StockA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "\n",
    "from textblob import TextBlob\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "scrolled": true
   },
   "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>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover (in lacs)</th>\n",
       "      <th>No. of Contracts</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-Apr-2017</td>\n",
       "      <td>2435.00</td>\n",
       "      <td>2408.20</td>\n",
       "      <td>2411.70</td>\n",
       "      <td>598444</td>\n",
       "      <td>1.447038e+04</td>\n",
       "      <td>6263</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05-Apr-2017</td>\n",
       "      <td>2419.90</td>\n",
       "      <td>2395.00</td>\n",
       "      <td>2401.25</td>\n",
       "      <td>1452784</td>\n",
       "      <td>3.489032e+04</td>\n",
       "      <td>8452</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06-Apr-2017</td>\n",
       "      <td>2410.00</td>\n",
       "      <td>2392.55</td>\n",
       "      <td>2400.05</td>\n",
       "      <td>860352</td>\n",
       "      <td>2.064533e+04</td>\n",
       "      <td>7025</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07-Apr-2017</td>\n",
       "      <td>2433.25</td>\n",
       "      <td>2392.15</td>\n",
       "      <td>2429.05</td>\n",
       "      <td>1138506</td>\n",
       "      <td>2.753949e+04</td>\n",
       "      <td>11681</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10-Apr-2017</td>\n",
       "      <td>2449.00</td>\n",
       "      <td>2405.00</td>\n",
       "      <td>2422.25</td>\n",
       "      <td>1271632</td>\n",
       "      <td>3.096273e+04</td>\n",
       "      <td>14843</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11-Apr-2017</td>\n",
       "      <td>2443.10</td>\n",
       "      <td>2406.00</td>\n",
       "      <td>2419.30</td>\n",
       "      <td>778892</td>\n",
       "      <td>1.892359e+04</td>\n",
       "      <td>8672</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12-Apr-2017</td>\n",
       "      <td>2428.00</td>\n",
       "      <td>2383.00</td>\n",
       "      <td>2393.25</td>\n",
       "      <td>525269</td>\n",
       "      <td>1.262435e+04</td>\n",
       "      <td>8995</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13-Apr-2017</td>\n",
       "      <td>2409.00</td>\n",
       "      <td>2321.05</td>\n",
       "      <td>2328.65</td>\n",
       "      <td>1250236</td>\n",
       "      <td>2.926459e+04</td>\n",
       "      <td>21646</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17-Apr-2017</td>\n",
       "      <td>2332.90</td>\n",
       "      <td>2295.15</td>\n",
       "      <td>2314.85</td>\n",
       "      <td>1135441</td>\n",
       "      <td>2.622516e+04</td>\n",
       "      <td>16021</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18-Apr-2017</td>\n",
       "      <td>2334.20</td>\n",
       "      <td>2302.05</td>\n",
       "      <td>2308.80</td>\n",
       "      <td>865310</td>\n",
       "      <td>2.007052e+04</td>\n",
       "      <td>36373</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19-Apr-2017</td>\n",
       "      <td>2348.70</td>\n",
       "      <td>2252.80</td>\n",
       "      <td>2299.15</td>\n",
       "      <td>2813406</td>\n",
       "      <td>6.463724e+04</td>\n",
       "      <td>92373</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20-Apr-2017</td>\n",
       "      <td>2332.00</td>\n",
       "      <td>2290.95</td>\n",
       "      <td>2325.50</td>\n",
       "      <td>634421</td>\n",
       "      <td>1.465052e+04</td>\n",
       "      <td>20835</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21-Apr-2017</td>\n",
       "      <td>2335.65</td>\n",
       "      <td>2301.00</td>\n",
       "      <td>2310.80</td>\n",
       "      <td>580577</td>\n",
       "      <td>1.343354e+04</td>\n",
       "      <td>13200</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>24-Apr-2017</td>\n",
       "      <td>2339.65</td>\n",
       "      <td>2282.50</td>\n",
       "      <td>2328.10</td>\n",
       "      <td>1000010</td>\n",
       "      <td>2.317274e+04</td>\n",
       "      <td>26382</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>25-Apr-2017</td>\n",
       "      <td>2344.45</td>\n",
       "      <td>2302.70</td>\n",
       "      <td>2311.50</td>\n",
       "      <td>1574355</td>\n",
       "      <td>3.641607e+04</td>\n",
       "      <td>36281</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>26-Apr-2017</td>\n",
       "      <td>2343.95</td>\n",
       "      <td>2303.05</td>\n",
       "      <td>2310.35</td>\n",
       "      <td>1093719</td>\n",
       "      <td>2.529562e+04</td>\n",
       "      <td>38144</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>27-Apr-2017</td>\n",
       "      <td>2327.80</td>\n",
       "      <td>2277.65</td>\n",
       "      <td>2302.65</td>\n",
       "      <td>2886593</td>\n",
       "      <td>6.659993e+04</td>\n",
       "      <td>32958</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>28-Apr-2017</td>\n",
       "      <td>2298.75</td>\n",
       "      <td>2268.00</td>\n",
       "      <td>2273.15</td>\n",
       "      <td>1270255</td>\n",
       "      <td>2.898077e+04</td>\n",
       "      <td>7853</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>02-May-2017</td>\n",
       "      <td>2302.00</td>\n",
       "      <td>2280.00</td>\n",
       "      <td>2289.90</td>\n",
       "      <td>1191348</td>\n",
       "      <td>2.729841e+04</td>\n",
       "      <td>6908</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>03-May-2017</td>\n",
       "      <td>2345.00</td>\n",
       "      <td>2268.50</td>\n",
       "      <td>2337.30</td>\n",
       "      <td>1757203</td>\n",
       "      <td>4.079635e+04</td>\n",
       "      <td>19274</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>04-May-2017</td>\n",
       "      <td>2350.00</td>\n",
       "      <td>2308.80</td>\n",
       "      <td>2330.10</td>\n",
       "      <td>1354104</td>\n",
       "      <td>3.150690e+04</td>\n",
       "      <td>12505</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>05-May-2017</td>\n",
       "      <td>2332.50</td>\n",
       "      <td>2302.50</td>\n",
       "      <td>2320.50</td>\n",
       "      <td>841237</td>\n",
       "      <td>1.951290e+04</td>\n",
