Bitcoin Price Predictor/Bitcoin_Price_Prediction.ipynb

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      "name": "Bitcoin Price Prediction Using RNN - LSTM.ipynb",
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        "<a href=\"https://colab.research.google.com/github/psyduck1203/Hands-on-ML-Basic-to-Advance-/blob/master/Bitcoin%20Price%20Predictor/Bitcoin_Price_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
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        "id": "j1YyRji0O_U2"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
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        "outputId": "8f02e326-76a6-4bae-ffa0-316086cf446c"
      },
      "source": [
        "data = pd.read_csv('/content/sample_data/BTC-USD.csv', date_parser = True)\n",
        "data.tail()"
      ],
      "execution_count": 2,
      "outputs": [
        {
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              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
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              "      <th>2075</th>\n",
              "      <td>2020-05-23</td>\n",
              "      <td>9185.062500</td>\n",
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              "      <th>2076</th>\n",
              "      <td>2020-05-24</td>\n",
              "      <td>9212.283203</td>\n",
              "      <td>9288.404297</td>\n",
              "      <td>8787.250977</td>\n",
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              "      <td>8790.368164</td>\n",
              "      <td>3.251880e+10</td>\n",
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              "      <th>2077</th>\n",
              "      <td>2020-05-25</td>\n",
              "      <td>8786.107422</td>\n",
              "      <td>8951.005859</td>\n",
              "      <td>8719.667969</td>\n",
              "      <td>8906.934570</td>\n",
              "      <td>8906.934570</td>\n",
              "      <td>3.128816e+10</td>\n",
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              "    <tr>\n",
              "      <th>2078</th>\n",
              "      <td>2020-05-26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2079</th>\n",
              "      <td>2020-05-27</td>\n",
              "      <td>8834.157227</td>\n",
              "      <td>8859.578125</td>\n",
              "      <td>8834.157227</td>\n",
              "      <td>8856.885742</td>\n",
              "      <td>8856.885742</td>\n",
              "      <td>2.914432e+10</td>\n",
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              "            Date         Open  ...    Adj Close        Volume\n",
              "2075  2020-05-23  9185.062500  ...  9209.287109  2.772787e+10\n",
              "2076  2020-05-24  9212.283203  ...  8790.368164  3.251880e+10\n",
              "2077  2020-05-25  8786.107422  ...  8906.934570  3.128816e+10\n",
              "2078  2020-05-26          NaN  ...          NaN           NaN\n",
              "2079  2020-05-27  8834.157227  ...  8856.885742  2.914432e+10\n",
              "\n",
              "[5 rows x 7 columns]"
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      "source": [
        "data_training = data[data['Date']< '2020-01-01'].copy()\n",
        "data_training"
      ],
      "execution_count": 3,
      "outputs": [
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          "output_type": "execute_result",
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              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-09-17</td>\n",
              "      <td>465.864014</td>\n",
              "      <td>468.174011</td>\n",
              "      <td>452.421997</td>\n",
              "      <td>457.334015</td>\n",
              "      <td>457.334015</td>\n",
              "      <td>2.105680e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-09-18</td>\n",
              "      <td>456.859985</td>\n",
              "      <td>456.859985</td>\n",
              "      <td>413.104004</td>\n",
              "      <td>424.440002</td>\n",
              "      <td>424.440002</td>\n",
              "      <td>3.448320e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-09-19</td>\n",
              "      <td>424.102997</td>\n",
              "      <td>427.834991</td>\n",
              "      <td>384.532013</td>\n",
              "      <td>394.795990</td>\n",
              "      <td>394.795990</td>\n",
              "      <td>3.791970e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-09-20</td>\n",
              "      <td>394.673004</td>\n",
              "      <td>423.295990</td>\n",
              "      <td>389.882996</td>\n",
              "      <td>408.903992</td>\n",
              "      <td>408.903992</td>\n",
