stock price prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stock Price Prediction Minor Project\n",
    "\n",
    "# By: Brajesh V Kulkarni"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.26.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import plot\n",
    "\n",
    "\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Date</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>267.899994</td>\n",
       "      <td>250.029999</td>\n",
       "      <td>254.259995</td>\n",
       "      <td>254.259995</td>\n",
       "      <td>11896100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>247.699997</td>\n",
       "      <td>266.700012</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>265.720001</td>\n",
       "      <td>265.720001</td>\n",
       "      <td>12595800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>266.579987</td>\n",
       "      <td>272.450012</td>\n",
       "      <td>264.329987</td>\n",
       "      <td>264.559998</td>\n",
       "      <td>264.559998</td>\n",
       "      <td>8981500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>267.079987</td>\n",
       "      <td>267.619995</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.100006</td>\n",
       "      <td>250.100006</td>\n",
       "      <td>9306700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>253.850006</td>\n",
       "      <td>255.800003</td>\n",
       "      <td>236.110001</td>\n",
       "      <td>249.470001</td>\n",
       "      <td>249.470001</td>\n",
       "      <td>16906900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-02-12</td>\n",
       "      <td>252.139999</td>\n",
       "      <td>259.149994</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>257.950012</td>\n",
       "      <td>257.950012</td>\n",
       "      <td>8534900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-02-13</td>\n",
       "      <td>257.290009</td>\n",
       "      <td>261.410004</td>\n",
       "      <td>254.699997</td>\n",
       "      <td>258.269989</td>\n",
       "      <td>258.269989</td>\n",
       "      <td>6855200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-02-14</td>\n",
       "      <td>260.470001</td>\n",
       "      <td>269.880005</td>\n",
       "      <td>260.329987</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>10972000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-02-15</td>\n",
       "      <td>270.029999</td>\n",
       "      <td>280.500000</td>\n",
       "      <td>267.630005</td>\n",
       "      <td>280.269989</td>\n",
       "      <td>280.269989</td>\n",
       "      <td>10759700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-02-16</td>\n",
       "      <td>278.730011</td>\n",
       "      <td>281.959991</td>\n",
       "      <td>275.690002</td>\n",
       "      <td>278.519989</td>\n",
       "      <td>278.519989</td>\n",
       "      <td>8312400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-02-20</td>\n",
       "      <td>277.739990</td>\n",
       "      <td>285.809998</td>\n",
       "      <td>276.609985</td>\n",
       "      <td>278.549988</td>\n",
       "      <td>278.549988</td>\n",
       "      <td>7769000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>282.070007</td>\n",
       "      <td>286.640015</td>\n",
       "      <td>280.010010</td>\n",
       "      <td>281.040009</td>\n",
