{"id":50729,"date":"2021-02-09T00:00:00","date_gmt":"2021-02-09T08:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/create-a-machine-learning-model-using-griddb\/"},"modified":"2025-11-14T07:54:25","modified_gmt":"2025-11-14T15:54:25","slug":"create-a-machine-learning-model-using-griddb","status":"publish","type":"post","link":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/create-a-machine-learning-model-using-griddb\/","title":{"rendered":"GridDB\u3092\u7528\u3044\u305f\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u4f5c\u6210"},"content":{"rendered":"

\u306f\u3058\u3081\u306b<\/h2>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001GridDB\u306b\u4fdd\u5b58\u3055\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u3001\u7c21\u5358\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002\u307e\u305a\u3001GridDB\u306epython-connector\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u306e\u633f\u5165\u3068\u30a2\u30af\u30bb\u30b9\u3092\u884c\u3044\u307e\u3059\u3002\u305d\u306e\u5f8c\u3001pandas\u3068numpy\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3057\u3001\u5909\u63db\u3059\u308b\u65b9\u6cd5\u3092\u898b\u3066\u3044\u304d\u307e\u3059\u3002\u6700\u5f8c\u306b\u3001scikit-learn\u3068matplotlib\u3092\u4f7f\u3063\u3066\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5b66\u7fd2\u3057\u3001\u305d\u306e\u7d50\u679c\u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/p>\n

\u30d5\u30eb\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9 <\/a><\/p>\n

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

\u4ee5\u4e0b\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306f\u3001Ubuntu \u30aa\u30da\u30ec\u30fc\u30c6\u30a3\u30f3\u30b0\u30b7\u30b9\u30c6\u30e0\uff08v.18.04\uff09\u3068 gcc (v7.5.0) \u3092\u4f7f\u7528\u3057\u3066\u884c\u308f\u308c\u307e\u3059\u3002GridDB (v.4.5.2) \u306f\u3001GitHub\u306b\u3042\u308b\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8<\/a>\u3092\u53c2\u8003\u306b\u3057\u3066\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3057\u305f\u3002<\/p>\n

GridDB \u306e python \u30b3\u30cd\u30af\u30bf\u3092\u6709\u52b9\u306b\u3059\u308b\u306b\u306f\u3001GridDB C \u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/a>\u3001SWIG<\/a>\u3001pcre \u304c\u5fc5\u8981\u3067\u3059\u3002\u8a73\u7d30\u306f\u3053\u3061\u3089<\/a>\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

\u307e\u305a\u5fc5\u8981\u306a\u30d1\u30b9\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002<\/p>\n

