credit_data.ipynb

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      "cell_type": "markdown",
      "source": [
        "# **Conjunto de dados de crédito**\n",
        "\n",
        "Este conjunto de dados contém colunas que simulam dados de agências de crédito\n",
        "\n"
      ],
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    {
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      "execution_count": 10,
      "metadata": {
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      "source": [
        "!pip -q install plotly"
      ]
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      "source": [
        "!pip -q install yellowbrick"
      ],
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        "id": "TBdSHpzOtWnl"
      },
      "execution_count": 11,
      "outputs": []
    },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "j6kK_OMOtZok"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit = pd.read_csv('/content/credit_data.csv')"
      ],
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        "id": "VGvWqm08tjCC"
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      "execution_count": 13,
      "outputs": []
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        "base_credit"
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      "execution_count": 14,
      "outputs": [
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              "      clientid        income        age         loan  default\n",
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              "1999      2000  69436.579552  56.152617  7378.833599        0\n",
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              "   clientid        income        age         loan  default\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "base_credit",
              "summary": "{\n  \"name\": \"base_credit\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 577,\n        \"min\": 1,\n        \"max\": 2000,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          1861,\n          354,\n          1334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14326.327118850028,\n        \"min\": 20014.4894700497,\n        \"max\": 69995.6855783239,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          40240.7275559381,\n          46706.4588610083,\n          51211.6540386342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.62446945559601,\n        \"min\": -52.4232799196616,\n        \"max\": 63.971795841120205,\n        \"num_unique_values\": 1997,\n        \"samples\": [\n          36.6687041711994,\n          43.0130229773931,\n          32.553030352108195\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3045.410024391518,\n        \"min\": 1.37762959325451,\n        \"max\": 13766.0512393337,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          7498.630446855849,\n          7084.263509070559,\n          4093.360006036261\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 15
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    {
      "cell_type": "code",
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      "execution_count": 16,
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            "text/plain": [
              "      clientid        income        age         loan  default\n",
              "1992      1993  30803.806165  23.250084   623.024153        0\n",
              "1993      1994  54421.410155  26.821928  3273.631823        0\n",
              "1994      1995  24254.700791  37.751622  2225.284643        0\n",
              "1995      1996  59221.044874  48.518179  1926.729397        0\n",
              "1996      1997  69516.127573  23.162104  3503.176156        0\n",
              "1997      1998  44311.449262  28.017167  5522.786693        1\n",
              "1998      1999  43756.056605  63.971796  1622.722598        0\n",
              "1999      2000  69436.579552  56.152617  7378.833599        0"
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              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>1996</td>\n",
              "      <td>59221.044874</td>\n",
              "      <td>48.518179</td>\n",
              "      <td>1926.729397</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>1997</td>\n",
              "      <td>69516.127573</td>\n",
              "      <td>23.162104</td>\n",
              "      <td>3503.176156</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>1998</td>\n",
              "      <td>44311.449262</td>\n",
              "      <td>28.017167</td>\n",
              "      <td>5522.786693</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>1999</td>\n",
              "      <td>43756.056605</td>\n",
              "      <td>63.971796</td>\n",
              "      <td>1622.722598</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>2000</td>\n",
              "      <td>69436.579552</td>\n",
              "      <td>56.152617</td>\n",
              "      <td>7378.833599</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "                                                    [key], {});\n",
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              "\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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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"base_credit\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1993,\n        \"max\": 2000,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1994,\n          1998,\n          1993\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16748.59730267154,\n        \"min\": 24254.7007906251,\n        \"max\": 69516.1275728606,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          54421.4101554826,\n          44311.44926231349,\n          30803.8061647676\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.925781749315888,\n        \"min\": 23.162104470655304,\n        \"max\": 63.971795841120205,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          26.8219283991493,\n          28.0171668957919,\n          23.250084119383203\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2224.117088594399,\n        \"min\": 623.024152762279,\n        \"max\": 7378.833598730591,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3273.63182294932,\n          5522.786693255141,\n          623.024152762279\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "VhVzedNSuDJG",
        "outputId": "a0dd0b62-043f-444a-c471-688f9885dbe3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          clientid        income          age          loan      default\n",
              "count  2000.000000   2000.000000  1997.000000   2000.000000  2000.000000\n",
              "mean   1000.500000  45331.600018    40.807559   4444.369695     0.141500\n",
              "std     577.494589  14326.327119    13.624469   3045.410024     0.348624\n",
              "min       1.000000  20014.489470   -52.423280      1.377630     0.000000\n",
              "25%     500.750000  32796.459717    28.990415   1939.708847     0.000000\n",
              "50%    1000.500000  45789.117313    41.317159   3974.719419     0.000000\n",
              "75%    1500.250000  57791.281668    52.587040   6432.410625     0.000000\n",
              "max    2000.000000  69995.685578    63.971796  13766.051239     1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6df62810-759e-45e2-ad27-6eaf64309153\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clientid</th>\n",
              "      <th>income</th>\n",
              "      <th>age</th>\n",
              "      <th>loan</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>1997.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1000.500000</td>\n",
              "      <td>45331.600018</td>\n",
              "      <td>40.807559</td>\n",
              "      <td>4444.369695</td>\n",
              "      <td>0.141500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>577.494589</td>\n",
              "      <td>14326.327119</td>\n",
              "      <td>13.624469</td>\n",
              "      <td>3045.410024</td>\n",
              "      <td>0.348624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>20014.489470</td>\n",
              "      <td>-52.423280</td>\n",
              "      <td>1.377630</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>500.750000</td>\n",
              "      <td>32796.459717</td>\n",
              "      <td>28.990415</td>\n",
              "      <td>1939.708847</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1000.500000</td>\n",
              "      <td>45789.117313</td>\n",
              "      <td>41.317159</td>\n",
              "      <td>3974.719419</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1500.250000</td>\n",
              "      <td>57791.281668</td>\n",
              "      <td>52.587040</td>\n",
              "      <td>6432.410625</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2000.000000</td>\n",
              "      <td>69995.685578</td>\n",
              "      <td>63.971796</td>\n",
              "      <td>13766.051239</td>\n",
              "      <td>1.000000</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6df62810-759e-45e2-ad27-6eaf64309153');\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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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-70f835f9-7a4d-4f3e-93e9-ec32a85a8708 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"base_credit\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 720.5049024934813,\n        \"min\": 1.0,\n        \"max\": 2000.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2000.0,\n          1000.5,\n          1500.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23006.270052327105,\n        \"min\": 2000.0,\n        \"max\": 69995.6855783239,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          45331.600017793244,\n          45789.11731252445,\n          2000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 697.4194419741473,\n        \"min\": -52.4232799196616,\n        \"max\": 1997.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          40.80755937840458,\n          41.3171591130085,\n          1997.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4225.971731499404,\n        \"min\": 1.37762959325451,\n        \"max\": 13766.0512393337,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4444.369694688258,\n          3974.7194188426347,\n          2000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 707.0316018525248,\n        \"min\": 0.0,\n        \"max\": 2000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1415,\n          1.0,\n          0.3486237502804848\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit[base_credit['income'] >= 69995.685578]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "dLXjeyfmuKCI",