       "      <td>11057</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>08-May-2017</td>\n",
       "      <td>2347.40</td>\n",
       "      <td>2317.70</td>\n",
       "      <td>2342.45</td>\n",
       "      <td>535891</td>\n",
       "      <td>1.252685e+04</td>\n",
       "      <td>9912</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>09-May-2017</td>\n",
       "      <td>2361.00</td>\n",
       "      <td>2332.55</td>\n",
       "      <td>2352.55</td>\n",
       "      <td>895135</td>\n",
       "      <td>2.103095e+04</td>\n",
       "      <td>10301</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10-May-2017</td>\n",
       "      <td>2356.15</td>\n",
       "      <td>2316.00</td>\n",
       "      <td>2332.45</td>\n",
       "      <td>603194</td>\n",
       "      <td>1.405627e+04</td>\n",
       "      <td>8036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11-May-2017</td>\n",
       "      <td>2360.00</td>\n",
       "      <td>2322.75</td>\n",
       "      <td>2349.45</td>\n",
       "      <td>1035651</td>\n",
       "      <td>2.421729e+04</td>\n",
       "      <td>7467</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>12-May-2017</td>\n",
       "      <td>2369.90</td>\n",
       "      <td>2320.05</td>\n",
       "      <td>2360.65</td>\n",
       "      <td>1157223</td>\n",
       "      <td>2.720590e+04</td>\n",
       "      <td>11051</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>15-May-2017</td>\n",
       "      <td>2377.95</td>\n",
       "      <td>2341.60</td>\n",
       "      <td>2365.10</td>\n",
       "      <td>908802</td>\n",
       "      <td>2.138043e+04</td>\n",
       "      <td>11193</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16-May-2017</td>\n",
       "      <td>2435.00</td>\n",
       "      <td>2373.45</td>\n",
       "      <td>2429.15</td>\n",
       "      <td>1800513</td>\n",
       "      <td>4.339703e+04</td>\n",
       "      <td>26804</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>17-May-2017</td>\n",
       "      <td>2460.75</td>\n",
       "      <td>2423.00</td>\n",
       "      <td>2455.35</td>\n",
       "      <td>1438099</td>\n",
       "      <td>3.513729e+04</td>\n",
       "      <td>17432</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>01-Feb-2018</td>\n",
       "      <td>3190.00</td>\n",
       "      <td>3088.80</td>\n",
       "      <td>3138.60</td>\n",
       "      <td>1200315</td>\n",
       "      <td>3.776474e+04</td>\n",
       "      <td>24827</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>02-Feb-2018</td>\n",
       "      <td>3190.00</td>\n",
       "      <td>3103.45</td>\n",
       "      <td>3153.65</td>\n",
       "      <td>1228245</td>\n",
       "      <td>3.890163e+04</td>\n",
       "      <td>20247</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>05-Feb-2018</td>\n",
       "      <td>3188.00</td>\n",
       "      <td>3076.85</td>\n",
       "      <td>3103.20</td>\n",
       "      <td>1378871</td>\n",
       "      <td>4.300356e+04</td>\n",
       "      <td>22474</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>06-Feb-2018</td>\n",
       "      <td>3077.15</td>\n",
       "      <td>2985.00</td>\n",
       "      <td>2997.25</td>\n",
       "      <td>1328653</td>\n",
       "      <td>4.013744e+04</td>\n",
       "      <td>25668</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>07-Feb-2018</td>\n",
       "      <td>3031.00</td>\n",
       "      <td>2947.45</td>\n",
       "      <td>2955.05</td>\n",
       "      <td>1090162</td>\n",
       "      <td>3.243512e+04</td>\n",
       "      <td>20014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>08-Feb-2018</td>\n",
       "      <td>3027.60</td>\n",
       "      <td>2959.00</td>\n",
       "      <td>2974.20</td>\n",
       "      <td>1377502</td>\n",
       "      <td>4.132716e+04</td>\n",
       "      <td>27852</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>09-Feb-2018</td>\n",
       "      <td>2994.00</td>\n",
       "      <td>2896.65</td>\n",
       "      <td>2971.70</td>\n",
       "      <td>1051229</td>\n",
       "      <td>3.091037e+04</td>\n",
       "      <td>24988</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>12-Feb-2018</td>\n",
       "      <td>3023.35</td>\n",
       "      <td>2955.55</td>\n",
       "      <td>2967.30</td>\n",
       "      <td>987281</td>\n",
       "      <td>2.943408e+04</td>\n",
       "      <td>18911</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>14-Feb-2018</td>\n",
       "      <td>2974.45</td>\n",
       "      <td>2892.50</td>\n",
       "      <td>2911.80</td>\n",
       "      <td>1423766</td>\n",
       "      <td>4.185367e+04</td>\n",
       "      <td>24136</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>15-Feb-2018</td>\n",
       "      <td>2943.00</td>\n",
       "      <td>2905.10</td>\n",
       "      <td>2925.85</td>\n",
       "      <td>1232618</td>\n",
       "      <td>3.605954e+04</td>\n",
       "      <td>24025</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>16-Feb-2018</td>\n",
       "      <td>2999.60</td>\n",
       "      <td>2923.00</td>\n",
       "      <td>2933.05</td>\n",
       "      <td>1257878</td>\n",
       "      <td>3.730256e+04</td>\n",
       "      <td>38392</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>19-Feb-2018</td>\n",
       "      <td>2953.30</td>\n",
       "      <td>2895.20</td>\n",
       "      <td>2923.60</td>\n",
       "      <td>784112</td>\n",
       "      <td>2.295955e+04</td>\n",
       "      <td>36660</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>20-Feb-2018</td>\n",
       "      <td>2985.05</td>\n",
       "      <td>2927.90</td>\n",
       "      <td>2944.75</td>\n",
       "      <td>944534</td>\n",
       "      <td>2.795126e+04</td>\n",
       "      <td>30308</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>21-Feb-2018</td>\n",
       "      <td>3062.60</td>\n",
       "      <td>2960.05</td>\n",
       "      <td>3042.30</td>\n",
       "      <td>1544614</td>\n",
       "      <td>4.658938e+04</td>\n",
       "      <td>62846</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>22-Feb-2018</td>\n",
       "      <td>3079.00</td>\n",
       "      <td>3022.50</td>\n",
       "      <td>3050.20</td>\n",
       "      <td>1731505</td>\n",
       "      <td>5.277342e+04</td>\n",
       "      <td>57167</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>23-Feb-2018</td>\n",
