CRYPTO_CURRENCY_FORECASTING_USING_LSTM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sarankumar1325/CRYPTO-PRICE-FORECASTING-USING-LSTM/blob/main/CRYPTO_CURRENCY_FORECASTING_USING_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dataset Analysis**"
      ],
      "metadata": {
        "id": "yptA_cItXHBB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import required libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Load the dataset\n",
        "data = pd.read_csv('/content/btc.csv')\n",
        "print(\"Dataset loaded. Total records:\", data.shape[0])\n",
        "\n",
        "# Display the first few rows\n",
        "data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "FRcy8raJXJI_",
        "outputId": "98009901-4d6e-4349-a155-5090babb6c5f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset loaded. Total records: 2713\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date        Open        High         Low       Close   Adj Close  \\\n",
              "0  2014-09-17  465.864014  468.174011  452.421997  457.334015  457.334015   \n",
              "1  2014-09-18  456.859985  456.859985  413.104004  424.440002  424.440002   \n",
              "2  2014-09-19  424.102997  427.834991  384.532013  394.795990  394.795990   \n",
              "3  2014-09-20  394.673004  423.295990  389.882996  408.903992  408.903992   \n",
              "4  2014-09-21  408.084991  412.425995  393.181000  398.821014  398.821014   \n",
              "\n",
              "     Volume  \n",
              "0  21056800  \n",
              "1  34483200  \n",
              "2  37919700  \n",
              "3  36863600  \n",
              "4  26580100  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b78c23c-7f12-4485-bcd0-4b64618f9f68\" 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>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>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>21056800</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>34483200</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>37919700</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>36863600</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>26580100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b78c23c-7f12-4485-bcd0-4b64618f9f68')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6b78c23c-7f12-4485-bcd0-4b64618f9f68 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-6b78c23c-7f12-4485-bcd0-4b64618f9f68');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2e93b0b9-8345-47f4-a0cd-277e1092a6fc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2e93b0b9-8345-47f4-a0cd-277e1092a6fc')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2e93b0b9-8345-47f4-a0cd-277e1092a6fc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 2713,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2713,\n        \"samples\": [\n          \"2016-02-05\",\n          \"2018-11-30\",\n          \"2019-12-05\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16106.428891479882,\n        \"min\": 176.897003,\n        \"max\": 67549.734375,\n        \"num_unique_values\": 2709,\n        \"samples\": [\n          36275.734375,\n          60701.886719,\n          381.733002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16537.390648731223,\n        \"min\": 211.731003,\n        \"max\": 68789.625,\n        \"num_unique_values\": 2710,\n        \"samples\": [\n          37247.519531,\n          35035.984375,\n          388.104004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15608.572560422952,\n        \"min\": 171.509995,\n        \"max\": 66382.0625,\n        \"num_unique_values\": 2712,\n        \"samples\": [\n          385.571991,\n          4145.765137,\n          7229.356934\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16110.365009695252,\n        \"min\": 178.102997,\n        \"max\": 67566.828125,\n        \"num_unique_values\": 2710,\n        \"samples\": [\n          36654.328125,\n          33572.117188,\n          382.299011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16110.365009695252,\n        \"min\": 178.102997,\n        \"max\": 67566.828125,\n        \"num_unique_values\": 2710,\n        \"samples\": [\n          36654.328125,\n          33572.117188,\n          382.299011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20016265866,\n        \"min\": 5914570,\n        \"max\": 350967941479,\n        \"num_unique_values\": 2713,\n        \"samples\": [\n          43825000,\n          6048016717,\n          18816085231\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert date column to datetime format\n",
        "data['Date'] = pd.to_datetime(data['Date'])\n",
        "data.set_index('Date', inplace=True)"
      ],
      "metadata": {
        "id": "N0wkymrWXRch"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Bitcoin closing prices over time\n",
        "plt.figure(figsize=(14, 7))\n",
        "plt.plot(data['Close'], color='blue', label='Closing Price')\n",
        "plt.title('Bitcoin Closing Price Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price in USD')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "JDWZtotkXVMP",
        "outputId": "040935f0-6801-417e-e773-3e37b20497a0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Preprocessing for LSTM**"
      ],
      "metadata": {
        "id": "35HJH56nXdkn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale data to the range (0,1)\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))"
      ],
      "metadata": {
        "id": "exMw3OmcXXXO"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create sequences\n",
        "sequence_length = 60\n",
        "X_train, y_train = [], []\n",
        "for i in range(sequence_length, len(scaled_data)):\n",
        "    X_train.append(scaled_data[i-sequence_length:i, 0])\n",
        "    y_train.append(scaled_data[i, 0])"
      ],
      "metadata": {
        "id": "uYRf59GAXgzO"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert to numpy arrays\n",
        "X_train, y_train = np.array(X_train), np.array(y_train)"
      ],
      "metadata": {
