ELAN_future_weeks.ipynb

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        "<a href=\"https://colab.research.google.com/github/YanjunLin-Andrie/elan_price_prediction/blob/main/ELAN_future_weeks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "pip install yfinance"
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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        "pip install yahoo_fin"
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: yahoo_fin in /usr/local/lib/python3.7/dist-packages (0.8.9.1)\n",
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      "execution_count": 102,
      "id": "c4b3d2eb-bcd0-4574-beee-4647cb2848fa",
      "metadata": {
        "id": "c4b3d2eb-bcd0-4574-beee-4647cb2848fa"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import yfinance as yf\n",
        "from yahoo_fin.stock_info import get_data\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pandas_datareader\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "id": "4e9f7e45-182c-412b-8803-f3b6f87098bf",
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          "height": 424
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        "outputId": "8d2e3c84-ba75-4025-cc56-c21bd18c5e36"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                 open       high        low      close   adjclose    volume\n",
              "2018-09-17  32.250000  36.009998  32.049999  34.099998  34.099998  49452200\n",
              "2018-09-24  34.099998  37.610001  33.700001  34.889999  34.889999  16028100\n",
              "2018-10-01  35.480000  35.480000  32.709999  34.340000  34.340000  10334300\n",
              "2018-10-08  34.000000  34.950001  31.760000  32.580002  32.580002   6534000\n",
              "2018-10-15  32.490002  32.930000  31.330000  31.610001  31.610001   6136800\n",
              "...               ...        ...        ...        ...        ...       ...\n",
              "2022-06-27  20.809999  20.910000  19.340000  20.620001  20.620001  24564300\n",
              "2022-07-04  20.250000  20.910000  20.030001  20.450001  20.450001  13488200\n",
              "2022-07-11  20.250000  20.500000  19.110001  20.080000  20.080000  14734000\n",
              "2022-07-18  20.090000  20.330000  19.930000  20.000000  20.000000   2536900\n",
              "2022-07-19  20.450001  20.650000  20.230000  20.549999  20.549999   1205424\n",
              "\n",
              "[202 rows x 6 columns]"
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              "      <th>open</th>\n",
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              "      <th>2018-09-17</th>\n",
              "      <td>32.250000</td>\n",
              "      <td>36.009998</td>\n",
              "      <td>32.049999</td>\n",
              "      <td>34.099998</td>\n",
              "      <td>34.099998</td>\n",
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              "      <th>2018-09-24</th>\n",
              "      <td>34.099998</td>\n",
              "      <td>37.610001</td>\n",
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              "      <th>2018-10-01</th>\n",
              "      <td>35.480000</td>\n",
              "      <td>35.480000</td>\n",
              "      <td>32.709999</td>\n",
              "      <td>34.340000</td>\n",
              "      <td>34.340000</td>\n",
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              "      <th>2018-10-08</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>34.950001</td>\n",
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              "      <th>2018-10-15</th>\n",
              "      <td>32.490002</td>\n",
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              "      <td>31.330000</td>\n",
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              "      <th>2022-06-27</th>\n",
              "      <td>20.809999</td>\n",
              "      <td>20.910000</td>\n",
              "      <td>19.340000</td>\n",
              "      <td>20.620001</td>\n",
              "      <td>20.620001</td>\n",
              "      <td>24564300</td>\n",
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              "      <th>2022-07-04</th>\n",
              "      <td>20.250000</td>\n",
              "      <td>20.910000</td>\n",
              "      <td>20.030001</td>\n",
              "      <td>20.450001</td>\n",
              "      <td>20.450001</td>\n",
              "      <td>13488200</td>\n",
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              "    <tr>\n",
              "      <th>2022-07-11</th>\n",
              "      <td>20.250000</td>\n",
              "      <td>20.500000</td>\n",
              "      <td>19.110001</td>\n",
              "      <td>20.080000</td>\n",
              "      <td>20.080000</td>\n",
              "      <td>14734000</td>\n",
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              "    <tr>\n",
              "      <th>2022-07-18</th>\n",
              "      <td>20.090000</td>\n",
              "      <td>20.330000</td>\n",
              "      <td>19.930000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>2536900</td>\n",
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              "    <tr>\n",
              "      <th>2022-07-19</th>\n",
              "      <td>20.450001</td>\n",
              "      <td>20.650000</td>\n",
              "      <td>20.230000</td>\n",
              "      <td>20.549999</td>\n",
              "      <td>20.549999</td>\n",
              "      <td>1205424</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ],
      "source": [
        "#get all ELAN trading history until today from yahoo finance\n",
        "df = get_data('ELAN', index_as_date = True, interval = '1wk').drop(columns = 'ticker')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "07ceeffd-2657-46f9-91cc-8c5419864a54",
      "metadata": {
        "id": "07ceeffd-2657-46f9-91cc-8c5419864a54"
      },
      "outputs": [],
      "source": [
        "# Defines function to split X and y data\n",
        "def window_data(df, window, feature_col_number, target_col_number):\n",
        "    # Creates empty X and y list\n",
        "    X = []\n",
        "    y = []\n",
        "    # loops through data\n",
        "    for i in range(len(df) - window - 1):\n",
        "        # splits the features by grouping window amount of previous close data\n",
        "        features = df.iloc[i:(i + window), feature_col_number]\n",
        "        # splits target by taking current close values\n",
        "        target = df.iloc[(i + window), target_col_number]\n",
        "        # Adds end of each loops data to respective X and y list\n",
        "        X.append(features)\n",
        "        y.append(target)\n",
        "    # returns X,y as arrays\n",
        "    return np.array(X), np.array(y).reshape(-1, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "c0290e24-f967-4add-88bd-8cd22f7818db",
      "metadata": {
        "id": "c0290e24-f967-4add-88bd-8cd22f7818db"
      },
      "outputs": [],
      "source": [
        "# Defines window size\n",
        "window_size=10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "id": "d614c3fa-5866-4b3c-ad81-468410a279d2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "d614c3fa-5866-4b3c-ad81-468410a279d2",
        "outputId": "8990fe67-2c6f-4f89-eb89-56fccfa02488"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[34.09999847, 34.88999939, 34.34000015, ..., 34.45999908,\n",
              "        34.        , 33.61000061],\n",
              "       [34.88999939, 34.34000015, 32.58000183, ..., 34.        ,\n",
              "        33.61000061, 33.40999985],\n",
              "       [34.34000015, 32.58000183, 31.61000061, ..., 33.61000061,\n",
              "        33.40999985, 33.56999969],\n",
              "       ...,\n",
              "       [25.30999947, 23.79000092, 22.27000046, ..., 20.54999924,\n",
              "        20.61000061, 20.62000084],\n",
              "       [23.79000092, 22.27000046, 24.27000046, ..., 20.61000061,\n",
              "        20.62000084, 20.45000076],\n",
              "       [22.27000046, 24.27000046, 23.87000084, ..., 20.62000084,\n",
              "        20.45000076, 20.07999992]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "       [24.04000092],\n",
              "       [19.84000015],\n",
              "       [19.12000084],\n",
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              "       [21.40999985],\n",
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              "       [28.37999916],\n",
              "       [29.75      ],\n",
              "       [30.10000038],\n",
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              "       [28.79000092],\n",
              "       [29.75      ],\n",
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              "       [36.5       ],\n",
              "       [36.47000122],\n",
              "       [35.36000061],\n",
              "       [31.        ],\n",
              "       [31.5       ],\n",
              "       [33.31000137],\n",
              "       [34.15000153],\n",
              "       [33.38000107],\n",
              "       [32.40000153],\n",
              "       [32.63000107],\n",
              "       [32.09000015],\n",
              "       [33.15000153],\n",
              "       [32.54000092],\n",
              "       [33.90000153],\n",
              "       [32.88000107],\n",
              "       [34.09000015],\n",
              "       [33.02999878],\n",
              "       [32.11000061],\n",
              "       [30.21999931],\n",
              "       [28.17000008],\n",
              "       [27.96999931],\n",
              "       [28.27000046],\n",
              "       [28.87999916],\n",
              "       [28.37999916],\n",
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              "       [27.03000069],\n",
              "       [26.77000046],\n",
              "       [25.51000023],\n",
              "       [24.96999931],\n",
              "       [25.48999977],\n",
              "       [25.51000023],\n",
              "       [27.87000084],\n",
              "       [27.95000076],\n",
              "       [26.48999977],\n",
              "       [27.42000008],\n",
              "       [26.59000015],\n",
              "       [26.12000084],\n",
              "       [26.25      ],\n",
              "       [26.25      ],\n",
              "       [25.28000069],\n",
              "       [25.30999947],\n",
              "       [23.79000092],\n",
              "       [22.27000046],\n",
              "       [24.27000046],\n",
              "       [23.87000084],\n",
              "       [24.19000053],\n",
              "       [22.27000046],\n",
              "       [20.54999924],\n",
              "       [20.61000061],\n",
              "       [20.62000084],\n",
              "       [20.45000076],\n",
              "       [20.07999992],\n",
              "       [20.        ]])"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Runs function to split X and y data\n",
        "X, y=window_data(df, window_size, 3, 3)\n",
        "# Displays X and y\n",
        "display(X)\n",
        "display(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "id": "f4fa42a0-81f8-4e02-9e12-20cbefe20492",
      "metadata": {
        "id": "f4fa42a0-81f8-4e02-9e12-20cbefe20492"
      },
      "outputs": [],
      "source": [
        "# Scales X and y data using MinMaxScaler\n",
        "X_scaler=MinMaxScaler()\n",
        "X_scaler.fit(X)\n",
        "X_scaled=X_scaler.transform(X)\n",
        "y_scaler=MinMaxScaler()\n",
        "y_scaler.fit(y)\n",
        "y_scaled=y_scaler.transform(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "id": "fcbce4bc-34ad-4ead-b40c-f42d24132a68",
      "metadata": {
        "id": "fcbce4bc-34ad-4ead-b40c-f42d24132a68"
      },
      "outputs": [],
      "source": [
        "# Defines where train and test data will be split (70% is train)\n",
        "split=int(0.7*len(X_scaled))\n",
        "# Splits train and test data\n",
        "X_train=X_scaled[:split]\n",
        "X_test=X_scaled[split:]\n",
        "y_train=y_scaled[:split]\n",
        "y_test=y_scaled[split:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "id": "60e9fc2d-1010-45e9-9acf-dbb6c338bc1f",
      "metadata": {
        "id": "60e9fc2d-1010-45e9-9acf-dbb6c338bc1f"
      },
      "outputs": [],
      "source": [
        "#X_scaled=X_scaled.reshape(X_scaled.shape[0], X_scaled.shape[1], 1)\n",
        "#display(X.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "id": "cc78138d-c9b8-4260-8e68-b36579263bee",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "cc78138d-c9b8-4260-8e68-b36579263bee",
        "outputId": "25273491-f25b-4b7b-82c6-949262d3ecd8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(133, 10, 1)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(58, 10, 1)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Reshapes data into size LSTM can handle\n",
        "X_train=X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
        "X_test=X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n",
        "display(X_train.shape)\n",
        "display(X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "id": "703a9741-a03d-4697-8530-b9a458f9e021",
      "metadata": {
