Stock_Market_Prediction_using_ML.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/yashlad27/LSTM_basics/blob/main/Stock_Market_Prediction_using_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "o1MIe8_cP_s-"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.layers import LSTM, Dense, Dropout\n",
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import matplotlib.dates as mandates\n",
        "\n",
        "from sklearn import linear_model\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "import keras.backend as K\n",
        "from keras.callbacks import EarlyStopping\n",
        "\n",
        "from keras.optimizers import Adam\n",
        "from keras.models import load_model\n",
        "from keras.layers import LSTM\n",
        "from keras.utils.vis_utils import plot_model"
      ],
      "metadata": {
        "id": "RPhe_k4CQGUn"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Code to read csv file into Colaboratory:\n",
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "# Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "metadata": {
        "id": "qyGTqNuDQMys"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
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      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
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          "height": 73
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              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
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              "\n",
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              "\n",
              "scope.google = scope.google || {};\n",
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            ]
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving MSFT.csv to MSFT (1).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "df2 = pd.read_csv(io.BytesIO(uploaded['MSFT.csv']))"
      ],
      "metadata": {
        "id": "jT7UF1tBSQAa"
      },
      "execution_count": 14,
      "outputs": []
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      "execution_count": 15,
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              "            Date        Open        High         Low       Close   Adj Close  \\\n",
              "0     1986-03-13    0.088542    0.101563    0.088542    0.097222    0.061434   \n",
              "1     1986-03-14    0.097222    0.102431    0.097222    0.100694    0.063628   \n",
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              "3     1986-03-18    0.102431    0.103299    0.098958    0.099826    0.063079   \n",
              "4     1986-03-19    0.099826    0.100694    0.097222    0.098090    0.061982   \n",
              "...          ...         ...         ...         ...         ...         ...   \n",
              "9078  2022-03-18  295.369995  301.000000  292.730011  300.429993  300.429993   \n",
              "9079  2022-03-21  298.890015  300.140015  294.899994  299.160004  299.160004   \n",
              "9080  2022-03-22  299.799988  305.000000  298.769989  304.059998  304.059998   \n",
              "9081  2022-03-23  300.510010  303.230011  297.720001  299.489990  299.489990   \n",
              "9082  2022-03-24  299.140015  304.200012  298.320007  304.100006  304.100006   \n",
              "\n",
              "          Volume  \n",
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              "2      133171200  \n",
              "3       67766400  \n",
              "4       47894400  \n",
              "...          ...  \n",
              "9078    43317000  \n",
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              "      <th>0</th>\n",
              "      <td>1986-03-13</td>\n",
              "      <td>0.088542</td>\n",
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              "      <td>1986-03-17</td>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fa02e7c0-f201-423c-9d38-7f4e4aa0d37b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fa02e7c0-f201-423c-9d38-7f4e4aa0d37b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check for null values \n",
        "print(\"Dataframe shape: \", df2.shape)\n",
        "print(\"Null values present: \", df2.isnull().values.any())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WiqD5FwTSefX",
        "outputId": "b9de663f-da24-4756-9478-1241a3f70147"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataframe shape:  (9083, 7)\n",
            "Null values present:  False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# adjusted close value: \n",
        "# final output value that will be forecasted \n",
        "\n",
        "df2[\"Adj Close\"].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "AnZjA-_4S0nX",
        "outputId": "33b33728-fe71-4895-b120-6ae096f4b904"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc1e0eb4910>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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YQSmMpu2U3EVEYrjp8ZX1y1PPHNzMnulJzTIiIhlIyV1EJAMpuYuIZCAldxGRDKTkLiLSwLw3tqQ6hLgpuYuINPDDZ9fVL7/27UtTF0gclNxFRJrwL1NOpWhAj1SH0S5K7iLS5S15fx9Fc56j5OAxAK6dNBKAr116UirDiouSu4h0ebPmLgHgzyt3cKQyxGNLtgFg1glm5WiC7lAVkS6tNhjhEeBnL73Hz156L4XRdBzV3EWkSxt92/OpDiEhWkzuZvawme0xszURZf3NbJGZbQqe+wXlZmb3m1mxma02swmJDF5EJFHu+8y4VIcQl9bU3B8BpjYomwMsdvcxwOJgHWAaMCZ4zAYe6JgwRUSSqyaiuaYzajG5u/vrwP4GxTOAR4PlR4GZEeW/97AlQF8z63zDqYlIl7Blb3mT2yaM7JfESDpee9vcB7p73WDHu4CBwfJQYHvEfjuCskbMbLaZLTez5aWlpe0MQ0Sk/RauCaexwX0KGm0rOqF7ssPpUHFfUHV3B9r8+8Xd57r7RHefWFhYGG8YIiJtcqC8ip++sBGA7310LJeMGRC1vTN3g4T2J/fddc0twXPdLLIlwPCI/YYFZSIiaWVfeVX98llD+/DYDRfUr5/YKz8VIXWo9ib3BcD1wfL1wDMR5dcFvWYmAYcimm9ERNLGNQ8tqV8ubJDMR3XSIQcitaYr5B+B/wVONbMdZnYDcA9wpZltAq4I1gGeB94HioGHgK8lJGoRkXYqq6imaM5z7CmrBOBj5wyhIDcbgInBRdRbrhiTsvg6Sot3qLr7Z5vYNDnGvg7cFG9QIiKJctYdL0Wt/+qz4+uXLzp5AMu3HWBY3859MRU0/ICIdCFlFdXNbr9l8hj+ceIwhvXr/Mldww+ISJex90hV1Pq0MwdFrWdlWUYkdlByF5Eu5IllH0St39vJhxhojpK7iHQZv3n9/aj1ugupmUjJXUS6hFBNbdT6L2dlbq0dlNxFpIs4+faF9cv9uucyY1zMkVEyhnrLiEjGOxBxN+qXLhrFbdNPS2E0yaGau4hkvOqIJpkLRvcnJzvzU1/mf0IR6fL2Hw3X3G+4eBRTzhjUwt6ZQcldRDLeW8X7ADivqHOP0d4WSu4iktE27DrMD55dB8BVY7tGrR10QVVEMpS785m5S1i25fhEcllZnXuM9rZQzV1EMtLDb26NSuw/mHFGCqNJPiV3Eck47s4Pg6YYgItPHsB1FxalLqAUUHIXkYzzP6uj5wj6zy9f0MSemUvJXUQyzoqtx5tjfnPtuSmMJHV0QVVEMsbv3tzCnf9zvDlm84+mk92FLqJGUs1dRDLC6++VRiX280f177KJHVRzF5EMUFZRzXUPL6tf/9M/fYhzR3adG5ZiUXIXkU5v0o8W1y8vuPkizh7WN4XRpIe4kruZbQXKgBog5O4Tzaw/8CRQBGwFPu3uB+ILU0Sksb8fPMaH7nklqkyJPawj2twvc/dx7j4xWJ8DLHb3McDiYF1EJC5lFdVs338UgJfW7uJwRXWjxP7X71yWitDSUiKaZWYAlwbLjwKvAf+agOOISBexYddhpt73VwBGD+jB+3vLG+3zXzdeyPD+mTG5dUeIN7k78JKZOfAbd58LDHT3ujsIdgEDY73QzGYDswFGjBgRZxgikqlCNbX1iR2Imdi33nN1MkPqFOJN7he7e4mZnQgsMrMNkRvd3YPE30jwh2AuwMSJE2PuIyJSVhFqdvvnJ6lyGEtcbe7uXhI87wH+DJwP7DazwQDB8554gxSRrqu6wcTWDX307CFJiqRzaXdyN7MeZtarbhm4ClgDLACuD3a7Hngm3iBFJHPV1Dof/umrTLzrZapCtWzZW07RnOe4Y8Falm3Zz8oPwp3t7vjYWHoX5DCsXzce/PzxIQXGDumdqtDTmrm3r0XEzEYTrq1DuHnncXe/28xOAJ4CRgDbCHeF3N/E2wDhZpnly5e3Kw4R6bxeWLOLG/9zRVTZt686hZ+99F6jfT8/aQR3zTwrWaF1Cma2IqKnYpR2t7m7+/vAOTHK9wGT2/u+ItI17D5c0SixA/xpZUnM/e/8+JmJDimjaGwZSSsbd5Xxi0WNa22SeS6IuKs00pYYvWGALj1OTHsouUtamXLf69y/eBPPv7uTPYcrUh2OdLCqUC0vrNnJeXe/HFXeIy+bb1xxSlTZ+z+aXr/82+titjxIMzS2jKSlr/1hJQAbfjiVgtzsFEcjHeWU7y6MWr/5spP5+hVjqHX4y3ulUduysowFN1/EC2t2ccXYmLfLSDOU3CWt9MjLpryqpn598fo9XH324BRGJPGKddEU4B8mDOPbU06tX79y7EBWfPcK+nTLrW+COXtYX40V005K7pISoZpassxYXXKImb9+kylnDGTW+SOoDEX3ae6Wp5bDzuiDfUd5Z8dBPnr24JiJ/a/fuSzmUAEn9MxPRnhdgpK7pMTJt0f/PH9x7W5eXLu70X7ZWUrunc3Eu15m75FKAP7fH99utF1DBSSH/udIUpVVVHPLE43/w0ca3r9b/fL8lTsSHVJaKi2r5MG/bGbhuzupqK5p+QVJUBWqpeTgMZq7N6YqVFuf2CP17Z7LI188jw0/nJrIECWCau6SVL96pZhnVv292X0e//IkXtmwh+8vWMtrG0spmvMcv7n2XKacMShJUabez1/ayBN/2w7AySf25OVvfiSl8bh71MXQS08t5LWN4QugL379w5w6qBcAbxSXxnz9stuuIC9Hdclk0tmWpJr7+vsxy88Y0pstP57OstsnM7x/d665IDwY1KFj1QB89bEVMWuM7s77pUeoqc2MsefKK0NUhWrZFdENtHjPEfYdqeTVjW0fpmnZlv0UzXmOu55dx8d+9QZFc55r17ma9su/Rq3XJXYId199bvVO/vz2Dr70SPhO8wU3X8T5Rf355IShrPvBFCX2FGj38AMdScMPdA01tc5Jtz3fqLypi2tFc56LWi/slU9pWSWTTzuReV84j71HKpl41/H+0m/NuZx+3fP4/oI1LF6/hyW3TSY3Oz2TSmWohq/8fgWvB93/brh4FPPe2NLi6wb0zGfZbZPJasUNPT95YQMPvLa5Ufm/TDmVmy47GYAD5VVc8KPFVNXUMmpAD1799qUAfPPJVZw2uBdvbd5HbnYWi9aFr4f8+zXjufnx5pvVADbeNZX8HHVhTbTmhh9QcpekufDHi9l5KFwjfe6fL+aMIX2a3b9hco90Yq989pQ1btttqKk/HPFyd8ysUVnxniOMOKE7r24oZV95JbW14bILTxrA1DPDzUqL1+/mhkdb931/cvYkPjN3ScxtN1w8iu99dGzM2GpqvdFF64Ye+NwE/im4n6A1pp81iP/43LkcOlbN4WPVFPbKJzvLGBNxnE9PHMY9nzy7VX98JH5K7pIyz6wqYcW2A3zrqlM5586XAPjdF87jstNObPG1D7y2mZ+8EJ4i4JPjhzL/7dhjjnTLzeZYMxcdi++eRk5Qg6+pdX747Do+ds5gzhzaJ6p2efBoFeN+sIjeBTksuW0yC9/dxczxQ8nOMg5XVHPTH1Zy98yzuPXPq3mzeB8AJxX24LtXj+UnL2xgw66yZj/Pk7MnYWZ8+jf/W1927aSRPLZkGwCPf/kC3ijey38Ete3NP5pOqLaWU7/7QpPv2fAmr+I9ZVzxi9cbff4/LP2AayeNZHSMX04At08/nbufX99s/M3dUBbrj50knpK7pERFdQ2nfa9xYmpLV7jHl37ApacW0rd7LmP/7cWY+2y952qu+MVfKN5zBIBRA3rEHJ9kzrTTuGdh1HwyfOycIWzaXdZiYu5oV44dyEPBLfVrSg7Rp1tuk78wQjW11Drk5WRx42MreGHtrqjtkeez4a+dZ266iHOGH78J6LnVO7np8eja+sJbLuH0wb1ZsW0/K7cdjEryj91wPr0KchlzYk965Kv/RbpRcpek+9enV/P0yh2NLt594UNF3PHxM9r1nk0102y95+qo9vctPw6PSTJ/ZQnf+q932nWslsz/2oc4fVBv5r3xfv3wtF/9yGhmjhvKM6v+ztMrtrP8u1cC4fb1yNp3Rwxd2/BcPP6VC7jmoaVRZZvunhbzmsPqHQcZ0b87PfJz0vaahLSOkrskjbsz6tbon/6DehfU9/6I5waWNSWHeGHNLv791WIg3KTx1Y+MZli/cI332nlLueqMQVw7aWT9a6be93qjWvlpg3rFrKn/9B/O5qozBrJ+Zxk7Dx3jE+OHcur3XmBInwL+/ZoJnDm0+WsEzdm2r5xZc5fw+Ukj6y9mxuNIZYgzvx/7l8xr376UogE94j6GpD8ld0mI6ppaLvjRYm78yGj+8dzh/GnlDjbtPsKTy7dH7bflx9PZsrecvJys+kTcXlv3lnPpz15j5rgh3DdrfKtfd/YdL3I4mIvzr9+5jGH9uvHYkm2cNbQP40f0iyumVLl1/mr+uCz6XD//z5doZqIuRMldOlxLPT7OHdmPx79yQUK6w1WGatTNLsL8lTv45lPvMOWMgfzmWg2N25UkZCYm6ZruWLCWp5Zv52hV071T/u2jY/nSxaMSFoMSe7RPThjGJ8YPVW8ViaLkLq2yavtBZv76zaiyS08t5HdfOK8+qVTX1PLB/qOcVNgzFSF2aUrs0pCSexe29u+HOGVgL3Kzs6itdbKyjPLKEO/sOMi7Ow4xf2UJG3eXNdmP/JEvnh+1npudpcQukiYSltzNbCrwSyAb+K2735OoY0k0d8cdKkO17D9aRWlZJRXVNeTlZLF1bzmLN+xh2Zb9lLbiDk+AY9U1jB7Qgy9eVMTnJ41ULVGkE0hIcjezbODXwJXADuBvZrbA3dcl4nipUHeLd6g2/FxRXcPRqvAjPyeLgtxsqkK1HK0Osf9IFQePVVMdTFAB4a5s+8urOFxRzba9R9lXHk60BbnZ9C4Iz0RTXhmirDLE0aoQ1SGne342x6pqcIfe3XLo1z2PwxXV5OVkUxWq4VhVDaFaZ/fhCvaVV9HctfLeBeF/+o+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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting the Target Variable and Selecting the Features:\n",
        "\n",
        "# 1. Target\n",
        "output_var = pd.DataFrame(df2['Adj Close'])\n",
        "\n",
        "# features:\n",
        "features = ['Open', 'High', 'Low', 'Volume']"
      ],
      "metadata": {
        "id": "yerCMyxbTHU3"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling:\n",
        "\n",
        "scalar = MinMaxScaler()\n",
        "feature_transform = scalar.fit_transform(df2[features])\n",
        "\n",
        "feature_transform = pd.DataFrame(columns=features, \n",
        "                                 data=feature_transform,\n",
        "                                 index=df2.index)\n",
        "\n",
        "feature_transform.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WW48WhBgTfxO",
        "outputId": "196eaaee-4899-4519-ae5b-6d0571e1fdc3"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Open      High       Low    Volume\n",
              "0  0.000000  0.000027  0.000000  1.000000\n",
              "1  0.000025  0.000030  0.000025  0.297096\n",
              "2  0.000035  0.000032  0.000036  0.127119\n",
              "3  0.000040  0.000032  0.000030  0.063588\n",
