knn-NaiveBayes-DecisionTree.ipynb

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        "import pandas as pd\n",
        "url = 'https://raw.githubusercontent.com/alura-cursos/ML_Classificacao_por_tras_dos_panos/main/Dados/Customer-Churn.csv'\n",
        "df = pd.read_csv(url)"
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        "df.describe()"
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              "       Maior65Anos  MesesDeContrato  ContaMensal\n",
              "count  7043.000000      7043.000000  7043.000000\n",
              "mean      0.162147        32.371149    64.761692\n",
              "std       0.368612        24.559481    30.090047\n",
              "min       0.000000         0.000000    18.250000\n",
              "25%       0.000000         9.000000    35.500000\n",
              "50%       0.000000        29.000000    70.350000\n",
              "75%       0.000000        55.000000    89.850000\n",
              "max       1.000000        72.000000   118.750000"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 18 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Maior65Anos              7043 non-null   int64  \n",
            " 1   Conjuge                  7043 non-null   object \n",
            " 2   Dependentes              7043 non-null   object \n",
            " 3   MesesDeContrato          7043 non-null   int64  \n",
            " 4   TelefoneFixo             7043 non-null   object \n",
            " 5   VariasLinhasTelefonicas  7043 non-null   object \n",
            " 6   ServicoDeInternet        7043 non-null   object \n",
            " 7   SegurancaOnline          7043 non-null   object \n",
            " 8   BackupOnline             7043 non-null   object \n",
            " 9   SeguroNoDispositivo      7043 non-null   object \n",
            " 10  SuporteTecnico           7043 non-null   object \n",
            " 11  TVaCabo                  7043 non-null   object \n",
            " 12  StreamingDeFilmes        7043 non-null   object \n",
            " 13  TipoDeContrato           7043 non-null   object \n",
            " 14  PagamentoOnline          7043 non-null   object \n",
            " 15  FormaDePagamento         7043 non-null   object \n",
            " 16  ContaMensal              7043 non-null   float64\n",
            " 17  Churn                    7043 non-null   object \n",
            "dtypes: float64(1), int64(2), object(15)\n",
            "memory usage: 990.5+ KB\n"
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              "   Maior65Anos Conjuge Dependentes  MesesDeContrato TelefoneFixo  \\\n",
              "0            0     Sim         Nao                1          Nao   \n",
              "1            0     Nao         Nao               34          Sim   \n",
              "2            0     Nao         Nao                2          Sim   \n",
              "3            0     Nao         Nao               45          Nao   \n",
              "4            0     Nao         Nao                2          Sim   \n",
              "\n",
              "  VariasLinhasTelefonicas ServicoDeInternet SegurancaOnline BackupOnline  \\\n",
              "0    SemServicoTelefonico               DSL             Nao          Sim   \n",
              "1                     Nao               DSL             Sim          Nao   \n",
              "2                     Nao               DSL             Sim          Sim   \n",
              "3    SemServicoTelefonico               DSL             Sim          Nao   \n",
              "4                     Nao       FibraOptica             Nao          Nao   \n",
              "\n",
              "  SeguroNoDispositivo SuporteTecnico TVaCabo StreamingDeFilmes TipoDeContrato  \\\n",
              "0                 Nao            Nao     Nao               Nao    Mensalmente   \n",
              "1                 Sim            Nao     Nao               Nao          UmAno   \n",
              "2                 Nao            Nao     Nao               Nao    Mensalmente   \n",
              "3                 Sim            Sim     Nao               Nao          UmAno   \n",
              "4                 Nao            Nao     Nao               Nao    Mensalmente   \n",
              "\n",
              "  PagamentoOnline FormaDePagamento  ContaMensal Churn  \n",
              "0             Sim    ChequeDigital        29.85   Nao  \n",
              "1             Nao      ChequePapel        56.95   Nao  \n",
              "2             Sim      ChequePapel        53.85   Sim  \n",
              "3             Nao    DebitoEmConta        42.30   Nao  \n",
              "4             Sim    ChequeDigital        70.70   Sim  "
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              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>UmAno</td>\n",
