RNN example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "91c75426",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2efc7b2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader as pdr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab145226",
   "metadata": {},
   "source": [
    "Pandas datareader:used to access the public financial data from the internet and import it into \n",
    "python as a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ae59f90f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pdr.DataReader(\"AMZN\",\"yahoo\", start='2005-04-05', end='2022-06-05')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "cdd2064b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-04-04</th>\n",
       "      <td>1.7590</td>\n",
       "      <td>1.6905</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>1.750</td>\n",
       "      <td>146844000</td>\n",
       "      <td>1.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-05</th>\n",
       "      <td>1.7670</td>\n",
       "      <td>1.7495</td>\n",
       "      <td>1.7615</td>\n",
       "      <td>1.764</td>\n",
       "      <td>98660000</td>\n",
       "      <td>1.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-06</th>\n",
       "      <td>1.7600</td>\n",
       "      <td>1.7310</td>\n",
       "      <td>1.7550</td>\n",
       "      <td>1.741</td>\n",
       "      <td>105522000</td>\n",
       "      <td>1.741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-07</th>\n",
       "      <td>1.7460</td>\n",
       "      <td>1.7250</td>\n",
       "      <td>1.7340</td>\n",
       "      <td>1.745</td>\n",
       "      <td>76146000</td>\n",
       "      <td>1.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-08</th>\n",
       "      <td>1.7465</td>\n",
       "      <td>1.7285</td>\n",
       "      <td>1.7375</td>\n",
       "      <td>1.730</td>\n",
       "      <td>60260000</td>\n",
       "      <td>1.730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              High     Low    Open  Close     Volume  Adj Close\n",
       "Date                                                           \n",
       "2005-04-04  1.7590  1.6905  1.7000  1.750  146844000      1.750\n",
       "2005-04-05  1.7670  1.7495  1.7615  1.764   98660000      1.764\n",
       "2005-04-06  1.7600  1.7310  1.7550  1.741  105522000      1.741\n",
       "2005-04-07  1.7460  1.7250  1.7340  1.745   76146000      1.745\n",
       "2005-04-08  1.7465  1.7285  1.7375  1.730   60260000      1.730"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "5c9b623e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x196913ad760>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4eNHGTh27dMt+Jnznn8x5ctlh96uub+leWN/oH8z92OG9Ox1rIn3rkomcP2lwws7foeTunFsItP7auwx40Ft+EPhoUPlDzu9NoI+ZDYlDrCKSBrbtb6mxf9vri95RK7YfBOCxt7ccdr+jh7Uk8toGf03/7KMGdupa3V0sbe6DnHM7vOWdQPM8VsOA4Hd+q1cWwsxmm1mZmZVVVFTEEIaIdCe1je03q0RS2r+wQ/uNHlAUWC7zmmPycjLrFmNcflrnn+akU7d8nXP3OeemOuemlpSUxCMMEekGGoPnvOuk+g4eG5yM/r50u3fd1BrTPdFiSe7lzc0t3r+7vPJtwIig/YZ7ZSIi7K2q79T+pXOeo3TOczjneGfTvkD54boQhus7vr6islPX7e5iSe7PAFd7y1cDTweVf9brNXMycCCo+UZEMlhDk4+3N+5rf8cw7nhxFb/8V0svmO37ayLuG+65oOBhdjNBR7tCPgosAsab2VYzuw6YC8w0szXAud46wPPAemAtcD/w33GPWkS6pQff2Bj1sRt2V4Ws769uCLvfwtUVfPnRd9uUjxrQsfb6dNGhabedc7MibDonzL4OuCGWoEQkPW3aUx2y3iP38LVpF1QFf2H5zpBtwd0dg3123uKw5R85dmhHQkwbmXX7WESSZu2uQzz85iYAJo/owzWnlpLTzoAvr6za1absux+ZCEB1Q9teN4drhx/RL7Nq7kruItIlHnmrpYf0nz9/EoV52RyqbaThMD1g5jz5fpuyk0f3B+BgTQO//ffaQD92SNw4Ld2RkruIdIl5r28ILPfMz6HMu7F61/zVEY9p3bNmwpBiivL8rck3PbaUn/5zFTN++kpgu5J7iw61uYuIxNv63f6uiWt2he+i+Ma63TT6HJ89ZRQPLfI352zfXxMy/yjArkN1vLdlP7Puf5PqDow7kylUcxeRpNhd6a+Vv7F2d9jtn77/LQAmDS3myS+dAvhvouaHuQl72W9eD0nsH5k8lIX/e1Zg/ZwMG3oAlNxFpIuM9G5oTh3VN6S8wed4dXUFayPU4PdVN9Cn0D9yYkOTa7eHDcDMiYMYGTRUQaImxEhlSu4iknCTv/8Sm/dWc8Gkwfz5CycB0KcwF/CP2nj1vMWc+/NXwx575vgS+ha2DIubm23tjvB46WR/t8dPTR3uHZN5qS7zfmIR6XIHavwPHI3qXxh4UrS9yvToEv/gX0cNLiY/aNAvM+OZG0/naxcc1e51F6z0d6V8aUV5NGF3a0ruIpJQwQOFNbezQ/jx1ddXVPLOZn8vmr6FeZw21t/tMTvM5NZfOnMMs6aNDCk7d8Ig5t8yI7A+oGd+bMF3Y0ruIpJQJ//kX4HlZVv3B5Z/+5m2k7SdfeerfOy3b/DIW5tZsmlfoH29Obm37ilz2/njA8uDivP5/dVTGTeoV6DsK+cdCcCMIzNv5Fl1hRSRhNpdWRdYnj1jdGC5V0FuxGO+8Tf/w0vNST03O4ubzx3XZuaivkV5rP/xRdz6xHtce+oRbc5TkNvcBJR5N1SV3EUkoQYV51N+0J/gP3HC8E4dW5TfkqJuPvfIsPtkZRk//9SUsNuyvUmxM/HhJiV3EUmIAzUN7Kuq54gBRQzu3YO/Xn8KZocfS6a1/BhnT8ryDldyFxGJ0Za91ZjB5x8s48Odhyjplc+McSVRTXOXF2MXxqmj+vHRKUO5KUKtP50puYtIXE0PGusFoOJQHf2KIrevH06foP7t0cjLyeLuK4+L6RzdlXrLiEjCNUQxf2n/ojyum972Jql0jGruIpJwx43s0+F9bzhrDH0L8/j89NHt7ywRRZ3czWw88HhQ0WjgO0Af4AtAhVf+Defc89FeR0S6t/MnDQoMB9Cx/Qdz7PA+iQsoQ0Sd3J1zq4ApAGaWDWwD/gZcC9zlnPtZPAIUke6j/GBtm7J7r5raqXNEc+NV2orXu3gOsM45tylO5xORbmj2w0tiPkesPWTEL17v4pXAo0HrN5rZMjObZ2Z9Ix0kIunlvS37Yz6Hau7xEfO7aGZ5wKXAX72ie4Ax+JtsdgB3RjhutpmVmVlZRUVFuF1EpBu57Yn3On1M8HAEEl/x+Iq8EHjHOVcO4Jwrd841Oed8wP3AtHAHOefuc85Ndc5NLSnJvEF9RNLNX8q2dvqYb1w0oU1ZcY/o+sRLqHgk91kENcmY2ZCgbZcDy+NwDRHphk4fO6DdfXKzQ4ckKD7MgGLScTEldzMrAmYCTwUV/9TM3jezZcBZwC2xXENEupeLj22p3/1qVvtPh75w03R+fPkxiQwpI8X0EJNzrgro36rsqpgiEpFurSCnZY7TvkXtDx8wdmAvxg7sFRjmV+JDt6VFJGa+oFEXWzezSHIouYtIzOoaW6bS60htXRJPY8uISMxqGpoCy18+eyz9i/KoOFR3mCMk0ZTcRSRmi9btAWDux46hMC8nqkG/Hp99MgOLC+IdWsZSs4yIxOyGR94B4O2N+6I+x0mj+3PEgKJ4hZTxlNxFJG5OGKXRRlKFkruIxGxaaT8APn3SyCRHIs2U3EUkZos37k12CNKKkruISBpSbxkRidqidXsY0DOPvOwsrj29NNnhSBAldxGJ2qz73wws99ZojilFzTIiEhXnXMh6+YG2U+xJ8ii5i0hUquubQta/oIk3UoqaZUQkKvctXB9YXva98zQOe4pRzV1EOs05xy8WrAms98pXPTHV6H9ERDrt1dUt8x5vnHtxEiORSFRzF5F2bdlbzf7q+sD6gZoGAD6jJ1JTlpK7iLRr+k9f4fy7FwbWG5r8PWWunzEmWSFJO2JuljGzjcAhoAlodM5NNbN+wONAKbAR+JRzLvrh4kQk6coPtozPXlPfCEBhfnak3SXJ4tXmfpZzbnfQ+hxggXNurpnN8da/FqdriUgXcM5x18traGzytdnWPPNStmlKvVSVqBuqlwFnessPAv9GyV2kWznlJ/9i58HQB5P2VNaxZNM+fvTcSgCyspTcU1U8krsDXjIzB9zrnLsPGOSc2+Ft3wkMan2Qmc0GZgOMHKmbMiKppnViBzjhRy+HrGcruaeseCT3051z28xsIDDfzD4M3uicc17ip1X5fcB9AFOnTm2zXUSSZ035oQ7tV5CjPhmpKub/GefcNu/fXcDfgGlAuZkNAfD+3RXrdUSka2zfX8PMuxaGlN1y7pFt9rv3qhPIyVZyT1Ux1dzNrAjIcs4d8pbPA34APANcDcz1/n061kBFpGs89vaWwPLS78ykT2EeABOG9GL2w0uYdkQ/zjiyhPMnDU5WiNIBsTbLDAL+Zv475jnAI865f5rZ28BfzOw6YBPwqRivIyJd4PW1u/mlN6zAL66cEkjsAOdNGsyGn1yEqYdMtxBTcnfOrQcmhynfA5wTy7lFpOs9sWRrYDlczVyJvftQg5mIAP5+7UXeQ0nXnlZKQa4eUOrONHCYSAZZtG4PxwzvTc9Wozg+vXQbNz22NLD+3Y9M6uLIJN6U3EUyxNsb9wamxXv0Cydzypj+AFxw90I+3Nmxro/SfSi5i2SIhUHD9M5fUc4pY/qzcXdVILH3KczlxrPGctzIPkmKUOJJyV0kA+w6VMuv/rU2sD7v9Q1cOW0E33l6eaDs/z5+rLo3phEld5E05pxj8Ya9XHHfm222nRf0oNLib5zDwOKCrgxNEkzJXSRN7TpUy7TbF4SUTR83gP+s2d1mXyX29KOukCJpaMPuKu58cXVIWd/CXO66YkqbfX/40aO7KCrpSqq5i6SR6vpGZj+0hNfWhtbOg+c5nXPhUcx94UPGDuzJizfP0MiOaUrJXaQbOlDdQFYW9CrIDSl/eun2kMTevyiPsm+dG7LPF88Yw8XHDGFEv8IuiVWSQ8ldpBtZtG5PoK96YV42K35wQcj297cdCFm/5tTSsEMGKLGnPyV3kW5iTfmhQGIHqK5varPPI29tBuD1OWdT29DEiL5K4plKyV2km/iFN1pjMOdcoGZe39gy1+mwPj26LC5JTeotI9JNBD9h2mxdRWVg+bvPfABAQa5+rUXJXaRb2F1Zx8HaRsDf8+W/zxwDwLk/X0h9ow+fz/HoYn+TTG2DL+J5JHMouYukoPpGHxWH6gLrdV6Ty5De/oeNPnf6EYFtb67fwyfvXRRYf33O2V0UpaQyJXeRFHTL40s58faXcc4/d/yO/TUAfOakkQAhQ/Z+dt5ilmzaB8APL5uk9nYBdENVJCX984OdAFTVN7Gnso5P/M5fM8/1JqSONJHGVaeUdkl8kvqirrmb2Qgze8XMVpjZB2Z2k1f+PTPbZmZLvddF8QtXJDM0+fw19qO/+yJn3PHvQPnsGaMDy+9+e2bIMSeW9u2S2KR7iKXm3gh81Tn3jpn1ApaY2Xxv213OuZ/FHp5IZhrRrwdb9taElBUX5IQ8kFSY31J7/8WVU7hsyrAui09SX9TJ3Tm3A9jhLR8ys5WAPl0icZCf07bZZe7Hj424jxK7tBaXG6pmVgocB7zlFd1oZsvMbJ6Zhf1b0cxmm1mZmZVVVLTtvyuSydbuqgxZf/fbM7nomCFJika6o5iTu5n1BJ4EbnbOHQTuAcYAU/DX7O8Md5xz7j7n3FTn3NSSkpJYwxBJGw1NPszgkmP9yfz4kX3oW5QXdt97PnM8D183rSvDk24ipt4yZpaLP7H/2Tn3FIBzrjxo+/3AszFFKJIBLv/t67y7eX9I2eljB3DtaaWMLekV8bgLVZuXCKJO7ua/s/MAsNI59/Og8iFeezzA5cDycMeLdEdVdY0U5mWHHWkxFq0TO8CpYwYwsr8G/pLoxFJzPw24CnjfzJZ6Zd8AZpnZFMABG4HrY7iGSNK9vXEvX3iojP3VDQDceNZYbj1/fNzO3/ygUrPzJg7i3qtOiPsXiGSWWHrLvAaE+/Q9H304Iqnnk79bFLL+61fW0rMgh3+8t51nv3x6VEl4dfmhkAmqAW46Zxy3zDwyplhFmmn4AZEIdh2qpXTOc4H1X846LrA894UP