Stock Price Predictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stock Price Predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import yfinance as yf\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>843.200012</td>\n",
       "      <td>843.200012</td>\n",
       "      <td>843.200012</td>\n",
       "      <td>834.900024</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-02</th>\n",
       "      <td>848.700012</td>\n",
       "      <td>857.799988</td>\n",
       "      <td>846.400024</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-03</th>\n",
       "      <td>863.000000</td>\n",
       "      <td>865.500000</td>\n",
       "      <td>856.299988</td>\n",
       "      <td>866.400024</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-04</th>\n",
       "      <td>861.400024</td>\n",
       "      <td>865.500000</td>\n",
       "      <td>860.200012</td>\n",
       "      <td>863.099976</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-07</th>\n",
       "      <td>860.900024</td>\n",
       "      <td>860.900024</td>\n",
       "      <td>857.400024</td>\n",
       "      <td>859.599976</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close  Volume\n",
       "Date                                                              \n",
       "2007-12-31  843.200012  843.200012  843.200012  834.900024       4\n",
       "2008-01-02  848.700012  857.799988  846.400024  857.000000     130\n",
       "2008-01-03  863.000000  865.500000  856.299988  866.400024     181\n",
       "2008-01-04  861.400024  865.500000  860.200012  863.099976      28\n",
       "2008-01-07  860.900024  860.900024  857.400024  859.599976       4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock = input('Enter the code of the Stock: ')\n",
    "data = yf.download(stock, \"2008-01-01\", \"2021-11-27\", auto_adjust=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3501, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 3501 entries, 2007-12-31 to 2021-11-26\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Open    3501 non-null   float64\n",
      " 1   High    3501 non-null   float64\n",
      " 2   Low     3501 non-null   float64\n",
      " 3   Close   3501 non-null   float64\n",
      " 4   Volume  3501 non-null   int64  \n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 164.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3501.000000</td>\n",
       "      <td>3501.000000</td>\n",
       "      <td>3501.000000</td>\n",
       "      <td>3501.000000</td>\n",
       "      <td>3501.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1346.093431</td>\n",
       "      <td>1353.284119</td>\n",
       "      <td>1338.217281</td>\n",
       "      <td>1345.850871</td>\n",
       "      <td>5577.000286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>279.904981</td>\n",
       "      <td>281.122225</td>\n",
       "      <td>278.552880</td>\n",
       "      <td>279.944893</td>\n",
       "      <td>29425.377313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>705.000000</td>\n",
       "      <td>715.500000</td>\n",
       "      <td>681.000000</td>\n",
       "      <td>704.900024</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1192.599976</td>\n",
       "      <td>1197.900024</td>\n",
       "      <td>1186.099976</td>\n",
       "      <td>1193.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1292.099976</td>\n",
       "      <td>1296.300049</td>\n",
       "      <td>1286.699951</td>\n",
       "      <td>1292.500000</td>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1569.300049</td>\n",
       "      <td>1577.500000</td>\n",
       "      <td>1558.300049</td>\n",
       "      <td>1567.400024</td>\n",
       "      <td>457.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2045.500000</td>\n",
       "      <td>2063.000000</td>\n",
       "      <td>2040.000000</td>\n",
       "      <td>2051.500000</td>\n",
       "      <td>386334.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close         Volume\n",
       "count  3501.000000  3501.000000  3501.000000  3501.000000    3501.000000\n",
       "mean   1346.093431  1353.284119  1338.217281  1345.850871    5577.000286\n",
       "std     279.904981   281.122225   278.552880   279.944893   29425.377313\n",