              "      <td>Nao</td>\n",
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              "      <td>56.95</td>\n",
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              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>2</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Mensalmente</td>\n",
              "      <td>Sim</td>\n",
              "      <td>ChequePapel</td>\n",
              "      <td>53.85</td>\n",
              "      <td>Sim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>45</td>\n",
              "      <td>Nao</td>\n",
              "      <td>SemServicoTelefonico</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>UmAno</td>\n",
              "      <td>Nao</td>\n",
              "      <td>DebitoEmConta</td>\n",
              "      <td>42.30</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>2</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>FibraOptica</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Mensalmente</td>\n",
              "      <td>Sim</td>\n",
              "      <td>ChequeDigital</td>\n",
              "      <td>70.70</td>\n",
              "      <td>Sim</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-9d746778-e837-4af1-91c6-dd4dd26c7b6a')\"\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",
              "      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-9d746778-e837-4af1-91c6-dd4dd26c7b6a 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-9d746778-e837-4af1-91c6-dd4dd26c7b6a');\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-282323f2-c64b-4832-b78a-a76a6fd7ebc5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-282323f2-c64b-4832-b78a-a76a6fd7ebc5')\"\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",
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              "    </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-282323f2-c64b-4832-b78a-a76a6fd7ebc5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preparação de Dados I"
      ],
      "metadata": {
        "id": "ytITwIgnsfwZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Transformando valores categóricos manualmente"
      ],
      "metadata": {
        "id": "n-kOgioVspkW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "map = {\n",
        "    'Sim': 1,\n",
        "    'Nao':0\n",
        "    }\n",
        "manual =  df[['Conjuge','Dependentes','TelefoneFixo','PagamentoOnline','Churn']].replace(map)"
      ],
      "metadata": {
        "id": "gn6FGqnLsv15"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "manual.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IV2DakpvubW5",
        "outputId": "fc01ddad-fc14-49da-b4da-7dd522097f12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Conjuge  Dependentes  TelefoneFixo  PagamentoOnline  Churn\n",
              "0        1            0             0                1      0\n",
              "1        0            0             1                0      0\n",
              "2        0            0             1                1      1\n",
              "3        0            0             0                0      0\n",
              "4        0            0             1                1      1"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Conjuge</th>\n",
              "      <th>Dependentes</th>\n",
              "      <th>TelefoneFixo</th>\n",
              "      <th>PagamentoOnline</th>\n",
              "      <th>Churn</th>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dcb5a950-5254-4592-b2ab-1b9805bb7cf7');\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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              "        element.appendChild(docLink);\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\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-327c53db-bdde-4600-b87c-77229de526a1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Transformando valores categores Usando get_dummies"
      ],
      "metadata": {
        "id": "Pp3K2LR9vB86"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#get_dummies pega as categorias presentes em cada coluna e transforma em novas colunas usando 1 ou 0\n",
        "dummie_data = pd.get_dummies(df.drop(columns = ['Conjuge','Dependentes','TelefoneFixo','PagamentoOnline','Churn']))\n",
        "\n",
        "#Unindo dois dataframes\n",
        "final_df = pd.concat( [dummie_data, manual], axis =1 )\n",
        "final_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-vKAdlgRvLzC",
        "outputId": "13524d89-03ab-4033-f397-ad5983682d7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 39 columns):\n",
            " #   Column                                        Non-Null Count  Dtype  \n",
            "---  ------                                        --------------  -----  \n",
