{"id":50756,"date":"2021-08-06T00:00:00","date_gmt":"2021-08-06T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/performing-real-time-predictions-using-machine-learning-griddb-and-python\/"},"modified":"2025-11-14T07:54:49","modified_gmt":"2025-11-14T15:54:49","slug":"performing-real-time-predictions-using-machine-learning-griddb-and-python","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/performing-real-time-predictions-using-machine-learning-griddb-and-python\/","title":{"rendered":"\u6a5f\u68b0\u5b66\u7fd2\u3001GridDB\u3001Python\u3092\u4f7f\u3063\u3066\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u306b\u4e88\u6e2c\u3092\u884c\u3046"},"content":{"rendered":"

\u6982\u8981<\/h2>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python\u3092\u4f7f\u3063\u3066\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092Web API\u306b\u3057\u3066\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u4e88\u6e2c\u3092\u884c\u3046\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\u6982\u8981\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n

    \n
  1. \u524d\u63d0\u6761\u4ef6\u3068\u74b0\u5883\u8a2d\u5b9a<\/li>\n
  2. \u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3059\u308b<\/li>\n
  3. \u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u30b7\u30ea\u30a2\u30eb\u5316\u3068\u975e\u30b7\u30ea\u30a2\u30eb\u5316<\/li>\n
  4. Python\u306eFlask\u3092\u4f7f\u3063\u3066API\u3092\u958b\u767a\u3059\u308b<\/li>\n
  5. \u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u4e88\u6e2c\u3092\u4f5c\u6210\u3059\u308b<\/li>\n<\/ol>\n

    \u524d\u63d0\u6761\u4ef6\u3068\u74b0\u5883\u8a2d\u5b9a<\/h2>\n

    \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306f\u3001Windows \u30aa\u30da\u30ec\u30fc\u30c6\u30a3\u30f3\u30b0\u30b7\u30b9\u30c6\u30e0\u4e0a\u306e Anaconda Navigator (Python version – 3.8.3) \u3092\u4f7f\u3044\u307e\u3059\u3002\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u9032\u3081\u308b\u524d\u306b\u3001\u4ee5\u4e0b\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

      \n
    1. Pandas<\/li>\n
    2. NumPy<\/li>\n
    3. Scikit-learn<\/li>\n
    4. Flask<\/li>\n
    5. Joblib<\/li>\n<\/ol>\n

      \u3053\u308c\u3089\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u3092Conda\u306e\u4eee\u60f3\u74b0\u5883\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u306b\u306f\u3001conda install package-name<\/code>\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u30bf\u30fc\u30df\u30ca\u30eb\u3084\u30b3\u30de\u30f3\u30c9\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u76f4\u63a5Python\u3092\u4f7f\u7528\u3059\u308b\u5834\u5408\u306f\u3001pip install package-name<\/code>\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u307e\u3059\u3002<\/p>\n

      \u306a\u304a\u3001Python\u3067GridDB\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b<\/a>\u305f\u3081\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u304c\u5fc5\u8981\u3068\u306a\u308a\u307e\u3059\u3002<\/p>\n

        \n
      1. GridDB C-client<\/li>\n
      2. SWIG (Simplified Wrapper and Interface Generator)<\/li>\n
      3. GridDB Python-client<\/li>\n<\/ol>\n

        \u3053\u308c\u3067\u3001\u74b0\u5883\u304c\u3059\u3079\u3066\u6574\u3044\u3001\u4f7f\u3048\u308b\u6e96\u5099\u304c\u3067\u304d\u307e\u3057\u305f\u3002\u305d\u308c\u3067\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n

        \u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3059\u308b<\/h2>\n

        \u3053\u3053\u3067\u306f\u3001\u904e\u53bb\u306e\u30d6\u30ed\u30b0\u3067\u7d39\u4ecb\u3057\u305fMachine Learning using GridDB<\/a>\u306eLinear Regression\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u5b8c\u5168\u306a\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u306fGithub<\/a>\u306b\u3042\u308a\u307e\u3059\u3002\u65b0\u3057\u3044\u5909\u66f4\u70b9\u3092\u53cd\u6620\u3055\u305b\u308b\u305f\u3081\u306b\u3001\u30b3\u30fc\u30c9\u3092\u4fee\u6b63\u3057\u3066\u3044\u304d\u307e\u3059\u3002\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u3066\u30d5\u30a9\u30ed\u30fc\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306e\u6700\u5f8c\u306bpython\u30d5\u30a1\u30a4\u30eb\u3092\u6dfb\u4ed8\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n