+WD7Qd7euC/kmDXlh7jxkXeoqms87Llv+PM7bcrOGK+OBRI/Su4iEfzjvR2B5VduPZNLJw9l8vDeIft86t5F1DY04fM5/rl8JzPvWsizy3Yw56n3GffN53l7417APxDYgpXl7DpUi3OO8oO19C3M5QfeWDAvf2UGx4/UE6YSP9a6vS8Zpk6d6srKypIdhkjAa2t2818P+B/bOG1sf/78+ZMB//jp59z5KqP6F7JpT3Vg/2OH92bZ1gNhzwVwxpElvLq6glPH9GfeNSdy1Lf/qWYYiZmZLXHOTQ27TcldxN+cMtNrA3/4umlc9cDiwLaNcy8Oe8xtT7zHX8q2Rn3NT54wnDs+OTnq40UOl9zVLCMZYcPuKp5btoMmn6O2IXTu0ZU7DnLtH98OrAcn9jkXHhXxnD++/BhOHdM/sH7koJ5snHsxcy48iksnD2Xj3ItD2ulbO3fioGh+FJEOUc1d0oZzjp+9tIpR/Yv42HHDyMnOorKukb+/u41v/b39xy1mTRvJuopKFm/YyyXHDuHXnz6+3WPqG3186U9LGD+4F7ddEP6L4IK7F9LQ5OOlW85g5Y6DfPJ3i6hpaGLDTy5Sd0eJiZplJOVV1TWyt6qe97cd4PxJg8nO6lzSq21o4vgfzqe6vqVW/odrT+TaP7x9mKNaTBxSzN9uOJX8nGwO1jZQXJDbqeuLJMPhkrsm65CkqW1o4kfPreDW88Yz5QfzA+XjB/XixVtmRDzuD69v4Kl3tvGLK6fwpzc3M+/1DWH3C07sXz57LBccPZjaBh/rdlXyzuZ9vLN5H7NnjOHSyUPJy2lpoVRil3Sgmrt0Geccizfs5ajBxWzZV80lv3ot4r5XnTyKH3706JCyqrpG7l24nl8uWBP2mNPHDuDh66ZRUVnHtNsXAHDtaaV8++KJAGR18q8BkVSnmrt0Gecc2/bXMPeFDxlUXMDuyjr+9/zxNPkcH7/nDXZX1nfoPA+/uYmdB2s5dUx/vv+PFYzsV8jmvdVt9svOMi46ZgjnTRzERyYPBWBgrwI2zr2YjburGNmvUEldMpJq7hIXL7y/g6/+9b2QNu/2TB83gJNH9+eOF1cB/i6HuyvruOff63jgtfBNLc3u+czxGhFRMp5q7tIhDU2+wATMwSoO1XGgpp6xA9sOPbu/uj6kvbxZ/6I89lSF1tL7Fuby0OdOYvzgXqyrqOSowb1wDso27uWbXtPJgJ75fOviCVxx4og2Y68M6JnHgq+cSa+CHNXGRdqhmrtQWdfIMd97keaPwlGDe3HvVSewcM1uvh3UhXD2jNFcceIIBhUXUJCTxe9f28DcFz4MbJ8wpJgLjx7M/5wzLuT8d7z4IcUFuVx/xpgu+XlEMoW6QkpEG3ZXcdbP/h3TOW4+dxw3n6vH6EW6mpplUsSBmgZe+mAn33vmAx667iRWlx/i3c37GF3Sk+tnjG73gRafz1HT0ERRfuf/2xqbfORkZ7Gvqp6i/BzycrJ4Y91uPn2/f/yUWdNG8NXzxuNzju//YwXPLdvBiH49uPuK45gyog/Ltu7n8t++0ea8w/v24Mtnj2tTLiLJpZp7gpRt3EuTz7Fs6wFuf35lh445d8JAvjJzPNf8YTG7DtUBMKxPD7btr2nTht1eX/Anlmzlr2VbeGvD3g5dO9L4Ka0553hz/V72VddH9bCRiMSPmmU8zjlqG3zk5WQlLCm9+MFOrn94ScTt37xoAmt3VfJ42RYA/vvMMTy9dDvb9tdEdb3/PX88/3XSKP5StoVnl21n097qwIxBkcycOIilW/bTqyCH71wykRnjSnSDUqQbSkpyN7MLgF8A2cDvnXNzI+0bbXKvqW9i2db9AFQ3NHGguoGe+Tn8fek2Ptx5iJKe+RTlZ7Nlbw3rd1diZtQ3+gB/z4v8nGzGD+7F9HEDaPI5hvctpCg/m6q6JqrqGulTmMv0cSVkZxkHahrYtKeKf324i817qxlcXADm9QqprMcBjU0u8LRk7x65zJo2ktL+hUwcWgzAMcN6R2x6+d2r61hTXsmT72zlmxdN4MJjBtMjN5t+RXnUNvg4445X+MZFE/joccNYX1HJ2Xe+2u778+DnpjFj3ABqG3z0yMvu9PsrIqmty5O7mWUDq4GZwFbgbWCWc25FuP2jTe5lG/fyiVZToAU7YVRfquoaMTNW7jjIiaV9OXP8wMBgUjsO1Hbw54Hgt6kwL5vGJofPORp9juwso8nnyMvOYtygnt6j7ontg/3q6gquntcyeuHg4gLOHF/C6eMGcMmxQxN6bRFJDclI7qcA33POne+tfx3AOfeTcPtHm9zrG328traC/JxsCnKz6FWQy76qekp65XPEgKJ2b1A2Nvk4UNMQePDmQE0De6rq6ZmfQ3FBDg8u2ohh9C3Ko29hLn0L8zhhVF9G9CsMOb5nQQ6GkZNlXd68UVXXSF5OVtj+6SKS3pLRW2YYsCVofStwUqugZgOzAUaOHBnVRfJysjj7qOjHxM7JzqJ/z3yaR+Qe0Wr7jz56TIeOT6Zoes6ISPpLWnXPOXefc26qc25qSYkmBhYRiadEJfdthFaEh3tlIiLSBRKV3N8GxpnZEWaWB1wJPJOga4mISCsJabB1zjWa2Y3Ai/i7Qs5zzn2QiGuJiEhbCbsb55x7Hng+UecXEZHI1H9ORCQNKbmLiKQhJXcRkTSUEgOHmVkFsKmLLzsA2N3F14wklWKB1IonlWKB1IonlWKB1IonU2IZ5ZwL+6BQSiT3ZDCzskiP7Xa1VIoFUiueVIoFUiueVIoFUisexaJmGRGRtKTkLiKShjI5ud+X7ACCpFIskFrxpFIskFrxpFIskFrxZHwsGdvmLiKSzjK55i4ikraU3EVE0lDaJHczG2Fmr5jZCjP7wMxu8sr7mdl8M1vj/dvXKzcz+6WZrTWzZWZ2fNC5RprZS2a20jtfaRJj+al3jpXePp2e6imKeI4ys0VmVmdmt7Y61wVmtsqLdU6yYol0nmS+N972bDN718yeTWYsZtbHzJ4wsw+9z84pSY7nFu8cy83sUTMrSHAsn/F+l943szfMbHLQuWL6DMcznnh9jsNyzqXFCxgCHO8t98I/h+tE4KfAHK98DvB/3vJFwAuAAScDbwWd69/ATG+5J1CYjFiAU4HX8Y+smQ0sAs7sgvdmIHAicDtwa9B5soF1wGggD3gPmJikWMKeJ1nvTdD5vgI8AjybzFiAB4HPe8t5QJ8kfm6GARuAHt76X4BrEhzLqUBfb/lCWn6nYv4MxzmeuHyOw8YYj5Ok4gt4Gv8E3auAIUFv5Cpv+V78k3Y377/K2z4ReC1FYjkFWAL0AAqBMmBCouMJ2u97rX5JTwFeDFr/OvD1ZMQS6TzJem+8suHAAuBsokjucfx/6o0/mVpXfo4PE0/ztJv98I9E+yxwXlfE4pX3BbYl6jMcSzyJ+hw759KnWSaY+ZtRjgPeAgY553Z4m3YCzZOuhpvndRhwJLDfzJ7y/ry+w8yykxGLc24R8Aqww3u96JxbGW0snYgnkkjvWTJiiXSeqMUhnruB2wBfLHHEIZYjgArgD95n+PdmVpSseJxz24CfAZvxf44POOde6sJYrsP/lzHE+TMch3ginSdmaZfczawn8CRws3PuYPA25/9qbK/vZw4wHbgV/5+Yo4FrkhGLmY0FJuCvEQ4Dzjaz6dHEEo944ilesRzuPF0Zj5ldAuxyzi2JNoZ4xYL/M3w8cI9z7jigCn8TQVLi8dqdL8P/pTMUKDKz/+qKWMzsLPzJ9GvRXK+r4onX5zhYWiV3M8vF/wb92Tn3lFdcbmZDvO1DgF1eeaR5XrcCS51z651zjcDf8f+iJCOWy4E3nXOVzrlK/N/2