K_Nearest_Neighbors.ipynb

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      "source": [
        "# **Modelagem K-Vizinhos Mais Próximos (K-Nearest Neighbors): aplicada a segmentação de clientes**\n",
        "\n",
        "O algoritmo K-Vizinhos Mais Próximos (KNN) é uma abordagem de classificação amplamente utilizada em aprendizado de máquina supervisionado, é flexível e fácil de implementar, sendo adequado para uma ampla gama de tarefas de classificação e regressão. Ele funciona ao classificar novos dados com base na similaridade com pontos rotulados em um conjunto de dados de treinamento. A ideia central é que pontos de dados semelhantes (ou vizinhos próximos) compartilhem as mesmas classes.\n",
        "\n",
        "Imagine um exemplo prático: uma empresa de telecomunicações categoriza seus clientes em quatro grupos com base em padrões de uso. Se informações demográficas como idade e renda puderem ser usadas para prever o grupo de um cliente, a empresa pode personalizar suas ofertas. O objetivo, nesse caso, seria construir um modelo de classificação usando os dados rotulados (categorias dos clientes) para prever a classe de novos clientes.\n",
        "\n",
        "O KNN realiza essa tarefa analisando a proximidade entre pontos de dados em termos de características, como idade e renda. A \"proximidade\" é calculada utilizando métricas como a [distância Euclidiana](https://github.com/SampMark/Computational-Thinking/blob/main/Minkowski_Manhattan_and_Euclidean_distance_metrics.ipynb), que mede a diferença geométrica entre os pontos.\n",
        "\n",
        "Embora simples em sua estrutura, o k-NN detém um poder expressivo para identificar padrões, tendo se tornado popularmente conhecido em diversas aplicações no campo da ciência da computação e áreas correlatas.\n",
        "\n",
        "Podemos ilustrar aqui alguma aplicações do k-NN, entre elas:\n",
        "1.\t**Reconhecimento de padrões**: O k-NN pode ser usado para classificar imagens, reconhecimento facial, diagnósticos médicos, com base nas características extraídas. O K-NN é aplicado na análise de dados hospitalares (resultados e imagens de exames, etc.), sendo treinado no diagnóstico de doenças com base em características de pacientes e sintomas. Por exemplo, pode ser usado para classificar tumores como benignos ou malignos, utilizando dados de imagens médicas ou resultados de biópsias.\n",
        "\n",
        "2.\t**Sistemas de Recomendação**: Em sistemas de recomendação de filmes, música, produtos em e-commerce etc., o k-NN pode ser usado para recomendar itens com base na similaridade entre usuários ou produtos. Por exemplo, se vários usuários com preferências semelhantes gostarem de um produto, esse produto pode ser recomendado a um novo usuário com preferências similares. Em aplicações de *marketing*, por exemplo, o k-NN pode ser aplicado para segmentar consumidores em grupos com base em características demográficas e comportamentais, permitindo campanhas mais direcionadas e personalizadas.\n",
        "\n",
        "3.\t**Detecção de Anomalias**: O k-NN pode ser usado em \"detecção de anomalias\", isto é, *outliers* em conjuntos de dados, especialmente em sistemas de monitoramento e segurança. As anomalias são detectadas com base em sua distância aos vizinhos mais próximos. Se um ponto de dados está muito distante de todos os seus vizinhos, ele pode ser considerado uma anomalia. Por exemplo, pode ser aplicado na detecção de **fraudes bancárias**, identificando transações  anômalas em relação ao histórico do cliente, ou ainda, para  identificar tráfegos incomuns ou suspeitos em redes de computadores, como ataques cibernéticos.\n",
        "\n",
        "O algoritmo KNN é simples, mas poderoso, oferecendo uma abordagem intuitiva para problemas de classificação e regressão. Apesar de ser sensível à escolha de\n",
        "𝐾 e à normalização dos dados, é eficaz em cenários onde os pontos de dados semelhantes compartilham características comuns. Tem aplicações em diversas áreas como *marketing*, economia, entre outras."