       "      <td>281.040009</td>\n",
       "      <td>9371100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-02-22</td>\n",
       "      <td>283.880005</td>\n",
       "      <td>284.500000</td>\n",
       "      <td>274.450012</td>\n",
       "      <td>278.140015</td>\n",
       "      <td>278.140015</td>\n",
       "      <td>8891500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-02-23</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>286.000000</td>\n",
       "      <td>277.809998</td>\n",
       "      <td>285.929993</td>\n",
       "      <td>285.929993</td>\n",
       "      <td>7301800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>288.750000</td>\n",
       "      <td>295.649994</td>\n",
       "      <td>287.010010</td>\n",
       "      <td>294.160004</td>\n",
       "      <td>294.160004</td>\n",
       "      <td>10268600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-02-27</td>\n",
       "      <td>294.769989</td>\n",
       "      <td>297.359985</td>\n",
       "      <td>290.589996</td>\n",
       "      <td>290.609985</td>\n",
       "      <td>290.609985</td>\n",
       "      <td>9416500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>293.100006</td>\n",
       "      <td>295.750000</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>291.380005</td>\n",
       "      <td>291.380005</td>\n",
       "      <td>7653500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>292.750000</td>\n",
       "      <td>295.250000</td>\n",
       "      <td>283.829987</td>\n",
       "      <td>290.390015</td>\n",
       "      <td>290.390015</td>\n",
       "      <td>11932100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>284.649994</td>\n",
       "      <td>301.179993</td>\n",
       "      <td>283.230011</td>\n",
       "      <td>301.049988</td>\n",
       "      <td>301.049988</td>\n",
       "      <td>13345300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>302.850006</td>\n",
       "      <td>316.910004</td>\n",
       "      <td>297.600006</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>18986100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date        Open        High         Low       Close   Adj Close  \\\n",
       "0   2018-02-05  262.000000  267.899994  250.029999  254.259995  254.259995   \n",
       "1   2018-02-06  247.699997  266.700012  245.000000  265.720001  265.720001   \n",
       "2   2018-02-07  266.579987  272.450012  264.329987  264.559998  264.559998   \n",
       "3   2018-02-08  267.079987  267.619995  250.000000  250.100006  250.100006   \n",
       "4   2018-02-09  253.850006  255.800003  236.110001  249.470001  249.470001   \n",
       "5   2018-02-12  252.139999  259.149994  249.000000  257.950012  257.950012   \n",
       "6   2018-02-13  257.290009  261.410004  254.699997  258.269989  258.269989   \n",
       "7   2018-02-14  260.470001  269.880005  260.329987  266.000000  266.000000   \n",
       "8   2018-02-15  270.029999  280.500000  267.630005  280.269989  280.269989   \n",
       "9   2018-02-16  278.730011  281.959991  275.690002  278.519989  278.519989   \n",
       "10  2018-02-20  277.739990  285.809998  276.609985  278.549988  278.549988   \n",
       "11  2018-02-21  282.070007  286.640015  280.010010  281.040009  281.040009   \n",
