LSTM_agile_V4.ipynb

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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
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      "source": [
        "df = pd.read_csv('10100125.csv')"
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              "  REF_DATE     GEO           DGUID  ... SYMBOL TERMINATED  DECIMALS\n",
              "0  1956-01  Canada  2016A000011124  ...    NaN        NaN         2\n",
              "1  1956-01  Canada  2016A000011124  ...    NaN          t         2\n",
              "2  1956-01  Canada  2016A000011124  ...    NaN          t         2\n",
              "3  1956-01  Canada  2016A000011124  ...    NaN          t         2\n",
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        "df = df.loc[df['Toronto Stock Exchange Statistics'] == 'Standard and Poor\\'s/Toronto Stock Exchange Composite Index, close']"
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              "   REF_DATE     GEO           DGUID  ... SYMBOL TERMINATED  DECIMALS\n",
              "0   1956-01  Canada  2016A000011124  ...    NaN        NaN         2\n",
              "10  1956-02  Canada  2016A000011124  ...    NaN        NaN         2\n",
              "20  1956-03  Canada  2016A000011124  ...    NaN        NaN         2\n",
              "30  1956-04  Canada  2016A000011124  ...    NaN        NaN         2\n",
              "40  1956-05  Canada  2016A000011124  ...    NaN        NaN         2\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
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      ]
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    {
      "cell_type": "code",
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        "df = df[['REF_DATE', 'VALUE']]"
      ],
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      "execution_count": 6,
      "outputs": []
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        "df.head()"
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            "text/plain": [
              "   REF_DATE   VALUE\n",
              "0   1956-01  525.63\n",
              "10  1956-02  545.15\n",
              "20  1956-03  588.91\n",
              "30  1956-04  583.61\n",
              "40  1956-05  560.09"
            ]
          },
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        }
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    },
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      "cell_type": "code",
      "source": [
        "Y = df['VALUE']\n",
        "date = df['REF_DATE']\n",
        "date = np.array(date,dtype=np.datetime64)\n",
        "Series = pd.DataFrame(Y)\n",
        "Series = Series.set_index(date)\n",
        "Series.head()"
      ],
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          "height": 206
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              "                                                     [key], {});\n",
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              "\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",
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            "text/plain": [
              "             VALUE\n",
              "1956-01-01  525.63\n",
              "1956-02-01  545.15\n",
              "1956-03-01  588.91\n",
              "1956-04-01  583.61\n",
              "1956-05-01  560.09"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['year']= pd.DatetimeIndex(df['REF_DATE']).year"
      ],
      "metadata": {
        "id": "jEQMcITT9T03"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "df.head()"
      ],
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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",
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            "text/plain": [
              "   REF_DATE   VALUE  year\n",
              "0   1956-01  525.63  1956\n",
              "10  1956-02  545.15  1956\n",
              "20  1956-03  588.91  1956\n",
              "30  1956-04  583.61  1956\n",
              "40  1956-05  560.09  1956"
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['REF_DATE'], axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8GZwaAtg_dVI",
        "outputId": "345b0d4a-a325-467a-fddb-ba41fd834cbf"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8287ea25-52cc-4883-a952-2b2f26ad47f1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>VALUE</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>525.63</td>\n",
              "      <td>1956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>545.15</td>\n",
              "      <td>1956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>588.91</td>\n",
              "      <td>1956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>583.61</td>\n",
              "      <td>1956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>560.09</td>\n",
              "      <td>1956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>11066</th>\n",
              "      <td>20582.94</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11080</th>\n",
              "      <td>20070.30</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11094</th>\n",
              "      <td>21037.10</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11108</th>\n",
              "      <td>20660.00</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11122</th>\n",
              "      <td>21223.00</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>792 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8287ea25-52cc-4883-a952-2b2f26ad47f1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8287ea25-52cc-4883-a952-2b2f26ad47f1 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-8287ea25-52cc-4883-a952-2b2f26ad47f1');\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",
              "  "
            ],
            "text/plain": [
              "          VALUE  year\n",
              "0        525.63  1956\n",
              "10       545.15  1956\n",
              "20       588.91  1956\n",
              "30       583.61  1956\n",
              "40       560.09  1956\n",
              "...         ...   ...\n",
              "11066  20582.94  2021\n",
              "11080  20070.30  2021\n",
              "11094  21037.10  2021\n",
              "11108  20660.00  2021\n",
              "11122  21223.00  2021\n",