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      "cell_type": "markdown",
      "source": [
        "## **Instalando e importando os pacotes Python**"
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      "source": [
        "!pip install scikit-learn\n",
        "!pip install matplotlib\n",
        "!pip install pandas\n",
        "!pip install numpy\n",
        "%matplotlib inline"
      ],
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    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
      "metadata": {
        "id": "nBejkC7za-If"
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      "execution_count": 4,
      "outputs": []
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      "source": [
        "## **Utilização do k-NN para segmentação da base de clientes de empresa de telecomunicações (hipotética)**\n",
        "\n",
        "Uma empresa de telecomunicações pretende oferecer planos de celular personalizados para seus clientes e novos em potencial, com base no perfil de segmentação de mercado. A estratégia de marketing  permitindo campanhas mais direcionadas e personalizadas.\n",
        "\n",
        "Para tal a seguir foi desenvolvida a utilização e análise dos dados de modo a segmentar consumidores em grupos com base em características demográficas, faixa etária, renda e até mesmo comportamentais. Para isso, a empresa de telecomunicações analisou o comportamento dos clientes atuais e os dividiu em quatro grupos, cada um com  perfil de usuário, isto é, plano de consumo diferente, a seguir:\n",
        "\n",
        "1. **Serviço Básico** (*Basic-service*): Clientes que usam pouco o celular, geralmente apenas para ligações e mensagens.\n",
        "2. **E-Serviço** (*E-Service customers*): Clientes que usam bastante a internet, principalmente para redes sociais e e-mails.\n",
        "3. **Serviço Plus** (*Plus Service*): Clientes que usam o celular de forma moderada, tanto para ligações e mensagens quanto para internet.\n",
        "4. **Serviço Total** (*Total Service*): Clientes que usam o celular intensamente, com alto consumo de internet, ligações e mensagens.\n",
        "\n",
        "A empresa dispõe dos dados necessários para classificação de caracteríticas comuns, como região onde o cliente mora, idade, estado civil, entre outras. As quais podem ajudar a prever qual grupo determinado tipo de cliente se encaixa. Com essas informações, a empresa pode criar ofertas personalizadas para atrair novos clientes.\n",
        "\n",
        "Para realizar previsões, a empresa vai modelar utilizando o k-NN de modo a  analisar as características de um potencial novo cliente e, simultanemante,  \"chutar\" em qual dos quatro grupos ele pertenceria e assim ofertar o serviço.\n",
        "Ou seja, comparando-se as características dos potenciais novos clientes com as características dos clientes atuais, o modelo encontrará os \"vizinhos\" mais parecidos com o novo cliente e, com base no grupo desses vizinhos, é possível prever a qual grupo pertencerá os clientes potenciais."
      ],
      "metadata": {
        "id": "LQIkpj4giSXF"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Extração, visualização e análise de dados**"
      ],
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      }
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              "    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-45b8b5be-911e-455e-b30a-f5828c0ca308 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",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 1,\n        \"max\": 72,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          23,\n          50,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 77,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          44,\n          22,\n          68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"address\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 0,\n        \"max\": 55,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          38,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 107.04416484903902,\n        \"min\": 9.0,\n        \"max\": 1668.0,\n        \"num_unique_values\": 218,\n        \"samples\": [\n          168.0,\n          269.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"employ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 0,\n        \"max\": 47,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          33,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"retire\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2117447421681024,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\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      \"column\": \"reside\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"custcat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Contagem absoluta e percentual de cada categoria\n",