       "min     705.000000   715.500000   681.000000   704.900024       0.000000\n",
       "25%    1192.599976  1197.900024  1186.099976  1193.000000      42.000000\n",
       "50%    1292.099976  1296.300049  1286.699951  1292.500000     140.000000\n",
       "75%    1569.300049  1577.500000  1558.300049  1567.400024     457.000000\n",
       "max    2045.500000  2063.000000  2040.000000  2051.500000  386334.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.Close.plot(figsize=(10,7), color='r')\n",
    "plt.xlabel(f\"{stock} Prices\")\n",
    "plt.ylabel(f\"{stock} Prices Series\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Open', ylabel='Density'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.Open)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='High', ylabel='Density'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.High)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Low', ylabel='Density'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.Low)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Close', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.Close)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conlusions\n",
    "- Shape the of the data\n",
    "- How our data is distributed\n",
    "- It's is very very non-linear "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Close', axis = 1)\n",
    "y = data['Close']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2800, 4) (701, 4) (2800,) (701,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, X_test.shape,y_train.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling Part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model_lr = LinearRegression()\n",
    "model_lr.fit(X_train, y_train)\n",
    "pred = model_lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1681.75095251,  902.41536579, 1283.96311199, 1293.66212428,\n",
       "       1268.60803071, 1270.35435153, 1277.52227286, 1221.09791399,\n",
       "       1196.76380802, 1779.54929814, 1721.48843551,  740.29241436,\n",
       "       1674.51540155,  978.64468957, 1197.41160432,  925.45610584,\n",
       "       1884.84233119, 1333.1044081 , 1638.8004372 , 1080.15970153,\n",
       "        942.41253267, 1302.1040397 , 1855.41875876, 1132.5698276 ,\n",
       "       1411.99841385,  838.07689447, 1206.2259597 , 1497.22461786,\n",
       "       1328.63048394, 1759.09211222, 1188.29092937, 1381.54175619,\n",
       "       1265.66405139, 1359.69547545, 1823.97918146,  786.15848141,\n",
       "       1105.59226302, 1234.10599141, 1266.23915772, 1283.5924742 ,\n",
       "       1308.68573032, 1298.75795658,  892.44613576, 1501.3294342 ,\n",
       "        956.35468615, 1726.4564187 , 1103.41961523, 1253.57858029,\n",
       "       1155.96904623, 1272.27607217, 1884.58418145, 1315.97233004,\n",
       "       1221.28234296, 1274.3748234 , 1650.45036801, 1694.23437298,\n",
       "       1280.75984317, 1387.20777894, 1268.83056156, 1170.6512293 ,\n",
       "       1084.13474733, 1387.20431768, 1083.730259  , 1412.57220074,\n",
       "       1575.50199121, 1736.21770301, 1254.92472708, 1315.84524053,\n",
       "       1168.13323523, 1229.31035136, 1102.39685474, 1154.45805067,\n",
       "       1089.3619396 , 1086.08729694, 1728.72140029, 1283.05474715,\n",
       "       1790.53610496,  935.25183737, 1710.43978835, 1687.94255331,\n",
       "       1721.53559644, 1280.83349761, 1258.07101953, 1075.25269531,\n",
       "       1377.147083  , 1273.86646062,  834.25725979, 1543.2145845 ,\n",
       "       1254.7901546 , 1137.12610207, 1236.3518701 ,  900.47750136,\n",
       "       1272.99142276, 1658.87474797, 1078.0331637 , 1499.37287259,\n",
       "       1119.46506298, 1114.37470752, 1489.3326925 , 1394.17363434,\n",
       "        920.16790079, 1293.14583546, 1503.94840374,  888.92664932,\n",
       "       1664.59921962, 1259.2996843 , 1331.26374344, 1740.13286923,\n",
       "        909.89807131, 1534.68403072, 1681.21194398, 1937.77098277,\n",
       "       1391.02803095, 1137.74793353, 1799.17947901, 1306.91488213,\n",
       "        962.1525548 , 1276.51722214,  939.83913229,  781.12376383,\n",
       "       1240.49369007, 1294.67799622, 1351.29934306, 1218.72495729,\n",
       "       1115.83522739, 1267.23054867, 1356.18328622, 1279.00986302,\n",
