{"id":50741,"date":"2021-05-11T00:00:00","date_gmt":"2021-05-11T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/bank-loan-classification\/"},"modified":"2025-11-14T07:54:35","modified_gmt":"2025-11-14T15:54:35","slug":"bank-loan-classification","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/bank-loan-classification\/","title":{"rendered":"\u9280\u884c\u30ed\u30fc\u30f3\u306e\u5206\u985e"},"content":{"rendered":"<h2>\u306f\u3058\u3081\u306b<\/h2>\n<p>\u3053\u306e\u30d6\u30ed\u30b0\u3067\u306f\u3001GridDB\u306b\u4fdd\u5b58\u3055\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u3001\u9280\u884c\u30ed\u30fc\u30f3\u306e\u5206\u985e\u30e2\u30c7\u30eb\u3092\u4e00\u304b\u3089\u69cb\u7bc9\u3057\u3001\u4ee5\u4e0b\u306e\u5185\u5bb9\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n<p><a href=\"#adding\">1&#46;GridDB\u3078\u306e\u30c7\u30fc\u30bf\u4fdd\u5b58<\/a><br \/>\n<a href=\"#extracting\">2&#46;GridDB\u304b\u3089\u306e\u30c7\u30fc\u30bf\u62bd\u51fa<\/a><br \/>\n<a href=\"#building\">3&#46;Pandas\u3092\u7528\u3044\u305f\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9<\/a><br \/>\n<a href=\"#evaluating\">4&#46;\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u3068\u76f8\u95a2\u884c\u5217\u3092\u7528\u3044\u305f\u30e2\u30c7\u30eb\u306e\u8a55\u4fa1<\/a><\/p>\n<p>\u307e\u305a\u3001\u524d\u63d0\u6761\u4ef6\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u74b0\u5883\u3092\u6574\u3048\u308b\u3068\u3053\u308d\u304b\u3089\u59cb\u3081\u307e\u3059\u3002\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30e2\u30c7\u30eb\u69cb\u7bc9\u306b\u4f7f\u7528\u3059\u308b\u4e3b\u8981\u8a00\u8a9e\u304cPython\u3067\u3042\u308b\u3053\u3068\u304b\u3089\u3001<a href=\"https:\/\/github.com\/griddb\/python_client\">GridDB\u306ePython\u30b3\u30cd\u30af\u30bf<\/a>\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002GridDB\u306f\u63a5\u7d9a\u3059\u308b<a href=\"https:\/\/github.com\/griddb\/griddb#client-and-connector\">\u30e9\u30a4\u30d6\u30e9\u30ea\u3084API<\/a>\u304c\u5145\u5b9f\u3057\u3066\u304a\u308a\u3001SQL\u3068NoSQL\u306e\u4e21\u65b9\u306e\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u3092\u7c21\u5358\u306b\u6271\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<h2>\u74b0\u5883\u3068\u524d\u63d0\u6761\u4ef6<\/h2>\n<p>\u4ee5\u4e0b\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306f\u3001Ubuntu OS (v. 18.04)\u3001gcc version 7.5.0\u3001GridDB (v. 4.5.2)\u3001Jupyter Notebooks (Anaconda Navigator)\u3092\u4f7f\u3063\u3066\u3044\u307e\u3059\u3002\u30b3\u30fc\u30c9\u306fPython 3\u3067\u66f8\u304b\u308c\u3066\u3044\u308b\u306e\u3067\u3001\u540c\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u8a00\u8a9e\u3067\u52d5\u4f5c\u3059\u308b\u3042\u3089\u3086\u308b\u30c7\u30d0\u30a4\u30b9\u30fb\u30b3\u30fc\u30c9\u30a8\u30c7\u30a3\u30bf\u3067\u5b9f\u884c\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u306a\u304a\u3001Python\u3084\u6a5f\u68b0\u5b66\u7fd2\u3092\u521d\u3081\u3066\u4f7f\u3046\u65b9\u306f\u3001\u4ee5\u4e0b\u306e\u30ea\u30f3\u30af\u3092\u53c2\u8003\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<ol>\n<li><a href=\"https:\/\/www.python.org\/downloads\/\">Installing Python<\/a><\/li>\n<li><a href=\"https:\/\/docs.anaconda.com\/anaconda\/install\/\">Installing Anaconda<\/a><\/li>\n<\/ol>\n<p>GridDB\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u969b\u306f\u3001<a href=\"https:\/\/github.com\/griddb\/griddb\">Github<\/a>\u3067\u516c\u958b\u3055\u308c\u3066\u3044\u308b\u30ac\u30a4\u30c9\u3092\u53c2\u8003\u306b\u3057\u3066\u304f\u3060\u3055\u3044\u3002GridDB\u306e<a href=\"https:\/\/github.com\/griddb\/python_client\">python\u30b3\u30cd\u30af\u30bf<\/a>\u3092\u5229\u7528\u3059\u308b\u305f\u3081\u306e\u524d\u63d0\u6761\u4ef6\u306f\u6b21\u306e3\u3064\u3067\u3059\u30021. <a href=\"https:\/\/github.com\/griddb\/c_client\">GridDB C-client<\/a> 2. <a href=\"https:\/\/github.com\/griddb\/python_client#preparations\">SWIG<\/a> 3. Pcre<\/p>\n<p>Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306b\u3064\u3044\u3066\u306f<a href=\"https:\/\/griddb.net\/en\/blog\/python-client\/\">\u3053\u3061\u3089<\/a>\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<h2>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8<\/h2>\n<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001<a