EBAC_Projeto_de_Parceria.ipynb

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      "source": [
        "<img src=\"https://raw.githubusercontent.com/andre-marcos-perez/ebac-course-utils/main/media/logo/newebac_logo_black_half.png\" alt=\"ebac-logo\">\n",
        "\n",
        "---\n",
        "\n",
        "# **Projeto de Parceria** | Falta de Igualdade na Educação pelo Mundo\n",
        "Aluna: [Maria Luiza Cordeiro](https://www.linkedin.com/in/malu-analise-de-dados/)\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9jDtUbDOE1-"
      },
      "source": [
        "# **Tópicos**\n",
        "\n",
        "<ol type=\"1\">\n",
        "  <li>Coleta de Dados;</li>\n",
        "  <li>Modelagem;</li>\n",
        "  <li>Conclusões.</li>\n",
        "</ol>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmoHgt-lwkpD"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Coleta de Dados"
      ],
      "metadata": {
        "id": "Bt5OVW_h5wE0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Contexto**"
      ],
      "metadata": {
        "id": "U-fAlYAVNc3N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A educação é algo imprescindível para o desenvolvimento humano, porém, mesmo com essa importância, muitas regiões do mundo enfrentam problemas para garantir uma igualdade no acesso ao ensino."
      ],
      "metadata": {
        "id": "AjANcg-nNmek"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nesse projeto, foi utilizada a base de dados [Inequality in Education Around the World](https://www.kaggle.com/datasets/iamsouravbanerjee/inequality-in-education-around-the-world), disponível no Kaggle. Nela, encontra-se o nível de desigualdade na educação entre os anos de 2010 e 2021 por país, bem como o seu Índice de Desenvolvimento Humano (IDH) em 2021 e outras informações relacionadas ao desenvolvimento dos países no mundo."
      ],
      "metadata": {
        "id": "mwOYN863PBas"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -q \"https://raw.githubusercontent.com/malucor/Projeto_Educacao_ML/main/Inequality%20in%20Education.csv\" -O education.csv"
      ],
      "metadata": {
        "id": "5uPwrzfPSArd"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Essa atividade terá como foco a relação do rank do IDH com a evolução do índice relacionado a falta de igualdade da educação por ano. O modelo de aprendizagem de máquina, no final, terá, como objetivo, prever a posição que o país está no rank do IDH, por meio do seu índice de destribuição da educação."
      ],
      "metadata": {
        "id": "uLK35B8ba9YZ"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Manipulação dos Dados**"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "HThLMGdQUtJy"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para começar, é necessário tratar os dados no arquivo original, que está no formato `.csv`."
      ],
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        "id": "nzyoNmx5UwhJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Conversão do arquivo de .csv para DataFrame\n",
        "\n",
        "education_df = pd.read_csv('education.csv')\n",
        "education_df.head()"
      ],
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              "  ISO3               Country Human Development Groups UNDP Developing Regions  \\\n",
              "0  AFG           Afghanistan                      Low                      SA   \n",
              "1  AGO                Angola                   Medium                     SSA   \n",
              "2  ALB               Albania                     High                     ECA   \n",
              "3  AND               Andorra                Very High                     NaN   \n",
              "4  ARE  United Arab Emirates                Very High                      AS   \n",
              "\n",
              "   HDI Rank (2021)  Inequality in Education (2010)  \\\n",
              "0            180.0                       42.809000   \n",
              "1            148.0                             NaN   \n",
              "2             67.0                       11.900000   \n",
              "3             40.0                       15.160302   \n",
              "4             26.0                             NaN   \n",
              "\n",
              "   Inequality in Education (2011)  Inequality in Education (2012)  \\\n",
              "0                       44.823380                       44.823380   \n",
              "1                             NaN                             NaN   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       15.160302                       15.160302   \n",
              "4                             NaN                             NaN   \n",
              "\n",
              "   Inequality in Education (2013)  Inequality in Education (2014)  \\\n",
              "0                       44.823380                       44.823380   \n",
              "1                             NaN                             NaN   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       15.160302                        9.965681   \n",
              "4                             NaN                             NaN   \n",
              "\n",
              "   Inequality in Education (2015)  Inequality in Education (2016)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       10.083815                       10.008154   \n",
              "4                             NaN                       18.241437   \n",
              "\n",
              "   Inequality in Education (2017)  Inequality in Education (2018)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       12.333440                       12.333440   \n",
              "3                       10.008154                       10.008154   \n",
              "4                       14.475335                       12.634355   \n",
              "\n",
              "   Inequality in Education (2019)  Inequality in Education (2020)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       12.333440                       12.333440   \n",
              "3                       10.008154                       10.008154   \n",
              "4                       12.634355                       12.634355   \n",
              "\n",
              "   Inequality in Education (2021)  \n",
              "0                       45.365170  \n",
              "1                       34.171440  \n",
              "2                       12.333440  \n",
              "3                       10.008154  \n",
              "4                       12.634355  "
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remoção das colunas que não são o foco do projeto\n",
        "\n",
        "education = education_df.drop(columns=['ISO3', 'Country', 'Human Development Groups', 'UNDP Developing Regions'])\n",
        "education.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "1Srqb3SseIQQ",
        "outputId": "cc6b467a-0b38-418e-87d0-f518ddc79d3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   HDI Rank (2021)  Inequality in Education (2010)  \\\n",
              "0            180.0                       42.809000   \n",
              "1            148.0                             NaN   \n",
              "2             67.0                       11.900000   \n",
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              "4             26.0                             NaN   \n",
              "\n",
              "   Inequality in Education (2011)  Inequality in Education (2012)  \\\n",
              "0                       44.823380                       44.823380   \n",
              "1                             NaN                             NaN   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       15.160302                       15.160302   \n",
              "4                             NaN                             NaN   \n",
              "\n",
              "   Inequality in Education (2013)  Inequality in Education (2014)  \\\n",
              "0                       44.823380                       44.823380   \n",
              "1                             NaN                             NaN   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       15.160302                        9.965681   \n",
              "4                             NaN                             NaN   \n",
              "\n",
              "   Inequality in Education (2015)  Inequality in Education (2016)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       10.083815                       10.008154   \n",
              "4                             NaN                       18.241437   \n",
              "\n",
              "   Inequality in Education (2017)  Inequality in Education (2018)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       12.333440                       12.333440   \n",
              "3                       10.008154                       10.008154   \n",
              "4                       14.475335                       12.634355   \n",
              "\n",
              "   Inequality in Education (2019)  Inequality in Education (2020)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       12.333440                       12.333440   \n",
              "3                       10.008154                       10.008154   \n",
              "4                       12.634355                       12.634355   \n",
              "\n",
              "   Inequality in Education (2021)  \n",
              "0                       45.365170  \n",
              "1                       34.171440  \n",
              "2                       12.333440  \n",
              "3                       10.008154  \n",
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              "      <td>NaN</td>\n",
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              "                                                    [key], {});\n",
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              "  @keyframes spin {\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "</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",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "education.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ptEv3KW_wl46",
        "outputId": "349714d1-21a7-421c-bf8b-a79048498000"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 195 entries, 0 to 194\n",
            "Data columns (total 13 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   HDI Rank (2021)                 191 non-null    float64\n",
            " 1   Inequality in Education (2010)  137 non-null    float64\n",
            " 2   Inequality in Education (2011)  150 non-null    float64\n",
            " 3   Inequality in Education (2012)  157 non-null    float64\n",
            " 4   Inequality in Education (2013)  165 non-null    float64\n",
            " 5   Inequality in Education (2014)  168 non-null    float64\n",
            " 6   Inequality in Education (2015)  168 non-null    float64\n",
            " 7   Inequality in Education (2016)  168 non-null    float64\n",
            " 8   Inequality in Education (2017)  168 non-null    float64\n",
            " 9   Inequality in Education (2018)  172 non-null    float64\n",
            " 10  Inequality in Education (2019)  174 non-null    float64\n",
            " 11  Inequality in Education (2020)  176 non-null    float64\n",
            " 12  Inequality in Education (2021)  176 non-null    float64\n",
            "dtypes: float64(13)\n",
            "memory usage: 19.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Visualização**"
      ],
      "metadata": {
        "id": "P0k0U8pEUd4Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para se ter uma ideia da distribuição dos dados, os ranks de IDH serão comparados, em um gráfico, com o índice de desigualdade do ano que foi divulgado resultado do IDH, nesse caso, em 2021."