            " 0   Maior65Anos                                   7043 non-null   int64  \n",
            " 1   MesesDeContrato                               7043 non-null   int64  \n",
            " 2   ContaMensal                                   7043 non-null   float64\n",
            " 3   VariasLinhasTelefonicas_Nao                   7043 non-null   uint8  \n",
            " 4   VariasLinhasTelefonicas_SemServicoTelefonico  7043 non-null   uint8  \n",
            " 5   VariasLinhasTelefonicas_Sim                   7043 non-null   uint8  \n",
            " 6   ServicoDeInternet_DSL                         7043 non-null   uint8  \n",
            " 7   ServicoDeInternet_FibraOptica                 7043 non-null   uint8  \n",
            " 8   ServicoDeInternet_Nao                         7043 non-null   uint8  \n",
            " 9   SegurancaOnline_Nao                           7043 non-null   uint8  \n",
            " 10  SegurancaOnline_SemServicoDeInternet          7043 non-null   uint8  \n",
            " 11  SegurancaOnline_Sim                           7043 non-null   uint8  \n",
            " 12  BackupOnline_Nao                              7043 non-null   uint8  \n",
            " 13  BackupOnline_SemServicoDeInternet             7043 non-null   uint8  \n",
            " 14  BackupOnline_Sim                              7043 non-null   uint8  \n",
            " 15  SeguroNoDispositivo_Nao                       7043 non-null   uint8  \n",
            " 16  SeguroNoDispositivo_SemServicoDeInternet      7043 non-null   uint8  \n",
            " 17  SeguroNoDispositivo_Sim                       7043 non-null   uint8  \n",
            " 18  SuporteTecnico_Nao                            7043 non-null   uint8  \n",
            " 19  SuporteTecnico_SemServicoDeInternet           7043 non-null   uint8  \n",
            " 20  SuporteTecnico_Sim                            7043 non-null   uint8  \n",
            " 21  TVaCabo_Nao                                   7043 non-null   uint8  \n",
            " 22  TVaCabo_SemServicoDeInternet                  7043 non-null   uint8  \n",
            " 23  TVaCabo_Sim                                   7043 non-null   uint8  \n",
            " 24  StreamingDeFilmes_Nao                         7043 non-null   uint8  \n",
            " 25  StreamingDeFilmes_SemServicoDeInternet        7043 non-null   uint8  \n",
            " 26  StreamingDeFilmes_Sim                         7043 non-null   uint8  \n",
            " 27  TipoDeContrato_DoisAnos                       7043 non-null   uint8  \n",
            " 28  TipoDeContrato_Mensalmente                    7043 non-null   uint8  \n",
            " 29  TipoDeContrato_UmAno                          7043 non-null   uint8  \n",
            " 30  FormaDePagamento_CartaoDeCredito              7043 non-null   uint8  \n",
            " 31  FormaDePagamento_ChequeDigital                7043 non-null   uint8  \n",
            " 32  FormaDePagamento_ChequePapel                  7043 non-null   uint8  \n",
            " 33  FormaDePagamento_DebitoEmConta                7043 non-null   uint8  \n",
            " 34  Conjuge                                       7043 non-null   int64  \n",
            " 35  Dependentes                                   7043 non-null   int64  \n",
            " 36  TelefoneFixo                                  7043 non-null   int64  \n",
            " 37  PagamentoOnline                               7043 non-null   int64  \n",
            " 38  Churn                                         7043 non-null   int64  \n",
            "dtypes: float64(1), int64(7), uint8(31)\n",
            "memory usage: 653.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#faz com que o .head() mostre a quantidade de colunas escolhidas\n",
        "pd.set_option('display.max_columns',39)"
      ],
      "metadata": {
        "id": "zTCq6O2I5bT-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Dados desabalanceados\n"
      ],
      "metadata": {
        "id": "B1jHwBLKBH1n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Quando uma categoria possui mais valores uq e a outra**"
      ],
      "metadata": {
        "id": "D6jrwLFoBMOt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "ax = sns.countplot(x='Churn', data=final_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "pR6pS8XCBLbM",
        "outputId": "fff5f914-3467-47b4-9fe5-9c646362e508"
      },
      "execution_count": null,
      "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": [
        "Um método que pode ser usado é o **oversampling**, que cria novas instâncias para a categoria com menos. Uma Técnica é o **SMOTE** e feita pegando duas instâncias próximas ja existentes e pegando os **dados intermediários**, não sendo necessariamente a média."
