{"id":50796,"date":"2022-05-05T00:00:00","date_gmt":"2022-05-05T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/building-a-linear-regression-model-for-housing-data-using-python-and-griddb\/"},"modified":"2025-11-14T07:55:22","modified_gmt":"2025-11-14T15:55:22","slug":"building-a-linear-regression-model-for-housing-data-using-python-and-griddb","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/building-a-linear-regression-model-for-housing-data-using-python-and-griddb\/","title":{"rendered":"Python\u3068GridDB\u3092\u7528\u3044\u3066\u4f4f\u5b85\u30c7\u30fc\u30bf\u306e\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b"},"content":{"rendered":"<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python\u3092\u4f7f\u7528\u3057\u3066\u4f4f\u5b85\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8abf\u67fb\u3057\u307e\u3059\u3002\u307e\u305a\u3001\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30d7\u30eb\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3044\u307e\u3059\u3002\u305d\u306e\u5f8c\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3059\u308b\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u3001\u5c06\u6765\u306e\u4e88\u6e2c\u3092\u884c\u3046\u65b9\u6cd5\u3092\u898b\u307e\u3059\u3002<\/p>\n<p>\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306e\u6982\u8981\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n<ol>\n<li>\u524d\u63d0\u6761\u4ef6<\/li>\n<li>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3064\u3044\u3066<\/li>\n<li>\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<\/li>\n<li>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aad\u307f\u8fbc\u307f<\/li>\n<li>\u30c7\u30fc\u30bf\u306e\u524d\u51e6\u7406<\/li>\n<li>\u30c7\u30fc\u30bf\u306e\u6b63\u898f\u5316<\/li>\n<li>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5206\u5272<\/li>\n<li>\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9<\/li>\n<li>\u4e88\u6e2c\u3059\u308b<\/li>\n<li>\u30e2\u30c7\u30eb\u306e\u8a55\u4fa1<\/li>\n<li>\u7d50\u8ad6<\/li>\n<\/ol>\n<h2>1&#46; \u524d\u63d0\u6761\u4ef6<\/h2>\n<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306f\u3001Jupyter Notebooks (Anaconda version 4.8.3) \u3068 Python version 3.8 on Windows 10 Operating system \u3092\u4f7f\u7528\u3057\u3066\u5b9f\u884c\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u5b9f\u884c\u524d\u306b\u4ee5\u4e0b\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ol>\n<li><a href=\"https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html\">Pandas<\/a><\/li>\n<li><a href=\"https:\/\/pypi.org\/project\/scikit-learn\/\">scikit-learn<\/a><\/li>\n<\/ol>\n<p>Anaconda \u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u5834\u5408\u3001\u30d1\u30c3\u30b1\u30fc\u30b8\u306f\u30e6\u30fc\u30b6\u30fc\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u3001\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u3001\u307e\u305f\u306f Jupyter Notebooks \u306a\u3069\u306e\u8907\u6570\u306e\u65b9\u6cd5\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002Python \u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u6700\u3082\u4e00\u822c\u7684\u306a\u65b9\u6cd5\u306f <code>pip<\/code> \u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3067\u3059\u3002\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u3084\u30bf\u30fc\u30df\u30ca\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u5834\u5408\u306f\u3001<code>pip install package-name<\/code>\u3068\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3082\u3046\u4e00\u3064\u306e\u65b9\u6cd5\u306f\u3001Anaconda \u74b0\u5883\u5185\u3067 <code>conda install package-name<\/code> \u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/p>\n<p>\u307e\u305f\u3001Python \u74b0\u5883\u3067\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30ed\u30fc\u30c9\u3059\u308b\u65b9\u6cd5\u3068\u3057\u3066\u3001<code>Pandas<\/code> \u3068 <code>GridDB<\/code> \u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002Python\u74b0\u5883\u3067GridDB\u3092\u4f7f\u7528\u3059\u308b\u305f\u3081\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n<ol>\n<li><a href=\"https:\/\/github.com\/griddb\/c_client\">GridDB C\u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/a><\/li>\n<li>SWIG (Simplified Wrapper and Interface Generator)<\/li>\n<li><a href=\"https:\/\/github.com\/griddb\/python_client\">GridDB Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/a><\/li>\n<\/ol>\n<h2>2&#46; \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3064\u3044\u3066<\/h2>\n<p>\u6211\u3005\u306f\u3001\u516c\u5171\u306e\u30ea\u30bd\u30fc\u30b9\u304b\u3089\u53ce\u96c6\u3055\u308c\u3001\u73fe\u5728<a href=\"https:\/\/www.kaggle.com\/dansbecker\/melbourne-housing-snapshot\">Kaggle<\/a>\u3067\u5229\u7528\u53ef\u80fd\u3067\u3042\u308bMelbourne Housing Dataset\u306e\u30b9\u30ca\u30c3\u30d7\u30b7\u30e7\u30c3\u30c8\u3092\u4f7f\u7528\u3059\u308b\u4e88\u5b9a\u3067\u3059\u3002\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u3042\u308b\u7a0b\u5ea6\u524d\u51e6\u7406\u3055\u308c\u3066\u304a\u308a\u3001\u5408\u8a0813580\u500b\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5b58\u5728\u3059\u308b\u5c5e\u6027\u306e\u6570\u306f21\u3067\u3059\u3002\u5f93\u5c5e\u5909\u6570\u306f\u7269\u4ef6\u306e\u4fa1\u683c\u3067\u3042\u308a\u3001\u4ed6\u306e20\u5c5e\u6027\u306f\u72ec\u7acb\u5909\u6570\u3067\u3059\u3002\u3067\u306f\u3001\u30b3\u30fc\u30c9\u3092\u66f8\u304d\u59cb\u3081\u307e\u3057\u3087\u3046\u3002<\/p>\n<h2>3&#46; \u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<\/h2>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">import