              "4  0.000033  0.000025  0.000025  0.044285"
            ],
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              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000027</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.297096</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>0.000035</td>\n",
              "      <td>0.000032</td>\n",
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              "      <td>0.127119</td>\n",
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              "      <td>0.000040</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.063588</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>0.000033</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.000025</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training and Testing data set for model:\n",
        "\n",
        "timesplit = TimeSeriesSplit(n_splits=10)\n",
        "\n",
        "for train_index, test_index in timesplit.split(feature_transform):\n",
        "  X_train, X_test = feature_transform[:len(train_index)], feature_transform[len(train_index): (len(train_index)+len(test_index))]\n",
        "  y_train, y_test = output_var[:len(train_index)].values.ravel(), output_var[len(train_index):(len(train_index)+len(test_index))].values.ravel()\n"
      ],
      "metadata": {
        "id": "7ICGQtxcUH4Z"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# DataPreprocessing from LSTM\n",
        "trainX = np.array(X_train)\n",
        "testX = np.array(X_test)\n",
        "\n",
        "X_train = trainX.reshape(X_train.shape[0], 1, X_train.shape[1])\n",
        "X_test = testX.reshape(X_test.shape[0], 1, X_test.shape[1])"
      ],
      "metadata": {
        "id": "rKcXF5t9VXM-"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Building the LSTM Model for Stock Market Prediction\n",
        "\n",
        "lstm = Sequential()\n",
        "\n",
        "lstm.add(LSTM(32, input_shape=(1, trainX.shape[1]), activation='relu', return_sequences=False))\n",
        "lstm.add(Dense(1))\n",
        "lstm.compile(loss=\"mean_squared_error\", optimizer=\"adam\")\n",
        "plot_model(lstm, show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "fywA79PFW0Xh",
        "outputId": "0d9578a3-0c98-4171-d72e-e021c7ce968a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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q1SpMmDBBY+1euHABP/zwQ6fyNWvWYPz48Zg9ezba2toAPL7rQ/XkWw8PD43FwJuklpycjIqKCn2HwVva7F9DXXc3btzAli1b8NFHH0EkEnWa7uPjg5CQENy5cwcbNmzQQ4T9N378eKSnp2PJkiW9fgv9syiVSmzcuBGJiYldTo+KikJeXl630zVFa0ktKSkJUqkUEokER48exWuvvQa5XI5hw4bh4MGDAB7/pxOJRLCzs8PKlSvh6OgIkUgEHx8fXL58GQAQHBwMU1NTODg4cG2vXr0aUqkUAoEAVVVVCAkJwfr161FSUgKBQAB3d/c+x/vtt9/Cy8sLFhYWEIlEGDduHL7++msAwNtvv80d9ysUCu5FtStWrIBEIoGFhQWOHTuG9vZ2REZGwtnZGWKxGC+88AJ3u84nn3wCiUQCmUyGiooKrF+/Hk5OTigqKhpQPz8LESE+Ph6jR4+GmZkZrKysMH/+fFy/fh1A//tX2+vuq6++0vu7QHft2gUigq+vb7d1YmJiMHLkSHzxxRc4c+ZMt/WetR5683sB0OM2pm8RERFYvXo194b2p1lZWWHGjBlITEzU7pOJnz4e1eQ5tYiICAJAZ8+epdraWqqoqKDp06eTVCqllpYWInp8PkMqldK1a9eoubmZCgsLafLkySSTyaisrIyIiJYsWUL29vZq89u5cycBoMrKSiIi8vf3J4VC0Smu3p5TO3z4MEVFRdHDhw/pwYMHNGXKFLVjfH9/fzI2NqY7d+6ofe+NN96gY8eOERHRhg0byMzMjI4cOULV1dUUHh5ORkZGdOXKFbX+WLNmDe3evZsWLFhA//d///fM2FT6c04tMjKSTE1Naf/+/VRTU0P5+fnk7e1NNjY23HtH+9u/2lx3J06cIJlMRtHR0X1aXk2+Is/NzY28vLy6/I5CoaCbN28SEdGFCxfIyMiIRowYQQ0NDUTU+Zxab9ZDb34vz9rG+uPFF18c8Dm1nJwc8vX1JaKe3/sZFhZGACg3N1etfNC9Is/HxwdyuRy2trYIDAxEY2MjysrKuOkmJibcfzAvLy8kJSWhvr4eKSkpuggPwOO7/T/88ENYWVnB2toavr6+ePDgASorKwEAq1atQnt7u1pMdXV1uHLlCmbPno3m5mYkJSXBz88P/v7+sLS0xObNmyEUCjstx44dO/Dee+8hPT0dnp6eWlsmpVKJ+Ph4LFiwAEuXLoWFhQXGjRuHvXv3oqqqCvv27RvwPLS17ubMmYO6ujps2bJlwDH2R2NjI27evAmFQvHMulOnTsXatWtRWlqKTZs2dZre1/XQ3e+lL9uYLimVSoSEhCApKemZdVXnzgoKCrQWj87PqZmamgIAWltbu60zadIkSCQSbtdcH1Sv8VJdhv7tb3+LkSNH4i9/+Qu363zo0CEEBgbC2NgYRUVFaGpqwtixY7k2xGIxHBwc9LYchYWFaGhowKRJk9TKJ0+eDFNTU+4wUZMMYd1pQkVFBYio16+Hi4mJwahRo7Bnzx7k5OSoTRvIenjy92KI2xgAhIeH45133oGTk9Mz66r68/79+1qLx2AvFJiZmXF7Sbpw8uRJvPzyy7C1tYWZmRk++OADtekCgQArV67ETz/9hLNnzwIA/va3v+H//b//B+Dxf3YA2Lx5s9rYm1u3bqGpqUlny/GkmpoaAIC5uXmnaZaWlqivr9fKfHW97rShubkZAHp9El0kEiElJQUCgQBvvfUWlEolN01T68EQt7GcnBwUFBTg7bff7lV9sVgM4Jf+1QaDTGqtra2oqanBsGHDtDqfb775BgkJCSgrK4Ofnx8cHBxw+fJl1NbWIjY2tlP95cuXQyQS4YsvvkBRURHkcjlcXFwAgDs5mpCQACJS+1y8eFGry9EdS0tLAOjyR6Ot/tXVutM21Y+vL2+lnzp1KtatW4fi4mJs3bqVK9fUejDEbSw5ORlnz57lBt8LBAIuzm3btkEgEOC7777j6re0tAD4pX+1wSCTWnZ2NogIU6ZMAfD4vE1Ph6v99Z///AdSqRQFBQVobW3Fu+++Czc3N4hEoi6fnGplZYXFixcjMzMTcXFx+OMf/8hNGz58OEQiEfLy8jQeZ3+NHTsW5ubmahsVAFy+fBktLS2YOHEiAM32r67WnbbZ2dlBIBD0efzZ1q1b4enpyV0hB3q/Hp7FELexlJSUTglWtZceEREBIlI77Fb1p729vdZiMoik1tHRgerqarS1tSE/Px8hISFwdnbG8uXLAQDu7u54+PAhMjMz0draisrKSty6dUutDWtra9y9exelpaWor6/v8YfU2tqK+/fvIzs7G1KpFM7OzgAe3wrT3NyM4uLibs9zrFq1Co8ePcKJEycwd+5crlwkEmHFihU4ePAgkpKSUFdXh/b2dpSXl+Pnn38eYA/1j0gkwvr165GRkYEDBw6grq4OBQUFWLVqFRwdHREUFARgYP2rrXWXlZWl1yEdEokEbm5uKC8v79P3VIehxsbGamW9WQ+9aftZ21hgYCDs7e01cmuWJttSUfXnuHHjNNZmJ09fDu3rJfFLly7RmDFjyMjIiACQg4MDbdu2jfbs2UMSiYQAkIeHB5WUlNC+fftILpcTAHJxcaEff/yRgoKCSCgUkpOTE5mYmJBcLqf58+dTSUkJN48HDx7QK6+8QiKRiFxdXen999+njRs3EgByd3ensrIyunr1Krm4uJBYLKZp06bRZ599RgqFggD0+MnIyCAiotDQULK2tiZLS0sKCAigTz/9lACQQqHghieo/OpXv6KwsLBOffHo0SMKDQ0lZ2dnMjExIVtbW/L396fCwkKKjY0lsVhMAGj48OG0f//+XvexSn+GdHR0dNDOnTvJw8ODhEIhWVlZkZ+fHxUVFQ2of+/du6e1dXfv3j06deoUyWQyiomJ6dPyanJIR3BwMAmFQmpqauLKMjIyuO3KxsaG3nvvvS7b3Lhxo9qQjmeth97+XnraxoiI/Pz8CABFRkb2uMwXL16kl156iRwdHbnfgoODA/n4+ND58+f71NbTehrSMWfOHHJycqKOjg61ck0O6dDYOLX+Ut13N5jMnj2bfvrpJ53P19Du/TTEdafJpFZcXEwmJib9+gekL+3t7TR9+nRKTk42qLaIiKqqqkgkElFcXFynaYNunNqz9OVkrD48eSibn58PkUgEV1dXPUZkOAx93fWWUqnE119/jeLiYu5ktru7O6KjoxEdHY2GhgY9R/hs7e3tyMzMRH19PQIDAw2mLZWoqChMmDABwcHBAB7fZXH37l