{"id":50740,"date":"2021-05-06T00:00:00","date_gmt":"2021-05-06T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/use-machine-learning-and-griddb-to-detect-phishing-websites\/"},"modified":"2025-11-14T07:54:34","modified_gmt":"2025-11-14T15:54:34","slug":"use-machine-learning-and-griddb-to-detect-phishing-websites","status":"publish","type":"post","link":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/use-machine-learning-and-griddb-to-detect-phishing-websites\/","title":{"rendered":"\u6a5f\u68b0\u5b66\u7fd2\u3068GridDB\u3092\u5229\u7528\u3057\u305f\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u30b5\u30a4\u30c8\u306e\u691c\u51fa"},"content":{"rendered":"<h2>\u306f\u3058\u3081\u306b\u3000\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u30b5\u30a4\u30c8\u3068\u306f\u4f55\u304b\uff1f<\/h2>\n<p>\u597d\u5947\u5fc3\u306e\u305b\u3044\u3067\u3001\u500b\u4eba\u60c5\u5831\u304c\u60aa\u8005\u306b\u6d41\u51fa\u3057\u3066\u3057\u307e\u3046\u5371\u967a\u306b\u3042\u3046\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u77e5\u308a\u5408\u3044\u304b\u3089\u9001\u3089\u308c\u3066\u304d\u305f\u30e1\u30fc\u30eb\u306b\u3042\u308b\u30ea\u30f3\u30af\u3092\u3001\u3064\u3044\u30af\u30ea\u30c3\u30af\u3057\u305d\u3046\u306b\u306a\u3063\u305f\u3053\u3068\u306f\u3042\u308a\u307e\u305b\u3093\u304b\uff1f\u305d\u308c\u306f\u3068\u3066\u3082\u5371\u967a\u306a\u3053\u3068\u3067\u3059\u3002\u30cf\u30c3\u30ab\u30fc\u306f\u60c5\u5831\u53ce\u96c6\u306e\u65b9\u6cd5\u3092\u305f\u304f\u3055\u3093\u6301\u3063\u3066\u304a\u308a\u3001\u30bd\u30fc\u30b7\u30e3\u30eb\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u306b\u3088\u3063\u3066\u4fe1\u983c\u3092\u5f97\u3066\u3001\u666e\u901a\u306f\u5358\u306a\u308b\u77e5\u308a\u5408\u3044\u306b\u983c\u307e\u308c\u3066\u3082\u3057\u306a\u3044\u306f\u305a\u306e\u3053\u3068\u3092\u3042\u306a\u305f\u306b\u3055\u305b\u3066\u3057\u307e\u3046\u65b9\u6cd5\u3092\u719f\u77e5\u3057\u3066\u3044\u308b\u306e\u3067\u3059\u304c\u3001\u305d\u306e\u4e00\u3064\u304c\u30ea\u30f3\u30af\u3092\u30af\u30ea\u30c3\u30af\u3055\u305b\u308b\u3053\u3068\u306a\u306e\u3067\u3059\u3002<\/p>\n<p>\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u3068\u306f\u3001\u30cf\u30c3\u30ab\u30fc\u304c\u30d1\u30b9\u30ef\u30fc\u30c9\u3001\u96fb\u5b50\u30e1\u30fc\u30eb\u3001\u540d\u524d\u306a\u3069\u306e\u60c5\u5831\u3092\u53ce\u96c6\u3059\u308b\u305f\u3081\u306b\u7528\u3044\u3089\u308c\u308b\u624b\u6cd5\u3067\u3059\u3002\u3053\u306e\u30d7\u30ed\u30bb\u30b9\u304c\u6210\u529f\u3059\u308b\u3068\u3001\u3042\u306a\u305f\u306e\u30c7\u30d0\u30a4\u30b9\u306f\u60aa\u7528\u3055\u308c\u3084\u3059\u3044\u72b6\u614b\u306b\u306a\u3063\u3066\u3057\u307e\u3044\u307e\u3059\u3002<\/p>\n<p>\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u306f\u3069\u306e\u3088\u3046\u306b\u884c\u308f\u308c\u308b\u306e\u3067\u3057\u3087\u3046\u304b\uff1f\u30cf\u30c3\u30ab\u30fc\u306f\u3001Facebook\u304c\u30d1\u30b9\u30ef\u30fc\u30c9\u306e\u5909\u66f4\u3092\u6c42\u3081\u308b\u30da\u30fc\u30b8\u3084\u3001\u30ed\u30b0\u30a4\u30f3\u3092\u6c42\u3081\u308b\u30da\u30fc\u30b8\u3092\u8907\u88fd\u3057\u307e\u3059\u3002\u30cf\u30c3\u30ab\u30fc\u306f\u30d0\u30c3\u30af\u30a8\u30f3\u30c9\u306b\u3001\u30e6\u30fc\u30b6\u30fc\u304c\u5165\u529b\u3057\u305f\u5185\u5bb9\u3092\u81ea\u5206\u305f\u3061\u304c\u30a2\u30af\u30bb\u30b9\u3067\u304d\u308b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u4fdd\u5b58\u3059\u308b\u3088\u3046\u306b\u8a2d\u5b9a\u3057\u307e\u3059\u3002\u30d1\u30b9\u30ef\u30fc\u30c9\u3092\u5909\u66f4\u3059\u308b\u969b\u306b\u306f\u3001\u65b0\u3057\u3044\u30d1\u30b9\u30ef\u30fc\u30c9\u3060\u3051\u3067\u306a\u304f\u3001\u53e4\u3044\u30d1\u30b9\u30ef\u30fc\u30c9\u3082\u5165\u529b\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002\u3057\u3070\u3089\u304f\u3057\u3066\u3001\u304a\u305d\u3089\u304fWhatsApp\u3067\u4fe1\u983c\u3092\u5f97\u305f\u5f8c\u3001\u30cf\u30c3\u30ab\u30fc\u306f\u30c4\u30fc\u30eb\u3092\u4f7f\u3063\u3066\u30ea\u30f3\u30af\u3092\u901a\u5e38\u306eFacebook\u30ea\u30f3\u30af\u306e\u5f62\u5f0f\u306b\u3057\u3066\u3001\u8208\u5473\u6df1\u3044\u30ad\u30e3\u30d7\u30b7\u30e7\u30f3\u3092\u4ed8\u3051\u3066\u30c6\u30b9\u30c8\u30b1\u30fc\u30b9\u3067\u3042\u308b\u3042\u306a\u305f\u306b\u9001\u308a\u307e\u3059\u3002\u3042\u306a\u305f\u306f\u305d\u306e\u30ea\u30f3\u30af\u3092\u30af\u30ea\u30c3\u30af\u3057\u3066\u60c5\u5831\u3092\u5165\u529b\u3057\u3001\u30cf\u30c3\u30ab\u30fc\u306f\u305d\u306e\u60c5\u5831\u306b\u30a2\u30af\u30bb\u30