        "outputId": "92eb499d-79b7-40f4-af01-c245cb42928b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     clientid        income        age         loan  default\n",
              "422       423  69995.685578  52.719673  2084.370861        0"
            ],
            "text/html": [
              "\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "\n",
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              "        document.querySelector('#df-9b697525-2dda-48d8-abc8-ab6cafe4d623 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-9b697525-2dda-48d8-abc8-ab6cafe4d623');\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",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"base_credit[base_credit['income'] >= 69995\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 423,\n        \"max\": 423,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          423\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 69995.6855783239,\n        \"max\": 69995.6855783239,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          69995.6855783239\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 52.719673210755,\n        \"max\": 52.719673210755,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          52.719673210755\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2084.37086090284,\n        \"max\": 2084.37086090284,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2084.37086090284\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit[base_credit['loan'] <= 1.377630]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "K2PI9rzKyhtm",
        "outputId": "792c0a08-8b4e-4ee0-d061-a07460fa4d62"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     clientid        income        age     loan  default\n",
              "865       866  28072.604355  54.142548  1.37763        0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-14c85461-8125-4004-a065-4904ad579077\" class=\"colab-df-container\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-14c85461-8125-4004-a065-4904ad579077 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-14c85461-8125-4004-a065-4904ad579077');\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",
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              "\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"base_credit[base_credit['loan'] <= 1\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 866,\n        \"max\": 866,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          866\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 28072.604355402294,\n        \"max\": 28072.604355402294,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          28072.604355402294\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 54.142548226751394,\n        \"max\": 54.142548226751394,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          54.142548226751394\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1.37762959325451,\n        \"max\": 1.37762959325451,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.37762959325451\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit['default'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6o8frT0jyvxj",
        "outputId": "e03c35cc-2859-4007-f71c-ca1462b6f6af"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "default\n",
              "0    1717\n",
              "1     283\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x = base_credit['default']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "EuS5xMOnzHU2",
        "outputId": "9b4e935c-1e73-4c4c-dcdb-35206005ef55"
      },
      "execution_count": 22,
      "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": [
        "plt.hist(x = base_credit['age']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "QrFzCrDCzRjJ",
        "outputId": "5ba8146e-a1e4-4eff-bc26-534ad2348ba9"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgYElEQVR4nO3df3RT9eH/8VdLaaBAUlppQqUFnMzSAaIgkIluSkdl1aOjetSDiB4OTBaY0A2xOwiKn1lOdeJgQNHjgE1YHe6oAwWscCznjPCrjFlBOtxwLdakOG0CTNrS3u8f+/a6WFQCxbxbn49z7jnm3neS932fSp4nvUnjLMuyBAAAYJD4WE8AAADg8wgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMZJiPUEzkdLS4tqa2vVq1cvxcXFxXo6AADgHFiWpRMnTig9PV3x8V/+HkmHDJTa2lplZGTEehoAAOA81NTUqF+/fl86pkMGSq9evST99wSdTmeMZwMAAM5FOBxWRkaG/Tr+ZTpkoLT+WsfpdBIoAAB0MOdyeQYXyQIAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgJsZ4AAADnasDDr8V6Ct8Y7y/Oi+nz8w4KAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADBOVIHy6KOPKi4uLmLLysqyj58+fVo+n0+pqanq2bOn8vPzFQwGIx6jurpaeXl5SkpKUlpamubOnaszZ860z9kAAIBOIeqPGX/nO9/Rm2+++dkDJHz2EHPmzNFrr72mDRs2yOVyaebMmZo4caL+8pe/SJKam5uVl5cnj8ejnTt36sMPP9S9996rrl276oknnmiH0wEAAJ1B1IGSkJAgj8fTZn8oFNLzzz+v9evX68Ybb5QkrV69WoMHD9auXbs0ZswYvfHGGzp06JDefPNNud1uDR8+XI8//rjmzZunRx99VImJiRd+RgAAoMOL+hqUI0eOKD09XZdddpkmTZqk6upqSVJFRYWampqUk5Njj83KylJmZqb8fr8kye/3a+jQoXK73faY3NxchcNhHTx48Aufs6GhQeFwOGIDAACdV1SBMnr0aK1Zs0ZbtmzRypUrdfToUV133XU6ceKEAoGAEhMTlZycHHEft9utQCAgSQoEAhFx0nq89dgXKSoqksvlsreMjIxopg0AADqYqH7FM2HCBPu/hw0bptGjR6t///764x//qO7du7f75FoVFhaqoKDAvh0Oh4kUAEbpiF/BHuuvMge+zAV9zDg5OVnf/va39d5778nj8aixsVH19fURY4LBoH3NisfjafOpntbbZ7uupZXD4ZDT6YzYAABA53VBgXLy5En94x//UN++fTVixAh17dpV27Zts49XVVWpurpaXq9XkuT1elVZWam6ujp7TFlZmZxOp7Kzsy9kKgAAoBOJ6lc8P//5z3XLLbeof//+qq2t1cKFC9WlSxfdfffdcrlcmjp1qgoKCpSSkiKn06lZs2bJ6/VqzJgxkqTx48crOztbkydPVnFxsQKBgObPny+fzyeHw3FRThAAAHQ8UQXKsWPHdPfdd+vf//63+vTpo7Fjx2rXrl3q06ePJGnJkiWKj49Xfn6+GhoalJubqxUrVtj379KlizZt2qQZM2bI6/WqR48emjJlihYtWtS+ZwUAADq0qAKltLT0S49369ZNy5cv1/Lly79wTP/+/fX6669H87QAAOAbhr/FAwAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAmxngAAIDYGPPxarKcAfCHeQQEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxrmgQFm8eLHi4uI0e/Zse9/p06fl8/mUmpqqnj17Kj8/X8FgMOJ+1dXVysvLU1JSktLS0jR37lydOXPmQqYCAAA6kfMOlL1792rVqlUaNmxYxP45c+Zo48aN2rBhg8rLy1VbW6uJEyfax5ubm5WXl6fGxkbt3LlTa9eu1Zo1a7RgwYLzPwsAANCpnFegnDx5UpMmTdJzzz2n3r172/tDoZCef/55Pf3007rxxhs1YsQIrV69Wjt37tSuXbskSW+88YYOHTqkF154QcOHD9eECRP0+OOPa/ny5WpsbGyfswIAAB3aeQWKz+dTXl6ecnJyIvZXVFSoqakpYn9WVpYyMzPl9/slSX6/X0OHDpXb7bbH5ObmKhwO6+DBg2d9voaGBoXD4YgNAAB0XgnR3qG0tFT79+/X3r172xwLBAJKTExUcnJyxH63261AIGCP+d84aT3eeuxsioqK9Nhjj0U7VQAA0EFF9Q5KTU2NHnzwQa1bt07dunW7WHNqo7CwUKFQyN5qamq+tucGAABfv6gCpaKiQnV1dbr66quVkJCghIQElZeXa+nSpUpISJDb7VZjY6Pq6+sj7hcMBuXxeCRJHo+nzad6Wm+3jvk8h8Mhp9MZsQEAgM4rqkAZN26cKisrdeDAAXsbOXKkJk2aZP93165dtW3bNvs+VVVVqq6ultfrlSR5vV5VVlaqrq7OHlNWVian06ns7Ox2Oi0AANCRRXUNSq9evTRkyJCIfT169FBqaqq9f+rUqSooKFBKSoqcTqdmzZolr9erMWPGSJLGjx+v7OxsTZ48WcXFxQoEApo/f758Pp8cDkc7nRYAAOjIor5I9qssWbJE8fHxys/PV0NDg3Jzc7VixQr7eJcuXbRp0ybNmDFDXq9XPXr00JQpU7Ro0aL2ngoAAOig4izLsmI9iWiFw2G5XC6FQiGuRwFghAEPvxbrKQDt6v3Fee3+mNG8fvO3eAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGCcqAJl5cqVGjZsmJxOp5xOp7xerzZv3mwfP336tHw+n1JTU9WzZ0/l5+crGAxGPEZ1dbXy8vKUlJSktLQ0zZ07V2fOnGmfswEAAJ1CVIHSr18/LV68WBUVFdq3b59uvPFG3XrrrTp48KAkac6cOdq4caM2bNig8vJy1dbWauLEifb9m5ublZeXp8bGRu3cuVNr167VmjVrtGDBgvY9KwAA0KHFWZZlXcgDpKSk6Mknn9Ttt9+uPn36aP369br99tslSYcPH9bgwYPl9/s1ZswYbd68WTfffLNqa2vldrslSSUlJZo3b56OHz+uxMTEc3rOcDgsl8ulUCgkp9N5IdMHgHYx4OHXYj0FoF29vziv3R8zmtfv874Gpbm5WaWlpTp16pS8Xq8qKirU1NSknJwce0xWVpYyMzPl9/slSX6/X0OHDrXjRJJyc3MVDoftd2HOpqGhQeFwOGIDAACdV9SBUllZqZ49e8rhcOiBBx7Qyy+/rOzsbAUCASUmJio5OTlivNvtViAQkCQFAoGIOGk93nrsixQVFcnlctlbRkZGtNMGAAAdSNSBcsUVV+jAgQPavXu3ZsyYoSlTpujQoUMXY262wsJChUIhe6upqbmozwcAAGIrIdo7JCYm6vLLL5ckjRgxQnv37tWvf/1r3XnnnWpsbFR9fX3EuyjBYFAej0eS5PF4tGfPnojHa/2UT+uYs3E4HHI4HNFOFQAAdFAX/D0oLS0tamho0IgRI9S1a1dt27bNPlZVVaXq6mp5vV5JktfrVWVlperq6uwxZWVlcjqdys7OvtCpAACATiKqd1AKCws1YcIEZWZm6sSJE1q/fr3eeustbd26VS6XS1OnTlVBQYFSUlLkdDo1a9Yseb1ejRkzRpI0fvx4ZWdna/LkySouLlYgEND8+fPl8/l4hwQAANiiCpS6ujrde++9+vDDD+VyuTRs2DBt3bpVP/jBDyRJS5YsUXx8vPLz89XQ0KDc3FytWLHCvn+XLl20adMmzZgxQ16vVz169NCUKVO0aNGi9j0rAADQoV3w96DEAt+DAsA0fA8KOpsO+z0oAAAAFwuBAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADBOVIFSVFSka665Rr169VJaWppuu+02VVVVRYw5ffq0fD6fUlNT1bNnT+Xn5ysYDEaMqa6uVl5enpKSkpSWlqa5c+fqzJkzF342AACgU4gqUMrLy+Xz+bRr1y6VlZWpqalJ48eP16lTp+wxc+bM0caNG7VhwwaVl5ertrZWEydOtI83NzcrLy9PjY2N2rlzp9auXas1a9ZowYIF7XdWAACgQ4uzLMs63zsfP35caWlpKi8v1/XXX69QKKQ+ffpo/fr1uv322yVJhw8f1uDBg+X3+zVmzBht3rxZN998s2pra+V2uyVJJSUlmjdvno4fP67ExMSvfN5wOCyXy6VQKCSn03m+0weAdjPg4ddiPQWgXb2/OK/dHzOa1+8LugYlFApJklJSUiRJFRUVampqUk5Ojj0mKytLmZmZ8vv9kiS/36+hQ4facSJJubm5CofDOnjw4Fmfp6GhQeFwOGIDAACd13kHSktLi2bPnq1rr71WQ4YMkSQFAgElJiYqOTk5Yqzb7VYgELDH/G+ctB5vPXY2RUVFcrlc9paRkXG+0wYAAB3AeQeKz+fTO++8o9LS0vacz1kVFhYqFArZW01NzUV/TgAAEDsJ53OnmTNnatOmTdqxY4f69etn7/d4PGpsbFR9fX3EuyjBYFAej8ces2fPnojHa/2UT+uYz3M4HHI4HOczVQAA0AFF9Q6KZVmaOXOmXn75ZW3fvl0DBw6MOD5ixAh17dpV27Zts/dVVVWpurpaXq9XkuT1elVZWam6ujp7TFlZmZxOp7Kzsy/kXAAAQCcR1TsoPp9P69ev16uvvqpevXrZ14y4XC51795dLpdLU6dOVUFBgVJSUuR0OjVr1ix5vV6NGTNGkjR+/HhlZ2dr8uTJKi4uViAQ0Pz58+Xz+XiXBAAASIoyUFauXClJ+v73vx+xf/Xq1brvvvskSUuWLFF8fLzy8/PV0NCg3NxcrVixwh7bpUsXbdq0STNmzJDX61WPHj00ZcoULVq06MLOBAAAdBoX9D0oscL3oAAwDd+Dgs6mQ38PCgAAwMVAoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjRB0oO3bs0C233KL09HTFxcXplVdeiThuWZYWLFigvn37qnv37srJydGRI0cixnz88ceaNGmSnE6nkpOTNXXqVJ08efKCTgQAAHQeUQfKqVOndOWVV2r58uVnPV5cXKylS5eqpKREu3fvVo8ePZSbm6vTp0/bYyZNmqSDBw+qrKxMmzZt0o4dOzR9+vTzPwsAANCpJER7hwkTJmjChAlnPWZZlp555hnNnz9ft956qyTpd7/7ndxut1555RXdddddevfdd7Vlyxbt3btXI0eOlCQtW7ZMP/zhD/XUU08pPT39Ak4HAAB0Bu16DcrRo0cVCASUk5Nj73O5XBo9erT8fr8kye/3Kzk52Y4TScrJyVF8fLx279591sdtaGhQOByO2AAAQOfVroESCAQkSW63O2K/2+22jwUCAaWlpUUcT0hIUEpKij3m84qKiuRyuewtIyOjPacNAAAM0yE+xVNYWKhQKGRvNTU1sZ4SAAC4iNo1UDwejyQpGAxG7A8Gg/Yxj8ejurq6iONnzpzRxx9/bI/5PIfDIafTGbEBAIDOq10DZeDAgfJ4PNq2bZu9LxwOa/fu3fJ6vZIkr9er+vp6VVRU2GO2b9+ulpYWjR49uj2nAwAAOqioP8Vz8uRJvffee/bto0eP6sCBA0pJSVFmZqZmz56t//u//9OgQYM0cOBAPfLII0pPT9dtt90mSRo8eLBuuukmTZs2TSUlJWpqatLMmTN111138QkeAAAg6TwCZd++fbrhhhvs2wUFBZKkKVOmaM2aNXrooYd06tQpTZ8+XfX19Ro7dqy2bNmibt262fdZt26dZs6cqXHjxik+Pl75+flaunRpO5wOAADoDOIsy7JiPYlohcNhuVwuhUIhrkcBYIQBD78W6ykA7er9xXnt/pjRvH53iE/xAACAbxYCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGCcmAbK8uXLNWDAAHXr1k2jR4/Wnj17YjkdAABgiJgFyosvvqiCggItXLhQ+/fv15VXXqnc3FzV1dXFakoAAMAQMQuUp59+WtOmTdP999+v7OxslZSUKCkpSb/97W9jNSUAAGCIhFg8aWNjoyoqKlRYWGjvi4+PV05Ojvx+f5vxDQ0NamhosG+HQiFJUjgcvviTBYBz0NLwn1hPAWhXF+M1tvUxLcv6yrExCZSPPvpIzc3NcrvdEfvdbrcOHz7cZnxRUZEee+yxNvszMjIu2hwBAPgmcz1z8R77xIkTcrlcXzomJoESrcLCQhUUFNi3W1pa9PHHHys1NVVxcXExnJl5wuGwMjIyVFNTI6fTGevpdAis2flh3aLHmp0f1i16pq6ZZVk6ceKE0tPTv3JsTALlkksuUZcuXRQMBiP2B4NBeTyeNuMdDoccDkfEvuTk5Is5xQ7P6XQa9UPZEbBm54d1ix5rdn5Yt+iZuGZf9c5Jq5hcJJuYmKgRI0Zo27Zt9r6WlhZt27ZNXq83FlMCAAAGidmveAoKCjRlyhSNHDlSo0aN0jPPPKNTp07p/vvvj9WUAACAIWIWKHfeeaeOHz+uBQsWKBAIaPjw4dqyZUubC2cRHYfDoYULF7b5lRi+GGt2fli36LFm54d1i15nWLM461w+6wMAAPA14m/xAAAA4xAoAADAOAQKAAAwDoECAACMQ6B0Qg0NDRo+fLji4uJ04MCBiGNvv/22rrvuOnXr1k0ZGRkqLi6OzSQN8P7772vq1KkaOHCgunfvrm9961tauHChGhsbI8axZm0tX75cAwYMULdu3TR69Gjt2bMn1lMyRlFRka655hr16tVLaWlpuu2221RVVRUx5vTp0/L5fEpNTVXPnj2Vn5/f5osrv+kWL16suLg4zZ49297HurX1wQcf6J577lFqaqq6d++uoUOHat++ffZxy7K0YMEC9e3bV927d1dOTo6OHDkSwxmfOwKlE3rooYfO+jXC4XBY48ePV//+/VVRUaEnn3xSjz76qJ599tkYzDL2Dh8+rJaWFq1atUoHDx7UkiVLVFJSol/84hf2GNasrRdffFEFBQVauHCh9u/fryuvvFK5ubmqq6uL9dSMUF5eLp/Pp127dqmsrExNTU0aP368Tp06ZY+ZM2eONm7cqA0bNqi8vFy1tbWaOHFiDGdtlr1792rVqlUaNmxYxH7WLdInn3yia6+9Vl27dtXmzZt16NAh/epXv1Lv3r3tMcXFxVq6dKlKSkq0e/du9ejRQ7m5uTp9+nQMZ36OLHQqr7/+upWVlWUdPHjQkmT99a9/tY+tWLHC6t27t9XQ0GDvmzdvnnXFFVfEYKZmKi4utgYOHGjfZs3aGjVqlOXz+ezbzc3NVnp6ulVUVBTDWZmrrq7OkmSVl5dblmVZ9fX1VteuXa0NGzbYY959911LkuX3+2M1TWOcOHHCGjRokFVWVmZ973vfsx588EHLsli3s5k3b541duzYLzze0tJieTwe68knn7T31dfXWw6Hw/rDH/7wdUzxgvAOSicSDAY1bdo0/f73v1dSUlKb436/X9dff70SExPtfbm5uaqqqtInn3zydU7VWKFQSCkpKfZt1ixSY2OjKioqlJOTY++Lj49XTk6O/H5/DGdmrlAoJEn2z1VFRYWampoi1jArK0uZmZmsoSSfz6e8vLyI9ZFYt7P585//rJEjR+qOO+5QWlqarrrqKj333HP28aNHjyoQCESsmcvl0ujRozvEmhEonYRlWbrvvvv0wAMPaOTIkWcdEwgE2nxTb+vtQCBw0edouvfee0/Lli3Tj3/8Y3sfaxbpo48+UnNz81nX5Ju4Hl+lpaVFs2fP1rXXXqshQ4ZI+u/PTWJiYps/eMoaSqWlpdq/f7+KioraHGPd2vrnP/+plStXatCgQdq6datmzJihn/70p1q7dq2kz/6N6qj/vxIohnv44YcVFxf3pdvhw4e1bNkynThxQoWFhbGecsyd65r9rw8++EA33XST7rjjDk2bNi1GM0dn4/P59M4776i0tDTWUzFeTU2NHnzwQa1bt07dunWL9XQ6hJaWFl199dV64okndNVVV2n69OmaNm2aSkpKYj21dhGzv8WDc/Ozn/1M991335eOueyyy7R9+3b5/f42f3dh5MiRmjRpktauXSuPx9PmivfW2x6Pp13nHUvnumatamtrdcMNN+i73/1um4tfvylrdq4uueQSdenS5axr8k1cjy8zc+ZMbdq0STt27FC/fv3s/R6PR42Njaqvr494N+CbvoYVFRWqq6vT1Vdfbe9rbm7Wjh079Jvf/EZbt25l3T6nb9++ys7Ojtg3ePBg/elPf5L02b9RwWBQffv2tccEg0ENHz78a5vneYv1RTBoH//617+syspKe9u6daslyXrppZesmpoay7I+u+CzsbHRvl9hYeE3+oLPY8eOWYMGDbLuuusu68yZM22Os2ZtjRo1ypo5c6Z9u7m52br00ku5SPb/a2lpsXw+n5Wenm79/e9/b3O89WLPl156yd53+PDhb/TFnpZlWeFwOOLfsMrKSmvkyJHWPffcY1VWVrJuZ3H33Xe3uUh29uzZltfrtSzrs4tkn3rqKft4KBTqMBfJEiid1NGjR9t8iqe+vt5yu93W5MmTrXfeeccqLS21kpKSrFWrVsVuojF07Ngx6/LLL7fGjRtnHTt2zPrwww/trRVr1lZpaanlcDisNWvWWIcOHbKmT59uJScnW4FAINZTM8KMGTMsl8tlvfXWWxE/U//5z3/sMQ888ICVmZlpbd++3dq3b5/l9XrtFxV85n8/xWNZrNvn7dmzx0pISLB++ctfWkeOHLHWrVtnJSUlWS+88II9ZvHixVZycrL16quvWm+//bZ16623WgMHDrQ+/fTTGM783BAondTZAsWyLOtvf/ubNXbsWMvhcFiXXnqptXjx4thM0ACrV6+2JJ11+1+sWVvLli2zMjMzrcTERGvUqFHWrl27Yj0lY3zRz9Tq1avtMZ9++qn1k5/8xOrdu7eVlJRk/ehHP4oIY/zX5wOFdWtr48aN1pAhQyyHw2FlZWVZzz77bMTxlpYW65FHHrHcbrflcDiscePGWVVVVTGabXTiLMuyvvbfKwEAAHwJPsUDAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwzv8D8d3lv9CZyfAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(x = base_credit['income']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "oYnWz0yTzWFw",
        "outputId": "746a555d-52e4-48ab-ad73-871e85f867a0"
      },
      "execution_count": 24,
      "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": [
        "plt.hist(x = base_credit['loan']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "jutu1FqqzaPs",
        "outputId": "67762407-5c34-43df-e763-73cd62911c03"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tratamento de Valores Inconsistentes"
      ],
      "metadata": {
        "id": "UEBBgzlwzlb4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.loc[base_credit['age'] < 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "-kZ3MlMZzh04",
        "outputId": "a0840453-f923-4f6b-8efd-dc1ab3971c73"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    clientid        income        age         loan  default\n",
              "15        16  50501.726689 -28.218361  3977.287432        0\n",
              "21        22  32197.620701 -52.423280  4244.057136        0\n",
              "26        27  63287.038908 -36.496976  9595.286289        0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c8e40736-72dc-4e63-8be9-f9ff15f74fd1\" 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>clientid</th>\n",
              "      <th>income</th>\n",
              "      <th>age</th>\n",
              "      <th>loan</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>50501.726689</td>\n",
              "      <td>-28.218361</td>\n",
              "      <td>3977.287432</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>32197.620701</td>\n",
              "      <td>-52.423280</td>\n",
              "      <td>4244.057136</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>63287.038908</td>\n",
              "      <td>-36.496976</td>\n",
              "      <td>9595.286289</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c8e40736-72dc-4e63-8be9-f9ff15f74fd1')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c8e40736-72dc-4e63-8be9-f9ff15f74fd1 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-c8e40736-72dc-4e63-8be9-f9ff15f74fd1');\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",
              "\n",
              "\n",
              "<div id=\"df-3e3106f8-7f52-42a4-a564-6cc3eb4838a0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e3106f8-7f52-42a4-a564-6cc3eb4838a0')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3e3106f8-7f52-42a4-a564-6cc3eb4838a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"base_credit\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 16,\n        \"max\": 27,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          16,\n          22,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15626.134246775633,\n        \"min\": 32197.6207010448,\n        \"max\": 63287.038907874405,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          50501.7266888171,\n          32197.6207010448,\n          63287.038907874405\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.302172635538561,\n        \"min\": -52.4232799196616,\n        \"max\": -28.218361321371003,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -28.218361321371003,\n          -52.4232799196616,\n          -36.4969755136408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3169.351423445414,\n        \"min\": 3977.28743247384,\n        \"max\": 9595.28628892989,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3977.28743247384,\n          4244.057136123401,\n          9595.28628892989\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit[base_credit['age'] < 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "yEmjpjiN0D5u",
        "outputId": "43a1d932-d7a4-4f55-f15e-db65bf370140"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    clientid        income        age         loan  default\n",
              "15        16  50501.726689 -28.218361  3977.287432        0\n",
              "21        22  32197.620701 -52.423280  4244.057136        0\n",
              "26        27  63287.038908 -36.496976  9595.286289        0"
            ],
            "text/html": [
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              "  <div id=\"df-e80d5929-0bf3-4e40-87de-cf8947764bc2\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clientid</th>\n",
              "      <th>income</th>\n",
              "      <th>age</th>\n",
              "      <th>loan</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>50501.726689</td>\n",
              "      <td>-28.218361</td>\n",
              "      <td>3977.287432</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>32197.620701</td>\n",
              "      <td>-52.423280</td>\n",
              "      <td>4244.057136</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>63287.038908</td>\n",
              "      <td>-36.496976</td>\n",
              "      <td>9595.286289</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e80d5929-0bf3-4e40-87de-cf8947764bc2')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-e80d5929-0bf3-4e40-87de-cf8947764bc2 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-e80d5929-0bf3-4e40-87de-cf8947764bc2');\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",
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              "\n",
              "\n",
              "<div id=\"df-9d1d6f5f-af2f-4cd2-b97d-384e662b76c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9d1d6f5f-af2f-4cd2-b97d-384e662b76c2')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"base_credit[base_credit['age'] < 0]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 16,\n        \"max\": 27,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          16,\n          22,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15626.134246775633,\n        \"min\": 32197.6207010448,\n        \"max\": 63287.038907874405,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          50501.7266888171,\n          32197.6207010448,\n          63287.038907874405\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.302172635538561,\n        \"min\": -52.4232799196616,\n        \"max\": -28.218361321371003,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -28.218361321371003,\n          -52.4232799196616,\n          -36.4969755136408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3169.351423445414,\n        \"min\": 3977.28743247384,\n        \"max\": 9595.28628892989,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3977.28743247384,\n          4244.057136123401,\n          9595.28628892989\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# OPÇÃO 1 - Apagar a coluna inteira (de todos os registros da base de dados)\n",