              "      <td>3.686360e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-09-21</td>\n",
              "      <td>408.084991</td>\n",
              "      <td>412.425995</td>\n",
              "      <td>393.181000</td>\n",
              "      <td>398.821014</td>\n",
              "      <td>398.821014</td>\n",
              "      <td>2.658010e+07</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1927</th>\n",
              "      <td>2019-12-27</td>\n",
              "      <td>7238.141113</td>\n",
              "      <td>7363.529297</td>\n",
              "      <td>7189.934082</td>\n",
              "      <td>7290.088379</td>\n",
              "      <td>7290.088379</td>\n",
              "      <td>2.277736e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928</th>\n",
              "      <td>2019-12-28</td>\n",
              "      <td>7289.031250</td>\n",
              "      <td>7399.041016</td>\n",
              "      <td>7286.905273</td>\n",
              "      <td>7317.990234</td>\n",
              "      <td>7317.990234</td>\n",
              "      <td>2.136567e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1929</th>\n",
              "      <td>2019-12-29</td>\n",
              "      <td>7317.647461</td>\n",
              "      <td>7513.948242</td>\n",
              "      <td>7279.865234</td>\n",
              "      <td>7422.652832</td>\n",
              "      <td>7422.652832</td>\n",
              "      <td>2.244526e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1930</th>\n",
              "      <td>2019-12-30</td>\n",
              "      <td>7420.272949</td>\n",
              "      <td>7454.824219</td>\n",
              "      <td>7276.308105</td>\n",
              "      <td>7292.995117</td>\n",
              "      <td>7292.995117</td>\n",
              "      <td>2.287413e+10</td>\n",
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              "    <tr>\n",
              "      <th>1931</th>\n",
              "      <td>2019-12-31</td>\n",
              "      <td>7294.438965</td>\n",
              "      <td>7335.290039</td>\n",
              "      <td>7169.777832</td>\n",
              "      <td>7193.599121</td>\n",
              "      <td>7193.599121</td>\n",
              "      <td>2.116795e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>1932 rows × 7 columns</p>\n",
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              "            Date         Open  ...    Adj Close        Volume\n",
              "0     2014-09-17   465.864014  ...   457.334015  2.105680e+07\n",
              "1     2014-09-18   456.859985  ...   424.440002  3.448320e+07\n",
              "2     2014-09-19   424.102997  ...   394.795990  3.791970e+07\n",
              "3     2014-09-20   394.673004  ...   408.903992  3.686360e+07\n",
              "4     2014-09-21   408.084991  ...   398.821014  2.658010e+07\n",
              "...          ...          ...  ...          ...           ...\n",
              "1927  2019-12-27  7238.141113  ...  7290.088379  2.277736e+10\n",
              "1928  2019-12-28  7289.031250  ...  7317.990234  2.136567e+10\n",
              "1929  2019-12-29  7317.647461  ...  7422.652832  2.244526e+10\n",
              "1930  2019-12-30  7420.272949  ...  7292.995117  2.287413e+10\n",
              "1931  2019-12-31  7294.438965  ...  7193.599121  2.116795e+10\n",
              "\n",
              "[1932 rows x 7 columns]"
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      "source": [
        "data_test = data[data['Date']> '2020-01-01'].copy()\n",
        "data_test"
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      "execution_count": 4,
      "outputs": [
        {
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              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1933</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>7202.551270</td>\n",
              "      <td>7212.155273</td>\n",
              "      <td>6935.270020</td>\n",
              "      <td>6985.470215</td>\n",
              "      <td>6985.470215</td>\n",
              "      <td>2.080208e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1934</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>6984.428711</td>\n",
              "      <td>7413.715332</td>\n",
              "      <td>6914.996094</td>\n",
              "      <td>7344.884277</td>\n",
              "      <td>7344.884277</td>\n",
              "      <td>2.811148e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1935</th>\n",
              "      <td>2020-01-04</td>\n",
              "      <td>7345.375488</td>\n",
              "      <td>7427.385742</td>\n",
              "      <td>7309.514160</td>\n",
              "      <td>7410.656738</td>\n",
