GOOGLE_STOCK_PRICE_PRED_USING_LSTM_RNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "14e7ab40-1bee-4e66-9d8c-6e7b22857241",
    "_uuid": "46dc7d54bd45771e43c5d9222458997b48a3c451"
   },
   "outputs": [],
   "source": [
    "dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "cf80eb5a-9626-4564-889a-7db854ad37ca",
    "_uuid": "1b8e0f00b71e1db7b00b0915dcadc68280114593"
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>325.25</td>\n",
       "      <td>332.83</td>\n",
       "      <td>324.97</td>\n",
       "      <td>663.59</td>\n",
       "      <td>7,380,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>331.27</td>\n",
       "      <td>333.87</td>\n",
       "      <td>329.08</td>\n",
       "      <td>666.45</td>\n",
       "      <td>5,749,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>329.83</td>\n",
       "      <td>330.75</td>\n",
       "      <td>326.89</td>\n",
       "      <td>657.21</td>\n",
       "      <td>6,590,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2012</td>\n",
       "      <td>328.34</td>\n",
       "      <td>328.77</td>\n",
       "      <td>323.68</td>\n",
       "      <td>648.24</td>\n",
       "      <td>5,405,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/9/2012</td>\n",
       "      <td>322.04</td>\n",
       "      <td>322.29</td>\n",
       "      <td>309.46</td>\n",
       "      <td>620.76</td>\n",
       "      <td>11,688,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/10/2012</td>\n",
       "      <td>313.70</td>\n",
       "      <td>315.72</td>\n",
       "      <td>307.30</td>\n",
       "      <td>621.43</td>\n",
       "      <td>8,824,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/11/2012</td>\n",
       "      <td>310.59</td>\n",
       "      <td>313.52</td>\n",
       "      <td>309.40</td>\n",
       "      <td>624.25</td>\n",
       "      <td>4,817,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/12/2012</td>\n",
       "      <td>314.43</td>\n",
       "      <td>315.26</td>\n",
       "      <td>312.08</td>\n",
       "      <td>627.92</td>\n",
       "      <td>3,764,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/13/2012</td>\n",
       "      <td>311.96</td>\n",
       "      <td>312.30</td>\n",
       "      <td>309.37</td>\n",
       "      <td>623.28</td>\n",
       "      <td>4,631,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/17/2012</td>\n",
       "      <td>314.81</td>\n",
       "      <td>314.81</td>\n",
       "      <td>311.67</td>\n",
       "      <td>626.86</td>\n",
       "      <td>3,832,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/18/2012</td>\n",
       "      <td>312.14</td>\n",
       "      <td>315.82</td>\n",
       "      <td>309.90</td>\n",
       "      <td>631.18</td>\n",
       "      <td>5,544,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/19/2012</td>\n",
       "      <td>319.30</td>\n",
       "      <td>319.30</td>\n",
       "      <td>314.55</td>\n",
       "      <td>637.82</td>\n",
       "      <td>12,657,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1/20/2012</td>\n",
       "      <td>294.16</td>\n",
       "      <td>294.40</td>\n",
       "      <td>289.76</td>\n",
       "      <td>584.39</td>\n",
       "      <td>21,231,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1/23/2012</td>\n",
       "      <td>291.91</td>\n",
       "      <td>293.23</td>\n",
       "      <td>290.49</td>\n",
       "      <td>583.92</td>\n",
       "      <td>6,851,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1/24/2012</td>\n",
       "      <td>292.07</td>\n",
       "      <td>292.74</td>\n",
       "      <td>287.92</td>\n",
       "      <td>579.34</td>\n",
       "      <td>6,134,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1/25/2012</td>\n",
       "      <td>287.68</td>\n",
       "      <td>288.27</td>\n",
       "      <td>282.13</td>\n",
       "      <td>567.93</td>\n",
       "      <td>10,012,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1/26/2012</td>\n",
       "      <td>284.92</td>\n",
       "      <td>286.17</td>\n",
       "      <td>281.22</td>\n",
       "      <td>566.54</td>\n",
       "      <td>6,476,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1/27/2012</td>\n",
       "      <td>284.32</td>\n",
       "      <td>289.08</td>\n",
       "      <td>283.60</td>\n",
       "      <td>578.39</td>\n",
       "      <td>7,262,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1/30/2012</td>\n",
       "      <td>287.95</td>\n",
       "      <td>288.92</td>\n",
       "      <td>285.63</td>\n",
       "      <td>576.11</td>\n",
       "      <td>4,678,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1/31/2012</td>\n",
       "      <td>290.41</td>\n",
       "      <td>290.91</td>\n",
       "      <td>286.50</td>\n",
       "      <td>578.52</td>\n",
       "      <td>4,300,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2/1/2012</td>\n",
       "      <td>291.38</td>\n",
       "      <td>291.66</td>\n",
       "      <td>288.49</td>\n",
       "      <td>579.24</td>\n",
       "      <td>4,658,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2/2/2012</td>\n",
       "      <td>291.34</td>\n",
       "      <td>292.11</td>\n",
       "      <td>289.95</td>\n",
       "      <td>583.51</td>\n",
       "      <td>4,847,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2/3/2012</td>\n",
       "      <td>294.23</td>\n",
       "      <td>297.42</td>\n",
       "      <td>292.93</td>\n",
       "      <td>594.7</td>\n",
       "      <td>6,360,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2/6/2012</td>\n",
       "      <td>296.39</td>\n",
       "      <td>304.27</td>\n",
       "      <td>295.90</td>\n",
       "      <td>607.42</td>\n",
       "      <td>7,386,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2/7/2012</td>\n",
       "      <td>302.44</td>\n",
       "      <td>303.56</td>\n",
       "      <td>300.75</td>\n",
       "      <td>605.11</td>\n",
       "      <td>4,199,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2/8/2012</td>\n",
       "      <td>303.18</td>\n",
       "      <td>304.53</td>\n",
       "      <td>301.24</td>\n",
       "      <td>608.18</td>\n",
