Stock_Price_Prediction.ipynb

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    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rq1Dka5OjDM",
        "outputId": "54c99e5a-7a98-4bf3-96b4-7900a8363ebb"
      },
      "execution_count": 1,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "OE0wjB1zL8xs"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Dropout\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import mean_absolute_percentage_error\n",
        "import matplotlib.pyplot as plt\n",
        "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
        "import plotly.graph_objs as go"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/drive/MyDrive/DataSets/WIPRO.csv\")\n",
        "df.head()"
      ],
      "metadata": {
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        "colab": {
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          "height": 206
        },
        "outputId": "b95b1262-9d2e-408f-bb5b-b981f0532fef"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date Symbol Series  Prev Close     Open     High      Low     Last  \\\n",
              "0  2000-01-03  WIPRO     EQ     2522.40  2724.00  2724.20  2724.00  2724.20   \n",
              "1  2000-01-04  WIPRO     EQ     2724.20  2942.15  2942.15  2942.15  2942.15   \n",
              "2  2000-01-05  WIPRO     EQ     2942.15  2942.15  3177.55  2715.00  3000.00   \n",
              "3  2000-01-06  WIPRO     EQ     2990.10  3144.70  3183.00  2790.00  2915.00   \n",
              "4  2000-01-07  WIPRO     EQ     2932.25  2751.00  2751.00  2697.70  2697.70   \n",
              "\n",
              "     Close     VWAP  Volume      Turnover  Trades  Deliverable Volume  \\\n",
              "0  2724.20  2724.17    1599  4.355942e+11     NaN                 NaN   \n",
              "1  2942.15  2942.15    4392  1.292192e+12     NaN                 NaN   \n",
              "2  2990.10  3063.86  132297  4.053390e+13     NaN                 NaN   \n",
              "3  2932.25  2962.41   72840  2.157822e+13     NaN                 NaN   \n",
              "4  2697.70  2697.95   10110  2.727630e+12     NaN                 NaN   \n",
              "\n",
              "   %Deliverble  \n",
              "0          NaN  \n",
              "1          NaN  \n",
              "2          NaN  \n",
              "3          NaN  \n",
              "4          NaN  "
            ],
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>Series</th>\n",
              "      <th>Prev Close</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Last</th>\n",
              "      <th>Close</th>\n",
              "      <th>VWAP</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Turnover</th>\n",
              "      <th>Trades</th>\n",
              "      <th>Deliverable Volume</th>\n",
              "      <th>%Deliverble</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2000-01-03</td>\n",
              "      <td>WIPRO</td>\n",
              "      <td>EQ</td>\n",
              "      <td>2522.40</td>\n",
              "      <td>2724.00</td>\n",
              "      <td>2724.20</td>\n",
              "      <td>2724.00</td>\n",
              "      <td>2724.20</td>\n",
              "      <td>2724.20</td>\n",
              "      <td>2724.17</td>\n",
              "      <td>1599</td>\n",
              "      <td>4.355942e+11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2000-01-04</td>\n",
              "      <td>WIPRO</td>\n",
              "      <td>EQ</td>\n",
              "      <td>2724.20</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>4392</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2000-01-05</td>\n",
              "      <td>WIPRO</td>\n",
              "      <td>EQ</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>3177.55</td>\n",
              "      <td>2715.00</td>\n",
              "      <td>3000.00</td>\n",
              "      <td>2990.10</td>\n",
              "      <td>3063.86</td>\n",
              "      <td>132297</td>\n",
              "      <td>4.053390e+13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000-01-06</td>\n",
              "      <td>WIPRO</td>\n",
              "      <td>EQ</td>\n",
              "      <td>2990.10</td>\n",
              "      <td>3144.70</td>\n",
              "      <td>3183.00</td>\n",
              "      <td>2790.00</td>\n",
              "      <td>2915.00</td>\n",
              "      <td>2932.25</td>\n",
              "      <td>2962.41</td>\n",
              "      <td>72840</td>\n",
              "      <td>2.157822e+13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000-01-07</td>\n",
              "      <td>WIPRO</td>\n",
              "      <td>EQ</td>\n",
              "      <td>2932.25</td>\n",
              "      <td>2751.00</td>\n",
              "      <td>2751.00</td>\n",
              "      <td>2697.70</td>\n",
              "      <td>2697.70</td>\n",
              "      <td>2697.70</td>\n",
              "      <td>2697.95</td>\n",
              "      <td>10110</td>\n",
              "      <td>2.727630e+12</td>\n",
              "      <td>NaN</td>\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9uXdeSaTzJ_",
        "outputId": "90c0ffbd-2753-4d0f-c752-c796fcebb09c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5306 entries, 0 to 5305\n",
            "Data columns (total 15 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Date                5306 non-null   object \n",
            " 1   Symbol              5306 non-null   object \n",
            " 2   Series              5306 non-null   object \n",
            " 3   Prev Close          5306 non-null   float64\n",
            " 4   Open                5306 non-null   float64\n",
            " 5   High                5306 non-null   float64\n",
            " 6   Low                 5306 non-null   float64\n",
            " 7   Last                5306 non-null   float64\n",
            " 8   Close               5306 non-null   float64\n",
            " 9   VWAP                5306 non-null   float64\n",
            " 10  Volume              5306 non-null   int64  \n",
            " 11  Turnover            5306 non-null   float64\n",
            " 12  Trades              2456 non-null   float64\n",
            " 13  Deliverable Volume  4797 non-null   float64\n",
            " 14  %Deliverble         4797 non-null   float64\n",
            "dtypes: float64(11), int64(1), object(3)\n",
            "memory usage: 621.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "print(f'Dataframe contains stock prices between {df.Date.min()} and {df.Date.max()}')\n",
        "print(f'Total days = {(df.Date.max() - df.Date.min()).days} days')"
      ],
      "metadata": {
        "id": "HC9d8n6eMmdl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataframe contains stock prices between 2000-01-03 00:00:00 and 2021-04-30 00:00:00\n",
            "Total days = 7788 days\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"number of NaN values for the column Close :\", df['Close'].isnull().sum())\n",
        "print(\"number of NaN values for the column Volume :\", df['Volume'].isnull().sum())\n",
        "print(\"number of NaN values for the column Trades :\", df['Trades'].isnull().sum())\n",
        "print(\"number of NaN values for the column Deliverable Volume :\", df['Deliverable Volume'].isnull().sum())\n",
        "print(\"number of NaN values for the column %Deliverble :\", df['%Deliverble'].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VkXxG_OFPpb1",
        "outputId": "97ad32ff-a492-4600-8965-c7d3ec4f2e71"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of NaN values for the column Close : 0\n",
            "number of NaN values for the column Volume : 0\n",
            "number of NaN values for the column Trades : 2850\n",
            "number of NaN values for the column Deliverable Volume : 509\n",
            "number of NaN values for the column %Deliverble : 509\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# If Trades, Deliverable Volume and %Deliverable are required, perform EDA\n",
        "mean = df['Trades'].mean()\n",
        "df['Trades'].replace(np.nan,mean, inplace=True)\n",
        "mean = df['Deliverable Volume'].mean()\n",
        "df['Deliverable Volume'].replace(np.nan,mean, inplace=True)\n",
        "mean = df['%Deliverble'].mean()\n",
        "df['%Deliverble'].replace(np.nan,mean, inplace=True)"
      ],
      "metadata": {
        "id": "pngJj5sVRKHc"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[['Open','High','Low','Last','Close','VWAP']].plot(kind='area')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "NMcmfpByRgFF",
        "outputId": "fab21514-60e9-4348-b88a-4a7c64edc6fb"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "bbd666ea-9436-4b78-b42a-1a54d570f688"