      ],
      "metadata": {
        "id": "TL406W_FBSyW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = final_df.drop(['Churn'],axis=1)\n",
        "y = final_df['Churn']\n",
        "\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "smt = SMOTE(random_state=5)\n",
        "x, y = smt.fit_resample(x,y)\n",
        "\n",
        "final_df = pd.concat([x,y], axis=1)"
      ],
      "metadata": {
        "id": "4eSfUsEaB9Hy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.countplot(x = 'Churn', data = final_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "g_9x5JEhDcUc",
        "outputId": "ab424353-2dee-4886-ada0-9a228d58c927"
      },
      "execution_count": null,
      "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": [
        "#kNN por trás do panos"
      ],
      "metadata": {
        "id": "AHHXbnLu4kg8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Xmaria = [[0,0,1,1,0,0,39.90,1,0,0,0,1,0,1,0,0,0,0,1,1,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,1]]"
      ],
      "metadata": {
        "id": "FfUht_eR7H_f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = final_df.drop('Churn', axis = 1)\n",
        "y = final_df['Churn']"
      ],
      "metadata": {
        "id": "pmwa810E4q4V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "norm = StandardScaler()\n",
        "X_norm = norm.fit_transform(x)\n",
        "\n",
        "#standardScaler.transform so aceita como parâmetro de entrada array de duas dimensões, por isso o pd.dataframe, transformando o Xmaria em um dataframe\n",
        "#um dataframe são dados organizados em uma estrutura bidimensional\n",
        "xmaria_norm = norm.transform(pd.DataFrame(Xmaria, columns = x.columns))\n",
        "\n",
        "#(Xmaria, columns = x.columns) --> ai transformar o vetor da Xmaria em um conjunto de linhas colunas contendo as mesmas colunas do nosso conjunto x"
      ],
      "metadata": {
        "id": "iAnDmAYF48NB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(Xmaria, columns = x.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "id": "dJz1DnFIVNYQ",
        "outputId": "04dbfa5e-714e-48c8-ec81-e15211c7d2be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Maior65Anos  MesesDeContrato  ContaMensal  VariasLinhasTelefonicas_Nao  \\\n",
              "0            0                0            1                            1   \n",
              "\n",
              "   VariasLinhasTelefonicas_SemServicoTelefonico  VariasLinhasTelefonicas_Sim  \\\n",
              "0                                             0                            0   \n",
              "\n",
              "   ServicoDeInternet_DSL  ServicoDeInternet_FibraOptica  \\\n",
              "0                   39.9                              1   \n",
              "\n",
              "   ServicoDeInternet_Nao  SegurancaOnline_Nao  \\\n",
              "0                      0                    0   \n",
              "\n",
              "   SegurancaOnline_SemServicoDeInternet  SegurancaOnline_Sim  \\\n",
              "0                                     0                    1   \n",
              "\n",
              "   BackupOnline_Nao  BackupOnline_SemServicoDeInternet  BackupOnline_Sim  \\\n",
              "0                 0                                  1                 0   \n",
              "\n",
              "   SeguroNoDispositivo_Nao  SeguroNoDispositivo_SemServicoDeInternet  \\\n",
              "0                        0                                         0   \n",
              "\n",
              "   SeguroNoDispositivo_Sim  SuporteTecnico_Nao  \\\n",
              "0                        0                   1   \n",
              "\n",
              "   SuporteTecnico_SemServicoDeInternet  SuporteTecnico_Sim  TVaCabo_Nao  \\\n",
              "0                                    1                   1            0   \n",
              "\n",
              "   TVaCabo_SemServicoDeInternet  TVaCabo_Sim  StreamingDeFilmes_Nao  \\\n",
              "0                             0            1                      0   \n",
              "\n",
              "   StreamingDeFilmes_SemServicoDeInternet  StreamingDeFilmes_Sim  \\\n",
              "0                                       1                      0   \n",
              "\n",
              "   TipoDeContrato_DoisAnos  TipoDeContrato_Mensalmente  TipoDeContrato_UmAno  \\\n",
              "0                        0                           0                     0   \n",
              "\n",