href=\"https:\/\/www.kaggle.com\/sriharipramod\/bank-loan-classification\">Kaggle<\/a>\u3067\u516c\u958b\u3055\u308c\u3066\u3044\u308b\u9280\u884c\u30ed\u30fc\u30f3\u5206\u985e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u3001\u5408\u8a085000\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u306814\u306e\u5c5e\u6027\u304c\u3042\u308a\u307e\u3059\u3002\u5c5e\u6027\u3068\u306f\u3001\u53ce\u5165\u3001\u5e74\u9f62\u3001\u7d4c\u9a13\u306a\u3069\u306e\u69d8\u3005\u306a\u57fa\u6e96\u3067\u8a55\u4fa1\u3055\u308c\u305f\u30e6\u30fc\u30b6\u30fb\u30c7\u30fc\u30bf\u3092\u610f\u5473\u3057\u307e\u3059\u3002\u3053\u306e\u5834\u5408\u306e\u5fdc\u7b54\u5909\u6570\u306f\u3001\u00e2\u20ac\u02dcPersonal Loan\u00e2\u20ac\u2122\u3068\u3044\u3046\u5909\u6570\u3067\u3001\u6027\u8cea\u306f\u4e8c\u5024\u3067\u3059\u30020\u306e\u30e9\u30d9\u30eb\u306f\u30ed\u30fc\u30f3\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u5374\u4e0b\u3092\u610f\u5473\u3057\u30011\u306f\u53d7\u3051\u5165\u308c\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/p>\n<p>\u3053\u306e\u76ee\u7684\u306f\u3001\u6b8b\u308a\u306e\u8aac\u660e\u5909\u6570\u306b\u57fa\u3065\u3044\u3066\u3001\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u30920\u307e\u305f\u306f1\uff08\u5374\u4e0b\u307e\u305f\u306f\u53d7\u3051\u5165\u308c\uff09\u306e\u3044\u305a\u308c\u304b\u306e\u30ab\u30c6\u30b4\u30ea\u306b\u5206\u985e\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u9054\u6210\u3067\u304d\u308b2\u30af\u30e9\u30b9\u306e\u5206\u985e\u30bf\u30b9\u30af\u3067\u3059\u3002\u3067\u306f\u3001\u65e9\u901f\u3084\u3063\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<h2>\u5fc5\u8981\u306a\u30d1\u30b9\u3092\u8a2d\u5b9a\u3059\u308b<\/h2>\n<p><a href=\"https:\/\/github.com\/griddb\/griddb\">GridDB<\/a>\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3001\u4e0a\u8a18\u306e\u524d\u63d0\u6761\u4ef6\u3092\u6e80\u305f\u3057\u305f\u4e0a\u3067\u3001Ubuntu Terminal\u306a\u3069\u306eOS\u3067\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/p>\n<pre><code>export CPATH=$CPATH:&lt;Python header file directory path&gt;\n\nexport PYTHONPATH=$PYTHONPATH:&lt;installed directory path&gt;\n\nexport LIBRARY_PATH=$LIBRARY_PATH:C client library file directory path\n<\/code><\/pre>\n<h2>\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b<\/h2>\n<p>\u6700\u521d\u306e\u30b9\u30c6\u30c3\u30d7\u306f\u3001\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u4f7f\u7528\u3059\u308b\u30e9\u30a4\u30d6\u30e9\u30ea\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n<ol>\n<li><a href=\"https:\/\/numpy.org\/\">NumPy<\/a><\/li>\n<li><a href=\"https:\/\/pandas.pydata.org\/\">Pandas<\/a><\/li>\n<li><a href=\"https:\/\/matplotlib.org\/\">Matplotlib<\/a><\/li>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/\">scikit-learn<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/griddb\/python_client\">griddb_python<\/a><\/li>\n<\/ol>\n<p>\u306a\u304a\uff0c\u3053\u308c\u3089\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u521d\u3081\u3066\u4f7f\u7528\u3059\u308b\u5834\u5408\u306b\u306f\uff0c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u30a8\u30e9\u30fc\u304c\u767a\u751f\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u306e\u5834\u5408\u306f\u3001<code>pip install package-name<\/code>\u307e\u305f\u306f<code>conda install package-name<\/code>\u3092\u8a66\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<p>\u3053\u308c\u3089\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u5b8c\u4e86\u3057\u305f\u3089\u3001\u4eca\u5ea6\u306f <code>griddb_python<\/code> \u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092python\u30b3\u30f3\u30bd\u30fc\u30eb\u4e0a\u3067\u500b\u5225\u306b\u5b9f\u884c\u3059\u308b\u304b\u3001\u90fd\u5408\u306b\u5408\u308f\u305b\u3066.py\u30d5\u30a1\u30a4\u30eb\u3092\u4f5c\u6210\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u6210\u529f\u3059\u308c\u3070\u3001import\u30b3\u30de\u30f3\u30c9\u306f\u30a8\u30e9\u30fc\u3092\u8fd4\u3055\u306a\u3044\u306f\u305a\u3067\u3059\u3002<\/p>\n<pre><code>import griddb_python\n<\/code><\/pre>\n<p><strong>\u53c2\u8003<\/strong>\u30d3\u30eb\u30c9\u304c\u3046\u307e\u304f\u3044\u304b\u306a\u3044\u5834\u5408\u306f\u3001\u30d1\u30b9\u3092\u8a2d\u5b9a\u3057\u305f\u5f8c\u306b <code>make<\/code> \u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u307e\u305f\u3001<a href=\"https:\/\/github.com\/griddb\/python_client#how-to-run-sample\">\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u5b9f\u884c<\/a>\u3057\u3066\u3001\u8a73\u7d30\u3092\u628a\u63e1\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n<h2><span id=\"adding\">Adding data to GridDB<\/span><\/h2>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">import griddb_python as griddb\nimport pandas as pd\n\nfactory = griddb.StoreFactory.get_instance()\n\n# Container Initialization\ntry:\n    gridstore = factory.get_store(host=your_host, port=ypur_port, \n            cluster_name=your_cluster_name, username=your_username, \n            password=your_password)\n\n    conInfo = griddb.ContainerInfo(\"Dataset_Name\",\n                    [[\"attribute1\", griddb.Type.INTEGER],[\"attribute2\",griddb.Type.FLOAT],\n                    ....],\n                    griddb.ContainerType.COLLECTION, True)\n    \n    cont = gridstore.put_container(conInfo)   \n    cont.create_index(\"id\", griddb.IndexType.DEFAULT)\n    \n    dataset = pd.read_csv(\"BankLoanClassification.csv\")\n    \n    #Adding data to container\n    for i in range(len(dataset)):\n        ret = cont.put(data.iloc[i, :])\n    print(\"Data has been added\")\n\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))<\/code><\/pre>\n<\/div>\n<p><strong>\u6ce8\u610f\u70b9<\/strong> 1. <code>factory.get_store()<\/code>\u95a2\u6570\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u3001\u304a\u4f7f\u3044\u306e\u30af\u30e9\u30b9\u30bf\u306e\u8a8d\u8a3c\u60c5\u5831\u306b\u7f6e\u304d\u63db\u3048\u3066\u304f\u3060\u3055\u3044\u3002\u307e\u305f\u306f\u3001\u65b0\u898f\u30af\u30e9\u30b9\u30bf\u306e\u4f5c\u6210\u65b9\u6cd5\u3092<a href=\"https:\/\/github.com\/griddb\/griddb#start-a-server-1\">\u53c2\u7167<\/a>\u3057\u3066\u304f\u3060\u3055\u3044\u3002 2. \u95a2\u6570 <code>griddb.ContainerInfo()<\/code> \u306b\u3001\u95a2\u9023\u3059\u308b\u5c5e\u6027\u540d\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u540d\u3001\u30c7\u30fc\u30bf\u30bf\u30a4\u30d7\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002<\/p>\n<h2><span id=\"extracting\">SQL\u3092\u4f7f\u3063\u3066GridDB\u304b\u3089\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3059\u308b<\/span><\/h2>\n<p><code>griddb_python<\/code>\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u3046\u3068\u3001Python\u3067SQL\u7d4c\u7531\u3067\u30c7\u30fc\u30bf\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u4e21\u65b9\u306e\u8a00\u8a9e\u3092\u4e0a\u624b\u306b\u4f7f\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002Python\u30d5\u30a1\u30a4\u30eb\u306e\u4e2d\u3067\u6b21\u306e\u3088\u3046\u306b\u30af\u30a8\u30ea\u3092\u6e21\u3059\u3060\u3051\u3067\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u4fdd\u5b58\u3055\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<pre><code>statement = ('SELECT * FROM Dataset_Name')\nsql_query = pd.read_sql_query(statement, cont)\n<\/code><\/pre>\n<p>\u306a\u304a\u3001<code>pd.read_sql_query()<\/code>\u95a2\u6570\u306f\u3001SQL\u30af\u30a8\u30ea\u304b\u3089\u306e\u30c7\u30fc\u30bf\u3092pandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u5909\u63db\u3057\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\uff0c\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u76f4\u63a5\u64cd\u4f5c\u3057\u3066\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\uff0e\u307e\u305f\u3001\u6b21\u306e\u3088\u3046\u306bcsv\u30d5\u30a1\u30a4\u30eb\u3092\u76f4\u63a5\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">import os\nimport numpy as np    \nimport pandas as pd   \nimport matplotlib.pyplot as plt    \nfrom sklearn.metrics import confusion_matrix,mean_squared_error,accuracy_score\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score \nimport seaborn as sns\nimport matplotlib.pyplot as plt \n<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">APP_PATH = os.getcwd()\nAPP_PATH\n<\/code><\/pre>\n<\/div>\n<pre><code>'C:\\Users\\SHRIPRIYA\\Desktop\\GridDB'\n<\/code><\/pre>\n<h2><span id=\"building\">\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u3080<\/span><\/h2>\n<p>Note that if you have loaded your dataset using GridDB&#8217;s <code>python_client<\/code>, you