       "      <td>3,686,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2/9/2012</td>\n",
       "      <td>304.87</td>\n",
       "      <td>306.10</td>\n",
       "      <td>303.36</td>\n",
       "      <td>609.79</td>\n",
       "      <td>4,546,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2/10/2012</td>\n",
       "      <td>302.81</td>\n",
       "      <td>302.93</td>\n",
       "      <td>300.87</td>\n",
       "      <td>604.25</td>\n",
       "      <td>4,667,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2/13/2012</td>\n",
       "      <td>304.11</td>\n",
       "      <td>305.77</td>\n",
       "      <td>303.87</td>\n",
       "      <td>610.52</td>\n",
       "      <td>3,646,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2/14/2012</td>\n",
       "      <td>304.63</td>\n",
       "      <td>304.86</td>\n",
       "      <td>301.25</td>\n",
       "      <td>608.09</td>\n",
       "      <td>3,620,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>11/17/2016</td>\n",
       "      <td>766.92</td>\n",
       "      <td>772.70</td>\n",
       "      <td>764.23</td>\n",
       "      <td>771.23</td>\n",
       "      <td>1,304,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>11/18/2016</td>\n",
       "      <td>771.37</td>\n",
       "      <td>775.00</td>\n",
       "      <td>760.00</td>\n",
       "      <td>760.54</td>\n",
       "      <td>1,547,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>11/21/2016</td>\n",
       "      <td>762.61</td>\n",
       "      <td>769.70</td>\n",
       "      <td>760.60</td>\n",
       "      <td>769.2</td>\n",
       "      <td>1,330,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>11/22/2016</td>\n",
       "      <td>772.63</td>\n",
       "      <td>776.96</td>\n",
       "      <td>767.00</td>\n",
       "      <td>768.27</td>\n",
       "      <td>1,593,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>11/23/2016</td>\n",
       "      <td>767.73</td>\n",
       "      <td>768.28</td>\n",
       "      <td>755.25</td>\n",
       "      <td>760.99</td>\n",
       "      <td>1,478,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>11/25/2016</td>\n",
       "      <td>764.26</td>\n",
       "      <td>765.00</td>\n",
       "      <td>760.52</td>\n",
       "      <td>761.68</td>\n",
       "      <td>587,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>11/28/2016</td>\n",
       "      <td>760.00</td>\n",
       "      <td>779.53</td>\n",
       "      <td>759.80</td>\n",
       "      <td>768.24</td>\n",
       "      <td>2,188,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>11/29/2016</td>\n",
       "      <td>771.53</td>\n",
       "      <td>778.50</td>\n",
       "      <td>768.24</td>\n",
       "      <td>770.84</td>\n",
       "      <td>1,616,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>11/30/2016</td>\n",
       "      <td>770.07</td>\n",
       "      <td>772.99</td>\n",
       "      <td>754.83</td>\n",
       "      <td>758.04</td>\n",
       "      <td>2,392,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>12/1/2016</td>\n",
       "      <td>757.44</td>\n",
       "      <td>759.85</td>\n",
       "      <td>737.03</td>\n",
       "      <td>747.92</td>\n",
       "      <td>3,017,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>12/2/2016</td>\n",
       "      <td>744.59</td>\n",
       "      <td>754.00</td>\n",
       "      <td>743.10</td>\n",
       "      <td>750.5</td>\n",
       "      <td>1,452,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>12/5/2016</td>\n",
       "      <td>757.71</td>\n",
       "      <td>763.90</td>\n",
       "      <td>752.90</td>\n",
       "      <td>762.52</td>\n",
       "      <td>1,394,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>12/6/2016</td>\n",
       "      <td>764.73</td>\n",
       "      <td>768.83</td>\n",
       "      <td>757.34</td>\n",
       "      <td>759.11</td>\n",
       "      <td>1,690,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>12/7/2016</td>\n",
       "      <td>761.00</td>\n",
       "      <td>771.36</td>\n",
       "      <td>755.80</td>\n",
       "      <td>771.19</td>\n",
       "      <td>1,761,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>12/8/2016</td>\n",
       "      <td>772.48</td>\n",
       "      <td>778.18</td>\n",
       "      <td>767.23</td>\n",
       "      <td>776.42</td>\n",
       "      <td>1,488,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>12/9/2016</td>\n",
       "      <td>780.00</td>\n",
       "      <td>789.43</td>\n",
       "      <td>779.02</td>\n",
       "      <td>789.29</td>\n",
       "      <td>1,821,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>12/12/2016</td>\n",
       "      <td>785.04</td>\n",
       "      <td>791.25</td>\n",
       "      <td>784.35</td>\n",
       "      <td>789.27</td>\n",
       "      <td>2,104,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>12/13/2016</td>\n",
       "      <td>793.90</td>\n",
       "      <td>804.38</td>\n",
       "      <td>793.34</td>\n",
       "      <td>796.1</td>\n",
       "      <td>2,145,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>12/14/2016</td>\n",
       "      <td>797.40</td>\n",
       "      <td>804.00</td>\n",
       "      <td>794.01</td>\n",
       "      <td>797.07</td>\n",
       "      <td>1,704,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>12/15/2016</td>\n",
       "      <td>797.34</td>\n",
       "      <td>803.00</td>\n",
       "      <td>792.92</td>\n",
       "      <td>797.85</td>\n",
       "      <td>1,626,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>12/16/2016</td>\n",
       "      <td>800.40</td>\n",
       "      <td>800.86</td>\n",
       "      <td>790.29</td>\n",
       "      <td>790.8</td>\n",
       "      <td>2,443,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>12/19/2016</td>\n",
       "      <td>790.22</td>\n",
       "      <td>797.66</td>\n",
       "      <td>786.27</td>\n",
       "      <td>794.2</td>\n",
       "      <td>1,232,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>12/20/2016</td>\n",
       "      <td>796.76</td>\n",
       "      <td>798.65</td>\n",
       "      <td>793.27</td>\n",
       "      <td>796.42</td>\n",