      ],
      "metadata": {
        "id": "ZvnlNGkrUiov"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with sns.axes_style('whitegrid'):\n",
        "\n",
        "  grafico = sns.scatterplot(x=education['HDI Rank (2021)'], y=education['Inequality in Education (2021)'])\n",
        "  grafico.set(title='Relação entre o rank de IDH e acesso à Educação em 2021', xlabel='HDI Rank', ylabel='Inequality in Education');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "vmWb-pQfdimO",
        "outputId": "aee77e1d-ebf7-47d3-8c33-47d33576c9f8"
      },
      "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": [
        "Como visto no gráfico, a medida que a posição no rank é pior (aumenta na linha $x$), a desigualdade na educação é maior (aumenta na linha $y$). Então, de acordo com essas informações e o visual do gráfico, percebe-se que há uma relação de proporcionalidade."
      ],
      "metadata": {
        "id": "stSPN2a5hAl_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Tratamento dos Dados**"
      ],
      "metadata": {
        "id": "6iDBVDfZw3hJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "É preciso tratar os dados para que eles estejam prontos para o machine learning."
      ],
      "metadata": {
        "id": "Ttr1aTjO26Op"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Dados Faltantes**"
      ],
      "metadata": {
        "id": "vq-_flSS5rbN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar se há valores nulos\n",
        "\n",
        "print(education.isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "emzFwe91fQpu",
        "outputId": "0ac5f106-6fd7-4c2a-e03c-a9f1ee6b5bc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HDI Rank (2021)                    4\n",
            "Inequality in Education (2010)    58\n",
            "Inequality in Education (2011)    45\n",
            "Inequality in Education (2012)    38\n",
            "Inequality in Education (2013)    30\n",
            "Inequality in Education (2014)    27\n",
            "Inequality in Education (2015)    27\n",
            "Inequality in Education (2016)    27\n",
            "Inequality in Education (2017)    27\n",
            "Inequality in Education (2018)    23\n",
            "Inequality in Education (2019)    21\n",
            "Inequality in Education (2020)    19\n",
            "Inequality in Education (2021)    19\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como há valores nulos, eles serão divididos em duas categorias:\n",
        "- Os que impedem de continuar com o Aprendizado de Máquina; nesse caso, toda a linha de dados será descartada (o IDH na base de dados que está sendo trabalhada)\n",
        "- Os que se podem inferir a partir de outros dados; nesse caso, será feita a média dos dados relacionados (os índices de desigualdade na base de dados que está sendo trabalhada)"
      ],
      "metadata": {
        "id": "_UfgNAz1fLjG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retirar os valores nulos da coluna de IDH\n",
        "\n",
        "education_clean_df = education.dropna(subset=['HDI Rank (2021)'])\n",
        "education_clean_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "nwhxF_e-godh",
        "outputId": "b9886d8c-a0b0-46f6-8966-d8e70a7b683d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   HDI Rank (2021)  Inequality in Education (2010)  \\\n",
              "0            180.0                       42.809000   \n",
              "1            148.0                             NaN   \n",
              "2             67.0                       11.900000   \n",
              "3             40.0                       15.160302   \n",
              "4             26.0                             NaN   \n",
              "\n",
              "   Inequality in Education (2011)  Inequality in Education (2012)  \\\n",
              "0                       44.823380                       44.823380   \n",
              "1                             NaN                             NaN   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       15.160302                       15.160302   \n",
              "4                             NaN                             NaN   \n",
              "\n",
              "   Inequality in Education (2013)  Inequality in Education (2014)  \\\n",
              "0                       44.823380                       44.823380   \n",
              "1                             NaN                             NaN   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       15.160302                        9.965681   \n",
              "4                             NaN                             NaN   \n",
              "\n",
              "   Inequality in Education (2015)  Inequality in Education (2016)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       10.083815                       10.008154   \n",
              "4                             NaN                       18.241437   \n",
              "\n",
              "   Inequality in Education (2017)  Inequality in Education (2018)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       12.333440                       12.333440   \n",
              "3                       10.008154                       10.008154   \n",
              "4                       14.475335                       12.634355   \n",
              "\n",
              "   Inequality in Education (2019)  Inequality in Education (2020)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       12.333440                       12.333440   \n",
              "3                       10.008154                       10.008154   \n",
              "4                       12.634355                       12.634355   \n",
              "\n",
              "   Inequality in Education (2021)  \n",
              "0                       45.365170  \n",
              "1                       34.171440  \n",
              "2                       12.333440  \n",
              "3                       10.008154  \n",
              "4                       12.634355  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HDI Rank (2021)</th>\n",
              "      <th>Inequality in Education (2010)</th>\n",
              "      <th>Inequality in Education (2011)</th>\n",
              "      <th>Inequality in Education (2012)</th>\n",
              "      <th>Inequality in Education (2013)</th>\n",
              "      <th>Inequality in Education (2014)</th>\n",
              "      <th>Inequality in Education (2015)</th>\n",
              "      <th>Inequality in Education (2016)</th>\n",
              "      <th>Inequality in Education (2017)</th>\n",
              "      <th>Inequality in Education (2018)</th>\n",
              "      <th>Inequality in Education (2019)</th>\n",
              "      <th>Inequality in Education (2020)</th>\n",
              "      <th>Inequality in Education (2021)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>180.0</td>\n",
              "      <td>42.809000</td>\n",
              "      <td>44.823380</td>\n",
              "      <td>44.823380</td>\n",
              "      <td>44.823380</td>\n",
              "      <td>44.823380</td>\n",
              "      <td>45.365170</td>\n",
              "      <td>45.365170</td>\n",
              "      <td>45.365170</td>\n",
              "      <td>45.365170</td>\n",
              "      <td>45.365170</td>\n",
              "      <td>45.365170</td>\n",
              "      <td>45.365170</td>\n",
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              "      <th>1</th>\n",
              "      <td>148.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.171440</td>\n",
              "      <td>34.171440</td>\n",
              "      <td>34.171440</td>\n",
              "      <td>34.171440</td>\n",
              "      <td>34.171440</td>\n",
              "      <td>34.171440</td>\n",
              "      <td>34.171440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>67.0</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>12.333440</td>\n",
              "      <td>12.333440</td>\n",
              "      <td>12.333440</td>\n",
              "      <td>12.333440</td>\n",
              "      <td>12.333440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40.0</td>\n",
              "      <td>15.160302</td>\n",
              "      <td>15.160302</td>\n",
              "      <td>15.160302</td>\n",
              "      <td>15.160302</td>\n",
              "      <td>9.965681</td>\n",
              "      <td>10.083815</td>\n",
              "      <td>10.008154</td>\n",
              "      <td>10.008154</td>\n",
              "      <td>10.008154</td>\n",
              "      <td>10.008154</td>\n",
              "      <td>10.008154</td>\n",
              "      <td>10.008154</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>26.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.241437</td>\n",
              "      <td>14.475335</td>\n",
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              "      <td>12.634355</td>\n",
              "      <td>12.634355</td>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(education_clean_df.isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VA6zaRwvg-Jt",
        "outputId": "3d888208-1050-4a56-e16c-503c442a6802"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HDI Rank (2021)                    0\n",
            "Inequality in Education (2010)    55\n",
            "Inequality in Education (2011)    42\n",
            "Inequality in Education (2012)    34\n",
            "Inequality in Education (2013)    26\n",
            "Inequality in Education (2014)    23\n",