              "   FormaDePagamento_CartaoDeCredito  FormaDePagamento_ChequeDigital  \\\n",
              "0                                 1                               0   \n",
              "\n",
              "   FormaDePagamento_ChequePapel  FormaDePagamento_DebitoEmConta  Conjuge  \\\n",
              "0                             0                               1        0   \n",
              "\n",
              "   Dependentes  TelefoneFixo  PagamentoOnline  \n",
              "0            0             0                1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b694d859-4ba4-47d8-81d3-6120b1c2bbe7\" 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>Maior65Anos</th>\n",
              "      <th>MesesDeContrato</th>\n",
              "      <th>ContaMensal</th>\n",
              "      <th>VariasLinhasTelefonicas_Nao</th>\n",
              "      <th>VariasLinhasTelefonicas_SemServicoTelefonico</th>\n",
              "      <th>VariasLinhasTelefonicas_Sim</th>\n",
              "      <th>ServicoDeInternet_DSL</th>\n",
              "      <th>ServicoDeInternet_FibraOptica</th>\n",
              "      <th>ServicoDeInternet_Nao</th>\n",
              "      <th>SegurancaOnline_Nao</th>\n",
              "      <th>SegurancaOnline_SemServicoDeInternet</th>\n",
              "      <th>SegurancaOnline_Sim</th>\n",
              "      <th>BackupOnline_Nao</th>\n",
              "      <th>BackupOnline_SemServicoDeInternet</th>\n",
              "      <th>BackupOnline_Sim</th>\n",
              "      <th>SeguroNoDispositivo_Nao</th>\n",
              "      <th>SeguroNoDispositivo_SemServicoDeInternet</th>\n",
              "      <th>SeguroNoDispositivo_Sim</th>\n",
              "      <th>SuporteTecnico_Nao</th>\n",
              "      <th>SuporteTecnico_SemServicoDeInternet</th>\n",
              "      <th>SuporteTecnico_Sim</th>\n",
              "      <th>TVaCabo_Nao</th>\n",
              "      <th>TVaCabo_SemServicoDeInternet</th>\n",
              "      <th>TVaCabo_Sim</th>\n",
              "      <th>StreamingDeFilmes_Nao</th>\n",
              "      <th>StreamingDeFilmes_SemServicoDeInternet</th>\n",
              "      <th>StreamingDeFilmes_Sim</th>\n",
              "      <th>TipoDeContrato_DoisAnos</th>\n",
              "      <th>TipoDeContrato_Mensalmente</th>\n",
              "      <th>TipoDeContrato_UmAno</th>\n",
              "      <th>FormaDePagamento_CartaoDeCredito</th>\n",
              "      <th>FormaDePagamento_ChequeDigital</th>\n",
              "      <th>FormaDePagamento_ChequePapel</th>\n",
              "      <th>FormaDePagamento_DebitoEmConta</th>\n",
              "      <th>Conjuge</th>\n",
              "      <th>Dependentes</th>\n",
              "      <th>TelefoneFixo</th>\n",
              "      <th>PagamentoOnline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39.9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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-b694d859-4ba4-47d8-81d3-6120b1c2bbe7')\"\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-b694d859-4ba4-47d8-81d3-6120b1c2bbe7 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-b694d859-4ba4-47d8-81d3-6120b1c2bbe7');\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",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xmaria_norm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T0nmgo3EXOPr",
        "outputId": "da0f31f6-1b97-481b-cfd0-5c0d569b72f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.40868545, -1.15137432, -2.29940252,  1.12398491, -0.3190435 ,\n",
              "        -0.83217125, 85.39097662,  0.97499366, -0.44819859, -1.13684809,\n",
              "        -0.44819859,  1.90579383, -0.95058171,  2.23115382, -0.63611502,\n",
              "        -0.95704832, -0.44819859, -0.64046351,  0.88187382,  2.23115382,\n",
              "         1.89390587, -0.8343353 , -0.44819859,  1.27870653, -0.83100748,\n",
              "         2.23115382, -0.78509228, -0.44695427, -1.33846016, -0.42148404,\n",
              "         2.33567894, -0.77068917, -0.47363024,  2.2828238 , -0.78992399,\n",
              "        -0.53025348, -3.00290276,  0.81860231]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Distância Euclidiana"
      ],
      "metadata": {
        "id": "LXlTYWkTYdYM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "a = xmaria_norm\n",
        "\n",
        "b = X_norm"
      ],
      "metadata": {