3k5OTgxo0bGpkHYCDn1AxdaGgoiouL8eOPP2LFihVqV7YYfnj48CF3Q/tbb73FlYeFhSEgIACBgYEGf9N6dnY20tPTkZWV1evxdbpoCwDi4+ORl5eHU6dOcWNAjx49yt3QfvLkyQHPg/P0rpsuDz/DwsLI1NSUANCIESPo8OHDOplvX0VERJCRkRENHz6cuyVKHwzp8NNQ1522tt+vv/6aQkNDNd7u8yAzM5O2b99ObW1tGm23u9+DgEj9ztK0tDQsXrxYuzecMv0SEBAAoPtXgzFs+32edPd7YIefDMPwCktqDMPwCktqDMPwCktqDMPwikl3E9LS0nQZB9MLqltM2LrpnurGbtZH/FdeXt71wwCevhyquiTOPuzDPuxj6J9eDelgGG0QCARITU3FokWL9B0Kw3PsnBrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLzCkhrDMLxiou8AGP75/PPP8fDhw07lR48exc2bN9XKVqxYATs7O12FxjwHBERE+g6C4ZeVK1fif/7nf2BmZtZtndbWVlhZWeHevXswMWH/WxnNYYefjMa9/vrrAIBHjx51+zE2NsYbb7zBEhqjcWxPjdE4IoKTkxN+/vnnHutduHABU6dO1VFUzPOC7akxGicQCLBkyRKYmpp2W2fo0KGYMmWKDqNinhcsqTFa8frrr6OlpaXLaaampvjDH/4AgUCg46iY5wE7/GS0xsPDAzdu3OhyWn5+PsaNG6fjiJjnAdtTY7Rm6dKlEAqFncrd3d1ZQmO0hiU1RmuWLl2KtrY2tTKhUIgVK1boKSLmecAOPxmtmjBhAvLz86HazAQCAUpKSuDq6qrnyBi+YntqjFa9+eabMDY2BvA4oU2cOJElNEarWFJjtOr1119HR0cHAMDY2BhvvvmmniNi+I4lNUarHB0d8dJLL0EgEKCjowMBAQH6DonhOZbUGK1btmwZiAgvv/wyHBwc9B0Ow3ekI6mpqQSAfdiHfZ7Dz8KFC3WVakjndxOnpqbqepYGLyEhAQCwdu1aPUeiPQkJCXjnnXcglUr79f2LFy8iMTGRbT+DkGr71hWdJ7VFixbpepYG7/DhwwD43TfTpk3D0KFDB9RGYmIir/uIr1Tbt66wc2qMTgw0oTFMb7GkxjAMr7CkxjAMr7CkxjAMr7CkxjAMrwzapBYXFwc7OzsIBALs3btX3+Ho3alTp2BhYYHjx4/rOxSDdObMGYSFhSE9PR1ubm4QCAQQCARYtmxZp7qzZs2CTCaDsbExxowZg6tXr+oh4t7p6OhAQkICfHx8NNZmc3MzPD09sXnzZgDAsWPHEBsbi/b2do3NQ5sGbVLbsGEDLly4oO8wDAaxh61068MPP8SuXbsQHh4Of39//PTTT1AoFBgyZAgOHDiAkydPqtU/ffo0Dh8+jLlz56KwsBDe3t56irxnxcXF+M1vfoN169ahqalJY+1GRESgqKiI+9vX1xcikQgzZ85ETU2NxuajLYM2qfWHUqnU6H80QzJnzhzU1tZi7ty5epm/ofbtjh07cOjQIaSlpUEmk6lN27VrF4yMjBAUFITa2lo9Rdg/33//PTZt2oRVq1ZhwoQJGmv3woUL+OGHHzqVr1mzBuPHj8fs2bM7PSPP0DxXSS05ORkVFRX6DoOXDLFvb9y4gS1btuCjjz6CSCTqNN3HxwchISG4c+cONmzYoIcI+2/8+PFIT0/HkiVLeny/al8olUps3LgRiYmJXU6PiopCXl5et9MNBe+S2vnz5/HrX/8aEokEcrkc48aNQ11dHUJCQrB+/XqUlJRAIBDA3d0diYmJkEqlMDIywsSJE2Fvbw+hUAipVApvb29Mnz4dw4cPh0gkgqWlJT744AN9L16XcnJy4OzsDIFAgE8//RQAkJSUBKlUColEgqNHj+K1116DXC7HsGHDcPDgQQCP91REIhHs7OywcuVKODo6QiQSwcfHB5cvXwYABAcHw9TUVO1G9NWrV0MqlUIgEKCqqqrLvgWAr776CnK5HNu2bdNxj4BbPiKCr69vt3ViYmIwcuRIfPHFFzhz5ky39YgI8fHxGD16NMzMzGBlZYX58+fj+vXrAHrX3wDQ3t6OyMhIODs7QywW44UXXjCYW78iIiKwevVq2NradjndysoKM2bMQGJiomGf7tDVTaaqG9o1qbi4mADQZ599RkREDQ0NJJfLKTY2lpRKJd27d48WLFhAlZWVRETk7+9PCoVCrY0PP/yQANDly5epsbGRqqqq6NVXXyUAdPLkSaqsrKTGxkYKDg4mAJSXl6fRZSAiWrhw4YBv+L19+zYBoN27d3NlERERBIDOnj1LtbW1VFFRQdOnTyepVEotLS1ERBQUFERSqZSuXbtGzc3NVFhYSJMnTyaZTEZlZWVERLRkyRKyt7dXm9/OnTsJQI99e+LECZLJZBQdHT2gZSPq3/bj5uZGXl5eXU5TKBR08+ZNIiK6cOECGRkZ0YgRI6ihoYGIiLKysmjevHlc/cjISDI1NaX9+/dTTYAEUOYAACAASURBVE0N5efnk7e3N9nY2NC9e/eIqHf9vWHDBjIzM6MjR45QdXU1hYeHk5GREV25cqWvXcJ58cUXafz48f3+PhFRTk4O+fr6EhFRZWUlAaCIiIhO9cLCwggA5ebm9rptTWzffcGrPbXS0lLU1dVhzJgxEIlEsLe3R3p6OmxsbJ75XS8vL0gkEgwZMoR7w7izszNsbGwgkUiwdOlSAOD+Mw8mPj4+kMvlsLW1RWBgIBobG1FWVsZNNzEx4fZAvLy8kJSUhPr6eqSkpAxovnPmzEFdXR22bNky0EXos8bGRty8eRMKheKZdadOnYq1a9eitLQUmzZt6jRdqVQiPj4eCxYswNKlS2FhYYFx48Zh7969qKqqwr59+9Tqd9ffzc3NSEpKgp+fH/z9/WFpaYnNmzdDKBQOuK8HQqlUIiQkBElJSc+s6+HhAQAoKCjQdlj9xquk5ubmBjs7OyxduhRRUVEoLS3tVzuql/A+eUJU9Vak1tbWAcepT6pl62k5Jk2aBIlEMigTuEpFRQWICBKJpFf1Y2JiMGrUKOzZswc5OTlq0woLC9HQ0IBJkyaplU+ePBmmpqbcoXpXnuzvoqIiNDU1YezYsdx0sVgMBwcHvfZ1eHg43nnnHTg5OT2zrqo/79+/r+2w+o1XSU0sFuPcuXOYNm0atm3bBjc3NwQGBkKpVOo7tEHHzMwMlZWV+g6j35qbmwGg1yfRRSIRUlJSIBAI8NZbb6ltM6phDObm5p2+Z2lpifr6+l7No7GxEQCwefNmbpycQCDArVu3NDokoy9ycnJQUFCAt99+u1f1xWIxgF/61xDxKqkBwJgxY3D8+HHcvXsXoaGhSE1NRVxcnL7DGlRaW1tRU1ODYcOG6TuUflP9+PoyYHTq1KlYt24diouLsXXrVq7c0tISALpMXn3pJ9UJ+ISEBBCR2ufixYu9jlOTkpOTcfbsWRgZGXFJVhXntm3bIBAI8N1333H1W1paAPzSv4aIV0nt7t27uHbtGoDHG9DHH38Mb29vrozpnezsbBARpkyZAuDxObfBdtitutukr+PPtm7dCk9PT+Tm5nJlY8eOhbm5udqPGwAuX76MlpYWTJw4sVdtq66k5+Xl9SkmbUpJSemUYFV76BERESAitcNuVX/a29vrJd7e4F1SW7lyJa5fv46Wlhbk5ubi1q1b3I/T2toad+/eRWlpKerr6wfdD1VbOjo6UF1djba2NuTn5yMkJATOzs5Yvnw5gMdvVH/48CEyMzPR2tqKyspK3Lp1S62Nrvo2KytLb0M6JBIJ3NzcUF5e3qfvqQ5DVa/1U5WtX78eGRkZOHDgAOrq6lBQUIBVq1bB0dERQUFBvW57xYoVOHjwIJKSklBXV4f29naUl5fj559/BgAEBgbC3t5eI7dmabItFVV/jhs3TmNtapyuLrNqekjHn/70J7K3tycAJJVKacGCBVRaWko+Pj5kZWVFxsbGNHToUIqIiKC2tjYiIrp69Sq5uLiQWCymadOmUVhYGEkkEgJAI0aMoG+//ZZ27NhBFhYWBIDs7e3pyy+/pEOHDnHzsrKyooMHD2psOYgGfsl79+7d5ODgQABIIpGQr68v7dmzh1s2Dw8PKikpoX379pFcLicA5OLiQj/++CMFBQWRUCgkJycnMjExIblcTvPnz6eSkhKu/QcPHtArr7xCIpGIXF1d6f3336eNGzcSAHJ3d6eysrJOfXvv3j06deoUyWQyiomJGXAf9Wf7CQ4OJqFQSE1NTVxZRkYGKRQKAkA2Njb03nvvdfndjRs3qg3p6OjooJ07d5KHhwcJhUKysrIiPz8/KioqIiLqdX8/evSIQkNDydnZmUxMTMjW1pb8/f2psLCQiIj8/PwIAEVGRva4bBcvXqSXXnqJHB0dufcAODg4kI+PD50/f75PbT2tpyEdc+bMIScnJ+ro6Oh1e7oe0jFokxqf6HqlPykoKIisra31Mu++6M/2U1xcTCYmJrR//34tRaV57e3tNH36dEpOTjaotoiIqqqqSCQSUVxcXJ++x8apMTo3WJ6+0Ffu7u6Ijo5GdHQ0Ghoa9B3OM7W3tyMzMxP19fUIDAw0mLZUoqKiMGHCBAQHB2ukPW1hSY3htbCwMAQEBCAwMNDgb1rPzs5Geno6srKyej2+ThdtAUB8fDzy8vJw6tQpbsymoWJJ7TkWHh6OlJQU1NbWwtXVFUeOHNF3SFqxbds2BAcH4+OPP9Z3KD2aOXMmvvzyS4288FmTbR09ehSPHj1CdnY2rKysBtyetun8FXmM4di+fTu2b9+u7zB0YtasWZg1a5a+wxiU5s2bh3nz5uk7jF5je2oMw/AKS2oMw/AKS2oMw/AKS2oMw/CKzi8UpKWl6XqWBk916wnrm+6pbvhmfTT4lJeX6/bhCLoa5asaEc4+7MM+z99Hl3cU6HxPjQz52eZ6EhAQAAA4fPiwniMxXGlpaVi8eDHbfgYh1fatK+ycGsMwvMKSGsMwvMKSGsMwvMKSGsMwvMKSGsMwvMKSGsMwvGKwSS09PR1ubm7cG24cHBy4Fwp35/vvv0dgYCBcXV1hZmYGGxsbjB8/HjExMQAeP7P9yVeT9fRZsWKF2vyf9ULe+Ph4CAQCGBkZwdPTE998843G+oLRjjNnziAsLKzTtrZs2bJOdWfNmgWZTAZjY2OMGTNGo8/915TY2Fh4enpCLBZDKpXC09MTW7ZsQV1dHVcnOjoaXl5ekMvlMDMzg7u7Oz744APuIZrHjh1DbGzs4H5wqK4GxPX3cd4KhYIsLCyeWS8/P58kEgmtWbOGbt68SUqlkoqKiuiDDz6gmTNnEhHR4sWL6fTp01RTU0Otra30888/EwDy9fWllpYWamxspIqKCvrjH/9Ix48f5+YPPH7+e0tLS5fzbmtrIxcXFwLAzasv9Pk478FC04+Dj4yMpLlz51JdXR1XplAoaMiQIQSATpw40ek7WVlZau8tMDRz5syhuLg4qqiooPr6ekpLSyOhUEi/+93vuDozZsygPXv20IMHD6iuro5SU1NJKBTSq6++ytVJTEykGTNmUHV1tUbiYo/z7qe4uDhYWloiMTERI0aMgEgkwsiRI7F161buHYUCgQAvvfQSLCwsYGLyy7hjgUAAoVAIiUQCW1vbTq88mzhxIu7du4fMzMwu552ent6rt1sbIqVSCR8fn0HX9kDs2LEDhw4dQlpaGmQymdq0Xbt2wcjICEFBQQb/pNynmZqaYvXq1bC1tYW5uTkCAgIwf/58/OMf/+DeVmVubo6goCBYW1tDJpNh0aJF8PPzw1dffYXbt28DANasWYPx48dj9uzZaGtr0+ci9QtvktqDBw9QW1uLhw8fqpWbmpri+PHjAICDBw/26tHGQUFB+P3vf8/9/e677wIAPvvssy7rx8fHY/369f0NXa+Sk5NRUVEx6Nrurxs3bmDLli346KOPIBKJOk338fFBSEgI7ty5gw0bNughwv7LyMjotEyqf7aqw8sTJ06ovf4PAGxsbABA7S3xUVFRyMvLQ2JiojZD1greJLXJkyejsbERv/3tb/Gvf/1Lo23/9re/xejRo/HPf/4TRUVFatP+9a9/oampSedPVSUixMfHY/To0TAzM4OVlRXmz5+P69evAwCCg4Nhamqq9jjn1atXQyqVQiAQoKqqCiEhIVi/fj1KSkogEAjg7u6OXbt2QSQSwc7ODitXroSjoyNEIhF8fHxw+fLlAbUNAF999ZXe3gUKPN4TIyL4+vp2WycmJgYjR47EF198gTNnznRb71nrICkpCVKpFBKJBEePHsVrr70GuVyOYcOG4eDBg1w77e3tiIyMhLOzM8RiMV544QWkpqZqZHmLi4thaWkJFxeXbuvcuXMHYrEYrq6uXJmVlRVmzJiBxMTEwXdrmq6Oc7V9Tq2pqYkmTZrE3UDr5eVFsbGx9ODBg26/ozqn1tN5EoVCQTdv3qQ///nPBIBCQkLUpvv5+VFKSgrV19fr9JxaZGQkmZqa0v79+6mmpoby8/PJ29ubbGxs6N69e0REtGTJErK3t1f73s6dOwkAVVZWEhGRv78/KRQKtTpBQUEklUrp2rVr1NzcTIWFhTR58mSSyWRUVlY2oLZPnDhBMpmMoqOj+7S8mjqn5ubmRl5eXl1OU61rIqILFy6QkZERjRgxghoaGoio8zm13qyDiIgIAkBnz56l2tpaqqiooOnTp5NUKuXO0W7YsIHMzMzoyJEjVF1dTeHh4WRkZERXrlzp1zK2tLRQeXk57d69m8zMzHp8RWBjYyPJZDIKDg7uNC0sLIwAUG5ubr/iUGHn1PpJLBbjwoUL+POf/wxPT09cu3YNoaGhGD16NM6fPz/g9v/whz9AKpXir3/9K5RKJQDgp59+wpUrV/DGG28MuP2+UCqViI+Px4IFC7B06VJYWFhg3Lhx2Lt3L6qqqrBv374Bz8PExITbA/Hy8kJSUhLq6+uRkpIyoHbnzJmDurq6Z15N1obGxkbcvHkTCoXimXWnTp2KtWvXorS0FJs2beo0va/rwMfHB3K5HLa2tggMDERjYyPKysrQ3NyMpKQk+Pn5wd/fH5aWlti8eTOEQmG/+3r48OEYNmwYoqKi8Mknn2Dx4sXd1t2+fTscHR25EQJP8vDwAAAUFBT0Kw594U1SAwChUIjg4GD83//9Hy5duoT58+ejoqICAQEBqK6uHlDbFhYWeOONN1BdXY1Dhw4BABISEvDuu+/C1NRUE+H3WmFhIRoaGjBp0iS18smTJ8PU1JQ7TNSkSZMmQSKRcIdWg1FFRQWIqNevjIuJicGoUaOwZ88e5OTkqE0byDpQbS+tra0oKipCU1MTxo4dy00Xi8VwcHDod1/fvn0bFRUV+Pvf/46//vWv+NWvftXluc2MjAykpaXh66+/7nTBBADXT/fv3+9XHPrCq6T2pBdffBH/+7//i1WrVqGyshL//Oc/B9ym6oLB3r17UVNTg8OHD2PlypUDbrevampqADy+kvU0S0tL1NfXa2W+ZmZmqKys1ErbutDc3Azg8XL0hkgkQkpKCgQCAd566y1uDx3Q3DpobGwEAGzevFltnOStW7fUTtz3hVAohK2tLWbNmoVDhw6hsLCw01vDDh06hB07diA7OxsjRozosh3VqAFVvw0WgzqpffPNN0hISAAA+Pv7d3n5WTWQsr8byJMmTJiAKVOm4N///jeCgoIQEBCgl/cgWlpaAkCXP5yamhqtPGW0tbVVa23riupH2peBpVOnTsW6detQXFyMrVu3cuWaWge2trYAHu/1E5HaR/W034Fwd3eHsbExCgsLubLdu3fjwIEDOHfuHIYOHdrtd1taWgD80m+DxaBOav/5z38glUoBAI8ePcK1a9c61VFdrXzhhRc0Mk/V3tqRI0ewdu1ajbTZV2PHjoW5uTm+++47tfLLly+jpaWFG2dnYmKC1tZWjcwzOzsbRIQpU6ZovG1dsbOzg0Ag6PP4s61bt8LT0xO5ublcWW/XwbMMHz4cIpEIeXl5fYrpaQ8ePOjy3G5xcTHa29sxfPhwEBFCQ0NRUFCAzMzMLvcyn6TqJ3t7+wHFpmuDMqm1trbi/v37yM7O5pIaAPj5+SEtLQ01NTWora3F0aNHsWnTJsybN09jSW3RokWwsbGBn58f3NzcNNJmX4lEIqxfvx4ZGRk4cOAA6urqUFBQgFWrVsHR0RFBQUEAHv+XfvjwITIzM9Ha2orKykrcunVLrS1ra2vcvXsXpaWlqK+v5xJVR0cHqqur0dbWhvz8fISEhMDZ2RnLly8fUNtZWVl6G9IhkUjg5ubGvROit1SHoU+O7+rtOuhN2ytWrMDBgweRlJSEuro6tLe3o7y8nBswGxgYCHt7+x5vzZJKpTh9+jTOnTuHuro6tLa2Ijc3l7vAtW7dOly7dg2ffPIJPv/8cwiFwk63BsbFxam1qeqncePG9am/9E5Xl1n7ekk+IyODu0Wpp09GRgYREZ0+fZoWL15MCoWCzMzMyNTUlEaNGkVRUVHU3Nys1nZdXR395je/IWtrawJARkZG5O7uTtu2bety/jY2NvTee+9x0z744AO6cOEC9/fmzZvJwcGBa8vLy4u+/fbbXi9rfy55d3R00M6dO8nDw4OEQiFZWVmRn58fFRUVcXUePHhAr7zyColEInJ1daX333+fNm7cSADI3d2dysrK6OrVq+Ti4kJisZimTZtG9+7do6CgIBIKheTk5EQmJiYkl8tp/vz5VFJSMuC2T506RTKZjGJiYvq0vJoa0hEcHExCoZCampq4sp7W9ZM2btyoNqTjWetgz549JJFICAB5eHhQSUkJ7du3j+RyOQEgFxcX+vHHH+nRo0cUGhpKzs7OZGJiQra2tuTv70+FhYVE9HjYEACKjIzscdl8fX3J1dWVzM3NyczMjBQKBQUGBlJBQQERERUUFPT4W9q5c6dae3PmzCEnJyfq6Ojoe0c/QddDOgw2qT1PDO3ez6CgILK2ttZ3GGo0tf0UFxeTiYlJj2O3DE17eztNnz6dkpOTdTbPqqoqEolEFBcXN+C22Dg1xiAM6qc09MDd3R3R0dGIjo7mbh0yZO3t7cjMzER9fT0CAwN1Nt+oqChMmDABwcHBOpunprCkxjx3wsLCEBAQgMDAQIO/aT07Oxvp6enIysrq9fi6gYqPj0deXh5OnToFoVCok3lqEktqjJrw8HCkpKSgtrYWrq6uOHLkiL5D0opt27YhODgYH3/8sb5D6dHMmTPx5Zdfqt1nq01Hjx7Fo0ePkJ2drZfhSpqg8/d+MoZt+/btnQZq8tWsWbN0/iACQzdv3jzMmzdP32EMCNtTYxiGV1hSYxiGV1hSYxiGV1hSYxiGV3R+oSAgIEDXszR4ly5dAsD6pieqW3ZYHw0+ly5d4u4Z1gUBkW6e1Xvx4kXEx8frYlaMATp79izGjh076G6OZjRD9bQTXdBZUmOebwKBAKmpqVi0aJG+Q2F4jp1TYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGV1hSYxiGVwRERPoOguGXN998E7m5uWplt2/fxpAhQyCRSLgyoVCIEydOYOjQoboOkeExE30HwPDPqFGjsH///k7ltbW1an97eXmxhMZoHDv8ZDRu6dKlEAgEPdYRCoVYvny5bgJinissqTEa5+LiAm9v7x4TW1tbGwICAnQYFfO8YEmN0Yo333wTxsbGXU4zMjLClClTMGLECN0GxTwXWFJjtCIwMBAdHR1dTjMyMsKbb76p44iY5wVLaoxW2NnZYcaMGV3urRERFixYoIeomOcBS2qM1ixbtgxPjxgyNjbGf/3Xf8HOzk5PUTF8x5IaozX+/v4wMVEfNUREWLp0qZ4iYp4HLKkxWiOXy/Haa6+pJTYTExP4+vrqMSqG71hSY7Rq6dKlaG9vB/A4oc2bNw9yuVzPUTF8xpIao1W///3vuVuj2tvbsWTJEj1HxPAdS2qMVolEIvj7+wMApFIpXn31VT1HxPCdQdz7efHiRdy+fVvfYTBaMmzYMADA5MmTcfToUT1Hw2jTokWL9B2CYTylIyAgAEeOHNF3GAzDDJABpBPDOfxcuHAhiOi5/wBAamqq3uPQ9CcmJgZtbW0aaWvhwoVsezGwT2pqqp4zyC8MJqkx/BYaGtrtvaAMo0ksqTE68fQgXIbRFpbUGIbhFZbUGIbhFZbUGIbhFZbUGIbhFd4ltbfffhsymQwCgQB5eXn6DkcvTp06BQsLCxw/flzfoRikM2fOICwsDOnp6XBzc4NAIIBAIMCyZcs61Z01axZkMhmMjY0xZswYXL16VQ8R9yw2Nhaenp4Qi8WQSqXw9PTEli1bUFdXx9WJjo6Gl5cX5HI5zMzM4O7ujg8++AANDQ0AgGPHjiE2Npa7T3cw411S++KLL/D555/rOwy9ItL/AEhD9eGHH2LXrl0IDw+Hv78/fvrpJygUCgwZMgQHDhzAyZMn1eqfPn0ahw8fxty5c1FYWAhvb289Rd69b7/9Fn/84x9RVlaG+/fvY+vWrYiNjcXChQu5OufOncN7772H0tJSVFVVYfv27UhMTOTeE+Hr6wuRSISZM2eipqZGX4uiEbxLagwwZ84c1NbWYu7cuXqZv1KphI+Pj17m3ZMdO3bg0KFDSEtLg0wmU5u2a9cuGBkZISgoqNOr/AydqakpVq9eDVtbW5ibmyMgIADz58/HP/7xD/z8888AAHNzcwQFBcHa2hoymQyLFi2Cn58fvvrqK+4WxTVr1mD8+PGYPXs22tra9LlIA8LLpPas17Mx2pWcnIyKigp9h6Hmxo0b2LJlCz766COIRKJO0318fBASEoI7d+5gw4YNeoiw/zIyMjotk5OTEwBwh5cnTpzoNPjZxsYGANDU1MSVRUVFIS8vD4mJidoMWasGfVIjIuzcuROjRo2CmZkZLCwssHHjRrU67e3tiIyMhLOzM8RiMV544QXuto6kpCRIpVJIJBIcPXoUr732GuRyOYYNG4aDBw9ybZw/fx6//vWvIZFIIJfLMW7cOO6cRU/t61pOTg6cnZ0hEAjw6aef9noZd+3aBZFIBDs7O6xcuRKOjo4QiUTw8fHB5cuXAQDBwcEwNTWFg4MDN7/Vq1dDKpVCIBCgqqoKISEhWL9+PUpKSiAQCODu7g4A+OqrryCXy7Ft2zYd9wi45SOiHh9QGRMTg5EjR+KLL77AmTNnuq1HRIiPj8fo0aNhZmYGKysrzJ8/H9evXwfQ+21Km9tNcXExLC0t4eLi0m2dO3fuQCwWw9XVlSuzsrLCjBkzkJiYOHhPY5ABWLhwIS1cuLBf342IiCCBQEB/+tOfqLq6mpqammjPnj0EgHJzc4mIaMOGDWRmZkZHjhyh6upqCg8PJyMjI7py5QrXBgA6e/Ys1dbWUkVFBU2fPp2kUim1tLRQQ0MDyeVyio2NJaVSSffu3aMFCxZQZWVlr9rvCwCUmprar75QuX37NgGg3bt3q/VTT8tIRBQUFERSqZSuXbtGzc3NVFhYSJMnTyaZTEZlZWVERLRkyRKyt7dXm9/OnTsJANcf/v7+pFAo1OqcOHGCZDIZRUdHD2jZiPq3vbi5uZGXl1eX0xQKBd28eZOIiC5cuEBGRkY0YsQIamhoICKirKwsmjdvHlc/MjKSTE1Naf/+/VRTU0P5+fnk7e1NNjY2dO/ePSLqXX9rcrshImppaaHy8nLavXs3mZmZ0f79+7ut29jYSDKZjIKDgztNCwsLU/v99EZqaioZSDohg4iiv0mtqamJJBIJ/e53v1MrP3jwILdSlEolSSQSCgwMVPuemZkZvfvuu0T0ywaoVCq5OqrEeOPGDfrhhx8IAJ04caJTDL1pvy+0ndS6W0aix0nNwsJCra0rV64QAProo4+IqP9JTZP6ur00NDSQQCCguXPndjn9yaRGRLR+/XoCQO+99x4RqSe1pqYmMjc3V1vfRET//ve/CQCXtJ/V35reboiI7O3tCQANGTKE/vznP3PJsysRERE0cuRIqqur6zTtL3/5CwGgv/3tb72etyEltUF9+Hnjxg00NTVh5syZ3dYpKipCU1MTxo4dy5WJxWI4ODhwhwtdMTU1BQC0trbCzc0NdnZ2WLp0KaKiolBaWjrg9g3Bk8vYnUmTJkEikRj8svSkoqICRMQ9gfdZYmJiMGrUKOzZswc5OTlq0woLC9HQ0IBJkyaplU+ePBmmpqbcoXpXnuxvbWw3t2/fRkVFBf7+97/jr3/9K371q191eW4zIyMDaWlp+PrrrztdMAHA9dP9+/f7FYe+DeqkVl5eDgCwtbXttk5jYyMAYPPmzdx4JIFAgFu3bqmdIO2JWCzGuXPnMG3aNGzbtg1ubm4IDAyEUqnUSPuGzszMDJWVlfoOo9+am5sBPF6O3hCJREhJSYFAIMBbb70FpVLJTVMNdzA3N+/0PUtLS9TX1/dqHtrYboRCIWxtbTFr1iwcOnQIhYWF2L59u1qdQ4cOYceOHcjOzsaIESO6bEcsFgP4pd8Gm0Gd1FRXfB49etRtHVXCS0hI6PQMqIsXL/Z6XmPGjMHx48dx9+5dhIaGIjU1FXFxcRpr31C1traipqaGe3rtYKT6kfZlYOnUqVOxbt06FBcXY+vWrVy5paUlAHSZvPrST9rebtzd3WFsbIzCwkKubPfu3Thw4ADOnTuHoUOHdvvdlpYWAL/022AzqJPa2LFjYWRkhPPnz3dbZ/jw4RCJRAO6u+Du3bu4du0agMcb48cffwxvb29cu3ZNI+0bsuzsbBARpkyZAuDxI4R6Olw1RHZ2dhAIBH0ef7Z161Z4enoiNzeXKxs7dizMzc3x3XffqdW9fPkyWlpaMHHixF61rant5sGDB3jjjTc6lRcXF6O9vR3Dhw8HESE0NBQFBQXIzMzsci/zSap+sre3H1Bs+jKok5qtrS38/f1x5MgRJCcno66uDvn5+di3bx9XRyQSYcWKFTh48CCSkpJQV1eH9vZ2lJeXcwMTn+Xu3btYuXIlrl+/jpaWFuTm5uLWrVuYMmWKRto3JB0dHaiurkZbWxvy8/MREhICZ2dnLF++HMDjPYCHDx8iMzMTra2tqKysxK1bt9TasLa2xt27d1FaWor6+nq0trYiKytLb0M6JBIJ3NzcuNMVvaU6DH1yfJdIJML69euRkZGBAwcOoK6uDgUFBVi1ahUcHR0RFBTU67aftd0EBgbC3t6+x1uzpFIpTp8+jXPnzqGurg6tra3Izc3Fs0FiSgAADgxJREFUH/7wB0ilUqxbtw7Xrl3DJ598gs8//xxCoVDtcFcgECAuLk6tTVU/jRs3rk/9ZTD0cHGik4EM6aivr6e3336bhgwZQubm5jRt2jSKjIwkADRs2DD6/vvv6dGjRxQaGkrOzs5kYmJCtra25O/vT4WFhbRnzx6SSCQEgDw8PKikpIT27dtHcrmcAJCLiwv94x//IB8fH7KysiJjY2MaOnQoRUREUFtbGxFRj+33FQZ49XP37t3k4OBAAEgikZCvr2+vlvHHH3+koKAgEgqF5OTkRCYmJiSXy2n+/PlUUlLCtf/gwQN65ZVXSCQSkaurK73//vu0ceNGAkDu7u5UVlZGV69eJRcXFxKLxTRt2jS6d+8enTp1imQyGcXExPR72VT6s70EBweTUCikpqYmriwjI4MUCgUBIBsbG+5q59M2btyoNqSjo6ODdu7cSR4eHiQUCsnKyor8/PyoqKiIiKjX/f2s7cbPz48AUGRkZI/L5uvrS66urmRubk5mZmakUCgoMDCQCgoKiIiooKCAAHT72blzp1p7c+bMIScnJ+ro6Oh1/xrS1U+DiGIgSY1vBprUBiIoKIisra31Mu++6M/2UlxcTCYmJj2O3TI07e3tNH36dEpOTtbZPKuqqkgkElFcXFyfvmdISW1QH34ymseHpzR0xd3dHdHR0YiOjuZuHTJk7e3tyMzMRH19PQIDA3U236ioKEyYMAHBwcE6m6emsaTGPDfCwsIQEBCAwMBAg79pPTs7G+np6cjKyur1+LqBio+PR15eHk6dOgWhUKiTeWoDS2oMACA8PBwpKSmora2Fq6srb9/Dum3bNgQHB+Pjjz/Wdyg9mjlzJr788ku1+2y16ejRo3j06BGys7NhZWWlk3lqC3vFDwMA2L59e6eBmnw1a9YszJo1S99hGJR58+Zh3rx5+g5DI9ieGsMwvMKSGsMwvMKSGsMwvMKSGsMwvGIwFwouXbrEvQTieZeQkIDDhw/rOwyDdenSJQBg24sB6estaNrE9tQYhuEVg9lTmzJlCts7weOXxqxduxaLFi3SdygGS7WHxrYXw5GWlobFixfrOwwAbE+NYRieYUmNYRheYUmNYRheYUmNYRheYUmNYRheGZRJLT09HW5ubp0eS2xqago7Ozu8/PLL2LlzJ6qrq/UdKjMInDlzBmFhYZ22q2XLlnWqO2vWLMhkMhgbG2PMmDE9Pmpb3zo6OpCQkAAfHx+18mPHjiE2Npa3z84blEnN398fP/30ExQKBSwsLEBE6OjoQEVFBdLS0uDq6orQ0FCMGTOm0wsyGOZJH374IXbt2oXw8HC17WrIkCE4cOAATp48qVb/9OnTOHz4MObOnYvCwkJ4e3vrKfKeFRcX4ze/+Q3WrVvX6ZV7vr6+EIlEmDlzJvfKPz4ZlEmtKwKBAJaWlnj55ZeRkpKCtLQ03L9/H3PmzDH4BwIaAqVS2ek/+mBoeyB27NiBQ4cOIS0trdNLfXft2gUjIyMEBQUNuu3n+++/x6ZNm7Bq1SpMmDChyzpr1qzB+PHjMXv2bLS1tek4Qu3iTVJ72sKFC7F8+XJUVFRg7969+g7H4CUnJ3f5Nm9Db7u/bty4gS1btuCjjz7i3h/7JB8fH4SEhODOnTvYsGGDHiLsv/HjxyM9PR1Llizp8QXOUVFRyMvLQ2Jiog6j0z7eJjUA3GvdsrKyADx+7ntkZCScnZ0hFovxwgsvIDU1FQCQlJQEqVQKiUSCo0eP4rX/396ZhkTVtnH8PzozHkc9OeGSuZQzY5tZUWquRARBBLlhDOSX+mJRiW3YYiLmQigWgRJR+MEi4q2wBRVaUIhEgpRCsUJSCzOX1Jmx0THnej/4Os8zb4szemZ55rl/MF/OfZ//fZ37vubiLNc5165d4HkeISEhuH37tkmzubkZsbGxkMlk4HkeUVFR0Gg08+rbCiJCZWUl1q5dCw8PD8jlcqSmpqKrqwsAkJOTA6lUavYF1cOHD8PLywsikQjDw8PIzc3FiRMn0N3dDZFIBJVKhStXroDjOAQEBODgwYMICgoCx3FISEhAa2vrorQBoLGx0WEl84DZMzEiwp49e37bp7i4GKtWrcL169fx9OnT3/abbw0s9S17+49cLse2bdtw+fJlEJHNxrE7Di378j8WWk1KqVTSkiVLftuu0WgIAIWGhhIR0cmTJ8nDw4Pu3r1Lo6OjdPbsWXJzc6NXr14REdG5c+cIAD179ozGx8dpcHCQkpOTycvLiwwGA+l0OuJ5ni5evEh6vZ4GBgYoPT2dhoaGLNK3BFhZTaqgoICkUinV1tbS2NgYvXnzhjZv3kx+fn40MDBARET79u2jwMBAs/3Ky8sJgMn2jIwMUiqVZn2ys7PJy8uLOjs7aXJykjo6OigmJoZ8fHyor69vUdqPHz8mHx8fKioqsvhY5xCi+phCoaB169b9sk2pVNLHjx+JiOjly5fk5uZGK1euJJ1OR0REDQ0NZiXzLFmD+XyLSBj/+X+2bt1KGzdu/G37mTNnCAC1tbUteAwiVk3Kbvj4+EAkEkGr1WJychLV1dVIS0tDRkYGfH19kZ+fD4lEgpqaGrP9EhISwPM8/P39oVarMTExgb6+PvT09ECj0SAyMhIcxyEwMBD37t2Dn5+fVfpCodfrUVlZifT0dGRlZWHJkiWIiorC1atXMTw8bFbUeaGIxWLTGci6detQXV0NrVa76GPavXs3NBoNzp8/v2gbrWViYgIfP36EUqmct298fDyOHTuGnp4enD59+qd2a9fgd77lCP8BgIiICADA27dvbTaGvXHpoDYxMQEiAs/zePfuHb5//47169eb2j09PbFs2TLTZcKvkEqlAIDp6WkoFAoEBAQgKysLhYWF6OnpMfVbqP5i6OjogE6nQ3R0tNn2mJgYSKVS02WikERHR0Mmk9nsmOzB4OAgiMjiKk3FxcVYvXo1qqqq8OLFC7O2xazB333LEf4DwDQHX79+tdkY9salg9r79+8BAGvWrMHExAQAID8/3yy3rbe396dH3r/D09MTz58/R1JSEkpKSqBQKKBWq6HX6wXRt5a5x/He3t4/tfn6+kKr1dpkXA8PDwwNDdlE2x5MTk4CwB9vov8djuNQU1MDkUiEAwcOQK/Xm9qEWgNH+A8w69PAX3PiCrh0UGtsbAQA7Nq1C/7+/gBmP8BIs5XpTb+WlhaLNSMjI/Ho0SP09/cjLy8Pd+7cQUVFhWD61uDr6wsAv/zjjI2NISQkRPAxp6enbaZtL+b+yNYkn8bHx+P48eP48OEDLly4YNou1Bo4wn8AwGAwAPhrTlwBlw1qAwMDuHTpEkJCQnDgwAGEhoaC4zi0t7cvWLO/vx+dnZ0AZp2wrKwMmzdvRmdnpyD61rJ+/Xp4e3v/lGDc2toKg8GALVu2AJi9LzY9PS3ImE1NTSAixMXFCa5tLwICAiASiazOP7tw4QLWrFmDtrY20zZL12A+HOE/AExzEBgYaNdxbck/PqgREXQ6HYxGI4gIQ0NDuHPnDhITE+Hu7o66ujrwPA+O47B//37cvn0b1dXV0Gg0mJmZwefPn/HlyxeLxurv78fBgwfR1dUFg8GAtrY29Pb2Ii4uThB9a+E4DidOnMD9+/dx8+ZNaDQavH37FocOHUJQUBCys7MBACqVCt++fUNdXR2mp6cxNDSE3t5eM62lS5eiv78fPT090Gq1pkBlNBoxOjqKHz9+4M2bN8jNzUVYWJgpXWah2g0NDQ5L6ZDJZFAoFFZ/gnruMtTd3d1smyVrYIn2fP6jVqsRGBgo6KtZc3MQFRUlmKbDsfvz1l9g7SP6hw8f0oYNG0gmk5FUKiU3NzcCQCKRiHx9fSk2NpaKiopoZGTEbL+pqSnKy8ujsLAwEovF5O/vTxkZGdTR0UFVVVUkk8kIAEVERFB3dzddu3aNeJ4nALRixQp68uQJJSQkkFwuJ3d3d1q+fDmdO3eOfvz4Ma++pcDKlA6j0Ujl5eUUERFBEomE5HI5paWl0bt370x9RkZGaPv27cRxHIWHh9PRo0fp1KlTBIBUKhX19fXR69evacWKFeTp6UlJSUk0MDBA2dnZJJFIKDg4mMRiMfE8T6mpqdTd3b1o7fr6evLx8aHi4mKLj3UOIVI6cnJySCKR0Pfv303b7t+/T0qlkgCQn58fHTly5Jf7njp1yiylY741sMS33r9/P6//pKWlEQAqKCj447G1tLRQYmIiBQUFEQACQMuWLaOEhARqbm4267t7924KDg4mo9G4oHmcw5lSOpzCCiGc1FWwNqjZkuzsbFq6dKmjzfgJIfzlw4cPJBaLqba2ViCrbM/MzAwlJyfTjRs3BNEbHh4mjuOooqJi0VrOFNT+8ZefDNviql9yUKlUKCoqQlFREXQ6naPNmZeZmRnU1dVBq9VCrVYLollYWIhNmzYhJydHED1ngQU1xr+WM2fOIDMzE2q12ulfWm9qasK9e/fQ0NBgcX7dn6isrER7ezvq6+shkUgEsNB5YEGN8UvOnj2LmpoajI+PIzw8HHfv3nW0STahpKQEOTk5KCsrc7Qpf2THjh24deuW2Xu2C+XBgweYmppCU1MT5HK5ANY5F05TIo/hXJSWlqK0tNTRZtiFnTt3YufOnY42w26kpKQgJSXF0WbYDHamxmAwXAoW1BgMhkvBghqDwXApWFBjMBguBQtqDAbDpRAROf47vpmZmS6bMsBg/JtwgnDiHEGtpaUFnz59crQZDAZjkezdu9fRJjhHUGMwGAyhYPfUGAyGS8GCGoPBcClYUGMwGC6FGMB/HG0Eg8FgCMV/ASziVAGNxUZHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# training the stock market prediction model:\n",
        "history = lstm.fit(X_train, y_train, epochs=100, batch_size=8, verbose=1, shuffle=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BAGcTH0QXYHt",
        "outputId": "a5349d4d-d48f-4bab-9499-182b7543a887"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1033/1033 [==============================] - 6s 3ms/step - loss: 54.7049\n",
            "Epoch 2/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 211.0509\n",
            "Epoch 3/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 124.9409\n",
            "Epoch 4/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 63.0289\n",
            "Epoch 5/100\n",
            "1033/1033 [==============================] - 4s 3ms/step - loss: 27.0159\n",
            "Epoch 6/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 9.3733\n",
            "Epoch 7/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 3.5001\n",
            "Epoch 8/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 1.9576\n",
            "Epoch 9/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 1.3777\n",
            "Epoch 10/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 1.0680\n",
            "Epoch 11/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8981\n",
            "Epoch 12/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8112\n",
            "Epoch 13/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 0.7693\n",
            "Epoch 14/100\n",
            "1033/1033 [==============================] - 4s 4ms/step - loss: 0.7498\n",
            "Epoch 15/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 0.7412\n",
            "Epoch 16/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7380\n",
            "Epoch 17/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7375\n",
            "Epoch 18/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7384\n",
            "Epoch 19/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7400\n",
            "Epoch 20/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 0.7420\n",
            "Epoch 21/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7441\n",
            "Epoch 22/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7463\n",
            "Epoch 23/100\n",
            "1033/1033 [==============================] - 4s 4ms/step - loss: 0.7485\n",
            "Epoch 24/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7507\n",
            "Epoch 25/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7528\n",
            "Epoch 26/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7549\n",
            "Epoch 27/100\n",
            "1033/1033 [==============================] - 5s 5ms/step - loss: 0.7569\n",
            "Epoch 28/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7589\n",
            "Epoch 29/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7608\n",
            "Epoch 30/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7627\n",
            "Epoch 31/100\n",
            "1033/1033 [==============================] - 5s 5ms/step - loss: 0.7645\n",
            "Epoch 32/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7662\n",
            "Epoch 33/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7679\n",
            "Epoch 34/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 0.7696\n",
            "Epoch 35/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7712\n",
            "Epoch 36/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7727\n",
            "Epoch 37/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7742\n",
            "Epoch 38/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7757\n",
            "Epoch 39/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7771\n",
            "Epoch 40/100\n",
            "1033/1033 [==============================] - 4s 3ms/step - loss: 0.7784\n",
            "Epoch 41/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7797\n",
            "Epoch 42/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 0.7810\n",
            "Epoch 43/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 0.7822\n",
            "Epoch 44/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7834\n",
            "Epoch 45/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7845\n",
            "Epoch 46/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7856\n",
            "Epoch 47/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7867\n",