can skip this step as it is redundant. GridDB\u306e<code>python_client<\/code>\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u3093\u3060\u5834\u5408\u306f\u3001\u3053\u306e\u30b9\u30c6\u30c3\u30d7\u306f\u5197\u9577\u306a\u306e\u3067\u7701\u7565\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n<p><code>APP_PATH<\/code> \u5909\u6570\u306b\u306f\uff0c\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067\u30d5\u30a1\u30a4\u30eb\u540d\u306b\u4ed8\u52a0\u3055\u308c\u308b\u73fe\u5728\u306e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002\u6df7\u4e71\u3092\u907f\u3051\u308b\u305f\u3081\u306b\uff0cpython\u30d5\u30a1\u30a4\u30eb\u3068csv\u30d5\u30a1\u30a4\u30eb\u304c\u540c\u3058\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3042\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u305d\u3046\u3067\u306a\u3044\u5834\u5408\u306f\u3001<code>FileNotFoundError<\/code>\u3092\u907f\u3051\u308b\u305f\u3081\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u30d5\u30a1\u30a4\u30eb\u306e\u30d5\u30eb\u30d1\u30b9\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">dataset = pd.read_csv(os.path.join(APP_PATH, 'UniversalBank.csv'))<\/code><\/pre>\n<\/div>\n<h2>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u3063\u3066\u307f\u308b<\/h2>\n<p>\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u524d\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6982\u8981\u3092\u628a\u63e1\u3059\u308b\u305f\u3081\u3001\u3044\u304f\u3064\u304b\u306e\u7c21\u5358\u306a\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u30c7\u30fc\u30bf\u306e\u524d\u51e6\u7406\u3068\u5206\u6790\u3092\u3057\u3066\u304a\u304f\u3053\u3068\u3067\u3001\u3088\u308a\u5f37\u56fa\u3067\u52b9\u679c\u7684\u306a\u30e2\u30c7\u30eb\u3092\u4f5c\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<code>head<\/code>\u30b3\u30de\u30f3\u30c9\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6700\u521d\u306e5\u884c\u3092\u8fd4\u3057\u307e\u3059\u3002\u3082\u3063\u3068\u591a\u304f\u306e\u884c\u3092\u8868\u793a\u3057\u305f\u3044\u5834\u5408\u306b\u306f\u3001<code>head<\/code>\u30b3\u30de\u30f3\u30c9\u306e\u5f15\u6570\u306b\u6570\u5b57\u3092\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u4f8b\u3048\u3070\uff0c<code>dataset.head(20)<\/code> \u3068\u3059\u308b\u3068\uff0c\u6700\u521d\u306e20\u884c\u304c\u8868\u793a\u3055\u308c\uff0c<code>dataset.head(-5)<\/code> \u3068\u3059\u308b\u3068\uff0c\u6700\u5f8c\u306e5\u884c\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">dataset.head()\n<\/code><\/pre>\n<\/div>\n<div>\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          ID\n        <\/th>\n<th>\n          Age\n        <\/th>\n<th>\n          Experience\n        <\/th>\n<th>\n          Income\n        <\/th>\n<th>\n          ZIP Code\n        <\/th>\n<th>\n          Family\n        <\/th>\n<th>\n          CCAvg\n        <\/th>\n<th>\n          Education\n        <\/th>\n<th>\n          Mortgage\n        <\/th>\n<th>\n          Personal Loan\n        <\/th>\n<th>\n          Securities Account\n        <\/th>\n<th>\n          CD Account\n        <\/th>\n<th>\n          Online\n        <\/th>\n<th>\n          CreditCard\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          25\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          49\n        <\/td>\n<td>\n          91107\n        <\/td>\n<td>\n          4\n        <\/td>\n<td>\n          1.6\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          2\n        <\/td>\n<td>\n          45\n        <\/td>\n<td>\n          19\n        <\/td>\n<td>\n          34\n        <\/td>\n<td>\n          90089\n        <\/td>\n<td>\n          3\n        <\/td>\n<td>\n          1.5\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          3\n        <\/td>\n<td>\n          39\n        <\/td>\n<td>\n          15\n        <\/td>\n<td>\n          11\n        <\/td>\n<td>\n          94720\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3\n        <\/th>\n<td>\n          4\n        <\/td>\n<td>\n          35\n        <\/td>\n<td>\n          9\n        <\/td>\n<td>\n          100\n        <\/td>\n<td>\n          94112\n        <\/td>\n<td>\n          1\n        <\/td>\n<td>\n          2.7\n        <\/td>\n<td>\n          