        "id": "CWJJI2hiYcEK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#quando se faz uma operação com duas instâncias de um dataframe, todas as features dessas são alteradas pela operação de forma ordenada\n",
        "a-b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NxYiNNm8anP1",
        "outputId": "e6700c7a-2f7d-43f6-e3f0-a7ad237cf4a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.        , -0.04150397, -0.99616184, ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [ 0.        , -1.41113514, -1.93189791, ...,  0.        ,\n",
              "        -3.33591387,  2.04019671],\n",
              "       [ 0.        , -0.08300795, -1.82485799, ...,  0.        ,\n",
              "        -3.33591387,  0.        ],\n",
              "       ...,\n",
              "       [-2.85555505, -2.03369476, -3.10037665, ...,  0.        ,\n",
              "        -3.33591387,  2.04019671],\n",
              "       [ 0.        , -0.12451192, -2.69106515, ...,  0.        ,\n",
              "        -3.33591387,  2.04019671],\n",
              "       [ 0.        , -0.70556757, -2.75742585, ...,  0.        ,\n",
              "        -3.33591387,  2.04019671]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sqrt(np.sum(np.square(a-b)))"
      ],
      "metadata": {
        "id": "JxGqsLOmu_NB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Implementando Modelo knn"
      ],
      "metadata": {
        "id": "greGOREnPsNW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import  train_test_split\n",
        "\n",
        "SEED = 123\n",
        "\n",
        "x_train, x_test, y_train, y_test= train_test_split(X_norm, y, random_state=SEED, test_size=0.3 )"
      ],
      "metadata": {
        "id": "RwA6Tfk1PxmN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "#manhattan , minkowski , chebyshev --> outra métricas de distância\n",
        "knn = KNeighborsClassifier(metric= 'euclidean')\n",
        "\n"
      ],
      "metadata": {
        "id": "FWE8G7QERCK0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "0cpT4AtyRu5d",
        "outputId": "edfa6f85-f385-493b-d45d-76cdd4e05935"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean')"
            ],
            "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>KNeighborsClassifier(metric=&#x27;euclidean&#x27;)</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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(metric=&#x27;euclidean&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "accuracy_score(y_test,knn.predict(x_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqL6Gz5-R9f1",
        "outputId": "6b622ff8-eb3d-4a1d-8644-23efdb5c6024"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8112721417069243"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,knn.predict(x_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OFn3F5UkTCOc",
        "outputId": "afb4e4a1-1828-4e36-d110-9c4fc911523c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1230,  339],\n",
              "       [ 247, 1289]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#distância do exemplo para o resto"
      ],
      "metadata": {
        "id": "xL00su5dtCwM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calcula_distancia(dados_clientes, dados_maria, numero_clientes):\n",
        "    distancias = []\n",
        "\n",
        "#loop para percorrer cliente por cliente\n",
        "    for i in range(numero_clientes):\n",
        "        dist1 = dados_maria - dados_clientes[i]            #subtração dos atributos\n",
        "        soma_quadrado = np.sum(np.square(dist1))    #exponenciação e soma de todos os atributos\n",
        "        distancias.append(np.sqrt(soma_quadrado))   #raiz quadrada da soma anterior\n",
        "\n",
        "    return distancias"
      ],
      "metadata": {
        "id": "18GwZTurtCPe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Naive bayes Bernouli model\n"
      ],
      "metadata": {
        "id": "8tQ5IiUx2Ma0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "np.median(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJyoMzWt3DHj",
        "outputId": "5e84df9b-c861-47ed-ebad-88b8f57bc1fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.4481985915711518"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import BernoulliNB\n",
        "#vai transforma em binário a partir do valor de 0.44\n",
        "bnb = BernoulliNB(binarize = 0.44 )\n",
        "\n",
        "\n",
        "\n",
        "bnb.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Mw-uoFWn2MKv",