        "counts = df['custcat'].value_counts()\n",
        "percentages = df['custcat'].value_counts(normalize=True) * 100\n",
        "\n",
        "# Criar DataFrame formatado\n",
        "table = pd.DataFrame({\n",
        "    'Planos': counts.index,\n",
        "    'Nº de Clientes': counts.values,\n",
        "    '% de Clientes': percentages.values\n",
        "})\n",
        "\n",
        "# Ordenar por categoria de plano (opcional, se precisar em ordem específica)\n",
        "table = table.sort_values(by='Planos').reset_index(drop=True)\n",
        "\n",
        "# Exibir a tabela formatada\n",
        "print(\"Tabela - Número e percentuais de clientes por categorias\")\n",
        "print(table)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MchrZEOVBgRr",
        "outputId": "2e583aca-4f32-4721-e379-adf6b926cafd"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tabela - Número e percentuais de clientes por categorias\n",
            "   Planos  Nº de Clientes  % de Clientes\n",
            "0       1             266           26.6\n",
            "1       2             217           21.7\n",
            "2       3             281           28.1\n",
            "3       4             236           23.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Output: 1. Basic-service (266), 2. E-Service customers (217), 3. Plus Service (281), 4. Total Service (236)"
      ],
      "metadata": {
        "id": "l5ztnvapiHHq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotar histograma de distribuição de renda dos clientes\n",
        "plt.figure(figsize=(8, 6))  # Ajustar o tamanho da figura\n",
        "df.hist(column='income', bins='auto', color='skyblue', edgecolor='black')\n",
        "plt.title('Histograma da Renda')\n",
        "plt.xlabel('Renda')\n",
        "plt.ylabel('Frequência')\n",
        "plt.xlim(0, df['income'].max())\n",
        "plt.grid(True)  # Adicionar grade ao gráfico\n",
        "\n",
        "# Calcular mínimo, máximo, média e desvio padrão\n",
        "minimo = df['income'].min()\n",
        "maximo = df['income'].max()\n",
        "media = df['income'].mean()\n",
        "desvio_padrao = df['income'].std()\n",
        "\n",
        "# Exibir média e desvio padrão no gráfico\n",
        "plt.text(0.6, 0.95, f'Renda Mínima: {minimo:.2f}', transform=plt.gca().transAxes)\n",
        "plt.text(0.6, 0.9, f'Renda Média: {media:.2f}', transform=plt.gca().transAxes)\n",
        "plt.text(0.6, 0.85, f'Renda Máxima: {maximo:.2f}', transform=plt.gca().transAxes)\n",
        "plt.text(0.6, 0.8, f'Desvio Padrão: {desvio_padrao:.2f}', transform=plt.gca().transAxes)\n",
        "\n",
        "# Adicionar linha vertical para a média\n",
        "plt.axvline(media, color='red', linestyle='dashed', linewidth=1)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# Imprimir os valores de mínimo, média, máximo e desvio padrão\n",
        "print(f\"Renda mínima: {minimo:.2f}\")\n",
        "print(f\"Renda média: {media:.2f}\")\n",
        "print(f\"Renda máxima: {maximo:.2f}\")\n",
        "print(f\"Desvio padrão: {desvio_padrao:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "hL5tO1RtthyO",
        "outputId": "269e0f40-53c5-49c2-9c90-764bb8d304d2"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Renda mínima: 9.00\n",
            "Renda média: 77.53\n",
            "Renda máxima: 1668.00\n",
            "Desvio padrão: 107.04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Análise Explicativa do Histograma de Distribuição de Renda dos Clientes**\n",
        "\n",
        "A distribuição da renda dos clientes concentra-se na faixa inicial (abaixo de $200) e a longa cauda à direita sugere  distribuição assimétrica, onde poucos clientes apresentam rendas muito superiores à média, ou seja, há um contraste significativo entre a maioria dos clientes e aqueles com rendas mais altas.\n",
        "\n",
        "A média (77.53) e o desvio padrão (107.04) indicam dispersão relativamente ampla dos dados. Como o desvio padrão é maior do que a média, a presença de rendas muito elevadas que puxam a média para cima, mas as rendas elevadas representam um número pequeno de clientes.\n",
        "\n",
        "Com um máximo de $1668, muito acima da média, evidencia-se os *outliers*, que aumentam a amplitude da distribuição."
      ],
      "metadata": {
        "id": "u3McBfsty5SF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotar histograma de distribuição de idade dos clientes\n",
        "plt.figure(figsize=(8, 6))  # Ajustar o tamanho da figura\n",