              "\n",
              "[792 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[['year', 'VALUE']]"
      ],
      "metadata": {
        "id": "RhOedbSA_08M"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(x='year',y='VALUE')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "g_Ms0WK7_8wH",
        "outputId": "b365368f-d207-4381-c74b-7e8c5ba6707e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb831d56ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "oy-8gnJYCBWj",
        "outputId": "a30b391e-62f1-4a0b-b88c-9a4aa8c3b49e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d8a8fed9-b4f3-4944-b3da-813eeb5b6706\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>year</th>\n",
              "      <th>VALUE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>792.00000</td>\n",
              "      <td>792.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1988.50000</td>\n",
              "      <td>5636.448270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>19.06241</td>\n",
              "      <td>5434.998279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1956.00000</td>\n",
              "      <td>432.110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1972.00000</td>\n",
              "      <td>1005.252500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1988.50000</td>\n",
              "      <td>3353.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2005.00000</td>\n",
              "      <td>9646.757500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2021.00000</td>\n",
              "      <td>21223.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8a8fed9-b4f3-4944-b3da-813eeb5b6706')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d8a8fed9-b4f3-4944-b3da-813eeb5b6706 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-d8a8fed9-b4f3-4944-b3da-813eeb5b6706');\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",
              "  "
            ],
            "text/plain": [
              "             year         VALUE\n",
              "count   792.00000    792.000000\n",
              "mean   1988.50000   5636.448270\n",
              "std      19.06241   5434.998279\n",
              "min    1956.00000    432.110000\n",
              "25%    1972.00000   1005.252500\n",
              "50%    1988.50000   3353.000000\n",
              "75%    2005.00000   9646.757500\n",
              "max    2021.00000  21223.000000"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_set = df.iloc[:, 1:2].values"
      ],
      "metadata": {
        "id": "n1IeWSlyCIf4"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "training_set_scaled = sc.fit_transform(training_set)\n",
        "print(training_set_scaled.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IsT9esHIC6bf",
        "outputId": "176dda1f-52a5-495a-95cc-48696614a1c1"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(792, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock_data_len = df['VALUE'].count()\n",
        "print(stock_data_len)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RE0OLNk1DVFR",
        "outputId": "fa29aa5b-9671-426f-86dd-150549d370e8"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "792\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = []\n",
        "labels = []\n",
        "for i in range(60, stock_data_len):\n",
        "    features.append(training_set_scaled[i-60:i, 0])\n",
        "    labels.append(training_set_scaled[i,0])\n",
        "\n",
        "features = np.array(features)\n",
        "labels = np.array(labels)\n",
        "\n",
        "features = np.reshape(features, (features.shape[0], features.shape[1], 1))"
      ],
      "metadata": {
        "id": "fF1c_ZUmDADa"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_iwW_S2rDPmM",
        "outputId": "5e23f4fb-b94d-441f-e2e4-c6e4c85c155a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.00690158 0.00780582 0.008345   0.00968357 0.01029201 0.0105123\n",
            " 0.01107408 0.01155939 0.01103801 0.01122655 0.01219669 0.01292585\n",
            " 0.01205913 0.01225056 0.01221208 0.01132419 0.00867207 0.00675055\n",
            " 0.00740565 0.00808864 0.00672073 0.00714688 0.0093339  0.00946953\n",
            " 0.01095576 0.00992454 0.01084177 0.01236647 0.01290469 0.0116551\n",
            " 0.01078405 0.01099568 0.01195283 0.01221304 0.01188213 0.01301532\n",
            " 0.01391619 0.01362376 0.01496232 0.01627395 0.01755721 0.01748458\n",
            " 0.0183566  0.01811996 0.01979136 0.01993902 0.02010448 0.02026945\n",
            " 0.02257191 0.02238192 0.02200146 0.02304615 0.02294274 0.01996018\n",
            " 0.01954269 0.0205287  0.0212242  0.02196876 0.02076102 0.02159744\n",
            " 0.02335831 0.02227418 0.02168835 0.02208227 0.02066434 0.02043347\n",
            " 0.01975192 0.01675109 0.01582424 0.01653513 0.01628838 0.01719022\n",
            " 0.01978415 0.02016845 0.0215402  0.02241559 0.02080094 0.02187497\n",
            " 0.02324768 0.02272774 0.02358677 0.0209967  0.02205678 0.02246609\n",
            " 0.02123574 0.01939407 0.01840614 0.0219019  0.02149836 0.02372578\n",
            " 0.02347326 0.02461847 0.02679058 0.02750532 0.02931332 0.03033877\n",
            " 0.03168263 0.02919115 0.03078512 0.03224249 0.03361713 0.0277256\n",
            " 0.02494554 0.02724366 0.02691948 0.02758804 0.02881791 0.02826526\n",
            " 0.02636347 0.02762219 0.02760584 0.02335686 0.01905161 0.01821327\n",
            " 0.01999289 0.02106115 0.02294659 0.02171913 0.02357186 0.02479115\n",
            " 0.02611817 0.02608787 0.02734948 0.02813203 0.02601813 0.02668188\n",
            " 0.02652027 0.02617589 0.0254852  0.02161043 0.02180907 0.02685936\n",
            " 0.03111026 0.03284034 0.03153352 0.03208184 0.03353873 0.03233051\n",
            " 0.03356037 0.03641884 0.03536789 0.03389129 0.03629619 0.03821241\n",
            " 0.03918591 0.03934031 0.03881652 0.03684691 0.03469212 0.03448097\n",
            " 0.03820375 0.03674157 0.03935714 0.04267013 0.03609562 0.03634621\n",
            " 0.03676274 0.0389435  0.03764966 0.03212032 0.028841   0.02775831\n",
            " 0.03005403 0.02346076 0.01996451 0.02229823 0.02011891 0.0193984\n",
            " 0.02633317 0.02761883 0.02681415 0.02772464 0.02879098 0.0299737\n",
            " 0.02979959 0.02918538 0.02617781 0.02399224 0.02639137 0.02507973\n",