        "base_credit2 = base_credit.drop('age', axis = 1)\n",
        "base_credit2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "JlqbpzeK0T-V",
        "outputId": "de1cc891-8b58-46be-8547-fa9ecb671e64"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      clientid        income         loan  default\n",
              "0            1  66155.925095  8106.532131        0\n",
              "1            2  34415.153966  6564.745018        0\n",
              "2            3  57317.170063  8020.953296        0\n",
              "3            4  42709.534201  6103.642260        0\n",
              "4            5  66952.688845  8770.099235        1\n",
              "...        ...           ...          ...      ...\n",
              "1995      1996  59221.044874  1926.729397        0\n",
              "1996      1997  69516.127573  3503.176156        0\n",
              "1997      1998  44311.449262  5522.786693        1\n",
              "1998      1999  43756.056605  1622.722598        0\n",
              "1999      2000  69436.579552  7378.833599        0\n",
              "\n",
              "[2000 rows x 4 columns]"
            ],
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              "      <th>1995</th>\n",
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              "      <th>1997</th>\n",
              "      <td>1998</td>\n",
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              "    <tr>\n",
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              "      <td>1999</td>\n",
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              "type": "dataframe",
              "variable_name": "base_credit2",
              "summary": "{\n  \"name\": \"base_credit2\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 577,\n        \"min\": 1,\n        \"max\": 2000,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          1861,\n          354,\n          1334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14326.327118850028,\n        \"min\": 20014.4894700497,\n        \"max\": 69995.6855783239,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          40240.7275559381,\n          46706.4588610083,\n          51211.6540386342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3045.410024391518,\n        \"min\": 1.37762959325451,\n        \"max\": 13766.0512393337,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          7498.630446855849,\n          7084.263509070559,\n          4093.360006036261\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1chbL_Br1oGg",
        "outputId": "ac72757d-2964-4978-8458-6848d57df9c0"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=2000, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit[base_credit['age'] < 0].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78AX5jmx1xhk",
        "outputId": "0d15f70e-1cc2-4de4-871f-e51984e06ec9"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index([15, 21, 26], dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# OPÇÃO 2 - Apagar somente os registros com valores inconsistentes\n",
        "base_credit3 = base_credit.drop(base_credit[base_credit['age'] < 0].index)\n",
        "base_credit3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "7laGF4vD1Md-",
        "outputId": "1d9a2340-2dc6-4fac-c58b-8cf8d144872a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      clientid        income        age         loan  default\n",
              "0            1  66155.925095  59.017015  8106.532131        0\n",
              "1            2  34415.153966  48.117153  6564.745018        0\n",
              "2            3  57317.170063  63.108049  8020.953296        0\n",
              "3            4  42709.534201  45.751972  6103.642260        0\n",
              "4            5  66952.688845  18.584336  8770.099235        1\n",
              "...        ...           ...        ...          ...      ...\n",
              "1995      1996  59221.044874  48.518179  1926.729397        0\n",
              "1996      1997  69516.127573  23.162104  3503.176156        0\n",
              "1997      1998  44311.449262  28.017167  5522.786693        1\n",
              "1998      1999  43756.056605  63.971796  1622.722598        0\n",
              "1999      2000  69436.579552  56.152617  7378.833599        0\n",
              "\n",
              "[1997 rows x 5 columns]"
            ],
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              "      <td>0</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>57317.170063</td>\n",
              "      <td>63.108049</td>\n",
              "      <td>8020.953296</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>42709.534201</td>\n",
              "      <td>45.751972</td>\n",
              "      <td>6103.642260</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
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              "      <td>8770.099235</td>\n",
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              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>1996</td>\n",
              "      <td>59221.044874</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>1999</td>\n",
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              "      <td>7378.833599</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>1997 rows × 5 columns</p>\n",
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              "summary": "{\n  \"name\": \"base_credit3\",\n  \"rows\": 1997,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 576,\n        \"min\": 1,\n        \"max\": 2000,\n        \"num_unique_values\": 1997,\n        \"samples\": [\n          260,\n          356,\n          302\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14327.97154972908,\n        \"min\": 20014.4894700497,\n        \"max\": 69995.6855783239,\n        \"num_unique_values\": 1997,\n        \"samples\": [\n          28199.601632077203,\n          60778.7650241517,\n          43677.6292207651\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.271802383260699,\n        \"min\": 18.055188510566897,\n        \"max\": 63.971795841120205,\n        \"num_unique_values\": 1994,\n        \"samples\": [\n          36.6745834872127,\n          55.5069526312873,\n          40.9114946299233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3045.494192393999,\n        \"min\": 1.37762959325451,\n        \"max\": 13766.0512393337,\n        \"num_unique_values\": 1997,\n        \"samples\": [\n          3871.688901934071,\n          10021.0492234587,\n          6739.8585984927895\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit3.loc[base_credit3['age'] < 0]"
      ],
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        },
        "id": "H28dZsel12Rp",
        "outputId": "a14298f7-aead-46ef-b29f-a48fa524ca3e"
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      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
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              "Empty DataFrame\n",
              "Columns: [clientid, income, age, loan, default]\n",
              "Index: []"
            ],
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              "      <th>clientid</th>\n",
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              "      <th>loan</th>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        const element = document.querySelector('#df-94c63881-8dcf-4f98-9888-aed78d8b0244');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' 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",
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              "\n",
              "\n",
              "    </div>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"base_credit3\",\n  \"rows\": 0,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# OPÇÃO 4 - Preencher os valores inconsistente manualmente"
      ],
      "metadata": {
        "id": "AhmbqUD52Dup"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# OPÇÃO 5 - Prencher a média"
      ],
      "metadata": {
        "id": "2GOI4dW42FO_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZKY__302Wxu",
        "outputId": "c78c12c4-0be3-4b3e-a62d-d557261bb583"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "clientid     1000.500000\n",
              "income      45331.600018\n",
              "age            40.807559\n",
              "loan         4444.369695\n",
              "default         0.141500\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit['age'].mean() #aqui esta considerando todos os valores, até mesmo as idades negativas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i81Yur812ZFY",
        "outputId": "528349e9-bf1f-49d4-b2fe-a924b07e3d20"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40.80755937840458"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit['age'][base_credit['age'] > 0].mean() # aqui só está considerando valores positivos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hOMk2ht62pci",
        "outputId": "a06b75bd-9a9a-4c47-cbcf-fe7b47b8d0fa"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40.92770044906149"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.loc[base_credit['age'] < 0, 'age'] = 40.92 #atribuindo a média aos valores negativos"
      ],
      "metadata": {
        "id": "y4pYiEvm27PT"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.loc[base_credit['age'] < 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "-OTrcTWz3NC3",
        "outputId": "60536b7a-c72d-4f2b-a789-68f8b65f32c7"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [clientid, income, age, loan, default]\n",