       "      <td>951,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>12/21/2016</td>\n",
       "      <td>795.84</td>\n",
       "      <td>796.68</td>\n",
       "      <td>787.10</td>\n",
       "      <td>794.56</td>\n",
       "      <td>1,211,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>12/22/2016</td>\n",
       "      <td>792.36</td>\n",
       "      <td>793.32</td>\n",
       "      <td>788.58</td>\n",
       "      <td>791.26</td>\n",
       "      <td>972,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>12/23/2016</td>\n",
       "      <td>790.90</td>\n",
       "      <td>792.74</td>\n",
       "      <td>787.28</td>\n",
       "      <td>789.91</td>\n",
       "      <td>623,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>12/27/2016</td>\n",
       "      <td>790.68</td>\n",
       "      <td>797.86</td>\n",
       "      <td>787.66</td>\n",
       "      <td>791.55</td>\n",
       "      <td>789,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>12/28/2016</td>\n",
       "      <td>793.70</td>\n",
       "      <td>794.23</td>\n",
       "      <td>783.20</td>\n",
       "      <td>785.05</td>\n",
       "      <td>1,153,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>12/29/2016</td>\n",
       "      <td>783.33</td>\n",
       "      <td>785.93</td>\n",
       "      <td>778.92</td>\n",
       "      <td>782.79</td>\n",
       "      <td>744,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>12/30/2016</td>\n",
       "      <td>782.75</td>\n",
       "      <td>782.78</td>\n",
       "      <td>770.41</td>\n",
       "      <td>771.82</td>\n",
       "      <td>1,770,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1258 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Open    High     Low   Close      Volume\n",
       "0       1/3/2012  325.25  332.83  324.97  663.59   7,380,500\n",
       "1       1/4/2012  331.27  333.87  329.08  666.45   5,749,400\n",
       "2       1/5/2012  329.83  330.75  326.89  657.21   6,590,300\n",
       "3       1/6/2012  328.34  328.77  323.68  648.24   5,405,900\n",
       "4       1/9/2012  322.04  322.29  309.46  620.76  11,688,800\n",
       "5      1/10/2012  313.70  315.72  307.30  621.43   8,824,000\n",
       "6      1/11/2012  310.59  313.52  309.40  624.25   4,817,800\n",
       "7      1/12/2012  314.43  315.26  312.08  627.92   3,764,400\n",
       "8      1/13/2012  311.96  312.30  309.37  623.28   4,631,800\n",
       "9      1/17/2012  314.81  314.81  311.67  626.86   3,832,800\n",
       "10     1/18/2012  312.14  315.82  309.90  631.18   5,544,000\n",
       "11     1/19/2012  319.30  319.30  314.55  637.82  12,657,800\n",
       "12     1/20/2012  294.16  294.40  289.76  584.39  21,231,800\n",
       "13     1/23/2012  291.91  293.23  290.49  583.92   6,851,300\n",
       "14     1/24/2012  292.07  292.74  287.92  579.34   6,134,400\n",
       "15     1/25/2012  287.68  288.27  282.13  567.93  10,012,700\n",
       "16     1/26/2012  284.92  286.17  281.22  566.54   6,476,500\n",
       "17     1/27/2012  284.32  289.08  283.60  578.39   7,262,000\n",
       "18     1/30/2012  287.95  288.92  285.63  576.11   4,678,400\n",
       "19     1/31/2012  290.41  290.91  286.50  578.52   4,300,700\n",
       "20      2/1/2012  291.38  291.66  288.49  579.24   4,658,700\n",
       "21      2/2/2012  291.34  292.11  289.95  583.51   4,847,400\n",
       "22      2/3/2012  294.23  297.42  292.93   594.7   6,360,700\n",
       "23      2/6/2012  296.39  304.27  295.90  607.42   7,386,700\n",
       "24      2/7/2012  302.44  303.56  300.75  605.11   4,199,700\n",
       "25      2/8/2012  303.18  304.53  301.24  608.18   3,686,400\n",
       "26      2/9/2012  304.87  306.10  303.36  609.79   4,546,300\n",
       "27     2/10/2012  302.81  302.93  300.87  604.25   4,667,700\n",
       "28     2/13/2012  304.11  305.77  303.87  610.52   3,646,100\n",
       "29     2/14/2012  304.63  304.86  301.25  608.09   3,620,900\n",
       "...          ...     ...     ...     ...     ...         ...\n",
       "1228  11/17/2016  766.92  772.70  764.23  771.23   1,304,000\n",
       "1229  11/18/2016  771.37  775.00  760.00  760.54   1,547,100\n",
       "1230  11/21/2016  762.61  769.70  760.60   769.2   1,330,600\n",
       "1231  11/22/2016  772.63  776.96  767.00  768.27   1,593,100\n",
       "1232  11/23/2016  767.73  768.28  755.25  760.99   1,478,400\n",
       "1233  11/25/2016  764.26  765.00  760.52  761.68     587,400\n",
       "1234  11/28/2016  760.00  779.53  759.80  768.24   2,188,200\n",
       "1235  11/29/2016  771.53  778.50  768.24  770.84   1,616,600\n",
       "1236  11/30/2016  770.07  772.99  754.83  758.04   2,392,900\n",
       "1237   12/1/2016  757.44  759.85  737.03  747.92   3,017,900\n",
       "1238   12/2/2016  744.59  754.00  743.10   750.5   1,452,500\n",
       "1239   12/5/2016  757.71  763.90  752.90  762.52   1,394,200\n",
       "1240   12/6/2016  764.73  768.83  757.34  759.11   1,690,700\n",
       "1241   12/7/2016  761.00  771.36  755.80  771.19   1,761,000\n",
       "1242   12/8/2016  772.48  778.18  767.23  776.42   1,488,100\n",
       "1243   12/9/2016  780.00  789.43  779.02  789.29   1,821,900\n",
       "1244  12/12/2016  785.04  791.25  784.35  789.27   2,104,100\n",
       "1245  12/13/2016  793.90  804.38  793.34   796.1   2,145,200\n",
       "1246  12/14/2016  797.40  804.00  794.01  797.07   1,704,200\n",
       "1247  12/15/2016  797.34  803.00  792.92  797.85   1,626,500\n",
       "1248  12/16/2016  800.40  800.86  790.29   790.8   2,443,800\n",
       "1249  12/19/2016  790.22  797.66  786.27   794.2   1,232,100\n",
       "1250  12/20/2016  796.76  798.65  793.27  796.42     951,000\n",
       "1251  12/21/2016  795.84  796.68  787.10  794.56   1,211,300\n",
       "1252  12/22/2016  792.36  793.32  788.58  791.26     972,200\n",
       "1253  12/23/2016  790.90  792.74  787.28  789.91     623,400\n",
       "1254  12/27/2016  790.68  797.86  787.66  791.55     789,100\n",
       "1255  12/28/2016  793.70  794.23  783.20  785.05   1,153,800\n",
       "1256  12/29/2016  783.33  785.93  778.92  782.79     744,300\n",
       "1257  12/30/2016  782.75  782.78  770.41  771.82   1,770,000\n",
       "\n",
       "[1258 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "5208b5a3-9765-44d1-90c9-5f8de532b012",
