gold price pridiction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "import yfinance as yf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj 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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>82.739998</td>\n",
       "      <td>82.830002</td>\n",
       "      <td>81.980003</td>\n",
       "      <td>82.459999</td>\n",
       "      <td>82.459999</td>\n",
       "      <td>3815600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-02</th>\n",
       "      <td>83.559998</td>\n",
       "      <td>85.139999</td>\n",
       "      <td>83.440002</td>\n",
       "      <td>84.860001</td>\n",
       "      <td>84.860001</td>\n",
       "      <td>12291100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-03</th>\n",
       "      <td>84.870003</td>\n",
       "      <td>85.940002</td>\n",
       "      <td>84.599998</td>\n",
       "      <td>85.570000</td>\n",
       "      <td>85.570000</td>\n",
       "      <td>9553900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-04</th>\n",
       "      <td>85.339996</td>\n",
       "      <td>85.550003</td>\n",
       "      <td>84.430000</td>\n",
       "      <td>85.129997</td>\n",
       "      <td>85.129997</td>\n",
       "      <td>8402200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-07</th>\n",
       "      <td>85.239998</td>\n",
       "      <td>85.260002</td>\n",
       "      <td>84.570000</td>\n",
       "      <td>84.769997</td>\n",
       "      <td>84.769997</td>\n",
       "      <td>6944300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                       \n",
       "2007-12-31  82.739998  82.830002  81.980003  82.459999  82.459999   3815600\n",
       "2008-01-02  83.559998  85.139999  83.440002  84.860001  84.860001  12291100\n",
       "2008-01-03  84.870003  85.940002  84.599998  85.570000  85.570000   9553900\n",
       "2008-01-04  85.339996  85.550003  84.430000  85.129997  85.129997   8402200\n",
       "2008-01-07  85.239998  85.260002  84.570000  84.769997  84.769997   6944300"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = yf.download('GLD','2008-01-01','2017-12-31')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df[['Close']] #only keeping close columns\n",
    "df = df.dropna() #Drop rows with missing values\n",
    "df.Close.plot(figsize=(10,5)) # Plot the closing price of GLD\n",
    "plt.ylabel('Gold ETF Prices')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S_3</th>\n",
       "      <th>S_9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-14</th>\n",
       "      <td>87.793335</td>\n",
       "      <td>85.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-15</th>\n",
       "      <td>88.790001</td>\n",
       "      <td>86.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-16</th>\n",
       "      <td>88.703334</td>\n",
       "      <td>87.017777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-17</th>\n",
       "      <td>88.076665</td>\n",
       "      <td>87.143333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-18</th>\n",
       "      <td>87.063332</td>\n",
       "      <td>87.295555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  S_3        S_9\n",
       "Date                            \n",
       "2008-01-14  87.793335  85.883333\n",
       "2008-01-15  88.790001  86.670000\n",
       "2008-01-16  88.703334  87.017777\n",
       "2008-01-17  88.076665  87.143333\n",
       "2008-01-18  87.063332  87.295555"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#defining explanatory variable and they are moving avg for past 3 days and 9  days\n",
    "df['S_3'] = df['Close'].shift(1).rolling(window=3).mean()\n",
    "df['S_9'] = df['Close'].shift(1).rolling(window=9).mean()\n",
    "df = df.dropna()\n",
    "X = df[['S_3','S_9']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2008-01-14    89.540001\n",
       "2008-01-15    87.989998\n",
       "2008-01-16    86.699997\n",
       "2008-01-17    86.500000\n",
       "2008-01-18    87.419998\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#defining dependent variable\n",
    "y = df['Close']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg=LinearRegression()\n",
    "reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-04-16</th>\n",
       "      <td>111.239998</td>\n",
       "      <td>113.289237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-06</th>\n",
       "      <td>134.149994</td>\n",
       "      <td>134.174856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-21</th>\n",
       "      <td>152.020004</td>\n",
       "      <td>156.903829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-08</th>\n",
       "      <td>118.190002</td>\n",
       "      <td>117.487167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-13</th>\n",
       "      <td>93.720001</td>\n",
       "      <td>92.857082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-15</th>\n",
       "      <td>110.160004</td>\n",
       "      <td>111.067826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-14</th>\n",
       "      <td>153.679993</td>\n",
