Rede_neural_Valor_do_ouro.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/DanielBrisch/RedeNeural-ValorDoOuro/blob/main/Rede_neural_Valor_do_ouro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BgCxaTsUKo0"
      },
      "source": [
        "# Utilização de Redes Neurais Recorrentes - Preço do Ouro\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OSp-1Fkol2D4"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UaugiF4ul6RO"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout\n",
        "import yfinance as yf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JLq5TDzvo05S"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7E_KS_xPpDAg"
      },
      "source": [
        "## Importando os dados da tabela\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n6uQhFN7pTST",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ],
      "source": [
        "df = yf.download('GC=F', start='2000-01-01', end='2023-04-30')\n",
        "df= df.dropna()\n",
        "Gold_set = df.iloc[:, 1:2].values"
      ]
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              "                   High\n",
              "Date                   \n",
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              "          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": 6
        }
      ],
      "source": [
        "df.iloc[:, 1:2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nAtKQnn6pB0J"
      },
      "source": [
        "## Dimensionamento dos dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BwNcupnqqP-a"
      },
      "outputs": [],
      "source": [
        "sc = MinMaxScaler(feature_range=(0, 1))\n",
        "Gold_set_scaled = sc.fit_transform(Gold_set)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXJgnbWRqy9C"
      },
      "source": [
        "## Criação de uma estrutura de dados com 20 timesteps e 1 um saída"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nwg5EhxuqxgW"
      },
      "outputs": [],
      "source": [
        "X_gold = []\n",
        "y_gold = []\n",
        "for i in range(20, len(Gold_set_scaled)):\n",
        "    X_gold.append(Gold_set_scaled[i-20:i, 0])\n",
        "    y_gold.append(Gold_set_scaled[i, 0])\n",
        "X_gold, y_gold = np.array(X_gold), np.array(y_gold)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlZ7QeWrrTKJ"
      },
      "source": [
        "## Reshaping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iM_RPskGrTn3"
      },
      "outputs": [],
      "source": [
        "X_gold = np.reshape(X_gold, (X_gold.shape[0], X_gold.shape[1], 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ASg5iIZt8ZA",
        "outputId": "a80083db-3bf2-4d46-b674-34b428b9e0f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "X_gold.shape[1], 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PtyggSsAmGdO"
      },
      "source": [
        "## Inicializa a RNA\n",
        "### Um modelo sequencial é apropriado para um conjunto simples de camadas onde cada camada tem exatamente um tensor de entrada e um tensor de saída."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "98bcM_Dkl_PW"
      },
      "outputs": [],
      "source": [
        "regressor = Sequential()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3xZW0UxnESh"
      },
      "source": [
        "## Adicionando a primeira camada LSTM (É uma arquitetura de rede neural recorrente) e alguma regularização de Dropout\n",
        "\n",
        "### O intuito é reduzir o overfitting\n",
        "\n",
        "### Dropout Ele consiste em aleatoriamente descartar algumas unidades (neurônios) durante o treinamento de cada época. Isso significa que cada unidade é mantida com uma certa probabilidade (normalmente 60%) e descartada com uma certa probabilidade (40%) -- Em nosso caso.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QdNuBUfDmDsK"
      },
      "outputs": [],
      "source": [
        "regressor.add(LSTM(units = 70, return_sequences = True, input_shape = (X_gold.shape[1], 1)))\n",
        "regressor.add(Dropout(0.4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUVdA1h4sJq3"
      },
      "source": [
        "## Adicionando uma segunda camada de LSTM com Regularização Dropout\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FBZ7menqsIO4"
      },
      "outputs": [],
      "source": [
        "regressor.add(LSTM(units = 70, return_sequences = True))\n",
        "regressor.add(Dropout(0.4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zq1MUkupsdH8"
      },
      "source": [
        "## Adicionando uma terceira camada de LSTM com Regularização Dropout\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uIl9BmQTscRT"
      },
      "outputs": [],
      "source": [
        "regressor.add(LSTM(units = 70, return_sequences = True))\n",
        "regressor.add(Dropout(0.4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXmLUIsEsmxx"
      },
      "source": [
        "## Adicionando uma quarta camada de LSTM com Regularização Dropout"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RwX_zGu1slej"
      },
      "outputs": [],
      "source": [
        "regressor.add(LSTM(units = 70))\n",
        "regressor.add(Dropout(0.4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ynh4UhOfs4TD"
      },
      "source": [
        "## Adicionando a camada de saída\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X4lozj1ns6lq"
      },
      "outputs": [],
      "source": [
        "regressor.add(Dense(units = 1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHTCvtaftmvX"
      },
      "source": [
        "## Compilando RNA\n",
        "### Utilizando a otimização ADAM: A otimização de Adam é um método de descida gradiente estocástico baseado na estimativa adaptativa de momentos de primeira e segunda ordem.\n",
        "\n",
        "### A função loss é por meio da média dos quadrados\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0kM0NFdktC3y"