        "id": "703a9741-a03d-4697-8530-b9a458f9e021"
      },
      "outputs": [],
      "source": [
        "# Creates neural network\n",
        "model=Sequential()\n",
        "# Defines and adds LSTM layer\n",
        "first_lstm_layer=LSTM(units=30, return_sequences=True, input_shape=(window_size, 1))\n",
        "model.add(first_lstm_layer)\n",
        "# Randomly drops 20% of defined nodes\n",
        "first_dropout_layer=Dropout(0.2)\n",
        "# Defines and adds LSTM layer\n",
        "model.add(first_dropout_layer)\n",
        "second_lstm_layer=LSTM(units=30, return_sequences=True)\n",
        "model.add(second_lstm_layer)\n",
        "# Randomly drops 20% of defined nodes\n",
        "second_dropout_layer=Dropout(0.2)\n",
        "# Defines and adds LSTM layer\n",
        "model.add(second_dropout_layer)\n",
        "third_lstm_layer=LSTM(units=30)\n",
        "model.add(third_lstm_layer)\n",
        "# Defines and adds output layer with linear activation\n",
        "output_layer=Dense(units=1, activation='linear')\n",
        "model.add(output_layer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "id": "8dbea1ae-7ae0-477b-9671-20d6a636c869",
      "metadata": {
        "id": "8dbea1ae-7ae0-477b-9671-20d6a636c869"
      },
      "outputs": [],
      "source": [
        "# Compiles model\n",
        "model.compile(loss='mean_squared_error', optimizer='adam', metrics='mean_squared_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "id": "c6b63a61-2ebf-4b39-9e4f-262db1d471ec",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6b63a61-2ebf-4b39-9e4f-262db1d471ec",
        "outputId": "52145db6-7bd5-4101-99ce-536356281adb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_15 (LSTM)              (None, 10, 30)            3840      \n",
            "                                                                 \n",
            " dropout_10 (Dropout)        (None, 10, 30)            0         \n",
            "                                                                 \n",
            " lstm_16 (LSTM)              (None, 10, 30)            7320      \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 10, 30)            0         \n",
            "                                                                 \n",
            " lstm_17 (LSTM)              (None, 30)                7320      \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 31        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 18,511\n",
            "Trainable params: 18,511\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Provides summary of model\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "id": "e750cfcc-e3af-44d6-9489-21e329ab69ac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e750cfcc-e3af-44d6-9489-21e329ab69ac",
        "outputId": "1728eb45-dddf-4c34-be0d-45ec6dcf8a2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "5/5 [==============================] - 5s 16ms/step - loss: 0.3972 - mean_squared_error: 0.3972\n",
            "Epoch 2/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2599 - mean_squared_error: 0.2599\n",
            "Epoch 3/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.1222 - mean_squared_error: 0.1222\n",
            "Epoch 4/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0352 - mean_squared_error: 0.0352\n",
            "Epoch 5/50\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0569 - mean_squared_error: 0.0569\n",
            "Epoch 6/50\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.0327 - mean_squared_error: 0.0327\n",
            "Epoch 7/50\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.0307 - mean_squared_error: 0.0307\n",
            "Epoch 8/50\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 0.0328 - mean_squared_error: 0.0328\n",
            "Epoch 9/50\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 0.0291 - mean_squared_error: 0.0291\n",
            "Epoch 10/50\n",
            "5/5 [==============================] - 0s 31ms/step - loss: 0.0275 - mean_squared_error: 0.0275\n",
            "Epoch 11/50\n",
            "5/5 [==============================] - 0s 29ms/step - loss: 0.0293 - mean_squared_error: 0.0293\n",
            "Epoch 12/50\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 0.0285 - mean_squared_error: 0.0285\n",
            "Epoch 13/50\n",
            "5/5 [==============================] - 0s 31ms/step - loss: 0.0272 - mean_squared_error: 0.0272\n",
            "Epoch 14/50\n",
            "5/5 [==============================] - 0s 35ms/step - loss: 0.0284 - mean_squared_error: 0.0284\n",
            "Epoch 15/50\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 0.0257 - mean_squared_error: 0.0257\n",
            "Epoch 16/50\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 0.0251 - mean_squared_error: 0.0251\n",
            "Epoch 17/50\n",
            "5/5 [==============================] - 0s 33ms/step - loss: 0.0260 - mean_squared_error: 0.0260\n",
            "Epoch 18/50\n",
            "5/5 [==============================] - 0s 30ms/step - loss: 0.0246 - mean_squared_error: 0.0246\n",
            "Epoch 19/50\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.0256 - mean_squared_error: 0.0256\n",
            "Epoch 20/50\n",
            "5/5 [==============================] - 0s 29ms/step - loss: 0.0256 - mean_squared_error: 0.0256\n",
            "Epoch 21/50\n",
            "5/5 [==============================] - 0s 28ms/step - loss: 0.0252 - mean_squared_error: 0.0252\n",
            "Epoch 22/50\n",
            "5/5 [==============================] - 0s 30ms/step - loss: 0.0255 - mean_squared_error: 0.0255\n",
            "Epoch 23/50\n",
            "5/5 [==============================] - 0s 32ms/step - loss: 0.0251 - mean_squared_error: 0.0251\n",
            "Epoch 24/50\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0242 - mean_squared_error: 0.0242\n",
            "Epoch 25/50\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0246 - mean_squared_error: 0.0246\n",
            "Epoch 26/50\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 0.0238 - mean_squared_error: 0.0238\n",
            "Epoch 27/50\n",
            "5/5 [==============================] - 0s 28ms/step - loss: 0.0238 - mean_squared_error: 0.0238\n",
            "Epoch 28/50\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 0.0225 - mean_squared_error: 0.0225\n",
            "Epoch 29/50\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0249 - mean_squared_error: 0.0249\n",
            "Epoch 30/50\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 0.0241 - mean_squared_error: 0.0241\n",
            "Epoch 31/50\n",
            "5/5 [==============================] - 0s 29ms/step - loss: 0.0257 - mean_squared_error: 0.0257\n",
            "Epoch 32/50\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0239 - mean_squared_error: 0.0239\n",
            "Epoch 33/50\n",
            "5/5 [==============================] - 0s 37ms/step - loss: 0.0265 - mean_squared_error: 0.0265\n",
            "Epoch 34/50\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 0.0246 - mean_squared_error: 0.0246\n",
            "Epoch 35/50\n",
            "5/5 [==============================] - 0s 22ms/step - loss: 0.0232 - mean_squared_error: 0.0232\n",
            "Epoch 36/50\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 0.0234 - mean_squared_error: 0.0234\n",
            "Epoch 37/50\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 0.0233 - mean_squared_error: 0.0233\n",
            "Epoch 38/50\n",
            "5/5 [==============================] - 0s 29ms/step - loss: 0.0234 - mean_squared_error: 0.0234\n",
            "Epoch 39/50\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.0240 - mean_squared_error: 0.0240\n",
            "Epoch 40/50\n",
            "5/5 [==============================] - 0s 28ms/step - loss: 0.0245 - mean_squared_error: 0.0245\n",
            "Epoch 41/50\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 0.0256 - mean_squared_error: 0.0256\n",
            "Epoch 42/50\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.0234 - mean_squared_error: 0.0234\n",
            "Epoch 43/50\n",
            "5/5 [==============================] - 0s 33ms/step - loss: 0.0253 - mean_squared_error: 0.0253\n",
            "Epoch 44/50\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 0.0243 - mean_squared_error: 0.0243\n",
            "Epoch 45/50\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.0236 - mean_squared_error: 0.0236\n",
            "Epoch 46/50\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 0.0239 - mean_squared_error: 0.0239\n",
            "Epoch 47/50\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 0.0226 - mean_squared_error: 0.0226\n",
            "Epoch 48/50\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0228 - mean_squared_error: 0.0228\n",
            "Epoch 49/50\n",
            "5/5 [==============================] - 0s 41ms/step - loss: 0.0236 - mean_squared_error: 0.0236\n",
            "Epoch 50/50\n",
            "5/5 [==============================] - 0s 30ms/step - loss: 0.0231 - mean_squared_error: 0.0231\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa372842c50>"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ],
      "source": [
        "# Initially fits model using training data\n",
        "model.fit(X_train, y_train, epochs=50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "id": "df1cbace-fc65-48f4-a350-c86254b293ba",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "df1cbace-fc65-48f4-a350-c86254b293ba",
        "outputId": "8c2e124f-6f2e-4d84-ce5f-6292b9274d98"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.018404]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 35ms/step - loss: 0.0208 - mean_squared_error: 0.0208\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 31ms/step - loss: 0.0223 - mean_squared_error: 0.0223\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 0.0217 - mean_squared_error: 0.0217\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 30ms/step - loss: 0.0218 - mean_squared_error: 0.0218\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 28ms/step - loss: 0.0222 - mean_squared_error: 0.0222\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.297218]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 30ms/step - loss: 0.0214 - mean_squared_error: 0.0214\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 37ms/step - loss: 0.0226 - mean_squared_error: 0.0226\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 28ms/step - loss: 0.0212 - mean_squared_error: 0.0212\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.0224 - mean_squared_error: 0.0224\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 31ms/step - loss: 0.0205 - mean_squared_error: 0.0205\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.979933]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 33ms/step - loss: 0.0227 - mean_squared_error: 0.0227\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 30ms/step - loss: 0.0213 - mean_squared_error: 0.0213\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 33ms/step - loss: 0.0225 - mean_squared_error: 0.0225\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 33ms/step - loss: 0.0203 - mean_squared_error: 0.0203\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0221 - mean_squared_error: 0.0221\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[32.090954]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 34ms/step - loss: 0.0211 - mean_squared_error: 0.0211\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 41ms/step - loss: 0.0216 - mean_squared_error: 0.0216\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 32ms/step - loss: 0.0201 - mean_squared_error: 0.0201\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 37ms/step - loss: 0.0210 - mean_squared_error: 0.0210\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 28ms/step - loss: 0.0209 - mean_squared_error: 0.0209\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.53026]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 45ms/step - loss: 0.0204 - mean_squared_error: 0.0204\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 0.0217 - mean_squared_error: 0.0217\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0215 - mean_squared_error: 0.0215\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 0.0223 - mean_squared_error: 0.0223\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 29ms/step - loss: 0.0205 - mean_squared_error: 0.0205\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.936348]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 41ms/step - loss: 0.0194 - mean_squared_error: 0.0194\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 44ms/step - loss: 0.0200 - mean_squared_error: 0.0200\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 34ms/step - loss: 0.0195 - mean_squared_error: 0.0195\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 31ms/step - loss: 0.0200 - mean_squared_error: 0.0200\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 30ms/step - loss: 0.0200 - mean_squared_error: 0.0200\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.866653]], dtype=float32)"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 40ms/step - loss: 0.0210 - mean_squared_error: 0.0210\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 33ms/step - loss: 0.0185 - mean_squared_error: 