            "Inequality in Education (2015)    23\n",
            "Inequality in Education (2016)    23\n",
            "Inequality in Education (2017)    23\n",
            "Inequality in Education (2018)    19\n",
            "Inequality in Education (2019)    17\n",
            "Inequality in Education (2020)    15\n",
            "Inequality in Education (2021)    15\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Criar um dataframe que só tenha as linhas com algum valor nulo\n",
        "\n",
        "colunas_valores_nulos = education_clean_df.drop(columns=['HDI Rank (2021)'])\n",
        "valores_nulos = colunas_valores_nulos.isna().any(axis=1)\n",
        "\n",
        "valores_nulos = colunas_valores_nulos[valores_nulos]\n",
        "valores_nulos.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "drAMBfZVhGAe",
        "outputId": "0ce5b4e4-caa3-4eb1-fdff-259fcd0a0ac7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d1ad8581-6129-4ebc-a4cb-17ef8ed56899 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-d1ad8581-6129-4ebc-a4cb-17ef8ed56899');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      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-6e744d25-c165-455c-a203-7aa780802e1d 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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformar as linhas em colunas\n",
        "\n",
        "valores_nulos_transposed = valores_nulos.transpose()\n",
        "valores_nulos_transposed.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "OxZqC5_Bnj6P",
        "outputId": "ec4ebe8e-079d-4b3c-e5d2-78d22a756aba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "        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-a34700e0-db72-4bce-a02c-6d5243e506a8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a34700e0-db72-4bce-a02c-6d5243e506a8')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    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-a34700e0-db72-4bce-a02c-6d5243e506a8 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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fazer a média de cada coluna\n",
        "\n",
        "valores_nulos_transposed_mean = valores_nulos_transposed.agg('mean')\n",
        "valores_nulos_transposed_mean = pd.DataFrame([valores_nulos_transposed_mean], columns=valores_nulos_transposed.columns)\n",
        "valores_nulos_transposed_mean.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "rpj-ik_FqQuF",
        "outputId": "482a73e7-7de6-4e1d-eaf5-f6b05cbe7e56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        1          4    7          13        20         21       24   25   \\\n",
              "0  34.17144  13.875699  NaN  43.983064  3.406076  15.191584  5.54685  NaN   \n",
              "\n",
              "       30        34   ...       173       175      176        177       179  \\\n",
              "0  2.07139  32.69893  ...  3.747624  4.810606  9.71398  33.027292  10.16849   \n",
              "\n",
              "        185       186       189      190        192  \n",
              "0  0.661381  9.166052  17.49305  7.02996  16.621268  \n",
              "\n",
              "[1 rows x 65 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3931762-c8ba-4665-a49b-a18df4ca618b\" class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>4</th>\n",
              "      <th>7</th>\n",
              "      <th>13</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>30</th>\n",
              "      <th>34</th>\n",
              "      <th>...</th>\n",
              "      <th>173</th>\n",
              "      <th>175</th>\n",
              "      <th>176</th>\n",
              "      <th>177</th>\n",
              "      <th>179</th>\n",
              "      <th>185</th>\n",
              "      <th>186</th>\n",
              "      <th>189</th>\n",
              "      <th>190</th>\n",
              "      <th>192</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>34.17144</td>\n",
              "      <td>13.875699</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.983064</td>\n",
              "      <td>3.406076</td>\n",
              "      <td>15.191584</td>\n",
              "      <td>5.54685</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.07139</td>\n",
              "      <td>32.69893</td>\n",
              "      <td>...</td>\n",
              "      <td>3.747624</td>\n",
              "      <td>4.810606</td>\n",
              "      <td>9.71398</td>\n",
              "      <td>33.027292</td>\n",
              "      <td>10.16849</td>\n",
              "      <td>0.661381</td>\n",
              "      <td>9.166052</td>\n",
              "      <td>17.49305</td>\n",
              "      <td>7.02996</td>\n",
              "      <td>16.621268</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 65 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3931762-c8ba-4665-a49b-a18df4ca618b')\"\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-b3931762-c8ba-4665-a49b-a18df4ca618b 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-b3931762-c8ba-4665-a49b-a18df4ca618b');\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformar as colunas em linhas\n",
        "\n",
        "valores_nulos_mean = valores_nulos_transposed_mean.transpose()\n",
        "valores_nulos_mean.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ak5CTT_Hq0Fj",
        "outputId": "e51a5e98-4c46-4162-b0a8-04b004a42662"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            0\n",
              "1   34.171440\n",
              "4   13.875699\n",
              "7         NaN\n",
              "13  43.983064\n",
              "20   3.406076"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f377ab7-22a3-4383-bf5a-089b8ad4f93d\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>1</th>\n",
              "      <td>34.171440</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.875699</td>\n",
              "    </tr>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>43.983064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>3.406076</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5f377ab7-22a3-4383-bf5a-089b8ad4f93d 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-5f377ab7-22a3-4383-bf5a-089b8ad4f93d');\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",
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              "\n",
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              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\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",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } 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-17b0dcd1-8a9d-4b9b-aa14-2ed0b7df67f4 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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adicionar a coluna de média ao dataframe com os valores nulos\n",
        "\n",
        "valores_nulos['Mean'] = valores_nulos_mean\n",
        "valores_nulos = valores_nulos.dropna(subset=['Mean'])\n",
        "\n",
        "valores_nulos.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "pIcL1sugrWFj",
        "outputId": "8fce25e9-2331-4d9b-abfd-ae0814e14c8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-6dc5a358e97c>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  valores_nulos['Mean'] = valores_nulos_mean\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "    Inequality in Education (2010)  Inequality in Education (2011)  \\\n",
              "1                              NaN                             NaN   \n",
              "4                              NaN                             NaN   \n",
              "13                             NaN                        44.79226   \n",
              "20                             NaN                             NaN   \n",
              "21                             NaN                        15.85449   \n",
              "\n",
              "    Inequality in Education (2012)  Inequality in Education (2013)  \\\n",
              "1                              NaN                             NaN   \n",
              "4                              NaN                             NaN   \n",
              "13                        44.79226                        44.79226   \n",
              "20                         3.67781                         3.67781   \n",
              "21                        15.85449                        15.85449   \n",
              "\n",
              "    Inequality in Education (2014)  Inequality in Education (2015)  \\\n",
              "1                              NaN                        34.17144   \n",
              "4                              NaN                             NaN   \n",
              "13                        43.65639                        43.65639   \n",
              "20                         3.67781                         3.67781   \n",
              "21                        15.85449                        14.81278   \n",
              "\n",
              "    Inequality in Education (2016)  Inequality in Education (2017)  \\\n",
              "1                        34.171440                       34.171440   \n",
              "4                        18.241437                       14.475335   \n",
              "13                       43.656390                       43.693550   \n",
              "20                        3.677810                        3.677810   \n",
              "21                       14.812780                       14.812780   \n",
              "\n",
              "    Inequality in Education (2018)  Inequality in Education (2019)  \\\n",