            "Epoch 48/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7877\n",
            "Epoch 49/100\n",
            "1033/1033 [==============================] - 4s 4ms/step - loss: 0.7887\n",
            "Epoch 50/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7897\n",
            "Epoch 51/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7906\n",
            "Epoch 52/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 0.7915\n",
            "Epoch 53/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7923\n",
            "Epoch 54/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7932\n",
            "Epoch 55/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7940\n",
            "Epoch 56/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7948\n",
            "Epoch 57/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7955\n",
            "Epoch 58/100\n",
            "1033/1033 [==============================] - 4s 4ms/step - loss: 0.7962\n",
            "Epoch 59/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7969\n",
            "Epoch 60/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7976\n",
            "Epoch 61/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7983\n",
            "Epoch 62/100\n",
            "1033/1033 [==============================] - 4s 3ms/step - loss: 0.7989\n",
            "Epoch 63/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.7995\n",
            "Epoch 64/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8001\n",
            "Epoch 65/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8006\n",
            "Epoch 66/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8012\n",
            "Epoch 67/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8017\n",
            "Epoch 68/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8022\n",
            "Epoch 69/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8027\n",
            "Epoch 70/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8032\n",
            "Epoch 71/100\n",
            "1033/1033 [==============================] - 4s 4ms/step - loss: 0.8036\n",
            "Epoch 72/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8041\n",
            "Epoch 73/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8045\n",
            "Epoch 74/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8049\n",
            "Epoch 75/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8053\n",
            "Epoch 76/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8057\n",
            "Epoch 77/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8061\n",
            "Epoch 78/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 0.8064\n",
            "Epoch 79/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8068\n",
            "Epoch 80/100\n",
            "1033/1033 [==============================] - 4s 4ms/step - loss: 0.8071\n",
            "Epoch 81/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8075\n",
            "Epoch 82/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8078\n",
            "Epoch 83/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8081\n",
            "Epoch 84/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8084\n",
            "Epoch 85/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8087\n",
            "Epoch 86/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8089\n",
            "Epoch 87/100\n",
            "1033/1033 [==============================] - 3s 2ms/step - loss: 0.8092\n",
            "Epoch 88/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8095\n",
            "Epoch 89/100\n",
            "1033/1033 [==============================] - 4s 4ms/step - loss: 0.8097\n",
            "Epoch 90/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8100\n",
            "Epoch 91/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8102\n",
            "Epoch 92/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8105\n",
            "Epoch 93/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8107\n",
            "Epoch 94/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8109\n",
            "Epoch 95/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8111\n",
            "Epoch 96/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8113\n",
            "Epoch 97/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8115\n",
            "Epoch 98/100\n",
            "1033/1033 [==============================] - 4s 4ms/step - loss: 0.8117\n",
            "Epoch 99/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8119\n",
            "Epoch 100/100\n",
            "1033/1033 [==============================] - 3s 3ms/step - loss: 0.8121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM_Prediction:\n",
        "y_pred = lstm.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNBSPOh-XoTR",
        "outputId": "06c07b5c-bf47-4c9d-deb8-9ff1ffc97c5a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26/26 [==============================] - 2s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicted vs True Adj Close Value – LSTM\n",
        "plt.plot(y_test, label='True Value')\n",
        "plt.plot(y_pred, label='LSTM Value')\n",
        "plt.title('Prediction by LSTM')\n",
        "plt.xlabel('Time Scale')\n",
        "plt.ylabel('Scaled USD')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "q7-5TPUHY2-X",
        "outputId": "0006b128-fb0f-4611-c431-4e41313a064d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "atdEERMJY7IZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}