PrevisaoDeLucros_Sorvetes.ipynb

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      "name": "PrevisaoDeLucros-Sorvetes.ipynb",
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    {
      "cell_type": "markdown",
      "source": [
        "# **Definição do problema**"
      ],
      "metadata": {
        "id": "zHFlk6KqI0X-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## - Base de dados\n",
        "\n",
        "*   Input(X): Temperatura\n",
        "*   Outpout(Y): Lucro diário em dólares"
      ],
      "metadata": {
        "id": "PP_rTSVqI7BA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "GuujOcGRI5hS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Etapa 1 - Importação das bibliotecas**"
      ],
      "metadata": {
        "id": "DLzLJCPlJNyw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "HDrKyAFyJSdR",
        "outputId": "3bf1d88e-cf0b-4279-e56b-efc573abafe1"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.8.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "_SNFBRpJPCUQ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Etapa 2 - Importação da base de dados**"
      ],
      "metadata": {
        "id": "ICNX9miHPLqG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados_df = pd.read_csv('/content/SalesData.csv')\n",
        "dados_df.reset_index(drop = True, inplace = True)"
      ],
      "metadata": {
        "id": "42bMeM0QQO_m"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados_df"
      ],
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      },
      "execution_count": 5,
      "outputs": [
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              "     Temperature     Revenue\n",
              "0      24.566884  534.799028\n",
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              "2      27.790554  660.632289\n",
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              "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",
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              "   Temperature     Revenue\n",
              "0    24.566884  534.799028\n",
              "1    26.005191  625.190122\n",
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              "     Temperature     Revenue\n",
              "490    23.824922  584.399945\n",
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              "    </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-dcc41b5d-2e66-46f9-9f4c-4e11a4270185')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          document.querySelector('#df-dcc41b5d-2e66-46f9-9f4c-4e11a4270185 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dcc41b5d-2e66-46f9-9f4c-4e11a4270185');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7iJnhi4Qytu",
        "outputId": "b4bfe34c-f490-4164-bd55-233d766d5e2c"
      },
      "execution_count": 8,
      "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",
      "source": [
        "dados_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "SL4lpkPJQ5O5",
        "outputId": "371c7461-f181-44d7-8aa8-3f75f0de2f11"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3837816b-4875-4294-936c-9ac862188bbb\">\n",
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              "  <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",
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              "  </tbody>\n",
              "</table>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\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-3837816b-4875-4294-936c-9ac862188bbb 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-3837816b-4875-4294-936c-9ac862188bbb');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Etapa 3 - Visualização da base de dados**"
      ],
      "metadata": {
        "id": "YJd21yANRBOH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(dados_df['Temperature'], dados_df['Revenue']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "e4bsj25LRFTO",
        "outputId": "5dbbf69f-d97c-4963-bc2d-911cfa8bfae7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Etapa 4 - Criação das variáveis da base de dados**"
      ],
      "metadata": {
        "id": "wCVYKsE1RcO5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = dados_df['Temperature']\n",
        "y_train = dados_df['Revenue']"
      ],
      "metadata": {
        "id": "br5hmbi7RhXn"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2MA3_ccvRvc2",
        "outputId": "477678b0-c5f0-4d32-85f9-7b0a0719821f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((500,), 0      24.566884\n",
              " 1      26.005191\n",
              " 2      27.790554\n",
              " 3      20.595335\n",
              " 4      11.503498\n",
              "          ...    \n",
              " 495    22.274899\n",
              " 496    32.893092\n",
              " 497    12.588157\n",
              " 498    22.362402\n",
              " 499    28.957736\n",
              " Name: Temperature, Length: 500, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape, y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YlOGta4cR0cG",
        "outputId": "43a72809-feff-4a38-82d8-bf9f705ad95b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((500,), 0      534.799028\n",
              " 1      625.190122\n",
              " 2      660.632289\n",
              " 3      487.706960\n",
              " 4      316.240194\n",
              "           ...    \n",
              " 495    524.746364\n",
              " 496    755.818399\n",
              " 497    306.090719\n",
              " 498    566.217304\n",
              " 499    655.660388\n",
              " Name: Revenue, Length: 500, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Etapa 5 - Criação e construção do modelo**"
      ],
      "metadata": {
        "id": "kYtD7d9tR-hd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential()\n",
        "#model.add(tf.keras.layers.Dense(units = 1, input_shape = [1]))    #uma saída, que é o lucro (input_shape = [1]), para um atributo de entrada (units = 1) que é a temperatura\n",
        "model.add(tf.keras.layers.Dense(units = 10, input_shape = [1]))    #o erro está alto, entao colocaremos mais camadas (neuronios) na rede para tentar diminuir o erro com as epocas\n",