pandas as pd\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split \nfrom sklearn.metrics import mean_absolute_error<\/code><\/pre>\n<\/div>\n<p>\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306b\u6210\u529f\u3057\u305f\u5834\u5408\u3001\u4e0a\u8a18\u306e\u30bb\u30eb\u306f\u4f55\u3082\u51fa\u529b\u3055\u308c\u305a\u306b\u5b9f\u884c\u3055\u308c\u308b\u306f\u305a\u3067\u3059\u3002\u4e07\u304c\u4e00\u3001\u30a8\u30e9\u30fc\u304c\u767a\u751f\u3057\u305f\u5834\u5408\u306f\u3001\u4ee5\u4e0b\u3092\u8a66\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<ol>\n<li>\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u6210\u529f\u3057\u305f\u304b\u3069\u3046\u304b\u518d\u78ba\u8a8d\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u3046\u307e\u304f\u3044\u304b\u306a\u3044\u5834\u5408\u306f\u3001\u3082\u3046\u4e00\u5ea6 <code>pip install package-name<\/code> \u3092\u5b9f\u884c\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/li>\n<li>\u304a\u4f7f\u3044\u306e\u30b7\u30b9\u30c6\u30e0\u304c\u3001\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u3068\u4e92\u63db\u6027\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n<h2>4&#46; \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aad\u307f\u8fbc\u307f<\/h2>\n<h3>GridDB\u3092\u5229\u7528\u3059\u308b<\/h3>\n<p><a href=\"https:\/\/griddb.net\/ja\/\">GridDB<\/a> \u306f\u3001\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u3092\u6271\u3046\u305f\u3081\u306b\u8a2d\u8a08\u3055\u308c\u305f\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u306e\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3067\u3059\u3002IoT\u306b\u6700\u9069\u5316\u3055\u308c\u3066\u304a\u308a\u3001\u30a4\u30f3\u30e1\u30e2\u30ea\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3092\u63a1\u7528\u3057\u3066\u3044\u308b\u305f\u3081\u3001\u9ad8\u3044\u52b9\u7387\u6027\u3092\u8a87\u308a\u307e\u3059\u3002\u30ed\u30fc\u30ab\u30eb\u3067\u30d5\u30a1\u30a4\u30eb\u3092\u6271\u3046\u3068\u3001\u30d7\u30ed\u30d5\u30a7\u30c3\u30b7\u30e7\u30ca\u30eb\u306a\u74b0\u5883\u3067\u306f\u7d71\u5408\u306e\u554f\u984c\u304c\u767a\u751f\u3059\u308b\u305f\u3081\u3001\u4fe1\u983c\u6027\u306e\u9ad8\u3044\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u4f7f\u3046\u3053\u3068\u304c\u91cd\u8981\u306b\u306a\u308b\u3002GridDB\u306f\u305d\u306e\u4fe1\u983c\u6027\u3068\u30d5\u30a9\u30fc\u30eb\u30c8\u30c8\u30ec\u30e9\u30f3\u30b9\u306b\u3088\u308b\u30b9\u30b1\u30fc\u30e9\u30d3\u30ea\u30c6\u30a3\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/p>\n<p>\u3055\u3089\u306b\u3001GridDB\u306e<a href=\"https:\/\/github.com\/griddb\/python_client\">python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/a>\u306b\u3088\u308a\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u30a4\u30f3\u30af\u30eb\u30fc\u30c9\u3057\u3066\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u74b0\u5883\u5185\u3067\u76f4\u63a5\u64cd\u4f5c\u3059\u308b\u3053\u3068\u304c\u975e\u5e38\u306b\u5bb9\u6613\u306b\u306a\u308a\u307e\u3057\u305f\u3002GriDB\u306eWebAPI\u306b\u3064\u3044\u3066\u306f<a href=\"https:\/\/griddb.net\/ja\/blog\/griddb-webapi\/\">\u3053\u3061\u3089<\/a>\u3092\u3054\u89a7\u304f\u3060\u3055\u3044\u3002<\/p>\n<p>\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30ed\u30fc\u30c9\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">import griddb_python as griddb\n\nsql_statement = ('SELECT * FROM melb_data')\ndataset = pd.read_sql_query(sql_statement, container)<\/code><\/pre>\n<\/div>\n<p>\u5909\u6570 <code>dataset<\/code> \u306b\u306f\u3001pandas \u306e dataframe \u5f62\u5f0f\u3067\u30c7\u30fc\u30bf\u304c\u683c\u7d0d\u3055\u308c\u307e\u3059\u3002GridDB\u3092\u521d\u3081\u3066\u4f7f\u3046\u5834\u5408\u306f\u3001<a href=\"https:\/\/griddb.net\/ja\/blog\/using-pandas-dataframes-with-griddb\/\">\u3053\u3061\u3089<\/a> \u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u304c\u53c2\u8003\u306b\u306a\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002<\/p>\n<h3>Pandas\u306e\u4f7f\u7528<\/h3>\n<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u3080\u3082\u3046\u4e00\u3064\u306e\u65b9\u6cd5\u306f\u3001pandas\u3092\u76f4\u63a5\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset = pd.read_csv(\"melb_data.csv\")<\/code><\/pre>\n<\/div>\n<h2>5&#46; \u30c7\u30fc\u30bf\u524d\u51e6\u7406<\/h2>\n<p>\u7d20\u6674\u3089\u3057\u3044\uff01\u3055\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u3067\u304d\u305f\u306e\u3067\u3001\u5b9f\u969b\u306b\u3069\u306e\u3088\u3046\u306b\u898b\u3048\u308b\u304b\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset.head()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>\n        <\/th>\n<th>\n          Suburb\n        <\/th>\n<th>\n          Address\n        <\/th>\n<th>\n          Rooms\n        <\/th>\n<th>\n          Type\n        <\/th>\n<th>\n          Price\n        <\/th>\n<th>\n          Method\n        <\/th>\n<th>\n          SellerG\n        <\/th>\n<th>\n          Date\n        <\/th>\n<th>\n          Distance\n        <\/th>\n<th>\n          Postcode\n        <\/th>\n<th>\n          &#8230;\n        <\/th>\n<th>\n          