{"id":50785,"date":"2022-02-24T00:00:00","date_gmt":"2022-02-24T08:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/imbalanced-classification-with-the-fraudulent-credit-card-transaction-dataset-using-python-and-griddb\/"},"modified":"2026-03-30T14:50:05","modified_gmt":"2026-03-30T21:50:05","slug":"imbalanced-classification-with-the-fraudulent-credit-card-transaction-dataset-using-python-and-griddb","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/imbalanced-classification-with-the-fraudulent-credit-card-transaction-dataset-using-python-and-griddb\/","title":{"rendered":"Python\u3068GridDB\u3092\u7528\u3044\u305f\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u4e0d\u6b63\u53d6\u5f15\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3088\u308b\u30a2\u30f3\u30d0\u30e9\u30f3\u30b9\u5206\u985e"},"content":{"rendered":"<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Kaggle\u3067\u516c\u958b\u3055\u308c\u3066\u3044\u308b\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u4e0d\u6b63\u53d6\u5f15\u691c\u51fa\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u63a2\u308a\u307e\u3059\u3002\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u4f1a\u793e\u306b\u3068\u3063\u3066\u3001\u640d\u5931\u3092\u907f\u3051\u308b\u305f\u3081\u306b\u4e0d\u6b63\u3092\u691c\u51fa\u3059\u308b\u3053\u3068\u306f\u975e\u5e38\u306b\u91cd\u8981\u3067\u3042\u308a\u3001\u540c\u6642\u306b\u9867\u5ba2\u304c\u5b9f\u969b\u306b\u8cfc\u5165\u3057\u3066\u3044\u306a\u3044\u3082\u306e\u3092\u8acb\u6c42\u3057\u306a\u3044\u3088\u3046\u306b\u3059\u308b\u3053\u3068\u3082\u91cd\u8981\u3067\u3059\u3002\u6211\u3005\u306f\u3001GridDB\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u3092\u62bd\u51fa\u3057\u3001\u4e0d\u6b63\u3092\u6b63\u78ba\u306b\u691c\u51fa\u3059\u308b\u305f\u3081\u306e\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\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>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u6982\u8981<\/li>\n<li>\u5fc5\u8981\u306a\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>\u63a2\u7d22\u7684\u30c7\u30fc\u30bf\u89e3\u6790<\/li>\n<li>\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u69cb\u7bc9<\/li>\n<li>\u30e2\u30c7\u30eb\u8a55\u4fa1<\/li>\n<li>\u7d50\u8ad6<\/li>\n<li>\u53c2\u8003\u6587\u732e<\/li>\n<\/ol>\n<h2>GridDB\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/h2>\n<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30ed\u30fc\u30c9\u3059\u308b\u969b\u306b\u3001GridDB\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3068\u3001Pandas\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u306e2\u3064\u3092\u53d6\u308a\u4e0a\u3052\u307e\u3059\u3002Python\u3092\u4f7f\u7528\u3057\u3066GridDB\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u305f\u3081\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u3082\u4e88\u3081\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304a\u304f\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ol>\n<li><a href=\"https:\/\/github.com\/griddb\/c_client\">GridDB C-\u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/a>\u3067\u3059\u3002<\/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>\u3067\u3059\u3002<\/li>\n<\/ol>\n<h2>1&#46; \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u6982\u8981<\/h2>\n<p>\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u30012013\u5e749\u6708\u306e2\u65e5\u9593\u306b\u30e8\u30fc\u30ed\u30c3\u30d1\u306e\u30ab\u30fc\u30c9\u4f1a\u54e1\u304c\u884c\u3063\u305f\u3059\u3079\u3066\u306e\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u53d6\u5f15\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002\u5408\u8a08284,407\u4ef6\u306e\u53d6\u5f15\u304c\u3042\u308a\u3001\u305d\u306e\u3046\u3061492\u4ef6\u304c\u4e0d\u6b63\u53d6\u5f15\u3067\u3059\u3002\u660e\u3089\u304b\u306b\u3001\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u975e\u5e38\u306b\u30a2\u30f3\u30d0\u30e9\u30f3