        "                                                                  #Faremos isso para tentar resolver esse problema, que apesar de ser resolvido com uma regressão linear (disposição\n",
        "                                                                  #das amostras), ele não consegue ajustar com apenas um neuronio (1 camada oculta), sendo necessário a acresção de \n",
        "                                                                  #mais neuronios (mais camadas ocultas)\n",
        "model.add(tf.keras.layers.Dense(units = 1))                       \n",
        "#as duas linhas acima indicam 10 neuronios na camada oculta, 1 neuronio na camada de entrada, e 1 neuronio na camada de saida.                                        "
      ],
      "metadata": {
        "id": "Sx9IqqQuSCnP"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VEgDj1QcScbH",
        "outputId": "9e427ff7-94bc-40a2-a832-08446b2e98cc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 10)                20        \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 31\n",
            "Trainable params: 31\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Total params nos retorna o valor 31, que é a quantidade de pesos obtidos pela rede neural criada**"
      ],
      "metadata": {
        "id": "u-1_SHVNgBIL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = tf.keras.optimizers.Adam(0.1), loss = 'mean_squared_error')"
      ],
      "metadata": {
        "id": "Gvw5q6yXSfL-"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs_hist = model.fit(X_train, y_train, epochs = 1000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e91gj2b5SfM8",
        "outputId": "b312a93f-6fda-453e-fa57-67218ba02657"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 137849.1875\n",
            "Epoch 2/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 14048.4043\n",
            "Epoch 3/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 4243.9067\n",
            "Epoch 4/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 1504.3092\n",
            "Epoch 5/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 833.9846\n",
            "Epoch 6/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 732.0997\n",
            "Epoch 7/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 702.8948\n",
            "Epoch 8/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 694.3812\n",
            "Epoch 9/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 689.5355\n",
            "Epoch 10/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 680.5339\n",
            "Epoch 11/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 674.8489\n",
            "Epoch 12/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 695.4411\n",
            "Epoch 13/1000\n",
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            "Epoch 827/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 637.3551\n",
            "Epoch 828/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 705.5131\n",
            "Epoch 829/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 655.6002\n",
            "Epoch 830/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 653.9598\n",
            "Epoch 831/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 643.4326\n",
            "Epoch 832/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 672.4573\n",
            "Epoch 833/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 636.8657\n",
            "Epoch 834/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 686.0085\n",
            "Epoch 835/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 679.8994\n",
            "Epoch 836/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 636.9601\n",
            "Epoch 837/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 692.4361\n",
            "Epoch 838/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 638.1058\n",
            "Epoch 839/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 646.0850\n",
            "Epoch 840/1000\n",
            "16/16 [==============================] - 0s 10ms/step - loss: 629.4780\n",
            "Epoch 841/1000\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 656.1731\n",
            "Epoch 842/1000\n",
            "16/16 [==============================] - 0s 9ms/step - loss: 637.3848\n",
            "Epoch 843/1000\n",
            "16/16 [==============================] - 0s 10ms/step - loss: 639.8612\n",
            "Epoch 844/1000\n",
            "16/16 [==============================] - 0s 9ms/step - loss: 667.1346\n",
            "Epoch 845/1000\n",
            "16/16 [==============================] - 0s 7ms/step - loss: 648.3752\n",
            "Epoch 846/1000\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 638.9482\n",
            "Epoch 847/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 649.2002\n",
            "Epoch 848/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 775.6786\n",
            "Epoch 849/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 727.7797\n",
            "Epoch 850/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 658.8767\n",
            "Epoch 851/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 639.6113\n",
            "Epoch 852/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 679.8664\n",
            "Epoch 853/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 687.4346\n",
            "Epoch 854/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 656.6276\n",
            "Epoch 855/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 641.8384\n",
            "Epoch 856/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 638.3654\n",
            "Epoch 857/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 633.9543\n",
            "Epoch 858/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 642.0712\n",
            "Epoch 859/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 711.4810\n",
            "Epoch 860/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 665.3513\n",
            "Epoch 861/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 682.9926\n",
            "Epoch 862/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 662.4608\n",
