Part 13 - Multivariate_Time_Series_Data_Preprocessing_with_Pandas.ipynb

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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ibadrather/pytorch_learn/blob/main/Part%2013%20-%20Multivariate_Time_Series_Data_Preprocessing_with_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --quiet pytorch-lightning\n",
        "!pip install --quiet tqdm"
      ],
      "metadata": {
        "id": "S2Y09Q8dhC6W"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import torch\n",
        "import torch.autograd as autograd\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import pytorch_lightning as pl\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "CnJLCSn83jsH"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Styling Settings"
      ],
      "metadata": {
        "id": "zXXSZ9d75jAU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
        "\n",
        "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#93D30C\", \"#8F00FF\"]\n",
        "\n",
        "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
        "\n",
        "rcParams['figure.figsize'] = 14, 10\n",
        "\n",
        "tqdm.pandas()\n"
      ],
      "metadata": {
        "id": "ohQhw77y4_a7"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Seed Pytorch Lightning\n",
        "pl.seed_everything(42)"
      ],
      "metadata": {
        "id": "n2VfHesC5pnO",
        "outputId": "5b14971b-5c7c-4c62-8a73-ee810d360d17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Global seed set to 42\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Data"
      ],
      "metadata": {
        "id": "ctNtEGq85-3F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mouting Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "XsBeTa686El7",
        "outputId": "d6f4e644-3701-454d-9201-a64774bf68ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Source: https://www.cryptodatadownload.com/data/binance/\n",
        "\n",
        "data_path = \"/content/drive/MyDrive/Colab Notebooks/Binance_BTCUSDT_minute.csv\"\n",
        "df = pd.read_csv(data_path, parse_dates=[\"date\"])\n",
        "df"
      ],
      "metadata": {
        "id": "00QlkyGz79w9",
        "outputId": "fa4f6b1e-9a41-4268-b912-f6defd07d286",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  unix                date    symbol      open      high  \\\n",
              "0        1655511420000 2022-06-18 00:17:00  BTC/USDT  20599.28  20626.34   \n",
              "1        1655511360000 2022-06-18 00:16:00  BTC/USDT  20597.03  20599.29   \n",
              "2        1655511300000 2022-06-18 00:15:00  BTC/USDT  20619.34  20645.13   \n",
              "3        1655511240000 2022-06-18 00:14:00  BTC/USDT  20619.22  20638.02   \n",
              "4        1655511180000 2022-06-18 00:13:00  BTC/USDT  20643.04  20643.05   \n",
              "...                ...                 ...       ...       ...       ...   \n",
              "1439168  1567965660000 2019-09-08 18:01:00  BTC/USDT  10000.00  10000.00   \n",
              "1439169  1567965600000 2019-09-08 18:00:00  BTC/USDT  10000.00  10000.00   \n",
              "1439170  1567965540000 2019-09-08 17:59:00  BTC/USDT  10000.00  10000.00   \n",
              "1439171  1567965480000 2019-09-08 17:58:00  BTC/USDT  10000.00  10000.00   \n",
              "1439172  1567965420000 2019-09-08 17:57:00  BTC/USDT  10000.00  10000.00   \n",
              "\n",
              "              low     close  Volume BTC   Volume USDT  tradecount  \n",
              "0        20599.28  20613.06    20.35142  4.195369e+05         429  \n",
              "1        20568.90  20599.28    64.92620  1.336134e+06         957  \n",
              "2        20597.02  20597.02    56.02534  1.155457e+06         856  \n",
              "3        20613.27  20619.33    67.57083  1.393749e+06         758  \n",
              "4        20617.26  20619.22    58.09482  1.198367e+06         821  \n",
              "...           ...       ...         ...           ...         ...  \n",
              "1439168  10000.00  10000.00     0.00000  0.000000e+00           0  \n",
              "1439169  10000.00  10000.00     0.00000  0.000000e+00           0  \n",
              "1439170  10000.00  10000.00     0.00100  1.000000e+01           1  \n",
              "1439171  10000.00  10000.00     0.00000  0.000000e+00           0  \n",
              "1439172  10000.00  10000.00     0.00100  1.000000e+01           1  \n",
              "\n",
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              "      <th>3</th>\n",
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              "      <td>2022-06-18 00:14:00</td>\n",
              "      <td>BTC/USDT</td>\n",
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              "      <td>20613.27</td>\n",
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              "      <td>1.393749e+06</td>\n",
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              "      <th>4</th>\n",
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              "    <tr>\n",
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              "      <td>1567965540000</td>\n",
              "      <td>2019-09-08 17:59:00</td>\n",
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          },