       "      <td>153.588601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-08</th>\n",
       "      <td>121.650002</td>\n",
       "      <td>122.697679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-16</th>\n",
       "      <td>121.870003</td>\n",
       "      <td>122.093763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-18</th>\n",
       "      <td>118.809998</td>\n",
       "      <td>118.451302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-04</th>\n",
       "      <td>160.639999</td>\n",
       "      <td>160.459476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-05</th>\n",
       "      <td>126.220001</td>\n",
       "      <td>124.319852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-25</th>\n",
       "      <td>114.320000</td>\n",
       "      <td>115.526045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-18</th>\n",
       "      <td>114.870003</td>\n",
       "      <td>115.015858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-15</th>\n",
       "      <td>124.529999</td>\n",
       "      <td>127.687161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-11</th>\n",
       "      <td>153.559998</td>\n",
       "      <td>154.166542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-28</th>\n",
       "      <td>149.820007</td>\n",
       "      <td>147.610799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-17</th>\n",
       "      <td>85.220001</td>\n",
       "      <td>87.099162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-06</th>\n",
       "      <td>125.669998</td>\n",
       "      <td>123.956235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-18</th>\n",
       "      <td>116.339996</td>\n",
       "      <td>116.797474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-09</th>\n",
       "      <td>135.369995</td>\n",
       "      <td>136.963563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-20</th>\n",
       "      <td>117.510002</td>\n",
       "      <td>116.948334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03-03</th>\n",
       "      <td>97.239998</td>\n",
       "      <td>96.170161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-22</th>\n",
       "      <td>113.830002</td>\n",
       "      <td>115.284877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-23</th>\n",
       "      <td>119.779999</td>\n",
       "      <td>120.419729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-10</th>\n",
       "      <td>110.820000</td>\n",
       "      <td>110.969627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-01</th>\n",
       "      <td>86.860001</td>\n",
       "      <td>91.963388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-03</th>\n",
       "      <td>160.960007</td>\n",
       "      <td>155.882779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-28</th>\n",
       "      <td>120.690002</td>\n",
       "      <td>119.706902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-06</th>\n",
       "      <td>89.540001</td>\n",
       "      <td>88.047269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-17</th>\n",
       "      <td>132.869995</td>\n",
       "      <td>133.685712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-10</th>\n",
       "      <td>161.100006</td>\n",
       "      <td>157.597153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-19</th>\n",
       "      <td>153.380005</td>\n",
       "      <td>153.454101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-22</th>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.112284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-26</th>\n",
       "      <td>125.410004</td>\n",
       "      <td>126.479617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03-31</th>\n",
       "      <td>90.410004</td>\n",
       "      <td>93.121260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-18</th>\n",
       "      <td>159.309998</td>\n",
       "      <td>160.397474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-30</th>\n",
       "      <td>127.910004</td>\n",
       "      <td>127.726607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-27</th>\n",
       "      <td>118.059998</td>\n",
       "      <td>118.676135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-22</th>\n",
       "      <td>115.599998</td>\n",
       "      <td>115.881821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>127.550003</td>\n",
       "      <td>127.750933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-14</th>\n",
       "      <td>143.809998</td>\n",
       "      <td>142.007492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-09</th>\n",
       "      <td>168.610001</td>\n",
       "      <td>163.563974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-10-20</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>79.331951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-10-13</th>\n",
       "      <td>81.989998</td>\n",
       "      <td>88.114008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-25</th>\n",
       "      <td>129.210007</td>\n",