nb4xFkU8kcRlbtw4xRLpPJ0Wp3hOAy41s43AY/i/iP+UpFi2Aludc801wCeI4jMcx3jOBTY45yqccw3AU/jboBMai5kdC/weuMw5t8crjtv8znGKJ26f49bSJrmbmQEPACudcz8P2vQMcLW3fDX+Nq3m8s+a38n4/1TcgX/+1z5m1jzS2tnAiiTFshk4w8xyvA/AGUCnm2WiiCeSmOfGjVcshzlPp8QrHufc151zw51zpfjfl3855zpVO41jLDuBLWY23is6h05+huMZD/7P8clmVuid8xw6+TnubCxmNhL/l8hVzrnVQfvHZX7neMUTr89xWPFouE+FF3A6/j+BlgFLvddFQH/8N7nWAC8D/bz9DfgN/jvn7wNTg8410zvP+8AfgbxkxIL/zv69+H8RVgA/76L3ZjD+2t9BYL+3XOxtuwj/Hf11wDeTFUuk8yTzvQk655lE11smnv9PU/DfgF+G/6/PvkmO5/vAh8By4GEgP8Gx/B7YF7RvWdC5YvoMxzOeSOeJJqbWLw0/ICKShtKmWUZERFoouYuIpCEldxGRNKTkLiKShpTcRUTSkJK7iEgaUnIXEUlD/w8rx8sUSb50SgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Open'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2c079bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19691aac2b0>]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7eb13b86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4324, 6)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "21ac68f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['High', 'Low', 'Open', 'Close', 'Volume', 'Adj Close'], dtype='object')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4946287d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4324.000000</td>\n",
       "      <td>4324.000000</td>\n",
       "      <td>4324.000000</td>\n",
       "      <td>4324.000000</td>\n",
       "      <td>4.324000e+03</td>\n",
       "      <td>4324.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.133630</td>\n",
       "      <td>41.156866</td>\n",
       "      <td>41.672494</td>\n",
       "      <td>41.654590</td>\n",
       "      <td>1.115269e+08</td>\n",
       "      <td>41.654590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>52.328629</td>\n",
       "      <td>51.088523</td>\n",
       "      <td>51.749466</td>\n",
       "      <td>51.702590</td>\n",
       "      <td>9.277685e+07</td>\n",
       "      <td>51.702590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.315000</td>\n",
       "      <td>1.288000</td>\n",
       "      <td>1.304500</td>\n",
       "      <td>1.303500</td>\n",
       "      <td>1.762600e+07</td>\n",
       "      <td>1.303500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.554000</td>\n",
       "      <td>4.425000</td>\n",
       "      <td>4.490125</td>\n",
       "      <td>4.492500</td>\n",
       "      <td>6.249900e+07</td>\n",
       "      <td>4.492500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.313750</td>\n",
       "      <td>15.011000</td>\n",
       "      <td>15.197500</td>\n",
       "      <td>15.193500</td>\n",
       "      <td>8.998200e+07</td>\n",
       "      <td>15.193500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>72.480001</td>\n",
       "      <td>69.360252</td>\n",
       "      <td>71.309628</td>\n",
       "      <td>71.001127</td>\n",
       "      <td>1.318690e+08</td>\n",
       "      <td>71.001127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>188.654007</td>\n",
       "      <td>184.839493</td>\n",
       "      <td>187.199997</td>\n",
       "      <td>186.570496</td>\n",
       "      <td>2.086584e+09</td>\n",
       "      <td>186.570496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              High          Low         Open        Close        Volume  \\\n",
       "count  4324.000000  4324.000000  4324.000000  4324.000000  4.324000e+03   \n",
       "mean     42.133630    41.156866    41.672494    41.654590  1.115269e+08   \n",
       "std      52.328629    51.088523    51.749466    51.702590  9.277685e+07   \n",
       "min       1.315000     1.288000     1.304500     1.303500  1.762600e+07   \n",
       "25%       4.554000     4.425000     4.490125     4.492500  6.249900e+07   \n",
       "50%      15.313750    15.011000    15.197500    15.193500  8.998200e+07   \n",