            "Epoch 863/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 645.6476\n",
            "Epoch 864/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 676.5834\n",
            "Epoch 865/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 711.3583\n",
            "Epoch 866/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 636.7485\n",
            "Epoch 867/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 648.3769\n",
            "Epoch 868/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 630.3988\n",
            "Epoch 869/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 638.7808\n",
            "Epoch 870/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 667.8807\n",
            "Epoch 871/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 633.1523\n",
            "Epoch 872/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 643.1318\n",
            "Epoch 873/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 630.6728\n",
            "Epoch 874/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 652.2158\n",
            "Epoch 875/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 642.7456\n",
            "Epoch 876/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 643.9268\n",
            "Epoch 877/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 638.3316\n",
            "Epoch 878/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 639.4117\n",
            "Epoch 879/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 634.3330\n",
            "Epoch 880/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 672.4812\n",
            "Epoch 881/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 712.5028\n",
            "Epoch 882/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 634.7305\n",
            "Epoch 883/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 632.9240\n",
            "Epoch 884/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 657.8478\n",
            "Epoch 885/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 669.9587\n",
            "Epoch 886/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 630.9218\n",
            "Epoch 887/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 649.4678\n",
            "Epoch 888/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 663.8792\n",
            "Epoch 889/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 641.1063\n",
            "Epoch 890/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 632.5831\n",
            "Epoch 891/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 644.4851\n",
            "Epoch 892/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 677.4313\n",
            "Epoch 893/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 651.8280\n",
            "Epoch 894/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 645.5315\n",
            "Epoch 895/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 684.3723\n",
            "Epoch 896/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 647.5765\n",
            "Epoch 897/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 671.3331\n",
            "Epoch 898/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 690.0588\n",
            "Epoch 899/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 658.3777\n",
            "Epoch 900/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 685.2607\n",
            "Epoch 901/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 702.6855\n",
            "Epoch 902/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 706.6145\n",
            "Epoch 903/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 667.5268\n",
            "Epoch 904/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 649.1822\n",
            "Epoch 905/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 656.1443\n",
            "Epoch 906/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 662.3870\n",
            "Epoch 907/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 669.7556\n",
            "Epoch 908/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 808.6731\n",
            "Epoch 909/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 683.7938\n",
            "Epoch 910/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 658.3221\n",
            "Epoch 911/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 689.2765\n",
            "Epoch 912/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 706.4354\n",
            "Epoch 913/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 846.1797\n",
            "Epoch 914/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 845.1458\n",
            "Epoch 915/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 702.2988\n",
            "Epoch 916/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 769.7210\n",
            "Epoch 917/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 674.3452\n",
            "Epoch 918/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 652.6171\n",
            "Epoch 919/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 640.8485\n",
            "Epoch 920/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 633.1277\n",
            "Epoch 921/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 684.1183\n",
            "Epoch 922/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 726.2818\n",
            "Epoch 923/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 654.5193\n",
            "Epoch 924/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 673.7805\n",
            "Epoch 925/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 713.5388\n",
            "Epoch 926/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 734.7350\n",
            "Epoch 927/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 692.9335\n",
            "Epoch 928/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 649.1716\n",
            "Epoch 929/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 647.3287\n",
            "Epoch 930/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 666.2354\n",
            "Epoch 931/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 683.3549\n",