Bathroom\n        <\/th>\n<th>\n          Car\n        <\/th>\n<th>\n          Landsize\n        <\/th>\n<th>\n          BuildingArea\n        <\/th>\n<th>\n          YearBuilt\n        <\/th>\n<th>\n          CouncilArea\n        <\/th>\n<th>\n          Lattitude\n        <\/th>\n<th>\n          Longtitude\n        <\/th>\n<th>\n          Regionname\n        <\/th>\n<th>\n          Propertycount\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          0\n        <\/th>\n<td>\n          Abbotsford\n        <\/td>\n<td>\n          85 Turner St\n        <\/td>\n<td>\n          2\n        <\/td>\n<td>\n          h\n        <\/td>\n<td>\n          1480000.0\n        <\/td>\n<td>\n          S\n        <\/td>\n<td>\n          Biggin\n        <\/td>\n<td>\n          3\/12\/2016\n        <\/td>\n<td>\n          2.5\n        <\/td>\n<td>\n          3067.0\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          202.0\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          Yarra\n        <\/td>\n<td>\n          -37.7996\n        <\/td>\n<td>\n          144.9984\n        <\/td>\n<td>\n          Northern Metropolitan\n        <\/td>\n<td>\n          4019.0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          Abbotsford\n        <\/td>\n<td>\n          25 Bloomburg St\n        <\/td>\n<td>\n          2\n        <\/td>\n<td>\n          h\n        <\/td>\n<td>\n          1035000.0\n        <\/td>\n<td>\n          S\n        <\/td>\n<td>\n          Biggin\n        <\/td>\n<td>\n          4\/02\/2016\n        <\/td>\n<td>\n          2.5\n        <\/td>\n<td>\n          3067.0\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          0.0\n        <\/td>\n<td>\n          156.0\n        <\/td>\n<td>\n          79.0\n        <\/td>\n<td>\n          1900.0\n        <\/td>\n<td>\n          Yarra\n        <\/td>\n<td>\n          -37.8079\n        <\/td>\n<td>\n          144.9934\n        <\/td>\n<td>\n          Northern Metropolitan\n        <\/td>\n<td>\n          4019.0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          Abbotsford\n        <\/td>\n<td>\n          5 Charles St\n        <\/td>\n<td>\n          3\n        <\/td>\n<td>\n          h\n        <\/td>\n<td>\n          1465000.0\n        <\/td>\n<td>\n          SP\n        <\/td>\n<td>\n          Biggin\n        <\/td>\n<td>\n          4\/03\/2017\n        <\/td>\n<td>\n          2.5\n        <\/td>\n<td>\n          3067.0\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          2.0\n        <\/td>\n<td>\n          0.0\n        <\/td>\n<td>\n          134.0\n        <\/td>\n<td>\n          150.0\n        <\/td>\n<td>\n          1900.0\n        <\/td>\n<td>\n          Yarra\n        <\/td>\n<td>\n          -37.8093\n        <\/td>\n<td>\n          144.9944\n        <\/td>\n<td>\n          Northern Metropolitan\n        <\/td>\n<td>\n          4019.0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3\n        <\/th>\n<td>\n          Abbotsford\n        <\/td>\n<td>\n          40 Federation La\n        <\/td>\n<td>\n          3\n        <\/td>\n<td>\n          h\n        <\/td>\n<td>\n          850000.0\n        <\/td>\n<td>\n          PI\n        <\/td>\n<td>\n          Biggin\n        <\/td>\n<td>\n          4\/03\/2017\n        <\/td>\n<td>\n          2.5\n        <\/td>\n<td>\n          3067.0\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          2.0\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          94.0\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          Yarra\n        <\/td>\n<td>\n          -37.7969\n        <\/td>\n<td>\n          144.9969\n        <\/td>\n<td>\n          Northern Metropolitan\n        <\/td>\n<td>\n          4019.0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          Abbotsford\n        <\/td>\n<td>\n          55a Park St\n        <\/td>\n<td>\n          4\n        <\/td>\n<td>\n          h\n        <\/td>\n<td>\n          1600000.0\n        <\/td>\n<td>\n          VB\n        <\/td>\n<td>\n          Nelson\n        <\/td>\n<td>\n          4\/06\/2016\n        <\/td>\n<td>\n          2.5\n        <\/td>\n<td>\n          3067.0\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          2.0\n        <\/td>\n<td>\n          120.0\n        <\/td>\n<td>\n          142.0\n        <\/td>\n<td>\n          2014.0\n        <\/td>\n<td>\n          Yarra\n        <\/td>\n<td>\n          -37.8072\n        <\/td>\n<td>\n          144.9941\n        <\/td>\n<td>\n          Northern Metropolitan\n        <\/td>\n<td>\n          4019.0\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\n    5 rows \u00d7 21 columns\n  <\/p>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">len(dataset)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">    13580<\/code><\/pre>\n<\/div>\n<p>\u5217\u304c\u305f\u304f\u3055\u3093\u3042\u308b\u306e\u304c\u308f\u304b\u308b\u306e\u3067\u3001\u72ec\u7acb\u5c5e\u6027\u3068\u5f93\u5c5e\u5c5e\u6027\u306e\u30a4\u30e1\u30fc\u30b8\u3092\u3064\u304b\u3080\u305f\u3081\u306b\u3001\u5217\u540d\u3092\u30d7\u30ea\u30f3\u30c8\u30a2\u30a6\u30c8\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset.columns<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">    Index(['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',\n           