       "12  2018-02-22  283.880005  284.500000  274.450012  278.140015  278.140015   \n",
       "13  2018-02-23  281.000000  286.000000  277.809998  285.929993  285.929993   \n",
       "14  2018-02-26  288.750000  295.649994  287.010010  294.160004  294.160004   \n",
       "15  2018-02-27  294.769989  297.359985  290.589996  290.609985  290.609985   \n",
       "16  2018-02-28  293.100006  295.750000  290.779999  291.380005  291.380005   \n",
       "17  2018-03-01  292.750000  295.250000  283.829987  290.390015  290.390015   \n",
       "18  2018-03-02  284.649994  301.179993  283.230011  301.049988  301.049988   \n",
       "19  2018-03-05  302.850006  316.910004  297.600006  315.000000  315.000000   \n",
       "\n",
       "      Volume  \n",
       "0   11896100  \n",
       "1   12595800  \n",
       "2    8981500  \n",
       "3    9306700  \n",
       "4   16906900  \n",
       "5    8534900  \n",
       "6    6855200  \n",
       "7   10972000  \n",
       "8   10759700  \n",
       "9    8312400  \n",
       "10   7769000  \n",
       "11   9371100  \n",
       "12   8891500  \n",
       "13   7301800  \n",
       "14  10268600  \n",
       "15   9416500  \n",
       "16   7653500  \n",
       "17  11932100  \n",
       "18  13345300  \n",
       "19  18986100  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk = pd.read_csv(r\"C:\\Users\\braje\\OneDrive\\Desktop\\corizo internship\\minor project\\Stock_Price_data_set.csv\")\n",
    "sk.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1009, 7)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1009 entries, 0 to 1008\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Date       1009 non-null   object \n",
      " 1   Open       1009 non-null   float64\n",
      " 2   High       1009 non-null   float64\n",
      " 3   Low        1009 non-null   float64\n",
      " 4   Close      1009 non-null   float64\n",
      " 5   Adj Close  1009 non-null   float64\n",
      " 6   Volume     1009 non-null   int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 55.3+ KB\n"
     ]
    }
   ],
   "source": [
    "sk.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sk['Date'] = pd.to_datetime(sk['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe contains stock prices between 2018-02-05 00:00:00 2022-02-04 00:00:00\n",
      "Total days = 1460 days\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataframe contains stock prices between {sk.Date.min()} {sk.Date.max()}') \n",
    "print(f'Total days = {(sk.Date.max()  - sk.Date.min()).days} days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Date</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1009</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>1.009000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2020-02-05 07:30:58.870168320</td>\n",
       "      <td>419.059673</td>\n",
       "      <td>425.320703</td>\n",
       "      <td>412.374044</td>\n",
       "      <td>419.000733</td>\n",
       "      <td>419.000733</td>\n",
       "      <td>7.570685e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2018-02-05 00:00:00</td>\n",
       "      <td>233.919998</td>\n",
       "      <td>250.649994</td>\n",
       "      <td>231.229996</td>\n",
       "      <td>233.880005</td>\n",
       "      <td>233.880005</td>\n",
       "      <td>1.144000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2019-02-06 00:00:00</td>\n",
