CodeBase.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
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              "      <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",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>08-10-2015 13.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>245.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>245.00</td>\n",
              "      <td>245.00</td>\n",
              "      <td>0.606654</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>08-10-2015 14.00</td>\n",
              "      <td>245.00</td>\n",
              "      <td>245.00</td>\n",
              "      <td>244.50</td>\n",
              "      <td>245.00</td>\n",
              "      <td>245.00</td>\n",
              "      <td>4.453649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>08-10-2015 15.00</td>\n",
              "      <td>245.00</td>\n",
              "      <td>245.00</td>\n",
              "      <td>244.92</td>\n",
              "      <td>244.92</td>\n",
              "      <td>244.92</td>\n",
              "      <td>3.016926</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>08-10-2015 16.00</td>\n",
              "      <td>244.92</td>\n",
              "      <td>244.92</td>\n",
              "      <td>244.25</td>\n",
              "      <td>244.25</td>\n",
              "      <td>244.25</td>\n",
              "      <td>3.895252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>08-10-2015 17.00</td>\n",
              "      <td>244.25</td>\n",
              "      <td>244.99</td>\n",
              "      <td>244.02</td>\n",
              "      <td>244.99</td>\n",
              "      <td>244.99</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-797855dc-4e65-4060-a3c8-1e22831a6570');\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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              "  "
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            "text/plain": [
              "               Date    Open    High     Low   Close  Adj Close    Volume\n",
              "0  08-10-2015 13.00    0.00  245.00    0.00  245.00     245.00  0.606654\n",
              "1  08-10-2015 14.00  245.00  245.00  244.50  245.00     245.00  4.453649\n",
              "2  08-10-2015 15.00  245.00  245.00  244.92  244.92     244.92  3.016926\n",
              "3  08-10-2015 16.00  244.92  244.92  244.25  244.25     244.25  3.895252\n",
              "4  08-10-2015 17.00  244.25  244.99  244.02  244.99     244.99  3.920632"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data2=pd.read_csv('Data.csv')\n",
        "#data2=data2.sort_values('Date')\n",
        "data2.head()"
      ]
    },
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              "                   Date     Open     High  ...    Close  Adj Close      Volume\n",
              "39460  08-04-2020 20.00  7320.11  7375.72  ...  7327.70    7327.70  153.655811\n",
              "39461  08-04-2020 21.00  7327.70  7370.00  ...  7337.19    7337.19   35.551553\n",
              "39462  08-04-2020 22.00  7337.19  7390.25  ...  7367.28    7367.28   88.486108\n",
              "39463  08-04-2020 23.00  7367.28  7398.92  ...  7369.60    7369.60   66.558293\n",
              "39464   09-04-2020 0.00  7369.60  7369.60  ...  7338.23    7338.23    7.788915\n",
              "\n",
              "[5 rows x 7 columns]"
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          },
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      "source": [
        "data2.tail()"
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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "price=data2[['Close']]\n",
        "plt.figure(figsize=(15,9))\n",
        "plt.plot(price)\n",
        "plt.title(\"Bitcoin Price\",fontsize=18,fontweight='bold')\n",
        "plt.xlabel('Date',fontsize=18)\n",
        "plt.ylabel('Close price',fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "eNKziTBq5see",
        "outputId": "995fda91-8641-4cee-fe24-1301a93a1bfc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
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              "      <td>245.00</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fb47f2d1-f537-45ac-b407-5b2ec84528a8');\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",
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            "text/plain": [
              "         Close\n",
              "0       245.00\n",
              "1       245.00\n",
              "2       244.92\n",
              "3       244.25\n",
              "4       244.99\n",
              "...        ...\n",
              "39460  7327.70\n",
              "39461  7337.19\n",
              "39462  7367.28\n",
              "39463  7369.60\n",
              "39464  7338.23\n",
              "\n",
              "[39465 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4GPiISrf6JZj",
        "outputId": "627b0922-2a8b-4a3e-c4af-44fbebf988e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 39465 entries, 0 to 39464\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Close   39465 non-null  float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 308.4 KB\n"
          ]
        }
      ],
      "source": [
        "price.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9lFv46oB6N09"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "min_max_scaler=MinMaxScaler()\n",
        "norm_data=min_max_scaler.fit_transform(price.values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPNYO-EY62Nz",