0.0185\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0194 - mean_squared_error: 0.0194\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 0.0181 - mean_squared_error: 0.0181\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.0195 - mean_squared_error: 0.0195\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.985687]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 37ms/step - loss: 0.0185 - mean_squared_error: 0.0185\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0188 - mean_squared_error: 0.0188\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 32ms/step - loss: 0.0164 - mean_squared_error: 0.0164\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 34ms/step - loss: 0.0189 - mean_squared_error: 0.0189\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 0.0166 - mean_squared_error: 0.0166\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.057312]], dtype=float32)"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.0184 - mean_squared_error: 0.0184\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 37ms/step - loss: 0.0177 - mean_squared_error: 0.0177\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 35ms/step - loss: 0.0178 - mean_squared_error: 0.0178\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 22ms/step - loss: 0.0186 - mean_squared_error: 0.0186\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 28ms/step - loss: 0.0182 - mean_squared_error: 0.0182\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.077768]], dtype=float32)"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.0194 - mean_squared_error: 0.0194\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 29ms/step - loss: 0.0164 - mean_squared_error: 0.0164\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 38ms/step - loss: 0.0165 - mean_squared_error: 0.0165\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 37ms/step - loss: 0.0159 - mean_squared_error: 0.0159\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 43ms/step - loss: 0.0169 - mean_squared_error: 0.0169\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.393219]], dtype=float32)"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 30ms/step - loss: 0.0172 - mean_squared_error: 0.0172\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 0.0206 - mean_squared_error: 0.0206\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.0186 - mean_squared_error: 0.0186\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 29ms/step - loss: 0.0164 - mean_squared_error: 0.0164\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 31ms/step - loss: 0.0168 - mean_squared_error: 0.0168\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.805155]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 0.0179 - mean_squared_error: 0.0179\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 0.0152 - mean_squared_error: 0.0152\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 34ms/step - loss: 0.0149 - mean_squared_error: 0.0149\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 31ms/step - loss: 0.0166 - mean_squared_error: 0.0166\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 28ms/step - loss: 0.0163 - mean_squared_error: 0.0163\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.894695]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0162 - mean_squared_error: 0.0162\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0147 - mean_squared_error: 0.0147\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0153 - mean_squared_error: 0.0153\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0163 - mean_squared_error: 0.0163\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0148 - mean_squared_error: 0.0148\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.20881]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0162 - mean_squared_error: 0.0162\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0150 - mean_squared_error: 0.0150\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0155 - mean_squared_error: 0.0155\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0155 - mean_squared_error: 0.0155\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0135 - mean_squared_error: 0.0135\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.67032]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0146 - mean_squared_error: 0.0146\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0157 - mean_squared_error: 0.0157\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0149 - mean_squared_error: 0.0149\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0155 - mean_squared_error: 0.0155\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0146 - mean_squared_error: 0.0146\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.670456]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0155 - mean_squared_error: 0.0155\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0135 - mean_squared_error: 0.0135\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0146 - mean_squared_error: 0.0146\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0135 - mean_squared_error: 0.0135\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 21ms/step - loss: 0.0134 - mean_squared_error: 0.0134\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.069845]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0130 - mean_squared_error: 0.0130\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0142 - mean_squared_error: 0.0142\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0139 - mean_squared_error: 0.0139\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0128 - mean_squared_error: 0.0128\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0134 - mean_squared_error: 0.0134\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.991829]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0140 - mean_squared_error: 0.0140\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0127 - mean_squared_error: 0.0127\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0130 - mean_squared_error: 0.0130\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0138 - mean_squared_error: 0.0138\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0128 - mean_squared_error: 0.0128\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.890495]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0128 - mean_squared_error: 0.0128\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.0134 - mean_squared_error: 0.0134\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0129 - mean_squared_error: 0.0129\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0131 - mean_squared_error: 0.0131\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0127 - mean_squared_error: 0.0127\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.82687]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.0125 - mean_squared_error: 0.0125\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 21ms/step - loss: 0.0141 - mean_squared_error: 0.0141\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0126 - mean_squared_error: 0.0126\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0122 - mean_squared_error: 0.0122\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0124 - mean_squared_error: 0.0124\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.706451]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0124 - mean_squared_error: 0.0124\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0124 - mean_squared_error: 0.0124\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0119 - mean_squared_error: 0.0119\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0123 - mean_squared_error: 0.0123\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0120 - mean_squared_error: 0.0120\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.934916]], dtype=float32)"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0121 - mean_squared_error: 0.0121\n",
            "Epoch 2/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0074 - mean_squared_error: 0.0074\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0074 - mean_squared_error: 0.0074\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.425001]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.0068 - mean_squared_error: 0.0068\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0075 - mean_squared_error: 0.0075\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0068 - mean_squared_error: 0.0068\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 20ms/step - loss: 0.0072 - mean_squared_error: 0.0072\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.047207]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0067 - mean_squared_error: 0.0067\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0073 - mean_squared_error: 0.0073\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.912859]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.469494]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0072 - mean_squared_error: 0.0072\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0076 - mean_squared_error: 0.0076\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0067 - mean_squared_error: 0.0067\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.89129]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0070 - mean_squared_error: 0.0070\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.661516]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0075 - mean_squared_error: 0.0075\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0070 - mean_squared_error: 0.0070\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[31.380444]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0069 - mean_squared_error: 0.0069\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.922508]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.534676]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0072 - mean_squared_error: 0.0072\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.742481]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.914528]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n"
          ]
        }
      ],
      "source": [
        "# Projects future data by using model to predict next close and then uses that value to retrain \n",
        "# the model before making next prediction\n",
        "for i in range(56): \n",
        "    # predicts next close value and reshapes it\n",
        "    prediction_scaled=model.predict(X_train[-1].reshape(1, window_size, 1))\n",
        "    # transforms prediction back to original scale\n",
        "    next_prediction=y_scaler.inverse_transform(prediction_scaled)\n",
        "    # displays predicted close value\n",
        "    display(next_prediction)\n",
        "    # adds predicted close value to list\n",
        "    new_train_sample=np.append(X_train[-1], prediction_scaled)[1:].reshape(1, window_size, 1)\n",
        "    # adds new list entry of predicted close to X train\n",
        "    X_train=np.append(X_train, new_train_sample, axis=0)\n",
        "    # adds new list entry of predicted close to y train\n",
        "    y_train=np.append(y_train, prediction_scaled, axis=0)\n",
        "    # refits model with new close value\n",
        "    model.fit(X_train, y_train, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "id": "5f56a9c9-96b1-4e17-8514-46d59c132ca2",
      "metadata": {
        "id": "5f56a9c9-96b1-4e17-8514-46d59c132ca2"
      },
      "outputs": [],
      "source": [
        "# predicts next close values\n",
        "prediction = model.predict(X_test)\n",
        "# rescales predicted values to original scale\n",
        "y_predicted=y_scaler.inverse_transform(prediction)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i3u1WWiwuVPT",
        "outputId": "8d46ad52-4e07-4113-fd7b-7dad84ce0717"
      },
      "id": "i3u1WWiwuVPT",
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "202"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lYh-Z4Anuq_C",
        "outputId": "c8db78bb-5c99-46bd-c2cf-bd2e1e0e71f6"
      },
      "id": "lYh-Z4Anuq_C",
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df) - len(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G3xuuFo0Tb4l",
        "outputId": "7d7e9427-f8dc-4a1c-d5eb-3a5de57f125b"
      },
      "id": "G3xuuFo0Tb4l",
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "144"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "id": "b63e03e2-d0da-4169-b262-c734ec5a0ef2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "b63e03e2-d0da-4169-b262-c734ec5a0ef2",
        "outputId": "4adb15cf-5811-4d6f-d207-12d2ac86eb84"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            predictions     actual\n",
              "2021-06-21    32.815453  35.040001\n",
              "2021-06-28    34.146427  34.939999\n",
              "2021-07-05    34.380753  34.639999\n",
              "2021-07-12    34.031372  34.259998\n",
              "2021-07-19    33.696766  36.500000\n",
              "2021-07-26    33.401535  36.470001\n",
              "2021-08-02    35.774689  35.360001\n",
              "2021-08-09    35.310661  31.000000\n",
              "2021-08-16    33.840504  31.500000\n",
              "2021-08-23    29.631077  33.310001\n",
              "2021-08-30    31.055893  34.150002\n",
              "2021-09-06    33.441872  33.380001\n",
              "2021-09-13    33.964020  32.400002\n",
              "2021-09-20    32.808769  32.630001\n",
              "2021-09-27    31.838539  32.090000\n",
              "2021-10-04    32.306072  33.150002\n",