       "      <td>331.489990</td>\n",
       "      <td>336.299988</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>331.619995</td>\n",
       "      <td>331.619995</td>\n",
       "      <td>4.091900e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2020-02-06 00:00:00</td>\n",
       "      <td>377.769989</td>\n",
       "      <td>383.010010</td>\n",
       "      <td>370.880005</td>\n",
       "      <td>378.670013</td>\n",
       "      <td>378.670013</td>\n",
       "      <td>5.934500e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2021-02-05 00:00:00</td>\n",
       "      <td>509.130005</td>\n",
       "      <td>515.630005</td>\n",
       "      <td>502.529999</td>\n",
       "      <td>509.079987</td>\n",
       "      <td>509.079987</td>\n",
       "      <td>9.322400e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2022-02-04 00:00:00</td>\n",
       "      <td>692.349976</td>\n",
       "      <td>700.989990</td>\n",
       "      <td>686.090027</td>\n",
       "      <td>691.690002</td>\n",
       "      <td>691.690002</td>\n",
       "      <td>5.890430e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>108.537532</td>\n",
       "      <td>109.262960</td>\n",
       "      <td>107.555867</td>\n",
       "      <td>108.289999</td>\n",
       "      <td>108.289999</td>\n",
       "      <td>5.465535e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Date         Open         High          Low  \\\n",
       "count                           1009  1009.000000  1009.000000  1009.000000   \n",
       "mean   2020-02-05 07:30:58.870168320   419.059673   425.320703   412.374044   \n",
       "min              2018-02-05 00:00:00   233.919998   250.649994   231.229996   \n",
       "25%              2019-02-06 00:00:00   331.489990   336.299988   326.000000   \n",
       "50%              2020-02-06 00:00:00   377.769989   383.010010   370.880005   \n",
       "75%              2021-02-05 00:00:00   509.130005   515.630005   502.529999   \n",
       "max              2022-02-04 00:00:00   692.349976   700.989990   686.090027   \n",
       "std                              NaN   108.537532   109.262960   107.555867   \n",
       "\n",
       "             Close    Adj Close        Volume  \n",
       "count  1009.000000  1009.000000  1.009000e+03  \n",
       "mean    419.000733   419.000733  7.570685e+06  \n",
       "min     233.880005   233.880005  1.144000e+06  \n",
       "25%     331.619995   331.619995  4.091900e+06  \n",
       "50%     378.670013   378.670013  5.934500e+06  \n",
       "75%     509.079987   509.079987  9.322400e+06  \n",
       "max     691.690002   691.690002  5.890430e+07  \n",
       "std     108.289999   108.289999  5.465535e+06  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xcSooKND06dPl4+Oj+++/X6GhoRo1apQmTpyoli1bymazKS0tTXa7XX379pUk3XnnnYqPj9fIkSM1d+5cORwOTZ06VampqQoICKiXEwQAAE2PWwHl66+/1v3336/vvvtOrVq10q233qodO3aoVatWkqT58+fL29tbycnJLh/UVs3Hx0dZWVkaM2aM7Ha7goODlZKSovT09Lo9KwAA0KS5FVBWrVr1s+WBgYHKyMhQRkZGjXXi4uL03nvvuXNYAABwlbmiOSgAAAD1gYACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHGuKKA8++yz8vLy0vjx453rzp07p9TUVIWHhyskJETJyckqLCx02S4/P19JSUlq1qyZIiIiNGnSJJ0/f/5KmgIAADxIrQPK7t279corr6hLly4u6ydMmKB3331Xa9as0datW1VQUKChQ4c6yysrK5WUlKTy8nJt375dy5cv17JlyzRt2rTanwUAAPAotQoopaWlGjFihF599VW1aNHCub