              "      <td>7410.656738</td>\n",
              "      <td>1.844427e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1936</th>\n",
              "      <td>2020-01-05</td>\n",
              "      <td>7410.451660</td>\n",
              "      <td>7544.497070</td>\n",
              "      <td>7400.535645</td>\n",
              "      <td>7411.317383</td>\n",
              "      <td>7411.317383</td>\n",
              "      <td>1.972507e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1937</th>\n",
              "      <td>2020-01-06</td>\n",
              "      <td>7410.452148</td>\n",
              "      <td>7781.867188</td>\n",
              "      <td>7409.292969</td>\n",
              "      <td>7769.219238</td>\n",
              "      <td>7769.219238</td>\n",
              "      <td>2.327626e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2075</th>\n",
              "      <td>2020-05-23</td>\n",
              "      <td>9185.062500</td>\n",
              "      <td>9302.501953</td>\n",
              "      <td>9118.108398</td>\n",
              "      <td>9209.287109</td>\n",
              "      <td>9209.287109</td>\n",
              "      <td>2.772787e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2076</th>\n",
              "      <td>2020-05-24</td>\n",
              "      <td>9212.283203</td>\n",
              "      <td>9288.404297</td>\n",
              "      <td>8787.250977</td>\n",
              "      <td>8790.368164</td>\n",
              "      <td>8790.368164</td>\n",
              "      <td>3.251880e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2077</th>\n",
              "      <td>2020-05-25</td>\n",
              "      <td>8786.107422</td>\n",
              "      <td>8951.005859</td>\n",
              "      <td>8719.667969</td>\n",
              "      <td>8906.934570</td>\n",
              "      <td>8906.934570</td>\n",
              "      <td>3.128816e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2078</th>\n",
              "      <td>2020-05-26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2079</th>\n",
              "      <td>2020-05-27</td>\n",
              "      <td>8834.157227</td>\n",
              "      <td>8859.578125</td>\n",
              "      <td>8834.157227</td>\n",
              "      <td>8856.885742</td>\n",
              "      <td>8856.885742</td>\n",
              "      <td>2.914432e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>147 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Date         Open  ...    Adj Close        Volume\n",
              "1933  2020-01-02  7202.551270  ...  6985.470215  2.080208e+10\n",
              "1934  2020-01-03  6984.428711  ...  7344.884277  2.811148e+10\n",
              "1935  2020-01-04  7345.375488  ...  7410.656738  1.844427e+10\n",
              "1936  2020-01-05  7410.451660  ...  7411.317383  1.972507e+10\n",
              "1937  2020-01-06  7410.452148  ...  7769.219238  2.327626e+10\n",
              "...          ...          ...  ...          ...           ...\n",
              "2075  2020-05-23  9185.062500  ...  9209.287109  2.772787e+10\n",
              "2076  2020-05-24  9212.283203  ...  8790.368164  3.251880e+10\n",
              "2077  2020-05-25  8786.107422  ...  8906.934570  3.128816e+10\n",
              "2078  2020-05-26          NaN  ...          NaN           NaN\n",
              "2079  2020-05-27  8834.157227  ...  8856.885742  2.914432e+10\n",
              "\n",
              "[147 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 4
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      ]
    },
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        "id": "nN2D2CCzO_VE",
        "outputId": "63e864e0-2fee-4108-b575-33d6c19e14ba"
      },
      "source": [
        "training_data = data_training.drop(['Date', 'Adj Close'], axis = 1)\n",
        "training_data.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "\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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>465.864014</td>\n",
              "      <td>468.174011</td>\n",
              "      <td>452.421997</td>\n",
              "      <td>457.334015</td>\n",
              "      <td>21056800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>456.859985</td>\n",
              "      <td>456.859985</td>\n",
              "      <td>413.104004</td>\n",
              "      <td>424.440002</td>\n",
              "      <td>34483200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>424.102997</td>\n",
              "      <td>427.834991</td>\n",
              "      <td>384.532013</td>\n",
              "      <td>394.795990</td>\n",
              "      <td>37919700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>394.673004</td>\n",