              "Index: []"
            ],
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-daca44db-a3cd-4a93-adcc-6606b024c978 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-daca44db-a3cd-4a93-adcc-6606b024c978');\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",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"base_credit\",\n  \"rows\": 0,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.head(27)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "vCLAWuqQ3apR",
        "outputId": "5bf18174-5c80-40ae-9c2e-8954c0a4c1dd"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    clientid        income        age         loan  default\n",
              "0          1  66155.925095  59.017015  8106.532131        0\n",
              "1          2  34415.153966  48.117153  6564.745018        0\n",
              "2          3  57317.170063  63.108049  8020.953296        0\n",
              "3          4  42709.534201  45.751972  6103.642260        0\n",
              "4          5  66952.688845  18.584336  8770.099235        1\n",
              "5          6  24904.064140  57.471607    15.498598        0\n",
              "6          7  48430.359613  26.809132  5722.581981        0\n",
              "7          8  24500.141984  32.897548  2971.003310        1\n",
              "8          9  40654.892537  55.496853  4755.825280        0\n",
              "9         10  25075.872771  39.776378  1409.230371        0\n",
              "10        11  64131.415372  25.679575  4351.028971        0\n",
              "11        12  59436.847123  60.471936  9254.244538        0\n",
              "12        13  61050.346079  26.355044  5893.264659        0\n",
              "13        14  27267.995458  61.576776  4759.787581        0\n",
              "14        15  63061.960174  39.201553  1850.369377        0\n",
              "15        16  50501.726689  40.920000  3977.287432        0\n",
              "16        17  43548.654711  39.574530  3935.544453        0\n",
              "17        18  43378.175194  60.848318  3277.737553        0\n",
              "18        19  20542.365073  61.690571  3157.442290        0\n",
              "19        20  58887.357549  26.076093  4965.516066        0\n",
              "20        21  23000.784002  31.761354  1148.118057        0\n",
              "21        22  32197.620701  40.920000  4244.057136        0\n",
              "22        23  23329.319414  48.576975   222.622299        0\n",
              "23        24  27845.800894  51.970624  4959.921226        0\n",
              "24        25  65301.984029  48.840922  5465.267886        0\n",
              "25        26  47451.630123  27.031741  5361.282716        0\n",
              "26        27  63287.038908  40.920000  9595.286289        0"
            ],
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              "\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>clientid</th>\n",
              "      <th>income</th>\n",
              "      <th>age</th>\n",
              "      <th>loan</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>66155.925095</td>\n",
              "      <td>59.017015</td>\n",
              "      <td>8106.532131</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>34415.153966</td>\n",
              "      <td>48.117153</td>\n",
              "      <td>6564.745018</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>57317.170063</td>\n",
              "      <td>63.108049</td>\n",
              "      <td>8020.953296</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>42709.534201</td>\n",
              "      <td>45.751972</td>\n",
              "      <td>6103.642260</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>66952.688845</td>\n",
              "      <td>18.584336</td>\n",
              "      <td>8770.099235</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>24904.064140</td>\n",
              "      <td>57.471607</td>\n",
              "      <td>15.498598</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>48430.359613</td>\n",
              "      <td>26.809132</td>\n",
              "      <td>5722.581981</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>24500.141984</td>\n",
              "      <td>32.897548</td>\n",
              "      <td>2971.003310</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>40654.892537</td>\n",
              "      <td>55.496853</td>\n",
              "      <td>4755.825280</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>25075.872771</td>\n",
              "      <td>39.776378</td>\n",
              "      <td>1409.230371</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>64131.415372</td>\n",
              "      <td>25.679575</td>\n",
              "      <td>4351.028971</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>59436.847123</td>\n",
              "      <td>60.471936</td>\n",
              "      <td>9254.244538</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>61050.346079</td>\n",
              "      <td>26.355044</td>\n",
              "      <td>5893.264659</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>27267.995458</td>\n",
              "      <td>61.576776</td>\n",
              "      <td>4759.787581</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>63061.960174</td>\n",
              "      <td>39.201553</td>\n",
              "      <td>1850.369377</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>50501.726689</td>\n",
              "      <td>40.920000</td>\n",
              "      <td>3977.287432</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>43548.654711</td>\n",
              "      <td>39.574530</td>\n",
              "      <td>3935.544453</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>43378.175194</td>\n",
              "      <td>60.848318</td>\n",
              "      <td>3277.737553</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>20542.365073</td>\n",
              "      <td>61.690571</td>\n",
              "      <td>3157.442290</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>58887.357549</td>\n",
              "      <td>26.076093</td>\n",
              "      <td>4965.516066</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>23000.784002</td>\n",
              "      <td>31.761354</td>\n",
              "      <td>1148.118057</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>32197.620701</td>\n",
              "      <td>40.920000</td>\n",
              "      <td>4244.057136</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>23329.319414</td>\n",
              "      <td>48.576975</td>\n",
              "      <td>222.622299</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>27845.800894</td>\n",
              "      <td>51.970624</td>\n",
              "      <td>4959.921226</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>65301.984029</td>\n",
              "      <td>48.840922</td>\n",
              "      <td>5465.267886</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>47451.630123</td>\n",
              "      <td>27.031741</td>\n",
              "      <td>5361.282716</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>63287.038908</td>\n",
              "      <td>40.920000</td>\n",
              "      <td>9595.286289</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "base_credit",
              "summary": "{\n  \"name\": \"base_credit\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 577,\n        \"min\": 1,\n        \"max\": 2000,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          1861,\n          354,\n          1334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14326.327118850028,\n        \"min\": 20014.4894700497,\n        \"max\": 69995.6855783239,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          40240.7275559381,\n          46706.4588610083,\n          51211.6540386342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.261824836740445,\n        \"min\": 18.055188510566897,\n        \"max\": 63.971795841120205,\n        \"num_unique_values\": 1995,\n        \"samples\": [\n          63.4878736236133,\n          55.5069526312873,\n          43.9410412409343\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3045.410024391518,\n        \"min\": 1.37762959325451,\n        \"max\": 13766.0512393337,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          7498.630446855849,\n          7084.263509070559,\n          4093.360006036261\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tratamento de Valores Faltantes"
      ],
      "metadata": {
        "id": "u1vw2xPj3s0U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.isnull()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9IEFxkyL3erD",
        "outputId": "87423377-54a2-4983-f1eb-4f5cb04ba5c6"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      clientid  income    age   loan  default\n",
              "0        False   False  False  False    False\n",
              "1        False   False  False  False    False\n",
              "2        False   False  False  False    False\n",
              "3        False   False  False  False    False\n",
              "4        False   False  False  False    False\n",
              "...        ...     ...    ...    ...      ...\n",
              "1995     False   False  False  False    False\n",
              "1996     False   False  False  False    False\n",
              "1997     False   False  False  False    False\n",
              "1998     False   False  False  False    False\n",
              "1999     False   False  False  False    False\n",
              "\n",
              "[2000 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d91352de-14c1-4da5-bbe1-71ec52225462\" 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>clientid</th>\n",
              "      <th>income</th>\n",
              "      <th>age</th>\n",
              "      <th>loan</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 5 columns</p>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"base_credit\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISPZWRjX4DeZ",