64uFiZmZl68cUXNWDAAPXs2VNLly7V9u3btWPHDknShg0bdPDgQf3pT39St27dNHjwYD399NPKyMhQeXl53ZwVAABo0moVUFJTU5WUlKSEhASX9Xv27FFFRYXL+o4dO6pt27bKycmRJOXk5Khz586KjIx01klMTFRJSYkOHDhw0eOVlZWppKTEZQEAAJ7L190NVq1apU8++US7d+++oMzhcMjf319hYWEu6yMjI+VwOJx1fhxOqsuryy5mzpw5mjlzprtNBQAATZRbd1COHz+ucePGacWKFQoMDKyvNl1gypQpKi4udi7Hjx9vsGMDAICG51ZA2bNnj4qKitSjRw/5+vrK19dXW7du1YIFC+Tr66vIyEiVl5fr9OnTLtsVFhYqKipKkhQVFXXBu3qqX1fX+amAgADZbDaXBQAAeC63AsrAgQO1f/9+7d2717ncfPPNGjFihPPvfn5+2rx5s3ObvLw85efny263S5Lsdrv279+voqIiZ52NGzfKZrMpPj6+jk4LAAA0ZW7NQWnevLluuukml3XBwcEKDw93rh81apQmTpyoli1bymazKS0tTXa7XX379pUk3XnnnYqPj9fIkSM1d+5cORwOTZ06VampqQoICKij0wIAAE2Z25NkL2X+/Pny9vZWcnKyysrKlJiYqIULFzrLfXx8lJWVpTFjxshutys4OFgpKSlKT0+v66YAAIAm6ooDSnZ2tsvrwMBAZWRkKCMjo8Zt4uLi9N57713poQEAgIfiu3gAAIBxCCgAAMA4BBQAAGAcAgoAADBOnb+LB6hrZZXn5B34jY6V5Mk7MKSxm1Nrx0pK5R34jcoqz0kKbezmAIDRCCgwXsHZrxR87R/0+12N3ZIrF3ytVHC2m3oq8tKVAeAqRkCB8WKC43T2WJpeHt5N10U03TsoR4tKNW71XsXcEdfYTQEA4xFQYLwAn0BVnWuta20dFB/edB+NVJ0rVtW5bxXg03BftAnPxaNPs3hCf5jWFwSUGvDLBsBkPPo0i6f0h0l9QUCpAb9sAEzGo0+zeEJ/mNYXBJQa8MsGwGQ8+jSLJ/SHaX1BQKkBv2wAADQePqgNAAAYhzsoAC6bJ0wel5hADjQFBBQAl81TJo9LTCAHTEdAAXDZPGHyuMQEcqApIKAAuGyeMHlcYgI50BQwSRYAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADj+DZ2A4BL+b6iUpL02TfF9XaMcxWV+vrU92rTIkiBfj71cozPi0rrZb8A4IkIKDDe0f9/YZ/85/2N3JK6ERzAPzsAuBRGShjvzhujJEnXRYQoqB7vboxfvVcvDe+m9hEh9XIM6Ydwcu01wfW2fwDwFAQUGK9lsL/u6922QY7VPiJEN7UObZBjAQBqRkCpgSfMe2DOA+C5PGGMkjxnnKrv/rga+4KAUgNPmvfAnAfA83jSGCU1/XHKk/rDlL4woxUG8pR5D8x5ADyTp4xRkmeMU/XdH1djXxBQasC8BwAmY4wyS0P1x9XUFwQUAJeNeQ8AGgoBBcBl86Tn7JI5z9oBXIh/nQAuG/MeADQUAgqAy8a8BwANhS8LBAAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBx3AooixYtUpcuXWSz2WSz2WS327Vu3Tpn+blz55Samqrw8HCFhIQoOTlZhYWFLvvIz89XUlKSmjVrpoiICE2aNEnnz5+vm7MBAAAewa2A0qZNGz377LPas2ePPv74Yw0YMED33nuvDhw4IEmaMGGC3n33Xa1Zs0Zbt25VQUGBhg4d6ty+srJSSUlJKi8v1/bt27V8+XItW7ZM06ZNq9uzAgAATZpb32Z8zz33uLyeNWuWFi1apB07dqhNmzbKzMzUypUrNWDAAEnS0qVL1alTJ+3YsUN9+/bVhg0bdPDgQW3atEmRkZHq1q2bnn76aT355JOaMWOG/P396+7MAABAk1XrOSiVlZVatWqVzp49K7vdrj179qiiokIJCQnOOh07dlTbtm2Vk5MjScrJyVHnzp0VGRnprJOYmKiSkhLnXZiLKSsrU0lJicsCAAA8l9sBZf/+/QoJCVFAQID++7//W2+//bbi4+PlcDjk7++vsLAwl/qRkZFyOBySJIfD4RJOqsury2oyZ84chYaGOpfY2Fh3mw0AAJoQtwNKhw4dtHfvXu3cuVNjxoxRSkqKDh48WB9tc5oyZYqKi4udy/Hjx+v1eAAAoHG5NQdFkvz9/dW+fXtJUs+ePbV79269/PLLGj58uMrLy3X69GmXuyiFhYWKioqSJEVFRWnXrl0u+6t+l091nYsJCAhQQECAu00FAABN1BV/DkpVVZXKysrUs2dP+fn5afPmzc6yvLw85efny263S5Lsdrv279+voqIiZ52NGzfKZrMpPj7+SpsCAAA8hFt3UKZMmaLBgwerbdu2OnPmjFauXKns7Gy9//77Cg0N1ahRozRx4kS1bNlSNptNaWlpstvt6tu3ryTpzjvvVHx8vEaOHKm5c+fK4XBo6tSpSk1N5Q4JAABwciugFBUV6cEHH9SJEycUGhqqLl266P3339egQYMkSfPnz5e3t7eSk5NVVlamxMRELVy40Lm9j4+PsrKyNGbMGNntdgUHByslJUXp6el1e1YAAKBJcyugZGZm/mx5YGCgMjIylJGRUWOduLg4vffee+4cFgAAXGX4Lh4AAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIzj29gNAOra9+WVOvptqVvbfF5U6vLn5bquVYiC/H3c2gYAcGkEFHico9+W6u4/fFSrbcev3utW/ay0W3VT69BaHQsAUDMCCjzOda1ClJV2q1vbnKuo1NenvlebFkEK9Lv8OyLXtQpxt3kAgMtAQIHHCfL3qdVdjZvb1X1bAAC1wyRZAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4fFBbHeH7XwCYzt1xqrZjlMQ4dSn0xaURUOoI3/8CXFxDDcRNdRBuSLUdp9wdoyTGqUuhLy7Ny7Isq7Eb4a6SkhKFhoaquLhYNputsZsjqXZ3UK7k+18YiNFUfPZNca3Duzua6iDckNwdp2o7RkmMU5dytfaFO9dvAgqAetVQA7FJgzCAi3Pn+s0jHgD1qjZf3sgXNwLgXTwAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjNMkv83YsixJP3xtMwAAaBqqr9vV1/Gf0yQDypkzZyRJsbGxjdwSAADgrjNnzig0NPRn63hZlxNjDFNVVaWCggI1b95