       "      <td>3089.00</td>\n",
       "      <td>2992.10</td>\n",
       "      <td>3075.80</td>\n",
       "      <td>1681250</td>\n",
       "      <td>5.118936e+04</td>\n",
       "      <td>24127</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>26-Feb-2018</td>\n",
       "      <td>3089.95</td>\n",
       "      <td>3021.20</td>\n",
       "      <td>3031.70</td>\n",
       "      <td>681941</td>\n",
       "      <td>2.075894e+04</td>\n",
       "      <td>13474</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>27-Feb-2018</td>\n",
       "      <td>3078.55</td>\n",
       "      <td>3032.00</td>\n",
       "      <td>3042.35</td>\n",
       "      <td>858900</td>\n",
       "      <td>2.623443e+04</td>\n",
       "      <td>14880</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>28-Feb-2018</td>\n",
       "      <td>3063.95</td>\n",
       "      <td>3021.45</td>\n",
       "      <td>3035.05</td>\n",
       "      <td>1353432</td>\n",
       "      <td>4.114652e+04</td>\n",
       "      <td>14848</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>01-Mar-2018</td>\n",
       "      <td>3061.20</td>\n",
       "      <td>3020.00</td>\n",
       "      <td>3037.70</td>\n",
       "      <td>859062</td>\n",
       "      <td>2.613481e+04</td>\n",
       "      <td>12984</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>05-Mar-2018</td>\n",
       "      <td>3113.65</td>\n",
       "      <td>3032.95</td>\n",
       "      <td>3105.85</td>\n",
       "      <td>1429084</td>\n",
       "      <td>4.395523e+04</td>\n",
       "      <td>25024</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>06-Mar-2018</td>\n",
       "      <td>3127.00</td>\n",
       "      <td>3030.15</td>\n",
       "      <td>3042.80</td>\n",
       "      <td>953382</td>\n",
       "      <td>2.943403e+04</td>\n",
       "      <td>19192</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>07-Mar-2018</td>\n",
       "      <td>3089.00</td>\n",
       "      <td>3018.50</td>\n",
       "      <td>3027.45</td>\n",
       "      <td>958886</td>\n",
       "      <td>2.920078e+04</td>\n",
       "      <td>15677</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>08-Mar-2018</td>\n",
       "      <td>3054.90</td>\n",
       "      <td>2987.50</td>\n",
       "      <td>3003.95</td>\n",
       "      <td>975913</td>\n",
       "      <td>2.944917e+04</td>\n",
       "      <td>13646</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>09-Mar-2018</td>\n",
       "      <td>3063.20</td>\n",
       "      <td>2977.20</td>\n",
       "      <td>3034.10</td>\n",
       "      <td>936218</td>\n",
       "      <td>2.837381e+04</td>\n",
       "      <td>18735</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>12-Mar-2018</td>\n",
       "      <td>3083.90</td>\n",
       "      <td>3035.50</td>\n",
       "      <td>3052.15</td>\n",
       "      <td>1045670</td>\n",
       "      <td>3.200669e+04</td>\n",
       "      <td>22258</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>13-Mar-2018</td>\n",
       "      <td>2939.70</td>\n",
       "      <td>2872.00</td>\n",
       "      <td>2886.80</td>\n",
       "      <td>44033577</td>\n",
       "      <td>1.268362e+06</td>\n",
       "      <td>124501</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>14-Mar-2018</td>\n",
       "      <td>2923.05</td>\n",
       "      <td>2875.05</td>\n",
       "      <td>2886.90</td>\n",
       "      <td>1834334</td>\n",
       "      <td>5.318880e+04</td>\n",
       "      <td>42759</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>15-Mar-2018</td>\n",
       "      <td>2902.55</td>\n",
       "      <td>2855.60</td>\n",
       "      <td>2869.70</td>\n",
       "      <td>1174453</td>\n",
       "      <td>3.378810e+04</td>\n",
       "      <td>31625</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>16-Mar-2018</td>\n",
       "      <td>2872.00</td>\n",
       "      <td>2805.00</td>\n",
       "      <td>2825.70</td>\n",
       "      <td>3598741</td>\n",
       "      <td>1.019998e+05</td>\n",
       "      <td>46414</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>238 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  High Price  Low Price  Close Price  Total Traded Quantity  \\\n",
       "0    03-Apr-2017     2435.00    2408.20      2411.70                 598444   \n",
       "1    05-Apr-2017     2419.90    2395.00      2401.25                1452784   \n",
       "2    06-Apr-2017     2410.00    2392.55      2400.05                 860352   \n",
       "3    07-Apr-2017     2433.25    2392.15      2429.05                1138506   \n",
       "4    10-Apr-2017     2449.00    2405.00      2422.25                1271632   \n",
       "5    11-Apr-2017     2443.10    2406.00      2419.30                 778892   \n",
       "6    12-Apr-2017     2428.00    2383.00      2393.25                 525269   \n",
       "7    13-Apr-2017     2409.00    2321.05      2328.65                1250236   \n",
       "8    17-Apr-2017     2332.90    2295.15      2314.85                1135441   \n",
       "9    18-Apr-2017     2334.20    2302.05      2308.80                 865310   \n",
       "10   19-Apr-2017     2348.70    2252.80      2299.15                2813406   \n",
       "11   20-Apr-2017     2332.00    2290.95      2325.50                 634421   \n",
       "12   21-Apr-2017     2335.65    2301.00      2310.80                 580577   \n",
       "13   24-Apr-2017     2339.65    2282.50      2328.10                1000010   \n",
       "14   25-Apr-2017     2344.45    2302.70      2311.50                1574355   \n",
       "15   26-Apr-2017     2343.95    2303.05      2310.35                1093719   \n",
       "16   27-Apr-2017     2327.80    2277.65      2302.65                2886593   \n",
       "17   28-Apr-2017     2298.75    2268.00      2273.15                1270255   \n",
       "18   02-May-2017     2302.00    2280.00      2289.90                1191348   \n",
       "19   03-May-2017     2345.00    2268.50      2337.30                1757203   \n",
       "20   04-May-2017     2350.00    2308.80      2330.10                1354104   \n",
       "21   05-May-2017     2332.50    2302.50      2320.50                 841237   \n",
       "22   08-May-2017     2347.40    2317.70      2342.45                 535891   \n",
       "23   09-May-2017     2361.00    2332.55      2352.55                 895135   \n",