'Date', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car',\n           'Landsize', 'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude',\n           'Longtitude', 'Regionname', 'Propertycount'],\n          dtype='object')<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset.describe()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>\n        <\/th>\n<th>\n          Rooms\n        <\/th>\n<th>\n          Price\n        <\/th>\n<th>\n          Distance\n        <\/th>\n<th>\n          Postcode\n        <\/th>\n<th>\n          Bedroom2\n        <\/th>\n<th>\n          Bathroom\n        <\/th>\n<th>\n          Car\n        <\/th>\n<th>\n          Landsize\n        <\/th>\n<th>\n          BuildingArea\n        <\/th>\n<th>\n          YearBuilt\n        <\/th>\n<th>\n          Lattitude\n        <\/th>\n<th>\n          Longtitude\n        <\/th>\n<th>\n          Propertycount\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          count\n        <\/th>\n<td>\n          13580.000000\n        <\/td>\n<td>\n          1.358000e+04\n        <\/td>\n<td>\n          13580.000000\n        <\/td>\n<td>\n          13580.000000\n        <\/td>\n<td>\n          13580.000000\n        <\/td>\n<td>\n          13580.000000\n        <\/td>\n<td>\n          13518.000000\n        <\/td>\n<td>\n          13580.000000\n        <\/td>\n<td>\n          7130.000000\n        <\/td>\n<td>\n          8205.000000\n        <\/td>\n<td>\n          13580.000000\n        <\/td>\n<td>\n          13580.000000\n        <\/td>\n<td>\n          13580.000000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          mean\n        <\/th>\n<td>\n          2.937997\n        <\/td>\n<td>\n          1.075684e+06\n        <\/td>\n<td>\n          10.137776\n        <\/td>\n<td>\n          3105.301915\n        <\/td>\n<td>\n          2.914728\n        <\/td>\n<td>\n          1.534242\n        <\/td>\n<td>\n          1.610075\n        <\/td>\n<td>\n          558.416127\n        <\/td>\n<td>\n          151.967650\n        <\/td>\n<td>\n          1964.684217\n        <\/td>\n<td>\n          -37.809203\n        <\/td>\n<td>\n          144.995216\n        <\/td>\n<td>\n          7454.417378\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          std\n        <\/th>\n<td>\n          0.955748\n        <\/td>\n<td>\n          6.393107e+05\n        <\/td>\n<td>\n          5.868725\n        <\/td>\n<td>\n          90.676964\n        <\/td>\n<td>\n          0.965921\n        <\/td>\n<td>\n          0.691712\n        <\/td>\n<td>\n          0.962634\n        <\/td>\n<td>\n          3990.669241\n        <\/td>\n<td>\n          541.014538\n        <\/td>\n<td>\n          37.273762\n        <\/td>\n<td>\n          0.079260\n        <\/td>\n<td>\n          0.103916\n        <\/td>\n<td>\n          4378.581772\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          min\n        <\/th>\n<td>\n          1.000000\n        <\/td>\n<td>\n          8.500000e+04\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          3000.000000\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          1196.000000\n        <\/td>\n<td>\n          -38.182550\n        <\/td>\n<td>\n          144.431810\n        <\/td>\n<td>\n          249.000000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          25%\n        <\/th>\n<td>\n          2.000000\n        <\/td>\n<td>\n          6.500000e+05\n        <\/td>\n<td>\n          6.100000\n        <\/td>\n<td>\n          3044.000000\n        <\/td>\n<td>\n          2.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          177.000000\n        <\/td>\n<td>\n          93.000000\n        <\/td>\n<td>\n          1940.000000\n        <\/td>\n<td>\n          -37.856822\n        <\/td>\n<td>\n          144.929600\n        <\/td>\n<td>\n          4380.000000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          50%\n        <\/th>\n<td>\n          3.000000\n        <\/td>\n<td>\n          9.030000e+05\n        <\/td>\n<td>\n          9.200000\n        <\/td>\n<td>\n          3084.000000\n        <\/td>\n<td>\n          3.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          2.000000\n        <\/td>\n<td>\n          440.000000\n        <\/td>\n<td>\n          126.000000\n        <\/td>\n<td>\n          1970.000000\n        <\/td>\n<td>\n          -37.802355\n        <\/td>\n<td>\n          145.000100\n        <\/td>\n<td>\n          6555.000000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          75%\n        <\/th>\n<td>\n          3.000000\n        <\/td>\n<td>\n          1.330000e+06\n        <\/td>\n<td>\n          13.000000\n        <\/td>\n<td>\n          3148.000000\n        <\/td>\n<td>\n          3.000000\n        <\/td>\n<td>\n          2.000000\n        <\/td>\n<td>\n          2.000000\n        <\/td>\n<td>\n          651.000000\n        <\/td>\n<td>\n          174.000000\n        <\/td>\n<td>\n          1999.000000\n        <\/td>\n<td>\n          -37.756400\n        <\/td>\n<td>\n          145.058305\n        <\/td>\n<td>\n          10331.000000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          max\n        <\/th>\n<td>\n          10.000000\n        <\/td>\n<td>\n          9.000000e+06\n        <\/td>\n<td>\n          48.100000\n        <\/td>\n<td>\n          3977.000000\n        <\/td>\n<td>\n          20.000000\n        <\/td>\n<td>\n          8.000000\n        <\/td>\n<td>\n          10.000000\n        <\/td>\n<td>\n          433014.000000\n        <\/td>\n<td>\n          44515.000000\n        <\/td>\n<td>\n          2018.000000\n        <\/td>\n<td>\n          -37.408530\n        <\/td>\n<td>\n          145.526350\n        <\/td>\n<td>\n          21650.000000\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><code>describe<\/code> \u95a2\u6570\u306e\u51fa\u529b\u306f\u3001\u5404\u5c5e\u6027\u306e\u5024\u304c\u7570\u306a\u308b\u30b9\u30b1\u30fc\u30eb\u3092\u6301\u3063\u3066\u3044\u308b\u3053\u3068\u3092\u4f1d\u3048\u3066\u3044\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u3001\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u524d\u306b\u6b63\u898f\u5316\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<p>\u6b63\u898f\u5316\u3059\u308b\u524d\u306b\u3001\u4fa1\u683c\u3068\u76f4\u63a5\u7684\u306b\u76f8\u95a2\u304c\u3042\u308b\u3068\u601d\u308f\u308c\u308b\u5c5e\u6027\u306e\u30b5\u30d6\u30bb\u30c3\u30c8\u3092\u53d6\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset = dataset[[\"Rooms\", \"Price\", \"Bedroom2\", \"Bathroom\",\"Landsize\", \"BuildingArea\", \"YearBuilt\"]]<\/code><\/pre>\n<\/div>\n<p>\u307e\u305f\u3001\u30e2\u30c7\u30eb\u69cb\u7bc9\u306b\u9032\u3080\u524d\u306b\u3001\u30c7\u30fc\u30bf\u306b\u30cc\u30eb\u5024\u304c\u542b\u307e\u308c\u3066\u3044\u306a\u3044\u3053\u3068\u3092\u78ba\u8a8d\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset.isna().sum()<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">    Rooms              0\n    Price              0\n    Bedroom2           0\n    Bathroom           0\n    Landsize           0\n    BuildingArea    6450\n    YearBuilt       5375\n    dtype: int64<\/code><\/pre>\n<\/div>\n<p>\u898b\u3066\u308f\u304b\u308b\u3088\u3046\u306b\u3001\u3053\u306e2\u3064\u306e\u5c5e\u6027\u306b\u306f\u3044\u304f\u3064\u304b\u306eNULL\u5024\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002\u305d\u308c\u3089\u3092\u53d6\u308a\u9664\u3044\u3066\u304b\u3089\u5148\u306b\u9032\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset = dataset.dropna()<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">len(dataset)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">    6858<\/code><\/pre>\n<\/div>\n<p>\u3053\u3053\u3067\u3001<code>HouseAge<\/code>\u3068\u3044\u3046\u65b0\u3057\u3044\u5c5e\u6027\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u3053\u306e\u5c5e\u6027\u306e\u5024\u306f\u3001<code>YearBuilt<\/code>\u5c5e\u6027\u304b\u3089\u73fe\u5728\u306e\u5e74\u3092\u5dee\u3057\u5f15\u304f\u3053\u3068\u3067\u5f97\u3089\u308c\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u3082\u3046\u65e5\u4ed8\u3092\u6271\u3046\u5fc5\u8981\u304c\u306a\u3044\u305f\u3081\u4fbf\u5229\u3067\u3059\u3002\u3059\u3079\u3066\u306e\u5c5e\u6027\u304c\u6570\u5024\u3067\u8868\u3055\u308c\u308b\u3088\u3046\u306b\u306a\u3063\u305f\u306e\u3067\u3001\u5f8c\u306e\u6a5f\u68b0\u5b66\u7fd2\u3067\u5f79\u306b\u7acb\u3061\u305d\u3046\u3067\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset['HouseAge'] = 2022 - dataset[\"YearBuilt\"].astype(int)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset.head()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>\n        <\/th>\n<th>\n          Rooms\n        <\/th>\n<th>\n          Price\n        <\/th>\n<th>\n          Bedroom2\n        <\/th>\n<th>\n          Bathroom\n        <\/th>\n<th>\n          Landsize\n        <\/th>\n<th>\n          BuildingArea\n        <\/th>\n<th>\n          YearBuilt\n        <\/th>\n<th>\n          HouseAge\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          2\n        <\/td>\n<td>\n          1035000.0\n        <\/td>\n<td>\n          2.0\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          156.0\n        <\/td>\n<td>\n          79.0\n        <\/td>\n<td>\n          1900.0\n        <\/td>\n<td>\n          122\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          3\n        <\/td>\n<td>\n          1465000.0\n        <\/td>\n<td>\n          3.0\n        <\/td>\n<td>\n          2.0\n        <\/td>\n<td>\n          134.0\n        <\/td>\n<td>\n          150.0\n        <\/td>\n<td>\n          1900.0\n        <\/td>\n<td>\n          122\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          4\n        <\/td>\n<td>\n          1600000.0\n        <\/td>\n<td>\n          3.0\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          120.0\n        <\/td>\n<td>\n          142.0\n        <\/td>\n<td>\n          2014.0\n        <\/td>\n<td>\n          8\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          6\n        <\/th>\n<td>\n          3\n        <\/td>\n<td>\n          1876000.0\n        <\/td>\n<td>\n          4.0\n        <\/td>\n<td>\n          2.0\n        <\/td>\n<td>\n          245.0\n        <\/td>\n<td>\n          210.0\n        <\/td>\n<td>\n          1910.0\n        <\/td>\n<td>\n          112\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          7\n        <\/th>\n<td>\n          2\n        <\/td>\n<td>\n          1636000.0\n        <\/td>\n<td>\n          2.0\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          256.0\n        <\/td>\n<td>\n          107.0\n        <\/td>\n<td>\n          