        CalCOFI\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n

        \n
        import numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nimport joblib\n\ndataset = pd.read_csv(\"bottle.csv\")\ndataset=dataset[[\"Salnty\",\"T_degC\"]]\ndataset = dataset[:500]\ndataset=dataset.dropna(axis=0)\ndataset.reset_index(drop=True,inplace=True)\n\nx_label=np.array(dataset['Salnty']).reshape(493,1)\ny_label=np.array(dataset['T_degC']).reshape(493,1)\nx_train, x_test, y_train, y_test = train_test_split(x_label, y_label, test_size = 0.2, random_state = 100)\nregression_model=LinearRegression()\nregression_model.fit(x_train,y_train)<\/code><\/pre>\n<\/div>\n
        \u306a\u304a\u3001\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4e88\u6e2c\u3092\u884c\u3063\u3066\u3044\u308b\u6700\u5f8c\u306e\u6570\u884c\u306f\u3001\u3053\u3053\u3067\u306f\u5fc5\u8981\u306a\u3044\u306e\u3067\u524a\u9664\u3057\u3066\u3044\u307e\u3059\u3002\u524d\u306e\u30d6\u30ed\u30b0\u3067\u8aac\u660e\u3057\u305f\u3088\u3046\u306b\u3001\u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u306f 87%<\/code> \u3067\u3059\u3002\u5f97\u3089\u308c\u305f\u30e2\u30c7\u30eb\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u3068\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
        \n
        plt.figure(figsize=(12,10))\nplt.scatter(x_label, y_label,  color='aqua')\nplt.plot(x_train, regression_model.predict(x_train),linewidth=\"4\")\nplt.xlabel(\"Temperature\",fontsize=22)\nplt.ylabel(\"Salinity\",fontsize=22)\nplt.title(\"Linear Regression\",fontsize=22)<\/code><\/pre>\n<\/div>\n
        \"\"<\/p>\n
        \u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u30b7\u30ea\u30a2\u30eb\u5316\u3068\u975e\u30b7\u30ea\u30a2\u30eb\u5316<\/h2>\n
        \u3053\u308c\u3067\u30e2\u30c7\u30eb\u304c\u4e88\u6e2c\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u304c\u3001\u30b5\u30fc\u30d0\u30fc\u304b\u3089\u30ea\u30af\u30a8\u30b9\u30c8\u3092\u53d7\u3051\u308b\u305f\u3073\u306b\u540c\u3058\u624b\u9806\u3092\u5b9f\u884c\u3057\u306a\u304f\u3066\u6e08\u3080\u3088\u3046\u306b\u3001\u5b66\u7fd2\u3057\u305f\u30e2\u30c7\u30eb\u3092\u4fdd\u5b58\u3057\u3066\u304a\u304f\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u3088\u3046\u306bPython\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u30d0\u30a4\u30c8\u30b9\u30c8\u30ea\u30fc\u30e0\u306b\u683c\u7d0d\u3057\u3001\u5f8c\u3067\u5229\u7528\u3067\u304d\u308b\u3088\u3046\u306b\u3059\u308b\u30d7\u30ed\u30bb\u30b9\u3092\u30b7\u30ea\u30a2\u30eb\u5316\u3068\u3044\u3044\u307e\u3059\u3002\u975e\u30b7\u30ea\u30a2\u30eb\u5316\u306f\u3001\u305d\u306e\u540d\u306e\u901a\u308a\u3001\u30b7\u30ea\u30a2\u30eb\u5316\u306e\u9006\u3067\u3001\u30d0\u30a4\u30c8\u30b9\u30c8\u30ea\u30fc\u30e0\u3092Python\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306b\u623b\u3059\u3053\u3068\u3092\u8a00\u3044\u307e\u3059\u3002Python\u306e\u30b7\u30ea\u30a2\u30eb\u5316\u3068\u975e\u30b7\u30ea\u30a2\u30eb\u5316\u306fjoblib, pickle<\/code>\u306a\u3069\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4ecb\u3057\u3066\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
        \u305d\u308c\u3067\u306f\u3001\u30e2\u30c7\u30eb\u3092\u4fdd\u5b58\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
        \n