       "75%      72.480001    69.360252    71.309628    71.001127  1.318690e+08   \n",
       "max     188.654007   184.839493   187.199997   186.570496  2.086584e+09   \n",
       "\n",
       "         Adj Close  \n",
       "count  4324.000000  \n",
       "mean     41.654590  \n",
       "std      51.702590  \n",
       "min       1.303500  \n",
       "25%       4.492500  \n",
       "50%      15.193500  \n",
       "75%      71.001127  \n",
       "max     186.570496  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e3a31930",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4324 entries, 2005-04-04 to 2022-06-03\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   High       4324 non-null   float64\n",
      " 1   Low        4324 non-null   float64\n",
      " 2   Open       4324 non-null   float64\n",
      " 3   Close      4324 non-null   float64\n",
      " 4   Volume     4324 non-null   int64  \n",
      " 5   Adj Close  4324 non-null   float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 236.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "02ecd67f",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainrows=df.iloc[:3000]\n",
    "testrows=df.iloc[3000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5c9102e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "window_size=40\n",
    "xtrain=[]\n",
    "ytrain=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "80a815e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 2960)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(len(trainrows)-window_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "25a9a13e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trainrows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e9dcbe66",
   "metadata": {},
   "outputs": [
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       " 2.861999988555908,\n",
       " 2.7885000705718994,\n",
       " 2.922499895095825,\n",
       " 2.5989999771118164,\n",
       " 2.3610000610351562,\n",
       " 2.4605000019073486,\n",
       " 2.4230000972747803,\n",
       " 2.315000057220459,\n",
       " 2.078000068664551,\n",
       " 2.246500015258789,\n",
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       " 1.9220000505447388,\n",
       " 1.7920000553131104,\n",
       " 1.7515000104904175,\n",
       " 1.8934999704360962,\n",
       " 2.125,\n",
       " 2.109499931335449,\n",
       " 2.197999954223633,\n",
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       " 2.570499897003174,\n",
       " 2.5625,\n",
       " 2.484999895095825,\n",
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       " 2.5625,\n",
       " 2.442500114440918,\n",
       " 2.631500005722046,\n",
       " 2.6589999198913574,\n",
       " 2.6040000915527344,\n",
       " 2.578000068664551,\n",
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       " 2.553999900817871,\n",
       " 2.572000026702881,\n",
       " 2.5889999866485596,\n",
       " 2.4700000286102295,\n",
       " 2.5380001068115234,\n",
       " 2.563999891281128,\n",
       " 2.7179999351501465,\n",
       " 2.703000068664551,\n",
       " 2.868000030517578,\n",
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       " 2.5959999561309814,\n",
       " 2.572499990463257,\n",
       " 2.424499988555908,\n",
       " 2.572000026702881,\n",
       " 2.5794999599456787,\n",
       " 2.421999931335449,\n",
       " 2.5269999504089355,\n",
       " 2.496999979019165,\n",
       " 2.5315001010894775,\n",
       " 2.4814999103546143,\n",
       " 2.421999931335449,\n",
       " 2.5179998874664307,\n",
       " 2.5,\n",
       " 2.940999984741211,\n",
       " 3.057499885559082,\n",
       " 3.179500102996826,\n",
       " 3.052999973297119,\n",
       " 3.1589999198913574,\n",
       " 3.327500104904175,\n",
       " 3.3355000019073486,\n",
       " 3.1654999256134033,\n",
       " 3.2174999713897705,\n",