\u30b9\u3067\u3042\u308a\u3001\u8a50\u6b3a\u306e\u30af\u30e9\u30b9\u306f\u5168\u53d6\u5f15\u306e0.172%\u3057\u304b\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u305b\u3093\u3002<\/p>\n<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306bPCA\uff08\u4e3b\u6210\u5206\u5206\u6790\uff09\u5909\u63db\u3092\u9069\u7528\u3057\u305f\u3002PCA\u5909\u63db\u3068\u306f\u3001\u76f8\u95a2\u306e\u3042\u308b\u53ef\u80fd\u6027\u306e\u3042\u308b\u5909\u6570\u3092\u3001\u4e3b\u6210\u5206\u3068\u547c\u3070\u308c\u308b\u7dda\u5f62\u76f8\u95a2\u306e\u306a\u3044\u5909\u6570\u306e\u5024\u306e\u96c6\u5408\u306b\u5909\u63db\u3059\u308b\u3053\u3068\u3092\u6307\u3057\u307e\u3059\u3002PCA\u3067\u306f\u3001V1, V2, &#8230;, V28\u304c\u7279\u5fb4\u91cf\u3068\u3057\u3066\u4e0e\u3048\u3089\u308c\u3001\u5909\u63db\u3055\u308c\u3066\u3044\u306a\u3044\u552f\u4e00\u306e\u7279\u5fb4\u91cf\u306f\u3001\u53d6\u5f15\u6642\u523b\u3068\u53d6\u5f15\u984d\u3067\u3059\u3002\u6b8b\u308a\u306e\u751f\u306e\uff08\u5143\u306e\uff09\u7279\u5fb4\u306f\u6a5f\u5bc6\u60c5\u5831\u3092\u542b\u3093\u3067\u3044\u308b\u306e\u3067\u3001\u3053\u308c\u3089\u306f\u6700\u7d42\u7684\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u542b\u307e\u308c\u307e\u305b\u3093\u3002<\/p>\n<p>\u6a5f\u80fd\u306e\u8aac\u660e<\/p>\n<ul>\n<li>Time\uff1a\u5404\u30c8\u30e9\u30f3\u30b6\u30af\u30b7\u30e7\u30f3\u3068\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6700\u521d\u306e\u30c8\u30e9\u30f3\u30b6\u30af\u30b7\u30e7\u30f3\u3068\u306e\u9593\u306e\u7d4c\u904e\u79d2\u6570\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<\/li>\n<li>Acount\uff1a\u53d6\u5f15\u91d1\u984d\u3067\u3059\u3002<\/p>\n<\/li>\n<li>\n<p>V1\u3001V2\u3001&#8230;\u3001V28\uff1a\u5909\u63db\u3055\u308c\u305f\u7279\u5fb4\u91cf\u3067\u3042\u308a\u3001\u5fc5\u305a\u3057\u3082\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u53d6\u5f15\u306b\u95a2\u9023\u3059\u308b\u751f\u306e\u7279\u5fb4\u91cf\u3067\u306f\u306a\u3044\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ul>\n<p>\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u4e00\u822c\u306b\u516c\u958b\u3055\u308c\u3066\u304a\u308a\u3001<a href=\"https:\/\/www.kaggle.com\/mlg-ulb\/creditcardfraud\">Kaggle<\/a>\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306b\u6cbf\u3063\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<h2>2&#46; \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<\/h2>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.metrics import precision_recall_curve\nfrom sklearn.metrics import auc\nfrom sklearn.metrics import make_scorer\nfrom sklearn.metrics import classification_report\n\nfrom sklearn.model_selection import cross_val_score<\/code><\/pre>\n<\/div>\n<p>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306b\u5931\u6557\u3057\u305f\u5834\u5408\u306f\u3001\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u306b <code>pip install package-name<\/code> \u3068\u5165\u529b\u3059\u308b\u3053\u3068\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001conda\u306e\u4eee\u60f3\u74b0\u5883\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u5834\u5408\u306f\u3001<code>conda install package-name<\/code>\u3068\u5165\u529b\u3059\u308b\u3053\u3068\u3067\u3082\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u307e\u3059\u3002<\/p>\n<h2>3&#46; \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aad\u307f\u8fbc\u307f<\/h2>\n<p>\u7d9a\u3051\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30ce\u30fc\u30c8\u30d6\u30c3\u30af\u306b\u30ed\u30fc\u30c9\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<h4>3&#46;a