              "2021-10-11    31.962324  32.540001\n",
              "2021-10-18    32.962616  33.900002\n",
              "2021-10-25    32.384914  32.880001\n",
              "2021-11-01    33.537880  34.090000\n",
              "2021-11-08    32.544418  33.029999\n",
              "2021-11-15    33.610409  32.110001\n",
              "2021-11-22    32.622963  30.219999\n",
              "2021-11-29    31.702873  28.170000\n",
              "2021-12-06    30.146265  27.969999\n",
              "2021-12-13    28.519560  28.270000\n",
              "2021-12-20    28.546883  28.879999\n",
              "2021-12-27    29.036980  28.379999\n",
              "2022-01-03    29.587559  27.049999\n",
              "2022-01-10    29.232298  27.030001\n",
              "2022-01-17    28.105726  26.770000\n",
              "2022-01-24    27.877247  25.510000\n",
              "2022-01-31    27.756227  24.969999\n",
              "2022-02-07    26.903053  25.490000\n",
              "2022-02-14    26.278416  25.510000\n",
              "2022-02-21    26.436176  27.870001\n",
              "2022-02-28    26.557726  27.950001\n",
              "2022-03-07    27.879868  26.490000\n",
              "2022-03-14    28.477512  27.420000\n",
              "2022-03-21    27.677374  26.590000\n",
              "2022-03-28    27.865000  26.120001\n",
              "2022-04-04    27.550686  26.250000\n",
              "2022-04-11    27.121933  26.250000\n",
              "2022-04-18    27.098036  25.280001\n",
              "2022-04-25    27.156065  25.309999\n",
              "2022-05-02    26.605244  23.790001\n",
              "2022-05-09    26.388868  22.270000\n",
              "2022-05-16    25.488369  24.270000\n",
              "2022-05-23    24.270170  23.870001\n",
              "2022-05-30    24.749437  24.190001\n",
              "2022-06-06    24.943748  22.270000\n",
              "2022-06-13    25.081034  20.549999\n",
              "2022-06-20    24.232201  20.610001\n",
              "2022-06-27    22.913565  20.620001\n",
              "2022-07-04    22.210331  20.450001\n",
              "2022-07-11    21.916794  20.080000\n",
              "2022-07-18    21.695837  20.000000\n",
              "2022-07-19    21.420383  20.549999"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-429c73dc-c06f-471b-b35f-e15de054f3e4\">\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>predictions</th>\n",
              "      <th>actual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-06-21</th>\n",
              "      <td>32.815453</td>\n",
              "      <td>35.040001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-28</th>\n",
              "      <td>34.146427</td>\n",
              "      <td>34.939999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-05</th>\n",
              "      <td>34.380753</td>\n",
              "      <td>34.639999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-12</th>\n",
              "      <td>34.031372</td>\n",
              "      <td>34.259998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-19</th>\n",
              "      <td>33.696766</td>\n",
              "      <td>36.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-26</th>\n",
              "      <td>33.401535</td>\n",
              "      <td>36.470001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-02</th>\n",
              "      <td>35.774689</td>\n",
              "      <td>35.360001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-09</th>\n",
              "      <td>35.310661</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-16</th>\n",
              "      <td>33.840504</td>\n",
              "      <td>31.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-23</th>\n",
              "      <td>29.631077</td>\n",
              "      <td>33.310001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-30</th>\n",
              "      <td>31.055893</td>\n",
              "      <td>34.150002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-06</th>\n",
              "      <td>33.441872</td>\n",
              "      <td>33.380001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-13</th>\n",
              "      <td>33.964020</td>\n",
              "      <td>32.400002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-20</th>\n",
              "      <td>32.808769</td>\n",
              "      <td>32.630001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-27</th>\n",
              "      <td>31.838539</td>\n",
              "      <td>32.090000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-04</th>\n",
              "      <td>32.306072</td>\n",
              "      <td>33.150002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-11</th>\n",
              "      <td>31.962324</td>\n",
              "      <td>32.540001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-18</th>\n",
              "      <td>32.962616</td>\n",
              "      <td>33.900002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-25</th>\n",
              "      <td>32.384914</td>\n",
              "      <td>32.880001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-01</th>\n",
              "      <td>33.537880</td>\n",
              "      <td>34.090000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-08</th>\n",
              "      <td>32.544418</td>\n",
              "      <td>33.029999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-15</th>\n",
              "      <td>33.610409</td>\n",
              "      <td>32.110001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-22</th>\n",
              "      <td>32.622963</td>\n",
              "      <td>30.219999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-29</th>\n",
              "      <td>31.702873</td>\n",
              "      <td>28.170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06</th>\n",
              "      <td>30.146265</td>\n",
              "      <td>27.969999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-13</th>\n",
              "      <td>28.519560</td>\n",
              "      <td>28.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-20</th>\n",
              "      <td>28.546883</td>\n",
              "      <td>28.879999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-27</th>\n",
              "      <td>29.036980</td>\n",
              "      <td>28.379999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-03</th>\n",
              "      <td>29.587559</td>\n",
              "      <td>27.049999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-10</th>\n",
              "      <td>29.232298</td>\n",
              "      <td>27.030001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-17</th>\n",
              "      <td>28.105726</td>\n",
              "      <td>26.770000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-24</th>\n",
              "      <td>27.877247</td>\n",
              "      <td>25.510000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-31</th>\n",
              "      <td>27.756227</td>\n",
              "      <td>24.969999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-07</th>\n",
              "      <td>26.903053</td>\n",
              "      <td>25.490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-14</th>\n",
              "      <td>26.278416</td>\n",
              "      <td>25.510000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-21</th>\n",
              "      <td>26.436176</td>\n",
              "      <td>27.870001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-28</th>\n",
              "      <td>26.557726</td>\n",
              "      <td>27.950001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-07</th>\n",
              "      <td>27.879868</td>\n",
              "      <td>26.490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-14</th>\n",
              "      <td>28.477512</td>\n",
              "      <td>27.420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-21</th>\n",
              "      <td>27.677374</td>\n",
              "      <td>26.590000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-28</th>\n",
              "      <td>27.865000</td>\n",
              "      <td>26.120001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-04</th>\n",
              "      <td>27.550686</td>\n",
              "      <td>26.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-11</th>\n",
              "      <td>27.121933</td>\n",
              "      <td>26.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-18</th>\n",
              "      <td>27.098036</td>\n",
              "      <td>25.280001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-25</th>\n",
              "      <td>27.156065</td>\n",
              "      <td>25.309999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-02</th>\n",
              "      <td>26.605244</td>\n",
              "      <td>23.790001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-09</th>\n",
              "      <td>26.388868</td>\n",
              "      <td>22.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-16</th>\n",
              "      <td>25.488369</td>\n",
              "      <td>24.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-23</th>\n",
              "      <td>24.270170</td>\n",
              "      <td>23.870001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-30</th>\n",
              "      <td>24.749437</td>\n",
              "      <td>24.190001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-06</th>\n",
              "      <td>24.943748</td>\n",
              "      <td>22.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-13</th>\n",
              "      <td>25.081034</td>\n",
              "      <td>20.549999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-20</th>\n",
              "      <td>24.232201</td>\n",
              "      <td>20.610001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-27</th>\n",
              "      <td>22.913565</td>\n",
              "      <td>20.620001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-07-04</th>\n",
              "      <td>22.210331</td>\n",
              "      <td>20.450001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-07-11</th>\n",
              "      <td>21.916794</td>\n",
              "      <td>20.080000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-07-18</th>\n",
              "      <td>21.695837</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-07-19</th>\n",
              "      <td>21.420383</td>\n",
              "      <td>20.549999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-429c73dc-c06f-471b-b35f-e15de054f3e4')\"\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",
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              "      \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",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-429c73dc-c06f-471b-b35f-e15de054f3e4 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-429c73dc-c06f-471b-b35f-e15de054f3e4');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ],
      "source": [
        "comparison_df = pd.DataFrame({'predictions':y_predicted.ravel(),'actual':df.iloc[144:]['close']})\n",
        "comparison_df\n",
        "#display(y_predicted)\n",
        "#display(df.iloc[760:]['close'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(comparison_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9nJkPr5CiOs",
        "outputId": "5987cc96-ea02-476c-fff4-6dea7ae509c3"
      },
      "id": "v9nJkPr5CiOs",
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "id": "7f8c4b76-378b-424c-af97-c7b60db7c4e5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "7f8c4b76-378b-424c-af97-c7b60db7c4e5",
        "outputId": "180969d0-e14f-496c-992c-f51ff251af46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa3702f71d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 122