      },
      "outputs": [],
      "source": [
        "regressor.compile(optimizer = 'Nadam', loss = 'mean_squared_error')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGCDxKV4vAQP"
      },
      "source": [
        "## Fitting da RNA para treinamento do set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9sqfMIGu04Y",
        "outputId": "e50e867d-4ec7-4818-9e11-4c10915d581e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "178/178 [==============================] - 40s 70ms/step - loss: 0.0129\n",
            "Epoch 2/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0045\n",
            "Epoch 3/100\n",
            "178/178 [==============================] - 10s 59ms/step - loss: 0.0040\n",
            "Epoch 4/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0037\n",
            "Epoch 5/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0034\n",
            "Epoch 6/100\n",
            "178/178 [==============================] - 12s 69ms/step - loss: 0.0030\n",
            "Epoch 7/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0028\n",
            "Epoch 8/100\n",
            "178/178 [==============================] - 12s 66ms/step - loss: 0.0027\n",
            "Epoch 9/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0025\n",
            "Epoch 10/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0022\n",
            "Epoch 11/100\n",
            "178/178 [==============================] - 12s 69ms/step - loss: 0.0022\n",
            "Epoch 12/100\n",
            "178/178 [==============================] - 10s 57ms/step - loss: 0.0021\n",
            "Epoch 13/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0020\n",
            "Epoch 14/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0018\n",
            "Epoch 15/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0018\n",
            "Epoch 16/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0017\n",
            "Epoch 17/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0017\n",
            "Epoch 18/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0016\n",
            "Epoch 19/100\n",
            "178/178 [==============================] - 10s 58ms/step - loss: 0.0015\n",
            "Epoch 20/100\n",
            "178/178 [==============================] - 11s 61ms/step - loss: 0.0015\n",
            "Epoch 21/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0016\n",
            "Epoch 22/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0016\n",
            "Epoch 23/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0015\n",
            "Epoch 24/100\n",
            "178/178 [==============================] - 11s 65ms/step - loss: 0.0015\n",
            "Epoch 25/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0014\n",
            "Epoch 26/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0014\n",
            "Epoch 27/100\n",
            "178/178 [==============================] - 10s 59ms/step - loss: 0.0014\n",
            "Epoch 28/100\n",
            "178/178 [==============================] - 11s 62ms/step - loss: 0.0015\n",
            "Epoch 29/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0014\n",
            "Epoch 30/100\n",
            "178/178 [==============================] - 11s 65ms/step - loss: 0.0014\n",
            "Epoch 31/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0014\n",
            "Epoch 32/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0014\n",
            "Epoch 33/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0014\n",
            "Epoch 34/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0014\n",
            "Epoch 35/100\n",
            "178/178 [==============================] - 10s 58ms/step - loss: 0.0013\n",
            "Epoch 36/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0013\n",
            "Epoch 37/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0014\n",
            "Epoch 38/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 39/100\n",
            "178/178 [==============================] - 11s 65ms/step - loss: 0.0013\n",
            "Epoch 40/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0013\n",
            "Epoch 41/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0013\n",
            "Epoch 42/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0013\n",
            "Epoch 43/100\n",
            "178/178 [==============================] - 11s 59ms/step - loss: 0.0013\n",
            "Epoch 44/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0014\n",
            "Epoch 45/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 46/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 47/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 48/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 49/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 50/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 51/100\n",
            "178/178 [==============================] - 11s 59ms/step - loss: 0.0013\n",
            "Epoch 52/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 53/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 54/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 55/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 56/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 57/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 58/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0013\n",
            "Epoch 59/100\n",
            "178/178 [==============================] - 11s 59ms/step - loss: 0.0013\n",
            "Epoch 60/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0012\n",
            "Epoch 61/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 62/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 63/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0012\n",
            "Epoch 64/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0012\n",