GridDB\u306e\u4f7f\u7528<\/h4>\n<p>\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u3092\u4fdd\u5b58\u3059\u308b\u5834\u5408\u3001CSV\u30d5\u30a1\u30a4\u30eb\u3067\u306f\u9762\u5012\u306a\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002GridDB\u306f\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u3067\u3042\u308a\u3001\u62e1\u5f35\u6027\u306e\u9ad8\u3044\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3067\u3042\u308b\u305f\u3081\u3001\u5b8c\u74a7\u306a\u4ee3\u66ff\u6848\u3068\u3057\u3066\u6a5f\u80fd\u3057\u307e\u3059\u3002GridDB\u306f\u3001\u30b9\u30b1\u30fc\u30e9\u30d6\u30eb\u3067\u30a4\u30f3\u30e1\u30e2\u30ea\u306aNo SQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3067\u3001\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u3092\u7c21\u5358\u306b\u4fdd\u5b58\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002GridDB\u3092\u521d\u3081\u3066\u4f7f\u3046\u5834\u5408\u306f\u3001\u300cGridDB\u3078\u306e\u8aad\u307f\u66f8\u304d\u300d<a href=\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-pandas-dataframes-with-griddb\/\">4<\/a>\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u304c\u5f79\u306b\u7acb\u3061\u307e\u3059\u3002<\/p>\n<p>\u3059\u3067\u306b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u8a2d\u5b9a\u304c\u6e08\u3093\u3067\u3044\u308b\u3068\u4eee\u5b9a\u3057\u3066\u3001\u4eca\u5ea6\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u3080\u305f\u3081\u306eSQL\u30af\u30a8\u30ea\u3092python\u3067\u66f8\u3044\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 credit_card_dataset')\nheart_dataset = pd.read_sql_query(sql_statement, cont)<\/code><\/pre>\n<\/div>\n<p>\u5909\u6570 <code>cont<\/code> \u306b\u306f\u3001\u30c7\u30fc\u30bf\u304c\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\u30b3\u30f3\u30c6\u30ca\u60c5\u5831\u304c\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002credit_card_dataset` \u3092\u30b3\u30f3\u30c6\u30ca\u306e\u540d\u524d\u306b\u7f6e\u304d\u63db\u3048\u3066\u304f\u3060\u3055\u3044\u3002\u8a73\u7d30\u306f\u3001\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb<a href=\"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-pandas-dataframes-with-griddb\/\">GridDB\u3078\u306e\u8aad\u307f\u66f8\u304d<\/a>\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<h4>3&#46;b Pandas\u306e\u4f7f\u7528<\/h4>\n<p>Pandas\u306e <code>read_csv<\/code> \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3080\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002\u3069\u3061\u3089\u306e\u65b9\u6cd5\u3067\u3082\u3001pandas\u306edataframe\u306e\u5f62\u3067\u30c7\u30fc\u30bf\u304c\u8aad\u307f\u8fbc\u307e\u308c\u308b\u306e\u3067\u3001\u4e0a\u8a18\u306e\u3069\u3061\u3089\u306e\u65b9\u6cd5\u3067\u3082\u540c\u3058\u51fa\u529b\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code>credit_card_dataset = pd.read_csv('creditcard.csv')<\/code><\/pre>\n<\/div>\n<h2>4&#46; \u63a2\u7d22\u7684\u30c7\u30fc\u30bf\u89e3\u6790<\/h2>\n<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u8aad\u307f\u8fbc\u307e\u308c\u305f\u3089\u3001\u6b21\u306f\u305d\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8abf\u3079\u3066\u307f\u307e\u3057\u3087\u3046\u3002head()\u95a2\u6570\u306e\u5f15\u6570\u306b10\u3092\u6e21\u3057\u3066\u3001\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6700\u521d\u306e10\u884c\u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code>credit_card_dataset.head(10)<\/code><\/pre>\n<\/div>\n<div style=\"overflow-x: auto;\">\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          Time\n        <\/th>\n<th>\n          V1\n        <\/th>\n<th>\n          V2\n        <\/th>\n<th>\n          V3\n        <\/th>\n<th>\n          V4\n        <\/th>\n<th>\n          V5\n        <\/th>\n<th>\n          V6\n        <\/th>\n<th>\n          V7\n        <\/th>\n<th>\n          V8\n        <\/th>\n<th>\n          V9\n        <\/th>\n<th>\n          &#8230;\n        <\/th>\n<th>\n          V21\n        <\/th>\n<th>\n          V22\n        <\/th>\n<th>\n          V23\n        <\/th>\n<th>\n          V24\n        <\/th>\n<th>\n          V25\n        <\/th>\n<th>\n          V26\n        <\/th>\n<th>\n          V27\n        <\/th>\n<th>\n          V28\n        <\/th>\n<th>\n          Amount\n        <\/th>\n<th>\n          Class\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          0\n        <\/th>\n<td>\n          0.0\n        <\/td>\n<td>\n          -1.359807\n        <\/td>\n<td>\n          -0.072781\n        <\/td>\n<td>\n          2.536347\n        <\/td>\n<td>\n          1.378155\n        <\/td>\n<td>\n          -0.338321\n        <\/td>\n<td>\n          0.462388\n        <\/td>\n<td>\n          0.239599\n        <\/td>\n<td>\n          0.098698\n        <\/td>\n<td>\n          0.363787\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          -0.018307\n        <\/td>\n<td>\n          0.277838\n        <\/td>\n<td>\n          -0.110474\n        <\/td>\n<td>\n          0.066928\n        <\/td>\n<td>\n          0.128539\n        <\/td>\n<td>\n          -0.189115\n        <\/td>\n<td>\n          0.133558\n        <\/td>\n<td>\n          -0.021053\n        <\/td>\n<td>\n          149.62\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          0.0\n        <\/td>\n<td>\n          1.191857\n        <\/td>\n<td>\n          0.266151\n        <\/td>\n<td>\n          0.166480\n        <\/td>\n<td>\n          0.448154\n        <\/td>\n<td>\n          0.060018\n        <\/td>\n<td>\n          -0.082361\n        <\/td>\n<td>\n          -0.078803\n        <\/td>\n<td>\n          0.085102\n        <\/td>\n<td>\n          -0.255425\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          -0.225775\n        <\/td>\n<td>\n          -0.638672\n        <\/td>\n<td>\n          0.101288\n        <\/td>\n<td>\n          -0.339846\n        <\/td>\n<td>\n          0.167170\n        <\/td>\n<td>\n          0.125895\n        <\/td>\n<td>\n          -0.008983\n        <\/td>\n<td>\n          0.014724\n        <\/td>\n<td>\n          2.69\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          1.0\n        <\/td>\n<td>\n          -1.358354\n        <\/td>\n<td>\n          -1.340163\n        <\/td>\n<td>\n          1.773209\n        <\/td>\n<td>\n          0.379780\n        <\/td>\n<td>\n          -0.503198\n        <\/td>\n<td>\n          1.800499\n        <\/td>\n<td>\n          0.791461\n        <\/td>\n<td>\n          0.247676\n        <\/td>\n<td>\n          -1.514654\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          0.247998\n        <\/td>\n<td>\n          0.771679\n        <\/td>\n<td>\n          0.909412\n        <\/td>\n<td>\n          -0.689281\n        <\/td>\n<td>\n          -0.327642\n        <\/td>\n<td>\n          -0.139097\n        <\/td>\n<td>\n          -0.055353\n        <\/td>\n<td>\n          -0.059752\n        <\/td>\n<td>\n          378.66\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3\n        <\/th>\n<td>\n          1.0\n        <\/td>\n<td>\n          -0.966272\n        <\/td>\n<td>\n          -0.185226\n        <\/td>\n<td>\n          1.792993\n        <\/td>\n<td>\n          -0.863291\n        <\/td>\n<td>\n          -0.010309\n        <\/td>\n<td>\n          1.247203\n        <\/td>\n<td>\n          0.237609\n        <\/td>\n<td>\n          0.377436\n        <\/td>\n<td>\n          -1.387024\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          -0.108300\n        <\/td>\n<td>\n          0.005274\n        <\/td>\n<td>\n          -0.190321\n        <\/td>\n<td>\n          -1.175575\n        <\/td>\n<td>\n          0.647376\n        <\/td>\n<td>\n          -0.221929\n        <\/td>\n<td>\n          0.062723\n        <\/td>\n<td>\n          0.061458\n        <\/td>\n<td>\n          123.50\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          2.0\n        <\/td>\n<td>\n          -1.158233\n        <\/td>\n<td>\n          0.877737\n        <\/td>\n<td>\n          1.548718\n        <\/td>\n<td>\n          0.403034\n        <\/td>\n<td>\n          -0.407193\n        <\/td>\n<td>\n          0.095921\n        <\/td>\n<td>\n          0.592941\n        <\/td>\n<td>\n          -0.270533\n        <\/td>\n<td>\n          0.817739\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          -0.009431\n        <\/td>\n<td>\n          0.798278\n        <\/td>\n<td>\n          -0.137458\n        <\/td>\n<td>\n          0.141267\n        <\/td>\n<td>\n          -0.206010\n        <\/td>\n<td>\n          0.502292\n        <\/td>\n<td>\n          0.219422\n        <\/td>\n<td>\n          0.215153\n        <\/td>\n<td>\n          69.99\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          5\n        <\/th>\n<td>\n          2.0\n        <\/td>\n<td>\n          -0.425966\n        <\/td>\n<td>\n          0.960523\n        <\/td>\n<td>\n          1.141109\n        <\/td>\n<td>\n          -0.168252\n        <\/td>\n<td>\n          0.420987\n        <\/td>\n<td>\n          -0.029728\n        <\/td>\n<td>\n          0.476201\n        <\/td>\n<td>\n          0.260314\n        <\/td>\n<td>\n          -0.568671\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          -0.208254\n        <\/td>\n<td>\n          -0.559825\n        <\/td>\n<td>\n          -0.026398\n        <\/td>\n<td>\n          -0.371427\n        <\/td>\n<td>\n          -0.232794\n        <\/td>\n<td>\n          0.105915\n        <\/td>\n<td>\n          0.253844\n        <\/td>\n<td>\n          0.081080\n        <\/td>\n<td>\n          3.67\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          6\n        <\/th>\n<td>\n          4.0\n        <\/td>\n<td>\n          1.229658\n        <\/td>\n<td>\n          0.141004\n        <\/td>\n<td>\n          0.045371\n        <\/td>\n<td>\n          1.202613\n        <\/td>\n<td>\n          0.191881\n        <\/td>\n<td>\n          0.272708\n        <\/td>\n<td>\n          -0.005159\n        <\/td>\n<td>\n          0.081213\n        <\/td>\n<td>\n          0.464960\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          -0.167716\n        <\/td>\n<td>\n          -0.270710\n        <\/td>\n<td>\n          -0.154104\n        <\/td>\n<td>\n          -0.780055\n        <\/td>\n<td>\n          0.750137\n        <\/td>\n<td>\n          -0.257237\n        <\/td>\n<td>\n          0.034507\n        <\/td>\n<td>\n          0.005168\n        <\/td>\n<td>\n          4.99\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          7\n        <\/th>\n<td>\n          7.0\n        <\/td>\n<td>\n          -0.644269\n        <\/td>\n<td>\n          1.417964\n        <\/td>\n<td>\n          1.074380\n        <\/td>\n<td>\n          -0.492199\n        <\/td>\n<td>\n          0.948934\n        <\/td>\n<td>\n          0.428118\n        <\/td>\n<td>\n          1.120631\n        <\/td>\n<td>\n          -3.807864\n        <\/td>\n<td>\n          0.615375\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1.943465\n        <\/td>\n<td>\n          -1.015455\n        <\/td>\n<td>\n          0.057504\n        <\/td>\n<td>\n          -0.649709\n        <\/td>\n<td>\n          -0.415267\n        <\/td>\n<td>\n          -0.051634\n        <\/td>\n<td>\n          -1.206921\n        <\/td>\n<td>\n          -1.085339\n        <\/td>\n<td>\n          40.80\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          8\n        <\/th>\n<td>\n          7.0\n        <\/td>\n<td>\n          -0.894286\n        <\/td>\n<td>\n          0.286157\n        <\/td>\n<td>\n          -0.113192\n        <\/td>\n<td>\n          -0.271526\n        <\/td>\n<td>\n          2.669599\n        <\/td>\n<td>\n          3.721818\n        <\/td>\n<td>\n          0.370145\n        <\/td>\n<td>\n          0.851084\n        <\/td>\n<td>\n          -0.392048\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          -0.073425\n        <\/td>\n<td>\n          -0.268092\n        <\/td>\n<td>\n          -0.204233\n        <\/td>\n<td>\n          1.011592\n        <\/td>\n<td>\n          0.373205\n        <\/td>\n<td>\n          -0.384157\n        <\/td>\n<td>\n          0.011747\n        <\/td>\n<td>\n          0.142404\n        <\/td>\n<td>\n          93.20\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          9\n        <\/th>\n<td>\n          9.0\n        <\/td>\n<td>\n          -0.338262\n        <\/td>\n<td>\n          1.119593\n        <\/td>\n<td>\n          1.044367\n        <\/td>\n<td>\n          -0.222187\n        <\/td>\n<td>\n          0.499361\n        <\/td>\n<td>\n          -0.246761\n        <\/td>\n<td>\n          0.651583\n        <\/td>\n<td>\n          0.069539\n        <\/td>\n<td>\n          -0.736727\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          -0.246914\n        <\/td>\n<td>\n          -0.633753\n        <\/td>\n<td>\n          -0.120794\n        <\/td>\n<td>\n          -0.385050\n        <\/td>\n<td>\n          -0.069733\n        <\/td>\n<td>\n          0.094199\n        <\/td>\n<td>\n          0.246219\n        <\/td>\n<td>\n          0.083076\n        <\/td>\n<td>\n          3.68\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>    10 rows \u00d7 31 columns<\/p>\n<\/div>\n<p>\u3053\u3053\u3067\u3001<code>Time<\/code>\u306f\u6570\u5024\u5909\u6570\u3067\u3059\u3002\u305d\u308c\u306f\u6709\u7528\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002\u53d6\u5f15\u91d1\u984d\u3092\u542b\u3080 <code>Amount<\/code> \u30ab\u30e9\u30e0\u304c\u3042\u308a\u307e\u3059\u3002\u6b21\u306b\u3001\u5bfe\u8c61\u5909\u6570 <code>Class<\/code> \u306e\u5206\u5e03\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f284,315\u4ef6\u306e\u975e\u4e0d\u6b63\u53d6\u5f15\u3068492\u4ef6\u306e\u4e0d\u6b63\u53d6\u5f15\u304c\u3042\u308a\u3001\u4e0d\u6b63\u3068\u5206\u985e\u3055\u308c\u308b\u53d6\u5f15\u306f0.172%\u3060\u3051\u306a\u306e\u3067\u3001\u975e\u5e38\u306b\u30a2\u30f3\u30d0\u30e9\u30f3\u30b9\u306a\u72b6\u614b\u306b\u306a\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code>credit_card_dataset.Class.value_counts()<\/code><\/pre>\n<\/div>\n<pre><code>    0    284315\n    1       492\n    Name: Class, dtype: int64\n<\/code><\/pre>\n<p>\u5909\u6570\u306e\u5206\u5e03\u3092\u8abf\u3079\u308b\u305f\u3081\u306b\u3001\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3068\u3057\u3066\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u30d7\u30ed\u30c3\u30c8\u7a7a\u9593\u3092\u6574\u7406\u3059\u308b\u305f\u3081\u306b\u3001\u30bf\u30fc\u30b2\u30c3\u30c8\u5909\u6570\u3068\u8ef8\u30e9\u30d9\u30eb\u3092\u524a\u9664\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">credit_card_dataset_without_target = credit_card_dataset.drop(columns = ['Class'], axis=1)\n\nvariable_hist = credit_card_dataset_without_target.hist(bins=100)\n\nfor axis in variable_hist.flatten():\n    axis.set_xticklabels([])\n    axis.set_yticklabels([])\n\nplt.figure(figsize = (20,15))\nplt.show()<\/code><\/pre>\n<\/div>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/02\/43aa0a7ecd4e82b1476db58bd40e1707a11f7084.png\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/02\/43aa0a7ecd4e82b1476db58bd40e1707a11f7084.png\" alt=\"\" width=\"352\" height=\"251\" class=\"aligncenter size-full wp-image-28070\" srcset=\"\/wp-content\/uploads\/2022\/02\/43aa0a7ecd4e82b1476db58bd40e1707a11f7084.png 352w, \/wp-content\/uploads\/2022\/02\/43aa0a7ecd4e82b1476db58bd40e1707a11f7084-300x214.png 300w\" sizes=\"(max-width: 352px) 100vw, 