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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EERPiT5o9mLGJ4e1+5sTkSEbEh/Lvb/K1M1OpLtIE7gGdbYE3NjnYW1LV6U0kbDbh6snJrNlb2q0VCt/bVEBNQxNXZiSdcnxaegzXT03h2ZV7+aKd8kkLf18bT16XwWNXT+hwsw0R4YZpqewoOE52/pkjXpRSHdME7gGdbYHnl1bT0GRc7sA82RUZifj5CK90Y6nW17IPMCwuhPGttK5/On8EiZFBNDpMu+WTFpNSIhmd0H7ru8Ul4wcRFujL8yv3dTZkpRSawD0iLNAXf1+by2PBW9ZA6co+nDEudGauyC3hp29ubnX2484jFWw6UMaVGUmttpqDA3z5+7WTuH3WkFYTfHcM8PflyowkPt56hMLjtW69tlL9gSZwDxCR5sk8LrbAW8ofQ+xd29jomszmzsxPtp3ZmbnzSAW3vZDNa9kHWPDESg6V1Zzy+mtrD+DnI1w+MbHN649JDOeB80d0aQ/SjnwvK4UmY3h5tQ4pVKqzXNnUOFBE1ojIJhHZJiK/dh5/SUR2ishWEXlGRPw6ulZ/EhPamQReSUJE0IkOxM6aNqS5M/Ol05Lg0co6bnl+LcEBvjx69XiOHK/l8r9/zfbDx4HmDaIXbzjI3JFxJ7Zu62mpMcGcM8zOy2v2U9/o8EoMSlmVKy3wOmCOMWYcMB6YLyJZwEvACGAMEATc6rEoLcge4u/y5sa5RZUM6UL9u8WpnZnN5Zj6Rgd3vLie4oo6nro+g0vGJ/DG7VMRhCuf/Iav80pYsr2QsuoGrpqc3OXPdofrp6VSXFHHx638BaGUapsrmxobY0zLsnd+zh9jjPnQ+ZoB1gBt/w3eD9ldbIE7HIbdxZVd6sA82YJJifjahFfX7McYwy/e3sqafaX8YcHYE7XrEfFhvHXnNBIigrjx2TU8/MlOBoUHMiO9extEd9esoXZSogfwwjf7vBqHUlbjUg1cRHxEZCNQBCwxxqw+6TU/4Drg4zbeu1BEskUku7i4/+xKbg8JoLSqjqYOptMfKquhtsHR7QRuDw1g3qh43lx/kCe+3MNr2Qe4a076ic0UWgwMD+L126cyKSWS/KPVLMhIanX7tp5kswnXZaWwdt8xth3W9VGUcpVLCdwY02SMGU9zKztTREaf9PLfga+MMcvbeO9TxpgMY0yG3W6N1QTdISY0AIeB0qr2yyi5zg7Mzo4Bb01LZ+ZDH+cwb1Qc935rWKvnhQf58fzNmTy8YCy3zUzr9ue6wxWTkgjy8+GFb/K9HYpSltGpUSjGmDJgGTAfQER+BdiBH7s/NGuzuziZp6Vm7Y4EPm1INMPiQhg1KIw/XzW+3Uk3Ab4+XJGR1OWOU3cLH+DHpRMSeHvjIcqqO7eSo1L9lSujUOwiEuF8HATMBXJE5FZgHnCNMUaHD5wmxsXJPLmFlcSEBBAxoPujQGw2YdEd03j7zukM8O8dibkzrp+aQm2DgzeydbMHpVzhSgt8ILBMRDYDa2mugb8PPAHEAd+IyEYR+aUH47QcVzc3znNDB+bJQgP9OtySrbc6a2AYE5MjeGfTIW+HopQldNhMM8ZsBia0ctx6Tbwe1DKdvr0SijGGvMJKLpvY/sp9/cnMYXYe/TyX8pqGM3bzUUqdyppNNQsIDvAlyM+n3RZ44fE6Kuoa3VL/7iumDI7GGMjeV+rtUJTq9TSBe5A9NKDdFrg7OzD7ignJEfj72li156i3Q1Gq19ME7kExIf7tLmjVMoRwaCfXAe/LAv18mJAUwao92gJXqiOawD2oo82N84oqCQ/yIybEO+uQ9FZZadFsO1zO8VrXt6RTqj/SBO5B9tCADlrgzSNQPLHKn5VNSYvCoXVwpTqkCdyDYkICKK2qp6Gp9WHyzduoaf37dBOTI/H3sWkZRakOaAL3oJahhK1Npz9aWUdpVb0m8FYE+vkwPjlCOzKV6oAmcA+KaWcyz+aDzYs2nTUwrEdjsoqstGi2HtI6uFLt0QTuQSf2xmylDv5VbjEBvjYmpUT2dFiWkOWsg6/bpxseK9UWTeAe1N50+hW5JWQOjiLQz6enw7KE/9TBtYyiVFs0gXtQTBsrEhaU15BbVMnMof1ned3OCvTzYXyS1sGVao8mcA8K8vchNMD3jBb48twSAGYM9e5OOL1dVloUWw6VU6F1cKVapQncw1rb3HhFbgkxIQGMiNcZmO3JSot2jgfXOrhSrdEE7mH2kFPXQ3E4DCvySpg5NEYn8HRgQnIkfj7Cqr1aRlGqNZrAPSwm1P+UFvj2guOUVtVr+cQFQf4tdXCd0KNUazSBe1hzC/w/E3m+ym3e2NnbO8FbRct4cK2DK3UmTeAeZg8NoLymgbrGJqC5/j0iPpTYsEAvR2YNWWnRNDkM2flaB1fqdJrAPaxlKOHRynqq6xvJ3neMs7V84rKJzjr4ai2jKHUG3RbNw+wnbW68s7CC+iYHZ+v4b5cF+fswLlHHgyvVGld2pQ8UkTUisklEtonIr53HB4vIahHJE5HXREQXtW7FyeuhrMgtwd/XRubgKC9HZS1ZadFsOVROZV2jt0NRqldxpYRSB8wxxowDxgPzRSQLeAj4szEmHTgG3OK5MK3r5M2Nl+cWk5mq0+c7K3NwFE0Ow4b9WgdX6mQdJnDTrNL51M/5Y4A5wJvO488Dl3okQouLdu62s+VQObsKK7X+3QUTUyKxCazdq3VwpU7mUiemiPiIyEagCFgC7AbKjDEtf9MeBBLaeO9CEckWkezi4mJ3xGwpAb4+hAf58cGWAgCtf3dBSIAvowaFs0Z36FHqFC4lcGNMkzFmPJAIZAIjXP0AY8xTxpgMY0yG3d4/k5c9NICy6gZiQvx1+nwXTU6NYsP+MuobW9/dSKn+qFPDCI0xZcAyYCoQISIto1gSgUNujq3PaNm0eEZ6DDabTp/viszBkdQ1OthyqNzboSjVa7gyCsUuIhHOx0HAXGAHzYl8gfO0G4B3PBWk1dlDmyftaPmk6zJSm0furNUyilInuNICHwgsE5HNwFpgiTHmfeCnwI9FJA+IBp72XJjW1rKxg3Zgdl1MSABp9mDWuLEjc+eRCmobmtx2PaV6WocTeYwxm4EJrRzfQ3M9XHXg2inJDI0L0enz3ZSZGsWHWwpwOEy3S1Ff7CzixmfXMiE5gmdvnEzEAJ3GoKxHp9L3gPTYEK7JTPZ2GJY3OTWK47WN7Cys6NZ1jlbW8V9vbiYxMohth49zxRPfUFBe46Yoleo5msCVZbTMYO1OHdwYwwOLt1Be3cA/r8/g+ZsyKSiv5Tt/X0leUWXHF1CqF9EEriwjMTKI+LDAbtXBX1t7gCXbC7l//nDOGhjG1CHRvLowi/omB1c8sZJNB8rcGLFSnqUJXFmGiDB5cBRr95VijOn0+/eWVPHr97YzPT2am6cPPnF8dEI4b94+jZBAX6755ypW5pW4M2ylPEYTuLKUzNRICo/XcaC0czXrhiYH97y2EX9fG3+8YtwZnaCpMcEsun0aA8MD+enizV36BaFUT9MErixlsrMO3tlp9X9dmsemA2X8/rIxDAwPavWc2LBAbp81hAOlNWw6qBOGVO+nCVxZyrDYUMKD/Dq1sFX2vlIeX5rL5RMTuHDswHbPPW9UPP4+Nt7deLi7oSrlcZrAlaXYbEJGSqTLI1GKjtfyg5fWkxQ1gF9fPKrD88OD/Jg13M77mw/T5NAyiurdNIEry5k8OIo9JVUUV9S1e159o4MfvLSeitpGnrxuEqGBfi5d/9vjBlFUUafT9lWvpwlcWc5k57oo2R0k2P/5YDvZ+cd4aMFYRsSHuXz9b50VS5CfD+9t0jKK6t00gSvLGZMQTqCfrd2OzDfXHeTf3+Rz64zBXDxuUKeuP8Dfl3PPiuWjrUdoaNLla1XvpQlcWY6/r43xSRFtlji2Hirnwbe2kJUWxQPnu7x0/Sm+PW4QpVX1rNytmymr3ksTuLKkzNQoth8+TkVtwynHS6vque2FdUQF+/P4tRPx9enaV/yc4XZCA321jKJ6tQ5XI1SqN5o8OArHUrj9xXUIQllNPWXVDRytrKfJYXjj9qnEOJfx7YoAXx/mjYrnk61H+N1lownwtc5G1MYYRHTjkP5AW+DKkialRDImIZyC8lqq6xuJDQ0kMzWKqzOTePamyYxLiuj2Z3x73CAq6hr5cqd19nL92eLN3PjsWp1J2k9oC1xZ0gB/X967a4ZHP2PakGiigv15d9NhzhsV79HPcoePtxbwypoDAHy5q5hzhsd6OSLladoCV6oNfj42zh8dz+c7iqiub/R2OO0qr27gF+9s46yBYSREBPHXpXnaCu8HNIEr1Y5vjxtETUMTn+0o8nYo7frdh9sprarn4QVjuX1WGuvyj7Fqj05E6utc2dQ4SUSWich2EdkmInc7j48XkVUislFEskVEt1dTfU5mahRxYQG9ejTKitwSXs8+yMKZaYxOCOeKjCTsoQE8vizX26EpD3OlBd4I3GeMGQlkAXeKyEjgD8CvjTHjgV86nyvVp9hswkVjB/HlzmLe2Xio15UlqusbeWDxZtJigrn73KEABPr5cNvMNL7OO8q6/GNejlB5UocJ3BhTYIxZ73xcAewAEgADtMxPDgd6bxNFqW64ZcZghseHcverG7nqqVVsP3zc2yGd8MdPdnHwWA0PLRhLoN9/hjpeOyWZyAF+/G1ZnhejU57WqRq4iKTSvEP9auAe4GEROQD8EfhZG+9Z6CyxZBcXW2c4llItBkUE8fad0/nfy8eQV1TJRX9dzi/f2UpZdb1X41q//xjPrtzL9VNTTqwP02KAvy+3np3G0pwith7Stc37KpcTuIiEAIuAe4wxx4E7gHuNMUnAvcDTrb3PGPOUMSbDGJNht9vdEbNSPc7HJlyTmcyy+87huqwUXlyVz+w/fsH/friDdfmlOHp46VmHw/DAos0MDAvk/vmtLxdw3dQUQgN9W22FF1XU8tfPc9mwX0ssViau1PRExA94H/jEGPOI81g5EGGMMdI87avcGNPukm8ZGRkmOzvbDWEr5V07Co7zx0928lVuMQ1NhpiQAOaOjOW8kfFMS4/2+MzNzQfLuPjxr3l4wViuyEhq87xHPt3JY0vz+PTemQyLC+VQWQ1PfrmbV9ceoL7Rgb+Pjd9eOoqrJid7NF7VPSKyzhiTcfrxDifyOJPz08COluTtdBiYBXwBzAG0y1v1G2cNDOPpGydzvLaBL3YW8+m2I7y3qXkiTVZaFK98P8uj09mX5hQhAueeFdfueTdNH8y/Vuzl/z7KwR4SwKL1BxGByyck8t2sZB7+ZCc/XbSF7YeP8/8uGolfF9eOUd7hykzM6cB1wBYR2eg89nPg+8CjIuIL1AILPROiUr1XWKAfF48bxMXjBlHX2MSTX+7hkSW7WLO3lClp0R773KU5RUxIiiAq2L/d8yKD/bkuK4Unv9qDv6+N705JZuGsISRENO8L+uyNk3no4xz+uXwvOwsr+Nu1E4nuxhoyqmd1mMCNMSuAtpoSk9wbjlLWFeDrw/fPTuO5lfv45/I9HkvgRRW1bD5Yzk/OG+bS+T+ck05iZBDzRscTGxp4ymu+PjYevHAkIweF8dNFW7j48a956vpJjBoU7onQlZvp30tKuVGQvw/XZaXw2Y4i8ooqPPIZXzgX15o9wrW1TkID/bhuauoZyftkl01I5I3bptLkMFz91CpyjvSeoZKqbZrAlXKz66emEOBr41/L93rk+styiogPC2TkQNe3iXPFuKQI3rxjKgP8fbjhmTUcPFbt1usr99MErpSbRYcEsGBSIovXH6Kootat165vdLA8t4TZI+we6SRNjBzAv2+eQk19E9c/s4bSKu+OdVft0wSulAfcenYaDQ4H/16Z79brZu8rpbKukdkeXCp2eHwo/7phMgeP1XDTc2t7/UqM/ZkmcKU8YHBMMOeNjOOFVfluTYBLc4rw97ExPT3GbddsTebgKP56zQS2HCzjBy+t182deynd0EEpD1k4M41PthXy+toD3Dh9sFuuuXRnEVPSoggO8Pz/uvNGxfO7y8bws8Vb+PHrm7h8YgJhgb6EBfoRFuRHSIAvxRV17C2pYndxJXtLqthbUkVS5AD+9/Ix2Gy6rZunaQJXykMmpUQxMTmCp7/ey/eyUrq8wXKLfZFNKvcAABFaSURBVCVV7Cmu4rqsFDdF2LFrMpMpqajjT0t2dbikbligLwPDg1i5+yjD40O5eYZ7fmmptmkCV8qDFs5M4/YX1/PJtkIuHDuwW9damtO8qcQcF4cPustd5w7l0gkJlFTWcby2keM1DVTUNnK8toGoYH/SYoJJs4cQOcAPgFufz+b/Ps5henoMw+NDezTW/kYTuFIeNHdkPKnRA3jqq91cMCa+WyNHlu0sIs0eTEp0sBsjdE1S1ACSoga4dO5DC8Yy/y9fcferG3jnh9M9vi5Mf6admEp5kI9NuPXsNDYdLOeTbYVdvk5VXSOr95Rybg+3vrsiJiSAh74zlpwjFfzp013eDqdP0wSulIddNTmJEfGh/Pe726is69qIlBV5JdQ3OVyefelt554Vx/eykvnn8j2szCvxdjh9liZwpTzMz8fG7y8fQ2FFLY90sUW6LKeI0ADfMzZu6M0evGAkg2OCue+NTZRXN3g7nD5JE7hSPWBiciTXZibz3Mq9nd4hxxjDsp1FnD0sxlLLvQb5+/CXq8ZTXFHHg29v6XX7ifYF1vk2KGVx988fQVRwAD9/awtNLu7gU1HbwMtr9lN4vM6jsy89ZWxiBPfOHcb7mwu46slVbDpQ5u2Q+hQdhaJUDwkP8uMXF53F3a9u5MVV+dwwLbXV84oqavlsexGfbj/Cyryj1Dc5SI4awLc62Lyht7pj1hAiBvjx5yW7uORvX3PJ+EH817zhJEa6NqpFtc2lLdXcRbdUU/2dMYbrn1nDhv1lfH7fLOLCmpd4rW908On2I7y0aj+r9h7FGEiOGsB5I+OYNzqeicmR+Fh8ZmNFbQNPfLmbfy3fiwFunj6Yu+ak98isUqtra0s1TeBK9bB9JVWc95evmDsyjgfmj+CVNft5PfsAJZX1JEQEcUVGIvNHxzM8LtSj27J5y+GyGv74yU4WbzjE1ZOT+L/vjPV2SL1el/fEVEq5V2pMMD+cnc4jS3bx4ZYCBJgzIo7vZiUzc6jd8i3tjgyKCOKRq8YT6O/Dm+sOOvsG2t8aTrVOE7hSXnDbrDT2Ha0iMSKIqzOTGeTco7I/uWlaKi+v3s8ra/Zz5+x0b4djSR2OQhGRJBFZJiLbRWSbiNx90mt3iUiO8/gfPBuqUn1HgK8Pj1w5nh+fN7xfJm+AoXGhzEiP4cVV+bpcbRe5MoywEbjPGDMSyALuFJGRIjIbuAQYZ4wZBfzRg3EqpfqgG6elUlBeyyfbjng7FEvqMIEbYwqMMeudjyuAHUACcAfwf8aYOudrRZ4MVCnV98weEUty1ACe+3qft0OxpE5N5BGRVGACsBoYBpwtIqtF5EsRmdzGexaKSLaIZBcXF3c3XqVUH+JjE66fmkJ2/jG2HOzcDFXViQQuIiHAIuAeY8xxmjtAo2guq/wX8Lq0MubJGPOUMSbDGJNht9vdFLZSqq+4cnISA/x9eG7lPm+HYjkuJXAR8aM5eb9kjFnsPHwQWGyarQEcgGc36lNK9TlhgX4smJTIe5sOU1xR5+1wLMWVUSgCPA3sMMY8ctJLbwOznecMA/wBXTdSKdVp109Npb7JwStr9ns7FEtxpQU+HbgOmCMiG50/FwDPAGkishV4FbjB6HJjSqkuSI8NYeYwOy+uyqe+UYcUuqrDiTzGmBVAW1PDvufecJRS/dVN01K56bm1fLS1gEvGJ3g7HEvQ5WSVUr3CrGF2BscE87x2ZrpME7hSqlew2YQrM5JYv7+MQ2U13g7HEjSBK6V6jXmjmtc8/1RnZrpEE7hSqtdIs4cwLC6Ej7dqAneFJnClVK8yb1Q8a/eVcrRSx4R3RBO4UqpXmTcqHoeBz3YUuuV69Y0Ocgsr3HKt3kYTuFKqVxk1KIyEiCA+2db1BO5wGNbuK+XBt7Yw5fefMffPX7Fku3t+IfQmuqGDUqpXERHmj47nhW/yqahtIDTQz+X37j9azStr9/PuxsMcKqsh0M/G3JHxrM8/xj++yGPuSGtuDN0WTeBKqV5n3qh4nl6xly92FvPtcYM6PD//aBWPL81j8YZDAMxIj+En84Zx3sh4ggN8eX7lPn717jay95WSkRrl6fB7jCZwpVSvMyklkpgQfz7edqTdBL7/aDWPL8tl0fpD+NqEG6amctusNOLCAk8574qMRP782S6e/GqPJnCllPIkH5swd2Qc7248TG1DE4F+Pqe87nAYfvP+dl5YlX9iTfE7Zg0h9rTE3WKAvy/XZ6Xw12V57C6uZIg9pCduw+O0E1Mp1SvNGxVPVX0TK3efucjpo5/n8tzKfVyZkcTy+2fzq2+PajN5t7h+Wir+Pjb+tXyvp0LucZrAlVK90rQhMYQG+J4xqefjrUd49PNcvjMxkd9fNvqMcklbYkIC+M6kRBatP9hn1h3XBK6U6pX8fW3MOSuWz3YU0ejctX7nkQp+/PpGxiVF8LvLRtPKJmDtunXGYBqaHPz7m33uD9gLNIErpXqteaPiKa2qZ+2+Y5RV1/P9f2cTHODLk9+bdEZd3BVp9hDmnhXHC6vyqa5v9EDEPUsTuFKq15o1zE6Ar40PtxRw1ysbKCiv4YnvTSQ+3LWySWtum5VGWXUDr6894MZIvUMTuFKq1woO8OXsoXZeWJXP8twS/ufS0UxK6d4wwEkpUUxKieRfK/aeKM1YlSZwpVSvdv7oeACun5rCVZOT3XLNhTPTOHisho8svuqhK5saJ4nIMhHZLiLbROTu016/T0SMiOiO9Eopt7tsQgLP3TSZX1w00m3XnHtWHImRQSxef9Bt1/QGV1rgjcB9xpiRQBZwp4iMhObkDpwH6FbSSimPsNmEc4bH4ufjvoKBzSZ866w4Vu4+Sk19k9uu29M6/DdijCkwxqx3Pq4AdgAtO47+Gbgf0N3olVKWMmdELHWNDr7Zc+ZEIavo1K80EUkFJgCrReQS4JAxZlMH71koItkikl1cXNzlQJVSyp2mpEUxwN+Hz3cUeTuULnM5gYtICLAIuIfmssrPgV929D5jzFPGmAxjTIbdbu9yoEop5U4Bvj5MT49hWU4RxliziOBSAhcRP5qT90vGmMXAEGAwsElE9gGJwHoRifdUoEop5W7njojlcHktOy26Y48ro1AEeBrYYYx5BMAYs8UYE2uMSTXGpAIHgYnGGGuPyVFK9SuzR8QCsDTHmmUUV1rg04HrgDkistH5c4GH41JKKY+LCwtk1KAwllq0Dt7heuDGmBVAuyvGOFvhSillOXNGxPK3ZXkcq6onMtjf2+F0is7EVEr1a3NGxOIw8FWu9UbJaQJXSvVr4xIjiA72t+RwQk3gSql+zWYTZg238+WuYsstbqUJXCnV7507Io7ymgY2HCjzdiidoglcKdXvnT0sBl+bWG44oSZwpVS/FxboR0ZqpOWGE2oCV0opmkej7Cys4FBZjbdDcZkmcKWUAuaMiAOsNStTE7hSSgFD7MEkRw1g6Y5Cb4fiMk3gSikFiAjnjYxjRV4J+UervB2OSzSBK6WU08KZafjabPzh453eDsUlmsCVUsopNiyQ789M44MtBazff8zb4XRIE7hSSp3ktplpxIQE8PsPdvT6jR40gSul1EmCA3y5d+5QsvOP8cm23t2hqQlcKaVOc1VGEumxITz0cQ4NvXh9FE3gSil1Gl8fGz87fwR7S6p4efV+b4fTJk3gSinVijkjYslKi+LRz3M5Xtvg7XBapQlcKaVaISI8eMFISqvqeeKL3d4Op1WawJVSqg1jEsO5ZPwgnl6xl62Hyr0dzhlc2ZU+SUSWich2EdkmInc7jz8sIjkisllE3hKRCM+Hq5RSPev++SOIHODP5f9YyUur83vV0EJXWuCNwH3GmJFAFnCniIwElgCjjTFjgV3AzzwXplJKeUdCRBAf/GgGUwZH8eBbW7nntY1U1TV6OyzAhQRujCkwxqx3Pq4AdgAJxphPjTEtd7EKSPRcmEop5T3RIQE8f1Mm980dxnubDnPx4yvYVVjh7bA6VwMXkVRgArD6tJduBj5q4z0LRSRbRLKLi62367NSSkHz3pl3nTuUF2+ZQnlNIxc/voLnV+7z6j6aLidwEQkBFgH3GGOOn3T8QZrLLC+19j5jzFPGmAxjTIbdbu9uvEop5VXT0mP48O4ZTE6N4lfvbuP8R5fz1S7vNE5dSuAi4kdz8n7JGLP4pOM3AhcB3zW9qbKvlFIeFBsayL9vzuTJ6yZR3+Tg+mfWcPNza8krquzROFwZhSLA08AOY8wjJx2fD9wPXGyMqfZciEop1fuICPNGxfPpvTP5+QUjWLu3lPl/+Yrfvr+d2oamnomho4aziMwAlgNbgJZiz8+Bx4AA4Kjz2CpjzO3tXSsjI8NkZ2d3K2CllOqNSirr+NOnu3hlzX7SY0N49OrxjBoU7pZri8g6Y0zGGcd7svKhCVwp1dctzy3mvtc3UVbdwH/NG84tMwZjs0m3rtlWAteZmEop5UZnD7Xz8T0zOWe4nd99uIPrnlnNkfJaj3yWJnCllHKzqGB/nrxuEv97+RjW55cx/9GvWLXnaMdv7CRN4Eop5QEiwjWZyXzwoxmMSQgnJXqA2z/D1+1XVEopdUKaPYQXbpnikWtrC1wppSxKE7hSSlmUJnCllLIoTeBKKWVRmsCVUsqiNIErpZRFaQJXSimL0gSulFIW1aOLWYlIMZDfxbfHACVuDKe36Iv3pfdkHX3xvvriPaUYY87YEadHE3h3iEh2a6txWV1fvC+9J+voi/fVF++pLVpCUUopi9IErpRSFmWlBP6UtwPwkL54X3pP1tEX76sv3lOrLFMDV0opdSortcCVUkqdRBO4UkpZlCUSuIjMF5GdIpInIg94O56uEJFnRKRIRLaedCxKRJaISK7zn5HejLGzRCRJRJaJyHYR2SYidzuPW/2+AkVkjYhsct7Xr53HB4vIauf38DUR8fd2rJ0lIj4iskFE3nc+7wv3tE9EtojIRhHJdh6z9HfQVb0+gYuID/A34HxgJHCNiIz0blRd8hww/7RjDwCfG2OGAp87n1tJI3CfMWYkkAXc6fxvY/X7qgPmGGPGAeOB+SKSBTwE/NkYkw4cA27xYoxddTew46TnfeGeAGYbY8afNP7b6t9Bl/T6BA5kAnnGmD3GmHrgVeASL8fUacaYr4DS0w5fAjzvfPw8cGmPBtVNxpgCY8x65+MKmhNDAta/L2OMqXQ+9XP+GGAO8KbzuOXuS0QSgQuBfzmfCxa/p3ZY+jvoKisk8ATgwEnPDzqP9QVxxpgC5+MjQJw3g+kOEUkFJgCr6QP35Sw1bASKgCXAbqDMGNPoPMWK38O/APcDDufzaKx/T9D8y/VTEVknIgudxyz/HXSFbmrcSxhjjIhYckyniIQAi4B7jDHHmxt2zax6X8aYJmC8iEQAbwEjvBxSt4jIRUCRMWadiJzj7XjcbIYx5pCIxAJLRCTn5Bet+h10hRVa4IeApJOeJzqP9QWFIjIQwPnPIi/H02ki4kdz8n7JGLPYedjy99XCGFMGLAOmAhEi0tLosdr3cDpwsYjso7kMOQd4FGvfEwDGmEPOfxbR/Ms2kz70HWyPFRL4WmCos7fcH7gaeNfLMbnLu8ANzsc3AO94MZZOc9ZQnwZ2GGMeOeklq9+X3dnyRkSCgLk01/eXAQucp1nqvowxPzPGJBpjUmn+f2ipMea7WPieAEQkWERCWx4D5wFbsfh30FWWmIkpIhfQXL/zAZ4xxvzOyyF1moi8ApxD81KXhcCvgLeB14FkmpfZvdIYc3pHZ68lIjOA5cAW/lNX/TnNdXAr39dYmju+fGhu5LxujPmNiKTR3HqNAjYA3zPG1Hkv0q5xllB+Yoy5yOr35Iz/LedTX+BlY8zvRCQaC38HXWWJBK6UUupMViihKKWUaoUmcKWUsihN4EopZVGawJVSyqI0gSullEVpAldKKYvSBK6UUhb1/wEAQxdDGbeyagAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plots the predicted test values\n",
        "plt.plot(y_predicted)\n",
        "# Plots the true historic close values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "id": "d0cb5b07-4c3e-4e34-bfb9-1785d69480b9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "d0cb5b07-4c3e-4e34-bfb9-1785d69480b9",
        "outputId": "0dba453f-efef-446e-b793-94370f7d2359"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([737972., 738034., 738095., 738156., 738215., 738276., 738337.]),\n",
              " <a list of 7 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 123
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plots the true historic close values\n",
        "plt.plot(df.iloc[144:]['close'])\n",
        "# Rotates X labels\n",
        "plt.xticks(rotation = 90)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "id": "9ba38db2-c1c1-4348-84cd-096bcff635e6",
      "metadata": {
        "id": "9ba38db2-c1c1-4348-84cd-096bcff635e6"
      },
      "outputs": [],
      "source": [
        "# Begins Future Projections Section"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "id": "01285ba3-329d-4d3b-b680-c6999f9ca1db",
      "metadata": {
        "id": "01285ba3-329d-4d3b-b680-c6999f9ca1db"
      },
      "outputs": [],
      "source": [
        "# Creates neural network\n",
        "model_prediction=Sequential()\n",
        "# Defines and adds LSTM layer\n",
        "first_lstm_layer=LSTM(units=30, return_sequences=True, input_shape=(window_size, 1))\n",
        "model_prediction.add(first_lstm_layer)\n",
        "# Randomly drops 20% of defined nodes\n",
        "first_dropout_layer=Dropout(0.2)\n",
        "# Defines and adds LSTM layer\n",
        "model_prediction.add(first_dropout_layer)\n",
        "second_lstm_layer=LSTM(units=30, return_sequences=True)\n",
        "model_prediction.add(second_lstm_layer)\n",
        "# Randomly drops 20% of defined nodes\n",
        "second_dropout_layer=Dropout(0.2)\n",
        "# Defines and adds LSTM layer\n",
        "model_prediction.add(second_dropout_layer)\n",
        "third_lstm_layer=LSTM(units=30)\n",
        "model_prediction.add(third_lstm_layer)\n",
        "# Defines and adds output layer with linear activation\n",
        "output_layer=Dense(units=1, activation='linear')\n",
        "model_prediction.add(output_layer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "id": "8307f72d-406e-4062-94c1-e340b8c6fe64",
      "metadata": {
        "id": "8307f72d-406e-4062-94c1-e340b8c6fe64"
      },
      "outputs": [],
      "source": [
        "# Compiles model\n",