          "metadata": {},
          "execution_count": 60
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    {
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      "source": [
        "df = df.sort_values(by=[\"date\"]).reset_index()\n",
        "df.head()"
      ],
      "metadata": {
        "id": "5ySkyu6A8k4H",
        "outputId": "d6ef5749-b022-4fc5-d0c1-a4acf854d3fc",
        "colab": {
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          "height": 206
        }
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     index           unix                date    symbol     open     high  \\\n",
              "0  1439172  1567965420000 2019-09-08 17:57:00  BTC/USDT  10000.0  10000.0   \n",
              "1  1439171  1567965480000 2019-09-08 17:58:00  BTC/USDT  10000.0  10000.0   \n",
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              "3  1439169  1567965600000 2019-09-08 18:00:00  BTC/USDT  10000.0  10000.0   \n",
              "4  1439168  1567965660000 2019-09-08 18:01:00  BTC/USDT  10000.0  10000.0   \n",
              "\n",
              "       low    close  Volume BTC  Volume USDT  tradecount  \n",
              "0  10000.0  10000.0       0.001         10.0           1  \n",
              "1  10000.0  10000.0       0.000          0.0           0  \n",
              "2  10000.0  10000.0       0.001         10.0           1  \n",
              "3  10000.0  10000.0       0.000          0.0           0  \n",
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              "            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": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "id": "c4Qgm5AS-i3-",
        "outputId": "147b4f58-8950-477b-9b66-3fc549d12022",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['index', 'unix', 'date', 'symbol', 'open', 'high', 'low', 'close',\n",
              "       'Volume BTC', 'Volume USDT', 'tradecount'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pre-Processing Data"
      ],
      "metadata": {
        "id": "D-ZWm8CT_c04"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding a previously_closed data column to our dataframe\n",
        "df[\"prev_close\"] = df.shift(1)[\"close\"]\n",
        "df.head()"
      ],
      "metadata": {
        "id": "BGn3EfVhEi9y",
        "outputId": "bb6607d2-62f8-46ae-f936-e20bbe93aff8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     index           unix                date    symbol     open     high  \\\n",
              "0  1439172  1567965420000 2019-09-08 17:57:00  BTC/USDT  10000.0  10000.0   \n",
              "1  1439171  1567965480000 2019-09-08 17:58:00  BTC/USDT  10000.0  10000.0   \n",
              "2  1439170  1567965540000 2019-09-08 17:59:00  BTC/USDT  10000.0  10000.0   \n",
              "3  1439169  1567965600000 2019-09-08 18:00:00  BTC/USDT  10000.0  10000.0   \n",
              "4  1439168  1567965660000 2019-09-08 18:01:00  BTC/USDT  10000.0  10000.0   \n",
              "\n",
              "       low    close  Volume BTC  Volume USDT  tradecount  prev_close  \n",
              "0  10000.0  10000.0       0.001         10.0           1         NaN  \n",
              "1  10000.0  10000.0       0.000          0.0           0     10000.0  \n",
              "2  10000.0  10000.0       0.001         10.0           1     10000.0  \n",
              "3  10000.0  10000.0       0.000          0.0           0     10000.0  \n",
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              "          const element = document.querySelector('#df-4da7525e-cf75-4a60-94d9-cde35319cf96');\n",
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              "            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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding another column that will be a difference from previous close\n",
        "# this will have the change in close price data per minute\n",
        "df[\"close_change\"] = df.progress_apply(\n",
        "    lambda row: 0 if np.isnan(row.prev_close) else row.close - row.prev_close,\n",
        "    axis=1\n",
        ")\n",
        "df.head()"
      ],
      "metadata": {
        "id": "IBUTf0jmErek",
        "outputId": "123e2838-1728-453a-ca99-c76f16dc447d",
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          "height": 238,
          "referenced_widgets": [
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      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1439173 [00:00<?, ?it/s]"
            ],
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              "     index           unix                date    symbol     open     high  \\\n",
              "0  1439172  1567965420000 2019-09-08 17:57:00  BTC/USDT  10000.0  10000.0   \n",
              "1  1439171  1567965480000 2019-09-08 17:58:00  BTC/USDT  10000.0  10000.0   \n",
              "2  1439170  1567965540000 2019-09-08 17:59:00  BTC/USDT  10000.0  10000.0   \n",
              "3  1439169  1567965600000 2019-09-08 18:00:00  BTC/USDT  10000.0  10000.0   \n",
              "4  1439168  1567965660000 2019-09-08 18:01:00  BTC/USDT  10000.0  10000.0   \n",
              "\n",
              "       low    close  Volume BTC  Volume USDT  tradecount  prev_close  \\\n",
              "0  10000.0  10000.0       0.001         10.0           1         NaN   \n",