        "outputId": "00832412-b1e6-4c8e-d801-ab866d7fb957"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "clientid    0\n",
              "income      0\n",
              "age         3\n",
              "loan        0\n",
              "default     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.loc[pd.isnull(base_credit['age'])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "OlVwy9Tb4H1U",
        "outputId": "97d66844-5614-4bf9-c603-ae716e9a82b1"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    clientid        income  age         loan  default\n",
              "28        29  59417.805406  NaN  2082.625938        0\n",
              "30        31  48528.852796  NaN  6155.784670        0\n",
              "31        32  23526.302555  NaN  2862.010139        0"
            ],
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          "metadata": {},
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        }
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    {
      "cell_type": "code",
      "source": [
        "base_credit['age'].fillna(base_credit['age'].mean(), inplace = True) #forma auto usando pandas"
      ],
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        "id": "jdPuITcf4OnO"
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      "outputs": []
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        "base_credit.loc[pd.isnull(base_credit['age'])]"
      ],
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          "height": 53
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        "id": "w1p2c3rt4cjM",
        "outputId": "32892156-e58c-4bf9-e1b4-4872862478ce"
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              "Empty DataFrame\n",
              "Columns: [clientid, income, age, loan, default]\n",
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        "base_credit.loc[base_credit['clientid'].isin([29, 31, 32])]"
      ],
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      },
      "execution_count": 45,
      "outputs": [
        {
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            "text/plain": [
              "    clientid        income        age         loan  default\n",
              "28        29  59417.805406  40.927689  2082.625938        0\n",
              "30        31  48528.852796  40.927689  6155.784670        0\n",
              "31        32  23526.302555  40.927689  2862.010139        0"
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8d411c45-d723-48cf-abcb-f7a697a37854 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"base_credit\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"clientid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 29,\n        \"max\": 32,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          29,\n          31,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18402.430928200996,\n        \"min\": 23526.3025551103,\n        \"max\": 59417.805406265,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          59417.805406265,\n          48528.8527957164,\n          23526.3025551103\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 40.92768888103586,\n        \"max\": 40.92768888103586,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          40.92768888103586\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2162.0596836405384,\n        \"min\": 2082.62593812344,\n        \"max\": 6155.78467025544,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2082.62593812344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Divisão entre previsores e classes"
      ],
      "metadata": {
        "id": "7b-4jbwP5RTb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(base_credit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "oXs7g8Kf5k7t",
        "outputId": "2af9490a-9bdd-46da-8a80-5f4292df95e4"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame</b><br/>def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py</a>Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\n",
              "    If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.dataframe&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [1, 2], &#x27;col2&#x27;: [3, 4]}\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d)\n",
              "&gt;&gt;&gt; df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d, dtype=np.int8)\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [0, 1, 2, 3], &#x27;col2&#x27;: pd.Series([2, 3], index=[2, 3])}\n",
              "&gt;&gt;&gt; pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              "&gt;&gt;&gt; data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[(&quot;a&quot;, &quot;i4&quot;), (&quot;b&quot;, &quot;i4&quot;), (&quot;c&quot;, &quot;i4&quot;)])\n",
              "&gt;&gt;&gt; df3 = pd.DataFrame(data, columns=[&#x27;c&#x27;, &#x27;a&#x27;])\n",
              "...\n",
              "&gt;&gt;&gt; df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              "&gt;&gt;&gt; from dataclasses import make_dataclass\n",
              "&gt;&gt;&gt; Point = make_dataclass(&quot;Point&quot;, [(&quot;x&quot;, int), (&quot;y&quot;, int)])\n",
              "&gt;&gt;&gt; pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              "&gt;&gt;&gt; ser = pd.Series([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=ser, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              "&gt;&gt;&gt; df1 = pd.DataFrame([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], columns=[&quot;x&quot;])\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(data=df1, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df2\n",
              "   x\n",
              "a  1\n",
              "c  3</pre>\n",
              "      <script>\n",
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              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 490);\n",
              "          };\n",
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              "        };\n",
              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit = base_credit.iloc[:, 1:4].values"
      ],
      "metadata": {
        "id": "MMK3RKpN5sxC"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pFauBhNn5yJk",
        "outputId": "67235c71-7d86-4a28-f112-7ea675ae287c"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.61559251e+04, 5.90170151e+01, 8.10653213e+03],\n",
              "       [3.44151540e+04, 4.81171531e+01, 6.56474502e+03],\n",
              "       [5.73171701e+04, 6.31080495e+01, 8.02095330e+03],\n",
              "       ...,\n",
              "       [4.43114493e+04, 2.80171669e+01, 5.52278669e+03],\n",
              "       [4.37560566e+04, 6.39717958e+01, 1.62272260e+03],\n",
              "       [6.94365796e+04, 5.61526170e+01, 7.37883360e+03]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(X_credit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "om_y-fNU51i_",
        "outputId": "a3e467d5-13e7-4bf3-9afd-065e18b2e38e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_credit = base_credit.iloc[:, 4].values"
      ],
      "metadata": {
        "id": "LsrF2Iaf53uk"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_credit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ud0E2Vn25-6x",
        "outputId": "5ec1f442-3147-4533-ce6d-c0a2d2fcd3e9"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(y_credit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "encE89VK6BSH",
        "outputId": "4339e3a5-e1d6-4b7b-9b05-6a82bebf79ea"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Escalonamento de Valores\n"
      ],
      "metadata": {
        "id": "Tldi4C_16m5l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Padronização (Standardisation) - para registros fora do padrão\n",
        "x = x - média(x)/desvio padrão(x)\n",
        "\n",
        "\n",
        " Normalização (Normalization)\n",
        "x = x - min(x)/ (max(x)-min(x))"
      ],
      "metadata": {
        "id": "kL907e-UBeR6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKQcD_Of7C5l",
        "outputId": "112d1ea7-64df-456e-863b-99335a6c53e4"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['clientid', 'income', 'age', 'loan', 'default'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit[:,0].min(), X_credit[:,1].min(), X_credit[:,2].min() #valores min de cada coluna"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YaaolDV6rXh",
        "outputId": "3b1646c8-9cc1-4737-9456-a32f08f2f56f"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20014.4894700497, 18.055188510566897, 1.37762959325451)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit[:,0].max(), X_credit[:,1].max(), X_credit[:,2].max() #valores max de cada coluna"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nve8JKAx6yH7",
        "outputId": "551df936-f9e9-46f0-cde5-8e1854203ff0"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(69995.6855783239, 63.971795841120205, 13766.0512393337)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler_credit = StandardScaler()\n",
        "X_credit = scaler_credit.fit_transform(X_credit)"
      ],
      "metadata": {
        "id": "dGEUVCVv7nnO"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit[:,0].min(), X_credit[:,1].min(), X_credit[:,2].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WafqKDar7uLD",
        "outputId": "4c5925ea-8fd7-4102-dfb4-176671840cfd"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.7676158019964077, -1.7264145408889917, -1.4592791099462408)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit[:,0].max(), X_credit[:,1].max(), X_credit[:,2].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQoHh7xq7w6S",
        "outputId": "ba9e559a-6f26-4946-8734-2612d0b85ee7"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.7220222385319197, 1.7393673928651967, 3.0616609141708273)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vWr0SWdw8OcA",
        "outputId": "665b55db-781e-4653-86b0-55313107baba"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.45393393,  1.36538093,  1.20281942],\n",
