Gold Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_dataset = pd.read_csv(\"gold_price.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>SPX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>USO</th>\n",
       "      <th>SLV</th>\n",
       "      <th>EUR/USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/2/2008</td>\n",
       "      <td>1447.160034</td>\n",
       "      <td>84.860001</td>\n",
       "      <td>78.470001</td>\n",
       "      <td>15.180</td>\n",
       "      <td>1.471692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/3/2008</td>\n",
       "      <td>1447.160034</td>\n",
       "      <td>85.570000</td>\n",
       "      <td>78.370003</td>\n",
       "      <td>15.285</td>\n",
       "      <td>1.474491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/4/2008</td>\n",
       "      <td>1411.630005</td>\n",
       "      <td>85.129997</td>\n",
       "      <td>77.309998</td>\n",
       "      <td>15.167</td>\n",
       "      <td>1.475492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/7/2008</td>\n",
       "      <td>1416.180054</td>\n",
       "      <td>84.769997</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>15.053</td>\n",
       "      <td>1.468299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/8/2008</td>\n",
       "      <td>1390.189941</td>\n",
       "      <td>86.779999</td>\n",
       "      <td>76.059998</td>\n",
       "      <td>15.590</td>\n",
       "      <td>1.557099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date          SPX        GLD        USO     SLV   EUR/USD\n",
       "0  1/2/2008  1447.160034  84.860001  78.470001  15.180  1.471692\n",
       "1  1/3/2008  1447.160034  85.570000  78.370003  15.285  1.474491\n",
       "2  1/4/2008  1411.630005  85.129997  77.309998  15.167  1.475492\n",
       "3  1/7/2008  1416.180054  84.769997  75.500000  15.053  1.468299\n",
       "4  1/8/2008  1390.189941  86.779999  76.059998  15.590  1.557099"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>SPX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>USO</th>\n",
       "      <th>SLV</th>\n",
       "      <th>EUR/USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>5/8/2018</td>\n",
       "      <td>2671.919922</td>\n",
       "      <td>124.589996</td>\n",
       "      <td>14.0600</td>\n",
       "      <td>15.5100</td>\n",
       "      <td>1.186789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>5/9/2018</td>\n",
       "      <td>2697.790039</td>\n",
       "      <td>124.330002</td>\n",
       "      <td>14.3700</td>\n",
       "      <td>15.5300</td>\n",
       "      <td>1.184722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>5/10/2018</td>\n",
       "      <td>2723.070068</td>\n",
       "      <td>125.180000</td>\n",
       "      <td>14.4100</td>\n",
       "      <td>15.7400</td>\n",
       "      <td>1.191753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>5/14/2018</td>\n",
       "      <td>2730.129883</td>\n",
       "      <td>124.489998</td>\n",
       "      <td>14.3800</td>\n",
       "      <td>15.5600</td>\n",
       "      <td>1.193118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>5/16/2018</td>\n",
       "      <td>2725.780029</td>\n",
       "      <td>122.543800</td>\n",
       "      <td>14.4058</td>\n",
       "      <td>15.4542</td>\n",
       "      <td>1.182033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date          SPX         GLD      USO      SLV   EUR/USD\n",
       "2285   5/8/2018  2671.919922  124.589996  14.0600  15.5100  1.186789\n",
       "2286   5/9/2018  2697.790039  124.330002  14.3700  15.5300  1.184722\n",
       "2287  5/10/2018  2723.070068  125.180000  14.4100  15.7400  1.191753\n",
       "2288  5/14/2018  2730.129883  124.489998  14.3800  15.5600  1.193118\n",
       "2289  5/16/2018  2725.780029  122.543800  14.4058  15.4542  1.182033"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2290, 6)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>USO</th>\n",
       "      <th>SLV</th>\n",
       "      <th>EUR/USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2290.000000</td>\n",
       "      <td>2290.000000</td>\n",
       "      <td>2290.000000</td>\n",
       "      <td>2290.000000</td>\n",
       "      <td>2290.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1654.315776</td>\n",