              "1                        34.171440                       34.171440   \n",
              "4                        12.634355                       12.634355   \n",
              "13                       43.693550                       43.693550   \n",
              "20                        3.677810                        2.772030   \n",
              "21                       14.812780                       14.812780   \n",
              "\n",
              "    Inequality in Education (2020)  Inequality in Education (2021)       Mean  \n",
              "1                        34.171440                       34.171440  34.171440  \n",
              "4                        12.634355                       12.634355  13.875699  \n",
              "13                       43.693550                       43.693550  43.983064  \n",
              "20                        2.772030                        2.772030   3.406076  \n",
              "21                       14.812780                       14.812780  15.191584  "
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-b4adcf21-460f-40f8-8588-85b0a0dab4de 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-b4adcf21-460f-40f8-8588-85b0a0dab4de');\n",
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              "          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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
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              "\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      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-465cfb48-c46a-460c-ad81-bf076562bb2d 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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Juntar o dataframe que tem os valores nulos, com o que tem a média e depois remover os dados nulos e desnecessário\n",
        "\n",
        "education_merged = education_clean_df.merge(valores_nulos, how='left', left_index=True, right_index=True)\n",
        "\n",
        "education_final = education_merged.apply(lambda coluna: coluna.fillna(coluna['Mean']), axis=1)\n",
        "education_final = education_final.drop(columns=['Inequality in Education (2010)_y', 'Inequality in Education (2011)_y', 'Inequality in Education (2012)_y', 'Inequality in Education (2013)_y', 'Inequality in Education (2014)_y', 'Inequality in Education (2015)_y', 'Inequality in Education (2016)_y', 'Inequality in Education (2017)_y', 'Inequality in Education (2018)_y', 'Inequality in Education (2019)_y', 'Inequality in Education (2020)_y', 'Inequality in Education (2021)_y'])\n",
        "education_final = education_final.drop(columns=['Mean']).dropna(subset=['Inequality in Education (2021)_x'])\n",
        "education_final = education_final.rename(columns={'Inequality in Education (2010)_x': 'Inequality in Education (2010)',\n",
        "                                                  'Inequality in Education (2011)_x': 'Inequality in Education (2011)',\n",
        "                                                  'Inequality in Education (2012)_x': 'Inequality in Education (2012)',\n",
        "                                                  'Inequality in Education (2013)_x': 'Inequality in Education (2013)',\n",
        "                                                  'Inequality in Education (2014)_x': 'Inequality in Education (2014)',\n",
        "                                                  'Inequality in Education (2015)_x': 'Inequality in Education (2015)',\n",
        "                                                  'Inequality in Education (2016)_x': 'Inequality in Education (2016)',\n",
        "                                                  'Inequality in Education (2017)_x': 'Inequality in Education (2017)',\n",
        "                                                  'Inequality in Education (2018)_x': 'Inequality in Education (2018)',\n",
        "                                                  'Inequality in Education (2019)_x': 'Inequality in Education (2019)',\n",
        "                                                  'Inequality in Education (2020)_x': 'Inequality in Education (2020)',\n",
        "                                                  'Inequality in Education (2021)_x': 'Inequality in Education (2021)'})\n",
        "\n",
        "education_completa = education_final\n",
        "education_final.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "9Dsz0I_XWdpg",
        "outputId": "e1526d70-bffa-4249-e626-6a464be42ab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   HDI Rank (2021)  Inequality in Education (2010)  \\\n",
              "0            180.0                       42.809000   \n",
              "1            148.0                       34.171440   \n",
              "2             67.0                       11.900000   \n",
              "3             40.0                       15.160302   \n",
              "4             26.0                       13.875699   \n",
              "\n",
              "   Inequality in Education (2011)  Inequality in Education (2012)  \\\n",
              "0                       44.823380                       44.823380   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       15.160302                       15.160302   \n",
              "4                       13.875699                       13.875699   \n",
              "\n",
              "   Inequality in Education (2013)  Inequality in Education (2014)  \\\n",
              "0                       44.823380                       44.823380   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       15.160302                        9.965681   \n",
              "4                       13.875699                       13.875699   \n",
              "\n",
              "   Inequality in Education (2015)  Inequality in Education (2016)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       10.083815                       10.008154   \n",
              "4                       13.875699                       18.241437   \n",
              "\n",
              "   Inequality in Education (2017)  Inequality in Education (2018)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       12.333440                       12.333440   \n",
              "3                       10.008154                       10.008154   \n",
              "4                       14.475335                       12.634355   \n",
              "\n",
              "   Inequality in Education (2019)  Inequality in Education (2020)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       12.333440                       12.333440   \n",
              "3                       10.008154                       10.008154   \n",
              "4                       12.634355                       12.634355   \n",
              "\n",
              "   Inequality in Education (2021)  \n",
              "0                       45.365170  \n",
              "1                       34.171440  \n",
              "2                       12.333440  \n",
              "3                       10.008154  \n",
              "4                       12.634355  "
            ],
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              "      <th>HDI Rank (2021)</th>\n",
              "      <th>Inequality in Education (2010)</th>\n",
              "      <th>Inequality in Education (2011)</th>\n",
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              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
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              "      <td>12.333440</td>\n",
              "      <td>12.333440</td>\n",
              "      <td>12.333440</td>\n",
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              "      <th>3</th>\n",
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              "      <td>15.160302</td>\n",
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              "      <td>10.008154</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>26.0</td>\n",
              "      <td>13.875699</td>\n",
              "      <td>13.875699</td>\n",
              "      <td>13.875699</td>\n",
              "      <td>13.875699</td>\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(education_final.isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPj6BBcta-Gp",
        "outputId": "eeee95ec-503b-43c3-e57a-829e98fe40d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HDI Rank (2021)                   0\n",
            "Inequality in Education (2010)    0\n",
            "Inequality in Education (2011)    0\n",
            "Inequality in Education (2012)    0\n",
            "Inequality in Education (2013)    0\n",
            "Inequality in Education (2014)    0\n",
            "Inequality in Education (2015)    0\n",
            "Inequality in Education (2016)    0\n",
            "Inequality in Education (2017)    0\n",
            "Inequality in Education (2018)    0\n",
            "Inequality in Education (2019)    0\n",
            "Inequality in Education (2020)    0\n",
            "Inequality in Education (2021)    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelagem"
      ],
      "metadata": {
        "id": "BaB4orFL55Mb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Variáveis Numéricas**"
      ],
      "metadata": {
        "id": "7S8BP-81-4Xb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para um melhor Machine Learning, é necessário trabalhar com os dados em escalas reduzidas. <br>\n",
        "Para isso, é necessário aplicar o método de padronização ($x' = \\frac{x - x_{m}}{\\sigma}$)."