b9\u3057\u307e\u3059\u3002\u6b21\u306bFacebook\u306b\u30ed\u30b0\u30a4\u30f3\u3059\u308b\u3068\u3001\u3042\u306a\u305f\u306f\u81ea\u5206\u306e\u30a2\u30ab\u30a6\u30f3\u30c8\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u306a\u304f\u306a\u3063\u3066\u3044\u308b\u3053\u3068\u306b\u6c17\u3065\u304f\u3067\u3057\u3087\u3046\u3002\u3053\u308c\u304c\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u306e\u4ed5\u7d44\u307f\u3067\u3059\u3002<\/p>\n<h3>\u5371\u967a\u306a\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u3092\u624b\u52d5\u3067\u691c\u51fa\u3059\u308b<\/h3>\n<p>\u4eca\u3084\u3001\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u30fb\u30b5\u30a4\u30c8\u3092\u691c\u77e5\u3059\u308b\u30c4\u30fc\u30eb\u3092\u4f7f\u3046\u3053\u3068\u306f\u975e\u5e38\u306b\u91cd\u8981\u3067\u3059\u3002\u3057\u304b\u3057\u3001\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u30fb\u30ea\u30f3\u30af\u3092\u898b\u5206\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3060\u3051\u3067\u3082\u5b89\u5168\u6027\u304c\u9ad8\u307e\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002\u4ee5\u4e0b\u306b\u3001\u30ea\u30f3\u30af\u306b\u898b\u3089\u308c\u308b\u6b20\u70b9\u3092\u6319\u3052\u3066\u304a\u304d\u307e\u3059\u306e\u3067\u3001\u602a\u3057\u3044\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u969b\u306e\u53c2\u8003\u306b\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<ul>\n<li>\u30ea\u30f3\u30af\u304c\u77ed\u304f\u306a\u3063\u3066\u3044\u308b<\/li>\n<li>\u30ea\u30f3\u30af\u306e\u30b9\u30da\u30eb\u304c\u9593\u9055\u3063\u3066\u3044\u308b<\/li>\n<li>\u30ea\u30f3\u30af\u5148\u306b\u5b9f\u5728\u3057\u306a\u3044\u6587\u5b57\u304c\u3042\u308b\uff08\u30ad\u30ea\u30eb\u6587\u5b57\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\uff09<\/li>\n<li>\u524d\u306b\u805e\u3044\u305f\u3053\u3068\u304c\u306a\u3044\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u3067\u3042\u3063\u305f\u308a\u3001\u30e9\u30f3\u30af\u4ed8\u3051\u304c\u4f4e\u3044\u3082\u306e\u3067\u3042\u308b<\/li>\n<li>\u30ea\u30f3\u30af\u304c\u975e\u5e38\u306b\u9577\u304f\u3001\u5408\u6cd5\u7684\u306a\u30ea\u30f3\u30af\u3068\u30aa\u30ea\u30b8\u30ca\u30eb\u3067\u306a\u3044\u30ea\u30f3\u30af\u306e\u7d44\u307f\u5408\u308f\u305b\u3092\u6301\u3063\u3066\u3044\u308b<\/li>\n<\/ul>\n<p>\u307e\u305f\u3001\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u306b\u30a2\u30af\u30bb\u30b9\u3057\u305f\u5f8c\u306b\u3082\u3001\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u30b5\u30a4\u30c8\u3092\u898b\u5206\u3051\u308b\u305f\u3081\u306e\u30d2\u30f3\u30c8\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ul>\n<li>\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u4e0a\u3067\u30de\u30a6\u30b9\u306e\u53f3\u30af\u30ea\u30c3\u30af\u30dc\u30bf\u30f3\u304c\u7121\u52b9\u306b\u306a\u3063\u3066\u3044\u308b\n<ul>\n<li>\u30de\u30a6\u30b9\u306e\u53f3\u30af\u30ea\u30c3\u30af\u30dc\u30bf\u30f3\u304c\u7121\u52b9\u306b\u306a\u3063\u3066\u3044\u3066\u3001\u30da\u30fc\u30b8\u306e\u30bd\u30fc\u30b9\u3092\u898b\u308b\u3053\u3068\u304c\u3067\u304d\u306a\u3044<\/li>\n<\/ul>\n<\/li>\n<li>\u30dd\u30c3\u30d7\u30a2\u30c3\u30d7\u30a6\u30a3\u30f3\u30c9\u30a6<\/li>\n<\/ul>\n<h2>\u524d\u63d0\u6761\u4ef6<\/h2>\n<ul>\n<li>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306b\u5fc5\u8981\u306a\u30c4\u30fc\u30eb\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002\u79c1\u306fCentOS\u306e\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059\u3002\n<ul>\n<li>Python 3.8\u307e\u305f\u306fPython 3.9\u3002\u6700\u65b0\u7248\u306ePython\u306f\u3001<a href=\"https:\/\/www.python.org\/downloads\">\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8<\/a>\u304b\u3089\u5165\u624b\u3067\u304d\u307e\u3059\u3002<\/li>\n<li>Jupyter Notebook\uff08IDLE for Python\u3092\u3059\u3067\u306b\u6301\u3063\u3066\u3044\u308b\u5834\u5408\u306f\u30aa\u30d7\u30b7\u30e7\u30f3\uff09\u3002Jupyter Notebook\u306f\u3001Python\u306e\u30b3\u30fc\u30c9\u3092\u66f8\u304f\u305f\u3081\u306e\u74b0\u5883\u3067\u3001\u30b3\u30fc\u30c9\u3084\u30c6\u30ad\u30b9\u30c8\u3092\u66f8\u304f\u306e\u306b\u9069\u3057\u305f\u30bb\u30eb\u304c\u7528\u610f\u3055\u308c\u3066\u3044\u307e\u3059\u3002https:\/\/www.anaconda.org \u304b\u3089 Anaconda3 \u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u3068\u3001Notebook \u3092\u5165\u624b\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u3001Jupyter Notebook \u3084 Spyder \u306a\u3069\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u304cPC \u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u307e\u3059\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3>\u30c7\u30fc\u30bf<\/h3>\n<p>\u4eca\u56de\u306e\u30e2\u30c7\u30eb\u306b\u4f7f\u7528\u3059\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u3001\u3053\u3061\u3089\u306e\u30ab\u30ea\u30d5\u30a9\u30eb\u30cb\u30a2\u5927\u5b66\u30a2\u30fc\u30d0\u30a4\u30f3\u6821\uff08UCI\uff09\u304c\u63d0\u4f9b\u3059\u308b<a href=\"https:\/\/archive.ics.uci.edu\/ml\/datasets\/phishing+websites\">\u516c\u5f0f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8<\/a>\u3067\u3059\u3002\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u3001\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u3092\u6b63\u898f\u306e\u3082\u306e\u3068\u507d\u306e\u3082\u306e\u306b\u5206\u985e\u3059\u308b\u306e\u306b\u5f79\u7acb\u3064\u591a\u304f\u306e\u6570\u5024\u7684\u7279\u5fb4\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f2456\u500b\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u306830\u500b\u306e\u5c5e\u6027\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002\u30c7\u30fc\u30bf\u3092\u5206\u6790\u3057\u3088\u3046\u3068\u3059\u308b\u3068\u304d\u306b\u306f\u3001\u5e38\u306b\u6b20\u640d\u5024\u3092\u30c1\u30a7\u30c3\u30af\u3059\u308b\u306e\u304c\u826f\u3044\u65b9\u6cd5\u3067\u3059\u304c\u3001\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u306b\u306f\u6b20\u640d\u5024\u304c\u306a\u3044\u3068\u66f8\u304b\u308c\u3066\u3044\u307e\u3059\u3002<a href=\"https:\/\/archive.ics.uci.edu\/ml\/machine-learning-databases\/00327\/Training%20Dataset.arff\">UCI\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u30da\u30fc\u30b8<\/a>\u306b\u30a2\u30af\u30bb\u30b9\u3057\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u76f4\u63a5\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>\u3059\u3079\u3066\u306e\u6a5f\u80fd\u3092\u898b\u308b\u306b\u306f\u3001<a href=\"https:\/\/archive.ics.uci.edu\/ml\/machine-learning-databases\/00327\/Phishing%20Websites%20Features.docx\">\u3053\u3061\u3089<\/a>\u304b\u3089Phishing Website Features.docx\u30d5\u30a1\u30a4\u30eb\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<h4>ARFF\u30d5\u30a1\u30a4\u30eb\u3092CSV\u306b\u5909\u63db\u3059\u308b<\/h4>\n<p>UCI\u306e\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306fARFF\u30d5\u30a1\u30a4\u30eb\u306a\u306e\u3067\u3001Python\u306e\u30b3\u30fc\u30c9\u3067\u4f7f\u7528\u3067\u304d\u308b\u3088\u3046\u306bCSV\u30d5\u30a1\u30a4\u30eb\u306b\u5909\u63db\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u305fARFF\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u30d5\u30a1\u30a4\u30eb\u304c\u3001Python\u30b3\u30fc\u30c9\u306e\u5165\u3063\u305f\u30d5\u30a9\u30eb\u30c0\u3068\u540c\u3058\u5834\u6240\u306b\u3042\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">import glob as gb # This builtin module would allow us to select only the file with .arff extension\n\nfiles = [f for f in gb.glob(\"*.arff\")] # We are using \"files\" because this selects all the files with that extension\n\n# This function would convert the ARFF file to CSV file\ndef convert(lines):\n    header = \"\"\n    file_content = []\n    data = not True\n    \n    for line in lines:\n        if not data:\n            if \"@attribute\" in line:\n                attributes = line.split()\n                columnName = attributes[attributes.index(\"@attribute\")+1]\n                header = header + columnName + \",\"\n            elif \"@data\" in line:\n                data = True\n                header = header[:-1]\n                header += \"n\"\n                file_content.append(header)\n        else:\n            file_content.append(line)\n    return file_content\n        \n\nfor file in files:\n    with open(file, \"r\") as inp:\n        lines = inp.readlines()\n        output = convert(lines)\n        with open(\"dataset\" + \".csv\", \"w\") as out:\n            out.writelines(output)\n\n<\/code><\/pre>\n<\/div>\n<h3>GridDB Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3092\u4f7f\u7528\u3059\u308b<\/h3>\n<p>https:\/\/griddb.net\/en\/downloads\/ \u306b\u30a2\u30af\u30bb\u30b9\u3057\u3066 GridDB Server \u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u3001\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u624b\u9806\u306b\u5f93\u3063\u3066\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u306a\u304a\u3001SWIG\u3068C_Client\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u306a\u3044\u3068Python\u306eGridDB