       "24   10-May-2017     2356.15    2316.00      2332.45                 603194   \n",
       "25   11-May-2017     2360.00    2322.75      2349.45                1035651   \n",
       "26   12-May-2017     2369.90    2320.05      2360.65                1157223   \n",
       "27   15-May-2017     2377.95    2341.60      2365.10                 908802   \n",
       "28   16-May-2017     2435.00    2373.45      2429.15                1800513   \n",
       "29   17-May-2017     2460.75    2423.00      2455.35                1438099   \n",
       "..           ...         ...        ...          ...                    ...   \n",
       "208  01-Feb-2018     3190.00    3088.80      3138.60                1200315   \n",
       "209  02-Feb-2018     3190.00    3103.45      3153.65                1228245   \n",
       "210  05-Feb-2018     3188.00    3076.85      3103.20                1378871   \n",
       "211  06-Feb-2018     3077.15    2985.00      2997.25                1328653   \n",
       "212  07-Feb-2018     3031.00    2947.45      2955.05                1090162   \n",
       "213  08-Feb-2018     3027.60    2959.00      2974.20                1377502   \n",
       "214  09-Feb-2018     2994.00    2896.65      2971.70                1051229   \n",
       "215  12-Feb-2018     3023.35    2955.55      2967.30                 987281   \n",
       "216  14-Feb-2018     2974.45    2892.50      2911.80                1423766   \n",
       "217  15-Feb-2018     2943.00    2905.10      2925.85                1232618   \n",
       "218  16-Feb-2018     2999.60    2923.00      2933.05                1257878   \n",
       "219  19-Feb-2018     2953.30    2895.20      2923.60                 784112   \n",
       "220  20-Feb-2018     2985.05    2927.90      2944.75                 944534   \n",
       "221  21-Feb-2018     3062.60    2960.05      3042.30                1544614   \n",
       "222  22-Feb-2018     3079.00    3022.50      3050.20                1731505   \n",
       "223  23-Feb-2018     3089.00    2992.10      3075.80                1681250   \n",
       "224  26-Feb-2018     3089.95    3021.20      3031.70                 681941   \n",
       "225  27-Feb-2018     3078.55    3032.00      3042.35                 858900   \n",
       "226  28-Feb-2018     3063.95    3021.45      3035.05                1353432   \n",
       "227  01-Mar-2018     3061.20    3020.00      3037.70                 859062   \n",
       "228  05-Mar-2018     3113.65    3032.95      3105.85                1429084   \n",
       "229  06-Mar-2018     3127.00    3030.15      3042.80                 953382   \n",
       "230  07-Mar-2018     3089.00    3018.50      3027.45                 958886   \n",
       "231  08-Mar-2018     3054.90    2987.50      3003.95                 975913   \n",
       "232  09-Mar-2018     3063.20    2977.20      3034.10                 936218   \n",
       "233  12-Mar-2018     3083.90    3035.50      3052.15                1045670   \n",
       "234  13-Mar-2018     2939.70    2872.00      2886.80               44033577   \n",
       "235  14-Mar-2018     2923.05    2875.05      2886.90                1834334   \n",
       "236  15-Mar-2018     2902.55    2855.60      2869.70                1174453   \n",
       "237  16-Mar-2018     2872.00    2805.00      2825.70                3598741   \n",
       "\n",
       "     Turnover (in lacs)  No. of Contracts  Unnamed: 7  \n",
       "0          1.447038e+04              6263         NaN  \n",
       "1          3.489032e+04              8452         NaN  \n",
       "2          2.064533e+04              7025         NaN  \n",
       "3          2.753949e+04             11681         NaN  \n",
       "4          3.096273e+04             14843         NaN  \n",
       "5          1.892359e+04              8672         NaN  \n",
       "6          1.262435e+04              8995         NaN  \n",
       "7          2.926459e+04             21646         NaN  \n",
       "8          2.622516e+04             16021         NaN  \n",
       "9          2.007052e+04             36373         NaN  \n",
       "10         6.463724e+04             92373         NaN  \n",
       "11         1.465052e+04             20835         NaN  \n",
       "12         1.343354e+04             13200         NaN  \n",
       "13         2.317274e+04             26382         NaN  \n",
       "14         3.641607e+04             36281         NaN  \n",
       "15         2.529562e+04             38144         NaN  \n",
       "16         6.659993e+04             32958         NaN  \n",
       "17         2.898077e+04              7853         NaN  \n",
       "18         2.729841e+04              6908         NaN  \n",
       "19         4.079635e+04             19274         NaN  \n",
       "20         3.150690e+04             12505         NaN  \n",
       "21         1.951290e+04             11057         NaN  \n",
       "22         1.252685e+04              9912         NaN  \n",
       "23         2.103095e+04             10301         NaN  \n",
       "24         1.405627e+04              8036         NaN  \n",
       "25         2.421729e+04              7467         NaN  \n",
       "26         2.720590e+04             11051         NaN  \n",
       "27         2.138043e+04             11193         NaN  \n",
       "28         4.339703e+04             26804         NaN  \n",
       "29         3.513729e+04             17432         NaN  \n",
       "..                  ...               ...         ...  \n",
       "208        3.776474e+04             24827         NaN  \n",
       "209        3.890163e+04             20247         NaN  \n",
       "210        4.300356e+04             22474         NaN  \n",
       "211        4.013744e+04             25668         NaN  \n",
       "212        3.243512e+04             20014         NaN  \n",
       "213        4.132716e+04             27852         NaN  \n",
       "214        3.091037e+04             24988         NaN  \n",
       "215        2.943408e+04             18911         NaN  \n",
       "216        4.185367e+04             24136         NaN  \n",
       "217        3.605954e+04             24025         NaN  \n",
       "218        3.730256e+04             38392         NaN  \n",
       "219        2.295955e+04             36660         NaN  \n",
       "220        2.795126e+04             30308         NaN  \n",