      },
      "outputs": [],
      "source": [
        "layout = go.Layout(\n",
        "    title='Stock Prices of Wipro',\n",
        "    xaxis=dict(\n",
        "        title='Date',\n",
        "        titlefont=dict(\n",
        "            family='Courier New, monospace',\n",
        "            size=18,\n",
        "            color='#7f7f7f'\n",
        "        )\n",
        "    ),\n",
        "    yaxis=dict(\n",
        "        title='Price',\n",
        "        titlefont=dict(\n",
        "            family='Courier New, monospace',\n",
        "            size=18,\n",
        "            color='#7f7f7f'\n",
        "        )\n",
        "    )\n",
        ")\n",
        "\n",
        "wipro_data = [{'x':df['Date'], 'y':df['Close']}]\n",
        "plot = go.Figure(data=wipro_data, layout=layout)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iplot(plot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "5C1H_9QvTCYt",
        "outputId": "0926428b-8814-4a92-9bcd-8f044dc29fc2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      "execution_count": 19,
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            "Epoch 1/100\n",
            "500/500 [==============================] - 6s 4ms/step - loss: 0.0310\n",
            "Epoch 2/100\n",
            "500/500 [==============================] - 2s 3ms/step - loss: 0.0025\n",
            "Epoch 3/100\n",
            "500/500 [==============================] - 2s 3ms/step - loss: 5.3803e-04\n",
            "Epoch 4/100\n",
            "500/500 [==============================] - 1s 3ms/step - loss: 4.9150e-04\n",
            "Epoch 5/100\n",
            "500/500 [==============================] - 1s 3ms/step - loss: 4.5479e-04\n",
            "Epoch 6/100\n",
            "500/500 [==============================] - 1s 3ms/step - loss: 4.6127e-04\n",
            "Epoch 7/100\n",
            "500/500 [==============================] - 1s 3ms/step - loss: 4.3892e-04\n",
            "Epoch 8/100\n",
            "500/500 [==============================] - 1s 3ms/step - loss: 4.2706e-04\n",
            "Epoch 9/100\n",
            "500/500 [==============================] - 1s 3ms/step - loss: 4.2669e-04\n",
            "Epoch 10/100\n",
            "500/500 [==============================] - 2s 4ms/step - loss: 4.0924e-04\n",
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            "Epoch 81/100\n",
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            "Epoch 94/100\n",
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            "Epoch 95/100\n",
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            "Epoch 96/100\n",
            "500/500 [==============================] - 1s 3ms/step - loss: 1.0511e-04\n",
            "Epoch 97/100\n",
            "500/500 [==============================] - 1s 3ms/step - loss: 1.0470e-04\n",
            "Epoch 98/100\n",
            "500/500 [==============================] - 1s 3ms/step - loss: 1.0361e-04\n",
            "Epoch 99/100\n",
            "500/500 [==============================] - 2s 4ms/step - loss: 9.8858e-05\n",
            "Epoch 100/100\n",
            "500/500 [==============================] - 2s 4ms/step - loss: 1.0717e-04\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e7551c072e0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set=pd.read_csv(\"/content/drive/MyDrive/DataSets/WIPRO.csv\")\n",
        "test_set.head()"
      ],
      "metadata": {
        "id": "mt2y01KxNaqx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c0770e4c-dfcc-43cf-e0fa-2bd73f147156"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date Symbol Series  Prev Close     Open     High      Low     Last  \\\n",
              "0  2000-01-03  WIPRO     EQ     2522.40  2724.00  2724.20  2724.00  2724.20   \n",
              "1  2000-01-04  WIPRO     EQ     2724.20  2942.15  2942.15  2942.15  2942.15   \n",
              "2  2000-01-05  WIPRO     EQ     2942.15  2942.15  3177.55  2715.00  3000.00   \n",
              "3  2000-01-06  WIPRO     EQ     2990.10  3144.70  3183.00  2790.00  2915.00   \n",
              "4  2000-01-07  WIPRO     EQ     2932.25  2751.00  2751.00  2697.70  2697.70   \n",
              "\n",
              "     Close     VWAP  Volume      Turnover  Trades  Deliverable Volume  \\\n",
              "0  2724.20  2724.17    1599  4.355942e+11     NaN                 NaN   \n",
              "1  2942.15  2942.15    4392  1.292192e+12     NaN                 NaN   \n",
              "2  2990.10  3063.86  132297  4.053390e+13     NaN                 NaN   \n",
              "3  2932.25  2962.41   72840  2.157822e+13     NaN                 NaN   \n",
              "4  2697.70  2697.95   10110  2.727630e+12     NaN                 NaN   \n",
              "\n",
              "   %Deliverble  \n",
              "0          NaN  \n",
              "1          NaN  \n",
              "2          NaN  \n",
              "3          NaN  \n",
              "4          NaN  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>Series</th>\n",
              "      <th>Prev Close</th>\n",
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              "      <th>VWAP</th>\n",
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              "      <th>Turnover</th>\n",
              "      <th>Trades</th>\n",
              "      <th>Deliverable Volume</th>\n",
              "      <th>%Deliverble</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2000-01-03</td>\n",
              "      <td>WIPRO</td>\n",
              "      <td>EQ</td>\n",
              "      <td>2522.40</td>\n",
              "      <td>2724.00</td>\n",
              "      <td>2724.20</td>\n",
              "      <td>2724.00</td>\n",
              "      <td>2724.20</td>\n",
              "      <td>2724.20</td>\n",
              "      <td>2724.17</td>\n",
              "      <td>1599</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2000-01-04</td>\n",
              "      <td>WIPRO</td>\n",
              "      <td>EQ</td>\n",
              "      <td>2724.20</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>4392</td>\n",
              "      <td>1.292192e+12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2000-01-05</td>\n",
              "      <td>WIPRO</td>\n",
              "      <td>EQ</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>2942.15</td>\n",
              "      <td>3177.55</td>\n",
              "      <td>2715.00</td>\n",
              "      <td>3000.00</td>\n",
              "      <td>2990.10</td>\n",
              "      <td>3063.86</td>\n",
              "      <td>132297</td>\n",
              "      <td>4.053390e+13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000-01-06</td>\n",
              "      <td>WIPRO</td>\n",
              "      <td>EQ</td>\n",
              "      <td>2990.10</td>\n",
              "      <td>3144.70</td>\n",
              "      <td>3183.00</td>\n",
              "      <td>2790.00</td>\n",
              "      <td>2915.00</td>\n",
              "      <td>2932.25</td>\n",
              "      <td>2962.41</td>\n",
              "      <td>72840</td>\n",
              "      <td>2.157822e+13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000-01-07</td>\n",
              "      <td>WIPRO</td>\n",
              "      <td>EQ</td>\n",
              "      <td>2932.25</td>\n",
              "      <td>2751.00</td>\n",
              "      <td>2751.00</td>\n",
              "      <td>2697.70</td>\n",
              "      <td>2697.70</td>\n",
              "      <td>2697.70</td>\n",
              "      <td>2697.95</td>\n",
              "      <td>10110</td>\n",
              "      <td>2.727630e+12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-50a2064c-9174-475a-9e69-f3d09dcb7b60 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set=df.iloc[5000:5305,8:9].values\n",
        "inputs = sc.fit_transform(test_set)\n",
        "inputs"
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      "metadata": {
        "id": "WUXbB_eXNatJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e21532c-7e38-4397-b8ba-73c338f3077f"
      },
      "execution_count": 21,
      "outputs": [
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          "metadata": {},
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      "cell_type": "code",
      "source": [
        "len(inputs)"
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            "text/plain": [
              "305"
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          "execution_count": 22
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      "metadata": {