              "1  10000.0  10000.0       0.000          0.0           0     10000.0   \n",
              "2  10000.0  10000.0       0.001         10.0           1     10000.0   \n",
              "3  10000.0  10000.0       0.000          0.0           0     10000.0   \n",
              "4  10000.0  10000.0       0.000          0.0           0     10000.0   \n",
              "\n",
              "   close_change  \n",
              "0           0.0  \n",
              "1           0.0  \n",
              "2           0.0  \n",
              "3           0.0  \n",
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          "metadata": {},
          "execution_count": 64
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      ]
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    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "RlZvEn83EsW8"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Converting Dataframe in Features"
      ],
      "metadata": {
        "id": "hfSq6zo9GPGv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rows = []\n",
        "\n",
        "for _, row in tqdm(df.iterrows(), total=df.shape[0]):\n",
        "  row_data = dict(\n",
        "      day_of_week = row.date.dayofweek,\n",
        "      day_of_month = row.date.day,\n",
        "      week_of_year = row.date.week,\n",
        "      month = row.date.month,\n",
        "      open = row.open,\n",
        "      high = row.high,\n",
        "      low = row.low,\n",
        "      close_change = row.close_change,\n",
        "      close = row.close\n",
        "      )\n",
        "  rows.append(row_data)\n",
        "\n",
        "# Convert this into Pandas DataFrame\n",
        "features_df = pd.DataFrame(rows)\n",
        "features_df"
      ],
      "metadata": {
        "id": "gZxwicBoGSNK",
        "outputId": "c1d9583a-4d17-49ee-d6d3-3b7321d45653",
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          "height": 455,
          "referenced_widgets": [
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            "84f1aaefdd3942a391746fb9201e7766",
            "2d71c4242d8e4c46b6c3ef7469646a29"
          ]
        }
      },
      "execution_count": 65,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "  0%|          | 0/1439173 [00:00<?, ?it/s]"
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            "text/plain": [
              "         day_of_week  day_of_month  week_of_year  month      open      high  \\\n",
              "0                  6             8            36      9  10000.00  10000.00   \n",
              "1                  6             8            36      9  10000.00  10000.00   \n",
              "2                  6             8            36      9  10000.00  10000.00   \n",
              "3                  6             8            36      9  10000.00  10000.00   \n",
              "4                  6             8            36      9  10000.00  10000.00   \n",
              "...              ...           ...           ...    ...       ...       ...   \n",
              "1439168            5            18            24      6  20643.04  20643.05   \n",
              "1439169            5            18            24      6  20619.22  20638.02   \n",
              "1439170            5            18            24      6  20619.34  20645.13   \n",
              "1439171            5            18            24      6  20597.03  20599.29   \n",
              "1439172            5            18            24      6  20599.28  20626.34   \n",
              "\n",
              "              low  close_change     close  \n",
              "0        10000.00          0.00  10000.00  \n",
              "1        10000.00          0.00  10000.00  \n",
              "2        10000.00          0.00  10000.00  \n",
              "3        10000.00          0.00  10000.00  \n",
              "4        10000.00          0.00  10000.00  \n",
              "...           ...           ...       ...  \n",
              "1439168  20617.26        -23.83  20619.22  \n",
              "1439169  20613.27          0.11  20619.33  \n",
              "1439170  20597.02        -22.31  20597.02  \n",
              "1439171  20568.90          2.26  20599.28  \n",
              "1439172  20599.28         13.78  20613.06  \n",
              "\n",
              "[1439173 rows x 9 columns]"
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              "    <tr>\n",
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              "      <td>20613.06</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>1439173 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ba9ca16-edb1-44fd-a56d-07b04328cb21')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      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",
              "    [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-8ba9ca16-edb1-44fd-a56d-07b04328cb21 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-8ba9ca16-edb1-44fd-a56d-07b04328cb21');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_df.to_csv(\"binance_btc_usd_dataset_processed.csv\", index=False)"
      ],
      "metadata": {
        "id": "exdsVRuJPFl5"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train-Test Split"