              "      <td>423.295990</td>\n",
              "      <td>389.882996</td>\n",
              "      <td>408.903992</td>\n",
              "      <td>36863600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>408.084991</td>\n",
              "      <td>412.425995</td>\n",
              "      <td>393.181000</td>\n",
              "      <td>398.821014</td>\n",
              "      <td>26580100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Open        High         Low       Close      Volume\n",
              "0  465.864014  468.174011  452.421997  457.334015  21056800.0\n",
              "1  456.859985  456.859985  413.104004  424.440002  34483200.0\n",
              "2  424.102997  427.834991  384.532013  394.795990  37919700.0\n",
              "3  394.673004  423.295990  389.882996  408.903992  36863600.0\n",
              "4  408.084991  412.425995  393.181000  398.821014  26580100.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WeMzT-B-O_VF",
        "outputId": "56d4894f-a9cf-41b0-f316-cb4c3de96c54"
      },
      "source": [
        "scaler = MinMaxScaler()\n",
        "training_data = scaler.fit_transform(training_data)\n",
        "training_data"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.49732345e-02, 1.29013200e-02, 1.49400698e-02, 1.44534769e-02,\n",
              "        3.35749244e-04],\n",
              "       [1.45066780e-02, 1.23321258e-02, 1.28489753e-02, 1.27508263e-02,\n",
              "        6.33453324e-04],\n",
              "       [1.28093283e-02, 1.08719155e-02, 1.13293978e-02, 1.12164013e-02,\n",
              "        7.09650970e-04],\n",
              "       ...,\n",
              "       [3.70008086e-01, 3.67365217e-01, 3.78051927e-01, 3.74990337e-01,\n",
              "        4.97548412e-01],\n",
              "       [3.75325771e-01, 3.64390763e-01, 3.77862744e-01, 3.68279031e-01,\n",
              "        5.07057851e-01],\n",
              "       [3.68805505e-01, 3.58377151e-01, 3.72197021e-01, 3.63134123e-01,\n",
              "        4.69226533e-01]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5THccptO_VH"
      },
      "source": [
        "X_train = []\n",
        "Y_train = []"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXfq0kdXO_VH",
        "outputId": "a2a96c8e-683b-4823-a660-a7d69890f69b"
      },
      "source": [
        "training_data.shape[0]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1932"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aAUWNkSO_VI"
      },
      "source": [
        "for i in range(60, training_data.shape[0]):\n",
        "    X_train.append(training_data[i-60:i])\n",
        "    Y_train.append(training_data[i,0])"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYI1PWZUO_VL"
      },
      "source": [
        "X_train, Y_train = np.array(X_train), np.array(Y_train)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RK_Dw1bqO_VM",
        "outputId": "826e46ce-70eb-49b0-ddf6-f3147b65fc12"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1872, 60, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8MRYA1SO_VP"
      },
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Dropout"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1R7D6GL8O_VQ"
      },
      "source": [
        "regressor = Sequential()\n",
        "regressor.add(LSTM(units = 50, activation = 'relu', return_sequences = True, input_shape = (X_train.shape[1], 5)))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exIMrC0VO_VR"
      },
      "source": [
        "regressor.add(LSTM(units = 60, activation = 'relu', return_sequences = True))\n",
        "regressor.add(Dropout(0.3))\n",
        "\n",
        "regressor.add(LSTM(units = 80, activation = 'relu', return_sequences = True))\n",
        "regressor.add(Dropout(0.4))\n",
        "\n",
        "regressor.add(LSTM(units = 120, activation = 'relu'))\n",
        "regressor.add(Dropout(0.5))\n",
        "\n",
        "regressor.add(Dense(units =1))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1MFd4GuOO_VR",
        "outputId": "f0d616d4-570d-478a-ef75-9ee6f63edf30"
      },
      "source": [
        "regressor.summary()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 60, 50)            11200     \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 60, 60)            26640     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 60, 60)            0         \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 60, 80)            45120     \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 60, 80)            0         \n",
            "_________________________________________________________________\n",
            "lstm_3 (LSTM)                (None, 120)               96480     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 120)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 121       \n",