       "      <td>122.732875</td>\n",
       "      <td>31.842221</td>\n",
       "      <td>20.084997</td>\n",
       "      <td>1.283653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>519.111540</td>\n",
       "      <td>23.283346</td>\n",
       "      <td>19.523517</td>\n",
       "      <td>7.092566</td>\n",
       "      <td>0.131547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>676.530029</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>7.960000</td>\n",
       "      <td>8.850000</td>\n",
       "      <td>1.039047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1239.874969</td>\n",
       "      <td>109.725000</td>\n",
       "      <td>14.380000</td>\n",
       "      <td>15.570000</td>\n",
       "      <td>1.171313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1551.434998</td>\n",
       "      <td>120.580002</td>\n",
       "      <td>33.869999</td>\n",
       "      <td>17.268500</td>\n",
       "      <td>1.303296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2073.010070</td>\n",
       "      <td>132.840004</td>\n",
       "      <td>37.827501</td>\n",
       "      <td>22.882499</td>\n",
       "      <td>1.369971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2872.870117</td>\n",
       "      <td>184.589996</td>\n",
       "      <td>117.480003</td>\n",
       "      <td>47.259998</td>\n",
       "      <td>1.598798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SPX          GLD          USO          SLV      EUR/USD\n",
       "count  2290.000000  2290.000000  2290.000000  2290.000000  2290.000000\n",
       "mean   1654.315776   122.732875    31.842221    20.084997     1.283653\n",
       "std     519.111540    23.283346    19.523517     7.092566     0.131547\n",
       "min     676.530029    70.000000     7.960000     8.850000     1.039047\n",
       "25%    1239.874969   109.725000    14.380000    15.570000     1.171313\n",
       "50%    1551.434998   120.580002    33.869999    17.268500     1.303296\n",
       "75%    2073.010070   132.840004    37.827501    22.882499     1.369971\n",
       "max    2872.870117   184.589996   117.480003    47.259998     1.598798"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2290 entries, 0 to 2289\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   Date     2290 non-null   object \n",
      " 1   SPX      2290 non-null   float64\n",
      " 2   GLD      2290 non-null   float64\n",
      " 3   USO      2290 non-null   float64\n",
      " 4   SLV      2290 non-null   float64\n",
      " 5   EUR/USD  2290 non-null   float64\n",
      "dtypes: float64(5), object(1)\n",
      "memory usage: 107.5+ KB\n"
     ]
    }
   ],
   "source": [
    "gold_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date       0\n",
       "SPX        0\n",
       "GLD        0\n",
       "USO        0\n",
       "SLV        0\n",
       "EUR/USD    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation = gold_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2702b3b4dc0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sns.heatmap(correlation, cbar = True, square = True, fmt = '.1f', annot = True, annot_kws = {'size': 9}, cmap = 'Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPX        0.049345\n",
      "GLD        1.000000\n",
      "USO       -0.186360\n",
      "SLV        0.866632\n",
      "EUR/USD   -0.024375\n",
      "Name: GLD, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(correlation['GLD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2702bd003a0>"
      ]
     },
     "execution_count": 50,
     "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": [
    "sns.distplot(gold_dataset['GLD'], color = 'Red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = gold_dataset.drop({'Date', 'GLD'}, axis = 1)\n",
    "Y = gold_dataset['GLD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              SPX        USO      SLV   EUR/USD\n",
      "0     1447.160034  78.470001  15.1800  1.471692\n",
      "1     1447.160034  78.370003  15.2850  1.474491\n",
      "2     1411.630005  77.309998  15.1670  1.475492\n",
      "3     1416.180054  75.500000  15.0530  1.468299\n",
      "4     1390.189941  76.059998  15.5900  1.557099\n",
      "...           ...        ...      ...       ...\n",