        "model_prediction.compile(loss='mean_squared_error', optimizer='adam', metrics='mean_squared_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "id": "9cd8c1fd-5243-4e62-b32a-999c6383262f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cd8c1fd-5243-4e62-b32a-999c6383262f",
        "outputId": "53e3e60e-6ec0-428e-e357-abb4f4ba414b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_18 (LSTM)              (None, 10, 30)            3840      \n",
            "                                                                 \n",
            " dropout_12 (Dropout)        (None, 10, 30)            0         \n",
            "                                                                 \n",
            " lstm_19 (LSTM)              (None, 10, 30)            7320      \n",
            "                                                                 \n",
            " dropout_13 (Dropout)        (None, 10, 30)            0         \n",
            "                                                                 \n",
            " lstm_20 (LSTM)              (None, 30)                7320      \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 1)                 31        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 18,511\n",
            "Trainable params: 18,511\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Provides summary of model\n",
        "model_prediction.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "id": "64894b6e-7d7c-48a9-82f8-571224e36ad1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "64894b6e-7d7c-48a9-82f8-571224e36ad1",
        "outputId": "1ba94166-a196-4f65-d94d-bc2447e690bf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(191, 10)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "X_scaled=X_scaled.reshape(X_scaled.shape[0], X_scaled.shape[1], 1)\n",
        "display(X.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "id": "090c190c-b545-464a-8879-cf3b0486dc7a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "090c190c-b545-464a-8879-cf3b0486dc7a",
        "outputId": "57273bcf-67d1-4cc3-a935-7e7430ac306f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "6/6 [==============================] - 5s 23ms/step - loss: 0.3133 - mean_squared_error: 0.3133\n",
            "Epoch 2/50\n",
            "6/6 [==============================] - 0s 23ms/step - loss: 0.1299 - mean_squared_error: 0.1299\n",
            "Epoch 3/50\n",
            "6/6 [==============================] - 0s 24ms/step - loss: 0.0441 - mean_squared_error: 0.0441\n",
            "Epoch 4/50\n",
            "6/6 [==============================] - 0s 23ms/step - loss: 0.0467 - mean_squared_error: 0.0467\n",
            "Epoch 5/50\n",
            "6/6 [==============================] - 0s 26ms/step - loss: 0.0296 - mean_squared_error: 0.0296\n",
            "Epoch 6/50\n",
            "6/6 [==============================] - 0s 27ms/step - loss: 0.0348 - mean_squared_error: 0.0348\n",
            "Epoch 7/50\n",
            "6/6 [==============================] - 0s 25ms/step - loss: 0.0272 - mean_squared_error: 0.0272\n",
            "Epoch 8/50\n",
            "6/6 [==============================] - 0s 26ms/step - loss: 0.0269 - mean_squared_error: 0.0269\n",
            "Epoch 9/50\n",
            "6/6 [==============================] - 0s 24ms/step - loss: 0.0262 - mean_squared_error: 0.0262\n",
            "Epoch 10/50\n",
            "6/6 [==============================] - 0s 28ms/step - loss: 0.0259 - mean_squared_error: 0.0259\n",
            "Epoch 11/50\n",
            "6/6 [==============================] - 0s 23ms/step - loss: 0.0259 - mean_squared_error: 0.0259\n",
            "Epoch 12/50\n",
            "6/6 [==============================] - 0s 27ms/step - loss: 0.0244 - mean_squared_error: 0.0244\n",
            "Epoch 13/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0243 - mean_squared_error: 0.0243\n",
            "Epoch 14/50\n",
            "6/6 [==============================] - 0s 20ms/step - loss: 0.0233 - mean_squared_error: 0.0233\n",
            "Epoch 15/50\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0250 - mean_squared_error: 0.0250\n",
            "Epoch 16/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0240 - mean_squared_error: 0.0240\n",
            "Epoch 17/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0227 - mean_squared_error: 0.0227\n",
            "Epoch 18/50\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0228 - mean_squared_error: 0.0228\n",
            "Epoch 19/50\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0220 - mean_squared_error: 0.0220\n",
            "Epoch 20/50\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0220 - mean_squared_error: 0.0220\n",
            "Epoch 21/50\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0220 - mean_squared_error: 0.0220\n",
            "Epoch 22/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0215 - mean_squared_error: 0.0215\n",
            "Epoch 23/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0224 - mean_squared_error: 0.0224\n",
            "Epoch 24/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0226 - mean_squared_error: 0.0226\n",
            "Epoch 25/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0219 - mean_squared_error: 0.0219\n",
            "Epoch 26/50\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0219 - mean_squared_error: 0.0219\n",
            "Epoch 27/50\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0216 - mean_squared_error: 0.0216\n",
            "Epoch 28/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0224 - mean_squared_error: 0.0224\n",
            "Epoch 29/50\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0220 - mean_squared_error: 0.0220\n",
            "Epoch 30/50\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0218 - mean_squared_error: 0.0218\n",
            "Epoch 31/50\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.0217 - mean_squared_error: 0.0217\n",
            "Epoch 32/50\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0225 - mean_squared_error: 0.0225\n",
            "Epoch 33/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0228 - mean_squared_error: 0.0228\n",
            "Epoch 34/50\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0221 - mean_squared_error: 0.0221\n",
            "Epoch 35/50\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0215 - mean_squared_error: 0.0215\n",
            "Epoch 36/50\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.0224 - mean_squared_error: 0.0224\n",
            "Epoch 37/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0217 - mean_squared_error: 0.0217\n",
            "Epoch 38/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0212 - mean_squared_error: 0.0212\n",
            "Epoch 39/50\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0212 - mean_squared_error: 0.0212\n",
            "Epoch 40/50\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0223 - mean_squared_error: 0.0223\n",
            "Epoch 41/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0212 - mean_squared_error: 0.0212\n",
            "Epoch 42/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0205 - mean_squared_error: 0.0205\n",
            "Epoch 43/50\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0219 - mean_squared_error: 0.0219\n",
            "Epoch 44/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0210 - mean_squared_error: 0.0210\n",
            "Epoch 45/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0214 - mean_squared_error: 0.0214\n",
            "Epoch 46/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0207 - mean_squared_error: 0.0207\n",
            "Epoch 47/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0203 - mean_squared_error: 0.0203\n",
            "Epoch 48/50\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0203 - mean_squared_error: 0.0203\n",
            "Epoch 49/50\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0214 - mean_squared_error: 0.0214\n",
            "Epoch 50/50\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0224 - mean_squared_error: 0.0224\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa376672850>"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ],
      "source": [
        "# Initially fits model using training data\n",
        "model_prediction.fit(X_scaled, y_scaled, epochs=50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "id": "2d185c03-5986-454d-b8f6-4f6a939c4b78",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2d185c03-5986-454d-b8f6-4f6a939c4b78",
        "outputId": "72993417-5724-4bb6-e988-abf11382fc58"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[23.055798]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6/6 [==============================] - 2s 13ms/step - loss: 0.0204 - mean_squared_error: 0.0204\n",
            "Epoch 2/5\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0202 - mean_squared_error: 0.0202\n",
            "Epoch 3/5\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0205 - mean_squared_error: 0.0205\n",
            "Epoch 4/5\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0199 - mean_squared_error: 0.0199\n",
            "Epoch 5/5\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0194 - mean_squared_error: 0.0194\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.686623]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0188 - mean_squared_error: 0.0188\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0225 - mean_squared_error: 0.0225\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0200 - mean_squared_error: 0.0200\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0191 - mean_squared_error: 0.0191\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0194 - mean_squared_error: 0.0194\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.490131]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 18ms/step - loss: 0.0173 - mean_squared_error: 0.0173\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0175 - mean_squared_error: 0.0175\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0179 - mean_squared_error: 0.0179\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0172 - mean_squared_error: 0.0172\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0170 - mean_squared_error: 0.0170\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.818346]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0222 - mean_squared_error: 0.0222\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 18ms/step - loss: 0.0182 - mean_squared_error: 0.0182\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0175 - mean_squared_error: 0.0175\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0179 - mean_squared_error: 0.0179\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0170 - mean_squared_error: 0.0170\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.462254]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0172 - mean_squared_error: 0.0172\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0179 - mean_squared_error: 0.0179\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0188 - mean_squared_error: 0.0188\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0163 - mean_squared_error: 0.0163\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 18ms/step - loss: 0.0165 - mean_squared_error: 0.0165\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.316767]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0168 - mean_squared_error: 0.0168\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0161 - mean_squared_error: 0.0161\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0151 - mean_squared_error: 0.0151\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0161 - mean_squared_error: 0.0161\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.0161 - mean_squared_error: 0.0161\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.840288]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.0181 - mean_squared_error: 0.0181\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0177 - mean_squared_error: 0.0177\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0159 - mean_squared_error: 0.0159\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0150 - mean_squared_error: 0.0150\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.0156 - mean_squared_error: 0.0156\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.893238]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0149 - mean_squared_error: 0.0149\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0153 - mean_squared_error: 0.0153\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0158 - mean_squared_error: 0.0158\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0145 - mean_squared_error: 0.0145\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0143 - mean_squared_error: 0.0143\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.717663]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0139 - mean_squared_error: 0.0139\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0152 - mean_squared_error: 0.0152\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0146 - mean_squared_error: 0.0146\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0141 - mean_squared_error: 0.0141\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.0140 - mean_squared_error: 0.0140\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.875635]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0131 - mean_squared_error: 0.0131\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 