        joblib.dump(regression_model, 'regression_model.pkl')\nprint('Model dumped')\nregression_model = joblib.load('regression_model.pkl')\nregression_model_columns = list(x_train)\njoblib.dump(regression_model_columns, 'regression_model_columns.pkl')<\/code><\/pre>\n<\/div>\n
        \u3053\u308c\u3089\u306e\u30b3\u30fc\u30c9\u30b9\u30cb\u30da\u30c3\u30c8\u3092\u8ffd\u52a0\u3057\u305f\u5f8c\u306e\u30d5\u30a1\u30a4\u30eb\u306f\u3001\u6700\u7d42\u7684\u306b\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
        \n
        import numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nimport joblib\n\ndataset = pd.read_csv(\"bottle.csv\")\ndataset=dataset[[\"Salnty\",\"T_degC\"]]\ndataset = dataset[:500]\ndataset=dataset.dropna(axis=0)\ndataset.reset_index(drop=True,inplace=True)\n\nx_label=np.array(dataset['Salnty']).reshape(493,1)\ny_label=np.array(dataset['T_degC']).reshape(493,1)\nx_train, x_test, y_train, y_test = train_test_split(x_label, y_label, test_size = 0.2, random_state = 100)\nregression_model=LinearRegression()\nregression_model.fit(x_train,y_train)\n\njoblib.dump(regression_model, 'regression_model.pkl')\nprint('Model dumped')\nregression_model = joblib.load('regression_model.pkl')\nregression_model_columns = list(x_train)\njoblib.dump(regression_model_columns, 'regression_model_columns.pkl')<\/code><\/pre>\n<\/div>\n
        \u6b21\u306b\u3001\u540c\u3058\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b regression_model.py<\/code> \u3092\u4fdd\u5b58\u3057\u307e\u3057\u3087\u3046\u3002\u3053\u306e\u30b3\u30fc\u30c9\u3092\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u3067\u5b9f\u884c\u3059\u308b\u306b\u306f\u3001python regression_model.py<\/code>\u3068\u5165\u529b\u3057\u307e\u3059\u3002\u5b9f\u884c\u5f8c\u306e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u69cb\u9020\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
        \"\"<\/a><\/p>\n
        \u30bf\u30fc\u30df\u30ca\u30eb\u3084\u30b3\u30de\u30f3\u30c9\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u30d5\u30a1\u30a4\u30eb\u3092\u5b9f\u884c\u3059\u308b\u5834\u5408\u306f\u3001ipynb<\/code>\u30d5\u30a1\u30a4\u30eb\u306f\u30aa\u30d7\u30b7\u30e7\u30f3\u3067\u3059\u3002 \u3053\u308c\u3067\u30e2\u30c7\u30eb\u304c\u4fdd\u5b58\u3055\u308c\u3001\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u4e88\u6e2c\u3092\u884c\u3046\u305f\u3081\u306eAPI\u3092\u4f5c\u6210\u3059\u308b\u6e96\u5099\u304c\u6574\u3044\u307e\u3057\u305f\u3002<\/p>\n
        Python\u306eFlask\u3092\u4f7f\u3063\u3066API\u3092\u958b\u767a\u3059\u308b<\/h2>\n
        \u307e\u305a\u3001\u30e2\u30c7\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u3001\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u8d77\u52d5\u6642\u306bpython\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306b\u5909\u63db\u3057\u307e\u3059\u3002\u305d\u308c\u306b\u306f\u3001\u6b21\u306e\u3088\u3046\u306a\u30b3\u30fc\u30c9\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n
        \n
        lr = joblib.load(\"regression_model.pkl\")\nprint ('Model loaded')\nmodel_columns = joblib.load(\"regression_model_columns.pkl\")\nprint ('Model columns loaded')<\/code><\/pre>\n<\/div>\n