    "_uuid": "54a836842467b7d6f0f1bc469b25c8ebc6929c1a"
   },
   "outputs": [],
   "source": [
    "training_set = dataset_train.iloc[:,1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "75381182-0748-499d-acd5-819d00ba540b",
    "_uuid": "600ac43312788e12da77674a04e8a060e0d662dc"
   },
   "outputs": [],
   "source": [
    "# Feature scaling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range =(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "e16f779e-76da-44a0-87be-2ab6b93e036a",
    "_uuid": "ab0b28a4eb15d451da6c0edbe4f5eb52c2f593ad"
   },
   "outputs": [],
   "source": [
    "training_set_scaled = sc.fit_transform(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "41ad7d40-bd82-47b8-911b-e42f34706533",
    "_uuid": "2ac9911184ffc16933b6dc81f15644b50a7e9320"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08581368],\n",
       "       [0.09701243],\n",
       "       [0.09433366],\n",
       "       ...,\n",
       "       [0.95725128],\n",
       "       [0.93796041],\n",
       "       [0.93688146]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "f7177060-936c-4991-8c9a-7e908dff773e",
    "_uuid": "769a99ff3d34fb1a3fa701050b072ae06203300c"
   },
   "outputs": [],
   "source": [
    "# Creating a data structure with 60 timesteps and 1 output\n",
    "X_train = []\n",
    "y_train = []\n",
    "for i in range(60, 1258):\n",
    "    X_train.append(training_set_scaled[i-60:i, 0])\n",
    "    y_train.append(training_set_scaled[i, 0])\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.09701243, 0.09433366, 0.09156187, 0.07984225, 0.0643277 ,\n",
       "       0.0585423 , 0.06568569, 0.06109085, 0.06639259, 0.0614257 ,\n",
       "       0.07474514, 0.02797827, 0.02379269, 0.02409033, 0.0159238 ,\n",
       "       0.01078949, 0.00967334, 0.01642607, 0.02100231, 0.02280676,\n",
       "       0.02273235, 0.02810849, 0.03212665, 0.0433812 , 0.04475779,\n",
       "       0.04790163, 0.0440695 , 0.04648783, 0.04745517, 0.04873875,\n",
       "       0.03936305, 0.04137213, 0.04034898, 0.04784582, 0.04325099,\n",
       "       0.04356723, 0.04286033, 0.04602277, 0.05398467, 0.05738894,\n",
       "       0.05714711, 0.05569611, 0.04421832, 0.04514845, 0.04605997,\n",
       "       0.04412531, 0.03675869, 0.04486941, 0.05065481, 0.05214302,\n",
       "       0.05612397, 0.05818885, 0.06540665, 0.06882953, 0.07243843,\n",
       "       0.07993526, 0.07846566, 0.08034452, 0.08497656, 0.08627874])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "d0979d0c-8bfd-4cbc-8715-60a58f5d7d13",
    "_uuid": "9ff491bbe0af28e8273354519d58888d2bc8e374"
   },
   "outputs": [],
   "source": [
    "# Reshaping\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "474cd352-f78b-4f0f-b91d-00b2837982c2",
    "_uuid": "6aaa6bca2541b74fcd49a0e016e029a98cce63ea"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Part -2 - Building the RNN\n",
    "# Importing the Keras libraries and packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_cell_guid": "c7e4a3a6-8cbd-43a2-8920-bd52ee20ac3e",
    "_uuid": "9738d6c1d6fc2757c198882725fa64cc2758a15d"
   },
   "outputs": [],
   "source": [
    "# Initialising the RNN\n",
    "regressor = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_cell_guid": "2f4095bd-65e3-42f4-b1ff-8e2d969ab236",
    "_uuid": "aff2e3898b13c21f294808c9ac07c96e926928a4"
   },
   "outputs": [],
   "source": [
    "# Adding the first LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_cell_guid": "056caecd-723b-4acd-972f-4021f4581cc8",
    "_uuid": "8d2be2950b2bba4f93e5e6f179562514a7425727"
   },
   "outputs": [],
   "source": [
    "# Adding a second LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_cell_guid": "cc7fa811-df76-400a-871c-19d6fc188236",
    "_uuid": "37cee8dfb2c4857cbdb82c1cf7151711a472bcc9"
   },
   "outputs": [],
   "source": [
    "# Adding a third LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_cell_guid": "0ad96c50-4ae1-40d5-8d49-98c9b0cbfdc5",
    "_uuid": "11b0e30e0e01c2e7a227d9a56ac100ae71f5d362"
   },
   "outputs": [],
   "source": [
    "# Adding a fourth LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_cell_guid": "9f4388c5-89ba-4130-8bdf-4700c2465413",
    "_uuid": "095840c13f9fe0a958a8bf0d85c4ecb8c69c694f"
   },
   "outputs": [],
   "source": [
    "# Adding the output layer\n",
    "regressor.add(Dense(units = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_cell_guid": "d9d623c0-776e-4371-9b92-ede03ba3c05a",
    "_uuid": "8784ee001da0006d8274095b16e29bdb52c0fd45"
   },
   "outputs": [],
   "source": [
    "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_13 (LSTM)               (None, 60, 50)            10400     \n",
      "_________________________________________________________________\n",
      "dropout_13 (Dropout)         (None, 60, 50)            0         \n",
      "_________________________________________________________________\n",
      "lstm_14 (LSTM)               (None, 60, 50)            20200     \n",
      "_________________________________________________________________\n",
      "dropout_14 (Dropout)         (None, 60, 50)            0         \n",
      "_________________________________________________________________\n",
      "lstm_15 (LSTM)               (None, 60, 50)            20200     \n",
      "_________________________________________________________________\n",
      "dropout_15 (Dropout)         (None, 60, 50)            0         \n",
      "_________________________________________________________________\n",
      "lstm_16 (LSTM)               (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dropout_16 (Dropout)         (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 71,051\n",
      "Trainable params: 71,051\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "regressor.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "_cell_guid": "2a38f25b-4f65-49f8-81ef-ac704de8bc84",
    "_uuid": "17561ed5b1c9ff58eee51875b87c5352e043e30c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0023 - accuracy: 0.0017\n",
      "Epoch 2/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0024 - accuracy: 0.0017\n",
      "Epoch 3/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0025 - accuracy: 0.0017\n",