            "Epoch 932/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 711.7460\n",
            "Epoch 933/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 705.2787\n",
            "Epoch 934/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 706.7402\n",
            "Epoch 935/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 633.9147\n",
            "Epoch 936/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 680.4799\n",
            "Epoch 937/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 679.9385\n",
            "Epoch 938/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 711.6737\n",
            "Epoch 939/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 664.2224\n",
            "Epoch 940/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 651.7244\n",
            "Epoch 941/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 703.2386\n",
            "Epoch 942/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 651.6968\n",
            "Epoch 943/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 712.5943\n",
            "Epoch 944/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 637.6198\n",
            "Epoch 945/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 645.9719\n",
            "Epoch 946/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 700.4209\n",
            "Epoch 947/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 727.8335\n",
            "Epoch 948/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 698.3884\n",
            "Epoch 949/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 640.7560\n",
            "Epoch 950/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 664.4083\n",
            "Epoch 951/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 697.9526\n",
            "Epoch 952/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 707.7313\n",
            "Epoch 953/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 709.3991\n",
            "Epoch 954/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 647.4993\n",
            "Epoch 955/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 648.1198\n",
            "Epoch 956/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 652.7930\n",
            "Epoch 957/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 724.0564\n",
            "Epoch 958/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 830.2179\n",
            "Epoch 959/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 676.0062\n",
            "Epoch 960/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 701.2050\n",
            "Epoch 961/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 666.4058\n",
            "Epoch 962/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 692.6642\n",
            "Epoch 963/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 632.8367\n",
            "Epoch 964/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 649.3825\n",
            "Epoch 965/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 648.0106\n",
            "Epoch 966/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 691.5262\n",
            "Epoch 967/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 649.3671\n",
            "Epoch 968/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 704.6360\n",
            "Epoch 969/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 688.5538\n",
            "Epoch 970/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 680.3873\n",
            "Epoch 971/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 667.4791\n",
            "Epoch 972/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 679.9332\n",
            "Epoch 973/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 670.2965\n",
            "Epoch 974/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 649.1376\n",
            "Epoch 975/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 683.5751\n",
            "Epoch 976/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 712.0479\n",
            "Epoch 977/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 665.7512\n",
            "Epoch 978/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 656.5576\n",
            "Epoch 979/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 643.8956\n",
            "Epoch 980/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 640.6848\n",
            "Epoch 981/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 680.6856\n",
            "Epoch 982/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 650.7208\n",
            "Epoch 983/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 670.7853\n",
            "Epoch 984/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 731.1970\n",
            "Epoch 985/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 641.3024\n",
            "Epoch 986/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 725.3879\n",
            "Epoch 987/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 662.8569\n",
            "Epoch 988/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 646.6006\n",
            "Epoch 989/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 633.1639\n",
            "Epoch 990/1000\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 679.3354\n",
            "Epoch 991/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 730.1925\n",
            "Epoch 992/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 694.8232\n",
            "Epoch 993/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 631.6189\n",
            "Epoch 994/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 710.6174\n",
            "Epoch 995/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 697.8445\n",
            "Epoch 996/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 647.9675\n",
            "Epoch 997/1000\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 634.9559\n",
            "Epoch 998/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 670.5034\n",
            "Epoch 999/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 639.3802\n",
            "Epoch 1000/1000\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 679.4614\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Etapa 6 - Avaliação do modelo**"