        "outputId": "0c47be25-e3de-4c59-fb95-18b047197f3b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7.13329998e-05],\n",
              "       [7.13329998e-05],\n",
              "       [6.72568284e-05],\n",
              "       ...,\n",
              "       [3.62966760e-01],\n",
              "       [3.63084969e-01],\n",
              "       [3.61486600e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "norm_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "InfdUob-68No",
        "outputId": "3d2ef7a5-f1ef-4151-cfaa-e6602a2c8977"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Real :[245.],Normalized: [7.13329998e-05]\n",
            "Real :[312.],Normalized: [0.00348513]\n",
            "Real :[351.16],Normalized: [0.00548041]\n"
          ]
        }
      ],
      "source": [
        "print(f'Real :{price.values[0]},Normalized: {norm_data[0]}')\n",
        "print(f'Real :{price.values[500]},Normalized: {norm_data[500]}')\n",
        "print(f'Real :{price.values[900]},Normalized: {norm_data[900]}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IZ7jScW27tbt"
      },
      "outputs": [],
      "source": [
        "def univariate_data(dataset, start_index, end_index, history_size,target_size):\n",
        "  data=[]\n",
        "  labels=[]\n",
        "\n",
        "  start_index=start_index + history_size\n",
        "  if end_index is None:\n",
        "    end_index=len(dataset) - target_size\n",
        "\n",
        "  for i in range(start_index ,end_index):\n",
        "    indices= range(i - history_size,i)\n",
        "\n",
        "    data.append(np.reshape(dataset[indices],(history_size,1)))\n",
        "    labels.append(dataset[i+target_size])\n",
        "  return np.array(data),np.array(labels)\n",
        "past_history=5\n",
        "future_target=0\n",
        "\n",
        "TRAIN_SPLIT=int(len(norm_data)*0.8)\n",
        "\n",
        "x_train,y_train=univariate_data(norm_data,0,TRAIN_SPLIT,past_history,future_target)\n",
        "\n",
        "x_test,y_test=univariate_data(norm_data,TRAIN_SPLIT,None,past_history,future_target)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TK9sz3pj-NhE"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,LSTM,LeakyReLU,Dropout\n",
        "import tensorflow as tf\n",
        "\n",
        "num_units=64\n",
        "learning_rate=0.00001\n",
        "activation_function='sigmoid'\n",
        "loss_function='mae'\n",
        "batch_size=5\n",
        "num_epochs=150\n",
        "model=Sequential()\n",
        "\n",
        "model.add(LSTM(units= num_units, activation=activation_function,input_shape=(None,1)))\n",
        "model.add(LeakyReLU(alpha=0.5))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(units=1))\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate),loss=loss_function)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6NtEizOH4Uc",
        "outputId": "1bb484ea-8e17-417c-9b6b-6e534cf45a2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_3 (LSTM)               (None, 64)                16896     \n",
            "                                                                 \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 64)                0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 16,961\n",
            "Trainable params: 16,961\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FgVM6uzbDth",
        "outputId": "9e8d5d7c-aa64-4c6a-dff1-415160186db6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.41156033],\n",
              "        [0.41151039],\n",
              "        [0.4078077 ],\n",
              "        [0.39437366],\n",
              "        [0.39490713]],\n",
              "\n",
              "       [[0.41151039],\n",
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              "        [0.40164249]],\n",
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              "        [0.40164249],\n",
              "        [0.39575141]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[0.36025967],\n",
              "        [0.36077786],\n",
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              "        [0.36143361]],\n",
              "\n",
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              "\n",
              "       [[0.36056335],\n",
              "        [0.36095007],\n",
              "        [0.36143361],\n",
              "        [0.36296676],\n",
              "        [0.36308497]]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SM2mRGzkIEsN",
        "outputId": "41ef273b-15fa-4f8a-a680-3121137631ee"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.1258 - val_loss: 0.0398\n",
            "Epoch 2/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.1185 - val_loss: 0.0368\n",
            "Epoch 3/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.1128 - val_loss: 0.0360\n",
            "Epoch 4/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.1068 - val_loss: 0.0341\n",
            "Epoch 5/150\n",
            "5682/5682 [==============================] - 15s 3ms/step - loss: 0.1017 - val_loss: 0.0361\n",
            "Epoch 6/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0962 - val_loss: 0.0333\n",
            "Epoch 7/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0920 - val_loss: 0.0356\n",
            "Epoch 8/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0878 - val_loss: 0.0332\n",
            "Epoch 9/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0845 - val_loss: 0.0350\n",