        "df.hist(column='age', bins='auto', color='skyblue', edgecolor='black')\n",
        "plt.title('Histograma da Idade')\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('Frequência')\n",
        "plt.xlim(0, df['age'].max())\n",
        "plt.grid(True)  # Adicionar grade ao gráfico\n",
        "\n",
        "# Calcular mínimo, máximo, média e desvio padrão\n",
        "minimo = int(df['age'].min())\n",
        "maximo = int(df['age'].max())\n",
        "media = int(df['age'].mean())\n",
        "desvio_padrao = df['age'].std()\n",
        "\n",
        "# Exibir média e desvio padrão no gráfico\n",
        "plt.text(0.6, 0.95, f'Idade Mínima: {minimo}', transform=plt.gca().transAxes)\n",
        "plt.text(0.6, 0.9, f'Idade Média: {media:.1f}', transform=plt.gca().transAxes)\n",
        "plt.text(0.6, 0.85, f'Idade Máxima: {maximo}', transform=plt.gca().transAxes)\n",
        "plt.text(0.6, 0.8, f'Desvio Padrão: {desvio_padrao:.2f}', transform=plt.gca().transAxes)\n",
        "\n",
        "# Adicionar linha vertical para a média\n",
        "plt.axvline(media, color='red', linestyle='dashed', linewidth=1)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# Imprimir os valores de mínimo, média, máximo e desvio padrão\n",
        "print(f\"Idade mínima: {minimo:.2f}\")\n",
        "print(f\"Idade média: {media:.2f}\")\n",
        "print(f\"Idade máxima: {maximo:.2f}\")\n",
        "print(f\"Desvio padrão: {desvio_padrao:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "LFZn_bOcwKu1",
        "outputId": "fa9a671a-3e4c-4c6a-b0b8-bb074f435625"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Idade mínima: 18.00\n",
            "Idade média: 41.00\n",
            "Idade máxima: 77.00\n",
            "Desvio padrão: 12.56\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Análise Explicativa do Histograma da Distribuição de Idade**\n",
        "\n",
        "O histograma de distribuição de idade apresenta predominância de clientes na faixa etária de adultos jovens a meia-idade, dos 30 aos 50 anos, seguindo um padrão aproximadamente simétrico com  leve concentração em torno da média, o que pode refletir o perfil demográfico da base de clientes para um público predominantemente de \"meia-idade\" - poucas ocorrências em idades mais jovens e mais avançadas. Ou seja, clientela em plena atividade econômica com necessidade de utilização de smartphones.  .\n",
        "\n",
        "A média de idade é 41 anos, enquanto o desvio padrão é 12.56, havendo dispersão moderada em torno da média. A idade mínima é 18 anos, indicando que a empresa atende clientes desde o início da idade adulta. A idade máxima é 77, portanto,há uma pequena parcela de clientes idosos.\n"
      ],
      "metadata": {
        "id": "4Q5Kyjtr0wnZ"
      }
    },
    {
      "source": [
        "# Distribuição Renda vs. Idade considerando o gênero\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(df['age'], df['income'], c=df['gender'], alpha=0.5)\n",
        "plt.title('Renda vs. Idade')\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('Renda')\n",
        "_ = plt.colorbar(label='Gender')"
      ],
      "cell_type": "code",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "wFp0Y0B0hX39",
        "outputId": "c752a277-6cdb-4a8a-bdc4-45cd89e49b2f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Análise Explicativa do Gráfico Renda vs. Idade com referência ao Gênero**\n",
        "\n",
        "O gráfico de dispersão mostra a relação entre idade e renda dos clientes, com a cor dos pontos representando o gênero (onde 0 e 1 indicam diferentes categorias de gênero).\n",
        "\n",
        "A maioria dos clientes está concentrada em rendas abaixo de 250, independentemente da idade, há uma leve tendência de aumento da renda com o avanço da idade. Observa-se uma mistura da variável gênero em todas as faixas etárias e níveis de renda, ou seja, a variável de gênero parece não ter  influência sobre a distribuição de renda e idade."
      ],
      "metadata": {
        "id": "XxOvQvaCDhJ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calcular Mediana e IQR para cada categoria\n",
        "categorias = df['custcat'].unique()\n",
        "medianas = [df['income'][df['custcat'] == i].median() for i in categorias]\n",
        "iqr_values = [df['income'][df['custcat'] == i].quantile(0.75) - df['income'][df['custcat'] == i].quantile(0.25) for i in categorias]\n",
        "\n",
        "# Criar o box plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.boxplot([df['income'][df['custcat'] == i] for i in categorias], labels=categorias)\n",
        "plt.xlabel('Categorias de Clientes')\n",
        "plt.ylabel('Renda')\n",