       "       1325.87527247, 1503.81325374, 1096.80105859, 1877.38495789,\n",
       "       1291.97332924, 1200.95352548, 1372.81148995, 1312.11057625,\n",
       "       1540.39687276,  917.2379546 , 1272.38465183, 1305.64154828,\n",
       "       1781.82373932, 1772.59193283, 1173.46891614,  876.88436953,\n",
       "       1881.44407685, 1732.04959537, 1757.66654672, 1248.76208087,\n",
       "       1351.93784298, 1203.5077914 , 1633.1530096 , 1177.08452079,\n",
       "       1209.09219465, 1529.14163986, 1754.62127431, 1345.36401865,\n",
       "       1592.32825349, 1762.61094133, 1330.14592351, 1238.19250273,\n",
       "       1253.66253817, 1250.90493072, 1216.07223038, 1871.37133046,\n",
       "       1233.10881354, 1338.64427344,  854.02753101, 1245.15650317,\n",
       "       1277.3785911 , 1419.45997004, 1244.58199072, 1247.61124659,\n",
       "       1287.75277935, 1153.4232237 , 1170.99806002, 1237.88420426,\n",
       "       1169.14415468, 1278.09437417, 1734.54930005, 1228.31510856,\n",
       "       1540.81277835, 1807.45503715, 1352.9178022 , 1309.91975842,\n",
       "       1333.05482417, 1182.53515375, 1255.56198682, 1088.32338418,\n",
       "       1749.01187579, 1327.59771079, 1350.82807325, 1246.83729934,\n",
       "       1128.35839156,  868.8116856 , 1283.37690926, 1296.57342976,\n",
       "       1285.17095198, 1805.3493295 , 1330.03433298,  860.81742547,\n",
       "       1317.95530528,  903.56884946, 1767.52452343, 1140.79859897,\n",
       "       1613.89069205, 1410.36603859, 1229.66733518, 1814.18135005,\n",
       "       1720.45713674, 1647.41563468, 1317.33212981, 1091.56589204,\n",
       "       1202.54102767, 1285.50664165, 1563.88534889, 1494.44965185,\n",
       "       1219.9718713 , 1898.71967648, 1314.01438354, 1226.902991  ,\n",
       "       1462.16698322,  978.10734231, 1370.28705901, 1279.83827743,\n",
       "       1376.30993123, 1874.92138077, 1078.60394356,  920.47281726,\n",
       "       1191.25892589, 1821.36942518, 1790.19448919, 1813.74114858,\n",
       "       1607.7705171 , 1263.66000852, 1201.33506997, 1261.7915346 ,\n",
       "       1140.59776972,  952.27727664,  964.90720717, 1168.36149938,\n",
       "       1294.39591943, 1730.27916583, 1798.44236629, 1309.63812211,\n",
       "       1065.20634817, 1358.75100963, 1717.17673188, 1642.7626161 ,\n",
       "        923.3740411 , 1951.15619031, 1651.20748331, 1806.47458661,\n",
       "       1307.89451174, 1410.35635231, 1709.65045033, 1267.63733151,\n",
       "       1307.79150529, 1258.8548901 , 1116.59480894, 1093.80554238,\n",
       "       1340.04475007, 1204.06509389,  947.38113268, 1864.82732345,\n",
       "       1826.43057858, 1386.90418487, 1851.72467813, 1038.29303389,\n",
       "       1341.99748004, 1350.26359892, 1269.20109902, 1116.62064067,\n",
       "       1881.26615358, 1534.60551452,  887.82720218, 1466.76425645,\n",
       "       1275.40076865, 1805.04902534,  858.38896655,  794.8822435 ,\n",
       "       1178.6914295 , 1294.07395966,  946.6134595 , 1225.64933122,\n",
       "        940.86761595, 1323.47208936, 1686.51717164, 1652.39922129,\n",
       "       1281.64600096, 1750.63288314, 1663.14573661, 1231.7973916 ,\n",
       "       1636.94648629, 1463.45592549, 1193.48814132, 1259.04108962,\n",
       "       1226.10038898, 1160.21651758, 1095.50237059,  966.3785701 ,\n",
       "       1231.15581925, 1372.9286805 , 1657.16310059, 1234.26144353,\n",
       "       1854.17626821, 1491.57104037, 1616.89225269, 1890.74094827,\n",
       "       1244.94847816, 1318.58930946, 1292.52210906, 1888.22655391,\n",
       "        732.00407847,  751.8114724 , 1858.23179548, 1130.28578465,\n",
       "       1216.30788734,  869.2794021 , 1203.67073028, 1288.08841191,\n",
       "       1113.51419676, 1266.97636367, 1367.50167776, 1220.46378039,\n",
       "       1253.66075123, 1298.5287421 ,  889.45024475, 1756.6131929 ,\n",
       "       1929.50601639, 1501.255845  , 1430.05860064,  950.73697971,\n",
       "       1251.35857981,  747.10584391, 1146.72253175, 1108.48984621,\n",
       "       1271.20095562, 1664.1517744 , 1274.06499694, 1156.17359077,\n",
       "       1847.89698393, 1709.69472893, 1089.62757296, 1571.54471806,\n",
       "       1290.85381752, 1213.14495299, 1238.63437503, 1649.16196068,\n",