cXl5ejd2cWispKVFsbKyOHz8um83W2M25qtEX5qAvzEFfmMNT+sKyLJ05c0YxMTHy9v75WSZN8g6Kt7e32rRp09jNqDM2m61J/8J5EvrCHPSFOegLc3hCX1zqzkk1JskCAADjEFAAAIBxCCiNKCAgQNOnT1dAQEBjN+WqR1+Yg74wB31hjquxL5rkJFkAAODZuIMCAACMQ0ABAADGIaAAAADjEFBw1Vm2bJnCwsLc2uahhx7SkCFD6qU9QGPy8vLSO++809jN8HgzZsxQt27danxdV/v1JASUK3T8+HE9/PDDiomJkb+/v+Li4jRu3Dh99913jd20q1JNQSI7O1teXl46ffq0hg8frsOHDzd8465iBLzG43A4lJaWpl/84hcKCAhQbGys7rnnHm3evLmxm9ak5eTkyMfHR0lJSbXa/ne/+91l9cHatWt1++23KzQ0VCEhIerSpYvS09N18uTJWh23KSGgXIEvvvhCN998s44cOaI333xTn3/+uRYvXqzNmzfLbrdfFb9ATVFQUJAiIiIauxlAvfvyyy/Vs2dP/f3vf9fzzz+v/fv3a/369brjjjuUmpra2M1r0jIzM5WWlqYPPvhABQUFbm8fEhKi8PDwn63z1FNPafjw4erVq5fWrVunzz77TPPmzdO+ffv0xhtv1LbpTQYB5QqkpqbK399fGzZsUP/+/dW2bVsNHjxYmzZt0jfffKOnnnpKktSuXTs9/fTTuv/++xUcHKzWrVsrIyPDZV+nT5/WI488olatWslms2nAgAHat2+fs7z6Nt4bb7yhdu3aKTQ0VPfdd5/zixNx+S72iOeZZ55RRESEmjdvrkceeUSTJ0++6G3TF154QdHR0QoPD1dqaqoqKioaptEebOvWrerdu7cCAgIUHR2tyZMn6/z585KkrKwshYWFqbKyUpK0d+9eeXl5afLkyc7tH3nkET3wwAON0nbTPfbYY/Ly8tKuXbuUnJysG264QTfeeKMmTpyoHTt2XHSb/fv3a8CAAQoKClJ4eLhGjx6t0tJSZ3l2drZ69+6t4OBghYWFqV+/fvrqq6+c5X/5y1/Uo0cPBQYG6he/+IVmzpzp7E9PUVpaqtWrV2vMmDFKSkrSsmXLLqjz7LPPKjIyUs2bN9eoUaN07tw5l/JLPZrZtWuXZs+erXnz5un555/XLbfconbt2mnQoEFau3atUlJSLrpdVVWV0tPT1aZNGwUEBKhbt25av369s7y8vFxjx45VdHS0AgMDFRcXpzlz5jjLL3UtakgElFo6efKk3n//fT322GMKCgpyKYuKitKIESO0evVq51dKP//88+ratav+8Y9/aPLkyRo3bpw2btzo3GbYsGEqKirSunXrtGfPHvXo0UMDBw50uQtz9OhRvfPOO8rKylJWVpa2bt2qZ599tmFO2IOtWLFCs2bN0nPPPac9e/aobdu2WrRo0QX1tmzZoqNHj2rLli1avny5li1bdtGBCZfvm2++0V133aVevXpp3759WrRokTIzM/XMM89Ikn75y1/qzJkz+sc//iHphzBzzTXXKDs727mPrVu36vbbb2+E1pvt5MmTWr9+vVJTUxUcHHxB+cXmYZ09e1aJiYlq0aKFdu/erTVr1mjTpk0aO3asJOn8+fMaMmSI+vfvr08//VQ5OTkaPXq080tbP/zwQz344IMaN26cDh48qFdeeUXLli3TrFmz6vVcG9pbb72ljh07qkOHDnrggQf02muv6ccfKfbWW29pxowZmj17tj7++GNFR0dr4cKFbh1jxYoVCgkJ0WOPPXbR8prm0b388suaN2+eXnjhBX366adKTEzUr3/9ax05ckSStGDBAv31r3/VW2+9pby8PK1YsULt2rVzbn8516IGY6FWduzYYUmy3n777YuWv/jii5Ykq7Cw0IqLi7N+9atfuZQPHz7cGjx4sGVZlvXhhx9aNpvNOnfunEud6667znrllVcsy7Ks6dOnW82aNbNKSkqc5ZMmTbL69OlTh2fV9KWkpFg+Pj5WcHCwyxIYGGhJsk6dOmUtXbrUCg0NdW7Tp08fKzU11WU//fr1s7p27eqy37i4OOv8+fPOdcOGDbOGDx9e36fkEVJSUqx77733gvW///3vrQ4dOlhVVVXOdRkZGVZISIhVWVlpWZZl9ejRw3r++ecty7KsIUOGWLNmzbL8/f2tM2fOWF9//bUlyTp8+HCDnEdTsnPnTkuS9ec///ln6/14HFuyZInVokULq7S01Fn+t7/9zfL29rYcDof13XffWZKs7Ozsi+5r4MCB1uzZs13WvfHGG1Z0dPSVnYxhbrnlFuull16yLMuyKioqrGuuucbasmWLs9xut1uPPfaYyzZ9+vRxGVOmT5/u8vqnBg8ebHXp0uWSbfnpfmJiYqxZs2a51OnVq5ezPWlpadaAAQNc/s1Vu5xrUUPiDsoVsi7zg3jtdvsFr3NzcyVJ+/btU2lpqcLDwxUSEuJcjh07pqNHjzq3adeunZo3b+58HR0draKiojo4C89yxx13aO/evS7L//7v/9ZYPy8vT71793ZZ99PXknTjjTfKx8fH+Zqf/5XLzc2V3W53/g9ckvr166fS0lJ9/fXXkqT+/fsrOztblmXpww8/1NChQ9WpUyd99NFH2rp1q2JiYnT99dc31ikY63LHph/Lzc1V165dXe649OvXT1VVVcrLy1PLli310EMPKTExUffcc49efvllnThxwll33759Sk9PdxnHHn30UZ04cUL/+te/6uS8GlteXp527dql+++/X5Lk6+ur4cOHKzMz01knNzdXffr0cdnup9eAS6lN/5WUlKigoED9+vVzWd+vXz/n9eahhx7S3r171aFDBz3++OPasGGDs97lXosaim+DH9FDtG/fXl5eXsrNzdV//Md/XFCem5urFi1aqFWrVpfcV2lpqaKjo11uW1f78W08Pz8/lzIvLy9VVVW53XZPFxwcrPbt27usq77YXQl+/o3j9ttv12uvvaZ9+/bJz89PHTt21O23367s7GydOnVK/fv3b+wmGun666+Xl5eXDh06VKf7Xbp0qR5//HGtX79eq1ev1tSpU7Vx40b17dtXpaWlmjlzpoYOHXrBdoGBgXXajsaSmZmp8+fPKyYmxrnOsiwFBAToj3/8o0JDQ+vkODfccIM++ugjVVRUXDD2XIkePXro2LFjWrdunTZt2qTf/OY3SkhI0P/93/9d9rWooXAHpZbCw8M1aNAgLVy4UN9//71LmcPh0IoVKzR8+HDn/wx/OiFtx44d6tSpk6QffmEcDod8fX3Vvn17l+Waa65pmBO6inXo0EG7d+92WffT16gfnTp1Uk5Ojsv/Frdt26bmzZurTZs2kv49D