      "Epoch 4/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0023 - accuracy: 0.0017\n",
      "Epoch 5/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0022 - accuracy: 0.0017\n",
      "Epoch 6/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0025 - accuracy: 0.0017: 3s - ETA: 0s - loss: 0.0025 - accuracy: 0.00\n",
      "Epoch 7/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0023 - accuracy: 0.0017\n",
      "Epoch 8/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0023 - accuracy: 0.0017\n",
      "Epoch 9/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0024 - accuracy: 0.0017\n",
      "Epoch 10/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0020 - accuracy: 0.0017\n",
      "Epoch 11/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0022 - accuracy: 0.0017\n",
      "Epoch 12/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0022 - accuracy: 0.0017\n",
      "Epoch 13/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0022 - accuracy: 0.0017\n",
      "Epoch 14/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0020 - accuracy: 0.0017: 1s - loss: 0.0020 \n",
      "Epoch 15/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0022 - accuracy: 0.0017\n",
      "Epoch 16/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0020 - accuracy: 0.0017: 2s - loss: 0\n",
      "Epoch 17/50\n",
      "1198/1198 [==============================] - 8s 6ms/step - loss: 0.0021 - accuracy: 0.0017\n",
      "Epoch 18/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0020 - accuracy: 0.0017\n",
      "Epoch 19/50\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0022 - accuracy: 0.0017\n",
      "Epoch 20/50\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0020 - accuracy: 0.0017\n",
      "Epoch 21/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0021 - accuracy: 0.0017\n",
      "Epoch 22/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0019 - accuracy: 0.0017\n",
      "Epoch 23/50\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0021 - accuracy: 0.0017\n",
      "Epoch 24/50\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0016 - accuracy: 0.0017\n",
      "Epoch 25/50\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0019 - accuracy: 0.0017\n",
      "Epoch 26/50\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0021 - accuracy: 0.0017\n",
      "Epoch 27/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0017 - accuracy: 0.0017\n",
      "Epoch 28/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018 - accuracy: 0.0017\n",
      "Epoch 29/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0019 - accuracy: 0.0017\n",
      "Epoch 30/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018 - accuracy: 0.0017\n",
      "Epoch 31/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0017 - accuracy: 0.0017\n",
      "Epoch 32/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015 - accuracy: 0.0017\n",
      "Epoch 33/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0019 - accuracy: 0.0017\n",
      "Epoch 34/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016 - accuracy: 0.0017\n",
      "Epoch 35/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018 - accuracy: 0.0017\n",
      "Epoch 36/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016 - accuracy: 0.0017\n",
      "Epoch 37/50\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0017 - accuracy: 0.0017\n",
      "Epoch 38/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015 - accuracy: 0.0017\n",
      "Epoch 39/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016 - accuracy: 0.0017\n",
      "Epoch 40/50\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0016 - accuracy: 0.0017\n",
      "Epoch 41/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0014 - accuracy: 0.0017\n",
      "Epoch 42/50\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0013 - accuracy: 0.0017\n",
      "Epoch 43/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016 - accuracy: 0.0017\n",
      "Epoch 44/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0017 - accuracy: 0.0017\n",
      "Epoch 45/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0017 - accuracy: 0.0017\n",
      "Epoch 46/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015 - accuracy: 0.0017\n",
      "Epoch 47/50\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016 - accuracy: 0.0017\n",
      "Epoch 48/50\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0015 - accuracy: 0.0017\n",
      "Epoch 49/50\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0016 - accuracy: 0.0017\n",
      "Epoch 50/50\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0017 - accuracy: 0.0017\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x296128e9828>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the RNN to the Training set\n",
    "regressor.fit(X_train, y_train, epochs = 50, batch_size = 32,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "7869ae7b-a648-4296-9192-c25c06ce9c8c",
    "_uuid": "6a4dbea6f09ce76acfc921c98c696f4125a8dd52"
   },
   "outputs": [],
   "source": [
    "# Part 3 - Making the predictions and visualising the results\n",
    "\n",
    "# Getting the real stock price of 2017\n",
    "dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "e901b859-7fce-4cb2-b212-cf41bc600bd5",
    "_uuid": "c056c8d3b56f27167a570530f4bea14677748ec8"
   },
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>778.81</td>\n",
       "      <td>789.63</td>\n",
       "      <td>775.80</td>\n",
       "      <td>786.14</td>\n",
       "      <td>1,657,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>788.36</td>\n",
       "      <td>791.34</td>\n",
       "      <td>783.16</td>\n",
       "      <td>786.90</td>\n",
       "      <td>1,073,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>786.08</td>\n",
       "      <td>794.48</td>\n",
       "      <td>785.02</td>\n",
       "      <td>794.02</td>\n",
       "      <td>1,335,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>795.26</td>\n",
       "      <td>807.90</td>\n",
       "      <td>792.20</td>\n",
       "      <td>806.15</td>\n",
       "      <td>1,640,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/9/2017</td>\n",
       "      <td>806.40</td>\n",
       "      <td>809.97</td>\n",