        "plt.title('Box Plot da Distribuição de Renda por Categorias de Clientes')\n",
        "\n",
        "# Imprimir no Box plot Mediana e Intervalo Interquartil (IQR)\n",
        "for i, (mediana, iqr) in enumerate(zip(medianas, iqr_values), start=1):\n",
        "    plt.text(i, mediana, f'Mediana: {mediana:.1f}\\nIQR: {iqr:.1f}',\n",
        "             ha='center', va='bottom', fontsize=9, color='blue')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Num7YGLXJG31",
        "outputId": "0310c1c5-03e2-4aa5-cacf-dc38c88142c4"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Análise Explicativa do Gráfico Box Plot da Distribuição de Renda por Categorias de Clientes**\n",
        "\n",
        "O gráfico de box plot apresenta a distribuição da renda dos clientes segmentada por quatro categorias/planos da operadora, em que é possível observar a dispersão, a mediana, o intervalo interquartil (IQR) e a presença de *outliers* em cada categoria.\n",
        "\n",
        "As quatro categorias de clientes exibem uma distribuição de renda semelhante, com a maioria das rendas concentrada em valores baixos. Em cada box plot, a mediana está próxima da base da escala de renda (abaixo de 100), indicando que as rendas na maioria dos casos são baixas, independentemente da categoria.\n",
        "\n",
        "O box plot revela que, apesar da segmentação em quatro categorias de clientes, não há diferenças significativas na distribuição de renda entre elas, indicando que o nível de renda não é necessariamente o fator decisivo para escolha dos planos (1) Serviço Básico, (2) E-Serviço, (3) Serviço Plus ou (4) Serviço Total. Ainda assim, observa-se que a mediana da categoria 4 (Serviço Total) é ligeiramente maior que as demais, indicando que clientes com renda um pouco maior tendem a optar por planos de telefonia mais completos.\n",
        "\n",
        "Todas as categorias apresentam uma maioria de rendas baixas, com uma pequena proporção de outliers em faixas de renda muito mais altas. Uma conclusão parcial é a de que a segmentação em categorias não está fortemente relacionada à renda dos clientes, mas que existem outros fatores além da renda a influenciar na decisão de escolha do respectivo plano, tais como, faixa etária, hábitos de consumo de Internet e necessidades de comunicação.\n",
        "\n",
        "Cada categoria apresenta um número significativo de valores atípicos (outliers), indicados por pontos fora do limite superior. Esses valores representam clientes com rendas muito acima da média do grupo.\n",
        "Alguns outliers estão acima de 500, e há casos excepcionais que ultrapassam 1000 em cada categoria, sugerindo a presença de rendas significativamente elevadas em todas as categorias, embora eles representem uma pequena proporção dos dados."
      ],
      "metadata": {
        "id": "ljvl1u7TFmUl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Definição do Conjunto de Variáveis**"
      ],
      "metadata": {
        "id": "NINkjuW624_t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Imprime os nomes das colunas do DataFrame 'df'\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JS39rovrlhU5",
        "outputId": "fb876726-c09d-42d3-aa2a-22bf95b02b06"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['region', 'tenure', 'age', 'marital', 'address', 'income', 'ed',\n",
              "       'employ', 'retire', 'gender', 'reside', 'custcat'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Usa a biblioteca scikit-learn para converter o quadro de dados Pandas em um array Numpy\n",
        "# Seleciona as colunas especificadas do DataFrame para conjuntos de recursos, X\n",
        "\n",
        "X = df[['region', 'tenure','age', 'marital', 'address', 'income', 'ed', 'employ','retire', 'gender', 'reside']] .values  #.astype(float)\n",
        "# Extrai os valores das colunas selecionadas como um array NumPy.\n",
        "X[0:5] #Imprime as primeiras 5 linhas do array X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VgWjrInmlpUc",
        "outputId": "c4f3adc5-3d43-4d7e-c935-aff68b77466c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  2.,  13.,  44.,   1.,   9.,  64.,   4.,   5.,   0.,   0.,   2.],\n",
              "       [  3.,  11.,  33.,   1.,   7., 136.,   5.,   5.,   0.,   0.,   6.],\n",
              "       [  3.,  68.,  52.,   1.,  24., 116.,   1.,  29.,   0.,   1.,   2.],\n",
              "       [  2.,  33.,  33.,   0.,  12.,  33.,   2.,   0.,   0.,   1.,   1.],\n",
              "       [  2.,  23.,  30.,   1.,   9.,  30.,   1.,   2.,   0.,   0.,   4.]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cria o array NumPy 'y' com os valores da coluna 'custcat', variável alvo (rótulo) no conjunto de dados.\n",