      ],
      "metadata": {
        "id": "FviHq41b-9CJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Padronização do IDH\n",
        "\n",
        "media_idh = education_final['HDI Rank (2021)'].mean()\n",
        "print(media_idh)\n",
        "\n",
        "desvio_padrao_idh = education_final['HDI Rank (2021)'].std()\n",
        "print(desvio_padrao_idh)\n",
        "\n",
        "education_final['HDI Rank (2021) - std'] = education_final['HDI Rank (2021)'].apply(lambda idh: (idh - media_idh) / desvio_padrao_idh)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YyF34tasAERa",
        "outputId": "e5c89b61-18cc-41f2-c6c5-cf21c4135fe9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "95.90055248618785\n",
            "55.806023666543744\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Padronização dos Índices de Desigualdade\n",
        "\n",
        "  # 2010\n",
        "media_10 = education_final['Inequality in Education (2010)'].mean()\n",
        "desvio_padrao_10 = education_final['Inequality in Education (2010)'].std()\n",
        "education_final['Inequality in Education (2010) - std'] = education_final['Inequality in Education (2010)'].apply(lambda id10: (id10 - media_10) / desvio_padrao_10)\n",
        "\n",
        "  # 2011\n",
        "media_11 = education_final['Inequality in Education (2011)'].mean()\n",
        "desvio_padrao_11 = education_final['Inequality in Education (2011)'].std()\n",
        "education_final['Inequality in Education (2011) - std'] = education_final['Inequality in Education (2011)'].apply(lambda id11: (id11 - media_11) / desvio_padrao_11)\n",
        "\n",
        "  # 2012\n",
        "media_12 = education_final['Inequality in Education (2012)'].mean()\n",
        "desvio_padrao_12 = education_final['Inequality in Education (2012)'].std()\n",
        "education_final['Inequality in Education (2012) - std'] = education_final['Inequality in Education (2012)'].apply(lambda id12: (id12 - media_12) / desvio_padrao_12)\n",
        "\n",
        "  # 2013\n",
        "media_13 = education_final['Inequality in Education (2013)'].mean()\n",
        "desvio_padrao_13 = education_final['Inequality in Education (2013)'].std()\n",
        "education_final['Inequality in Education (2013) - std'] = education_final['Inequality in Education (2013)'].apply(lambda id13: (id13 - media_13) / desvio_padrao_13)\n",
        "\n",
        "  # 2014\n",
        "media_14 = education_final['Inequality in Education (2014)'].mean()\n",
        "desvio_padrao_14 = education_final['Inequality in Education (2014)'].std()\n",
        "education_final['Inequality in Education (2014) - std'] = education_final['Inequality in Education (2014)'].apply(lambda id14: (id14 - media_14) / desvio_padrao_14)\n",
        "\n",
        "  # 2015\n",
        "media_15 = education_final['Inequality in Education (2015)'].mean()\n",
        "desvio_padrao_15 = education_final['Inequality in Education (2015)'].std()\n",
        "education_final['Inequality in Education (2015) - std'] = education_final['Inequality in Education (2015)'].apply(lambda id15: (id15 - media_15) / desvio_padrao_15)\n",
        "\n",
        "  # 2016\n",
        "media_16 = education_final['Inequality in Education (2016)'].mean()\n",
        "desvio_padrao_16 = education_final['Inequality in Education (2016)'].std()\n",
        "education_final['Inequality in Education (2016) - std'] = education_final['Inequality in Education (2016)'].apply(lambda id16: (id16 - media_16) / desvio_padrao_16)\n",
        "\n",
        "  # 2017\n",
        "media_17 = education_final['Inequality in Education (2017)'].mean()\n",
        "desvio_padrao_17 = education_final['Inequality in Education (2017)'].std()\n",
        "education_final['Inequality in Education (2017) - std'] = education_final['Inequality in Education (2017)'].apply(lambda id17: (id17 - media_17) / desvio_padrao_17)\n",
        "\n",
        "  # 2018\n",
        "media_18 = education_final['Inequality in Education (2018)'].mean()\n",
        "desvio_padrao_18 = education_final['Inequality in Education (2018)'].std()\n",
        "education_final['Inequality in Education (2018) - std'] = education_final['Inequality in Education (2018)'].apply(lambda id18: (id18 - media_18) / desvio_padrao_18)\n",
        "\n",
        "  # 2019\n",
        "media_19 = education_final['Inequality in Education (2019)'].mean()\n",
        "desvio_padrao_19 = education_final['Inequality in Education (2019)'].std()\n",
        "education_final['Inequality in Education (2019) - std'] = education_final['Inequality in Education (2019)'].apply(lambda id19: (id19 - media_19) / desvio_padrao_19)\n",
        "\n",
        "  # 2020\n",
        "media_20 = education_final['Inequality in Education (2020)'].mean()\n",
        "desvio_padrao_20 = education_final['Inequality in Education (2020)'].std()\n",
        "education_final['Inequality in Education (2020) - std'] = education_final['Inequality in Education (2020)'].apply(lambda id20: (id20 - media_20) / desvio_padrao_20)\n",
        "\n",
        "  # 2021\n",
        "media_21 = education_final['Inequality in Education (2021)'].mean()\n",
        "desvio_padrao_21 = education_final['Inequality in Education (2021)'].std()\n",
        "education_final['Inequality in Education (2021) - std'] = education_final['Inequality in Education (2021)'].apply(lambda id21: (id21 - media_21) / desvio_padrao_21)\n",
        "\n",
        "education_final.head()"
      ],
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        "id": "lMgEvCzMA2is",
        "outputId": "0a803cad-a56f-4ab7-bc5d-1c06ff061bf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   HDI Rank (2021)  Inequality in Education (2010)  \\\n",
              "0            180.0                       42.809000   \n",
              "1            148.0                       34.171440   \n",
              "2             67.0                       11.900000   \n",
              "3             40.0                       15.160302   \n",
              "4             26.0                       13.875699   \n",
              "\n",
              "   Inequality in Education (2011)  Inequality in Education (2012)  \\\n",
              "0                       44.823380                       44.823380   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       15.160302                       15.160302   \n",
              "4                       13.875699                       13.875699   \n",
              "\n",
              "   Inequality in Education (2013)  Inequality in Education (2014)  \\\n",
              "0                       44.823380                       44.823380   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       15.160302                        9.965681   \n",
              "4                       13.875699                       13.875699   \n",
              "\n",
              "   Inequality in Education (2015)  Inequality in Education (2016)  \\\n",
              "0                       45.365170                       45.365170   \n",
              "1                       34.171440                       34.171440   \n",
              "2                       11.900000                       11.900000   \n",
              "3                       10.083815                       10.008154   \n",
              "4                       13.875699                       18.241437   \n",
              "\n",
              "   Inequality in Education (2017)  Inequality in Education (2018)  ...  \\\n",
              "0                       45.365170                       45.365170  ...   \n",
              "1                       34.171440                       34.171440  ...   \n",
              "2                       12.333440                       12.333440  ...   \n",
              "3                       10.008154                       10.008154  ...   \n",
              "4                       14.475335                       12.634355  ...   \n",
              "\n",
              "   Inequality in Education (2012) - std  Inequality in Education (2013) - std  \\\n",
              "0                              1.753449                              1.776017   \n",