       "      <td>802.83</td>\n",
       "      <td>806.65</td>\n",
       "      <td>1,272,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/10/2017</td>\n",
       "      <td>807.86</td>\n",
       "      <td>809.13</td>\n",
       "      <td>803.51</td>\n",
       "      <td>804.79</td>\n",
       "      <td>1,176,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/11/2017</td>\n",
       "      <td>805.00</td>\n",
       "      <td>808.15</td>\n",
       "      <td>801.37</td>\n",
       "      <td>807.91</td>\n",
       "      <td>1,065,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/12/2017</td>\n",
       "      <td>807.14</td>\n",
       "      <td>807.39</td>\n",
       "      <td>799.17</td>\n",
       "      <td>806.36</td>\n",
       "      <td>1,353,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/13/2017</td>\n",
       "      <td>807.48</td>\n",
       "      <td>811.22</td>\n",
       "      <td>806.69</td>\n",
       "      <td>807.88</td>\n",
       "      <td>1,099,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/17/2017</td>\n",
       "      <td>807.08</td>\n",
       "      <td>807.14</td>\n",
       "      <td>800.37</td>\n",
       "      <td>804.61</td>\n",
       "      <td>1,362,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/18/2017</td>\n",
       "      <td>805.81</td>\n",
       "      <td>806.21</td>\n",
       "      <td>800.99</td>\n",
       "      <td>806.07</td>\n",
       "      <td>1,294,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/19/2017</td>\n",
       "      <td>805.12</td>\n",
       "      <td>809.48</td>\n",
       "      <td>801.80</td>\n",
       "      <td>802.17</td>\n",
       "      <td>919,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1/20/2017</td>\n",
       "      <td>806.91</td>\n",
       "      <td>806.91</td>\n",
       "      <td>801.69</td>\n",
       "      <td>805.02</td>\n",
       "      <td>1,670,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1/23/2017</td>\n",
       "      <td>807.25</td>\n",
       "      <td>820.87</td>\n",
       "      <td>803.74</td>\n",
       "      <td>819.31</td>\n",
       "      <td>1,963,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1/24/2017</td>\n",
       "      <td>822.30</td>\n",
       "      <td>825.90</td>\n",
       "      <td>817.82</td>\n",
       "      <td>823.87</td>\n",
       "      <td>1,474,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1/25/2017</td>\n",
       "      <td>829.62</td>\n",
       "      <td>835.77</td>\n",
       "      <td>825.06</td>\n",
       "      <td>835.67</td>\n",
       "      <td>1,494,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1/26/2017</td>\n",
       "      <td>837.81</td>\n",
       "      <td>838.00</td>\n",
       "      <td>827.01</td>\n",
       "      <td>832.15</td>\n",
       "      <td>2,973,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1/27/2017</td>\n",
       "      <td>834.71</td>\n",
       "      <td>841.95</td>\n",
       "      <td>820.44</td>\n",
       "      <td>823.31</td>\n",
       "      <td>2,965,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1/30/2017</td>\n",
       "      <td>814.66</td>\n",
       "      <td>815.84</td>\n",
       "      <td>799.80</td>\n",
       "      <td>802.32</td>\n",
       "      <td>3,246,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1/31/2017</td>\n",
       "      <td>796.86</td>\n",
       "      <td>801.25</td>\n",
       "      <td>790.52</td>\n",
       "      <td>796.79</td>\n",
       "      <td>2,160,600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    Open    High     Low   Close     Volume\n",
       "0    1/3/2017  778.81  789.63  775.80  786.14  1,657,300\n",
       "1    1/4/2017  788.36  791.34  783.16  786.90  1,073,000\n",
       "2    1/5/2017  786.08  794.48  785.02  794.02  1,335,200\n",
       "3    1/6/2017  795.26  807.90  792.20  806.15  1,640,200\n",
       "4    1/9/2017  806.40  809.97  802.83  806.65  1,272,400\n",
       "5   1/10/2017  807.86  809.13  803.51  804.79  1,176,800\n",
       "6   1/11/2017  805.00  808.15  801.37  807.91  1,065,900\n",
       "7   1/12/2017  807.14  807.39  799.17  806.36  1,353,100\n",
       "8   1/13/2017  807.48  811.22  806.69  807.88  1,099,200\n",
       "9   1/17/2017  807.08  807.14  800.37  804.61  1,362,100\n",
       "10  1/18/2017  805.81  806.21  800.99  806.07  1,294,400\n",
       "11  1/19/2017  805.12  809.48  801.80  802.17    919,300\n",
       "12  1/20/2017  806.91  806.91  801.69  805.02  1,670,000\n",
       "13  1/23/2017  807.25  820.87  803.74  819.31  1,963,600\n",
       "14  1/24/2017  822.30  825.90  817.82  823.87  1,474,000\n",
       "15  1/25/2017  829.62  835.77  825.06  835.67  1,494,500\n",
       "16  1/26/2017  837.81  838.00  827.01  832.15  2,973,900\n",
       "17  1/27/2017  834.71  841.95  820.44  823.31  2,965,800\n",
       "18  1/30/2017  814.66  815.84  799.80  802.32  3,246,600\n",
       "19  1/31/2017  796.86  801.25  790.52  796.79  2,160,600"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "2856736d-05ef-404a-8f2f-227bb352fe34",
    "_uuid": "0e26b8be72e4c2c2fd40f1871888ed419bf4e0cf"
   },
   "outputs": [],
   "source": [
    "real_stock_price = dataset_test.iloc[:,1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "5fdf1959-67df-4eb1-8d56-0bfbf3e5f912",
    "_uuid": "05aee4987660bc3e68224b20e13a89a6dadc2156"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[778.81],\n",
       "       [788.36],\n",
       "       [786.08],\n",
       "       [795.26],\n",
       "       [806.4 ],\n",
       "       [807.86],\n",
       "       [805.  ],\n",
       "       [807.14],\n",
       "       [807.48],\n",
       "       [807.08],\n",
       "       [805.81],\n",
       "       [805.12],\n",
       "       [806.91],\n",
       "       [807.25],\n",
       "       [822.3 ],\n",
       "       [829.62],\n",
       "       [837.81],\n",
       "       [834.71],\n",
       "       [814.66],\n",
       "       [796.86]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_stock_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "0f81909a-518a-448c-ba8c-13acbc7c687e",
    "_uuid": "ecf108ab62efb2e1a676cf6d18799519d31d6479"
   },
   "outputs": [],
   "source": [
    "# Getting the predicted stock price of 2017\n",