              "       [-0.76217555,  0.5426602 ,  0.69642695],\n",
              "       [ 0.83682073,  1.67417189,  1.17471147],\n",
              "       ...,\n",
              "       [-0.07122592, -0.97448519,  0.35420081],\n",
              "       [-0.11000289,  1.73936739, -0.92675625],\n",
              "       [ 1.682986  ,  1.14917639,  0.96381038]])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Divisão da base em treinamento e teste"
      ],
      "metadata": {
        "id": "YY-gXYtv8tWD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "9B4l58Mp9i_h"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "YglWGPdjeXb1"
      },
      "outputs": [],
      "source": [
        "X_credit_treinamento, X_credit_teste, y_credit_treinamento, y_credit_teste = train_test_split(X_credit, y_credit, test_size = 0.25, random_state = 0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit_treinamento.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFXMOCyH-5ev",
        "outputId": "7b18dcb8-ee69-4ca8-8405-9559ca9d4718"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1500, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_credit_treinamento.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fiI9-FyT-8Qs",
        "outputId": "88d0e600-2588-4a53-d600-681aa98f089a"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1500,)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit_teste.shape, y_credit_teste.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0KD_AGeL-_rb",
        "outputId": "06ceeca6-1100-48c8-89a5-842d55532f90"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((500, 3), (500,))"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "lQdoM2u4_Fdc"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('credit.pkl', mode = 'wb') as f:\n",
        "  pickle.dump([X_credit_treinamento, y_credit_treinamento, X_credit_teste, y_credit_teste], f)"
      ],
      "metadata": {
        "id": "PYSvbdFd_Itg"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Naïve Bayes"
      ],
      "metadata": {
        "id": "h_HEmnT-_N7h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB"
      ],
      "metadata": {
        "id": "2Cse61f5_SOb"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('/content/credit.pkl', 'rb') as f:\n",
        "  X_credit_treinamento, y_credit_treinamento, X_credit_teste, y_credit_teste = pickle.load(f)"
      ],
      "metadata": {
        "id": "d0S7xQRM_mdj"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit_treinamento.shape, y_credit_treinamento.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xBINwsV_yQB",
        "outputId": "424cd39a-5c53-429b-db98-14fb274993c7"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1500, 3), (1500,))"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit_teste.shape, y_credit_teste.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btyM03Xg_5cd",
        "outputId": "511e6779-b701-4659-fee5-19b3a9e09173"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((500, 3), (500,))"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "naive_credit_data = GaussianNB()\n",
        "naive_credit_data.fit(X_credit_treinamento, y_credit_treinamento)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "MPrY9JigAABV",
        "outputId": "a64f3383-65c5-4baa-e2b9-9aa49980141c"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsoes = naive_credit_data.predict(X_credit_teste)"
      ],
      "metadata": {
        "id": "Gp54piPxADKk"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "previsoes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WzEDrQ93AF0v",
        "outputId": "9537a44e-dae3-41df-89f7-00fbcc1dd84c"
      },
      "execution_count": 81,
      "outputs": [
        {
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            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_credit_teste"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7q6kXlIIAIOb",
        "outputId": "16caa3d4-eb99-4203-e12c-aaf464a781dd"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "ReWGjhXgAS_V"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_credit_teste, previsoes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TlKK2XTtAWOF",
        "outputId": "32a967ea-115f-4f98-8775-28cfee44de73"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.938"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_credit_teste, previsoes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m8MT6fRsAZVC",
        "outputId": "76b939f7-d074-433e-bcb3-622bea8e9f72"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[428,   8],\n",
              "       [ 23,  41]])"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from yellowbrick.classifier import ConfusionMatrix"
      ],
      "metadata": {
        "id": "NCsr-6RIAcD1"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm = ConfusionMatrix(naive_credit_data)\n",
        "cm.fit(X_credit_treinamento, y_credit_treinamento)\n",
        "cm.score(X_credit_teste, y_credit_teste)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "Y3wQ1t_OAg8N",
        "outputId": "8dc95880-21e9-474c-9640-0f35cf2b4bdd"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.938"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_credit_teste, previsoes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KL5teaZvAko9",
        "outputId": "4eb03977-bf4e-469c-a161-dd291e707b03"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.98      0.97       436\n",
            "           1       0.84      0.64      0.73        64\n",
            "\n",
            "    accuracy                           0.94       500\n",
            "   macro avg       0.89      0.81      0.85       500\n",
            "weighted avg       0.93      0.94      0.93       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Árvore de decisão"
      ],
      "metadata": {
        "id": "hJecKUKZA7CV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "8S4dsbRQB2Y8"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('credit.pkl', 'rb') as f:\n",
        "  X_credit_treinamento, y_credit_treinamento, X_credit_teste, y_credit_teste = pickle.load(f)"
      ],
      "metadata": {
        "id": "-XYxVKrFBEZ5"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit_treinamento.shape, y_credit_treinamento.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1pqvXSIBsX3",
        "outputId": "deb5cd35-6ed9-47b7-b6e6-3c077669200e"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1500, 3), (1500,))"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_credit_teste.shape, y_credit_teste.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UOfg5fRZBvMB",
        "outputId": "48ae9d08-7e24-405d-a3c9-e30fef2bd263"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((500, 3), (500,))"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arvore_credit = DecisionTreeClassifier(criterion='entropy', random_state = 0)\n",
        "arvore_credit.fit(X_credit_treinamento, y_credit_treinamento)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "l_OiNpPFBxx7",
        "outputId": "309cfc54-ca6a-4413-8fe2-a93d04238660"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsoes = arvore_credit.predict(X_credit_teste)\n",
        "previsoes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-5U4-VkB9CU",
        "outputId": "ac4f151c-6ed9-4890-8bce-335893f5fa37"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_credit_teste"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nmVTARMACAV0",
        "outputId": "5663c4f0-f9dd-4dd6-c8fc-853854e43874"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report"
      ],
      "metadata": {
        "id": "n8AHNtDuCGAy"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_credit_teste, previsoes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_JlAMrPCI47",
        "outputId": "1537dcaa-1340-4fb9-b218-c8a5a22c73d5"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.982"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from yellowbrick.classifier import ConfusionMatrix\n",
        "cm = ConfusionMatrix(arvore_credit)\n",
        "cm.fit(X_credit_treinamento, y_credit_treinamento)\n",
        "cm.score(X_credit_teste, y_credit_teste)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "w0vye3_QCMQy",
        "outputId": "fe08a662-d308-4b4b-840a-294ae3c8c2e1"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.982"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_credit_teste, previsoes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T5FMZPc0CR5R",
        "outputId": "edb1e942-24eb-4b10-c578-68a8ed73fbdf"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       436\n",
            "           1       0.91      0.95      0.93        64\n",
            "\n",
            "    accuracy                           0.98       500\n",
            "   macro avg       0.95      0.97      0.96       500\n",
            "weighted avg       0.98      0.98      0.98       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arvore_credit.classes_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIWMxurRCWBn",
        "outputId": "25d33797-bfe4-4b48-aee3-ca1755e76424"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "previsores = ['income', 'age', 'loan']\n",
        "fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (20,20))\n",
        "tree.plot_tree(arvore_credit, feature_names=previsores, class_names=['0','1'], filled=True);\n",
        "fig.savefig('arvore_credit.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "awuEmuxJCZsz",
        "outputId": "2d40af80-09da-4403-e2ed-bc7854e17f6a"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest"
      ],
      "metadata": {
        "id": "boEJo40bCvRp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "I8QSFiG2DFcq"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('credit.pkl', 'rb') as f:\n",
        "  X_credit_treinamento, y_credit_treinamento, X_credit_teste, y_credit_teste = pickle.load(f)"
      ],
      "metadata": {
        "id": "G2_0NZcHC0_v"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_forest_credit = RandomForestClassifier(n_estimators=40, criterion='entropy', random_state = 0)\n",