       "       1291.33940654, 1226.20116546,  754.85291373, 1112.62640119,\n",
       "        897.16788496, 1364.30587653, 1523.50406155, 1538.32170851,\n",
       "       1285.04648865, 1573.5881373 , 1802.02140537, 1283.37023099,\n",
       "       1957.66605012, 1547.71452255, 1326.31063776, 1325.54109549,\n",
       "       1176.92339947, 1716.83834549, 1891.48256214, 1729.78055628,\n",
       "        996.16307438, 1929.69168762, 1212.52069447, 1218.79701814,\n",
       "        904.82154089, 1197.65666101, 1694.93841989, 1485.34032269,\n",
       "       1194.11624836, 1282.42089976, 1327.47795932, 1218.88963687,\n",
       "       1340.14659698,  872.04839711,  911.44781398, 1931.24656442,\n",
       "       1844.99149476, 1273.95179841, 1208.43838949, 1229.84270886,\n",
       "        915.17707167, 1228.59369302, 1155.50215728, 1291.76016403,\n",
       "       1319.38559286, 1725.19595322, 1079.52041907, 1655.50605814,\n",
       "       1278.78698661,  915.93059609, 1049.27259338, 1010.44599511,\n",
       "       1201.14126385, 1885.94796694, 1858.62104361, 1701.44792621,\n",
       "        808.22688683, 1226.6662901 , 1221.34785283, 1334.03573278,\n",
       "       1752.82653646, 1182.26053756, 1105.12669952, 1325.35249377,\n",
       "       1408.71652572, 1283.82116194,  807.81879041, 1234.45978661,\n",
       "       1856.47607909, 1777.61846059, 1051.41860655, 1195.59535363,\n",
       "       1258.43662624, 1130.63864714, 1544.87594472, 1719.37559454,\n",
       "       1186.72932596, 1258.92348459, 1955.07063493,  799.55604606,\n",
       "       1183.46517224, 1290.57276233,  903.9023438 , 1766.95731723,\n",
       "       1219.93960307, 1607.89681865,  958.30991818, 1902.79471488,\n",
       "       1322.75381617, 1294.41397624, 1233.90345032, 1777.66672105,\n",
       "       1107.70673593, 1740.76672465, 1212.84186066, 1786.69849771,\n",
       "        904.76442848, 1346.8064221 , 1566.01431275,  768.29730292,\n",
       "       1817.06294241, 1634.13125766, 1311.879152  , 1332.56571283,\n",
       "       1107.60334629, 1728.69882081, 1783.29313851, 1530.82624083,\n",
       "       1333.80607566,  824.91475424,  905.53978795, 1674.21747687,\n",
       "        922.47179304, 1336.60417422, 1198.43122333, 1191.01799156,\n",
       "       1120.44997698, 1115.8014806 , 1647.40611775, 1282.83275743,\n",
       "       1081.36784977, 1271.83993681, 1550.85842475, 1106.10870833,\n",
       "       1317.08875579, 1416.083776  , 1643.07490241,  838.57524922,\n",
       "        933.67040819,  877.66446532, 1155.15113108, 1292.96575727,\n",
       "       1200.84677632, 1195.86368652, 1244.35636895, 1237.40499307,\n",
       "       1178.61305305, 1251.13705314, 1239.85937817, 1186.67498767,\n",
       "        934.20508623, 1574.02804271, 1826.53689089, 1129.53135426,\n",
       "       1287.12770538, 1730.95285629, 1223.75738993, 1744.56549619,\n",
       "       1213.40085796, 1676.65452925, 1310.84042759, 1196.92005235,\n",
       "        925.55883069, 1303.67762981, 1739.16850527, 1264.06039326,\n",
       "       1823.03710157, 1824.33026198, 1186.66874011, 1311.48036571,\n",
       "       1557.6996335 ,  949.24189317, 1405.9146088 , 1121.06739654,\n",
       "       1291.96251682, 1483.79547502, 1107.12710457, 1598.69209472,\n",
       "        958.51884024, 1188.53066438, 1240.55076443,  930.21228468,\n",
       "       1645.67585151, 1793.60967701, 1164.5668402 , 1573.45128446,\n",
       "       1463.7301817 , 1825.37900234, 1471.53952341, 1650.59555075,\n",
       "        754.24400088, 1678.41185677, 1282.56034996, 1672.01706795,\n",
       "       1156.21127814, 1244.87020399, 1391.18701287,  895.55318   ,\n",
       "       1299.86529241, 1071.53817243,  947.11948961, 1396.5080779 ,\n",
       "       1365.426527  , 1565.19475326, 1211.25548657, 1290.16282372,\n",
       "       1330.7035614 , 1383.87721233, 1663.34268989, 1229.85093447,\n",
       "        909.24347554, 1170.4507983 , 1281.28648006, 1921.65016444,\n",
       "        957.20427269, 1890.52706172,  913.64478083, 1128.02044159,\n",
       "       1198.45496419, 1619.46509966, 1725.11846816, 1713.26945206,\n",
       "       1674.30787356, 1182.43076151, 1184.98291617, 1802.6170457 ,\n",
