RegressãoLinear_Multipla_Sklearn.ipynb

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        "Regressão Linear Multipla no Sklearn"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics"
      ]
    },
    {
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      "source": [
        "credito = pd.read_excel(\"Limite_Credito_Dummy.xlsx\")\n",
        "credito.head()"
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              "   Idade  RendimentoTotal  Salario  LimitedeCreditoImediato  \\\n",
              "0     70            10736     5214                      400   \n",
              "1     68             5000     5000                      380   \n",
              "2     44             5800     5800                      500   \n",
              "3     72             4300     4300                     2000   \n",
              "4     75             4400     4400                     3000   \n",
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              "        Escolaridade Gênero Região  LimitedoChequeEspecial  \n",
              "0  Fundamental_Medio     RJ      F                     500  \n",
              "1  Fundamental_Medio     RJ      F                     600  \n",
              "2  Fundamental_Medio     RJ      F                     800  \n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ],
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      },
      "execution_count": 3,
      "outputs": []
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        "le.fit(credito.Escolaridade)\n",
        "credito.Escolaridade = le.transform(credito.Escolaridade)\n",
        "\n",
        "le.fit(credito.Gênero)\n",
        "credito.Gênero = le.transform(credito.Gênero)\n",
        "\n",
        "le.fit(credito.Região)\n",
        "credito.Região = le.transform(credito.Região)"
      ],
      "metadata": {
        "id": "oz_7qfYX86I0"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "credito.head()"
      ],
      "metadata": {
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          "height": 250
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        "id": "1eNS3lhH9gq3",
        "outputId": "15ec55d6-261e-4a95-ae06-997f9632144a"
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              "   Idade  RendimentoTotal  Salario  LimitedeCreditoImediato  Escolaridade  \\\n",
              "0     70            10736     5214                      400             0   \n",
              "1     68             5000     5000                      380             0   \n",
              "2     44             5800     5800                      500             0   \n",
              "3     72             4300     4300                     2000             0   \n",
              "4     75             4400     4400                     3000             0   \n",
              "\n",
              "   Gênero  Região  LimitedoChequeEspecial  \n",
              "0       0       0                     500  \n",
              "1       0       0                     600  \n",
              "2       0       0                     800  \n",
              "3       0       0                    1000  \n",
              "4       0       0                    1000  "
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-8123c9ed-879c-4d85-bd14-81d44771ab14 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
              "      cursor: pointer;\n",
              "      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-ab9ddbc7-f6f6-4d48-b2af-86a80a21d09c 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-ab9ddbc7-f6f6-4d48-b2af-86a80a21d09c');\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"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(credito, x=\"LimitedoChequeEspecial\", kde=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "4kuOarsT9ifN",
        "outputId": "1a92a026-56fe-480d-e83a-5eeff716ff8c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(credito, x=\"Idade\", kde=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "ukOpE9dd-BQY",
        "outputId": "cbe6cfce-d9b8-4ce1-827e-daee89917a79"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "credito.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "FlyzxEh7-Ceb",
        "outputId": "0f4fbcb9-8993-4120-f208-3cfb4a7defe6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Idade  RendimentoTotal       Salario  LimitedeCreditoImediato  \\\n",
              "count  48.000000        48.000000     48.000000                48.000000   \n",
              "mean   61.458333     12275.083333  10507.125000              2175.208333   \n",
              "std    15.284477     12640.654241   9332.173617               938.094469   \n",
              "min    30.000000      4300.000000   4027.000000               340.000000   \n",
              "25%    47.000000      6380.000000   5950.000000              1925.000000   \n",
              "50%    66.500000      8400.000000   7400.000000              2000.000000   \n",
              "75%    70.000000     12378.250000  10163.000000              3000.000000   \n",
              "max    94.000000     81000.000000  54500.000000              3500.000000   \n",
              "\n",
              "       Escolaridade     Gênero     Região  LimitedoChequeEspecial  \n",
              "count     48.000000  48.000000  48.000000               48.000000  \n",
              "mean       0.375000   0.458333   0.500000             5620.729167  \n",
              "std        0.489246   0.503534   0.505291             6131.900367  \n",
              "min        0.000000   0.000000   0.000000              500.000000  \n",
              "25%        0.000000   0.000000   0.000000             2262.500000  \n",
              "50%        0.000000   0.000000   0.500000             3550.000000  \n",
              "75%        1.000000   1.000000   1.000000             6500.000000  \n",
              "max        1.000000   1.000000   1.000000            35000.000000  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-bc817e1a-5fb2-4a97-8e6e-cef68fc67db9\">\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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Idade</th>\n",
              "      <th>RendimentoTotal</th>\n",
              "      <th>Salario</th>\n",
              "      <th>LimitedeCreditoImediato</th>\n",
              "      <th>Escolaridade</th>\n",
              "      <th>Gênero</th>\n",
              "      <th>Região</th>\n",
              "      <th>LimitedoChequeEspecial</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>61.458333</td>\n",
              "      <td>12275.083333</td>\n",
              "      <td>10507.125000</td>\n",
              "      <td>2175.208333</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>5620.729167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>15.284477</td>\n",
              "      <td>12640.654241</td>\n",
              "      <td>9332.173617</td>\n",
