NYSE_stock_price_prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/abhijitguinkatwa/machine-learning-project/blob/main/NYSE_stock_price_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "RwJIYJ6jSvRK"
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      "source": [
        ""
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      "execution_count": 1,
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        "id": "RxY3NobKdUhv"
      },
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      "source": [
        "#importing important libraries\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import os \n",
        "import matplotlib.pyplot as plt "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#calling the file in nyse named prices.csv\n",
        "df =pd.read_csv(\"/content/prices.csv\", header=0)\n",
        "df"
      ],
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      "outputs": [
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              "                       date symbol        open       close         low  \\\n",
              "0       2016-01-05 00:00:00   WLTW  123.430000  125.839996  122.309998   \n",
              "1       2016-01-06 00:00:00   WLTW  125.239998  119.980003  119.940002   \n",
              "2       2016-01-07 00:00:00   WLTW  116.379997  114.949997  114.930000   \n",
              "3       2016-01-08 00:00:00   WLTW  115.480003  116.620003  113.500000   \n",
              "4       2016-01-11 00:00:00   WLTW  117.010002  114.970001  114.089996   \n",
              "...                     ...    ...         ...         ...         ...   \n",
              "106572           2010-11-24    TEL   31.150000   31.750000   31.150000   \n",
              "106573           2010-11-24   TGNA   12.620000   12.920000   12.610000   \n",
              "106574           2010-11-24    TGT   55.470001   57.250000   55.470001   \n",
              "106575           2010-11-24    TIF   60.410000   61.330002   59.750000   \n",
              "106576           2010-11-24    NaN         NaN         NaN         NaN   \n",
              "\n",
              "              high     volume  \n",
              "0       126.250000  2163600.0  \n",
              "1       125.540001  2386400.0  \n",
              "2       119.739998  2489500.0  \n",
              "3       117.440002  2006300.0  \n",
              "4       117.330002  1408600.0  \n",
              "...            ...        ...  \n",
              "106572   31.750000  1971200.0  \n",
              "106573   12.920000  2513300.0  \n",
              "106574   57.340000  6958600.0  \n",
              "106575   61.580002  6792200.0  \n",
              "106576         NaN        NaN  \n",
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              "      <th>0</th>\n",
              "      <td>2016-01-05 00:00:00</td>\n",
              "      <td>WLTW</td>\n",
              "      <td>123.430000</td>\n",
              "      <td>125.839996</td>\n",
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              "      <td>126.250000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-01-06 00:00:00</td>\n",
              "      <td>WLTW</td>\n",
              "      <td>125.239998</td>\n",
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              "      <th>2</th>\n",
              "      <td>2016-01-07 00:00:00</td>\n",
              "      <td>WLTW</td>\n",
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              "      <td>WLTW</td>\n",
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              "      <td>31.150000</td>\n",
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              "      <td>2010-11-24</td>\n",
              "      <td>TGNA</td>\n",
              "      <td>12.620000</td>\n",
              "      <td>12.920000</td>\n",
              "      <td>12.610000</td>\n",
              "      <td>12.920000</td>\n",
              "      <td>2513300.0</td>\n",
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              "      <th>106574</th>\n",
              "      <td>2010-11-24</td>\n",
              "      <td>TGT</td>\n",
              "      <td>55.470001</td>\n",
              "      <td>57.250000</td>\n",
              "      <td>55.470001</td>\n",
              "      <td>57.340000</td>\n",
              "      <td>6958600.0</td>\n",
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              "      <th>106575</th>\n",
              "      <td>2010-11-24</td>\n",
              "      <td>TIF</td>\n",
              "      <td>60.410000</td>\n",
              "      <td>61.330002</td>\n",
              "      <td>59.750000</td>\n",
              "      <td>61.580002</td>\n",
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              "      <th>106576</th>\n",
              "      <td>2010-11-24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "        buttonEl.style.display =\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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        }
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    },
    {
      "cell_type": "code",
      "source": [
        "print(df.shape)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2b8Gw5uySaYp",
        "outputId": "78ff2026-9d09-455f-f6d7-7a1d7bd24e8a"
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          "text": [
            "(106577, 7)\n"
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    {
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      "source": [
        "# takes 20 different and unique from symbol\n",
        "df.symbol.unique()[0:20]"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ac6XKhQgSjLn",
        "outputId": "c8511f9b-376a-4fcd-9997-9b59408194ce"
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      "outputs": [
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          "data": {
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              "array(['WLTW', 'A', 'AAL', 'AAP', 'AAPL', 'ABC', 'ABT', 'ACN', 'ADBE',\n",
              "       'ADI', 'ADM', 'ADP', 'ADS', 'ADSK', 'AEE', 'AEP', 'AES', 'AET',\n",
              "       'AFL', 'AGN'], dtype=object)"
            ]
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          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# finind the length of column named symbol\n",
        "print(len(df.symbol.values))"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5DCBrAZSqlu",
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      "outputs": [
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          "text": [
            "106577\n"
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      "source": [
        "#gives the details of each columns of the dataset like mean, max etc\n",
        "df.describe()"
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          "height": 344
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        "id": "Xl1oHXnvSv7-",
        "outputId": "a68a22b6-487c-416b-8b97-3cd48576eff1"
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              "                open          close            low           high  \\\n",
              "count  106576.000000  106576.000000  106576.000000  106576.000000   \n",
              "mean       47.918877      47.939703      47.332197      48.478802   \n",
              "std        47.735584      47.714709      47.177159      48.242702   \n",
              "min         0.850000       0.860000       0.830000       0.880000   \n",
              "25%        24.990000      24.987499      24.660000      25.299999   \n",
              "50%        38.770000      38.790047      38.280024      39.240002   \n",
              "75%        56.650002      56.720001      55.992491      57.340000   \n",
              "max       630.001057     626.771094     624.241073     630.851095   \n",
              "\n",
              "             volume  \n",
              "count  1.065760e+05  \n",
              "mean   7.338217e+06  \n",
              "std    1.710604e+07  \n",
              "min    0.000000e+00  \n",
              "25%    1.545875e+06  \n",
              "50%    3.256750e+06  \n",
              "75%    6.802000e+06  \n",
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              "      <th>count</th>\n",
              "      <td>106576.000000</td>\n",
              "      <td>106576.000000</td>\n",
              "      <td>106576.000000</td>\n",
              "      <td>106576.000000</td>\n",
              "      <td>1.065760e+05</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>47.918877</td>\n",
              "      <td>47.939703</td>\n",
              "      <td>47.332197</td>\n",
              "      <td>48.478802</td>\n",
              "      <td>7.338217e+06</td>\n",
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              "      <th>std</th>\n",
              "      <td>47.735584</td>\n",
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              "      <td>47.177159</td>\n",
              "      <td>48.242702</td>\n",
              "      <td>1.710604e+07</td>\n",
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              "      <th>min</th>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.860000</td>\n",
              "      <td>0.830000</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>25%</th>\n",
              "      <td>24.990000</td>\n",
              "      <td>24.987499</td>\n",
              "      <td>24.660000</td>\n",
              "      <td>25.299999</td>\n",
              "      <td>1.545875e+06</td>\n",
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              "      <th>50%</th>\n",
              "      <td>38.770000</td>\n",
              "      <td>38.790047</td>\n",
              "      <td>38.280024</td>\n",
              "      <td>39.240002</td>\n",
              "      <td>3.256750e+06</td>\n",
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              "      <th>75%</th>\n",
              "      <td>56.650002</td>\n",
              "      <td>56.720001</td>\n",
              "      <td>55.992491</td>\n",
              "      <td>57.340000</td>\n",
              "      <td>6.802000e+06</td>\n",
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              "      <th>max</th>\n",
              "      <td>630.001057</td>\n",
              "      <td>626.771094</td>\n",
              "      <td>624.241073</td>\n",
              "      <td>630.851095</td>\n",
              "      <td>6.551166e+08</td>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d8dc4a69-7ff7-46e4-847c-b60e1c21fa29 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-d8dc4a69-7ff7-46e4-847c-b60e1c21fa29');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking whether their is any null value in the dataset\n",
        "# .sum() will give the total no. of null value column vise \n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "peDbfgPtS10X",
        "outputId": "97a431d6-e13d-4608-c5f3-bc0f08eba9c5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date      0\n",
              "symbol    1\n",
              "open      1\n",
              "close     1\n",
              "low       1\n",
              "high      1\n",
              "volume    1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# taking all the unique or one time value in the date column \n",
        "df.date.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7URgYhOITEPP",
        "outputId": "f6692a98-24a8-474c-cbe7-95336c9f7f5c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       '2016-01-07 00:00:00', '2016-01-08 00:00:00',\n",
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              "       '2016-01-15 00:00:00', '2016-01-19 00:00:00',\n",
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              "       '2016-01-22 00:00:00', '2016-01-25 00:00:00',\n",
              "       '2016-01-26 00:00:00', '2016-01-27 00:00:00',\n",
              "       '2016-01-28 00:00:00', '2016-01-29 00:00:00',\n",
              "       '2016-02-01 00:00:00', '2016-02-02 00:00:00',\n",
              "       '2016-02-03 00:00:00', '2016-02-04 00:00:00',\n",
              "       '2016-02-05 00:00:00', '2016-02-08 00:00:00',\n",
              "       '2016-02-09 00:00:00', '2016-02-10 00:00:00',\n",
              "       '2016-02-11 00:00:00', '2016-02-12 00:00:00',\n",
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              "       '2016-03-17 00:00:00', '2016-03-18 00:00:00',\n",
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              "       '2016-05-05 00:00:00', '2016-05-06 00:00:00',\n",
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              "       '2016-05-25 00:00:00', '2016-05-26 00:00:00',\n",
              "       '2016-05-27 00:00:00', '2016-05-31 00:00:00',\n",
              "       '2016-06-01 00:00:00', '2016-06-02 00:00:00',\n",
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              "       '2016-06-21 00:00:00', '2016-06-22 00:00:00',\n",
              "       '2016-06-23 00:00:00', '2016-06-24 00:00:00',\n",
              "       '2016-06-27 00:00:00', '2016-06-28 00:00:00',\n",
              "       '2016-06-29 00:00:00', '2016-06-30 00:00:00',\n",
              "       '2016-07-01 00:00:00', '2016-07-05 00:00:00',\n",
              "       '2016-07-06 00:00:00', '2016-07-07 00:00:00',\n",
              "       '2016-07-08 00:00:00', '2016-07-11 00:00:00',\n",
              "       '2016-07-12 00:00:00', '2016-07-13 00:00:00',\n",
              "       '2016-07-14 00:00:00', '2016-07-15 00:00:00',\n",
              "       '2016-07-18 00:00:00', '2016-07-19 00:00:00',\n",
              "       '2016-07-20 00:00:00', '2016-07-21 00:00:00',\n",
              "       '2016-07-22 00:00:00', '2016-07-25 00:00:00',\n",
              "       '2016-07-26 00:00:00', '2016-07-27 00:00:00',\n",
              "       '2016-07-28 00:00:00', '2016-07-29 00:00:00',\n",
              "       '2016-08-01 00:00:00', '2016-08-02 00:00:00',\n",
              "       '2016-08-03 00:00:00', '2016-08-04 00:00:00',\n",
              "       '2016-08-05 00:00:00', '2016-08-08 00:00:00',\n",
              "       '2016-08-09 00:00:00', '2016-08-10 00:00:00',\n",
              "       '2016-08-11 00:00:00', '2016-08-12 00:00:00',\n",
              "       '2016-08-15 00:00:00', '2016-08-16 00:00:00',\n",
              "       '2016-08-17 00:00:00', '2016-08-18 00:00:00',\n",
              "       '2016-08-19 00:00:00', '2016-08-22 00:00:00',\n",
              "       '2016-08-23 00:00:00', '2016-08-24 00:00:00',\n",
              "       '2016-08-25 00:00:00', '2016-08-26 00:00:00',\n",
              "       '2016-08-29 00:00:00', '2016-08-30 00:00:00',\n",
              "       '2016-08-31 00:00:00', '2016-09-01 00:00:00',\n",
              "       '2016-09-02 00:00:00', '2016-09-06 00:00:00',\n",
              "       '2016-09-07 00:00:00', '2016-09-08 00:00:00',\n",
              "       '2016-09-09 00:00:00', '2016-09-12 00:00:00',\n",
              "       '2016-09-13 00:00:00', '2016-09-14 00:00:00',\n",
              "       '2016-09-15 00:00:00', '2016-09-16 00:00:00',\n",
              "       '2016-09-19 00:00:00', '2016-09-20 00:00:00',\n",
              "       '2016-09-21 00:00:00', '2016-09-22 00:00:00',\n",
              "       '2016-09-23 00:00:00', '2016-09-26 00:00:00',\n",
              "       '2016-09-27 00:00:00', '2016-09-28 00:00:00',\n",
              "       '2016-09-29 00:00:00', '2016-09-30 00:00:00',\n",
              "       '2016-10-03 00:00:00', '2016-10-04 00:00:00',\n",
              "       '2016-10-05 00:00:00', '2016-10-06 00:00:00',\n",
              "       '2016-10-07 00:00:00', '2016-10-10 00:00:00',\n",
              "       '2016-10-11 00:00:00', '2016-10-12 00:00:00',\n",
              "       '2016-10-13 00:00:00', '2016-10-14 00:00:00',\n",
              "       '2016-10-17 00:00:00', '2016-10-18 00:00:00',\n",
              "       '2016-10-19 00:00:00', '2016-10-20 00:00:00',\n",
              "       '2016-10-21 00:00:00', '2016-10-24 00:00:00',\n",
              "       '2016-10-25 00:00:00', '2016-10-26 00:00:00',\n",
              "       '2016-10-27 00:00:00', '2016-10-28 00:00:00',\n",
              "       '2016-10-31 00:00:00', '2016-11-01 00:00:00',\n",
              "       '2016-11-02 00:00:00', '2016-11-03 00:00:00',\n",
              "       '2016-11-04 00:00:00', '2016-11-07 00:00:00',\n",
              "       '2016-11-08 00:00:00', '2016-11-09 00:00:00',\n",
              "       '2016-11-10 00:00:00', '2016-11-11 00:00:00',\n",
              "       '2016-11-14 00:00:00', '2016-11-15 00:00:00',\n",
              "       '2016-11-16 00:00:00', '2016-11-17 00:00:00',\n",
              "       '2016-11-18 00:00:00', '2016-11-21 00:00:00',\n",
              "       '2016-11-22 00:00:00', '2016-11-23 00:00:00',\n",
              "       '2016-11-25 00:00:00', '2016-11-28 00:00:00',\n",