Client\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u305b\u3093\u3002Windows\u306e\u5834\u5408\u306f<a href=\"http:\/\/prdownloads.sourceforge.net\/swig\/swigwin-4.0.2.zip\">\u3053\u3061\u3089<\/a>\u3001CentOS\u3084Ubuntu\u306a\u3069\u306e\u5229\u7528\u53ef\u80fd\u306aOS\u306e\u5834\u5408\u306f<a href=\"http:\/\/prdownloads.sourceforge.net\/swig\/swig-4.0.2.tar.gz\">\u3053\u3061\u3089<\/a>\u306b\u30a2\u30af\u30bb\u30b9\u3057\u3066\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<p>Python \u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3092\u4f7f\u7528\u3059\u308b\u524d\u306b\u3001\u4e8b\u524d\u306b\u30b5\u30fc\u30d0\u30fc\u3092\u8d77\u52d5\u3057\u3066\u3001\u30db\u30b9\u30c8\u3001\u30dd\u30fc\u30c8\u3001\u30af\u30e9\u30b9\u30bf\u30fc\u540d\u3001\u30e6\u30fc\u30b6\u30fc\u540d\u3092\u30e1\u30e2\u3057\u3066\u304a\u3044\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<p>If you&#8217;re using Jupyter notebook, you may need to follow the instructions <a href=\"https:\/\/griddb.net\/en\/blog\/using-python-to-interface-with-griddb-via-jdbc-with-jaydebeapi\/\">here<\/a> to get GridDB running on your notebook. Jupyter notebook\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u5834\u5408\u3001Notebook\u4e0a\u3067GridDB\u3092\u52d5\u4f5c\u3055\u305b\u308b\u306b\u306f\u3001<a href=\"https:\/\/griddb.net\/en\/blog\/using-python-to-interface-with-griddb-via-jdbc-with-jaydebeapi\/\">\u3053\u3061\u3089<\/a>\u306e\u624b\u9806\u306b\u5f93\u3046\u5fc5\u8981\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002<\/p>\n<p>\u79c1\u305f\u3061\u306f\u30c7\u30fc\u30bf\u306e\u30b3\u30f3\u30c6\u30ca\u3068\u3057\u3066GridDB\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u7a7a\u306e\u5f15\u7528\u7b26\u304c\u3042\u308b\u3068\u3053\u308d\u306b\u3001\u3042\u306a\u305f\u306eGridDB\u30b5\u30fc\u30d0\u30fc\u306e\u8a73\u7d30\u3092\u8a18\u5165\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">\nimport griddb_python as griddb\nfactory = griddb.StoreFactory.get_instance()\nyour_host = \"\"\nyour_port = \"\"\nyour_cluster_name = \"\"\nyour_username = \"\"\nyour_password = \"\"\ntry:\n    gridstore = factory.get_store(host=your_host, port=your_port, \n            cluster_name=your_cluster_name, username=your_username, \n            password=your_password)\nconInfo = griddb.ContainerInfo(\"Phishing Websites\",\n                    [              \n                     [\"having_IP_Address\", griddb.Type.INTEGER],\n                     [\"URL_Length\", griddb.Type.INTEGER],\n                     [\"Shortining_Service\", griddb.Type.INTEGER],\n                     [\"having_At_Symbol\", griddb.Type.INTEGER],\n                     [\"double_slash_redirecting\", griddb.Type.INTEGER],\n                     [\"Prefix_Suffix\", griddb.Type.INTEGER],\n                     [\"having_Sub_Domain\", griddb.Type.INTEGER],\n                     [\"SSLfinal_State\", griddb.Type.INTEGER],\n                     [\"Domain_registeration_length\", griddb.Type.INTEGER],\n                     [\"Favicon\", griddb.Type.INTEGER],\n                     [\"port\", griddb.Type.INTEGER],\n                     [\"HTTPS_token\", griddb.Type.INTEGER],\n                     [\"Request_URL\", griddb.Type.INTEGER],\n                     [\"URL_of_Anchor\", griddb.Type.INTEGER],\n                     [\"Links_in_tags\", griddb.Type.INTEGER],\n                     [\"SFH\", griddb.Type.INTEGER],\n                     [\"Submitting_to_email\", griddb.Type.INTEGER],\n                     [\"Abnormal_URL\", griddb.Type.INTEGER],\n                     [\"Redirect\", griddb.Type.INTEGER],\n                     [\"on_mouseover\", griddb.Type.INTEGER],\n                     [\"RightClick\", griddb.Type.INTEGER],\n                     [\"popUpWidnow\", griddb.Type.INTEGER],\n                     [\"Iframe\", griddb.Type.INTEGER],\n                     [\"age_of_domain\", griddb.Type.INTEGER],\n                     [\"DNSRecord\", griddb.Type.INTEGER],\n                     [\"web_traffic\", griddb.Type.INTEGER],\n                     [\"Page_Rank\", griddb.Type.INTEGER],\n                     [\"Google_Index\", griddb.Type.INTEGER],\n                     [\"Links_pointing_to_page\", griddb.Type.INTEGER],\n                     [\"Statistical_report\", griddb.Type.INTEGER],\n                     [\"Result\", griddb.Type.INTEGER],                    \n                    ],\n                    griddb.ContainerType.COLLECTION, True)\n    