            "Epoch 10/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0812 - val_loss: 0.0319\n",
            "Epoch 11/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0781 - val_loss: 0.0320\n",
            "Epoch 12/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0748 - val_loss: 0.0296\n",
            "Epoch 13/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0727 - val_loss: 0.0290\n",
            "Epoch 14/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0703 - val_loss: 0.0292\n",
            "Epoch 15/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0685 - val_loss: 0.0282\n",
            "Epoch 16/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0664 - val_loss: 0.0275\n",
            "Epoch 17/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0635 - val_loss: 0.0282\n",
            "Epoch 18/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0620 - val_loss: 0.0269\n",
            "Epoch 19/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0605 - val_loss: 0.0268\n",
            "Epoch 20/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0586 - val_loss: 0.0266\n",
            "Epoch 21/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0570 - val_loss: 0.0258\n",
            "Epoch 22/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0550 - val_loss: 0.0253\n",
            "Epoch 23/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0541 - val_loss: 0.0250\n",
            "Epoch 24/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0524 - val_loss: 0.0250\n",
            "Epoch 25/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0512 - val_loss: 0.0255\n",
            "Epoch 26/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0499 - val_loss: 0.0240\n",
            "Epoch 27/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0488 - val_loss: 0.0235\n",
            "Epoch 28/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0472 - val_loss: 0.0233\n",
            "Epoch 29/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0461 - val_loss: 0.0228\n",
            "Epoch 30/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0452 - val_loss: 0.0222\n",
            "Epoch 31/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0440 - val_loss: 0.0223\n",
            "Epoch 32/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0430 - val_loss: 0.0219\n",
            "Epoch 33/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0417 - val_loss: 0.0229\n",
            "Epoch 34/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0405 - val_loss: 0.0213\n",
            "Epoch 35/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0397 - val_loss: 0.0203\n",
            "Epoch 36/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0386 - val_loss: 0.0197\n",
            "Epoch 37/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0379 - val_loss: 0.0214\n",
            "Epoch 38/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0370 - val_loss: 0.0192\n",
            "Epoch 39/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0364 - val_loss: 0.0191\n",
            "Epoch 40/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0351 - val_loss: 0.0185\n",
            "Epoch 41/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0343 - val_loss: 0.0191\n",
            "Epoch 42/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0335 - val_loss: 0.0174\n",
            "Epoch 43/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0328 - val_loss: 0.0172\n",
            "Epoch 44/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0321 - val_loss: 0.0174\n",
            "Epoch 45/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0313 - val_loss: 0.0184\n",
            "Epoch 46/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0303 - val_loss: 0.0162\n",
            "Epoch 47/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0296 - val_loss: 0.0166\n",
            "Epoch 48/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0289 - val_loss: 0.0151\n",
            "Epoch 49/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0280 - val_loss: 0.0156\n",
            "Epoch 50/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0275 - val_loss: 0.0146\n",
            "Epoch 51/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0267 - val_loss: 0.0154\n",
            "Epoch 52/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0261 - val_loss: 0.0138\n",
            "Epoch 53/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0256 - val_loss: 0.0144\n",
            "Epoch 54/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0249 - val_loss: 0.0134\n",
            "Epoch 55/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0244 - val_loss: 0.0138\n",
            "Epoch 56/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0235 - val_loss: 0.0152\n",
            "Epoch 57/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0230 - val_loss: 0.0136\n",
            "Epoch 58/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0224 - val_loss: 0.0130\n",
            "Epoch 59/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0220 - val_loss: 0.0138\n",
            "Epoch 60/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0213 - val_loss: 0.0136\n",
            "Epoch 61/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0210 - val_loss: 0.0131\n",
            "Epoch 62/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0204 - val_loss: 0.0127\n",
            "Epoch 63/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0200 - val_loss: 0.0139\n",