1890.0\n        <\/td>\n<td>\n          132\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>\u7d20\u6674\u3089\u3057\u3044! <code>YearBuilt<\/code> \u5c5e\u6027\u306f\u3082\u3046\u5fc5\u8981\u306a\u3044\u306e\u3067\u524a\u9664\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset = dataset.drop(\"YearBuilt\", axis=1)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">dataset.head()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>\n        <\/th>\n<th>\n          Rooms\n        <\/th>\n<th>\n          Price\n        <\/th>\n<th>\n          Bedroom2\n        <\/th>\n<th>\n          Bathroom\n        <\/th>\n<th>\n          Landsize\n        <\/th>\n<th>\n          BuildingArea\n        <\/th>\n<th>\n          HouseAge\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          2\n        <\/td>\n<td>\n          1035000.0\n        <\/td>\n<td>\n          2.0\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          156.0\n        <\/td>\n<td>\n          79.0\n        <\/td>\n<td>\n          122\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          3\n        <\/td>\n<td>\n          1465000.0\n        <\/td>\n<td>\n          3.0\n        <\/td>\n<td>\n          2.0\n        <\/td>\n<td>\n          134.0\n        <\/td>\n<td>\n          150.0\n        <\/td>\n<td>\n          122\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          4\n        <\/td>\n<td>\n          1600000.0\n        <\/td>\n<td>\n          3.0\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          120.0\n        <\/td>\n<td>\n          142.0\n        <\/td>\n<td>\n          8\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          6\n        <\/th>\n<td>\n          3\n        <\/td>\n<td>\n          1876000.0\n        <\/td>\n<td>\n          4.0\n        <\/td>\n<td>\n          2.0\n        <\/td>\n<td>\n          245.0\n        <\/td>\n<td>\n          210.0\n        <\/td>\n<td>\n          112\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          7\n        <\/th>\n<td>\n          2\n        <\/td>\n<td>\n          1636000.0\n        <\/td>\n<td>\n          2.0\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          256.0\n        <\/td>\n<td>\n          107.0\n        <\/td>\n<td>\n          132\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2>6&#46; \u30c7\u30fc\u30bf\u6b63\u898f\u5316<\/h2>\n<p>\u5148\u306b\u898b\u305f\u3088\u3046\u306b\u3001\u5c5e\u6027\u306e\u5024\u306f\u7570\u306a\u308b\u30b9\u30b1\u30fc\u30eb\u3092\u6301\u3063\u3066\u3044\u308b\u305f\u3081\u3001\u5024\u306e\u5927\u304d\u3044\u7279\u5fb4\u304c\u5c0f\u3055\u3044\u7279\u5fb4\u3088\u308a\u3082\u512a\u52e2\u306b\u306a\u308a\u3001\u683c\u5dee\u304c\u751f\u3058\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u3059\u3079\u3066\u306e\u5024\u30921\u3064\u306e\u30b9\u30b1\u30fc\u30eb\u306b\u3059\u308b\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002\u305d\u306e\u305f\u3081\u306b\u3001\u300cMin-Max Normalization\u300d\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002\u3053\u308c\u306f\u6700\u3082\u4e00\u822c\u7684\u306a\u624b\u6cd5\u306e\u4e00\u3064\u3067\u3001\u6700\u5c0f\u5024\u306f0\u306b\u3001\u6700\u5927\u5024\u306f1\u306b\u5909\u63db\u3055\u308c\u307e\u3059\u3002\u4ed6\u306e\u3059\u3079\u3066\u306e\u5024\u306f0\u30681\u306e\u9593\u306b\u5e83\u304c\u308a\u307e\u3059\u3002<\/p>\n<p>\u6b63\u898f\u5316\u306e\u305f\u3081\u306e\u76f4\u63a5\u7684\u306a\u65b9\u6cd5\u306f\u5b58\u5728\u3057\u307e\u3059\u304c\u3001\u305d\u308c\u3089\u306f\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092NumPy\u306e\u914d\u5217\u306b\u5909\u63db\u3057\u307e\u3059\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u6211\u3005\u306f\u3001\u5217\u540d\u3092\u5931\u3044\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u3001\u6211\u3005\u306f\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u53d6\u308a\u8fbc\u307f\u3001\u65b0\u3057\u3044\u6b63\u898f\u5316\u3055\u308c\u305f\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u8fd4\u3059\u72ec\u81ea\u306e\u30e1\u30bd\u30c3\u30c9\u3092\u5b9a\u7fa9\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">def normalize(df):\n    result = df.copy()\n    for feature_name in df.columns:\n        max_value = df[feature_name].max()\n        min_value = df[feature_name].min()\n        result[feature_name] = (df[feature_name] - min_value) \/ (max_value - min_value)\n    return result<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">df = normalize(dataset)<\/code><\/pre>\n<\/div>\n<p>\u6b63\u898f\u5316\u3055\u308c\u305f\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">df.head()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>\n        <\/th>\n<th>\n          Rooms\n        <\/th>\n<th>\n          Price\n        <\/th>\n<th>\n          Bedroom2\n        <\/th>\n<th>\n          Bathroom\n        <\/th>\n<th>\n          Landsize\n        <\/th>\n<th>\n          BuildingArea\n        <\/th>\n<th>\n          HouseAge\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          0.142857\n        <\/td>\n<td>\n          0.101928\n        <\/td>\n<td>\n          0.222222\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          0.004216\n        <\/td>\n<td>\n          0.025386\n        <\/td>\n<td>\n          0.143552\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          