2X+/PnOMFIdULKzs5l/UoOWLVsqMTFRGRkZOnv27AXlp0+fvmBdp06dtG/fPpf627Ztk7e3tzp06OBc1717d02ZMkXbt2/XTTfdpJUrV0r6YSzLy8u7YBxr3769vL2b/uXm/Pnzev311zVv3jyXO7T79u1TTEyM3nzzTUk//Bx37tzpsm1Nk5Jr8l//9V8qLS2tce7KxfrPZrMpJiZG27Ztc1m/bds2xcfHu9QbPny4Xn31Va1evVpr167VyZMnjbsWNf3fmEb0xz/+UWVlZUpMTNQHH3yg48ePa/369Ro0aJBat27tMjFs27Ztmjt3rg4fPqyMjAytWbNG48aNkyQlJCTIbrdryJAh2rBhg7788ktt375dTz31lD7++OPGOr2rRlpamjIzM7V8+XIdOXJEzzzzjD799FOXxw64csXFxRc8ehs9erSOHz+utLQ0HTp0SH/5y180ffp0TZw40XlBa9Gihbp06aIVK1Y4w8htt92mTz75RIcPH+YOys/IyMhQZWWlevfurbVr1+rIkSPKzc3VggULLvrIYcSIEQoMDFRKSoo+++wzbdmyRWlpaRo5cqQiIyN17NgxTZkyRTk5Ofrqq6+0YcMGHTlyxPmfrWnTpun111/XzJkzdeDAAeXm5mrVqlWaOnVqQ596vcjKytKpU6c0atQo3XTTTS5LcnKy8zHPuHHj9Nprr2np0qU6fPiwpk+frgMHDrh1rD59+uiJJ57Qb3/7Wz3xxBPOn/nmzZs1bNgwLV++/KLbTZo0Sc8995xWr16tvLw8TZ48WXv37nVeb1588UW9+eabOnTokA4fPqw1a9YoKipKYWFh5l2LGnzWi4f58ssvrZSUFCsyMtLy8/OzYmNjrbS0NOuf//yns05cXJw1c+ZMa9iwYVazZs2sqKgo6+WXX3bZT0lJiZWWlmbFxMQ49zNixAgrPz/fsqyLT6iaP3++FRcXV9+n2KTUNBlzy5YtNU6StSzLSk9Pt6655horJCTEevjhh63HH3/c6tu378/ud9y4cVb//v3r/iQ8UEpKiiXpgmXUqFFWdna21atXL8vf39+KioqynnzySauiosJl+3HjxlmSrNzcXOe6rl27WlFRUQ19Kk1OQUGBlZqaasXFxVn+/v5W69atrV//+tfOSZ36yWT/Tz/91LrjjjuswMBAq2XLltajjz5qnTlzxrIsy3I4HNaQIUOs6Ohoy9/f34qLi7OmTZvmnNBsWZa1fv1665ZbbrGCgoIsm81m9e7d21qyZElDnnK9ufvuu6277rrromXVk5L37dtnWZZlzZo1yzmmpKSkWE888YRbk2SrrV692rrtttus5s2bW8HBwVaXLl2s9PR069SpUxfdT2VlpTVjxgyrdevWlp+fn9W1a1dr3bp1zvIlS5ZY3bp1s4KDgy2bzWYNHDjQ+uSTT5zll7oWNSQvy6rFTBy4pV27dho/frzGjx/f2E3BZRo0aJCioqKuig9DAtDwpkyZog8//FAfffRRYzfFWEySxVXvX//6lxYvXqzExET5+PjozTff1KZNm1w+pwYA6oJlWfriiy+0efNmde/evbGbYzTmoOCq5+Xlpffee0+33XabevbsqXfffVdr165VQkJCYzcNgIcpLi5WfHy8/P399fvf/76xm2M0HvEAAADjcAcFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABjn/wHvwZ5Ttoxv3QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sk[['Open','High','Low','Close','Adj Close']].plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout = go.Layout(\n",
    "    title='Stock Prices',\n",
    "    xaxis=dict(\n",
    "        title='Date',\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=18,\n",
    "            color='#7f7f7f'\n",
    "        )\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='Price',\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=18,\n",
    "            color='#7f7f7f'\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "sk_data = [{'x':sk['Date'], 'y':sk['Close']}]\n",
    "plot = go.Figure(data=sk_data, layout=layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "type": "scatter",
         "x": [
          "2018-02-05T00:00:00",
          "2018-02-06T00:00:00",
          "2018-02-07T00:00:00",
          "2018-02-08T00:00:00",
          "2018-02-09T00:00:00",
          "2018-02-12T00:00:00",
          "2018-02-13T00:00:00",
          "2018-02-14T00:00:00",
          "2018-02-15T00:00:00",
          "2018-02-16T00:00:00",
          "2018-02-20T00:00:00",
          "2018-02-21T00:00:00",
          "2018-02-22T00:00:00",
          "2018-02-23T00:00:00",
          "2018-02-26T00:00:00",
          "2018-02-27T00:00:00",
          "2018-02-28T00:00:00",
          "2018-03-01T00:00:00",
          "2018-03-02T00:00:00",
          "2018-03-05T00:00:00",
          "2018-03-06T00:00:00",
          "2018-03-07T00:00:00",
          "2018-03-08T00:00:00",
          "2018-03-09T00:00:00",
          "2018-03-12T00:00:00",
          "2018-03-13T00:00:00",
