datamining_12.ipynb

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          "evalue": "\n`load_boston` has been removed from scikit-learn since version 1.2.\n\nThe Boston housing prices dataset has an ethical problem: as\ninvestigated in [1], the authors of this dataset engineered a\nnon-invertible variable \"B\" assuming that racial self-segregation had a\npositive impact on house prices [2]. Furthermore the goal of the\nresearch that led to the creation of this dataset was to study the\nimpact of air quality but it did not give adequate demonstration of the\nvalidity of this assumption.\n\nThe scikit-learn maintainers therefore strongly discourage the use of\nthis dataset unless the purpose of the code is to study and educate\nabout ethical issues in data science and machine learning.\n\nIn this special case, you can fetch the dataset from the original\nsource::\n\n    import pandas as pd\n    import numpy as np\n\n    data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n    raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n    data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n    target = raw_df.values[1::2, 2]\n\nAlternative datasets include the California housing dataset and the\nAmes housing dataset. You can load the datasets as follows::\n\n    from sklearn.datasets import fetch_california_housing\n    housing = fetch_california_housing()\n\nfor the California housing dataset and::\n\n    from sklearn.datasets import fetch_openml\n    housing = fetch_openml(name=\"house_prices\", as_frame=True)\n\nfor the Ames housing dataset.\n\n[1] M Carlisle.\n\"Racist data destruction?\"\n<https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8>\n\n[2] Harrison Jr, David, and Daniel L. Rubinfeld.\n\"Hedonic housing prices and the demand for clean air.\"\nJournal of environmental economics and management 5.1 (1978): 81-102.\n<https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air>\n",
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            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-170ef283db27>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_boston\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Importando o conjunto de dados Boston Housing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/datasets/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m    154\u001b[0m             \"\"\"\n\u001b[1;32m    155\u001b[0m         )\n\u001b[0;32m--> 156\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mglobals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: \n`load_boston` has been removed from scikit-learn since version 1.2.\n\nThe Boston housing prices dataset has an ethical problem: as\ninvestigated in [1], the authors of this dataset engineered a\nnon-invertible variable \"B\" assuming that racial self-segregation had a\npositive impact on house prices [2]. Furthermore the goal of the\nresearch that led to the creation of this dataset was to study the\nimpact of air quality but it did not give adequate demonstration of the\nvalidity of this assumption.\n\nThe scikit-learn maintainers therefore strongly discourage the use of\nthis dataset unless the purpose of the code is to study and educate\nabout ethical issues in data science and machine learning.\n\nIn this special case, you can fetch the dataset from the original\nsource::\n\n    import pandas as pd\n    import numpy as np\n\n    data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n    raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n    data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n    target = raw_df.values[1::2, 2]\n\nAlternative datasets include the California housing dataset and the\nAmes housing dataset. You can load the datasets as follows::\n\n    from sklearn.datasets import fetch_california_housing\n    housing = fetch_california_housing()\n\nfor the California housing dataset and::\n\n    from sklearn.datasets import fetch_openml\n    housing = fetch_openml(name=\"house_prices\", as_frame=True)\n\nfor the Ames housing dataset.\n\n[1] M Carlisle.\n\"Racist data destruction?\"\n<https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8>\n\n[2] Harrison Jr, David, and Daniel L. Rubinfeld.\n\"Hedonic housing prices and the demand for clean air.\"\nJournal of environmental economics and management 5.1 (1978): 81-102.\n<https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air>\n",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.datasets import load_boston\n",
        "\n",
        "# Importando o conjunto de dados Boston Housing\n",
        "boston = load_boston()\n",
        "df_boston = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
        "df_boston['MEDV'] = boston.target\n",
        "\n",
        "# Exibindo as primeiras linhas do conjunto de dados\n",
        "print(df_boston.head())\n",
        "\n",
        "# Informações básicas sobre o conjunto de dados\n",
        "print(df_boston.info())\n",
        "\n",
        "# Estatísticas descritivas\n",
        "print(df_boston.describe())\n",
        "\n",
        "# Distribuição dos preços das casas (MEDV)\n",
        "sns.histplot(df_boston['MEDV'], bins=30, kde=True)\n",
        "plt.title('Distribuição dos Preços das Casas')\n",
        "plt.xlabel('MEDV (Valor Mediano das Casas em $1000)')\n",
        "plt.ylabel('Frequência')\n",
        "plt.show()\n",
        "\n",
        "# Heatmap da matriz de correlação\n",
        "corr_matrix = df_boston.corr()\n",
        "plt.figure(figsize=(12, 10))\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
        "plt.title('Matriz de Correlação')\n",
        "plt.show()\n",
        "\n",
        "# Relação entre variáveis mais correlacionadas e MEDV\n",
        "sns.pairplot(df_boston, vars=['RM', 'LSTAT', 'PTRATIO', 'MEDV'])\n",
        "plt.show()\n",
        "\n",
        "# Boxplot para visualizar a distribuição de RM por MEDV\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.boxplot(x='MEDV', y='RM', data=df_boston)\n",
        "plt.title('Número de Quartos por Valor Mediano das Casas')\n",
        "plt.xlabel('Valor Mediano das Casas (MEDV)')\n",
        "plt.ylabel('Número de Quartos (RM)')\n",
        "plt.show()\n",
        "\n",
        "# Seleção de variáveis independentes (X) e dependente (y)\n",
        "X = df_boston.drop('MEDV', axis=1)\n",
        "y = df_boston['MEDV']\n",
        "\n",
        "# Divisão dos dados em conjuntos de treino e teste\n",
        "X_treino, X_teste, y_treino, y_teste = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Treinamento do modelo de regressão linear\n",
        "modelo = LinearRegression()\n",
        "modelo.fit(X_treino, y_treino)\n",
        "\n",
        "# Previsões no conjunto de teste\n",
        "y_pred = modelo.predict(X_teste)\n",
        "\n",
        "# Avaliação do modelo\n",
        "mse = mean_squared_error(y_teste, y_pred)\n",
        "r2 = r2_score(y_teste, y_pred)\n",
        "print(f'Erro Quadrático Médio (MSE): {mse}')\n",
        "print(f'Coeficiente de Determinação (R^2): {r2}')\n"
      ]
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