        "y = df['custcat'].values\n",
        "y[0:5] #Imprime os primeiros 5 valores do array 'y'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UF0jpJ7amB_B",
        "outputId": "c09fd139-c06b-459c-b162-6dda33df593d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 4, 3, 1, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Padronizar as features usando StandardScaler\n",
        "X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))\n",
        "X[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dVvuR4_vmKx9",
        "outputId": "8a1f6078-6f9e-4b10-a874-20ab29de9484"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.02696767, -1.055125  ,  0.18450456,  1.0100505 , -0.25303431,\n",
              "        -0.12650641,  1.0877526 , -0.5941226 , -0.22207644, -1.03459817,\n",
              "        -0.23065004],\n",
              "       [ 1.19883553, -1.14880563, -0.69181243,  1.0100505 , -0.4514148 ,\n",
              "         0.54644972,  1.9062271 , -0.5941226 , -0.22207644, -1.03459817,\n",
              "         2.55666158],\n",
              "       [ 1.19883553,  1.52109247,  0.82182601,  1.0100505 ,  1.23481934,\n",
              "         0.35951747, -1.36767088,  1.78752803, -0.22207644,  0.96655883,\n",
              "        -0.23065004],\n",
              "       [-0.02696767, -0.11831864, -0.69181243, -0.9900495 ,  0.04453642,\n",
              "        -0.41625141, -0.54919639, -1.09029981, -0.22207644,  0.96655883,\n",
              "        -0.92747794],\n",
              "       [-0.02696767, -0.58672182, -0.93080797,  1.0100505 , -0.25303431,\n",
              "        -0.44429125, -1.36767088, -0.89182893, -0.22207644, -1.03459817,\n",
              "         1.16300577]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Padronização dos Dados/Variáveis**\n",
        "\n",
        "Em modelagens em Machine Learning a padronização dos dados é uma técnica importante para garantir que todas as features tenham a mesma escala. A padronização de dados fornece média zero e variância unitária aos dados, é uma boa prática, especialmente para algoritmos como k-NN, que se baseia na distância dos pontos de dados, permitindo-se melhorar o desempenho do algoritmo.\n",
        "\n",
        "1.   `preprocessing.StandardScaler()`: Cria um objeto StandardScaler da biblioteca scikit-learn, que é usado para padronizar os dados, ou seja, transformar os dados para que tenham média zero e desvio padrão igual a 1.\n",
        "2. `.fit(X)`: Ajusta o objeto StandardScaler aos dados do array X, serve para calcular a média e o desvio padrão de cada feature.\n",
        "3. `.transform(X.astype(float))`: Transforma em `float` os dados do array X usando a média e o desvio padrão calculados na etapa anterior."
      ],
      "metadata": {
        "id": "IfQVgY1x6Itt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def split_data(X, y, test_size=0.2, random_state=4):\n",
        "    \"\"\"\n",
        "    Divide um conjunto de dados em conjuntos de treinamento e teste.\n",
        "\n",
        "    Args:\n",
        "        X: DataFrame ou array NumPy contendo as features.\n",
        "        y: Série Pandas ou array NumPy contendo os rótulos.\n",
        "        test_size: Proporção dos dados a ser usada para teste (padrão: 0.2).\n",
        "        random_state: Semente para o gerador de números aleatórios (padrão: 4).\n",
        "\n",
        "    Returns:\n",
        "        Uma tupla contendo quatro objetos: X_train, X_test, y_train, y_test.\n",
        "    \"\"\"\n",
        "    return train_test_split(X, y, test_size=test_size, random_state=random_state)\n",
        "\n",
        "# Chamada da função para dividir os dados\n",
        "X_train, X_test, y_train, y_test = split_data(X, y)\n",
        "\n",
        "# Exibir as dimensões dos conjuntos de treino e teste\n",
        "print('Formato do conjunto de treinamento:', X_train.shape, y_train.shape)\n",
        "print('Formato do conjunto de teste:', X_test.shape, y_test.shape)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-TUxhPYMB3R",
        "outputId": "83d293ae-dac1-49de-9e45-96caad21df1d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Formato do conjunto de treinamento: (800, 11) (800,)\n",
            "Formato do conjunto de teste: (200, 11) (200,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nesta etapa os dados foram divididos em subconjuntos aleatórios de treino (80%) e teste (20%), a divisão permite que o modelo seja treinado num conjunto de dados e testado noutro, simulando-se como se comportaria com entrada de novos dados. A utilização do `random_state` garante que a divisão seja consistente em diferentes execuções do código."