0.285714\n        <\/td>\n<td>\n          0.150412\n        <\/td>\n<td>\n          0.333333\n        <\/td>\n<td>\n          0.142857\n        <\/td>\n<td>\n          0.003622\n        <\/td>\n<td>\n          0.048201\n        <\/td>\n<td>\n          0.143552\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          0.428571\n        <\/td>\n<td>\n          0.165633\n        <\/td>\n<td>\n          0.333333\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          0.003243\n        <\/td>\n<td>\n          0.045630\n        <\/td>\n<td>\n          0.004866\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          6\n        <\/th>\n<td>\n          0.285714\n        <\/td>\n<td>\n          0.196753\n        <\/td>\n<td>\n          0.444444\n        <\/td>\n<td>\n          0.142857\n        <\/td>\n<td>\n          0.006622\n        <\/td>\n<td>\n          0.067481\n        <\/td>\n<td>\n          0.131387\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          7\n        <\/th>\n<td>\n          0.142857\n        <\/td>\n<td>\n          0.169692\n        <\/td>\n<td>\n          0.222222\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          0.006919\n        <\/td>\n<td>\n          0.034383\n        <\/td>\n<td>\n          0.155718\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>\u898b\u3066\u308f\u304b\u308b\u3088\u3046\u306b\u3001\u3059\u3079\u3066\u306e\u5024\u306f0\u30681\u306e\u9593\u306b\u3042\u308a\u307e\u3059\u3002\u6b21\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092 <code>train<\/code> \u3068 <code>test<\/code> \u306b\u5206\u5272\u3057\u307e\u3059\u3002<\/p>\n<h2>7&#46; \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5206\u5272<\/h2>\n<p>\u3053\u3053\u3067\u306f\u3001\u300c70-30\u300d\u306e\u5272\u5408\u3067 train \u3068 test \u306b\u5206\u5272\u3059\u308b\u3053\u3068\u306b\u3057\u307e\u3059\u3002\u3088\u308a\u5c0f\u3055\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5834\u5408\u306f\u3001\u300c80-20\u300d\u306e\u3088\u3046\u306b\u3059\u308b\u3053\u3068\u3082\u53ef\u80fd\u3067\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">train, test = train_test_split(df, test_size=0.3)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">len(train)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">    4800<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">len(test)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">    2058<\/code><\/pre>\n<\/div>\n<p>\u3053\u3053\u3067\u3001\u5f93\u5c5e\u5909\u6570\u3068\u72ec\u7acb\u5909\u6570\u3092\u5206\u96e2\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">train_y = train[[\"Price\"]]\ntrain_x = train.drop([\"Price\"], axis=1)\ntest_y = test[[\"Price\"]]\ntest_x = test.drop([\"Price\"], axis=1)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">train_x.head()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>\n        <\/th>\n<th>\n          Rooms\n        <\/th>\n<th>\n          Bedroom2\n        <\/th>\n<th>\n          Bathroom\n        <\/th>\n<th>\n          Landsize\n        <\/th>\n<th>\n          BuildingArea\n        <\/th>\n<th>\n          HouseAge\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          4860\n        <\/th>\n<td>\n          0.285714\n        <\/td>\n<td>\n          0.333333\n        <\/td>\n<td>\n          0.142857\n        <\/td>\n<td>\n          0.222054\n        <\/td>\n<td>\n          0.041774\n        <\/td>\n<td>\n          0.027981\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3434\n        <\/th>\n<td>\n          0.285714\n        <\/td>\n<td>\n          0.333333\n        <\/td>\n<td>\n          0.142857\n        <\/td>\n<td>\n          0.018459\n        <\/td>\n<td>\n          0.064267\n        <\/td>\n<td>\n          0.027981\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          6048\n        <\/th>\n<td>\n          0.285714\n        <\/td>\n<td>\n          0.333333\n        <\/td>\n<td>\n          0.285714\n        <\/td>\n<td>\n          0.005973\n        <\/td>\n<td>\n          0.049807\n        <\/td>\n<td>\n          0.008516\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          9918\n        <\/th>\n<td>\n          0.142857\n        <\/td>\n<td>\n          0.222222\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          0.007135\n        <\/td>\n<td>\n          0.031170\n        <\/td>\n<td>\n          0.131387\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          7855\n        <\/th>\n<td>\n          0.142857\n        <\/td>\n<td>\n          0.222222\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          0.030848\n        <\/td>\n<td>\n          0.155718\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">train_y.head()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>\n        <\/th>\n<th>\n          Price\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          4860\n        <\/th>\n<td>\n          0.103619\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3434\n        <\/th>\n<td>\n          0.064494\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          6048\n        <\/th>\n<td>\n          0.055136\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          9918\n        <\/th>\n<td>\n          0.174879\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          