              "      <td>938.094469</td>\n",
              "      <td>0.489246</td>\n",
              "      <td>0.503534</td>\n",
              "      <td>0.505291</td>\n",
              "      <td>6131.900367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>4300.000000</td>\n",
              "      <td>4027.000000</td>\n",
              "      <td>340.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>6380.000000</td>\n",
              "      <td>5950.000000</td>\n",
              "      <td>1925.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2262.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>66.500000</td>\n",
              "      <td>8400.000000</td>\n",
              "      <td>7400.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>3550.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>70.000000</td>\n",
              "      <td>12378.250000</td>\n",
              "      <td>10163.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>94.000000</td>\n",
              "      <td>81000.000000</td>\n",
              "      <td>54500.000000</td>\n",
              "      <td>3500.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>35000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc817e1a-5fb2-4a97-8e6e-cef68fc67db9')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-95e57502-1eca-4c78-998f-ea1ca97d97ea\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-95e57502-1eca-4c78-998f-ea1ca97d97ea')\"\n",
              "              title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-95e57502-1eca-4c78-998f-ea1ca97d97ea button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
              "      cursor: pointer;\n",
              "      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-bc817e1a-5fb2-4a97-8e6e-cef68fc67db9 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-bc817e1a-5fb2-4a97-8e6e-cef68fc67db9');\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"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "credito_norm = pd.DataFrame(scaler.fit_transform(credito), index=credito.index, columns=credito.columns)"
      ],
      "metadata": {
        "id": "yU5ZhnaJ-HJz"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "credito_norm.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "vLY6EXSl_EsT",
        "outputId": "889d1a67-d10f-47bc-8474-594a7e3b94e0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Idade  RendimentoTotal    Salario  LimitedeCreditoImediato  \\\n",
              "count  48.000000        48.000000  48.000000                48.000000   \n",
              "mean    0.491536         0.103978   0.128388                 0.580762   \n",
              "std     0.238820         0.164806   0.184894                 0.296865   \n",
              "min     0.000000         0.000000   0.000000                 0.000000   \n",
              "25%     0.265625         0.027119   0.038100                 0.501582   \n",
              "50%     0.570312         0.053455   0.066828                 0.525316   \n",
              "75%     0.625000         0.105323   0.121570                 0.841772   \n",
              "max     1.000000         1.000000   1.000000                 1.000000   \n",
              "\n",
              "       Escolaridade     Gênero     Região  LimitedoChequeEspecial  \n",
              "count     48.000000  48.000000  48.000000               48.000000  \n",
              "mean       0.375000   0.458333   0.500000                0.148427  \n",
              "std        0.489246   0.503534   0.505291                0.177736  \n",
              "min        0.000000   0.000000   0.000000                0.000000  \n",
              "25%        0.000000   0.000000   0.000000                0.051087  \n",
              "50%        0.000000   0.000000   0.500000                0.088406  \n",
              "75%        1.000000   1.000000   1.000000                0.173913  \n",
              "max        1.000000   1.000000   1.000000                1.000000  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-3c1bdcea-ada9-4021-8587-446bbeda410a\">\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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Idade</th>\n",
              "      <th>RendimentoTotal</th>\n",
              "      <th>Salario</th>\n",
              "      <th>LimitedeCreditoImediato</th>\n",
              "      <th>Escolaridade</th>\n",
              "      <th>Gênero</th>\n",
              "      <th>Região</th>\n",
              "      <th>LimitedoChequeEspecial</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.491536</td>\n",
              "      <td>0.103978</td>\n",
              "      <td>0.128388</td>\n",
              "      <td>0.580762</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.458333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.148427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.238820</td>\n",
              "      <td>0.164806</td>\n",
              "      <td>0.184894</td>\n",
              "      <td>0.296865</td>\n",
              "      <td>0.489246</td>\n",
              "      <td>0.503534</td>\n",
              "      <td>0.505291</td>\n",
              "      <td>0.177736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.265625</td>\n",
              "      <td>0.027119</td>\n",
              "      <td>0.038100</td>\n",
              "      <td>0.501582</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.051087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.570312</td>\n",
              "      <td>0.053455</td>\n",
              "      <td>0.066828</td>\n",
              "      <td>0.525316</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.088406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.105323</td>\n",
              "      <td>0.121570</td>\n",
              "      <td>0.841772</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.173913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c1bdcea-ada9-4021-8587-446bbeda410a')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-e4930d2e-01f6-470e-b305-77caabb974de\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e4930d2e-01f6-470e-b305-77caabb974de')\"\n",
              "              title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-e4930d2e-01f6-470e-b305-77caabb974de button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
              "      cursor: pointer;\n",
              "      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-3c1bdcea-ada9-4021-8587-446bbeda410a 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-3c1bdcea-ada9-4021-8587-446bbeda410a');\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"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "É notório que os números ficaram mais próximos uns dos outros, agora vamos inciar o processo de treino do modelo, para avaliar o peso de cada variável paro o modelo."