        \u6b21\u306b\u3001\u30ea\u30af\u30a8\u30b9\u30c8\u3092\u7a4d\u6975\u7684\u306b\u53d7\u3051\u4ed8\u3051\u308bAPI\u30a8\u30f3\u30c9\u30dd\u30a4\u30f3\u30c8\u3092\u4f5c\u6210\u3057\u3001\u30ea\u30af\u30a8\u30b9\u30c8\u3092Python\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3067\u51e6\u7406\u3057\u3066\u3001\u4e88\u6e2c\u3092\u884c\u3046\u30e2\u30c7\u30eb\u306b\u6e21\u3059\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u307e\u305f\u3001\u5165\u529b\u304c\u671b\u307e\u3057\u3044JSON\u30d5\u30a9\u30fc\u30de\u30c3\u30c8\u3067\u306a\u3044\u5834\u5408\u306e\u30e9\u30f3\u30bf\u30a4\u30e0\u4f8b\u5916\u306b\u3082\u5bfe\u5fdc\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3067\u305d\u308c\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n
        \n
        @app.route('\/predict', methods=['POST'])\ndef predict():\n    if lr:\n        try:\n            json_ = request.json\n            query = pd.DataFrame(json_)\n            print(query)\n\n            prediction = list(lr.predict(query))\n\n            return jsonify({'prediction': str(prediction)})\n\n        except:\n\n            return jsonify({'trace': traceback.format_exc()})\n    else:\n        print ('Train the model first')\n        return ('No model here to use')<\/code><\/pre>\n<\/div>\n
        \u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u306f\u3001flask\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306f http:\/\/127.0.0.1:5000 \u3067\u5b9f\u884c\u3055\u308c\u307e\u3059\u3002\u5b9f\u884c\u6642\u306b\u30e6\u30fc\u30b6\u30fc\u304c\u7279\u5b9a\u306e\u30dd\u30fc\u30c8\u756a\u53f7\u3092\u63d0\u4f9b\u3057\u305f\u3044\u5834\u5408\u306f\u3001\u3053\u306e\u8a2d\u5b9a\u3092\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u3057\u307e\u3057\u3087\u3046\u3002\u30b3\u30f3\u30d1\u30a4\u30eb\u3055\u308c\u305f\u30d5\u30a1\u30a4\u30eb\u306f\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
        \n
        from flask import Flask, request, jsonify\nimport joblib\nimport traceback\nimport pandas as pd\nimport numpy as np\nimport sklearn\n\n# Your API definition\napp = Flask(__name__)\n\n@app.route('\/predict', methods=['POST'])\ndef predict():\n    if lr:\n        try:\n            json_ = request.json\n            query = pd.DataFrame(json_)\n            print(query)\n\n            prediction = list(lr.predict(query))\n\n            return jsonify({'prediction': str(prediction)})\n\n        except:\n\n            return jsonify({'trace': traceback.format_exc()})\n    else:\n        print ('Train the model first')\n        return ('No model here to use')\n\nif __name__ == '__main__':\n    try:\n        port = int(sys.argv[1])\n    except:\n        port = 12345\n\n    lr = joblib.load(\"regression_model.pkl\")\n    print ('Model loaded')\n    model_columns = joblib.load(\"regression_model_columns.pkl\")\n    print ('Model columns loaded')\n\n    app.run(port=port, debug=True)<\/code><\/pre>\n<\/div>\n
        load()<\/code>\u95a2\u6570\u3067\u306f\uff0c\u3059\u3079\u3066\u3092\u540c\u3058\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u4fdd\u5b58\u3057\u3066\u3044\u308b\u305f\u3081\uff0c\u30d5\u30a1\u30a4\u30eb\u540d\u3060\u3051\u3092\u6307\u5b9a\u3057\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u30b3\u30f3\u30d1\u30a4\u30eb\u6642\u306b FileNotFoundError<\/code> \u304c\u767a\u751f\u3057\u305f\u5834\u5408\u306b\u306f\uff0c\u4ee3\u308f\u308a\u306b\u30d5\u30eb\u30d1\u30b9\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
        \u3053\u306e\u30d5\u30a1\u30a4\u30eb\u3092\u30b3\u30f3\u30d1\u30a4\u30eb\u3057\u3066\u3001\u3059\u3079\u3066\u304c\u6b63\u3057\u304f\u52d5\u4f5c\u3057\u3066\u3044\u308b\u304b\u3069\u3046\u304b\u3092\u78ba\u8a8d\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u6b21\u306e\u3088\u3046\u306a\u51fa\u529b\u304c\u5f97\u3089\u308c\u307e\u3057\u305f\u3002<\/p>\n