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              "(305, 1)"
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          "metadata": {},
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    {
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        "inputs=np.reshape(inputs,(305,1,1))\n",
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      "execution_count": 24,
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              "\n",
              "       [[1.        ]]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scaling the Test Data"
      ],
      "metadata": {
        "id": "uHNFlqUFSC2H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_values=model.predict(inputs)\n",
        "predicted_values"
      ],
      "metadata": {
        "id": "bWKT5H8hO_IN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6822cf47-7f94-49b4-b096-6e36d49a8183"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 1s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_values.shape"
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      "metadata": {
        "id": "0thmziLpO_Kz",
        "colab": {
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        },
        "outputId": "e634eaf2-3000-4281-e70e-5c4efbc5bb63"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(305, 1, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_values=np.reshape(predicted_values,(305,1))\n",
        "predicted_values"
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      "metadata": {
        "id": "Lf1OnwYEO_NS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58e5db6e-e35d-49d4-f721-fe2b33056d91"
      },
      "execution_count": 27,
      "outputs": [
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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reverse Scaling to get back the Original Data"
      ],
      "metadata": {
        "id": "PDdjIe_JSLMr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_pred=sc.inverse_transform(predicted_values)\n",
        "test_pred"
      ],
      "metadata": {
        "id": "Z8HUAKmVO_Pm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e572ed0c-4113-4f12-c78d-b6573d444432"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The Error Rate of Prediction Model:\",mean_absolute_percentage_error(test_pred,test_set))\n",
        "print(\"The Accuracy of Prediction Model:\",(1-mean_absolute_percentage_error(test_pred,test_set)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjT9bLLjY3XJ",
        "outputId": "4833c6c4-ce39-4d85-ba85-03eae5aea90d"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Error Rate of Prediction Model: 0.014395070990139635\n",
            "The Accuracy of Prediction Model: 0.9856049290098604\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing the Data"
      ],
      "metadata": {
        "id": "OIV4i9s1RSJd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_set,color='red',label='original test data')\n",
        "plt.plot(test_pred,color='green',label='predicted data')\n",
        "plt.xlabel('time')\n",
        "plt.ylabel('google stock price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "5Nn0MNv9O_SS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "680e426a-9a8b-4d42-8773-4753e4a360f2"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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RgrZt2xIREcGmTZt0/VyEEEI8wnJz4fr1gv3oaPV7BWrCAqnZEffh7e3Nrl27DB2GEEKIiujWLbUz8n9VkFFY+aRmRwghhBClU9TintWrwz0mrDUESXaEEEIIUTr5yU7t2mCUl1L06aNOwFuBSLIjhBBCiNLJH3bu5QX584g9+6zh4rkL6bMjhBBCiNLJr9lxd4evvlInFWzXzrAxFUGSHSGEEEKUTE4OzJ0LW7ao+25uas1OoVniKxJpxhJlokaNGsydO1e3r9FoWLFixUOPY+rUqSVet2vr1q1oNBoSExPLJSYhhKhyvvsOxo+HNWvU/Qqwsvm9SLIjykVMTAw9e/Ys1rmlSVAMrVOnTiVaVFUIIaoMRYGvv9Yvc3c3TCzFJM1YQicrK0u38veD8vDwKJP7CCGEqGB274bjx/XLnJwME0sxSc1OFdWpUydGjhzJyJEjsbe3x8XFhcmTJ1N4KbQaNWowY8YMBg0ahJ2dHUOHDgVg586dtG/fHktLS7y9vRk1ahS3b9/WXRcfH88TTzyBpaUlfn5+/Prrr3c8/7/NWFevXqVfv344OTlhbW1N8+bN2bdvH4sWLWLatGkcPXoUjUaDRqPRrSCemJjIq6++iqurK3Z2dnTp0oWjR4/qPefDDz/E3d0dW1tbhgwZQkZGxn1/NmvWrKFOnTpYWlrSuXNnLl26pHf85s2b9OvXDy8vL6ysrAgMDOS3337THX/ppZfYtm0b8+bN08V86dIlcnNzGTJkCH5+flhaWlK3bl3mzZt333iEEKJS+e479fvLL0PLlmBqWuHm1fkvqdkpIUVRSMtOM8izrUyt0JRg7oKffvqJIUOGsH//fg4ePMjQoUPx8fHhtdde053zySefMGXKFN577z0AIiMjeeyxx3j//ff58ccfuX79ui5pWrhwIaB+2EdHR7NlyxZMTU0ZNWoU8UVNLJUnNTWVjh074uXlxcqVK/Hw8ODQoUNotVqef/55jh8/zrp169i0aRMA9vb2ADz77LNYWlqydu1a7O3t+fbbb+natStnz57FycmJP/74g6lTpzJ//nzatWvHL7/8wueff07NmjXvGsuVK1fo06cPYWFhDB06lIMHDzJu3Di9czIyMmjWrBkTJkzAzs6O1atXM3DgQGrVqkXLli2ZN28eZ8+epWHDhkyfPh0AV1dXtFot1atX588//8TZ2Zndu3czdOhQqlWrxnPPPVfsPzchhKjQDh9Wvz/zDMyfD0lJUMFr8yXZKaG07DRsZtkY5NmpE1OxNrMu9vne3t589tlnaDQa6tatS0REBJ999plestOlSxe9D/tXX32VAQMG6Pqj+Pv78/nnn9OxY0e+/vproqKiWLt2Lfv376dFixYALFiw4J5rZS1ZsoTr169z4MABnPKqOmvXrq07bmNjg4mJiV7T186dO9m/fz/x8fGYm5sDamK2YsUK/vrrL4YOHcrcuXMZMmQIQ4YMAeD9999n06ZN96zd+frrr6lVqxaffvopgO7n8tFHH+nO8fLy4q233tLtv/HGG6xfv54//viDli1bYm9vj5mZGVZWVnoxGxsbM23aNN2+n58fe/bs4Y8//pBkRwhRdaSnq98dHMDSUv2q4CTZqcJat26tVxMUHBzMp59+Sm5uLsbGxgA0/0/V49GjRzl27Jhe05SiKGi1Wi5evMjZs2cxMTGhWbNmuuP16tXDwcHhrnEcOXKEJk2a6BKd4jh69Cipqak4OzvrlaenpxMZGQnAqVOnGD58uN7x4OBgtuQPhSzCqVOnaNWq1R3XFJabm8vMmTP5448/uHbtGllZWWRmZmJlZXXfuOfPn8+PP/5IVFQU6enpZGVlVbrO10IIcU9pea0blSDJySfJTglZmVqROjHVYM8ua9bW+jVFqampDBs2jFGjRt1xro+PD2fPni3xMyxL8Q8iNTWVatWqsXXr1juO3SuxKgsff/wx8+bNY+7cuQQGBmJtbc3o0aPJysq653VLly7lrbfe4tNPPyU4OBhbW1s+/vhj9u3bV67xCiHEQ5Wf7BTjF8CKQpKdEtJoNCVqSjKk/37I7t27F39/f12tTlGaNm3KyZMn9ZqZCqtXrx45OTmEh4frmrHOnDlzzzlqgoKC+OGHH7h161aRtTtmZmbk5ubeEUdsbCwmJibUqFGjyPvWr1+fffv2MWjQIL13vJf69euzcuVKvbL/XrNr1y6eeuopXnzxRQC0Wi1nz54loNBkWUXFvGvXLtq0acPrr7+uK8uvhRJCiCojvxmrEtXsyGisKiwqKoqxY8dy5swZfvvtN7744gvefPPNe14zYcIEdu/ezciRIzly5Ajnzp3j//7v/xg5ciSg9nF57LHHGDZsGPv27SM8PJxXX331nrU3/fr1w8PDg969e7Nr1y4uXLjAsmXL2LNnD6COCrt48SJHjhzhxo0bZGZm0q1bN4KDg+nduzcbNmzg0qVL7N69m//9738cPHgQgDfffJMff/yRhQsXcvbsWd577z1OnDhxz/cbPnw4586dY/z48Zw5c4YlS5boRn/l8/f3Z+PGjezevZtTp04xbNgw4vLXf8lTo0YN9u3bx6VLl7hx4wZarRZ/f38OHjzI+vXrOXv2LJMnT+bAgQP3jEcIISqV3FzIzFS3K1HNjiQ7VdigQYNIT0+nZcuWhIWF8eabb+qGl99NUFAQ27Zt4+zZs7Rv354mTZowZcoUPD09decsXLgQT09POnbsSJ8+fRg6dChu95g908zMjA0bNuDm5kavXr0IDAzkww8/1NUw9e3bl8cee4zOnTvj6urKb7/9hkajYc2aNXTo0IGXX36ZOnXq8MILL3D58mXc8yavev7555k8eTJvv/02zZo14/Lly4wYMeKe7+fj48OyZctYsWIFjRo14ptvvmHmzJl650yaNImmTZsSEhJCp06ddIlaYW+99RbGxsYEBATg6upKVFQUw4YNo0+fPjz//PO0atWKmzdv6tXyCCFEpVd4AEglSnY0SuGJVx5RycnJ2Nvbk5SUhJ2dnd6xjIwMLl68iJ+fHxYWFgaKsOQ6depE48aN9ZZwEFVTZf07KoSohK5fL1gaIjcXjAxbZ3Kvz+/CpGZHCCGEEMWT3znZwsLgiU5JVJ5IhRBCiKKcPQu//AJaraEjqfoqYedkkNFYVVZRQ7aFEKLK+fdf6N0bUlLU2oZnnzV0RFVbJRx2DlKzI4QQorK6cgV69VITHYBlywwbz6Mgv2ZHkp2qSfpxi4pK/m6KR9beveow6Lz19FizpmBYtCgfpZw9OSv33pOyljdJdu4jf3j0/WbPFcJQ0vL+8zE1NTVwJEI8ZBcuqN8ffxyqVVNreKQJv3yVoBnrStIVXvjrBfy/8MfqAysS0hPKObi7kz4792FiYoKVlRXXr1/H1NQUo0rU+1xUbYqikJaWRnx8PA4ODvecGVuIKik/2aldG2xs4NtvYcUKCAkxaFhVWhEdlHO0OczbO49e/r2o76ouCp2alcrjvz3OsbhjuvNO3zhNsLf+WoQPiyQ796HRaKhWrRoXL17k8uXLhg5HiDs4ODjorb4uxCMjP9mpWROaNlWTnV27DBtTVVdEzc5vEb/x1sa3+PPkn+x9dS+KojBk5RCOxR3D3dqdn3r/RKB7INVsqhko6AqU7Hz44YdMnDiRN998UzcRXqdOndi2bZveecOGDeObb77R7UdFRTFixAi2bNmCjY0NgwcPZtasWZiYlN2rmZmZ4e/vL01ZosIxNTWVGh3x6Cqc7ORPKBcba7h4HgVFJDv7r+0HYN+1fcSkxLAhcgN/nPgDEyMTlj23jLY+bQ0RqZ4KkewcOHCAb7/9lqCgoDuOvfbaa0yfPl23b1XoB5ybm0toaCgeHh7s3r2bmJgYBg0ahKmp6R1LADwoIyMjmZ1WCCEqipwcyK9tr1kT8pP+GzfUY2X4C68opIhmrMOxh3XbXx34inn75gEwvdP0CpHoQAXooJyamsqAAQP4/vvvcXR0vOO4lZUVHh4euq/C00Fv2LCBkydPsnjxYho3bkzPnj2ZMWMG8+fPl1oYIYSoyq5cUZcrsLAADw9wcVFn9FUUdUkD8eASE9VmwcIjPv9Ts6NVtByNO6o7/P6O90nJSqGNdxvebvv2Qwz23gye7ISFhREaGkq3bt2KPP7rr7/i4uJCw4YNmThxom7kCcCePXsIDAzULQwJEBISQnJy8j1Xv87MzCQ5OVnvSwghRCWS34Tl56cmOcbG4OqqlsXFGS6uqmTYMGjXDjZvLij7T83O+VvnSc1K1bvM2tSan3v/jLFRxWliN2g939KlSzl06BAHDhwo8nj//v3x9fXF09OTY8eOMWHCBM6cOcPff/8NQGxsrF6iA+j2Y+/Rbjtr1iymTZtWRm8hhBDioSvcXyefu7ua6Ei/nbKxc6f6/fRpyK+QKFSzczPtJuHR4QC09GpJ/O14LiVeYu5jc6nlVMsAAd+dwZKdK1eu8Oabb7Jx48a79oUZOnSobjswMJBq1arRtWtXIiMjqVWr9D/IiRMnMnbsWN1+cnIy3t7epb6fEEKIh6yoZMfDA44dk5qdsnDjBkRHq9uFmwXzkp0/LSIZ8Gk1srXZADT1aMrIliM5d+scT9V96mFHe18Ga8YKDw8nPj6epk2bYmJigomJCdu2bePzzz/HxMSE3NzcO65p1aoVAOfPnwfAw8ODuP/8pc7fv9dQXHNzc+zs7PS+hBBCVCJ3q9mB4tXspKTAtGlqrYW407GC+XH0kp30dA56wqDcv3SJDkCTak1o4NaA3vV6o9FoHmKgxWOwZKdr165ERERw5MgR3Vfz5s0ZMGAAR44cKXI47ZEjRwCoVk0dqx8cHExERATx8fG6czZu3IidnR0BAQEP5T2EEEIYwNWr6vfCtfL5yc5/a3bOn4e+faFJk4J5eH7+GaZOhffeK/dQK6W7JTtpaUzoBhnk0NWvK7Uca2GsMaZTjU4PPcSSMFgzlq2tLQ0bNtQrs7a2xtnZmYYNGxIZGcmSJUvo1asXzs7OHDt2jDFjxtChQwfdEPUePXoQEBDAwIEDmT17NrGxsUyaNImwsDDMzc0N8VpCCCEehvyEpnC/zfwa/cLJzrFj0KoVZGSo+x07wq+/wvHj6r5MFlu0e9TsROXll9M6TaOZZzOu376Ot33F7gpi8NFYd2NmZsamTZvo0aMH9erVY9y4cfTt25d//vlHd46xsTGrVq3C2NiY4OBgXnzxRQYNGqQ3L48QQogqKL9Gv3CyU1Qz1p49aqJTr55au5ObC599BqdOqcdjYh5OvJXNPWp2EvO62TpaOmJhYlHhEx2oIJMK5ttaaAE3b2/vO2ZPLoqvry9r1qwpx6iEEEJUKOnpap8bADe3gvKianbyO9l27Ahjx8KyZeoHuY2NWh4TA1qtOnxdqHJyCmq+QC/ZUdJu65IdBwuHhxvXA5A/XSGEEJVLfq2OmVnBMhFQdM1Ofs1NtWoFC4ampxd8gGdnw82bpY9l4UJwcoJCrQ6V3unTkJlZkADevKnWiAFpWWnk5HWplWRHCCGEKC/5yY6bGyeun6Tfsn40/645FyzzJry7eVNNYqAg2fH0VD+8GzW68375tT+l8eWXkJAAQ4Y8WNJUkSxfrn7v1En9rtXCrVsAJOSqEwiaakywNLEs4uKKSZIdIYQQlUtesnOxhgMtf2jJ0uNLCY8J562DswrWyMqvuSlcswPq6uj/VZJk5+JFOHBATaZiY+HQoYLnjR5d8nepaBRF7cANMHgwODio23k/z0RFTSgdTG0r5BDzu5FkRwghROWS1ydnTW0tadlp1HaqjZHGiOVnVrAj0F49J78p67/JTpMmd96vuMnOzZvQuDG0bKkuTTFunFqeX2u0eDGURR/S336DN97QNR09VIcPw5kz6ppjvXsX9InSJTvqpIIO5vYPP7YHIMmOEEKIyiWvZme3i1rLMDBoIK81fQ2AGW3ymq/yFwrNT3qKSnbyV0YvbrLz55+Qv5ZiUhIsWaJuv/pqQa3OsGEF55TGzZvQv7/aPPbvv6W/T2n99pv6/Ykn1P5Q+euN5Sc7ZAKVq78OSLIjhBCisslPdqzUfiRtvNswrNkwAA47Z6nnHDqkfkBrtaDRFNRQBASAqam6nTcrf7GTnfzmnZkzoUWLgvJevWDGDHU256tXYdGi0r4ZfPddwXZCQunvU1p796rfn8pb8qFwspOdTaKZFpBkRwghhChfcXFE28IloySMNEa09GqJv7M/ADeMM7llidqvJr8Jy82toBbHzAzefhtCQuDZZ9Wy4iQ7ly+rC2NqNDBokFoD4uwMdetC8+ZgZQUvvaSee5fFre8rO1ut0cl340bp7vMg8pfhqFOHPVf2MKb2eTJMUJOdQnPsOFg7P/zYHkCFmmdHCCGEuK/4ePZUVzcD3QKxM1eHn3vZenEt5RrnnKBV4WQnvwkr3/vvq9/zh4sXJ9n54w/1e+fO4OWlbkdGqrVE+Z2i8zs/53daLqlVq/RjKTyZ38OQnl7w/Jo1GfpHJ47bHKdpAAy8fh3S0wuSHSvHhxvbA5KaHSGEEJVLfDy7fNTNNt5tdMV1nOsAcNbNWK0V2bNHPeDpecctFEVhtfEFtRaoqGTn2jW4dKlgPz+BCQkpKLO3V2t08uUnO6dPw+3bJXwp7uzc/LBrdvLf186OKONUjserEwued+LOmh0LSXaEEEKI8hMXx6G8yprW1VvrinXJTv28fiYrV5JjBHP8bxD4dSDV51Rn8r+TSclM4aejP/H4gdFM6IbaibnwyKesLHXEVVBQQQfns2fV73Xr3j2uatXUL60W5s2DAQOKvxyFosDatep2r17q94ddsxMZqX6vVYvV5woSr4uOqP2k9JIdh4cb2wOSZiwhhBCVh1YL169zIa9iIT/BKbx91ttaLTh2jP+rD+Mc90PePITv73ifi4kXydaqo7b2e+XdMz6+oLlr166C2p6FC+GddwqSnToFzytS06awejX873/qvoUFLFhw//c6cUKtTbK0hD591Fqeh12zk99fp2ZN1pwvlOw4AGfj9ZuxKlmyIzU7QgghKo9bt8jUaLmat0pETceaukO6ZMcuS1d2PG8QVi//XvzwxA8ALD+9nK2XtgJwzkWDVoN+U9a6dQXb33+vHktNVefSqVnwPIArSVf4aOdH3EjLS0yaNdOP9+efISrq/u+VX6vTuTN45y2s+bBrdvKSnfSa3my+sFlXfMkBtYaqEtfsSLIjhBCi8oiL47IDKBqwMrXC1cpVd8jfSR2RdVa5ieJfW93OGzTUwacDrzR5BS9bL9Ky04i/rVb1pJsoauJ0t2Tn4kWYP1/drlEDzM31whm2ahjvbH6HLj914frt6/ozNDs5qYtqzp59//fasEH9/thj4OKibhso2fnFI570nHQc8/rlXLODzKRbcP26JDtCCCFEubt5U21WQa3VKbxkgZ+jH8YaY9Jy0ohevwzatOGcq/oxV8e5DhqNhsfrPH7HLc84U5DsREerq6JrNOrkfgBz5wKgrePP6HWj6flrTyZsnMC/F/9l7Xm1RiYiPoKuP3flRrP6apLTogX88ot6/V9/3f+9zp1Tv7doQZRlFv/6oTZjKcrdr9m2Ta0F+v33+9+/OC5c4LYpvJehJnvvdXwPK1MrFA1E2QP79kmyI4QQQpS7xERdf53CTVgAZsZm+Dn6AXBSG4eyYwdna9gCBU1cRSY7LhQkO/k1LM2bw6hR6na6OlPzXw01zNs3j3Xn1zF792y6/dwNgHY+7fCw8VATnlXPknguAnbsgOBg9fq4uHuPzlIU3RIYtxzM8f0jmK6D4bhj9t1nY1YUdaHOq1fVpSUelKLAhQt83gpis2/h5+DH8ObD8XNQf54XHYFduyTZEUIIIcpdUpIu2cn/IC4suLqaYKyPXM/19BskZSahQUMtp1oAdPHrgoWJ+ondtJra5HS2cM3Orl3q965d1RFZPuoY92wj+J9dOAAvNHyBWo61UFBrXT7o8gFbBm/B3dqdY3HH+OrEIrW5y9FRHZ4O+sPY/yslBTIyABh7/FNd8XE39Juyrl2DiAh1e9WqgnKt9u73Lq5r1yA9nRX11d1327+LuYk5NRxqAGonZSX8oCQ7QgghRLm7R80OwJN1nwTgn7P/cPamOoLKx95Hl+BYmVrxwxM/MKHtBN0SE3rNWPv3q99btVKbsp55BoBfGsF57XVcrVz57vHv2PnKTnrU6sFLjV+ivU976rnUY1qnaQCsKTRsG7+8hOzixbu/U16tzs66lvx04ldd8VU79Edkde+uDof/+GN1hFi+pCR19uXSysmBoUPJMYJjHmqzYAffDmr4eQnlJQe4TTa5eVlDZUt2ZOi5EEKIyiMxUW1SoehkJ6RWCGbGZpy9eZZVZ9Xaj8LD0wEGBA0AYMflHUBeM9aJGLWp6bg6kd4nFodI2HyAGc/0xWjOHNaq/Z15o+Ub2JrbYmtuy/oX1+vdt6d/TwD2XN3D5cTLnL91ni5+NdAcOVKsZOfzNsZ6xVfsKKjZiYuDU6fU7bffVr87OcGtW2qycuHCvecAupePPoK1aznjbU6GcSY2ZjbUdlJfOL9Z8KIjulodU40JliaWpXuWgUjNjhBCiEpDSUwg8h7Jjq25LZ1qdALg0z1qk9B/k518dV3U5CDKHtLjrqmzJGu1nGrgxvh9M5i5cyZLrCKhf3/211FnSm7n0+6usfnY+xDgGoBW0dLk2yZ0+6Ub79fNm+DnPslOjA0s91b79eSv4H7FnoJk5/Bh/WsaNFAX7WzcWN3PnweoNPJGnx1+83kAGrk3wkijpgeFm7EKN2EV7hheGUiyI4QQotJISLlOct6Hbv4H8X89WUdtysrR5gB3T3ZcrVxxMLNH0cD53Hhdf53vO9nqznln80Qufvk+UaZpaNDQzLNZkffK17O2WruTkKGuWD7dYi/h1Sjos7NvH0yapN/xODaWBU0hx0ihjXcbQv1DgbyanfxmrPxk54UX1O2DB8Hfv6A258yZe8Z1T3k1Rofd1L4/TTya6A7VclT7Op13KpTsWFaupSJAkh0hhBCVyKU0tW+Nu8YWK1OrIs8Z3Hgwbb3b6vbrOhfdvKPRaKidt1p6pCPwzz9kmMBPruozrEytuJZyjReXvwhAfdf6ukVH76aXfy/ddiP3RuSgZWQvCmp23ngDPvgAunQp6FgcF8eyvI7BQ5sOxdtenVRQr2Ynf22upk3V2hwLCy4lXuLVemc56k7pk53r1+HmTdBoOJyjTn7YpFqhZCevY/ctKziZN6WRi5VL6Z5lQJLsCCGEqDTiMm8CUM3U6a7n2JjZsO2lbXz3+HeMbT2WLn5d7npu/od5pCOwezfL68Et0qluV51vH/8WgN1XdgPQwrPFfePrVKMT77R9h29Cv2HFCysAOOAFGVEX1L41Bw6oJ4aHw6xZeS8Vp+t03dKrJd52arITZwOZN9T+PLqanSZqIpKjzaHhVw1ZoDnM290pfTNWXq2O4uvD4fhj6iMK1ezYmNlQzVKdhvr/8nLG+i71S/csA5JkRwghRKVxIzsJABfzezelGBsZ81qz1/g05FNMjU3vel5+R9zzebnTigZqJ+FBQYN4oeELek1lLb1a3jc+I40Rs7rNYljzYfja++Jg7kCuEZwxTVb72BS2cCEAiTeu6prmfOx9cLFywQI15mtx59TRVpGRfNYaXrz+LdEp0UzfNp3b2Wofn601KH3NTl6ys6KNE4kZiZgamdLArYHeKf55fZs25XWRCnANKN2zDEiSHSGEEJXGjdxUAFysnMvkfoX7pOQYwYY6arITWicUEyMTxgWP051bnJqdwjQaDYHugUDenDnLl6sH8ubu4fJlyM0lKvkKAM7GtlibWaPRaKhu4wnAldP7Yd060kzhrR7w69m/8JrjxYztM3TPyTKBhKQ4NSkqqVOnWFYfnvE/AsDzDZ/HzNhM7xR/lzq65wB3JEOVgSQ7QgghKo0bqLUZrrbuZXK//JqdSCfY5wWJxlk4WjjqanFebvwytRxrUdOxJo08GpX4/g3dGgIQ4Q4sWwaANrQXmJmpzVpXr3I5Ux2x5WvlqbvO20WtRrlqo4WRI9nlDdpCn9iWJpbM6jpLV/N0xIPiN2WtW1dQy3TqFJ+3Aq1GYWDQQH588sc7Ts9fcyyf1OwIIYQQ5UVRuGGkzjTsYletTG6Zn+xctoeVeX1SutfqjomRWo1hbWbNsRHHOPH6iTtqPIojP9k57gZcvszkzuDi8TMr2ubVTF24wGXtLQB8HHx01+l1Ur5xgy15TUjPBjzLr31+5czIM7zT7h2aVVNHhx2qRvGasmJjoWdPdSmLpCRyT58kPC/HmtB2QpFNfvk/IwAbUxtdn6LKRJIdIYQQlUNaGtct1SUaXBy9yuSWHjYeWJlYojWCrzuqo7vyh4/nszK10s3AXFKFkx0FeL8jJChpPN0xhr8CgGPHiLJSZz/2dS2oQclPKK44qB/T/7b2ACDUP5T+gf11yVD+khfFTnYKz/ezaBGnMq5y2wxsTK2p51KvyEv8nQviCnALqHRz7IAkO0IIISqLpCRu5I02d3HwvPe5xaTRaKiVV3ORkpuGiZEJj9V+rEzuDQXJzmUHdDUo+SZ3Bvbs4XLe8lm+LgU1KPnJzolWfiS/+CwHTdUh6J39OuvdQy/ZKdyMFRsL/frB8OH6K6fnL4sBMGEC+/NyxmaezTE20p/BOV/hmp0GrpWvvw5IsiOEEKKySEzUJTuu1m5ldtv84ecArzR+BQ8bjzK7t5OlE562apbzVSv1Izd/ksOzzpBxYA9RecmOj31BM1b3Wt0x0hixLTeSD1+qTa6SSy3HWnrnQMEw8TMukHr+pFp49Ki6htbSpfDttwWLhwJER6PVqJ2xyczkQF4Cdq+RZlamVnjZqllRZeyvA5LsCCGEqCwKJTtlObGdubG5bntyx8lldt98jdzVjs2Lm6g1J883eB4HI2u0RnD2dhSXHdTzfB18ddfUdKzJcw2eA2DWTnU+nr71+95xb3cbd7ws3VE0cDT5rDpR4dSp6mSB+c1NeR2jAdKjL1N7FLR/GXI1sD9AnSTxfiPNWldvDaA3WWNlIsmOEEKISkGbcIubeetPlmWyk78W1bRO06huV73M7pvvzVZvApCtVfvmdPTtSAMHtR/MYQ+IyVudwtfeV++6d9oWrGzewrMF0zpPK/L+Tas3B+CQU5Zaq7N6tXpg/Hj1+99/6849eeM0Fx1hrzcsntiLY7ZpwP3nEFrUexFHhh0h2Dv4fq9bIUmyI4QQolJISIjWDb92LqN5dgC61uzK7XdvM6XjlDK7Z2EhtUN0612ZGJkQ7B1MgKda27M+rzuMpbHFHQlcI49GvNHyDZp7Nufv5/++ayfppp55yU41YMYMlOxs/nmsJo191tBjkIbcE8d1/Xkup1zRXfeqxQZytDl423nf0Tz2XzZmNqUael9RSLIjhBCiUriRoHautcs1KdUw8Hu52zpbZWVOyBzcrN14oeELWJlaEeDVGIANed2FfBx8ixzl9HnPzznw2oF71jjld1IO9wSWL+e7ZvBk6wscvXGcjTUV1tUGVq4E4FJmnO66HG0ORhojvn/i+0o5wqokTAwdgBBCCFEcN5JjAXDVWho4kpKr41yH6LHRuhFP+aOabublWK2qtyr1vfOTnZOukGKmDm8HdVX4S4mX+K4ZhJ4+DcBlJUHv2tndZhNSO6TUz64spGZHCCFEpXDjtjrTsIvG2sCRlE7hod3/HdU0ssXIUt/Xy9YLVwtnco1g8tvNuWoH7tbuLH9eXZ5idR24du00pKVxyTILgC86zebqmKuMazPuXreuMiTZEUIIUSncSFdnGnYxsTNwJA/O09YTO3P1PYKrB9PCq2TrbhWm0Who6qX225lnchCAkS1H0tijMe3tg8g1gt9MTkFMjG7kV81qAXjZlc3EjJWBJDtCCCEqrlu34LvvYPp0rl9Q54txMXMwbExlQKPR0MpLbboaGzz2ge+Xfy8AO3M7hjcfDkBPf3U26GNmCRAVxSUH9Zwajn4P/MzKRPrsCCGEqLjGj4cf1cUpb/RQi1xdfO9xQeWx8KmFnLx+ku61uj/wvcYEj8HK1Ap3G3e6+HXRjeyqU6MZHISzTgqJ+7eTlDeg67/D3Ks6SXaEEEJUWNrDhwj3hOudWrCv3nXQXsKlfjNDh1UmvOy8yqwpycHCgQntJtxRXidvvaszznDp8BaoDy455libVc5+T6UlyY4QQoiKSVF4p9oJPn4K4ABo1eL7zQkjCuSva5VoCeHRB6E+1NA4GDYoA6gwfXY+/PBDNBoNo0eP1pVlZGQQFhaGs7MzNjY29O3bl7i4OL3roqKiCA0NxcrKCjc3N8aPH09OTs5Djl4IIUSZi4tjq5c667C/Y2361O/D3JC59Knfx8CBVR6Wppb45NgAsNH9NgA1rB+djsn5KkTNzoEDB/j2228JCgrSKx8zZgyrV6/mzz//xN7enpEjR9KnTx927doFQG5uLqGhoXh4eLB7925iYmIYNGgQpqamzJw50xCvIoQQooxoz5zmpKu6/X/9VlLftb5hA6qk6ph6EKWcZ1NNdd+3bunn9KmsDF6zk5qayoABA/j+++9xdHTUlSclJbFgwQLmzJlDly5daNasGQsXLmT37t3s3bsXgA0bNnDy5EkWL15M48aN6dmzJzNmzGD+/PlkZWUZ6pWEEEKUgSun93PbDEy1Gl1zjCi5OvbqyKv8CQxb1O5owGgMw+DJTlhYGKGhoXTr1k2vPDw8nOzsbL3yevXq4ePjw549ewDYs2cPgYGBuLu7684JCQkhOTmZEydO3PWZmZmZJCcn630JIYSoWE5ePgBAHa0jpsamBo6m8qrj0UC37aix4ql6TxkwGsMwaLKzdOlSDh06xKxZs+44Fhsbi5mZGQ4ODnrl7u7uxMbG6s4pnOjkH88/djezZs3C3t5e9+Xt7f2AbyKEEKKsnbipLnEQYCkdkh9EHf/Wuu0XAwfcdUHRqsxgyc6VK1d48803+fXXX7GweLg/+IkTJ5KUlKT7unLlyv0vEkII8VCdzLwKQAO3Bvc5U9xLnRrNddtDgsMMGInhGKyDcnh4OPHx8TRt2lRXlpuby/bt2/nyyy9Zv349WVlZJCYm6tXuxMXF4eHhAYCHhwf79+/Xu2/+aK38c4pibm6Oubl5Gb6NEEKIMqXVctIsCYCAmo9eh9qyVNOxJm+2ehMrUysaeTQydDgGYbBkp2vXrkREROiVvfzyy9SrV48JEybg7e2Nqakpmzdvpm/fvgCcOXOGqKgogoODAQgODuaDDz4gPj4eNzc3ADZu3IidnR0BAfqLrAkhhKg8lCtXOOmiANCg/qPXobYsaTQa5j4219BhGJTBkh1bW1saNmyoV2ZtbY2zs7OufMiQIYwdOxYnJyfs7Ox44403CA4OpnVrtf2xR48eBAQEMHDgQGbPnk1sbCyTJk0iLCxMam6EEKISu3J4KynmYKKF2m71DB2OqOQqxDw7d/PZZ59hZGRE3759yczMJCQkhK+++kp33NjYmFWrVjFixAiCg4OxtrZm8ODBTJ8+3YBRCyGEeFDHTm0FoF6WPWbGZoYNRlR6GkVRFEMHYWjJycnY29uTlJSEnZ2docMRQohH3vujGjPZ+Sgvahrxy5Qjhg5HVFDF/fw2+Dw7QgghxH8dzbgEQONqTQwbiKgSJNkRQghRseTkcMRSney1Uf3OBg5GVAWS7AghhKhQUk4d5byT2sOiUaMQA0cjqgJJdoQQQlQoxw6vBcAr3RRXW/f7nC3E/UmyI4QQokI5GrkbgEZIoiPKhiQ7QgghKozcyPP8dW0jAI2cZZkIUTYk2RFCCPFwZWcXXZ6ezv/ea8sW7xwsc40Y2P+jhxuXqLJKlexERkYyadIk+vXrR3x8PABr167lxIkTZRqcEEKIquXc0vm8MMCcE999oCu78ev33Pj2M5aOC+Ejf/UzZWG3L6jv9Wiu4yTKXomTnW3bthEYGMi+ffv4+++/SU1NBeDo0aO89957ZR6gEEKIquN/W6fwewOFhjGTICeHyN+/xj9iKB7RYxnsvAOACdVf4PkOrxs4UlGVlDjZeeedd3j//ffZuHEjZmYFU3h36dKFvXv3lmlwQgghqpZkG1Pd9oHPJzBwyygSLSHXCLJM4DHjenzw8mIDRiiqohKvjRUREcGSJUvuKHdzc+PGjRtlEpQQQoiqyapQd50e8XNIrAZ22Ub8OeD/uBx/jn7Br2FsZGy4AEWVVOJkx8HBgZiYGPz8/PTKDx8+jJeXV5kFJoQQoupJIF23nWgJploNCzrNoUf9x6G+AQMTVVqJm7FeeOEFJkyYQGxsLBqNBq1Wy65du3jrrbcYNGhQecQohBCiikjQZALQ06Q+7/oO5MLYyzzT7U0DRyWquhLX7MycOZOwsDC8vb3Jzc0lICCA3Nxc+vfvz6RJk8ojRiGEEFVEgonajvVe23dp1elFA0cjHhUlTnbMzMz4/vvvmTJlChEREaSmptKkSRP8/f3LIz4hhBBVhaKQYKYFwNHF28DBiEdJiZOdfN7e3nh7y19WIcSjTUlL4/s32uDrXJOQD/8CI5mr9W5yUpJIMVe3Hd18DBuMeKSU+F9l3759+eijO2e1nD17Ns8++2yZBCWEEJXF5h8nMcznKL0sl/P3uF6gKIYOqcJKjLus23ZwkgEt4uEpcbKzfft2evXqdUd5z5492b59e5kEJYQQlYKi8E3EQgC0RtDPZj2nln9n4KAqroTrUQDYZGkwNTG7z9lClJ0SJzupqal6kwnmMzU1JTk5uUyCEkKIyiBm+xpWeCQCUCvdkiwT+Pf8RsMGVYEl3LgKgGO2zKMjHq4SJzuBgYH8/vvvd5QvXbqUgICAMglKCCEqg4X/TCfXCNpmuNFBUfswpqQnGTiqiishMQYAR63U6oiHq8QdlCdPnkyfPn2IjIykS5cuAGzevJnffvuNP//8s8wDFEKIimpv1gUAXqj+GOeijwOQkpliyJAqtITkOAAcFQsDRyIeNSVOdp544glWrFjBzJkz+euvv7C0tCQoKIhNmzbRsWPH8ohRCCEqpFjUhZB9Xf2Ju3kZFEjOlmTnbhJSrgPgaGRt4EjEo6ZUQ89DQ0MJDQ0t61iEEKJSiTVVZwP28KqD7aUDkA4pOWkGjqriSkxLABNwNLExdCjiESMTQgghRCkoaWnEWqvDzD18ArCzdAAgOVeSnbtJyEgAwNHc3sCRiEdNsWp2nJycOHv2LC4uLjg6OqLRaO567q1bt8osOCGEqKgSLp8mf1CRm0dtbK0d4RakKJmGDawCS8hr4nO0dDJwJOJRU6xk57PPPsPW1haAuXPnlmc8QghRKcRePgGAU6YR5qYW2Nm6AJCskWTnbhK0ah8nR2tnA0ciHjXFSnYGDx4MQE5ODhqNhpCQENzd3cs1MCGEqMhiY88D4JGtjiyytXMFIMUox2AxVXQJSjoAjrZuBo5EPGpK1GfHxMSE4cOHk5GRUV7xCCFEpRB7/SIAHqidbe0c1F8Ak01yDRZTRZeQV+vl6OBh4EjEo6bEHZRbtmzJ4cOHyyMWIYSoNGKT1NmAPUwdAbDNS3ZSTBXQag0WV0WWYKLWejnKuljiISvx0PPXX3+dcePGcfXqVZo1a4a1tf58CUFBQWUWnBBCVFSxqXHgCB5WavOVnYsnACnmoCQno3FwMGB05eiff2D3bpg+HUxN73/+tm2QnAy9epFgriaBji7VyzlIIfSVONl54YUXABg1apSuTKPRoCgKGo2G3FypwhVCVH2x2erIUw87tZbCNq+DsqKB27disamKyY5Wy88f9WebcypfLq2D5cCX731+RgZ/jgnhumkWQ523k5Q3cbKjV+3yj1WIQkqc7Fy8eLE84hBCiEolVqsOo/Zw8QXA0tQKYy3kGkHyzWhsatYzZHjlIjf8AG+2SyXREjpu/pxB90l20g/u5cXQTLJMoOGqr8EczHM1uNjKABfxcJU42fH19S2POIQQolKJNVEHanh4qLUUGo0G22wjEs21JCfG4WnI4MrSiROweDEkJHDEKZNES7X476yjDEpIAEfHu156dO8KsvI+ZTZc2Aj1wTfLCiONzGcrHq5SLRdx5swZvvjiC06dOgVA/fr1eeONN6hbt26ZBieEEBVSRgaxlmqTvUf1+rpiu1xjEtGSkhhvqMjK1ldfkTI2jGX14ZYlZBkD3dRD62sq3F62FOtXR9z18vDIHZA38Gqjrboulp+JSzkHLcSdSpxeL1u2jIYNGxIeHk6jRo1o1KgRhw4domHDhixbtqw8YhRCiAolO/oKN6zUbQ/POrpyW63aYTc5b8HLSu2HH9jxURg+o+Hl3jAuBKYXWus5wxTWbZhf9LW3bsGVK4Qnn9EVHcyr6vKz8S63kIW4mxInO2+//TYTJ05kz549zJkzhzlz5rB7927effdd3n777fKIUQghKpTrcRdRNGCsBWfrgpoKO8wBSEmt/MvmhH87ldABkGgJbtbqJIDpeYOvulRrA8Bf2hNw7pz+hYoCbdqAjw8H7W/rirV5nzZ+bnUQ4mErcbITExPDoEGD7ih/8cUXiYmJKZOghBCiIrt1U51jxzHLWK//ia2ROtwo+XYlT3aio3kz6Bop5tCpensujLpAY5eGADga2/B+z08A+L96kPLdl/rXnj7NmBpnaP8yRBTRD7lmjcblHLwQdypxstOpUyd27NhxR/nOnTtp3759mQQlhBAVWXKS2ifHTqvf7dHOWG3bSklPeugxlUhEBIweDd99B/F39i/K2LaZ/Xnz/v3w9EKszaz58olvsDSxZEDTl2hdvTV1zD1JN4VlexZAoVn1EzavZm4w7LzLWBa/2i3L4YWEuLcSd1B+8sknmTBhAuHh4bRu3RqAvXv38ueffzJt2jRWrlypd64QQlQ1yclqnxw7xUyv3NZEnWQ1ObOCJzuTJqGsXIkG4Msv4ehRmDsXmjSBTp04vO//yHYEV60lNR1rAtDWpy03376JhYkFGo2GQa2HM2nbFH6ufZuXPvsMJk4EYE/4CqhR8KgWHs0IjwlHq1H3/Vz9H+KLCqEq1QzKAF999RVfffVVkccAmWBQCFFlpeQ1U9lhoVduZ2YLuZCSlWqIsIptfs5uJrwLQXEwdk8Ez3zzDYwdC87OEBPD3qjd4Ait7Rug0Wh011maWuq2BzYZzKRtU9jiB+e/nE7tAQOgenV23TgENaCOlTeuzj5M7jCZYYv6ctn0NnY5Jjha3H2ouhDlpcTNWFqttlhfkugIIaqq/D45dsaWeuW25nbq8Zzbd1xTYWRl8bv7DW6bwR5veO0JSHx/Et0GwfsNbsLy5ewzVvtftq7f/a638bH3oWftngBMDs6AKVPgxAl2uagrm4/v+C47X9lJSO0QaluoQ7H8jJz0kichHhaDzuz09ddfExQUhJ2dHXZ2dgQHB7N27Vrd8U6dOqHRaPS+hg8frnePqKgoQkNDsbKyws3NjfHjx5OTk/OwX0UI8QhJzlCbqexM9NcGtLNyACAlN638g7h5E955B65eLdl1kZFEFqpcSbSE9xveYnNNeL8D3B4zkr15S1e1qtv1nrea2XUmGjQsDYQDO38n+4/fdH192voVjFOvFdgBAL860l9HGIZBk53q1avz4YcfEh4ezsGDB+nSpQtPPfUUJ06c0J3z2muvERMTo/uaPXu27lhubi6hoaFkZWWxe/dufvrpJxYtWsSUKVMM8TpCiEdEcmYyALZmNnrltlZqFpGsZNxxTZn74AP46COYObNEl6WfOka0WgFFUxu1/8xXLdT9TBP4xfM6lx1Ag4YWXi3uea/GHo0ZGDQQgJnNMzi86EPSTcHJyJq6LgWTzHbw6wxA+5qdSxSrEGXFoMnOE088Qa9evfD396dOnTp88MEH2NjYsHfvXt05VlZWeHh46L7s7Ox0xzZs2MDJkydZvHgxjRs3pmfPnsyYMYP58+eTlZV11+dmZmaSnJys9yWEEMWV3yfHztxer9zOxhmAZCWz3GNI2LuFKZ0h+vzhEl134ew+AOxyTendpB9QMH8OwLt5lTmB7oHYmdv99/I7jA0eC8D62rCirgJAG7+OekPyBwQNIHpsNGNajylRrEKUlQqzQElubi5Lly7l9u3bBAcH68p//fVXXFxcaNiwIRMnTiQtraB6eM+ePQQGBuLuXjCZQ0hICMnJyXq1Q/81a9Ys7O3tdV/e3jKjpxCi+JLzmqnsLPWTHVs39f+SFCUDyrM5PTWVWbZHmdERZtkfu/t52dmwb5860V+eyKsRANQydi6ypiUhrxvS+DbjixVKkHsQNay8SDeFj9W5Bnk28Pk7zqtmW0366wiDKdNkJz09vcTXREREYGNjg7m5OcOHD2f58uUEBAQA0L9/fxYvXsyWLVuYOHEiv/zyCy+++KLu2tjYWL1EB9Dtx8bG3vWZEydOJCkpSfd15cqVEscthHh0JWvV/+vsrJ30yu28a6nHzRS4eLH8Ati3jz1eagJzxjINUlKKPm/MGGjdGj7/XFcUees8ALVsfGnl1QpTI7Vap0uNLrqanKbVmtI/sH+xQtFoNDwd9BwAOcZgZ2rLMwHPlOq1hCgvJU52Ro0aVWT57du36dWrV4kDqFu3LkeOHGHfvn2MGDGCwYMHc/LkSQCGDh1KSEgIgYGBDBgwgJ9//pnly5cTGRlZ4ucUZm5urusUnf8lhBDFld8nx9baWa/c1VZd9TLGBpS8/8d0jh+HhIQyeX7Ozm0cqqZuRzqirky+e7dak5MvPh5++EHdnjMH8kbIRmaqvwjW8qiHpamlrl9OL/9evNL4FSxNLPn8sc9LtDJ573q9ddv9gwZgZWpV6ncTojyUONlZvXo17733nl7Z7du3eeyxx0o1CsrMzIzatWvTrFkzZs2aRaNGjZg3b16R57Zq1QqA8+fV30w8PDyIi4vTOyd/38PDo8SxCCFEcaRo1KTCzs5Vr7ymY000CiRbwPWTBwoO7NgBQUEQGqrXpFQqWi0nD64jLW8+w8sOkDP0VWjbFrp0KZgR+ZtvWOWbSeAICM+Ogn/+gVu3iLRUE7Vafs0AmBsyl7GtxzK8+XDmhMwhYUICbX3aliiktt5t8bL1QoOG15q99mDvJ0Q5KHGys2HDBr7//nvmzp0LQEpKCt27d0ej0bBu3boHDkir1ZKZWXTnviNHjgBQrZr6K01wcDARERHEF5rufOPGjdjZ2emawoQQoqwlG6u/2Nk56DejW5hY4KNxAODcxYMFBz7/XE1y9uyBnTtL/+Ddu8HTk/3RBYlUrhFcuZLXR3HnTrXZassWlC+/YHx3OO4OMzrmxbB2LRfyhp3XqtYAgBZeLfg05FOszazRaDSYm5iXOCxjI2M2D9rMtpe20bRa09K/nxDlpMQzKNeqVYt169bRuXNnjIyM+O233zA3N2f16tVYW1vf/waFTJw4kZ49e+Lj40NKSgpLlixh69atrF+/nsjISJYsWUKvXr1wdnbm2LFjjBkzhg4dOhAUFARAjx49CAgIYODAgcyePZvY2FgmTZpEWFgY5uYl/wcrhBD3lZGh9skB7BzvrEH2t/HhcmoiZ6+fpi1ATAxZK5czpRt0vgghb74JmZnw3HPwn1rye8rKgpdfhrg4DrQxAQpq0jfUgkPVjXj9mieNDl6ELl3Y6QOn8yqeVtWBuH+24BJ5jouD1bJajrVK9/53Udelrt5wcyEqklJ1UA4KCmLVqlW8++67WFlZsXbt2hInOgDx8fEMGjSIunXr0rVrVw4cOMD69evp3r07ZmZmbNq0iR49elCvXj3GjRtH3759+eeff3TXGxsbs2rVKoyNjQkODubFF19k0KBBTJ8+vTSvJYQQ95eURHLe71K2jncu613HQ10d/Fz6NdBqYcECfmqYy0ft4MU+kHXsMJw8CVOnwqpVxX/u3Llw9izn/Z3Z1skPADON+vvq2BD4romWDk8nsb2DugLnd90cdJfmGsHiILiUcpVsYzA1MqW6XfUSv7oQlVWxanaaNGlS5JBBc3NzoqOjadu2oH330KFDxX74ggUL7nrM29ubbdu23fcevr6+rFmzptjPFEKIB6EkJpKSl+zYWTrccdzfrymcX8JZ+xx1RNZ337E4b+6aG9awsp6GvtbN0ew/AK+8onYudnW94z56tFr48EOWNoQBz9xCm3ATgFDPTiy/tknXfyc5O4XHeuQw+/nB/HlrKeTCK41f4ccjP7KgmQYNao1UC68WGBsZl8WPQ4hKoVjJTu/evcs5DCGEqBwyb8WTnZcnFDXpnr9LPQDOOQHTp3Mp5QrbaxQcf3doLV7PvMQAf2c++/U6TJ8OX3xx54Nyc8E470HnzpGeksBbIaBFoXX11gxrNozE1Bssv7YJAFtTG4J92rAhcgNvXP8JgJ61ezInZA5/nPyDUy6pat8doF/DfmXxoxCi0ihWsvPf0VdCCPGoSr4Vo9u2+c9yEQD+zuoSDOedQPvtz/zaTi0PcA3g5PWTnEtQR5PO9YfHakHIN9/AyJFQt1B/l61bISQEpk1T178KD+erFnDNFrztvNkyeAsWJhasPLNSd0n32j34uffPdPulG3uv7qVTjU78+eyfWJtZM6L5CD7e/TGJFmCkMeLZgGfL/gcjRAVW4j47Bw4cYN++fXeU79u3j4MHDxZxhRBCVB3Jier0FrY5xkXORePn4Iexxpg0M4i2hV8aqeXj24znuQbq5Hv5I5ZGPGdFmiZHrd0p7L331A7JP6k1NEr4QT7Jm514aqepWJhYAPqdjEP9Q7E2s2bTwE2s7r+atQPWYm2m9qUc03oM5sZq21u3mt1wt7mzr5EQVVmJk52wsLAiZxy+du0aYWFhZRKUEEJUVCnJNwCw1RZdMW5qbIqfo9qBeElra864gKWJJX3q92Hx04tJmJDA1sFbqW5XnYvmaXzbDFi3Tu2XA3DwIGzfrm6fPg1xcVw9sYdYWzDGiAGBA3TP8nP0w9LEEhMjEx6r/RgA1mbW9PLvpUuIQF2qYXTr0QC83vz1MvxpCFE5lDjZOXnyJE2b3jmPQpMmTXQzHwshRFWVnHIdADvuPr1FY4/GAExur04++FS9p7Azt8PU2BQHCwdszW2Z2nEqALPbaUhPvgUR6ppVfPwxNy1hRCgc8AS2beN4zFEA6trW0JsHx8rUitX9V7Om/xo8bT3vGfesrrOIHRfLU/WeKsVbC1G5lTjZMTc3v2PWYoCYmBhMTEo8bY8QQlQqyam3ALDTWNz1nKkdp2JmbEZWbhYAA4MG3nHOwEYD8bX3JdZG4ftmwJYt8PPP8McffNABvmkBE7sBP/5IhK26Fldg9WZ33KezX2e61+p+37g1Go00X4lHVomTnR49eugW0syXmJjIu+++S/fu9/8HJ4QQlVlyurq+lZ3x3dd/auDWgCkdpgDgZu1Gj1o97jjHzNiMt9u+DcDPjYAFC2DoUDKN4efW6tLj+70gd8N6ItzUawI9GpXhmwjx6ChxVcwnn3xChw4d8PX1pUmTJoC6jIO7uzu//PJLmQcohBAGpyiQlAQODqQkxoONOtT7Xia0m4CVqRXNPZtjYlT0f7Wh/qGEEcZRd0g7cxyrbFg+qCk3Nep8ZSnmcNoFIvIqZALdA8v0tYR4VJS4ZsfLy4tjx44xe/ZsAgICaNasGfPmzSMiIgJvb+/yiFEIIQzr22/B0RF++onki6cBsHP3ueclJkYmjAkeQ3vf9nc9x8feh2o21cgxhoOeoLRswfz2+n2BdjR14pSHmiwFukmyI0RplKqTjbW1NUOHDi3rWIQQomJ66y31+0svkdxF3bTzqvnAt9VoNAR7B/P3qb/Z0yuQiGf7snP7O5gbm9M3oC9LIpaw+Ln6ZF3ZhY2ZDb4Ovg/8TCEeRaVaGysyMpI33niDbt260a1bN0aNGkVkZGRZxyaEEBVDQIBuM39dLDsL+zK5dXD1YAC+r5fGW7unAjC7+2zdxH+7ruwCoKFbwyLn9RFC3F+J/+WsX7+egIAA9u/fT1BQEEFBQezbt48GDRqwcePG8ohRCCEMy7hgHan4vDWP7c3LJtlp463OFhiZEElGTgaP1X6MN1q+QSuvVnrn9ah5ZydnIUTxlLgZ65133mHMmDF8+OGHd5RPmDBBRmQJIaqc7SbXmDAEvlkFe/IWC29SrUmZ3Dt/NmUAa1NrFj+9GI1GQzXbajT2aMzx+ONM7jCZie0mlsnzhHgUaRRFUUpygYWFBREREfj7++uVnz17lqCgIDIyMso0wIchOTkZe3t7kpKSsLO7c2E/IcSjreZYYy7aa3X7JkYmJE5I1C3H8KAGLh/I+vPr2ThwI40KDS+/nXWb29m3cbN2K5PnCFHVFPfzu8Q1O66urhw5cuSOZOfIkSO4uck/SCFEFaMo5CpavaLmns3LLNEB+OXpX8jR5twxRN3azLpMnyPEo6rEyc5rr73G0KFDuXDhAm3aqG3Nu3bt4qOPPmLs2LFlHqAQQhhUaiq1EiDKoaCog0+HMn/M3ebiEUI8uBL/65o8eTK2trZ8+umnTJyotiF7enoydepURo0aVeYBCiGEQd26RdJ/lsHqWKOjYWIRQpRKifvsFJaSkgKAra1tmQVkCNJnRwhxV4cPU3tRUyKd1F0jjRG33r6FfRkNPRdClF5xP79LPPS8S5cuJCYmAmqSk5/oJCcn06VLl9JFK4QQFdWtWyTmrfk5MGgg3z3+nSQ6QlQyJW7G2rp1K1lZWXeUZ2RksGPHjjIJSgghKgqlULLzYbcP8bT1NGxAQogSK3ayc+zYMd32yZMniY2N1e3n5uaybt06vLy8yjY6IYQwsNs3Y8jNqwN3sHAwaCxCiNIpdrLTuHFjNBoNGo2myOYqS0tLvvjiizINTgghDC0xIQYAU8UISxNLA0cjhCiNYic7Fy9eRFEUatasyf79+3F1ddUdMzMzw83NDeNCU6oLIURVkJgUB5bgoJij0WgMHY4QohSKnez4+qqr7Wq12vucKYQQVUdiynWwBHsjqdURorIq8Wisn376idWrV+v23377bRwcHGjTpg2XL18u0+CEEMLQkm7fBMDBxMbAkQghSqvEyc7MmTOxtFR/w9mzZw9ffvkls2fPxsXFhTFjxpR5gEIIUWZOnYIiRpPeS2J6IgAOZjIHlxCVVYmTnStXrlC7dm0AVqxYwTPPPMPQoUOZNWuWDD0XQlRcf/0FAQEwfnyJLkvMSgbAwcKxPKISQjwEJU52bGxsuHlTrdbdsGED3bt3B9TV0NPT08s2OiGEKCu//KJ+X7wYcnLU7dWrYcmSe16WmJMKgIO1U3lGJ4QoRyWeVLB79+68+uqrNGnShLNnz9KrVy8ATpw4QY0aNco6PiGEeHDp6bBxo7p96xbs2AHu7vD442pZ+/Zw4AAkJ8NLL6llWi0cP05itrosjoON6533FUJUCiWu2Zk/fz7BwcFcv36dZcuW4ezsDEB4eDj9+vUr8wCFEOKB/fuvmvDkW7ECJkwo2D98GPr2hZdfhv/7P7Vs9Gho1IhEM3UEqr2920MLVwhRtkpcs+Pg4MCXX355R/m0adPKJCAhhCgzMTHw6qtw8aK6X7s2nD8PP/wAaWkF5xUaYcqbb0JiIuRNkpqUt1SEg4PHw4lZCFHmSlyzI4QQlcaXX8KaNeooLIDZs8HWVj/RAVi1qmD78uWCpqxRo0js0BIAB0vpoCxEZVXimh0hhKg0/vlH/d6tG9ca+fF6xkJG/zKdzpFaaNMGDh2CsDCIjlbPq1EDjIzUmp2mTeGjj0j8pQMg62IJUZlJsiOEqJouX4aICDV5WbqUzw/NZuXu74mvfp09Y/eo56Sk6F8TFgZvvaVXlJiRCEiyI0RlJs1YQoiqKb9Wp21bcHZmw4UNAOy7uo+baTdRFIWX4r7liX6Qm7/kVf36d9xGkh0hKr9S1ezk5OSwdetWIiMj6d+/P7a2tkRHR2NnZ4eNjUypLoQwoIQEGDECNm9W9594grjUOI7EHgFAQWFD5AZszGz4KXIZ1IXTLvBjE7hxawELtCGYGJmQlJHEHyf+4HradQDsze0N9EJCiAdV4mTn8uXLPPbYY0RFRZGZmUn37t2xtbXlo48+IjMzk2+++aY84hRCiOJZsQJ+/13dNjODvn3ZdGGT3ilrzq/hRPwJ3X64J8xpA1xYTouD3xDWIoz+f/dnzbk1unOkZkeIyqvEyc6bb75J8+bNOXr0qG6OHYCnn36a1157rUyDE0KIErt6Vf3++ONc+vh/9NsygKvJalnr6q3Ze3Uvv0X8Rq6Sq7tkQ62Cy6dvm052bjZrzq3BWGMMQA2HGtiYSa21EJVViZOdHTt2sHv3bszMzPTKa9SowbVr18osMCGEKJX8/4eaNOHbq//H3qt7dYemdpxK/7/7cyv9FgCmRqZka7P1kp3radcZu2EsAO91fI+wlmGYGpmi0WgQQlROJU52tFotubm5d5RfvXoVW1vbMglKCCFKLX8YuZcX6yO/BaCXfy+aVWtG91rd2fDiBo7GHaWDbwc2Rm7k9TWvc91avcTfyR9jI2Nupd+iuWdzJrSbgJmx2V0eJISoLEo8GqtHjx7MnTtXt6/RaEhNTeW9997TrZNVXF9//TVBQUHY2dlhZ2dHcHAwa9eu1R3PyMggLCwMZ2dnbGxs6Nu3L3FxcXr3iIqKIjQ0FCsrK9zc3Bg/fjw5+Yv8CSEePXk1O3FuVhyOPQzAj0/+yPTO0zHSGNHMsxmvNHmF2k61qelYU+/SIU2GcCrsFHFvxbG6/2pJdISoIkqc7Hz66afs2rWLgIAAMjIy6N+/v64J66OPPirRvapXr86HH35IeHg4Bw8epEuXLjz11FOcOKF2HBwzZgz//PMPf/75J9u2bSM6Opo+ffrors/NzSU0NJSsrCx2797NTz/9xKJFi5gyZUpJX0sIUVXk1exsMr4MQGOPxrjbuBd5qp+jn95+Hec65RubEMIgNIqiKCW9KCcnh6VLl3Ls2DFSU1Np2rQpAwYMwNLS8oEDcnJy4uOPP+aZZ57B1dWVJUuW8MwzzwBw+vRp6tevz549e2jdujVr167l8ccfJzo6Gnd39T+zb775hgkTJnD9+vU7+hXdTXJyMvb29iQlJWFnZ/fA7yCEMJDsbLTmZqyrDR+91ZrtMXuZ0HYCH3b7sMjTM3MysfzAEgX1v8GIERE0dGv4MCMWQjyA4n5+l2qeHRMTE1588cVSB1eU3Nxc/vzzT27fvk1wcDDh4eFkZ2fTrVs33Tn16tXDx8dHl+zs2bOHwMBAXaIDEBISwogRIzhx4gRNmjQp8lmZmZlkZmbq9pOTk8v0XYQQBhIby/paEDoAiFE7Jves3fOup5ubmONl56UbrVXLsdZdzxVCVF7FSnZWrlxZ7Bs++eSTJQogIiKC4OBgMjIysLGxYfny5QQEBHDkyBHMzMxwcHDQO9/d3Z3Y2FgAYmNj9RKd/OP5x+5m1qxZskq7EFVRdDSnXNVNfyd/RrYcSQffDve8pKZjTa4mX8XbzhtL0wevnRZCVDzFSnZ69+5drJtpNJoiR2rdS926dTly5AhJSUn89ddfDB48mG3btpXoHiU1ceJExo4dq9tPTk7G29u7XJ8phHgIrl0jOm9Q6ON1HmdUq1H3vcTPwY/tl7fj7+xfzsEJIQylWMmOVqsttwDMzMyoXbs2AM2aNePAgQPMmzeP559/nqysLBITE/Vqd+Li4vDw8ADAw8OD/fv3690vf7RW/jlFMTc3x9zcvIzfRAhhcNHRxOTN/edp61msSxq4NgAgyC2ovKISQhhYhVsIVKvVkpmZSbNmzTA1NWVz/vo2wJkzZ4iKiiI4OBiA4OBgIiIiiI+P152zceNG7OzsCAgIeOixCyEM7No1YvJqdqrZVCvWJSNajGDBkwv4X4f/lWNgQghDKnEH5c8//7zIco1Gg4WFBbVr16ZDhw4YGxvf914TJ06kZ8+e+Pj4kJKSwpIlS9i6dSvr16/H3t6eIUOGMHbsWJycnLCzs+ONN94gODiY1q1bA+qcPwEBAQwcOJDZs2cTGxvLpEmTCAsLk5obIR5F0dEFyY5t8ZIdGzMbXmnySjkGJYQwtBInO5999hnXr18nLS0NR0dHABISErCyssLGxob4+Hhq1qzJli1b7tsPJj4+nkGDBhETE4O9vT1BQUGsX7+e7t27655lZGRE3759yczMJCQkhK+++kp3vbGxMatWrWLEiBEEBwdjbW3N4MGDmT59eklfSwhRFVy7RnQLdbO4zVhCiKqvxPPs/Pbbb3z33Xf88MMP1KqlDtM8f/48w4YNY+jQobRt25YXXngBDw8P/vrrr3IJuqzJPDtCVA1pAf5YP38egMQJidhb2Bs4IiFEeSru53eJk51atWqxbNkyGjdurFd++PBh+vbty4ULF9i9ezd9+/YlJiamVME/bJLsCFEFZGYSWd2K2iO1WJlYkvrubVm8U4gqrrif3yXuoBwTE1Pk2lM5OTm6uW08PT1JSUkp6a2FEKL0zp0j2kodOVrN1lMSHSGETomTnc6dOzNs2DAOHz6sKzt8+DAjRoygS5cugDpRoJ+f391uIYQQZe/EiRJ3ThZCPBpKnOwsWLAAJycnmjVrppuvpnnz5jg5ObFgwQIAbGxs+PTTT8s8WCGEuKuTJ0s8x44Q4tFQ4tFYHh4ebNy4kdOnT3P27FlAnQW5bt26unM6d+5cdhEKIURxnDypmz25uHPsCCEeDaVaCBTURTnzExxpGxdCGNyJE8TUUzcl2RFCFFaqGZR//vlnAgMDsbS0xNLSkqCgIH755Zeyjk0IIYonKwvOnZNmLCFEkUpcszNnzhwmT57MyJEjadu2LQA7d+5k+PDh3LhxgzFjxpR5kEIIcU/nz0NODtccjACtdFAWQugpcbLzxRdf8PXXXzNo0CBd2ZNPPkmDBg2YOnWqJDtCVAXjxsHff8OWLVCjhqGjuTdFgTlzyDaC847qtGH+TrKCuRCiQKnm2WnTps0d5W3atKk0kwgKIe5Bq4UFC+DSJfjoowe/3/798N57EBf3YPf591947TVITVX3FQV+/hmeew4WLCDSWUO2kYK1qTXe9vdeqkYI8WgpcbJTu3Zt/vjjjzvKf//9d/z95bcpISq9yEhISlK3Fy6E9eth+XJITi75vUaOhFatYPp0GDr0weKaOBF++AHy+weuWAGDB0PesjQn334ZgPqu9THSlKo7ohCiiipxM9a0adN4/vnn2b59u67Pzq5du9i8eXORSZAQopI5eLBgOzMTHntM3TY3h6++gleKuUL4hQswfz7kj9ZcuRKOHoVGjUoek6LAqVPq9v79MGKEWvsE8MQTMG4cJzU7YAsEuAaU/P5CiCqtxL/+9O3bl3379uHi4sKKFStYsWIFLi4u7N+/n6effro8YhRCPEz5yU5AXtJgbg61aqmJz5tvwrVr8PvvcONGwTUHDsDGjfr32bRJ/d6uHTz/vLr9/vuliykmBvKXoNm/H6KjYe1adf+TT6BjR05ePwlAA9cGpXuGEKLKKtU8O82aNWPx4sVlHYsQoiI4cED9PmECNGsGHh7g5KRuHz4MgYGQkKDW0Bw4AJcvQ4cOkJGhHs9fJDg/2enWDfr2haVLYdkyNUlycSlZTPm1Ovnb8+erfYvatYM6dQA4cf0EIDU7Qog7lSrZyc3NZcWKFZzK+w+oQYMGPPnkkxgbG5dpcEKIhyw3Fw4dUrebNy+o3QGYNg2efFJNdEBtkpo+HXbuVBMdUJOQ779XE5F//1XLunWDBg2gYUM4flwd4fXssyWL6/Tpgm1FUWtzAF5W++nkaHM4c+MMIMmOEOJOJU52zp8/T2hoKFevXtXNoDxr1iy8vb1ZvXo1tWrVKvMghRAPyYkTcPs2WFtDoSVgAHj8cejSBcLD4aWXYN68gmYpY2M1Ufr1V7Czg4sX4eZNsLWFFi3Uc7p2VZOdzZtLnuwUrtkBdRLB6tWhf38ALiZcJDM3E0sTS3ztfUv+3kKIKq3EfXZGjRpFzZo1uXLlCocOHeLQoUNERUXh5+fHqFGjyiNGIcTDkJAAAwcCMOcZLzov7kb87fiC4xqN2i8nNhY++0xNeBwdwc0Nvv1Wbd5KT4c5c9TRWwCdOoGpqbrdtav6Pb/GpyTya3YK1zRNmQIWFgAsOrIIgHou9TA2khpmIYS+EtfsbNu2jb179+Lk5KQrc3Z25sMPP9SNzhJCVEIjRsCxYyge7sysd52bl87y8a6P+bjHxwXnGBnpEgwWLtS/3s5OnfOmRQto00atAZowgchbkWyI3MCAVo9jZ2QE587BlSvgXYK5cE6dIrwarHzJl/9NPImZfz146SXWn1/P6nOr+WL/FwCMbj36wX4GQogqqcTJjrm5OSn5oyIKSU1NxczMrEyCEkI8ZFqtbnTTxUVzubm3HwDfhH9D34C+3M66TVuftliYWNz9Hs8+C4mJatKTN9x8ypYpzPxyJrlKLtsbbue3Fi1g3z61CWzWrIJan3tJToboaMa8DDvS1uKzdCJD2r/J+sv/8tivj+lOm9huIoMaDbrHjYQQj6oSN2M9/vjjDB06lH379qEoCoqisHfvXoYPH86TTz5ZHjEKIcrbqVNqUmFtzX6PXF1xalYqwQuC6fZLN9w+duPXY7/e+z729rpE51LiJWZsn0Guot7v9+O/c+KZjup5n36qNkl9+62aaN3LmjUowDEP9b67TePA3Z2vD34NQAffDvzW9zfe71LKYe1CiCqvxMnO559/Tq1atQgODsbCwgILCwvatm1L7dq1mTdvXnnEKIQob3v3qt9btGB/bDhQsL6UscYYd2t3UrJSGLF6BFeSrrD42GK9/jx7r+5l7bm1erdcf349AG2929K3fl8UFKb6RMJ336lDz8+fh+HD1U7OR4+qHaDd3cHPT+14vHev2tw1ciTRtpBkrq57tffaXqJToll1dhUA34R+wwsNX5BZk4UQd1XiZiwHBwf+7//+j3PnznE6r9Ng/fr1qV27dpkHJ4QoIzdvqp2Kly+H0FCYPVstVxR19FV+stO6NfuubQdgcofJ1Hetj6etJx42HjT7rhlHYo/Q8OuGJGcm08C1AeFDw4lMiKTTok5k5mZy8LWDNPNsBsD6SDXZCakVwtP1n2bZqWUsO/U38W99hVu/furw8WnT4OOP1QTo2rWCeC9dgt9+0+2e7FYTuKBuXz/JZ3s+I1fJpa13W+q71i/XH50QovIr9a9C/v7+PPHEEzzxxBOS6AhR0Y0eDR98ACdPqklPfr+74cPVWpYffgAgu1VzDsWo8+y09GpJc8/meNp6YqQxYmaXmQAkZ6prZJ24foLR60bz0oqXyMzNBGDuvrnqfXKz2XxxMwAhtUNo6NaQRu6NUFDYdGET2Nioi4M2a6Yu7HntGvj7q0nX5s3q/DlGef89BQZyYuRzeq/z2d7PAHit6Wvl8uMSQlQtJa7ZGTt2bJHlGo0GCwsLateuzVNPPaU3WksIYWCHDxds5+TAtm3Qti0sWqTOWZNnj48RGUczsDe3x99Zf2Hfx2o/xsCggey9upcXg17kva3v8U34NwBYmVqRlp3G0uNLScxIJCopiuTMZJwsnWhWTa3pCakVwtG4o6yPXE//wP5q356ZMyEkRE1sfv5ZXTQU1Pl88mufXFw4+Y/+IqK5Si61nWrTL7Bf2f6chBBVUomTncOHD3Po0CFyc3N1kwqePXsWY2Nj6tWrx1dffcW4cePYuXMnAQEyk6kQBpebq/aPATWxWL8eNmxQa1MKJTqn6rnw7L/DAehRq8cdfWA0Gg0/9f5Jt52rzWXl2ZWYGpkyucNkPt79MTuiduj60gB0q9lNN+9Nj1o9mL17NhsiN6AoChqNBrp3V4ewOztD69b6cRdaUiJ/KYi23m3ZdWUXAB92/RAzYxkBKoS4P42iKEpJLpg7dy47duxg4cKF2NnZAZCUlMSrr75Ku3bteO211+jfvz/p6emsX7++XIIua8nJydjb25OUlKR7JyGqjAsX1IU8LSzU2pPnnoN69dT1rnbvVifnS0yko89mtqeeoIlHEzYN2oSTZclqZ3dF7WLg8oF0qtGJll4tORJ7hHHB43Q1RJk5mTh+5Eh6TjpHhx8lyD2oWPdVFAXHjxxJykxi+fPL6besHx19O7J2wFo1YRJCPLKK+/ld4mTHy8uLjRs33lFrc+LECXr06MG1a9c4dOgQPXr04EbhVZErMEl2RJW2Zo3aKTkwUG2+cnEpGO5tZARXr3Lb2Q6HjxzI0eZw/o3z1HIqn2Vfev3ai7Xn19LCswWzu8+mo2/H+yYs15KvUf2z6hhpjLj97m2yc7OxMLHA1LgYc/QIIaq04n5+l7iDclJSEvHx8XeUX79+neRkteOig4MDWYWqx4UQBnRGXSCTunXV5R1atiw4NmwYVKvGvmv7yNHmUN2uOjUda5ZbKOPbjMfK1IoD0Qfo/FNnmnzbhLM3zwJwPP44vxz9hb9O/sWlxEu6a6ZunQpAQ7eGWJhYYGtuK4mOEKJEStxn56mnnuKVV17h008/pUXeAn8HDhzgrbfeonfv3gDs37+fOnXqlGmgQohSykt24up6se7ITzSb/RYN1x2CJ57QdQjeGbUTgHY+7cq1aaizX2fOvXGOGdtm8NPRnzgad5QhK4fQ1KMpn+//XO/cx2o/RjWbaiw8shANGub0mFNucQkhqrYSN2OlpqYyZswYfv75Z3JycgAwMTFh8ODBfPbZZ1hbW3PkyBEAGjduXNbxlgtpxhJVTno6mJmpq5F37sw3qVsZ+bgRuWipbledy6MvY6QxYs25NSw7uYwD0QeIiI9gfq/5vN7i9YcS4qXESwTMDyA9Jx0ADRra+7YnLTuNQzGH0CoFMytP6TCFaZ2nPZS4hBCVR7n12cmXmprKhQvqJF81a9bExsamdJFWAJLsiCrl9m21Q7KRkTp8e9w42jwRz55C624eeO0AjT0a4zvXl+iUaF35seHHCHQPfGihTt06lWnb1CRmcofJTO88HYALCRf49uC35Cq5tPdpz5N1n5TOyEKIO5R7slOVSLIjqpRTp9R1p/IogMM7kGwBdZ3rcubmGd7r+B6NPRrz9O9P612aOyX3oS67cDvrNr1/742rlSu/PP2Lbpi6EEIUR3E/v0vcZ0cIUcGlpRVs29pyVZNCsgWYGJkwNngsw1YNY9XZVey7tg+AvvX7cubmGZ6p/8xDX1/K2syajQM3PtRnCiEePZLsCFHVpKt9YPD3h4gIjv/fJ3BqEnWc6/BU3acYvmo44THhaFCbhT7s9iG1nWTJFyFE1SXLBAtR1eTX7FhZgbk5J6qbA+rQbXcbd1pVV0dgKSi80vgVSXSEEFWe1OwIUdUUTnZQ568BaOjaEIDvn/iev07+RY9aPQiuHmyQEIUQ4mGSZEeIqiYtjT8DwKz6bZ5QtLp1pRq4NQDUGp6Gbg0NGaEQQjxUkuwIUcXEpETz3HMAxwj8pjER8REAkuAIIR5Z0mdHiCrmelrBmnT5iY65sTm1HMtnvSshhKjopGZHiComNSNvjbpcM6b0/JA159fQo2YPmcNGCPHIMmjNzqxZs2jRogW2tra4ubnRu3dvzuQvWpinU6dOaDQava/hw4frnRMVFUVoaChWVla4ubkxfvx43VIWQjxqbuclOz5aW8YEj2HjwI2MbzvewFEJIYThGLRmZ9u2bYSFhdGiRQtycnJ499136dGjBydPnsTa2lp33muvvcb06dN1+1Z5o0wAcnNzCQ0NxcPDg927dxMTE8OgQYMwNTVl5syZD/V9hKgIbmemgiXYGJkbOhQhhKgQDJrsrFu3Tm9/0aJFuLm5ER4eTocOHXTlVlZWeHh4FHmPDRs2cPLkSTZt2oS7uzuNGzdmxowZTJgwgalTp2JmZlau7yBERZOapSY71kYWhg5FCCEqhArVQTkpKQkAJycnvfJff/0VFxcXGjZsyMSJE0krNB3+nj17CAwMxN3dXVcWEhJCcnIyJ06cKPI5mZmZJCcn630JUVXczlH/fVgbS7IjhBBQgTooa7VaRo8eTdu2bWnYsGCIbP/+/fH19cXT05Njx44xYcIEzpw5w99//w1AbGysXqID6PZjY2OLfNasWbOYNm1aOb2JEIaVmpfs2JhY3edMIYR4NFSYZCcsLIzjx4+zc+dOvfKhQ4fqtgMDA6lWrRpdu3YlMjKSWrVKN5R24sSJjB07VrefnJyMt7d36QIvL8uWQWoqDB5s6EhEJXM7NwMAa0l2hBACqCDJzsiRI1m1ahXbt2+nevXq9zy3VSt1XZ/z589Tq1YtPDw82L9/v945cXFxAHft52Nubo65eQXuvHnpEjz7LCiKui8JjygBXbJjZn2fM4UQ4tFg0D47iqIwcuRIli9fzr///oufn999rzly5AgA1apVAyA4OJiIiAji4+N152zcuBE7OzsCAgLKJe5y9/33BYnOiBFw7Jhh4xGVSqqiJjs25rYGjkQIISoGgyY7YWFhLF68mCVLlmBra0tsbCyxsbGkp6cDEBkZyYwZMwgPD+fSpUusXLmSQYMG0aFDB4KCggDo0aMHAQEBDBw4kKNHj7J+/XomTZpEWFhYxa69URTYtg2uX9cvz8qCH35Qt318ID0dQkLgxAnQah9+nKLSua1kAWAtyY4QQgAGTna+/vprkpKS6NSpE9WqVdN9/f777wCYmZmxadMmevToQb169Rg3bhx9+/bln3/+0d3D2NiYVatWYWxsTHBwMC+++CKDBg3Sm5fHoNLS4IMPoEcP6N1b7YcD8OWX0KkT1KgBb70FMTEwfTq0bg3x8SjVPNDu3wdBQRAbCw0bgq0trF5twJcRlUGqJhsAawtJdoQQAkCjKPntJY+u5ORk7O3tSUpKws7OruxunJICoaGwY0dB2ezZMHo01KoFV64UeVm2EbR5rzrJTlZs6rUU7xfDYM8e9WDXrrBpU9nFKKqcnq9asM47k0WNpjK493uGDkcIIcpNcT+/K9Q8O1VKcrJam7NjB9jbwyuvqOVz5sDixWqi4+YGK1ZA/frqserV4Ycf+PWfDzioXOXszbP0XDeQhM2r4exZ9ZwtW9SaHiHu4rZRLgDWVvYGjkQIISoGSXbKi4UFuLiAkxNs3gxffw3e3mqiMnQoCrAv7Clu9WgPR4/Cv//CqVPkvvwSsy78BICxxpgT10/QcVFHzjtryG3VUu238+efhn03UaGlGqvJjo21o4EjEUKIikGSnfJiZgZ//sn1f1cx+vovBP/Ske7DrUm0AHJy+PJJD1or3+M1x4tX145gT20L3tzxPwK/DuTszbM4WDiw/eXteNh4EBEfgf8X/tj0PMz/1QWWLjX024mKSqvltonaMm1t43Sfk4UQ4tFQIebZqaqOJZ3lqa39uZR4SVf2wdw+TGo0imnb+0I6ZORksODwAhYcXqB37cR2E2nj3Ya9Q/by7J/PciD6ABlk80EHeOr73Wp/IFvpgCr+IyOD23nLwVnbSM2OEEKA1OyUmxtpN2i/sD2XEi9R26k2UztOBeDz66sYdvVrbqbfpJ5LPba/tJ1Q/1AAgqsH8/dzf3Nh1AXebvs2AL4Ovux/bT8x42IwMzbjgBeEV0Mdii7Ef6WlkZqX7NjYOhs2FiGEqCCkZqecuFi5MLnDZNZHruf3Z37H0cKRnVd2sunCJn4/oQ6t/6DLB7T3bU973/YkpCfgYOGARqMp8n4eNh70rd+X347/xrfN4bvjx9Vh6kIUoty+zW1TddvaogxHFgohRCUmNTvlaFzwONYOWIuTpRMajYYve35JK69W9Kzdkx+e+IGn6z2tO9fR0vGuiU6+4c2HA7AkELIjjpRn6KKSykpNIsdY3ZblIoQQQiU1O+VIo9Fgoin4Edd1qcveV/eW+n7tfdpjr7EkySydExf20bgMYhRVy+2Um7pta1NJdoQQAqRmp1LRaDQ0dw4E4GDyaQNHIyqi27cTADDLBVNjUwNHI4QQFYMkO5VM85rtADhglwp5q7sLkS81VU12rHONDRyJEEJUHJLsVDLNfYMBOOgJREQYNhhR4dxOSwTARpIdIYTQkWSnkmnu2RyACHfIOHbIwNGIiuZ2WhIA1op0xxNCiHyS7FQyvva+OCuWZBtDxKX9RZ907VrB6urikZKanp/sSH8dIYTIJ8lOJaPRaGhuWQuAAwnH9Q8mJ8Prr4OPD3TubIDohKHdzkgGwAYzA0cihBAVhyQ7lVAT90YAHM+J1j8wbhzK11+z11PLlbMHZXX0R1BqZgoA1hpJdoQQIp8kO5VQ/ZotAThlkQKZmWphbi5ntvxFy9cg+FXoNgiUHTsMGKV4qNLToUEDbq9cBoC1kYWBAxJCiIpDkp1KqH5NdZmIUy7AxYtqYXg4YW0TOeil7p51gdN7/jFMgFVdZib07QszZxo6kgLbt7Mx4ySbaqq7NsaS7AghRD5Jdiqheq71AYizgVunDwNwc91yttZQj/ubegCwKWqrAaJ7BOzYwbbwvzkzfxrk5pbNPa9cga5dYe3aoo8nJUFiorodFQWXL+sdvvjvMh57EVbWU/dlnh0hhCggyU4lZGtui3eWJQCnzu0BYFXEX+QaQaCJF0MavQzAJrMrkJJisDirqtOHNtBlMDzeNwvOny+Te2b/8C1fpfzLtdmT9A+kpsKgQeDmBrVrw44dKA0boATUhyNHdKetilyLttC/ZitzmzKJSwghqgJJdiqp+iZq7c2p2Ai4fp3lJuqHbp+Gz9KtSV8AttSAnD27DBVilbXj0ja0RnDeGWLCt5XJPb+LWk5YKIRVO6w3bYD2g/f58swvtBycRbfQm8Q80Yl2z6bSun86cf2ehBs34OZNVlle1btfRlBAmcQlhBBVgSQ7lVR9e3X4+amUCyTPmcV6dZenW71EY4/GOOWYkWIO+8NXGjDKqulA6lnd9qHT/xb/wpgY2LfvznJFYVtOJAAb/RQyd2zRHfrs7M+80QsOeMHmmtBwmJbdPrC/OoR2uELK4z1I/e0nXRPm/9r/j9pOtRkcPKI0ryaEEFWSJDuVVH1Pdfj5qZxYPjn0JRmmUM+8OkHuQRgbGdPRRO2puif2oCHDrHrS0zlglajbPRR3tHjXKQpX+nRl2/OtYdd/atuio9nrqo6qSzODXTuW6Mp/c4kB4AX/pzHGiFtWYIQGR1M7wj0hpOFhFv4yjiwTqJlrx4zOMzj3xjmaVmv6oG8qhBBVhiQ7lVRAA3XSwP2uWXzaPBuAD56ci0ajASDIpSEAJ9IuGSS+qir96EEi3Av2w3OiinWddv8+ejQ7RaeXYcuvH8CUKdC9O9y6xbU9G7hiX3Duumtq09iV1b8R7gkaBeY++TUf9/gEgOmdZ7Dhpc04mNqyxxtG9VKvC23YR/fnL4QQooAsoFNJBfm3w9rYkptW6QC0tWvI0/X76I43rNkabvzFCZNboCggH4Jl4nD4anIL/YpwyD5N7QRua3vP6/79bSanXdXtrxLW8/mttexrBDsmh3HUJVfv1441NjG8Ws+V9b450AaCqY67jTtjgsfwSpNXsLdQM6NtQ3bSa3FPEjIS6VWnF2+HzCjr1xVCiCpBkp1Kyt7CniOvH+OrA19xMPogX4V+pfdbfcOgbrAfTjjloo2LxcijmgGjrToOnN8ODtAp15ttRle4Yg/Xw7fj2in07hddvcq3N9aBo7r7Vz2t7tCrN5bS7IYDBEAf0yCWZx3jhBvU7XcDjaKe81SdJ3Xn5yc6AEHuQVwYrc6zZGYsMyYLIcTdSDNWJVbbqTZzQuaw/eXtNHRrqH+sWgPMcuG2GVw+vNUwAVYxysWLrE5QOxh38elInUx1ePfaT0dAXFyR10SNe5X3B3izoqba1Oit2Okd3+oHX/knAvBEnccZ03o0npbuWBqZo2jUJqze3d+4a0xmxmaS6AghxH1IslNFmRiZUC9TbVo5cXangaOpGv5+fwAb/bSYajU833sSPRs+DcCQJldYEtbhzguys3kudSGTu0COMbS3DmBq56kA1Dfz4pO20wBINwUTxYhOXV/l08c+49rbsdx8J4E/nvmDNS+upY5rvYf1ikIIUSVJM1YV1tDUi2Oc5nj0ER43dDCVjVZLxk8L+PP3KdR0q0vTCfN400GdwHFC/Vep41KXD5//nhu/JrH40kpe9z/L0xGHsQxsortF7PY17PNUm6w+6zGH/kEDcLVyxdrZk/a+7almU40W/p1IzEikpmNNajj66a61NLXk2QbPPtx3FkKIKkqSnSqsoXN9SD7N8dsXDR1KpbP7nQG8kLOUK8FglRXL11+P5JoreGWa827feQCYm5jz06DlbP+fDVEW6az8632eD1ymu8eGHYtAA83SHRkdPEZX/nzD53XbHXyLqBESQghRpqQZqwprWLstAFvNY7j6/acGjqbySNy+gedyl+qGg6eZwduWalPgM/bBWJpa6s410hgx0KMHAD9FrwVF4dTcSXzT15f/u7IRgMfcgh/uCwghhNAjyU4V1qHbEKrlWHLNDlqdfovrO9YbOqSKT6tlzI/Pc80OamfbMtq2O6AuugrwVIfX7rhk0NNTAVjvmU7M9jW8cnImI4Ki+NvnNgCPtX3pYUQuhBDiLiTZqcLsLR3YM/YkfukWRNvByn0/GTqkCu/GmcMs8ksEYOHzv/FyaMHCnI6ZRrRreWc/mjrejQlOc0ZrBAv/nMheL0V3zD7LiNYtni73uIUQQtydJDtVnK9jDbpp1IWzohOv3udscfnCYQA80oxpFxhKYJ321E2zAqCXcV1MjU2LvK539W4AzLaN0JUF4Mq4ei9jYiRd44QQwpAk2XkEeFqrK6RHpxU9F4wocC3mNABeOWq/HI1Gw/86TMIz15pR/ebd9brQ7q8DkGSh7r+W25gT78UzeeAP5RuwEEKI+5Jk5xHg6egDQHTOLQNHUvFdu6GOXPPSFMxUPDB0Itemp9IyoPtdrwuo254aaQWT+3WqG1J+QQohhCgRSXYeAZ5uec1YpBo4korvWpLa1Odl4Vqi6zQaDaFWjXT7HTu/XKZxCSGEKD1Jdh4Bnl7qDLzR5pkGjqTiu5YeD4CXrWeJr+3dYRgAAek2eHnWLdO4hBBClJ70nHwEeNZUaxxirRRyk5MwtrO/zxWPrmu5CQBUd65Z4mu7dR7CH2a5NPBuVtZhCSGEeABSs/MIcHXzw1gLWiOIvxhx/wseYdeM0wDwquZfquufbTuUAB9JdoQQoiKRZOcRYGxkjEeGWokXffmEgaOpwLRarlmqq5N7+QYaOBghhBBlRZKdR4Rn3lDq6NizBo6k4rp97ZJu6LiXX6N7nyyEEKLSMGiyM2vWLFq0aIGtrS1ubm707t2bM2fO6J2TkZFBWFgYzs7O2NjY0LdvX+Li9OeLiYqKIjQ0FCsrK9zc3Bg/fjw5OTkP81UqPE8jtZ9O9A1ZFPRurkWqEwraZGmws3YycDRCCCHKikGTnW3bthEWFsbevXvZuHEj2dnZ9OjRg9u3b+vOGTNmDP/88w9//vkn27ZtIzo6mj59+uiO5+bmEhoaSlZWFrt37+ann35i0aJFTJkyxRCvVGF5mrsAcC35moEjqYC0Wv56pj6fffIMAF5Z5gYOSAghRFky6GisdevW6e0vWrQINzc3wsPD6dChA0lJSSxYsIAlS5bQpUsXABYuXEj9+vXZu3cvrVu3ZsOGDZw8eZJNmzbh7u5O48aNmTFjBhMmTGDq1KmYmZnd8dzMzEwyMwuGYScnJ5fvi1YAnraekHGE6Lyh1aJA8oVTPBt4WrfvpdgYMBohhBBlrUL12UlKSgLAyUltQggPDyc7O5tu3brpzqlXrx4+Pj7s2bMHgD179hAYGIi7u7vunJCQEJKTkzlxoujOuLNmzcLe3l735e3tXV6vVGF4OvkCEJ2TYOBIKp6oyEN6+2ZGRa9/JYQQonKqMMmOVqtl9OjRtG3bloYNGwIQGxuLmZkZDg4Oeue6u7sTGxurO6dwopN/PP9YUSZOnEhSUpLu68qVK2X8NhWPp39TAI5aJJH01WcGjqZiuXZFPykOrnf3ZSGEEEJUPhUm2QkLC+P48eMsXbq03J9lbm6OnZ2d3ldV167NC1THjmg7GLhrLNqTDzgE/fx5OHy46GM5ObB5M2RlPdgzHpKrcecBaJrpxOxuswl7YY6BIxJCCFGWKkSyM3LkSFatWsWWLVuoXr26rtzDw4OsrCwSExP1zo+Li8PDw0N3zn9HZ+Xv558jwMbMhuWvbsI8V8M/dWDLjp9LfzNF4fdXWvL5yBYQd+dK6rGTxzBxVjdiP5vxABE/PFcTowBobuLD+LbjcbZyNnBEQgghypJBkx1FURg5ciTLly/n33//xc/PT+94s2bNMDU1ZfPmzbqyM2fOEBUVRXBwMADBwcFEREQQH1/Q8Xbjxo3Y2dkREBDwcF6kkmju1YJu6dUAuJRQ+iHo2oRbvNI+gTd75HJu23L9g1lZjLv8PR+2h9kXf3mQcB+aq+lqwuZlX/X7bgkhxKPIoKOxwsLCWLJkCf/3f/+Hra2tro+Nvb09lpaW2NvbM2TIEMaOHYuTkxN2dna88cYbBAcH07p1awB69OhBQEAAAwcOZPbs2cTGxjJp0iTCwsIwN5chxP/lamoPRHMjtfSjsq6fO0pa3iC3Qyc24s9w3bHUlX+xwk8d6XZYG/0goT40V/PXw3KtZeBIhBBClAeD1ux8/fXXJCUl0alTJ6pVq6b7+v3333XnfPbZZzz++OP07duXDh064OHhwd9//607bmxszKpVqzA2NiY4OJgXX3yRQYMGMX36dEO8UoXnYqE20dxIv1nqe1y7fEy3fST2iN6xlf98okuEjjlmo0RX/ITnqok6r1N1r/oGjkQIIUR5MGjNjqIo9z3HwsKC+fPnM3/+/Lue4+vry5o1a8oytCrLxcYVUuBGVlKp73H1WsGcNEeyr4KiQHo6vPYav1LQafmWFUTv24jX04MfKOZylZrKNWstANVrNjZsLEIIIcpFheigLB4eF3u1z84NbUqp73H1ZkF/n6NOWXDtGlnzP+eNhCWsqaOWO+Wq1TvHjm8u6hbFpyjw++9w5MiD3ecubl88Q4K6bBjVPeuVyzOEEEIYliQ7jxgXJ3W02w1NeqnvUXjJiRhbiNu7me8u/82XrdSyqR2n0sNc7Rx+7Nqhom5RfLt3c+KNF0gIbgJvvgla7f2viY+H3Nxi3f5a5BEAbLONsDOv+lMQCCHEo0iSnUeMi2sNAG6YZJf6HlezruvtHz22nkPZ6vDtCY5P8F6n9wiq3hyAY+mXSv0cgOOH19EwDPxGw9J/P4cVK+59wfLl4OEBb71193Nu3oT33oNZs7i64S8AvPJWhRdCCFH1SLLziHGpVhOAGxZaKLQ+WElcVdS1xMy06l+fI3HHiELtAxTg2QiAoCB1iY9j1rdh5064eBGulXwR0oNXDwCQZAH9noG92369+8mZmZycNpKmQxUW7/wK7rLmmXb6NA5/N53Tc97l7AF1fbbqRg4ljk0IIUTlIMnOI8bFXU12Ei0hOz6m5DfIzeWaWQYAHS3VPi4RmVFEmatlPr5BAATVbAPAKVeIGdYf6tYFPz+YMAGyi1+rFJmkPx/Q2pgdcO4c/P232p+nsK+/ZkxgNIerwbvts9AuKSIxysnhm4iFNB0O9UfCiMfV4uq1mhY7JiGEEJWLJDuPGAcrJzR5OcKta5Elvl6JieFqXteWLvV7AXDGLIUreWU+NZsAUN2uOsHuzcg1ggl1r/D4s9n06ZPN+R9mw1dfFft5FzLVCf9qm6rrne20vA6dOkHfvlB4aZH4eHZ+P5kNtdXdK/awdcXcO2+4ZQuLaqcCYGZspivu0PTpYsckhBCicpFk5xFjbGSMU5YxADfiLpT4+qSLp7idlyN0btoHgCMekGEKGqVgFmKNRsOMHh8B8EsjWF0HlteHhq/D/h3FXP9MqyXSRG2KGhzQH4C91WFEk2hqjIZLc6boOiwrb43jfy3VJMbcWJ1M8mfLs7B+vd4tL/3xLQe8wEjRcGXMFZLeSSJ6bDQvN3m5xD8LIYQQlYMkO48gl1w1GbhxParE1147Fw6AY7YJQR6NAchWcyc8sswwNymYtbqLXxc61egEgJetF60cG5JpAgu04epiofcTHU2kg1oNFdq8H05ac9LM4JsWcNkBZnmch8mTYehQ/gpfzPYaYGFkzqLeiwD4KwCSwl6FlBR14dLgYP46uQyAjo6NcbN2w87cjmq21Ur8cxBCCFF5SLLzCHLBCoAbCSXoMHz9OgwZwtUP/wdAda0NlqaW+GRZ/X979x8XVZnvAfxzBpgRBGbAAYZRh5+C4A9ESnbW/BWsDrmmgjc0Xq+wvFoKW5taabd0s3uj2n1tqde1rXalWq+27kvraubNxIEsRCRZTY2EMDJBEuK3AjLP/WPy1Ig/ZgsYmPm8X695NXOeZ57zPN/OkS/nPJxHrmKA2uYrkiRha+pWrJu6Dh8/8DGe/JV1YdACfaddz81pKjuOi4Ot7yMCojFRPcamPHcccH7jc/h262tYMcO67YlJq5A+Kh0j/aPQqgSeijoHPPggsGABDp87jNe/n5ozz7jI/rETEdGAxmTHBWndfAAAF5tr7PvCl18CMTGoffuv2Dr6+6cNh1gTjyj3ILmaQRXQ7at6Hz2envI0QjQhmBgyCQDweQDwrfm96++rqQkoKrLu9gvrf7WdHvBV+eKOCfPkahGacHS4A3dkeSJspTu+VgMGtQGPT3wckiRh40zrE7c3TQAKP9qGV3AUxn8HyrSAj9IHqbFp9o2diIgGPCY7Lkir0gAALrZ+e/OK3xNvvYlXQ+sQ9YgCb1n/shwTou8EAERpwuV6Bt+brxo+xGsIRknfTzQ+/qNkRwjgO+tinFi0CPjFL4A//QlfnjsBAIiAPwBgTsxceLp7IjMuE3/69WYAQOWgS2hVXMH44PHYlb4LXh7WK03J4cnIjMuEkIB59wArp1ubv2fUPfjo/o+g89bZNXYiIhr4HLo2FjmG1lMLXAIutn9nV/13z/4fHpwFABbE6+Lx1OSnMGfkHABA1NCxQJN1SQhDwIhbtjVp2ESc/HonPmr4J+Y2NwM+PkBWFvDnPwN/+xvySndh691AzronUDFFA8QC4V5DrfsaEoWGVQ1wV7hDISlwOus0zjachbfSGxOHT4QkSTb7emnGSyg+X4xTOAUAmGyYjG1p26CQmOMTEbkS/qvvgrQ+1qsrF69c89A9iwXYvRu4cMFm8/52a7KQEZCE4sXFSI1JlROGqKhfyvUMw0bdct+TEqx/4l0Q3AG8+ipgNuPM25vxgtGCS0vuxzJTF/46Hlg6pQUVbecAABFhPzwDR+mmlPc9UjsSpkgT7jDc0S3RAQA/Tz/sy9iHEHUI/Ab54bW7X2OiQ0TkgnhlxwVpdeFALXCxo8F6C+lqovDBB8DddwNz5liXXQCA5mZ86mVNimaOuwduCjebtkYYxsnvDVG333LfU8PvhAQJJXqB0r8+h+hX1bgrAygfAhSEtKNMa623Mxbw7pQACETETPzJYx2uHo4vfvMFWjta4efp95PbISKigYu/5rqggCjrg/++GdQJVPzwYMHzZUcx8QEgtzFf3nbleCn++f30lvFRU7q1FaoJhWaQBio3FcL9I265b72PHukx/wYAeGZMPdaEVKJ8iLXs6orpXm7WdapaPATG6cZhXuy86zVlN6WbkokOEZEL45UdFxRvmAAA+CwQaPgkD5pI62OH99QV4hMD0Kz6DgsbGoCdO1FWUYhLSsC7yw0jhnSfk+OucEfefXlo62yDZpDGrv0/PW0t3j69A+/E/LDcQ4DKH9+21wMAXp/9Fxz55ggMagOyJ2TDw83j5w2YiIhcGq/suCC9jx4jujQQEnCo9H/l7WfbzgMATgUAl59aha8fXYTiva8DAMYh+IbzXeKD4zHRYP+tptiAWGSMzQAADHIfhCfveBIbf21dQkKtUiM1JhUvmV7Co8ZHmegQEdHPxis7Lmqq/3icacyDue4ovl8LE191XgQAdCmAl0/+BauX/1A/YcjoHt3/a7New4MJDyJeF4/BysGwCAvqLtUhRhtj8xRmIiKin4tXdlzUlLGzAAD5g2qBy9YVy7+SGuXy/zLaLucwflRyj+5/kPsg3GG4A4OV1kckKyQFlt2+DNPCpvXofoiIiJjsuKgpCdYnCH+qE2g89CEA4CvlJbm85UcXVxRQ4JfjZ/dp/4iIiHoKkx0XNUw9HBGdPrAogP/ZsQYdXR34xsv2ao5vpwJnfnMGhx44hEj/SAf1lIiI6OdhsuPCHr4tCwDwpN8xlHy8A+Ka5/JNt4Qh0j8SxuFGB/SOiIioZzDZcWFZd/8nElp90eAJ3LtvMQAgsh7w7rCW/9rwKwf2joiIqGcw2XFhbgo3bEh4GgBwVmWdrxPeqsJTbtOQ8q0GqWn/4cjuERER9QgmOy7OmLQQkXU/fA7p8sYT6/Kw97+/g0/AMMd1jIiIqIcw2XFxklaLBVW+8ucQicsqEBGRc2GyQ5ivSpDfcw0pIiJyNkx2CLEjJyHsO+v7aV6xju0MERFRD+NyEQTEx6P4XqDaB4hZHuPo3hAREfUoJjsExMdjyCVgyCUAgYGO7g0REVGP4m0sAgwGwO/7uTpMdoiIyMkw2SFAkoBly4DYWMDIpyUTEZFzkYQQwtGdcLSmpiao1Wo0NjbC19f31l8gIiIih7P35zev7BAREZFTY7JDRERETo3JDhERETk1JjtERETk1JjsEBERkVNjskNEREROjckOEREROTWHJjsFBQWYNWsW9Ho9JEnCO++8Y1O+cOFCSJJk8zKZTDZ16uvrkZGRAV9fX2g0GixatAgtLS19OAoiIiLqzxya7LS2tiIuLg6bNm26YR2TyYTq6mr5tW3bNpvyjIwMnDx5Evv378eePXtQUFCAJUuW9HbXiYiIaIBw6EKgKSkpSElJuWkdlUoFnU533bLTp09j3759KC4uxm233QYA2LhxI+666y784Q9/gF6v7/E+ExER0cDS7+fsmM1mBAYGIjo6GkuXLkVdXZ1cVlhYCI1GIyc6AJCcnAyFQoGioqIbttne3o6mpiabFxERETmnfp3smEwmvPnmmzhw4ABeeOEF5OfnIyUlBV1dXQCAmpoaBF6zSre7uzv8/f1RU1Nzw3ZzcnKgVqvl1/Dhw3t1HEREROQ4Dr2NdSvz58+X348ZMwZjx45FREQEzGYzkpKSfnK7q1evxvLly+XPTU1NTHiIiIicVL++snOt8PBwaLValJeXAwB0Oh1qa2tt6ly5cgX19fU3nOcDWOcB+fr62ryIiIjIOfXrKzvXOnfuHOrq6hAcHAwAMBqNaGhoQElJCRISEgAAeXl5sFgsSExMtLtdIQQAcO4OERHRAHL15/bVn+M34tBkp6WlRb5KAwCVlZUoLS2Fv78//P398cwzzyAtLQ06nQ4VFRV4/PHHERkZiRkzZgAAYmJiYDKZsHjxYrzyyivo7OxEdnY25s+f/y/9JVZzczMA8FYWERHRANTc3Ay1Wn3DckncKh3qRWazGdOmTeu2PTMzE5s3b8acOXNw7NgxNDQ0QK/XY/r06Xj22WcRFBQk162vr0d2djZ2794NhUKBtLQ0bNiwAd7e3nb3w2Kx4Pz58/Dx8YEkST0yNuCHuUBff/01b5XdBONkP8bKPoyTfRgn+zBO9nFEnIQQaG5uhl6vh0Jx45k5Dk12nF1TUxPUajUaGxt5gtwE42Q/xso+jJN9GCf7ME726c9xGlATlImIiIj+VUx2iIiIyKkx2elFKpUKa9euhUqlcnRX+jXGyX6MlX0YJ/swTvZhnOzTn+PEOTtERETk1Hhlh4iIiJwakx0iIiJyakx2iIiIyKkx2SEiIiKnxmSnF23atAmhoaEYNGgQEhMTceTIEUd3yaF+97vfQZIkm9fIkSPl8suXLyMrKwtDhgyBt7c30tLScOHCBQf2uG8UFBRg1qxZ0Ov1kCQJ77zzjk25EAJr1qxBcHAwPD09kZycjDNnztjUqa+vR0ZGBnx9faHRaLBo0SK0tLT04Sh6363itHDhwm7Hl8lksqnjCnHKycnB7bffDh8fHwQGBmLOnDkoKyuzqWPPuVZVVYWZM2fCy8sLgYGBeOyxx3DlypW+HEqvsidOU6dO7XZMPfTQQzZ1nD1OmzdvxtixY+VFs41GI95//325fKAcS0x2esnbb7+N5cuXY+3atfj0008RFxeHGTNmdFul3dWMGjUK1dXV8uvQoUNy2aOPPordu3djx44dyM/Px/nz55GamurA3vaN1tZWxMXFYdOmTdctf/HFF7Fhwwa88sorKCoqwuDBgzFjxgxcvnxZrpORkYGTJ09i//792LNnDwoKCrBkyZK+GkKfuFWcAMBkMtkcX9u2bbMpd4U45efnIysrC4cPH8b+/fvR2dmJ6dOno7W1Va5zq3Otq6sLM2fOREdHBz755BO88cYbyM3NxZo1axwxpF5hT5wAYPHixTbH1IsvviiXuUKchg0bhueffx4lJSU4evQo7rzzTsyePRsnT54EMICOJUG9YsKECSIrK0v+3NXVJfR6vcjJyXFgrxxr7dq1Ii4u7rplDQ0NwsPDQ+zYsUPedvr0aQFAFBYW9lEPHQ+A2LVrl/zZYrEInU4nfv/738vbGhoahEqlEtu2bRNCCHHq1CkBQBQXF8t13n//fSFJkvjmm2/6rO996do4CSFEZmammD179g2/44pxEkKI2tpaAUDk5+cLIew71/bu3SsUCoWoqamR62zevFn4+vqK9vb2vh1AH7k2TkIIMWXKFPHII4/c8DuuGCchhPDz8xOvv/76gDqWeGWnF3R0dKCkpATJycnyNoVCgeTkZBQWFjqwZ4535swZ6PV6hIeHIyMjA1VVVQCAkpISdHZ22sRs5MiRMBgMLh2zyspK1NTU2MRFrVYjMTFRjkthYSE0Gg1uu+02uU5ycjIUCgWKior6vM+OZDabERgYiOjoaCxduhR1dXVymavGqbGxEQDg7+8PwL5zrbCwEGPGjLFZdHnGjBloamqSf6N3NtfG6aqtW7dCq9Vi9OjRWL16Ndra2uQyV4tTV1cXtm/fjtbWVhiNxgF1LLn32Z5cyMWLF9HV1WXzPxcAgoKC8PnnnzuoV46XmJiI3NxcREdHo7q6Gs888wwmTZqEzz77DDU1NVAqldBoNDbfCQoKQk1NjWM63A9cHfv1jqWrZTU1NQgMDLQpd3d3h7+/v0vFzmQyITU1FWFhYaioqMCTTz6JlJQUFBYWws3NzSXjZLFY8Nvf/hYTJ07E6NGjAcCuc62mpua6x9zVMmdzvTgBwL333ouQkBDo9XocP34cTzzxBMrKyrBz504ArhOnEydOwGg04vLly/D29sauXbsQGxuL0tLSAXMsMdmhPpOSkiK/Hzt2LBITExESEoK///3v8PT0dGDPyBnMnz9ffj9mzBiMHTsWERERMJvNSEpKcmDPHCcrKwufffaZzdw46u5GcfrxfK4xY8YgODgYSUlJqKioQERERF9302Gio6NRWlqKxsZG/OMf/0BmZiby8/Md3a1/CW9j9QKtVgs3N7duM9IvXLgAnU7noF71PxqNBlFRUSgvL4dOp0NHRwcaGhps6rh6zK6O/WbHkk6n6zbx/cqVK6ivr3fp2IWHh0Or1aK8vByA68UpOzsbe/bswcGDBzFs2DB5uz3nmk6nu+4xd7XMmdwoTteTmJgIADbHlCvESalUIjIyEgkJCcjJyUFcXBzWr18/oI4lJju9QKlUIiEhAQcOHJC3WSwWHDhwAEaj0YE9619aWlpQUVGB4OBgJCQkwMPDwyZmZWVlqKqqcumYhYWFQafT2cSlqakJRUVFclyMRiMaGhpQUlIi18nLy4PFYpH/cXZF586dQ11dHYKDgwG4TpyEEMjOzsauXbuQl5eHsLAwm3J7zjWj0YgTJ07YJIf79++Hr68vYmNj+2YgvexWcbqe0tJSALA5ppw9TtdjsVjQ3t4+sI6lPpsK7WK2b98uVCqVyM3NFadOnRJLliwRGo3GZka6q1mxYoUwm82isrJSfPzxxyI5OVlotVpRW1srhBDioYceEgaDQeTl5YmjR48Ko9EojEajg3vd+5qbm8WxY8fEsWPHBADxxz/+URw7dkx89dVXQgghnn/+eaHRaMS7774rjh8/LmbPni3CwsLEpUuX5DZMJpOIj48XRUVF4tChQ2LEiBFiwYIFjhpSr7hZnJqbm8XKlStFYWGhqKysFB9++KEYP368GDFihLh8+bLchivEaenSpUKtVguz2Syqq6vlV1tbm1znVufalStXxOjRo8X06dNFaWmp2LdvnwgICBCrV692xJB6xa3iVF5eLtatWyeOHj0qKisrxbvvvivCw8PF5MmT5TZcIU6rVq0S+fn5orKyUhw/flysWrVKSJIkPvjgAyHEwDmWmOz0oo0bNwqDwSCUSqWYMGGCOHz4sKO75FDp6ekiODhYKJVKMXToUJGeni7Ky8vl8kuXLolly5YJPz8/4eXlJebOnSuqq6sd2OO+cfDgQQGg2yszM1MIYf3z86effloEBQUJlUolkpKSRFlZmU0bdXV1YsGCBcLb21v4+vqK+++/XzQ3NztgNL3nZnFqa2sT06dPFwEBAcLDw0OEhISIxYsXd/vlwhXidL0YARBbtmyR69hzrp09e1akpKQIT09PodVqxYoVK0RnZ2cfj6b33CpOVVVVYvLkycLf31+oVCoRGRkpHnvsMdHY2GjTjrPH6YEHHhAhISFCqVSKgIAAkZSUJCc6QgycY0kSQoi+u45ERERE1Lc4Z4eIiIicGpMdIiIicmpMdoiIiMipMdkhIiIip8Zkh4iIiJwakx0iIiJyakx2iIiIyKkx2SEiIiKnxmSHiAYks9kMSZK6LUJIRHQtPkGZiAaEqVOnYty4cXj55ZcBAB0dHaivr0dQUBAkSXJs54ioX3N3dAeIiH4KpVIJnU7n6G4Q0QDA21hE1O8tXLgQ+fn5WL9+PSRJgiRJyM3NtbmNlZubC41Ggz179iA6OhpeXl6YN28e2tra8MYbbyA0NBR+fn54+OGH0dXVJbfd3t6OlStXYujQoRg8eDASExNhNpsdM1Ai6hW8skNE/d769evxxRdfYPTo0Vi3bh0A4OTJk93qtbW1YcOGDdi+fTuam5uRmpqKuXPnQqPRYO/evfjyyy+RlpaGiRMnIj09HQCQnZ2NU6dOYfv27dDr9di1axdMJhNOnDiBESNG9Ok4iah3MNkhon5PrVZDqVTCy8tLvnX1+eefd6vX2dmJzZs3IyIiAgAwb948vPXWW7hw4QK8vb0RGxuLadOm4eDBg0hPT0dVVRW2bNmCqqoq6PV6AMDKlSuxb98+bNmyBc8991zfDZKIeg2THSJyGl5eXnKiAwBBQUEIDQ2Ft7e3zbba2loAwIkTJ9DV1YWoqCibdtrb2zFkyJC+6TQR9TomO0TkNDw8PGw+S5J03W0WiwUA0NLSAjc3N5SUlMDNzc2m3o8TJCIa2JjsENGAoFQqbSYW94T4+Hh0dXWhtrYWkyZN6tG2iaj/4F9jEdGAEBoaiqKiIpw9exYXL16Ur878HFFRUcjIyMB9992HnTt3orKyEkeOHEFOTg7ee++9Hug1EfUHTHaIaEBYuXIl3NzcEBsbi4CAAFRVVfVIu1u2bMF9992HFStWIDo6GnPmzEFxcTEMBkOPtE9EjscnKBMREZFT45UdIiIicmpMdoiIiMipMdkhIiIip8Zkh4iIiJwakx0iIiJyakx2iIiIyKkx2SEiIiKnxmSHiIiInBqTHSIiInJqTHaIiIjIqTHZISIiIqf2/6HkAn+lI3yJAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "today_price=float(input(\"Enter Today's Stock Price: \"))"
      ],
      "metadata": {
        "id": "hW1xx9SMO_VM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a678e6c-da0c-468e-f248-29031aadcab1"
      },
      "execution_count": 52,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter Today's Stock Price: 521.27\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converting into 2D array and Scaling the Data"
      ],
      "metadata": {
        "id": "JtBWknruSdUM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr=np.array(today_price).reshape(-1,1)\n",
        "arr1=sc.transform(arr)\n",
        "arr1=arr1.reshape(-1,1,1)"
      ],
      "metadata": {
        "id": "ZfVN2PTLO_X_"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result=model.predict(arr1)\n",
        "print(result)\n",
        "result=np.reshape(result,(1,1))"
      ],
      "metadata": {
        "id": "2XlKU2YCO_au",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e429a069-9ee9-4ae3-fb2a-ef86c3909b5a"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 20ms/step\n",
            "[[[0.9282093]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converting back the Scaled Data to Original data"
      ],
      "metadata": {
        "id": "UfHlouDsSvWb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tomorrow_price=sc.inverse_transform(result)\n",
        "print(\"Predicted price tomorrow=%.2f\" % tomorrow_price, end='')"
      ],
      "metadata": {
        "id": "rF0jf_gkO_eB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47bfa55e-af9a-4774-fe11-3a779fc1b281"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted price tomorrow=466.34"
          ]
        }
      ]
    }
  ]
}