      ],
      "metadata": {
        "id": "B9-INVRgIZK6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "split_ratio = 0.9\n",
        "train_size = int(len(features_df) * split_ratio)\n",
        "print(train_size)"
      ],
      "metadata": {
        "id": "pO6LuPPvIWtB",
        "outputId": "66b7d6f8-934d-4524-e127-4c8312729e13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1295255\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df, test_df = features_df[:train_size], features_df[train_size+1:]\n",
        "train_df.shape, test_df.shape"
      ],
      "metadata": {
        "id": "szvEWko1Hpbh",
        "outputId": "5ffffcb1-5683-4dd6-8c66-2f63aabf6dc5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1295255, 9), (143917, 9))"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalising the Data\n",
        "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
        "scaler = scaler.fit(train_df)"
      ],
      "metadata": {
        "id": "gk1WxPSUJPie"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.DataFrame(\n",
        "    scaler.transform(train_df),\n",
        "    index = train_df.index,\n",
        "    columns = train_df.columns\n",
        ")\n",
        "train_df.head()"
      ],
      "metadata": {
        "id": "6IWPot6TJl26",
        "outputId": "3ba6777f-f4cc-4487-cdb8-5c3a683d6bac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   day_of_week  day_of_month  week_of_year     month      open      high  \\\n",
              "0          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
              "1          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
              "2          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
              "3          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
              "4          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
              "\n",
              "        low  close_change     close  \n",
              "0 -0.804245       0.38329 -0.807237  \n",
              "1 -0.804245       0.38329 -0.807237  \n",
              "2 -0.804245       0.38329 -0.807237  \n",
              "3 -0.804245       0.38329 -0.807237  \n",
              "4 -0.804245       0.38329 -0.807237  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d6ab247d-a941-4b09-9fee-fb98bb601416\">\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>day_of_week</th>\n",
              "      <th>day_of_month</th>\n",
              "      <th>week_of_year</th>\n",
              "      <th>month</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close_change</th>\n",
              "      <th>close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.533333</td>\n",
              "      <td>0.346154</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>-0.807237</td>\n",
              "      <td>-0.809233</td>\n",
              "      <td>-0.804245</td>\n",
              "      <td>0.38329</td>\n",
              "      <td>-0.807237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.533333</td>\n",
              "      <td>0.346154</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>-0.807237</td>\n",
              "      <td>-0.809233</td>\n",
              "      <td>-0.804245</td>\n",
              "      <td>0.38329</td>\n",
              "      <td>-0.807237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.533333</td>\n",
              "      <td>0.346154</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>-0.807237</td>\n",
              "      <td>-0.809233</td>\n",
              "      <td>-0.804245</td>\n",
              "      <td>0.38329</td>\n",
              "      <td>-0.807237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.533333</td>\n",
              "      <td>0.346154</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>-0.807237</td>\n",
              "      <td>-0.809233</td>\n",
              "      <td>-0.804245</td>\n",
              "      <td>0.38329</td>\n",
              "      <td>-0.807237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.533333</td>\n",
              "      <td>0.346154</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>-0.807237</td>\n",
              "      <td>-0.809233</td>\n",
              "      <td>-0.804245</td>\n",
              "      <td>0.38329</td>\n",
              "      <td>-0.807237</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6ab247d-a941-4b09-9fee-fb98bb601416')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      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",
              "    [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-d6ab247d-a941-4b09-9fee-fb98bb601416 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-d6ab247d-a941-4b09-9fee-fb98bb601416');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Cutting DataFrame in Multiple Sequences"
      ],
      "metadata": {
        "id": "vnwd9vXgKZui"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_sequences(input_data: pd.DataFrame, target_column, sequence_length=3):\n",
        "  sequences = []\n",
        "  data_size = len(input_data)\n",
        "\n",
        "  for i in tqdm(range(data_size - sequence_length)):\n",
        "    sequence = input_data[i:i+sequence_length]\n",
        "    label_position = i + sequence_length\n",