      "2285  2671.919922  14.060000  15.5100  1.186789\n",
      "2286  2697.790039  14.370000  15.5300  1.184722\n",
      "2287  2723.070068  14.410000  15.7400  1.191753\n",
      "2288  2730.129883  14.380000  15.5600  1.193118\n",
      "2289  2725.780029  14.405800  15.4542  1.182033\n",
      "\n",
      "[2290 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        84.860001\n",
      "1        85.570000\n",
      "2        85.129997\n",
      "3        84.769997\n",
      "4        86.779999\n",
      "           ...    \n",
      "2285    124.589996\n",
      "2286    124.330002\n",
      "2287    125.180000\n",
      "2288    124.489998\n",
      "2289    122.543800\n",
      "Name: GLD, Length: 2290, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor_model = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_prediction = regressor_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[163.9108001  127.47029925 124.26989942 119.70019894 125.94579896\n",
      "  86.61929809 171.85790192 135.95210034 106.84410073 132.90370195\n",
      " 120.77279956 124.80730029 143.58999757 104.50039946  80.83119924\n",
      " 119.96379979 140.55939964  90.38140008  91.48959941 128.35570261\n",
      " 125.64359987 147.84129953 116.76150193 153.16809903 129.4989982\n",
      " 130.09480103 118.40940094 111.39330036 168.90160277 157.96899747\n",
      " 125.76080163 154.03020156 127.07379955 122.58919786 116.1170016\n",
      " 154.81710007 115.00089965 120.60910112 103.83840218 105.38479998\n",
      " 134.94239901  85.7506992  156.55360167 121.23950013 122.44960101\n",
      " 155.54349978 120.28909987 116.58799956 168.98400289 116.33790055\n",
      " 137.80480204 162.07489876 157.96229981 153.24180067 124.2428999\n",
      " 116.95290008 167.11500082 117.25649984 165.75930038  84.44519979\n",
      " 121.3573992  122.32950004 120.10690019 171.98099932 118.87179966\n",
      " 180.50389687 162.27030292 138.74780169 115.65030006  92.5785007\n",
      " 171.90420001 117.66639949 117.89689839 125.55680011 147.40579913\n",
      "  91.19300091  83.85809883  88.72309993 102.70279927 124.30710055\n",
      " 122.87870021 169.40850266 121.14669968 125.76579928 160.36170603\n",
      " 120.59050066 170.14510162 148.27299786  86.95709915 126.5801005\n",
      " 118.78100038  89.89440013 118.82489979 116.62299783  89.33559895\n",
      " 138.44520244 160.41640099 130.93880369 105.75550114 122.63179966\n",
      " 120.4785006  124.06510125 108.30250012 115.35690067 113.55409933\n",
      " 120.94650092 158.28870281 126.47719929 119.92189953  91.13659982\n",
      " 124.85869934 161.12759931 159.23030287  87.57679908 138.70530029\n",
      " 166.81079854 154.92940087 108.75740045 109.21319802 124.45019995\n",
      " 113.90200187 117.35189961 160.6858014  121.71010024 143.06619706\n",
      " 116.06820081 163.60570223 119.99689962 112.73800105 148.60219949\n",
      " 104.24849924 121.87830011  78.2226989  161.0152978  125.47060059\n",
      " 132.07640081 120.02150004 107.87379987 153.11120059 114.26990022\n",
      " 161.91020065 128.62980218  91.90640105 121.30359954 131.06480018\n",
      "  89.63819987 120.42069979 120.69110043 112.56040059  90.57300139\n",
      "  91.15289924 118.09210056 150.88160018 113.2280997  132.16910122\n",
      " 117.2200011  119.29340082 156.34579864 127.89260351 114.35790028\n",
      " 119.53420029 142.64559839 115.65520116  92.32119915  90.07030054\n",
      " 165.8941032  112.28899982 115.61960098 120.00200083 114.58439993\n",
      "  92.35539803 117.4790989  154.63860244 151.21580025  93.05320011\n",
      " 125.94670068 104.37169948 155.30840065 115.50960029 119.96779817\n",
      "  91.66739939  91.76069989 152.89400108 173.17779745  79.24479934\n",
      " 125.94309998 119.26890059 151.58880066  90.91450099 163.94800169\n",
      " 138.60999816 117.96510095 141.99459991  82.57089772 124.74729939\n",
      " 119.10120071  90.98170082  94.20759982 173.45159712 164.39760116\n",
      " 168.0367007  139.256703   118.46890084 130.63490128 124.28780029\n",
      " 129.34209839 121.48959987 121.86689925 127.18470038 103.31340052\n",
      " 116.10390068 133.4336993  165.06449915 125.54050033 113.65400196\n",
      " 123.19770042  82.21460048 102.92999862 121.7359989  163.16510022\n",
      " 121.31209942  87.35690067 127.42850044 108.13229942 158.19850181\n",