      ],
      "metadata": {
        "id": "TUHtDNVm_QBV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.formula.api as smf"
      ],
      "metadata": {
        "id": "LOWhpA8N_pbE"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "function = \"LimitedoChequeEspecial~Idade+RendimentoTotal+Salario+LimitedeCreditoImediato+Escolaridade+Gênero+Região\""
      ],
      "metadata": {
        "id": "Q4iYujzI_z9Q"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = smf.ols(formula=function, data=credito_norm).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLv9rwZSAI9s",
        "outputId": "e7c69f1e-6385-45d0-8deb-3f48df60f238"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.886\n",
            "Model:                                OLS   Adj. R-squared:                  0.865\n",
            "Method:                     Least Squares   F-statistic:                     44.20\n",
            "Date:                    Fri, 04 Aug 2023   Prob (F-statistic):           7.44e-17\n",
            "Time:                            19:24:15   Log-Likelihood:                 67.328\n",
            "No. Observations:                      48   AIC:                            -118.7\n",
            "Df Residuals:                          40   BIC:                            -103.7\n",
            "Df Model:                               7                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                  -0.0321      0.028     -1.129      0.266      -0.089       0.025\n",
            "Idade                      -0.0093      0.042     -0.220      0.827      -0.094       0.076\n",
            "RendimentoTotal             0.3010      0.225      1.336      0.189      -0.154       0.756\n",
            "Salario                     0.4095      0.207      1.977      0.055      -0.009       0.828\n",
            "LimitedeCreditoImediato     0.0223      0.038      0.589      0.559      -0.054       0.099\n",
            "Escolaridade                0.0245      0.028      0.867      0.391      -0.033       0.082\n",
            "Gênero                      0.0676      0.023      2.974      0.005       0.022       0.114\n",
            "Região                      0.0960      0.030      3.153      0.003       0.034       0.158\n",
            "==============================================================================\n",
            "Omnibus:                       15.742   Durbin-Watson:                   1.724\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               41.550\n",
            "Skew:                          -0.679   Prob(JB):                     9.50e-10\n",
            "Kurtosis:                       7.351   Cond. No.                         49.7\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "function = \"LimitedoChequeEspecial~RendimentoTotal+Salario+Gênero+Região\"\n",
        "model = smf.ols(formula=function, data=credito_norm).fit()\n",
        "print(model.summary())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n05MA0yfAo_f",
        "outputId": "ecd42216-47e6-4c68-b001-21f21835e89e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.883\n",
            "Model:                                OLS   Adj. R-squared:                  0.872\n",
            "Method:                     Least Squares   F-statistic:                     80.75\n",
            "Date:                    Fri, 04 Aug 2023   Prob (F-statistic):           2.02e-19\n",
            "Time:                            19:25:31   Log-Likelihood:                 66.709\n",
            "No. Observations:                      48   AIC:                            -123.4\n",
            "Df Residuals:                          43   BIC:                            -114.1\n",
            "Df Model:                               4                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "===================================================================================\n",
            "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------\n",
            "Intercept          -0.0286      0.017     -1.731      0.091      -0.062       0.005\n",
            "RendimentoTotal     0.2291      0.208      1.103      0.276      -0.190       0.648\n",
            "Salario             0.4739      0.192      2.469      0.018       0.087       0.861\n",
            "Gênero              0.0743      0.020      3.631      0.001       0.033       0.116\n",
            "Região              0.1166      0.021      5.450      0.000       0.073       0.160\n",
            "==============================================================================\n",
            "Omnibus:                       21.294   Durbin-Watson:                   1.615\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               65.346\n",
            "Skew:                          -0.973   Prob(JB):                     6.46e-15\n",
            "Kurtosis:                       8.374   Cond. No.                         38.1\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "function = \"LimitedoChequeEspecial~Salario+Gênero+Região -1\"\n",
        "model = smf.ols(formula=function, data=credito_norm).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCMV0lNZAz7n",
        "outputId": "7f36591b-e0e5-48de-d0b7-7f3ccfc4a695"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                   OLS Regression Results                                  \n",
