Machine Learning/Bitcoin__Price_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Bitcoin _Price_Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmMO__YhieOb"
      },
      "source": [
        "import json\n",
        "import requests\n",
        "import tensorflow as tf\n",
        "from tensorflow import  keras\n",
        "from tensorflow.keras import layers\n",
        "#from tensorflow.contrib import lite\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Activation, Dense, Dropout, LSTM\n",
        "from keras.callbacks import EarlyStopping\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.offline as py\n",
        "import plotly.graph_objs as go\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBVKRHGfigX6"
      },
      "source": [
        "endpoint = 'https://min-api.cryptocompare.com/data/histoday'\n",
        "res = requests.get(endpoint + '?fsym=BTC&tsym=USD&limit=500')\n",
        "data = pd.DataFrame(json.loads(res.content)['Data'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBY6ytPNik0L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "outputId": "9a29c462-99a5-4081-8849-f19db1a131f5"
      },
      "source": [
        "data = data.set_index('time')\n",
        "data.index = pd.to_datetime(data.index, unit='s')\n",
        "data.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>high</th>\n",
              "      <th>low</th>\n",
              "      <th>open</th>\n",
              "      <th>volumefrom</th>\n",
              "      <th>volumeto</th>\n",
              "      <th>close</th>\n",
              "      <th>conversionType</th>\n",
              "      <th>conversionSymbol</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-12-21</th>\n",
              "      <td>7213.86</td>\n",
              "      <td>7132.82</td>\n",
              "      <td>7203.41</td>\n",
              "      <td>12101.12</td>\n",
              "      <td>8.686484e+07</td>\n",
              "      <td>7159.47</td>\n",
              "      <td>direct</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-22</th>\n",
              "      <td>7527.40</td>\n",
              "      <td>7143.17</td>\n",
              "      <td>7159.47</td>\n",
              "      <td>27325.26</td>\n",
              "      <td>2.005866e+08</td>\n",
              "      <td>7517.58</td>\n",
              "      <td>direct</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-23</th>\n",
              "      <td>7694.65</td>\n",
              "      <td>7275.28</td>\n",
              "      <td>7517.58</td>\n",
              "      <td>44770.93</td>\n",
              "      <td>3.364250e+08</td>\n",
              "      <td>7326.60</td>\n",
              "      <td>direct</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-24</th>\n",
              "      <td>7429.42</td>\n",
              "      <td>7175.50</td>\n",
              "      <td>7326.60</td>\n",
              "      <td>27770.75</td>\n",
              "      <td>2.030134e+08</td>\n",
              "      <td>7260.91</td>\n",
              "      <td>direct</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-25</th>\n",
              "      <td>7280.52</td>\n",
              "      <td>7132.30</td>\n",
              "      <td>7260.91</td>\n",
              "      <td>15012.21</td>\n",
              "      <td>1.085133e+08</td>\n",
              "      <td>7202.72</td>\n",
              "      <td>direct</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               high      low     open  ...    close  conversionType  conversionSymbol\n",
              "time                                   ...                                           \n",
              "2019-12-21  7213.86  7132.82  7203.41  ...  7159.47          direct                  \n",
              "2019-12-22  7527.40  7143.17  7159.47  ...  7517.58          direct                  \n",
              "2019-12-23  7694.65  7275.28  7517.58  ...  7326.60          direct                  \n",
              "2019-12-24  7429.42  7175.50  7326.60  ...  7260.91          direct                  \n",
              "2019-12-25  7280.52  7132.30  7260.91  ...  7202.72          direct                  \n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AnPPwQsXiliC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21546cfe-08d4-49b0-e5bd-f06af964fb6e"
      },
      "source": [
        "target_col = data['close']\n",
        "len(target_col)\n",
        "target_col"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "time\n",
              "2019-12-21     7159.47\n",
              "2019-12-22     7517.58\n",
              "2019-12-23     7326.60\n",
              "2019-12-24     7260.91\n",
              "2019-12-25     7202.72\n",
              "                ...   \n",
              "2021-04-30    57757.97\n",
              "2021-05-01    57843.16\n",
              "2021-05-02    56623.51\n",
              "2021-05-03    57195.07\n",
              "2021-05-04    56108.65\n",
              "Name: close, Length: 501, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LakZtj3riThB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78ff2ab8-b313-45fe-d53c-7e99658abe1f"
      },
      "source": [