       " 3.197999954223633,\n",
       " 3.1630001068115234,\n",
       " 3.0834999084472656,\n",
       " 3.117500066757202,\n",
       " 3.0975000858306885,\n",
       " 3.193000078201294,\n",
       " 3.0855000019073486,\n",
       " 3.2799999713897705,\n",
       " 3.185499906539917,\n",
       " 3.117000102996826,\n",
       " 3.239500045776367,\n",
       " 3.0994999408721924,\n",
       " 3.0850000381469727,\n",
       " 3.240499973297119,\n",
       " 3.2385001182556152,\n",
       " 3.0845000743865967,\n",
       " 3.0244998931884766,\n",
       " 3.2855000495910645,\n",
       " 3.427000045776367,\n",
       " 3.4790000915527344,\n",
       " 3.43149995803833,\n",
       " 3.3489999771118164,\n",
       " 3.567500114440918,\n",
       " 3.5625,\n",
       " 3.505000114440918,\n",
       " 3.497999906539917,\n",
       " 3.7790000438690186,\n",
       " ...]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainrows=trainrows['Close'].tolist()\n",
    "trainrows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "751f18c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19691218c70>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(trainrows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "22f207d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(trainrows)-window_size):\n",
    "    xtrain.append(trainrows[i:i+window_size])\n",
    "    ytrain.append(trainrows[i+window_size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "34cb2527",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain=np.array(xtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9fff3cd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.75      ,  1.76400006,  1.74100006, ...,  1.76250005,\n",
       "         1.77250004,  1.77499998],\n",
       "       [ 1.76400006,  1.74100006,  1.745     , ...,  1.77250004,\n",
       "         1.77499998,  1.77550006],\n",
       "       [ 1.74100006,  1.745     ,  1.73000002, ...,  1.77499998,\n",
       "         1.77550006,  1.82500005],\n",
       "       ...,\n",
       "       [38.25749969, 37.49349976, 37.68349838, ..., 42.60950089,\n",
       "        42.26200104, 42.43199921],\n",
       "       [37.49349976, 37.68349838, 37.85900116, ..., 42.26200104,\n",
       "        42.43199921, 42.2519989 ],\n",
       "       [37.68349838, 37.85900116, 39.02249908, ..., 42.43199921,\n",
       "        42.2519989 , 42.65399933]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "919b033b",
   "metadata": {},
   "outputs": [
    {
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       " ...]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fe4d2cd3",
   "metadata": {},
   "outputs": [
    {
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       " 159.03700256347656,\n",
       " ...]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testrows=testrows['Close'].tolist()\n",
    "testrows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "35218849",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtest=[]\n",
    "ytest=[]\n",
    "for i in range(len(testrows)-window_size):\n",
    "    xtest.append(testrows[i:i+window_size])\n",
    "    ytest.append(testrows[i+window_size])\n",
    "xtest=np.array(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "17ad4faa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 42.49399948,  42.33050156,  42.30099869, ...,  45.46450043,\n",
       "         45.91899872,  46.24950027],\n",
       "       [ 42.33050156,  42.30099869,  42.52500153, ...,  45.91899872,\n",
       "         46.24950027,  47.41149902],\n",
       "       [ 42.30099869,  42.52500153,  42.65000153, ...,  46.24950027,\n",
       "         47.41149902,  47.34700012],\n",
       "       ...,\n",
       "       [168.34649658, 164.05499268, 158.7559967 , ..., 111.07749939,\n",
       "        115.14649963, 120.20950317],\n",