       "221        4.658938e+04             62846         NaN  \n",
       "222        5.277342e+04             57167         NaN  \n",
       "223        5.118936e+04             24127         NaN  \n",
       "224        2.075894e+04             13474         NaN  \n",
       "225        2.623443e+04             14880         NaN  \n",
       "226        4.114652e+04             14848         NaN  \n",
       "227        2.613481e+04             12984         NaN  \n",
       "228        4.395523e+04             25024         NaN  \n",
       "229        2.943403e+04             19192         NaN  \n",
       "230        2.920078e+04             15677         NaN  \n",
       "231        2.944917e+04             13646         NaN  \n",
       "232        2.837381e+04             18735         NaN  \n",
       "233        3.200669e+04             22258         NaN  \n",
       "234        1.268362e+06            124501         NaN  \n",
       "235        5.318880e+04             42759         NaN  \n",
       "236        3.378810e+04             31625         NaN  \n",
       "237        1.019998e+05             46414         NaN  \n",
       "\n",
       "[238 rows x 8 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Tcs.csv\")\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>24-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>25-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>26-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>27-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>28-Apr-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>02-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>03-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>04-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>05-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>08-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>09-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>12-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>15-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>17-May-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>01-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>02-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>05-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>06-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>07-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>08-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>09-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>12-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>14-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>15-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>16-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>19-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>20-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>21-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>22-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>23-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>26-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>27-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>28-Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>01-Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>05-Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>06-Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>07-Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>08-Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>09-Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>12-Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>13-Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>14-Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>15-Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>16-Mar-2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>238 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date\n",
       "0    03-Apr-2017\n",
       "1    05-Apr-2017\n",
       "2    06-Apr-2017\n",
       "3    07-Apr-2017\n",
       "4    10-Apr-2017\n",
       "5    11-Apr-2017\n",
       "6    12-Apr-2017\n",
       "7    13-Apr-2017\n",
       "8    17-Apr-2017\n",
       "9    18-Apr-2017\n",
       "10   19-Apr-2017\n",
       "11   20-Apr-2017\n",
       "12   21-Apr-2017\n",
       "13   24-Apr-2017\n",
       "14   25-Apr-2017\n",
       "15   26-Apr-2017\n",
       "16   27-Apr-2017\n",
       "17   28-Apr-2017\n",
       "18   02-May-2017\n",
       "19   03-May-2017\n",
       "20   04-May-2017\n",
       "21   05-May-2017\n",
       "22   08-May-2017\n",
       "23   09-May-2017\n",
       "24   10-May-2017\n",
       "25   11-May-2017\n",
       "26   12-May-2017\n",
       "27   15-May-2017\n",
       "28   16-May-2017\n",
       "29   17-May-2017\n",
       "..           ...\n",
       "208  01-Feb-2018\n",
       "209  02-Feb-2018\n",
       "210  05-Feb-2018\n",
       "211  06-Feb-2018\n",
       "212  07-Feb-2018\n",
       "213  08-Feb-2018\n",
       "214  09-Feb-2018\n",
       "215  12-Feb-2018\n",
       "216  14-Feb-2018\n",
       "217  15-Feb-2018\n",
       "218  16-Feb-2018\n",
       "219  19-Feb-2018\n",
       "220  20-Feb-2018\n",
       "221  21-Feb-2018\n",
       "222  22-Feb-2018\n",
       "223  23-Feb-2018\n",
       "224  26-Feb-2018\n",
       "225  27-Feb-2018\n",
       "226  28-Feb-2018\n",
       "227  01-Mar-2018\n",
       "228  05-Mar-2018\n",
       "229  06-Mar-2018\n",
       "230  07-Mar-2018\n",
       "231  08-Mar-2018\n",
       "232  09-Mar-2018\n",
       "233  12-Mar-2018\n",
       "234  13-Mar-2018\n",
       "235  14-Mar-2018\n",
       "236  15-Mar-2018\n",
       "237  16-Mar-2018\n",
       "\n",
       "[238 rows x 1 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = data[['Date']]\n",
    "date\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=data[[\"Close Price\"]]\n",
    "dataset = data1.values\n",
    "dataset = data1.astype('float32')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSentiment(text):\n",
    "    cleanedtext = ' '.join([word for word in text.split(' ') if len(word) > 0 and word[0] != '@' and word[0] != '#' and 'http' not in word and word != 'RT'])\n",
    "    analysis = TextBlob(cleanedtext)\n",
    "    sentiment = analysis.sentiment.polarity\n",
    "    return sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "sent = pd.read_csv('file3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "senti = pd.DataFrame([getSentiment(x) for x in sent['Headlines']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124 62 52\n"