            "=================================================================\n",
            "Total params: 179,561\n",
            "Trainable params: 179,561\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwQ7xPmrO_VT"
      },
      "source": [
        "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAS76L2HO_VU",
        "outputId": "64da34f8-acef-4c4e-8866-bb0a111fe60d"
      },
      "source": [
        "regressor.fit(X_train, Y_train, epochs = 20, batch_size =50)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "38/38 [==============================] - 11s 185ms/step - loss: 0.0191\n",
            "Epoch 2/20\n",
            "38/38 [==============================] - 7s 183ms/step - loss: 0.0051\n",
            "Epoch 3/20\n",
            "38/38 [==============================] - 7s 183ms/step - loss: 0.0046\n",
            "Epoch 4/20\n",
            "38/38 [==============================] - 7s 184ms/step - loss: 0.0046\n",
            "Epoch 5/20\n",
            "38/38 [==============================] - 7s 184ms/step - loss: 0.0037\n",
            "Epoch 6/20\n",
            "38/38 [==============================] - 7s 182ms/step - loss: 0.0052\n",
            "Epoch 7/20\n",
            "38/38 [==============================] - 7s 184ms/step - loss: 0.0036\n",
            "Epoch 8/20\n",
            "38/38 [==============================] - 7s 185ms/step - loss: 0.0033\n",
            "Epoch 9/20\n",
            "38/38 [==============================] - 7s 182ms/step - loss: 0.0031\n",
            "Epoch 10/20\n",
            "38/38 [==============================] - 7s 183ms/step - loss: 0.0028\n",
            "Epoch 11/20\n",
            "38/38 [==============================] - 7s 183ms/step - loss: 0.0027\n",
            "Epoch 12/20\n",
            "38/38 [==============================] - 7s 183ms/step - loss: 0.0028\n",
            "Epoch 13/20\n",
            "38/38 [==============================] - 7s 183ms/step - loss: 0.0026\n",
            "Epoch 14/20\n",
            "38/38 [==============================] - 7s 183ms/step - loss: 0.0025\n",
            "Epoch 15/20\n",
            "38/38 [==============================] - 7s 183ms/step - loss: 0.0026\n",
            "Epoch 16/20\n",
            "38/38 [==============================] - 7s 184ms/step - loss: 0.0026\n",
            "Epoch 17/20\n",
            "38/38 [==============================] - 7s 183ms/step - loss: 0.0025\n",
            "Epoch 18/20\n",
            "38/38 [==============================] - 7s 183ms/step - loss: 0.0022\n",
            "Epoch 19/20\n",
            "38/38 [==============================] - 7s 183ms/step - loss: 0.0022\n",
            "Epoch 20/20\n",
            "38/38 [==============================] - 7s 184ms/step - loss: 0.0024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7faddaf8ced0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "P9m3OMuBO_VV",
        "outputId": "e58e6577-f796-43bd-b1e6-23b74e535926"
      },
      "source": [
        "past_60_days = data_training.tail(60)\n",
        "df= past_60_days.append(data_test, ignore_index = True)\n",
        "df = df.drop(['Date', 'Adj Close'], axis = 1)\n",
        "df.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9259.783203</td>\n",
              "      <td>9377.486328</td>\n",
              "      <td>9249.587891</td>\n",
              "      <td>9324.717773</td>\n",
              "      <td>2.124268e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9324.787109</td>\n",
              "      <td>9379.806641</td>\n",
              "      <td>9141.251953</td>\n",
              "      <td>9235.354492</td>\n",
              "      <td>2.113222e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9235.607422</td>\n",
              "      <td>9505.051758</td>\n",
              "      <td>9191.485352</td>\n",
              "      <td>9412.612305</td>\n",
              "      <td>2.617026e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9413.004883</td>\n",
              "      <td>9457.417969</td>\n",
              "      <td>9256.931641</td>\n",
              "      <td>9342.527344</td>\n",
              "      <td>2.619861e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9340.864258</td>\n",
              "      <td>9423.237305</td>\n",
              "      <td>9305.909180</td>\n",
              "      <td>9360.879883</td>\n",
              "      <td>2.313390e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Open         High          Low        Close        Volume\n",
              "0  9259.783203  9377.486328  9249.587891  9324.717773  2.124268e+10\n",
              "1  9324.787109  9379.806641  9141.251953  9235.354492  2.113222e+10\n",