    "dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "ccee0065-f198-4f2d-aef0-7c2376f2d348",
    "_uuid": "84ce8e68a82332a6a48b458ee94aacb3fcf5b15e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       325.25\n",
       "1       331.27\n",
       "2       329.83\n",
       "3       328.34\n",
       "4       322.04\n",
       "5       313.70\n",
       "6       310.59\n",
       "7       314.43\n",
       "8       311.96\n",
       "9       314.81\n",
       "10      312.14\n",
       "11      319.30\n",
       "12      294.16\n",
       "13      291.91\n",
       "14      292.07\n",
       "15      287.68\n",
       "16      284.92\n",
       "17      284.32\n",
       "18      287.95\n",
       "19      290.41\n",
       "20      291.38\n",
       "21      291.34\n",
       "22      294.23\n",
       "23      296.39\n",
       "24      302.44\n",
       "25      303.18\n",
       "26      304.87\n",
       "27      302.81\n",
       "28      304.11\n",
       "29      304.63\n",
       "         ...  \n",
       "1248    800.40\n",
       "1249    790.22\n",
       "1250    796.76\n",
       "1251    795.84\n",
       "1252    792.36\n",
       "1253    790.90\n",
       "1254    790.68\n",
       "1255    793.70\n",
       "1256    783.33\n",
       "1257    782.75\n",
       "0       778.81\n",
       "1       788.36\n",
       "2       786.08\n",
       "3       795.26\n",
       "4       806.40\n",
       "5       807.86\n",
       "6       805.00\n",
       "7       807.14\n",
       "8       807.48\n",
       "9       807.08\n",
       "10      805.81\n",
       "11      805.12\n",
       "12      806.91\n",
       "13      807.25\n",
       "14      822.30\n",
       "15      829.62\n",
       "16      837.81\n",
       "17      834.71\n",
       "18      814.66\n",
       "19      796.86\n",
       "Name: Open, Length: 1278, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "7b3f2dbc-83ef-4597-b6c9-80b666843a11",
    "_uuid": "734fe3bf2af54ecee713addbae3205f9bab6ed89"
   },
   "outputs": [],
   "source": [
    "inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "f3c64a9c-2937-4359-8ac9-559cbc71a471",
    "_uuid": "4330d69a1d9b21120359f2b01a02ec4a1564e64c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([779.  , 779.66, 777.71, 786.66, 783.76, 781.22, 781.65, 779.8 ,\n",
       "       787.85, 798.24, 803.3 , 795.  , 804.9 , 816.68, 806.34, 801.  ,\n",
       "       808.35, 795.47, 782.89, 778.2 , 767.25, 750.66, 774.5 , 783.4 ,\n",
       "       779.94, 791.17, 756.54, 755.6 , 746.97, 755.2 , 766.92, 771.37,\n",
       "       762.61, 772.63, 767.73, 764.26, 760.  , 771.53, 770.07, 757.44,\n",
       "       744.59, 757.71, 764.73, 761.  , 772.48, 780.  , 785.04, 793.9 ,\n",
       "       797.4 , 797.34, 800.4 , 790.22, 796.76, 795.84, 792.36, 790.9 ,\n",
       "       790.68, 793.7 , 783.33, 782.75, 778.81, 788.36, 786.08, 795.26,\n",
       "       806.4 , 807.86, 805.  , 807.14, 807.48, 807.08, 805.81, 805.12,\n",
       "       806.91, 807.25, 822.3 , 829.62, 837.81, 834.71, 814.66, 796.86])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "90e023b8-793d-4369-9211-3f23db144b72",
    "_uuid": "67fcb70ce5a798609d7dbd61d1155447d8880577"
   },
   "outputs": [],
   "source": [
    "inputs = inputs.reshape(-1,1)\n",
    "inputs = sc.transform(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "66ff2f98-926f-4ecd-84e7-a7a17b7547bb",
    "_uuid": "5a67cf5528799d344ff02c45a2bb6930c6f95ba4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.9299055 ],\n",
       "       [0.93113327],\n",
       "       [0.92750577],\n",
       "       [0.94415507],\n",
       "       [0.93876032],\n",
       "       [0.93403527],\n",
       "       [0.93483518],\n",
       "       [0.9313937 ],\n",
       "       [0.94636878],\n",
       "       [0.96569685],\n",
       "       [0.97510976],\n",
       "       [0.95966962],\n",
       "       [0.97808617],\n",
       "       [1.        ],\n",
       "       [0.98076494],\n",
       "       [0.97083116],\n",
       "       [0.98450406],\n",
       "       [0.96054394],\n",
       "       [0.9371419 ],\n",
       "       [0.92841729],\n",
       "       [0.90804747],\n",
       "       [0.8771858 ],\n",
       "       [0.92153434],\n",
       "       [0.93809063],\n",
       "       [0.93165414],\n",
       "       [0.95254483],\n",
       "       [0.88812412],\n",
       "       [0.88637547],\n",
       "       [0.87032145],\n",
       "       [0.88563137],\n",
       "       [0.90743359],\n",
       "       [0.91571173],\n",
       "       [0.89941588],\n",
       "       [0.91805566],\n",
       "       [0.9089404 ],\n",
       "       [0.9024853 ],\n",
       "       [0.89456061],\n",
       "       [0.91600938],\n",
       "       [0.9132934 ],\n",
       "       [0.88979835],\n",
       "       [0.86589404],\n",
       "       [0.89030062],\n",
       "       [0.90335962],\n",
       "       [0.89642086],\n",
       "       [0.91777662],\n",
       "       [0.93176576],\n",
       "       [0.94114145],\n",
       "       [0.95762334],\n",
       "       [0.96413424],\n",
       "       [0.96402262],\n",
       "       [0.96971501],\n",
       "       [0.95077759],\n",
       "       [0.96294367],\n",
       "       [0.96123223],\n",
       "       [0.95475854],\n",
       "       [0.95204256],\n",
       "       [0.95163331],\n",
       "       [0.95725128],\n",
       "       [0.93796041],\n",
       "       [0.93688146],\n",
       "       [0.92955205],\n",
       "       [0.94731751],\n",
       "       [0.94307612],\n",
       "       [0.96015329],\n",
       "       [0.98087655],\n",
       "       [0.98359253],\n",
       "       [0.97827219],\n",
       "       [0.98225314],\n",
       "       [0.98288563],\n",
       "       [0.98214153],\n",
       "       [0.979779  ],\n",
       "       [0.97849542],\n",
       "       [0.98182528],\n",
       "       [0.98245777],\n",
       "       [1.01045465],\n",
       "       [1.02407173],\n",
       "       [1.03930724],\n",
       "       [1.03354044],\n",
       "       [0.99624228],\n",
       "       [0.9631297 ]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_cell_guid": "c371b737-47ab-42cb-81d8-c8071dfdb608",