     ]
    }
   ],
   "source": [
    "train_size = 124\n",
    "cross_size = 62\n",
    "test_size = 52\n",
    "train, cv, test = dataset[0:train_size], dataset[train_size:train_size+cross_size], dataset[train_size+cross_size:]\n",
    "print(len(train), len(cv), len(test))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "date1 = date[train_size:train_size+51]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i:(i+look_back)]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "look_back = 10\n",
    "trainX, trainY = create_dataset(train, look_back=look_back)\n",
    "crossX, crossY =  create_dataset(cv, look_back=look_back)\n",
    "testX, testY = create_dataset(test, look_back=look_back)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113, 10, 1)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113, 1, 10)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = np.reshape(trainX, (trainX.shape[0], 1,trainX.shape[1]))\n",
    "crossX = np.reshape(crossX,(crossX.shape[0],1,crossX.shape[1]))\n",
    "testX = np.reshape(testX, (testX.shape[0], 1,testX.shape[1]))\n",
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 1s - loss: 0.0175\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.0168\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.0160\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.0153\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.0146\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.0140\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.0133\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.0127\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.0121\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.0110\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.0105\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.0100\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.0095\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.0090\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.0086\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.0082\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.0078\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.0074\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.0071\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.0068\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.0065\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.0062\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.0057\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.0055\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.0053\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.0052\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.0050\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.0049\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.0047\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.0046\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.0045\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.0044\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.0044\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0038\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fccd5bf1fd0>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(5, input_shape=(1,look_back)))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(trainX, trainY, epochs=100, batch_size=256, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.3471968 ],\n",
       "       [0.33860876],\n",
       "       [0.33569184],\n",
       "       [0.34001289],\n",
       "       [0.32975039],\n",
       "       [0.30976552],\n",
       "       [0.28702618],\n",
       "       [0.3361241 ],\n",
       "       [0.3705844 ],\n",
       "       [0.3790105 ],\n",
       "       [0.35616304],\n",
       "       [0.38133296],\n",
       "       [0.37479758],\n",
       "       [0.4238955 ],\n",
       "       [0.47196722],\n",
       "       [0.49891974],\n",
       "       [0.500432  ],\n",
       "       [0.47056282],\n",
       "       [0.52479199],\n",
       "       [0.47688247],\n",
       "       [0.46683597],\n",
       "       [0.5115047 ],\n",
       "       [0.46899649],\n",
       "       [0.46483738],\n",
       "       [0.43188934],\n",
       "       [0.44020732],\n",
       "       [0.44085558],\n",
       "       [0.44820142],\n",
       "       [0.4458249 ],\n",
       "       [0.4452307 ],\n",
       "       [0.41590139],\n",
       "       [0.39305393],\n",
       "       [0.38543802],\n",
       "       [0.38803081],\n",
       "       [0.3914876 ],\n",
       "       [0.38905701],\n",
       "       [0.37171873],\n",
       "       [0.35470458],\n",
       "       [0.41908838],\n",
       "       [0.36620928],\n",
       "       [0.38376356],\n",
       "       [0.30749712],\n",
       "       [0.29431796],\n",
       "       [0.33201905],\n",
       "       [0.32640173],\n",
       "       [0.34114722],\n",
       "       [0.348439  ],\n",
       "       [0.4035865 ],\n",
       "       [0.40650315],\n",
       "       [0.37458132],\n",
       "       [0.38219723]])"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainPredict = model.predict(trainX)\n",
    "cvPredict = model.predict(crossX)\n",
    "#crossY.shape\n",
    "trainY.reshape(-1,1)\n",
    "crossY.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02808685],\n",
       "       [0.05655189],\n",
       "       [0.04067207],\n",
       "       [0.05936068],\n",
       "       [0.04142821],\n",
       "       [0.04018601],\n",
       "       [0.03186777],\n",
       "       [0.        ],\n",
       "       [0.01809441],\n",
       "       [0.06929905],\n",
       "       [0.06152121],\n",
       "       [0.05115058],\n",
       "       [0.0748623 ],\n",
       "       [0.08577307],\n",
       "       [0.06405967],\n",
       "       [0.08242415],\n",
       "       [0.09452304],\n",
       "       [0.09933043],\n",
       "       [0.16852108],\n",
       "       [0.19682419],\n",
       "       [0.28416333],\n",
       "       [0.25278163],\n",
       "       [0.27892421],\n",
       "       [0.26849951],\n",
       "       [0.30895532],\n",
       "       [0.37965877],\n",
       "       [0.32958846],\n",
       "       [0.32375514],\n",
       "       [0.30025941],\n",
       "       [0.29539823],\n",
       "       [0.30296007],\n",
       "       [0.31516683],\n",
       "       [0.35735117],\n",
