TAREFA_1 - Previsão_de_Lucros_(sorvetes).ipynb

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    "colab": {
      "name": "Projeto #2 - TAREFA: Previsão de Lucros (sorvetes).ipynb",
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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/DanielMSousa/curso-deeplearning-na-pratica/blob/main/TAREFA_1%20-%20Previs%C3%A3o_de_Lucros_(sorvetes).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M27qF7CTrBqc"
      },
      "source": [
        "# Definição do problema"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iCj2rwwHBB1I"
      },
      "source": [
        "- Base de dados:\n",
        "  - Input (X): Temperatura\n",
        "  - Output (Y): Lucro diário em dólares "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKmFmyaGunc7"
      },
      "source": [
        "# Etapa 1: Importação das bibliotecas"
      ]
    },
    {
      "cell_type": "code",
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        "id": "Y_LnJrYYAr7b",
        "outputId": "7de5dbe4-f8b8-4a11-beed-ec6a29c6537f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "tf.__version__"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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            "text/plain": [
              "'2.7.0'"
            ]
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          "execution_count": 31
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uAE6Icc0uylP"
      },
      "source": [
        "# Etapa 2: Importação da base de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLjnytV6eMA0"
      },
      "source": [
        "# Leitura do arquivo csv\n",
        "df = pd.read_csv('SalesData.csv')"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "# Visualização de todos os registros\n",
        "df"
      ],
      "execution_count": 33,
      "outputs": [
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          "output_type": "execute_result",
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              "     Temperature     Revenue\n",
              "0      24.566884  534.799028\n",
              "1      26.005191  625.190122\n",
              "2      27.790554  660.632289\n",
              "3      20.595335  487.706960\n",
              "4      11.503498  316.240194\n",
              "..           ...         ...\n",
              "495    22.274899  524.746364\n",
              "496    32.893092  755.818399\n",
              "497    12.588157  306.090719\n",
              "498    22.362402  566.217304\n",
              "499    28.957736  655.660388\n",
              "\n",
              "[500 rows x 2 columns]"
            ]
          },
          "metadata": {},
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "# Visualização dos 5 primeiros registros\n",
        "df.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1</th>\n",
              "      <td>26.005191</td>\n",
              "      <td>625.190122</td>\n",
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              "      <th>3</th>\n",
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              "                                                     [key], {});\n",
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            "text/plain": [
              "   Temperature     Revenue\n",
              "0    24.566884  534.799028\n",
              "1    26.005191  625.190122\n",
              "2    27.790554  660.632289\n",
              "3    20.595335  487.706960\n",
              "4    11.503498  316.240194"
            ]
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          "metadata": {},
          "execution_count": 34
        }
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "# Visualização dos 10 últimos registros\n",
        "df.tail(10)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>Revenue</th>\n",
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              "      <td>22.274899</td>\n",
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              "      <th>496</th>\n",
              "      <td>32.893092</td>\n",
              "      <td>755.818399</td>\n",
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              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>12.588157</td>\n",
              "      <td>306.090719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>22.362402</td>\n",
              "      <td>566.217304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>28.957736</td>\n",
              "      <td>655.660388</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84d6135f-51ca-459a-91b8-e582aea7b04a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-84d6135f-51ca-459a-91b8-e582aea7b04a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-84d6135f-51ca-459a-91b8-e582aea7b04a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Temperature     Revenue\n",
              "490    23.824922  584.399945\n",
              "491    34.472169  809.352520\n",
              "492    23.056214  552.819351\n",
              "493    14.931506  377.430928\n",
              "494    25.112066  571.434257\n",
              "495    22.274899  524.746364\n",
              "496    32.893092  755.818399\n",
              "497    12.588157  306.090719\n",
              "498    22.362402  566.217304\n",
              "499    28.957736  655.660388"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMq3-KWOx0e1",
        "outputId": "35ba8a0e-7f93-4b91-95ea-56d726bfb62a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Visualização de informações da base de dados\n",
        "df.info()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 500 entries, 0 to 499\n",
            "Data columns (total 2 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Temperature  500 non-null    float64\n",
            " 1   Revenue      500 non-null    float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 7.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0E9xPLdx2Ok",
        "outputId": "e115df77-44f1-4980-f3a6-da91b0765fc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "# Descrição da base de dados\n",
        "df.describe()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3e521be0-04c4-4ac6-9656-b55ee7e304ec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>Temperature</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>500.000000</td>\n",
              "      <td>500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.232225</td>\n",
              "      <td>521.570777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.096388</td>\n",
              "      <td>175.404751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.122258</td>\n",
              "      <td>405.558681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>22.392791</td>\n",
              "      <td>529.368565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>27.740674</td>\n",
              "      <td>642.257922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e521be0-04c4-4ac6-9656-b55ee7e304ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3e521be0-04c4-4ac6-9656-b55ee7e304ec button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3e521be0-04c4-4ac6-9656-b55ee7e304ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       Temperature      Revenue\n",