            " 0.02921087 0.0308919  0.02991599 0.03092268 0.03096597 0.02998621\n",
            " 0.02972263 0.0296375  0.02869959 0.02681174 0.02347134 0.02786846\n",
            " 0.02715083 0.0277434  0.02837781 0.02706378 0.02640964 0.02881695\n",
            " 0.02892709 0.02747309 0.02731773 0.02589692 0.02815512 0.03018053\n",
            " 0.02723933 0.02758756 0.03035705 0.03123435 0.03350746 0.03338241\n",
            " 0.03663528 0.0384832  0.04100594 0.03769584 0.0402912  0.04217665\n",
            " 0.04440839 0.04584941 0.04974583 0.0506087  0.05230945 0.05705864\n",
            " 0.05415785 0.0607569  0.06347876 0.05517609 0.06096468 0.06642621\n",
            " 0.07674467 0.08467507 0.06567636 0.06915625 0.07405263 0.07836317\n",
            " 0.08490594 0.0858982  0.08791976 0.08696309 0.09475881 0.08833629\n",
            " 0.08631425 0.08404691 0.09143283 0.09015102 0.09326729 0.09278198\n",
            " 0.08762444 0.08391175 0.06980509 0.06784269 0.07599386 0.07321139\n",
            " 0.06516316 0.05960447 0.05558781 0.05368024 0.05250232 0.04495671\n",
            " 0.04712593 0.05681383 0.05626984 0.06454413 0.06763539 0.07339609\n",
            " 0.076926   0.07975897 0.08291853 0.09180463 0.09564478 0.09691071\n",
            " 0.09838492 0.09864801 0.09944115 0.09277958 0.10142808 0.10197928\n",
            " 0.09796454 0.09560533 0.0937906  0.09096436 0.08646287 0.08603913\n",
            " 0.08214559 0.09411334 0.09429803 0.09240345 0.09313839 0.09466742\n",
            " 0.10403306 0.1040297  0.10488728 0.10596949 0.11082739 0.10968217\n",
            " 0.11286145 0.11485175 0.10582231 0.1078708  0.11664099 0.11872941\n",
            " 0.11595704 0.11657606 0.12578346 0.1273048  0.12937686 0.12762272\n",
            " 0.12039985 0.12486671 0.12251472 0.12538088 0.12576133 0.12669347\n",
            " 0.14028933 0.14750787 0.15907737 0.1579841  0.15646901 0.159112\n",
            " 0.17306811 0.17130051 0.16691253 0.12443719 0.12246854 0.13120843\n",
            " 0.12626251 0.13336226 0.13860301 0.1398526  0.13549733 0.14474465\n",
            " 0.1416303  0.13725771 0.13715623 0.14253406 0.13768386 0.14226808\n",
            " 0.15316949 0.15102817 0.15132156 0.15370867 0.15753679 0.16010859\n",
            " 0.17020387 0.17209893 0.16887156 0.16769797 0.16885713 0.17015529\n",
            " 0.15739538 0.15654404 0.15427382 0.13991176 0.15150289 0.14967565\n",
            " 0.15050294 0.14016668 0.13117716 0.12741879 0.13077795 0.13586191\n",
            " 0.1366411  0.14574941 0.14735252 0.14606349 0.14978147 0.14591535\n",
            " 0.14946594 0.14841981 0.14216659 0.14831736 0.14508277 0.14815383\n",
            " 0.15218252 0.15150049 0.14333345 0.14061207 0.142169   0.14215794\n",
            " 0.14483796 0.14288999 0.13783777 0.13967704 0.1371139  0.1403658\n",
            " 0.13820284 0.14523573 0.1524865  0.16147938 0.16596548 0.1699908\n",
            " 0.17003216 0.17822421 0.17115669 0.18389929 0.18027607 0.18706847\n",
            " 0.1982955  0.19199419 0.18746239 0.18445627 0.18732531 0.17282281\n",
            " 0.18021643 0.18841858 0.18864368 0.18563611 0.17610117 0.18188255\n",
            " 0.17244909 0.17761144 0.18669234 0.18505028 0.19318702 0.19696319\n",
            " 0.201191   0.19646057 0.19708824 0.19369301 0.20340976 0.20592817\n",
            " 0.21818787 0.21651839 0.2183033  0.22675124 0.23155767 0.221826\n",
            " 0.21629858 0.22660502 0.2337062  0.24850836 0.26860611 0.2642946\n",
            " 0.27307489 0.2753961  0.2606002  0.26668026 0.28618352 0.28885873\n",
            " 0.31001896 0.29723018 0.31783728 0.30832013 0.292468   0.30144597\n",
            " 0.30148252 0.32035088 0.34276503 0.34788698 0.34426953 0.33354897\n",
            " 0.31260422 0.24523241 0.24924426 0.27782216 0.28434377 0.29117705\n",
            " 0.30289468 0.28284407 0.29655681 0.31660934 0.3082932  0.31638665\n",
            " 0.31979968 0.31449832 0.31386872 0.32822597 0.34106861 0.38390083\n",
            " 0.38714071 0.41830244 0.43433831 0.42881762 0.42421849 0.46959702\n",
            " 0.47973896 0.52021823 0.47837346 0.44286031 0.40343679 0.40890842\n",
            " 0.42757958 0.36778656 0.34514588 0.3614333  0.37178591 0.35131926\n",
            " 0.349075   0.33510398 0.30813736 0.3104047  0.33637521 0.34901344\n",
            " 0.34709337 0.34656477 0.3568563  0.34781003 0.34746084 0.32290585\n",
            " 0.2969238  0.29723788 0.27648215 0.27977061 0.29524037 0.29736245\n",
            " 0.29519564 0.29450447 0.28431587 0.2959931  0.30915896 0.31509137\n",
            " 0.32830773 0.34044767 0.33615781 0.35306762 0.35723723 0.37462466\n",
            " 0.3890781  0.40192507 0.39218235 0.37573476 0.38407254 0.39024159\n",
            " 0.38603254 0.38213468 0.3961437  0.4058922  0.41354362 0.42396165\n",
            " 0.42191267 0.44424313 0.44155253 0.42986087 0.44130819 0.4555197\n",
            " 0.48053835 0.49237094 0.50886326 0.47863319 0.49983575 0.52138942\n",
            " 0.55377764 0.54140203 0.56171237 0.56621241 0.54410417 0.53777207\n",
            " 0.54826176 0.55993947 0.54490981 0.57296633 0.59258021 0.60008398\n",
            " 0.60613134 0.60665561 0.61245045 0.62453171 0.65531923 0.64809443\n",
            " 0.64626959 0.636258   0.65734464 0.68264947 0.63763552 0.64455875\n",
            " 0.61195023 0.63251645 0.62133078 0.64955998 0.6869653  0.67505143\n",
            " 0.63300801 0.64158581 0.54450723 0.4487855  0.42511456 0.41150667\n",
            " 0.39742358 0.36991731 0.3986496  0.42772195 0.47799589 0.47822869\n",
            " 0.4980566  0.50195542 0.52729104 0.50400151 0.52980368 0.54418065\n",
            " 0.51283038 0.53857819 0.55820698 0.56652649 0.54499254 0.52245527\n",
            " 0.54260881 0.55224909 0.57412357 0.58891803 0.60222386 0.62580823\n",
            " 0.63103984 0.6591536  0.6581724  0.64993273 0.64310715 0.61896148\n",
            " 0.60187515 0.59336517 0.53829971 0.56851583 0.56620953 0.55423217\n",
            " 0.57813975 0.58736783 0.57525532 0.57047005 0.53297863 0.53698759\n",
            " 0.54026547 0.55395175 0.57166143 0.57673337 0.56790498 0.57724417\n",
            " 0.58935091 0.59592062 0.59246093 0.57834898 0.58767614 0.56260218\n",
            " 0.57979865 0.58784352 0.59425498 0.62186612 0.62350818 0.63438554\n",
            " 0.63791545 0.66266908 0.66871596 0.68394186 0.68164711 0.707709\n",