        \"\"<\/a><\/p>\n
        \u3053\u308c\u306f\u3001\u3053\u306e\u30b5\u30fc\u30d0\u30fc\u304c http:\/\/127.0.0.1:12345 \u3067\u30a2\u30af\u30c6\u30a3\u30d6\u306b\u306a\u3063\u305f\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002\u6b21\u306b\u3001\u30b5\u30fc\u30c9\u30d1\u30fc\u30c6\u30a3\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3067\u3001\u30b5\u30fc\u30d0\u30fc\u306b\u30ea\u30af\u30a8\u30b9\u30c8\u3092\u9001\u308a\u3001\u3069\u306e\u3088\u3046\u306a\u51fa\u529b\u304c\u5f97\u3089\u308c\u308b\u304b\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/p>\n
        \u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u4e88\u6e2c\u3092\u4f5c\u6210\u3059\u308b<\/h2>\n
        \u30b5\u30fc\u30d0\u30fc\u3078\u306e\u30ea\u30af\u30a8\u30b9\u30c8\u9001\u4fe1\u306b\u306f\u3001Postman’s Desktop application<\/a>\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002API\u30c6\u30b9\u30c8\u7528\u306e\u30c4\u30fc\u30eb\u306f\u3044\u304f\u3064\u304b\u3042\u308b\u306e\u3067\u3001\u597d\u304d\u306a\u3082\u306e\u3092\u4f7f\u3063\u3066\u304f\u3060\u3055\u3044\u3002\u30b3\u30f3\u30bd\u30fc\u30eb\u306f\u901a\u5e38\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
        \"\"<\/a><\/p>\n
        \u4ee5\u4e0b\u306e\u3088\u3046\u306b\u5909\u66f4\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
          \n
        1. \u30e1\u30bd\u30c3\u30c9\u3092 POST<\/code> \u306b\u5909\u66f4\u3059\u308b<\/li>\n
        2. Body<\/code>\u30bf\u30d6\u3067\u3001\u30ea\u30af\u30a8\u30b9\u30c8\u30d5\u30a9\u30fc\u30de\u30c3\u30c8\u3068\u3057\u3066raw<\/code> \u3068 JSON<\/code>\u3092\u9078\u629e\u3059\u308b\u3002<\/li>\n
        3. \u5165\u529b\u3092 [{\"Salnty\":34}, {...}]<\/code> \u306e\u3088\u3046\u306a\u30ea\u30b9\u30c8\u3068\u3057\u3066\u6e21\u3059\u3002<\/li>\n
        4. Send<\/code> \u3092\u62bc\u3059\u3002<\/li>\n<\/ol>\n
          \u6ce8\u610f\u70b9<\/h3>\n
            \n
          1. api.py<\/code> \u306f\u3001\u30d0\u30c3\u30af\u30b0\u30e9\u30a6\u30f3\u30c9\u3067\u5b9f\u884c\u3055\u308c\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u3046\u3057\u306a\u3044\u3068\u3001\u30b5\u30fc\u30d0\u30fc\u304c\u30a2\u30af\u30c6\u30a3\u30d6\u306b\u306a\u3089\u305a\u3001\u30b3\u30f3\u30bd\u30fc\u30eb\u306b\u30a8\u30e9\u30fc\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/li>\n
          2. \u4eca\u56de\u9001\u308b\u5165\u529b\u306f\u3001\u30ea\u30b9\u30c8\u3068\u8f9e\u66f8\u3092\u7d44\u307f\u5408\u308f\u305b\u3066\u4f7f\u3046JSON\u5f62\u5f0f\u3067\u3059\u3002\u305d\u306e\u305f\u3081\u3001\u5165\u529b\u306f {key: value}<\/code> \u306e\u30da\u30a2\u3067\u6e21\u3059\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002\u8907\u6570\u306e\u5c5e\u6027\u304c\u3042\u308b\u5834\u5408\u306b\u306f\u3001\u5165\u529b\u306f {\"attribute1\": value1, \"attribute2\": value2, ...}<\/code> \u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/li>\n<\/ol>\n
            \u6b21\u306e\u3088\u3046\u306a\u51fa\u529b\u304c\u30b3\u30f3\u30bd\u30fc\u30eb\u306b\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/p>\n
            \"\"<\/a><\/p>\n