        "    label = input_data.iloc[label_position][target_column]\n",
        "\n",
        "    sequences.append((sequence, label))\n",
        "\n",
        "  return sequences"
      ],
      "metadata": {
        "id": "3YSp4cu2KZG4"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Creating Training and Testing Sequences"
      ],
      "metadata": {
        "id": "Ntkc7ml2MZO3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SEQUENCE_LENGTH = 60"
      ],
      "metadata": {
        "id": "JHzbuwJGNW0T"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_sequences = create_sequences(train_df, \"close\", sequence_length=SEQUENCE_LENGTH)\n",
        "test_sequences = create_sequences(test_df, \"close\", sequence_length=SEQUENCE_LENGTH)"
      ],
      "metadata": {
        "id": "_4josB6JLwQJ",
        "outputId": "9518a997-d74a-4a6c-c773-a340d5c81586",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "b5e6377e491b46418c6615f84bf48dbb",
            "77ef43f15c584ea3830269b394661583",
            "245ed94b00c74af6a297067dfd270281",
            "fdc43b3f59294e43a6792df3915eac36",
            "dd596b8311ff4cc692722fd6cb300d6d",
            "16e65634dc5a48539fb65e1f03c450ca",
            "e8645c7f644742de944bd1d0cec88cba",
            "6c60042f82c94f158b6ef75b4846e98a",
            "ecd2db03c56a421db67df6862beae746",
            "76d33665e11c48c8b7fe183b0e45e898",
            "177a8be6353c4a6ebf5715de9efded6f",
            "940556bcbdc04a69aa2473c67fd94207",
            "15bee28bdc8d49938cd00b9ab17d5d5d",
            "227c648b10db4f95b570797c6ce38a29",
            "c1a400a3e9c9448c8bd433ca73f332b2",
            "df22898363d24700abd8bc2c7b6bd150",
            "1df156cfd7cf4251ab84624f651ee6c6",
            "d0fddc02e08e4fb89fd3846a7352a67d",
            "e195748e21644917815a4cf8c097eaf5",
            "7ae159ff569543ee8163d10f81d36434",
            "3c7a2b9b7c8d4fc5828e39b6f467d2fe",
            "fa488cafead2457ea858e3cb7aa84172"
          ]
        }
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1295195 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5e6377e491b46418c6615f84bf48dbb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/143857 [00:01<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "940556bcbdc04a69aa2473c67fd94207"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's check first sequence label and data\n",
        "print(\"Label: \", train_sequences[0][1])\n",
        "print(\"\")\n",
        "print(\"Sequence: \",train_sequences[0][0])"
      ],
      "metadata": {
        "id": "G6peMcOWNtYu",
        "outputId": "65459cfe-8526-4f3a-cce9-f53dd4813bf1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label:  -0.8072370347589881\n",
            "\n",
            "Sequence:      day_of_week  day_of_month  week_of_year     month      open      high  \\\n",
            "0           1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "1           1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "2           1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "3           1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "4           1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "5           1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "6           1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "7           1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "8           1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "9           1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "10          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "11          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "12          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "13          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "14          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "15          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "16          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "17          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "18          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "19          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "20          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "21          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "22          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "23          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "24          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "25          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "26          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "27          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "28          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "29          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "30          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "31          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "32          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "33          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "34          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "35          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "36          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "37          