352px\" \/><\/a><\/p>\n<p>\u4e0a\u8a18\u306e\u3088\u3046\u306b\u3001\u307b\u3068\u3093\u3069\u306e\u5909\u6570\u304c\u30ac\u30a6\u30b9\u5206\u5e03\u3067\u3042\u308a\u3001\u305d\u306e\u591a\u304f\u304c\u30bc\u30ed\u3092\u4e2d\u5fc3\u3068\u3057\u305f\u5206\u5e03\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u5909\u6570\u304cPCA\u5909\u63db\u3092\u7d4c\u305f\u3068\u304d\u306b\u6a19\u6e96\u5316\u3055\u308c\u305f\u53ef\u80fd\u6027\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<h2>5&#46; \u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u69cb\u7bc9<\/h2>\n<p>\u305d\u308c\u3067\u306f\u3001\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u3063\u3066\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u3001\u8a55\u4fa1\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u307e\u305a\u3001\u30e2\u30c7\u30eb\u306e\u7279\u5fb4\uff08features\uff09\u3068\u30e9\u30d9\u30eb\uff08labels\uff09\u3092\u4f5c\u6210\u3057\u3001\u8a13\u7df4\u7528\uff08train\uff09\u3068\u30c6\u30b9\u30c8\u7528\uff08test\uff09\u306e\u30b5\u30f3\u30d7\u30eb\u306b\u5206\u5272\u3057\u307e\u3059\u3002\u30c6\u30b9\u30c8\u30b5\u30f3\u30d7\u30eb\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5168\u4f53\u306e20%\u3067\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">features = credit_card_dataset.drop(columns = ['Time', 'Class'], axis = 1)\nlabels = credit_card_dataset[['Class']]\n\nX_train, X_test, y_train, y_test = train_test_split(features, labels, test_size = 0.2, random_state = 0)<\/code><\/pre>\n<\/div>\n<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306e\u76ee\u7684\u306f\u3001<strong>k-Nearest Neighbors<\/strong> \u5206\u985e\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u3053\u3068\u3067\u3059\u3002k-\u6700\u8fd1\u508d\u30e2\u30c7\u30eb\u3067\u306f\u3001\u4e88\u6e2c\u306e\u305f\u3081\u306b\u3001\u6700\u3082\u4f3c\u3066\u3044\u308b\uff08\u8fd1\u3044\uff09k\u500b\u306e\u70b9\u3092\u898b\u3066\u3001\u305d\u308c\u306b\u5fdc\u3058\u3066\u4e88\u6e2c\u3092\u884c\u3044\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001\u300cn_neighbors\u300d\u30d1\u30e9\u30e1\u30fc\u30bf\u30923\u3068\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">model = KNeighborsClassifier(n_neighbors = 3)\nmodel.fit(X_train, y_train)<\/code><\/pre>\n<\/div>\n<pre><code> {.output .stream .stderr} \/Users\/aniket\/opt\/anaconda3\/lib\/python3.9\/site-packages\/sklearn\/neighbors\/_classification.py:179: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel(). return self._fit(X, y)\n\n {.output .execute_result execution_count=\"7\"} KNeighborsClassifier(n_neighbors=3)\n<\/code><\/pre>\n<p>\u5b66\u7fd2\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3066\u30e2\u30c7\u30eb\u306e\u9069\u5408\u304c\u884c\u308f\u308c\u305f\u5f8c\u3001\u30e2\u30c7\u30eb\u306e\u6027\u80fd\u3092\u8a55\u4fa1\u3059\u308b\u305f\u3081\u306b\u3001\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u306b\u5bfe\u3059\u308b\u4e88\u6e2c\u306b\u9032\u3080\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u4e88\u6e2c\u5024\u3092 <code>predicted<\/code> \u306b\u683c\u7d0d\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code>predicted = model.predict(X_test)<\/code><\/pre>\n<\/div>\n<h2>6&#46; \u30e2\u30c7\u30eb\u8a55\u4fa1<\/h2>\n<p>\u6211\u3005\u306f\u3001classification report` \u30e1\u30c8\u30ea\u30c3\u30af\u3092\u4f7f\u7528\u3057\u3066\u3001modelpha\u306e\u6027\u80fd\u3092\u8a55\u4fa1\u3057\u307e\u3059\u3002Classification report\u306f\u5206\u985e\u30e2\u30c7\u30eb\u3092\u8a55\u4fa1\u3059\u308b\u305f\u3081\u306b\u5e83\u304f\u4f7f\u308f\u308c\u3066\u3044\u308b\u6307\u6a19\u3067\u3059\u3002\u3053\u308c\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u51fa\u529b\u3055\u308c\u307e\u3059\u3002<\/p>\n<ol>\n<li><strong>Precision<\/strong>: \u4e88\u6e2c\u3055\u308c\u305f\u6b63\u306e\u30aa\u30d6\u30b6\u30d9\u30fc\u30b7\u30e7\u30f3\u306e\u5408\u8a08\u306b\u5bfe\u3059\u308b\uff0c\u6b63\u3057\u304f\u4e88\u6e2c\u3055\u308c\u305f\u6b63\u306e\u30aa\u30d6\u30b6\u30d9\u30fc\u30b7\u30e7\u30f3\u306e\u6bd4\u7387<\/li>\n<li><strong>Recall<\/strong>: \u5b9f\u969b\u306e\u30af\u30e9\u30b9\u3067\u306e\u5168\u30aa\u30d6\u30b6\u30d9\u30fc\u30b7\u30e7\u30f3\u306b\u5bfe\u3059\u308b\u6b63\u3057\u304f\u4e88\u6e2c\u3055\u308c\u305f\u30dd\u30b8\u30c6\u30a3\u30d6\u30aa\u30d6\u30b6\u30d9\u30fc\u30b7\u30e7\u30f3\u306e\u6bd4\u7387<\/li>\n<li><strong>F1 