      " 164.34899996 124.00950035 124.52940008 147.11909776  89.81159908\n",
      "  88.39240054 115.38490058 108.32810009 128.425999   108.32309999\n",
      " 114.70509928 167.82090028 119.9919004  106.71529922 158.11149867\n",
      " 125.14049886 114.38450108 103.49510037 150.1494987  166.36059839\n",
      " 171.32870369  92.40109925 114.68860043 118.96009946 113.54880082\n",
      " 160.34190547 120.12909917  72.7097022  118.89829964 115.26440044\n",
      " 166.60379848 118.82820046 114.49009975  94.33589973 117.18330147\n",
      " 128.25929883 114.45450006 156.47130037 119.5440004  118.7083011\n",
      " 161.31310072 118.57690041  94.99099915 118.31090103 166.77019991\n",
      " 111.06739871 146.62900246 118.86870066  86.63959867 171.74370184\n",
      " 155.77849764 162.5308004   84.50219833 119.83590033 108.31179998\n",
      "  92.52050014  72.51909993  93.16439991 117.63079991 176.9504994\n",
      " 115.9307012  123.63809967  87.14079889 113.51820059 112.16909979\n",
      " 161.66070424 118.9687011   87.23489811 169.18759902 125.31720016\n",
      " 126.07499961 121.22789983 116.6791004   90.04139995 146.5540998\n",
      " 109.00089876  91.84089826 118.55039926 152.95930226 123.16659947\n",
      " 142.19119903 148.05020058 108.47429992 129.34150252 112.0281002\n",
      " 114.05690013 115.19460022 157.308398    83.70929872 124.07479995\n",
      "  91.41589961 113.744699   127.1299979   92.21110035 129.87420123\n",
      " 124.37679935 128.00679902 121.7643993   90.0558998   87.12779831\n",
      " 136.78739906 165.53080077 169.33180023 159.85660324 123.98349999\n",
      " 106.52190022 116.50260001 124.53809981  91.77729887 167.1441984\n",
      "  91.69679922 128.02690128 117.53210053 166.75019908 114.91639971\n",
      " 163.22970297 169.20629873  84.32499959 117.32780046 101.83099895\n",
      " 130.76910135 112.73359945  80.92809877  92.18190021 135.62019995\n",
      "  98.59259913 117.45119861 116.42200102 118.90170011 125.69460071\n",
      " 115.27000022  97.14670136  73.08069923 118.04669987  89.74150102\n",
      " 135.17679936 113.49799932 114.47119976 110.59550109  86.55569858\n",
      " 112.96590099 126.4925003  156.33489854 119.94709997  77.52090019\n",
      " 113.50360061  87.15129789 114.51689917  96.24890186 115.75320065\n",
      " 124.92469926 114.11130033 118.48400026 119.1615004  125.46089862\n",
      " 111.87990194 108.91679999 165.51450203  96.16329847 121.53540114\n",
      "  92.07200045 116.05210112 110.84129931 155.76460376 117.47239934\n",
      " 154.68190022  86.50769866 141.73309863 110.34969847 106.29629997\n",
      " 127.9128985  118.75519978 119.86710023 117.94740029 149.0827012\n",
      " 109.43679947 135.62210166 158.52249923 107.37279959 102.58669964\n",
      " 166.80569939 119.81960065 140.89590054 104.37650022  94.17339938\n",
      " 114.14070143 123.46500103 109.82299908 167.26090086 170.99200288\n",
      " 117.82469938 168.9292976  126.2857013  160.83000511  95.59819973\n",
      "  99.09999879 125.31889939  73.16830063  88.94889955  93.84780084\n",
      " 148.57349699 114.33520132 108.37679919 123.08829857  90.05109968\n",
      " 149.34790109  93.76509809  86.05569884 106.73430079  74.39070016\n",
      " 134.25199932 167.22700235 117.57049968 117.74429915  96.57800157\n",
      " 126.29660006 128.1096009  113.90329987 126.77439959 127.43709926\n",
      " 116.19620073  95.64769954 123.33009946 126.16010093  89.87440051\n",
      " 133.66760003 118.29350062 115.8411979  144.62779973  93.971701\n",
      " 116.45279958  87.90080048 119.60250014]\n"
     ]
    }
   ],
   "source": [
    "print(test_data_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "error = metrics.r2_score(Y_test, test_data_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared Error:  0.9853978925288737\n"
     ]
    }
   ],
   "source": [
    "print(\"R Squared Error: \", error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test = list(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(Y_test, color = 'Red', label = 'Actual Values')\n",
    "plt.plot(test_data_prediction, color = 'Green', label = 'Predicted Value')\n",
    "plt.title('Actual Price vs Predicted Price')\n",
    "plt.xlabel('No of Values')\n",
    "plt.ylabel('Gold Price')\n",
    "#plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}