7855\n        <\/th>\n<td>\n          0.053219\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2>8&#46; \u30e2\u30c7\u30eb\u306e\u69cb\u7bc9<\/h2>\n<p>\u4eca\u56de\u306f\u3001\u300c\u7dda\u5f62\u56de\u5e30\u300d\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u3053\u308c\u306f\u5358\u7d14\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306a\u306e\u3067\u3001\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3067\u5341\u5206\u3067\u3057\u3087\u3046\u3002\u3088\u308a\u6d17\u7df4\u3055\u308c\u305f\u30e2\u30c7\u30eb\u3092\u4f5c\u308b\u306b\u306f\u3001\u6c7a\u5b9a\u6728\uff08Decision Trees\uff09\u3092\u4f7f\u3046\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>GridDB\u3068Python\u306b\u3088\u308b\u7dda\u5f62\u56de\u5e30\u306e\u8a73\u7d30\u306f<a href=\"https:\/\/griddb.net\/ja\/blog\/create-a-machine-learning-model-using-griddb\/\">\u3053\u3061\u3089<\/a>\u3092\u3054\u89a7\u304f\u3060\u3055\u3044\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">model =  LinearRegression()\nmodel.fit(train_x, train_y)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">    LinearRegression()<\/code><\/pre>\n<\/div>\n<h2>9&#46; \u4e88\u6e2c\u3059\u308b<\/h2>\n<p>\u305d\u308c\u3067\u306f\u3001<code>test<\/code>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5bfe\u3057\u3066\u4e88\u6e2c\u3092\u884c\u3063\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">predictions = model.predict(test_x)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">predictions<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">    array([[0.0890521 ],\n           [0.06244483],\n           [0.13166691],\n           ...,\n           [0.09182388],\n           [0.20981148],\n           [0.1077662 ]])<\/code><\/pre>\n<\/div>\n<h2>10&#46; \u30e2\u30c7\u30eb\u8a55\u4fa1<\/h2>\n<p>\u4e88\u6e2c\u304c\u3069\u306e\u7a0b\u5ea6\u512a\u308c\u3066\u3044\u308b\u304b\u3092\u5b9a\u91cf\u5316\u3059\u308b\u305f\u3081\u306b\u3001<code>sklearn<\/code>\u30e9\u30a4\u30d6\u30e9\u30ea\u304c\u63d0\u4f9b\u3059\u308b\u3044\u304f\u3064\u304b\u306e<a href=\"https:\/\/scikit-learn.org\/stable\/modules\/model_evaluation.html\">\u30e1\u30c8\u30ea\u30af\u30b9<\/a>\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3067\u3088\u304f\u4f7f\u308f\u308c\u308b<a href=\"https:\/\/scikit-learn.org\/stable\/modules\/model_evaluation.html#mean-squared-error\">mean_absolute_error<\/a>\u30e1\u30c8\u30ea\u30af\u30b9\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u306b\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">mean_absolute_error(predictions, test_y)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">    0.035125149637253696<\/code><\/pre>\n<\/div>\n<p>\u7d20\u6674\u3089\u3057\u3044\uff01\u6211\u3005\u306e\u30e2\u30c7\u30eb\u306f\u5e73\u5747\u7d76\u5bfe\u8aa4\u5dee\u304c<code>0.03<\/code>\u3067\u3001\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3068\u3057\u3066\u306f\u60aa\u3044\u30b9\u30bf\u30fc\u30c8\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/p>\n<h2>11&#46; \u7d50\u8ad6<\/h2>\n<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u4f4f\u5b85\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5bfe\u3057\u3066\u3069\u306e\u3088\u3046\u306b\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u304b\u3092\u898b\u3066\u304d\u307e\u3057\u305f\u3002\u6700\u521d\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u74b0\u5883\u306b\u8aad\u307f\u8fbc\u3080\u305f\u3081\u306e2\u3064\u306e\u65b9\u6cd5\u3001<code>GridDB<\/code>\u3068Pandas\u3092\u53d6\u308a\u4e0a\u3052\u307e\u3057\u305f\u3002\u307e\u305f\u3001\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30d7\u30eb\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3044\u307e\u3057\u305f\u3002\u305d\u306e\u5f8c\u3001<code>sklearn<\/code>\u30e9\u30a4\u30d6\u30e9\u30ea\u304c\u63d0\u4f9b\u3059\u308b <code>Linear Regression<\/code> \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u30d5\u30a3\u30c3\u30c8\u3055\u305b\u307e\u3057\u305f\u3002<\/p>\n<p>GridDB\u3068Python\u306b\u3088\u308b\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u4e88\u6e2c\u306b\u3064\u3044\u3066\u8a73\u3057\u304f\u77e5\u308a\u305f\u3044\u65b9\u306f<a href=\"https:\/\/griddb.net\/ja\/blog\/performing-real-time-predictions-using-machine-learning-griddb-and-python\/\">\u3053\u3061\u3089\u306e\u30d6\u30ed\u30b0<\/a>\u3082\u3054\u89a7\u304f\u3060\u3055\u3044\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python\u3092\u4f7f\u7528\u3057\u3066\u4f4f\u5b85\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8abf\u67fb\u3057\u307e\u3059\u3002\u307e\u305a\u3001\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30d7\u30eb\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3044\u307e\u3059\u3002\u305d\u306e\u5f8c\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3059\u308b\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u3001\u5c06\u6765\u306e\u4e88\u6e2c\u3092\u884c\u3046\u65b9\u6cd5\u3092\u898b\u307e\u3059\u3002 \u30c1 [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":49159,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50796","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Python\u3068GridDB\u3092\u7528\u3044\u3066\u4f4f\u5b85\u30c7\u30fc\u30bf\u306e\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" 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