            "===========================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared (uncentered):                   0.925\n",
            "Model:                                OLS   Adj. R-squared (uncentered):              0.920\n",
            "Method:                     Least Squares   F-statistic:                              185.0\n",
            "Date:                    Fri, 04 Aug 2023   Prob (F-statistic):                    2.56e-25\n",
            "Time:                            19:26:23   Log-Likelihood:                          64.573\n",
            "No. Observations:                      48   AIC:                                     -123.1\n",
            "Df Residuals:                          45   BIC:                                     -117.5\n",
            "Df Model:                               3                                                  \n",
            "Covariance Type:                nonrobust                                                  \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Salario        0.6801      0.061     11.241      0.000       0.558       0.802\n",
            "Gênero         0.0497      0.016      3.019      0.004       0.017       0.083\n",
            "Região         0.0938      0.018      5.339      0.000       0.058       0.129\n",
            "==============================================================================\n",
            "Omnibus:                       10.368   Durbin-Watson:                   1.545\n",
            "Prob(Omnibus):                  0.006   Jarque-Bera (JB):               28.212\n",
            "Skew:                          -0.066   Prob(JB):                     7.48e-07\n",
            "Kurtosis:                       6.753   Cond. No.                         5.47\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
            "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Incialmente o modelo operava com um Adj. R-squared (uncentered): de 86%, agora com 92%, chegamos a conclusão que a variabilidade dessas três variaveis (Salário, Gênero e Região) explica 92% da viriação no Limite do Cheque Especial (Target)"
      ],
      "metadata": {
        "id": "2A06GIydBgB4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# O X receber as variaveis auxiliares e o Y recebe a variável Target\n",
        "\n",
        "x = credito_norm[[\"Salario\", \"Gênero\", \"Região\"]]\n",
        "\n",
        "y = credito_norm[[\"LimitedoChequeEspecial\"]]"
      ],
      "metadata": {
        "id": "AaH54-eABLhQ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Vamos criar o modelo\n",
        "\n",
        "x_treino, x_test, y_treino,y_test = train_test_split(x,y, test_size = 0.2, random_state = 42)\n",
        "lr = LinearRegression()\n",
        "lr.fit(x,y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "jjXk7aAACK_2",
        "outputId": "84e6fca8-3bda-4e97-fa27-5945d71b7bc1"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r_sqr = lr.score(x,y)\n",
        "r_sqr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25kzAvo2Del6",
        "outputId": "d7690635-a79d-48cf-845e-850a985d9f15"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8791882143098557"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "87% é a nossa métrica de confiabilidade o que é uma boa métrica.\n"
      ],
      "metadata": {
        "id": "q6yERPpiDxuy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_treino = lr.predict(x_treino)\n",
        "print(\"MAE:\", metrics.mean_absolute_error(y_treino, y_pred_treino))\n",
        "print(\"MSE:\", metrics.mean_squared_error(y_treino, y_pred_treino))\n",
        "print(\"RMSE:\", np.sqrt(metrics.mean_squared_error(y_treino, y_pred_treino)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2PBZZzJgDlWT",
        "outputId": "c22917e8-3bb7-4ef7-bdf9-19505ffbf39d"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 0.03535260038720454\n",
            "MSE: 0.0024122510423403173\n",
            "RMSE: 0.04911467237333786\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O Nível de Erro ficou bem baixo, tudo abaixo de 1%"
      ],
      "metadata": {
        "id": "EnHzO6ZoEvFr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Já que o modelo performou bem em uma base de treino, vamos ver como se comporta em uma base de teste\n",
        "y_pred_test = lr.predict(x_test)\n",
        "print(\"MAE:\", metrics.mean_absolute_error(y_test, y_pred_test))\n",
        "print(\"MSE:\", metrics.mean_squared_error(y_test, y_pred_test))\n",
        "print(\"RMSE:\", np.sqrt(metrics.mean_squared_error(y_test, y_pred_test)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lFmtownKEuwJ",
        "outputId": "e0344def-2c48-4973-b905-c29d6081dcc2"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 0.06543044287056327\n",
            "MSE: 0.008770831909357938\n",
            "RMSE: 0.09365271971148482\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vimos que as métricas permaneceram abaixo de 1%, logo o modelo se comportou bem na base de teste também."
      ],
      "metadata": {
        "id": "ObOrwgVHFeft"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xPKUtVoNFxJG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}