       "       [0.45678947],\n",
       "       [0.3695582 ],\n",
       "       [0.26839164],\n",
       "       [0.25261969],\n",
       "       [0.23123042],\n",
       "       [0.19271914],\n",
       "       [0.21286595],\n",
       "       [0.14913041],\n",
       "       [0.13670738],\n",
       "       [0.17127581],\n",
       "       [0.18564336],\n",
       "       [0.14383722],\n",
       "       [0.13146826],\n",
       "       [0.09565737],\n",
       "       [0.07529457],\n",
       "       [0.08307241],\n",
       "       [0.06492393],\n",
       "       [0.0963597 ],\n",
       "       [0.10851267],\n",
       "       [0.10122089],\n",
       "       [0.08949992],\n",
       "       [0.08037175],\n",
       "       [0.06351954],\n",
       "       [0.17478666],\n",
       "       [0.21891553],\n",
       "       [0.17964785],\n",
       "       [0.18715563],\n",
       "       [0.13427679],\n",
       "       [0.13211626],\n",
       "       [0.14605182],\n",
       "       [0.19061268],\n",
       "       [0.18531923],\n",
       "       [0.23555148],\n",
       "       [0.27822188],\n",
       "       [0.32229668],\n",
       "       [0.3055526 ],\n",
       "       [0.22528898],\n",
       "       [0.22809777],\n",
       "       [0.23619974],\n",
       "       [0.24684018],\n",
       "       [0.216755  ],\n",
       "       [0.23366102],\n",
       "       [0.26968791],\n",
       "       [0.2487847 ],\n",
       "       [0.2483527 ],\n",
       "       [0.25802101],\n",
       "       [0.25742681],\n",
       "       [0.24079086],\n",
       "       [0.21200168],\n",
       "       [0.23679394],\n",
       "       [0.22955596],\n",
       "       [0.25996553],\n",
       "       [0.27616949],\n",
       "       [0.24241126],\n",
       "       [0.23366102],\n",
       "       [0.24138506],\n",
       "       [0.23133856],\n",
       "       [0.21356828],\n",
       "       [0.22836783],\n",
       "       [0.24111499],\n",
       "       [0.19801233],\n",
       "       [0.19774226],\n",
       "       [0.22815183],\n",
       "       [0.19450147],\n",
       "       [0.20195545],\n",
       "       [0.21329821],\n",
       "       [0.1958518 ],\n",
       "       [0.23258075],\n",
       "       [0.23339095],\n",
       "       [0.23096036],\n",
       "       [0.25159349],\n",
       "       [0.24403165],\n",
       "       [0.24316739],\n",
       "       [0.2380902 ],\n",
       "       [0.2719563 ],\n",
       "       [0.2489469 ],\n",
       "       [0.25667068],\n",
       "       [0.21767307],\n",
       "       [0.23965653],\n",
       "       [0.21961759]])"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY = scaler.inverse_transform(trainY)\n",
    "cvPredict = scaler.inverse_transform(cvPredict)\n",
    "crossY = scaler.inverse_transform(crossY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.97229602e+09, 1.99487597e+09, 1.98227927e+09, 1.99710405e+09,\n",
       "       1.98287908e+09, 1.98189370e+09, 1.97529524e+09, 1.95001608e+09,\n",
       "       1.96436950e+09, 2.00498768e+09, 1.99881789e+09, 1.99059136e+09,\n",
       "       2.00940074e+09, 2.01805572e+09, 2.00083153e+09, 2.01539918e+09,\n",
       "       2.02499665e+09, 2.02881012e+09, 2.08369573e+09, 2.10614723e+09,\n",
       "       2.17542915e+09, 2.15053556e+09, 2.17127321e+09, 2.16300380e+09,\n",
       "       2.19509544e+09, 2.25118108e+09, 2.21146271e+09, 2.20683543e+09,\n",
       "       2.18819740e+09, 2.18434125e+09, 2.19033970e+09, 2.20002274e+09,\n",
       "       2.23348554e+09, 2.31236514e+09, 2.24316879e+09, 2.16291823e+09,\n",
       "       2.15040710e+09, 2.13344003e+09, 2.10289088e+09, 2.11887237e+09,\n",
       "       2.06831404e+09, 2.05845945e+09, 2.08588092e+09, 2.09727801e+09,\n",
       "       2.06411522e+09, 2.05430351e+09, 2.02589646e+09, 2.00974363e+09,\n",
       "       2.01591342e+09, 2.00151711e+09, 2.02645359e+09, 2.03609395e+09,\n",
       "       2.03030973e+09, 2.02101205e+09, 2.01377112e+09, 2.00040307e+09,\n",
       "       2.08866591e+09, 2.12367121e+09, 2.09252206e+09, 2.09847762e+09,\n",
       "       2.05653138e+09, 2.05481754e+09, 2.06587195e+09, 2.10121993e+09,\n",
       "       2.09702089e+09, 2.13686771e+09, 2.17071609e+09, 2.20567850e+09,\n",
       "       2.19239623e+09, 2.12872696e+09, 2.13095504e+09, 2.13738195e+09,\n",
       "       2.14582249e+09, 2.12195737e+09, 2.13536810e+09, 2.16394649e+09,\n",
       "       2.14736499e+09, 2.14702231e+09, 2.15469171e+09, 2.15422036e+09,\n",
       "       2.14102386e+09, 2.11818680e+09, 2.13785330e+09, 2.13211176e+09,\n",
       "       2.15623421e+09, 2.16908802e+09, 2.14230924e+09, 2.13536810e+09,\n",
       "       2.14149521e+09, 2.13352580e+09, 2.11942950e+09, 2.13116927e+09,\n",
       "       2.14128098e+09, 2.10708971e+09, 2.10687548e+09, 2.13099793e+09,\n",
       "       2.10430472e+09, 2.11021760e+09, 2.11921527e+09, 2.10537587e+09,\n",
       "       2.13451118e+09, 2.13515387e+09, 2.13322580e+09, 2.14959307e+09,\n",
       "       2.14359462e+09, 2.14290905e+09, 2.13888156e+09, 2.16574590e+09,\n",
       "       2.14749366e+09, 2.15362056e+09, 2.12268563e+09, 2.14012405e+09,\n",
       "       2.12422813e+09])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainY[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.06 RMSE\n",
      "Test Score: 0.11 RMSE\n"
     ]
    }
   ],
   "source": [
    "trainScore = math.sqrt(mean_squared_error(trainY[:,0], trainPredict[:, 0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "cvScore = math.sqrt(mean_squared_error(crossY[:,0], cvPredict[:, 0]))\n",
    "print('Test Score: %.2f RMSE' % (cvScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62, 1)"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date1.shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fccd4953860>]"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fccd4953748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.scatter(date1,cv[0:51],color=\"Red\")\n",