              "       '2016-11-29 00:00:00', '2016-11-30 00:00:00',\n",
              "       '2016-12-01 00:00:00', '2016-12-02 00:00:00',\n",
              "       '2016-12-05 00:00:00', '2016-12-06 00:00:00',\n",
              "       '2016-12-07 00:00:00', '2016-12-08 00:00:00',\n",
              "       '2016-12-09 00:00:00', '2016-12-12 00:00:00',\n",
              "       '2016-12-13 00:00:00', '2016-12-14 00:00:00',\n",
              "       '2016-12-15 00:00:00', '2016-12-16 00:00:00',\n",
              "       '2016-12-19 00:00:00', '2016-12-20 00:00:00',\n",
              "       '2016-12-21 00:00:00', '2016-12-22 00:00:00',\n",
              "       '2016-12-23 00:00:00', '2016-12-27 00:00:00',\n",
              "       '2016-12-28 00:00:00', '2016-12-29 00:00:00',\n",
              "       '2016-12-30 00:00:00', '2010-01-04', '2010-01-04 00:00:00',\n",
              "       '2010-01-05', '2010-01-05 00:00:00', '2010-01-06',\n",
              "       '2010-01-06 00:00:00', '2010-01-07', '2010-01-07 00:00:00',\n",
              "       '2010-01-08', '2010-01-08 00:00:00', '2010-01-11',\n",
              "       '2010-01-11 00:00:00', '2010-01-12', '2010-01-12 00:00:00',\n",
              "       '2010-01-13', '2010-01-13 00:00:00', '2010-01-14',\n",
              "       '2010-01-14 00:00:00', '2010-01-15', '2010-01-15 00:00:00',\n",
              "       '2010-01-19', '2010-01-19 00:00:00', '2010-01-20',\n",
              "       '2010-01-20 00:00:00', '2010-01-21', '2010-01-21 00:00:00',\n",
              "       '2010-01-22', '2010-01-22 00:00:00', '2010-01-25',\n",
              "       '2010-01-25 00:00:00', '2010-01-26', '2010-01-26 00:00:00',\n",
              "       '2010-01-27', '2010-01-27 00:00:00', '2010-01-28',\n",
              "       '2010-01-28 00:00:00', '2010-01-29', '2010-01-29 00:00:00',\n",
              "       '2010-02-01', '2010-02-01 00:00:00', '2010-02-02',\n",
              "       '2010-02-02 00:00:00', '2010-02-03', '2010-02-03 00:00:00',\n",
              "       '2010-02-04', '2010-02-04 00:00:00', '2010-02-05',\n",
              "       '2010-02-05 00:00:00', '2010-02-08', '2010-02-08 00:00:00',\n",
              "       '2010-02-09', '2010-02-09 00:00:00', '2010-02-10',\n",
              "       '2010-02-10 00:00:00', '2010-02-11', '2010-02-11 00:00:00',\n",
              "       '2010-02-12', '2010-02-12 00:00:00', '2010-02-16',\n",
              "       '2010-02-16 00:00:00', '2010-02-17', '2010-02-17 00:00:00',\n",
              "       '2010-02-18', '2010-02-18 00:00:00', '2010-02-19',\n",
              "       '2010-02-19 00:00:00', '2010-02-22', '2010-02-22 00:00:00',\n",
              "       '2010-02-23', '2010-02-23 00:00:00', '2010-02-24',\n",
              "       '2010-02-24 00:00:00', '2010-02-25', '2010-02-25 00:00:00',\n",
              "       '2010-02-26', '2010-02-26 00:00:00', '2010-03-01',\n",
              "       '2010-03-01 00:00:00', '2010-03-02', '2010-03-02 00:00:00',\n",
              "       '2010-03-03', '2010-03-03 00:00:00', '2010-03-04',\n",
              "       '2010-03-04 00:00:00', '2010-03-05', '2010-03-05 00:00:00',\n",
              "       '2010-03-08', '2010-03-08 00:00:00', '2010-03-09',\n",
              "       '2010-03-09 00:00:00', '2010-03-10', '2010-03-10 00:00:00',\n",
              "       '2010-03-11', '2010-03-11 00:00:00', '2010-03-12',\n",
              "       '2010-03-12 00:00:00', '2010-03-15', '2010-03-15 00:00:00',\n",
              "       '2010-03-16', '2010-03-16 00:00:00', '2010-03-17',\n",
              "       '2010-03-17 00:00:00', '2010-03-18', '2010-03-18 00:00:00',\n",
              "       '2010-03-19', '2010-03-19 00:00:00', '2010-03-22',\n",
              "       '2010-03-22 00:00:00', '2010-03-23', '2010-03-23 00:00:00',\n",
              "       '2010-03-24', '2010-03-24 00:00:00', '2010-03-25',\n",
              "       '2010-03-25 00:00:00', '2010-03-26', '2010-03-26 00:00:00',\n",
              "       '2010-03-29', '2010-03-29 00:00:00', '2010-03-30',\n",
              "       '2010-03-30 00:00:00', '2010-03-31', '2010-03-31 00:00:00',\n",
              "       '2010-04-01', '2010-04-01 00:00:00', '2010-04-05',\n",
              "       '2010-04-05 00:00:00', '2010-04-06', '2010-04-06 00:00:00',\n",
              "       '2010-04-07', '2010-04-07 00:00:00', '2010-04-08',\n",
              "       '2010-04-08 00:00:00', '2010-04-09', '2010-04-09 00:00:00',\n",
              "       '2010-04-12', '2010-04-12 00:00:00', '2010-04-13',\n",
              "       '2010-04-13 00:00:00', '2010-04-14', '2010-04-14 00:00:00',\n",
              "       '2010-04-15', '2010-04-15 00:00:00', '2010-04-16',\n",
              "       '2010-04-16 00:00:00', '2010-04-19', '2010-04-19 00:00:00',\n",
              "       '2010-04-20', '2010-04-20 00:00:00', '2010-04-21',\n",
              "       '2010-04-21 00:00:00', '2010-04-22', '2010-04-22 00:00:00',\n",
              "       '2010-04-23', '2010-04-23 00:00:00', '2010-04-26',\n",
              "       '2010-04-26 00:00:00', '2010-04-27', '2010-04-27 00:00:00',\n",
              "       '2010-04-28', '2010-04-28 00:00:00', '2010-04-29',\n",
              "       '2010-04-29 00:00:00', '2010-04-30', '2010-04-30 00:00:00',\n",
              "       '2010-05-03', '2010-05-03 00:00:00', '2010-05-04',\n",
              "       '2010-05-04 00:00:00', '2010-05-05', '2010-05-05 00:00:00',\n",
              "       '2010-05-06', '2010-05-06 00:00:00', '2010-05-07',\n",
              "       '2010-05-07 00:00:00', '2010-05-10', '2010-05-10 00:00:00',\n",
              "       '2010-05-11', '2010-05-11 00:00:00', '2010-05-12',\n",
              "       '2010-05-12 00:00:00', '2010-05-13', '2010-05-13 00:00:00',\n",
              "       '2010-05-14', '2010-05-14 00:00:00', '2010-05-17',\n",
              "       '2010-05-17 00:00:00', '2010-05-18', '2010-05-18 00:00:00',\n",
              "       '2010-05-19', '2010-05-19 00:00:00', '2010-05-20',\n",
              "       '2010-05-20 00:00:00', '2010-05-21', '2010-05-21 00:00:00',\n",
              "       '2010-05-24', '2010-05-24 00:00:00', '2010-05-25',\n",
              "       '2010-05-25 00:00:00', '2010-05-26', '2010-05-26 00:00:00',\n",
              "       '2010-05-27', '2010-05-27 00:00:00', '2010-05-28',\n",
              "       '2010-05-28 00:00:00', '2010-06-01', '2010-06-01 00:00:00',\n",
              "       '2010-06-02', '2010-06-02 00:00:00', '2010-06-03',\n",
              "       '2010-06-03 00:00:00', '2010-06-04', '2010-06-04 00:00:00',\n",
              "       '2010-06-07', '2010-06-07 00:00:00', '2010-06-08',\n",
              "       '2010-06-08 00:00:00', '2010-06-09', '2010-06-09 00:00:00',\n",
              "       '2010-06-10', '2010-06-10 00:00:00', '2010-06-11',\n",
              "       '2010-06-11 00:00:00', '2010-06-14', '2010-06-14 00:00:00',\n",
              "       '2010-06-15', '2010-06-15 00:00:00', '2010-06-16',\n",
              "       '2010-06-16 00:00:00', '2010-06-17', '2010-06-17 00:00:00',\n",
              "       '2010-06-18', '2010-06-18 00:00:00', '2010-06-21',\n",
              "       '2010-06-21 00:00:00', '2010-06-22', '2010-06-22 00:00:00',\n",
              "       '2010-06-23', '2010-06-23 00:00:00', '2010-06-24',\n",
              "       '2010-06-24 00:00:00', '2010-06-25', '2010-06-25 00:00:00',\n",
              "       '2010-06-28', '2010-06-28 00:00:00', '2010-06-29',\n",
              "       '2010-06-29 00:00:00', '2010-06-30', '2010-06-30 00:00:00',\n",
              "       '2010-07-01', '2010-07-01 00:00:00', '2010-07-02',\n",
              "       '2010-07-02 00:00:00', '2010-07-06', '2010-07-06 00:00:00',\n",
              "       '2010-07-07', '2010-07-07 00:00:00', '2010-07-08',\n",
              "       '2010-07-08 00:00:00', '2010-07-09', '2010-07-09 00:00:00',\n",
              "       '2010-07-12', '2010-07-12 00:00:00', '2010-07-13',\n",
              "       '2010-07-13 00:00:00', '2010-07-14', '2010-07-14 00:00:00',\n",
              "       '2010-07-15', '2010-07-15 00:00:00', '2010-07-16',\n",
              "       '2010-07-16 00:00:00', '2010-07-19', '2010-07-19 00:00:00',\n",
              "       '2010-07-20', '2010-07-20 00:00:00', '2010-07-21',\n",
              "       '2010-07-21 00:00:00', '2010-07-22', '2010-07-22 00:00:00',\n",
              "       '2010-07-23', '2010-07-23 00:00:00', '2010-07-26',\n",
              "       '2010-07-26 00:00:00', '2010-07-27', '2010-07-27 00:00:00',\n",
              "       '2010-07-28', '2010-07-28 00:00:00', '2010-07-29',\n",
              "       '2010-07-29 00:00:00', '2010-07-30', '2010-07-30 00:00:00',\n",
              "       '2010-08-02', '2010-08-02 00:00:00', '2010-08-03',\n",
              "       '2010-08-03 00:00:00', '2010-08-04', '2010-08-04 00:00:00',\n",
              "       '2010-08-05', '2010-08-05 00:00:00', '2010-08-06',\n",
              "       '2010-08-06 00:00:00', '2010-08-09', '2010-08-09 00:00:00',\n",
              "       '2010-08-10', '2010-08-10 00:00:00', '2010-08-11',\n",
              "       '2010-08-11 00:00:00', '2010-08-12', '2010-08-12 00:00:00',\n",
              "       '2010-08-13', '2010-08-13 00:00:00', '2010-08-16',\n",
              "       '2010-08-16 00:00:00', '2010-08-17', '2010-08-17 00:00:00',\n",
              "       '2010-08-18', '2010-08-18 00:00:00', '2010-08-19',\n",
              "       '2010-08-19 00:00:00', '2010-08-20', '2010-08-20 00:00:00',\n",
              "       '2010-08-23', '2010-08-23 00:00:00', '2010-08-24',\n",
              "       '2010-08-24 00:00:00', '2010-08-25', '2010-08-25 00:00:00',\n",
              "       '2010-08-26', '2010-08-26 00:00:00', '2010-08-27',\n",
              "       '2010-08-27 00:00:00', '2010-08-30', '2010-08-30 00:00:00',\n",
              "       '2010-08-31', '2010-08-31 00:00:00', '2010-09-01',\n",
              "       '2010-09-01 00:00:00', '2010-09-02', '2010-09-02 00:00:00',\n",
              "       '2010-09-03', '2010-09-03 00:00:00', '2010-09-07',\n",
              "       '2010-09-07 00:00:00', '2010-09-08', '2010-09-08 00:00:00',\n",
              "       '2010-09-09', '2010-09-09 00:00:00', '2010-09-10',\n",
              "       '2010-09-10 00:00:00', '2010-09-13', '2010-09-13 00:00:00',\n",
              "       '2010-09-14', '2010-09-14 00:00:00', '2010-09-15',\n",
              "       '2010-09-15 00:00:00', '2010-09-16', '2010-09-16 00:00:00',\n",
              "       '2010-09-17', '2010-09-17 00:00:00', '2010-09-20',\n",
              "       '2010-09-20 00:00:00', '2010-09-21', '2010-09-21 00:00:00',\n",
              "       '2010-09-22', '2010-09-22 00:00:00', '2010-09-23',\n",
              "       '2010-09-23 00:00:00', '2010-09-24', '2010-09-24 00:00:00',\n",
              "       '2010-09-27', '2010-09-27 00:00:00', '2010-09-28',\n",
              "       '2010-09-28 00:00:00', '2010-09-29', '2010-09-29 00:00:00',\n",
              "       '2010-09-30', '2010-09-30 00:00:00', '2010-10-01',\n",
              "       '2010-10-01 00:00:00', '2010-10-04', '2010-10-04 00:00:00',\n",
              "       '2010-10-05', '2010-10-05 00:00:00', '2010-10-06',\n",
              "       '2010-10-06 00:00:00', '2010-10-07', '2010-10-07 00:00:00',\n",
              "       '2010-10-08', '2010-10-08 00:00:00', '2010-10-11',\n",
              "       '2010-10-11 00:00:00', '2010-10-12', '2010-10-12 00:00:00',\n",
              "       '2010-10-13', '2010-10-13 00:00:00', '2010-10-14',\n",
              "       '2010-10-14 00:00:00', '2010-10-15', '2010-10-15 00:00:00',\n",
              "       '2010-10-18', '2010-10-18 00:00:00', '2010-10-19',\n",
              "       '2010-10-19 00:00:00', '2010-10-20', '2010-10-20 00:00:00',\n",
              "       '2010-10-21', '2010-10-21 00:00:00', '2010-10-22',\n",
              "       '2010-10-22 00:00:00', '2010-10-25', '2010-10-25 00:00:00',\n",
              "       '2010-10-26', '2010-10-26 00:00:00', '2010-10-27',\n",
              "       '2010-10-27 00:00:00', '2010-10-28', '2010-10-28 00:00:00',\n",
              "       '2010-10-29', '2010-10-29 00:00:00', '2010-11-01',\n",
              "       '2010-11-01 00:00:00', '2010-11-02', '2010-11-02 00:00:00',\n",
              "       '2010-11-03', '2010-11-03 00:00:00', '2010-11-04',\n",
              "       '2010-11-04 00:00:00', '2010-11-05', '2010-11-05 00:00:00',\n",
              "       '2010-11-08', '2010-11-08 00:00:00', '2010-11-09',\n",
              "       '2010-11-09 00:00:00', '2010-11-10', '2010-11-10 00:00:00',\n",
              "       '2010-11-11', '2010-11-11 00:00:00', '2010-11-12',\n",
              "       '2010-11-12 00:00:00', '2010-11-15', '2010-11-15 00:00:00',\n",
              "       '2010-11-16', '2010-11-16 00:00:00', '2010-11-17',\n",
              "       '2010-11-17 00:00:00', '2010-11-18', '2010-11-18 00:00:00',\n",
              "       '2010-11-19', '2010-11-19 00:00:00', '2010-11-22',\n",
              "       '2010-11-22 00:00:00', '2010-11-23', '2010-11-23 00:00:00',\n",
              "       '2010-11-24'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#calling the file in nyse named securities.csv\n",
        "comp_info = pd.read_csv('securities.csv')\n",
        "comp_info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "5CCxkmQkTLyY",
        "outputId": "35e9a287-a125-4d59-b2ec-7ba3cb8f2cd7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Ticker symbol                Security SEC filings             GICS Sector  \\\n",
              "0             MMM              3M Company     reports             Industrials   \n",
              "1             ABT     Abbott Laboratories     reports             Health Care   \n",
              "2            ABBV                  AbbVie     reports             Health Care   \n",
              "3             ACN           Accenture plc     reports  Information Technology   \n",
              "4            ATVI     Activision Blizzard     reports  Information Technology   \n",
              "..            ...                     ...         ...                     ...   \n",
              "500          YHOO              Yahoo Inc.     reports  Information Technology   \n",
              "501           YUM         Yum! Brands Inc     reports  Consumer Discretionary   \n",
              "502           ZBH  Zimmer Biomet Holdings     reports             Health Care   \n",
              "503          ZION           Zions Bancorp     reports              Financials   \n",
              "504           ZTS                  Zoetis     reports             Health Care   \n",
              "\n",
              "                  GICS Sub Industry   Address of Headquarters  \\\n",
              "0          Industrial Conglomerates       St. Paul, Minnesota   \n",
              "1             Health Care Equipment   North Chicago, Illinois   \n",
              "2                   Pharmaceuticals   North Chicago, Illinois   \n",
              "3    IT Consulting & Other Services           Dublin, Ireland   \n",
              "4       Home Entertainment Software  Santa Monica, California   \n",
              "..                              ...                       ...   \n",
              "500    Internet Software & Services     Sunnyvale, California   \n",
              "501                     Restaurants      Louisville, Kentucky   \n",
              "502           Health Care Equipment           Warsaw, Indiana   \n",
              "503                  Regional Banks      Salt Lake City, Utah   \n",
              "504                 Pharmaceuticals  Florham Park, New Jersey   \n",
              "\n",
              "    Date first added      CIK  \n",
              "0                NaN    66740  \n",
              "1         31-03-1964     1800  \n",
              "2         31-12-2012  1551152  \n",
              "3         06-07-2011  1467373  \n",
              "4         31-08-2015   718877  \n",
              "..               ...      ...  \n",
              "500              NaN  1011006  \n",
              "501       06-10-1997  1041061  \n",
              "502              NaN  1136869  \n",
              "503              NaN   109380  \n",
              "504       21-06-2013  1555280  \n",
              "\n",
              "[505 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e6b54a23-3d44-4327-975c-628013816eb3\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ticker symbol</th>\n",
              "      <th>Security</th>\n",
              "      <th>SEC filings</th>\n",
              "      <th>GICS Sector</th>\n",
              "      <th>GICS Sub Industry</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MMM</td>\n",
              "      <td>3M Company</td>\n",
              "      <td>reports</td>\n",
              "      <td>Industrials</td>\n",
              "      <td>Industrial Conglomerates</td>\n",