            "Epoch 64/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0194 - val_loss: 0.0140\n",
            "Epoch 65/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0188 - val_loss: 0.0132\n",
            "Epoch 66/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0187 - val_loss: 0.0131\n",
            "Epoch 67/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0184 - val_loss: 0.0141\n",
            "Epoch 68/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0178 - val_loss: 0.0140\n",
            "Epoch 69/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0176 - val_loss: 0.0128\n",
            "Epoch 70/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0172 - val_loss: 0.0142\n",
            "Epoch 71/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0169 - val_loss: 0.0132\n",
            "Epoch 72/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0165 - val_loss: 0.0137\n",
            "Epoch 73/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0163 - val_loss: 0.0144\n",
            "Epoch 74/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0160 - val_loss: 0.0131\n",
            "Epoch 75/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0159 - val_loss: 0.0128\n",
            "Epoch 76/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0155 - val_loss: 0.0136\n",
            "Epoch 77/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0153 - val_loss: 0.0133\n",
            "Epoch 78/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0150 - val_loss: 0.0138\n",
            "Epoch 79/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0149 - val_loss: 0.0127\n",
            "Epoch 80/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0146 - val_loss: 0.0127\n",
            "Epoch 81/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0145 - val_loss: 0.0134\n",
            "Epoch 82/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0143 - val_loss: 0.0138\n",
            "Epoch 83/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0140 - val_loss: 0.0128\n",
            "Epoch 84/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0141 - val_loss: 0.0131\n",
            "Epoch 85/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0140 - val_loss: 0.0134\n",
            "Epoch 86/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0137 - val_loss: 0.0123\n",
            "Epoch 87/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0136 - val_loss: 0.0128\n",
            "Epoch 88/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0135 - val_loss: 0.0123\n",
            "Epoch 89/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0135 - val_loss: 0.0122\n",
            "Epoch 90/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0132 - val_loss: 0.0131\n",
            "Epoch 91/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0131 - val_loss: 0.0129\n",
            "Epoch 92/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0131 - val_loss: 0.0126\n",
            "Epoch 93/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0129 - val_loss: 0.0132\n",
            "Epoch 94/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0129 - val_loss: 0.0131\n",
            "Epoch 95/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0128 - val_loss: 0.0120\n",
            "Epoch 96/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0128 - val_loss: 0.0114\n",
            "Epoch 97/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0128 - val_loss: 0.0116\n",
            "Epoch 98/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0126 - val_loss: 0.0114\n",
            "Epoch 99/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0124 - val_loss: 0.0115\n",
            "Epoch 100/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0123 - val_loss: 0.0115\n",
            "Epoch 101/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0124 - val_loss: 0.0121\n",
            "Epoch 102/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0123 - val_loss: 0.0115\n",
            "Epoch 103/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0122 - val_loss: 0.0111\n",
            "Epoch 104/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0122 - val_loss: 0.0111\n",
            "Epoch 105/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0121 - val_loss: 0.0116\n",
            "Epoch 106/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0121 - val_loss: 0.0109\n",
            "Epoch 107/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0120 - val_loss: 0.0105\n",
            "Epoch 108/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0120 - val_loss: 0.0103\n",
            "Epoch 109/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0119 - val_loss: 0.0101\n",
            "Epoch 110/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0119 - val_loss: 0.0105\n",
            "Epoch 111/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0117 - val_loss: 0.0103\n",
            "Epoch 112/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0118 - val_loss: 0.0107\n",
            "Epoch 113/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0117 - val_loss: 0.0098\n",
            "Epoch 114/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0116 - val_loss: 0.0099\n",
            "Epoch 115/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0117 - val_loss: 0.0100\n",
            "Epoch 116/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0115 - val_loss: 0.0099\n",