            Postman\u30b3\u30f3\u30bd\u30fc\u30eb\u304b\u3089\u5f97\u3089\u308c\u305f\u5165\u529b\u3092\u518d\u78ba\u8a8d\u3059\u308b\u305f\u3081\u306b\u3001\u30bf\u30fc\u30df\u30ca\u30eb\u3092\u30c1\u30a7\u30c3\u30af\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u4ee5\u4e0b\u306e\u3088\u3046\u306aJSON\u30af\u30a8\u30ea\u304c\u51fa\u529b\u3055\u308c\u3066\u3044\u308b\u306f\u305a\u3067\u3059\u3002<\/p>\n
            \"\"<\/a><\/p>\n
            \u3053\u308c\u3067\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u5c55\u958b\u3057\u3066\u3001\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u3067\u4e88\u6e2c\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002\u79c1\u305f\u3061\u306e\u30a6\u30a7\u30d6\u30da\u30fc\u30b8\u306e\u95a2\u9023\u8a18\u4e8b<\/a>\u306b\u3064\u3044\u3066\u3082\u3054\u89a7\u304f\u3060\u3055\u3044\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
            \u6982\u8981 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python\u3092\u4f7f\u3063\u3066\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092Web API\u306b\u3057\u3066\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u4e88\u6e2c\u3092\u884c\u3046\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\u6982\u8981\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002 \u524d\u63d0\u6761\u4ef6\u3068\u74b0\u5883\u8a2d\u5b9a \u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3059\u308b \u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u30b7\u30ea\u30a2 […]<\/p>\n","protected":false},"author":41,"featured_media":50140,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50756","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"\n\u6a5f\u68b0\u5b66\u7fd2\u3001GridDB\u3001Python\u3092\u4f7f\u3063\u3066\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u306b\u4e88\u6e2c\u3092\u884c\u3046 | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"\u6982\u8981 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python\u3092\u4f7f\u3063\u3066\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092Web API\u306b\u3057\u3066\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u4e88\u6e2c\u3092\u884c\u3046\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\u6982\u8981\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/griddb.net\/ja\/\u672a\u5206\u985e\/performing-real-time-predictions-using-machine-learning-griddb-and-python\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u6a5f\u68b0\u5b66\u7fd2\u3001GridDB\u3001Python\u3092\u4f7f\u3063\u3066\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u306b\u4e88\u6e2c\u3092\u884c\u3046 | GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"og:description\" content=\"\u6982\u8981 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python\u3092\u4f7f\u3063\u3066\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092Web API\u306b\u3057\u3066\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u4e88\u6e2c\u3092\u884c\u3046\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\u6982\u8981\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002\" \/>\n<meta property=\"og:url\" content=\"https:\/\/griddb.net\/ja\/\u672a\u5206\u985e\/performing-real-time-predictions-using-machine-learning-griddb-and-python\/\" \/>\n<meta property=\"og:site_name\" content=\"GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/griddbcommunity\/\" \/>\n<meta property=\"article:published_time\" content=\"2021-08-06T07:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-14T15:54:49+00:00\" \/>\n<meta property=\"og:image\" 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