              "      <td>St. Paul, Minnesota</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ABT</td>\n",
              "      <td>Abbott Laboratories</td>\n",
              "      <td>reports</td>\n",
              "      <td>Health Care</td>\n",
              "      <td>Health Care Equipment</td>\n",
              "      <td>North Chicago, Illinois</td>\n",
              "      <td>31-03-1964</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ABBV</td>\n",
              "      <td>AbbVie</td>\n",
              "      <td>reports</td>\n",
              "      <td>Health Care</td>\n",
              "      <td>Pharmaceuticals</td>\n",
              "      <td>North Chicago, Illinois</td>\n",
              "      <td>31-12-2012</td>\n",
              "      <td>1551152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ACN</td>\n",
              "      <td>Accenture plc</td>\n",
              "      <td>reports</td>\n",
              "      <td>Information Technology</td>\n",
              "      <td>IT Consulting &amp; Other Services</td>\n",
              "      <td>Dublin, Ireland</td>\n",
              "      <td>06-07-2011</td>\n",
              "      <td>1467373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ATVI</td>\n",
              "      <td>Activision Blizzard</td>\n",
              "      <td>reports</td>\n",
              "      <td>Information Technology</td>\n",
              "      <td>Home Entertainment Software</td>\n",
              "      <td>Santa Monica, California</td>\n",
              "      <td>31-08-2015</td>\n",
              "      <td>718877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>YHOO</td>\n",
              "      <td>Yahoo Inc.</td>\n",
              "      <td>reports</td>\n",
              "      <td>Information Technology</td>\n",
              "      <td>Internet Software &amp; Services</td>\n",
              "      <td>Sunnyvale, California</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1011006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>YUM</td>\n",
              "      <td>Yum! Brands Inc</td>\n",
              "      <td>reports</td>\n",
              "      <td>Consumer Discretionary</td>\n",
              "      <td>Restaurants</td>\n",
              "      <td>Louisville, Kentucky</td>\n",
              "      <td>06-10-1997</td>\n",
              "      <td>1041061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>ZBH</td>\n",
              "      <td>Zimmer Biomet Holdings</td>\n",
              "      <td>reports</td>\n",
              "      <td>Health Care</td>\n",
              "      <td>Health Care Equipment</td>\n",
              "      <td>Warsaw, Indiana</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1136869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>ZION</td>\n",
              "      <td>Zions Bancorp</td>\n",
              "      <td>reports</td>\n",
              "      <td>Financials</td>\n",
              "      <td>Regional Banks</td>\n",
              "      <td>Salt Lake City, Utah</td>\n",
              "      <td>NaN</td>\n",
              "      <td>109380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>ZTS</td>\n",
              "      <td>Zoetis</td>\n",
              "      <td>reports</td>\n",
              "      <td>Health Care</td>\n",
              "      <td>Pharmaceuticals</td>\n",
              "      <td>Florham Park, New Jersey</td>\n",
              "      <td>21-06-2013</td>\n",
              "      <td>1555280</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>505 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6b54a23-3d44-4327-975c-628013816eb3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-e6b54a23-3d44-4327-975c-628013816eb3 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-e6b54a23-3d44-4327-975c-628013816eb3');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# taking total no. of unique values in column Ticket symbol\n",
        "comp_info[\"Ticker symbol\"].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spKUEXVPT45i",
        "outputId": "cff41aa6-0887-43f6-acff-4b8bff4a22ab"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "505"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for locating specific data here.... in security column of string that starts with \"Face\"\n",
        "comp_info.loc[comp_info.Security.str.startswith('Face') , :]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "oQ3S1X8_T_MH",
        "outputId": "3a762bf4-3344-4810-9313-f72d47c9455f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Ticker symbol  Security SEC filings             GICS Sector  \\\n",
              "181            FB  Facebook     reports  Information Technology   \n",
              "\n",
              "                GICS Sub Industry Address of Headquarters Date first added  \\\n",
              "181  Internet Software & Services  Menlo Park, California       23-12-2013   \n",
              "\n",
              "         CIK  \n",
              "181  1326801  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d32be79-9c3f-4f60-8a91-91d470e2953f\">\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>Ticker symbol</th>\n",
              "      <th>Security</th>\n",
              "      <th>SEC filings</th>\n",
              "      <th>GICS Sector</th>\n",
              "      <th>GICS Sub Industry</th>\n",
              "      <th>Address of Headquarters</th>\n",
              "      <th>Date first added</th>\n",
              "      <th>CIK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>FB</td>\n",
              "      <td>Facebook</td>\n",
              "      <td>reports</td>\n",
              "      <td>Information Technology</td>\n",
              "      <td>Internet Software &amp; Services</td>\n",
              "      <td>Menlo Park, California</td>\n",
              "      <td>23-12-2013</td>\n",
              "      <td>1326801</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d32be79-9c3f-4f60-8a91-91d470e2953f')\"\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",
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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",
              "      cursor: pointer;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\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-4d32be79-9c3f-4f60-8a91-91d470e2953f 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-4d32be79-9c3f-4f60-8a91-91d470e2953f');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# here we locate Ticker symbol of company with security like Yahoo, Xerox, Adobe etc \n",
        "# then taking their no. of times entry and thier \"Ticker symbol\"\n",
        "comp_plot = comp_info.loc[(comp_info[\"Security\"] == 'Yahoo Inc.') | (comp_info[\"Security\"] == 'Xerox Corp.') | (comp_info[\"Security\"] == 'Adobe Systems Inc')\n",
        "              | (comp_info[\"Security\"] == 'Microsoft Corp.') | (comp_info[\"Security\"] == 'Adobe Systems Inc') \n",
        "              | (comp_info[\"Security\"] == 'Facebook') | (comp_info[\"Security\"] == 'Goldman Sachs Group') , [\"Ticker symbol\"] ][\"Ticker symbol\"] \n",
        "print(comp_plot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfcc9GqSUHVu",
        "outputId": "98323f01-141e-443d-9607-75a27dc30ca1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6      ADBE\n",
            "181      FB\n",
            "212      GS\n",
            "306    MSFT\n",
            "496     XRX\n",
            "500    YHOO\n",
            "Name: Ticker symbol, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plotter(code):\n",
        "    \n",
        "    global closing_stock ,opening_stock\n",
        "    \n",
        "    f, axs = plt.subplots(2,2,figsize=(8,8))\n",
        "    \n",
        "    plt.subplot(212)\n",
        "    \n",
        "    company = df[df['symbol']==code]\n",
        "    \n",
        "    company = company.open.values.astype('float32')\n",
        "    \n",
        "    company = company.reshape(-1, 1)\n",
        "     \n",
        "    opening_stock = company\n",
        "    \n",
        "    \n",
        "    plt.grid(True)\n",
        "    plt.xlabel('Time') \n",
        "    plt.ylabel(code + \" open stock prices\")\n",
        "    plt.title('prices Vs Time')\n",
        "    plt.plot(company , 'g') \n",
        "    \n",
        "    \n",
        "    plt.subplot(211)\n",
        "    \n",
        "    company_close = df[df['symbol']==code]\n",
        "    \n",
        "    company_close = company_close.close.values.astype('float32')\n",
        "    \n",
        "    company_close = company_close.reshape(-1, 1)\n",
        "   \n",
        "    closing_stock = company_close\n",
        "    \n",
        "    \n",
        "    plt.xlabel('Time') \n",
        "    plt.ylabel(code + \" close stock prices\")\n",
        "    plt.title('prices Vs Time') \n",
        "    plt.grid(True)\n",
        "    plt.plot(company_close , 'b')\n",
        "    plt.show() \n",
        "\n",
        "# calling the graphs through the function    \n",
        "for i in comp_plot:\n",
        "    plotter(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eqeghzeQUUJO",
        "outputId": "15e36475-d4e8-4583-bdf3-30d8d6368ccc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Lets take a single stock as a sample to forecast further stock prices.**"
      ],
      "metadata": {
        "id": "f4KdQ6ouUnHI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#taking the values of closing_stock in a single list\n",
        "closing_stock[:,0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdleFv6wUoLA",
        "outputId": "ae91e180-68a5-47be-f9fc-c2a8d8753c75"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([17.1 , 17.23, 17.17, 16.7 , 16.7 , 16.74, 16.68, 16.9 , 17.12,\n",
              "       16.82, 16.75, 16.38, 16.2 , 15.88, 15.86, 15.99, 15.98, 15.44,\n",
              "       15.01, 15.05, 15.17, 15.46, 15.01, 15.19, 14.99, 15.07, 14.8 ,\n",
              "       15.22, 15.17, 15.41, 15.44, 15.54, 15.58, 15.49, 15.38, 15.59,\n",
              "       15.24, 15.31, 15.79, 15.73, 15.57, 15.81, 16.06, 16.52, 16.53,\n",
              "       16.79, 16.53, 16.32, 16.46, 16.36, 16.5 , 16.56, 16.44, 16.34,\n",
              "       16.03, 16.09, 16.32, 16.54, 16.56, 16.61, 16.53, 16.29, 16.51,\n",
              "       16.92, 16.87, 17.35, 17.52, 17.64, 18.18, 18.38, 18.97, 18.17,\n",
              "       18.39, 18.38, 17.45, 17.72, 17.64, 17.39, 16.92, 16.75, 16.97,\n",
              "       16.53, 16.95, 16.32, 16.49, 15.92, 15.29, 16.33, 16.41, 16.47,\n",
              "       16.14, 16.39, 16.27, 16.03, 15.79, 15.1 , 15.48, 15.54, 15.31,\n",
              "       15.45, 15.69, 15.34, 15.02, 15.18, 15.43, 15.  , 14.94, 14.79,\n",
              "       14.69, 15.1 , 15.29, 15.17, 15.65, 15.49, 15.6 , 15.54, 15.21,\n",
              "       15.09, 15.23, 14.83, 14.81, 14.73, 14.04, 13.84, 14.09, 14.07,\n",
              "       14.13, 14.4 , 14.6 , 14.89, 14.94, 15.52, 15.37, 15.37, 14.9 ,\n",
              "       15.1 , 15.2 , 13.91, 13.88, 13.99, 14.15, 13.95, 13.87, 13.76,\n",
              "       13.88, 14.  , 13.94, 14.18, 14.16, 14.34, 14.4 , 14.35, 13.87,\n",
              "       13.85, 13.83, 13.79, 13.94, 13.99, 13.85, 13.79, 13.65, 13.4 ,\n",
              "       13.26, 13.21, 13.43, 13.18, 13.11, 13.37, 13.51, 13.62, 13.53,\n",
              "       13.75, 13.65, 13.68, 13.73, 13.63, 14.27, 14.19, 13.89, 13.86,\n",
              "       14.18, 14.04, 14.17, 14.5 , 14.28, 14.39, 14.34, 14.17, 14.27,\n",
              "       14.28, 14.61, 14.52, 14.23, 14.49, 14.41, 14.43, 15.25, 15.93,\n",
              "       16.25, 15.93, 15.49, 15.8 , 15.97, 16.31, 16.4 , 16.46, 16.42,\n",
              "       16.4 , 16.49, 16.15, 16.19, 16.17, 16.2 , 16.27, 16.44, 16.97,\n",
              "       16.94, 16.8 , 16.55, 16.6 , 16.24, 16.15, 16.99, 16.57, 16.56,\n",
              "       16.19], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#taking the values of closing_stock in a single list called stocks\n",
        "stocks = closing_stock[: , 0]\n",
        "print(stocks)\n",
        "#reshaping the stocks in 1D array form\n",
        "stocks = stocks.reshape(len(stocks) , 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sbu7g_ErUt5P",
        "outputId": "ac8cd9c9-320c-4023-afa3-9af7c7256424"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[17.1  17.23 17.17 16.7  16.7  16.74 16.68 16.9  17.12 16.82 16.75 16.38\n",
            " 16.2  15.88 15.86 15.99 15.98 15.44 15.01 15.05 15.17 15.46 15.01 15.19\n",
            " 14.99 15.07 14.8  15.22 15.17 15.41 15.44 15.54 15.58 15.49 15.38 15.59\n",
            " 15.24 15.31 15.79 15.73 15.57 15.81 16.06 16.52 16.53 16.79 16.53 16.32\n",
            " 16.46 16.36 16.5  16.56 16.44 16.34 16.03 16.09 16.32 16.54 16.56 16.61\n",
            " 16.53 16.29 16.51 16.92 16.87 17.35 17.52 17.64 18.18 18.38 18.97 18.17\n",
            " 18.39 18.38 17.45 17.72 17.64 17.39 16.92 16.75 16.97 16.53 16.95 16.32\n",
            " 16.49 15.92 15.29 16.33 16.41 16.47 16.14 16.39 16.27 16.03 15.79 15.1\n",
            " 15.48 15.54 15.31 15.45 15.69 15.34 15.02 15.18 15.43 15.   14.94 14.79\n",
            " 14.69 15.1  15.29 15.17 15.65 15.49 15.6  15.54 15.21 15.09 15.23 14.83\n",
            " 14.81 14.73 14.04 13.84 14.09 14.07 14.13 14.4  14.6  14.89 14.94 15.52\n",
            " 15.37 15.37 14.9  15.1  15.2  13.91 13.88 13.99 14.15 13.95 13.87 13.76\n",
            " 13.88 14.   13.94 14.18 14.16 14.34 14.4  14.35 13.87 13.85 13.83 13.79\n",
            " 13.94 13.99 13.85 13.79 13.65 13.4  13.26 13.21 13.43 13.18 13.11 13.37\n",
            " 13.51 13.62 13.53 13.75 13.65 13.68 13.73 13.63 14.27 14.19 13.89 13.86\n",
            " 14.18 14.04 14.17 14.5  14.28 14.39 14.34 14.17 14.27 14.28 14.61 14.52\n",
            " 14.23 14.49 14.41 14.43 15.25 15.93 16.25 15.93 15.49 15.8  15.97 16.31\n",
            " 16.4  16.46 16.42 16.4  16.49 16.15 16.19 16.17 16.2  16.27 16.44 16.97\n",
            " 16.94 16.8  16.55 16.6  16.24 16.15 16.99 16.57 16.56 16.19]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature scaling the vector for better model performance.**"
      ],
      "metadata": {
        "id": "9gJtf3xEU5R3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0, 1)) #scaling features between 0 and 1\n",
        "stocks = scaler.fit_transform(stocks) # it will start learning algo and then provide scaled and dimension reduced output"
      ],
      "metadata": {
        "id": "BG3qIUwfU6Rg"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = int(len(stocks) * 0.80) #creating sizes of train and taking 80% percentage of the part\n",
        "test = len(stocks) - train #creating sizes of test as total minus train"
      ],
      "metadata": {
        "id": "HKwFbr18YCy4"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#sizes of train and test\n",
        "print(train , test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r398lbMlYIff",
        "outputId": "cdefda0b-6aaf-4368-e860-0fd0e5e500a3"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "180 46\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#divinding the values of stocks data to train from 0 to 1409 i.e 80% data\n",
        "train = stocks[0:train]\n",
        "print(train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOHfHW76YOIo",
        "outputId": "95c6d248-f30b-4d57-f4cd-bba5a666eb63"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.68088746]\n",
            " [0.7030716 ]\n",
            " [0.6928327 ]\n",
            " [0.6126282 ]\n",
            " [0.6126282 ]\n",
            " [0.6194539 ]\n",
            " [0.609215  ]\n",
            " [0.6467576 ]\n",
            " [0.6843004 ]\n",
            " [0.63310575]\n",
            " [0.6211605 ]\n",
            " [0.55802035]\n",
            " [0.52730393]\n",
            " [0.4726963 ]\n",
            " [0.46928334]\n",
            " [0.49146748]\n",
            " [0.4897611 ]\n",
            " [0.3976109 ]\n",
            " [0.3242321 ]\n",
            " [0.33105803]\n",
            " [0.3515358 ]\n",
            " [0.40102386]\n",
            " [0.3242321 ]\n",
            " [0.35494876]\n",
            " [0.32081914]\n",
            " [0.334471  ]\n",
            " [0.28839588]\n",
            " [0.36006832]\n",
            " [0.3515358 ]\n",
            " [0.39249134]\n",
            " [0.3976109 ]\n",
            " [0.4146757 ]\n",
            " [0.42150164]\n",
            " [0.40614343]\n",
            " [0.38737202]\n",
            " [0.42320824]\n",
            " [0.36348128]\n",
            " [0.37542677]\n",
            " [0.45733786]\n",
            " [0.44709897]\n",
            " [0.41979527]\n",
            " [0.46075106]\n",
            " [0.50341296]\n",
            " [0.5819113 ]\n",
            " [0.5836179 ]\n",
            " [0.62798643]\n",
            " [0.5836179 ]\n",
            " [0.54778147]\n",