    "_uuid": "1b5800d2d464316e65f82556aae992d808ea4614"
   },
   "outputs": [],
   "source": [
    "X_test = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_cell_guid": "f17da072-c0b5-45b9-88b3-d767237c3e5e",
    "_uuid": "fca2096af2ae7296201fa458149fcb9ca45ccb90"
   },
   "outputs": [],
   "source": [
    "for i in range(60, 80):\n",
    "    X_test.append(inputs[i-60:i, 0])\n",
    "X_test = np.array(X_test)\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "predicted_stock_price = regressor.predict(X_test)\n",
    "predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_cell_guid": "1cbc7c05-efd6-4055-ab39-948a80f06bab",
    "_uuid": "5fd9d3c891b6dd8ca3a1fc16879ff612e41d7660"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[778.81],\n",
       "       [788.36],\n",
       "       [786.08],\n",
       "       [795.26],\n",
       "       [806.4 ],\n",
       "       [807.86],\n",
       "       [805.  ],\n",
       "       [807.14],\n",
       "       [807.48],\n",
       "       [807.08],\n",
       "       [805.81],\n",
       "       [805.12],\n",
       "       [806.91],\n",
       "       [807.25],\n",
       "       [822.3 ],\n",
       "       [829.62],\n",
       "       [837.81],\n",
       "       [834.71],\n",
       "       [814.66],\n",
       "       [796.86]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_stock_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_cell_guid": "ad24c9e1-1587-4222-87a6-2fbeaf398bf9",
    "_uuid": "49cac9f704efbf26ad0f077231483c659159dd3f"
   },
   "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": [
    "# Visualising the results\n",
    "plt.plot(real_stock_price, color = 'red', label = 'Real Google Stock Price')\n",
    "plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Google Stock Price')\n",
    "plt.title('Google Stock Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Google Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.9299055 ],\n",
       "       [0.93113327],\n",
       "       [0.92750577],\n",
       "       [0.94415507],\n",
       "       [0.93876032],\n",
       "       [0.93403527],\n",
       "       [0.93483518],\n",
       "       [0.9313937 ],\n",
       "       [0.94636878],\n",
       "       [0.96569685],\n",
       "       [0.97510976],\n",
       "       [0.95966962],\n",
       "       [0.97808617],\n",
       "       [1.        ],\n",
       "       [0.98076494],\n",
       "       [0.97083116],\n",
       "       [0.98450406],\n",
       "       [0.96054394],\n",
       "       [0.9371419 ],\n",
       "       [0.92841729],\n",
       "       [0.90804747],\n",
       "       [0.8771858 ],\n",
       "       [0.92153434],\n",
       "       [0.93809063],\n",
       "       [0.93165414],\n",
       "       [0.95254483],\n",
       "       [0.88812412],\n",
       "       [0.88637547],\n",
       "       [0.87032145],\n",
       "       [0.88563137],\n",
       "       [0.90743359],\n",
       "       [0.91571173],\n",
       "       [0.89941588],\n",
       "       [0.91805566],\n",
       "       [0.9089404 ],\n",
       "       [0.9024853 ],\n",
       "       [0.89456061],\n",
       "       [0.91600938],\n",
       "       [0.9132934 ],\n",
       "       [0.88979835],\n",
       "       [0.86589404],\n",
       "       [0.89030062],\n",
       "       [0.90335962],\n",
       "       [0.89642086],\n",
       "       [0.91777662],\n",
       "       [0.93176576],\n",
       "       [0.94114145],\n",
       "       [0.95762334],\n",
       "       [0.96413424],\n",
       "       [0.96402262],\n",
       "       [0.96971501],\n",
       "       [0.95077759],\n",
       "       [0.96294367],\n",
       "       [0.96123223],\n",
       "       [0.95475854],\n",
       "       [0.95204256],\n",
       "       [0.95163331],\n",
       "       [0.95725128],\n",
       "       [0.93796041],\n",
       "       [0.93688146]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([778.81])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_stock_price[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 1)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[766.18085]], dtype=float32)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.inverse_transform(regressor.predict(X_test[0].reshape(1,60,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(array([778.81]), array([766.1809], dtype=float32)),\n",
       " (array([788.36]), array([763.51855], dtype=float32)),\n",
       " (array([786.08]), array([763.1026], dtype=float32)),\n",
       " (array([795.26]), array([764.13855], dtype=float32)),\n",
       " (array([806.4]), array([767.1646], dtype=float32)),\n",
       " (array([807.86]), array([772.6408], dtype=float32)),\n",
       " (array([805.]), array([778.2005], dtype=float32)),\n",
       " (array([807.14]), array([781.1533], dtype=float32)),\n",
       " (array([807.48]), array([781.8903], dtype=float32)),\n",
       " (array([807.08]), array([781.52716], dtype=float32)),\n",
       " (array([805.81]), array([780.9421], dtype=float32)),\n",
       " (array([805.12]), array([780.44366], dtype=float32)),\n",
       " (array([806.91]), array([780.18115], dtype=float32)),\n",
       " (array([807.25]), array([780.6167], dtype=float32)),\n",
       " (array([822.3]), array([781.5582], dtype=float32)),\n",
       " (array([829.62]), array([785.5048], dtype=float32)),\n",
       " (array([837.81]), array([791.90125], dtype=float32)),\n",
       " (array([834.71]), array([799.2505], dtype=float32)),\n",
       " (array([814.66]), array([804.00977], dtype=float32)),\n",
       " (array([796.86]), array([801.5161], dtype=float32))]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(real_stock_price,predicted_stock_price))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(zip(real_stock_price,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ca62cc1d-2ef7-43e7-84af-f96efcc1e447",
    "_uuid": "7a0df4f137254d50c26abff0aa4c8407cc0cd51d"
   },
   "source": [
    "## Congratulation...!!!!! Awesome prediction .... We are very close to the real google stock price. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "d631b66c-93a6-4fb6-b29b-e0212268c404",
    "_uuid": "27565cffc2ec3d8c880bc3cf436852d0335547ea",
    "collapsed": true
   },
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}