        "target_col.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      501.000000\n",
              "mean     20289.575749\n",
              "std      17327.126371\n",
              "min       4916.780000\n",
              "25%       9160.390000\n",
              "50%      10929.400000\n",
              "75%      28889.610000\n",
              "max      63558.480000\n",
              "Name: close, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8mK8uToX7uKJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "51c951a7-cfbf-47ce-b2e2-c932eda94e5c"
      },
      "source": [
        "plt.rcParams['figure.figsize'] = [10,6]\n",
        "plt.plot(target_col)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f518cd31e90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPzobcXnqlyq"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "target_col=scaler.fit_transform(np.array(target_col).reshape(-1,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ez8_5IvogUP"
      },
      "source": [
        "##splitting dataset into train and test split\n",
        "training_size=int(len(target_col)*0.80)\n",
        "test_size=len(target_col)-training_size\n",
        "train_data,test_data=target_col[0:training_size,:],target_col[training_size:len(target_col),:1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMon8VWLr6jO"
      },
      "source": [
        "# convert an array of values into a dataset matrix\n",
        "def create_dataset(dataset, time_step=1):\n",
        "\tdataX, dataY = [], []\n",
        "\tfor i in range(len(dataset)-time_step-1):\n",
        "\t\ta = dataset[i:(i+time_step), 0]   ###i=0, 0,1,2,3-----99   100 \n",
        "\t\tdataX.append(a)\n",
        "\t\tdataY.append(dataset[i + time_step, 0])\n",
        "\treturn np.array(dataX), np.array(dataY)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "583kizUCsA7u"
      },
      "source": [
        "# reshape into X=t,t+1,t+2,t+3 and Y=t+4\n",
        "time_step=1\n",
        "X_train, y_train = create_dataset(train_data, time_step)\n",
        "X_test, ytest = create_dataset(test_data, time_step)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePpTIUHOkAt3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9700ea2a-1782-4b5b-fc1a-e87a0a881a7e"
      },
      "source": [
        "print(X_train.shape), print(y_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(398, 1)\n",
            "(398,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNaEHZOwkIj3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33081e98-08c2-44e9-f78b-09776dd0fc6f"
      },
      "source": [
        "print(X_test.shape), print(ytest.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(99, 1)\n",
            "(99,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJ-XaSzikOqB"
      },
      "source": [
        "# reshape input to be [samples, time steps, features] which is required for LSTM\n",
        "X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
        "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVGFeeABkYCl"
      },
      "source": [
        "model=keras.Sequential()\n",
        "model.add(LSTM(500,return_sequences=True,input_shape=(X_train.shape[1], X_train.shape[2])))\n",
        "model.add(LSTM(500,return_sequences=True))\n",
        "model.add(LSTM(500,return_sequences=False))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Activation('sigmoid'))\n",
        "model.add(Dense(1))\n",
        "#opt = keras.optimizers.Adam(learning_rate=0.01)\n",
        "#model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['mse'])\n",
        "model.compile(loss='mean_squared_error',optimizer='adam', metrics=['mae'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xF8-LcpsqMt6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ef342e9-a93f-4a70-9316-72d3a0938303"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 1, 500)            1004000   \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 1, 500)            2002000   \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 500)               2002000   \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 500)               0         \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 500)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 501       \n",
            "=================================================================\n",
            "Total params: 5,008,501\n",
            "Trainable params: 5,008,501\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLH_xT7qqdpT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "446bc1f0-ddc3-4a52-c174-15ea63bf63a8"
      },
      "source": [
        "history=model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=20,batch_size=32,verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "13/13 [==============================] - 6s 162ms/step - loss: 0.0422 - mae: 0.1598 - val_loss: 0.3443 - val_mae: 0.5704\n",
            "Epoch 2/20\n",
            "13/13 [==============================] - 1s 42ms/step - loss: 0.0216 - mae: 0.1215 - val_loss: 0.4032 - val_mae: 0.6202\n",
            "Epoch 3/20\n",
            "13/13 [==============================] - 1s 42ms/step - loss: 0.0168 - mae: 0.0962 - val_loss: 0.3642 - val_mae: 0.5901\n",
            "Epoch 4/20\n",