cont = gridstore.put_container(conInfo)   \n    data = pd.read_csv(\"dataset.csv\")\n    \n    #Add data\n    for i in range(len(data)):\n        ret = cont.put(data.iloc[i, :])\n    print(\"Successfully added the data\")\nexcept griddb.GSException as e:\n    for i in range(e.get_error_stack_size()):\n        print(\"[\", i, \"]\")\n        print(e.get_error_code(i))\n        print(e.get_location(i))\n        print(e.get_message(i))\n<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">\nimport pandas as pd\ndataset = pd.read_csv(\"dataset.csv\")\n<\/code><\/pre>\n<\/div>\n<h3>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5148\u982d\u3092\u5370\u5237\u3059\u308b\uff08\u30c7\u30fc\u30bf\u306e\u4e0a\u4f4d5\u30ec\u30b3\u30fc\u30c9\u3092\u8868\u793a\u3059\u308b\uff09<\/h3>\n<p>\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3064\u3044\u3066\u7406\u89e3\u3057\u3066\u304a\u304f\u3079\u304d\u3053\u3068\u306f\uff0c\u3059\u3079\u3066\u306e\u7279\u5fb4\u304c\u6570\u5024\u3067\u3042\u308b\u305f\u3081\uff0c\u30c7\u30fc\u30bf\u3092\u30a8\u30f3\u30b3\u30fc\u30c9\u3059\u308b\u5fc5\u8981\u304c\u306a\u3044\u3068\u3044\u3046\u3053\u3068\u3067\u3059\u3002\u307e\u305f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f <code>1<\/code>, <code>-1<\/code>, <code>0<\/code> \u3068\u3044\u30463\u3064\u306e\u5024\u3057\u304b\u3042\u308a\u307e\u305b\u3093\u304c\u3001\u3053\u308c\u3089\u306f\u30c7\u30fc\u30bf\u306e\u771f\u507d\u3092\u8868\u3059\u5f62\u5f0f\u306b\u904e\u304e\u307e\u305b\u3093\u3002\u5024\u304c<code>1<\/code>\u3067\u3042\u308c\u3070\u3001\u305d\u306e\u7279\u5fb4\u306b\u3064\u3044\u3066\u306e\u6761\u4ef6\uff08\u5c5e\u6027\u30fb\u5217\u540d\uff09\u306f\u771f\u3067\u3042\u308a\u3001\u5024\u304c<code>0<\/code>\u307e\u305f\u306f<code>-1<\/code>\u3067\u3042\u308c\u3070\u507d\u3067\u3042\u308b\u3068\u3044\u3048\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001<code>popUpWindow<\/code>\u5217\u306b<code>1<\/code>\u306e\u5024\u304c\u3042\u308c\u3070\u3001popUpWindow\u304c\u3042\u3063\u305f\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002\u3053\u308c\u3089\u306e\u5024\u306f\u3059\u3079\u3066\u7d50\u679c\u3092\u6c7a\u5b9a\u3057\u3001<code>Result<\/code>\u5217\u306b\u306f<code>1<\/code>\u3068<code>-1<\/code>\u306e\u5024\u304c\u3042\u308a\u3001\u305d\u308c\u305e\u308c<code>Phishing Website<\/code>\u3068<code>Not a Phishing Website<\/code>\u3092\u8868\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>Python IDLE\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u5834\u5408\u306f\u3001<code>print(dataset.head())<\/code>\u3092\u4f7f\u7528\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">\n\"\"\" You can also decide to show the bottom five records of the data by \ntyping dataset.tail()\"\"\"\ndataset.head()\n<\/code><\/pre>\n<\/div>\n<div style=\"overflow-y: hidden; 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          having_IP_Address\n        <\/th>\n<th>\n          URL_Length\n        <\/th>\n<th>\n          Shortining_Service\n        <\/th>\n<th>\n          having_At_Symbol\n        <\/th>\n<th>\n          double_slash_redirecting\n        <\/th>\n<th>\n          Prefix_Suffix\n        <\/th>\n<th>\n          having_Sub_Domain\n        <\/th>\n<th>\n          SSLfinal_State\n        <\/th>\n<th>\n          Domain_registeration_length\n        <\/th>\n<th>\n          Favicon\n        <\/th>\n<th>\n          &#8230;\n        <\/th>\n<th>\n          popUpWidnow\n        <\/th>\n<th>\n          Iframe\n        <\/th>\n<th>\n          age_of_domain\n        <\/th>\n<th>\n          DNSRecord\n        <\/th>\n<th>\n          web_traffic\n        <\/th>\n<th>\n          Page_Rank\n        <\/th>\n<th>\n          Google_Index\n        <\/th>\n<th>\n          Links_pointing_to_page\n        <\/th>\n<th>\n          Statistical_report\n        <\/th>\n<th>\n          Result\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          0\n        <\/th>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3\n        <\/th>\n<td>\n          1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          -1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\n    5 rows \u00c3\u2014 31 columns\n  <\/p>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">\ndataset.shape # Number of rows and columns\n<\/code><\/pre>\n<\/div>\n<pre><code>(11055, 31)\n<\/code><\/pre>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">\ndataset.describe() # Describing the data to gain insights