+02,\n",
       "       -8.36554775e+01,  9.44374834e+01,  1.03713532e+02, -6.97917929e+01,\n",
       "       -4.56224430e+01,  5.53192285e+01, -7.23502457e+01, -8.12485377e+01,\n",
       "        6.80180568e+01, -5.24007214e+01, -1.04324357e+02, -7.51520269e+01,\n",
       "        3.75763829e+01, -1.94588535e+02,  2.58133171e+01,  1.23790545e+02,\n",
       "        9.01172535e+01, -3.25146907e+01, -1.22309937e+02, -1.12760144e+02,\n",
       "       -1.74878544e+01, -1.74612865e+01, -1.44490153e+02, -2.16090554e+01,\n",
       "        9.89828079e+01, -1.06156110e+02, -3.77760425e+01,  4.56401519e+01,\n",
       "       -2.76537723e+00, -8.40503697e+01,  1.95801228e+02, -2.49118458e+02,\n",
       "       -9.94150050e+01, -2.83127332e+01, -1.20395556e+02, -1.47628830e+02,\n",
       "       -1.03125641e+02,  1.42314291e+02,  1.63793698e+01, -2.90432468e+01,\n",
       "       -7.09289887e+01, -1.81196786e+02,  1.22417907e+01, -1.07139324e+02,\n",
       "        2.08003806e+02,  1.45809310e+02,  1.33454534e+02, -4.66900478e+00,\n",
       "       -1.03051964e+02,  3.15506814e+01, -1.10841423e+02, -2.14473946e+01,\n",
       "        8.03506174e+00,  7.52548336e+01, -1.26700266e+02, -4.52993244e+01,\n",
       "        2.66778684e+02,  2.23765872e+02,  7.87228993e+01, -9.46347917e+01,\n",
       "       -2.04428208e+02, -8.30641619e+01, -1.80863872e+02, -6.82050643e+01,\n",
       "       -1.72438488e+02, -2.79323754e+01,  1.30476083e+02, -7.29985923e+01,\n",
       "        7.49914281e+01,  2.04882045e+02,  8.86662804e+01,  6.84392673e+01,\n",
       "        1.11130937e+02, -1.26666375e+01, -1.69120400e+01, -5.72204587e+01,\n",
       "        4.92521404e+01, -6.41889530e+01, -7.22261557e+01, -5.70536464e+01,\n",
       "       -8.26773512e+01, -1.94413526e+02, -7.13204706e+01,  7.17893205e+01,\n",
       "        1.57374741e+02,  4.83831440e+01, -8.41689797e+01, -2.95108751e+02,\n",
       "       -2.69682010e+01,  1.33250388e+02, -8.11308436e+00, -5.21533234e+00,\n",
       "       -1.98821463e+02, -9.60234411e+01,  6.41611842e+01, -1.32532276e+02,\n",
       "       -2.01035950e+02, -1.28230557e+02, -2.74463270e+02, -2.89445406e+01,\n",
       "        3.74330879e+01, -8.39727973e+01, -2.32457773e+01,  3.19535307e+01,\n",
       "       -9.38227160e+01, -2.54069408e+00,  2.10487710e+01,  1.43982336e+02,\n",
       "        6.47304035e+01, -1.11288894e+02, -1.81283150e+02,  1.63529342e+02,\n",
       "        1.23838713e+02, -3.73353161e-02,  6.96481605e+01, -3.65959895e+01,\n",
       "       -4.80086738e+01,  1.45734510e+01, -1.94277114e+02,  4.16977433e+01,\n",
       "        1.13960305e+02,  3.70446634e+00,  1.07716810e+02, -1.11521836e+02,\n",
       "        9.31584415e+01, -2.43760029e+01, -1.58438536e+02, -9.68838622e+01,\n",
       "        1.62617911e+01,  1.54213390e+02, -4.84616196e+01, -1.22825703e+02,\n",
       "        1.24079766e+02, -9.51856838e+01, -2.35214717e+02, -1.51082135e+02,\n",
       "       -7.98514765e+01, -1.73525331e+02])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predict the model\n",
    "y_pred = svr.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9df1bbd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9992761682604338\n"
     ]
    }
   ],
   "source": [
    "#printing and finding the r2_score\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "#define the r2_score\n",
    "score = r2_score(y_pred, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c61c696",
   "metadata": {},
   "source": [
    "### Hyper Parameter Tuning with SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4f91f6d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the grid search cv\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#define the parameters\n",
    "parameters = {\n",
    "    \"C\" : [0.1, 1, 10, 100, 1000],\n",
    "    \"gamma\" : [1, 0.1, 0.01, 0.001, 0.0001],\n",
    "    \"kernel\" : [\"linear\"],\n",
    "    \"epsilon\" : [0.1, 0.2, 0.3]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d765a136",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n",
      "[CV 1/5] END C=0.1, epsilon=0.1, gamma=1, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.1, gamma=1, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.1, gamma=1, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.1, gamma=1, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.1, gamma=1, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.1, gamma=0.1, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.1, gamma=0.1, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.1, gamma=0.1, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.1, gamma=0.1, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.1, gamma=0.1, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.1, gamma=0.01, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.1, gamma=0.01, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.1, gamma=0.01, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.1, gamma=0.01, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.1, gamma=0.01, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.2, gamma=1, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.2, gamma=1, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.2, gamma=1, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.2, gamma=1, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.2, gamma=1, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.3, gamma=1, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.3, gamma=1, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.3, gamma=1, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.3, gamma=1, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.3, gamma=1, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=0.1, epsilon=0.3, gamma=0.0001, kernel=linear;, score=0.711 total time=   0.0s\n",