            " [0.5716722 ]\n",
            " [0.5546076 ]\n",
            " [0.57849836]\n",
            " [0.588737  ]\n",
            " [0.5682595 ]\n",
            " [0.55119467]\n",
            " [0.49829364]\n",
            " [0.5085325 ]\n",
            " [0.54778147]\n",
            " [0.5853243 ]\n",
            " [0.588737  ]\n",
            " [0.5972698 ]\n",
            " [0.5836179 ]\n",
            " [0.5426624 ]\n",
            " [0.5802047 ]\n",
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            " [0.7235496 ]\n",
            " [0.7525599 ]\n",
            " [0.77303743]\n",
            " [0.8651879 ]\n",
            " [0.89931726]\n",
            " [1.        ]\n",
            " [0.8634813 ]\n",
            " [0.90102386]\n",
            " [0.89931726]\n",
            " [0.7406144 ]\n",
            " [0.7866893 ]\n",
            " [0.77303743]\n",
            " [0.7303753 ]\n",
            " [0.65017056]\n",
            " [0.6211605 ]\n",
            " [0.6587031 ]\n",
            " [0.5836179 ]\n",
            " [0.65529037]\n",
            " [0.54778147]\n",
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            " [0.54948807]\n",
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            " [0.5733788 ]\n",
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            " [0.49829364]\n",
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            " [0.33959055]\n",
            " [0.40443683]\n",
            " [0.4146757 ]\n",
            " [0.37542677]\n",
            " [0.3993175 ]\n",
            " [0.44027305]\n",
            " [0.3805461 ]\n",
            " [0.3259387 ]\n",
            " [0.3532424 ]\n",
            " [0.39590454]\n",
            " [0.3225255 ]\n",
            " [0.31228662]\n",
            " [0.28668952]\n",
            " [0.26962447]\n",
            " [0.33959055]\n",
            " [0.37201357]\n",
            " [0.3515358 ]\n",
            " [0.43344712]\n",
            " [0.40614343]\n",
            " [0.42491484]\n",
            " [0.4146757 ]\n",
            " [0.35836172]\n",
            " [0.33788395]\n",
            " [0.36177468]\n",
            " [0.29351544]\n",
            " [0.29010248]\n",
            " [0.2764504 ]\n",
            " [0.15870309]\n",
            " [0.12457347]\n",
            " [0.16723561]\n",
            " [0.16382241]\n",
            " [0.17406154]\n",
            " [0.2201364 ]\n",
            " [0.25426626]\n",
            " [0.30375433]\n",
            " [0.31228662]\n",
            " [0.411263  ]\n",
            " [0.38566542]\n",
            " [0.38566542]\n",
            " [0.3054607 ]\n",
            " [0.33959055]\n",
            " [0.35665536]\n",
            " [0.13651872]\n",
            " [0.1313994 ]\n",
            " [0.15017056]\n",
            " [0.17747426]\n",
            " [0.14334464]\n",
            " [0.1296928 ]\n",
            " [0.11092162]\n",
            " [0.1313994 ]\n",
            " [0.15187716]\n",
            " [0.14163804]\n",
            " [0.18259382]\n",
            " [0.17918086]\n",
            " [0.20989776]\n",
            " [0.2201364 ]\n",
            " [0.21160412]\n",
            " [0.1296928 ]\n",
            " [0.12627983]\n",
            " [0.12286687]\n",
            " [0.11604095]\n",
            " [0.14163804]\n",
            " [0.15017056]\n",
            " [0.12627983]\n",
            " [0.11604095]\n",
            " [0.09215021]\n",
            " [0.04948807]\n",
            " [0.02559733]\n",
            " [0.01706481]\n",
            " [0.05460763]\n",
            " [0.01194549]\n",
            " [0.        ]\n",
            " [0.04436851]\n",
            " [0.06825948]\n",
            " [0.08703065]\n",
            " [0.0716722 ]\n",
            " [0.10921502]\n",
            " [0.09215021]\n",
            " [0.09726977]\n",
            " [0.10580206]\n",
            " [0.08873725]\n",
            " [0.19795227]\n",
            " [0.18430018]\n",
            " [0.13310575]\n",
            " [0.1279862 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#divinding the values of stocks data to test from train ending to stock data ending i.e rest 20% data\n",
        "test = stocks[len(train) : ]"
      ],
      "metadata": {
        "id": "bQItEcV4YVdY"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reshaping train data in 1D array form\n",
        "train = train.reshape(len(train) , 1)\n",
        "#reshaping test data in 1D array form\n",
        "test = test.reshape(len(test) , 1)"
      ],
      "metadata": {
        "id": "VxzVXx7_YWk3"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#new train and test array shape\n",
        "print(train.shape , test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCpNsBcKYf2H",
        "outputId": "fb825241-0baf-4e25-9d1d-74a87d19fc2a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(180, 1) (46, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating function to create trainX,testX and target(trainY, testY)\n",
        "def process_data(data , n_features):\n",
        "    dataX, dataY = [], [] \n",
        "    for i in range(len(data)-n_features-1):\n",
        "        \n",
        "        a = data[i:(i+n_features), 0]\n",
        "        \n",
        "        dataX.append(a) \n",
        "        \n",
        "        dataY.append(data[i + n_features, 0])\n",
        "        \n",
        "    return np.array(dataX), np.array(dataY)\n",
        "\n",
        "#so the stucture of trainX and trainY is somehow like this\n",
        "# trainX=[[i1 , i2,...., i n_features ]] and trainY=[i + n_features]  \n",
        "# trainY will show the future value of trainX values"
      ],
      "metadata": {
        "id": "x7QoLtVzY7Yx"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_features = 2\n",
        "\n",
        "trainX, trainY = process_data(train, n_features)\n",
        "\n",
        "testX, testY = process_data(test, n_features)\n"
      ],
      "metadata": {
        "id": "ACC3N-2_ZFOf"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# printing the structure of train X,Y and test X,Y\n",
        "print(trainX.shape , trainY.shape , testX.shape , testY.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wfpq2dYPZGdH",
        "outputId": "0f207af6-c9c1-4ba1-e91e-07501bb46886"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(177, 2) (177,) (43, 2) (43,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reshaping trainX and testX to use in deeplearning model\n",
        "trainX = trainX.reshape(trainX.shape[0] , 1 ,trainX.shape[1])\n",
        "testX = testX.reshape(testX.shape[0] , 1 ,testX.shape[1])"
      ],
      "metadata": {
        "id": "RmJag2DZZUq7"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import math \n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense , BatchNormalization , Dropout , Activation # types of layers\n",
        "from keras.layers import LSTM , GRU \n",
        "from sklearn.preprocessing import MinMaxScaler \n",
        "from sklearn.metrics import mean_squared_error \n",
        "from tensorflow.keras.optimizers import Adam , SGD , RMSprop "
      ],
      "metadata": {
        "id": "0QHS0aTHaG5j"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filepath=\"stock_weights1.hdf5\"\n",
        "\n",
        "from keras.callbacks import ReduceLROnPlateau , ModelCheckpoint\n",
        "\n",
        "lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.1, epsilon=0.0001, patience=1, verbose=1)\n",
        "\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='max')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2F8NEGDgbtGS",
        "outputId": "eae61e73-4d1e-4a83-cb43-121fb8e48123"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:`epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating model for training data using sequential to give series wise output between layers\n",
        "model = Sequential()\n",
        "\n",
        "model.add(GRU(256 , input_shape = (1 , n_features) , return_sequences=True))\n",
        "\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(LSTM(256))\n",
        "\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(Dense(64 ,  activation = 'relu'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1qSAYRMbw2n",
        "outputId": "d6a1bd59-cb72-47d7-c28b-c73f1e0b80e2"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " gru (GRU)                   (None, 1, 256)            199680    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1, 256)            0         \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 256)               525312    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                16448     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 741,505\n",
            "Trainable params: 741,505\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# selecting the loss measurement metrics and optimizer for our model , to find out mean square error\n",
        "model.compile(loss='mean_squared_error', optimizer=Adam(lr = 0.0005) , metrics = ['mean_squared_error'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KIYJBZZxcBoo",
        "outputId": "216034fd-3485-4c30-a490-96cdef2526bb"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fitting the data i.e training the trainX, to relate to trainY\n",
        "history = model.fit(trainX, trainY, epochs=100 , batch_size = 128 , \n",
        "          callbacks = [checkpoint , lr_reduce] , validation_data = (testX,testY))    \n",
        "#callbacks are proper"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gc5FwHLzcLfQ",
        "outputId": "5ef51984-2868-45db-e067-61fe6f51e098"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/2 [==============>...............] - ETA: 4s - loss: 0.2149 - mean_squared_error: 0.2149\n",
            "Epoch 1: val_loss improved from -inf to 0.19645, saving model to stock_weights1.hdf5\n",
            "2/2 [==============================] - 6s 1s/step - loss: 0.2022 - mean_squared_error: 0.2022 - val_loss: 0.1964 - val_mean_squared_error: 0.1964 - lr: 5.0000e-04\n",
            "Epoch 2/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.1859 - mean_squared_error: 0.1859\n",
            "Epoch 2: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 69ms/step - loss: 0.1811 - mean_squared_error: 0.1811 - val_loss: 0.1750 - val_mean_squared_error: 0.1750 - lr: 5.0000e-04\n",
            "Epoch 3/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.1558 - mean_squared_error: 0.1558\n",
            "Epoch 3: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 93ms/step - loss: 0.1604 - mean_squared_error: 0.1604 - val_loss: 0.1517 - val_mean_squared_error: 0.1517 - lr: 5.0000e-04\n",
            "Epoch 4/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.1467 - mean_squared_error: 0.1467\n",
            "Epoch 4: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 77ms/step - loss: 0.1387 - mean_squared_error: 0.1387 - val_loss: 0.1272 - val_mean_squared_error: 0.1272 - lr: 5.0000e-04\n",
            "Epoch 5/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.1116 - mean_squared_error: 0.1116\n",
            "Epoch 5: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 93ms/step - loss: 0.1158 - mean_squared_error: 0.1158 - val_loss: 0.1017 - val_mean_squared_error: 0.1017 - lr: 5.0000e-04\n",
            "Epoch 6/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0963 - mean_squared_error: 0.0963\n",
            "Epoch 6: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 53ms/step - loss: 0.0922 - mean_squared_error: 0.0922 - val_loss: 0.0761 - val_mean_squared_error: 0.0761 - lr: 5.0000e-04\n",
            "Epoch 7/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0634 - mean_squared_error: 0.0634\n",
            "Epoch 7: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 77ms/step - loss: 0.0670 - mean_squared_error: 0.0670 - val_loss: 0.0514 - val_mean_squared_error: 0.0514 - lr: 5.0000e-04\n",
            "Epoch 8/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0496 - mean_squared_error: 0.0496\n",
            "Epoch 8: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 73ms/step - loss: 0.0473 - mean_squared_error: 0.0473 - val_loss: 0.0296 - val_mean_squared_error: 0.0296 - lr: 5.0000e-04\n",
            "Epoch 9/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0300 - mean_squared_error: 0.0300\n",
            "Epoch 9: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 78ms/step - loss: 0.0276 - mean_squared_error: 0.0276 - val_loss: 0.0137 - val_mean_squared_error: 0.0137 - lr: 5.0000e-04\n",
            "Epoch 10/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0170 - mean_squared_error: 0.0170\n",
            "Epoch 10: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 93ms/step - loss: 0.0156 - mean_squared_error: 0.0156 - val_loss: 0.0066 - val_mean_squared_error: 0.0066 - lr: 5.0000e-04\n",
            "Epoch 11/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0130 - mean_squared_error: 0.0130\n",
            "Epoch 11: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 5.0000002374872565e-05.\n",
            "2/2 [==============================] - 0s 71ms/step - loss: 0.0127 - mean_squared_error: 0.0127 - val_loss: 0.0083 - val_mean_squared_error: 0.0083 - lr: 5.0000e-04\n",
            "Epoch 12/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0145 - mean_squared_error: 0.0145\n",
            "Epoch 12: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 12: ReduceLROnPlateau reducing learning rate to 5.000000237487257e-06.\n",
            "2/2 [==============================] - 0s 104ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-05\n",
            "Epoch 13/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0169 - mean_squared_error: 0.0169\n",
            "Epoch 13: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 13: ReduceLROnPlateau reducing learning rate to 5.000000328436726e-07.\n",
            "2/2 [==============================] - 0s 73ms/step - loss: 0.0164 - mean_squared_error: 0.0164 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-06\n",
            "Epoch 14/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0139 - mean_squared_error: 0.0139\n",
            "Epoch 14: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 5.000000555810402e-08.\n",
            "2/2 [==============================] - 0s 73ms/step - loss: 0.0160 - mean_squared_error: 0.0160 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-07\n",
            "Epoch 15/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0178 - mean_squared_error: 0.0178\n",
            "Epoch 15: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 5.000000413701855e-09.\n",
            "2/2 [==============================] - 0s 74ms/step - loss: 0.0164 - mean_squared_error: 0.0164 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-08\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0168 - mean_squared_error: 0.0168\n",
            "Epoch 16: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 16: ReduceLROnPlateau reducing learning rate to 5.000000413701855e-10.\n",
            "2/2 [==============================] - 0s 171ms/step - loss: 0.0168 - mean_squared_error: 0.0168 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-09\n",
            "Epoch 17/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0179 - mean_squared_error: 0.0179\n",
            "Epoch 17: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 17: ReduceLROnPlateau reducing learning rate to 5.000000413701855e-11.\n",
            "2/2 [==============================] - 0s 92ms/step - loss: 0.0168 - mean_squared_error: 0.0168 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-10\n",
            "Epoch 18/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0149 - mean_squared_error: 0.0149\n",
            "Epoch 18: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 5.000000413701855e-12.\n",
            "2/2 [==============================] - 0s 95ms/step - loss: 0.0153 - mean_squared_error: 0.0153 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-11\n",
            "Epoch 19/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0177 - mean_squared_error: 0.0177\n",
            "Epoch 19: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 19: ReduceLROnPlateau reducing learning rate to 5.000000413701855e-13.\n",
            "2/2 [==============================] - 0s 143ms/step - loss: 0.0166 - mean_squared_error: 0.0166 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-12\n",
            "Epoch 20/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0166 - mean_squared_error: 0.0166\n",
            "Epoch 20: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 5.0000005221220725e-14.\n",
            "2/2 [==============================] - 0s 74ms/step - loss: 0.0166 - mean_squared_error: 0.0166 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-13\n",