            "Epoch 117/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0117 - val_loss: 0.0101\n",
            "Epoch 118/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0116 - val_loss: 0.0099\n",
            "Epoch 119/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0114 - val_loss: 0.0099\n",
            "Epoch 120/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0115 - val_loss: 0.0092\n",
            "Epoch 121/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0114 - val_loss: 0.0093\n",
            "Epoch 122/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0113 - val_loss: 0.0092\n",
            "Epoch 123/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0114 - val_loss: 0.0094\n",
            "Epoch 124/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0112 - val_loss: 0.0092\n",
            "Epoch 125/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0113 - val_loss: 0.0090\n",
            "Epoch 126/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0113 - val_loss: 0.0084\n",
            "Epoch 127/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0113 - val_loss: 0.0092\n",
            "Epoch 128/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0112 - val_loss: 0.0086\n",
            "Epoch 129/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0111 - val_loss: 0.0091\n",
            "Epoch 130/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0112 - val_loss: 0.0092\n",
            "Epoch 131/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0111 - val_loss: 0.0090\n",
            "Epoch 132/150\n",
            "5682/5682 [==============================] - 18s 3ms/step - loss: 0.0111 - val_loss: 0.0087\n",
            "Epoch 133/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0112 - val_loss: 0.0084\n",
            "Epoch 134/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0110 - val_loss: 0.0088\n",
            "Epoch 135/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0110 - val_loss: 0.0081\n",
            "Epoch 136/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0110 - val_loss: 0.0085\n",
            "Epoch 137/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0109 - val_loss: 0.0085\n",
            "Epoch 138/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0111 - val_loss: 0.0086\n",
            "Epoch 139/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0109 - val_loss: 0.0078\n",
            "Epoch 140/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0110 - val_loss: 0.0084\n",
            "Epoch 141/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0109 - val_loss: 0.0083\n",
            "Epoch 142/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0109 - val_loss: 0.0077\n",
            "Epoch 143/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0107 - val_loss: 0.0084\n",
            "Epoch 144/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0109 - val_loss: 0.0083\n",
            "Epoch 145/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0109 - val_loss: 0.0076\n",
            "Epoch 146/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0108 - val_loss: 0.0080\n",
            "Epoch 147/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0108 - val_loss: 0.0074\n",
            "Epoch 148/150\n",
            "5682/5682 [==============================] - 17s 3ms/step - loss: 0.0108 - val_loss: 0.0079\n",
            "Epoch 149/150\n",
            "5682/5682 [==============================] - 18s 3ms/step - loss: 0.0107 - val_loss: 0.0076\n",
            "Epoch 150/150\n",
            "5682/5682 [==============================] - 16s 3ms/step - loss: 0.0107 - val_loss: 0.0070\n"
          ]
        }
      ],
      "source": [
        "history=model.fit(x_train,y_train,validation_split=0.1,batch_size=batch_size,epochs=num_epochs,shuffle=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "W0bqyABgQD57",
        "outputId": "09a0c315-4f6f-48f3-ac96-2338d0345ed0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "loss=history.history['loss']\n",
        "val_loss=history.history['val_loss']\n",
        "\n",
        "epochs=range(len(loss))\n",
        "\n",
        "plt.figure(figsize=(15,9))\n",
        "\n",
        "plt.plot(epochs,loss,'b',label='Training loss')\n",
        "plt.plot(epochs,val_loss,'r',label='validation loss')\n",
        "plt.title(\"Training and validation loss\")\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "VkTbjzZgRsyj",
        "outputId": "293135ee-4e32-45e3-b13d-937aed86004b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cost (USD)')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 986.4x739.44 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "original=pd.DataFrame(min_max_scaler.inverse_transform(y_test))\n",
        "t=len(y_test)\n",
        "predictions=pd.DataFrame(min_max_scaler.inverse_transform(model.predict(x_test)))\n",
        "sns.set(rc={'figure.figsize':(11.7+2,8.27+2)})\n",
        "testc = pd.DataFrame()\n",
        "\n",
        "ax=sns.lineplot(x=original.index,y=original[0],label=\"Test Data\",color='blue')\n",
        "ax=sns.lineplot(x=predictions.index,y=predictions[0],label=\"Prediction\",color='red')\n",
        "ax.set_title('Bitcoin price',size=14,fontweight='bold')\n",
        "ax.set_xlabel(\"Days\",size=14)\n",
        "ax.set_ylabel(\"Cost (USD)\",size=14)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "W8qmhwo0he3F"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Untitled1.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}