\n
\nexport CPATH=$CPATH:<Python header file directory path>\nexport LIBRARY_PATH=$LIBRARY_PATH:<C client library file directory path>\nexport PYTHONPATH=$PYTHONPATH:<installed directory path>\n<\/code><\/pre>\n<\/div>\n
GridDB Python \u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u306e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u5185\u3067make\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002\u305d\u3057\u3066\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002<\/p>\n
\n
\n$ pip3 install numpy\n$ pip3 install pandas\n$ pip3 install matplotlib\n<\/code><\/pre>\n<\/div>\n
\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u6b63\u5e38\u306b\u5b8c\u4e86\u3057\u305f\u304b\u3092\u78ba\u8a8d\u3059\u308b\u305f\u3081\u306bpython\u30b3\u30f3\u30bd\u30fc\u30eb\u5185\u3067\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/p>\n
import griddb_python<\/code><\/p>\n
\u3082\u3057\u304f\u306f\u3001\/python-client\/sample\/ \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u5185\u306b\u3042\u308b\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u3067\u3082\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/p>\n
python3 sample1.py <GridDB notification address> <GridDB notification port> <GridDB cluster name> <GridDB user> <GridDB password><\/code><\/p>\n
\u2192 Person: name=name02 status=False count=2 lob=[65, 66, 67, 68, 69, 70, 71, 72, 73, 74]<\/pre>\n
\u5099\u8003\uff1aPython\u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u30d0\u30fc\u30b8\u30e7\u30f3\u304cPython3\u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u308b\u5834\u5408\u306f\u3001python\u3068pip\u3060\u3051\u3067\u524d\u8ff0\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002Ubuntu\u306b\u306fPython2\u304c\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u305f\u3081\u3001\u5b9f\u884c\u6642\u306bpython3\u3068pip3\u3092\u660e\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
GridDB\u3078\u306e\u30a2\u30af\u30bb\u30b9\u3068\u30c7\u30fc\u30bf\u306e\u53d6\u5f97<\/h2>\n
\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u4e00\u822c\u306b\u516c\u958b\u3055\u308c\u3066\u3044\u308bCalCOFI \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8<\/a>\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u3001\u4e16\u754c\u4e2d\u306e\u6d77\u6d0b\u751f\u7269\u3068\u5e7c\u9b5a\u306e\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002CalCOFI\u30c7\u30fc\u30bf\u306f\u3001\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067GridDB\u306b\u633f\u5165\u3055\u308c\u307e\u3059\u3002<\/p>\n
\n
\nimport griddb_python as griddb\nimport pandas as pd\n\nfactory = griddb.StoreFactory.get_instance()\n\n# Initialize container\ntry:\n    gridstore = factory.get_store(host=your_host, port=ypur_port, \n                                  cluster_name=your_cluster_name, \n                                  username=your_username, \n                                  password=your_password)\n\n    conInfo = griddb.ContainerInfo(\"Ocean_Data\", \n                                   [[\"id\", griddb.Type.INTEGER], \n                                    [\"T_degC\",griddb.Type.FLOAT], \n                                    [\"Salnty\", griddb.Type.FLOAT]],\n                                   griddb.ContainerType.COLLECTION, True)\n    cont = gridstore.put_container(conInfo)   \n    cont.create_index(\"id\", griddb.IndexType.DEFAULT)\n    data = pd.read_csv(\"bottle.csv\")\n    #Add data\n    for i in range(len(data)):\n        ret = cont.put(data.iloc[i, :])\n    print(\"Data added successfully\")\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))\n\n# After the data insertion is successful, we can now access the data using SQL queries. \n\nsql_statement = ('SELECT * FROM Ocean_Data')\nsql_query = pd.read_sql_query(sql_statement, cont)\n<\/code><\/pre>\n<\/div>\n
pandas\u30e9\u30a4\u30d6\u30e9\u30ea\u304c\u63d0\u4f9b\u3059\u308bread_sql_query<\/code>\u95a2\u6570\u306f\u3001\u30e6\u30fc\u30b6\u30fc\u304c\u4f5c\u696d\u3057\u3084\u3059\u3044\u3088\u3046\u306b\u3001\u53d6\u5f97\u3057\u305f\u30c7\u30fc\u30bf\u3092pandas\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u5909\u63db\u3057\u307e\u3059\u3002<\/p>\n
\nsql_query.info()\n<class 'pandas.core.frame.DataFrame'>\n<\/code><\/pre>\n
CalCOFI\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u7528\u3044\u305f\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb<\/h2>\n