       "       [164.05499268, 158.7559967 , 157.78450012, ..., 115.14649963,\n",
       "        120.20950317, 121.68399811],\n",
       "       [158.7559967 , 157.78450012, 154.460495  , ..., 120.20950317,\n",
       "        121.68399811, 125.51100159]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "70d6847d",
   "metadata": {},
   "outputs": [
    {
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       " 166.7344970703125,\n",
       " ...]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1801e244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2960, 40)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "75996c77",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Input, Dense, SimpleRNN\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.losses import MeanAbsoluteError\n",
    "from tensorflow.keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ef34158d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential([\n",
    "    SimpleRNN(100,input_shape=(window_size,1),return_sequences=True),\n",
    "    SimpleRNN(10),\n",
    "    Dense(100,activation='relu'),\n",
    "    Dense(1,activation='relu')\n",
    "              \n",
    "]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "a7b24500",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=model.compile(loss=MeanAbsoluteError(),optimizer=SGD(),metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "416cee76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " simple_rnn (SimpleRNN)      (None, 40, 100)           10200     \n",
      "                                                                 \n",
      " simple_rnn_1 (SimpleRNN)    (None, 10)                1110      \n",
      "                                                                 \n",
      " dense (Dense)               (None, 100)               1100      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12,511\n",
      "Trainable params: 12,511\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0f6ae9d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "93/93 [==============================] - 4s 20ms/step - loss: 6.2773 - mae: 6.2773\n",
      "Epoch 2/50\n",
      "93/93 [==============================] - 2s 19ms/step - loss: 2.2819 - mae: 2.2819\n",
      "Epoch 3/50\n",
      "93/93 [==============================] - 2s 19ms/step - loss: 1.4199 - mae: 1.4199\n",
      "Epoch 4/50\n",
      "93/93 [==============================] - 2s 19ms/step - loss: 1.2369 - mae: 1.2369\n",
      "Epoch 5/50\n",
      "93/93 [==============================] - 2s 19ms/step - loss: 1.1161 - mae: 1.1161\n",
      "Epoch 6/50\n",
      "93/93 [==============================] - 2s 19ms/step - loss: 0.9375 - mae: 0.9375\n",
      "Epoch 7/50\n",
      "93/93 [==============================] - 2s 20ms/step - loss: 0.8741 - mae: 0.8741\n",
      "Epoch 8/50\n",
      "93/93 [==============================] - 2s 21ms/step - loss: 0.8698 - mae: 0.8698\n",
      "Epoch 9/50\n",
      "93/93 [==============================] - 2s 20ms/step - loss: 0.8050 - mae: 0.8050\n",
      "Epoch 10/50\n",
      "93/93 [==============================] - 2s 19ms/step - loss: 0.7351 - mae: 0.7351\n",
      "Epoch 11/50\n",
      "93/93 [==============================] - 2s 19ms/step - loss: 0.7563 - mae: 0.7563\n",
      "Epoch 12/50\n",
      "93/93 [==============================] - 2s 19ms/step - loss: 0.7323 - mae: 0.7323\n",
      "Epoch 13/50\n",
      "93/93 [==============================] - 2s 19ms/step - loss: 0.6904 - mae: 0.6904\n",
      "Epoch 14/50\n",
      "93/93 [==============================] - 2s 20ms/step - loss: 0.6732 - mae: 0.6732\n",
      "Epoch 15/50\n",
      "93/93 [==============================] - 2s 19ms/step - loss: 0.6517 - mae: 0.6517\n",
      "Epoch 16/50\n",