      ],
      "metadata": {
        "id": "EE3bLl32NC43"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Classificação usando o K-Nearest Neighbor (K-NN)**\n",
        "\n",
        "O K-Nearest Neighbor (K-NN) classifica os pontos de dados com base na classe dos seus \"`k`\" vizinhos mais próximos, o parâmetro define quantos vizinhos são considerados na classificação.\n",
        "\n",
        "### **Funcionamento do 𝐾-NN**\n",
        "\n",
        "O KNN segue os seguintes passos:\n",
        "\n",
        "1. Escolher um valor para 𝐾, que representa o número de vizinhos mais próximos a considerar.\n",
        "2. Calcular a distância entre o novo ponto (não rotulado) e todos os pontos no conjunto de dados.\n",
        "3. Identificar os 𝐾 vizinhos mais próximos.\n",
        "4. Classificar o novo ponto com base na votação majoritária dos rótulos desses\n",
        "𝐾 vizinhos.\n",
        "\n",
        "### **Importância de Escolher o Valor de 𝐾**\n",
        "\n",
        "* **𝐾 muito baixo** (e.g., 𝐾=1): O modelo se torna sensível a *outliers* e ruídos nos dados. Por exemplo, ao usar apenas o vizinho mais próximo, um ponto atípico pode levar a uma classificação incorreta.\n",
        "* **𝐾 muito alto** (e.g., 𝐾=20): O modelo se torna excessivamente generalizado, perdendo detalhes importantes e podendo agrupar erroneamente dados de diferentes classes.\n",
        "\n",
        "A escolha ideal de 𝐾 é feita utilizando um conjunto de validação, onde diferentes valores de 𝐾 são testados para identificar aquele que maximiza a precisão do modelo.\n",
        "\n",
        "### **Observações importantes**\n",
        "\n",
        "* **Normalização dos Dados**: para evitar que características com escalas diferentes (e.g., idade e renda) dominem o cálculo da distância, é essencial normalizar os dados antes de aplicar o KNN.\n",
        "* **Métricas de Distância**: A distância Euclidiana é comumente usada, mas outras métricas correlatas, como [Manhattan ou Minkowski](https://github.com/SampMark/Computational-Thinking/blob/main/Minkowski_Manhattan_and_Euclidean_distance_metrics.ipynb), podem ser aplicadas dependendo da natureza dos dados.\n",
        "* **Domínio do Problema**: A escolha das características e da métrica de distância deve levar em conta o domínio do problema, garantindo que os dados sejam representativos e relevantes."
      ],
      "metadata": {
        "id": "pZoqKs2mp4wr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importa a classe KNeighborsClassifier do módulo neighbors da biblioteca sklearn para implementar o algoritmo KNN\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "5ObSQljAmzJn"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k = 4 # Define o número de vizinhos mais próximos (k)\n",
        "# Treina modelo para k = 4\n",
        "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
        "neigh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "Kfs0oVBrm7k6",
        "outputId": "89aa8231-97f7-479f-bea6-3d942c1e6de3"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=4)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=4)</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 fitted 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 fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=4)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Usa o modelo treinado (neigh) para prever os rótulos (yhat) para o conjunto de teste (X_test)\n",
        "yhat = neigh.predict(X_test)\n",
        "yhat[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2CsEmq5znBFS",
        "outputId": "1705fc62-4634-480d-b0d8-043307e02971"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 3, 2, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nesta etapa, utiliza-se o algoritmo KNN para classificação e, avalia-se sua acurácia (nível de exatidão dos resultados obtidos), supondo-se diferentes valores de `k`. Para tal, foi criado o objeto `KNeighborsClassifier` para definição do número de vizinhos `(n_neighbors = k)`. O método `fit()` ajusta o modelo K-NN aos dados de treinamento (`X_train` para as features e `y_train` para os rótulos). Para o cálculo da acurácia do modelo tanto para conjunto de treinamento como de teste, utilizando-se a função `accuracy_score()`, conforme se segue."
      ],
      "metadata": {
        "id": "Kh0yes94QoYG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importa o módulo metrics da biblioteca sklearn para calcular a acurácia\n",
        "from sklearn import metrics\n",
        "# Imprime os valores de acurácia para os conjuntos de treinamento e teste\n",
        "yhat = neigh.predict(X_test)\n",
        "yhat[0:5]\n",
        "print(\"Acurácia do conjunto de treino: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
        "print(\"Acurácia do conjunto de teste: \", metrics.accuracy_score(y_test, yhat))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XtSwBLXKYu1Y",
        "outputId": "9f75b12c-81e0-4033-e410-157c580638b0"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia do conjunto de treino:  0.5475\n",
            "Acurácia do conjunto de teste:  0.32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Treinar modelo para k = 6, nova previsão\n",
        "k = 6\n",
        "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
        "neigh\n",
        "yhat = neigh.predict(X_test)\n",
        "yhat[0:5]\n",
        "print(\"Acurácia do conjunto de treino: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
        "print(\"Acurácia do conjunto de teste: \", metrics.accuracy_score(y_test, yhat))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkFDhY8FnegN",
        "outputId": "51cf21de-3bf3-4294-e085-f2732dba8c39"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia do conjunto de treino:  0.51625\n",
            "Acurácia do conjunto de teste:  0.31\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Análise da Acurácia**\n",
        "\n",
        " A análise da acurácia para diferentes valores de k é crucial para otimizar o modelo e garantir que ele generalize bem para novos dados. Para encontrar o melhor valor de k, é necessário testar diferentes valores e comparar as acurácias no conjunto de teste. Técnicas como validação cruzada podem ajudar a escolher o valor \"ideal\" de k. Vamos analisar os resultados obtidos:\n",
        "\n",
        "*   Para `k=4`, a acurácia no conjunto de treinamento é 0.5475 (54.75%) e no\n",
        "conjunto de teste é 0.32 (32%).\n",
        "\n",
        "* Para `k=6`, a acurácia no conjunto de treinamento é 0.51625 (51.625%) e no conjunto de teste é 0.31 (31%).\n",
        "\n",
        "Como se observa, a acurácia no conjunto de teste é significativamente menor do que no conjunto de treinamento para ambos os valores de `k` escolhidos. Isto significa que o modelo pode estar se ajustando \"muito bem\" aos dados de treinamento (todavia, isto não é legal, o modelo está sofrendo *overfitting*), o que implica que não generalizará bem para novos dados.\n",
        "\n",
        "Como as configurações de `k` utilizadas obteve-se baixa acurácia no conjunto de teste, portanto, o modelo KNN não se mostra  eficaz para classificar esses dados. É possível que outros algoritmos de classificação ou um pré-processamento diferente dos dados produzam melhores resultados."