2\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          5\n        <\/td>\n<td>\n          35\n        <\/td>\n<td>\n          8\n        <\/td>\n<td>\n          45\n        <\/td>\n<td>\n          91330\n        <\/td>\n<td>\n          4\n        <\/td>\n<td>\n          1.0\n        <\/td>\n<td>\n          2\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          0\n        <\/td>\n<td>\n          1\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>\u5f8c\u306b\u30c6\u30b9\u30c8\u3068\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306b\u5206\u3051\u3089\u308c\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5168\u9577\u3092\u78ba\u8a8d\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">len(dataset)<\/code><\/pre>\n<\/div>\n<pre><code>5000\n<\/code><\/pre>\n<p><code>dataset.dtypes<\/code>\u30b3\u30de\u30f3\u30c9\u306f\u3001\u5404\u5c5e\u6027\u304c\u3069\u306e\u3088\u3046\u306a\u30c7\u30fc\u30bf\u578b\u3092\u6301\u3063\u3066\u3044\u308b\u304b\u3092\u793a\u3057\u307e\u3059\u3002\u3053\u306e\u30b9\u30c6\u30c3\u30d7\u306f\u3001\u6570\u5024\u5909\u6570\u306e\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3084\u3001\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u306e\u5834\u5408\u306f\u30c0\u30df\u30fc\u5909\u6570\u306e\u4f5c\u6210\u3084\u30a8\u30f3\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u30b9\u30ad\u30fc\u30e0\u3092\u4f7f\u7528\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u305f\u3081\u3001\u4e0d\u53ef\u6b20\u3067\u3059\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u30e2\u30c7\u30eb\u69cb\u7bc9\u306e\u524d\u306b\u3053\u308c\u3089\u306e\u30b9\u30c6\u30c3\u30d7\u3092\u8a08\u753b\u3067\u304d\u308b\u3088\u3046\u306b\u3001\u30c7\u30fc\u30bf\u3092\u898b\u3066\u304a\u304f\u3068\u3088\u3044\u3067\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">dataset.dtypes<\/code><\/pre>\n<\/div>\n<pre><code>ID                      int64\nAge                     int64\nExperience              int64\nIncome                  int64\nZIP Code                int64\nFamily                  int64\nCCAvg                 float64\nEducation               int64\nMortgage                int64\nPersonal Loan           int64\nSecurities Account      int64\nCD Account              int64\nOnline                  int64\nCreditCard              int64\ndtype: object\n<\/code><\/pre>\n<p>\u305d\u308c\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u6b20\u640d\u5024\u304c\u306a\u3044\u304b\u3069\u3046\u304b\u3092\u8abf\u3079\u3066\u307f\u307e\u3057\u3087\u3046\u3002<code>isnull()<\/code>\u30b3\u30de\u30f3\u30c9\u306f\uff0c\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4e2d\u306b\u5c11\u306a\u304f\u3068\u3082\u4e00\u3064\u306eNULL\u5024\u304c\u3042\u308c\u3070\uff0c<code>True<\/code>\u3092\u8fd4\u3057\u307e\u3059\uff0eNULL\u5024\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u5b66\u7fd2\u7528\u306b\u56de\u3059\u524d\u306b\u3001\u524a\u9664\u3059\u308b\u304b\u3001\u3042\u3089\u304b\u3058\u3081\u6c7a\u3081\u3089\u308c\u305f\u5024\u3067\u7f6e\u304d\u63db\u3048\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002\u4eca\u56de\u306e\u30b1\u30fc\u30b9\u3067\u306fNULL\u5024\u304c\u306a\u3044\u306e\u3067\u3001\u305d\u306e\u307e\u307e\u5148\u306b\u9032\u307f\u307e\u3059\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u542b\u307e\u308c\u308bNULL\u5024\u306e\u7dcf\u6570\u3092\u5f97\u308b\u306b\u306f\u3001<code>dataset[key].isnull().sum()<\/code>\u3068\u5165\u529b\u3057\u307e\u3059\u3002<code>[key]<\/code>\u306f\u5bfe\u8c61\u3068\u306a\u308b\u5c5e\u6027\u540d\u3067\u3059\u3002<code>dataset.dropna()<\/code>\u30b3\u30de\u30f3\u30c9\u306f\u3001\u5c11\u306a\u304f\u3068\u3082\u4e00\u3064\u306eNULL\u5024\u3092\u542b\u3080\u884c\u3092\u524a\u9664\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">dataset.isnull().values.any()<\/code><\/pre>\n<\/div>\n<pre><code>False\n<\/code><\/pre>\n<p><code>ID<\/code>\u3068<code>ZIP Code<\/code>\u306f\u3001\u30ed\u30fc\u30f3\u7533\u8acb\u306e\u7d50\u679c\u3092\u4e88\u6e2c\u3059\u308b\u4e0a\u3067\u307b\u3068\u3093\u3069\u5f79\u5272\u3092\u679c\u305f\u3055\u306a\u3044\u305f\u3081\u3001\u4eca\u5f8c\u306f\u3053\u308c\u3089\u306e\u5217\u3092\u524a\u9664\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">dataset.drop([\"ID\",\"ZIP