from it\n<\/code><\/pre>\n<\/div>\n<div style=\"overflow-y: hidden; 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          having_IP_Address\n        <\/th>\n<th>\n          URL_Length\n        <\/th>\n<th>\n          Shortining_Service\n        <\/th>\n<th>\n          having_At_Symbol\n        <\/th>\n<th>\n          double_slash_redirecting\n        <\/th>\n<th>\n          Prefix_Suffix\n        <\/th>\n<th>\n          having_Sub_Domain\n        <\/th>\n<th>\n          SSLfinal_State\n        <\/th>\n<th>\n          Domain_registeration_length\n        <\/th>\n<th>\n          Favicon\n        <\/th>\n<th>\n          &#8230;\n        <\/th>\n<th>\n          popUpWidnow\n        <\/th>\n<th>\n          Iframe\n        <\/th>\n<th>\n          age_of_domain\n        <\/th>\n<th>\n          DNSRecord\n        <\/th>\n<th>\n          web_traffic\n        <\/th>\n<th>\n          Page_Rank\n        <\/th>\n<th>\n          Google_Index\n        <\/th>\n<th>\n          Links_pointing_to_page\n        <\/th>\n<th>\n          Statistical_report\n        <\/th>\n<th>\n          Result\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          count\n        <\/th>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<td>\n          11055.000000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          mean\n        <\/th>\n<td>\n          0.313795\n        <\/td>\n<td>\n          -0.633198\n        <\/td>\n<td>\n          0.738761\n        <\/td>\n<td>\n          0.700588\n        <\/td>\n<td>\n          0.741474\n        <\/td>\n<td>\n          -0.734962\n        <\/td>\n<td>\n          0.063953\n        <\/td>\n<td>\n          0.250927\n        <\/td>\n<td>\n          -0.336771\n        <\/td>\n<td>\n          0.628584\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          0.613388\n        <\/td>\n<td>\n          0.816915\n        <\/td>\n<td>\n          0.061239\n        <\/td>\n<td>\n          0.377114\n        <\/td>\n<td>\n          0.287291\n        <\/td>\n<td>\n          -0.483673\n        <\/td>\n<td>\n          0.721574\n        <\/td>\n<td>\n          0.344007\n        <\/td>\n<td>\n          0.719584\n        <\/td>\n<td>\n          0.113885\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          std\n        <\/th>\n<td>\n          0.949534\n        <\/td>\n<td>\n          0.766095\n        <\/td>\n<td>\n          0.673998\n        <\/td>\n<td>\n          0.713598\n        <\/td>\n<td>\n          0.671011\n        <\/td>\n<td>\n          0.678139\n        <\/td>\n<td>\n          0.817518\n        <\/td>\n<td>\n          0.911892\n        <\/td>\n<td>\n          0.941629\n        <\/td>\n<td>\n          0.777777\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          0.789818\n        <\/td>\n<td>\n          0.576784\n        <\/td>\n<td>\n          0.998168\n        <\/td>\n<td>\n          0.926209\n        <\/td>\n<td>\n          0.827733\n        <\/td>\n<td>\n          0.875289\n        <\/td>\n<td>\n          0.692369\n        <\/td>\n<td>\n          0.569944\n        <\/td>\n<td>\n          0.694437\n        <\/td>\n<td>\n          0.993539\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          min\n        <\/th>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          25%\n        <\/th>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          50%\n        <\/th>\n<td>\n          1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          0.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          75%\n        <\/th>\n<td>\n          1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          -1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          max\n        <\/th>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<td>\n          1.000000\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\n    8 rows \u2014 31 columns\n  <\/p>\n<\/div>\n<h2>\u7d50\u679c\u3092\u4e88\u6e2c\u3059\u308b\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b<\/h2>\n<p>\u3053\u3053\u304b\u3089\u306f\u3001Decision Trees Classifier\u3092\u4f7f\u7528\u3057\u3066\u3001\u3042\u308b\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u304c\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u30b5\u30a4\u30c8\u3067\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u4e88\u6e2c\u3059\u308b\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/p>\n<h3>Decision Trees