        "outputId": "8a0674ea-a005-4395-ca78-051be858bd4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BernoulliNB(binarize=0.44)"
            ],
            "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>BernoulliNB(binarize=0.44)</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\">BernoulliNB</label><div class=\"sk-toggleable__content\"><pre>BernoulliNB(binarize=0.44)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,bnb.predict(x_test))\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JLW_8G3X4F57",
        "outputId": "a7d2b40c-425f-4b39-a2ea-edde0ed126b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7578099838969404"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Decision Tree"
      ],
      "metadata": {
        "id": "-9zXllYolJm2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dtc = DecisionTreeClassifier(criterion = 'entropy', random_state= SEED)\n",
        "\n",
        "dtc.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "DoLjc3a2lJOP",
        "outputId": "30811ba8-cca0-486c-a07e-3431e329c51e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=123)"
            ],
            "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>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=123)</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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=123)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dtc.feature_importances_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pBZr_Drilrqy",
        "outputId": "ce9c9174-a9a0-4ec8-bd57-143397dc5b58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.01231443, 0.16771507, 0.22973259, 0.0101721 , 0.0033813 ,\n",
              "       0.0116499 , 0.00060713, 0.02504051, 0.        , 0.01656304,\n",
              "       0.        , 0.01553491, 0.00610711, 0.        , 0.00821932,\n",
              "       0.01067592, 0.00043522, 0.00838202, 0.01403334, 0.        ,\n",
              "       0.01767918, 0.00738091, 0.00027777, 0.00734466, 0.00366511,\n",
              "       0.00092243, 0.00404312, 0.0416655 , 0.14996678, 0.08326924,\n",
              "       0.01577121, 0.01836651, 0.02055659, 0.02289949, 0.02428843,\n",
              "       0.0146941 , 0.00712703, 0.01951802])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,dtc.predict(x_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "po9Cv59Ll6S1",
        "outputId": "b9387c8a-f089-4855-ccac-00742059d42c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8041867954911434"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#Validação de Modelo"
      ],
      "metadata": {
        "id": "osxVG8EbvQIO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "cf_matrix = confusion_matrix(y_test,knn.predict(x_test))\n",
        "\n",
        "print(cf_matrix)\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "ax = plt.axes()\n",
        "sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True,\n",
        "            fmt='.2%', cmap='Blues',ax=ax)\n",
        "\n",
        "ax.set_title('---knn---')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "64pYDLkHvPoF",
        "outputId": "c9984539-7410-4e8a-9a8c-8e385e086721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1230  339]\n",
            " [ 247 1289]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test,bnb.predict(x_test))\n",
        "\n",
        "print(cf_matrix)\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "ax = plt.axes()\n",
        "sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True,\n",
        "            fmt='.2%', cmap='Blues',ax=ax)\n",
        "\n",
        "ax.set_title('---bernouli---')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "fkVmvxqox54c",
        "outputId": "2afa8fbe-1266-459a-cb6b-b03836274e1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1073  496]\n",
            " [ 256 1280]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test,dtc.predict(x_test))\n",
        "\n",
        "print(cf_matrix)\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "ax = plt.axes()\n",
        "sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True,\n",
        "            fmt='.2%', cmap='Blues',ax=ax)\n",
        "\n",
        "ax.set_title('---Decision tree---')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "9udwelImyyq3",
        "outputId": "f013d2c4-29f0-4a60-c16f-5f2b91e7959d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1260  309]\n",
            " [ 299 1237]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}