            " 0.71659414 0.73078257 0.69878682 0.68208768 0.6884068  0.68300732\n",
            " 0.68498126 0.7119575  0.69599377 0.71148517 0.70136391 0.67920229\n",
            " 0.67511925 0.64581218 0.61925439 0.62994225 0.62708811 0.60496881\n",
            " 0.59593505 0.59777335 0.62826796 0.65025307 0.65575211 0.65569247\n",
            " 0.68061685 0.68134842 0.68750063 0.69045433 0.70467113 0.71451871\n",
            " 0.71925011 0.71988886 0.72703189 0.72887789 0.71751618 0.70944919\n",
            " 0.70760607 0.71087673 0.73122555 0.75001503 0.75202986 0.75884294\n",
            " 0.74645963 0.72197823 0.71835212 0.72992402 0.75174223 0.76214246\n",
            " 0.76965921 0.7614282  0.75230209 0.70199833 0.71020096 0.66811714\n",
            " 0.72668799 0.74873659 0.75369453 0.77671615 0.7505874  0.76716725\n",
            " 0.76833892 0.77004688 0.78046298 0.77202323 0.79881573 0.79993305\n",
            " 0.81220092 0.76143638 0.62270735 0.69014025 0.70996095 0.72546726\n",
            " 0.75692238 0.77352773 0.75462234 0.72861383 0.80603283 0.81772594\n",
            " 0.81309218 0.84787857 0.878681   0.89828863 0.92823732 0.94914022\n",
            " 0.95501876 0.9692144  0.94455745 0.99105858 0.97292083 1.        ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEmb-tgcDk0U",
        "outputId": "53fe4e32-33c5-4d79-c973-77b0f718496d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[0.00449812]\n",
            "  [0.005437  ]\n",
            "  [0.00754176]\n",
            "  ...\n",
            "  [0.00327499]\n",
            "  [0.00419992]\n",
            "  [0.00541728]]\n",
            "\n",
            " [[0.005437  ]\n",
            "  [0.00754176]\n",
            "  [0.00728685]\n",
            "  ...\n",
            "  [0.00419992]\n",
            "  [0.00541728]\n",
            "  [0.00690158]]\n",
            "\n",
            " [[0.00754176]\n",
            "  [0.00728685]\n",
            "  [0.00615558]\n",
            "  ...\n",
            "  [0.00541728]\n",
            "  [0.00690158]\n",
            "  [0.00780582]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.69045433]\n",
            "  [0.70467113]\n",
            "  [0.71451871]\n",
            "  ...\n",
            "  [0.95501876]\n",
            "  [0.9692144 ]\n",
            "  [0.94455745]]\n",
            "\n",
            " [[0.70467113]\n",
            "  [0.71451871]\n",
            "  [0.71925011]\n",
            "  ...\n",
            "  [0.9692144 ]\n",
            "  [0.94455745]\n",
            "  [0.99105858]]\n",
            "\n",
            " [[0.71451871]\n",
            "  [0.71925011]\n",
            "  [0.71988886]\n",
            "  ...\n",
            "  [0.94455745]\n",
            "  [0.99105858]\n",
            "  [0.97292083]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(features.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_AmBhHBuDoHO",
        "outputId": "8d0b7bbc-3bcb-4f74-9c2b-9f6dd78ceab6"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(732, 60, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "1bTurM3hDswa"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.LSTM(units = 50, return_sequences = True, input_shape = (features.shape[1], 1)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.LSTM(units = 50, return_sequences = True),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.LSTM(units = 50, return_sequences = True),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.LSTM(units = 50),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(units = 1)\n",
        "])"
      ],
      "metadata": {
        "id": "Ocoe0aQLDvkY"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rEFucYgiDyN1",
        "outputId": "791305e7-2c76-4ef2-e165-f2dd7b569818"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 60, 50)            10400     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 60, 50)            0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 60, 50)            20200     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 60, 50)            0         \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 60, 50)            20200     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 60, 50)            0         \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 50)                20200     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 50)                0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 51        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 71,051\n",
            "Trainable params: 71,051\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 953
        },
        "id": "L5D2IYOXD8qU",
        "outputId": "f517379e-a41e-44a6-81f2-d757117a0c9c"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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NDZcvX05MTDRoyaZmwbkUCoWurq7JycmbNm1qbGzUcC0Gg4EQ6u/vpybpdDpC6On3s8ZGp9NXr15948aNCxcuREVFyWSyuLi49vZ2HTbF4/ESExPb29sLCwsRQnl5eatWraJ203JZcC7ZbPbJkyfDw8O3bt0qFArFYjF1wLAsr7zyyvfff79y5cqWlpZTp07pthHq3c+ePXs6OjoOHz5MndktmgXnEiEUGBhYVlbW1NSUlZUllUpzcnLMXdGIzpw5k5eXR72OjY1VHbAp1KMJe3t7ddv4Sy+9FBoaeunSJYlEEhcX5+TkpGe1ZmfBuWxqaqqpqUEI8fn8zz77bNq0adQknn799VfVT/s8efJkSKnUu+kpU6bovH3qkFlcXPzee+/pUSYuLDuXaWlptbW1CoWisrLy7t27oaGhCCFnZ+empqbGxsauri7TXzg+TalUPnz48PTp06pcIoSio6OLioo6Ojo6OzuPHj36wQcfvP766/rkcsmSJS4uLtHR0UKh0BBVm5u5nz7yLyKR6JnPgdm5cyf1fGgulxsTE9PY2BgWFubk5GRjYzN27Nj169f39/eTJHnlyhVvb282mx0eHr5u3TrqHvX48ePPnj37+eefUw+nFAgE3333XWFhIbVBJyengoKCZxaJRn0ODEmSpaWl1N8Gh1VaWko1O3HiRHx8vK+vL5PJZDAYkyZN2rRp0+PHj9U3JZfLIyIinJ2dEUI0Gs3Pz2/r1q1Pd+Ti4vL2229TMzMzM8+fP0+93rBhg5ubG7VuQEDA2bNnn7l31HNgntnMNDB6bhZC6PDhw+YuZDQEQUilUkyeeGZwRUVF8fHxmOTBgs/jwIpBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHGH0k4YPHjwoKioydxXPUFFRYe4SjAWrXcPoexTw++M4wCQPuOTSClj3t39MDK4vAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOMHq+usXZu3dve3u7+pyjR4/euXNHNZmamioQCExelzWA51jrTiKR7N27l8lkUpMkSRIEQb3u7+/n8XjNzc10Ot18BVowOI/rLiEhASH05N8UCoXqNY1GS0hIgFDqDI6XuhscHHR3d5fJZMMu/fnnn2fOnGnikqwGHC91R6PRkpOTGQzG04vc3d3DwsJMX5LVgFzqJSEhQaFQDJlJp9NTUlJU15pAB3Ae15dQKFR/D065evXq1KlTzVKPdYDjpb5SUlKGvL8RCoUQSj1BLvWVnJysVCpVk3Q6/Y033jBjPdYBzuMGMGXKlN9++001krdu3ZowYYJ5S7J0cLw0gJSUFBsbG4QQQRAvv/wyhFJ/kEsDSExMHBgYQAjZ2NgsW7bM3OVYA8ilAYwdOzYsLIwgiMHBwbi4OHOXYw0gl4axdOlSkiQjIiLGjh1r7lqsAmk4IpHI3HsDzMmAWTLw59xCQ0Pfe+89w24Tf/Hx8enp6efPn5dIJHZ2duYuxwwqKiry8/MNuEED59LT03PJkiWG3Sb+4uPjZ8yYkZGR4enpae5azMawuYTrS4N5nkNpcJBLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4MnMuly9fbm9vTxDE1atXzVuJUZWUlAiFQkINg8FwdXWdPXt2dnb2kIcVAmT2XO7bt++bb74xbw0mEBsb29DQ4Ovry+PxSJIcHByUyWRFRUU+Pj5ZWVmBgYG//PKLuWvEC5zHR9TX12ekZ18RBOHo6Dh79uwDBw4UFRU9fPhw0aJFnZ2dxuhLH8YbgWcyfy6xfb7U/v37R3qGoAGJRKLU1FSZTLZnzx5j96Ut04zAsMyQS5Iks7OzJ02axGQyeTze2rVrVYt27NjB4XDs7e1lMllGRoaHh8fNmzdJkszNzZ08eTKTyXRycoqKiqqtraXaf/nllywWy9XVNS0tzd3dncVihYWFXbx4Ub2vkdZdvXo1g8Fwc3OjJt966y0ul0sQxKNHjxBC6enpGRkZ9fX1BEH4+fkZdUBSU1MRQj/++ONzOwLDMOB32EQikUgkemaz9evXEwSxc+fO9vb23t7er776CiFUWVmpWooQevfdd3ft2hUTE3Pjxo2NGzcyGIyDBw92dHRUV1dPmzbNxcWlubmZai+RSLhcbk1NzePHj69fvx4SEmJvb3/v3j1q6ejrJiUlCQQCVWHZ2dkIoZaWFmoyNjbW19dXkx1HCEml0mc2U11fDiGXyxFCXl5eljsCUqnUsFkydS57e3s5HM68efNUcwoKCp7OZV9fn6q9nZ2dWCxWtb906RJCaPPmzdSkRCJR/8e+fPkyQuiTTz7RZF1MckmSJHXFSb22xBEweC5NfR6/fft2b2/vq6++qmH769evd3d3BwcHq+aEhIQwGAz1U5W64OBgDodDnaq0Xddcenp6SJJ0cHAYdunzMAJPM3UuHzx4gBDi8/katu/o6EAIDflStqOjY1dX10irMJnMlpYW3dY1i1u3biGE/P39h136PIzA00ydSxaLhRB68uSJhu0dHR0RQkPGsaOjY6QvxSqVStVSbdc1l+PHjyOEFi5cOOzS52EEnmbqXL7wwgs0Gq28vFzz9nZ2duq3nS9evKhQKIKCgoZtf/r0aZIkQ0NDNVnX1tZW/ZGqZtHc3JyXl+fp6fnmm28O28DqR2BYps4ln8+PjY0tLi7ev3+/XC6vrq7eu3fvKO1ZLFZGRkZpaemhQ4fkcvm1a9dWrlzp7u4ukUhUbQYHB9vb2/v7+6urq9PT08eNG0fdeXnmun5+fm1tbUeOHFEqlS0tLXfv3lXv2tnZuampqbGxsaury1D/eCRJdnd3Dw4OkiTZ0tIilUpnzpxpY2Nz5MiRka4vrWwENGXA91Aa3ifq6upavnz5mDFj7OzswsPDN27ciBDy9PSsqqravn07m81GCHl5eR08eJBqPzg4mJ2dPWHCBDqd7uTkFB0dTd3So0gkEjqd7uHhYWtr6+DgEBUVVV9fr1o6+rqtra1z5sxhsVg+Pj7vvPMOdSfVz8+Pusly5coVb29vNpsdHh6uurEyLPSs9+PHjh2bMmUKh8NhMBg0Gg39+08+06dP37x5c2trq6qlhY6Axd8nMjiJROLs7GziTod4Zi6NCocRsPj7RMZAPav3eWZ9I2ANuQTWx7Jz+eGHHx44cKCzs9PHx6e4uNjc5ZiBtY6AZf/++LZt27Zt22buKszJWkfAso+XwFpBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAkgE/Ywy/P/6cM2CWCJIkDVVWRUXF/fv3DbU1i0P9CvmMGTPMXYjZGPAnvg2Zy+ccQRBSqfQ5/Pl1Y4DrS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHtuYuwILdvXt3YGBAfc7Dhw8bGhpUk+7u7mw22+R1WQN4XrDuFi5cePz48ZGW2traNjc3jxkzxpQlWQ04j+tOLBYTBDHsIhqNNm/ePAilziCXuouJiaHT6SMtXbp0qSmLsTKQS93Z29u/9tprw0aTTqcvXrzY9CVZDcilXpKSkvr7+4fMtLW1jY6OtrOzM0tJ1gFyqZdFixZxudwhMwcGBpKSksxSj9WAXOqFyWSKRCIGg6E+087Obv78+eYqyTpALvWVmJioUChUk3Q6XSwWD0kq0Bbcv9TX4OCgQCB49OiRas6pU6dmz55tvoqsARwv9UWj0RITE1UHSD6fP2vWLPOWZAUglwaQkJBAncoZDEZKSoqNjY25K7J4cB43AJIkvb29qd8Svnz5cnBwsLkrsnhwvDQAgiBSUlIQQt7e3hBKgzDd54lyc3MrKipM1p2JyeVyhBCXy42LizN3LUZ0+PBh03RkuuNlRUXFhQsXTNadiTk4OCCERvlzuaV78OBBcXGxyboz3fUldSAx2X840yMIQiqVLlmyxNyFGEVRUVF8fLzJ0gLXlwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDjCK5c5OTmurq4EQezZs8eMZQwODubl5YWFhRl2syUlJUKhkCAIgiDc3NySk5NHallVVSUWi318fJhMpouLy9SpUz/99FNqEfWwrlH88MMP6h199NFHw3aRm5tLEASNRvP39z9z5oxh91R/eOXy/fffP3/+vHlrqKuri4iIWLNmTW9vr2G3HBsb29DQ4Ovry+PxmpubDx06NGyza9euhYWFubm5nTp1qrOz8/z58wsWLDh9+rSqwYkTJzo6OpRK5R9//IEQioyMVCgUPT09MplsxYoV6h0hhPbt26dUKod0MTAw8OWXXyKE5s6dW1tbGxERYdg91R9eudRQX1+fwQ9mlKqqqg8++GDlypUvvfSSMbaviZycHEdHx/z8/PHjx7NYrIkTJ27ZskX1fFeCIGbOnMnj8WxtbVVz6HQ6h8Ph8/lBQUHqmwoKCmpubj5y5MiQLkpKSjw8PEywLzqzyFzu379fJpMZY8tTp04tKSlJSkpiMpnG2L4mWltbOzs729raVHMYDEZZWRn1uqCggMPhjLSuRCJ57bXXVJOrVq1CCO3evXtIs9zc3IyMDEMWbWi457K8vHz69OkcDsfBweHFF1+Uy+Xp6ekZGRn19fUEQfj5+eXn53O5XBqNFhQUJBAI6HQ6l8udNm3arFmzvLy8WCyWo6NjZmamufdDCyEhIT09PXPnzj137pyem5o7d+7kyZNPnTp18+ZN1cxz58719vZi/gQlrHPZ09MTGRkpEona2trq6uomTpyoUCjy8/MXL17s6+tLkuTt27fT09PXrl1LkuTu3bvv3LnT3NwcERFRWVm5bt26ysrKtra2ZcuWZWdnV1VVmXtvNJWZmRkcHFxVVRUeHh4YGLhjxw71Y6e20tLSEELq7yN37ty5Zs0aAxRqTFjnsrGxUS6XBwYGslgsgUBQUlLi4uIyUuOAgAAOhzNmzJiEhASE0Lhx41xcXDgcDvW2t7a21nR164fNZp8/f/6LL77w9/evqanJysqaPHlyeXm5bltbtmwZl8v99ttv+/r6EEINDQ2XL19OTEw0aMmGh3UuhUKhq6trcnLypk2bGhsbNVyLelSQ6nGp1Hdnn35PijM6nb569eobN25cuHAhKipKJpPFxcW1t7frsCkej5eYmNje3l5YWIgQysvLW7VqFf6Pm8M6l2w2++TJk+Hh4Vu3bhUKhWKxmPpP//x45ZVXvv/++5UrV7a0tJw6dUq3jVDvfvbs2dPR0XH48GHqzI45rHOJEAoMDCwrK2tqasrKypJKpTk5OeauyCjOnDmTl5dHvY6NjR3ybGzqJwR0vp/60ksvhYaGXrp0SSKRxMXFOTk56VmtCWCdy6amppqaGoQQn8//7LPPpk2bRk1an19//VX1POwnT54M2U3q3fSUKVN03j51yCwuLn7vvff0KNN0cM9lWlpabW2tQqGorKy8e/duaGgoQsjZ2bmpqamxsbGrq8uyLhyfplQqHz58ePr0afXntEdHRxcVFXV0dHR2dh49evSDDz54/fXX9cnlkiVLXFxcoqOjhUKhIao2PtJURCKRSCQavc3OnTsFAgFCiMvlxsTENDY2hoWFOTk52djYjB07dv369f39/SRJXrlyxdvbm81mh4eHr1u3jrrPPH78+LNnz6V2VQgAACAASURBVH7++ec8Hg8hJBAIvvvuu8LCQmqDTk5OBQUFzyyyoqJi5syZ7u7u1OC4ubmFhYWVl5drsoMIIalUOkqD0tJS6m+DwyotLaWanThxIj4+3tfXl8lkMhiMSZMmbdq06fHjx+qbksvlERERzs7OCCEajebn57d169anO3JxcXn77bepmZmZmefPn6deb9iwwc3NjVo3ICDg7Nmzz9w7qVRqyrTA84kMBp5PZEBYn8fBc+s5ymVtbe0oHw8Ti8XmLhD8n+fod579/f1NdhoCenqOjpfAgkAuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgyKSfc7tw4YJ1/zx3Xl6etX4g/8GDB6bsznS5nDFjhsn6MgsGg0GjWe35x9PTUyQSmaw7032/x+pZ9/d7TMxq/38Diwa5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEzwvW3dKlS69evaqabGxs5PP5XC6XmqTT6WVlZR4eHmaqzrKZ9Ln/VmbSpEmHDh1Sn9Pd3a167e/vD6HUGZzHdZeQkEAQxLCL6HR6amqqacuxKnAe10tQUNDVq1cHBweHzCcIoqGhYfz48eYoyhrA8VIvKSkpT/82CkEQ06dPh1DqA3Kpl/j4+KcPljQaLSUlxSz1WA3IpV7c3NxmzZplY2MzZH5sbKxZ6rEakEt9LV26VH2SRqPNmTNHIBCYqx7rALnUV1xc3JBLzCFJBTqAXOrLwcFhwYIFtrb/uhNsY2Pz+uuvm7ckKwC5NIDk5OSBgQGEkK2tbWRkJI/HM3dFFg9yaQCRkZFsNhshNDAwkJSUZO5yrAHk0gBYLFZMTAxCiMPhLFy40NzlWAPt/j5eUVFx//59I5Vi0by8vBBCISEhx44dM3ctmNLuF7BJbZjyl9GBldEqaVqfx0UikVYdPD8+/vhjpVL59HyEkFQqNX09+JBKpdrGDK4vDWbDhg2qu0VAT5BLg4FQGhDkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcGT2Xy5cvt7e3JwhC/dFnlmtwcDAvLy8sLMyA2ywpKREKhYQaBoPh6uo6e/bs7Ozs9vZ2A/ZlKYyey3379n3zzTfG7sU06urqIiIi1qxZ09vba8DNxsbGNjQ0+Pr68ng8kiQHBwdlMllRUZGPj09WVlZgYOAvv/xiwO4swnN9Hu/r69P8yFdVVfXBBx+sXLnypZdeMmpVBEE4OjrOnj37wIEDRUVFDx8+XLRoUWdnp1E71YFWo6ctU+RypIfxmd3+/ftlMpmGjadOnVpSUpKUlMRkMo1alTqRSJSamiqTyfbs2WOyTjWk1ehpyyi5JEkyOzt70qRJTCaTx+OtXbtWtWjHjh0cDsfe3l4mk2VkZHh4eNy8eZMkydzc3MmTJzOZTCcnp6ioqNraWqr9l19+yWKxXF1d09LS3N3dWSxWWFjYxYsX1fsaad3Vq1czGAw3Nzdq8q233uJyuQRBPHr0CCGUnp6ekZFRX19PEISfn58xxsEgqOdo/vjjj+i5Gj2tvqghEok0+X7P+vXrCYLYuXNne3t7b2/vV199hRCqrKxULUUIvfvuu7t27YqJiblx48bGjRsZDMbBgwc7Ojqqq6unTZvm4uLS3NxMtZdIJFwut6am5vHjx9evXw8JCbG3t7937x61dPR1k5KSBAKBqrDs7GyEUEtLCzUZGxvr6+ur1QiQJPnKK69MnTpV8/ZIs+/3qK4vh5DL5QghLy8vatISR4/6fo8mLVUMn8ve3l4OhzNv3jzVnIKCgqdz2dfXp2pvZ2cnFotV7S9duoQQ2rx5MzUpkUjU/8EuX76MEPrkk080WdcKckmSJHXFSb22xNHTIZeGP4/fvn27t7f31Vdf1bD99evXu7u7g4ODVXNCQkIYDIb66UZdcHAwh8OhTjfarmuJenp6SJJ0cHAYdqm1jp7hc/ngwQOEEJ/P17B9R0cHQsjOzk59pqOjY1dX10irMJnMlpYW3da1OLdu3UII+fv7D7vUWkfP8LlksVgIoSdPnmjY3tHRESE0ZCw6Ojo8PT2Hba9UKlVLtV3XEh0/fhwhNNLjZax19AyfyxdeeIFGo5WXl2ve3s7OTv3W8cWLFxUKRVBQ0LDtT58+TZJkaGioJuva2toqlUod9wQDzc3NeXl5np6eb7755rANrHX0DJ9LPp8fGxtbXFy8f/9+uVxeXV29d+/eUdqzWKyMjIzS0tJDhw7J5fJr166tXLnS3d1dIpGo2gwODra3t/f391dXV6enp48bN466e/LMdf38/Nra2o4cOaJUKltaWu7evavetbOzc1NTU2NjY1dXFw7xJUmyu7t7cHCQJMmWlhapVDpz5kwbG5sjR46MdH1ptaOn1bskDe8TdXV1LV++fMyYMXZ2duHh4Rs3bkQIeXp6VlVVbd++nXokn5eX18GDB6n2g4OD2dnZEyZMoNPpTk5O0dHR1G05ikQiodPpHh4etra2Dg4OUVFR9fX1qqWjr9va2jpnzhwWi+Xj4/POO+9Qd1L9/PyoGyVXrlzx9vZms9nh4eGqmyMjqaiomDlzpru7OzVubm5uYWFh5eXlzxwN9Kz348eOHZsyZQqHw2EwGNSjh6k34NOnT9+8eXNra6uqpYWOHhb3iQxOIpE4OzubuFMDemYujQqH0cPiPpExUE/jBbqxxNGzjFwaW21tLTEysVhs7gKfO7jn8sMPPzxw4EBnZ6ePj09xcbGRevH39x/lnFJYWGikfo3NNKNnDLg/gmzbtm3btm0zdxWWynJHD/fjJXg+QS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwJHWnyd68OBBUVGRMUqxYhUVFeYuwZx02X2tPt0Ovz8OdKZV0giSJM1dsJUgCEIqlS5ZssTchVgDuL4EOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDjS+vnqQGXv3r3t7e3qc44ePXrnzh3VZGpqqkAgMHld1gCeY607iUSyd+9eJpNJTZIkSRAE9bq/v5/H4zU3N9PpdPMVaMHgPK67hIQEhNCTf1MoFKrXNBotISEBQqkzOF7qbnBw0N3dXSaTDbv0559/njlzpolLshpwvNQdjUZLTk5mMBhPL3J3dw8LCzN9SVYDcqmXhIQEhUIxZCadTk9JSVFdawIdwHlcX0KhUP09OOXq1atTp041Sz3WAY6X+kpJSRny/kYoFEIo9QS51FdycrJSqVRN0un0N954w4z1WAc4jxvAlClTfvvtN9VI3rp1a8KECeYtydLB8dIAUlJSbGxsEEIEQbz88ssQSv1BLg0gMTFxYGAAIWRjY7Ns2TJzl2MNIJcGMHbs2LCwMIIgBgcH4+LizF2ONYBcGsbSpUtJkoyIiBg7dqy5a7EKWv1auT5EIpG59xXoy2RpMenn3EJDQ9977z1T9mhK8fHxaWlpc+bMMXchRlFRUZGfn2+y7kyaS09PzyVLlpiyR1OKj4+fM2eOFe+gKXMJ15cAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4wiuXOTk5rq6uBEHs2bPHLAVs3rw5ICDAwcGByWT6+fllZmZ2d3cbauMlJSVCoZAgCIIg3NzckpOTR2pZVVUlFot9fHyYTKaLi8vUqVM//fRTapFYLCZG9cMPP6h39NFHHw3bRW5uLkEQNBrN39//zJkzhtpHgzHZJ5BFIpFIJHpms7q6OoTQ7t27TVDS0/7jP/7jq6++am1tlcvlUqmUTqcvWLBAw3URQlKp9JnNfH19eTzeKA2qq6s5HM677757586dvr6+mzdvZmZmvvrqq9TS+Pj4EydOdHR0KJXKP/74AyEUGRmpUCh6enpkMtmKFSvKyspUHSGE3NzcFArFkC76+/u9vb0RQqrNPpNUKjVlWvA6Xmqor6/PSE+lsrOzk0gkzs7O9vb2S5YsiY6OPn78+P37943R10hycnIcHR3z8/PHjx/PYrEmTpy4ZcsWNptNLSUIYubMmTwez9bWVjWHTqdzOBw+nx8UFKS+qaCgoObm5iNHjgzpoqSkxMPDwwT7ojOLzOX+/ftHerqfnn744Qfqm+AUFxcXhFBvb68x+hpJa2trZ2dnW1ubag6DwSgrK6NeFxQUcDickdaVSCSvvfaaanLVqlUIod27dw9plpubm5GRYciiDQ33XJaXl0+fPp3D4Tg4OLz44otyuTw9PT0jI6O+vp4gCD8/v/z8fC6XS6PRgoKCBAIBnU7ncrnTpk2bNWuWl5cXi8VydHTMzMzUrffff/+dzWb7+PgYdqdGFxIS0tPTM3fu3HPnzum5qblz506ePPnUqVM3b95UzTx37lxvb+/8+fP13LhRYZ3Lnp6eyMhIkUjU1tZWV1c3ceJEhUKRn5+/ePFiX19fkiRv376dnp6+du1akiR37959586d5ubmiIiIysrKdevWVVZWtrW1LVu2LDs7u6qqStvee3t7T548uWLFimGfcGk8mZmZwcHBVVVV4eHhgYGBO3bsUD92aistLQ0hpP4+cufOnWvWrDFAocaEdS4bGxvlcnlgYCCLxRIIBCUlJdSJdVgBAQEcDmfMmDHU46XHjRvn4uLC4XCot721tbXa9r5t2zZ3d3fVG2GTYbPZ58+f/+KLL/z9/WtqarKysiZPnlxeXq7b1pYtW8blcr/99tu+vj6EUENDw+XLlxMTEw1asuFhnUuhUOjq6pqcnLxp06bGxkYN16IOb/39/dQk9RBA9UeuaaK0tLSoqOinn36yt7fXakWDoNPpq1evvnHjxoULF6KiomQyWVxc3JDfvtAQj8dLTExsb28vLCxECOXl5a1atcrEZwAdYJ1LNpt98uTJ8PDwrVu3CoVCsVhM/ac3tsLCws8///z06dPjx483QXejeOWVV77//vuVK1e2tLScOnVKt41Q73727NnT0dFx+PBh6syOOaxziRAKDAwsKytramrKysqSSqU5OTnG7nHXrl2HDh06efKkKZ/ocubMmby8POp1bGys6mBPWbp0KdLjtsBLL70UGhp66dIliUQSFxfn5OSkZ7UmgHUum5qaampqEEJ8Pv+zzz6bNm0aNWkkJElmZWVdu3btyJEjdnZ2xuvoab/++iuXy6VeP3nyZMhuUu+mp0yZovP2qUNmcXGxpTzvBPdcpqWl1dbWKhSKysrKu3fvhoaGIoScnZ2bmpoaGxu7urq0vXAcRU1NzY4dO7755hs6na7+lz2jHqSVSuXDhw9Pnz6tyiVCKDo6uqioqKOjo7Oz8+jRox988MHrr7+uTy6XLFni4uISHR0tFAoNUbXxmewvS5r8HXLnzp3UD9dxudyYmJjGxsawsDAnJycbG5uxY8euX7++v7+fJMkrV654e3uz2ezw8PB169ZR95nHjx9/9uzZzz//nMfjIYQEAsF3331XWFhIbdDJyamgoGD03q9duzbsEGVnZ2uyg+hZf4csLS2l/jY4rNLSUqrZiRMn4uPjfX19mUwmg8GYNGnSpk2bHj9+rL4puVweERHh7OyMEKLRaH5+flu3bn26IxcXl7fffpuamZmZef78eer1hg0b3NzcqHUDAgLOnj37zL0z8d8hTfcca+rBkIcPHzZNd6ZHEIRUKrXW5xMVFRXFx8ebLC1Yn8fBc+s5ymVtbe0oHw8Ti8XmLhD8n+fod579/f1NdhoCenqOjpfAgkAuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BJgyWSfjIffH7cCJkuL6b5HUVFRYeIHo5lYfHx8enr6jBkzzF2IEZnsWyKmy6XVs+7v95gYXF8CHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOLI1dwEW7O7duwMDA+pzHj582NDQoJp0d3dns9kmr8sawPOCdbdw4cLjx4+PtNTW1ra5uXnMmDGmLMlqwHlcd2KxmCCIYRfRaLR58+ZBKHUGudRdTEwMnU4faenSpUtNWYyVgVzqzt7e/rXXXhs2mnQ6ffHixaYvyWpALvWSlJTU398/ZKatrW10dLSdnZ1ZSrIOkEu9LFq0iMvlDpk5MDCQlJRklnqsBuRSL0wmUyQSMRgM9Zl2dnbz5883V0nWAXKpr8TERIVCoZqk0+lisXhIUoG24P6lvgYHBwUCwaNHj1RzTp06NXv2bPNVZA3geKkvGo2WmJioOkDy+fxZs2aZtyQrALk0gISEBOpUzmAwUlJSbGxszF2RxYPzuAGQJOnt7U39WvDly5eDg4PNXZHFg+OlARAEkZKSghDy9vaGUBqEdp8nys3NraioMFIpFk0ulyOEuFxuXFycuWvB1OHDhzVvrN3xsqKi4sKFC1rW81xwcHDg8Xienp5PLyouLn7w4IHpS8LHgwcPiouLtVuH1IZIJBKJRFqt8vw4fvz4sPMRQlKp1MTFYEUqlWqbNLi+NJg//elP5i7BekAuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgyOi5XL58ub29PUEQV69eNXZfRrV58+aAgAAHBwcmk+nn55eZmdnd3W2QLZeUlAiFQkINg8FwdXWdPXt2dnZ2e3u7QXqxLEbP5b59+7755htj92ICJ0+efPvttxsbGx89erRt27b8/HxDfTQ9Nja2oaHB19eXx+ORJDk4OCiTyYqKinx8fLKysgIDA3/55ReDdGRBnuvzeF9fX1hYmIaN7ezsJBKJs7Ozvb39kiVLoqOjjx8/Tn3XzLAIgnB0dJw9e/aBAweKiooePny4aNGizs5Og3ekJ61GT1umyOVID4k0u/3798tkMg0b//DDD+pfwHVxcUEI9fb2GqWyfxOJRKmpqTKZbM+ePUbtSAdajZ62jJJLkiSzs7MnTZrEZDJ5PN7atWtVi3bs2MHhcOzt7WUyWUZGhoeHx82bN0mSzM3NnTx5MpPJdHJyioqKqq2tpdp/+eWXLBbL1dU1LS3N3d2dxWKFhYVdvHhRva+R1l29ejWDwXBzc6Mm33rrLS6XSxAE9WyM9PT0jIyM+vp6giD8/Py03cfff/+dzWb7+PjoPEoaSk1NRQj9+OOPyIpG79m0+taFht/vWb9+PUEQO3fubG9v7+3t/eqrrxBClZWVqqUIoXfffXfXrl0xMTE3btzYuHEjg8E4ePBgR0dHdXX1tGnTXFxcmpubqfYSiYTL5dbU1Dx+/Pj69eshISH29vb37t2jlo6+blJSkkAgUBWWnZ2NEGppaaEmY2NjfX19tRoBSk9Pj729/erVqzVpjDT7fo/q+nII6puWXl5e1KQljp4O3+8xfC57e3s5HM68efNUcwoKCp7OZV9fn6q9nZ2dWCxWtb906RJCaPPmzdSkRCJR/we7fPkyQuiTTz7RZF0j5XL9+vUTJ06Uy+WaNNYzlyRJUlecqq4tbvR0yKXhf4/i9u3bvb29r776qobtr1+/3t3drf44gJCQEAaDoX66URccHMzhcKjTjbbrGkRpaWlRUdGJEyfs7e2N14tKT08PSZIODg7DLrW40dOQ4XNJfVeaz+dr2L6jowMhNOTpuo6Ojl1dXSOtwmQyW1padFtXT4WFhbm5uadPnx47dqyRuhji1q1bCCF/f/9hl1rW6GnO8LlksVgIoSdPnmjY3tHRESE0ZCw6OjqGfUYAQkipVKqWaruunnbt2vXTTz+dPHnSlM+opn6KZeHChcMutaDR04rh34+/8MILNBqtvLxc8/Z2dnbqt44vXryoUCiCgoKGbX/69GmSJENDQzVZ19bWVqlU6rgnakiSzMrKunbt2pEjR0wZyubm5ry8PE9PzzfffHPYBhYxerrQ6mpUw/fjcXFxNjY2+/bt6+zsrKqqmjNnDhr5fQ9Jkh9//DGdTj948GBnZ2d1dfXLL7/s7u7e3d1NLZVIJPb29m1tbUqlsqqqKiAgYNy4cY8fP9Zk3S1btiCEvv/+e4VCIZPJ3n77baR25b5ixQo2m33nzh25XK5QKEbZo99++23Y0cvOzn7maCCN3/c4ODh0dXUNDAxQf/IpLCwUCoVubm6//PKLqpkljh4W78dJkuzq6lq+fPmYMWPs7OzCw8M3btyIEPL09Kyqqtq+fTv103ReXl4HDx6k2g8ODmZnZ0+YMIFOpzs5OUVHR1O35SgSiYROp3t4eNja2jo4OERFRdXX16uWjr5ua2vrnDlzWCyWj4/PO++8Q91J9fPzo26UXLlyxdvbm81mh4eHq26ODOvatWvGy+WxY8emTJnC4XAYDAaNRkP//pPP9OnTN2/e3NraqmppoaOHSy4Ni/rrn4k7NSANj5dGgsPoWe3ziYb8PCjQiiWOnmXk0thqa2uJkYnFYnMX+NzBPZcffvjhgQMHOjs7fXx8tH6Gosb8/f1HOacUFhYaqV9jM83oGQPuvz++bdu2bdu2mbsKS2W5o4f78RI8nyCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwBHkEuBI688TXbhwAX5iW1t5eXla/fq2ldHhZ661y+WMGTO07eD5cezYseDg4Ke/Vy4SicxSDz48PT21HQSCJEkjVfO8IQhCKpUuWbLE3IVYA7i+BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4gucF627p0qVXr15VTTY2NvL5fC6XS03S6fSysjIPDw8zVWfZtH7uP1CZNGnSoUOH1Od0d3erXvv7+0ModQbncd0lJCQQBDHsIjqdnpqaatpyrAqcx/USFBR09erVwcHBIfMJgmhoaBg/frw5irIGcLzUS0pKCo02dAwJgpg+fTqEUh+QS73Ex8c/fbCk0WgpKSlmqcdqQC714ubmNmvWLBsbmyHzY2NjzVKP1YBc6mvp0qXqkzQabc6cOQKBwFz1WAfIpb7i4uKGXGIOSSrQAeRSXw4ODgsWLLC1/dedYBsbm9dff928JVkByKUBJCcnDwwMIIRsbW0jIyN5PJ65K7J4kEsDiIyMZLPZCKGBgYGkpCRzl2MNIJcGwGKxYmJiEEIcDmfhwoXmLscamO7v4xUVFffv3zdZdybm5eWFEAoJCTl27Ji5azEi0/2KNWkq8PPwVsBkaTHpeVwkEplsx0wPIfTXv/7V3FUYi1QqNWVU4PrSkJ7+ww/QDeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGO8MplTk6Oq6srQRB79uwxSwHbt2/39/dns9lcLtff3/+jjz6Sy+WG2nhJSYlQKCQIgiAINze35OTkkVpWVVWJxWIfHx8mk+ni4jJ16tRPP/2UWiQWi4lR/fDDD+odffTRR8N2kZubSxAEjUbz9/c/c+aMofbRYEz2AT6RSKTJ5y/r6uoQQrt37zZBSU9btGhRTk6OTCbr6uoqKiqi0+nz5s3TcF2EkFQqfWYzX19fHo83SoPq6moOh/Puu+/euXOnr6/v5s2bmZmZr776KrU0Pj7+xIkTHR0dSqXyjz/+QAhFRkYqFIqenh6ZTLZixYqysjJVRwghNzc3hUIxpIv+/n5vb2+EkGqzz0R9/lLDxvrD63ipob6+vrCwMGNsmcFgvPXWW3w+387OLi4uLioq6u9//zv1z28yOTk5jo6O+fn548ePZ7FYEydO3LJlC/W9NoQQQRAzZ87k8XiqbwYTBEGn0zkcDp/PDwoKUt9UUFBQc3PzkSNHhnRRUlKC+TMQLTKX+/fvl8lkxthyaWkpi8VSTVL/eOpPtTSB1tbWzs7OtrY21RwGg1FWVka9Ligo4HA4I60rkUhee+011eSqVasQQrt37x7SLDc3NyMjw5BFGxruuSwvL58+fTqHw3FwcHjxxRflcnl6enpGRkZ9fT1BEH5+fvn5+Vwul0ajBQUFCQQCOp3O5XKnTZs2a9YsLy8vFovl6OiYmZmpW+91dXWOjo7UKc9kQkJCenp65s6de+7cOT03NXfu3MmTJ586dermzZuqmefOnevt7Z0/f76eGzcqrHPZ09MTGRkpEona2trq6uomTpyoUCjy8/MXL17s6+tLkuTt27fT09PXrl1LkuTu3bvv3LnT3NwcERFRWVm5bt26ysrKtra2ZcuWZWdnV1VVad6vUqn8/fff//KXv/zjH//YtWsXg8Ew3j4+LTMzMzg4uKqqKjw8PDAwcMeOHerHTm2lpaUhhNTfR+7cuXPNmjUGKNSYsM5lY2OjXC4PDAxksVgCgaCkpMTFxWWkxgEBARwOZ8yYMQkJCQihcePGubi4cDgc6m1vbW2t5v16eXl5enpu2rRpx44d8fHx+u+IVths9vnzcr2ZiwAAE15JREFU57/44gt/f/+ampqsrKzJkyeXl5frtrVly5Zxudxvv/22r68PIdTQ0HD58uXExESDlmx4WOdSKBS6uromJydv2rSpsbFRw7Wow1t/fz81SafTEUJKpVLzfu/fvy+Tyf76179+++23L7/8spGuZUdBp9NXr15948aNCxcuREVFyWSyuLi49vZ2HTbF4/ESExPb29sLCwsRQnl5eatWrTLxGUAHWOeSzWafPHkyPDx869atQqFQLBZT/+mNjU6n8/n8+fPnFxYWXr9+fdu2bSbodFivvPLK999/v3LlypaWllOnTum2Eerdz549ezo6Og4fPkyd2TGHdS4RQoGBgWVlZU1NTVlZWVKpNCcnx5S9+/n52djYXL9+3dgdnTlzJi8vj3odGxurOthTqAcX9vb26rbxl156KTQ09NKlSxKJJC4uzsnJSc9qTQDrXDY1NdXU1CCE+Hz+Z599Nm3aNGrSSFpbW4dceNXV1Q0MDFDPeDGqX3/9VfXDP0+ePBmym9S76SlTpui8feqQWVxc/N577+lRpungnsu0tLTa2lqFQlFZWXn37t3Q0FCEkLOzc1NTU2NjY1dXl1YXjqPjcrknTpw4efKkXC5XKpWVlZXUmwajvntVKpUPHz48ffq0KpcIoejo6KKioo6Ojs7OzqNHj37wwQevv/66PrlcsmSJi4tLdHS0UCg0RNXGZ7K/LGnyd8idO3dST4DmcrkxMTGNjY1hYWFOTk42NjZjx45dv359f38/SZJXrlzx9vZms9nh4eHr1q2j7jOPHz/+7Nmzn3/+OfX4SYFA8N133xUWFlIbdHJyKigoeGaRkZGRPj4+dnZ2TCbT19dXLBZfu3ZNwx1Ez/o7ZGlpKfW3wWGVlpZSzU6cOBEfH+/r68tkMhkMxqRJkzZt2vT48WP1Tcnl8oiICGdnZ4QQjUbz8/PbunXr0x25uLi8/fbb1MzMzMzz589Trzds2ODm5katGxAQcPbs2WfunYn/Dmm63++Ji4tDCB0+fNg03ZkeQRBSqdR0TzwzraKiovj4eJOlBevzOHhuPUe5rK2tHeXjYWKx2NwFgv/zHP1uqb+/v8lOQ0BPz9HxElgQyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhyZ9HNuDx48KCoqMmWPJlZRUWHuEozF1Ltmsm9swO+PWwGTpcV03++xetb9/R4Tg+tLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4Ajkz5f3crs3bu3vb1dfc7Ro0fv3LmjmkxNTRUIBCavyxrAc6x1J5FI9u7dy2QyqUmSJAmCoF739/fzeLzm5mY6nW6+Ai0YnMd1l5CQgBB68m8KhUL1mkajJSQkQCh1BsdL3Q0ODrq7u8tksmGX/vzzzzNnzjRxSVYDjpe6o9FoycnJDAbj6UXu7u5hYWGmL8lqQC71kpCQoFAohsyk0+kpKSmqa02gAziP60soFKq/B6dcvXp16tSpZqnHOsDxUl8pKSlD3t8IhUIIpZ4gl/pKTk5WKpWqSTqd/sYbb5ixHusA53EDmDJlym+//aYayVu3bk2YMMG8JVk6OF4aQEpKio2NDUKIIIiXX34ZQqk/yKUBJCYmDgwMIIRsbGyWLVtm7nKsAeTSAMaOHRsWFkYQxODgYFxcnLnLsQaQS8NYunQpSZIRERFjx441dy1WQatfK4fftgc60yppWn/OLTQ09L333jNG3ZZu586dEonEzs5uyPz4+Pj09PQZM2aYpSocVFRU5Ofna7WK1rn09PRcsmSJtms9D8LCwjw9PZ+eHx8fP2PGjOd80LTNJVxfGsywoQS6gVwCHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcCR0XO5fPlye3t7giCuXr1q7L6Mavv27f7+/mw2m8vl+vv7f/TRR3K53CBbLikpEQqFhBoGg+Hq6jp79uzs7OwhjzJ8Thg9l/v27fvmm2+M3YsJnD17dsWKFffu3Xv48OGWLVu2b99uqE/vx8bGNjQ0+Pr68ng8kiQHBwdlMllRUZGPj09WVlZgYOAvv/xikI4syHN9Hu/r69P86VYMBuOtt97i8/l2dnZxcXFRUVF///vf//jjD4NXRRCEo6Pj7NmzDxw4UFRU9PDhw0WLFnV2dhq8Iz1pNXraMkUusX2C1P79+0d6SuDTSktLWSyWatLDwwMh1N3dbZTK/k0kEqWmpspksj179hi1Ix1oNXraMkouSZLMzs6eNGkSk8nk8Xhr165VLdqxYweHw7G3t5fJZBkZGR4eHjdv3iRJMjc3d/LkyUwm08nJKSoqqra2lmr/5ZdfslgsV1fXtLQ0d3d3FosVFhZ28eJF9b5GWnf16tUMBsPNzY2afOutt7hcLkEQjx49Qgilp6dnZGTU19cTBOHn56ftPtbV1Tk6Onp7e+s8ShpKTU1FCP3444/Iikbv2bT9PqRIJHpms/Xr1xMEsXPnzvb29t7e3q+++gohVFlZqVqKEHr33Xd37doVExNz48aNjRs3MhiMgwcPdnR0VFdXT5s2zcXFpbm5mWovkUi4XG5NTc3jx4+vX78eEhJib29/7949auno6yYlJQkEAlVh2dnZCKGWlhZqMjY21tfXV6sRUCgUDx482LVrF5PJPHjwoCarIISkUukzm6muL4eg3l15eXlRk5Y4elKpVNukGT6Xvb29HA5n3rx5qjkFBQVP57Kvr0/V3s7OTiwWq9pfunQJIbR582ZqUiKRqP+DXb58GSH0ySefaLKuwXNJPcd/zJgxX3zxhUKh0GQVPXNJkiR1xUm9tsTR0yGXhj+P3759u7e399VXX9Ww/fXr17u7u4ODg1VzQkJCGAyG+ulGXXBwMIfDoU432q6rv/v378tksr/+9a/ffvvtyy+/bLwLLJWenh6SJB0cHIZdalmjpznD5/LBgwcIIT6fr2H7jo4OhNCQr107Ojp2dXWNtAqTyWxpadFtXT3R6XQ+nz9//vzCwsLr169v27bNSB2p3Lp1CyHk7+8/7FLLGj3NGT6X1JvWJ0+eaNje0dERITRkLDo6Okb62qtSqVQt1XZdA/Lz87Oxsbl+/bqxOzp+/DhCaOHChcMutdDReybD5/KFF16g0Wjl5eWat7ezs1O/dXzx4kWFQhEUFDRs+9OnT5MkGRoaqsm6tra26g9N1Vlra2tiYqL6nLq6uoGBAS8vL/03Porm5ua8vDxPT88333xz2AYWMXo6MHwu+Xx+bGxscXHx/v375XJ5dXX13r17R2nPYrEyMjJKS0sPHTokl8uvXbu2cuVKd3d3iUSiajM4ONje3t7f319dXZ2enj5u3Djq7skz1/Xz82trazty5IhSqWxpabl79656187Ozk1NTY2NjV1dXaP/A3C53BMnTpw8eVIulyuVysrKymXLlnG53DVr1ug8UE8jSbK7u3twcJAkyZaWFqlUOnPmTBsbmyNHjox0fWkRo6fjWGhOw/tEXV1dy5cvHzNmjJ2dXXh4+MaNGxFCnp6eVVVV27dvZ7PZCCEvLy/VfZbBwcHs7OwJEybQ6XQnJ6fo6GjqthxFIpHQ6XQPDw9bW1sHB4eoqKj6+nrV0tHXbW1tnTNnDovF8vHxeeedd6g7qX5+ftSNkitXrnh7e7PZ7PDwcNXNkZFERkb6+PjY2dkxmUxfX1+xWHzt2jVNBg096/34sWPHpkyZwuFwGAwGjUZD//6Tz/Tp0zdv3tza2qpqaaGjh8V9IoOTSCTOzs4m7tSAnplLo8Jh9LC4T2QM1NN4gW4scfQsI5fGVltbS4xMLBabu8DnDu65/PDDDw8cONDZ2enj41NcXGykXvz9/Uc5pxQWFhqpX2MzzegZA+6/P75t2zYT3Lu2VpY7ergfL8HzCXIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAZa0+hQx/P440JlWSSNIktR80xUVFffv3zde6RYNfmd8dFr90rV2uQSjIAhCKpU+578zbihwfQlwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgCHIJcAS5BDiCXAIcQS4BjiCXAEeQS4AjyCXAEeQS4AhyCXAEuQQ4glwCHEEuAY4glwBHkEuAI8glwBHkEuAIcglwBLkEOIJcAhxBLgGOIJcAR5BLgCPIJcAR5BLgyNbcBViwu3fvDgwMqM95+PBhQ0ODatLd3Z3NZpu8LmsAzwvW3cKFC48fPz7SUltb2+bm5jFjxpiyJKsB53HdicVigiCGXUSj0ebNmweh1BnkUncxMTF0On2kpUuXLjVlMVYGcqk7e3v71157bdho0un0xYsXm74kqwG51EtSUlJ/f/+Qmba2ttHR0XZ2dmYpyTpALvWyaNEiLpc7ZObAwEBSUpJZ6rEakEu9MJlMkUjEYDDUZ9rZ2c2fP99cJVkHyKW+EhMTFQqFapJOp4vF4iFJBdqC+5f6GhwcFAgEjx49Us05derU7NmzzVeRNYDjpb5oNFpiYqLqAMnn82fNmmXekqwA5NIAEhISqFM5g8FISUmxsbExd0UWD87jBkCSpLe3N/VLw5cvXw4ODjZ3RRYPjpcGQBBESkoKQsjb2xtCaRBm/jxRRUVFbm6ueWswCLlcjhDicrlxcXHmrsUAZsyYsWbNGjMWYObj5f3794uLi81bg/6Ki4vlcjmPx/P09DR3LQZw4f+1d3eh7P1xHMC/Z7SHMzbSJG1kEmVcuNAwpSQtdyx24QK5kDtFK5OkJKFdkYZc6gzlqVxRrijKQwghSsOk5WFnzdP3f3H6Lf3/+83DpvM9+39ed3y/++zztXc752zOOevra2tr/PZAxP9fTk1N8d1CSCiKamlpUSqV5eXlfPcSBiS85cP+ZdhERigJAbkEJIJcAhJBLgGJIJeARJBLQCLIJSAR5BKQCHIJSAS5BCSCXAISQS4BiSCXgESQS0Ai4eWysbExNjaWoqjt7W2+e/mqmZkZrVZLfSAWixMTE0tKSvr7+91uN98NEkd4uRwbGxsdHeW7i++pqqo6OztLT09XKpUY4/f3d5fL5XA40tLSLBZLdnb25uYm3z2SRXi5jAAURcXFxZWUlExMTDgcjpubm4qKivv7e777Ioggc/m3q04Kkclkqqurc7lcIyMjfPdCEGHkEmPc39+fmZkpkUiUSmVbW9vH0be3t87OzpSUFJlMlpubyzAMQmh4eFgul9M0PTc3ZzQaFQqFWq2enJz0P2p1dTU/P5+maYVCkZOTw504FrDUb6urq0MILS0tRcZywgPzivtLfTrNarVSFDU4OOh2u1mWHRoaQghtbW1xo62trRKJZHp62u12t7e3i0SijY0N7lEIoeXl5fv7e5fLVVxcLJfLn5+fMcZPT08KhaKvr8/r9V5fX1dWVt7e3gYpFRxCiGGYT6f59y//hcuQRqMhZDkmk8lkMn067VcJIJcsy9I0XVZW5v8N9z7B5dLr9dI0bTab/ZMlEklzczP+80J6vV5uiEvzyckJxnhvbw8htLi4+PGJgpQKLsRcYoy5PU5ClkNCLgWwHT85OWFZtrS0NODo0dERy7I6nY77USaTJSUlHR4e/ncmdwmhl5cXhJBWq01MTKytre3q6jo/P/9uqfDyeDwYY4VCERnLCQsB5PLy8hIhpFKpAo56PB6EUEdHh/+jwYuLC5Zlg9eUyWQrKysGg6Gnp0er1ZrNZq/X+7NSoTs+PkYIZWVlRcZywkIAuZRKpQghn88XcJTLq81m+7gV+Mpp+dnZ2QsLC06n02KxMAwzMDDw41Ih4m62YjQaUUQsJywEkEudTicSiVZXVwOOajQaqVT63e9+nE7nwcEBQkilUvX29ubl5R0cHPysVIiur69tNptarW5oaEDCX064CCCXKpWqqqpqenp6fHz84eFhd3fXbrf7R6VSaX19/eTk5PDw8MPDw9vb2+Xl5dXVVfCaTqezqanp8PDw+fl5a2vr4uJCr9f/rNS3YIyfnp7e398xxre3twzDFBUVRUVFzc7OcvuXwlrOL/ql46kv+uLnRI+Pj42NjQkJCTExMQaDobOzEyGkVqt3dnYwxj6fz2KxpKSkREdHcyHe398fGhqiaRohlJGRcXp6arfbuRc+NTX1+Pj4/Py8sLAwPj4+KioqOTnZarW+vr7+rdSn7aHPjsfn5+dzc3NpmhaLxSKRCP35yic/P7+7u/vu7u7jZN6XQ8LxOM/Xv3Q4HDU1Nfz2EDqKohiGqa6u5ruR8OCuT8TvRaMEsB0H/0OQS0AiyCUgEeQSkAhyCUgEuQQkglwCEkEuAYkgl4BEkEtAIsglIBHkEpAIcglIBLkEJIJcAhJBLgGJIJeARETcT5eEG7iGyGazCf2uwH7r6+t6vZ7fHnjOpUajMZlM/PYQughYwkd6vb6goIDfHng+vweAgGD/EpAIcglIBLkEJIJcAhL9A5AwsaIbxK09AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = 'adam', loss = 'mean_squared_error')"
      ],
      "metadata": {
        "id": "d1nkbEmZELUE"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from time import time\n",
        "start = time()\n",
        "history = model.fit(features, labels, epochs = 20, batch_size = 32, verbose = 1)\n",
        "end = time()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXQHwomTES78",
        "outputId": "5dd028b2-c51d-42ca-abe4-ed6c42e3ca0d"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "23/23 [==============================] - 12s 61ms/step - loss: 0.0315\n",
            "Epoch 2/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0051\n",
            "Epoch 3/20\n",
            "23/23 [==============================] - 1s 62ms/step - loss: 0.0047\n",
            "Epoch 4/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0041\n",
            "Epoch 5/20\n",
            "23/23 [==============================] - 1s 62ms/step - loss: 0.0047\n",
            "Epoch 6/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0038\n",
            "Epoch 7/20\n",
            "23/23 [==============================] - 1s 60ms/step - loss: 0.0041\n",
            "Epoch 8/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0039\n",
            "Epoch 9/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0038\n",
            "Epoch 10/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0038\n",
            "Epoch 11/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0038\n",
            "Epoch 12/20\n",
            "23/23 [==============================] - 1s 60ms/step - loss: 0.0043\n",
            "Epoch 13/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0037\n",
            "Epoch 14/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0031\n",
            "Epoch 15/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0031\n",
            "Epoch 16/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0033\n",
            "Epoch 17/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0030\n",
            "Epoch 18/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0030\n",
            "Epoch 19/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0038\n",
            "Epoch 20/20\n",
            "23/23 [==============================] - 1s 61ms/step - loss: 0.0032\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Total training time {} seconds'.format(end - start))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "he9p8dq6EV9Y",
        "outputId": "822b03f6-0c33-4494-bf9c-75508e1162b4"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total training time 38.753117084503174 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(features.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-98DmX4EiCU",
        "outputId": "6e6b5dae-6633-442e-bc4d-c2e8040bf423"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(732, 60, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = df.iloc[:60, 1:2].values"
      ],
      "metadata": {
        "id": "yvG3xXZc1ixD"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XIC37vuX2mYz",
        "outputId": "e597e654-f9a0-46a0-b3bd-70c689824fa7"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_predicted = model.predict(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3OwtaAIj3iyM",
        "outputId": "4b3b201e-523d-4973-9b61-fe4f3671d4c3"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Model was constructed with shape (None, 60, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 60, 1), dtype=tf.float32, name='lstm_input'), name='lstm_input', description=\"created by layer 'lstm_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_inverse_predicted = sc.inverse_transform(test_predicted)"
      ],
      "metadata": {
        "id": "vcRGjhoU3w1l"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))  \n",
        "plt.plot(test_stock_data_processed, color='blue', label='Actual Price')  \n",
        "plt.plot(test_predicted , color='red', label='Predicted Price')  \n",
        "plt.title('Price Prediction')  \n",
        "plt.xlabel('Date')  \n",
        "plt.ylabel('Price')  \n",
        "plt.legend()  \n",
        "plt.show()  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "C6T07KN64rw3",
        "outputId": "36a1fa68-31ac-49cd-b169-941de9380e01"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "gbuu5bA35lGi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}