      "93/93 [==============================] - 2s 20ms/step - loss: 0.6593 - mae: 0.6593\n",
      "Epoch 17/50\n",
      "93/93 [==============================] - 2s 19ms/step - loss: 0.6393 - mae: 0.6393\n",
      "Epoch 18/50\n",
      "93/93 [==============================] - 2s 21ms/step - loss: 0.6511 - mae: 0.6511\n",
      "Epoch 19/50\n",
      "93/93 [==============================] - 2s 21ms/step - loss: 0.6264 - mae: 0.6264\n",
      "Epoch 20/50\n",
      "93/93 [==============================] - 2s 20ms/step - loss: 0.6266 - mae: 0.6266\n",
      "Epoch 21/50\n",
      "93/93 [==============================] - 2s 20ms/step - loss: 0.6220 - mae: 0.6220\n",
      "Epoch 22/50\n",
      "93/93 [==============================] - 2s 20ms/step - loss: 0.5913 - mae: 0.5913\n",
      "Epoch 23/50\n",
      "93/93 [==============================] - 2s 20ms/step - loss: 0.5872 - mae: 0.5872\n",
      "Epoch 24/50\n",
      "93/93 [==============================] - 4s 38ms/step - loss: 0.6196 - mae: 0.6196\n",
      "Epoch 25/50\n",
      "93/93 [==============================] - 3s 30ms/step - loss: 0.5167 - mae: 0.5167\n",
      "Epoch 26/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.5710 - mae: 0.5710\n",
      "Epoch 27/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.5656 - mae: 0.5656\n",
      "Epoch 28/50\n",
      "93/93 [==============================] - 3s 29ms/step - loss: 0.5371 - mae: 0.5371\n",
      "Epoch 29/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.5064 - mae: 0.5064\n",
      "Epoch 30/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.5445 - mae: 0.5445\n",
      "Epoch 31/50\n",
      "93/93 [==============================] - 3s 29ms/step - loss: 0.5085 - mae: 0.5085\n",
      "Epoch 32/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.5179 - mae: 0.5179\n",
      "Epoch 33/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.5146 - mae: 0.5146\n",
      "Epoch 34/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.5312 - mae: 0.5312\n",
      "Epoch 35/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4737 - mae: 0.4737\n",
      "Epoch 36/50\n",
      "93/93 [==============================] - 3s 27ms/step - loss: 0.4917 - mae: 0.4917\n",
      "Epoch 37/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4910 - mae: 0.4910\n",
      "Epoch 38/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4590 - mae: 0.4590\n",
      "Epoch 39/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4910 - mae: 0.4910\n",
      "Epoch 40/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4538 - mae: 0.4538\n",
      "Epoch 41/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4550 - mae: 0.4550\n",
      "Epoch 42/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4892 - mae: 0.4892\n",
      "Epoch 43/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4912 - mae: 0.4912\n",
      "Epoch 44/50\n",
      "93/93 [==============================] - 3s 29ms/step - loss: 0.4758 - mae: 0.4758\n",
      "Epoch 45/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4712 - mae: 0.4712\n",
      "Epoch 46/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4555 - mae: 0.4555\n",
      "Epoch 47/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4198 - mae: 0.4198\n",
      "Epoch 48/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4504 - mae: 0.4504\n",
      "Epoch 49/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4894 - mae: 0.4894\n",
      "Epoch 50/50\n",
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4603 - mae: 0.4603\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1969ad36ee0>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrain.tolist(),ytrain,epochs=50,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37188058",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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