              "1                              1.017433                              1.035942   \n",
              "2                             -0.521455                             -0.511434   \n",
              "3                             -0.296178                             -0.284915   \n",
              "4                             -0.384940                             -0.374166   \n",
              "\n",
              "   Inequality in Education (2014) - std  Inequality in Education (2015) - std  \\\n",
              "0                              1.779577                              1.820592   \n",
              "1                              1.038627                              1.046603   \n",
              "2                             -0.510576                             -0.493353   \n",
              "3                             -0.645128                             -0.618933   \n",
              "4                             -0.373146                             -0.356744   \n",
              "\n",
              "   Inequality in Education (2016) - std  Inequality in Education (2017) - std  \\\n",
              "0                              1.844718                              1.847966   \n",
              "1                              1.066671                              1.074392   \n",
              "2                             -0.481357                             -0.434785   \n",
              "3                             -0.612855                             -0.595480   \n",
              "4                             -0.040581                             -0.286763   \n",
              "\n",
              "   Inequality in Education (2018) - std  Inequality in Education (2019) - std  \\\n",
              "0                              1.867245                              1.866333   \n",
              "1                              1.093994                              1.094499   \n",
              "2                             -0.414551                             -0.411282   \n",
              "3                             -0.575180                             -0.571616   \n",
              "4                             -0.393765                             -0.390534   \n",
              "\n",
              "   Inequality in Education (2020) - std  Inequality in Education (2021) - std  \n",
              "0                              1.864860                              1.864860  \n",
              "1                              1.096325                              1.096325  \n",
              "2                             -0.403020                             -0.403020  \n",
              "3                             -0.562668                             -0.562668  \n",
              "4                             -0.382360                             -0.382360  \n",
              "\n",
              "[5 rows x 26 columns]"
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              "      <th></th>\n",
              "      <th>HDI Rank (2021)</th>\n",
              "      <th>Inequality in Education (2010)</th>\n",
              "      <th>Inequality in Education (2011)</th>\n",
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              "      <th>Inequality in Education (2014) - std</th>\n",
              "      <th>Inequality in Education (2015) - std</th>\n",
              "      <th>Inequality in Education (2016) - std</th>\n",
              "      <th>Inequality in Education (2017) - std</th>\n",
              "      <th>Inequality in Education (2018) - std</th>\n",
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              "      <td>180.0</td>\n",
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              "      <td>44.823380</td>\n",
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              "      <td>1.844718</td>\n",
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              "      <td>1.866333</td>\n",
              "      <td>1.864860</td>\n",
              "      <td>1.864860</td>\n",
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              "      <th>1</th>\n",
              "      <td>148.0</td>\n",
              "      <td>34.171440</td>\n",
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              "      <td>34.171440</td>\n",
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              "      <td>34.171440</td>\n",
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              "      <td>1.017433</td>\n",
              "      <td>1.035942</td>\n",
              "      <td>1.038627</td>\n",
              "      <td>1.046603</td>\n",
              "      <td>1.066671</td>\n",
              "      <td>1.074392</td>\n",
              "      <td>1.093994</td>\n",
              "      <td>1.094499</td>\n",
              "      <td>1.096325</td>\n",
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              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>12.333440</td>\n",
              "      <td>12.333440</td>\n",
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              "      <td>-0.511434</td>\n",
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              "    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-86bb5a74-745e-4e15-a36b-c305f110edea 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": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "education_padrao = education_final.drop(columns=['HDI Rank (2021)', 'Inequality in Education (2010)', 'Inequality in Education (2011)', 'Inequality in Education (2012)', 'Inequality in Education (2013)', 'Inequality in Education (2014)', 'Inequality in Education (2015)', 'Inequality in Education (2016)', 'Inequality in Education (2017)', 'Inequality in Education (2018)', 'Inequality in Education (2019)', 'Inequality in Education (2020)', 'Inequality in Education (2021)'])\n",
        "education_padrao.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "KUsACs91D3Ka",
        "outputId": "7741e454-be7e-4b0b-f799-485b6094b458"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   HDI Rank (2021) - std  Inequality in Education (2010) - std  \\\n",
              "0               1.506996                              1.600717   \n",
              "1               0.933581                              1.000790   \n",
              "2              -0.517875                             -0.546087   \n",
              "3              -1.001694                             -0.319641   \n",
              "4              -1.252563                             -0.408864   \n",
              "\n",
              "   Inequality in Education (2011) - std  Inequality in Education (2012) - std  \\\n",
              "0                              1.754446                              1.753449   \n",
              "1                              1.013840                              1.017433   \n",
              "2                             -0.534645                             -0.521455   \n",
              "3                             -0.307963                             -0.296178   \n",
              "4                             -0.397279                             -0.384940   \n",
              "\n",
              "   Inequality in Education (2013) - std  Inequality in Education (2014) - std  \\\n",
              "0                              1.776017                              1.779577   \n",
              "1                              1.035942                              1.038627   \n",
              "2                             -0.511434                             -0.510576   \n",
              "3                             -0.284915                             -0.645128   \n",
              "4                             -0.374166                             -0.373146   \n",
              "\n",
              "   Inequality in Education (2015) - std  Inequality in Education (2016) - std  \\\n",
              "0                              1.820592                              1.844718   \n",
              "1                              1.046603                              1.066671   \n",
              "2                             -0.493353                             -0.481357   \n",