Classifier\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b<\/h3>\n<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u591a\u304f\u306e\u5217\u304c\u3042\u308a\u307e\u3059\u304c\u3001\u4ed6\u306e\u3059\u3079\u3066\u306e\u5217\u306e\u5024\u304c\u771f\u3067\u3042\u308b\u5834\u5408\u3001\u6700\u5f8c\u306e\u5217\u306e\u307f\u304c\u4e88\u6e2c\u7d50\u679c\u3092\u8868\u3057\u307e\u3059\u3002\u8a00\u3044\u63db\u3048\u308c\u3070\u3001\u6700\u5f8c\u306e\u5217\u306f\u6700\u521d\u306e\u5217\u304b\u3089\u6700\u5f8c\u306e\u5217\u307e\u3067\u306e\u4ed6\u306e\u3059\u3079\u3066\u306e\u5217\u306b\u4f9d\u5b58\u3057\u3066\u304a\u308a\u3001\u3053\u308c\u3092<code>Dependent Feature<\/code>\u3068\u547c\u3070\u308c\u307e\u3059\u3002\u4ed6\u306e\u3059\u3079\u3066\u306e\u5217\u306f<code>Independent Features<\/code>\u3068\u547c\u3070\u308c\u307e\u3059\u3002\u3053\u3053\u3067\u306f\uff0c\u72ec\u7acb\u7279\u5fb4\u3068\u5f93\u5c5e\u7279\u5fb4\u306e\u5024\u3092\u683c\u7d0d\u3059\u308b\u305f\u3081\u306b\u3001\u305d\u308c\u305e\u308c<code>X<\/code>\u3068<code>y<\/code>\u3068\u3044\u30462\u3064\u306e\u5909\u6570\u3092\u5b9a\u7fa9\u3057\u307e\u3059\u3002<code>X<\/code>\u306f <code>Independent Variable<\/code>\u3068\u547c\u3070\u308c\uff0c<code>y<\/code>\u306f <code>Dependent Variable<\/code>\u3068\u547c\u3070\u308c\u307e\u3059\u3002<\/p>\n<p>\u5024\u3092\u9078\u629e\u3059\u308b\u305f\u3081\u306e\u69cb\u6587\u306f\uff0c<code>data.iloc[number_of_rows_to_select, number_of_columns_to_select].values<\/code>\u3067\u3059\u3002\u3059\u3079\u3066\u306e\u884c\u3092\u9078\u629e\u3057\u3066\u3044\u308b\u306e\u3067\u3001Python\u3067\u306f\u30b9\u30e9\u30a4\u30b9\u6f14\u7b97\u5b50 <code>:<\/code> \u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5024\u3092\u6700\u521d\u306e\u884c\u304b\u3089\u6700\u5f8c\u306e\u884c\u307e\u3067\u30b9\u30e9\u30a4\u30b9\u3057\u307e\u3059\u3002\u3057\u304b\u3057\u3001X\u306b\u3064\u3044\u3066\u306f\u6700\u5f8c\u306e\u5217\uff08\u7d50\u679c\uff09\u3092\u9664\u304f\u3059\u3079\u3066\u306e\u5217\u304c\u5fc5\u8981\u306a\u306e\u3067\u3001\u6700\u5f8c\u306e\u5217\u3092\u6b8b\u3057\u3066\u30b9\u30e9\u30a4\u30b9\u3057\u307e\u3059\u3002y\u306b\u3064\u3044\u3066\u306f\u3001\u6700\u5f8c\u306e\u5217\u3060\u3051\u304c\u5fc5\u8981\u306a\u306e\u3067\u3001\u30b9\u30e9\u30a4\u30b9\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">X = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, -1].values\n<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">print(X)\n<\/code><\/pre>\n<\/div>\n<pre><code>[[-1  1  1 ...  1  1 -1]\n [ 1  1  1 ...  1  1  1]\n [ 1  0  1 ...  1  0 -1]\n ...\n [ 1 -1  1 ...  1  0  1]\n [-1 -1  1 ...  1  1  1]\n [-1 -1  1 ... -1  1 -1]]\n<\/code><\/pre>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">print(y)\n<\/code><\/pre>\n<\/div>\n<pre><code>[-1 -1 -1 ... -1 -1 -1]\n<\/code><\/pre>\n<h3>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u306b\u5206\u5272\u3059\u308b<\/h3>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">from sklearn.model_selection import train_test_split as tts\n<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">X_train, X_test, y_train, y_test = tts(X, y, test_size=0.30, random_state=0)\n<\/code><\/pre>\n<\/div>\n<p>\u30c7\u30fc\u30bf\u306e70\uff05\u3092\u5b66\u7fd2\u3055\u305b\u3001\u6b8b\u308a\u306e30\uff05\u3092\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u30b5\u30a4\u30c8\u304b\u3069\u3046\u304b\u3092\u4e88\u6e2c\u3055\u305b\u307e\u3059\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u6a5f\u68b0\u5b66\u7fd2\u306b\u3088\u3063\u3066\u7d041719\u306e\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u3092\u5b66\u7fd2\u3057\u3001\u7d04739\u306e\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u3092\u4e88\u6e2c\u3057\u3066\u3044\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<h2>\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\u3059\u308b<\/h2>\n<p>GridDB\u306f\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u6a5f\u80fd\u3082\u5099\u3048\u3066\u304a\u308a\u3001<a href=\"https:\/\/griddb.net\/en\/blog\/data-visualization-with-python-matplotlib-and-griddb\/\">\u3053\u3061\u3089<\/a>\u306e\u8aac\u660e\u306b\u5f93\u3063\u3066\u3001GridDB\u3068Python\u306ematplotlib\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\u3057\u307e\u3059\u3002\u3053\u3053\u3067\u306fSeaborn\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059\u304c\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u591a\u6570\u306e\u5c5e\u6027\u304c\u3042\u308b\u305f\u3081\u3001\u53ef\u8996\u5316\u306b\u306f\u975e\u5e38\u306b\u6642\u9593\u304c\u304b\u304b\u308a\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">import