      ],
      "metadata": {
        "id": "-GAf9kzzUb6Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Como encontrar o valor ideal de k que maximize a performance do modelo K-Nearest Neighbors (KNN)?**\n",
        "\n",
        "Para tornar dinâmico o processo de descoberta do parâmetro `k` em que se  obtém melhor acurácia. O código a seguir cria um loop que testará diferentes valores para o parâmetro `k` (neste caso, no intervalo de 1 a 9). Assim, avalia-se rapidamente a acurácia do modelo para cada valor obtido."
      ],
      "metadata": {
        "id": "Jr5XkaTtZsly"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Teste de um loop para diferentes valores do parâmetro k (1 a 9) de modo a avaliar a acurácia do modelo para cada valor de k\n",
        "Ks = 10\n",
        "mean_acc = np.zeros((Ks-1))\n",
        "std_acc = np.zeros((Ks-1))\n",
        "\n",
        "# Loop para testar diferentes valores de k\n",
        "for n in range(1,Ks):\n",
        "\n",
        "    #Treinar Modelo e Prever\n",
        "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
        "    yhat=neigh.predict(X_test)\n",
        "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
        "\n",
        "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
        "\n",
        "mean_acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_ig_xQdo1Ji",
        "outputId": "42905e07-b615-4cdb-efc7-2e2014a510d5"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.3  , 0.29 , 0.315, 0.32 , 0.315, 0.31 , 0.335, 0.325, 0.34 ])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O loop ao itera sobre os valores de `n` de 1 a 9 (usando `range(1,Ks)`). Para cada valor, o código acima  treina e testa o modelo KNN e calcula a acurácia média. O resultado final é um array contendo a acurácia para cada k.\n",
        "\n",
        "Observando o array `mean_acc`, identifica-se qual valor de `k` resultou em maior acurácia no conjunto de teste. No resultado, o array `mean_acc` calculado encontrou o melhor valor (0.34), que corresponde à maior acurácia para `k = 9`.\n",
        "\n",
        "O gráfico a seguir nos permite visualizar e nos ajuda a entender como a performance do modelo KNN varia conforme a escolha do parâmetro k"
      ],
      "metadata": {
        "id": "7oS5QcI5ceGt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1,Ks),mean_acc,'g')\n",
        "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
        "plt.fill_between(range(1,Ks),mean_acc - 3 * std_acc,mean_acc + 3 * std_acc, alpha=0.10,color=\"green\")\n",
        "plt.legend(('Acurácia ', '+/- 1xstd','+/- 3xstd'))\n",
        "plt.ylabel('Acurácia ')\n",
        "plt.xlabel('Número de Vizinhos (K)')\n",
        "plt.title('Performance do modelo KNN conforme variação do parâmetro k')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "--aYK5l3pARX",
        "outputId": "5e2ae64b-b959-4169-92be-1003bba2ec5e"
      },
      "execution_count": 41,
      "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": [
        "print( \"A melhor acurácia foi\", mean_acc.max(), \"para k =\", mean_acc.argmax()+1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9qV9ygSLpHtF",
        "outputId": "47f60735-edb5-47bf-9e5f-750b3e82f13c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A melhor acurácia foi 0.34 para k = 9\n"
          ]
        }
      ]
    }
  ]
}