        "random_forest_credit.fit(X_credit_treinamento, y_credit_treinamento)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "bZyGruH6C7WI",
        "outputId": "a44ad92b-19db-4ec1-a127-b5e627557569"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', n_estimators=40, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=40, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=40, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsoes = random_forest_credit.predict(X_credit_teste)\n",
        "previsoes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZVUfkm0DMdY",
        "outputId": "bbc683c4-016c-4c0a-e7bf-d2cea978a12b"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1])"
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_credit_teste"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ilJ6tFA6DWAG",
        "outputId": "1b550938-b467-45c9-b716-c86c5491ac13"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "accuracy_score(y_credit_teste, previsoes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2NCPckZDaBZ",
        "outputId": "7424a8da-3bff-46fc-9e4a-c28acb5619c3"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.984"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from yellowbrick.classifier import ConfusionMatrix\n",
        "cm = ConfusionMatrix(random_forest_credit)\n",
        "cm.fit(X_credit_treinamento, y_credit_treinamento)\n",
        "cm.score(X_credit_teste, y_credit_teste)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "5eaxz2jwDfF5",
        "outputId": "3c5e1fd3-f60d-430a-aeae-9c238ed87ea3"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.984"
            ]
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAApIAAAHOCAYAAAArLOl3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAWuUlEQVR4nO3df5TXdZ3o8dfEDD8ERH4tGA3jIoohrmK71d5iEA0F+RGnzGxTGMujSBomrICGSi56RVm1i67WsqiYnSXpUNoxECRSuxuEgnhF0oARRAIEFERogLl/YOydqyLzcpivMI/HXzPv73vm8/qe8z0zz/P5fr7fb1F1dXV1AABALX2i0AMAAHB4EpIAAKQISQAAUoQkAAApQhIAgBQhCQBAipAEACBFSAIAkFJc3wd87rnnorq6OkpKSur70AAAHISqqqooKiqKnj17HnBfvYdkdXV1VFVVxbp16+r70ACHRFlZWaFHAKhTB/vBh/UekiUlJbFu3bpYPGhUfR8a4JAYWL3i3a8WF3QOgLqybFnjg9rnGkkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkOWxfOnho3VK+IVmWd9q+dOLBPVPz2JzFm6x9i3LZnY9j8B6Os92dr/Fz7k0+Irz3ywxi1/pm4bueyuHzZo/GZS79e3+MDHLS9e/fG5MkPRY8e50ezZl+Idu3OivPPHxuVla8XejQaOCHJYem0i78ax/X5XI21boPPigt+cU9U/mZh/Pgfzotp5RfG7l1/iQtnT4323btGRESrsk5RsWB6fKK4OH7S75L4t1MGxSuPPxUD7/tB9Pz2eYW4KwAfavToO+PGG38UY8dWxIsvzoif/nRiLF68PPr0GR5/+UtVocejAUuF5M9+9rM499xzo0ePHtGrV6+49dZbo6rKA5n60aJj+zh78phYfN9/1ljv8Y0BsXLu72L+9XfF5pdXx/rnXoxffvu6KG7SOLr2L4+IiG6D+kT13uqYecH3Yv2S5bH55dXxxDWTYvMrlXHSkC8V4u4AHNDu3btj5swn45prhsaFF54bf/u3naJv38/HhAmXxapVr8Xzz79c6BFpwIpr+wOzZs2K8ePHx9ixY+Oss86KFStWxPjx42PHjh0xYcKEQzEj1HDu3dfHmt89Fy8+Mjs+e8WF+9dnfuPq9+yt3lsdERF7q3ZHRMTCKQ/FwikPvc++vbHn3T0AHyfFxcVRWfnYe9Y/8Yl954JKSmr9rxzqTK0ffVOmTIkBAwZERUVFRESUlpbGpk2bYsKECTFixIjo0KFDXc8I+3U/r1906fuFuKf7udH6+M4H3NuyU4fod9d1sWXV2nj+oV++757GLZrH568aFi07dYhffvu6QzEyQJ177rmX4qab/j0GDeoVp556YqHHoQGr1VPbq1evjjVr1kTv3r1rrJeXl8fevXvjqaeeqtPh4P/VtHWr6P+/vh/zxk2Ot9au/8B9Jww4I67dsTSuXvvbaNKyeUz74jfinc1ba+xpcnSLGLft2Ri37dn4u4sGx/S+34pXn158iO8BwEczZswPo3Hjz8ff//3Q6Nv3czFz5m2FHokGrlYhuWrVqoiI6Ny55pmgY489NkpKSmLlypV1Nxn8f/rdeW1sWbkmFt3z8AH3rZ7/+7jvtCHxUL9Lorhpk7j4qYfj6NJja+zZte3tuPe0ITH1H78elQsWxUVP/Eccf/YXD+X4AB/ZP//z0Fiy5OF48MEJMWvWb2LgwKtiz549hR6LBqxWIbl9+/aIiGjevHmN9aKiomjevPn+26GuHX9Or/j0V8/e9/RzdfUB91bteCfe+OOq+NPsp+KhfpdE4xZHxRfHXlpzU3V1bPnTq7H2v5bEo5eOj9Xzfx/n3DHuEN4DgI+uXbtjonv3LvHNb/aPn//8tpgz579i5swnCz0WDZgrdDksnPz1/lHSrGlcvuzR/14sKoqIiO++Micqn1ocv7/rgdi6+rX489KX9m/Z/c7O2LJyTbTvfnxERJR+4TNRclTTWPnEMzV+/4YXXo4Tzu2973d+SKgC1KdNm7bGvHkLo3fv06Njx3b713v02Pd37cUXPRtI4dQqJI8++uiIiPeceayuro633357/+1Q1+Z//87435On1Vjr9A+nxJen3RI/OffS2PxyZVw0d1q8sWJVPDzgv88+FjdtEm1OKItXfv10RET0vPgrceLgM+POsj6x+52d+/d1OPWk2Pb6RhEJfOy8887OuOCCa+PWW6+Ma64Ztn996dJ9b/vTqdPfFGo0qF1IdunSJSIiKisro2fPnvvX165dG1VVVdG1a9e6nQ7etW3dhti2bkONtaPatY6IiDf+uDrerHwtfvuDu2PIA7fGmRO/F89P/0U0atI4ysePiKatWsYf3r2u8pnbpkaPfxoYX5txZ/zmximx663t0eOCAXFC//KYO/b2er9fAB+mtLRjVFQMin/5l/+I9u1bR3n56VFZ+XqMHHl7dOzYNr72Ne+BS+HUKiRLS0ujS5cuMX/+/BgyZMj+9Xnz5kVxcXH06tWrrueDg7b0wVkREfG5q4bFP159ceza9nb8+fkV8UCfobHmd89GRMQbK1bGg2cOizMmXBnDnnwgoqgoNr+8On7xrWtjybSZBZwe4IPde++46NSpfdx009RYu/bm6NixbfTq1TMmThwRxxzTstDj0YAVVVfX7rm8X//613HVVVfFmDFj4uyzz47ly5fHuHHj4rzzzosxY8Z86M8vW7YsKisrY/GgUemhAT5Obqhe8e5X3kIKODIsW9Y4IiJOOeWUA+6r9Ytt+vXrF5MmTYr77rsvJk+eHO3atYthw4bFiBEjcpMCAHBYSr1qe/DgwTF48OC6ngUAgMNIrd5HEgAA/kpIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIKW4UAe+q/XGQh0aoE7dsP+rzxRwCoC6tOygdjkjCfARtWnTptAjABREQc5IlpWVxebNTxTi0AB1rk2bvtGmTZvY/ModhR4FoE5UVraNsrKyD93njCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAAClCEgCAFCEJAECKkAQAIEVIAgCQIiQBAEgRkgAApAhJAABShCQAACnFhR4A6tpxxw2KysrX37N+8sld4oUXZhRgIoCDd9xpo6JyzRvvWT/5pE7xwjMTIyLiV3OWxIRJv4jnX1wTR7dsFud/+bNx6w3nR/PmTep7XBo4IckRadSoC2P06AtrrJWUeLgDh4dR3+kXo7/Tr8baX/+GzX5yWQz6p7vikovK4/4pl8T6DW/GN4ffF+s3vBmP3H9FIcalAUv9Z73//vvj9ttvj759+8Ydd9xR1zPBR9aiRbPo2LFdoccASGnRvEl07HDM+9428V8fjW5dO8Z9/1oRRUVF0f2kTnHv7cNiyEU/jBeWr40en/5U/Q5Lg1arayS3bt0aw4cPj6lTp0aTJk6fA0B9W7x0dZT/j25RVFS0f23A2adGcXGjeOI3/6eAk9EQ1SokH3vssdixY0fMmjUrWrVqdahmAgA+QElxoyhuVPPfd3Fxo2h9zFHx8p/+XKCpaKhqFZK9e/eOadOmRdu2bQ/VPFAnFi9+Kfr3/2588pP9orR0QFx22cTYsGFzoccCOCiLl66O/udPjk92vypKT7k6Lrv6/tiw8a2IiOjW9dhY+OyqGvvXvb4lNm7aFtu2v1OIcWnAahWSpaWl0ahRo0M1C9SJ9u1bx1tvvR0jRpwXc+ZMiVtu+U786lfPRJ8+w2Pnzl2FHg/ggNq3PTre2rYzRnzrzJgzc3TcMv68+NWcpdHny/8zdu78S3z30i/FH5asilvueCx27NgVr659I4aO+HG0Pqa5FxVS7zziOOIsWvRgje979OgaHTu2jb59vxMzZjwRQ4cOLNBkAB9u0bwbanzf49Ofio5/0yr6fvW2mDFrUQy94Auxdt2WuHHSrPj+zTOj9THNY8KYIfHmW+9E+7YtCzQ1DZWQpEE47bRuERHx2msbCzwJQO2ddkrniIh47fUtERExZuSA+O6lfeONzdujY4d9r1kYd9Mj8b3Lzy7YjDRMPtmGI8ry5ati6NDr46WXVtdYX7Ro3ysZTzyxcwGmAjg4y1esi6GX/yhe+uO6GuuLntt3TeSJx3eI3//hTzFj1sJo1qxxfKpTmygubhS/fPy52L1nb/T/0t8VYmwaMGckOaKUlnaIBQuejSVL/hiTJ18VXbuWxtKlL8fIkbfHySd3icGDexd6RIAPVNqpTSz43YpY8sKrMfkHF0TXLh1i6QuvxshrH46TT+oUg/v3jB8/uCCuHPtQbHpjWww4+9RY+sKaGD76gbh+9OBofUzzQt8FGhghyRGlRYujYsGCH8X48f8WF1/8g9iwYXO0a3dMDBzYKyZOHOFCdOBjrUWLprHg0bEx/uafx8VXTo0Nm96Kdm1axsBzTo2J1301SkqK4/JvnRlb39wRt015PL73/Z9G50+1jfGjBseVl/Yt9Pg0QLX6r7p169aoqqqKiIg9e/bErl27YuPGfdectWzZMpo2bVr3E0ItHXfcJ2P69JsKPQZAynGd28f0ey/7wNuLiori2qsHxbVXD6rHqeD91Sokr7zyyli4cOH+79evXx/z5s2LiIhbbrklvvKVr9TtdAAAfGzVKiSnT59+qOYAAOAw41XbAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkFFVXV1fX5wGfffbZqK6ujsaNG9fnYQEOmcrKykKPAFCn2rdvHyUlJXH66acfcF9xPc2zX1FRUX0fEuCQKisrK/QIAHWqqqrqoJqt3s9IAgBwZHCNJAAAKUISAIAUIQkAQIqQBAAgRUgCAJAiJAEASBGSAACkCEkAAFKEJAAAKfX+EYlwKGzYsCGefvrpWLlyZWzbti0iIlq1ahXHH3989OrVK9q0aVPgCQHgyCMkOazt3r07Jk6cGDNmzIg9e/ZESUlJNG/ePCIi3n777aiqqori4uKoqKiI0aNHF3hagLq1a9euePzxx2PIkCGFHoUGymdtc1ibNGlSzJo1K0aOHBnl5eVx7LHH1rh97dq1MXfu3LjnnnuioqIiRowYUaBJAerepk2bolevXrF8+fJCj0IDJSQ5rJWXl8eNN94YZ5555gH3zZ07N26++eZ48skn62kygENPSFJontrmsLZly5bo1q3bh+7r3r17bNq0qR4mAvjoRo0adVD7du3adYgngQMTkhzWOnfuHPPmzYuhQ4cecN+cOXOirKysnqYC+Ghmz54dzZo1i5YtWx5w3969e+tpInh/QpLDWkVFRVx//fWxbNmy6N27d3Tu3Hn/i222b98elZWVMX/+/Jg9e3ZMmjSpwNMCHJzRo0fHtGnT4pFHHjngu05s3LgxysvL63EyqMk1khz2Zs2aFXfffXesWbMmioqKatxWXV0dXbp0iZEjR8Y555xToAkBam/48OGxc+fOmDZt2nv+tv2VayQpNCHJEaOysjJWrVoV27dvj4iIli1bRpcuXaK0tLTAkwHU3ptvvhmPPfZYnHHGGdGpU6cP3HPFFVfE9OnT63k62EdIAgCQ4iMSAQBIEZIAAKQISQAAUoQkAAApQhIAgBQhCQBAipAEACBFSAIAkPJ/AUwOWWFv+kAdAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_credit_teste, previsoes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wm_7TqpiDk4j",
        "outputId": "19b7b82e-d02a-4e19-bb45-6814ecb1b89f"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       436\n",
            "           1       0.95      0.92      0.94        64\n",
            "\n",
            "    accuracy                           0.98       500\n",
            "   macro avg       0.97      0.96      0.96       500\n",
            "weighted avg       0.98      0.98      0.98       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# K-Nearest Neighbour(kNN) - K vizinhos mais próximos"
      ],
      "metadata": {
        "id": "zsqCYA4OHlOG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "wQETIeyvH3ny"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_credit = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p = 2)\n",
        "knn_credit.fit(X_credit_treinamento, y_credit_treinamento)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "j8Hi_Y2RIOQS",
        "outputId": "1babf1ec-a885-4efd-ffbd-4df38219715f"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsoes = knn_credit.predict(X_credit_teste)\n",
        "previsoes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ET2s9Vs1IW4K",
        "outputId": "1cb43169-b737-4f2c-b61f-8b6d84e4f6a6"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "accuracy_score(y_credit_teste, previsoes) # padronização"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bywv7GXtIvoN",
        "outputId": "882e94d3-723e-491a-82d1-c02990581f9d"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.986"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from yellowbrick.classifier import ConfusionMatrix\n",
        "cm = ConfusionMatrix(knn_credit)\n",
        "cm.fit(X_credit_treinamento, y_credit_treinamento)\n",
        "cm.score(X_credit_teste, y_credit_teste)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "UVTV4tNOI2Ab",
        "outputId": "80887d31-d144-428e-b245-090c863f0325"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.986"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_credit_teste, previsoes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mv2nD5XvI7io",
        "outputId": "35e03373-ca5c-4c98-e936-52f63067aa3b"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       436\n",
            "           1       0.94      0.95      0.95        64\n",
            "\n",
            "    accuracy                           0.99       500\n",
            "   macro avg       0.97      0.97      0.97       500\n",
            "weighted avg       0.99      0.99      0.99       500\n",
            "\n"
          ]
        }
      ]
    }
  ]
}