dataming_14_ipyn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPo86umSdZdN9hQ32RKNEeU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nathalylorena/Data-mining/blob/main/dataming_14_ipyn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "5rUdcrGxOGN9",
        "outputId": "c34ce15b-95c2-418d-d9ff-a84377aaeb16"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "HTTPError",
          "evalue": "HTTP Error 403: Forbidden",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-aa4a4352d123>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\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 California Housing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mcalifornia\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfetch_california_housing\u001b[0m\u001b[0;34m(\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     11\u001b[0m \u001b[0mdf_california\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalifornia\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcalifornia\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mdf_california\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'MedHouseVal'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalifornia\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\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/_california_housing.py\u001b[0m in \u001b[0;36mfetch_california_housing\u001b[0;34m(data_home, download_if_missing, return_X_y, as_frame)\u001b[0m\n\u001b[1;32m    136\u001b[0m         )\n\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m         \u001b[0marchive_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_fetch_remote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mARCHIVE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_home\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    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtarfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"r:gz\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marchive_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\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/_base.py\u001b[0m in \u001b[0;36m_fetch_remote\u001b[0;34m(remote, dirname)\u001b[0m\n\u001b[1;32m   1322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m     \u001b[0mfile_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mremote\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdirname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremote\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1324\u001b[0;31m     \u001b[0murlretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremote\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_path\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   1325\u001b[0m     \u001b[0mchecksum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_sha256\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1326\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mremote\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecksum\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mchecksum\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0murl_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_splittype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mcontextlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\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    242\u001b[0m         \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m         \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\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    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    523\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mprocessor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_response\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m             \u001b[0mmeth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\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    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36mhttp_response\u001b[0;34m(self, request, response)\u001b[0m\n\u001b[1;32m    632\u001b[0m         \u001b[0;31m# request was successfully received, understood, and accepted.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m200\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mcode\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 634\u001b[0;31m             response = self.parent.error(\n\u001b[0m\u001b[1;32m    635\u001b[0m                 'http', request, response, code, msg, hdrs)\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36merror\u001b[0;34m(self, proto, *args)\u001b[0m\n\u001b[1;32m    561\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_err\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m             \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'default'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'http_error_default'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0morig_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_chain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m \u001b[0;31m# XXX probably also want an abstract factory that knows when it makes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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    497\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;31mHTTPError\u001b[0m: HTTP Error 403: Forbidden"
          ]
        }
      ],
      "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 fetch_california_housing\n",
        "\n",
        "# Importando o conjunto de dados California Housing\n",
        "california = fetch_california_housing()\n",
        "df_california = pd.DataFrame(california.data, columns=california.feature_names)\n",
        "df_california['MedHouseVal'] = california.target\n",
        "\n",
        "# Exibindo as primeiras linhas do conjunto de dados\n",
        "print(df_california.head())\n",
        "\n",
        "# Informações básicas sobre o conjunto de dados\n",
        "print(df_california.info())\n",
        "\n",
        "# Estatísticas descritivas\n",
        "print(df_california.describe())\n",
        "\n",
        "# Distribuição dos preços das casas (MedHouseVal)\n",
        "sns.histplot(df_california['MedHouseVal'], bins=30, kde=True)\n",
        "plt.title('Distribuição dos Preços das Casas')\n",
        "plt.xlabel('MedHouseVal (Valor Mediano das Casas em $100,000)')\n",
        "plt.ylabel('Frequência')\n",
        "plt.show()\n",
        "\n",
        "# Heatmap da matriz de correlação\n",
        "corr_matrix = df_california.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 MedHouseVal\n",
        "sns.pairplot(df_california, vars=['MedInc', 'AveRooms', 'AveOccup', 'MedHouseVal'])\n",
        "plt.show()\n",
        "\n",
        "# Boxplot para visualizar a distribuição de MedInc por MedHouseVal\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.boxplot(x='MedHouseVal', y='MedInc', data=df_california)\n",
        "plt.title('Renda Mediana por Valor Mediano das Casas')\n",
        "plt.xlabel('Valor Mediano das Casas (MedHouseVal)')\n",
        "plt.ylabel('Renda Mediana (MedInc)')\n",
        "plt.show()\n",
        "\n",
        "# Seleção de variáveis independentes (X) e dependente (y)\n",
        "X = df_california.drop('MedHouseVal', axis=1)\n",
        "y = df_california['MedHouseVal']\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",
        "\n"
      ]
    }
  ]
}