CalCOFI\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u30b5\u30a4\u30ba\u304c\u5927\u304d\u3044\u306e\u3067\u3001\u7c21\u5358\u306b\u3059\u308b\u305f\u3081\u306b\u3001\u3053\u306e\u30c7\u30fc\u30bf\u306e\u6700\u521d\u306e500\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4f7f\u7528\u3057\u3066\u3001\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/p>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f74\u306e\u5c5e\u6027\u307e\u305f\u306f\u5217\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u304c\u3001\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u5869\u5206(Salinity)\u3068\u6e29\u5ea6(Temperature)\u306e2\u3064\u3060\u3051\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n
\u307e\u305a\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u3059\u3079\u3066\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n
\n
\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nimport matplotlib.pyplot as plt\n<\/code><\/pre>\n<\/div>\n
\u305d\u308c\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u307f\u307e\u3059\u3002python\u30d5\u30a1\u30a4\u30eb\u3068\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u540c\u3058\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3042\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u3042\u308b\u3044\u306f\u3001csv\u30d5\u30a1\u30a4\u30eb\u306e\u5168\u30d1\u30b9\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
dataset = pd.read_csv(\"bottle.csv\")<\/code><\/p>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u898b\u3066\u3001\u3069\u306e\u3088\u3046\u306a\u3082\u306e\u3092\u6271\u3063\u3066\u3044\u308b\u306e\u304b\u3092\u628a\u63e1\u3059\u308b\u306e\u306f\u3001\u975e\u5e38\u306b\u826f\u3044\u8003\u3048\u3067\u3059\u3002<\/p>\n
dataset.head()<\/code><\/p>\n
\u3053\u308c\u304c\u305d\u306e\u51fa\u529b\u3067\u3059\u3002<\/p>\n
\"\"<\/a><\/p>\n
\u30c7\u30fc\u30bf\u306e\u6b21\u5143\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3057\u3066\u78ba\u8a8d\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\ndataset.shape\nOut []: (499, 74)\n<\/code><\/pre>\n
\u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u3088\u308a\u6df1\u304f\u7406\u89e3\u3059\u308b\u305f\u3081\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8981\u7d04\u7d71\u8a08\u91cf\u3092\u78ba\u8a8d\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n
dataset.describe()<\/code><\/p>\n
\"\"<\/a><\/p>\n
\u524d\u8ff0\u306e\u901a\u308a\u3001\u4f7f\u7528\u3059\u308b\u306e\u306f2\u3064\u306e\u5c5e\u6027\u306e\u307f\u306a\u306e\u3067\u3001\u3053\u306e\u56de\u5e30\u30e2\u30c7\u30eb\u3067\u306f\u4ed6\u306e\u5c5e\u6027\u3092\u4fdd\u6301\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/p>\n
\ndataset=dataset[[\"Salnty\",\"T_degC\"]]\ndataset.head()\n<\/code><\/pre>\n
\u3053\u308c\u3067\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u671f\u5f85\u901a\u308a2\u3064\u306e\u5c5e\u6027\u3057\u304b\u8868\u793a\u3055\u308c\u306a\u304f\u306a\u308a\u307e\u3057\u305f\u3002<\/p>\n
\"\"<\/a><\/p>\n
\u30e2\u30c7\u30eb\u3092\u5b66\u7fd2\u3059\u308b\u524d\u306b\u3001\u6b20\u843d\u3057\u3066\u3044\u308b\u5024\u3084\u7121\u52b9\u306a\u5024\u304c\u306a\u3044\u304b\u78ba\u8a8d\u3057\u3066\u304a\u304f\u3053\u3068\u304c\u671b\u307e\u3057\u3044\u3067\u3059\u3002\u3053\u308c\u3089\u306e\u5024\u304c\u5b58\u5728\u3059\u308b\u3068\u4ee5\u964d\u306e\u624b\u9806\u306b\u652f\u969c\u3092\u304d\u305f\u3059\u53ef\u80fd\u6027\u304c\u3042\u308b\u305f\u3081\u3067\u3059\u3002<\/p>\n
\ndataset.Salnty.isnull().value_counts()\nOut[]: False    493\n       True       6\n       Name: Salnty, dtype: int64\n<\/code><\/pre>\n
\ndataset.T_degC.isnull().value_counts()\nOut[]: False    498\n       True       1\n       Name: T_degC, dtype: int64\n<\/code><\/pre>\n
\u5b66\u7fd2\u6bb5\u968e\u306b\u79fb\u308b\u524d\u306b\u3001NULL\u5024\u3092\u524a\u9664\u3057\u3066\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u30ea\u30bb\u30c3\u30c8\u3057\u307e\u3059\u3002<\/p>\n
\ndataset=dataset.dropna(axis=0)\ndataset.reset_index(drop=True,inplace=True)\ndataset.shape\nOut[]: (492,2)\n<\/code><\/pre>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u3001\u5869\u5206(Salinity)\u3068\u6e29\u5ea6(Temperature)\u3068\u306e\u9593\u306b\u3069\u306e\u3088\u3046\u306a\u95a2\u4fc2\u304c\u3042\u308b\u304b\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
\nplt.figure(figsize=(12,10))\nplt.scatter(dataset.Salnty,dataset.T_degC, color='aqua')\nplt.xlabel(\"Temperature\",fontsize=22)\nplt.ylabel(\"Salinity\",fontsize=22)\n<\/code><\/pre>\n
\"\"<\/a><\/p>\n
\u3053\u3053\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30921\u6b21\u5143\u306e\u914d\u5217\u306b\u6574\u5f62\u3057\u307e\u3059\u3002<\/p>\n
\nx_label=np.array(dataset['Salnty']).reshape(492,1)\ny_label=np.array(dataset['T_degC']).reshape(492,1)\n<\/code><\/pre>\n