            "13/13 [==============================] - 1s 42ms/step - loss: 0.0096 - mae: 0.0663 - val_loss: 0.1141 - val_mae: 0.3296\n",
            "Epoch 5/20\n",
            "13/13 [==============================] - 1s 41ms/step - loss: 0.0023 - mae: 0.0315 - val_loss: 0.0232 - val_mae: 0.1477\n",
            "Epoch 6/20\n",
            "13/13 [==============================] - 1s 43ms/step - loss: 0.0011 - mae: 0.0186 - val_loss: 0.0129 - val_mae: 0.1058\n",
            "Epoch 7/20\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 3.7233e-04 - mae: 0.0123 - val_loss: 0.0049 - val_mae: 0.0604\n",
            "Epoch 8/20\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.6180e-04 - mae: 0.0084 - val_loss: 0.0041 - val_mae: 0.0538\n",
            "Epoch 9/20\n",
            "13/13 [==============================] - 1s 43ms/step - loss: 1.6337e-04 - mae: 0.0091 - val_loss: 0.0064 - val_mae: 0.0707\n",
            "Epoch 10/20\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.9074e-04 - mae: 0.0077 - val_loss: 0.0035 - val_mae: 0.0487\n",
            "Epoch 11/20\n",
            "13/13 [==============================] - 1s 41ms/step - loss: 1.1686e-04 - mae: 0.0067 - val_loss: 0.0044 - val_mae: 0.0561\n",
            "Epoch 12/20\n",
            "13/13 [==============================] - 1s 41ms/step - loss: 1.7812e-04 - mae: 0.0083 - val_loss: 0.0033 - val_mae: 0.0474\n",
            "Epoch 13/20\n",
            "13/13 [==============================] - 1s 42ms/step - loss: 1.6930e-04 - mae: 0.0077 - val_loss: 0.0055 - val_mae: 0.0643\n",
            "Epoch 14/20\n",
            "13/13 [==============================] - 1s 42ms/step - loss: 1.1692e-04 - mae: 0.0069 - val_loss: 0.0028 - val_mae: 0.0425\n",
            "Epoch 15/20\n",
            "13/13 [==============================] - 1s 42ms/step - loss: 1.2750e-04 - mae: 0.0074 - val_loss: 0.0043 - val_mae: 0.0553\n",
            "Epoch 16/20\n",
            "13/13 [==============================] - 1s 41ms/step - loss: 1.9787e-04 - mae: 0.0098 - val_loss: 0.0059 - val_mae: 0.0675\n",
            "Epoch 17/20\n",
            "13/13 [==============================] - 1s 41ms/step - loss: 2.1901e-04 - mae: 0.0115 - val_loss: 0.0030 - val_mae: 0.0441\n",
            "Epoch 18/20\n",
            "13/13 [==============================] - 1s 42ms/step - loss: 1.0347e-04 - mae: 0.0061 - val_loss: 0.0027 - val_mae: 0.0419\n",
            "Epoch 19/20\n",
            "13/13 [==============================] - 1s 41ms/step - loss: 1.2707e-04 - mae: 0.0068 - val_loss: 0.0032 - val_mae: 0.0466\n",
            "Epoch 20/20\n",
            "13/13 [==============================] - 1s 42ms/step - loss: 1.6478e-04 - mae: 0.0088 - val_loss: 0.0037 - val_mae: 0.0503\n"
          ],
          "name": "stdout"
        }
      ]
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        },
        "outputId": "bdbb3f64-e100-4436-d95d-169bc0e13d50"
      },
      "source": [
        "history.history['loss']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 0.0001174519638880156,\n",
              " 0.00013711779320146888,\n",
              " 0.00019837942090816796]"
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          "metadata": {
            "tags": []
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          "execution_count": 17
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    {
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          "height": 616
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        "    x = np.arange(0, len(history.history['loss']), 1),\n",
        "    y = history.history['loss'],\n",
        "    mode = 'lines',\n",
        "    name = 'Train loss',\n",
        "    line = dict(color=('rgb(255,0,0)'), width=2, dash='dash')\n",
        ")\n",
        "trace2 = go.Scatter(\n",
        "    x = np.arange(0, len(history.history['val_loss']), 1),\n",
        "    y = history.history['val_loss'],\n",
        "    mode = 'lines',\n",
        "    name = 'Test loss',\n",
        "    line = dict(color=('rgb(1, 0, 255)'), width=2)\n",
        ")\n",
        "\n",
        "data = [trace1, trace2]\n",
        "layout = go.Layout(\n",
        "    autosize=False,\n",
        "    width=750,\n",
        "    height=600,\n",
        "\n",
        "    title = 'Train and Test Loss during training',\n",
        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
        "                          linewidth = 1,\n",
        "                          mirror = True,\n",
        "                          title = \"Epoch number\",\n",
        "                          color = 'black'\n",
        "                          ),\n",
        "\n",
        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
        "                          linewidth = 1,\n",
        "                          mirror = True,\n",
        "                          title = \"Loss\",\n",
        "                           color = 'black'),\n",
        "\n",
        "    margin=go.layout.Margin(\n",
        "        l=50,\n",
        "        r=50,\n",
        "        b=100,\n",
        "        t=100,\n",
        "        pad = 4\n",
        "    )\n",
        ")\n",