score<\/strong>: Precision\u3068Recall\u306e\u8abf\u548c\u5e73\u5747<\/li>\n<li><strong>Support<\/strong>: \u5404\u30af\u30e9\u30b9\u3067\u4e88\u6e2c\u3055\u308c\u305f\u30aa\u30d6\u30b6\u30d9\u30fc\u30b7\u30e7\u30f3\u306e\u6570<\/li>\n<\/ol>\n<div class=\"clipboard\">\n<pre><code>print(classification_report(predicted,y_test))<\/code><\/pre>\n<\/div>\n<pre><code> {.output .stream .stdout}\n                  precision    recall  f1-score   support\n\n               0       1.00      1.00      1.00     56882\n               1       0.72      0.91      0.81        80\n\n        accuracy                           1.00     56962\n       macro avg       0.86      0.96      0.90     56962\n    weighted avg       1.00      1.00      1.00     56962\n<\/code><\/pre>\n<h2>7&#46; \u7d50\u8ad6<\/h2>\n<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001GriDB\u3068Python\u3092\u4f7f\u7528\u3057\u3066\u3001\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u8a50\u6b3a\u691c\u51fa\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5206\u985e\u5668\u3092\u69cb\u7bc9\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u898b\u3066\u304d\u307e\u3057\u305f\u3002\u30c7\u30fc\u30bf\u306e\u30a4\u30f3\u30dd\u30fc\u30c8\u65b9\u6cd5\u3068\u3057\u3066\u3001(1)GridDB\u3001(2)Pandas\u306e2\u3064\u306e\u65b9\u6cd5\u3092\u691c\u8a0e\u3057\u307e\u3057\u305f\u3002GridDB\u306f\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u3067\u62e1\u5f35\u6027\u304c\u9ad8\u3044\u305f\u3081\u3001\u5927\u898f\u6a21\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5834\u5408\u3001\u30ce\u30fc\u30c8\u30d6\u30c3\u30af\u306b\u30c7\u30fc\u30bf\u3092\u53d6\u308a\u8fbc\u3080\u305f\u3081\u306e\u512a\u308c\u305f\u4ee3\u66ff\u624b\u6bb5\u3068\u306a\u308a\u307e\u3059\u3002<a href=\"https:\/\/griddb.net\/ja\/downloads\/\">GridDB\u3092\u4eca\u3059\u3050\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/a>\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<h2>8&#46; \u53c2\u8003\u6587\u732e<\/h2>\n<ol>\n<li><a href=\"https:\/\/www.kaggle.com\/mlg-ulb\/creditcardfraud\">https:\/\/www.kaggle.com\/mlg-ulb\/creditcardfraud<\/a><\/li>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.neighbors.KNeighborsClassifier.html\">https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.neighbors.KNeighborsClassifier.html<\/a><\/li>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.train_test_split.html\">https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.train_test_split.html<\/a><\/li>\n<li><a href=\"https:\/\/www.scribd.com\/document\/557372014\/Accuracy-Precision-Recall-F1-Score-Interpretation-of-Performance-Measures\">https:\/\/blog.exsilio.com\/all\/accuracy-precision-recall-f1-score-interpretation-of-performance-measures\/<\/a><\/li>\n<\/ol>\n<p><!-- Created with Elementor --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Kaggle\u3067\u516c\u958b\u3055\u308c\u3066\u3044\u308b\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u4e0d\u6b63\u53d6\u5f15\u691c\u51fa\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u63a2\u308a\u307e\u3059\u3002\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u4f1a\u793e\u306b\u3068\u3063\u3066\u3001\u640d\u5931\u3092\u907f\u3051\u308b\u305f\u3081\u306b\u4e0d\u6b63\u3092\u691c\u51fa\u3059\u308b\u3053\u3068\u306f\u975e\u5e38\u306b\u91cd\u8981\u3067\u3042\u308a\u3001\u540c\u6642\u306b\u9867\u5ba2\u304c\u5b9f\u969b\u306b\u8cfc\u5165\u3057\u3066\u3044 [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":49367,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50785","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\u305f\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u4e0d\u6b63\u53d6\u5f15\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3088\u308b\u30a2\u30f3\u30d0\u30e9\u30f3\u30b9\u5206\u985e | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" 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