        "id": "Lc8n-1HwXjBJ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape the data to 3D format for LSTM [samples, time steps, features]\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
        "print(\"Data reshaped for LSTM:\", X_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tlKjGCGbXlqK",
        "outputId": "2041a234-7b9f-4ab3-ada9-29da604f9f6e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data reshaped for LSTM: (2653, 60, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Building the LSTM Model**"
      ],
      "metadata": {
        "id": "GP98s3DnXo6I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
        "\n",
        "# Initialize LSTM model\n",
        "model = Sequential()\n",
        "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(units=50, return_sequences=False))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(units=25))\n",
        "model.add(Dense(units=1))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "print(\"LSTM model compiled.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgQxbp9FXrct",
        "outputId": "1fe1d178-79ac-474b-ca73-83354bdad40a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LSTM model compiled.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train the Model**"
      ],
      "metadata": {
        "id": "FXfin-ukX0cr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "epochs = 50\n",
        "batch_size = 32\n",
        "history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1)\n",
        "print(\"Model training completed.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cKTkLZbGXtrt",
        "outputId": "6f79f82d-8757-48ab-b610-f231d9006c5a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 21ms/step - loss: 0.0059 - val_loss: 0.0036\n",
            "Epoch 2/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - loss: 0.0010 - val_loss: 0.0028\n",
            "Epoch 3/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 8.5567e-04 - val_loss: 0.0097\n",
            "Epoch 4/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 16ms/step - loss: 9.9091e-04 - val_loss: 0.0027\n",
            "Epoch 5/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 6.4456e-04 - val_loss: 0.0022\n",
            "Epoch 6/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 6.4597e-04 - val_loss: 0.0023\n",
            "Epoch 7/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 6.8488e-04 - val_loss: 0.0070\n",
            "Epoch 8/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 7.0031e-04 - val_loss: 0.0021\n",
            "Epoch 9/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 6.7441e-04 - val_loss: 0.0028\n",
            "Epoch 10/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 4.8210e-04 - val_loss: 0.0015\n",
            "Epoch 11/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 6.6288e-04 - val_loss: 0.0020\n",
            "Epoch 12/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 4.2714e-04 - val_loss: 0.0014\n",
            "Epoch 13/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 4.8398e-04 - val_loss: 0.0015\n",
            "Epoch 14/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 6.2582e-04 - val_loss: 0.0014\n",
            "Epoch 15/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 5.2430e-04 - val_loss: 0.0016\n",
            "Epoch 16/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 5.6741e-04 - val_loss: 0.0014\n",
            "Epoch 17/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 5.1460e-04 - val_loss: 0.0012\n",
            "Epoch 18/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 4.4027e-04 - val_loss: 0.0025\n",
            "Epoch 19/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 4.4971e-04 - val_loss: 0.0017\n",
            "Epoch 20/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 5.5276e-04 - val_loss: 0.0025\n",
            "Epoch 21/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 6.7541e-04 - val_loss: 0.0011\n",
            "Epoch 22/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 5.1814e-04 - val_loss: 0.0032\n",
            "Epoch 23/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 7.2409e-04 - val_loss: 0.0011\n",
            "Epoch 24/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 5.3155e-04 - val_loss: 0.0047\n",
            "Epoch 25/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 4.8799e-04 - val_loss: 0.0011\n",
            "Epoch 26/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 4.7288e-04 - val_loss: 0.0010\n",
            "Epoch 27/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 4.8931e-04 - val_loss: 0.0047\n",
            "Epoch 28/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 5.2662e-04 - val_loss: 0.0012\n",
            "Epoch 29/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 4.1911e-04 - val_loss: 0.0017\n",
            "Epoch 30/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 5.2979e-04 - val_loss: 0.0024\n",
            "Epoch 31/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 4.0001e-04 - val_loss: 9.8220e-04\n",
            "Epoch 32/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 3.7156e-04 - val_loss: 0.0010\n",
            "Epoch 33/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 4.3404e-04 - val_loss: 0.0027\n",
            "Epoch 34/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 4.9511e-04 - val_loss: 0.0011\n",
            "Epoch 35/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 4.5788e-04 - val_loss: 9.4644e-04\n",
            "Epoch 36/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 5.1771e-04 - val_loss: 0.0012\n",
            "Epoch 37/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 4.8488e-04 - val_loss: 0.0024\n",
            "Epoch 38/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 5.7104e-04 - val_loss: 0.0019\n",
            "Epoch 39/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 5.0305e-04 - val_loss: 0.0020\n",
            "Epoch 40/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 4.2288e-04 - val_loss: 0.0017\n",
            "Epoch 41/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 3.8153e-04 - val_loss: 9.4617e-04\n",
            "Epoch 42/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 5.1109e-04 - val_loss: 0.0015\n",