              "3                             -0.618933                             -0.612855   \n",
              "4                             -0.356744                             -0.040581   \n",
              "\n",
              "   Inequality in Education (2017) - std  Inequality in Education (2018) - std  \\\n",
              "0                              1.847966                              1.867245   \n",
              "1                              1.074392                              1.093994   \n",
              "2                             -0.434785                             -0.414551   \n",
              "3                             -0.595480                             -0.575180   \n",
              "4                             -0.286763                             -0.393765   \n",
              "\n",
              "   Inequality in Education (2019) - std  Inequality in Education (2020) - std  \\\n",
              "0                              1.866333                              1.864860   \n",
              "1                              1.094499                              1.096325   \n",
              "2                             -0.411282                             -0.403020   \n",
              "3                             -0.571616                             -0.562668   \n",
              "4                             -0.390534                             -0.382360   \n",
              "\n",
              "   Inequality in Education (2021) - std  \n",
              "0                              1.864860  \n",
              "1                              1.096325  \n",
              "2                             -0.403020  \n",
              "3                             -0.562668  \n",
              "4                             -0.382360  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-60cdb833-6842-4da2-9467-b458ab365447\" 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>HDI Rank (2021) - std</th>\n",
              "      <th>Inequality in Education (2010) - std</th>\n",
              "      <th>Inequality in Education (2011) - std</th>\n",
              "      <th>Inequality in Education (2012) - std</th>\n",
              "      <th>Inequality in Education (2013) - std</th>\n",
              "      <th>Inequality in Education (2014) - std</th>\n",
              "      <th>Inequality in Education (2015) - std</th>\n",
              "      <th>Inequality in Education (2016) - std</th>\n",
              "      <th>Inequality in Education (2017) - std</th>\n",
              "      <th>Inequality in Education (2018) - std</th>\n",
              "      <th>Inequality in Education (2019) - std</th>\n",
              "      <th>Inequality in Education (2020) - std</th>\n",
              "      <th>Inequality in Education (2021) - std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.506996</td>\n",
              "      <td>1.600717</td>\n",
              "      <td>1.754446</td>\n",
              "      <td>1.753449</td>\n",
              "      <td>1.776017</td>\n",
              "      <td>1.779577</td>\n",
              "      <td>1.820592</td>\n",
              "      <td>1.844718</td>\n",
              "      <td>1.847966</td>\n",
              "      <td>1.867245</td>\n",
              "      <td>1.866333</td>\n",
              "      <td>1.864860</td>\n",
              "      <td>1.864860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.933581</td>\n",
              "      <td>1.000790</td>\n",
              "      <td>1.013840</td>\n",
              "      <td>1.017433</td>\n",
              "      <td>1.035942</td>\n",
              "      <td>1.038627</td>\n",
              "      <td>1.046603</td>\n",
              "      <td>1.066671</td>\n",
              "      <td>1.074392</td>\n",
              "      <td>1.093994</td>\n",
              "      <td>1.094499</td>\n",
              "      <td>1.096325</td>\n",
              "      <td>1.096325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.517875</td>\n",
              "      <td>-0.546087</td>\n",
              "      <td>-0.534645</td>\n",
              "      <td>-0.521455</td>\n",
              "      <td>-0.511434</td>\n",
              "      <td>-0.510576</td>\n",
              "      <td>-0.493353</td>\n",
              "      <td>-0.481357</td>\n",
              "      <td>-0.434785</td>\n",
              "      <td>-0.414551</td>\n",
              "      <td>-0.411282</td>\n",
              "      <td>-0.403020</td>\n",
              "      <td>-0.403020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.001694</td>\n",
              "      <td>-0.319641</td>\n",
              "      <td>-0.307963</td>\n",
              "      <td>-0.296178</td>\n",
              "      <td>-0.284915</td>\n",
              "      <td>-0.645128</td>\n",
              "      <td>-0.618933</td>\n",
              "      <td>-0.612855</td>\n",
              "      <td>-0.595480</td>\n",
              "      <td>-0.575180</td>\n",
              "      <td>-0.571616</td>\n",
              "      <td>-0.562668</td>\n",
              "      <td>-0.562668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.252563</td>\n",
              "      <td>-0.408864</td>\n",
              "      <td>-0.397279</td>\n",
              "      <td>-0.384940</td>\n",
              "      <td>-0.374166</td>\n",
              "      <td>-0.373146</td>\n",
              "      <td>-0.356744</td>\n",
              "      <td>-0.040581</td>\n",
              "      <td>-0.286763</td>\n",
              "      <td>-0.393765</td>\n",
              "      <td>-0.390534</td>\n",
              "      <td>-0.382360</td>\n",
              "      <td>-0.382360</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-60cdb833-6842-4da2-9467-b458ab365447')\"\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-60cdb833-6842-4da2-9467-b458ab365447 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-60cdb833-6842-4da2-9467-b458ab365447');\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-b0d9f0bb-ff48-4bf8-a02b-2c25d510baf3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b0d9f0bb-ff48-4bf8-a02b-2c25d510baf3')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b0d9f0bb-ff48-4bf8-a02b-2c25d510baf3 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": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Treino / Teste**"
      ],
      "metadata": {
        "id": "HtzTYMxb30z2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para criar um modelo de aprendizagem de máquina funcional, é necessário separar a base de dados em dois conjuntos, um maior e outro menor. O maior será usado para treinar o modelo e o menor será usado para testá-lo."
      ],
      "metadata": {
        "id": "bX0w-xLw5isH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Separação da base de dados em 75% (treino) e 25% (teste)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "predictors_train, predictors_test, target_train, target_test = train_test_split(\n",
        "    education_padrao.drop(['HDI Rank (2021) - std'], axis=1),\n",
        "    education_padrao['HDI Rank (2021) - std'],\n",
        "    test_size=0.25,\n",
        "    random_state=123\n",
        ")\n",
        "\n",
        "print(f'predictors_train.shape = {predictors_train.shape}')\n",
        "print(f'predictors_test.shape = {predictors_test.shape}')\n",
        "print(f'target_train.shape = {target_train.shape}')\n",
        "print(f'target_test.shape = {target_test.shape}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fuh6HqlS4Jhg",
        "outputId": "f53ff970-a64e-4a7c-8dd5-371744bc47ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predictors_train.shape = (135, 12)\n",
            "predictors_test.shape = (46, 12)\n",
            "target_train.shape = (135,)\n",
            "target_test.shape = (46,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Treino**"
      ],
      "metadata": {
        "id": "jYcrZ9qt6YbD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "O treino serve para calcular os coeficiente, tanto linear quanto angular, da fórmula matemática que será usada no modelo de machine learning."