    "plt.plot(date1,cvPredict,color=\"Blue\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 2s - loss: 0.0426\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.0421\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.0416\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.0411\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.0406\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.0401\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.0396\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.0391\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.0386\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.0381\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.0376\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.0371\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.0366\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.0362\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.0357\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.0352\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.0347\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.0343\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.0338\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.0333\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.0329\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.0324\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.0320\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.0315\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.0311\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.0307\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.0302\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.0298\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.0294\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.0290\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.0285\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.0281\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.0277\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.0273\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.0269\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.0265\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.0261\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.0257\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.0254\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.0250\n",
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    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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      " - 0s - loss: 0.0043\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0043\n",
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      "Epoch 82/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 1/100\n",
      " - 4s - loss: 0.0312\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.0290\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.0270\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.0250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/100\n",
      " - 0s - loss: 0.0231\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.0212\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.0195\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.0179\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.0163\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.0149\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.0135\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.0123\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.0100\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.0091\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.0082\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.0074\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.0067\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.0061\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.0055\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.0051\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.0047\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.0044\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.0036\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.0036\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.0036\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.0036\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0033\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0032\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0032\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0032\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0032\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0032\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0032\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0032\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0032\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0032\n"
     ]
    }
   ],
   "source": [
    "mintrain = 10000\n",
    "valtrain = 0\n",
    "mincv = 10000\n",
    "valcv = 0\n",
    "for j in range(1,31):\n",
    "    look_back = 10\n",
    "    trainX, trainY = create_dataset(train, look_back=look_back)\n",
    "    crossX, crossY =  create_dataset(cv, look_back=look_back)\n",
    "    testX, testY = create_dataset(test, look_back=look_back)\n",
    "    trainX = np.reshape(trainX, (trainX.shape[0], 1,trainX.shape[1]))\n",
    "    crossX = np.reshape(crossX,(crossX.shape[0],1,crossX.shape[1]))\n",
    "    testX = np.reshape(testX, (testX.shape[0], 1,testX.shape[1]))\n",
    "    trainX.shape\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(j, input_shape=(1,look_back)))\n",
    "    model.add(Dense(1))\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    model.fit(trainX, trainY, epochs=100, batch_size=256, verbose=2)\n",
    "    trainPredict = model.predict(trainX)\n",
    "    cvPredict = model.predict(crossX)\n",
    "    #crossY.shape\n",
    "    trainY.reshape(-1,1)\n",
    "    crossY.reshape(-1,1)\n",
    "    trainPredict = scaler.inverse_transform(trainPredict)\n",
    "    trainY = scaler.inverse_transform([trainY])\n",
    "    cvPredict = scaler.inverse_transform(cvPredict)\n",
    "    crossY = scaler.inverse_transform([crossY])\n",
    "    trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:, 0]))\n",
    "    cvScore = math.sqrt(mean_squared_error(crossY[0], cvPredict[:, 0]))\n",
    "    if mintrain > trainScore:\n",
    "        mintrain = trainScore\n",
    "        valtrain = j\n",
    "    if mincv > cvScore:\n",
    "        mincv = cvScore\n",
    "        valcv = j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51.32226742826828, 10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mintrain, valtrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61.43255102571813, 30)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mincv, valcv"
   ]
  }
 ],
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