      "[CV 2/5] END C=0.1, epsilon=0.3, gamma=0.0001, kernel=linear;, score=0.724 total time=   0.0s\n",
      "[CV 3/5] END C=0.1, epsilon=0.3, gamma=0.0001, kernel=linear;, score=0.700 total time=   0.0s\n",
      "[CV 4/5] END C=0.1, epsilon=0.3, gamma=0.0001, kernel=linear;, score=0.703 total time=   0.0s\n",
      "[CV 5/5] END C=0.1, epsilon=0.3, gamma=0.0001, kernel=linear;, score=0.714 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.1, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END C=1, epsilon=0.1, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END C=1, epsilon=0.1, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END C=1, epsilon=0.1, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END C=1, epsilon=0.1, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.1, gamma=0.1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END C=1, epsilon=0.1, gamma=0.1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END C=1, epsilon=0.1, gamma=0.1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END C=1, epsilon=0.1, gamma=0.1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END C=1, epsilon=0.1, gamma=0.1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.1, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END C=1, epsilon=0.1, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END C=1, epsilon=0.1, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END C=1, epsilon=0.1, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END C=1, epsilon=0.1, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END C=1, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END C=1, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END C=1, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 5/5] END C=1, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.0s\n",
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      "[CV 5/5] END C=1, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.2, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END C=1, epsilon=0.2, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END C=1, epsilon=0.2, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END C=1, epsilon=0.2, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END C=1, epsilon=0.2, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.999 total time=   0.0s\n",
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      "[CV 4/5] END C=1, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END C=1, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END C=1, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END C=1, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END C=1, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END C=1, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END C=1, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END C=1, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END C=1, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END C=1, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END C=1, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END C=1, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END C=1, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END C=1, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.3, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END C=1, epsilon=0.3, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END C=1, epsilon=0.3, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END C=1, epsilon=0.3, gamma=1, kernel=linear;, score=0.999 total time=   0.0s\n",
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      "[CV 1/5] END C=1, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.999 total time=   0.0s\n",
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      "[CV 3/5] END C=1, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.999 total time=   0.0s\n",
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      "[CV 5/5] END C=1, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END C=1, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END C=1, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
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      "[CV 4/5] END C=1, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.999 total time=   0.0s\n",
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      "[CV 1/5] END C=1, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END C=1, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.999 total time=   0.0s\n",
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      "[CV 2/5] END C=1000, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 3/5] END C=1000, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 4/5] END C=1000, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.999 total time=   0.7s\n",
      "[CV 5/5] END C=1000, epsilon=0.1, gamma=0.001, kernel=linear;, score=0.999 total time=   0.7s\n",
      "[CV 1/5] END C=1000, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 2/5] END C=1000, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 3/5] END C=1000, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 4/5] END C=1000, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.7s\n",