      ],
      "metadata": {
        "id": "4lX9Yy-Ircoj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "model = LinearRegression()\n",
        "\n",
        "model = model.fit(predictors_train, target_train)\n",
        "\n",
        "model.__dict__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNk4E5c56b4m",
        "outputId": "e1247d16-c40f-448c-fb03-9975bb6e04c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'fit_intercept': True,\n",
              " 'copy_X': True,\n",
              " 'n_jobs': None,\n",
              " 'positive': False,\n",
              " 'feature_names_in_': array(['Inequality in Education (2010) - std',\n",
              "        'Inequality in Education (2011) - std',\n",
              "        'Inequality in Education (2012) - std',\n",
              "        'Inequality in Education (2013) - std',\n",
              "        'Inequality in Education (2014) - std',\n",
              "        'Inequality in Education (2015) - std',\n",
              "        'Inequality in Education (2016) - std',\n",
              "        'Inequality in Education (2017) - std',\n",
              "        'Inequality in Education (2018) - std',\n",
              "        'Inequality in Education (2019) - std',\n",
              "        'Inequality in Education (2020) - std',\n",
              "        'Inequality in Education (2021) - std'], dtype=object),\n",
              " 'n_features_in_': 12,\n",
              " 'coef_': array([-0.43427931,  0.34166188,  0.14772933,  0.02866962, -0.56006311,\n",
              "        -0.42809524,  0.2927623 ,  0.74302404,  3.1463301 , -4.51198674,\n",
              "         0.9989692 ,  0.9989692 ]),\n",
              " 'rank_': 11,\n",
              " 'singular_': array([3.88664891e+01, 3.57011256e+00, 1.46497653e+00, 8.88581965e-01,\n",
              "        7.69241350e-01, 5.79583113e-01, 5.13725829e-01, 4.35992672e-01,\n",
              "        3.65467637e-01, 3.10681914e-01, 1.83494986e-01, 3.17949257e-15]),\n",
              " 'intercept_': -0.041588463285132146}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Coeficiente angular\n",
        "\n",
        "a = model.coef_\n",
        "print(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDLttCMR624P",
        "outputId": "f47ce473-e273-4929-f3c4-44a6fd44c44d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.43427931  0.34166188  0.14772933  0.02866962 -0.56006311 -0.42809524\n",
            "  0.2927623   0.74302404  3.1463301  -4.51198674  0.9989692   0.9989692 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Coeficiente linear\n",
        "\n",
        "b = model.intercept_\n",
        "print(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oiUJ7VJA67Cv",
        "outputId": "4b2a58ab-e9a3-4c55-ab9f-400c5b5f7ffd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.041588463285132146\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusões"
      ],
      "metadata": {
        "id": "klqq8Xe86BDD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Avaliação**"
      ],
      "metadata": {
        "id": "UaCrky_669I8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cálculo da margem de erro da predição do modelo."
      ],
      "metadata": {
        "id": "aa_-vCb2vmQe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cálculo do RMSE (root mean square error)\n",
        "\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "target_predicted = model.predict(predictors_test)\n",
        "\n",
        "rmse = np.sqrt(mean_squared_error(target_test, target_predicted))\n",
        "print(rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Thi5DJm16_ZV",
        "outputId": "fcd44c29-77b7-4231-f065-8b81ec90d6b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.620650562574459\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Predição**"
      ],
      "metadata": {
        "id": "GaxbX3BT7Wt7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Com o modelo treinado, é possível colocar os dados de desigualdade na educação e prever em que rank o país estará em questão de IDH."
      ],
      "metadata": {
        "id": "cEaC4brR9An8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Receber os valores dos índice\n",
        "\n",
        "indice_2010 = float(input(\"Índice de Desigualdade em 2010: \"))\n",
        "indice_2011 = float(input(\"Índice de Desigualdade em 2011: \"))\n",
        "indice_2012 = float(input(\"Índice de Desigualdade em 2012: \"))\n",
        "indice_2013 = float(input(\"Índice de Desigualdade em 2013: \"))\n",
        "indice_2014 = float(input(\"Índice de Desigualdade em 2014: \"))\n",
        "indice_2015 = float(input(\"Índice de Desigualdade em 2015: \"))\n",
        "indice_2016 = float(input(\"Índice de Desigualdade em 2016: \"))\n",
        "indice_2017 = float(input(\"Índice de Desigualdade em 2017: \"))\n",
        "indice_2018 = float(input(\"Índice de Desigualdade em 2018: \"))\n",
        "indice_2019 = float(input(\"Índice de Desigualdade em 2019: \"))\n",
        "indice_2020 = float(input(\"Índice de Desigualdade em 2020: \"))\n",
        "indice_2021 = float(input(\"Índice de Desigualdade em 2021: \"))"
      ],
      "metadata": {
        "id": "zgtx1oXi_d87"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Padronizar os valores dos índices\n",
        "\n",
        "padrao_2010 = (indice_2010 - media_10) / desvio_padrao_10\n",
        "padrao_2011 = (indice_2011 - media_11) / desvio_padrao_11\n",
        "padrao_2012 = (indice_2012 - media_12) / desvio_padrao_12\n",
        "padrao_2013 = (indice_2013 - media_13) / desvio_padrao_13\n",
        "padrao_2014 = (indice_2014 - media_14) / desvio_padrao_14\n",
        "padrao_2015 = (indice_2015 - media_15) / desvio_padrao_15\n",
        "padrao_2016 = (indice_2016 - media_16) / desvio_padrao_16\n",
        "padrao_2017 = (indice_2017 - media_17) / desvio_padrao_17\n",
        "padrao_2018 = (indice_2018 - media_18) / desvio_padrao_18\n",
        "padrao_2019 = (indice_2019 - media_19) / desvio_padrao_19\n",
        "padrao_2020 = (indice_2020 - media_20) / desvio_padrao_20\n",
        "padrao_2021 = (indice_2021 - media_21) / desvio_padrao_21"
      ],
      "metadata": {
        "id": "UNZv3cMRGooL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Criação de um array com dados para fazer a predição\n",
        "\n",
        "idh_pais_novo = np.array([padrao_2010, padrao_2011, padrao_2012, padrao_2013, padrao_2014, padrao_2015, padrao_2016,padrao_2017, padrao_2018, padrao_2019, padrao_2020, padrao_2021])\n",
        "\n",
        "print(idh_pais_novo)"
      ],
      "metadata": {
        "id": "u3JSqXf-GbBK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resultado do rank do IDH do novo país padronizado\n",
        "\n",
        "idh_pais_novo_padrao = model.predict(idh_pais_novo.reshape(1, -1))\n",
        "print(idh_pais_novo_padrao)"
      ],
      "metadata": {
        "id": "Hkd4vD1JJFnV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Rank do IDH do país hipotético sem estar padronizado\n",
        "\n",
        "rank_idh_pais = idh_pais_novo_padrao * desvio_padrao_idh + media_idh\n",
        "print(rank_idh_pais)"
      ],
      "metadata": {
        "id": "E2sxi66tJbCp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Um país que teve nos anos de 2010 até 2021 os IDHs:\\n{indice_2010}\\n{indice_2011}\\n{indice_2012}\\n{indice_2013}\\n{indice_2014}\\n{indice_2015}\\n{indice_2016}\\n{indice_2017}\\n{indice_2018}\\n{indice_2019}\\n{indice_2020}\\n{indice_2021}\\nFicará, no rank de IDH, na posição {rank_idh_pais}')"
      ],
      "metadata": {
        "id": "0o6Cc20MOAoR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Considerações finais:**\n",
        "\n",
        "Com esse modelo, além de prever a posição que um país estará no rank do Indíce de Desenvolvimento Humano (IDH) de acordo com a evolução do nível de desigualdade na educação, também é possível constatar que, um melhor acesso ao ensino está relacionado com uma melhor posição na tebela de IDH's."
      ],
      "metadata": {
        "id": "_M0POhwJlBww"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "lqiCvp28mf4x"
      }
    }
  ]
}