       "       1031.38900664, 1210.87221419, 1714.01693794, 1650.36846901,\n",
       "       1227.99143157, 1331.13435214, 1723.472519  , 1135.11646966,\n",
       "       1564.72947953, 1230.72057373, 1222.37059883,  904.73930705,\n",
       "       1075.81863157, 1263.15789929, 1589.60864167,  910.73572975,\n",
       "       1186.47988032, 1105.82932903, 1080.13991545, 1235.17994816,\n",
       "       1268.27131277, 1086.78983691, 1139.36557534, 1658.94491269,\n",
       "       1755.45764496, 1336.20973368, 1303.87687652, 1186.56705894,\n",
       "       1091.00187606, 1282.27700197, 1945.7653412 , 1241.12815859,\n",
       "       1281.10227627,  971.65975321, 1295.86832499, 1223.54114676,\n",
       "       1607.40450086, 1214.39732124, 1163.01166105, 1137.79135896,\n",
       "       1658.67728158, 1823.32516365, 1191.35552784, 1519.8286646 ,\n",
       "       1735.15387134, 1306.39575345, 1355.84448447, 1341.26969632,\n",
       "       1482.59277349, 1154.50250597, 1608.56572371, 1193.51918792,\n",
       "       1255.41972913,  901.4975976 , 1728.90305712, 1276.08092404,\n",
       "       1836.43875028, 1666.83520131, 1622.02601547, 1234.69362574,\n",
       "       1176.34897348, 1273.05849364,  737.04794085, 1409.52855691,\n",
       "       1290.52370038, 1819.49144342, 1323.78482648, 1464.74895817,\n",
       "       1797.16216705, 1168.85907851, 1407.38801185, 1790.83580783,\n",
       "       1429.33489411, 1224.5360027 , 1407.75998634, 1572.04684656,\n",
       "       1338.18902946, 1181.08773948, 1252.92238615, 1134.68659991,\n",
       "       1481.42139151, 1696.25539545, 1099.60379359, 1262.40133516,\n",
       "       1463.53522289,  917.44127282, 1215.60863765, 1310.02665423,\n",
       "       1260.83502225, 1470.12915336, 1433.4222582 , 1763.71113744,\n",
       "       1298.39553703, 1267.49195808, 1617.51464723, 1259.24586914,\n",
       "       1908.98809467, 1420.83708182, 1231.3829908 ,  973.07662143,\n",
       "       1217.58156446, 1105.41258625, 1238.54520035, 1778.53230107,\n",
       "       1766.18726601,  859.89199252, 1188.76999484,  928.64408685,\n",
       "       1402.45273756, 1117.40173403, 1287.82277123,  777.34201219,\n",
       "       1267.82587607, 1410.17719593, 1090.72034777, 1227.84705151,\n",
       "       1263.79555733,  895.17932812, 1230.26303232, 1729.20168843,\n",
       "       1552.94852864, 1348.74319313,  830.966427  , 1904.58195329,\n",
       "        898.78666704, 1664.26503584, 1265.68541244, 1251.98712487,\n",
       "       1316.15115575, 1291.80923988, 1250.57656594, 1186.3056968 ,\n",
       "       1289.42502789, 1128.22637459, 1301.65829655, 1780.27429914,\n",
       "       1743.66287701])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict value print\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************\n",
      "*  Mean Square Error: 30.603609421197696\n",
      "*  Root Mean Square Error: 5.532052912002713\n",
      "*  r2_score: 0.9996200981240283\n",
      "**************************************************\n"
     ]
    }
   ],
   "source": [
    "# MSE (Mean Square Error)\n",
    "# RMSE (Root Mean Square Error)\n",
    "# r2_square --> 1.0 -> Very Good Model\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "def calculate_metrics(y_test, y_pred):\n",
    "    '''\n",
    "        y_test: ground truth\n",
    "        y_pred: model predict value\n",
    "    '''\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "    r2_scores = r2_score(y_test, y_pred)\n",
    "    print('*'*50)\n",
    "    print(f\"*  Mean Square Error: {mse}\")\n",
    "    print(f\"*  Root Mean Square Error: {rmse}\")\n",
    "    print(f\"*  r2_score: {r2_scores}\")\n",
    "    print('*'*50)\n",
    "\n",
    "calculate_metrics(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso and Ridge Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso, Ridge\n",
    "\n",
    "model_lasso = Lasso().fit(X_train, y_train)\n",
    "model_ridge = Ridge().fit(X_train, y_train)\n",
    "\n",
    "model_lasso_pred = model_lasso.predict(X_test)\n",
    "model_ridge_pred = model_ridge.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************\n",
      "*  Mean Square Error: 60.07663515535179\n",
      "*  Root Mean Square Error: 7.750911891858389\n",