              "2  9235.607422  9505.051758  9191.485352  9412.612305  2.617026e+10\n",
              "3  9413.004883  9457.417969  9256.931641  9342.527344  2.619861e+10\n",
              "4  9340.864258  9423.237305  9305.909180  9360.879883  2.313390e+10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XnUyBq48O_VX",
        "outputId": "e7002d14-2ab6-4aae-fe42-97b25320dea8"
      },
      "source": [
        "inputs = scaler.transform(df)\n",
        "inputs"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.4706426 , 0.46111744, 0.48280998, 0.47344448, 0.47088353],\n",
              "       [0.47401087, 0.46123417, 0.47704822, 0.46881889, 0.4684344 ],\n",
              "       [0.4693899 , 0.46753509, 0.47971984, 0.47799406, 0.58014293],\n",
              "       ...,\n",
              "       [0.44609842, 0.43966175, 0.45462663, 0.45181931, 0.69362236],\n",
              "       [       nan,        nan,        nan,        nan,        nan],\n",
              "       [0.44858819, 0.43506214, 0.46071565, 0.44922869, 0.64608693]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gogtVVwtO_VY"
      },
      "source": [
        "X_test = []\n",
        "Y_test = []\n",
        "for i in range (60, inputs.shape[0]):\n",
        "    X_test.append(inputs[i-60:i])\n",
        "    Y_test.append(inputs[i, 0])"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ZKlbWeIO_VZ",
        "outputId": "3aa38a11-b8fd-42c9-fc78-f700edf07fd4"
      },
      "source": [
        "X_test, Y_test = np.array(X_test), np.array(Y_test)\n",
        "X_test.shape, Y_test.shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((147, 60, 5), (147,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hllRzjkYO_Va",
        "outputId": "ff3e956f-a944-40d2-d8a6-bd77fe818d13"
      },
      "source": [
        "Y_pred = regressor.predict(X_test)\n",
        "Y_pred, Y_test"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[0.34013247],\n",
              "        [0.33989665],\n",
              "        [0.33954954],\n",
              "        [0.33907995],\n",
              "        [0.33859783],\n",
              "        [0.338462  ],\n",
              "        [0.33942494],\n",
              "        [0.34210616],\n",
              "        [0.346132  ],\n",
              "        [0.3510932 ],\n",
              "        [0.35645616],\n",
              "        [0.36162573],\n",
              "        [0.36619073],\n",
              "        [0.37126786],\n",
              "        [0.37740648],\n",
              "        [0.3841092 ],\n",
              "        [0.39104384],\n",
              "        [0.39767748],\n",
              "        [0.40361473],\n",
              "        [0.4081161 ],\n",
              "        [0.41078222],\n",
              "        [0.41159534],\n",
              "        [0.41082487],\n",
              "        [0.40875536],\n",
              "        [0.40560764],\n",
              "        [0.40185475],\n",
              "        [0.39843935],\n",
              "        [0.3965401 ],\n",
              "        [0.39676225],\n",
              "        [0.3992415 ],\n",
              "        [0.4034778 ],\n",
              "        [0.40861621],\n",
              "        [0.41405722],\n",
              "        [0.41929102],\n",
              "        [0.42376894],\n",
              "        [0.42758217],\n",
              "        [0.43117058],\n",
              "        [0.43467793],\n",
              "        [0.43814507],\n",
              "        [0.44168335],\n",
              "        [0.44542167],\n",
              "        [0.4491947 ],\n",
              "        [0.45334393],\n",
              "        [0.4582405 ],\n",
              "        [0.46345794],\n",
              "        [0.46842483],\n",
              "        [0.47250915],\n",
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        "scale = 1/5.18164146e-05\n",
        "scale"
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      "execution_count": 25,
      "outputs": []
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    },
    {
      "cell_type": "code",
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          "height": 350
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        "id": "ykp-633vO_Vd",
        "outputId": "0d661269-6c6b-493c-e312-1e63ea72dbde"
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      "source": [
        "plt.figure(figsize=(14,5))\n",
        "plt.plot(Y_test, color = 'red', label = 'Real Bitcoin Price')\n",
        "plt.plot(Y_pred, color = 'green', label = 'Predicted Bitcoin Price')\n",
        "plt.title('Bitcoin Price Prediction using RNN-LSTM')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HRkz2uTO_Ve"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}