18ms/step - loss: 0.0131 - mean_squared_error: 0.0131\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0143 - mean_squared_error: 0.0143\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0195 - mean_squared_error: 0.0195\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0152 - mean_squared_error: 0.0152\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.813814]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0160 - mean_squared_error: 0.0160\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 18ms/step - loss: 0.0159 - mean_squared_error: 0.0159\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 19ms/step - loss: 0.0146 - mean_squared_error: 0.0146\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 18ms/step - loss: 0.0137 - mean_squared_error: 0.0137\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0158 - mean_squared_error: 0.0158\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.909975]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 19ms/step - loss: 0.0131 - mean_squared_error: 0.0131\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0135 - mean_squared_error: 0.0135\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0138 - mean_squared_error: 0.0138\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0140 - mean_squared_error: 0.0140\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0128 - mean_squared_error: 0.0128\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[23.368155]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 19ms/step - loss: 0.0140 - mean_squared_error: 0.0140\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0139 - mean_squared_error: 0.0139\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0137 - mean_squared_error: 0.0137\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 18ms/step - loss: 0.0132 - mean_squared_error: 0.0132\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0138 - mean_squared_error: 0.0138\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[23.339409]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.0125 - mean_squared_error: 0.0125\n",
            "Epoch 2/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0125 - mean_squared_error: 0.0125\n",
            "Epoch 3/5\n",
            "7/7 [==============================] - 0s 19ms/step - loss: 0.0135 - mean_squared_error: 0.0135\n",
            "Epoch 4/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0130 - mean_squared_error: 0.0130\n",
            "Epoch 5/5\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.0123 - mean_squared_error: 0.0123\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[23.254261]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
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          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0067 - mean_squared_error: 0.0067\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0070 - mean_squared_error: 0.0070\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0072 - mean_squared_error: 0.0072\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0071 - mean_squared_error: 0.0071\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0069 - mean_squared_error: 0.0069\n"
          ]
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          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0076 - mean_squared_error: 0.0076\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0078 - mean_squared_error: 0.0078\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0073 - mean_squared_error: 0.0073\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0075 - mean_squared_error: 0.0075\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0083 - mean_squared_error: 0.0083\n"
          ]
        },
        {
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              "array([[24.821995]], dtype=float32)"
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          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0073 - mean_squared_error: 0.0073\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0081 - mean_squared_error: 0.0081\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0073 - mean_squared_error: 0.0073\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0083 - mean_squared_error: 0.0083\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0081 - mean_squared_error: 0.0081\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "array([[25.021118]], dtype=float32)"
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          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0069 - mean_squared_error: 0.0069\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n"
          ]
        },
        {
          "output_type": "display_data",
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              "array([[25.082664]], dtype=float32)"
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          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n"
          ]
        },
        {
          "output_type": "display_data",
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              "array([[25.04514]], dtype=float32)"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0067 - mean_squared_error: 0.0067\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n"
          ]
        },
        {
          "output_type": "display_data",
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              "array([[25.004925]], dtype=float32)"
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          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0067 - mean_squared_error: 0.0067\n"
          ]
        },
        {
          "output_type": "display_data",
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              "array([[25.2723]], dtype=float32)"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0067 - mean_squared_error: 0.0067\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0069 - mean_squared_error: 0.0069\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[25.224192]], dtype=float32)"
            ]
          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n"
          ]
        },
        {
          "output_type": "display_data",
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              "array([[25.700943]], dtype=float32)"
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          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n"
          ]
        },
        {
          "output_type": "display_data",
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              "array([[25.57471]], dtype=float32)"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n"
          ]
        },
        {
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              "array([[25.234612]], dtype=float32)"
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          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n"
          ]
        },
        {
          "output_type": "display_data",
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              "array([[25.824705]], dtype=float32)"
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          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[25.591751]], dtype=float32)"
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          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[25.56305]], dtype=float32)"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[25.743378]], dtype=float32)"
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[25.62024]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[25.904497]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0050 - mean_squared_error: 0.0050\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[25.90487]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n",
            "Epoch 2/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n",
            "Epoch 3/5\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n",
            "Epoch 4/5\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n",
            "Epoch 5/5\n",
            "8/8 [==============================] - 0s 20ms/step - loss: 0.0046 - mean_squared_error: 0.0046\n"
          ]
        }
      ],
      "source": [
        "# Projects future data by using model to predict next close and then uses that value to retrain \n",
        "# the model before making next prediction\n",
        "# Creates empty list where future predictions will be added to\n",
        "future_predictions = []\n",
        "for i in range(56): \n",
        "    # predicts next close value and reshapes it\n",
        "    prediction_scaled=model_prediction.predict(X_scaled[-1].reshape(1, window_size, 1))\n",
        "    # transforms prediction back to original scale\n",
        "    next_prediction=y_scaler.inverse_transform(prediction_scaled)\n",
        "    # displays predicted close value\n",
        "    display(next_prediction)\n",
        "    # Adds future predictions to list\n",
        "    future_predictions.append(next_prediction)\n",
        "    # adds predicted close value to list\n",
        "    new_train_sample=np.append(X_scaled[-1], prediction_scaled)[1:].reshape(1, window_size, 1)\n",
        "    # adds new list entry of predicted close to X train\n",
        "    X_scaled=np.append(X_scaled, new_train_sample, axis=0)\n",
        "    # adds new list entry of predicted close to y train\n",
        "    y_scaled=np.append(y_scaled, prediction_scaled, axis=0)\n",
        "    # refits model with new close value\n",
        "    model_prediction.fit(X_scaled, y_scaled, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "id": "90b67549-c059-4bb1-8655-60e4a4b35c8d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90b67549-c059-4bb1-8655-60e4a4b35c8d",
        "outputId": "dd527a2c-21bb-4e77-cbe4-0df79c57cce0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[23.055798]], dtype=float32),\n",
              " array([[22.686623]], dtype=float32),\n",
              " array([[22.490131]], dtype=float32),\n",
              " array([[21.818346]], dtype=float32),\n",
              " array([[22.462254]], dtype=float32),\n",
              " array([[22.316767]], dtype=float32),\n",
              " array([[22.840288]], dtype=float32),\n",
              " array([[22.893238]], dtype=float32),\n",
              " array([[22.717663]], dtype=float32),\n",
              " array([[22.875635]], dtype=float32),\n",
              " array([[22.813814]], dtype=float32),\n",
              " array([[22.909975]], dtype=float32),\n",
              " array([[23.368155]], dtype=float32),\n",
              " array([[23.339409]], dtype=float32),\n",
              " array([[23.254261]], dtype=float32),\n",
              " array([[23.769268]], dtype=float32),\n",
              " array([[23.743093]], dtype=float32),\n",
              " array([[23.642849]], dtype=float32),\n",
              " array([[23.848583]], dtype=float32),\n",
              " array([[23.888977]], dtype=float32),\n",
              " array([[23.994278]], dtype=float32),\n",
              " array([[24.07642]], dtype=float32),\n",
              " array([[24.303738]], dtype=float32),\n",
              " array([[24.270767]], dtype=float32),\n",
              " array([[24.32881]], dtype=float32),\n",
              " array([[24.516132]], dtype=float32),\n",
              " array([[24.710676]], dtype=float32),\n",
              " array([[24.888847]], dtype=float32),\n",
              " array([[24.836628]], dtype=float32),\n",
              " array([[25.02108]], dtype=float32),\n",
              " array([[24.92344]], dtype=float32),\n",
              " array([[25.038189]], dtype=float32),\n",
              " array([[24.947262]], dtype=float32),\n",
              " array([[24.767195]], dtype=float32),\n",
              " array([[25.219988]], dtype=float32),\n",
              " array([[25.16198]], dtype=float32),\n",
              " array([[25.183731]], dtype=float32),\n",
              " array([[25.377506]], dtype=float32),\n",
              " array([[24.809828]], dtype=float32),\n",
              " array([[24.821995]], dtype=float32),\n",
              " array([[25.021118]], dtype=float32),\n",
              " array([[25.082664]], dtype=float32),\n",
              " array([[25.04514]], dtype=float32),\n",
              " array([[25.004925]], dtype=float32),\n",
              " array([[25.2723]], dtype=float32),\n",
              " array([[25.224192]], dtype=float32),\n",
              " array([[25.700943]], dtype=float32),\n",
              " array([[25.57471]], dtype=float32),\n",
              " array([[25.234612]], dtype=float32),\n",
              " array([[25.824705]], dtype=float32),\n",
              " array([[25.591751]], dtype=float32),\n",
              " array([[25.56305]], dtype=float32),\n",
              " array([[25.743378]], dtype=float32),\n",
              " array([[25.62024]], dtype=float32),\n",
              " array([[25.904497]], dtype=float32),\n",
              " array([[25.90487]], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ],
      "source": [
        "future_predictions"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Future 56 weeks prediction\n",
        "len(future_predictions) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9QOEpq6xeNw3",
        "outputId": "6a2d9d7b-2a12-412e-c1e6-825f66aefac7"
      },
      "id": "9QOEpq6xeNw3",
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "56"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index[-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ohkIddQSfRfw",