      "*  r2_score: 0.9992542308953343\n",
      "**************************************************\n",
      "**************************************************\n",
      "*  Mean Square Error: 30.603630749258734\n",
      "*  Root Mean Square Error: 5.532054839682877\n",
      "*  r2_score: 0.9996200978592696\n",
      "**************************************************\n"
     ]
    }
   ],
   "source": [
    "calculate_metrics(y_test, model_lasso_pred)\n",
    "\n",
    "calculate_metrics(y_test, model_ridge_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
      "[CV 1/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.056 total time=   3.5s\n",
      "[CV 2/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.040 total time=   4.1s\n",
      "[CV 3/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.020 total time=   4.3s\n",
      "[CV 4/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.018 total time=   4.9s\n",
      "[CV 5/5] END .......C=0.1, gamma=1, kernel=rbf;, score=-0.051 total time=   5.8s\n",
      "[CV 1/5] END .....C=0.1, gamma=0.1, kernel=rbf;, score=-0.056 total time=   4.1s\n",
      "[CV 2/5] END .....C=0.1, gamma=0.1, kernel=rbf;, score=-0.040 total time=   5.0s\n",
      "[CV 3/5] END .....C=0.1, gamma=0.1, kernel=rbf;, score=-0.020 total time=   3.8s\n",
      "[CV 4/5] END .....C=0.1, gamma=0.1, kernel=rbf;, score=-0.018 total time=   5.8s\n",
      "[CV 5/5] END .....C=0.1, gamma=0.1, kernel=rbf;, score=-0.051 total time=   4.0s\n",
      "[CV 1/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.056 total time=   3.9s\n",
      "[CV 2/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.040 total time=   5.7s\n",
      "[CV 3/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.019 total time=   8.7s\n",
      "[CV 4/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.017 total time=   4.0s\n",
      "[CV 5/5] END ....C=0.1, gamma=0.01, kernel=rbf;, score=-0.051 total time=   3.7s\n",
      "[CV 1/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.053 total time=   3.3s\n",
      "[CV 2/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.036 total time=   3.2s\n",
      "[CV 3/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.016 total time=   3.2s\n",
      "[CV 4/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.014 total time=   3.0s\n",
      "[CV 5/5] END ...C=0.1, gamma=0.001, kernel=rbf;, score=-0.049 total time=   3.1s\n",
      "[CV 1/5] END ..C=0.1, gamma=0.0001, kernel=rbf;, score=-0.033 total time=   2.9s\n",
      "[CV 2/5] END ..C=0.1, gamma=0.0001, kernel=rbf;, score=-0.017 total time=   3.0s\n",
      "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.004 total time=   3.0s\n",
      "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.007 total time=   3.9s\n",
      "[CV 5/5] END ..C=0.1, gamma=0.0001, kernel=rbf;, score=-0.027 total time=   3.4s\n",
      "[CV 1/5] END .........C=1, gamma=1, kernel=rbf;, score=-0.056 total time=   3.8s\n",
      "[CV 2/5] END .........C=1, gamma=1, kernel=rbf;, score=-0.040 total time=   3.7s\n",
      "[CV 3/5] END .........C=1, gamma=1, kernel=rbf;, score=-0.020 total time=   3.4s\n",
      "[CV 4/5] END .........C=1, gamma=1, kernel=rbf;, score=-0.018 total time=   2.9s\n",
      "[CV 5/5] END .........C=1, gamma=1, kernel=rbf;, score=-0.052 total time=   2.8s\n",
      "[CV 1/5] END .......C=1, gamma=0.1, kernel=rbf;, score=-0.056 total time=   2.8s\n",
      "[CV 2/5] END .......C=1, gamma=0.1, kernel=rbf;, score=-0.040 total time=   3.4s\n",
      "[CV 3/5] END .......C=1, gamma=0.1, kernel=rbf;, score=-0.019 total time=   5.3s\n",
      "[CV 4/5] END .......C=1, gamma=0.1, kernel=rbf;, score=-0.017 total time=   3.3s\n",
      "[CV 5/5] END .......C=1, gamma=0.1, kernel=rbf;, score=-0.051 total time=   3.6s\n",
      "[CV 1/5] END ......C=1, gamma=0.01, kernel=rbf;, score=-0.052 total time=   2.9s\n",
      "[CV 2/5] END ......C=1, gamma=0.01, kernel=rbf;, score=-0.036 total time=   3.2s\n",
      "[CV 3/5] END ......C=1, gamma=0.01, kernel=rbf;, score=-0.016 total time=   3.4s\n",
      "[CV 4/5] END ......C=1, gamma=0.01, kernel=rbf;, score=-0.014 total time=   3.5s\n",
      "[CV 5/5] END ......C=1, gamma=0.01, kernel=rbf;, score=-0.049 total time=   3.0s\n",
      "[CV 1/5] END .....C=1, gamma=0.001, kernel=rbf;, score=-0.027 total time=   4.3s\n",