       "      <td>128.341076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-02</th>\n",
       "      <td>120.050003</td>\n",
       "      <td>117.962273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-24</th>\n",
       "      <td>89.720001</td>\n",
       "      <td>87.998307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-25</th>\n",
       "      <td>126.410004</td>\n",
       "      <td>126.936449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-03</th>\n",
       "      <td>131.570007</td>\n",
       "      <td>132.584422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-30</th>\n",
       "      <td>91.309998</td>\n",
       "      <td>89.019358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-12</th>\n",
       "      <td>167.449997</td>\n",
       "      <td>167.520960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-29</th>\n",
       "      <td>157.699997</td>\n",
       "      <td>156.066510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-24</th>\n",
       "      <td>124.559998</td>\n",
       "      <td>123.579611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-24</th>\n",
       "      <td>161.020004</td>\n",
       "      <td>158.349856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>162.100006</td>\n",
       "      <td>161.154171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-13</th>\n",
       "      <td>121.019997</td>\n",
       "      <td>119.118720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-16</th>\n",
       "      <td>123.540001</td>\n",
       "      <td>122.547880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-15</th>\n",
       "      <td>174.399994</td>\n",
       "      <td>176.731794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-08</th>\n",
       "      <td>167.119995</td>\n",
       "      <td>161.516434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>502 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Actual   Predicted\n",
       "Date                              \n",
       "2010-04-16  111.239998  113.289237\n",
       "2013-09-06  134.149994  134.174856\n",
       "2012-06-21  152.020004  156.903829\n",
       "2017-02-08  118.190002  117.487167\n",
       "2009-08-13   93.720001   92.857082\n",
       "2015-07-15  110.160004  111.067826\n",
       "2013-03-14  153.679993  153.588601\n",
       "2017-06-08  121.650002  122.697679\n",
       "2017-08-16  121.870003  122.093763\n",
       "2017-05-18  118.809998  118.451302\n",
       "2011-08-04  160.639999  160.459476\n",
       "2014-05-05  126.220001  124.319852\n",
       "2017-01-25  114.320000  115.526045\n",
       "2017-01-18  114.870003  115.015858\n",
       "2014-07-15  124.529999  127.687161\n",
       "2012-05-11  153.559998  154.166542\n",
       "2011-04-28  149.820007  147.610799\n",
       "2009-04-17   85.220001   87.099162\n",
       "2014-08-06  125.669998  123.956235\n",
       "2015-02-18  116.339996  116.797474\n",
       "2010-12-09  135.369995  136.963563\n",
       "2017-03-20  117.510002  116.948334\n",
       "2008-03-03   97.239998   96.170161\n",
       "2015-04-22  113.830002  115.284877\n",
       "2010-08-23  119.779999  120.419729\n",
       "2009-12-10  110.820000  110.969627\n",
       "2008-04-01   86.860001   91.963388\n",
       "2011-10-03  160.960007  155.882779\n",
       "2017-07-28  120.690002  119.706902\n",
       "2009-05-06   89.540001   88.047269\n",
       "...                ...         ...\n",
       "2013-04-17  132.869995  133.685712\n",
       "2012-04-10  161.100006  157.597153\n",
       "2012-07-19  153.380005  153.454101\n",
       "2016-01-22  105.000000  105.112284\n",
       "2014-03-26  125.410004  126.479617\n",
       "2008-03-31   90.410004   93.121260\n",
       "2012-04-18  159.309998  160.397474\n",
       "2010-09-30  127.910004  127.726607\n",
       "2014-10-27  118.059998  118.676135\n",
       "2015-05-22  115.599998  115.881821\n",
       "2016-09-26  127.550003  127.750933\n",
       "2011-04-14  143.809998  142.007492\n",
       "2011-08-09  168.610001  163.563974\n",
       "2008-10-20   78.500000   79.331951\n",
       "2008-10-13   81.989998   88.114008\n",
       "2014-02-25  129.210007  128.341076\n",
       "2013-07-02  120.050003  117.962273\n",
       "2009-04-24   89.720001   87.998307\n",
       "2014-03-25  126.410004  126.936449\n",
       "2010-11-03  131.570007  132.584422\n",
       "2009-01-30   91.309998   89.019358\n",
       "2012-11-12  167.449997  167.520960\n",
       "2011-09-29  157.699997  156.066510\n",
       "2014-04-24  124.559998  123.579611\n",
       "2011-10-24  161.020004  158.349856\n",
       "2011-10-11  162.100006  161.154171\n",
       "2014-01-13  121.019997  119.118720\n",
       "2013-10-16  123.540001  122.547880\n",
       "2011-09-15  174.399994  176.731794\n",
       "2011-08-08  167.119995  161.516434\n",
       "\n",
       "[502 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=reg.predict(X_test)\n",
    "pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9951234943513206"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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