        "outputId": "193183bc-0cf3-4c31-bf3b-4b614ec1a80d"
      },
      "id": "ohkIddQSfRfw",
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2022-07-19 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "future_dates = pd.date_range(start='2022-07-20', periods=56, freq = 'W')\n",
        "datetime_index=df.index.append(future_dates)\n",
        "datetime_index.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZHensNWfOn4",
        "outputId": "d3bd89f0-db1a-4222-cc27-096dae743b46"
      },
      "id": "QZHensNWfOn4",
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2018-09-17T00:00:00.000000000', '2018-09-24T00:00:00.000000000',\n",
              "       '2018-10-01T00:00:00.000000000', '2018-10-08T00:00:00.000000000',\n",
              "       '2018-10-15T00:00:00.000000000', '2018-10-22T00:00:00.000000000',\n",
              "       '2018-10-29T00:00:00.000000000', '2018-11-05T00:00:00.000000000',\n",
              "       '2018-11-12T00:00:00.000000000', '2018-11-19T00:00:00.000000000',\n",
              "       '2018-11-26T00:00:00.000000000', '2018-12-03T00:00:00.000000000',\n",
              "       '2018-12-10T00:00:00.000000000', '2018-12-17T00:00:00.000000000',\n",
              "       '2018-12-24T00:00:00.000000000', '2018-12-31T00:00:00.000000000',\n",
              "       '2019-01-07T00:00:00.000000000', '2019-01-14T00:00:00.000000000',\n",
              "       '2019-01-21T00:00:00.000000000', '2019-01-28T00:00:00.000000000',\n",
              "       '2019-02-04T00:00:00.000000000', '2019-02-11T00:00:00.000000000',\n",
              "       '2019-02-18T00:00:00.000000000', '2019-02-25T00:00:00.000000000',\n",
              "       '2019-03-04T00:00:00.000000000', '2019-03-11T00:00:00.000000000',\n",
              "       '2019-03-18T00:00:00.000000000', '2019-03-25T00:00:00.000000000',\n",
              "       '2019-04-01T00:00:00.000000000', '2019-04-08T00:00:00.000000000',\n",
              "       '2019-04-15T00:00:00.000000000', '2019-04-22T00:00:00.000000000',\n",
              "       '2019-04-29T00:00:00.000000000', '2019-05-06T00:00:00.000000000',\n",
              "       '2019-05-13T00:00:00.000000000', '2019-05-20T00:00:00.000000000',\n",
              "       '2019-05-27T00:00:00.000000000', '2019-06-03T00:00:00.000000000',\n",
              "       '2019-06-10T00:00:00.000000000', '2019-06-17T00:00:00.000000000',\n",
              "       '2019-06-24T00:00:00.000000000', '2019-07-01T00:00:00.000000000',\n",
              "       '2019-07-08T00:00:00.000000000', '2019-07-15T00:00:00.000000000',\n",
              "       '2019-07-22T00:00:00.000000000', '2019-07-29T00:00:00.000000000',\n",
              "       '2019-08-05T00:00:00.000000000', '2019-08-12T00:00:00.000000000',\n",
              "       '2019-08-19T00:00:00.000000000', '2019-08-26T00:00:00.000000000',\n",
              "       '2019-09-02T00:00:00.000000000', '2019-09-09T00:00:00.000000000',\n",
              "       '2019-09-16T00:00:00.000000000', '2019-09-23T00:00:00.000000000',\n",
              "       '2019-09-30T00:00:00.000000000', '2019-10-07T00:00:00.000000000',\n",
              "       '2019-10-14T00:00:00.000000000', '2019-10-21T00:00:00.000000000',\n",
              "       '2019-10-28T00:00:00.000000000', '2019-11-04T00:00:00.000000000',\n",
              "       '2019-11-11T00:00:00.000000000', '2019-11-18T00:00:00.000000000',\n",
              "       '2019-11-25T00:00:00.000000000', '2019-12-02T00:00:00.000000000',\n",
              "       '2019-12-09T00:00:00.000000000', '2019-12-16T00:00:00.000000000',\n",
              "       '2019-12-23T00:00:00.000000000', '2019-12-30T00:00:00.000000000',\n",
              "       '2020-01-06T00:00:00.000000000', '2020-01-13T00:00:00.000000000',\n",
              "       '2020-01-20T00:00:00.000000000', '2020-01-27T00:00:00.000000000',\n",
              "       '2020-02-03T00:00:00.000000000', '2020-02-10T00:00:00.000000000',\n",
              "       '2020-02-17T00:00:00.000000000', '2020-02-24T00:00:00.000000000',\n",
              "       '2020-03-02T00:00:00.000000000', '2020-03-09T00:00:00.000000000',\n",
              "       '2020-03-16T00:00:00.000000000', '2020-03-23T00:00:00.000000000',\n",
              "       '2020-03-30T00:00:00.000000000', '2020-04-06T00:00:00.000000000',\n",
              "       '2020-04-13T00:00:00.000000000', '2020-04-20T00:00:00.000000000',\n",
              "       '2020-04-27T00:00:00.000000000', '2020-05-04T00:00:00.000000000',\n",
              "       '2020-05-11T00:00:00.000000000', '2020-05-18T00:00:00.000000000',\n",
              "       '2020-05-25T00:00:00.000000000', '2020-06-01T00:00:00.000000000',\n",
              "       '2020-06-08T00:00:00.000000000', '2020-06-15T00:00:00.000000000',\n",
              "       '2020-06-22T00:00:00.000000000', '2020-06-29T00:00:00.000000000',\n",
              "       '2020-07-06T00:00:00.000000000', '2020-07-13T00:00:00.000000000',\n",
              "       '2020-07-20T00:00:00.000000000', '2020-07-27T00:00:00.000000000',\n",
              "       '2020-08-03T00:00:00.000000000', '2020-08-10T00:00:00.000000000',\n",
              "       '2020-08-17T00:00:00.000000000', '2020-08-24T00:00:00.000000000',\n",
              "       '2020-08-31T00:00:00.000000000', '2020-09-07T00:00:00.000000000',\n",
              "       '2020-09-14T00:00:00.000000000', '2020-09-21T00:00:00.000000000',\n",
              "       '2020-09-28T00:00:00.000000000', '2020-10-05T00:00:00.000000000',\n",
              "       '2020-10-12T00:00:00.000000000', '2020-10-19T00:00:00.000000000',\n",
              "       '2020-10-26T00:00:00.000000000', '2020-11-02T00:00:00.000000000',\n",
              "       '2020-11-09T00:00:00.000000000', '2020-11-16T00:00:00.000000000',\n",
              "       '2020-11-23T00:00:00.000000000', '2020-11-30T00:00:00.000000000',\n",
              "       '2020-12-07T00:00:00.000000000', '2020-12-14T00:00:00.000000000',\n",
              "       '2020-12-21T00:00:00.000000000', '2020-12-28T00:00:00.000000000',\n",
              "       '2021-01-04T00:00:00.000000000', '2021-01-11T00:00:00.000000000',\n",
              "       '2021-01-18T00:00:00.000000000', '2021-01-25T00:00:00.000000000',\n",
              "       '2021-02-01T00:00:00.000000000', '2021-02-08T00:00:00.000000000',\n",
              "       '2021-02-15T00:00:00.000000000', '2021-02-22T00:00:00.000000000',\n",
              "       '2021-03-01T00:00:00.000000000', '2021-03-08T00:00:00.000000000',\n",
              "       '2021-03-15T00:00:00.000000000', '2021-03-22T00:00:00.000000000',\n",
              "       '2021-03-29T00:00:00.000000000', '2021-04-05T00:00:00.000000000',\n",
              "       '2021-04-12T00:00:00.000000000', '2021-04-19T00:00:00.000000000',\n",
              "       '2021-04-26T00:00:00.000000000', '2021-05-03T00:00:00.000000000',\n",
              "       '2021-05-10T00:00:00.000000000', '2021-05-17T00:00:00.000000000',\n",
              "       '2021-05-24T00:00:00.000000000', '2021-05-31T00:00:00.000000000',\n",
              "       '2021-06-07T00:00:00.000000000', '2021-06-14T00:00:00.000000000',\n",
              "       '2021-06-21T00:00:00.000000000', '2021-06-28T00:00:00.000000000',\n",
              "       '2021-07-05T00:00:00.000000000', '2021-07-12T00:00:00.000000000',\n",
              "       '2021-07-19T00:00:00.000000000', '2021-07-26T00:00:00.000000000',\n",
              "       '2021-08-02T00:00:00.000000000', '2021-08-09T00:00:00.000000000',\n",
              "       '2021-08-16T00:00:00.000000000', '2021-08-23T00:00:00.000000000',\n",
              "       '2021-08-30T00:00:00.000000000', '2021-09-06T00:00:00.000000000',\n",
              "       '2021-09-13T00:00:00.000000000', '2021-09-20T00:00:00.000000000',\n",
              "       '2021-09-27T00:00:00.000000000', '2021-10-04T00:00:00.000000000',\n",
              "       '2021-10-11T00:00:00.000000000', '2021-10-18T00:00:00.000000000',\n",
              "       '2021-10-25T00:00:00.000000000', '2021-11-01T00:00:00.000000000',\n",
              "       '2021-11-08T00:00:00.000000000', '2021-11-15T00:00:00.000000000',\n",
              "       '2021-11-22T00:00:00.000000000', '2021-11-29T00:00:00.000000000',\n",
              "       '2021-12-06T00:00:00.000000000', '2021-12-13T00:00:00.000000000',\n",
              "       '2021-12-20T00:00:00.000000000', '2021-12-27T00:00:00.000000000',\n",
              "       '2022-01-03T00:00:00.000000000', '2022-01-10T00:00:00.000000000',\n",
              "       '2022-01-17T00:00:00.000000000', '2022-01-24T00:00:00.000000000',\n",
              "       '2022-01-31T00:00:00.000000000', '2022-02-07T00:00:00.000000000',\n",
              "       '2022-02-14T00:00:00.000000000', '2022-02-21T00:00:00.000000000',\n",
              "       '2022-02-28T00:00:00.000000000', '2022-03-07T00:00:00.000000000',\n",
              "       '2022-03-14T00:00:00.000000000', '2022-03-21T00:00:00.000000000',\n",
              "       '2022-03-28T00:00:00.000000000', '2022-04-04T00:00:00.000000000',\n",
              "       '2022-04-11T00:00:00.000000000', '2022-04-18T00:00:00.000000000',\n",
              "       '2022-04-25T00:00:00.000000000', '2022-05-02T00:00:00.000000000',\n",
              "       '2022-05-09T00:00:00.000000000', '2022-05-16T00:00:00.000000000',\n",
              "       '2022-05-23T00:00:00.000000000', '2022-05-30T00:00:00.000000000',\n",
              "       '2022-06-06T00:00:00.000000000', '2022-06-13T00:00:00.000000000',\n",
              "       '2022-06-20T00:00:00.000000000', '2022-06-27T00:00:00.000000000',\n",
              "       '2022-07-04T00:00:00.000000000', '2022-07-11T00:00:00.000000000',\n",
              "       '2022-07-18T00:00:00.000000000', '2022-07-19T00:00:00.000000000',\n",
              "       '2022-07-24T00:00:00.000000000', '2022-07-31T00:00:00.000000000',\n",
              "       '2022-08-07T00:00:00.000000000', '2022-08-14T00:00:00.000000000',\n",
              "       '2022-08-21T00:00:00.000000000', '2022-08-28T00:00:00.000000000',\n",
              "       '2022-09-04T00:00:00.000000000', '2022-09-11T00:00:00.000000000',\n",
              "       '2022-09-18T00:00:00.000000000', '2022-09-25T00:00:00.000000000',\n",
              "       '2022-10-02T00:00:00.000000000', '2022-10-09T00:00:00.000000000',\n",
              "       '2022-10-16T00:00:00.000000000', '2022-10-23T00:00:00.000000000',\n",
              "       '2022-10-30T00:00:00.000000000', '2022-11-06T00:00:00.000000000',\n",
              "       '2022-11-13T00:00:00.000000000', '2022-11-20T00:00:00.000000000',\n",
              "       '2022-11-27T00:00:00.000000000', '2022-12-04T00:00:00.000000000',\n",
              "       '2022-12-11T00:00:00.000000000', '2022-12-18T00:00:00.000000000',\n",
              "       '2022-12-25T00:00:00.000000000', '2023-01-01T00:00:00.000000000',\n",
              "       '2023-01-08T00:00:00.000000000', '2023-01-15T00:00:00.000000000',\n",
              "       '2023-01-22T00:00:00.000000000', '2023-01-29T00:00:00.000000000',\n",
              "       '2023-02-05T00:00:00.000000000', '2023-02-12T00:00:00.000000000',\n",
              "       '2023-02-19T00:00:00.000000000', '2023-02-26T00:00:00.000000000',\n",
              "       '2023-03-05T00:00:00.000000000', '2023-03-12T00:00:00.000000000',\n",
              "       '2023-03-19T00:00:00.000000000', '2023-03-26T00:00:00.000000000',\n",
              "       '2023-04-02T00:00:00.000000000', '2023-04-09T00:00:00.000000000',\n",
              "       '2023-04-16T00:00:00.000000000', '2023-04-23T00:00:00.000000000',\n",
              "       '2023-04-30T00:00:00.000000000', '2023-05-07T00:00:00.000000000',\n",
              "       '2023-05-14T00:00:00.000000000', '2023-05-21T00:00:00.000000000',\n",
              "       '2023-05-28T00:00:00.000000000', '2023-06-04T00:00:00.000000000',\n",
              "       '2023-06-11T00:00:00.000000000', '2023-06-18T00:00:00.000000000',\n",
              "       '2023-06-25T00:00:00.000000000', '2023-07-02T00:00:00.000000000',\n",
              "       '2023-07-09T00:00:00.000000000', '2023-07-16T00:00:00.000000000',\n",
              "       '2023-07-23T00:00:00.000000000', '2023-07-30T00:00:00.000000000',\n",
              "       '2023-08-06T00:00:00.000000000', '2023-08-13T00:00:00.000000000'],\n",
              "      dtype='datetime64[ns]')"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "id": "8dff4952-83b3-40af-afff-2252c09d5cb1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "8dff4952-83b3-40af-afff-2252c09d5cb1",
        "outputId": "bb3f58d2-36ea-4284-989e-d8b8f096746a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([736695., 737060., 737425., 737791., 738156., 738521.]),\n",
              " <a list of 6 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 135
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(datetime_index.values, np.concatenate((df['close'].values, np.array(future_predictions).ravel())))\n",
        "plt.xticks(rotation=90)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "F-R1iqSjDQV-"
      },
      "id": "F-R1iqSjDQV-",
      "execution_count": 135,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "colab": {
      "name": "ELAN_future_weeks.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}