            "Epoch 43/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 4.6470e-04 - val_loss: 0.0023\n",
            "Epoch 44/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 6.5702e-04 - val_loss: 9.7880e-04\n",
            "Epoch 45/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 3.9808e-04 - val_loss: 0.0014\n",
            "Epoch 46/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 5.3873e-04 - val_loss: 0.0014\n",
            "Epoch 47/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 5.6934e-04 - val_loss: 0.0015\n",
            "Epoch 48/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 3.1897e-04 - val_loss: 0.0037\n",
            "Epoch 49/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 5.5340e-04 - val_loss: 0.0026\n",
            "Epoch 50/50\n",
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 3.3008e-04 - val_loss: 9.8284e-04\n",
            "Model training completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training and Validation Loss Visualization**"
      ],
      "metadata": {
        "id": "WVh3DzrOYBVd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training history\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss During Training')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "7-_XZhfIX3_k",
        "outputId": "f2e3337d-e7d8-4673-f8d3-8bfd95794c71"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predict Future Prices**"
      ],
      "metadata": {
        "id": "JKa76XW3YMDT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare test dataset (use a new dataset or split from original)\n",
        "test_data = data[-sequence_length:]\n",
        "inputs = scaler.transform(test_data['Close'].values.reshape(-1, 1))"
      ],
      "metadata": {
        "id": "iYj70VyPYF7X"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test, y_test = [], []\n",
        "for i in range(sequence_length, len(inputs)):\n",
        "    X_test.append(inputs[i-sequence_length:i, 0])"
      ],
      "metadata": {
        "id": "1IPv2AoVYRSC"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if X_test is populated and has the correct shape\n",
        "if len(X_test) == 0:\n",
        "    print(\"Error: X_test is empty. Check sequence_length or test data preparation.\")\n",
        "else:\n",
        "    X_test = np.array(X_test)\n",
        "    print(\"X_test shape before reshaping:\", X_test.shape)  # Debugging statement\n",
        "\n",
        "    # Reshape if X_test is populated correctly\n",
        "    try:\n",
        "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "        print(\"X_test reshaped for prediction:\", X_test.shape)\n",
        "    except IndexError:\n",
        "        print(\"Error: Reshaping failed. Check dimensions of X_test.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GuMhBPYMYTmD",
        "outputId": "af6ce71f-71e7-4694-f7d5-b7b1d3fbda37"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error: X_test is empty. Check sequence_length or test data preparation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the test data for sequences\n",
        "sequence_length = 60  # Ensure this is not larger than the available test data points\n",
        "test_data = data['Close'].values[-(sequence_length + 100):]  # Adjust to ensure enough data\n",
        "\n",
        "# Scale and transform test data\n",
        "inputs = scaler.transform(test_data.reshape(-1, 1))\n",
        "\n",
        "# Populate X_test based on sequence_length\n",
        "X_test = []\n",
        "for i in range(sequence_length, len(inputs)):\n",
        "    X_test.append(inputs[i-sequence_length:i, 0])\n",
        "\n",
        "# Confirm data population\n",
        "if len(X_test) == 0:\n",
        "    print(\"Error: X_test is still empty. Check data range and sequence length.\")\n",
        "else:\n",
        "    X_test = np.array(X_test)\n",
        "    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "    print(\"X_test prepared successfully:\", X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qROAK8hXYV60",
        "outputId": "1c6c37dd-6f68-40a0-f4b9-17294de43ad0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_test prepared successfully: (100, 60, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = np.array(X_test)\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))"
      ],
      "metadata": {
        "id": "JipNjOMaYxfw"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "predicted_prices = model.predict(X_test)\n",
        "predicted_prices = scaler.inverse_transform(predicted_prices)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jhei2FYVY1PP",
        "outputId": "6c0dac3b-1f5e-411f-99ff-a97edbe7535a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot predictions vs actual\n",
        "plt.figure(figsize=(14, 7))\n",
        "plt.plot(data['Close'].values, color='blue', label='Actual Bitcoin Price')\n",
        "plt.plot(range(len(data) - len(predicted_prices), len(data)), predicted_prices, color='red', label='Predicted Bitcoin Price')\n",
        "plt.title('Bitcoin Price Prediction')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price in USD')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "dCXeqQCfY3xQ",
        "outputId": "c82e29b0-1a8d-459c-ff58-73307ea66063"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluation**"
      ],
      "metadata": {
        "id": "0qbY1XlfY-He"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "\n",
        "# Calculate RMSE and MAE\n",
        "rmse = np.sqrt(mean_squared_error(data['Close'][-len(predicted_prices):], predicted_prices))\n",
        "mae = mean_absolute_error(data['Close'][-len(predicted_prices):], predicted_prices)\n",
        "\n",
        "print(f'Root Mean Squared Error: {rmse}')\n",
        "print(f'Mean Absolute Error: {mae}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "geQwLXOlY6aU",
        "outputId": "c4fcab3c-9c41-4790-ccc8-440de413df33"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error: 1810.4501354423396\n",
            "Mean Absolute Error: 1455.7246094224995\n"
          ]
        }
      ]
    }
  ]
}