      "[CV 2/5] END .....C=1, gamma=0.001, kernel=rbf;, score=-0.011 total time=   3.4s\n",
      "[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.011 total time=   3.1s\n",
      "[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.013 total time=   3.0s\n",
      "[CV 5/5] END .....C=1, gamma=0.001, kernel=rbf;, score=-0.022 total time=   4.0s\n",
      "[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.125 total time=   2.7s\n",
      "[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.153 total time=   4.1s\n",
      "[CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.168 total time=   2.7s\n",
      "[CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.177 total time=   2.6s\n",
      "[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.149 total time=   2.7s\n",
      "[CV 1/5] END ........C=10, gamma=1, kernel=rbf;, score=-0.055 total time=   3.3s\n",
      "[CV 2/5] END ........C=10, gamma=1, kernel=rbf;, score=-0.039 total time=   3.7s\n",
      "[CV 3/5] END ........C=10, gamma=1, kernel=rbf;, score=-0.019 total time=   3.3s\n",
      "[CV 4/5] END ........C=10, gamma=1, kernel=rbf;, score=-0.018 total time=   3.3s\n",
      "[CV 5/5] END ........C=10, gamma=1, kernel=rbf;, score=-0.051 total time=   3.3s\n",
      "[CV 1/5] END ......C=10, gamma=0.1, kernel=rbf;, score=-0.054 total time=   3.3s\n",
      "[CV 2/5] END ......C=10, gamma=0.1, kernel=rbf;, score=-0.038 total time=   3.3s\n",
      "[CV 3/5] END ......C=10, gamma=0.1, kernel=rbf;, score=-0.017 total time=   3.2s\n",
      "[CV 4/5] END ......C=10, gamma=0.1, kernel=rbf;, score=-0.016 total time=   3.4s\n",
      "[CV 5/5] END ......C=10, gamma=0.1, kernel=rbf;, score=-0.050 total time=   3.3s\n",
      "[CV 1/5] END .....C=10, gamma=0.01, kernel=rbf;, score=-0.031 total time=   5.3s\n",
      "[CV 2/5] END .....C=10, gamma=0.01, kernel=rbf;, score=-0.014 total time=   3.0s\n",
      "[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.006 total time=   2.9s\n",
      "[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.006 total time=   2.9s\n",
      "[CV 5/5] END .....C=10, gamma=0.01, kernel=rbf;, score=-0.027 total time=   2.8s\n",
      "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.154 total time=   2.7s\n",
      "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.187 total time=   2.7s\n",
      "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.199 total time=   3.0s\n",
      "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.208 total time=   2.6s\n",
      "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.179 total time=   2.7s\n",
      "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.596 total time=   2.6s\n",
      "[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.682 total time=   2.5s\n",
      "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.677 total time=   2.5s\n",
      "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.670 total time=   2.5s\n",
      "[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.686 total time=   2.5s\n",
      "[CV 1/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.045 total time=   3.8s\n",
      "[CV 2/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.031 total time=   4.4s\n",
      "[CV 3/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.012 total time=   5.0s\n",
      "[CV 4/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.010 total time=   3.1s\n",
      "[CV 5/5] END .......C=100, gamma=1, kernel=rbf;, score=-0.038 total time=   3.8s\n",
      "[CV 1/5] END .....C=100, gamma=0.1, kernel=rbf;, score=-0.033 total time=   3.3s\n",
      "[CV 2/5] END .....C=100, gamma=0.1, kernel=rbf;, score=-0.018 total time=   3.2s\n",
      "[CV 3/5] END .....C=100, gamma=0.1, kernel=rbf;, score=-0.000 total time=   3.1s\n",
      "[CV 4/5] END .....C=100, gamma=0.1, kernel=rbf;, score=-0.000 total time=   3.1s\n",
      "[CV 5/5] END .....C=100, gamma=0.1, kernel=rbf;, score=-0.025 total time=   3.1s\n",
      "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.116 total time=   3.5s\n",
      "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.147 total time=   3.6s\n",
      "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.142 total time=   3.5s\n",