\u3044\u3088\u3044\u3088\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u5b66\u7fd2\u7528\u3068\u30c6\u30b9\u30c8\u7528\u306b\u5206\u3051\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u300180%-20%\u306e\u5272\u5408\u3067\u5206\u5272\u3057\u307e\u3059\u3002\u5272\u5408\u306f\u304a\u597d\u307f\u3067\u81ea\u7531\u306b\u5909\u3048\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\nx_train, x_test, y_train, y_test = train_test_split(x_label, y_label, test_size = 0.2, random_state = 100)\n<\/code><\/pre>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5206\u96e2\u304c\u5b8c\u4e86\u3057\u305f\u306e\u3067\u3001\u4eca\u5ea6\u306f\u5b66\u7fd2\u7528\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u7528\u3044\u3066\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
\nregression_model=LinearRegression()\nregression_model.fit(x_train,y_train)\nprint('Coefficients: ', regression_model.coef_)\nprint('Intercept: ',regression_model.intercept_)\n\nOut[]: Coefficients:  [[-4.80500593]]\n       Intercept:  [169.4247854]\n<\/code><\/pre>\n
\u6b21\u306b\u3001\u69cb\u7bc9\u3057\u305f\u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u3092\u78ba\u8a8d\u3059\u308b\u305f\u3081\u306b\u3001\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4e0e\u3048\u3066\u307f\u307e\u3059\u3002<\/p>\n
\naccuracy = regression_model.score(x_test, y_test)\nprint(accuracy)\n0.876504976866808\n<\/code><\/pre>\n
\u69cb\u7bc9\u3057\u305f\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u3088\u308b\u4e88\u6e2c\u7d50\u679c\u304c\u3069\u306e\u7a0b\u5ea6\u5408\u3063\u3066\u3044\u308b\u304b\u3092\u8996\u899a\u5316\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
\nplt.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)\n<\/code><\/pre>\n
\"\"<\/a><\/p>\n
\u30e2\u30c7\u30eb\u306e\u5e73\u5747\u8aa4\u5dee\u3092\u78ba\u8a8d\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n
\ny_predicted_value = regression_model.predict(x_test)\nprint(\"Mean absolute error:\",np.mean(np.absolute(y_predicted_value - y_test)))\nOut []: Mean absolute error: 0.8090316355920698\n<\/code><\/pre>\n
\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30c7\u30fc\u30bf\u3092GridDB\u306b\u633f\u5165\u3059\u308b\u65b9\u6cd5\u3068\u3001pandas\u30e9\u30a4\u30d6\u30e9\u30ea\u3068SQL\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u65b9\u6cd5\u3092\u898b\u307e\u3057\u305f\u3002\u305d\u306e\u5f8c\u3001\u3053\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3057\u305f\u3002GridDB\u3068Jupyter Notebooks\u3092\u63a5\u7d9a\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u306f\u3001\u3053\u3061\u3089<\/a>\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u3054\u89a7\u304f\u3060\u3055\u3044\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4ed6\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u3064\u3044\u3066\u3082\u3001\u81ea\u7531\u306b\u8a66\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002\u3053\u306e\u3088\u3046\u306a\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u3082\u3063\u3068\u898b\u305f\u3044\u65b9\u306f\u3001\u79c1\u305f\u3061\u306e\u30d6\u30ed\u30b0<\/a>\u3092\u3054\u89a7\u304f\u3060\u3055\u3044\u3002<\/p>\n
\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9<\/h3>\n
Jupyter Notebook \u30d5\u30a1\u30a4\u30eb<\/h4>\n
 GitHub <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u306f\u3058\u3081\u306b \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001GridDB\u306b\u4fdd\u5b58\u3055\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u3001\u7c21\u5358\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002\u307e\u305a\u3001GridDB\u306epython-connector\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u306e\u633f\u5165\u3068\u30a2\u30af\u30bb\u30b9\u3092\u884c\u3044\u307e\u3059\u3002\u305d\u306e\u5f8c\u3001p […]<\/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-50729","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"\nGridDB\u3092\u7528\u3044\u305f\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u4f5c\u6210 | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"\u306f\u3058\u3081\u306b\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.griddb.net\/ja\/\u672a\u5206\u985e\/create-a-machine-learning-model-using-griddb\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GridDB\u3092\u7528\u3044\u305f\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u4f5c\u6210 | GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"og:description\" content=\"\u306f\u3058\u3081\u306b\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.griddb.net\/ja\/\u672a\u5206\u985e\/create-a-machine-learning-model-using-griddb\/\" \/>\n<meta property=\"og:site_name\" content=\"GridDB: Open Source Time Series Database for IoT\" \/>\n<meta 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