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "38          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "39          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "40          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "41          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "42          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "43          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "44          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "45          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "46          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "47          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "48          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "49          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "50          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "51          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "52          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "53          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "54          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "55          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "56          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "57          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "58          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "59          1.0     -0.533333      0.346154  0.454545 -0.807237 -0.809233   \n",
            "\n",
            "         low  close_change     close  \n",
            "0  -0.804245       0.38329 -0.807237  \n",
            "1  -0.804245       0.38329 -0.807237  \n",
            "2  -0.804245       0.38329 -0.807237  \n",
            "3  -0.804245       0.38329 -0.807237  \n",
            "4  -0.804245       0.38329 -0.807237  \n",
            "5  -0.804245       0.38329 -0.807237  \n",
            "6  -0.804245       0.38329 -0.807237  \n",
            "7  -0.804245       0.38329 -0.807237  \n",
            "8  -0.804245       0.38329 -0.807237  \n",
            "9  -0.804245       0.38329 -0.807237  \n",
            "10 -0.804245       0.38329 -0.807237  \n",
            "11 -0.804245       0.38329 -0.807237  \n",
            "12 -0.804245       0.38329 -0.807237  \n",
            "13 -0.804245       0.38329 -0.807237  \n",
            "14 -0.804245       0.38329 -0.807237  \n",
            "15 -0.804245       0.38329 -0.807237  \n",
            "16 -0.804245       0.38329 -0.807237  \n",
            "17 -0.804245       0.38329 -0.807237  \n",
            "18 -0.804245       0.38329 -0.807237  \n",
            "19 -0.804245       0.38329 -0.807237  \n",
            "20 -0.804245       0.38329 -0.807237  \n",
            "21 -0.804245       0.38329 -0.807237  \n",
            "22 -0.804245       0.38329 -0.807237  \n",
            "23 -0.804245       0.38329 -0.807237  \n",
            "24 -0.804245       0.38329 -0.807237  \n",
            "25 -0.804245       0.38329 -0.807237  \n",
            "26 -0.804245       0.38329 -0.807237  \n",
            "27 -0.804245       0.38329 -0.807237  \n",
            "28 -0.804245       0.38329 -0.807237  \n",
            "29 -0.804245       0.38329 -0.807237  \n",
            "30 -0.804245       0.38329 -0.807237  \n",
            "31 -0.804245       0.38329 -0.807237  \n",
            "32 -0.804245       0.38329 -0.807237  \n",
            "33 -0.804245       0.38329 -0.807237  \n",
            "34 -0.804245       0.38329 -0.807237  \n",
            "35 -0.804245       0.38329 -0.807237  \n",
            "36 -0.804245       0.38329 -0.807237  \n",
            "37 -0.804245       0.38329 -0.807237  \n",
            "38 -0.804245       0.38329 -0.807237  \n",
            "39 -0.804245       0.38329 -0.807237  \n",
            "40 -0.804245       0.38329 -0.807237  \n",
            "41 -0.804245       0.38329 -0.807237  \n",
            "42 -0.804245       0.38329 -0.807237  \n",
            "43 -0.804245       0.38329 -0.807237  \n",
            "44 -0.804245       0.38329 -0.807237  \n",
            "45 -0.804245       0.38329 -0.807237  \n",
            "46 -0.804245       0.38329 -0.807237  \n",
            "47 -0.804245       0.38329 -0.807237  \n",
            "48 -0.804245       0.38329 -0.807237  \n",
            "49 -0.804245       0.38329 -0.807237  \n",
            "50 -0.804245       0.38329 -0.807237  \n",
            "51 -0.804245       0.38329 -0.807237  \n",
            "52 -0.804245       0.38329 -0.807237  \n",
            "53 -0.804245       0.38329 -0.807237  \n",
            "54 -0.804245       0.38329 -0.807237  \n",
            "55 -0.804245       0.38329 -0.807237  \n",
            "56 -0.804245       0.38329 -0.807237  \n",
            "57 -0.804245       0.38329 -0.807237  \n",
            "58 -0.804245       0.38329 -0.807237  \n",
            "59 -0.804245       0.38329 -0.807237  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_sequences), len(test_sequences)"
      ],
      "metadata": {
        "id": "xzc23o_bNnUO",
        "outputId": "49da5a5f-3917-4221-cdf0-6d3d8bd1add0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1295195, 143857)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "QLnED-QKO1a9"
      },
      "execution_count": 75,
      "outputs": []
    }
  ]
}