      "[CV 5/5] END C=1000, epsilon=0.1, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 1/5] END C=1000, epsilon=0.2, gamma=1, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 2/5] END C=1000, epsilon=0.2, gamma=1, kernel=linear;, score=0.999 total time=   0.7s\n",
      "[CV 3/5] END C=1000, epsilon=0.2, gamma=1, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 4/5] END C=1000, epsilon=0.2, gamma=1, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 5/5] END C=1000, epsilon=0.2, gamma=1, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 1/5] END C=1000, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 2/5] END C=1000, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.999 total time=   0.7s\n",
      "[CV 3/5] END C=1000, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 4/5] END C=1000, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 5/5] END C=1000, epsilon=0.2, gamma=0.1, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 1/5] END C=1000, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 2/5] END C=1000, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.999 total time=   0.7s\n",
      "[CV 3/5] END C=1000, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 4/5] END C=1000, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 5/5] END C=1000, epsilon=0.2, gamma=0.01, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 1/5] END C=1000, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 2/5] END C=1000, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.999 total time=   0.7s\n",
      "[CV 3/5] END C=1000, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 4/5] END C=1000, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 5/5] END C=1000, epsilon=0.2, gamma=0.001, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 1/5] END C=1000, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.6s\n",
      "[CV 2/5] END C=1000, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.8s\n",
      "[CV 3/5] END C=1000, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 4/5] END C=1000, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 5/5] END C=1000, epsilon=0.2, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 1/5] END C=1000, epsilon=0.3, gamma=1, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 2/5] END C=1000, epsilon=0.3, gamma=1, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 3/5] END C=1000, epsilon=0.3, gamma=1, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 4/5] END C=1000, epsilon=0.3, gamma=1, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 5/5] END C=1000, epsilon=0.3, gamma=1, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 1/5] END C=1000, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 2/5] END C=1000, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 3/5] END C=1000, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 4/5] END C=1000, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 5/5] END C=1000, epsilon=0.3, gamma=0.1, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 1/5] END C=1000, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 2/5] END C=1000, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 3/5] END C=1000, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 4/5] END C=1000, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.999 total time=   0.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 5/5] END C=1000, epsilon=0.3, gamma=0.01, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 1/5] END C=1000, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 2/5] END C=1000, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 3/5] END C=1000, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 4/5] END C=1000, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 5/5] END C=1000, epsilon=0.3, gamma=0.001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 1/5] END C=1000, epsilon=0.3, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 2/5] END C=1000, epsilon=0.3, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 3/5] END C=1000, epsilon=0.3, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.4s\n",
      "[CV 4/5] END C=1000, epsilon=0.3, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.5s\n",
      "[CV 5/5] END C=1000, epsilon=0.3, gamma=0.0001, kernel=linear;, score=0.999 total time=   0.5s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVR(kernel='linear'),\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
       "                         'epsilon': [0.1, 0.2, 0.3],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
       "                         'kernel': ['linear']},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define the grid search cv\n",
    "grid = GridSearchCV(svr, param_grid = parameters, cv = 5, refit = True, verbose = 3)\n",
    "\n",
    "#fit the model\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9ba32dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'epsilon': 0.3, 'gamma': 1, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "00d4ce74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9993062064990029\n"
     ]
    }
   ],
   "source": [
    "#predict the model\n",
    "y_pred = grid.predict(X_test)\n",
    "\n",
    "#print the accuracy\n",
    "print(r2_score(y_pred, y_test))"
   ]
  }
 ],
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