              "count   500.000000   500.000000\n",
              "mean     22.232225   521.570777\n",
              "std       8.096388   175.404751\n",
              "min       0.000000    10.000000\n",
              "25%      17.122258   405.558681\n",
              "50%      22.392791   529.368565\n",
              "75%      27.740674   642.257922\n",
              "max      45.000000  1000.000000"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LlszUhNNyrl_"
      },
      "source": [
        "# Etapa 3: Visualização da base de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYcWopZVyvAs",
        "outputId": "8a1d763b-8781-4a05-de51-4146afd5e707",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "# Scatter plot do Seborn\n",
        "sns.scatterplot(data=df, x='Temperature', y='Revenue')\n",
        "plt.title('relation between revenue and temperature')"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'relation between revenue and temperature')"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y0GmpAjG3GiH"
      },
      "source": [
        "# Etapa 4: Criação das variáveis da base de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-maV8hb9rYM"
      },
      "source": [
        "# Criação das variáveis X_train e y_train\n",
        "X_train = df['Temperature']\n",
        "y_train = df['Revenue']"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqIp8uvJ3cGi",
        "outputId": "01c44fa3-010d-4bcf-e6ac-7d05ae44f68e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Formato da variável X_train\n",
        "X_train.shape"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500,)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4wfApza3cEc",
        "outputId": "93b2c7e2-4cdd-4471-b8a9-51a44cf25dde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Formato da variável y_train\n",
        "y_train.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500,)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53qDZFRn3-S1"
      },
      "source": [
        "# Etapa 5: Criação e construção do modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yl6fmPJj3cCX"
      },
      "source": [
        "# Construção do modelo sequencial\n",
        "model = tf.keras.Sequential(layers = tf.keras.layers.Dense(1, input_shape=[1]))"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUd71odB4v-i",
        "outputId": "2918ffd4-6aaa-4f90-ac67-0ee8094698bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Sumário do modelo\n",
        "model.summary()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 1)                 2         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2\n",
            "Trainable params: 2\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYmvNpNw5Act"
      },
      "source": [
        "# Compilar o modelo\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1), loss='mean_squared_error')"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpia0VBS5Ljw"
      },
      "source": [
        "# Treinamento\n",
        "hist = model.fit(X_train, y_train, epochs=500)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-wRQqOeB5Zh5"
      },
      "source": [
        "# Etapa 6: Avaliação do modelo "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfUb63035TJL",
        "outputId": "06eddeee-eab4-43c0-cb09-a73d802985ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Visualização do dicionário com os resultados\n",
        "hist.history.keys()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss'])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eXiDop_l5erJ",
        "outputId": "bb8cfaff-72c2-4737-bd05-2025540a3301",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "# Gráfico com os resultados da loss function\n",
        "plt.plot(hist.history['loss']);\n",
        "plt.title('Loss function history')\n"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss function history')"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFYpuUJWBn6L",
        "outputId": "fcfd34d8-372a-4058-c51e-94bc20ffb6bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Visualização dos pesos\n",
        "model.weights"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Variable 'dense_3/kernel:0' shape=(1, 1) dtype=float32, numpy=array([[21.521578]], dtype=float32)>,\n",
              " <tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32, numpy=array([44.77571], dtype=float32)>]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isfjfT735fjD",
        "outputId": "2ace99cb-bc6c-46b6-8402-e4d06ec5c6c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Previsões com o modelo treinado, com a temperatura de 5 graus\n",
        "model.predict([5])"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[152.3836]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnbDTixOaB-V",
        "outputId": "ea0bd4ef-d4b7-4d08-f67b-f86d6ed35284",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# Gráfico com a reta da regressão linear\n",
        "plt.scatter(X_train, y_train, color='gray')\n",
        "plt.plot(X_train, model.predict(X_train), color='red')\n",
        "plt.ylabel('revenue')\n",
        "plt.ylabel('temperature')\n",
        "plt.title('Regression with tensorflow');"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_dXoZAtVcbfA"
      },
      "source": [
        "# Etapa 7: Confirmar os resultados usando sklearn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybYof7hSdM5I"
      },
      "source": [
        "# Usar o sklearn para obter os mesmos resultados\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import numpy as np"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()"
      ],
      "metadata": {
        "id": "YM86zNW5z-y2"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.array(X_train).reshape(-1, 1)\n",
        "y_train = np.array(y_train).reshape(-1, 1)"
      ],
      "metadata": {
        "id": "8AhAet1n0TmZ"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7bz5vuIu0Bk1",
        "outputId": "2ec006ee-e7ee-4a38-94f4-966d6f10e6bd"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.predict([[5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yif5j0FE0gpw",
        "outputId": "a7775e8a-0ade-4b36-e3ba-0b2e254b911f"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[152.04939464]])"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X_train, y_train, color='gray')\n",
        "plt.plot(X_train, model.predict(X_train), color='red')\n",
        "plt.plot(X_train, lr.predict(X_train), color='blue')\n",
        "plt.ylabel('revenue')\n",
        "plt.ylabel('temperature')\n",
        "plt.legend(labels=['tensorflow', 'linear-regressor'])\n",
        "plt.title('Comparing both Regressors');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "DIYH0upF0ua8",
        "outputId": "913b36bc-d467-4efc-e2b4-434ec5a0fb4c"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjhqqHwa2XI_",
        "outputId": "61ba96e9-feb7-4d51-bdd3-e04e66996d0b"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[21.44362551]])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9PCjVkO2ZIV",
        "outputId": "2b91a0c6-003d-4482-a3dc-6084fc4fd778"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([44.83126709])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    }
  ]
}