      ],
      "metadata": {
        "id": "qzUZCMQXTy_m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "epochs_hist.history.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GVGYMebmT22o",
        "outputId": "e82596b7-a88f-40b6-a770-68aaa15140be"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss'])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(epochs_hist.history['loss']);\n",
        "plt.title('Model Loss Progress During Training');\n",
        "plt.xlabel('Epoch');\n",
        "plt.ylabel('Training Loss');\n",
        "plt.legend(['Training Loss']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "T4YnsKWiUlUe",
        "outputId": "95560843-4845-4322-f252-a05f63f9e0e2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2riWd3uLWaaZ",
        "outputId": "69630032-2350-42c5-e2ac-2dfee1de5b36"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[-5.80919720e-03, -1.57917123e-02,  2.70615406e-02,\n",
              "         -3.81465914e-04, -3.45635321e-03, -7.42491148e-03,\n",
              "          1.65557575e+00, -6.84679449e-02, -4.20244408e+00,\n",
              "          1.18881725e-02]], dtype=float32),\n",
              " array([ 0.28425834,  0.41537255,  0.04440954,  0.01507026,  0.36822522,\n",
              "         0.27799976,  3.776212  ,  2.0650465 , -6.130694  , -0.5520356 ],\n",
              "       dtype=float32),\n",
              " array([[ 1.8757065e-03],\n",
              "        [ 9.1498671e-03],\n",
              "        [-1.2554887e-02],\n",
              "        [ 4.4482826e-05],\n",
              "        [ 8.0403946e-03],\n",
              "        [ 8.6735394e-03],\n",
              "        [ 1.7309335e+00],\n",
              "        [ 1.5451161e-02],\n",
              "        [-4.2920160e+00],\n",
              "        [-1.8149570e-02]], dtype=float32),\n",
              " array([11.336976], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Previsões com o modelo treinado**"
      ],
      "metadata": {
        "id": "xGumrHb-be8u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp = 5\n",
        "revenue = model.predict([temp])\n",
        "print(\"Renevue Predictions Using Trained ANN = \",revenue[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BHbcoDkWp3X",
        "outputId": "19ce9026-d618-45d8-ac41-0885fb85890c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Renevue Predictions Using Trained ANN =  [148.7414]\n"
          ]
        }
      ]
    },
    {
      "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.ylabel('Revenue [dollars]');\n",
        "plt.xlabel('Temperature [degC]');\n",
        "plt.title('Revenue Generated vs. Temperature @Ice Cream Stand');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "g-O_W3e2cXh4",
        "outputId": "f58b8623-4959-483d-92b0-6074ff2395be"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Etapa 7 - Confirmar os resultados usando o sklearn**"
      ],
      "metadata": {
        "id": "ycvf4Om6dQrW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLfm71Z3dnpc",
        "outputId": "22afa7dd-42ec-4999-881d-33782f5e833b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500,)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.values.reshape(-1,1)\n",
        "#quando usamos o .values, estamos acessando os valores no formato do numpy array, e colocando em formato de matriz com o reshape\n",
        "#o sklearn só trabalha com matriz"
      ],
      "metadata": {
        "id": "M8W7SNggdWcl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cy9JeHe7fHi9",
        "outputId": "1a160fb7-e56d-42fd-aaeb-41837c77affa"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = y_train.values.reshape(-1,1)"
      ],
      "metadata": {
        "id": "TOczzp-ZfaQt"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWRfD9eefgma",
        "outputId": "49b351f0-3193-42fb-dde9-562280ead366"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u40QKPEsfkU2",
        "outputId": "1edbdbda-96dc-49ed-ec1b-1a08558d1999"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XpPPHJh0f9sZ",
        "outputId": "cfd32cde-b619-453d-b6b6-47346c5b7e73"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[21.44362551]])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4KOc733gVY-",
        "outputId": "98e32e76-35ff-48e3-df87-312bc8dde632"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([44.83126709])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**os valores regressor.coef_ e regressor.intercept_ são os valores encontrados com a rede neural, que formam a equação da reta que se ajusta aos pontos, valores esses obtidos pela rede neural com 31 pesos**"
      ],
      "metadata": {
        "id": "0Fr2mSz3gY6U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X_train, y_train, color = 'gray');\n",
        "plt.plot(X_train, regressor.predict(X_train), color = 'red');\n",
        "plt.ylabel('Revenue [dollars]');\n",
        "plt.xlabel('Temperature [degC]');\n",
        "plt.title('Revenue Generated vs. Temperature @Ice Cream Stand');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "qUPll67pgsGc",
        "outputId": "6e4806d2-040c-424e-818c-6e860d0c1c0c"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Previsões com o modelo treinado**"
      ],
      "metadata": {
        "id": "utdQ5QTphpB8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp = 5\n",
        "revenue = regressor.predict([[temp]])       #como foi realizada a transformação para o formato de matriz, precisamos colocar o temp entre dois cochetes\n",
        "print(\"Renevue Predictions Using Trained ANN = \",revenue[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a76bf93c-a5a5-4479-e2ca-2a5a05426786",
        "id": "SAp9wsgDhDzj"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Renevue Predictions Using Trained ANN =  [152.04939464]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assim comparamos os resultados obtidos pelo modelo treinado por uma rede neural com 10 neuronios na camada oculta, e o resultado obtido por uma regressão linear, para estimativa do lucro da venda de sorvetes com relação a temperatura.**"
      ],
      "metadata": {
        "id": "6fMNkYNyhvus"
      }
    }
  ]
}