            "Epoch 21/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0166 - mean_squared_error: 0.0166\n",
            "Epoch 21: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 21: ReduceLROnPlateau reducing learning rate to 5.000000589884709e-15.\n",
            "2/2 [==============================] - 0s 75ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-14\n",
            "Epoch 22/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0161 - mean_squared_error: 0.0161\n",
            "Epoch 22: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 22: ReduceLROnPlateau reducing learning rate to 5.000000759291298e-16.\n",
            "2/2 [==============================] - 0s 81ms/step - loss: 0.0162 - mean_squared_error: 0.0162 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-15\n",
            "Epoch 23/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0180 - mean_squared_error: 0.0180\n",
            "Epoch 23: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 23: ReduceLROnPlateau reducing learning rate to 5.000000547533061e-17.\n",
            "2/2 [==============================] - 0s 132ms/step - loss: 0.0170 - mean_squared_error: 0.0170 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-16\n",
            "Epoch 24/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0166 - mean_squared_error: 0.0166\n",
            "Epoch 24: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 24: ReduceLROnPlateau reducing learning rate to 5.000000415184163e-18.\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0168 - mean_squared_error: 0.0168 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-17\n",
            "Epoch 25/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0149 - mean_squared_error: 0.0149\n",
            "Epoch 25: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 25: ReduceLROnPlateau reducing learning rate to 5.000000332466102e-19.\n",
            "2/2 [==============================] - 0s 131ms/step - loss: 0.0159 - mean_squared_error: 0.0159 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-18\n",
            "Epoch 26/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0171 - mean_squared_error: 0.0171\n",
            "Epoch 26: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 26: ReduceLROnPlateau reducing learning rate to 5.000000229068525e-20.\n",
            "2/2 [==============================] - 0s 88ms/step - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-19\n",
            "Epoch 27/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0172 - mean_squared_error: 0.0172\n",
            "Epoch 27: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 27: ReduceLROnPlateau reducing learning rate to 5.00000016444504e-21.\n",
            "2/2 [==============================] - 0s 183ms/step - loss: 0.0167 - mean_squared_error: 0.0167 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-20\n",
            "Epoch 28/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0140 - mean_squared_error: 0.0140\n",
            "Epoch 28: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 28: ReduceLROnPlateau reducing learning rate to 5.000000245224397e-22.\n",
            "2/2 [==============================] - 0s 158ms/step - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-21\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0169 - mean_squared_error: 0.0169\n",
            "Epoch 29: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 29: ReduceLROnPlateau reducing learning rate to 5.0000003461985925e-23.\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0169 - mean_squared_error: 0.0169 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-22\n",
            "Epoch 30/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0171 - mean_squared_error: 0.0171\n",
            "Epoch 30: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 30: ReduceLROnPlateau reducing learning rate to 5.000000472416337e-24.\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0172 - mean_squared_error: 0.0172 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-23\n",
            "Epoch 31/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0149 - mean_squared_error: 0.0149\n",
            "Epoch 31: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 31: ReduceLROnPlateau reducing learning rate to 5.000000393530247e-25.\n",
            "2/2 [==============================] - 0s 118ms/step - loss: 0.0154 - mean_squared_error: 0.0154 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-24\n",
            "Epoch 32/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0169 - mean_squared_error: 0.0169\n",
            "Epoch 32: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 32: ReduceLROnPlateau reducing learning rate to 5.000000590745473e-26.\n",
            "2/2 [==============================] - 0s 146ms/step - loss: 0.0160 - mean_squared_error: 0.0160 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-25\n",
            "Epoch 33/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0192 - mean_squared_error: 0.0192\n",
            "Epoch 33: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 33: ReduceLROnPlateau reducing learning rate to 5.000000714004989e-27.\n",
            "2/2 [==============================] - 0s 131ms/step - loss: 0.0177 - mean_squared_error: 0.0177 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-26\n",
            "Epoch 34/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0154 - mean_squared_error: 0.0154\n",
            "Epoch 34: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 34: ReduceLROnPlateau reducing learning rate to 5.00000071400499e-28.\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-27\n",
            "Epoch 35/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0152 - mean_squared_error: 0.0152\n",
            "Epoch 35: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 35: ReduceLROnPlateau reducing learning rate to 5.000000617708492e-29.\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 0.0153 - mean_squared_error: 0.0153 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-28\n",
            "Epoch 36/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0173 - mean_squared_error: 0.0173\n",
            "Epoch 36: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 36: ReduceLROnPlateau reducing learning rate to 5.0000006177084924e-30.\n",
            "2/2 [==============================] - 0s 141ms/step - loss: 0.0167 - mean_squared_error: 0.0167 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-29\n",
            "Epoch 37/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0167 - mean_squared_error: 0.0167\n",
            "Epoch 37: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 37: ReduceLROnPlateau reducing learning rate to 5.0000007681717695e-31.\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-30\n",
            "Epoch 38/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0172 - mean_squared_error: 0.0172\n",
            "Epoch 38: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 38: ReduceLROnPlateau reducing learning rate to 5.000000956250865e-32.\n",
            "2/2 [==============================] - 0s 128ms/step - loss: 0.0159 - mean_squared_error: 0.0159 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-31\n",
            "Epoch 39/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0170 - mean_squared_error: 0.0170\n",
            "Epoch 39: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 39: ReduceLROnPlateau reducing learning rate to 5.0000010738003005e-33.\n",
            "2/2 [==============================] - 0s 130ms/step - loss: 0.0166 - mean_squared_error: 0.0166 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-32\n",
            "Epoch 40/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0157 - mean_squared_error: 0.0157\n",
            "Epoch 40: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 40: ReduceLROnPlateau reducing learning rate to 5.000001220737094e-34.\n",
            "2/2 [==============================] - 0s 204ms/step - loss: 0.0168 - mean_squared_error: 0.0168 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-33\n",
            "Epoch 41/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0143 - mean_squared_error: 0.0143\n",
            "Epoch 41: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 41: ReduceLROnPlateau reducing learning rate to 5.000001037066102e-35.\n",
            "2/2 [==============================] - 0s 98ms/step - loss: 0.0149 - mean_squared_error: 0.0149 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-34\n",
            "Epoch 42/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0153 - mean_squared_error: 0.0153\n",
            "Epoch 42: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 42: ReduceLROnPlateau reducing learning rate to 5.000000807477361e-36.\n",
            "2/2 [==============================] - 0s 97ms/step - loss: 0.0160 - mean_squared_error: 0.0160 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-35\n",
            "Epoch 43/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0181 - mean_squared_error: 0.0181\n",
            "Epoch 43: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 43: ReduceLROnPlateau reducing learning rate to 5.000000807477361e-37.\n",
            "2/2 [==============================] - 0s 77ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-36\n",
            "Epoch 44/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0156 - mean_squared_error: 0.0156\n",
            "Epoch 44: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 44: ReduceLROnPlateau reducing learning rate to 5.000000628111158e-38.\n",
            "2/2 [==============================] - 0s 121ms/step - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-37\n",
            "Epoch 45/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0163 - mean_squared_error: 0.0163\n",
            "Epoch 45: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 45: ReduceLROnPlateau reducing learning rate to 5.000000516007281e-39.\n",
            "2/2 [==============================] - 0s 130ms/step - loss: 0.0160 - mean_squared_error: 0.0160 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-38\n",
            "Epoch 46/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0159 - mean_squared_error: 0.0159\n",
            "Epoch 46: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 46: ReduceLROnPlateau reducing learning rate to 5.000001076526666e-40.\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0166 - mean_squared_error: 0.0166 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-39\n",
            "Epoch 47/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0149 - mean_squared_error: 0.0149\n",
            "Epoch 47: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 47: ReduceLROnPlateau reducing learning rate to 5.000001076526667e-41.\n",
            "2/2 [==============================] - 0s 180ms/step - loss: 0.0164 - mean_squared_error: 0.0164 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-40\n",
            "Epoch 48/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0164 - mean_squared_error: 0.0164\n",
            "Epoch 48: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 48: ReduceLROnPlateau reducing learning rate to 4.99997305055738e-42.\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0162 - mean_squared_error: 0.0162 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0000e-41\n",
            "Epoch 49/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0171 - mean_squared_error: 0.0171\n",
            "Epoch 49: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 49: ReduceLROnPlateau reducing learning rate to 4.9998329207109475e-43.\n",
            "2/2 [==============================] - 0s 200ms/step - loss: 0.0166 - mean_squared_error: 0.0166 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 4.9998e-42\n",
            "Epoch 50/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0167 - mean_squared_error: 0.0167\n",
            "Epoch 50: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 50: ReduceLROnPlateau reducing learning rate to 5.002635517639597e-44.\n",
            "2/2 [==============================] - 0s 100ms/step - loss: 0.0164 - mean_squared_error: 0.0164 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0026e-43\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0166 - mean_squared_error: 0.0166\n",
            "Epoch 51: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 51: ReduceLROnPlateau reducing learning rate to 5.0446744715693416e-45.\n",
            "2/2 [==============================] - 0s 163ms/step - loss: 0.0166 - mean_squared_error: 0.0166 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.0447e-44\n",
            "Epoch 52/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0145 - mean_squared_error: 0.0145\n",
            "Epoch 52: val_loss did not improve from 0.19645\n",
            "\n",
            "Epoch 52: ReduceLROnPlateau reducing learning rate to 5.6051938572992686e-46.\n",
            "2/2 [==============================] - 0s 195ms/step - loss: 0.0160 - mean_squared_error: 0.0160 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 5.6052e-45\n",
            "Epoch 53/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0163 - mean_squared_error: 0.0163\n",
            "Epoch 53: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 119ms/step - loss: 0.0167 - mean_squared_error: 0.0167 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 54/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0146 - mean_squared_error: 0.0146\n",
            "Epoch 54: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 97ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 55/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0163 - mean_squared_error: 0.0163\n",
            "Epoch 55: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 163ms/step - loss: 0.0164 - mean_squared_error: 0.0164 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 56/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0154 - mean_squared_error: 0.0154\n",
            "Epoch 56: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.0167 - mean_squared_error: 0.0167 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 57/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0157 - mean_squared_error: 0.0157\n",
            "Epoch 57: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 86ms/step - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 58/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0174 - mean_squared_error: 0.0174\n",
            "Epoch 58: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 184ms/step - loss: 0.0169 - mean_squared_error: 0.0169 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 59/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0161 - mean_squared_error: 0.0161\n",
            "Epoch 59: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 138ms/step - loss: 0.0162 - mean_squared_error: 0.0162 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 60/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0160 - mean_squared_error: 0.0160\n",
            "Epoch 60: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 155ms/step - loss: 0.0159 - mean_squared_error: 0.0159 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 61/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0173 - mean_squared_error: 0.0173\n",
            "Epoch 61: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 95ms/step - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 62/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0164 - mean_squared_error: 0.0164\n",
            "Epoch 62: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 73ms/step - loss: 0.0169 - mean_squared_error: 0.0169 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 63/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0162 - mean_squared_error: 0.0162\n",
            "Epoch 63: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 83ms/step - loss: 0.0156 - mean_squared_error: 0.0156 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 64/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0180 - mean_squared_error: 0.0180\n",
            "Epoch 64: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 118ms/step - loss: 0.0175 - mean_squared_error: 0.0175 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 65/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0177 - mean_squared_error: 0.0177\n",
            "Epoch 65: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 124ms/step - loss: 0.0165 - mean_squared_error: 0.0165 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0157 - mean_squared_error: 0.0157\n",
            "Epoch 66: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 127ms/step - loss: 0.0157 - mean_squared_error: 0.0157 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 67/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0169 - mean_squared_error: 0.0169\n",