      "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.132 total time=   3.5s\n",
      "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.129 total time=   3.4s\n",
      "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.562 total time=   6.1s\n",
      "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.625 total time=   5.7s\n",
      "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.658 total time=   6.4s\n",
      "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.643 total time=   5.6s\n",
      "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.638 total time=   5.5s\n",
      "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.839 total time=   5.6s\n",
      "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.870 total time=   5.4s\n",
      "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.887 total time=   5.9s\n",
      "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.873 total time=   5.1s\n",
      "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.910 total time=   5.9s\n",
      "[CV 1/5] END ......C=1000, gamma=1, kernel=rbf;, score=-0.004 total time=   4.1s\n",
      "[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.000 total time=   4.0s\n",
      "[CV 3/5] END ......C=1000, gamma=1, kernel=rbf;, score=-0.003 total time=   3.9s\n",
      "[CV 4/5] END ......C=1000, gamma=1, kernel=rbf;, score=-0.004 total time=   4.3s\n",
      "[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.001 total time=   3.9s\n",
      "[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.023 total time=   4.0s\n",
      "[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.030 total time=   4.0s\n",
      "[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.027 total time=   4.1s\n",
      "[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.016 total time=   4.0s\n",
      "[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.031 total time=   3.9s\n",
      "[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.256 total time=   6.0s\n",
      "[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.301 total time=   7.7s\n",
      "[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.280 total time=   8.1s\n",
      "[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.252 total time=   7.4s\n",
      "[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.282 total time=   5.9s\n",
      "[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.687 total time=  39.9s\n",
      "[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.750 total time=  36.6s\n",
      "[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.771 total time=  34.9s\n",
      "[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.764 total time=  36.1s\n",
      "[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.778 total time=  43.6s\n",
      "[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.890 total time=  52.0s\n",
      "[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.904 total time= 1.1min\n",
      "[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.905 total time=  52.0s\n",
      "[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.906 total time=  48.7s\n",
      "[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.935 total time= 1.1min\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVR(),\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
       "                         'kernel': ['rbf']},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params = {\n",
    "    'C': [0.1, 1, 10, 100, 1000],\n",
    "    'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
    "    'kernel': ['rbf']\n",
    "}\n",
    "\n",
    "params_grid = GridSearchCV(SVR(), params, refit=True, verbose=3)\n",
    "params_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_svr = SVR(C=10, gamma=0.1, kernel='rbf')\n",
    "model_svr.fit(X_train, y_train)\n",
    "svr_pred = model_svr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib  \n",
    "\n",
    "joblib.dump(model_ridge, 'model.pkl') \n",
    "\n",
    "ridge_from_joblib = model = joblib.load(\"model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('myenv': conda)",
   "language": "python",
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  "language_info": {
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