            "Epoch 65/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 66/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0012\n",
            "Epoch 67/100\n",
            "178/178 [==============================] - 11s 60ms/step - loss: 0.0013\n",
            "Epoch 68/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0012\n",
            "Epoch 69/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0012\n",
            "Epoch 70/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0013\n",
            "Epoch 71/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0012\n",
            "Epoch 72/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0012\n",
            "Epoch 73/100\n",
            "178/178 [==============================] - 11s 64ms/step - loss: 0.0012\n",
            "Epoch 74/100\n",
            "178/178 [==============================] - 11s 61ms/step - loss: 0.0012\n",
            "Epoch 75/100\n",
            "178/178 [==============================] - 11s 61ms/step - loss: 0.0011\n",
            "Epoch 76/100\n",
            "178/178 [==============================] - 12s 66ms/step - loss: 0.0011\n",
            "Epoch 77/100\n",
            "178/178 [==============================] - 12s 69ms/step - loss: 0.0011\n",
            "Epoch 78/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0011\n",
            "Epoch 79/100\n",
            "178/178 [==============================] - 11s 65ms/step - loss: 0.0012\n",
            "Epoch 80/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0012\n",
            "Epoch 81/100\n",
            "178/178 [==============================] - 11s 65ms/step - loss: 0.0011\n",
            "Epoch 82/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0011\n",
            "Epoch 83/100\n",
            "178/178 [==============================] - 11s 61ms/step - loss: 0.0011\n",
            "Epoch 84/100\n",
            "178/178 [==============================] - 11s 63ms/step - loss: 0.0011\n",
            "Epoch 85/100\n",
            "178/178 [==============================] - 12s 66ms/step - loss: 0.0011\n",
            "Epoch 86/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0011\n",
            "Epoch 87/100\n",
            "178/178 [==============================] - 12s 66ms/step - loss: 0.0011\n",
            "Epoch 88/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0011\n",
            "Epoch 89/100\n",
            "178/178 [==============================] - 12s 66ms/step - loss: 0.0011\n",
            "Epoch 90/100\n",
            "178/178 [==============================] - 11s 65ms/step - loss: 0.0011\n",
            "Epoch 91/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0012\n",
            "Epoch 92/100\n",
            "178/178 [==============================] - 12s 66ms/step - loss: 0.0011\n",
            "Epoch 93/100\n",
            "178/178 [==============================] - 11s 61ms/step - loss: 0.0011\n",
            "Epoch 94/100\n",
            "178/178 [==============================] - 12s 67ms/step - loss: 0.0011\n",
            "Epoch 95/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0011\n",
            "Epoch 96/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0011\n",
            "Epoch 97/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0012\n",
            "Epoch 98/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0011\n",
            "Epoch 99/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0011\n",
            "Epoch 100/100\n",
            "178/178 [==============================] - 12s 65ms/step - loss: 0.0011\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd0796535b0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "regressor.fit(X_gold, y_gold, epochs = 100, batch_size = 32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QbFtVdJP1kVr"
      },
      "source": [
        "## Obtendo o preço real"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MA8BXnVSvQ7H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a1733d9-e59b-4cd4-a674-7056e5e68f52"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ],
      "source": [
        "dataset_test = yf.download('GC=F', start='2023-05-01', end='2023-05-31')\n",
        "real_preco_acao = dataset_test.iloc[:, 1:2].values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCxu8_cT1xP2"
      },
      "source": [
        "## Previsão"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ntha8aWD1psX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afc17b90-17cb-46a4-fb08-1919d386aa18"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n"
          ]
        }
      ],
      "source": [
        "dataset_total = pd.concat((df['Close'], dataset_test['Close']), axis = 0)\n",
        "inputs = dataset_total[len(dataset_total) - len(dataset_test) - 20:].values\n",
        "inputs = inputs.reshape(-1,1)\n",
        "inputs = sc.transform(inputs)\n",
        "X_test = []\n",
        "for i in range(20, 40):\n",
        "    X_test.append(inputs[i-20:i, 0])\n",
        "X_test = np.array(X_test)\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "predicted_stock_price = regressor.predict(X_test)\n",
        "predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcI8UBps2TQe"
      },
      "source": [
        "## Visualizando"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N1cDHdvW2Q9m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "6f19c422-64ea-4a00-8e14-3efe19b56461"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(real_preco_acao, color = 'red', label = 'Preço Real')\n",
        "plt.plot(predicted_stock_price, color = 'blue', label = 'Previsão')\n",
        "plt.title('Preço do Ouro')\n",
        "plt.xlabel('Tempo')\n",
        "plt.ylabel('Preço')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e1tBH8qx8etz"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}