          "2018-03-14T00:00:00",
          "2018-03-15T00:00:00",
          "2018-03-16T00:00:00",
          "2018-03-19T00:00:00",
          "2018-03-20T00:00:00",
          "2018-03-21T00:00:00",
          "2018-03-22T00:00:00",
          "2018-03-23T00:00:00",
          "2018-03-26T00:00:00",
          "2018-03-27T00:00:00",
          "2018-03-28T00:00:00",
          "2018-03-29T00:00:00",
          "2018-04-02T00:00:00",
          "2018-04-03T00:00:00",
          "2018-04-04T00:00:00",
          "2018-04-05T00:00:00",
          "2018-04-06T00:00:00",
          "2018-04-09T00:00:00",
          "2018-04-10T00:00:00",
          "2018-04-11T00:00:00",
          "2018-04-12T00:00:00",
          "2018-04-13T00:00:00",
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Prices\"},\"xaxis\":{\"title\":{\"font\":{\"color\":\"#7f7f7f\",\"family\":\"Courier New, monospace\",\"size\":18},\"text\":\"Date\"}},\"yaxis\":{\"title\":{\"font\":{\"color\":\"#7f7f7f\",\"family\":\"Courier New, monospace\",\"size\":18},\"text\":\"Price\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('5115efbd-1b29-48e7-898c-32b583cb1018');\n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iplot(plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(sk.index).reshape(-1,1)\n",
    "Y = sk['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290    367.720001\n",
       "167    377.049988\n",
       "949    646.909973\n",
       "835    499.079987\n",
       "885    512.400024\n",
       "          ...    \n",
       "575    451.040009\n",
       "838    494.739990\n",
       "337    352.010010\n",
       "523    372.779999\n",
       "863    537.309998\n",
       "Name: Close, Length: 706, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "740    507.790009\n",
       "617    502.410004\n",
       "531    319.750000\n",
       "522    383.790009\n",
       "462    302.859985\n",
       "          ...    \n",
       "941    690.309998\n",
       "830    502.899994\n",
       "399    290.170013\n",
       "864    540.679993\n",
       "746    565.169983\n",
       "Name: Close, Length: 303, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "trace0 = go.Scatter(\n",
    "    x = X_train.T[0],\n",
    "    y = Y_train,\n",
    "    mode = 'markers',\n",
    "    name = 'Actual'\n",
    ")\n",
    "trace1 = go.Scatter(\n",
    "    x = X_train.T[0],\n",
    "    y = lm.predict(X_train).T,\n",
    "    mode = 'lines',\n",
    "    name = 'Predicted'\n",
    ")\n",
    "sk_data = [trace0,trace1]\n",
    "layout.xaxis.title.text = 'Day'\n",
    "plot2 = go.Figure(data=sk_data, layout=layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iplot(plot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Metric           Train                Test        \n",
      "r2_score  0.6992669032944174\t0.7261648669848495\n",
      "MSE       3403.0038800025172\t3460.988580958064\n",
      "\n"
     ]
    }
   ],
   "source": [
    "scores = f'''\n",
    "{'Metric'.ljust(10)}{'Train'.center(20)}{'Test'.center(20)}\n",
    "{'r2_score'.ljust(10)}{r2_score(Y_train, lm.predict(X_train))}\\t{r2_score(Y_test, lm.predict(X_test))}\n",
    "{'MSE'.ljust(10)}{mse(Y_train, lm.predict(X_train))}\\t{mse(Y_test, lm.predict(X_test))}\n",
    "'''\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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