            "Epoch 67: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 121ms/step - loss: 0.0168 - mean_squared_error: 0.0168 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 68/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0149 - mean_squared_error: 0.0149\n",
            "Epoch 68: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0160 - mean_squared_error: 0.0160 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 69/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0174 - mean_squared_error: 0.0174\n",
            "Epoch 69: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 83ms/step - loss: 0.0174 - mean_squared_error: 0.0174 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 70/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0149 - mean_squared_error: 0.0149\n",
            "Epoch 70: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 74ms/step - loss: 0.0162 - mean_squared_error: 0.0162 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 71/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0158 - mean_squared_error: 0.0158\n",
            "Epoch 71: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 128ms/step - loss: 0.0156 - mean_squared_error: 0.0156 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 72/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0147 - mean_squared_error: 0.0147\n",
            "Epoch 72: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 90ms/step - loss: 0.0158 - mean_squared_error: 0.0158 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 73/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0171 - mean_squared_error: 0.0171\n",
            "Epoch 73: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 126ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 74/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0157 - mean_squared_error: 0.0157\n",
            "Epoch 74: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 122ms/step - loss: 0.0160 - mean_squared_error: 0.0160 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 75/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0148 - mean_squared_error: 0.0148\n",
            "Epoch 75: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 198ms/step - loss: 0.0166 - mean_squared_error: 0.0166 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 76/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0161 - mean_squared_error: 0.0161\n",
            "Epoch 76: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 77/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0166 - mean_squared_error: 0.0166\n",
            "Epoch 77: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 198ms/step - loss: 0.0166 - mean_squared_error: 0.0166 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 78/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0171 - mean_squared_error: 0.0171\n",
            "Epoch 78: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 138ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 79/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0183 - mean_squared_error: 0.0183\n",
            "Epoch 79: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 148ms/step - loss: 0.0176 - mean_squared_error: 0.0176 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 80/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0152 - mean_squared_error: 0.0152\n",
            "Epoch 80: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 105ms/step - loss: 0.0162 - mean_squared_error: 0.0162 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 81/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0147 - mean_squared_error: 0.0147\n",
            "Epoch 81: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 126ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 82/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0151 - mean_squared_error: 0.0151\n",
            "Epoch 82: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 131ms/step - loss: 0.0158 - mean_squared_error: 0.0158 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 83/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0153 - mean_squared_error: 0.0153\n",
            "Epoch 83: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 128ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 84/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0174 - mean_squared_error: 0.0174\n",
            "Epoch 84: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 147ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 85/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0168 - mean_squared_error: 0.0168\n",
            "Epoch 85: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 156ms/step - loss: 0.0168 - mean_squared_error: 0.0168 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 86/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0177 - mean_squared_error: 0.0177\n",
            "Epoch 86: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 151ms/step - loss: 0.0171 - mean_squared_error: 0.0171 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 87/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0162 - mean_squared_error: 0.0162\n",
            "Epoch 87: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 142ms/step - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 88/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0149 - mean_squared_error: 0.0149\n",
            "Epoch 88: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 102ms/step - loss: 0.0179 - mean_squared_error: 0.0179 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 89/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0166 - mean_squared_error: 0.0166\n",
            "Epoch 89: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 101ms/step - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 90/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0171 - mean_squared_error: 0.0171\n",
            "Epoch 90: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 153ms/step - loss: 0.0170 - mean_squared_error: 0.0170 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 91/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0164 - mean_squared_error: 0.0164\n",
            "Epoch 91: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 195ms/step - loss: 0.0160 - mean_squared_error: 0.0160 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 92/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0169 - mean_squared_error: 0.0169\n",
            "Epoch 92: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 155ms/step - loss: 0.0167 - mean_squared_error: 0.0167 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 93/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0151 - mean_squared_error: 0.0151\n",
            "Epoch 93: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 99ms/step - loss: 0.0165 - mean_squared_error: 0.0165 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 94/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0180 - mean_squared_error: 0.0180\n",
            "Epoch 94: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 84ms/step - loss: 0.0168 - mean_squared_error: 0.0168 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 95/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0168 - mean_squared_error: 0.0168\n",
            "Epoch 95: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 120ms/step - loss: 0.0164 - mean_squared_error: 0.0164 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 96/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0161 - mean_squared_error: 0.0161\n",
            "Epoch 96: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 96ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 97/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0149 - mean_squared_error: 0.0149\n",
            "Epoch 97: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 72ms/step - loss: 0.0159 - mean_squared_error: 0.0159 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 98/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0168 - mean_squared_error: 0.0168\n",
            "Epoch 98: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 175ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 99/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0165 - mean_squared_error: 0.0165\n",
            "Epoch 99: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 171ms/step - loss: 0.0154 - mean_squared_error: 0.0154 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n",
            "Epoch 100/100\n",
            "1/2 [==============>...............] - ETA: 0s - loss: 0.0169 - mean_squared_error: 0.0169\n",
            "Epoch 100: val_loss did not improve from 0.19645\n",
            "2/2 [==============================] - 0s 133ms/step - loss: 0.0174 - mean_squared_error: 0.0174 - val_loss: 0.0087 - val_mean_squared_error: 0.0087 - lr: 0.0000e+00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predicting the value for testX\n",
        "pred = model.predict(testX)\n",
        "\n",
        "pred = scaler.inverse_transform(pred)\n",
        "\n",
        "pred[:10]\n",
        "# taking pred from 1 to 10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CEt5_8zicZa5",
        "outputId": "7b798697-5c75-4478-bdfc-11eb287a809a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[15.0511  ],\n",
              "       [15.047194],\n",
              "       [15.196658],\n",
              "       [15.233918],\n",
              "       [15.197189],\n",
              "       [15.217176],\n",
              "       [15.145622],\n",
              "       [15.122155],\n",
              "       [15.158246],\n",
              "       [15.26862 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reshaping testY in single array\n",
        "testY = testY.reshape(testY.shape[0] , 1)\n",
        "\n",
        "testY = scaler.inverse_transform(testY)\n",
        "\n",
        "testY[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5fz92eocabt",
        "outputId": "5c7a939d-1926-4406-d2c3-cd295616e7b0"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[14.17    ],\n",
              "       [14.500001],\n",
              "       [14.28    ],\n",
              "       [14.39    ],\n",
              "       [14.340001],\n",
              "       [14.17    ],\n",
              "       [14.27    ],\n",
              "       [14.28    ],\n",
              "       [14.61    ],\n",
              "       [14.52    ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ploting the graph of stock prices with time\n",
        "print(\"Red - Predicted Stock Prices  ,  Blue - Actual Stock Prices\")\n",
        "plt.rcParams[\"figure.figsize\"] = (15,7)\n",
        "\n",
        "plt.plot(testY , 'b')\n",
        "\n",
        "plt.plot(pred , 'r')\n",
        "\n",
        "plt.xlabel('Time')\n",
        "\n",
        "plt.ylabel('Stock Prices')\n",
        "\n",
        "plt.title('Check the accuracy of the model with time')\n",
        "\n",
        "plt.grid(True)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "bE2_sqJCdA5y",
        "outputId": "00d4de09-72c3-4ebc-b05b-5a65e4e67173"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Red - Predicted Stock Prices  ,  Blue - Actual Stock Prices\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ],
            "image/png": 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yy23e6bhx8NhjVmlmyhQ74aBBUKsWNGoEb78NO3bk8AQSLCUFTjwRChRI5aqrLAkcHZKmcEceJX4iIiIiEtWSkuD886FIkbwdp0QJaNwYxoyxaaNPP22JYWpqNg+wZg20aWNDjJMn2wHBspFWrWD8eFixAp57Dv75x0b/ypeHLl3gxx/zFvwRKnh079hjoUULq+6piq45p8RPRERERKLWihWwcGHm0zxzIyHBmoLfcw8MGWLL89KKw2Rqxw5bw7dmDUycmHlBl3LloE8fWLwYkpPh4ovh9dehbl1bYDh8OGzZEronE8e2bYNlyw6e1tmhAyxfDt9+619csUqJn4iIiIhErenT7etFF4X2uM7BwIHwyCNWp6V9e9izJ5ONU1PhxhthzhwbbgquLHO4E1xwgc0zXb0ahg61PhSdO8Nxx8Ett8DXX2vo6jAWBhq6BSd+7dpBoULq6ZcbSvxEREREJGolJVlvvjp1Qn9s5+DRRy0B/OADW7q3c2cGG/btCx9+CM8/b5lHTpUsaVVJfvoJvv8err/e1geed549sf/9DzZvzuvTiTtpFT2DE79ixWzgdezYA/0cJXuU+ImIiIhIVNq3D2bMsG4J4WzK3quXTf2cMsWW8G3dGvTgiBEwYICt0+vRI28ncg7OOsuOuXq1TQE9+mgLoGFDm8Mo/0lJsXWdVaocfH+HDrB2LXz6qT9xxSolfiIiIiISlebMsUqcoZ7mmZHbb4d33oEvvrACIhs3YsONXbpA69ZWrTOU2WfRotYH8JtvrD3EP/9Ya4i0YS4hJcUGRPOly1guusjyZU33zBklfiIiIiISlZKS7E1/8+aROd9119kMzB9/hE4N5pN65VU2z3DMGEgMY/vrJk2sWaFzVr7088/Dd64Y4XmZ9+srWBCuvBImTFC3jJxQ4iciIiIiUWnaNJsZecwxkTtnu3Yw461VDFrShn93FWfVK5NsYVm4nXyylaosX97mto4bF/5zRrE1a2DduswbtXfoYFU/J02KbFyxTImfiIiIiESdDRtsqmckpnkeZPt2Gg24hOMKbeTKgpM456oKLFkSoXNXrAhffWXZ7tVXw7BhETpx9MmosEuwRo0sR9Z0z+xT4iciIiIiUWfmTOuiEMr+fVnav9/6Ovz0EwkfvM/gz09n2zabfblgQYRiOOYYq2hzySVWCfSBB47Ilg9ZJX4JCXDttVaQZ+PGyMUVy5T4iYiIiEjUmTbNuiCcdVYET9qrlzVnHzIE2rThzDNtuV1aS765cyMUR+HC1j7i9tvh6aehY8cjrndBSgqULQtlymS+TYcO9rJ8+GHk4vrP/v0+nDRvlPiJiIiISFTxPCvs0rx5eGuqHGToUBg8GHr2tEqeAXXqWN2VYsWgaVP7PiISE2H4cGs0+MYbtvhw+/YIndx/mRV2CVa3Lpx0kg/TPX/5BU491aoAxRAlfiIiIiISVRYuhFWrIjjNc9Ik69HXtq317EunWjVbele+vMU0fXqE4nIOHnnE+v5NmwbNmlnFkzi3f79dA1klfs7ZqF9yMqxcGZHQbM5vkya2CLVQoQidNDSU+ImIiIhIVJk2zb5GJPH74QdbLFa3Lrz3ni0ey8Dxx1uPv5NOsuV3EyZEILY0t99u8xl//hnOPRf+/DOCJ4+8pUth166sEz+wJZmeB++/H/64+PlnS/oSEy3brFUrAicNHSV+IiIiIhJVkpJsiuXxx4f5RCtWwMUXQ6lStravSJHDbn7ssTBrFpxxBlx1Fbz7bpjjC9aunRV9+fdfOPtsS0LiVFaFXYKddBLUqxeB6Z4//mhzfQsVsoWfNWqE+YShp8RPRERERKLG9u02shb20b6tWy3p274dJk+GcuWytVvJkpZ/NWoEN94Y4cIi551nc04TEy2AWbMiePLISUmxaZy1a2dv++uug3nzYPHiMAU0d64lfUWLWtJXvXqYThReSvxEREREJGp8/jns2RPm/n379lmfvIUL4YMPrHl6DhQrZm0E6taFbt0sh4yYOnWs0XvFivYijR0bwZNHRkqK5VZHHZW97a+5xhLF0aPDEMz331uVoaOPtouzatUwnCQylPiJiIiISNRISrJuBuefH6YTeB7cfbctJHz5ZWjZMleHKVTI+quvXg1PPhniGLNy/PFWXrRBA1ufOHRohAMIr+xU9Ax23HE2IPfeeyFuefjtt9CiBZQubUlf5cohPHjkKfETERERkaiRlGQ988JWMPHDDy3h69MHOnXK06EaNICbb4YXXgjjNMPMlCxpL1a7dpbI3n9/XDR637EDlizJWeIHVt1zyZIQ9lr86iv7UKBsWSvkUqlSiA7sHyV+IiIiIhIV/vzTEqiwTfPcvNmSpDPOgKeeCskhn33WRii7d/ch7ypc2Kaq3nmnBXLLLTHf6P2XX+x1zGnid/nlUKBAiIq8fP65XYQVKljSF/YqQ5GhxE9EREREokJSkn0NW2GXBx+Ef/6xvngh6gxftiw89pjF/sknITlkziQkwEsvwRNPwFtvQcOGMG6cNcOLQTmp6Bns6KOhTRsYMyaPT/2zz6BVKxvhS0625o1xQomfiIiIiESFpCR7vx2WSvlz5tiivK5d4ayzQnroLl2sAmXPnrBzZ0gPnT3OWVI7Zgxs2WK9JmrVgldesYZ4MSQlxQYyq1XL+b4dOlhen5ycy5PPmGHZY7VqdpBsVnqNFUr8RERERMR3e/fCzJk2w865EB983z5rgn7ccWGpxJI/v9VX+eMPGDgw5IfPvmuugUWLrNJn8eJwxx1WkOSZZ2DTJh8Dy76UFEuiExJyvm+bNlZxNVfTPadNg0susU8dZs2ypo1xRomfiIiIiPjuu++sLUJYpnkOHQo//QRDhlhCFAZNm9pA2zPPwPLlYTlF9iQkWCBz5sCnn8Jpp0G/fjaU2rs3rFzpY3BZy2lFz2CFC9tavw8/zOFA5+TJ0LatZZyffmpVPOOQEj8RERER8V1SkuUszZqF+MB//QUPPWTDQZdfHuKDHyxttK9Xr7CeJnucs2w0KQl++MGa1f/vf1ClCnTsaCODUWbtWlizJveJH9h0z82bYerUbO7wySdw2WVw6qmW9JUqlfuTRzklfiIiIiLiu2nT4OyzoUSJEB/47rshNRVefDEMc0gPVqmSDa59+KHlEFHjjDNs/uOSJTblddQoWwPYrp31qosSuS3sEqxpU5ulma3pnuPHwxVXQN26tr6vZMncnzgGKPETEREREV+tXWuDUiGf5vnRR/Dxx1Z2M0LNt++9F6pWhW7dorCzQpUqlgCnjYJ+8QWccw40agSTJlmC7KNQJH6JibbUceJEq3OTqQ8+gKuvhvr1Yfp0Kwsa55T4iYiIiIivZsyw3m0hTfy2brXs65RToEePEB748AoVgkGD4NdfLceKSmXKwOOPWwI4aJA1ULzkEpvu+PbbvmWsKSm2vK5s2bwdp0MH2L0bJkzIZIPRo6F9e0t6p00L27rPaKPET0RERER8NW2aveE/88wQHvSRR6yQyYgRVnYzgi6+2FrBPfKItReIWkWLWuf5pUst4XMObrrJ2hm88krEO9KnFXbJ64zcBg1scDPD6Z7vvgvXXw/nn28LAYsVy9vJYogSPxERERHxTWqqzbRr0QLyheqd6Q8/wODB1s7g7LNDdNDsc84G0nbtgr59I376nMufH264AebPtwqXlSrZa9emTcQy19RUWLgwb9M80zhno34zZ1qxmP8kJcGNN0KTJvY8ixTJ+8liiBI/EREREfHN/Pn25jxk0zz377ekpUwZ663gk5NOsuqeb70VVfVTDs85aN0avvzS5qnOmmXTPydODPup//gDtm8PTeIHlvilplpLQ8DmfnbrZr+YiRPhqKNCc6IYosRPRERERHwzbZp9bdkyRAd86SWYO9eG3Hwu2PHAA1ChguUb+/f7GkrOOAddusC8eVC+PFx6KXTuDDt2hO2UoSjsEqx2bWth+N90z8GD4fff7booXDg0J4kxSvxERERExDdJSfYG/bjjQnCwlSst22rZ0ko7+qxoURgwwPKn11/3O5pcqF0bZs+2UqUjRljbg3nzwnKqtMSvTp3QHbNDB/juO1j+3Wp44glLYC+6KHQniDFK/ERERETEF1u3wtdfh3CaZ48eVpHypZfC3rMvu6691rol9OsHGzb4HU0uFCxo2evMmbBtGzRsCM8+G/IhzJQUa4NRtGjojnnttfZ1c+e+sGePNbA/ginxExERERFfzJpleVpIBmEmT4Zx46w/XbVqIThgaDgHQ4fCxo3w8MN+R5MHTZvagszLLoP774dmzawdRIikVfQMpUqV4M7TvuXUn97Gu6dXVF0XflDiJyIiIiK+SEqywornnpvHA23fbmvSate2aYlR5tRT4a674OWX4eef/Y4mD445Bt5/H95806Z8nnqq9cTLo127bPldqBM/UlN5bGM3VlKeBZf2C/HBY48SPxERERHxRVKSVdYvUCCPB3rsMVi+3Nah5flg4fH445Y3desW8fZ4oZXW6++nn6BWLVtId8MNsHlzrg/56682czTkid8bb3DsX/Pom28A734UwjmkMUqJn4iIiIhE3JIl1jc8z9M858+3tVu33QbnnReS2MKhZEl4+mnrlBCCQTL/VatmT+bRR+0JnXYafPVVrg4V6oqeAGzaZFNSzz2XjRe1Z/Roa+9wJFPiJyIiIiIRl5RkX/NU2CU11Xr2HXMMPPdcSOIKp1tvhXr1oHdvq5MS8xIT4ZFHLAFMSIALLoAHH7SFmzmQkmI1ZE48MYSxPf44rFsHQ4fS4TrHihVWSOhIluh3ACIiIhJHUlNtvdW2bYfedu2ypmbVqtkb9UhXXdyyBRYtsnllv/5qw00XXGCJQ/78kY1FSEqyKo7Vq+fhIK+8YvX6337brqkol5BgfdEbNoQnn7TimKGSmmrtCytUsFtEnX22Tf3s3h2eegqmT4f33st2JpeSYrNGE0OVmfzyi1XU6dQJzjiDS0+0fu2jRsH554foHDFIiZ+IiIhkzPPsneTMmTZtKqNkLv0tuw2eS5Swd/zVqtkt7fvq1a2hW75cTkryPFiz5kByl3ZbtMh6vKXJnx/KlbMqkMOGwfPPQ6tWUdMCIN7t2QOffWZLxXLtn3+gb1+rNnn99SGLLdwaNICbb7bZqbfcAjVq5P5Yngdz5li9lQ8+gBUr4Kyz4PvvQxZu9hUrBiNHQuvWcPvtcPrp1jS9Y8cs/65SUqxIaEh4niWgRYtado1927YtjB1rIUXpMtCwU+InIiIiB1uwwNbsjBkDy5bZfQUL2run9LcyZQ58X6xYxtuk3QoUsHemS5ceWOD1ww8wfjzs23fg/IUKHRgKSp8YnnCCJW3798Off2ac4G3adOBYxYpBzZr2rrJWLfu+Vi07fmIiTJwIvXpBmzY25/D550PbQVoy9PXXNjCcp2mePXvCzp1WKjPGEvZnn7XLvkcPmDIlZ+F7Hvz4oyV7Y8fan0H+/PZaNmlig59z59qUUl9ceaUNad50k424TZ8O776baba1YQOsWhXC9X0ff2wfVg0ZYv8