        "\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "py.iplot(fig, filename='training_process')"
      ],
      "execution_count": null,
      "outputs": [
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "bc8520e0-71b8-42f3-9d7e-f9f340911ba2"
      },
      "source": [
        "trace1 = go.Scatter(\n",
        "    x = np.arange(0, len(history.history['mae']), 1),\n",
        "    y = history.history['mae'],\n",
        "    mode = 'lines',\n",
        "    name = 'Train Mean Absolute Error',\n",
        "    line = dict(color=('rgb(0,0,139)'), width=2, dash='dash')\n",
        ")\n",
        "trace2 = go.Scatter(\n",
        "    x = np.arange(0, len(history.history['val_mae']), 1),\n",
        "    y = history.history['val_mae'],\n",
        "    mode = 'lines',\n",
        "    name = 'Test Mean Absolute Error',\n",
        "    line = dict(color=('rgb(1, 0, 0)'), width=2)\n",
        ")\n",
        "\n",
        "data = [trace1, trace2]\n",
        "layout = go.Layout(\n",
        "    autosize=False,\n",
        "    width=750,\n",
        "    height=600,\n",
        "\n",
        "    title = 'Train and Test Mean Squared Error during training',\n",
        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
        "                          linewidth = 1,\n",
        "                          mirror = True,\n",
        "                          title = \"Epoch number\",\n",
        "                          color = 'black'\n",
        "                          ),\n",
        "\n",
        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
        "                          linewidth = 1,\n",
        "                          mirror = True,\n",
        "                          title = \"Mean Absolute Error\",\n",
        "                           color = 'black'),\n",
        "\n",
        "    margin=go.layout.Margin(\n",
        "        l=50,\n",
        "        r=50,\n",
        "        b=100,\n",
        "        t=100,\n",
        "        pad = 4\n",
        "    )\n",
        ")\n",
        "\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "py.iplot(fig, filename='training_process')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                        {\"responsive\": true}\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgbBKA6NClxf"
      },
      "source": [
        "# add one additional data point to align shapes of the predictions and true labels\n",
        "#X_test = np.append(X_test, scaler.transform(data.iloc[-1][0]))\n",
        "#X_test = np.reshape(X_test, (len(X_test), 1, 1))\n",
        "\n",
        "# get predictions and then make some transformations to be able to calculate RMSE properly in USD\n",
        "prediction = model.predict(X_test)\n",
        "prediction_inverse = scaler.inverse_transform(prediction.reshape(-1, 1))\n",
        "Y_test_inverse = scaler.inverse_transform(ytest.reshape(-1, 1))\n",
        "prediction2_inverse = np.array(prediction_inverse[:,0][1:])\n",
        "Y_test2_inverse = np.array(Y_test_inverse[:,0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8SXDEzbDPzM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8613de7-48ba-4dc6-d1b1-618cca8b5402"
      },
      "source": [
        "trace1 = go.Scatter(\n",
        "    x = np.arange(0, len(prediction2_inverse), 1),\n",
        "    y = prediction2_inverse,\n",
        "    mode = 'lines',\n",
        "    name = 'Predicted Labels',\n",
        "    line = dict(color=('rgb(0,0,139)'), width=2)\n",
        ")\n",
        "trace2 = go.Scatter(\n",
        "    x = np.arange(0, len(Y_test2_inverse), 1),\n",
        "    y = Y_test2_inverse,\n",
        "    mode = 'lines',\n",
        "    name = 'True labels',\n",
        "    line = dict(color=('rgb(255, 0, 0)'), width=2)\n",
        "\n",
        ")\n",
        "data = [trace1, trace2]\n",
        "\n",
        "layout = go.Layout(\n",
        "    autosize=False,\n",
        "    width=750,\n",
        "    height=600,\n",
        "\n",
        "    title = 'Prediction Graph',\n",
        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
        "                          linewidth = 1,\n",
        "                          mirror = True,\n",
        "                          title = \"Number of days\",\n",
        "                          color = 'black'\n",
        "                          ),                          \n",
        "\n",
        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
        "                          linewidth = 1,\n",
        "                          mirror = True,\n",
        "                          title = \"Price in USD\",\n",
        "                           color = 'black'),\n",
        "\n",
        "    margin=go.layout.Margin(\n",
        "        l=50,\n",
        "        r=50,\n",
        "        b=100,\n",
        "        t=100,\n",
        "        pad = 4\n",
        "    )\n",
        ")\n",
        "\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "py.iplot(fig, filename='results_demonstrating0')\n",
        "plt.savefig('demonstrating0.png', dpi=300, transparent=False, bbox_inches='tight')\n"
      ],
      "execution_count": null,
      "outputs": [
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