Code\"],axis=1,inplace=True)<\/code><\/pre>\n<\/div>\n<p>\u524d\u306e\u30b9\u30c6\u30c3\u30d7\u3092\u691c\u8a3c\u3059\u308b\u305f\u3081\u306b\u30b3\u30e9\u30e0\u3092\u8868\u793a\u3059\u308b<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">dataset.keys()<\/code><\/pre>\n<\/div>\n<pre><code>Index(['Age', 'Experience', 'Income', 'Family', 'CCAvg', 'Education',\n       'Mortgage', 'Personal Loan', 'Securities Account', 'CD Account',\n       'Online', 'CreditCard'],\n      dtype='object')\n<\/code><\/pre>\n<p>\u30e2\u30c7\u30eb\u306e\u4f5c\u6210\u306b\u79fb\u308b\u524d\u306b\u3001\u5e8f\u5217\u30c7\u30fc\u30bf\u3092\u30c0\u30df\u30fc\u30c7\u30fc\u30bf\u306b\u5909\u63db\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u30e2\u30c7\u30eb\u304c\u7c21\u5358\u306b\u3053\u308c\u3089\u306e\u5c5e\u6027\u3092\u89e3\u91c8\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u6570\u5024\u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u306f\u3001\u30e2\u30c7\u30eb\u304c\u305d\u306e\u307e\u307e\u5229\u7528\u3059\u308b\u305f\u3081\u3001\u3053\u3053\u3067\u306f\u7701\u7565\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">cat_cols = [\"Family\",\"Education\",\"Personal Loan\",\"Securities Account\",\"CD Account\",\"Online\",\"CreditCard\"]\ndataset = pd.get_dummies(dataset,columns=cat_cols,drop_first=True,)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">dataset.keys()<\/code><\/pre>\n<\/div>\n<pre><code>Index(['Age', 'Experience', 'Income', 'CCAvg', 'Mortgage', 'Family_2',\n       'Family_3', 'Family_4', 'Education_2', 'Education_3', 'Personal Loan_1',\n       'Securities Account_1', 'CD Account_1', 'Online_1', 'CreditCard_1'],\n      dtype='object')\n<\/code><\/pre>\n<p><code>Family<\/code>\u5c5e\u6027\u306b3\u3064\u306e\u30c0\u30df\u30fc\u30ab\u30e9\u30e0\u304c\u4f5c\u6210\u3055\u308c\u3066\u3044\u308b\u306e\u304c\u5206\u304b\u308a\u307e\u3059\u3002\u540c\u69d8\u306b\u3001<code>Education, Personal Loan<\/code>\u306a\u3069\u306b\u3082\u30c0\u30df\u30fc\u306e\u30ab\u30e9\u30e0\u304c\u4f5c\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u5217\u306e\u6570\u306f\u3001\u7279\u5b9a\u306e\u5c5e\u6027\u304c\u6301\u3064\u30e9\u30d9\u30eb\u306b\u4f9d\u5b58\u3057\u307e\u3059\u3002\u7c21\u5358\u306b\u8a00\u3046\u3068\u30010\u30011\u30012\u306e3\u6bb5\u968e\u306e\u5909\u6570\u304c\u3042\u308c\u3070\u3001\u4f5c\u6210\u3055\u308c\u308b\u30c0\u30df\u30fc\u30ab\u30e9\u30e0\u306e\u6570\u306f3\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<p>\u3053\u308c\u3067\u3001\u30c7\u30fc\u30bf\u3092X\u3068Y\uff08\u72ec\u7acb\u5909\u6570\u3068\u5fdc\u7b54\u5909\u6570\uff09\u306b\u5206\u5272\u3059\u308b\u6e96\u5099\u304c\u6574\u3044\u307e\u3057\u305f\u3002<code>y<\/code> \u306b\u306f\u30e9\u30d9\u30eb\u304c\u5165\u308a\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001<code>Personal_Loan_1<\/code> \u3068\u3044\u3046\u5c5e\u6027\u304c\u5165\u308a\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">X = dataset.copy().drop(\"Personal Loan_1\",axis=1)\ny = dataset[\"Personal Loan_1\"]<\/code><\/pre>\n<\/div>\n<p>\u5404\u30ab\u30c6\u30b4\u30ea\u30fc\u306e\u30e9\u30d9\u30eb\u6570\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">dataset[\"Personal Loan_1\"].value_counts()<\/code><\/pre>\n<\/div>\n<pre><code>0    4520\n1     480\nName: Personal Loan_1, dtype: int64\n<\/code><\/pre>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">sns.countplot(x ='Personal Loan_1', data=dataset, palette='hls')\nplt.show()<\/code><\/pre>\n<\/div>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2021\/05\/output_50_0.png\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2021\/05\/output_50_0.png\" alt=\"\" width=\"395\" height=\"263\" class=\"aligncenter size-full wp-image-27465\" srcset=\"\/wp-content\/uploads\/2021\/05\/output_50_0.png 395w, \/wp-content\/uploads\/2021\/05\/output_50_0-300x200.png 300w\" sizes=\"(max-width: 395px) 100vw, 395px\" \/><\/a><\/p>\n<h2>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30c6\u30b9\u30c8\u3068\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306b\u5206\u5272\u3059\u308b<\/h2>\n<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3068\u30c6\u30b9\u30c8\u306b\u5206\u3051\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u3053\u3053\u3067\u306f\u300180\u5bfe20\u306e\u6bd4\u7387\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059\u3002\u4e00\u822c\u7684\u306b\u306f66\u5bfe33\u306e\u5272\u5408\u3067\u3082\u3088\u3044\u3067\u3057\u3087\u3046\u3002\u4eca\u56de\u306f1000\u500b\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4f7f\u3044\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">trainx, testx, trainy, testy = train_test_split(X, y, test_size=0.20)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">print(trainx.shape)\nprint(testx.shape)\nprint(trainy.shape)\nprint(testy.shape)<\/code><\/pre>\n<\/div>\n<pre><code>(4000, 14)\n(1000, 14)\n(4000,)\n(1000,)\n<\/code><\/pre>\n<h2>\u6a5f\u80fd\u3092\u62e1\u5f35\u3059\u308b<\/h2>\n<p>\u30c7\u30fc\u30bf\u306e\u6b63\u898f\u5316\u3084\u6a19\u6e96\u5316\u306f\u3001\u6570\u5024\u30c7\u30fc\u30bf\u3092\u6271\u3046\u969b\u306b\u3088\u304f\u884c\u308f\u308c\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u5404\u5c5e\u6027\u304c\u540c\u3058\u7bc4\u56f2\u306b\u3042\u308b\u3053\u3068\u3092\u4fdd\u8a3c\u3059\u308b\u305f\u3081\u306b\u91cd\u8981\u3067\u3059\u3002\u30c7\u30fc\u30bf\u306e\u6a19\u6e96\u5316\u3068\u306f\u3001\u5168\u4f53\u306e\u5e73\u5747\u5024\u304c0\u3001\u6a19\u6e96\u504f\u5dee\u304c1\u306b\u306a\u308b\u3088\u3046\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u518d\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u3053\u3067\u306f\u3001<code>scikit-learn StandardScaler()<\/code>\u3092\u7528\u3044\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">scaler = StandardScaler()\nscaler.fit(trainx.iloc[:,:5])\n\ntrainx.iloc[:,:5] = scaler.transform(trainx.iloc[:,:5])\ntestx.iloc[:,:5] = scaler.transform(testx.iloc[:,:5])<\/code><\/pre>\n<\/div>\n<pre><code>C:UsersSHRIPRIYAanaconda3libsite-packagespandascoreindexing.py:966: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https:\/\/pandas.pydata.org\/pandas-docs\/stable\/user_guide\/indexing.html#returning-a-view-versus-a-copy\n  self.obj[item] = s\nC:UsersSHRIPRIYAanaconda3libsite-packagespandascoreindexing.py:966: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https:\/\/pandas.pydata.org\/pandas-docs\/stable\/user_guide\/indexing.html#returning-a-view-versus-a-copy\n  self.obj[item] = s\n<\/code><\/pre>\n<h2>\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b<\/h2>\n<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u524d\u51e6\u7406\u3001\u5206\u5272\u3001\u6a19\u6e96\u5316\u304c\u7d42\u308f\u3063\u305f\u3089\u3001\u6b21\u306f\u5206\u985e\u30e2\u30c7\u30eb\u306b\u6e21\u3057\u307e\u3059\u3002\u5b66\u7fd2\u30bb\u30c3\u30c8\u3068\u51fa\u529b\u30e9\u30d9\u30eb\u306f\uff0c\u30e2\u30c7\u30eb\u69cb\u7bc9\u306e\u305f\u3081\u306b\u3001<code>model.fit<\/code>\u95a2\u6570\u306b\u6e21\u3055\u308c\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">X = trainx\ny = trainy\n\nmodel = LogisticRegression()\nmodel.fit(X , y)\npredicted_classes = model.predict(X)\naccuracy = accuracy_score(y,predicted_classes)\nparameters = model.coef_<\/code><\/pre>\n<\/div>\n<h2><span id=\"evaluating\">Model Evaluation<\/span><\/h2>\n<h3>\u7cbe\u5ea6\u3068\u4fc2\u6570<\/h3>\n<p>\u3053\u3053\u3067\u306e<code>accuracy<\/code>\u306f\u5b66\u7fd2\u7cbe\u5ea6\u3001<code>parameters<\/code>\u306f\u69cb\u7bc9\u3057\u305f\u30e2\u30c7\u30eb\u306e\u4fc2\u6570\u3092\u8868\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">print(accuracy)\nprint(parameters)\nprint(model)<\/code><\/pre>\n<\/div>\n<pre><code>0.9615\n[[-0.11052202  0.21417872  2.70013875  0.30873518  0.04262124 -0.19654911\n   1.68376406  1.39637464  3.44973088  3.7034019  -0.56957366  3.35123915\n  -0.64710272 -0.72719671]]\nLogisticRegression()\n<\/code><\/pre>\n<p>\u3053\u3053\u3067\u306f\u3001\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u7528\u3044\u3066\u30011000\u4ef6\u306e\u672a\u51e6\u7406\u30c7\u30fc\u30bf\u306b\u5bfe\u3059\u308b\u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u3092\u8a55\u4fa1\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">model.fit(testx , testy)\npredicted_classes_test = model.predict(testx)\naccuracy = accuracy_score(testy,predicted_classes_test)\nprint(accuracy)<\/code><\/pre>\n<\/div>\n<pre><code>0.962\n<\/code><\/pre>\n<h3>\u6df7\u4e71\u30de\u30c8\u30ea\u30c3\u30af\u30b9\u3068\u30d2\u30fc\u30c8\u30de\u30c3\u30d7<\/h3>\n<p>\u30b3\u30f3\u30d5\u30e5\u30fc\u30b8\u30e7\u30f3\u30fb\u30de\u30c8\u30ea\u30af\u30b9\u3067\u306f\u30014\u3064\u306e\u30ab\u30c6\u30b4\u30ea\u30fc\u304c\u3042\u308a\u307e\u3059\u30021. <strong>\u771f\u306e\u5426\u5b9a\uff1a<\/strong> \u6b63\u3057\u304f\u4e88\u6e2c\u3055\u308c\u305f\u30bc\u30ed\uff08\u5b9f\u969b\u3068\u4e88\u6e2c-0\uff09 2. <strong>False Negative\uff1a<\/strong> \u30bc\u30ed\u3068\u3057\u3066\u8aa4\u3063\u3066\u4e88\u6e2c\u3055\u308c\u305f\u3082\u306e \uff08Actual &#8211; 1, Predicted &#8211; 0\uff093. <strong>False Positive\uff1a<\/strong> \u30bc\u30ed\u3092\u8aa4\u3063\u3066\u30bc\u30ed\u3068\u4e88\u6e2c\u3057\u305f\u5834\u5408\uff08\u5b9f\u969b &#8211; 0, \u4e88\u6e2c &#8211; 1\uff09 4. <strong>\u771f\u6b63\uff1a<\/strong>\u6b63\u3057\u304f\u4e88\u6e2c\u3055\u308c\u305f\u3082\u306e\uff08\u5b9f\u969b\u3068\u4e88\u6e2c-1\uff09<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-py\">cm = confusion_matrix(testy,predicted_classes_test)\nfig, ax = plt.subplots(figsize=(6, 6))\nax.imshow(cm)\nax.grid(False)\nax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))\nax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))\nax.set_ylim(1.5, -0.5)\nfor i in range(2):\n    for j in range(2):\n        ax.text(j, i, cm[i, j], ha='center', va='center', color='red')\nplt.show()<\/code><\/pre>\n<\/div>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2021\/05\/output_69_0.png\"><img decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2021\/05\/output_69_0.png\" alt=\"\" width=\"393\" height=\"357\" class=\"aligncenter size-full wp-image-27466\" srcset=\"\/wp-content\/uploads\/2021\/05\/output_69_0.png 393w, \/wp-content\/uploads\/2021\/05\/output_69_0-300x273.png 300w\" sizes=\"(max-width: 393px) 100vw, 393px\" \/><\/a><\/p>\n<h2>\u307e\u3068\u3081\u3068\u4eca\u5f8c\u306e\u5c55\u671b<\/h2>\n<p>\u4eca\u56de\u306e\u9280\u884c\u30ed\u30fc\u30f3\u5206\u985e\u30e2\u30c7\u30eb\u306f\u30015000\u4ef6\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306795.3%\u306e\u7cbe\u5ea6\u3092\u9054\u6210\u3057\u3001\u307e\u305a\u307e\u305a\u306e\u7d50\u679c\u3068\u306a\u308a\u307e\u3057\u305f\u3002\u307e\u305f\u3001\u6df7\u540c\u884c\u5217\u306b\u3088\u3063\u3066\u3001\u507d\u9670\u6027\u3068\u507d\u967d\u6027\u306e\u8a73\u7d30\u304c\u660e\u3089\u304b\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u7cbe\u5ea6\u3092\u4e0a\u3052\u308b\u305f\u3081\u306e\u4ed6\u306e\u9078\u629e\u80a2\u3068\u3057\u3066\u306f\u3001<code>Naive Bayes, KNN Classifier, Decision Trees, SVM, etc.<\/code> \u304c\u3042\u308a\u307e\u3059\u3002\u8a73\u3057\u304f\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30db\u30fc\u30e0\u30da\u30fc\u30b8<a href=\"https:\/\/www.kaggle.com\/sriharipramod\/bank-loan-classification\">\u3053\u3061\u3089<\/a>\u3067\u8abf\u3079\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001GridDB\u306b\u30c7\u30fc\u30bf\u3092\u633f\u5165\u3057\u3001<code>python-client<\/code>\u3092\u4f7f\u3063\u3066\u30a2\u30af\u30bb\u30b9\u3059\u308b\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002\u307e\u305f\u3001\u7c21\u5358\u306aSQL\u30af\u30a8\u30ea\u3092\u4f7f\u3063\u3066\u3001GridDB\u304b\u3089pandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3057\u307e\u3057\u305f\u3002\u3053\u306e\u3088\u3046\u306b\u3001GridDB\u306b\u306f\u69d8\u3005\u306a\u6a5f\u80fd\u304c\u3042\u308a\u3001\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u4fdd\u5b58\u306b\u9069\u3057\u3066\u3044\u307e\u3059\u3002GridDB\u3067\u3067\u304d\u308b\u3053\u3068\u306f\u307e\u3060\u307e\u3060\u305f\u304f\u3055\u3093\u3042\u308a\u307e\u3059\u3002\u305c\u3072<a href=\"https:\/\/griddb.net\/en\/blog\/\">\u30aa\u30f3\u30e9\u30a4\u30f3\u30b3\u30df\u30e5\u30cb\u30c6\u30a3<\/a>\u3092\u30c1\u30a7\u30c3\u30af\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u306f\u3058\u3081\u306b \u3053\u306e\u30d6\u30ed\u30b0\u3067\u306f\u3001GridDB\u306b\u4fdd\u5b58\u3055\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u3001\u9280\u884c\u30ed\u30fc\u30f3\u306e\u5206\u985e\u30e2\u30c7\u30eb\u3092\u4e00\u304b\u3089\u69cb\u7bc9\u3057\u3001\u4ee5\u4e0b\u306e\u5185\u5bb9\u3092\u8aac\u660e\u3057\u307e\u3059\u3002 1&#46;GridDB\u3078\u306e\u30c7\u30fc\u30bf\u4fdd\u5b58 2&#46;GridDB\u304b\u3089\u306e\u30c7\u30fc\u30bf\u62bd\u51fa 3&#038; 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