+BXToYP+szZhhf+5HIiV+IiIiYonYmDF2W7jQRtyaNYMHHoB27UI3je6MMw69b98+6wcWnBCmfT9jhr25T5OQAOXLw7//wu7dB+4vV84Suvbt7WvarXz5w7+rvvRSqy4ybJhVhjztNButeOyxg940SmglJVne3aRJHg4wZoz9nk46KaSxRULZshZ6z5722cOllx5+e8+zUbG0ZG/JEnv9mje3JXZt21rxmM2bbRD75Zfh9dcj81wydPzx9rc7YICNyu7bZ8FnMKU6pIVddu2Ce+6xD286dz7ooZYt7Z+xUaOU+ImIiMiRZsUKexc5erT15AKrijhsGFxxhb07jYTERBuBq1oVWrQ4+DHPg9WrDySDS5da2f5y5Q6M3tWsae96c6tAAXsHfsMNVqFw+HB7d/jww9C165E7LyyMpk2zS61YsVzsvHOnNcWrUQPuuy/ksUVKly7w6qs26teypQ10p/fLL5Yvvf8+LF5sn8c0bWpP+7LLoFSpg7cvUcJGtt57DwYOzNufRZ7ly2eBFi5sUy9vvNFG/hISDtospInf88/DH3/Ap58esmCwQAG46iobEe3SxfaeQwoAACAASURBVF6bkiUtGUz7Pvjno46KuYHkLCnxExEROZKsWWNDAmPGHCi9Xq+evUu8+mqoWNHf+NJzzkbtypcPf1WG0qWt8sZdd9moQa9eNnQycKANycTbu0Cf/POPNTF/5plcHuDJJ20K8mef2RTkGJU/v9UfadbMBsYeesju/+23A8newoV22V1wgSWIl18Oxx57+ON27gyvvWYJTvfu4X8eWbr7bhuJu+8+y25ff/2gNbwpKZZolS+fx/OsWGH9Mq64wrLjDHTuDJ9/bv/8bdp0+PYO+fMfnBRm9H3btlC5ch7jjiAlfiIiIvFu40aYMMHe7Xz6qb3bqV0bnngCrr02j2UV41Dt2jYkNXWqJX/t2tmcxBdesKmgkifTp9vXXK3vW7gQ+ve39WO5nicaPZo2tSVxzzxjy1Y/+siSYrAR0aFDLY857rjsH7NuXahf3wau7747Sj6v6NPHRmoffdSSv5de+i+wlBQb7ctznH362L9tAwdmuslpp9lSYLBNt261NYYbN9rtcN+vWmWX34YNViAY7J8KJX4iIiLir717bWRv9GhLYvbutamU999vyd7JJ/sdYfRr1coWUY0YYQupzjjDKhQ++WTkpsHGoWnT7OXLcQ6dmgp33gnFix/2zX2sef55K/Dy2GNWE+WFFywZPP743B+zc2erGJqcHEX58cMP28jfs8/aSO0LL+DhWLDAZoHmyZdf2gdbjzyS7UwsXz6bGluiBFSpkrPT7dtn6ymLFs15qH5S4iciIhJvdu60BUBJSdbQq1s3S/bq1YuSj/9jSP78ts7vuuusIfSLL9ocvH79bO5dRguzJFOpqVbzo1WrHHbs+OILuPde613w+us2LTdOVKpkxW0LFbKitaFwzTU2W/nll6Mo8XPOpmLu3Gk9FQoXZvntT7N1q8vb+r79++3fuIoVbdQvAhITD11fGQty2SRHREREotKOHbbwZPp0e9f31182pHDWWUr68qJkSRuKWbAAGje2kdNatWxU1fP8ji5m/PADrFuXg2mev/5q6ysvuMDm2r35pg1lxZkaNUKX9IHVU7n5ZpvhvXp16I6bZ87Z39Gdd8Kzz7LrwSeAPBZ2efVVmx/7/PNWkUUypcRPREQkXuzYAZdcYj2sRo60N1e5bYQuGatRAz75xIatiha1MoEXXAA//eR3ZDFh2jR779+yZRYb/vMP3HGHTUlOTraRot9+s7V9+gAjW+6806Yk+trWISPOWeXgm2+m5qhH6E3/3M8837DBWs40bmzzY+Wwwva/gXNupHPuX+fcgnT3d3POLXLOLXTO9c9k3z+dcynOuZ+cc3PDFaOIiEjc2L7dmlPNmmWjIjff7HdE8a15c+uiPXy4NY2vX9+Sk+BG9HKIpCQrPpJpi8Rt26wASPXq9uFFly7WwuP++zWak0MnnWQVQ195xWZDRpV8+eC11/im0rX05z6Kvzkkd8d5+GErzzlkiD4QyIZwfgz4JnBR8B3OuSZAW+A0z/PqAIdbmdvE87zTPc+rF74QRURE4sC2bdC6ta2DeuedEFRKkGxJTLRRqV9/tcqfDzwAjRpZd205xObN8O23mUzz3LfPiuhUr25VTlq1std1yJDDZImSlc6drcvB5Ml+R5KBhATuKvo235S7zPpOvPJKzvZPSbHp7HfdFaImgPEvbImf53lfABvS3d0ZeNbzvN2Bbf4N1/lFRESOCFu32pvkr7+2rs3XXed3REeeUqWs4Mu771rH7dNPtyRGa/8O8umnNvJ0UfCwgOfZ1NlTTrG5idWrW3b4wQdqMxICl15qrSBeftnvSA61ezcs/C0/U28cY7MV7rwT3norezt7nvWqKFnSPiiQbIl0Vc+TgPOdc08Bu4B7Pc+bk8F2HjDdOecBIzzPy/QjAOfc7cDtAGXLliU5OTn0UefRtm3bojIukdzSNS3xJlav6YTt2zn1vvso/uuv/PLQQ6wtV87WQ8WxefNKMmpUJW699Q/q1NnidzgHq1CBgq+8Qs3nnqPknXey/o03WNy7N3t8KP8Xjdf0G2+cxFFHHcvu3V+TnOxR7NdfqTZ8OEfPn8+OihVZ9sQTrDv3XCv5H2Wxx7IWLSrzzjsnMGrUbMqX3+V3OP9ZsqQI+/adBQWW8MXdd3PyP/9Q8tZb+WXpUtZm0IA9+Jouk5xMneRkfuvZk1Xz50c48hjmeV7YbkBlYEHQzwuAoYAD6gN/AC6D/SoEvh4L/Aw0ys75zjzzTC8azZo1y+8QREJK17TEm5i8pjdt8ryGDT0vMdHzxo3zO5qwW7nS8665xvPso37Pa9bM74gOY/9+zxs82PMKFfK8UqU874MPIh5CtF3Tqamed8IJnteuned5S5Yc+GUee6znvfSS5+3Z43eIcWvFCs9LSPC8Pn38juRg77xjl0BKSuCObds8r1EjC3b8+EO2/++a3r7d8ypW9LzTTvO8ffsiFm8sAeZ6GeRKkS719TeQ9pv8HkgFDmnE4nneysDXf4EJWJIoIiIiYMUMWraEuXNh7Fi44gq/Iwqbfftg0CCoWRM++sjqfjzyiE0bXLAgy939kS+fTUP78UdrJn3VVXDDDfZ7O0ItXgxbl6/n8S09rA3GxInw0EO2HrJzZ+uXKGFx/PFW7HfkSJteGS1SUuzXXqNG4I4iRWDSJGs9c8011tU+I889ZwsXhw6FhISIxRsPIp34fQQ0AXDOnQQUANYFb+CcK+KcK5b2PdASGykUERGRjRuhRQtLKj780Bq1Z2D/fquIH8u++QbOPBN69oRzz4WFCy3p69bN+pQNyWUhwIipWdPWqz38MIweDaeeCp995ndUkbdqFet6P8dSqnFy8lCrOPv77/D441CsmN/RHRE6d7b+iePG+R3JASkp9idyUM5frBhMnWprPi+/3D7hCfbnn9C/P1x7LZx/fiTDjQvhbOcwGvgWqOGc+9s51xEYCVQNtHgYA9zkeZ7nnCvvnEtL68sCXznnfga+ByZ7njctXHGKiIjEjA0brI3A/PkwfrxVbkhn1Sp44gmoUsWKOlStajUTPvzQcsZYsG4d3HabJXsbNljsU6ZAtWr2eKlScP31VsB0/Xp/Y81S/vxWfOKbbyxbbdbMMtmdO/2OLDz27bMPJYYNs0JDlStDhQqcN6kvPxQ5Hzd/vlVvLF/e70iPKM2b299PNBV5SUnJpBjn0UfD9OnWj+KSS+DLLw88du+9NqLeP8OOcJKFsBV38TyvfSYPXZ/BtquA1oHvlwGnhSsuERGRmLR+vb17+/VXm/PYqtV/D6Wm2gfjw4fDxx/baF+LFtYC7ZtvrNjniBH2fumss+yxFi2gYUMoUMDH55ROaqpNR7vvPtiyBXr3tsGyokUP3fbuu+HVV+3Wt2/kY82x+vUtIbrvPpu7mpRkVUDr1vU7srzZvBm++84utK+/htmzrb0IWHJ37rns7dKD8x9qTIOOp9O0jr/hHqny5bMPgHr3PkzCFUEbN8Lffx8mjlKlYMYMa8zeujXMmMHR8+bZp0BPPgkVK0Yy3LgR6ameIiIiklNr10LTptYo/OOP/0v61q6FAQPsg/GWLeHzz+Gee2wW3fTplmN8/LGNmn35JTz4oL0BfPppuOACe291ySW2VGbRIn+7D/z0k43wdeoEdepYjtS/f8ZJH8DJJ9vg2Ysvwt69kY011446yl7spCRLmBo0sDexsdL03fOsmfo771gWceqpVk7/oovseWzYADfdBKNG2ZS8v/+GsWOZVrMHs3efnnH/PomYW26BggWjY9QvbX3uYRPQsmXtE62yZeGii6jxv//ZVIZevSISYzyKdDsHERERyYl//7UMZ8kSmDgRr1lzvv7K3ryNGwd79sB559lswiuugEKFDj1E/vy2Tdp2mzbBrFn2gfr06VZPAawIRIsWlkQ2axaZvtlbttio3tChloi+9ZbVQXEu63179LDEdfx4qwURM1q2tGGXLl2swMnkyfD223Diif7E43k2TLxnz4Hb3r32dfVqG81Lu61ZY/sULw5nnw1XXmkZe/36Ga7X++MPm7ZbpQo0aRLh5yUHKVUKrr7a8vbnnvN3eWVKin3NcuSxfHlbF9uoEYWXL7fZDhn9IyfZosRPREQkWq1ZYyN9f/zBtvcn8+aipgzvYUVOiheHO+6wW50cTp87+mirCZNWF+aPPw4kgRMmwBtv2P1nnGE5StOmVoTh+ONtxDAUPM96nt9zjxWhufNOeOopG0DKrtatrcf34MExlvgBHHOMFXxp29Yqb5x+OgwcaC+EczYKuG0bbN9uX7N7C2x/+qpVNsKYlsAFJ3MZ/ZyVqlXtYjjnHEv0atfOsqLixo3Wl3vPHhuNLlw4RK+d5Frnzpb4vfeeXWp+SUmBEiWyOWOzUiX48kt+eeUVamewrlmyT4mfiIhINFq9Gpo2Zf+ff/G/plN4tH1jduywNXqvv26JTpEioTlVlSpw++1227/fukTMmGG355+30QGw9YBVqlgOUK2a3dK+r1o1+2/sFy+2wa5PP7WqnR9/bM8rp/Llswqf3bvDnDm5O4bv0qoT3nor3HUX3H+/NTDPSd39xEQbvila9MDN8+wCKVDAhnwLFDj0+8M9lvZ9yZI2JbVcuRw9rd27rSjj0qX2gULNmjl8XSQsGja0zxheftk+NMrOyHo4pKTYdO1sn79iRf5t1ozafgUcJ5T4iYiIRJkdS1ax57wmFFi7kotSpzIvuRHXXWdv1M48M7znTkiw9/kNGtiawK1b4fvvbabpsmX2Rn7pUvjqK3ss2HHHHZoQpn1fpowVsnz6aVu7d9RRVvjxjjvy1orr5pstzsGDrVZKTKpQAaZNs6HWuXMPTeKKFrUkLv19abcMKvT8lJxM48aNI/9csJzzttsgOdlGli64wJcwJAPO2ajfHXdYp5Fzzol8DJ5na/zaZ1YGUsJGiZ+IiEiU2LkThvVbSbshTSibupo7q0zj2nvPY+J1Ni3KD8WK2Xq/Zs0Ovt/zrNDo0qUHJ4TLlsHMmbBy5cHbFy1qhSXWr7c1fAMGWM2GvCpe3AbLhg2zhDJmuwQ4Z0/k1lv9jiTPHnnEkvAnn4QOHfyORtLr0MG6Irz8sj+J34oVVtvI78qiRyIlfiIiIlFgyhToe9cW3lt+EeUT/2HJsCTeuuMc36ZiZcU5KF3abg0aHPr4zp1W2DE4IVy71kYaQj0C1K2bNXN/+WXrYSj+eeMN+x107Aj9+vkdjWSkaFG48UZrhfLCC/Y3HEnZLuwiIafET0RExEd//WXVKSdO2MusIldRJ2ER+aZO5dTmPnwUH0KFC0OtWnYLt2rVrLrniBHwwAMq+ueXmTNtnWiLFpaER+uHFmLTPYcNs0S9d+/Injst8Tv55MieV9THT0RExBd79tjUxFq1YNpUjzlndeG87dPJ98oIa9QuOdK9u40ojh7tdyRHppQUaydSq5a1Gcmf3++I5HDq1LGaQiNGQGpqZM+dkmIVgnNSwVdCQyN+IiKSPb//bvXZ9++3dwpZ3TLbLjHRarwfwbXdP//cCjj+8otV8x9ZcwDHPPeqzY2LgzVefmjSxEYQBg+2gi8abYqcVavsT7poUWtJWLy43xFJdnTubOv9ZsyACy+M3HlTUjTN0y9K/EREJGs//mjlJD0vNMe79tojcmhmzRqbVvXOO1C5MnzyCVyyexxcdZ/1Z9ACtVxzzkb9OnWyxNqngpZHnG3b4OKL7TOhL7/MZl82iQqXX27Vdl9+OXKJ3969sGgRtGoVmfPJwZT4iYhI1gYNsnLyo0bZHK58+awGf758Wd/Sb/faa9YcrksXOO88v59ZROzfb1Oq+vWDHTtsHVq/fnDU/O+gyQ1WWu/NN0PXHf0Idd110Levjfop8Qu/ffvs84r582HiROsPJ7GjYEErwtO/v1XajETSvnixJX+nnhr+c8mhlPiJiMjhrV5to3OdO1sFjbx67DEYM+ZA1+04T3bmzLGXbt48a4kwbBjUqIGVubz0Uuvh9vHHqkgSAoULW9XQZ56BP/6wZvMSHp5n1VSnTLEPNTSCE5vuuAOee84qfD7+ePjPp4qe/orv/21FRCTvXn7ZPtrv1i00xytSxD5i/uEHKykXpzZutHV8DRrYGqjRo20tTY0agQfbtLHXdcqUyNdTj2N33WWDzC++6Hck8W3gQBg+HO67zyp5SmyqXNmS9tdes5G4cEtJsWXeNWuG/1xyKCV+IiKSuV27LPG75BKoXj10x23f3qY39utnnXzjiOfB229bgjdihA1sLlpkyxqdw8p5Xn65jfh99BGcdJLfIceVChXgyivh9ddh61a/o4lPH3wAffrYNM+nn/Y7Gsmrzp1tYsfHH4f/XCkp9m9jgQLhP5ccSomfiIhk7r33YN066NkztMd1zhZirV0LTz4Z2mP7aMECa05+002WJ8+bZw2S/6ty6HlWfSQ5GUaOhEaN/Aw3bnXvbp8nvPWW35HEn2++gRtugHPP1bLUeNGqFZxwgn3GF26q6Okv/bmKiEjGPM+Kupx2mmUzoVavHtxyiyWAv/0W+uNH2IgRcMYZsHChTZv66qsMil08+aQNBz72mFUikbBo2NCm2A4ZEvkeZfHs999tWWqlSlqWGk8SEmy67mefWfGVcNmyBZYvV+LnJyV+IiKSsc8+syGsHj3C1xTtqafs3WOvXuE5foRs3w7332+jIIsXW6W8Q0ZC3nsPHn4YbrwRHnrIlziPJN27W6IybZrfkcSHdeugdWv7p2DKFChVyu+IJJQ6drSCzcOHh+8cCxbYVyV+/lHiJyIiGXvhBTj2WFuPFy7lylkSNGlSTL9Df/ddq9fyxBOZ1Gn54gtrzN64sZXPU3fxsLvySihf3gaUJW927YK2ba3k/yefhHa5r0SHsmVt6fGbb1rLmXBQRU//KfETEZFD/fYbTJ5sJRILFgzvue6+295J9uwZmbJyIeZ5NqWwbt1M2hL+9htcdpn1Fhg/XlUNIiR/frt8p0+HX37xO5rYlZpqg9TffmsfcJx9tt8RSbh07gybNsH774fn+CkpUKyYrScUfyjxExGRQw0ZYgnKnXeG/1wFC8L//melL196KfznC7GZMy2x6N49g4G8tWttflxCgs2PK1nSlxiPVLffbpfXkCF+RxK77r/fqngOGGCjqBK/GjWC2rXDV+QlJQVOPlkTHvykxE9ERA62caP117vuOpv/EwkXXwwtW8Ijj1iyFEMGDbKX6Zpr0j2waxe0awcrV9r8uKpVfYnvSFamDFx/vdXT2bDB72hiy59/WnPv/v1t5PSee/yOSMLNOfusb84cq0gcSp6nip7RQImfiIgc7LXXbJFH9+6RO6dztqZw27aYKnzy2282kHfnnelmxKamws03W+37t9+2MpPii+7dYedOu6wlawsX2tTO6tXt85+uXW2dpEZpjgw33ghHHRX6Ub9Vq+wzRSV+/lLiJyISx/bvh4kT7Wu27NsHQ4dCkybWxiGSateGLl3glVfgp58ie+5cGjo0kxmxDz1kC2Weew6uusqX2MSccopdzi++aJe3ZGz2bBugPvlk+PBDW3q7bJld44mJfkcnkVKiBHToAKNG2Xq/UFFhl+igxE9EJI69+qr13XrzzWzuMGGCle7r0SOcYWXu0UfhmGPs/J7nTwzZtHmzva7XXmvFSf8zciQ8/bQ1au/d26/wJEj37nZZf/SR35FEF8+zNarNmtmg9Bdf2Gzrv/6yZbfHH+93hOKHzp1tlPytt0J3TCV+0UGJn4hInNq/HwYOtO8HDsxmI+sXXoBq1WzNnR9KlrQm559/bsMOUWzkSJuZetCM2JkzbWFUy5YwbJjmx0WJiy+2oqqDBvkdSXRITbXPeBo0gBYt4Ndf7d+I5cvtsxf16Duy1a1r18ZDD9mIbyhGylNSrL3KMcfk/ViSe0r8RETi1IQJsHQpXH21FcycNCmLHWbPtprt3btn0H08gjp1glNPhXvvtY+do9D+/VYp8rzz7E0SYIujrrgCataEsWOtn4BEhYQEm7r49dehL1oRS/butSWnJ59sPds2bIARI+CPP6BXLyu1LwIwZoy17rj7bqhf3/57yAsVdokOSvxEROKQ51k1vurV4Z13oHJlW252WIMHQ/HiVpTETwkJNjSzfDk8/7y/sWRi4kSrevjfaN+KFXDRRVYVYfJkWygjUeWWW6Bo0SOzofvOnbbG8cQT4aab7DOJ0aPtA6G0lhciwSpXhmnT7DOsNWssCbzjjtxVx923z0aVlfj5T4mfiEgcSk62ktz33mvFR3r1sgKTX3+dyQ5//23Nujp1io6P/Zs0sdGzZ56x2KLM4MFQqZIVw2D9epvauWULTJ1qD0jUKVHCkr8xY+Cff/yOJjI2b7Y/oRNOgG7doEIFG/n/6Sdbm6qiLXI4zlltqkWLoGdPeP11qFHDqr1ma+lAwO+/w+7dSvyigRI/EZE41L8/HHusleYGe8NbqpTdn6Fhw+x/8q5dIxZjlgYMsDmV993ndyQHmT/fEuuuXSFx1zZo08bmyk2cCKef7nd4chjdutnow/DhfkcSPqmp9kb91VerUKkS9OsHZ55py2a/+souVy09lZwoVswmX/zwA5x0Etx6qzV7nz8/e/ursEv0UOInIhJn5s+3KTrdu0PhwnZfkSKWqHzyiU25OciOHbbQ57LLbH5PtKhSxYYsR406zFBl5A0ebDM6b7txj41KzpljrRsaNfI7NMnCiSdC69bWo2z37tAdNzXVnyK0nmfreN9/3wrINmkCRx8NtWrB6NGVuPBCW9M4dapdnkr4JC9OPRW+/NIKWy1aZOube/WCrVsPv19Kis3gr1UrMnFK5pT4iYjEmQEDLNHr3Png+7t2tUQwrdLnf955xzrr+tXC4XD69rVScN2752xuUZisXQvvvQc33ZBKyR43wfTp1jOjbVu/Q5Ns6tED/v3XpnzmlufBb7/Zurm2bS3ZKlIE6tSx9ik9e9pjU6bA4sWhSTI9z5a9fvgh3H+/VeM85hhbx3vttVZ9cedOG+V/4w14993ZjB0bVHxIJATy5bMZJIsXQ8eO1vajZk1bKZDZhx8pKfahS6FCkY1VDqXZ3SIicWT5civacPfd1hkhWOnS9h/1iBHwxBOWT5GaaoVU6tWDc8/1JebDKlrU5qdef701lbrlFl/DGTECdu/2eHJrd8scnnvO5j1JzGjWzBK0wYMtScruKNj69fDpp5brz5hhve7ABqbbt7dLdelSu336qQ2kp3HOeuJVq2a3qlUP/j59iXvPg5UrbbRu7twDt3Xr7PH8+W305Zpr7E+3Xj17TsGFZJOTd+X+RRLJQqlS9u/hLbfAXXdZ9eiWLQ8UEQqWkmLTjcV/SvxEROLIoEH2JjOzwbt77oGXXrI3vc89h72LXbQI3n03eueBdehgaxDvv9+mVhYv7ksYe/bYa/dW9Sc5ZtSLNsdJDdpjjnP2wcgdd9iat/PPz3i73butINKMGXabN88SshIloGnTA6Nu1aoduq/nWSXEpUth2bIDCeGyZVZcZc2ag7c/+ugDSeDOnZbkpRWgSUiw9gtt2x5I8k45RZU4JTo0bAjff2/Tpx980K7Vvn3tVriw9Tpdtsz/YtFilPiJiMSJDRts1mH79pkXlqxSxT6ZHT7cij6UGDQIjjvOSrdFK+csU61fH556Kht9KcJj3Di4dPVwbuRhq4nfv3/0JstyWNdfb4nb4MEHEj/Pg19+OTCi9/nnNmqXmGhvbh991EY06tXLuhqmc1CunN0yGkjfts3qAQUnhEuXwo8/WkJ34YUHkrzTTjuwVlckGiUmWuGkK6+0ZdmPP25T4ocOPTCarcIu0UGJn4hInHjpJdi+PetBqN69bZbiuMd/oWNSkiVTBQpEJsjcOuss+8j4hRfgttsOnUsUZp4HKQ9/wEvchdfmYtyrr/rb5F7y5KijrHPJgAE2UvHddzBzJqxaZY/XqGEzeFu0gMaNQz/IXLSovRHWm2GJJ8cdZwlfx442/bN16wP/VOtajw76X0tEJA7s3AlDhkCrVln/B1u3LjRvDgWGD8YrVMg6OMeCp5+24ZB77434qX8dOpPHll7HP9XOxY19/+DFVBKTunSx3P2uu2DyZBv5e+01Wye7aJGNVlx6qW8zi0ViVtOmVl366aetDWuJEjbbRPynET8RkTjw1ltWcTK7Le8euHM9DWa+zW+NbqBG6dLhDS5UjjvOFpH07Wvz8Vq2jMx5586lSq/L+D1fTU6Y9YkNF0nMq1jRStPnzw9nnKEBXJFQKlDAplNffz1s2qS/r2ihX4OISIzbv99aNNSvn/1WchcsGkFhdtFrRY9o6JKQfT16WBWMHj1g797wn2/xYvZf2Ip/9pXmw07TKFqxZNb7SMxo2NCqDepNqUh4VKyoaZ7RRP/UiYjEuAkTrDBEnz7ZrDWyZw/upWGsPrUlk/+ozaRJYQ8xdAoWhOefty70L78c3nOtXAktW7JzVz4uctO5sW/58J5PREQkjJT4iYjEMM+z4pLVq0O7dtncadw4WLWKY5/qQeXKvhXJzL1LL7WqG488cqCxWaht2AAtW+Jt3Mil+adSp92JVK4cnlOJiIhEghI/EZEYlpwMc+ZYvZOEhGzs4HlWGbNGDRJaX0ivXtar7Ouvwx1pCDlnz2HrVqtUc/fdVpJxz57QHH/7drj4YliyhMmdPmbW5rp07x6aQ4uIiPhFiZ+ISAzr3x+OPRZuvDGbO3zzjXWH7tED8uXjllugVCk7TkypUwc++siqcrz2mo0AlikD114Lo0bBxo25O+7evdbTcPZsvFGj6ZvUhNNPz/7aSRERkWilxE9EJEbNnw/TpkH37jlo8DxoEJQsCTfcAECRItC1K3zyiS2biykXXwwff2zTPT/5xDrTJyfDdddZEti0qT3fZcuyd7zUVLjlFpg6FYYP57OjL2fhQnt91addRERinRI/EZEYNWCAJW6dO2dzh+XLYfx469tXNBXyvQAAIABJREFUpMh/d3ftaonjwIHhiTPsjjoKLrkEXn3VOnB/+61Vuvn3X+jZ06qAnnIKPPAAzJ5NhmVMPQ/uuce6Dz/1FHTqxODBBwYRRUREYp0SPxGRGLR8OYwebTlcyex2GBg61IauunY96O7SpaFjR3jnHcubYlq+fFaj/+mnYcECWLLE1gOWKWNVbBo2hPLloVMnmDgRduwAoNJ778HgwTYF9v77WbIEJk2CO++EQoV8fk4iIiIhoMRPRCQGDRpkOVyPHtncYetWWwt31VVw/PGHPHzPPdYPcPDg0Mbpu7Sef599Zh3u33sPGjeGsWOtOmjp0tCkCVVff92miD7/PDjH0KGQmJiD0VQREZEop8RPRCTGbNhgsxrbt4dKlbK501tvwebNmWaKVarYErnhw22zuFSyJHToAGPGWBI4YwbcdhssX87a886DN96AfPnYssW+veYaOO44v4MWEREJDSV+IiIx5qWXrONA797Z3CE11YbyGjaEBg0y3ax3b9iyBUaMCE2cUa1AAWjeHIYMgWXLWPjEE5A/P2BJ39atqIWDiIjEFSV+IiIxZOdOy1Vat7Z6JdkyebKtdevZ87Cb1a1rudCgQbB7d95jjUX799tSyHPOgXr1/I5GREQkdJT4iYjEkLfeslmKffrkYKdBg6BiRbj88iw37dMHVq+2pXBHoilTYOlSjfaJiEj8UeInIhIj9u+3lgv16+egofjChVbYpGtXq1aShebNrSf6gAEZdz2Id4MGWe2byy7zOxIREZHQClvi55wb6Zz71zm3IN393Zxzi5xzC51z/TPZ9yLn3GLn3BLnXN9wxSgiEksmTLDRqD59ctBQfPx42/imm7K1uXN2/EWLrJ3BkSQl5UCOHFjuJyIiEjfCOeL3JnBR8B3OuSZAW+A0z/PqAIe0C3bOJQDDgFZAbaC9c652GOMUEYl6ngf9+0P16tCuXQ52nDTJhgjLls32LldeCZUrW9u7I8mQIdbIvlMnvyMREREJvbAlfp7nfQFsSHd3Z+BZz/N2B7b5N4Nd6wNLPM9b5nneHmAMliyKiByxkpNhzhy4915ISMjmTmvWwPffw8UX5+hciYnQqxd88w18/XWOQ41Jmzfn59134YYb4Jhj/I5GREQk9LJe8BFaJwHnO+eeAnYB93qeNyfdNhWAFUE//w1kWn/cOXc7cDtA2bJlSU5ODmnAobBt27aojEskt3RNR17fvqdQsmQxqlT5juTk7C2+Kzd1KjWBueXKsS2Hv69q1fJRvPjZ9OmzmaeeWpD1DjFu/Piy7NoFDRt+T3LyDr/DEckz/Tst8UbXdN5FOvFLBI4BGgJnAWOdc1U9z/Nye0DP814BXgGoV6+e17hx41DEGVLJyclEY1wiuaVrOrLmz7eBu6eegpYts1vVBetLUKEC9Tp2zMGiwAN69oTHHitN2bKNqVUrx7vHjL174aqrdtOiBdxyS32/wxEJCf07LfFG13TeRbqq59/AeM98D6QCpdNtsxKoGPTz8YH7RESOSAMGQJEi0LlzDnbaswemT4c2bXKV9AF06WJr3gYesho7vnz4IaxbV1AtHEREJK5FOvH7CGgC4Jw7CSgArEu3zRzgROdcFedcAeBa4JOIRikiEiWWL4fRo+H226FkyRzs+MUXsG1bjtf3BStTBm69Fd55B1atyvVhotqWLfD441Chwg5atfI7GhERkfAJZzuH0cC3QA3n3N/OuY7ASKBqoMXDGOAmz/M851x559wUAM/z9gFdgSTgV2Cs53kLwxWniEg0GzTIBux69MjhjpMmQaFC0KxZns7fq5f1Dxw8OE+HiUp798LVV8Nvv0GPHr+TT51tRUQkjoVtjZ/nee0zeej6DLZdBbQO+nkKMCVMoYmIxIQNG+DVV6F9e6hUKQc7eh5MnAhNm8JRR+UphipVLDkaPhz69YMSJfJ0uKjheXDXXZCUBK+9BtWqbfQ7JBERkbDS55siIlHqpZdg+3bo3TuHOy5eDMuW5WmaZ7DevW1K5IgRITlcVHj2WUv4HngAOnb0OxoREZHwU+InIhKFdu60huKtW8Mpp+Rw58mT7WubNiGJpW5daN7cpp3u3h2SQ/pq9GgbvezQAZ54wu9oREREIkOJn4hIFBo/HtautYbtOTZpkmWLOZofenh9+sDq1fDBByE7pC+++AJuvhkaNYKRI3Nd8FRERCTmKPETEYlCX30FxYvDBRfkcMdNm+DLL0M2zTNN8+ZQsSK8/35IDxtRixdDu3a2bnHCBChY0O+IREREIkeJn4hIFPruO6hfn5xXmkxKsjKcIU78nIOrrrLDb9oU0kNHxL//2rTZxESYMgWOOcbviERERCJLiZ+ISJTZvh3mz4eGDXOx86RJUKoUNGgQ8riuvtpaIHz8ccgPHVY7dsCll9pU1UmToGpVvyMSERGJPCV+IiJRZt48SE3NReK3fz9MnQqtWkFCQsjjql/flg2OHRvyQ4fN/v1w/fXw/fcwapQ9BxERkSOREj8RkSjz3Xf2NcdJyuzZsH59yKd5pnHORv1mzICNMdL2rndvW8/3wgu2vk9ERORIpcRPRCTKzJ4N1apBmTI53HHSJBvpu/DCsMQFsTXdc+hQS/juvhu6d/c7GhEREX8p8RMRiSKeB99+m8slepMmwfnnw9FHhzyuNPXqQeXK0T/d85NPoEcPaNsW/vc/v6MRERHxnxI/EZEo8vffVoQkx+v7/voLUlLCNs0zTfB0zw0bwnqqXJs7F9q3hzPPhPfeC8tyRxERkZijxE9EJIrMnm1fc5z4TZ5sX9u0CWk8Gbn6ati3Dz76KOynyrE//7Tc99hjYeJEKFLE74hERESigxI/EZEo8t131lj8tNNyuOOkSbYwsEaNsMQVrG5da4kQbdM9N260Xn27d1uvvrJl/Y5IREQkeijxExGJIrNnW2JVoEAOdtq+HT791Ia6nAtbbGnSpnvOnGlFRKPBnj1w+eWwZIlV8axVy++IREREoosSPxGRKLF3r61Py3Fhl88+s2GuMK/vC3b11dYjb8KEiJ0yU54Ht90GyckwciQ0bux3RCIiItFHiZ+ISJSYPx927crF+r5Jk6BoUWjUKCxxZeT0021maTRM93z0UXjnHXjiCWvWLiIiIodS4iciEiVyVdjF86ywS8uWOZwfmjdp0z0/+wzWro3YaQ/x5pvw+ONw663wwAP+xSEiIhLtlPiJiESJ776zgiSVKuVgp59/hpUrIzrNM43f0z1nzoROnaB5cxg+PCLLG0VERGJWlomfc+4q51yxwPcPOufGO+fqhj80EZEjy+zZNtqXowRm0iT72rp1WGI6nNNOgxNP9Ge657Zt1quvZk0YNw7y5498DCIiIrEkOyN+D3met9U5dx7QHHgdeDm8YYmIHFnWr4fffstFYZdJk6B+fV96F6RN95w1K/LTPV99Fdats68lSkT23CIiIrEoO4nf/sDXNsArnudNBiK3kERE5Ajw/ff2NUfr+/7913b0YZpnmquvhtRUGD8+cufcvRsGDoQmTXJRCEdEROQIlZ3Eb6VzbgRwDTDFOVcwm/uJiEg2zZ4N+fJBvXo52GnqVCvu0qZN2OLKyimnWM/4SE73fPttWLUK+vWL3DlFRERiXXYSuKuBpP+3d99hUlbn/8c/N12wAUoTpKoUUVBUYomosRsTjSKYxERFsURRUcHeCyogsaOxxYiKRoNgRFEW/KmLosIAgoBIkCYWRGlS9vz+uGe/LLC7zO7OzDPl/bquuWbm2Wdm7tUnZj57zrmPpGNDCD9KaiDpqpRWBQB5prBQ6tRJ2mGHCrxo9GipWTOpa9eU1bUtxdM9Cwqkb75J/edt2CANGiQdcIB01FGp/zwAAHLFNoNfCGG1pGWSDo0f2iBpTiqLAoB8UlTkMzYrNG1x3Tpp7Fgf7Yu4nWU6p3uOHCl9+aWP9tHFEwCAxCXS1fMmSQMkXRM/VFPSc6ksCgDyyZw50vLlFWzs8t570s8/R7q+r1inTlKHDqmf7llUJN15p9Sxo3Tyyan9LAAAck0iUz1PkXSypFWSFEJYLKkik5EAAOUoLPT7Co34jR4t1a6dEfMdi6d7TpggLV2aus8ZM0aaPl265hpfDwkAABKXyP91rgshBElBksysXmpLAoD8MmmStOOOPmqWsDFjvK1lvcz4T/Lpp3ufmVdeSc37hyDdcYfUqpXUq1dqPgMAgFyWSPB7Kd7Vc2czO0/SOEmPp7YsAMgfhYXerCThUazZs31+aAZM8yzWqZNPwUzVdM+CAg/IAwZINWqk5jMAAMhliTR3uU/Sy5JekbSXpBtDCA+kujAAyAerV0uxWCWmeUqRbuNQmp49fenh4sXJf+8775SaNJH++tfkvzcAAPkgkeYurSW9F0K4KoRwpaT/Z2atUl0YAOSDTz6RNm6sYGOX0aOlvff2eY8ZJFXTPT/6SBo3TurfX6pTJ7nvDQBAvkhkYtFISUUlnm+MHwMAVFFxY5eEg9+KFT6slkHTPIt17Oh5NNnTPe+6S6pfX+rbN7nvCwBAPkkk+NUIIawrfhJ/XCt1JQFA/pg0SWrTRmrUKMEXvPWW72KeYdM8i/XsKb3/vrRoUXLeb8YM6bXXpEsvreDm9gAAYDOJBL9vzez/dkwys99J+i51JQFA/igsrMQ0zwYNKrgoMH2SPd3z7ru9cekllyTn/QAAyFeJBL8LJF1rZgvM7Gv5Zu5MuAGAKlq40EfGEs5wGzdKb7whHX98xra2bN9e2mef5Ez3nDdPGjFCuuACqWHDqr8fAAD5LJGunl+GELpL6iipQwjh4BDC3NSXBgC5bdIkv094xO+jj6TvvsvI9X0lFU/3XLiwau9z771S9erSFVckpy4AAPJZmcHPzP4Uv7/CzK6QdL6k80s8BwBUQWGhVKuW1KVLgi8YPdqT0LHHprSuqjr9dL9/+eXKv8eSJdKTT/r2Dc2aJaUsAADyWnkjfvXi9zuUcQMAVMGkSdJ++0m1ayf4gjFjpEMO8RaXGWzPPT3MVmW655Ah3sPm6quTVxcAAPmszEUiIYTHzKy6pJ9CCEPTWBMA5Lz166XJk6Xzz0/wBV9/LU2dKt1zT0rrSpaePaVrr5UWLJB2371ir/3hB+mRR6RevaS2bVNTHwAA+abcNX4hhI2SeqepFgDIG9OnS2vWVKCxy5gxfp/h6/uKVWW65wMPSKtWSQMHJrcmAADyWSJdPd83swfN7DAz26/4lvLKACCHVXjj9tGjfcO/9u1TVlMytWsnde1a8emeP/8sDRsmnXyy1LlzamoDACAfJdIPvLjtwK0ljgVJRya/HADID4WFvml7q1YJnLx6tfTOOz4v1CzVpSVNz57SNddI8+cn+HtKGj5cWr7cXwcAAJInkRG/00MIR2xxI/QBQBVMmuTTPBPKce++K61dK514YsrrSqaKTvdcu1YaPFg68siM3Z8eAICsVd52Dr81s28lxcxsoZkdnMa6ACBnLV8uffFFBaZ5jhkj1asnHX54SutKtrZtpf33T3y65zPP+DYO116b2roAAMhH5Y343SHpsBBCM0l/kHRXekoCgNz20Ud+n9CoVgi+vu+YYyqw70Pm6NlT+vhjn+5Zng0bpEGDpAMP9BE/AACQXOUFvw0hhFmSFEKYJPbuA4CkKCz0KZ7duiVwciwmLVyYNd08t1Q83XPkyPLPe/FF6auvfLQvi5YxAgCQNcpr7tLIzK4o63kIYUjqygKA3FVYKHXqJO24YwInjx7t9yeckNKaUqV1a+mAA3y651VXlX5OUZF0113+z+S3v01vfQAA5IvyRvwel4/yFd+2fA4AqKAQNjV2Scjo0T402KRJSutKpZ49fbP6efNK//nrr0szZngnz2qJtBwDAAAVVuaIXwjhlnQWAgD5YM4cb+6SUGOXb7/1lHjTTSmvK5VOP91H+0aOlAYM2PxnIUh33ukjg2ecEU19AADkA/62CgBpVLxxe0Ijfv/9ryejLF3fV6xlSw+6pXX3fPddb3YzYIBUI5GdZQEAQKUQ/AAgjSZNkrbfXurQIYGTR4+WmjaVunZNeV2p1rOn9Omn0ty5mx+/807/Ff/yl2jqAgAgX2wz+JnZVv3DzaxBasoBgNxWWOhbFlSvvo0T162Txo71TdtzYOHbaaf5fcnunoWFPuLXv79Up040dQEAkC8S+TbxbzOrWfzEzJpKejt1JQFAblq92ndnSGia58SJ0k8/efDLAbvv7r93yemed90l1a8v9e0bXV0AAOSLRILfa5JeMrPqZtZK0lhJ12zrRWb2pJktM7PpJY7dbGaLzGxK/FZqf3Izm29m0+LnTE7sVwGAzPbpp75R+TYbuxR3PNllF+noo9NSWzr07ClNmSLNni1NmyaNGiX16+dTXwEAQGptcyl9COFxM6slD4CtJPUNIXyQwHs/LelBSc9ucXxoCOG+BF5/RAjhuwTOA4CsUNzYZZvB7623pPHjpWHDpHr1Ul5Xupx2mnTFFT7d8/PP/Ve75JKoqwIAID+UGfy22LzdJO0uaYqk7mbWfVsbuIcQJsZHCAEA8sYurVpJjRuXc1JRkTRwoJ+YY3MgW7SQDj5YGj5cWrjQQ2ADVowDAJAW5Y34bblJ+7/LOF5RfzOzsyRNltQ/hLC8lHOCpLfMLEh6LIQwvKw3M7PzJZ0vSY0bN1ZBQUEVy0u+lStXZmRdQGVxTVfOhAnd1bnzChUUzCzznEbjxqnjlCn6/NprtezDD9NYXXp07bqbPvhgD9WsWaSDDipUQcG6qEuSxDWN3MM1jVzDNV11FkJI3Zv7iN/oEMLe8eeNJX0nD3a3SWoaQjinlNftFkJYZGaN5I1kLgkhTNzW53Xr1i1Mnpx5SwILCgrUo0ePqMsAkoZruuIWLZKaN5fuv9/XtZXql1+k9u2lnXbyBYE50M1zS4sW+b5+ffpIjz4adTWbcE0j13BNI9dwTSfOzD4JIXTb8ngi2zm8bWY7l3he38zGVqaIEMI3IYSNIYQiSY9LOrCM8xbF75dJerWs8wAgW0ya5Pflru977DFp/nxp0KCcDH2StNtuvmH7kHIXCwAAgGRL5JvFriGEH4ufxKdmNqrMh8W3gih2iqTppZxTz8x2KH4s6ZjSzgOAbFJYKNWqVc5e7D/9JN12m3TkkdIxx6S1tnTbbz+pbt2oqwAAIL9ss6unpI1mtnsIYYEkmVlL+VTNcpnZCEk9JO1iZgsl3SSph5l1ib9+vqS+8XObSXoihHCCpMaSXjWz4vqeDyG8WcHfCwAyyqRJHvpq1y7jhPvuk777Trr7bsn/+wcAAJA0iQS/6yT9PzObIO/ueZjizVTKE0LoXcrhf5Rx7mJJJ8Qfz5O0bwJ1AUBW2LBBmjzZ17WVaulSafBg3+jugAPSWhsAAMgPiezj96aZ7Sepe/zQZeyvBwCJmz5dWr1a6t69jBNuvVVat066/fa01gUAAPJHIiN+knSwpF+XeD46BbUAQE4qd+P22bN9Y7u+faU99khrXQAAIH8k0tXzbkn9JH0ev/UzsztTXRgA5IrCQmnXXaXWrUv54fXXS3XqSDfckPa6AABA/khkxO8ESV3iWzDIzJ6R9Jmka1NZGADkikmTfJrnVj1bPv5YGjlSuvFGqUmTSGoDAAD5IdGNonYu8XinVBQCALlo+XJp1qxSpnmGIA0Y4EOB/ftHUhsAAMgfiYz43SXpMzMbL+/q+WtJ16S0KgDIER9/7PdbNXZ56y1p/Hhp2DBpxx3TXhcAAMgviXT1HGFmBZKKe4wPCCEsTWlVAJAjCgt9iudmuzQUFfloX+vW3tQFAAAgxbYZ/MzsnRDCUZJGlXIMAFCOwkKpY8ctBvVGjJCmTpX+9a9ydnQHAABInjKDn5nVkVRX0i5mVl8+zVOSdpS0WxpqA4CsFoI3djnllBIHf/nFO3l26SL16hVZbQAAIL+UN+LXV9JlkppJ+kSbgt9Pkh5McV0AkPXmzpV++GGLxi6PPirNny+NHStVS7S/FgAAQNWUGfxCCMMkDTOzS0IID6SxJgDICZMm+f3/NXZZsUK67TbpqKOko4+OrC4AAJB/yvxzs5kdYGZNikOfmZ1lZv8xs7+bWYP0lQgA2amwUNp+e1/jJ0m67z7p+++lu+8uZVM/AACA1ClvntFjktZJkpn9WtLdkp6VtELS8NSXBiDjfPKJdNppajZq1LbPhQoLvZtn9eqSliyRhgyRzjhD6tYt6tIAAECeKW+NX/UQwg/xx2dIGh5CeEXSK2Y2JfWlAcgYc+d6Q5IXX5Rq1tSe69d7k5Jhw6QaiWwHmn/WrPHGnVddFT9w663SunXS7bdHWhcAAMhP5Y34VTez4m90R0l6t8TP+KYH5INvvpH+9jepQwfp9delG26Qli3TgjPOkB5+WDrhBOnHH6OuMiN9+qm0YUO8scvs2dLjj/uefe3aRV0aAADIQ+UFvxGSJpjZfyStkfSeJJlZO/l0TwC56uefpZtvltq29S6U553no3633irtvLPmXXCB9MQT0vjx0q9+5T/DZoobuxx0kHy0tE4dD84AAAARKDP4hRDukNRf0tOSDg0hhBKvuST1pQFIu3XrpIce8lGpW26Rjj9e+vxzH91r2nTzc889Vxo3Tlq2zNPNhAnR1JyhCgulli2lJgs+kkaOlK68UmrcOOqyAABAnip3E6kQQmEI4dUQwqoSx2aHED5NfWkA0qaoyNfvdey4aWpnYaEHlj33LPt1hx/uQ1uNGvn2BE89lb6aM1xhodT9oCANGCDtuqvUv3/UJQEAgDzG7sFAvnvnHenAA6VevaS6daU33vApnJvtOl6Odu2kDz+UevSQzjnHu5ls3JjSkjPd4sXS119LZ+w8Vioo8CmeO+wQdVkAACCPEfyAfPXZZ9Kxx0q/+Y307bfSs8/6seOPr/geczvv7IHxoot8r7pTT5VWrkxN3Vlg0iTJVKRj3h0gtW7tTV0AAAAiRPAD8s1XX0l/+pO0337S5MnS4MHSF19If/5zfMO5SqpRw9cHPvigNGaMdMgh0oIFyas7i0yaJJ1V/XnVmxuT7rhDqlUr6pIAAECeY1sGIF98+62HkIcf9pB2zTXS1Vf7aF0yXXyxtMce0umn+xTS116TundP7mck27p1XucXX/gIXZs23tG0UaOKj35K+uSDX/RM9eulfbr6hu0AAAARI/gB+eDll6U+fXybhnPPlW66Sdptt9R93jHHeHeTk07ytX9PP+1rCDPNvHnS8OHelGbZsq1/XrfuphC45X3LllLt2lu9ZMMGqWvhI2q2/n/SoMelakysAAAA0SP4Abls7VrvJvnww96s5amnvGNnOnTo4HMe//AHqXdvaeZM3xuwEiNoSbV+vTR6tO9P+NZbPr31t7/1dXi//rV3ZfnySw+Fxfdz5/q5a9Zseh8zqXnzrULh/LXNdfX627Vk79+o6dFHR/d7AgAAlEDwA3LV3LlSz57esKV/f+nOO9O/1myXXaS335YuuMA3f581y0f/ttsuvXVIvt7wiSf8tmSJh7ZbbvER0JKjn3vt5bcthSAtXbp5ICy+HzNG+uYbSVK7+Olf33536n8nAACABBH8gFz04ovSeef5Wr5Ro3xEKyq1akn/+IePAA4Y4M1l/vOfrTeET4WNG6X//ld67DHvOhqCdy197DG/r1GB/wSaec1Nm3rjmi2tWiXNm6dHrpqncf+vjl4+ef/k/R4AAABVRPBD4kLwEZvx46UJE3yK3OGH+xquPfeMfgoffCri5Zd7sPnVr6QXXpB23z3qqvzauOoqH0k780xv+jJqlNS1a2o+b/FiD5uPP+5TN5s08WY2ffpIrVql5jPr1ZM6d9Y/f+qsWt34nwMAAMgsBD+ULQRp9mzfgHr8eL+PT2dT8+Y+mjJihD9v0mRTCOzRw7/g8803vWbP9qmdU6d6t87bb5dq1oy6qs2dfLL0/vs+AnnoodKFF0qNG0sNG/ptl102PW7QoGLbSxQVSePG+dq9UaP8+jz6aGnoUP/cNPyzKCqSYjHfxx4AACCTEPywSQi+Zmn8+E1Bb8kS/1mzZr7Rd48e0hFHeDMLydeRFRT4CGBBgU8xlPzLfMkg2L598oJgCNIPP3itJdda1awpXXqp1KlTcj4nmzz/vDcnqV3bG5eceGLUFZVt332ljz6S/vhH6YEHfCuFsuy88+ZhsLSA2LCh9PHH3p1z3jz/Wf/+PtW1Xbuy3zsFvvrKZ3zus09aPxYAAGCbCH75LAT/ploc8saPlxYt8p81abIp5B1xhH+BLi247bGH3847b1NwLA6BBQXSSy/5eY0abQqChx8udexYfhDcsMGn6BWHupIB78svpZ9+2vz8pk392PDh0qmnStdd5xuU57o1a6R+/XxK4yGH+AhsixZRV7VtTZpI77zj18zKldL3329+++67rY8tXSrNmOGPV67c+j0PP9z3KTzllFK3WUiHWMzvCX4AACDTEPzyzdKl0ptvbgp7Cxb48UaNNo3OHXFE5aZqmnlAbNfOOyUWB8viEFhQII0c6efuuqt/UT/8cB9N/OqrzQPe/Pke/orVqrVpY+1DDtnUPr9tWz9et64HgmHDpL//Xfr3v6UTTpCuv97XuuWiWbN8aue0adLAgd41M9Omdm6LmbTDDn6ryNq7X37xUd/igNisma8zjVgs5r9SPg46AwCAzEbwyycff+xrnlas8OlxPXr4WrAjjvCOi8lek2fmAa1NG1/0FIIHupJB8OWXN51fv74Huf33l04/fVOwa9PG2+1va71Xw4Yefvr3lx56SBoyRDr4YOnIIz0A9uiRO+sOn3vOt0jYbjvvWnnccVFXlF61a2/qsJlBYjH/u0e9elFXAgAAsDmCX7745BPpmGM8XL3zjndTrFYtvTWY+ehc69bS2Wf7sfnzfeSmdWuvLRl22km69lqfAvnYY9K993r4O/hgD4DHHZe9AXD1aumSS6Qnn5QOO8yndpY69YoFAAAgAElEQVTcgw6RisV8CSMAAECmSfM3f0Tis898pG/HHX2K5/77pz/0laVVK1+Ll6zQV1K9etIVV/g00ocekhYu9Omf3bpJr77qLRizyeef+zYITz3laxjffZfQl0FWrvSZygQ/AACQiTLk2z9SZupU78a5/fY+tTJVe5hlsjp1pIsukubM8b3dVqzwBjD77OMjZhs3Rl3htj3zjHTAAdKyZb5G8/bbK7b5OFJuxgyfzUxjFwAAkIkIfrls2jTpqKO88cn48T6dMp/VquVrDWfNkv71L/+WfuaZvr7xqaek9eujrnBza9dKEydKf/6z9Ne/evCbMsWn7CLj0NETAABkMoJfrpoxw0Nf7doe+tq2jbqizFGjhge+adOkV17x0dBzzvFtKR55xANXFFat8g3Ib7zRu53uvLPfP/+8dMMN/rNmzaKpDdsUi3lz0pYto64EAABga8wVy0Wff+7NTGrU8NCX5k2ss0a1aj7l85RTvDPmbbf5lNDrrvNhm/btfTSwQwd/3KJFcpvCrFghvf++j+pNmCBNnuxbWFSr5useL77Yg9+hh0oNGiTvc5ESU6dKnTtnzvJZAACAkgh+uWbWLA99Zt78IwP2Nst4Zt705fjj/Z/Z889LM2dKL74o/fjjpvPq1fMAuGUgbNfOp5Fuy/ffS++9tynoTZniDWZq1PBpnFde6UHv4IO9EQ+yRgg+4te7d9SVAAAAlI7gl0tmz/bQF4I3cmnfPuqKsouZT4896ih/HoI3U5k50wP1zJl+mzjR1wgWq1HDp9JuGQgbN5Y++shD3sSJPrVU8um33bv71hK//rVvMF+3bvp/XyTN11/7AC7r+wAAQKYi+OWKOXN8I/YNG3x6Z4cOUVeU/cw8vDVu7Ju/l/Tzz9IXX2weCGfOlMaM8X8HJdWtKx1yiNSzp4/oHXCAdxpFzqCxCwAAyHQEv1zw5Zce+tat86mKnTpFXVHu22EH3w+wW7fNj69f7/8+Zs2SFi/2PRP320+qWTOaOpEWxcGvc+do6wAAACgLwS/bffWVh761az308c0zWjVrbloHiLwRi/luKSzNBAAAmYrgl83mz/fQt2qV9M47zDMDIhKL8T8/AACQ2Wg8nq0WLPDQt2KF9PbbUpcuUVcE5KW1a325J8EPAABkMkb8stHXX3voW77cR/r22y/qioC89fnnvisHwQ8AAGQygl+2WbTIQ99330njxnnzEACRoaMnAADIBgS/bLJ4sYe+Zct8eucBB0RdEZD3pk6VttvOt3IEAADIVAS/VFq1Sho9WrvMmSOtWeMbd9eq5fclb1seq1HD95ArackS35x9yRJp7FjpoIOi+Z0AbCYWk/beW6pePepKAAAAykbwS6WlS6VevbR3RV9ntnUYXLnS94h7803p4INTUS2ACgrBR/x+//uoKwEAACgfwS+VWrSQZszQ5PffV7fOnaVffvFN1n/5ZfPblsdKO6eoSLrgAql796h/KwBxS5dK33/P+j4AAJD5CH6pVKuW1LGjVi5bRmADchCNXQAAQLZgHz8AqCSCHwAAyBYpC35m9qSZLTOz6SWO3Wxmi8xsSvx2QhmvPc7MvjCzuWY2MFU1AkBVxGJS8+ZSgwZRVwIAAFC+VI74PS3puFKODw0hdInf3tjyh2ZWXdJDko6X1FFSbzPrmMI6AaBSYjFG+wAAQHZIWfALIUyU9EMlXnqgpLkhhHkhhHWSXpD0u6QWBwBVtG6dNHMmwQ8AAGSHKJq7/M3MzpI0WVL/EMLyLX6+m6SvSzxfKKnMTevM7HxJ50tS48aNVVBQkNxqk2DlypUZWRdQWVzT0rx59bR+/QGqUeNzFRQsi7ocVBHXNHIN1zRyDdd01aU7+D0i6TZJIX4/WNI5VXnDEMJwScMlqVu3bqFHjx5VLDH5CgoKlIl1AZXFNS0tXOj3vXp1VKdOzEbPdlzTyDVc08g1XNNVl9auniGEb0IIG0MIRZIel0/r3NIiSS1KPG8ePwYAGSMW8x1b9twz6koAAAC2La3Bz8yalnh6iqTppZz2saQ9zKy1mdWS1EvSqHTUBwCJisWkjh2lmjWjrgQAAGDbUjbV08xGSOohaRczWyjpJkk9zKyLfKrnfEl94+c2k/RECOGEEMIGM/ubpLGSqkt6MoQwI1V1AkBlxGLS0UdHXQUAAEBiUhb8Qgi9Szn8jzLOXSzphBLP35C01VYPAJAJvv1WWrJE2nffqCsBAABITFqnegJALpg2ze/ZygEAAGQLgh8AVFAs5vcEPwAAkC0IfgBQQbGY1Lix1KhR1JUAAAAkhuAHABUUizHaBwAAsgvBDwAqYMMGacYMgh8AAMguBD8AqIA5c6S1awl+AAAguxD8AKACaOwCAACyEcEPACogFpOqV5c6dIi6EgAAgMQR/ACgAmIxqX17qXbtqCsBAABIHMEPACogFpP23TfqKgAAACqG4AcACfrxR2nBAtb3AQCA7EPwA4AETZvm9wQ/AACQbQh+AJAgOnoCAIBsRfADgATFYlKDBlKzZlFXAgAAUDEEPwBIUCzmo31mUVcCAABQMQQ/AEhAUZGv8WOaJwAAyEYEPwBIwLx50qpVBD8AAJCdCH4AkAAauwAAgGxG8AOABMRiUrVqUqdOUVcCAABQcQQ/AEhALCbtsYdUt27UlQAAAFQcwQ8AElDc0RMAACAbEfwAYBtWrpS+/JLgBwAAshfBDwC2Yfp0vyf4AQCAbEXwA4BtoKMnAADIdgQ/ANiGWEzaYQepZcuoKwEAAKgcgh8AbMPUqT7aZxZ1JQAAAJVD8AOAcoRAR08AAJD9CH4AUI4FC6SffiL4AQCA7EbwA4ByFDd22XffaOsAAACoCoIfAJSjOPjtvXe0dQAAAFQFwQ8AyhGLSW3aeFdPAACAbEXwA4By0NgFAADkAoIfAJRhzRpp9myCHwAAyH4EPwAow+efS0VFBD8AAJD9CH4AUIbixi4EPwAAkO0IfgBQhqlTpbp1vbkLAABANiP4AUAZYjHfxqF69agrAQAAqBqCHwCUIgQPfmzcDgAAcgHBDwBKsWSJ9P33rO8DAAC5geAHAKWgsQsAAMglBD8gwy1Y4FsKIL2Kg1/nztHWAQAAkAwEPyCDTZggtWwpHXbYpiCC9IjFpBYtpPr1o64EAACg6gh+QAa7914PHrNnS/vtJ11xhfTzz1FXlR9iMaZ5AgCA3EHwAzLUzJnSmDHSZZdJX3wh9ekj3X+/1L699NJL3nUSqbFunf/zJ/gBAIBcQfADMtT990t16kgXXig1aCA9+qj04YdS48bSGWdIxx7rI4FIvlmzpA0bCH4AACB3EPyADPTtt9Kzz0pnnSXtuuum4wcdJH38sfTgg9KkSd545MYbpTVroqs1F02d6vcEPwAAkCsIfkAGeuQRae1a6fLLt/5Z9erSxRf79M+ePaXbbpM6dZLeeCP9deaqWEyqXVvac8+oKwEAAEgOgh+QYdaulR56SDrxRF/PV5YmTaR//lN6910PKSeeKJ16qm//gKqJxTxM16gRdSUAAADJQfADMsxzz0nLlkn9+yd2/hFH+NTEu+6S3nxT6tBBuuceb1CCyqGjJwAAyDUEPyCDhCANGSJ16SL16JH462rVkgYO9E6UxxwjDRggde3q+wCiYpYtk5YuJfgBAIDcQvADMsibb3p4699fMqv461u2lF59VXr9dWn1ag+PZ50lffNN0kvNWdOm+T3BDwAA5BKCH5BBhgyRmjXzpi1VcdJJ0owZ0vXXSy+8IO21l/Tww1JRUXLqzGWxmN8T/AAAQC5JWfAzsyfNbJmZTS/lZ/3NLJjZLmW8dqOZTYnfRqWqRiCTTJ0qjRsnXXqpT92sqrp1vePntGlSt27eCfSee6r+vrkuFvPGOSW30QAAAMh2qRzxe1rScVseNLMWko6RVF7vwTUhhC7x28kpqg/IKEOHSvXqSeefn9z33Wsv6e23fRTwnnukFSuS+/65hsYuAAAgF6Us+IUQJkr6oZQfDZV0taSQqs/ONUVF3qzjgw+irgSpsnix9Pzz0jnnSPXrJ//9zaRbb5WWL5fuvz/5758rNmzwKbIEPwAAkGvSukuVmf1O0qIQwlQrv3NFHTObLGmDpLtDCK+V857nSzpfkho3bqyCgoIkVpwcK1eurFJdH3zQUPfc01lPPvmLnnzyY+2ww4bkFYeM8PjjrbVhw+466KBJKihYm7LPOeywTrr33vrab7/CKl1HVb2mJemjj+qrWjVp//2XV6qRTSrMn19Xv/xyoGrWnKmCAjri5JNkXNNAJuGaRq7hmq46CyF1A29m1krS6BDC3mZWV9J4SceEEFaY2XxJ3UII35Xyut1CCIvMrI2kdyUdFUL4cluf161btzB58uSk/g7JUFBQoB4V6c2/hSOO8FGIH36Q/vhH6ZlnklcbordqldSihf97fuWV1H7WtGk+mnX99b7+r7Kqek3Pm+f7Da5b57/3Pff4OsSovfCC1Lu3r7dk1C+/VPWaBjIN1zRyDdd04szskxDCVt+s0tnVs62k1pKmxkNfc0mfmlmTLU8MISyK38+TVCCpa/rKzCyffioVFPgebdddJz37rDSKdjc55emnfQpmohu2V0Xnzt4x9P77pe+2+pNL+lx9tVSjhm86P326dMAB0hlnSHPnRleT5Ov7atSQ2rePtg4AAIBkS1vwCyFMCyE0CiG0CiG0krRQ0n4hhKUlzzOz+mZWO/54F0mHSPo8XXVmmiFDpB12kM4914PfvvtKfftK338fdWVIho0bPYQddJD0q1+l5zNvvtlHGe+7Lz2ft6WJE31kc+BAv82dK91wgzR6tI8CXnKJb6IehVjMa0hGV1UAAIBMksrtHEZI+lDSXma20MzOLefcbmb2RPxpB0mTzWyqfGro3SGEvAx+CxdKL74onXeetNNO/mX06ad9pObSS6OuDsnw+usefCq7YXtldOggnXmm9MAD6Q9YRUXS5ZdLzZtvGuHccUdvPPPll1KfPtIjj0ht2/qxlSvTWx8dPQEAQK5KZVfP3iGEpiGEmiGE5iGEf2zx81bF6/tCCJNDCH3ijz8IIXQOIewbv/9Hae+fD/7+dymEzUNely4+OvL889K//x1dbUiOIUOkli2lU05J7+feeKO0dq00aFB6P/fZZ3368qBBvs9gSU2aeOibMUM69ljpppukdu382Pr1qa9t+XLp668JfgAAIDelc40fKuDnn6Xhw6XTTvNgUNI110hdu0oXXhjtOi1UzccfS++9J112ma8rS6c995TOOkt6+GFpyZL0fObKldK11/q01t69yz5vr72kl1+WPvzQ67zoIqlTJz+Wwl5UmjbN7wl+AAAgFxH8MtSTT/pG21dcsfXPatb0zp7Ll0t/+1v6a0NyDB7s0xzPOSeaz7/hBh9Ju+uu9HzeoEEeMocOTWxaa/fu0oQJ3syoZk3p9NN9HeTEiamp79NP/Z7gBwAAchHBLwMVN/w49FDpwANLP6dzZ2/S8eKL0siRaS0PSfC///kI1vnne/iLQps20tlnS4895utJU2nBAm8m07t3xZrYmEm//a2vvfvHP7zOww/3Y9OnV6yGoiKvo6DA3+u666RevbyjaMOGvvawUSOpadOKvS8AAEA2SPMEMyTi1Vel+fN9/Vd5rr7az73oIv8y3KhRWspDEvz9735/ySXR1nH99T56fOedPu0zVa65xu/vvrtyr69e3UdGe/XypjR33eUdbv/yF+mWW3wfRElavVr66itvFPPll75fYPH9V1/5voHFatTwadRt23r4a9NG6tEjfU12AAAA0ongl4EGD/YvoyefXP55NWp4l8/99vPwN3IkX1qzwU8/SY8/7vvp7b57tLW0bOmdNJ94QhowYOv1pMlQWOjNiK6/vuq/b926XmefPh5WH3xQGjHC17zOn7/1esUdd/T/LXXuLP3ud/64TRu/b9Ei/WsrAQAAosLXngzz4Yf+RfmBB3yUY1s6dfK29wMH+rTPXr1SXyOq5oknvHlPaes3o3Dttb6m9PbbPZAmUwjevKZpUw9sydKwof+B5JJLpNtu8y0xjjtu82DXtq3UoAF/DAEAAJAIfhln8GCpfn1fe5Wo/v19yufFF/tUtSZNUlbe/1m+3LcDYD1UxWzYIA0bJv3611K3blFX45o3l/r2lR56yP+A0LZt8t57xAhp0iQPlttvn7z3Ldaqla/XAwAAQPlo7pJB5s3zAHfBBVK9eom/rnjK56pV/tpUtryXvClJq1ZSs2bSYYf5dLulS1P7mbnilVe8wUjx5uWZYuBA75x5223Je8/Vq/19u3b1tXgAAACIDsEvgwwb5tM7K7NFQ/v20h13SP/5j/SvfyW/NslH+C6+2Nvqd+jgTTV+/NGn2zVrJh1xhPToo9KyZan5/GwXgo/o7rGHdNJJUVezuaZN/d/tP/8pzZ6dnPccPNg3RL//fqka/6UBAACIFF/HMsSPP/qUtd69PURVxmWXSQcf7EFs8eLk1jdnjrfhf/hh6corfePxG2/0Ta9nzPA94ZYs8U3lmzaVjj7a17J9/31y68hm77/vm7ZffnlmBqGrr5bq1PFAX1WLF3sHzz/8wae1AgAAIFoZ+PUzPw0f7lM1q9Lwo3p16amnfGSub9/kTfkcMcI7h379tTR6tHTvvT4tsFjHjh4WZs6Upk716X1ffSWdd56vNzz+eJ+K+uOPyaknWw0e7M1GMnXaY6NG/keDESOkzz+v2ntdd52vZ7znnuTUBgAAgKoh+GWAdet8X7ejjvK9yapizz19j7PRo6Vnn63ae61Z4+HtzDO9rilTpBNPLPt8M2mffXzK6Zw50iefeJCdNcub1TRq5BtvP/ecb2mQT+bO9Wm4F17oWxJkqquu8vWlVRn1++QTD/r9+nmHTQAAAESP4JcBRo6UFi1KXnv/Sy/1piv9+kkLF1buPWbOlA480KdrXnONVFDg3R8TZeajhIMGedOaSZN8NGnKFOnPf/YQeMopPrq0alXlaswm99/vo6QXXxx1JeVr2NCnDL/0khSLVfz1IfhU1l139VE/AAAAZAaCX8SKG3506OD7kCVDtWrePn/9eh+xq+iUz2ee8a0GvvlGevNN3yi7Khtdm3mIHDxY+t//fK1b374eBotHE3O5IcwPP/gU3DPPzI7tL664QtppJ+nmmyv+2lde8fWft93m7wEAAIDMQPCL2IQJ0mefJb/hR7t2Ptr25pseAhOxapX017/67cADfXTu2GOTV5Pkv+PBB3sH0+I1g4sX+xTQ1auT+1mZ4rHH/He7/PKoK0lM/foe/l59Vfr008Rft3atN4jp3Fk699zU1QcAAICKI/hFbPBgnxb3pz8l/70vusg3dL/8ct87rjzTp/so37PPerfOceMq3100UdWr+5rB55/3bpd/+pO0cWNqPzPd1q2THnjAu5zus0/U1SSuXz8PgDfdlPhrhg3zpj5DhlRthBgAAADJR/CL0Bdf+IjXRRdJ222X/PevVs23iCgq8hGY0qZ8huDr+A44QFq+3APfLbd4KEuX3/9eGjrUR5iuuip9n5sOL7zg21xk2obt27LTTr5tx+jR0kcfbfv8b77xpj4nnST95jeprw8AAAAVQ/CL0NChUu3aHvxSpU0b335h3DjfMqKkn3/2UbbzzpMOPdS3YjjyyNTVUp5+/fw2dKiPkOWC4vWbnTpJxxwTdTUVd8kl0i67+Ajwttx4o3eBve++1NcFAACAiiP4ReTbb72JSnGHy1Tq29e3irjySmn+fD82ZYq0//4+InX77b4WsHHj1NaxLYMH++hfv36+9UG2e/dd74x5xRXe4Cbb7LCDr9kbO9Yb8pQlFvNR44svlvbaK331AQAAIHEEv4g8+qg3w0jWFg7lKZ7yKUnnnCM98ojUvbs3cxk/3tvup3NqZ1mqV5f+9S9fa9i7t6/7y2aDB3uoP/PMqCupvIsu8t+hrLV+Ifg1vPPOiY0MAgAAIBq0YIjA2rXSgw9KJ5zg2zikQ8uW3nTj/PM97B13nDdy2XXX9Hx+ourWlV5/3YPpSSdJhYVS69ZRV7XJxo3SihW+RcPy5X4r7fEPP0j//a90661SnTpRV1159er5Po6XX+4daA8/fPOfv/669M470t//LjVoEE2NAAAA2DaCXwSef973rUvHaF9JffpIs2b5Ruz9+iV3+4hkatxYeuMN3/bhxBN9mmH9+un57NmzfQrud9+VHupWrCj/9dtt5wGofn0P16lcv5kuffv6OtEbb5QKCjZNW123zqcPt28vXXBBpCUCAABgGwh+aRaCj7ztu2/6G6mY+fTDbNChg/Taa74Nwqmn+hrE2rVT93lFRT5qdc01vvF9w4abAlyTJlLHjv64fv1Nx4vvSz5OZY1R2W476dprpb/9zdctHnWUH3/4YWnOHGnMGKlmzWhrBAAAQPkIfmk2dqw0Y4aPKmVjw490Ovxw6amnvPNonz4+NTUV/8zmzZPOPluaONGnlw4fLjVtmvzPyWZ9+kh33y3dcIP/wWLFihq65RbvVnr88VFXBwAAgG3J0Ml+uWvIEA8VvXpFXUl2+OMfvevoc89VbDPxRITgTXb22ce7nD71lDRqFKGvNLVrS9dfL334of/x4plnWumnn/x65g8YAAAAmY/gl0axmPT2274/Wq1aUVeTPa691jegv+02D2fJ8PXX0rHHShde6GsJp0+X/vpXQkx5zj5batWqeLuN3dS3r+9RCAAAgMxH8EujoUO9a2XfvlFXkl3MfAuKo4/2rqTjxlX+vUKQnn5a2ntv6YMP/H3HjpVatEhauTmrVi2f6jl7trTddht1yy1RVwQAAIBEEfzSZMkS36Pu7LNpe18ZNWtKI0d605c//EGaNq3i77FkiXTyyf7voEsXH4G94AJG+Sriz3+Wfv97qV+/ORm3FQgAAADKRvBLk4cekjZskC67LOpKstdOO3kHye23920eFi9O7HUhSC+84KN848b5yOv48VKbNqmtNxfVrCm9+qp09NHfRF0KAAAAKoDglwZr1lTTI49Iv/ud1K5d1NVktxYtPPwtX+4dOFeuLP/8b7+VevaUeveW9tzTm7hcdlnm7mEIAAAApAJff9Ng7Ngm+uEHqX//qCvJDV26SC+95FM1zzjDR1JL89prPso3apR0113Se+9Je+2V3loBAACATEDwS7GiIunll5vrwAOlQw6JuprccfzxvoH4G294l9QQNv1s+XJfi3bKKdJuu0mffCINHCjVYNdKAAAA5Cm+CqfY669LixbV1eDBNBFJtvPP983XBw3y9XpXXSW9+aZv/bBsme/7d911vi4NAAAAyGcEvxQbMkRq3Hit/vCHOlGXkpPuvFOaP1+6+mqpoMBHADt18umd++8fdXUAAABAZmCqZwpNnSpNnCideupCphmmSLVqvi/foYf6aN+AAT61k9AHAAAAbEIcSaF99vHNwdevXyKJdp6pUqeO9NZbvk8fWzQAAAAAW2PEL4XMpGOOkerV2xh1KTlvu+0IfQAAAEBZCH4AAAAAkOMIfgAAAACQ4wh+AAAAAJDjCH4AAAAAkOMIfgAAAACQ4wh+AAAAAJDjCH4AAAAAkOMIfgAAAACQ4wh+AAAAAJDjCH4AAAAAkOMIfgAAAACQ4wh+AAAAAJDjCH4AAAAAkOMIfgAAAACQ4wh+AAAAAJDjLIQQdQ1JY2bfSvpf1HWUYhdJ30VdBJBEXNPINVzTyDVc08g1XNOJaxlC2HXLgzkV/DKVmU0OIXSLug4gWbimkWu4ppFruKaRa7imq46pngAAAACQ4wh+AAAAAJDjCH7pMTzqAoAk45pGruGaRq7hmkau4ZquItb4AQAAAECOY8QPAAAAAHIcwQ8AAAAAchzBL4XM7Dgz+8LM5prZwKjrASrDzJ40s2VmNr3EsQZm9raZzYnf14+yRiBRZtbCzMab2edmNsPM+sWPc00ja5lZHTP7yMymxq/rW+LHW5vZpPj3kBfNrFbUtQKJMrPqZvaZmY2OP+d6riKCX4qYWXVJD0k6XlJHSb3NrGO0VQGV8rSk47Y4NlDSOyGEPSS9E38OZIMNkvqHEDpK6i7p4vh/m7mmkc1+kXRkCGFfSV0kHWdm3SUNkjQ0hNBO0nJJ50ZYI1BR/STNLPGc67mKCH6pc6CkuSGEeSGEdZJekPS7iGsCKiyEMFHSD1sc/p2kZ+KPn5H0+7QWBVRSCGFJCOHT+OOf5V8qdhPXNLJYcCvjT2vGb0HSkZJejh/nukbWMLPmkk6U9ET8uYnrucoIfqmzm6SvSzxfGD8G5ILGIYQl8cdLJTWOshigMsyslaSukiaJaxpZLj4tboqkZZLelvSlpB9DCBvip/A9BNnkfklXSyqKP28orucqI/gBqJLge8KwLwyyipltL+kVSZeFEH4q+TOuaWSjEMLGEEIXSc3ls47aR1wSUClmdpKkZSGET6KuJdfUiLqAHLZIUosSz5vHjwG54BszaxpCWGJmTeV/YQaygpnVlIe+f4UQ/h0/zDWNnBBC+NHMxkv6laSdzaxGfJSE7yHIFodIOtnMTpBUR9KOkoaJ67nKGPFLnY8l7RHvQFRLUi9JoyKuCUiWUZL+En/8F0n/ibAWIGHxdSL/kDQzhDCkxI+4ppG1zGxXM9s5/ng7SUfL16+Ol3Ra/DSua2SFEMI1IYTmIYRW8u/P74YQ/iiu5yozn9GCVIj/peJ+SdUlPRlCuCPikoAKM7MRknpI2kXSN5JukvSapJck7S7pf5J6hhC2bAADZBwzO1TSe5KmadPakWvl6/y4ppGVzGwfebOL6vI/6r8UQrjVzNrIm8s1kPSZpD+FEH6JrlKgYsysh6QrQwgncT1XHcEPAAAAAHIcUz0BAAAAIMcR/AAAAAAgxxH8AAAAACDHEfwAAAAAIMcR/AAAAAAgxxH8AAAog5k1NLMp8dtSM1sUf7zSzB6Ouj4AABLFdg4AACTAzG6WtCx09bIAAAE2SURBVDKEcF/UtQAAUFGM+AEAUEFm1sPMRscf32xmz5jZe2b2PzM71czuMbNpZvammdWMn7e/mU0ws0/MbKyZNY32twAA5BOCHwAAVddW0pGSTpb0nKTxIYTOktZIOjEe/h6QdFoIYX9JT0q6I6piAQD5p0bUBQAAkAP+G0JYb2bTJFWX9Gb8+DRJrSTtJWlvSW+bmeLnLImgTgBAniL4AQBQdb9IUgihyMzWh00L6Ivk/19rkmaEEH4VVYEAgPzGVE8AAFLvC0m7mtmvJMnMappZp4hrAgDkEYIfAAApFkJYJ+k0SYPMbKqkKZIOjrYqAEA+YTsHAAAAAMhxjPgBAAAAQI4j+AEAAABAjiP4AQAAAECOI/gBAAAAQI4j+AEAAABAjiP4AQAAAECOI/gBAAAAQI77/5Tp96/Pcuv9AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "NYSE stock price prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNOK0m1UHvxbWU0HwDnvjs7",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}