seaborn as sns\nsns.pairplot(dataset)\n<\/code><\/pre>\n<\/div>\n<pre><code>&lt;seaborn.axisgrid.PairGrid at 0xeced8d0&gt;\n<\/code><\/pre>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2021\/05\/output_35_1.png\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2021\/05\/output_35_1.png\" alt=\"\" width=\"5594\" height=\"5590\" class=\"aligncenter size-full wp-image-27444\" \/><\/a><\/p>\n<p>\u4e0a\u8a18\u306e\u53ef\u8996\u5316\u7d50\u679c\u304b\u3089\u3001<code>age_of_domain<\/code>\u5217\u304c<code>Result<\/code>\u5217\u3068\u76f8\u95a2\u95a2\u4fc2\u306b\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n<h3>\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u30b5\u30a4\u30c8\u304b\u3089\u306e\u30ea\u30f3\u30af\u304b\u3069\u3046\u304b\u3092\u5206\u985e\u3059\u308b\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b<\/h3>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">from sklearn.tree import DecisionTreeClassifier\n<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">dtClassifier = DecisionTreeClassifier()\n<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">dtClassifier.fit(X_train, y_train)\n<\/code><\/pre>\n<\/div>\n<pre><code>DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n                       max_features=None, max_leaf_nodes=None,\n                       min_impurity_decrease=0.0, min_impurity_split=None,\n                       min_samples_leaf=1, min_samples_split=2,\n                       min_weight_fraction_leaf=0.0, presort=False,\n                       random_state=None, splitter='best')\n<\/code><\/pre>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">dty_pred = dtClassifier.predict(X_test)\n<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">print(dty_pred)\n<\/code><\/pre>\n<\/div>\n<pre><code>[-1 -1 -1 ... -1  1  1]\n<\/code><\/pre>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">from sklearn.metrics import confusion_matrix\ncMatrix = confusion_matrix(y_test, dty_pred)\nprint(cMatrix)\n<\/code><\/pre>\n<\/div>\n<pre><code>[[1426   72]\n [  48 1771]]\n<\/code><\/pre>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\"># Accuracy of the model\ndtacc = dtClassifier.score(X_test, y_test)\ndtacc *= 100\ndtaccu = round(dtacc, 2)\ndtaccuracy = str(dtaccu)+\"%\"\nprint(\nf\"\"\"\nThe Accuracy of our model, using the Decision Tree Classifier\n{dtaccuracy}\n\"\"\"\n)\n<\/code><\/pre>\n<\/div>\n<pre><code>The Accuracy of our model, using the Decision Tree Classifier\n96.38%\n<\/code><\/pre>\n<h2>\u307e\u3068\u3081<\/h2>\n<p>\u3053\u306e\u30e2\u30c7\u30eb\u306e\u4f7f\u3044\u65b9\u306e\u4f8b\u3068\u3057\u3066\u3001\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u306eURL\u304c\u3042\u308a\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5f93\u3063\u3066\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u306e\u7279\u5fb4\u3092\u6700\u5f8c\u306e1\u3064\u3092\u9664\u3044\u3066\u3059\u3079\u3066\u8a18\u5165\u3057\u305f\u5f8c\u3001\u30e2\u30c7\u30eb\u3092\u4f7f\u3063\u3066\u305d\u308c\u304c\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u30b5\u30a4\u30c8\u304b\u3069\u3046\u304b\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/p>\n<p>\u3053\u306e\u30d6\u30ed\u30b0\u3092\u8aad\u3093\u3067\u3001\u6a5f\u68b0\u5b66\u7fd2\u306bGridDB\u3092\u4f7f\u7528\u3059\u308b\u3068\u4fbf\u5229\u3067\u3042\u308b\u3068\u611f\u3058\u3066\u3044\u305f\u3060\u3051\u305f\u3089\u5e78\u3044\u3067\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u306f\u3058\u3081\u306b\u3000\u30d5\u30a3\u30c3\u30b7\u30f3\u30b0\u30b5\u30a4\u30c8\u3068\u306f\u4f55\u304b\uff1f \u597d\u5947\u5fc3\u306e\u305b\u3044\u3067\u3001\u500b\u4eba\u60c5\u5831\u304c\u60aa\u8005\u306b\u6d41\u51fa\u3057\u3066\u3057\u307e\u3046\u5371\u967a\u306b\u3042\u3046\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u77e5\u308a\u5408\u3044\u304b\u3089\u9001\u3089\u308c\u3066\u304d\u305f\u30e1\u30fc\u30eb\u306b\u3042\u308b\u30ea\u30f3\u30af\u3092\u3001\u3064\u3044\u30af\u30ea\u30c3\u30af\u3057\u305d\u3046\u306b\u306a\u3063\u305f\u3053\u3068\u306f\u3042\u308a\u307e\u305b\u3093\u304b\uff1f\u305d\u308c\u306f\u3068\u3066\u3082\u5371\u967a [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":50234,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50740","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 - 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